diff --git a/04. Presentation - P2.pptx b/04. Presentation - P2.pptx new file mode 100644 index 0000000..76a4c88 Binary files /dev/null and b/04. Presentation - P2.pptx differ diff --git a/04. Presentation - P3.pptx b/04. Presentation - P3.pptx new file mode 100644 index 0000000..1a7a28d Binary files /dev/null and b/04. Presentation - P3.pptx differ diff --git a/05. Jupyter Notebook _ Code - P2 b/05. Jupyter Notebook _ Code - P2 new file mode 100644 index 0000000..85cf8c6 --- /dev/null +++ b/05. Jupyter Notebook _ Code - P2 @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Importing Libraries and reading in CSV file"],"metadata":{"id":"5N3wcJiYIo_3"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"c6uRrmEsOMEg"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","df = pd.read_csv('/content/salary_model.csv')"]},{"cell_type":"markdown","source":["## Initial Data Exploration of count and Dtypes"],"metadata":{"id":"FyP7cw3CI20o"}},{"cell_type":"code","source":["print(df.info())\n","print()\n","df.head(15)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"B1c4hTFaIjts","executionInfo":{"status":"ok","timestamp":1714700826473,"user_tz":300,"elapsed":204,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"271ec487-e395-46e2-8530-8b61e4f2b6d9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 14199 entries, 0 to 14198\n","Data columns (total 12 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 work_year 14199 non-null int64 \n"," 1 experience_level 14199 non-null object\n"," 2 employment_type 14199 non-null object\n"," 3 job_title 14199 non-null object\n"," 4 salary 14191 non-null object\n"," 5 salary_currency 14199 non-null object\n"," 6 salary_in_usd 14199 non-null int64 \n"," 7 employee_residence 14199 non-null object\n"," 8 work_setting 14199 non-null object\n"," 9 company_location 14199 non-null object\n"," 10 company_size 14199 non-null object\n"," 11 job_category 14199 non-null object\n","dtypes: int64(2), object(10)\n","memory usage: 1.3+ MB\n","None\n","\n"]},{"output_type":"execute_result","data":{"text/plain":[" work_year experience_level employment_type \\\n","0 2024 Entry-level Freelance \n","1 2024 Executive Full-time \n","2 2024 Executive Full-time \n","3 2024 Senior Full-time \n","4 2024 Senior Full-time \n","5 2024 Mid-level Full-time \n","6 2024 Mid-level Full-time \n","7 2024 Entry-level Full-time \n","8 2024 Entry-level Full-time \n","9 2024 Senior Full-time \n","10 2024 Senior Full-time \n","11 2024 Senior Full-time \n","12 2024 Senior Full-time \n","13 2024 Entry-level Full-time \n","14 2024 Entry-level Full-time \n","\n"," job_title salary salary_currency salary_in_usd \\\n","0 Applied Data Scientist 30000 USD 30000 \n","1 Business Intelligence 230000 USD 230000 \n","2 Business Intelligence 176900 USD 176900 \n","3 Data Architect 171210 USD 171210 \n","4 Data Architect 92190 USD 92190 \n","5 Data Science 46203 GBP 57753 \n","6 Data Science 38280 GBP 47850 \n","7 Insight Analyst 50000 USD 50000 \n","8 Insight Analyst 40000 USD 40000 \n","9 Data Engineer 276000 USD 276000 \n","10 Data Engineer 148000 USD 148000 \n","11 Research Scientist 234000 USD 234000 \n","12 Research Scientist 146000 USD 146000 \n","13 Business Intelligence Analyst 192300 USD 192300 \n","14 Business Intelligence Analyst 120200 USD 120200 \n","\n"," employee_residence work_setting company_location company_size \\\n","0 United Kingdom Remote United Kingdom M \n","1 United States In-person United States M \n","2 United States In-person United States M \n","3 Canada In-person Canada M \n","4 Canada In-person Canada M \n","5 United Kingdom In-person United Kingdom M \n","6 United Kingdom In-person United Kingdom M \n","7 United States Remote United States M \n","8 United States Remote United States M \n","9 United States In-person United States M \n","10 United States In-person United States M \n","11 United States In-person United States M \n","12 United States In-person United States M \n","13 United States In-person United States M \n","14 United States In-person United States M \n","\n"," job_category \n","0 Data Science and Research \n","1 BI and Visualization \n","2 BI and Visualization \n","3 Data Architecture and Modeling \n","4 Data Architecture and Modeling \n","5 Data Science and Research \n","6 Data Science and Research \n","7 Data Analysis \n","8 Data Analysis \n","9 Data Engineering \n","10 Data Engineering \n","11 Data Science and Research \n","12 Data Science and Research \n","13 BI and Visualization \n","14 BI and Visualization "],"text/html":["\n","
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work_yearexperience_levelemployment_typejob_titlesalarysalary_currencysalary_in_usdemployee_residencework_settingcompany_locationcompany_sizejob_category
02024Entry-levelFreelanceApplied Data Scientist30000USD30000United KingdomRemoteUnited KingdomMData Science and Research
12024ExecutiveFull-timeBusiness Intelligence230000USD230000United StatesIn-personUnited StatesMBI and Visualization
22024ExecutiveFull-timeBusiness Intelligence176900USD176900United StatesIn-personUnited StatesMBI and Visualization
32024SeniorFull-timeData Architect171210USD171210CanadaIn-personCanadaMData Architecture and Modeling
42024SeniorFull-timeData Architect92190USD92190CanadaIn-personCanadaMData Architecture and Modeling
52024Mid-levelFull-timeData Science46203GBP57753United KingdomIn-personUnited KingdomMData Science and Research
62024Mid-levelFull-timeData Science38280GBP47850United KingdomIn-personUnited KingdomMData Science and Research
72024Entry-levelFull-timeInsight Analyst50000USD50000United StatesRemoteUnited StatesMData Analysis
82024Entry-levelFull-timeInsight Analyst40000USD40000United StatesRemoteUnited StatesMData Analysis
92024SeniorFull-timeData Engineer276000USD276000United StatesIn-personUnited StatesMData Engineering
102024SeniorFull-timeData Engineer148000USD148000United StatesIn-personUnited StatesMData Engineering
112024SeniorFull-timeResearch Scientist234000USD234000United StatesIn-personUnited StatesMData Science and Research
122024SeniorFull-timeResearch Scientist146000USD146000United StatesIn-personUnited StatesMData Science and Research
132024Entry-levelFull-timeBusiness Intelligence Analyst192300USD192300United StatesIn-personUnited StatesMBI and Visualization
142024Entry-levelFull-timeBusiness Intelligence Analyst120200USD120200United StatesIn-personUnited StatesMBI and Visualization
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n \"name\": \"df\",\n \"rows\": 14199,\n \"fields\": [\n {\n \"column\": \" work_year \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 2020,\n \"max\": 2024,\n \"num_unique_values\": 5,\n \"samples\": [\n 2023,\n 2021,\n 2022\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"experience_level\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Executive\",\n \"Mid-level\",\n \"Entry-level\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"employment_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"Freelance\",\n \"Full-time\",\n \"full-time\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"job_title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 149,\n \"samples\": [\n \"AI Product Manager\",\n \"BI Analyst\",\n \"Staff Data Scientist\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"salary\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2230,\n \"samples\": [\n \"192500\",\n \"207760\",\n \"398900\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"salary_currency\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 12,\n \"samples\": [\n \"SGD\",\n \"TRY\",\n \"USD\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"salary_in_usd\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 64379,\n \"min\": 15000,\n \"max\": 450000,\n \"num_unique_values\": 2578,\n \"samples\": [\n 342400,\n 104220,\n 240500\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"employee_residence\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 86,\n \"samples\": [\n \"Indonesia\",\n \"United Kingdom\",\n \"Chile\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"work_setting\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Remote\",\n \"In-person\",\n \"Hybrid\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company_location\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 74,\n \"samples\": [\n \"Poland\",\n \"Bahamas\",\n \"Switzerland\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company_size\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"M\",\n \"S\",\n \"s\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"job_category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Cloud and Database\",\n \"BI and Visualization\",\n \"Leadership and Management\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":33}]},{"cell_type":"markdown","source":["## Checking salary column as to why the total count differs from all of the other columns"],"metadata":{"id":"bHFqmMTEEyOY"}},{"cell_type":"code","source":["# Trying to convert salary to float as it should be numerical, not an object\n","\n","df['salary'] = df['salary'].astype('float')"],"metadata":{"id":"_Igz7MKwEvpY","executionInfo":{"status":"error","timestamp":1714700839841,"user_tz":300,"elapsed":172,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"colab":{"base_uri":"https://localhost:8080/","height":339},"outputId":"f6e0c411-df94-4d84-9351-2bd0d785e2c0"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"could not convert string to float: '238,000'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Trying to convert salary to float as it should be numerical, not an object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'salary'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'salary'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6322\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6323\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6324\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6325\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 449\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 452\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 453\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 351\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m 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137\u001b[0m \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: could not convert string to float: '238,000'"]}]},{"cell_type":"code","source":["# Removing comma from 238,000 and trying to convert to float again\n","\n","df['salary'].replace({\",\":\"\"}, regex=True, inplace=True)\n","df['salary'] = df['salary'].astype('float')"],"metadata":{"id":"GiEmW-XSFG8j","colab":{"base_uri":"https://localhost:8080/","height":339},"executionInfo":{"status":"error","timestamp":1714700846660,"user_tz":300,"elapsed":294,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"58993f86-0dca-4419-b989-9df4635a2c1e"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"could not convert string to float: '$154000 '","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m 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\u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 513\u001b[0m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;31m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_astype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: could not convert string to float: '$154000 '"]}]},{"cell_type":"code","source":["# Removing \"$\" from $154000 and trying to convert to float again\n","\n","df['salary'].replace({\"\\$\":\"\"}, regex=True, inplace=True)\n","df['salary'] = df['salary'].astype('float')"],"metadata":{"id":"OwOQkWyFFwEQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Checking for missing values for all columns\n","\n","df.isnull().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"luNqJ6OkGG1N","executionInfo":{"status":"ok","timestamp":1714700852192,"user_tz":300,"elapsed":196,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"f150933f-7bde-4d7f-9b09-de75d3273e23"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" work_year 0\n","experience_level 0\n","employment_type 0\n","job_title 0\n","salary 8\n","salary_currency 0\n","salary_in_usd 0\n","employee_residence 0\n","work_setting 0\n","company_location 0\n","company_size 0\n","job_category 0\n","dtype: int64"]},"metadata":{},"execution_count":39}]},{"cell_type":"code","source":["# Replacing missing values with the mean of the salary column and rounding to 2 decimal places\n","\n","df['salary'] = df['salary'].transform(lambda x: x.fillna(x.mean())).round(2)"],"metadata":{"id":"bbXGYCLVGj1V"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Re-checking the count of missing values in the salary column\n","\n","print(\"The number of missing values in the salary column is:\", df['salary'].isnull().sum())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FgTDTpV5HFqi","executionInfo":{"status":"ok","timestamp":1714700857312,"user_tz":300,"elapsed":303,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"f81237e9-b57c-4015-fd42-7ea80baff6cf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The number of missing values in the salary column is: 0\n"]}]},{"cell_type":"code","source":["df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vfEmJIAGIqU_","executionInfo":{"status":"ok","timestamp":1714700858664,"user_tz":300,"elapsed":240,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"53a4f2d3-23a5-4859-faf4-017d69c268e4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 14199 entries, 0 to 14198\n","Data columns (total 12 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 work_year 14199 non-null int64 \n"," 1 experience_level 14199 non-null object \n"," 2 employment_type 14199 non-null object \n"," 3 job_title 14199 non-null object \n"," 4 salary 14199 non-null float64\n"," 5 salary_currency 14199 non-null object \n"," 6 salary_in_usd 14199 non-null int64 \n"," 7 employee_residence 14199 non-null object \n"," 8 work_setting 14199 non-null object \n"," 9 company_location 14199 non-null object \n"," 10 company_size 14199 non-null object \n"," 11 job_category 14199 non-null object \n","dtypes: float64(1), int64(2), object(9)\n","memory usage: 1.3+ MB\n"]}]},{"cell_type":"markdown","source":["## Running Descriptive Statistics on Salary"],"metadata":{"id":"7B0wYG2CLqLQ"}},{"cell_type":"code","source":["df['salary'].describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pJYIH5sWLpf4","executionInfo":{"status":"ok","timestamp":1714700860694,"user_tz":300,"elapsed":198,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"5acc85d4-08bd-40be-8bbd-976381b8ad31"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 14199.000000\n","mean 149006.380452\n","std 64875.217695\n","min 0.000000\n","25% 102100.000000\n","50% 142000.000000\n","75% 185900.000000\n","max 450000.000000\n","Name: salary, dtype: float64"]},"metadata":{},"execution_count":43}]},{"cell_type":"markdown","source":["## Pulling row where salary value is 0"],"metadata":{"id":"48mswf4kME9l"}},{"cell_type":"code","source":["df.loc[df['salary'] == 0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"id":"ePYWVdHqMKik","executionInfo":{"status":"ok","timestamp":1714700863640,"user_tz":300,"elapsed":194,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"c7d2a7b0-aa04-44dc-b597-66179407ab1a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" work_year experience_level employment_type job_title salary \\\n","10126 2023 Senior Full-time Data Analyst 0.0 \n","\n"," salary_currency salary_in_usd employee_residence work_setting \\\n","10126 USD 116000 United States Remote \n","\n"," company_location company_size job_category \n","10126 United States M Data Analysis "],"text/html":["\n","
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work_yearexperience_levelemployment_typejob_titlesalarysalary_currencysalary_in_usdemployee_residencework_settingcompany_locationcompany_sizejob_category
101262023SeniorFull-timeData Analyst0.0USD116000United StatesRemoteUnited StatesMData Analysis
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"df\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \" work_year \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 2023,\n \"max\": 2023,\n \"num_unique_values\": 1,\n \"samples\": [\n 2023\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"experience_level\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Senior\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"employment_type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Full-time\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"job_title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Data Analyst\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"salary\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"salary_currency\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"USD\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"salary_in_usd\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 116000,\n \"max\": 116000,\n \"num_unique_values\": 1,\n \"samples\": [\n 116000\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"employee_residence\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"United States\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"work_setting\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Remote\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company_location\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"United States\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company_size\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"M\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"job_category\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Data Analysis\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":44}]},{"cell_type":"markdown","source":["## Salary should be 116000 based on salary_in_usd column, so replacing the 0 with that value"],"metadata":{"id":"Z4ChM4V1M_gi"}},{"cell_type":"code","source":["df['salary'] = df['salary'].replace(0, 116000)"],"metadata":{"id":"aZ7PPX47MP-y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df['salary'].describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VQ5_vQkJNEXQ","executionInfo":{"status":"ok","timestamp":1714700866913,"user_tz":300,"elapsed":215,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"6d0a8409-dd0f-4806-9d67-31d5b2e84449"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 14199.000000\n","mean 149014.550042\n","std 64863.755133\n","min 14000.000000\n","25% 102150.000000\n","50% 142000.000000\n","75% 185900.000000\n","max 450000.000000\n","Name: salary, dtype: float64"]},"metadata":{},"execution_count":46}]},{"cell_type":"markdown","source":["## Checking unique values for each column"],"metadata":{"id":"ReTBcxmKJDWc"}},{"cell_type":"code","source":["# Printing this column's unique values out individually as its dtype is not object\n","\n","print(f\"WORK YEAR Unique Values: {df.work_year.unique()}\")\n","print()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":315},"id":"MV3a7ICjJCn1","executionInfo":{"status":"error","timestamp":1714700903741,"user_tz":300,"elapsed":155,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"20a8857f-9380-4038-e496-ad50efdcf27c"},"execution_count":null,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"'DataFrame' object has no attribute 'work_year'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Printing this column's unique values out individually as its dtype is not object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"WORK YEAR Unique Values: {df.work_year.unique()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5987\u001b[0m ):\n\u001b[1;32m 5988\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5989\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5991\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'work_year'"]}]},{"cell_type":"markdown","source":["## Checking column names to explore why work_year is throwing an error"],"metadata":{"id":"nvADk_N0CQDU"}},{"cell_type":"code","source":["df.columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Cnq7N29ZCOx2","executionInfo":{"status":"ok","timestamp":1714700905776,"user_tz":300,"elapsed":166,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"e1964e3a-b8e0-4f9d-a0e1-adf32087f609"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index([' work_year ', 'experience_level', 'employment_type', 'job_title',\n"," 'salary', 'salary_currency', 'salary_in_usd', 'employee_residence',\n"," 'work_setting', 'company_location', 'company_size', 'job_category'],\n"," dtype='object')"]},"metadata":{},"execution_count":48}]},{"cell_type":"code","source":["# Removing the spaces in the work_year column name\n","\n","df = df.rename(columns={' work_year ': 'work_year'})"],"metadata":{"id":"Q9_vw17YCO9C"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Re-running code to print out unique values\n","\n","print(f\"WORK YEAR Unique Values: {df.work_year.unique()}\")\n","print()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z2N5tcUmDwVh","executionInfo":{"status":"ok","timestamp":1714700908046,"user_tz":300,"elapsed":300,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"77268c71-2383-49d4-a50b-acb20d7795e1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["WORK YEAR Unique Values: [2024 2023 2022 2020 2021]\n","\n"]}]},{"cell_type":"markdown","source":["## Checking Unique Values of columns where dtype is \"Object\""],"metadata":{"id":"BoyQAFCKjjw7"}},{"cell_type":"code","source":["# This loop checks the dtype of each column and produces the unique values if the column dtype matches object\n","# Otherwise it passes and moves on to the next column to check\n","\n","for column in df.columns:\n"," if df.dtypes[column] == \"object\":\n"," print (f\"{column.upper()} Unique Values: {df[column].unique()}\")\n"," print()\n"," else:\n"," pass"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qsQm4QXRCPAB","executionInfo":{"status":"ok","timestamp":1714700909830,"user_tz":300,"elapsed":245,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"581d8e65-f4ea-40ff-c0dd-97b4b08fd9a0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["EXPERIENCE_LEVEL Unique Values: ['Entry-level' 'Executive' 'Senior' 'Mid-level']\n","\n","EMPLOYMENT_TYPE Unique Values: ['Freelance' 'Full-time' 'Fulltime' 'Contract' 'Part-time' 'full-time']\n","\n","JOB_TITLE Unique Values: ['Applied Data Scientist' 'Business Intelligence' 'Data Architect'\n"," 'Data Science' 'Insight Analyst' 'Data Engineer' 'Research Scientist'\n"," 'Business Intelligence Analyst' 'Analytics Engineer' 'Data Scientist'\n"," 'Research Engineer' 'BI Developer' 'Data Analyst'\n"," 'Business Intelligence Engineer' 'Data Quality Engineer'\n"," 'Machine Learning Engineer' 'Cloud Database Engineer' 'Head of Data'\n"," 'BI Analyst' 'Data Manager' 'Computational Biologist'\n"," 'Data Integration Specialist' 'Data Science Manager' 'AI Engineer'\n"," 'Applied Scientist' 'BI Data Analyst' 'AI Research Scientist'\n"," 'MLOps Engineer' 'Research Analyst' 'Admin & Data Analyst'\n"," 'Business Intelligence Manager' 'Data Developer' 'Prompt Engineer'\n"," 'Data Specialist' 'Data Integration Engineer' 'Data Science Analyst'\n"," 'Data Analytics Associate' 'Data Reporting Analyst'\n"," 'Business Intelligence Developer' 'Machine Learning Scientist'\n"," 'Data Science Consultant' 'Data Management Analyst'\n"," 'Data Operations Analyst' 'Data Management Consultant'\n"," 'Data Quality Analyst' 'ML Engineer' 'Robotics Software Engineer'\n"," 'Machine Learning Researcher' 'Data DevOps Engineer'\n"," 'AI Software Engineer' 'Data Operations Specialist' 'AI Architect'\n"," 'Data Product Manager' 'Data Science Director' 'Data Strategist'\n"," 'Big Data Developer' 'Quantitative Research Analyst'\n"," 'Lead Machine Learning Engineer' 'Machine Learning Research Engineer'\n"," 'Data Infrastructure Engineer' 'Data Analytics Lead'\n"," 'Data Analytics Manager' 'Data Analytics Consultant'\n"," 'AI Research Engineer' 'Data Analytics Specialist' 'ETL Developer'\n"," 'Data Science Engineer' 'Big Data Engineer' 'Data Modeler'\n"," 'Robotics Engineer' 'Business Intelligence Lead' 'AI Programmer'\n"," 'ETL Engineer' 'AI Product Manager' 'Data Management Specialist'\n"," 'Data Operations Associate' 'AI Developer' 'AI Scientist'\n"," 'Computer Vision Engineer' 'Head of Machine Learning' 'Data Analyst Lead'\n"," 'Machine Learning Operations Engineer' 'Data Lead'\n"," 'Data Science Practitioner' 'Data Integration Developer'\n"," 'ML Ops Engineer' 'Data Pipeline Engineer' 'Data Science Lead'\n"," 'Director of Data Science' 'Managing Director Data Science'\n"," 'Data Visualization Specialist' 'Data Quality Manager'\n"," 'Data Product Owner' 'Machine Learning Infrastructure Engineer'\n"," 'Business Data Analyst' 'NLP Engineer' 'Marketing Data Scientist'\n"," 'Deep Learning Engineer' 'Machine Learning Modeler'\n"," 'Business Intelligence Specialist' 'Decision Scientist'\n"," 'Financial Data Analyst' 'Data Strategy Manager'\n"," 'Data Visualization Engineer' 'Azure Data Engineer'\n"," 'Principal Data Scientist' 'Staff Data Analyst'\n"," 'Machine Learning Software Engineer' 'Applied Machine Learning Scientist'\n"," 'Data Operations Engineer' 'Machine Learning Manager'\n"," 'Lead Data Scientist' 'Principal Machine Learning Engineer'\n"," 'Principal Data Engineer' 'Power BI Developer' 'Head of Data Science'\n"," 'Staff Machine Learning Engineer' 'Staff Data Scientist'\n"," 'Consultant Data Engineer' 'Machine Learning Specialist'\n"," 'Business Intelligence Data Analyst' 'Data Operations Manager'\n"," 'Data Modeller' 'Finance Data Analyst' 'Software Data Engineer'\n"," 'Compliance Data Analyst' 'Cloud Data Engineer'\n"," 'Analytics Engineering Manager' 'AWS Data Architect'\n"," 'Product Data Analyst' 'Machine Learning Developer'\n"," 'Data Visualization Analyst' 'Autonomous Vehicle Technician'\n"," 'Sales Data Analyst' 'Applied Machine Learning Engineer'\n"," 'Lead Data Analyst' 'BI Data Engineer' 'Deep Learning Researcher'\n"," 'Big Data Architect' 'Computer Vision Software Engineer'\n"," 'Marketing Data Engineer' 'Manager Data Management'\n"," 'Data Science Tech Lead' 'Data Scientist Lead' 'Marketing Data Analyst'\n"," 'Data Analytics Engineer' 'Cloud Data Architect' 'Lead Data Engineer'\n"," 'Principal Data Analyst']\n","\n","SALARY_CURRENCY Unique Values: ['USD' 'GBP' 'EUR' 'CAD' 'CHF' 'NZD' 'AUD' 'PLN' 'BRL' 'TRY' 'SGD' 'DKK']\n","\n","EMPLOYEE_RESIDENCE Unique Values: ['United Kingdom' 'United States' 'Canada' 'Lithuania' 'Poland' 'France'\n"," 'Germany' 'Saudi Arabia' 'India' 'Australia' 'United Arab Emirates'\n"," 'Ukraine' 'Netherlands' 'Egypt' 'Austria' 'Spain' 'Philippines' 'Finland'\n"," 'Türkiye' 'Switzerland' 'Oman' 'New Zealand' 'Mexico' 'Portugal'\n"," 'Bosnia and Herzegovina' 'Brazil' 'Argentina' 'Latvia' 'South Africa'\n"," 'Italy' 'Ireland' 'Estonia' 'Malta' 'Croatia' 'Lebanon' 'Romania'\n"," 'Hungary' 'Viet Nam' 'Nigeria' 'Czechia' 'Pakistan' 'Uganda' 'Colombia'\n"," 'Slovenia' 'Greece' 'Mauritius' 'Armenia' 'Thailand' 'Korea, Republic of'\n"," 'Qatar' 'Russian Federation' 'Kenya' 'Tunisia' 'Ghana' 'Belgium'\n"," 'Andorra' 'Ecuador' 'Peru' 'Moldova, Republic of' 'Uzbekistan' 'Georgia'\n"," 'Central African Republic' 'Singapore' 'Sweden' 'Kuwait' 'Cyprus'\n"," 'Iran, Islamic Republic of' 'American Samoa' 'China' 'Costa Rica' 'Chile'\n"," 'Puerto Rico' 'Denmark' 'Bolivia, Plurinational State of'\n"," 'Dominican Republic' 'Indonesia' 'Malaysia' 'Japan' 'Honduras' 'Algeria'\n"," 'Iraq' 'Bulgaria' 'Jersey' 'Serbia' 'Hong Kong' 'Luxembourg']\n","\n","WORK_SETTING Unique Values: ['Remote' 'In-person' 'Hybrid']\n","\n","COMPANY_LOCATION Unique Values: ['United Kingdom' 'United States' 'Canada' 'Lithuania' 'Poland' 'France'\n"," 'Germany' 'Saudi Arabia' 'Australia' 'United Arab Emirates' 'Ukraine'\n"," 'Netherlands' 'Egypt' 'Austria' 'Spain' 'Philippines' 'Finland'\n"," 'Türkiye' 'Switzerland' 'Oman' 'New Zealand' 'Mexico' 'Portugal'\n"," 'Bosnia and Herzegovina' 'Brazil' 'Argentina' 'Latvia' 'South Africa'\n"," 'Italy' 'American Samoa' 'Ireland' 'Estonia' 'India' 'Malta' 'Hungary'\n"," 'Lebanon' 'Romania' 'Viet Nam' 'Nigeria' 'Luxembourg' 'Gibraltar'\n"," 'Colombia' 'Slovenia' 'Greece' 'Mauritius' 'Russian Federation'\n"," 'Korea, Republic of' 'Czechia' 'Qatar' 'Kenya' 'Denmark' 'Ghana' 'Sweden'\n"," 'Andorra' 'Ecuador' 'Israel' 'Japan' 'Central African Republic'\n"," 'Singapore' 'Croatia' 'Armenia' 'Pakistan' 'Iran, Islamic Republic of'\n"," 'Bahamas' 'Puerto Rico' 'Thailand' 'Belgium' 'Indonesia' 'Malaysia'\n"," 'Honduras' 'Algeria' 'Iraq' 'China' 'Moldova, Republic of']\n","\n","COMPANY_SIZE Unique Values: ['M' 'S' 'm' 'L' 'l' 's']\n","\n","JOB_CATEGORY Unique Values: ['Data Science and Research' 'BI and Visualization'\n"," 'Data Architecture and Modeling' 'Data Analysis' 'Data Engineering'\n"," 'Leadership and Management' 'Data Quality and Operations'\n"," 'Machine Learning and AI' 'Cloud and Database'\n"," 'Data Management and Strategy']\n","\n"]}]},{"cell_type":"markdown","source":["## Cleaning of Employment Type column values"],"metadata":{"id":"gCuZVz65jpt3"}},{"cell_type":"code","source":["# Fixing column values in the Employment Type for consistency by capitalizing lower case values and fixing the value that is missing a \"-\"\n","\n","df['employment_type'] = df['employment_type'].str.capitalize()\n","df['employment_type'] = df['employment_type'].str.replace('Fulltime', 'Full-time')\n","\n","# Verifying the changes applied correctly\n","\n","df['employment_type'].value_counts()"],"metadata":{"id":"JRLeK1Wvo6dZ","executionInfo":{"status":"ok","timestamp":1714700911259,"user_tz":300,"elapsed":195,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"ccaad558-0da2-4320-a7cd-ee67a90c11c9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["employment_type\n","Full-time 14139\n","Contract 26\n","Part-time 22\n","Freelance 12\n","Name: count, dtype: int64"]},"metadata":{},"execution_count":52}]},{"cell_type":"markdown","source":["## Cleaning of Company Size column values"],"metadata":{"id":"JsPbLNmEjvfY"}},{"cell_type":"code","source":["# Fixing the Company Size column to make sure each value is consistent in casing\n","\n","df['company_size'] = df['company_size'].str.capitalize()\n","\n","# Verifying the changes were applied correctly\n","\n","df['company_size'].value_counts()"],"metadata":{"id":"ln6Azml7o6--","executionInfo":{"status":"ok","timestamp":1714700911993,"user_tz":300,"elapsed":190,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"74ce95f1-1747-430b-f71b-13360ca9f7ad"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["company_size\n","M 13112\n","L 919\n","S 168\n","Name: count, dtype: int64"]},"metadata":{},"execution_count":53}]},{"cell_type":"markdown","source":["## Filtering out data based on USD salary info"],"metadata":{"id":"1ygwoVWOj8oF"}},{"cell_type":"code","source":["# This filters out only rows that are pertaining to USD salary information and the company being located in the United States\n","\n","usd_salary_df = df[((df['salary_currency'] == \"USD\") & (df['employee_residence'] == 'United States'))]\n","\n","# Checking value counts for various columns to determine if any can be removed from the dataset\n","\n","print(usd_salary_df['company_location'].value_counts())\n","print()\n","print(usd_salary_df['employment_type'].value_counts())\n","print()\n","print(usd_salary_df['employee_residence'].value_counts())\n","print()\n","print(usd_salary_df['salary_currency'].value_counts())\n","print()\n","print(usd_salary_df['experience_level'].value_counts())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k5hm8KVmiCid","executionInfo":{"status":"ok","timestamp":1714700912893,"user_tz":300,"elapsed":195,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"326a4a62-a902-4f92-bd5d-abce691bb4a8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["company_location\n","United States 12398\n","Canada 2\n","Japan 1\n","Australia 1\n","Germany 1\n","France 1\n","Name: count, dtype: int64\n","\n","employment_type\n","Full-time 12381\n","Contract 14\n","Part-time 9\n","Name: count, dtype: int64\n","\n","employee_residence\n","United States 12404\n","Name: count, dtype: int64\n","\n","salary_currency\n","USD 12404\n","Name: count, dtype: int64\n","\n","experience_level\n","Senior 8555\n","Mid-level 2693\n","Entry-level 793\n","Executive 363\n","Name: count, dtype: int64\n"]}]},{"cell_type":"markdown","source":["## Filtering out non-United States company locations"],"metadata":{"id":"x2zOARrLEbzJ"}},{"cell_type":"code","source":["usd_salary_df = df[((df['company_location'] == \"United States\"))]\n","\n","print(usd_salary_df['company_location'].value_counts())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hJ-VyrHhEiFR","executionInfo":{"status":"ok","timestamp":1714700914013,"user_tz":300,"elapsed":366,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"6b8f84f2-d958-44a6-bc41-23b814e57a3f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["company_location\n","United States 12465\n","Name: count, dtype: int64\n"]}]},{"cell_type":"markdown","source":["## Dropping columns that are no longer needed"],"metadata":{"id":"cPMboPtEmXkN"}},{"cell_type":"code","source":["# Dropping the company_location as we are only looking at locations located in the United States\n","# Dropping the employee_residence column as we are only looking at employees who reside in the United States\n","# Dropping the salary_currency column as this column only contains the value \"USD\"\n","# Dropping the salary_in_usd column as we already filtered the salary column by salaries in the United States, so this column is redundant data\n","\n","usd_salary_df = usd_salary_df.drop({'company_location', 'employee_residence', 'salary_currency', 'salary_in_usd'}, axis=1)"],"metadata":{"id":"J2icSnhbmNTJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_u=usd_salary_df\n","usd_salary_df.head(15)"],"metadata":{"id":"vF0OL2RHmNiF","executionInfo":{"status":"ok","timestamp":1714700915320,"user_tz":300,"elapsed":197,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"colab":{"base_uri":"https://localhost:8080/","height":869},"outputId":"cb7fc514-f1c3-46c5-ad47-c44aa8243491"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" work_year experience_level employment_type job_title \\\n","1 2024 Executive Full-time Business Intelligence \n","2 2024 Executive Full-time Business Intelligence \n","7 2024 Entry-level Full-time Insight Analyst \n","8 2024 Entry-level Full-time Insight Analyst \n","9 2024 Senior Full-time Data Engineer \n","10 2024 Senior Full-time Data Engineer \n","11 2024 Senior Full-time Research Scientist \n","12 2024 Senior Full-time Research Scientist \n","13 2024 Entry-level Full-time Business Intelligence Analyst \n","14 2024 Entry-level Full-time Business Intelligence Analyst \n","15 2024 Entry-level Full-time Analytics Engineer \n","16 2024 Entry-level Full-time Analytics Engineer \n","17 2024 Entry-level Full-time Data Engineer \n","18 2024 Entry-level Full-time Data Engineer \n","19 2024 Senior Full-time Data Scientist \n","\n"," salary work_setting company_size job_category \n","1 230000.00 In-person M BI and Visualization \n","2 176900.00 In-person M BI and Visualization \n","7 50000.00 Remote M Data Analysis \n","8 40000.00 Remote M Data Analysis \n","9 276000.00 In-person M Data Engineering \n","10 148000.00 In-person M Data Engineering \n","11 234000.00 In-person M Data Science and Research \n","12 146000.00 In-person M Data Science and Research \n","13 192300.00 In-person M BI and Visualization \n","14 120200.00 In-person M BI and Visualization \n","15 132500.00 In-person M Leadership and Management \n","16 111500.00 In-person M Leadership and Management \n","17 234000.00 In-person M Data Engineering \n","18 146000.00 In-person M Data Engineering \n","19 149006.38 In-person M Data Science and Research "],"text/html":["\n","
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work_yearexperience_levelemployment_typejob_titlesalarywork_settingcompany_sizejob_category
12024ExecutiveFull-timeBusiness Intelligence230000.00In-personMBI and Visualization
22024ExecutiveFull-timeBusiness Intelligence176900.00In-personMBI and Visualization
72024Entry-levelFull-timeInsight Analyst50000.00RemoteMData Analysis
82024Entry-levelFull-timeInsight Analyst40000.00RemoteMData Analysis
92024SeniorFull-timeData Engineer276000.00In-personMData Engineering
102024SeniorFull-timeData Engineer148000.00In-personMData Engineering
112024SeniorFull-timeResearch Scientist234000.00In-personMData Science and Research
122024SeniorFull-timeResearch Scientist146000.00In-personMData Science and Research
132024Entry-levelFull-timeBusiness Intelligence Analyst192300.00In-personMBI and Visualization
142024Entry-levelFull-timeBusiness Intelligence Analyst120200.00In-personMBI and Visualization
152024Entry-levelFull-timeAnalytics Engineer132500.00In-personMLeadership and Management
162024Entry-levelFull-timeAnalytics Engineer111500.00In-personMLeadership and Management
172024Entry-levelFull-timeData Engineer234000.00In-personMData Engineering
182024Entry-levelFull-timeData Engineer146000.00In-personMData Engineering
192024SeniorFull-timeData Scientist149006.38In-personMData Science and Research
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"usd_salary_df","summary":"{\n \"name\": \"usd_salary_df\",\n \"rows\": 12465,\n \"fields\": [\n {\n \"column\": \"work_year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 2020,\n \"max\": 2024,\n \"num_unique_values\": 5,\n \"samples\": [\n 2023,\n 2020,\n 2022\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"experience_level\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Entry-level\",\n \"Mid-level\",\n \"Executive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"employment_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Contract\",\n \"Freelance\",\n \"Full-time\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"job_title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 127,\n \"samples\": [\n \"Data Integration Engineer\",\n \"BI Data Engineer\",\n \"NLP Engineer\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"salary\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 61523.413918642145,\n \"min\": 20000.0,\n \"max\": 450000.0,\n \"num_unique_values\": 2006,\n \"samples\": [\n 146115.0,\n 195200.0,\n 331640.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"work_setting\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"In-person\",\n \"Remote\",\n \"Hybrid\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company_size\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"M\",\n \"L\",\n \"S\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"job_category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Data Architecture and Modeling\",\n \"Data Analysis\",\n \"Data Quality and Operations\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":57}]},{"cell_type":"markdown","source":["## Exploratory Data Analysis (EDA)"],"metadata":{"id":"Ijy3cqNoU4s0"}},{"cell_type":"markdown","source":["## Removing 'job Title' column"],"metadata":{"id":"El--_dZ6-Iz4"}},{"cell_type":"code","source":["usd_salary_df = usd_salary_df.drop('job_title', axis=1)"],"metadata":{"id":"_4EI_4OBueNr"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Ensuring the column has been removed"],"metadata":{"id":"wJ9-vYI6_nGB"}},{"cell_type":"code","source":["usd_salary_df.head(15)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":606},"id":"x5qXNrLEqccG","executionInfo":{"status":"ok","timestamp":1714700917755,"user_tz":300,"elapsed":222,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"abf9662e-e516-4c70-8f15-58764965b271"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" work_year experience_level employment_type salary work_setting \\\n","1 2024 Executive Full-time 230000.00 In-person \n","2 2024 Executive Full-time 176900.00 In-person \n","7 2024 Entry-level Full-time 50000.00 Remote \n","8 2024 Entry-level Full-time 40000.00 Remote \n","9 2024 Senior Full-time 276000.00 In-person \n","10 2024 Senior Full-time 148000.00 In-person \n","11 2024 Senior Full-time 234000.00 In-person \n","12 2024 Senior Full-time 146000.00 In-person \n","13 2024 Entry-level Full-time 192300.00 In-person \n","14 2024 Entry-level Full-time 120200.00 In-person \n","15 2024 Entry-level Full-time 132500.00 In-person \n","16 2024 Entry-level Full-time 111500.00 In-person \n","17 2024 Entry-level Full-time 234000.00 In-person \n","18 2024 Entry-level Full-time 146000.00 In-person \n","19 2024 Senior Full-time 149006.38 In-person \n","\n"," company_size job_category \n","1 M BI and Visualization \n","2 M BI and Visualization \n","7 M Data Analysis \n","8 M Data Analysis \n","9 M Data Engineering \n","10 M Data Engineering \n","11 M Data Science and Research \n","12 M Data Science and Research \n","13 M BI and Visualization \n","14 M BI and Visualization \n","15 M Leadership and Management \n","16 M Leadership and Management \n","17 M Data Engineering \n","18 M Data Engineering \n","19 M Data Science and Research "],"text/html":["\n","
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work_yearexperience_levelemployment_typesalarywork_settingcompany_sizejob_category
12024ExecutiveFull-time230000.00In-personMBI and Visualization
22024ExecutiveFull-time176900.00In-personMBI and Visualization
72024Entry-levelFull-time50000.00RemoteMData Analysis
82024Entry-levelFull-time40000.00RemoteMData Analysis
92024SeniorFull-time276000.00In-personMData Engineering
102024SeniorFull-time148000.00In-personMData Engineering
112024SeniorFull-time234000.00In-personMData Science and Research
122024SeniorFull-time146000.00In-personMData Science and Research
132024Entry-levelFull-time192300.00In-personMBI and Visualization
142024Entry-levelFull-time120200.00In-personMBI and Visualization
152024Entry-levelFull-time132500.00In-personMLeadership and Management
162024Entry-levelFull-time111500.00In-personMLeadership and Management
172024Entry-levelFull-time234000.00In-personMData Engineering
182024Entry-levelFull-time146000.00In-personMData Engineering
192024SeniorFull-time149006.38In-personMData Science and Research
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"usd_salary_df","summary":"{\n \"name\": \"usd_salary_df\",\n \"rows\": 12465,\n \"fields\": [\n {\n \"column\": \"work_year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 2020,\n \"max\": 2024,\n \"num_unique_values\": 5,\n \"samples\": [\n 2023,\n 2020,\n 2022\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"experience_level\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Entry-level\",\n \"Mid-level\",\n \"Executive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"employment_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Contract\",\n \"Freelance\",\n \"Full-time\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"salary\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 61523.413918642145,\n \"min\": 20000.0,\n \"max\": 450000.0,\n \"num_unique_values\": 2006,\n \"samples\": [\n 146115.0,\n 195200.0,\n 331640.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"work_setting\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"In-person\",\n \"Remote\",\n \"Hybrid\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company_size\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"M\",\n \"L\",\n \"S\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"job_category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Data Architecture and Modeling\",\n \"Data Analysis\",\n \"Data Quality and Operations\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":59}]},{"cell_type":"markdown","source":["## Summary statistics of numerical variables"],"metadata":{"id":"2xE9ibaqKori"}},{"cell_type":"code","source":["summary_stats = usd_salary_df.describe()\n","print(summary_stats)"],"metadata":{"id":"ZmsZAoOavLjr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714700919043,"user_tz":300,"elapsed":186,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"ec93c791-c8f9-4d30-ecf5-186fcd2d798a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" work_year salary\n","count 12465.000000 12465.000000\n","mean 2023.141998 156454.438752\n","std 0.644336 61523.413919\n","min 2020.000000 20000.000000\n","25% 2023.000000 112000.000000\n","50% 2023.000000 148350.000000\n","75% 2024.000000 191200.000000\n","max 2024.000000 450000.000000\n"]}]},{"cell_type":"markdown","source":["## Salary Histogram and Box plot"],"metadata":{"id":"F-L1KJ41DRvY"}},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Set the style of seaborn for better visuals\n","sns.set(style=\"whitegrid\")\n","\n","# Create a figure with specified size\n","plt.figure(figsize=(12, 6))\n","\n","# Create a histogram\n","plt.subplot(1, 2, 1) # 1 row, 2 columns, 1st subplot\n","sns.histplot(usd_salary_df['salary'], kde=True, color='blue')\n","plt.title('Histogram of Salaries')\n","plt.xlabel('Salary')\n","plt.ylabel('Frequency')\n","\n","# Create a boxplot\n","plt.subplot(1, 2, 2) # 1 row, 2 columns, 2nd subplot\n","sns.boxplot(x=df['salary'], color='green')\n","plt.title('Boxplot of Salaries')\n","plt.xlabel('Salary')\n","\n","# Display the plots\n","plt.tight_layout()\n","plt.show()"],"metadata":{"id":"3LH3_oR4vLdU","colab":{"base_uri":"https://localhost:8080/","height":440},"executionInfo":{"status":"ok","timestamp":1714700921481,"user_tz":300,"elapsed":1232,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"a17a46c1-ae34-404e-e26a-cdd476ce3345"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Key Insights for Predicting Salary:\n","\n","The histogram indicates that the majority of salaries fall within a specific range, providing a baseline for salary prediction.\n","The box plot allows us to identify outliers, which may need to be addressed in the salary prediction model."],"metadata":{"id":"_NgdU6mRNVo4"}},{"cell_type":"markdown","source":["## Average Salary Distribution by Category"],"metadata":{"id":"soNFLqnuKtqB"}},{"cell_type":"code","source":["# Define a function to create bar plots for salary across different categories\n","def plot_salary_by_category(category, data, rotation=0):\n"," plt.figure(figsize=(10, 6))\n"," sns.barplot(\n"," x=category,\n"," y='salary',\n"," data=data,\n"," estimator=np.mean,\n"," ci=None,\n"," palette='viridis',\n"," order=data.groupby(category)['salary'].mean().sort_values(ascending=False).index\n"," )\n"," plt.title(f'Average Salary by {category.title().replace(\"_\", \" \")}')\n"," plt.xlabel(category.title().replace(\"_\", \" \"))\n"," plt.ylabel('Average Salary')\n"," plt.xticks(rotation=rotation)\n"," plt.tight_layout()\n"," plt.show()\n","\n","# Plotting bar plots for each category\n","categories = ['experience_level', 'employment_type', 'work_year', 'work_setting', 'company_size', 'job_category']\n","\n","for category in categories:\n"," plot_salary_by_category(category, usd_salary_df, rotation=90 if category in ['job_title', 'job_category'] else 0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"1BrTXYB_AS2T","executionInfo":{"status":"ok","timestamp":1714700926764,"user_tz":300,"elapsed":3432,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"4ca07624-53ac-47e5-c833-d15c56dadc40"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n","\n"," sns.barplot(\n",":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n","\n"," sns.barplot(\n",":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n","\n"," sns.barplot(\n",":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n","\n"," sns.barplot(\n",":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n","\n"," sns.barplot(\n",":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n","\n"," sns.barplot(\n",":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Salary Distribution by Category"],"metadata":{"id":"lBLZqD6tDw2q"}},{"cell_type":"code","source":["# Define a function to create boxplots for salary distribution across different categories\n","def plot_salary_distribution_by_category(category, data, rotation=0):\n"," plt.figure(figsize=(14, 8))\n"," sns.boxplot(\n"," x=category,\n"," y='salary',\n"," data=data,\n"," palette='muted'\n"," )\n"," plt.title(f'Salary Distribution by {category.title().replace(\"_\", \" \")}')\n"," plt.xlabel(category.title().replace(\"_\", \" \"))\n"," plt.ylabel('Salary')\n"," plt.xticks(rotation=rotation)\n"," plt.tight_layout()\n"," plt.show()\n","\n","# Plotting boxplots for each category\n","categories = ['job_category', 'work_year', 'experience_level', 'work_setting', 'company_size', 'employment_type']\n","\n","for category in categories:\n"," plot_salary_distribution_by_category(category, usd_salary_df, rotation=90 if category in ['job_category'] else 0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"HR3JXmIrD5BU","executionInfo":{"status":"ok","timestamp":1714700930013,"user_tz":300,"elapsed":3251,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"3734821e-6d5b-4f02-91ff-24e8c7536f09"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.boxplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.boxplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.boxplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.boxplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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1EhhYWFavny5Tp8+rZEjRzrOFRMTowULFmjMmDGaMGGCioqKNHPmTPXq1UuRkZHV8ZIBuAKLxaJ77rlHa9eu1e7du7Vr1y7HWNlvse655x5ZLBajWgQAAAAAAKg9Ae3Vio2NVVFRkRYuXKikpCS1bNlSb7zxhmPGa5nZs2frpZde0gsvvKCSkhL16NFDU6dOddoM6JZbblFycrJefvll/eEPf1CjRo00duxYPfLII07neuyxx2S327V48WKdPHlSbdq0UXJysmNJBeliGLRo0SJNnz5dEyZMkKenp/r27atnn322el8QAJcVHx8vSUpNTZXdbnccN5lMuueeexzjAAAAAAAARjHZL00tUOvs27dP0sWNjgBUzoULF7R06VL98MMPuvnmmxUfH++06R8AAAAAAEBVupZM74abQQsA1yItLU0pKSnKy8uTJO3du1fp6elKSEi47LIqAAAAAAAANaXWbBIGAFUtLS1NiYmJCgkJ0YwZM7RkyRLNmDFDISEhSkxMVFpamtEtAgAAAAAAN0dAC8AllZaWKiUlRZ07d9bkyZMVFhYmb29vhYWFafLkyercubOWLFmi0tJSo1sFAAAAAABujIAWgEvKyMhQXl6ehg4dKrPZ+Z86s9msIUOG6Pjx48rIyDCoQwAAAAAAAAJaAC7q1KlTkqTg4OAKx0NCQpzqAAAAAAAAjEBAC8Al+fv7S5JycnIqHM/OznaqAwAAAAAAMAIBLQCXFBERoYCAAK1evVo2m81pzGazac2aNQoMDFRERIRBHQIAAAAAABDQAnBRHh4eSkhIUHp6umbOnKkDBw6oqKhIBw4c0MyZM5Wenq7hw4fLw8PD6FYBAAAAAIAbM9ntdrvRTeDy9u3bJ0lq3769wZ0AN6a0tDSlpKQoLy/PcSwwMFDDhw9XdHS0gZ0BAAAAAABXdS2ZHjNoAbi8n/8e6udLHgAAAAAAABiFgBaAy0pLS1NiYqICAwNlsVhkMplksVgUGBioxMREpaWlGd0iAMBNlZaWav/+/dq2bZv279+v0tJSo1sCAACAQTyNbgAAqkNpaalSUlJkt9v19ddfO45brVbH/SVLlqhLly6sQwsAqFEVLb8TEBCghIQElt8BAABwQ8ygBeCSMjIynP7HtyLHjx9XRkZGDXUEAMD/Pt0REhKiGTNmaMmSJZoxY4ZCQkL4dAcAAICbYgYtAJf03XffOW63a9dO0dHR8vLyUnFxsdLS0vTVV1856tq2bWtUmwAAN1L26Y7OnTtr8uTJMpsvzpUICwvT5MmTNXPmTD7dAQAA4IaYQQvAJa1cuVKSZDKZ9OOPPyo5OVlvvfWWkpOT9eOPP8pkMjnVAQBQ3co+3TF06FBHOFvGbDZryJAhfLoDAADADTGDFoBLslqtkiS73a7mzZura9euKi4ulpeXl44dO6affvrJqQ4AgOp26tQpSVJwcHCF4yEhIU51AAAAcA8EtABckqenp0pKSiRJu3fv1u7duy9bBwBATfD395ck5eTkKCwsrNx4dna2Ux0AAADcA0scAHBJ99xzT5XWAQBwvSIiIhQQEKDVq1erpKRE+/fv17Zt27R//36VlJRozZo1CgwMVEREhNGtAgAAoAYxdQyAS7rpppuc7tetW1ddu3bVF198ofPnz1+2DgCA6uLh4aGEhATNmjVLCQkJKi4udoyVbWQ5adIkNggDAABwM8ygBeCSMjMzne6fP39eW7dudQpnK6oDAAAAAACoScygBeDSQkJCdObMGZ08edJxrFGjRmrQoIFjrT8AAGpCaWmpUlJSFBUVpYkTJ+rgwYM6deqU/P39FRYWpsTERC1ZskRdunRhFi0AAIAbIaAF4JLKli7Izs5W586dNXjwYNWpU0cXLlzQ7t27lZ6e7lQHAEB1y8jIUF5ensaNGyeLxaK2bds6jQ8ZMkRTp05VRkZGuTEAAAC4LgJaAC4pJiZGS5cuVd26dfX99987AllJatKkierVq6fz588rJibGwC4BAO7k1KlTkqTg4OAKx0NCQpzqAAAA4B4IaAG4JIvFotjYWK1du9ZxOygoSLm5ufrXv/6lc+fOaeDAgbJYLEa3CgBwE/7+/pKknJwctWzZUps2bVJubq6CgoIUExPjWHqnrA4AAADugYAWgMuKj4+XJKWmpio1NdVx3Gw2a+DAgY5xAABqQkREhAICAjRnzhzl5uY6jaWkpCgoKEiBgYGKiIgwqEMAgDsrLS1VRkaGY330iIgI1kQHaggBLQCXFh8fr7i4uHKzlJg5CwCoaR4eHrrlllv05ZdfVjiem5vLBmEAAEOkpaUpJSVFeXl5jmMBAQFKSEhQdHS0gZ0B7oGAFoDLM5vNatmypRo2bCh/f3+ZzWajWwIAuCGr1aqdO3dKkry8vFRcXOwYK7u/c+dOWa1WfpEIAKgxaWlpSkxMVOfOnTVu3DgFBwcrJydHq1evVmJioiZOnEhIC1QzAloALo3fBAMAaosNGzbIbrfrlltu0YwZM7RlyxbHpzv69u2rZ599VtnZ2dqwYYMGDhxodLsAADdQWlqqlJQUde7cWZMnT3ZMZgkLC9PkyZM1c+ZMLVmyhE94ANWMgBaAy7r0N8EDBw50zE7avXs3vwkGANS4jIwMSVLnzp01fvx4p18erl+/Xt27d1d2drYyMjIIaAEANSIjI0N5eXkaN25cuU8ams1mDRkyRFOnTlVGRobatm1rUJeA6yOgBeCSyn4THBoaqu+//97xkVJJatKkiUJDQ/lNMACgRtWtW1eStGbNGkVFRZX7GOn777/vVAcAQHU7deqUJCk4OLjC8ZCQEKc6ANWDhRgBuKSy3wQfOnTI8VHSJUuWaMaMGbrlllt06NAhHT9+3DGbCQCA6nbHHXdIurhZ2IQJExQWFiZvb2+FhYVpwoQJjl8YltUBAFDd/P39JUk5OTkVjmdnZzvVAagezKAF4JJOnDghSerYsaMefPBBTZo0STabTWazWbNmzVJpaal2797tqAMAoLp5el780bu0tFSPP/644uLiFBUVpZ07d2rFihUqLS11qgMAoLpFREQoICBAq1evdlqDVpJsNpvWrFmjwMBARUREGNgl4Pr46Q+ASyooKJAk7d69W7t373Yct9lsmjBhQrk6AACqW35+vuN2QUGBkpKSfrEOAIDq5OHhoYSEBCUmJmrmzJkaMmSIQkJClJ2drTVr1ig9PV0TJ05kWTigmrHEAQCX5Ovr63TfYrHovvvuk8ViuWIdAADVpezjoQ888IACAgKcxgICAnT//fc71QEAUBOio6M1ceJEZWdna+rUqRo+fLimTp2qnJwcNlYGaggzaAG4pAsXLjhu33bbbXrggQcUEhKiyMhI/fOf/9TXX39drg4AgOpU9jHSAwcOaM6cOTpw4IBOnTolf39/hYeHa9asWXyMFABgiOjoaHXp0kUZGRmO96aIiAhmzgI1xGS32+1GN4HL27dvnySpffv2BncC3FiGDRsmm80m6eKspLy8PMdYYGCgjh8/Lkkym81auXKlIT0CANxPWlqaEhMT1blz58t+jJSZSgAAADe+a8n0mEELwCWVhbOSFBwcrHvuuUd16tTRhQsXtHv3bkdAe2kdAADVrexjpCkpKZo6darjeGBgIOEsAACAmyKgBeCSzGazbDabzGazcnJylJ6e7hgLDAx0GgcAoCbxMVIAAABcioAWgEt69dVXNW7cONlsNj399NPau3evcnNzFRQUpMjISE2cONFRBwBATfPw8FDbtm2NbgMAAAC1AAEtAJfUrFkzx+2yMPaX6gAAAAAAAGoan+0F4LImTZp0XeMAAAAAAADVjYAWgEsqLS1VUlKSJKl169YymUySJJPJpNatW0uSFi5cqNLSUsN6BAAAAAAAYIkDAC5p//79KigoUHh4uJ5//nlt2bLFsQZt37599de//lUHDhzQ/v37FRkZaXS7AAAAAADATRHQAnBJX3/9tSSpUaNGGj58uGw2m2Ns6dKl6tq1q6OOgBYAAAAAABiFgBaAS9uxY4f8/PwUFxenqKgo7dy5UytWrNB//vMfo1sDAAAAAAAgoAXgmsLDwyVJHh4eevPNN+Xl5SVJ6tOnj3r27Knhw4ertLTUUQcAAAAAAGAEAloALiknJ0fSxc3CZs2aJS8vL505c0YNGjRQcXGxY3OwnJwcderUychWAQAAAACAGyOgBeCS8vLyHLd37dp1VXUAANQUq9WqTZs2OTawjImJkcViMbotAAAAGICAFoBLCgoKqtI6AACqytKlS5WamlpuA8vY2FjFx8cb2BkAAACMYDa6AQCoDr1793a67+XlpYiICMdatJerAwCgOi1dulRr166Vj4+PRo0apaSkJI0aNUo+Pj5au3atli5danSLAAAAqGEEtABc0qJFi5zuFxcXKyMjQ8XFxVesAwCgulitVqWmpsrPz0/z589Xnz595O/vrz59+mj+/Pny8/NTamqqrFar0a0CAACgBhHQAnBJ27Ztq9I6AACu16ZNm2Sz2RQXFydPT+eVxjw9PTVs2DDZbDZt2rTJoA4BAABgBNagBeDSGjRooPnz52vLli2OjVj69u2rUaNG6ezZs0a3BwBwI7m5uZKkqKioCsfLjpfVAQAAwD0Q0AJwSRaLRVarVWfOnJHJZFJsbKxjrLi42BHOsmM2AKCmlG1MuXPnTvXp06fc+M6dO53qAAAA4B5Y4gCAS7rvvvsctx988EEtW7ZMx44d07Jly/Tggw9WWAcAQHWKiYmR2WzWihUrVFxcrP3792vbtm3av3+/iouLtXLlSpnNZsXExBjdKgAAAGoQM2gBuKTWrVs73f/ggw/0wQcf/GIdAADVxWKxKDY2VmvXrtVDDz0ku93uGDOZTLLb7Ro4cCCf7gAAAHAzzKAF4JIiIiIUEBBwxZrAwEBFRETUUEcAAEhhYWGS5BTOXnq/bBwAAADug4AWgEvy8PBQQkLCFWuGDx8uDw+PGuoIAODuSktLlZKSoqioKC1btkwJCQnq16+fEhIStGzZMkVFRWnJkiUqLS01ulUAAADUIAJaAC7r4MGDki5+bPRSZffLxgEAqAkZGRnKy8vT0KFDVadOHcXGxmrkyJGKjY1VnTp1NGTIEB0/flwZGRlGtwoAAIAaREALwCVZrValpqbKz89PKSkpTrOUUlJS5Ofnp9TUVFmtVqNbBQC4iVOnTkmSgoODKxwPCQlxqgMAAIB7YJMwAC5p06ZNstlsio6O1sSJE5WXl+cYW79+vbp27aotW7Zo06ZNio2NNbBTAIC78Pf3lyTl5ORUuNZsdna2Ux0AAADcAwEtAJeUm5srSdq8ebM6dOigJk2aqLCwUD4+PvLy8tKWLVuc6gAAqG5lG1iuXr1a48eP15YtW5Sbm6ugoCD17dtXa9asYQNLAAAAN0RAC8AlBQQESJIsFov27NlTbtxischqtTrqAACobmUbWM6aNUsPPfSQ01hKSookadKkSWxgCQAA4GZYgxaAS7rlllskybHGbM+ePfX3v/9dPXv2dDpeVgcAQE1gA0sAAAD8HDNoAbikS9ec9fHxUUREhHx9fRUREaFdu3apsLCwXB0AANXp0g0s33zzTX377bc6deqU/P391bp1a/3xj39Uamqq4uLiZLFYjG4XAAAANYSAFoBL+uyzzyRdXOrgxIkTSkpKcoyZzWY1adJEP/30kz777DP16dPHoC4BAO6kbAPLuLg4eXl5qW3btk7jw4YNU1JSEhtYAgAAuBkCWgAu6dy5c5Kkxo0ba/bs2dq8ebNjI5a77rpLf/nLX/TTTz856gAAqG5lG1NGRUVVOF52nA0sAQAA3AsBLQCXdNNNNyknJ0cZGRn6+9//rgsXLqiwsFDff/+99uzZowMHDjjqAACoCUFBQZKknTt3VvjpjZ07dzrVAQAAwD2wSRgAlzRmzBjH7d27d+ubb77RkSNH9M0332j37t0V1gEAUJ1iYmJkNpu1YsUKlZSUOI2VlJRo5cqVMpvNiomJMahDAAAAGIGAFoBL8vb2lpeX1xVrvLy85O3tXUMdAQDcncViUWxsrPLz8zV69Ght2bJFJ0+e1JYtWzR69Gjl5+crNjaWDcIAAADcDEscAHBJRUVFKi4uvmJNcXGxioqKCGkBADUmPj5ekpSamlpuA8uBAwc6xgEAAOA+mEELwCXNnj1bkuTp6ank5GSFh4erUaNGCg8PV3Jysjw9PZ3qAACoKfHx8Vq2bJkSEhLUr18/JSQkaNmyZYSzAAAAbooZtABcUmZmpiSpS5cumjJlin766SdJ0smTJzVlyhRFRUUpLS3NUQcAQE0qW+4AAAAAIKAF4JLK1u/7z3/+U27sp59+cgS2rPMHAAAAAACMxBIHAFzSnXfe6bjt4+OjUaNGKSkpSaNGjZKPj0+FdQAAAAAAADWNGbQAXFLLli0dtwsLC7Vlyxbt2bNHx48fV2FhYYV1AADUFKvVqk2bNik3N1dBQUGKiYnhUx0AAABuioAWgEv697//7XQ/MzOzwvVm//3vfysqKqqm2gIAQEuXLlVqaqpsNpvTsdjYWDYKAwAAcEMEtABc0vnz56u0DgCAqrB06VKtXbtWfn5+iouLU1RUlHbu3KkVK1Zo7dq1kkRICwAA4GYIaAG4pFtvvVVffPGFJKlDhw5q1qyZiouL5eXlpaNHj2rPnj2OOgAAaoLValVqaqr8/Pw0f/58eXpe/FG8T58+6tWrl0aPHq3U1FTFxcWx3AEAAIAbYZMwAC7PZDLJbrdLkux2u0wmk8EdAQDc0aZNm2Sz2RQXF+cIZ8t4enpq2LBhstls2rRpk0EdAgAAwAjMoAXgkr777jvH7d27d2v37t2/WAcAQHXKzc2VpMuufV52vKwOAAAA7oEZtABcUt26dau0DgCA6xUUFCRJ2rlzZ4XjZcfL6gAAAOAeCGgBuKRf//rXjtsVfYy0ojoAAKpTTEyMzGazVqxYoZKSEqexkpISrVy5UmazWTExMQZ1CAAAACMQ0AJwSTk5OY7bdrtdgwYN0pw5czRo0CDHerQ/rwMAoDpZLBbFxsYqPz9fo0eP1pYtW3Ty5Elt2bJFo0ePVn5+vmJjY9kgDAAAwM2wBi0Al3Tw4EHH7dLSUn3wwQf64IMPrlgHAEB1i4+PlySlpqYqKSnJcdxsNmvgwIGOcQAAALgPAloALunChQuSLm64cuDAAZ05c8Yx5uPjo9atWys9Pd1RBwBATYmPj1dcXJw2bdqk3NxcBQUFKSYmhpmzAAAAboqAFoBLCg0N1d69eyvciKWwsFDp6emOOgAAalrZcgcAAAAAa9ACcEmRkZFVWgcAAAAAAFAdCGgBuKTmzZtXaR0AAAAAAEB1IKAF4JKee+65Kq0DAAAAAACoDqxBC8Al5eXlVWkdAABV6cKFC1q6dKl++OEH3XzzzYqPj1edOnWMbgsAAAAGIKAFAAAAatArr7yiL7/80nF/79692rRpk7p06aIpU6YY2BkAAACMwBIHANxCnTp1dMcddzA7CQBgqLJw1tPTUz169FBCQoJ69OghT09Pffnll3rllVeMbhEAAAA1jBm0ANzChQsX9K9//cvoNgAAbuzChQv68ssv5eHhIV9fX23btk3btm2TJDVq1Ej5+fn68ssvdeHCBX6hCAAA4EaYQQsAAADUgKVLl0qSSktL1bJlS82YMUNLlizRjBkz1LJlS5WWljrVAQAAwD0Q0AIAAAA14IcffpAk3XbbbZo8ebLCwsLk7e2tsLAwTZ48WbfddptTHQAAANwDSxwAcElms1k2m+2q6gAAqAllyxY0adJEdrtd+/fv16lTp+Tv76+IiAg1btzYqQ4AAADugYAWgEvq3Lmz0w7ZV6oDAKAmdOnSRf/973+1fft27d27V6dPn3aMNWzYUIWFhY46AAAAuA8CWgAu6WpnxjKDFgBQU4KCgiRdXIP20nBWktP9sjoAAAC4B5IJAC4pIiKiSusAALheYWFhVVoHAAAA10BAC8Al3XXXXVVaBwDA9fr666+rtA4AAACugYAWgEv69NNPq7QOAIDrdel7jsVicRq79D7vTQAAAO6FgBaAS0pOTq7SOgAArtf3338vSapbt66sVqvTmNVqVd26dZ3qAAAA4B7YJAyASzObzVq2bJk2bdqk3NxcBQUFKSYmRg8++KDsdrvR7QEA3IjJZJIknT9/Xn5+furZs6eCgoKUm5urrVu3Kj8/36kOAAAA7oGAFoBLs9ls8vDwUGxsrNMxwlkAQE0LDg5WTk6OJKmwsFDr1q1zjJnNZqc6AAAAuA+WOADgkjw8PBy3n3/+eR04cEBFRUU6cOCAnn/++QrrAACoTvXq1XPcttlsTmOX3r+0DgAAAK6PGbQAXNLrr7+uJ598UpJ08OBBTZ069bJ1AADUhEtnyVZFHQAAAFwDP/0BcElBQUG/uIafyWRSUFBQDXUEAHB3V7u2LGvQAgAAuBcCWgAu65133rns/+SaTCa98847NdwRAMCd3XrrrVVaBwAAANfAEgcAXNo777yj3NxcjR8/XlarVRaLRa+99hozZwEANa5x48ZO9yMjI9W2bVvt379fe/fuvWwdAAAAXBsBLVCL5ebm6uzZs0a34RKmT5/uuH327FllZmYa2I3rqF+/PmE3AFwlq9Uq6eKnOOx2u/bu3esUzJYdL6sDAACAeyCgBWqpgoICjRkzRna73ehWgMsym81auHChfH19jW4FAGq97du3S5Lsdrs6duwoLy8vnTt3TvXq1VNxcbF2797tqOvcubOBnQIAAKAmEdACtZSvr6/mzp3LDNoqcvToUc2ZM0djx45Vs2bNjG7HZdSvX59wFgCu0vnz5yVJ0dHR+uKLL5x+CWsymdS1a1d98cUXjjoAAAC4BwJaoBbjo+NVr1mzZgoNDTW6DQCAGwoPD9cXX3yhtLS0cmN2u11ffPGFow4AAADuw2x0AwAAAIA7iImJcdz28PDQ4MGD9frrr2vw4MHy8PCosA4AAACujxm0AAAAQA345ptvHLdLS0v1/vvv6/3336+wrmPHjjXXGAAAAAzFDFoAAACgBvzrX/+SJDVu3LjC8bLjZXUAAABwD8ygBQAAAGpA2eZfJ06ckI+Pj9q1a6e6devq/Pnz+uqrr3TixAmnOgAAALgHAloAAACgBrRu3VpffPGFzGaz5s+fLy8vL8dYcXGx4uPjZbPZ1Lp1awO7BAAAQE0joAUAAABqkM1m09/+9jcdPXpURUVF8vb2VrNmzWSz2YxuDQAAAAYgoAUAAABqQNkSBpK0f/9+x+0LFy7o9OnTFdYBAADA9bFJGAAAAFADgoKCqrQOAAAAroEZtAAAAEAN6Natm1JSUiRJ8+bN0xdffKHc3FwFBQWpa9eueuKJJxx1AAAAcB/MoAUAAABqwPPPP++4/cwzz+jEiRNq1qyZTpw4oWeeeabCOgAAALg+ZtACAAAANaCgoECSFBoaqszMTKWmpjqNt2zZUllZWY46AAAAuAdm0AIAAAA1wNfXV5KUmZmpyMhIBQYGqn79+goMDFRkZKSysrKc6gAAAOAeCGgBAACAGjBjxgzH7b179+r48eM6e/asjh8/rr1791ZYBwAAANdHQAsAAADUgGPHjlVpHQAAAFwDAS0AAABQA3744YcqrQMAAIBrIKAFAAAAasCnn34qSTKbzeXWmfX19ZXZbHaqAwAAgHvwNLoBAAAAwB3k5uZKkmw2mwoKCpzGLr1fVgcAAAD3wAxaAAAAoAZYLBbHbQ8PDw0ePFhz5szR4MGD5eHhUWEdAAAAXF+tCmg///xzPfTQQ7r99tvVrl07/fa3v9VLL72kwsJCp7pPPvlEAwcOVPv27RUTE6P33nuv3LmKi4v1yiuvqHv37urYsaMefvhhZWZmlqs7dOiQHn74YXXs2FHdu3fXzJkzVVxcXK5u1apViomJUfv27TVw4MAKP3pWWFioZ599Vl27dlWnTp00duxYHT9+/DpeEQAAALiKdu3aOW43aNBA77//vsaOHav3339fPj4+FdYBAADA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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.boxplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":[":4: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.boxplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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**ENCODING**"],"metadata":{"id":"fFvjwE-QFdYL"}},{"cell_type":"markdown","source":["## ORDINAL: Work year, Experience Level, Employment type, Work Setting, Company size"],"metadata":{"id":"uR4UiubctFeE"}},{"cell_type":"code","source":["from sklearn.preprocessing import OrdinalEncoder\n","\n","# Define mappings\n","experience_levels = ['Entry-level', 'Mid-level', 'Senior', 'Executive']\n","employment_types = ['Freelance', 'Part-time', 'Full-time', 'Contract']\n","work_settings = ['Remote', 'Hybrid', 'In-person']\n","company_sizes = ['S', 'M', 'L']\n","\n","# Create the encoder instance\n","ordinal_encoder = OrdinalEncoder(categories=[sorted(usd_salary_df['work_year'].unique()), experience_levels, employment_types, work_settings, company_sizes])\n","\n","# Select the columns to encode\n","ordinal_cols = ['work_year', 'experience_level', 'employment_type', 'work_setting', 'company_size']\n","usd_salary_df[ordinal_cols] = ordinal_encoder.fit_transform(usd_salary_df[ordinal_cols])\n","\n","# Check the transformation\n","print(usd_salary_df[ordinal_cols].head())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JcI2zrMUzhk1","executionInfo":{"status":"ok","timestamp":1714691870480,"user_tz":300,"elapsed":210,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"ca5a5060-2a6f-4815-dbaf-a70ef9e4284e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" work_year experience_level employment_type work_setting company_size\n","1 4.0 3.0 2.0 2.0 1.0\n","2 4.0 3.0 2.0 2.0 1.0\n","7 4.0 0.0 2.0 0.0 1.0\n","8 4.0 0.0 2.0 0.0 1.0\n","9 4.0 2.0 2.0 2.0 1.0\n"]}]},{"cell_type":"markdown","source":["## ONE-HOT ENCODING: Job Category"],"metadata":{"id":"GwnBRI9q97Mj"}},{"cell_type":"code","source":["# Apply one-hot encoding to 'job_category'\n","job_category_one_hot = pd.get_dummies(usd_salary_df['job_category'], prefix='category')\n","\n","# Concatenate the new columns to the original DataFrame\n","usd_salary_df = pd.concat([usd_salary_df, job_category_one_hot], axis=1)\n","\n","# drop the original 'job_category' column\n","usd_salary_df.drop('job_category', axis=1, inplace=True)\n","\n","# Check the DataFrame with new one-hot encoded columns\n","print(usd_salary_df.head())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fx6gRaSp-TGM","executionInfo":{"status":"ok","timestamp":1714691870481,"user_tz":300,"elapsed":9,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"eb2eb213-ac04-479b-ceaf-2c7025d7e155"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" work_year experience_level employment_type salary work_setting \\\n","1 4.0 3.0 2.0 230000.0 2.0 \n","2 4.0 3.0 2.0 176900.0 2.0 \n","7 4.0 0.0 2.0 50000.0 0.0 \n","8 4.0 0.0 2.0 40000.0 0.0 \n","9 4.0 2.0 2.0 276000.0 2.0 \n","\n"," company_size category_BI and Visualization category_Cloud and Database \\\n","1 1.0 True False \n","2 1.0 True False \n","7 1.0 False False \n","8 1.0 False False \n","9 1.0 False False \n","\n"," category_Data Analysis category_Data Architecture and Modeling \\\n","1 False False \n","2 False False \n","7 True False \n","8 True False \n","9 False False \n","\n"," category_Data Engineering category_Data Management and Strategy \\\n","1 False False \n","2 False False \n","7 False False \n","8 False False \n","9 True False \n","\n"," category_Data Quality and Operations category_Data Science and Research \\\n","1 False False \n","2 False False \n","7 False False \n","8 False False \n","9 False False \n","\n"," category_Leadership and Management category_Machine Learning and AI \n","1 False False \n","2 False False \n","7 False False \n","8 False False \n","9 False False \n"]}]},{"cell_type":"markdown","source":["## Correlation Analysis"],"metadata":{"id":"3TnvtrIwa5kY"}},{"cell_type":"code","source":["import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","# Calculate the correlation Matrix\n","corr_matrix = usd_salary_df.corr()\n","\n","# Print the correlation with target column to see the numerical values\n","print(corr_matrix['salary'].sort_values(ascending=False))\n","\n","# Set up the matplotlib figure\n","plt.figure(figsize=(10,8))\n","\n","# Draw the heatmap\n","sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', cbar=True)\n","\n","# Add labels and title for clarity\n","plt.title('Correlation Matrix Heatmap')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"wKgWHRSBbIKN","executionInfo":{"status":"ok","timestamp":1714691872239,"user_tz":300,"elapsed":1765,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"b5f6310f-73d3-4775-83f3-1186fba4a385"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["salary 1.000000\n","experience_level 0.336828\n","category_Machine Learning and AI 0.239611\n","category_Data Science and Research 0.159244\n","work_setting 0.047011\n","company_size 0.042575\n","employment_type 0.028643\n","work_year 0.019330\n","category_Data Architecture and Modeling 0.012728\n","category_Cloud and Database -0.006587\n","category_Data Engineering -0.026469\n","category_Leadership and Management -0.042647\n","category_Data Quality and Operations -0.064502\n","category_BI and Visualization -0.083977\n","category_Data Management and Strategy -0.087297\n","category_Data Analysis -0.309971\n","Name: salary, dtype: float64\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# **BUILDING MODELS**"],"metadata":{"id":"q5JqpntQhnqF"}},{"cell_type":"markdown","source":["# Ridge Regression"],"metadata":{"id":"ovF_GCLE-17b"}},{"cell_type":"markdown","source":["## 1st Iteration:\n","Steps:\n","Prepare Data: Split the data into training and testing sets.\n","\n","Train Model: Ridge regression model.\n","\n","Evaluate Model: Assess its performance using metrics"],"metadata":{"id":"ngLWr8IN_iHV"}},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import Ridge\n","from sklearn.metrics import mean_squared_error, r2_score\n","\n","# Split data into features and target\n","X = usd_salary_df.drop('salary', axis=1)\n","y = usd_salary_df['salary']\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Initialize and train the Ridge regression model\n","ridge_model = Ridge(alpha=1.0)\n","ridge_model.fit(X_train, y_train)\n","\n","# Predict on the test set\n","y_pred = ridge_model.predict(X_test)\n","\n","# Evaluate the model\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(\"RMSE:\", rmse)\n","print(\"R^2 Score:\", r2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CEitdAUt_L6c","executionInfo":{"status":"ok","timestamp":1714691872420,"user_tz":300,"elapsed":212,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"77184d52-5737-4567-cda2-d11c53d3505c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE: 53059.83424031529\n","R^2 Score: 0.2498462266201683\n"]}]},{"cell_type":"markdown","source":["## 2nd Iteration: Handling Outliers in Salary\n","Steps:\n","Modify Data: Replace outliers in the salary column.\n","\n","Train and Evaluate Model: Using the same steps as the first iteration."],"metadata":{"id":"ILnGyF4sA9oa"}},{"cell_type":"code","source":["# Replace outliers in the salary column\n","usd_salary_df['salary'] = usd_salary_df['salary'].apply(lambda x: min(x, 310000))\n","\n","# Re-split the data (this step is necessary as the target has changed)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Re-train the Ridge model\n","ridge_model.fit(X_train, y_train)\n","\n","# Re-predict and re-evaluate\n","y_pred = ridge_model.predict(X_test)\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(\"RMSE with Outlier Adjustment:\", rmse)\n","print(\"R^2 Score with Outlier Adjustment:\", r2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zScSW3DnA6lG","executionInfo":{"status":"ok","timestamp":1714691872420,"user_tz":300,"elapsed":10,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"8097c7ab-1d3f-4fc5-8980-7e788598e885"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE with Outlier Adjustment: 53059.83424031529\n","R^2 Score with Outlier Adjustment: 0.2498462266201683\n"]}]},{"cell_type":"markdown","source":["## 3rd Iteration: Feature Importance and Model Simplification\n","\n","Steps:\n","Identify Feature Importance: Using model coefficients.\n","\n","Select Top 3 Features: Based on the largest coefficients.\n","\n","Rebuild and Train Model: Using only top features.\n","\n","Evaluate Model Performance."],"metadata":{"id":"sGmnRmc6Cb-6"}},{"cell_type":"code","source":["# Plotting feature importance for Ridge regression\n","feature_importance_ridge = pd.Series(index=X_train.columns, data=ridge_model.coef_).sort_values(ascending=False)\n","feature_importance_ridge.plot(kind='bar', figsize=(10, 6))\n","plt.title('Feature Importance in Ridge Regression')\n","plt.ylabel('Coefficient Magnitude')\n","plt.show()\n","\n","\n","# Filter to get only features with a positive impact\n","positive_feature_importance_ridge = feature_importance_ridge[feature_importance_ridge > 0]\n","\n","# Get the top 3 features with the highest positive impact\n","top_3_positive_features_ridge = positive_feature_importance_ridge.nlargest(3).index\n","print(\"Top 3 features with positive impact from Ridge Regression:\", top_3_positive_features_ridge)\n","\n","\n","# Split the dataset with only the top 3 features\n","X_top3 = X[top_3_positive_features_ridge]\n","\n","# Split the data into training and testing sets with only the top 3 features\n","X_train_top3, X_test_top3, y_train, y_test = train_test_split(X_top3, y, test_size=0.2, random_state=42)\n","\n","# Initialize the Ridge regression model\n","ridge_model_top3 = Ridge(alpha=1.0)\n","\n","# Train the model using the top 3 positive features\n","ridge_model_top3.fit(X_train_top3, y_train)\n","\n","# Predict on the test set using the top 3 positive features\n","y_pred_top3 = ridge_model_top3.predict(X_test_top3)\n","\n","# Evaluate the model\n","rmse_top3 = np.sqrt(mean_squared_error(y_test, y_pred_top3))\n","r2_top3 = r2_score(y_test, y_pred_top3)\n","\n","print(\"RMSE with Top 3 Positive Features:\", rmse_top3)\n","print(\"R^2 Score with Top 3 Positive Features:\", r2_top3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":909},"id":"8Td5GvOGC10P","executionInfo":{"status":"ok","timestamp":1714691873691,"user_tz":300,"elapsed":766,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"a2697508-3a4f-4a1f-e240-77d086fa8750"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Top 3 features with positive impact from Ridge Regression: Index(['category_Machine Learning and AI',\n"," 'category_Data Science and Research', 'experience_level'],\n"," dtype='object')\n","RMSE with Top 3 Positive Features: 54160.19601462955\n","R^2 Score with Top 3 Positive Features: 0.2184100340321904\n"]}]},{"cell_type":"markdown","source":["# LASSO REGRESSION"],"metadata":{"id":"NFn5KxA_DzQA"}},{"cell_type":"markdown","source":["## 1st Iteration:\n","Steps:\n","\n","Prepare Data: Split the data into training and testing sets.\n","\n","Train Model: Use a LASSO regression model.\n","\n","Evaluate Model: Assess its performance using evaluation metrics"],"metadata":{"id":"MkwvtsrCECtV"}},{"cell_type":"code","source":["from sklearn.linear_model import Lasso\n","from sklearn.metrics import mean_squared_error, r2_score\n","\n","# Split data into features and target\n","X = usd_salary_df.drop('salary', axis=1)\n","y = usd_salary_df['salary']\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Initialize and train the LASSO regression model\n","lasso_model = Lasso(alpha=10, max_iter=15000)\n","lasso_model.fit(X_train, y_train)\n","\n","# Predict on the test set\n","y_pred = lasso_model.predict(X_test)\n","\n","# Evaluate the model\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(\"RMSE:\", rmse)\n","print(\"R^2 Score:\", r2)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tv02t9AeEe_C","executionInfo":{"status":"ok","timestamp":1714691874066,"user_tz":300,"elapsed":166,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"6139de60-15b7-4f5b-9f08-d10b57d53eb1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE: 50540.560299716315\n","R^2 Score: 0.26220267260895525\n"]}]},{"cell_type":"markdown","source":["## 2nd Iteration: Handling Outliers in Salary\n","Steps:\n","\n","Modify Data: Replace outliers in the salary column.\n","\n","Train and Evaluate Model: Using the same steps as the first iteration."],"metadata":{"id":"AnaOMl1oFdlz"}},{"cell_type":"code","source":["# Replace outliers in the salary column\n","usd_salary_df['salary'] = usd_salary_df['salary'].apply(lambda x: min(x, 310000))\n","\n","# Re-split the data (this step is necessary as the target has changed)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Re-train the LASSO model\n","lasso_model.fit(X_train, y_train)\n","\n","# Re-predict and re-evaluate\n","y_pred = lasso_model.predict(X_test)\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(\"RMSE with Outlier Adjustment:\", rmse)\n","print(\"R^2 Score with Outlier Adjustment:\", r2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"obMbd7XNGCIu","executionInfo":{"status":"ok","timestamp":1714691874259,"user_tz":300,"elapsed":9,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"4502c4fb-a377-46e0-a4d4-fdc3bf341975"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE with Outlier Adjustment: 50540.560299716315\n","R^2 Score with Outlier Adjustment: 0.26220267260895525\n"]}]},{"cell_type":"markdown","source":["## 3rd Iteration: Feature Importance and Model Simplification\n","Steps:\n","\n","Identify Feature Importance: Using model coefficients from the LASSO model.\n","\n","Select Top 3 Features: Based on the largest positive coefficients.\n","\n","Rebuild and Train Model: Using only top features.\n","\n","Evaluate Model Performance."],"metadata":{"id":"cL_XRWGOQzWR"}},{"cell_type":"code","source":["\n","# Get feature importances (coefficients) from the LASSO model\n","feature_importance_df = pd.DataFrame({\n"," 'Feature': X_train.columns,\n"," 'Coefficient': lasso_model.coef_\n","})\n","\n","# Sorting the DataFrame based on coefficient values for better visualization\n","sorted_feature_importance_df = feature_importance_df.sort_values('Coefficient', ascending=False)\n","\n","# Creating the bar chart for all coefficients\n","plt.figure(figsize=(10, 6))\n","plt.barh(sorted_feature_importance_df['Feature'], sorted_feature_importance_df['Coefficient'], color='dodgerblue')\n","plt.xlabel('Coefficient Value')\n","plt.ylabel('Features')\n","plt.title('Feature Importance in LASSO Regression')\n","plt.show()\n","\n","# Filter to get only features with a positive impact\n","positive_feature_importance = feature_importance_df[feature_importance_df['Coefficient'] > 0]\n","top_3_positive_features = positive_feature_importance.nlargest(3, 'Coefficient')['Feature']\n","print(\"Top 3 features with positive impact from LASSO Regression:\", top_3_positive_features)\n","\n","# Train a new model using only the top 3 features\n","X_train_top3 = X_train[top_3_positive_features]\n","X_test_top3 = X_test[top_3_positive_features]\n","\n","lasso_model_top3 = Lasso(alpha=0.01, max_iter=10000)\n","lasso_model_top3.fit(X_train_top3, y_train)\n","\n","# Predict on the test set using the top 3 positive features\n","y_pred_top3 = lasso_model_top3.predict(X_test_top3)\n","\n","# Evaluate the model\n","rmse_top3 = np.sqrt(mean_squared_error(y_test, y_pred_top3))\n","r2_top3 = r2_score(y_test, y_pred_top3)\n","\n","print(\"RMSE with Top 3 Features:\", rmse_top3)\n","print(\"R² Score with Top 3 Features:\", r2_top3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":534},"id":"tZAf-J7WRN9Q","executionInfo":{"status":"ok","timestamp":1714691875698,"user_tz":300,"elapsed":811,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"398ab9cb-1ca9-40db-f888-2e183c634de7"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Top 3 features with positive impact from LASSO Regression: 14 category_Machine Learning and AI\n","1 experience_level\n","12 category_Data Science and Research\n","Name: Feature, dtype: object\n","RMSE with Top 3 Features: 51675.35321658346\n","R² Score with Top 3 Features: 0.22869902409319398\n"]}]},{"cell_type":"markdown","source":["# RANDOM FOREST REGRESSION"],"metadata":{"id":"ENOBf9U7Sekj"}},{"cell_type":"markdown","source":["## 1st Iteration:\n","Steps:\n","\n","Prepare Data: Split the data into training and testing sets.\n","\n","Train Model: Use a Random Forest regression model.\n","\n","Evaluate Model: Assess its performance using evaluation metrics"],"metadata":{"id":"cQvKKkOJSp_O"}},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestRegressor\n","from sklearn.metrics import mean_squared_error, r2_score\n","\n","# Split data into features and target\n","X = usd_salary_df.drop('salary', axis=1)\n","y = usd_salary_df['salary']\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Initialize and train the Random Forest regression model\n","random_forest_model = RandomForestRegressor(n_estimators=1000, random_state=42)\n","random_forest_model.fit(X_train, y_train)\n","\n","# Predict on the test set\n","y_pred = random_forest_model.predict(X_test)\n","\n","# Evaluate the model\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(\"RMSE:\", rmse)\n","print(\"R^2 Score:\", r2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gIXGyeocTAfi","executionInfo":{"status":"ok","timestamp":1714691883896,"user_tz":300,"elapsed":7753,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"1ef526ac-7b28-4dab-8866-f0bf49d0b482"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE: 50654.62167388542\n","R^2 Score: 0.2588687508126324\n"]}]},{"cell_type":"markdown","source":["## 2nd Iteration: Handling Outliers in Salary\n","Steps:\n","\n","Modify Data: Replace outliers in the salary column.\n","\n","Train and Evaluate Model: Using the same steps as the first iteration."],"metadata":{"id":"qFbP5APOUhzI"}},{"cell_type":"code","source":["# Replace outliers in the salary column\n","usd_salary_df['salary'] = usd_salary_df['salary'].apply(lambda x: min(x, 310000))\n","\n","# Re-split the data (this step is necessary as the target has changed)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Re-train the Random Forest model\n","random_forest_model.fit(X_train, y_train)\n","\n","# Re-predict and re-evaluate\n","y_pred = random_forest_model.predict(X_test)\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(\"RMSE with Outlier Adjustment:\", rmse)\n","print(\"R^2 Score with Outlier Adjustment:\", r2)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ULOlZWtwVAfi","executionInfo":{"status":"ok","timestamp":1714691890697,"user_tz":300,"elapsed":6821,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"31fcbc73-4ff7-47c5-fc49-d0b1f7749caa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE with Outlier Adjustment: 50654.62167388542\n","R^2 Score with Outlier Adjustment: 0.2588687508126324\n"]}]},{"cell_type":"markdown","source":["## 3rd Iteration: Feature Importance and Model Simplification\n","Steps:\n","\n","Identify Feature Importance: Using model feature importances.\n","\n","Select Top 3 Features: Based on the highest importances.\n","\n","Rebuild and Train Model: Using only top features.\n","\n","Evaluate Model Performance."],"metadata":{"id":"ZOULVBWeVbom"}},{"cell_type":"code","source":["# Display feature importance from the Random Forest model\n","feature_importances = pd.Series(random_forest_model.feature_importances_, index=X_train.columns).sort_values(ascending=False)\n","\n","# Plot the feature importances\n","plt.figure(figsize=(10, 6))\n","feature_importances.plot(kind='bar')\n","plt.title('Feature Importance in Random Forest Regression')\n","plt.ylabel('Importance Score')\n","plt.xlabel('Features')\n","plt.show()\n","\n","# Select the top 3 features\n","top_3_features = feature_importances.nlargest(3).index\n","print(\"Top 3 features:\", top_3_features)\n","\n","# Train a new model with the top 3 features\n","X_train_top3 = X_train[top_3_features]\n","X_test_top3 = X_test[top_3_features]\n","\n","random_forest_top3 = RandomForestRegressor(n_estimators=100, random_state=42)\n","random_forest_top3.fit(X_train_top3, y_train)\n","\n","y_pred_top3 = random_forest_top3.predict(X_test_top3)\n","rmse_top3 = np.sqrt(mean_squared_error(y_test, y_pred_top3))\n","r2_top3 = r2_score(y_test, y_pred_top3)\n","\n","print(\"RMSE with Top 3 Features:\", rmse_top3)\n","print(\"R^2 Score with Top 3 Features:\", r2_top3)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":957},"id":"oQrPwrR8VtGe","executionInfo":{"status":"ok","timestamp":1714691891978,"user_tz":300,"elapsed":1300,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"973ab5e1-69c8-44ba-9e78-a8c31b376444"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Top 3 features: Index(['experience_level', 'category_Data Analysis',\n"," 'category_Machine Learning and AI'],\n"," dtype='object')\n","RMSE with Top 3 Features: 51472.08975251387\n","R^2 Score with Top 3 Features: 0.2347548692505219\n"]}]},{"cell_type":"markdown","source":["# Gradient BOOSTING REGRESSION MODEL"],"metadata":{"id":"j7jMRrCgWpBT"}},{"cell_type":"markdown","source":["## 1st Iteration:\n","Steps:\n","\n","Prepare Data: Split the data into training and testing sets, using salary as the target variable.\n","\n","Train Model: Use a GBM regression model.\n","\n","Evaluate Model: Assess its performance using RMSE and R²."],"metadata":{"id":"ERmRQk7VXBFo"}},{"cell_type":"code","source":["from sklearn.ensemble import GradientBoostingRegressor\n","from sklearn.metrics import mean_squared_error, r2_score\n","\n","# Split data into features and target\n","X = usd_salary_df.drop('salary', axis=1)\n","y = usd_salary_df['salary']\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Initialize and train the GBM regression model\n","gbm_model = GradientBoostingRegressor(random_state=42)\n","gbm_model.fit(X_train, y_train)\n","\n","# Predict on the test set\n","y_pred = gbm_model.predict(X_test)\n","\n","# Evaluate the model\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(\"Root Mean Squared Error:\", rmse)\n","print(\"R² Score:\", r2)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dU1c7cBsXPb8","executionInfo":{"status":"ok","timestamp":1714691892559,"user_tz":300,"elapsed":583,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"994c8d91-4970-46d5-d6e8-826f67ad9549"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Root Mean Squared Error: 50341.34819642685\n","R² Score: 0.2680074555352997\n"]}]},{"cell_type":"markdown","source":["## 2nd Iteration: Handling Outliers in Salary\n","Steps:\n","\n","Modify Data: Replace outliers in the salary column.\n","\n","Train and Evaluate Model: Using the same steps as the first iteration."],"metadata":{"id":"jYEuGWd9YM6U"}},{"cell_type":"code","source":["# Replace outliers in the salary column\n","usd_salary_df.loc[usd_salary_df['salary'] > 310000, 'salary'] = 310000\n","y = usd_salary_df['salary']\n","\n","# Re-split the data (since the target has changed)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Re-train the GBM model\n","gbm_model.fit(X_train, y_train)\n","\n","# Re-predict and re-evaluate\n","y_pred = gbm_model.predict(X_test)\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(\"RMSE with Outlier Adjustment:\", rmse)\n","print(\"R² Score with Outlier Adjustment:\", r2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rRIBY1DPYbfa","executionInfo":{"status":"ok","timestamp":1714691893010,"user_tz":300,"elapsed":457,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"1c656b02-ed38-4337-f0a5-aef7f5374085"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE with Outlier Adjustment: 50341.34819642685\n","R² Score with Outlier Adjustment: 0.2680074555352997\n"]}]},{"cell_type":"markdown","source":["## 3rd Iteration: Feature Importance and Model Simplification\n","Steps:\n","\n","Identify Feature Importance: Using model feature importances.\n","\n","Select Top 3 Features: Based on the highest importances.\n","\n","Rebuild and Train Model: Using only top features.\n","\n","Evaluate Model Performance."],"metadata":{"id":"iLZY8X02YuqH"}},{"cell_type":"code","source":["# Display feature importance from the GBM model\n","feature_importances = pd.Series(gbm_model.feature_importances_, index=X_train.columns)\n","\n","# Plot the feature importances for visual inspection\n","import matplotlib.pyplot as plt\n","plt.figure(figsize=(12, 8))\n","feature_importances.sort_values(ascending=True).plot(kind='barh')\n","plt.title('Feature Importance in GBM Regression')\n","plt.xlabel('Importance Score')\n","plt.ylabel('Features')\n","plt.show()\n","\n","# Select top 3 features based on importance\n","top_3_features = feature_importances.nlargest(3).index\n","print(\"Top 3 features:\", top_3_features)\n","\n","# Train a new model with the top 3 features\n","X_train_top3 = X_train[top_3_features]\n","X_test_top3 = X_test[top_3_features]\n","\n","gbm_model_top3 = GradientBoostingRegressor(random_state=42)\n","gbm_model_top3.fit(X_train_top3, y_train)\n","\n","# Predict and evaluate with top 3 features\n","y_pred_top3 = gbm_model_top3.predict(X_test_top3)\n","rmse_top3 = np.sqrt(mean_squared_error(y_test, y_pred_top3))\n","r2_top3 = r2_score(y_test, y_pred_top3)\n","\n","print(\"RMSE with Top 3 Features:\", rmse_top3)\n","print(\"R² Score with Top 3 Features:\", r2_top3)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"O2LzXXRLZB7z","executionInfo":{"status":"ok","timestamp":1714691893837,"user_tz":300,"elapsed":830,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"0b4c9ebb-d1e7-4f17-a199-cf457e6c7c77"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Top 3 features: Index(['experience_level', 'category_Data Analysis',\n"," 'category_Machine Learning and AI'],\n"," dtype='object')\n","RMSE with Top 3 Features: 51468.58712375065\n","R² Score with Top 3 Features: 0.23485901417334965\n"]}]},{"cell_type":"markdown","source":["# **HYPERPARAMETER TUNING**"],"metadata":{"id":"TJO5bPN9sVr1"}},{"cell_type":"markdown","source":["## Hyper-tuning the GBM Regression Model\n","\n","This the best performing modelcompared to the others used in this project"],"metadata":{"id":"CJtM_Doa23Ky"}},{"cell_type":"code","source":["from sklearn.model_selection import GridSearchCV\n","from sklearn.ensemble import GradientBoostingRegressor\n","from sklearn.metrics import mean_squared_error, r2_score\n","from sklearn.model_selection import train_test_split\n","import numpy as np\n","\n","# Define the parameter grid to search over\n","param_grid = {\n"," 'n_estimators': [100, 200, 300],\n"," 'max_depth': [3, 4, 5],\n"," 'learning_rate': [0.01, 0.1, 0.2],\n"," 'subsample': [0.8, 0.9, 1.0],\n"," 'min_samples_split': [2, 4],\n"," 'min_samples_leaf': [1, 2],\n"," 'max_features': [None, 'sqrt', 'log2']\n","}\n","\n","# Initialize the GBM regressor\n","gbm = GradientBoostingRegressor(random_state=42)\n","\n","# Setup the grid search\n","grid_search = GridSearchCV(estimator=gbm, param_grid=param_grid, scoring='neg_mean_squared_error', cv=3, verbose=2, n_jobs=-1)\n","\n","# Split data into features and target\n","X = usd_salary_df.drop('salary', axis=1)\n","y = usd_salary_df['salary']\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Fit the grid search to the data\n","grid_search.fit(X_train, y_train)\n","\n","# Best estimator found by grid search\n","best_gbm = grid_search.best_estimator_\n","\n","# Predict on the test set using the best estimator\n","y_pred = best_gbm.predict(X_test)\n","\n","# Calculate RMSE and R² Score\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(f\"Optimized RMSE: {rmse}\")\n","print(f\"Optimized R² Score: {r2}\")\n","\n","# print out the best parameters found\n","print(f\"Best hyperparameters: {grid_search.best_params_}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sJrnvZlZ3YuJ","executionInfo":{"status":"ok","timestamp":1714484749035,"user_tz":300,"elapsed":1178139,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"834667da-2f11-442b-bf18-5596ea76d8a0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 3 folds for each of 972 candidates, totalling 2916 fits\n","Optimized RMSE: 52888.07754321244\n","Optimized R² Score: 0.2546949186646855\n","Best hyperparameters: {'learning_rate': 0.1, 'max_depth': 3, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100, 'subsample': 0.9}\n"]}]},{"cell_type":"markdown","source":["## Hyper-tuning the Lasso Regression Model"],"metadata":{"id":"nxVKmRnLAc8g"}},{"cell_type":"code","source":["from sklearn.model_selection import GridSearchCV\n","from sklearn.linear_model import Lasso\n","from sklearn.metrics import mean_squared_error, r2_score\n","from sklearn.model_selection import train_test_split\n","import numpy as np\n","\n","# Split data into features and target\n","X = usd_salary_df.drop('salary', axis=1)\n","y = usd_salary_df['salary']\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Initialize the LASSO regression model\n","lasso = Lasso(max_iter=10000, random_state=42)\n","\n","# Define the parameter grid\n","param_grid = {\n"," 'alpha': np.logspace(-4, 1, 50) # Generates 50 values between 10^-4 to 10^1\n","}\n","\n","# Setup the grid search\n","grid_search = GridSearchCV(estimator=lasso, param_grid=param_grid, scoring='neg_mean_squared_error', cv=5, verbose=2)\n","\n","# Fit the grid search to the data\n","grid_search.fit(X_train, y_train)\n","\n","# Best estimator found by grid search\n","best_lasso = grid_search.best_estimator_\n","\n","# Predict on the test set\n","y_pred = best_lasso.predict(X_test)\n","\n","# Evaluate the model\n","rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n","r2 = r2_score(y_test, y_pred)\n","\n","print(f\"Optimized RMSE: {rmse}\")\n","print(f\"Optimized R² Score: {r2}\")\n","print(f\"Best alpha: {best_lasso.alpha}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IiIoh_PbAqHa","executionInfo":{"status":"ok","timestamp":1714359690871,"user_tz":-60,"elapsed":393692,"user":{"displayName":"Serge Nane","userId":"05651198543281429008"}},"outputId":"dc3ebbc6-cf50-4635-c4a6-c71f404ce09d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................................alpha=0.0001; total time= 2.2s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................................alpha=0.0001; total time= 3.6s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................................alpha=0.0001; total time= 2.1s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................................alpha=0.0001; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.657e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................................alpha=0.0001; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00012648552168552957; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00012648552168552957; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00012648552168552957; total time= 2.1s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00012648552168552957; total time= 3.4s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.657e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00012648552168552957; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00015998587196060574; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00015998587196060574; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00015998587196060574; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00015998587196060574; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.657e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00015998587196060574; total time= 2.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00020235896477251576; total time= 2.9s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00020235896477251576; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00020235896477251576; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00020235896477251576; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.657e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00020235896477251576; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0002559547922699536; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0002559547922699536; total time= 3.2s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0002559547922699536; total time= 2.3s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0002559547922699536; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.657e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0002559547922699536; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00032374575428176434; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00032374575428176434; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00032374575428176434; total time= 2.2s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00032374575428176434; total time= 3.5s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.657e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00032374575428176434; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00040949150623804275; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00040949150623804275; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00040949150623804275; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00040949150623804275; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.657e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .......................alpha=0.00040949150623804275; total time= 2.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0005179474679231213; total time= 2.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0005179474679231213; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0005179474679231213; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0005179474679231213; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.657e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0005179474679231213; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0006551285568595509; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0006551285568595509; total time= 3.6s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0006551285568595509; total time= 2.0s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0006551285568595509; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0006551285568595509; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0008286427728546842; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0008286427728546842; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0008286427728546842; total time= 2.1s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0008286427728546842; total time= 3.4s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0008286427728546842; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0010481131341546852; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0010481131341546852; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0010481131341546852; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0010481131341546852; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0010481131341546852; total time= 2.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0013257113655901094; total time= 2.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0013257113655901094; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0013257113655901094; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0013257113655901094; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0013257113655901094; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0016768329368110084; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0016768329368110084; total time= 3.6s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0016768329368110084; total time= 2.0s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0016768329368110084; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0016768329368110084; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0021209508879201904; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0021209508879201904; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0021209508879201904; total time= 2.1s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0021209508879201904; total time= 3.5s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ........................alpha=0.0021209508879201904; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.002682695795279727; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.002682695795279727; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.002682695795279727; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.002682695795279727; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.002682695795279727; total time= 2.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.00339322177189533; total time= 2.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.00339322177189533; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.00339322177189533; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.00339322177189533; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.00339322177189533; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.004291934260128779; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.004291934260128779; total time= 3.6s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.004291934260128779; total time= 1.9s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.004291934260128779; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.004291934260128779; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.005428675439323859; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.005428675439323859; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.005428675439323859; total time= 2.5s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.005428675439323859; total time= 3.1s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.005428675439323859; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.006866488450042998; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.006866488450042998; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.006866488450042998; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.006866488450042998; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.006866488450042998; total time= 3.1s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.008685113737513529; total time= 2.5s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.008685113737513529; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.008685113737513529; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.008685113737513529; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.008685113737513529; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.010985411419875584; total time= 1.9s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.010985411419875584; total time= 4.0s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.010985411419875584; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.010985411419875584; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.010985411419875584; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.013894954943731374; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.013894954943731374; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.013894954943731374; total time= 2.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.013894954943731374; total time= 2.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.013894954943731374; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.01757510624854793; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.01757510624854793; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.736e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.01757510624854793; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.01757510624854793; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.01757510624854793; total time= 3.4s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.022229964825261957; total time= 1.9s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.022229964825261957; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.737e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.022229964825261957; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.022229964825261957; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.022229964825261957; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.028117686979742307; total time= 2.0s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.028117686979742307; total time= 3.3s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.737e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.028117686979742307; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.028117686979742307; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END .........................alpha=0.028117686979742307; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.03556480306223128; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.03556480306223128; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.737e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.03556480306223128; total time= 2.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.03556480306223128; total time= 2.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.03556480306223128; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.04498432668969444; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.04498432668969444; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.737e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.04498432668969444; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.04498432668969444; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.04498432668969444; total time= 3.5s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.05689866029018299; total time= 2.0s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.05689866029018299; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.737e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.05689866029018299; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.05689866029018299; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.05689866029018299; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.07196856730011521; total time= 2.4s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.07196856730011521; total time= 3.1s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.761e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.07196856730011521; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.07196856730011521; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.07196856730011521; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.227e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.09102981779915217; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.692e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.09102981779915217; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.761e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.09102981779915217; total time= 3.0s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.09102981779915217; total time= 2.3s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.09102981779915217; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.227e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.11513953993264481; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.523e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.11513953993264481; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.761e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.11513953993264481; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.733e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.11513953993264481; total time= 2.0s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.564e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.11513953993264481; total time= 3.5s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.550e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.14563484775012445; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.523e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.14563484775012445; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.613e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.14563484775012445; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.555e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.14563484775012445; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.564e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.14563484775012445; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.550e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.18420699693267165; total time= 2.6s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.523e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.18420699693267165; total time= 3.0s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.613e+12, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.18420699693267165; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.555e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.18420699693267165; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.565e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ..........................alpha=0.18420699693267165; total time= 1.7s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.550e+12, tolerance: 2.760e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ...........................alpha=0.2329951810515372; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.523e+12, tolerance: 2.789e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ...........................alpha=0.2329951810515372; total time= 1.8s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+10, tolerance: 2.820e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ...........................alpha=0.2329951810515372; total time= 3.1s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.555e+12, tolerance: 2.787e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ...........................alpha=0.2329951810515372; total time= 2.4s\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.565e+12, tolerance: 2.809e+09\n"," model = cd_fast.enet_coordinate_descent(\n"]},{"output_type":"stream","name":"stdout","text":["[CV] END ...........................alpha=0.2329951810515372; total time= 1.8s\n","[CV] END ..........................alpha=0.29470517025518095; total time= 1.5s\n","[CV] END ..........................alpha=0.29470517025518095; total time= 1.6s\n","[CV] END ..........................alpha=0.29470517025518095; total time= 1.5s\n","[CV] END ..........................alpha=0.29470517025518095; total time= 1.6s\n","[CV] END ..........................alpha=0.29470517025518095; total time= 2.5s\n","[CV] END ...........................alpha=0.3727593720314942; total time= 2.0s\n","[CV] END ...........................alpha=0.3727593720314942; total time= 1.2s\n","[CV] END ...........................alpha=0.3727593720314942; total time= 1.3s\n","[CV] END ...........................alpha=0.3727593720314942; total time= 1.3s\n","[CV] END ...........................alpha=0.3727593720314942; total time= 1.1s\n","[CV] END ..........................alpha=0.47148663634573945; total time= 0.8s\n","[CV] END ..........................alpha=0.47148663634573945; total time= 0.9s\n","[CV] END ..........................alpha=0.47148663634573945; total time= 0.9s\n","[CV] END ..........................alpha=0.47148663634573945; total time= 0.9s\n","[CV] END ..........................alpha=0.47148663634573945; total time= 0.9s\n","[CV] END ...........................alpha=0.5963623316594643; total time= 1.0s\n","[CV] END ...........................alpha=0.5963623316594643; total time= 1.3s\n","[CV] END ...........................alpha=0.5963623316594643; total time= 1.6s\n","[CV] END ...........................alpha=0.5963623316594643; total time= 0.8s\n","[CV] END ...........................alpha=0.5963623316594643; total time= 0.7s\n","[CV] END ...........................alpha=0.7543120063354622; total time= 0.5s\n","[CV] END ...........................alpha=0.7543120063354622; total time= 0.6s\n","[CV] END ...........................alpha=0.7543120063354622; total time= 0.7s\n","[CV] END ...........................alpha=0.7543120063354622; total time= 0.6s\n","[CV] END ...........................alpha=0.7543120063354622; total time= 0.5s\n","[CV] END ...........................alpha=0.9540954763499944; total time= 0.3s\n","[CV] END ...........................alpha=0.9540954763499944; total time= 0.4s\n","[CV] END ...........................alpha=0.9540954763499944; total time= 0.6s\n","[CV] END ...........................alpha=0.9540954763499944; total time= 0.5s\n","[CV] END ...........................alpha=0.9540954763499944; total time= 0.3s\n","[CV] END ...........................alpha=1.2067926406393288; total time= 0.3s\n","[CV] END ...........................alpha=1.2067926406393288; total time= 0.3s\n","[CV] END ...........................alpha=1.2067926406393288; total time= 0.5s\n","[CV] END ...........................alpha=1.2067926406393288; total time= 0.3s\n","[CV] END ...........................alpha=1.2067926406393288; total time= 0.3s\n","[CV] END ...........................alpha=1.5264179671752334; total time= 0.3s\n","[CV] END ...........................alpha=1.5264179671752334; total time= 0.3s\n","[CV] END ...........................alpha=1.5264179671752334; total time= 0.5s\n","[CV] END ...........................alpha=1.5264179671752334; total time= 0.3s\n","[CV] END ...........................alpha=1.5264179671752334; total time= 0.2s\n","[CV] END ...........................alpha=1.9306977288832496; total time= 0.2s\n","[CV] END ...........................alpha=1.9306977288832496; total time= 0.2s\n","[CV] END ...........................alpha=1.9306977288832496; total time= 0.7s\n","[CV] END ...........................alpha=1.9306977288832496; total time= 0.3s\n","[CV] END ...........................alpha=1.9306977288832496; total time= 0.3s\n","[CV] END .............................alpha=2.44205309454865; total time= 0.3s\n","[CV] END .............................alpha=2.44205309454865; total time= 0.3s\n","[CV] END .............................alpha=2.44205309454865; total time= 0.5s\n","[CV] END .............................alpha=2.44205309454865; total time= 0.2s\n","[CV] END .............................alpha=2.44205309454865; total time= 0.2s\n","[CV] END ............................alpha=3.088843596477485; total time= 0.2s\n","[CV] END ............................alpha=3.088843596477485; total time= 0.3s\n","[CV] END ............................alpha=3.088843596477485; total time= 0.3s\n","[CV] END ............................alpha=3.088843596477485; total time= 0.1s\n","[CV] END ............................alpha=3.088843596477485; total time= 0.2s\n","[CV] END ............................alpha=3.906939937054621; total time= 0.1s\n","[CV] END ............................alpha=3.906939937054621; total time= 0.1s\n","[CV] END ............................alpha=3.906939937054621; total time= 0.2s\n","[CV] END ............................alpha=3.906939937054621; total time= 0.2s\n","[CV] END ............................alpha=3.906939937054621; total time= 0.2s\n","[CV] END ............................alpha=4.941713361323838; total time= 0.1s\n","[CV] END ............................alpha=4.941713361323838; total time= 0.2s\n","[CV] END ............................alpha=4.941713361323838; total time= 0.2s\n","[CV] END ............................alpha=4.941713361323838; total time= 0.2s\n","[CV] END ............................alpha=4.941713361323838; total time= 0.2s\n","[CV] END ............................alpha=6.250551925273976; total time= 0.1s\n","[CV] END ............................alpha=6.250551925273976; total time= 0.1s\n","[CV] END ............................alpha=6.250551925273976; total time= 0.1s\n","[CV] END ............................alpha=6.250551925273976; total time= 0.1s\n","[CV] END ............................alpha=6.250551925273976; total time= 0.1s\n","[CV] END ...........................alpha=7.9060432109077015; total time= 0.1s\n","[CV] END ...........................alpha=7.9060432109077015; total time= 0.1s\n","[CV] END ...........................alpha=7.9060432109077015; total time= 0.1s\n","[CV] END ...........................alpha=7.9060432109077015; total time= 0.1s\n","[CV] END ...........................alpha=7.9060432109077015; total time= 0.1s\n","[CV] END .........................................alpha=10.0; total time= 0.1s\n","[CV] END .........................................alpha=10.0; total time= 0.1s\n","[CV] END .........................................alpha=10.0; total time= 0.1s\n","[CV] END .........................................alpha=10.0; total time= 0.1s\n","[CV] END .........................................alpha=10.0; total time= 0.1s\n","Optimized RMSE: 50540.560299716315\n","Optimized R² Score: 0.26220267260895525\n","Best alpha: 10.0\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"dh_QHk23Q613"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"oSuktPW59aj7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"urmUjCDIBzus"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"PIZYsl6HBzpK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"tlHp8I7yBzls"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"-1VhBIGZBziM"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## DATA VISUALIZATION"],"metadata":{"id":"_S5tnOribp-Q"}},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","import os\n","%matplotlib inline"],"metadata":{"id":"RJptc9N1bukh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Shows the average/mean amount of salary based on year with color for experience_level\n","bar=sns.barplot(data=df_u,x='work_year',y='salary',hue='experience_level',palette='viridis',errorbar=None)\n","plt.xlabel('Year')\n","plt.ylabel('Salary in dollars')\n","plt.title('5 YEAR SALARY SNAPSHOT')\n","plt.savefig('/content/salary_snapshot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":480},"id":"PN6YLYgldR2f","executionInfo":{"status":"ok","timestamp":1714700977698,"user_tz":300,"elapsed":998,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"85feac9e-a9a0-4fa2-91e8-028643184201"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["#Shows the average/mean salary by experience level with color for work setting\n","sns.barplot(data=df_u,y='salary',hue='work_setting',x='experience_level',palette='viridis',errorbar=None)\n","plt.xlabel('Experience Level')\n","plt.ylabel('Salary')\n","plt.title('Optional Worksetting Employment Salary')\n","plt.savefig('/content/worksetting_experience')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":480},"id":"_vL9lsh5dVgJ","executionInfo":{"status":"ok","timestamp":1714700980869,"user_tz":300,"elapsed":861,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"8aae41b1-f2b4-4ed5-df5d-3e161c02155d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"dFewZmnzhlkE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#show average/mean salary by experience level with color showing each year comparision\n","\n","orderlist=['Executive','Senior','Mid-level','Entry-level']\n","fig=plt.figure(figsize=(7,5))\n","a=sns.barplot(data=df_u,x='experience_level',y='salary',hue='work_year',\n"," dodge=True,errorbar=None,palette='viridis',order=orderlist)\n","a.set(xlabel=\"Experience Level\",ylabel=\"Average Salary\",title='Average Salary by Experience and Year')\n","#a.title.set_size(15)\n","sns.move_legend(a,'upper right',title='Year')\n","plt.savefig('/content/Ordered Experience with Year')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":496},"id":"1MlOqDLqy1q2","executionInfo":{"status":"ok","timestamp":1714702566887,"user_tz":300,"elapsed":2086,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"c6b58ab5-eed6-4e7c-8b08-7dd88bf4dedc"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"wiqFJgPEc4cp","executionInfo":{"status":"ok","timestamp":1715124901741,"user_tz":300,"elapsed":3,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n"],"metadata":{"id":"bbWoYtxV4Sps","executionInfo":{"status":"ok","timestamp":1715124909584,"user_tz":300,"elapsed":402,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Xgs7Hb3XzK67"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"C0VoQucxTYUJ","executionInfo":{"status":"ok","timestamp":1715124923919,"user_tz":300,"elapsed":238,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["fig=plt.figure(figsize=(15,15))\n","h=sns.FacetGrid(data=df_u,col='work_year',row='experience_level',\n"," palette='viridis',margin_titles=True)\n","h.map(sns.histplot,'salary','job_category')\n","h.set_titles(col_template=\"{col_name}\",row_template=\"{row_name}\")\n","h.set_axis_labels('Salary','Category')\n","h.fig.suptitle(\"Salary Information by Year, Category and Experience Level\")\n","h.fig.subplots_adjust(top=.9)\n","plt.savefig('/content/YearSalaryCategoryExperience')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":715},"id":"wRjvXeqCzDi5","executionInfo":{"status":"ok","timestamp":1714701743902,"user_tz":300,"elapsed":15913,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"86c7fa9a-f037-4380-975e-1d805e0e43bc"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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QgsLC+GsWbMktovWh7m5uXDPnj1y9T0vgwcPFpqbmwv37dsnd90XL15IbUtPTxcOHjxY2LhxY2FsbKzEPtH5IEtmZqawTZs2QktLS4k1IRR+macWLVoIXV1dJeZLtB6WLFkiUf7BgwfCJk2aCM3NzYVr1qwRb8/JyRG2b99eaG5uLrW2jh07JjQ3Nxd6eXkJs7OzxdtFa8Ha2lp47949qX7/73//E5qbmwsXL14stU90zl6+fFnmmL/28uVLYU5OjtT2lStXyrxmyXudy8nJEXp5ecm8hmzbtk18buVe9/kRHX/QoEHCNWvWyPzv5s2bEnWioqKEtra2QkdHR2FsbKwwNTVV2KlTJ6GFhYXwn3/+kSib+7Pk6/mNiIgQNm7cWNisWTOJzyfR2h4zZozw8+fPMvu7bds2ie2iY/zyyy9Sn0lC4f+9j69evRJve/bsmfga8PV5/s8//wgtLCyEo0ePltgu7/UwISFBaGdnJ2zSpInUmhAKv3yWiyhy/cmLaN49PDwKLCua07lz50p8BmVlZcn8vFq3bp3Q3NxcuGPHDqm2fvvtN6G5ubnwzJkz4m1XrlwRmpubC/v27Sv8+PGjRHnRe71gwQKJ7R4eHoXqOxERUVnC1CtERKRSGjdujCVLlsDAwABHjhzBuHHj0Lp1azg6OmLMmDE4e/asVB0dHR2ZeaaNjY3Rvn17PHv2TOoOZ1mqVasm824tCwsL8d2UmZmZUvsNDAwk7hwVqVChAnr06IH4+HicOXNGYl9wcDAAoF+/fgX2qzBEP3XPrXfv3gCAO3fuFKqN2NhYXL58GTVr1sTw4cMl9tnZ2aFTp05ITEzEqVOnpOp6enrme9d+p06dJB6Cp66ujm7dugEAGjZsiK5du0qU7969OwDg4cOHEttr1aol807TXr16QVtbO98UBYV15MgRZGZmYtCgQTAzM5PYN3HiRFSuXBmHDx+WmT5gypQpEncIVqpUCV26dEFOTo7M1DX5GTx4sMRdyOrq6pg6dSrU1dWxf/9+8fbq1avD09MT9+7dkzpGcHAw1NXVxedCYc2dOxdGRkY4cOAAcnJysHTpUlSuXBkAcOjQISQlJWH8+PFo0KCBRD1zc3P07t0b9+/fl0hXZGRkJHWXNgC0aNECDRo0yPN9a9SoUZGtkfj4eHFf5FW3bl2pbeXLl8fAgQORlZWFK1euFLqt8+fP4+XLlxg0aJDUgyGNjIwwfPhwxMfHi9vMyMjAsWPHoKOjg59++kmivIWFhXit5Hbjxg08e/YMtra2UmurY8eOsLe3x/Pnz3H9+nWpun369EHjxo2ltrdp0waGhoY4cOCAxLmflJSE//3vf6hbty5cXFwKNQd16tSRmed+yJAhAL6kn5ClsNe5Gzdu4Pnz52jevDnatGkjUX7QoEEy38/CuHbtGgIDA2X+d+vWLYmy9erVw7x58/DhwwdMnjwZc+fOxZMnT+Dr6wtnZ2eZ7evo6Eil67CyskKXLl2QlJQkce3dvn07ypUrh4ULF6JixYoSdUaPHg09PT0cPXpU6hiampqYNm2a1J3medmzZw8yMzMxffp0qbXj7OyM1q1b49y5cxIpoUQKez08dOgQUlJS0K9fP5kPSzU2NpYoK+/151vl5ORg586dMDQ0hL+/v8RnkIaGBvz8/KCmpiYx3926dYO6ujoOHjwo0VZGRgaOHz+OatWqSXxm7tixA8CXVFlf/1qpR48eaNSokcz3k4iISNUw9QoREamcjh07om3btggPD8f169fx4MEDXL9+HadPn8bp06fRvXt3LF68WCLQcv36dWzfvh23bt1CQkKCVEA7Li5O5k/Dv3b+/Hns3bsXd+/exYcPH5CVlSWx/8OHD1KpBSwsLGQGAQFgwIAB2Lp1K4KDg+Hl5QUAeP/+PU6dOgUzMzM0b968UHNSECsrK6ltNWrUAACp3N15uX//PgDA3t5eZnoLJycnHDlyBPfv35cKzslK4ZCbpaWl1DbRPMr6ibwoIPN1/urMzEwEBwfj2LFjiIyMRHJyskRu2LxyFctDNA+y0jNUqVIFjRs3xr///otnz55JpeKRNU553wcRWQGjOnXqoEaNGoiJiUFSUpI4oDJgwACcPHkSwcHB4j48evQIt27dQsuWLVG7dm25jl2lShWMGjUKc+bMQbt27WBjYyPeJwoIPnz4MM+82MCXlEiiQJZQKMSRI0dw8OBBPHz4EElJScjOzhbXySudSkHnVUl5/fo1Nm7ciCtXruDNmzdIS0uT2C/PeSeav9evXxc4f+7u7nj+/DnS0tJgaWkp84s8e3t7hISESGwTncOOjo4y++Dk5ITr16/j/v37UtegvOa8XLly6NOnD9auXYuTJ0+KH/Z6+PBhpKWloU+fPoV+yGtqaiq2b9+OU6dOISoqCp8+fYJQKBTvzyvPeWGvc6Lxy7q+amhowN7eHi9fvixUX3MbO3asXA/z7NSpE65cuYKQkBD8+++/sLe3x/jx4/Ms37hxY5nvsYODAw4ePIj79+/D29sbnz9/xsOHD1G1alUEBQXJbKt8+fKIjIyU2l6rVq0CU63kJjpfr127JvNL14SEBGRnZyMqKkrq+lfY66HoGIVJkabI9edbPX/+HImJiTAxMcGff/4ps0zFihUl8qMbGxvD2dkZYWFhePr0qbgv586dQ2JiIoYMGSLxZcWtW7egqamJEydOyHyeRGZmJt6/fy8zlQsREZEqYaCciIhUkqamJlq0aIEWLVoA+PJAwZMnT2L69Ok4dOgQ2rZtK75T8NSpUxg/fjwqVKgAFxcX1K1bF1paWlBXV8e1a9dw7dq1Qj1MMSgoCAsXLkSVKlXg4uKCGjVqQEtLC2pqajh9+jQePnwosx0DA4M826xTpw5atGiBy5cv4+XLl6hbty4OHTqEjIwM9O3bV8HZkSbrwaeiu95yB5LzI3pIpaGhocz9ou2yHmaZ3xwU1L/89n39RcXEiRNx6tQp1KlTB56enjAwMBB/SREUFCTzjn95FXYeZD1cTVbecnnfB5G8glkGBgaIiYlBcnKy+HhOTk4wMzNDaGgopk2bBm1tbezbtw8AFD7PRHepfn23qujhc6L285Kamir+86JFixAUFARDQ0O0aNECRkZG4nZF+bxlKei8koehoSEiIyPl/jLl1atX6NWrF5KSktCsWTO0aNEC2tra0NDQQExMDA4ePCjXw1pF81fQw1VF8yc6H/M6H2RtF9XJK1+4omu5b9++WLduHYKDg8WB8n379kFTUxM9e/bMs15umZmZ+OGHHxAREQFzc3N07NgR+vr64qBhYGBgnvNZ2OucaFx5jaUoz6uCeHl5ib/IGDRokMxfxIgU1F/RXdtJSUkQCoV4//49AgMD5epPXte1vIjO14IeOp17vYsU9nooer8K82sPRa4/30p0zKioqHzn++sHKnt7eyMsLAwHDx7EL7/8AgDiO8y/fjZJYmIisrKyCnw/U1NTGSgnIiKVxkA5ERERvvzjumPHjnj8+DH+/PNPXL16VRwoX716NTQ1NbF//36pVBmzZs3CtWvXCmxf9A9UUWqBrwNMX/+sPreC7qLs378/Ll26hH379mHKlCkIDg5GhQoVZKZMUCZREOrdu3cy94tSV8i647Gwd5J+izt37uDUqVNwcXHBxo0bJe7Gy8nJwaZNm4rkOLnnoWHDhlL7RfMgK2hXlBISElC/fn2p7aL35+vj9+vXDwsWLMDRo0fh7e2NI0eOwMjISObDV7+F6LiHDx+W+XDbryUkJGDHjh0wNzfHnj17pM6f0NDQPOsW5Xllb2+Pq1ev4urVq3Klotm6dSsSExOxaNEi9OjRQ2JfaGioVGqFgojm748//ijUA1ZF85WQkCBzv6ztomOIztWvKbqWjYyM0Lp1a5w6dQqRkZH4+PEjHj9+LA52F8aZM2cQERGBHj16SD3Q8e3bt3IHfmUp6FqW1/ai9v79e0yfPh1aWloAvnxh5OTklOdcFdRf0fsl+n/jxo3lPv/kXVOiY12/fr3YHiQper/i4uKkUuvkVbaw15+iIDpm27Zt5To/27ZtC21tbRw5cgSTJk1CYmIiLl26BAsLC6m+a2trQygUFurvK0RERKqMOcqJiIhyEeVJzv0z/RcvXqBBgwZSQfKcnByZOXhl+fDhA5KSkmBraysVJP/06RPu3buncJ89PDxQs2ZNHDhwAJcvX0ZUVBQ6dOiAKlWqKNxmcRDlJb5+/brUndwAEB4eDkB2qpSSIEqV0Lp1a6n8uhEREVLpMIAveb0BSKT5KEijRo0A/N94c0tKSsKDBw9QoUIFqfOtqMkKmLx69Qpv3rxBrVq1pO7W9Pb2hpaWFvbt24fjx48jKSkJvXr1yvcOVkVYW1sDQKHX1qtXr5CTkwNXV1epQFtsbCyio6OLtH956dGjBzQ1NXHy5MkC8xfnvqP5xYsXAIB27dpJlcsrqJXfeSeav//++69Q/a5fvz4qVqyIR48eycwDLet9EJ3DefXvW9bygAEDAHzJf6/IrxZE67ht27ZS+/7991+5+yOL6Fomq73s7OxCn7vfQigUws/PD3FxcZg+fTqmT5+Ot2/fYurUqRKfX7ndv39f5nsseh9F46pcuTIaNmyIJ0+eiO92Li6itEuFPV+/5RgXL14ssKy815+iUL9+fejq6uLWrVty/WqpYsWK6NChA96+fYt//vkHR48eRVZWltTd5MCXOfj48SOePHlSlF0nIiIqcxgoJyIilRIaGoqwsDCZaSri4+PFP2Fv1qyZeHutWrUQFRUlkVJBKBQiICCg0A/0qlatGrS0tHDv3j2Jn09nZmZiwYIF+PDhg6JDgrq6Ovr06YOEhAT8+uuvAIruIZ5FydjYGK6uroiJiZHKe3v79m2EhoaiSpUqUg/HKymiB1t+HfxLSEjA3LlzZdYR/UT9zZs3hT5O165doampiZ07d4qDpCKrV69GSkoKunbtmmde+qKyfft2iZQkOTk5WLJkCXJycqTubAa+3PXYuXNn3L9/H6tWrYKGhgb69OlT5P3q0aMHdHV1ERgYiIiICKn9OTk5El8yiN6369evSwSOP336hBkzZsj8UqYwDhw4AIFAAD8/v0KVr127NsaOHYvMzEz4+vrm+ZDbixcvSjzMNq/z7tKlS/jrr79ktiF6gKGshwh7enqibt262L17Ny5cuCCz/s2bN/H582cAX3JNd+zYEcnJyVL5kR8+fIhDhw5J1be3t4epqSmuX78uleLlxIkT+O+//2BiYgJ7e3uZx8+Ps7MzTExMcOjQIfzvf/+DqampzHz+eclrPl+9eoVly5bJ3R9Z7OzsYGpqin///RenT5+W2Ldz506F8pPLa+vWrbhw4QI6duyI3r17o3fv3ujYsSMuXbqU569fkpOTsXbtWoltd+7cwdGjR6GjoyPx5cKQIUOQmZmJX3/9VWYaqI8fP37TF7wiAwcOhKamJhYtWoTnz59L7c/IyPjmIHr37t2hra2NvXv3yvxyI/ezKuS9/hSFcuXKYdCgQYiPj8f8+fNlfin79u1bmX/fEAXFDx06hMOHD6NcuXLitEW5iR5kO3PmTJnpoVJTU/P9ZRsREZGqYOoVIiJSKbdv38b27dthaGgIOzs78UMIo6OjceHCBaSlpcHT0xPt27cX1xkyZAhmz54Nb29vtGvXDuXKlcONGzcQGRkJDw8PnDt3rsDjqqurw8fHBxs2bECXLl3g6emJzMxMhIeH4+PHj3B0dPymf3z37t0ba9euRVxcHMzNzWFra6twW8Vpzpw56N+/P5YsWYKwsDBYWlrizZs3OHHiBNTV1bFw4cJi+/l9QaysrGBnZ4e///4b/fr1g52dHRISEnDx4kWYmprKzMdsY2MDLS0tBAUFITExUZzr18fHJ8/UKbVr14a/vz/mzp0Lb29vdOjQAfr6+vj3339x8+ZN1K9fH1OmTCnWsQJfgn3du3dHhw4doKOjg8uXL+Phw4do0qQJRowYIbPOgAEDEBISgri4OHh4eMDY2LjI+1W1alWsWbMGY8aMQZ8+feDs7IwGDRpATU0NsbGxuHnzJhITE8WBaENDQ3Tq1AnHjh1D9+7d4erqiuTkZPzzzz8oX748GjVqhAcPHsjdD9GXafLcMT9q1ChkZWVh7dq16NWrF2xtbWFpaYnKlSvj3bt3+O+//6QeSjhgwAAcOHAAEyZMgJeXF6pXr44nT57g0qVL6NChA44fPy51HGdnZ5w4cQLjxo2Du7s7KlSogJo1a6J79+7Q1NREQEAAhg8fDl9fX9ja2qJRo0aoWLEiYmNjcefOHbx69QqXL18Wp+yYPHkyrl69ik2bNiEiIgK2traIj4/H//73P7i7u+P06dMSKTXU1NTw+++/48cff8TEiRMRGhqK+vXr4/nz5zh9+jQqV66MJUuWiO98l4eamhr69+8vTpsibw58Dw8P1KtXD1u3bsXjx4/RqFEjvHnzBufOnUOrVq1kfrmgSB8XLFiAoUOHYvz48Wjbti3q1auHBw8e4MqVK3Bzc8OlS5fkbvfatWsyHyAJfPmiShTsjIiIwIoVK1C7dm2JL/HmzZuHO3fuYNWqVWjevLnEQ3KBLw8f/euvvxAREQE7OzvEx8fj+PHjyMnJwdy5cyWuvb169cK9e/ewe/dutG3bFi1atECNGjXw8eNHREdH499//0WPHj3y/BKxsMzMzLBgwQJMnz4dnTt3hpubG0xMTJCVlYXXr1/j+vXrqFq1aoE59/Ojr6+P5cuXY/z48Rg8eDBatmwJgUCAlJQUPHr0CG/evMHZs2cByH/9KYwPHz7k+YVbxYoV8dtvv2H06NF4+PAh9u7di3PnzsHJyQlGRkZISEjAixcvcOPGDUycOFHqAaL29vaoV68eTp48iczMTHh4eMh8roCzszMmT56MFStWwMvLS/wQ5tTUVLx+/Rr//vsv7OzsCswVT0REVNYxUE5ERCpl6NChMDExwT///INHjx7h8uXLyMjIgJ6eHhwcHNC5c2d06dJFIijUr18/lC9fHkFBQTh06BAqVKiAZs2aYdGiRfj7778LFSgHgAkTJkBfXx8hISEIDg6Gjo4OXFxc8PPPP+cZHCksAwMDcUCrNN5NLlKnTh3s378ff/zxBy5evIhr166hcuXKcHNzw6hRo9C0aVOl9U1DQwN//vknVq1ahYsXL2LHjh0wMjJC79698dNPP6FTp05SdapUqYI1a9Zg7dq1OHjwoPgBb127ds03x/jAgQNRr149bNmyBX///Tc+f/6MGjVqYNiwYRg1apTMh9QVtV9//RWnTp3Cvn37EBMTAz09PQwePBgTJkxAhQoVZNZp3LixOPBcnOeZs7Mzjhw5gi1btuDy5cv477//oKmpierVq8PJyQleXl4S5RcsWIA6derg+PHj2LVrF/T19dG6dWuMHz8e48ePV6gPjx8/BgCZ73t+xo4diw4dOmD37t0IDw/HgQMHxNcYCwsLDB8+HN26dROXt7CwwPbt27Fq1SpcuHABWVlZsLCwQGBgIHR0dGQGynv37o3Xr1/j2LFj2LRpE7KysuDg4CB+LoGFhQUOHz6MrVu34vz58zhw4ADU1dVhaGiIxo0bY9y4cRIP7DMwMMDevXuxYsUKXLhwAbdv34apqSlmz54NLS0tnD59WuoLLGtra/z111/4888/ceXKFZw7dw5Vq1ZFp06dMHr0aJn57wvL29sbv//+OzQ1NeV+1kKlSpUQFBSEZcuW4dq1a/jvv/9Qp04djB49Gj/++KPM+VSEvb09du3ahZUrV4pTelhbW2PHjh24fPmywoHyvNLZ1KpVC0OGDEFycjImTpwIAFi5cqXEdUZbWxsrV65E//79MWnSJBw6dEjiWlK7dm3MmTMHy5Ytw969e5GRkYHGjRtjzJgxcHNzkzrm7Nmz0bJlS+zduxf//PMPkpOTUaVKFfG1qmvXrnKPUZZu3brBwsICW7duRXh4OC5fvoxKlSqhevXq8PLyQocOHb75GK1atcL+/fuxceNGXLlyBWFhYdDV1UX9+vUxcuRIibLyXn8Kkpqammeudx0dHfz222/Q1NTEH3/8gcOHD+PgwYM4f/68+MGatWvXxoQJE2TeKQ58uWN+9erVAKQf4pmbr68v7OzssGPHDly/fh1nz56FtrY2jIyM0KdPH3Tu3FmucREREZVFasK8ktgRERHRdyMnJwdt27ZFQkICLl++rLS7sqlsS0lJgZubG/T09HDmzBmF7hj+Xnh7e6NcuXLidEyqauXKlVi3bh02bdokM5haHMLDwzF48GB07doVS5cuLZFjlmXR0dHw9PSEt7c3Fi9erOzuEBEREZVaZfdfN0RERCrkxIkTiI6ORrdu3Rgkp2KzZ88epKamon///mU6SJ6cnIyHDx9K3WlalsnKW/zo0SNs375d/IubkiLKsT1o0KASOyYREREREVOvEBERfcc2bNiAxMRE7Nu3D5UqVVKpwB6VjOTkZOzZswdxcXHYt28fDA0NMWDAAGV3q1jp6OgolNf8e9azZ0/Uq1cPDRs2hJaWFl68eIELFy6I81fnlY6nqDx69Ajnz5/HvXv3cPHiRXh4eMDa2rpYj0lERERElBsD5URERN+x5cuXQ1NTE2ZmZpg6dSpq1qyp7C5RGfPx40csX74c5cuXR5MmTTBz5kz+aqEM6tevH06fPo1jx47h06dP0NHRQYsWLTB06FA4OjoW+/Hv3buHFStWQFtbG+3bt8fs2bOL/ZhERERERLkxRzkRERERERERERERqbSym1ySiIiIiIiIiIiIiKgQGCgnIiIiIiIiIiIiIpXGQDkRERERERERERERqTQGyomIiIiIiIiIiIhIpTFQTkREREREREREREQqjYFyIiIiIiIiIiIiIlJpDJQTERERERERERERkUpjoJyIiIiIiIiIiIiIVBoD5URERERERERERESk0hgoJyIiIiIiIiIiIiKVxkA5EREREREREREREak0BsqJiIiIiIiIiIiISKUxUE5EREREREREREREKo2BciIiIiIiIiIiIiJSaQyUExEREREREREREZFKY6CciIiIiIiIiIiIiFQaA+VEREREREREREREpNIYKCciIiIiIiIiIiIilcZAORERERERERERERGpNAbKiYiIiIiIiIiIiEilMVBORERERERERERERCqNgXIiIiIiIiIiIiIiUmkMlBMRERERERERERGRSmOgnIiIiIiIiIiIiIhUGgPlRERERERERERERKTSGCgnIiIiIiIiIiIiIpXGQDkRERERERERERERqTQGyomIiIiIiIiIiIhIpTFQTkREREREREREREQqjYFyIiIiIiIiIiIiIlJpDJQTERERERERERERkUpjoJyIiIiIiIiIiIiIVBoD5URERERERERERESk0hgoJyIiIiIiIiIiIiKVxkA5EREREREREREREak0BsqJiIiIiIiIiIiISKUxUE5EREREREREREREKo2BciIiIiIiIiIiIiJSaQyUExEREREREREREZFKY6CciIiIiIiIiIiIiFQaA+VEREREREREREREpNIYKCciIiIiIiIiIiIilcZAORERERERERERERGpNAbKiYiIiIiIiIiIiEilMVBORERERERERERERCqNgXIiIiIiIiIiIiIiUmkMlBMRERERERERERGRSmOgnIiIiIiIiIiIiIhUGgPlRERERERERERERKTSGCgnIiIiIiIiIiIiIpXGQDkRERERERERERERqTQGyomIiIiIiIiIiIhIpTFQTkREREREREREREQqjYFyIiIiIiIiIiIiIlJpDJQTERERERERERERkUorp+wOEBFRyYiIiMChQ4cQHh6OmJgY6OnpwdraGj///DNMTU0lykZGRmLhwoW4ceMGNDU14e7uDn9/f+jr60uU2b9/P8LCwvDy5UtUrlwZjRs3xrhx42BlZSV1/Li4OCxcuBBhYWHIycmBo6Mjfv31V9SpU6fYx070vVHmen327Bn27t2LiIgI3Lt3DxkZGThz5gxq165dImMn+t4oc73+/fffOH78OO7cuYN3797B2NgYHh4eGD16NHR1dUtk/ETfE2Wu11OnTmHv3r149OgREhMToa+vDxsbG4wdOxbm5uYlMn6i74my//2a248//oh//vkHAwcOxKxZs4plvFQ6qAmFQqGyO0FERMVv/PjxuHHjBtq3bw+BQID4+Hjs2rULqampCA4OFv8FPTY2Ft27d4eOjg58fHyQmpqKLVu2oEaNGggJCUH58uUBAL///jv++usvtGvXDk2bNkVycjKCg4MRExODTZs2wcXFRXzsT58+oUePHkhOTsaPP/4ITU1NbNu2DUKhEIcOHULVqlWVMidEpZUy1+uBAwcwffp0NGjQABoaGnjw4AED5UT5UOZ6dXR0RPXq1dGmTRvUrFkTjx49wt69e1GnTh0cPHgQFStWVMqcEJVWylyvgYGBiIyMRKNGjVC1alW8e/cO+/fvR3x8PIKDg2FhYaGUOSEqrZS5XnP7+++/MW3aNKSmpjJQrgqERESkEq5fvy5MT0+X2Pb8+XOhpaWlcPLkyeJts2fPFjZt2lQYExMj3hYWFiY0NzcX7t27V7ztzp07wpSUFIn23r9/L3RychL269dPYvuGDRuE5ubmwtu3b4u3PX36VNioUSPh8uXLi2R8RGWJMtfrhw8fhMnJyUKhUCjctGmT0NzcXPjq1asiGxtRWaPM9Xr16lWp/hw8eFBobm4u3Ldv3zeNi6gsUuZ6lSU+Pl7YuHFj4cyZMxUdElGZVRrWa1pamtDDw0MYGBgoNDc3F86ZM6cohkalGHOUExGpCDs7O/G36SImJiZo2LAhnj17Jt72999/o1WrVqhZs6Z4m4uLC0xMTPC///1PvM3S0hKVK1eWaK9q1apo1qyZRHsAcPLkSVhZWaFp06bibWZmZnB2dpZok4i+UOZ61dPTg7a2dlEOh6hMU+Z6dXR0lOpPmzZtAHz5iTkRSVLmepWlWrVqqFixIpKTkxUdElGZVRrW68aNGyEUCjFs2LCiGBJ9BxgoJyJSYUKhEO/evROnPomLi0NCQgIsLS2lyjZt2hQPHjwosM34+Hjo6emJX+fk5ODRo0cy27SyssLLly+RkpKi+CCIVERJrFciKhrKXK/v3r0DAKY1Iyqkkl6vSUlJeP/+PR49eoTp06cjJSUFzs7O3zQGIlVRkuv19evX2LhxI6ZMmcJUZiqEgXIiIhV25MgRxMXFoUOHDgCAt2/fAgAMDQ2lyhoaGiIxMREZGRl5tvfff//h1q1b6Nixo3ibqE5ebeY+LhHlrSTWKxEVDWWu140bN0JDQwNeXl4K9p5ItZT0eu3Tpw+cnZ3RtWtX/O9//8NPP/2EXr16FcFIiMq+klyvixcvRqNGjdCpU6ci6j19D8opuwNERKQckZGRmDt3LmxtbeHt7Q0ASE9PBwCpn7gBQIUKFQAAaWlpMvcnJCRg8uTJqF27NoYPHy7eXpg2RWWISLaSWq9E9O2UuV6PHj2Kv/76C8OHD4eJick3joSo7FPGel20aBFSUlLw6tUrHDhwAOnp6cjOzoa6Ou9jJMpPSa7Xq1ev4u+//8a+ffuKehhUyjFQTkSkguLj4zFy5Ejo6Ohg9erV0NDQAPB/f5mQ9a276C8hsn52lpqaipEjR+LTp0/YvXu3RO63wrQpKkNE0kpyvRLRt1Hmev3vv/8wffp0tGjRAhMnTiyK4RCVacpar7a2tuI/d+rUSXwn67Rp075tQERlWEmu16ysLCxYsADdunWTeMYWqQZ+ZUlEpGKSk5MxYsQIJCcnY9OmTTAyMhLvq169OoAvfxH5mih329ffxmdkZGDcuHF49OgR/vjjD5ibm0vsF9XJq83cxyUiSSW9XolIccpcrw8fPsRPP/2Ehg0bYs2aNShXjvdDEeWntHy+VqlSBU5OTjh69Og3jIaobCvp9Xro0CE8f/4cffv2RXR0tPg/APj06ROio6Px+fPnoh4mlRL8GxQRkQpJT0/HqFGjEBUVha1bt6JBgwYS+42MjKCvr4+7d+9K1Y2IiICFhYXEtpycHEybNg1XrlzBqlWr4ODgIFVPXV0d5ubmebZZp04daGtrf+PIiMoeZaxXIlKMMtfry5cvMXz4cOjr62Pjxo38lQhRAUrb52taWhqSk5PlHwiRClDGen3z5g0yMzPRv39/qX2HDh3CoUOHsHbtWrRp0+YbR0elEe8oJyJSEdnZ2fj5559x69YtrF69WuJnn7m1a9cO58+fx5s3b8Tbrly5gqioKLRv316i7Lx583D8+HHMnj0b7dq1y/PYXl5euHPnDu7cuSPe9uzZM1y9elWqTSJS7nolIvkoc73Gx8dj6NChUFNTw+bNm6Gvr180gyIqo5S5XhMSEqS2RUdH48qVK7C0tFRwRERll7LWa8eOHbF27Vqp/wDA3d0da9euZUqWMkxNKBQKld0JIiIqfgsWLMD27dvh4eEhfkp4bt26dQPw5Rv07t27Q1dXF4MHD0Zqaio2b94MIyMj7N+/X/zTtW3btmHRokWwtbWV+W1727ZtUalSJQBASkoKvL298enTJwwdOhTlypXDtm3bkJ2djcOHD/Mf9kRfUeZ6TU5Oxo4dOwAAN27cwKVLlzB06FDo6OhAV1cXgwYNKq5hE32XlLleu3XrhocPH2L48OFSPx03MDCAq6trUQ+X6LumzPXq4uICZ2dnWFhYoEqVKoiKisL+/fvx+fNnbNu2DXZ2dsU4cqLvjzLXqywCgQADBw7ErFmzimiEVBoxUE5EpCJ8fHxw7dq1PPc/evRI/OcnT55g8eLFuH79OjQ1NeHu7g4/Pz8YGBiIy/j5+eHgwYN5tnfmzBnUrl1b/Do2NhYLFy5EWFgYcnJy4OjoCH9/f9SrV+8bR0ZU9ihzvUZHR8PT01NmuVq1auHs2bPyDoeoTFPmehUIBHmWc3BwEH/pRURfKHO9BgQE4Pz583j16hU+ffoEfX19NG/eHCNHjsx3LROpKmX/+/VrDJSrBgbKiYiIiIiIiIiIiEilMUc5EREREREREREREak0BsqJiIiIiIiIiIiISKUxUE5EREREREREREREKo2BciIiIiIiIiIiIiJSaQyUExEREREREREREZFKY6CciIiIiIiIiIiIiFQaA+VEVGo8evQIjx49UnY3iKgQuF6Jvh9cr0TfD65Xou8H1ytR2cNAORGVGhkZGUhPT8f169eRnp6u7O6UaZznklNW55rrteRwnktOWZ1rrteSw3kuOWV1rrleSw7nuWSU5XnOyMhASkpKmRxbaVOWz6PShPPMQDkRlVLZ2dnK7kKZJppfznPxU4W5LstjKw1U4RwqLVRhrsvy2EoDVTiHSgtVmOuyPLbSQBXOodJAFea5LI+ttFCF86g04DwzUE5EREREREREREREKo6BciIiIiIiIiIiIiJSaQyUExEREREREREREZFKY6CciIiIiIiIiIiIiFQaA+VEREREREREREREpNIYKCciIiIiIiIiIiIilcZAORERERERERERERGpNAbKiYiIiIiIiIiIiEillVN2B4iIiArLq+c4ucqf3B9QTD0h+j7Ju4ZIEq8pyrciOEqherdfphRtR/JVATj7TKGa1nW15a4zqa+JQsci1Xbk/MsSPd7z+By568QlyV8HAO5dC5WzRhh0Td0UOlZ2+ie56/Rzr6vQsd4mCRWq9yld/nqmhordU9m1lWJjo5Iza6t8n0/WdeULG55/mC5XefnoAldjEPP4H7lqdW7TSq7yVSqpyVVekTHb1i0vV/mhXerJVX7/Gfmv8T09uX4B3lFORERERERERERERCqOgXIiIiIiIiIiIiIiUmkMlBMRERERERERERGRSmOgPB8BAQEQCAQQCASwsLCAvb09unTpgrlz5yIyMlKhNsPDw7Fu3boi7efTp08xfvx4tGzZElZWVmjZsiVGjhyJCxcuyNWOQCDA5s2bi7RvZcGDBw8gEAgQHh6ebznRuSIQCGBpaYm2bdti/vz5SExMLJmOlrDw8HAIBALcuXNH2V0hIiIiIiIiIiL6JnyYZwEqVqyIoKAgAMCnT5/w+PFjBAcHY9++fViwYAG6desmV3vXrl3Dli1bMGrUqCLp38uXL9G7d28IBAL4+flBX18fMTExuHDhAq5duwZ3d/dCtxUcHIyaNWsWSb9UlY+PDzp37oz09HRcu3YN69atQ1RUFDZt2qTsrhEREREREREREVEeGCgvgLq6OmxsbMSvXV1dMWDAAPj6+mL69Omws7NDnTp1lNa//fv3AwC2bt0KLS0t8faePXsiJ0e+p4TnHicppkaNGuJ5dHR0xNu3b7Fv3z68ffsW1atXV27nCik7Oxs5OTnQ1NRUdleIiIiIiIiIiIhKBFOvKKBChQqYOXMmMjMzERISIt5+6NAh9O/fHw4ODmjevDl8fHwQEREh3h8QEIDAwECkpqaKU3T4+PgAACIjIzFx4kS4u7vD2toaHTt2xJYtWwoMdiclJUFbW1siSC6iri759kZGRmLs2LFwcHCAtbU1unbtitDQUPF+WalXzp8/j969e6Np06ZwcnLC7NmzkZqaKt4vSr8RFhaGyZMnw9bWFh4eHti4caNUf27evImhQ4fCzs4Otra26N27N8LCwsT7MzIysGLFCnh4eMDS0hIdOnTA0aNH8x2/qI8//vgjnJ2dYWdnh969e+PixYsSZQ4cOACBQID79+9j+PDhsLGxQbt27XDo0CGp9v744w+4urrC1tYWY8eORUJCQoF9yEujRo0AAG/evJFrnE+ePMGIESPg6OgIa2treHl5Sc3pzZs3MXjwYNjY2MDe3h6TJ0+W6uuyZcvQpUsX2Nraws3NDZMmTcLbt28lyvj4+GDkyJE4ePAgvLy8YGVlhYcPHwL4Mrf9+vWDtbW1+Jy+f/++RP2kpKQC33siIiIiIiIiIqLSjHeUK6hBgwYwMjLCzZs3xduio6PRvXt31K1bFxkZGTh27BgGDhyII0eOwNTUFL1790ZsbCxCQ0PF6Vy0tbUBAG/fvoWpqSm6dOmCypUr48GDBwgICEBqairGjh2bZz+aNGmC3bt3Y9asWejfvz8EAoFUgBwAoqKi0LdvX9SoUQPTp0+HoaEhHj9+jNevX+fZ9okTJzBx4kT06NED48aNQ3x8PJYvX46kpCSsXLlSouzs2bPRrVs3rF27FqdPn8ayZcsgEAjQsmVLAMD169fxww8/wMbGBvPnz4euri7u3r0rcfwJEybgxo0bGDNmDMzMzHDhwgX88ssv0NXVzTeFTHR0NDw8PDB06FCoq6vj4sWL8PX1RVBQEBwdHSXKTpkyBX369MGPP/6Iffv2wc/PD1ZWVjAzMwMA7Ny5E6tXr8bQoUPh4uKCf/75B9OnT8/z2AV5/fo11NXVJVLaFGaco0aNgoGBARYsWABtbW28fPkSsbGx4jZu3rwJHx8fuLu7Y+XKlfj8+TNWrVqF0aNHIzg4WFwuISEBI0eORPXq1fH+/Xts3boVPj4+OHbsGMqV+7/lf/fuXcTExGDChAnQ1dVFjRo1cPz4cUyaNAmenp5Yvnw5NDU1cePGDcTFxaFx48biugW990RERERERERERKUdA+XfoEaNGnj37p34de6Adk5ODlxdXREREYGDBw9i0qRJMDY2hrGxsVQ6FwBwdnaGs7MzAEAoFMLe3h5paWnYuXNnvoFyb29vXLlyBcHBwQgODkblypXh5OSEnj17wtPTU1wuICAAmpqa2LNnjzg47+Likme7QqEQS5YsQceOHbFgwQLxdkNDQ/j6+mL06NFo2LCheHu7du0wbtw48VjOnz+PkydPioOlS5cuRb169RAUFAQNDQ0AQIsWLcT1r169irNnz2Lz5s3i7a6uroiPj0dAQEC+gfJBgwaJ/5yTkwNHR0c8ffoU+/btkwqUDxw4EAMHDgQA2Nra4sKFCzh58iRGjx6N7OxsrF+/Ht26dcO0adMAAG5ubkhISMDhw4fzPH5uOTk5yMrKQkZGBsLDw7Fnzx707dsXhoaGhR7n+/fvER0djenTp6N169YAACcnJ4njLF++HJaWlggMDISamhoAwNzcHJ07d8aFCxfE87Vo0SJxnezsbNja2qJly5a4evWqxPx//PgRf/31F2rUqAHgy/v/+++/w9XVFWvXrhWXk/U+FPTey0soFAIAPn/+rFB9KhzR/Jb1ec79CxhlKQ1zXalSpWJpl+u1ZJSGc4i+KIlrCtcryas0fNYpQ2m4NnK9EkmSdT0qDWsVKL71Cih/bFT2lcRnfWpqqkqs14IwUP4NhEKhOEgJfEltsmLFCty8eVMiBUZUVFSBbaWnp2P9+vU4evQo3rx5g8zMTPG+T58+oXLlyjLraWhoYPny5Rg1ahTOnTuH//77D2FhYThz5gxGjx6NCRMmAPgSoPXy8hIHyQvy/PlzxMTE4Ndff0VWVpZ4u4ODA9TV1XH37l2JQHnuoKuamhrMzMzEd0B//vwZt2/fxqRJk8RB8q+FhYVBT08PTk5OEsdzcXHBb7/9huzs7DzrxsbGYuXKlfjnn38QHx8v/otlkyZNpMrm7melSpVQs2ZNcT9jY2Px9u1btG3bVqKOl5dXoQPly5Ytw7Jly8Sv7e3tMWPGDLnGWbVqVdSqVQsrVqzAx48f4ezsDGNjY3HZz58/48aNG5g6dSqys7PF201MTFCjRg3cuXNHHNC+cOEC/vzzTzx58gQpKSnislFRURJzYW5uLg6SA8CzZ88QGxsr/sIgP/m994oQzUth1g19u7I+zw8ePFB2F8SUOdf29vbF0i7Xa8niPCtfSVxTuF4LUk3ZHSh1StNnnTLw81VRhsXYNqmq/K5Hyv78Ka71Cih/bPKpquwOkALk/6yX/xqf+xjKPqeLc70WhIHybxAbGwsTExMAQEpKCoYOHQp9fX34+fmhZs2aqFChAmbMmIH09PQC21q6dClCQkIwZswYWFpaQkdHB2fOnMGff/6J9PT0PAPlIg0bNkTDhg3h6+uL9+/fY9iwYdiwYQN++OEH6OnpITExUa6HSX748AEAMGbMGJn7c+fcBgAdHR2J15qamkhOTgbwJYd1Tk5Ovsf/8OEDEhMTZQa3ASA+Pl4iWCySk5ODn376CcnJyRg/fjzq1asHLS0trFmzRqqPefUzIyNDfAwA0NfXlyhjYGCQZ7+/NnjwYHTt2hWfP3/GkSNHEBISgtWrV2Py5MlyjXPz5s1YuXIl5s6di9TUVDRp0gT+/v5o3rw5kpKSkJ2djUWLFkncMS4iGndERARGjx4NT09PjBgxAtWqVYOamhr69OkjdU5+PcbExEQAKNQ5k997r4hy5cohKysLJiYmMnPvU9H4/PkzoqKiyvw8i54ToExlea65XktGWT6Hvjel4ZqiqLKyXi88fVtwIRXzPZ+X36IsXxtLYr3GXHtXcCEiOcm6HpXltSryPY3tcITiN7WR8sj7WR91Rf5rfKNGjVRivRaEgXIFPXnyBHFxcfD29gYA3Lp1C7GxsVi/fj0sLCzE5ZKTk2UGeL924sQJ9O3bF76+vuJtFy5cUKhv+vr66NGjB+bPn48XL15AT08Penp6Ug9xzI+enh4AYNasWWjatKnUfnmC7jo6OlBXV8/3+FWqVIG+vj42bNggc//XwWuRFy9e4P79+1i7di3atGkj3p6Wllbo/omI0qO8f/9eYnvu9DoFMTY2hpWVFYAvd9+/e/cOW7duxYABA1CjRo1Cj9PU1BRr1qxBZmYmbt68iRUrVmDUqFG4ePEidHR0oKamhpEjR0qMWaRq1S/fEJ8+fRra2tpYtWqVOG99TEyMzOPm/mUE8H/vvzznTFER9UVLS0upP7dRFWV9nkvT2MriXHO9lizOs/J9z/PP9Vp2qfr7WRbPaa5X+l7ld76W5fO5LI+NSoeSOL9yH0OVz2nppz5SgdLT0zFv3jyUL18evXv3BvB/gVlNTU1xuRs3bkgFJnPfwfx1m7nrZmdn49ixYwX2Ja8gruhnEqI7hZ2dnXHy5EmJ9Bv5qV+/PoyNjfHq1StYWVlJ/WdkZFSodoAvi83GxgaHDx+WSBWSm4uLC96/fw9NTU2ZxytfvrzMeqI7o3PPXUxMjMRDVgvL2NgYhoaGOHXqlMT2kydPyt2WyNSpU5GTk4PNmzcDkH+cmpqacHBwgK+vL1JSUvD27VvxfD579kxmG7Vr1wbw5ZzU1NSUCIIfPXq0UP0Wvf8HDhxQeOxERERERERERETfC95RXoCcnBzcunULwJfE9o8fP0ZwcDBevXqFxYsXi4OSNjY2qFSpEubMmQNfX1/ExcUhICBAKqBsZmaGrKwsBAUFwdbWFtra2qhfvz5cXFwQEhKCBg0aoGrVqti9e7fMgPrX/vjjDzx48ACdO3dGgwYNkJ6ejrCwMOzevRtt2rRBrVq1AHx50Oj58+cxYMAADB8+HIaGhoiMjMTnz58xYsQIqXbV1NTg5+eHKVOmIDU1Fa1atYKWlhZev36NCxcuYOLEiTA1NS30PE6ePBlDhgzBkCFDMGDAAFSpUgX37t1D1apV0atXL7i6usLDwwPDhw/H8OHDIRAI8PnzZzx9+hQvXryQeKBobqKA7vLly5GTk4PU1FSsWbNGrjveRTQ0NODr64sFCxagWrVqcHV1RVhYGMLDw+VuK3f/OnbsiL/++gtjxowp1DgfPnyI33//HR07dkSdOnWQkpKC9evXo1atWqhbty6ALwH4H374AT///DM6deoEXV1dxMbG4p9//kGPHj3g6OgIV1dXBAUFYd68eWjbti1u3rxZ6FzrampqmDZtGiZNmoRx48ahW7duKF++PG7dugUrKyt4eHgoPCdERERERERERESlDQPlBUhLS0Pfvn0BfLkzunbt2nB2dkZgYCDMzMzE5QwMDLB69WosWbIEo0ePhomJCebMmYNNmzZJtOfh4YEBAwZgw4YNSEhIQPPmzbFjxw7MnDkTs2fPxrx586ClpQVvb2+0bdtW4kGQsnTt2hXp6enYsWMH4uLioKGhgVq1amHq1KkYMGCAuJyJiQn27t2L5cuXY86cOcjOzoaJiYlEqpevdejQAbq6uli3bp34TuRatWrBzc1NrrzdANCsWTNs374dq1atgr+/P9TV1dGwYUP8/PPP4jJr1qzBhg0bsGfPHsTExEBHRwcNGzZEjx498my3fPnyCAgIwNy5czFhwgTUqFEDP/30E65evYq7d+/K1UcA8PHxQVJSEnbv3o09e/bA2dkZ8+fPx/Dhw+VuS2T06NE4fvw4du7ciXHjxhU4TkNDQxgYGGD9+vWIi4uDjo4OmjVrhqVLl4ofaGpnZ4fdu3cjICAA/v7+yMzMhLGxMZycnFCvXj0AgLu7O6ZMmYKdO3fiwIEDsLOzw/r16+Hl5VWofnfs2BEVK1bEunXrMGnSJFSoUAGNGzeWetgpERERERERERHR905NKBQKld0JIiIAuHPnDoRCITIzM9GoUSOVzYlVElJTU/HgwYPvbp69eo6Tq/zJ/QHF1JPC+17nuiBcryWnKM8hedcQSSoN1xRFlKX1uiI4SqF6t18WLv2gslnX1Za7zqS+JkXfke8AP1+/zZHzL4ul3bw8j8+Ru05ckvx1AODetVC56+iauil0rOz0T3LX6edeV6FjvU1SLHTzKV3+eqaGimXp7dpKemxlda0CX9ZrRkbGdzW2WVufyVXeuq5899eef5guV3lFxDz+R67yndu0kqt8lUpqBRfKRZEx29aVnV44L0O71JOr/P4z8l/je3rWLdPrtbCYo5yIiIiIiIiIiIiIVBoD5URERERERERERESk0hgoJyIiIiIiIiIiIiKVxkA5EREREREREREREak0+bLyExERKdH3+iA9Uh2l/WGZXEP54wOMyq7k6OsK1dOpbV/EPcmfqj6Yk0re1cgsheo5mSkWQqhcQb6H4ylaBwAc3LooVE8R2hWqyl1H0blfOKy+QvVWh0TJXUfWQzmpbJj7o2LnUWF9TH1RrO0DgG3dVnKVj3qXLVd5eR9g2sqiglzlgS8PzixOxd1+WcY7yomIiIiIiIiIiIhIpTFQTkREREREREREREQqjYFyIiIiIiIiIiIiIlJpKhEoDwgIgK2trVL70KxZMwQElFxe0NatW2Pu3Ln5lgkPD4dAIMCdO3dKqFffplu3bvDz88u3jJ+fHwQCAfr06SO1TygUwt3dHQKBoETfC1UREBCAGzduKLsbREREREREREREclOJQDnJ1qRJEwQHB8PMzEzZXSlSlSpVwu3bt/Hq1SuJ7f/99x8SEhJQvnx5JfWsbAsMDMTNmzeV3Q0iIiIiIiIiIiK5MVD+nUlLSyuytrS1tWFjY4NKlSoVWZulQa1atWBhYYHjx49LbA8NDUWLFi1QoYL8TyQmIiIiIiIiIiKisouB8v8vNjYWU6ZMgaOjI5o2bYqBAwfi7t27EmUOHTqE/v37w8HBAc2bN4ePjw8iIiKk2jp9+jTat28PKysr9OrVS2YZADh//jx69+6Npk2bwsnJCbNnz0Zqaqp4vyg1yvnz5zF+/HjY2dlhwoQJAIAzZ86gR48esLW1RbNmzdCjRw9cuHBB6hi7du2Ch4cH7O3tMXr0aLx//16q/dypVwQCATZs2IAlS5bAyckJtra28PPzQ0pKSr7z9/btW/j7+8PT0xNNmzZFu3btsGLFCmRkZEiUEwgE2LhxIwICAuDi4gJHR0f4+/tLjBsAbty4gR49esDKygqdO3eWObb8dOrUCaGhoeLXWVlZOHnyJDp37ixV9ubNmxg1ahRatGgBGxsbdOvWDYcOHZIoI5qrsLAwTJ48Gba2tvDw8MDGjRvlbgsAnjx5goEDB8LKygrt2rXDkSNHMHr0aPj4+EiUi4yMxE8//QR7e3vY2NjA19cXL1++lCgjes9WrlwJZ2dnNGvWDEuWLIFQKMSVK1fQrVs32Nra4ocffsCbN28k6mZkZGDFihXw8PCApaUlOnTogKNHj0qU8fPzQ+fOnREeHo7u3bvDxsYGvXr1klgfAoEAALBkyRIIBAIIBAKEh4dLjZuIiIiIiIiIiKg0KqfsDpQGHz9+xIABA1CpUiXMnDkTOjo62LFjB3744Qf8/fffqFatGgAgOjoa3bt3R926dZGRkYFjx45h4MCBOHLkCExNTQEADx48wPjx49GyZUv4+/sjOjoaP//8s1TA+MSJE5g4cSJ69OiBcePGIT4+HsuXL0dSUhJWrlwpUXbmzJno2rUr1q5dC3V1dbx8+RITJkxAp06dMHnyZOTk5ODhw4f4+PGjRL2zZ8/ixYsXmDVrFj58+IBFixZh3rx5Uu1/bceOHWjSpAl+//13REdHY9myZUhPT8+33ocPH6Cnpwd/f3/o6uoiKioKAQEBiI+Px6JFiyTK7tq1C/b29li8eDGioqKwZMkSVKtWDVOmTAEAxMfHY9iwYRAIBFi1ahWSkpIwZ84cpKamolGjRvn2XaRTp05YsWIFnj59igYNGiAsLAzp6elo3bo1fvvtN4myr1+/hp2dHfr374/y5cvjxo0bmDFjBoRCIby9vSXKzp49G926dcPatWtx+vRpLFu2DAKBAC1btix0W2lpaRg6dCh0dXWxdOlSAMDatWuRlJSEunXrio/16tUr9OvXDw0bNsTixYuhpqaGdevWYciQIThx4oRECpldu3bBwcEBS5Yswe3btxEQEICcnByEhYXhp59+gqamJubPn4/p06djy5Yt4noTJkzAjRs3MGbMGJiZmeHChQv45ZdfoKurC3d3d3G5+Ph4zJ8/H76+vtDR0cHy5csxduxYnDp1CpqamggODkbfvn3h4+Mj/jKiQYMGhXqviIiIiIiIiIiIlI2BcgBBQUFISkpCSEiIOCju7OwMLy8vbN68GVOnTgUAjB07VlwnJycHrq6uiIiIwMGDBzFp0iQAwIYNG1CjRg2sXbsWGhoaAIAKFSpg+vTp4rpCoRBLlixBx44dsWDBAvF2Q0ND+Pr6YvTo0WjYsKF4e+vWrfHLL7+IX584cQKZmZmYOXMmtLW1AQBubm5S4xIKhfjzzz/FAdWYmBisX78eOTk5UFfP+8cE5cuXl+r/jBkzMHbs2DzzmQsEAkybNk382s7ODlpaWvDz88OsWbOgpaUlMc7ly5cDAFq2bIn79+/j5MmT4kB5UFAQ1NTUsHHjRujo6AAAjI2NMWTIkDz7/LVatWrBxsYGoaGh+PnnnxEaGorWrVvLTDPTqVMn8Z+FQiGaN2+OuLg4BAcHSwXK27Vrh3HjxgH4co6cP38eJ0+eFAfKC9PW/v37kZCQgD179qB27doAAEtLS7Rr104iUB4YGIgqVapg69at4nQxdnZ28PT0REhICAYOHCguW716dXHQ3c3NDWfPnsW2bdtw7Ngx8XsWFxeHefPmISkpCbq6urh69SrOnj2LzZs3o0WLFgAAV1dXxMfHIyAgQCJQ/vHjR+zcuVN8XmppaWHw4MG4ffs2mjVrBhsbGwBAjRo1xH9WlFAoBAB8/vz5m9qh/Inml/Nc/ErDXBdXii2u1+/P17/gIklcr6RsXKOFx/VKqqYkrw9FeazSsFaB4luvgPLHRt+f0vp5rwrrtSAMlAMICwuDo6MjqlSpgqysLACAuro6mjdvLpGWJDIyEitWrMDNmzeRkJAg3h4VFSX+8+3bt9G6dWtxkBkA2rdvLxEof/78OWJiYvDrr7+KjwcADg4OUFdXx927dyUC5a1atZLor0AggIaGBqZMmYI+ffqgefPm4oBybs2bN5e469jMzAyZmZlISEiAoaFhnvPh4eEhs/937tzJM1AuFAoRFBSEffv2ITo6Gunp6eJ9r169grm5ufi1i4uLRF0zMzMcO3ZM/Pr27dtwdHSUGJOzszP09PTy7LMsnTt3xvbt2zFq1CicOXMGy5Ytk1nu48ePCAgIwJkzZxAXF4fs7GwAkHk8UUAZANTU1GBmZobY2Fi52rp79y7Mzc3FQXIAqF27NiwsLCSOFRYWho4dO0JDQ0N8nujq6qJx48ZSaYG+nlNTU1O8e/dO4v0yMTEB8CXNkK6uLsLCwqCnpwcnJyeJ89DFxQW//fYbsrOzxedB9erVJc5J0d3icXFxUnP0rUR9yb2uqPhwnkuOMufa3t6+WNrlev3+PHjwQNld+C5wvZZm1ZTdgWLFNSo/rldFVS3Gtqk4KH59kP+6WRzXImV//hTXegWUP7bSxUDZHfgulPbPe2Wf08W5XgvCQDm+pA25desWmjRpIrVPdIdvSkoKhg4dCn19ffj5+aFmzZriO61zB4Xj4+PFd6WLaGtrSzxA8sOHDwCAMWPGyOzP13mkv27P1NQU69atw/r16zF27Fioq6ujRYsWmDVrFmrWrCkup6urK1FPFDTP3V9Z8ur/27dv86wTFBSE33//HcOHD4ejoyN0dXVx584dzJ07V+p4X/dLU1NTIjVNfHw86tWrJ3UMfX39fPv9tfbt22PhwoVYvXo1NDU1Zd51D3zJwX3z5k2MGTMGDRo0gLa2Nvbs2YP//e9/UmW//kJCU1MTycnJcrX19u1bmWPR19eXmKsPHz4gKCgIQUFBUmU1NTUlXsuaU1nbgP97/z98+IDExESZ5z3w5X0wNjbOs/3cbRWlcuXKISsrCyYmJhK/RKCi9fnzZ0RFRXGeS0BZnmuu1+9PYVOYqSqu19LvwtO8/z5aFnCNFh7X6ze6FVtwGSpVFL0+XH4m/3WzKK9FZXmtipTlscnr9ut4ZXfhu1BaP+9VYb0WhIFyAFWqVIGbm5v4QZm5iYLLt27dQmxsLNavXy9x529ycrI4mAh8SSuS+25z4EuQPXdAUXR38axZs9C0aVOpY1avXl3itZqamlSZli1bomXLlkhJScHFixexaNEi+Pv7ywyqyiuv/n/dr9xOnDiB1q1bY/LkyeJtkZGRCh1f1hwCkHgQaWEYGBjAyckJ27ZtQ69evaSCy8CXQO/58+fh5+cn8SDN3bt3y93vwrZVvXp1md8evn//HpUrVxa/rlKlCtzd3TFgwACpsrnLKapKlSrQ19fHhg0bZO6X94uJoiI637W0tJT6cxtVwXkuOWVxrrlevz98nwqnLJ7TXK/fB7438iuL5zTXK8lSkudCcRyrLJ/PZXlsVDxK+/miyuc0A+X4kmriyJEjMDMzy/NESEtLAyB5J++NGzcQExMjkZKiadOmOHfuHPz9/cVpK06cOCHRVv369WFsbIxXr15J5JlWhLa2Njp27IiIiAiEhoZ+U1sisvqvpqYGKyurPOukpaVJBaKPHj2q0PGbNm2KPXv2IDk5WXwH95UrV5CYmCh3Wz4+PqhYsSJ69+4tc39GRgZycnIk+p6SkoKzZ8/KfazCtmVpaYlDhw7h1atXqFOnDoAvD4p9+PChxM9LnJ2d8eTJEzRu3FgiFU5RcXFxwaZNm6CpqSmV9kURmpqaxXKHORERERERERERUXFTmUB5dna2VMAa+BKUHTJkCI4ePYpBgwZh8ODBqFmzJt6/f4/bt2/DyMgIQ4YMgY2NDSpVqoQ5c+bA19cXcXFxCAgIgJGRkUR7vr6+6NWrF8aMGYP+/fsjOjoamzdvlki9oqamBj8/P0yZMgWpqalo1aoVtLS08Pr1a1y4cAETJ06EqalpnmPZu3cvbt26BTc3NxgaGiI6OhpHjhyBq6trkcxVRkaGRP+XLVsGLy+vPPOTA1+Crtu3b8fOnTthYmKCI0eO4MWLFwod/4cffsDu3bsxYsQIjBgxAklJSQgICJA7RznwJd+6h4dHnvt1dHRgZWWFjRs3Ql9fH+XKlcOGDRugra0t9x3shW2rZ8+eWLduHUaNGiV+MGhgYCAMDAwkfj0wfvx49OrVC8OGDUOfPn1gYGCAd+/e4dq1a2jWrBk6d+4s52xIcnV1hYeHB4YPH47hw4dDIBDg8+fPePr0KV68eCHxoNnCqF+/Ps6cOYNmzZpBS0sLpqam4ofNEhERERERERERlWYqEyhPT0+XmVplyZIl6NatG4KDg7Fq1SosW7YMiYmJqFatGqytrdG2bVsAX9J4rF69GkuWLMHo0aNhYmKCOXPmYNOmTRLtNW7cGKtXr8ayZcswduxYNGzYECtXrsSwYcMkynXo0AG6urpYt26d+M7rWrVqwc3NDQYG+T/8QCAQ4Ny5c1i0aBESExNhaGiITp06yRyfInx8fPD+/XtMnToVGRkZaNu2LWbNmpVvnTFjxuDDhw9Ys2YNAMDLywszZszAqFGj5D5+9erVsXHjRsyfPx8TJkxA3bp1MWvWLKxcuVKh8RRk+fLlmDVrFvz8/KCnpwcfHx+kpqZiy5YtxdJWxYoVsWXLFsyePRtTpkyBkZERRo8ejUOHDknkQK9Xrx5CQkKwatUqzJkzB6mpqTA0NETz5s0hEAiKZOxr1qzBhg0bsGfPHsTExEBHRwcNGzZEjx495G5r1qxZWLhwIUaMGIG0tDRs374djo6ORdJPIiIiIiIiIiKi4qQmFAqFyu4ElR4CgQBTp06VCuxT8UpMTESbNm0wZMgQjB07VtndUZo7d+5AKBQiMzMTjRo1UtmcWCUhNTUVDx484DyXgLI611yvsnn1HKfsLuTr5P4AZXehVON6Lf1WBEcpVO/yPxcUqqdT277gQkUo6BfLEj3e94zr9dv8uvmZQvWczBS71+5tkvxhh7ikHIWOVZK0K0g/T6wgio5r4bD6CtVbHRIld50JvU0UOpYsZXWtAl/Wa0ZGRpkcm6K2HFUss0BxinqXLVd567rFf09xT8+6xX4MRZTl9VpYKnNHOVFpsmHDBhgYGKBWrVqIj4/Hli1bkJ2djZ49eyq7a0RERERERERERCqHgXIiJVBXV8eff/6JuLg4aGhowNraGkFBQahRo4ayu0ZERERERERERKRyGCgnCY8ePVJ2F1SC6AGaREREREREREREpHwMlBMRERER0Xfh9ssUheodWP1DEfdENub2pO+Fka66QvU+pSt2vMoK5PJWtI/VdeU/VtdWpTNfcFEoynzjVLrsP/NS7jofU+V7XkCVSvKvJ3kM7VKvUOX4+UolRbFPHiIiIiIiIiIiIiKiMoKBciIiIiIiIiIiIiJSaQyUExEREREREREREZFKY6CcvnsBAQEQCAQQCASwsLCAvb09unTpgrlz5yIyMlKhNsPDw7Fu3boi7ukX27Ztg0AgwK+//los7efWunVrzJ07t0jb9PPzQ+fOnYu0TSIiIiIiIiIiImVioJzKhIoVKyI4OBh79+7FmjVr0KNHD/zzzz/o1q0bDh8+LHd7165dw/r164uhp8CRI0cAAKdOnUJGRkaxHKM4jR49GsuWLVN2N4iIiIiIiIiIiIoMA+VUJqirq8PGxgY2NjZwdXXFjz/+iMOHD8Pe3h7Tp0/Hq1evlN1FAMDz589x7949uLi4ICkpCefPn1d2l+RWt25dWFhYKLsbRERERERERERERYaBciqzKlSogJkzZyIzMxMhISHi7YcOHUL//v3h4OCA5s2bw8fHBxEREeL9AQEBCAwMRGpqqjili4+PDwAgMjISEydOhLu7O6ytrdGxY0ds2bIFOTk5hepTaGgo1NTUMHfuXBgYGODo0aNSZUTpUnbt2gUPDw/Y29tj9OjReP/+vbhMamoq5s6dCy8vL1hbW6N169aYNWsWkpOT8zz22bNnIRAIEBUVJbH948ePaNq0KXbt2gUAePLkCUaMGAFHR0dYW1vDy8sLGzduFJf/OvVKUlISZsyYATc3N1hZWcHd3R0TJ04s1HwQERERERERERGVBuWU3QGi4tSgQQMYGRnh5s2b4m3R0dHo3r076tati4yMDBw7dgwDBw7EkSNHYGpqit69eyM2NhahoaEICgoCAGhrawMA3r59C1NTU3Tp0gWVK1fGgwcPEBAQgNTUVIwdO7bA/oSGhqJZs2aoU6cOOnTogODgYCQnJ0NHR0ei3NmzZ/HixQvMmjULHz58wKJFizBv3jysXLkSAJCWlobs7GxMnDgR+vr6ePPmDdatW4fRo0djx44dMo/t7u4OIyMj7N+/H5MnT5boEwB06dIFADBq1CgYGBhgwYIF0NbWxsuXLxEbG5vnmBYtWoRLly5h8uTJqFWrFuLj43Hx4sUC54KIiIiIiIiIiKi0YKCcyrwaNWrg3bt34te5A9o5OTlwdXVFREQEDh48iEmTJsHY2BjGxsbidC65OTs7w9nZGQAgFAphb2+PtLQ07Ny5s8BAeUREBKKiovDjjz8CADp37owdO3bg5MmT6NWrl0RZoVCIP//8E+XLlwcAxMTEYP369cjJyYG6ujr09fUxZ84ccfmsrCzUrl0bAwYMwPPnz2Fqaip1fA0NDfTo0QP79+/Hzz//DA0NDQDA/v370bZtW+jq6uL9+/eIjo7G9OnT0bp1awCAk5NTvuO6c+cOOnfuDG9vb/G2Tp065VsnP0KhEADw+fNnhduggonml/Nc/ErDXFeqVKlY2uV6/f6kpqYquwulGtdr2VVS535pOIdURWmYa67XsoOfj8WnNKxVoPjWK6D8sX3PCrv2Sst5VNaVlnkuzvVaEAbKqcwTCoVQU1MTv46MjMSKFStw8+ZNJCQkiLd/nZJElvT0dKxfvx5Hjx7FmzdvkJmZKd736dMnVK5cOc+6oaGh0NTURPv27QEANjY2qFOnDo4ePSoVKG/evLk4SA4AZmZmyMzMREJCAgwNDQF8SSGzbds2vHjxQuLDJSoqSmagHAB69eqFdevW4dKlS2jVqhUePnyIe/fu4ZdffgEAVK1aFbVq1cKKFSvw8eNHODs7w9jYON85ady4MQ4ePAhDQ0O4ubnB3Nw83/IFycrKEo+Dih/nueQoc67t7e2LpV2u1+/PgwcPlN2F7wLXa2lWQaFaJX3uf//z/P3gelVUtWJs+/vDz8fip+zrYnGtV0CZYzNU0nGLjrxrT9nnkapQ9jwX53otCAPlVObFxsbCxMQEAJCSkoKhQ4dCX18ffn5+qFmzJipUqIAZM2YgPT29wLaWLl2KkJAQjBkzBpaWltDR0cGZM2fw559/Ij09Pc9AeU5ODo4fPw4HBweoq6sjKSkJAODp6Ynt27cjLi4ORkZG4vK6uroS9UVBc1EfT506hWnTpqFv376YOHEi9PT0EB8fjzFjxuQ7jtq1a8PV1RV//fUXWrVqhf3796N27driu8bV1NSwefNmrFy5EnPnzkVqaiqaNGkCf39/NG/eXGabM2fORJUqVbB161YsWbIENWrUgK+vLwYMGFDgfMpSrlw5ZGVlwcTEBFpaWgq1QQX7/PkzoqKiOM8loCzPNdfr96dRo0bK7kKpxvX6HTj7TKFqJXXul+VzqLQpy3NdEuv18rO3xdLu94qfj8WnLK9VEWWNLerKu4ILlXKFXXuqcB6VBpxnBsqpjHvy5Ani4uLEaUFu3bqF2NhYrF+/HhYWFuJyycnJBd45DQAnTpxA37594evrK9524cKFAutdvXoV8fHxiI+PlxlwPn78uDglS2GcOHECjRo1wty5c8Xbrl27Vqi6vXv3xpQpUxAXF4ejR4/Cx8dH4o57U1NTrFmzBpmZmbh58yZWrFiBUaNG4eLFizK/CNDR0cH06dMxffp0PHr0CNu3b8ecOXNgbm6OZs2aFXpMIqK+aGlpKfXnNqqC81xyyuJcc71+f/g+FU5ZPKdVfb2W9JhVdZ6VoSzOtaqvV2XgPBe/snw+l+WxFTd5541zXTJUeZ7Vld0BouKSnp6OefPmoXz58ujduzeALw/BBABNTU1xuRs3biAmJkairqamJjIyMmS2mbtudnY2jh07VmBfjh49ikqVKmHbtm3Yvn27xH8WFhY4evSoXGNLS0uT6IfoGIXh6ekJXV1dTJ48GR8/fkSPHj1kltPU1ISDgwN8fX2RkpKCt28LvutEIBDA398fwJcUN0RERERERERERN8D3lFOZUJOTg5u3boF4MvDIB4/fozg4GC8evUKixcvRu3atQF8yQteqVIlzJkzB76+voiLi0NAQIBE2hPgS07wrKwsBAUFwdbWFtra2qhfvz5cXFwQEhKCBg0aoGrVqti9e7fMgHpu6enpOHXqFNq1ayd+EGhuPXv2xIIFC/Ds2TPUr1+/UON1cXHB3LlzsXbtWtja2uLChQu4cuVKoepqamqie/fu2Lx5M1q0aIEaNWqI9z18+BC///47OnbsiDp16iAlJQXr169HrVq1ULduXZnt9evXD23btkXDhg2hoaGBQ4cOQVNTU6G7yYmIiIiIiIiIiJSBgXIqE9LS0tC3b18AX366U7t2bTg7OyMwMBBmZmbicgYGBli9ejWWLFmC0aNHw8TEBHPmzMGmTZsk2vPw8MCAAQOwYcMGJCQkoHnz5tixYwdmzpyJ2bNnY968edDS0oK3tzfatm2LGTNm5Nm38+fPIzk5Gd27d5e5v3PnzliyZAmOHj2KCRMmFGq8/fr1Q3R0NHbu3CkOeC9fvhx9+vQpVP22bdti8+bN6Nmzp8R2Q0NDGBgYYP369YiLi4OOjg6aNWuGpUuXQkNDQ2ZbdnZ2OHToEKKjo6Gurg5zc3OsW7dOYt6JiIiIiIiIiIhKMwbK6bs3btw4jBs3rtDlW7ZsiZYtW0psc3d3l3hdrlw5zJ49G7Nnz5bYbmBggLVr10q1KUrtIouXlxcePXqU5359fX3cvXtX/Prs2bNSZdq0aSPRhoaGBqZNm4Zp06ZJlPv6OLLaAoCLFy9CT08Pbdq0kdherVo1LF26NM++AsDixYslXk+dOhVTp07Ntw4REREREREREVFpxkA5kQp59uwZnj9/jp07d2LAgAEoX768srtERERERERERESkdAyUE6mQ2bNn49atW3Bzc8PIkSOV3R0iIiIiIiIiIqJSgYFyIhWyY8cOZXeBiIiIiIiIiIio1FFXdgeIiIiIiIiIiIiIiJSJd5QTERERFZGT+wOKvM3U1FQ8ePAAjRo1QqVKlYq8faLviXVdbYXqrQiOUqje5X8uKFSvhUs1heqd3Ldc/joKXne8eo5TqF5xXOeo5JkaKnbPXMjVBIXqJT78n9x1MpLfKHSs8jo15K6zPbRhiR2riVkthY6lXUFNoXrVdeV/rytXUOhQ6NqqrmIVSSE9PeWf7y1HX8hV/vzDdLnKmxpoylVevs/narjw9K3cn5X83CJ5MFBORERERERERERERN+VgwcPyl3H29s7z30MlBMRERERERERERHRd+XXX3+FUCiEmpr0r25kbRcKhQyUExEREREREREREVHZMn78eLi7u0tsu3PnDn777Tds3boVOjo6AIBLly5h1apV+balcKD87du3qF69uqLVqYwKCAhAYGAgAEBNTQ2VK1dGzZo10bx5cwwcOBBmZmZytxkeHo6bN29i1KhRRdLH6OhoeHp6ytxnamqKEydOFMlxcgsPD8fgwYPx119/wcrKqsjb/1YCgQBTp07FsGHDlN0VIiIiIiIiIiKiQqlduzaaNGkisS0pKQnAl3hX1apVAQDPnj0rsC2FA+WtWrWCk5MTunbtinbt2vHhUiRWsWJFBAUFAQA+ffqEx48fIzg4GPv27cOCBQvQrVs3udq7du0atmzZUmSBcpFJkybB0dFRYlvFihWL9BgiTZo0QXBwsEJfFJSE4OBg1KxZU9ndICIiIiIiIiIiUgqFA+Xjx49HaGgo/Pz8MGfOHHh6eqJr165o0aIF1NUVe4I2lQ3q6uqwsbERv3Z1dcWAAQPg6+uL6dOnw87ODnXq1FFeB/+/evXqSfSzOGlra5fYseSRlpaGihUrlsq+ERERERERERERlRSFI9qjRo1CaGgoDhw4gH79+uHatWvw9fWFm5sbFi5ciDt37hRlP+k7V6FCBcycOROZmZkICQkRbz906BD69+8PBwcHNG/eHD4+PoiIiBDvF6VySU1NhUAggEAggI+PDwAgMjISEydOhLu7O6ytrdGxY0ds2bIFOTk5RdJnPz8/dO7cGeHh4ejevTtsbGzQq1cv3L17V6JccnIypkyZAltbWzg7O2PFihXYsmULBAKBuEx4eDgEAoHEuhAIBNi4cSMCAgLg4uICR0dH+Pv7IzU1VaL92NhYTJkyBY6OjmjatCkGDhwo1QcAOHDgALp06QIrKyu4ublh5cqVyM7OltgvEAhw8+ZN/Pjjj7CxscGSJUvEfdm8ebO4rI+PD0aOHIkTJ07Ay8sLtra2GDx4MF6+fCnVt5EjR8La2hru7u7Ytm0bFixYgNatWysw40RERERERERERMrxzQ/zbNy4MRo3boypU6fi6tWrOHr0KA4cOIAdO3bA1NQUXbt2RdeuXZnWgdCgQQMYGRnh5s2b4m3R0dHo3r076tati4yMDBw7dgwDBw7EkSNHYGpqit69eyM2NhahoaHidC7a2toAvuTJNzU1RZcuXVC5cmU8ePAAAQEBSE1NxdixYwvsT05ODrKysiS2qaurS/wiIj4+HvPnz4evry90dHSwfPlyjB07FqdOnYKmpiYAwN/fH1evXsUvv/yCWrVqYd++fbh3716h5mTXrl2wt7fH4sWLERUVhSVLlqBatWqYMmUKAODjx48YMGAAKlWqhJkzZ0JHRwc7duzADz/8gL///hvVqlUDAGzduhVLly7FDz/8AD8/P0RGRooD5aK2RCZPnoy+ffti5MiR0NLSyrNvDx48wPv37zFlyhRkZ2dj8eLF+OWXXxAcHAzgy5OCR48ejXfv3mHOnDnQ0dHB5s2b8fr1a/6qhIiIiIiIiIiIlEJDQwMVKlSAmpqaxPavX3/tmwPluQ9kb2+PpKQkxMXFISwsDC9evEBgYCDWrFmDNm3aYMaMGXwAqIqrUaMG3r17J36dO6Cdk5MDV1dXRERE4ODBg5g0aRKMjY1hbGwslc4FAJydneHs7AzgS9DW3t4eaWlp2LlzZ6EC5RMnTpTa1qtXLyxYsED8+uPHj9i5cycaNmwIANDS0sLgwYNx+/ZtNGvWDE+fPsWpU6fw+++/o3v37gAANzc3dOjQoVDzYWhoiOXLlwMAWrZsifv37+PkyZPi4HZQUBCSkpIQEhIiDoo7OzvDy8sLmzdvxtSpU5GSkoI1a9Zg+PDhmDRpEoAv6W40NTWxePFiDBs2TPzgAgDo168ffH19C+xbcnIyDh06BH19fQBAamoq/P39ERsbC2NjY1y8eBH37t3Drl270KxZMwCAk5MT3N3doaurW6jxyyIUCgEAnz9/VrgNKphofjnPxa80zHVxPUeE67VklIZzSFWUhrnmeiV5ff1rxLJ2vLxwvRKVPrKuD6VhrQLFt14B5Y+N8ldaPre+B9/jep0/fz5sbW2ltjs4OOD27dsS29zc3MQ3f+alSALlojvJ//77b6SkpMDc3BzTpk1Dly5doKGhgQMHDmD9+vWYOnUqtm3bVhSHpO+UUCiU+PYmMjISK1aswM2bN5GQkCDeHhUVVWBb6enpWL9+PY4ePYo3b94gMzNTvO/Tp0+oXLlyvvWnTJkCJycniW2ioLBI9erVxUFy4Mtd8QAQFxcHAOJUKp6enuIy6urq8PDwwNatWwscg4uLi8RrMzMzHDt2TPw6LCwMjo6OqFKlivjud3V1dTRv3lx87Js3byI1NRXt27eXuEPexcUFaWlpePLkCRwcHMTbW7VqVWC/AMDCwkJiPkRjFwXK79y5A11dXXGQHAAqV64MZ2fnQt9RL4toDIU5B+jbcZ5LjjLn2t7evlja5XotWZznksP1WppVU3YHSp0HDx6U6eMVhOtVUYbF2DapqvyuD8r+/Cmu9Qoof2zyMVB2B0pcafvc+h4o+5yWZ73u3bs33+fuZWVl4c8//8S4ceOgp6cHPT29fNtTOFD+8OFDHDlyBMeOHcPbt29hYGCAXr16oXv37hK5mQFg2LBhqFChAn7//XdFD0dlRGxsLExMTAAAKSkpGDp0KPT19eHn54eaNWuiQoUKmDFjBtLT0wtsa+nSpQgJCcGYMWNgaWkJHR0dnDlzBn/++SfS09MLDJTXqVMHVlZW+Zb5+s5oUboVUf/i4+OhqakJHR0diXJfB9zlaT8jI0P8+sOHD7h16xaaNGkiVbdu3briMgDg7e0t8xhv3ryReG1gULgPxoLG/vbtW5njLOzY81KuXDlkZWXBxMQk39Qw9G0+f/6MqKgoznMJKMtzzfVaMsryOVTalOW5Livr9cLTt8ruQqnTqFGjMn28vHC9fpuYa+8KLkQkJ1nXh7K8VkW+p7Hdfh2v7C6UuNLyufU9+B7Xa0JCAnr06IHx48dj6NChEjfnPnnyBNOmTcODBw8wbty4QrWncKC8e/fuqFixIjw9PdG9e3e4urrmm5e4QYMG+Ub4qex78uQJ4uLixAHdW7duITY2FuvXr4eFhYW4XHJyMoyNjQts78SJE+jbt69EGpELFy4UfcfzYWhoiMzMTCQnJ0sEy9+/f18k7VepUgVubm6YMGGC1L7y5cuLywBAYGCgzHmrXbt2kfTla9WrV5c5zm8du+iipqWlVaw/j6MvOM8lpyzONddryeI8l5yyONdcr2VXSb+fpe38KYvnNNcrfa/yO1/L8vlclsdWFvC9kd/3dE4fOXIEixYtwrJly3Dq1CksXrwYdevWxaZNmxAQEIDq1asXKuODiMKB8oULF8LLy6vAu3ZFnJycpNJckOpIT0/HvHnzUL58efTu3RsAkJaWBuD/7lQGgBs3biAmJkYi3cnXd1nnbjN33ezsbIm0JSXB0tISAHDmzBlxjvKcnBycO3euSNp3cXHBkSNHYGZmludFytbWFlpaWoiNjUXbtm2L5LiFYWVlhaSkJPz7779o3rw5gC8pb65cufJNOcqJiIiIiIiIiIgKoq2tjQULFqBdu3aYMWMGvL29Ua9ePTx8+BB9+vSBn5+fXEF/hQLlnz9/xs6dO5Geno7+/fsr0gSVYTk5Obh16xaALw9NePz4MYKDg/Hq1SssXrxYfIezjY0NKlWqhDlz5sDX1xdxcXEICAiAkZGRRHtmZmbIyspCUFAQbG1toa2tjfr168PFxQUhISFo0KABqlatit27d8sMqOflxYsX4n6KqKmpwdrautBtNGzYEG3btsX8+fPx+fNn1KxZE/v27UNaWlqBT9ItjCFDhuDo0aMYNGgQBg8ejJo1a+L9+/e4ffs2jIyMMGTIEOjq6mL8+PFYunQpYmNj4eDgAA0NDbx69QpnzpxBQEBAsfxkpmXLlmjSpAkmT56MSZMmQVdXF5s2bULlypWLZOxEREREREREREQFadmyJXr27In169fj0aNHaNq0KX755Re574xXKFCupaWF6OhoBsNIprS0NPTt2xfAl5+41K5dG87OzggMDISZmZm4nIGBAVavXo0lS5Zg9OjRMDExwZw5c7Bp0yaJ9jw8PDBgwABs2LABCQkJaN68OXbs2IGZM2di9uzZmDdvHrS0tODt7Y22bdtixowZhernihUrpLZpaGjg/v37co134cKFmDt3LpYsWYLy5cvD29sbDRs2xK5du+RqR5aqVasiODgYq1atwrJly5CYmIhq1arB2tpa4u7xoUOHwsjICFu3bsXOnTtRrlw51K1bF61atZK4674oqamp4Y8//sCsWbMwa9Ys6OrqYvDgwXj+/DkflkFERERERERERMXuxYsX8PPzw82bN9G5c2eYm5tj7dq16NSpExYsWAA3N7dCt6Vw6hU3NzdcvnwZ/fr1U7QJKoPGjRtX6AT5wJdvfFq2bCmxzd3dXeJ1uXLlMHv2bMyePVtiu4GBAdauXSvVpii1S15q166NR48eFdi3xYsXS23T1dWVqqurq4tly5ZJbBs4cKBE3nVHR0eperL6MGTIEAwZMkRim6GhIRYsWFBgfzt16oROnTrlub9Hjx7o0aOHzH1f92XHjh1SZRo1aiRVztjYGBs2bBC/zsjIQKdOndCsWbMC+0tERERERERERKSoHTt2YMWKFahcuTICAwPRpk0bAECbNm3g5+eHESNGoHfv3pg3b16h2lM4UD569GhMmDABv/zyC/r27Ys6deqgQoUKUuX09PQUPQTRd+HkyZN48+YNzM3N8fnzZ4SGhuK///6TGcQva4KDg5GTkwNTU1MkJSVhz549iImJkXm3PhERERERERERUVERPUPzt99+k4hB169fH3v37sXGjRsREBBQ/IFy0Z2rT58+RWhoaJ7lmIKByrpKlSrh8OHDiIqKQmZmJurXr4+lS5eKv8UqyypUqIANGzYgJiYGAGBhYYH169fDyspKyT0jIiIiIiIiIqKybOXKlWjfvr3Mferq6hg5ciRatWpV6PYUDpSPGTOGOcqJ8CUNkTz5jsqS7t27o3v37sruBhERERERERERqRhRkFwoFOLu3bt49eoV1NTUYGJigkaNGgEABAJBodtTOFAuTx5qIiIiIiKibzWpr0mJHu/yPxcUqjeqS3VUqlRJ7nqT+gYodDxFnNxfcsei0qdrq7olWg+wlbvG6pAohY6Uki6Uu45VbQ2FjqX4fMjvyPmXJXaskhwXlayhXerJVX7/GfnOu4+p8q8/eXn1mSxX+Vlbn8lV3rquwqHSQuvpWbxrTN73DZC/T/IeozjHfO3aNcyYMQMvX0r2qV69evjtt9/g7Oxc6LbUi6pTaWlpSEtLK6rmiIiIiIiIiIiIiIhkevjwIXx9faGuro558+bBz88PAPD777+jXr168PX1xe3btwvd3jd9TfL69WsEBATgwoUL+PDhAwCgatWqcHd3x9ixY1GrVq1vaZ6IiIiIiIiIiIiISMoff/wBfX19HDhwAJUqVcKVK1cAAC1btkS3bt3Qv39/rFmzBps3by5UewoHyiMjIzFgwAAkJyfDxcUFZmZmAIBnz57h8OHDOHfuHHbv3o369esreggiIiIiIiIiIiIiIin//vsvBg4cmGfKOy8vLwQGBha6PYVTryxfvhzq6uo4ePAgNm3aBH9/f/j7+2Pjxo04dOgQ1NXVsXz5ckWbL7MCAgIgEAjg5uaGnJwcqf39+vWDQCAQ/1SgKPn5+aFz584FlhMIBIX+pqUoFLZfpUHr1q0xd+5cZXejSHXr1q3Q51tmZiYcHR1hYWGB169fS+0PDw+HQCDAnTt3irqbREREREREREREYqmpqTAyMspzv7q6OrKysgrdnsKB8n///Rc+Pj4ynxxqbm6OgQMH4tq1a4o2X6Zpamriw4cP+PfffyW2x8TE4NatWwo9+KcoBQcHo0uXLkrtQ2kVGBiIoUOHKrsbSnP58mUkJiZCKBQiNDRU2d0hIiIiIiIiIiIVZWBggLi4OKntQqEQGRkZOHDgAKysrArdnsKB8qysLFSsWDHP/VpaWnJF7FWJpqYmWrZsiWPHjklsP3bsGBo2bIi6dZX7hGkbGxtUr15dqX0oKaKFU1iNGzdG7dq1i7FHpVtoaCh0dXXRpEkTHD16VNndISIiIiIiIiIiFWVtbY3w8HCp7StXrkTHjh0RGRmJiRMnFro9hQPljRo1QkhICJKTk6X2paSk4K+//kLjxo0Vbb7M69y5M06ePInMzEzxttDQUJkpSERvqru7O6ytrdGxY0ds2bJFKnVLRkYGVq5cCU9PT1haWqJly5YyU2qEh4eje/fusLGxQa9evXD37l2J/V+nXvHx8cHIkSNx4sQJeHl5wdbWFoMHD8bLly+ljr9ixQp4eHjA0tISHTp0KLJgamRkJH766SfY29vDxsYGvr6+UsffsmULevbsCXt7ezg7O2PkyJF4/vy5RBlRmpcLFy6ga9eusLKywtmzZxEQEABbW1s8evQI/fv3h7W1NTp37oxLly5J1P869YqovYLmNDk5GVOmTIGtrS2cnZ2xYsUKbNmyReYvMnJ7+/Yt/P394enpiaZNm6Jdu3ZYsWKFVHBfIBBg48aNCAgIgIuLCxwdHeHv74/U1FSJcjdu3ECPHj1gZWUlnofCSk1NxdmzZ+Hl5YUePXrg8ePHePToUaHrExERERERERERFZW+fftCW1sb79+/F29TV1fHkSNHUK9ePezevRt2dnaFbk/hh3mOGzcOI0aMQIcOHdCjRw+YmJgAAJ4/f46DBw8iMTERs2bNUrT5Ms/DwwPTp09HWFgYWrVqhadPn+LRo0dYu3Ytjh8/LlH27du3MDU1RZcuXVC5cmU8ePAAAQEBSE1NxdixY8Xlxo0bh6tXr2LkyJGwsbHB+/fv8ffff0u0FR8fj/nz58PX1xc6OjpYvnw5xo4di1OnTkFTUzPP/j548ADv37/HlClTkJ2djcWLF+OXX35BcHCwuMyECRNw48YNjBkzBmZmZrhw4QJ++eUX6Orqwt3dXeG5evXqFfr164eGDRti8eLFUFNTw7p16zBkyBCcOHEC5cuXBwDExsZi0KBBqFmzJlJSUrB3717069cPJ0+ehJ6ensR8zp8/Hz/99BNq1KiBmjVr4smTJ8jMzMSUKVMwePBgjB49Ghs3bsT48eNx9uxZVK1aNc/+FWZO/f39cfXqVfzyyy+oVasW9u3bh3v37hU49g8fPkBPTw/+/v7Q1dVFVFQUAgICEB8fj0WLFkmU3bVrF+zt7bF48WJERUVhyZIlqFatGqZMmSLu57BhwyAQCLBq1SokJSVhzpw5SE1NRaNGjQrsy+nTp5GamorOnTujYcOGWLRoEUJDQwsM9hMRERERERERERU1R0dHODo6il87ODggPDwcOjo6CrWncKDc2dkZGzZswJIlS7BhwwaJfY0aNcLSpUvh5OSkaPNlnpaWFlq3bo1jx46hVatWCA0Nha2tLerUqSNV1tnZGc7OzgC+pAqxt7dHWloadu7cKQ6Uh4WF4fz581i+fLnEXelf36H+8eNH7Ny5Ew0bNhT3Y/Dgwbh9+zaaNWuWZ3+Tk5Nx6NAh6OvrA/hyd7G/vz9iY2NhbGyMq1ev4uzZs9i8eTNatGgBAHB1dUV8fDwCAgK+KVAeGBiIKlWqYOvWrahQoQIAwM7ODp6enggJCcHAgQMBAL/++qu4TnZ2NlxdXeHs7IyTJ0+ib9++EnOwceNGWFtbSxxHFCgX9dXU1BSenp64ePEiunXrlmf/CprTp0+f4tSpU/j999/RvXt3AICbmxs6dOhQ4NgFAgGmTZsmfm1nZwctLS34+flh1qxZ0NLSEu8zNDQUP0C3ZcuWuH//Pk6ePCkOlAcFBUFNTQ0bN24UXzCMjY0xZMiQAvsBfPnFg5GRERwcHKCurg4nJyeEhoZi0qRJUFNTK1QbhSEUCgEAnz9/LrI2SZpofjnPxa80zHVxPfuC67VklIZzSFWUhrnmei0bOM/Fj+uVSoOvf8FbVhTluErDWgWKb70Cyh8bfX9K47UjNTW1WNerPGNWdL0KhUIkJiaK/6xIrErhQDkAuLi44NChQ4iPj8fr168BADVr1oShoeG3NKsyOnfujMmTJyMtLQ3Hjx+Hj4+PzHLp6elYv349jh49ijdv3kika/n06RMqV66MK1euQEtLC506dcr3mNWrVxcHdAGgQYMGACAz8X1uFhYW4iB57nqiQHlYWBj09PTg5OQkkZvexcUFv/32G7Kzs6GhoZHvMfISFhaGjh07QkNDQ9y2rq4uGjduLJHi5NatW1i9ejXu378vXhgAEBUVJdGenp6eVJAc+PLTDNEXEgBQu3ZtVKxYscC5KWhO79y5AwDw9PSUOJaHhwe2bt2ab9tCoRBBQUHYt28foqOjkZ6eLt736tUrmJubi1+7uLhI1DUzM5PIg3/79m04OjpKfKvm7Owscbd9Xt6/f4+wsDD4+PhAXf1LxqYuXbpg2rRpuH79er5fsshL9B5//b5R8eA8lxxlzrW9vX2xtMv1WrI4zyWH65W+Fee55HC9lmXVlN2BAj148KAEj1ZycZbiGJeyz+fiWq+A8sdWvBjfKw7Ff+2Q/33L3afCndPyHUOeMcu7XuPi4rBy5UqcPn0aKSkpAABtbW14enpi8uTJcj2H8ZsC5SKGhoYMjiugRYsW0NTUxOrVqxEdHZ3nHcZLly5FSEgIxowZA0tLS+jo6ODMmTP4888/kZ6ejsqVKyMxMRGGhoYFfluiq6sr8VqUGiR3AFaReh8+fEBiYiKaNGkis358fDyMjY3zPUZePnz4gKCgIAQFBUntE/Xj9evXGDp0KCwtLTFnzhxUr14dmpqaGDlypNTYDAwMZB6nYsWK4jQuudv/1rmJj4+Hpqam1M8+cn/xkJegoCD8/vvvGD58OBwdHaGrq4s7d+5g7ty5Uv2S1Y/cuczj4+NRr149qWMUph//+9//kJWVBXd3dyQlJQH48vOW8uXL4+jRo0UaKC9XrhyysrJgYmIiccc8Fa3Pnz8jKiqK81wCyvJcc72WjLJ8DpU2ZXmuuV4VdbfgIjJwnosf12vZd/nZW2V3oUCFSWFZVGKuvSuxYxXluMryWhUpy2OLulJy550qKe5rhyLvW6NGjeRar/Ieo7jGHBsbi969e+P9+/fw8PAQ38j69OlThIaG4p9//sFff/0FIyOjQrWncKD80KFDBZapUKECjI2N0aRJE6kAJH0JZLZr1w7btm2Ds7NzngHcEydOoG/fvvD19RVv+/ohjHp6eoiPj1f4pwXfqkqVKtDX15dKwyNSmGBsfm27u7tjwIABUvsqV64MALh06RJSU1MRGBgoDhhnZWXh48ePUnVKen4MDQ2RmZmJ5ORkiWB57gcN5OXEiRNo3bo1Jk+eLN4WGRmpcD8SEhKkthemH6GhoQAgM03LiRMnMGPGjHxz3MtD9P5oaWkV68/j6AvOc8kpi3PN9VqyOM8lpyzONddryeI8l5yyONdcr9+Psvr+FMe4yvL5XJbHRsWjNJ4vuftUHOd0cY159erVSElJwb59+6Ru3r1//z4GDhyI1atXY+HChYVqT+FAuZ+fn/gDXJRDTST3djU1NWhra8PX1xcjRoxQ9HBlVu/evZGQkIA+ffrkWSY9PV0iCJmdnS2RUgP4knZj48aN+N///oeOHTsWW3/z4uLigk2bNkFTUxMWFhZF2razszOePHmCxo0b55m+JS0tDWpqaihX7v9OadFd0MpmaWkJADhz5ow4R3lOTg7OnTtXYN20tDSpAPTRo0cV6kfTpk2xZ88eiYD9lStXJNLUyBITE4ObN2+iX79+UufWgwcPsGjRIly6dAmtW7dWqF9ERERERERERETyunjxIvr06SMzw0Xjxo3Rr18/8c2fhfFNd5T7+flBT08PAwcORN26dQEAL168wK5du5CcnIyZM2ciISEBO3bswIoVK1C5cmWZdwWrsqZNm+KPP/7It4yLiwtCQkLQoEEDVK1aFbt375ZIqSEq4+7ujl9//RUvX76EtbU1EhMTcfLkSaxataoYR/CFq6srPDw8MHz4cAwfPhwCgQCfP3/G06dP8eLFCyxYsCDf+ikpKThx4oTUdkdHR4wfPx69evXCsGHD0KdPHxgYGODdu3e4du0amjVrhs6dO4sfHOvv749+/frhyZMn2Lp1q1Q6EmVo2LAh2rZti/nz5+Pz58+oWbMm9u3bJw7u58fFxQXbt2/Hzp07YWJigiNHjuDFixcK9eOHH37A7t27MWLECIwYMQJJSUkICAgoMEe56IIyfPhwqYfN2tvbY8OGDQgNDWWgnIiIiIiIiIiISkxKSgrMzMzy3G9qalrgDaK5KRwoDwoKQrVq1bB582aJ7QKBAG3btsXw4cPx119/YcGCBWjdujUGDBiAPXv2MFCugJkzZ2L27NmYN28etLS04O3tjbZt22LGjBkS5QICAhAYGIjg4GAEBgaiWrVqcHV1LbF+rlmzBhs2bMCePXsQExMDHR0dNGzYED169Ciw7ps3bzBhwgSp7bt27UKzZs0QEhKCVatWYc6cOUhNTYWhoSGaN28OgUAA4Mt5t2jRIgQGBmLkyJFo1KgRVq9ejZ9//rmoh6mQhQsXYu7cuViyZAnKly8Pb29vNGzYELt27cq33pgxY/DhwwesWbMGAODl5YUZM2Zg1KhRcvehevXq2LhxI+bPn48JEyagbt26mDVrFlauXJlvvdDQUNjZ2UkFyYEv+RM7d+6Mffv24dOnT3L3iYiIiIiIiIiISBFGRkZ4+zbvZ1rExcXJ9TBPNeHXeVMKqXnz5pg4cWKege9d/4+9+46K6lr/Bv6lCgioKIoNwTY2pIlIURSjKGLs/WKXKLZYA1Hxihp7HSzYUaMiiRUjxljv1YjJT4yNJMYEFSOIoAKhw7x/8HIuIwPMIAzjzPezlms55+yzzz7P2VtZzxye8/XX2LJlC27fvg0A2L9/PzZt2oR79+5V5HREamfMmDHQ1tbGoUOHqnsoKuP+/fuQSCTIzc1F27ZtVbJul7rIyMhAbGws46wE6hprrlflUdc5pIrUNdZcrxU3eHbJl8nL4/CqYYxzFeN6VX9bIuIqdFx6tuIpDpsmskt8lufT7pYVOq4izlx9prRzVeZ1qetaBQrXa05OjlpeW5FvLyk2795lVCjFqJC3Cp7jbUaBQu1tLSv8TLHchvSs2n87FL1vQOGYFFmvip6jqq55xYoViI6OLrVMsY+PDzp16oR///vfcvVX4btfUFCAv/76q9T9f/31FwoK/jcZ9fX1UaNGjYqejuijduHCBbx8+RKtW7dGZmYmIiMj8fPPP2Pbtm3VPTQiIiIiIiIiIqKPTkBAALKyskrdf+zYMRgYGMjdX4UT5Z6enjh69CiaNWuGYcOGCUnw7OxsHD9+HMeOHUPfvn2F9jExMUIdcyJNY2RkhNOnTyMuLg65ublo3rw51q1bh08++aS6h0ZERERERERERPTR0dXVhbGxcan7y9ons7+KDmTRokV4/vw5VqxYgTVr1sDc3BwAkJSUhNzcXHTs2BGLFi0CUJg8NzAwwIQJEyp6OqKPWteuXdG1a9fqHgYREREREREREZHayMvLw40bN/Ds2TO8e/euxH6JRIKZM2fK1VeFE+W1a9fG0aNHcfHiRfznP//B33//DQBwd3eHu7s7PvnkE2hrawMAatSogRUrVlT0VEREREREREREREREgtjYWEyfPh1///03tLS0ZLZRSqIcALS0tNC7d2/07t37Q7ohIiIiIiJSOSe2jFOofdFLsIjUVUVfKFmRl0PWrCE74VGe2cOs5GpXGS+ZPHpB8XiM8qpYSVplvjiU1FfQ/j8Vaq/oiy1rGSm2bp++VuxFmxURPKF5lZ9D1VT1y0KVdQ55LFu2DJmZmdixYwecnJwULrXyvg9+lWtiYiJ++uknJCcnw8vLCxYWFigoKEBqaipMTEygo1Oxt0cTEREREREREREREcny6NEjzJo1Cz169KiU/iqcKJdIJFi9ejW+/vpr5OXlQUtLC61bt4aFhQX++ecfeHp6YtasWRg/fnylDJSIiIiIiIiIiIiICADq1asnlP6uDBXuac+ePTh48CAmTpyI/fv3QyKRCPtMTEzQu3dvfP/995UySCIiIiIiIiIiIiKiIuPHj8eJEyeQkZFRKf1VOFEeERGBgQMHYu7cuWjTpk2J/SKRCHFxcR8yNlIysVgMkUgk/LGxsUHfvn2xe/duFBRI140SiUTYu3dvNY20UGxsLEQiEaKjo2Xuf/DgAUQiEU6fPi1zf35+PlxdXbFw4ULEx8dDJBIhKiqqKodcKk9PTwQHBwufAwIC4OPjU+nnEYvFuHPnTontqnA/iYiIiIiIiIiI5GViYgIDAwP4+Phgy5Yt+Oabb3Dy5EmpPydOnJC7vwqXXnn58iXs7e1L3W9oaIj09PSKdk/VxMDAAGFhYQCArKwsREdHY8OGDZBIJPDz86vm0SmmQ4cOsLa2xrlz5zBgwIAS+3/88UckJyejf//+qF+/PsLDw2FlZaX8gcrg7+9fad+GFRcSEgIjIyM4ODhIbQ8PD0ejRo0q/XxERERERERERERV4csvvxT+vnPnTpltJBIJBg8eLFd/FU6U161bFy9fvix1/8OHD9GwYcOKdk/VRFtbG3Z2dsLnLl264Pfff8f333//0SXKAcDHxwc7d+7EmzdvUKdOHal9kZGRqFu3LlxdXaGjoyN13dXN0lK5bw9WpWsnIiIiIiIiIiIqz6VLlyq1vwqXXunVqxeOHTuG58+fC9u0tLQAAP/9739x8uRJ9OnT58NHSNWuZs2ayMvLU/i4q1evYsKECXBxcYGDgwOGDRuG69evS7U5ceIERCIRHj16hMmTJ8POzg69e/fGqVOnSvS3fft2uLm5wd7eHjNmzEBycnK5Y+jfvz9yc3NLlFTJzs7GxYsX4e3tDR0dHZmlVy5duoTBgwfD3t4enTp1wuDBg3Ht2jVhv6xyJQcOHIBIJBI+Z2RkIDg4GF5eXrC1tYWnpyeCgoKQlpZW5rjfL73i6ekpVRan6E9AQAAA4NWrVwgMDETPnj3RsWNH9O7dGxs3bkROTo7UeAFg7dq1wvFFZWtkXcuxY8fg5eWFDh06wNPTE9u3b5cqwaPIvSMiIiIiIiIiIqpMjRo1kuuPvCr8RPmsWbMQHR2NAQMGoFOnTtDS0sLu3buxZcsW3L17F23btsXUqVMr2j1Vo6KkeFHple+//x6fffaZwv3Ex8ejR48emDhxIrS1tXH9+nX4+fkhLCwMzs7OUm3nz5+P4cOHY8KECTh+/DgCAgJgY2ODFi1aAAAOHz6MLVu2YOLEiXB1dcXNmzexaNGicsfQrFkz2NjYIDIyEqNGjRK2X716Fenp6ejfv7/M4549e4bZs2ejX79+mDdvHgoKCvDrr7/i3bt3CsUgKysL+fn5mDNnDszMzPDy5Uvs3LkT/v7+OHTokNz9hISESCW9f/vtNyxduhTW1tYAgDdv3qB27doIDAyEqakp4uLiIBaLkZSUhFWrVgEoLK8yYsQI+Pr6Ckn4li1byjzfoUOHsGLFCvj6+qJ79+6IiYlBSEgI0tLS8MUXX0i1Le/eERERERERERERKdulS5ewcuVKXL58Wa72FU6Um5iY4Pjx49i3bx8uXLiAGjVq4KeffoKlpSWmT5+OyZMnw8DAoKLdUzXJyMhA+/btpbZ5e3tXqOzKv/71L+HvBQUFcHZ2xh9//IHjx4+XSJSPGTMGY8aMAQDY29vj2rVruHDhAvz9/ZGfn4/Q0FAMGDBASNJ27doVycnJpb6os7j+/ftj1apVePnypVAO6Ny5c7C0tIStra3MYx49eoTc3FwsWbIExsbGwjkVZWZmhmXLlgmf8/Ly0KRJE4wePRp//fWXkOguT7t27YS/p6SkYO7cuXBzc8OUKVMAFD4RXjyB7eDgAENDQwQEBCAoKAiGhoZCeZWGDRuWWWolPz8f27ZtQ79+/bB48WIAgLu7O3Jzc7Fv3z74+flJlbEp695VhEQiAQBkZmZW6HiST1F8GeeqpwqxNjIyqpJ+uV6VQxXmkKZQhVhzvX7cVGEOaQpViDXXq/yq4v1LH3qu6ppDyoyFKlCFtQpU3XoFqv/aqGyatuY+xMe4XpOTk7Ft2zY8fPhQ5r1OS0tDQkKC8KDsqFGjMHr06FL7q3CiHCh88aO/v3+FE2KkegwMDHD48GEAQE5ODh4+fIitW7di8eLFwpPJ8kpISMCmTZtw8+ZNJCUlCT/wvZ+IBwoTsUWMjIzQqFEjJCQkCP28evUKvXr1kjrGy8tLrkS5t7c31qxZg++++w6TJk1Ceno6rl69ikmTJpV6jEgkgo6OjvC0tJOTE0xMTOS67vedOnUKBw4cwNOnT6UWbVxcnNyJ8iK5ubmYPXs2dHR0sHHjRmhrF1ZPkkgkCAsLw/HjxxEfH4/s7GzhmOfPn6N169Zyn+PPP//EmzdvSpRO8vb2RmhoKO7duwcPDw9he1n3riKKfqMhLi6uwn2Q/Bhn5anOWDs6OlZJv1yvysU4Kw/XK30oxll5uF6VzbxCR8XGxlbgqHpKOdeHxVnxeFQsFh+/6p7PVbVegeq/NsXUKb+JmtHUNfchqntOK7JeFy1ahOvXr0MkEpV4NyEAoYRw0T5DQ8My+6twonzs2LGYNm0aXFxcZO6/desWtm/fjoMHD1b0FFQNtLW1YWNjI3x2dHREfn4+Vq9ejQkTJsidcC0oKMC0adOQlpaGWbNmoVmzZjA0NMTWrVtlvgT2/SS0np6eUGokKSkJQOHT2cXVqyffD07m5uZwdnZGZGQkJk2ahIsXLyI7O7vUsisAYG1tjZ07dyI0NBQzZsyAtrY23N3dERQUpFBto4sXL+KLL77AiBEjMGfOHNSuXRtJSUmYPn26VDJbXitXrsSDBw9w/Phx1KpVS9geFhaGNWvWYPLkyXB2doapqSnu37+P4OBghc9TVF6mbt26UtuLPr9ffqase1cRurq6yMvLg5WVVbn/gFHFZWZmIi4ujnFWAnWONdercqjzHFI16hxrrlflUOc5pGrUOdaqvF5f3H5doePatm2r8DExL5Kq9FyVMYd+/4/i8ahILD5m6rxWi3xM13b6XsUfavtYadqa+xAf43r9+eefMXPmTEybNk3m/sjISMyfP1/u/HSFE+W3b9/GsGHDSt2fkpKCn376qaLdkwpp3rw5AOCPP/6QO1H+9OlTPHr0CNu2bcMnn3wibM/KylL4/Obmhd/Sp6SkSG1//Vr+H0r69++PwMBA/Pnnn4iMjET79u2F6ypNt27d0K1bN6Snp+P69etYtWoVAgMDERYWBgDQ19dHbm6u1DGpqalSn6OiotC2bVsEBwcL227fvi33uIs7duwYjh07hq1bt6JVq1YlzuPp6Yl58+YJ2548eVKh89SuXRtAyXgXvTy1eIK+KhS9FNjQ0LBKfz2OCjHOyqOOseZ6VS7GWXnUMdZcr8rFOCuPOsZaHderMq9D0XMpO87qck8VpU7z+X3qfG3qgPdGcR/TnP7nn3/QrFmzUvcXVbeQl/aHDKboP3BZnj59ipo1a35I96QiHj9+DAAyf4WhNEVPMevp6QnbXrx4gZiYGIXPb2FhAXNzc1y8eFFq+4ULF+Tuo3fv3qhRowbCwsJw69atMp8mf5+xsTG8vb3Rr18/qeSzhYVFiWT0zZs3pT5nZWVJxQAAzp49K/e5i/z8889YsWIFpk6dit69e5fYL+959PT0yn3C3NraGmZmZoiKipLafv78eejp6aFjx44Kj5+IiIiIiIiIiKgyOTk5lahAUVy9evXQuXNnuftT6InykydP4uTJk8LnHTt24Pjx4yXapaWl4bfffkO3bt0U6Z5UQEFBAe7evQugsB72w4cPsWPHDrRs2RKdOnWSu5/mzZvDwsICGzZsQEFBATIyMrB161bUr19f4THp6OjAz88PK1euRN26deHm5oYbN24gOjpa7j6MjY3RvXt3hIeHQ0tLC97e3mW2P3bsGO7evYuuXbvC3Nwc8fHxOHPmDNzc3IQ2Xl5eCAsLg42NDaytrXHmzBkkJiZK9ePq6org4GBs27ZNeNHljz/+qND1p6enY+bMmWjWrBk8PDyE+wMUlqOxtLSEq6srDh48iMOHD8PKygpnzpzB06dPS/TVvHlzXLp0CZ06dYKhoSGsra2Fl5UW0dHRgb+/P1asWAEzMzPhnLt378a4ceMU+sKEiIiIiIiIiIioKpRXUsXFxaXUsuGyKJQoz8zMxJs3b4TP//zzj/AyweKMjIwwcuRITJ8+XZHuSQVkZWVhxIgRAApr41lYWODTTz/FjBkzSjyxXBZ9fX2IxWIEBwdj9uzZaNiwIaZNm4Zbt27hwYMHCo/L19cXqampOHLkCI4ePQoXFxesWLECkydPlruP/v3748KFC3B2dkaDBg3KbCsSiXDlyhWsWrUKb9++hbm5Ofr164fZs2cLbfz9/YW362ppaWHEiBEYO3YsVq9eLbQZOXIk4uPjcfjwYezduxfu7u7YsGEDhg8fLve43759i5SUFKSkpGDkyJFS+wYNGoTVq1dj+vTpePPmDbZu3QqgMIm/ePFiTJ06Vap9UFAQvvrqK0yZMgVZWVk4ePAgnJ2dS5zT19cXurq6OHDgAI4ePQpzc3PMmDGjRH9ERERERERERETVQd7yxkVPlRe1L+0pcy2JosVa/j9PT08sWrQIPXv2rMjhREQl3L9/HxKJBLm5uWjbtu1HUxPrY5SRkYHY2FjGWQnUNdZcr8qjrnNIFalrrLlelUdd55AqUtdYq/p6PXP1WYWO+7S7pcLH7DlT8jdk5TH509Jr1RZXGXPo6AXF4zHKS/FYfMzUda0Ches1Jyfno7q2oP1/KtTe1rLCrzaUy9PXBVXaPwDMHWFV5edQFx/jem3bti0kEkmZ5cElEgl+/fVX5OTkwNbWVvgsS4Vn/OXLlyt6KBERERERERERERFRhZVXeqU4fX39cttXyldD6enpSE9PR0FByW+CGjVqVBmnICIiIiIiIiIiIiICUPgyz8ps/0GJ8iNHjuDAgQN4/vx5qW1iY2M/5BRERERERERERERERAr5+eefcf78eSxZskSu9hVOlB89ehTBwcFwd3fHkCFDsGnTJowfPx41atTAiRMnUK9ePfj6+la0eyIiIiIiIiJSIRWpNV5R9U1LrzdbFsXqqJvjxe3XFb4uTas3Th+/4AnNq3sIUr69VLH3HhCV5ZdffsF3332HqKgovHr1CgYGBlWfKD98+DDc3d2xZ88evHnzBps2bYKHhwdcXFwwefJkDBkyBG/fvq1o90REREREREREREREZXr06BG+++47nD9/Hi9evIChoSG6desGb29vdO/eXe5+Kpwof/bsGUaPHg0A0NPTAwDk5uYCAExMTDB06FAcOXIEEydOrOgpiIiIiIiIiIiIiIhK2Lx5M86fP49nz55BX18fXbt2xdy5c+Hp6QlDQ0OF+6twotzExAT5+fkAAGNjYxgaGiIhIUHYX7NmTbx+/bqi3RMRERERERERERERyRQaGgojIyMsWrQIgwYNQs2aNT+oP+2KHtiqVSv8+uuvwmdbW1scPXoUiYmJePnyJcLDw2FlZVXhgYnFYohEIohEIrRp0waOjo7o378/goOD8eTJkwr1GR0djZ07d1Z4TGU5cOAARCIRvvzyy0rt98SJExCJREhJSSmzna+vLz777DPhc1VeqzL6V0WdOnWCWCwus42vry9EIhHmzJlTYl96ejo6duwIkUiEEydOVMqYBgwYgICAAIWOiY+Ph0gkQlRUlLDN09MTwcHBlTImIiIiIiIiIiKiqtasWTNkZGRg7dq1WLBgASIjI5GRkVHh/iqcKP/000/x+PFj5OTkAABmzpyJJ0+eoHv37vD09MRff/2Fzz//vMIDAwADAwOEh4fj2LFj2Lp1KwYPHoybN29iwIABOH36tML93b59G6GhoR80ptKcOXMGAHDx4kUhJsq0dOlSfPHFF8LnqrxWZfT/MTMyMsKVK1dKLMwffvgBOjo61TSqsoWEhLBMEhERERERERERfTSioqJw4sQJjB8/Hr///jsWLFgAV1dXzJo1C+fPn0dWVpZC/VW49MqQIUMwZMgQ4bOjoyPOnTuHy5cvQ0dHB25ubrC2tq5o9wAAbW1t2NnZCZ/d3NwwevRo+Pn5YdGiRXBwcEDTpk0/6ByV4a+//sLDhw/h6uqKmzdv4urVq+jdu3eZx+Tk5EBXVxfa2hX+rkJKy5YtK6Wf6lLZ8ahODg4OePjwIS5fvgwfHx9he2RkJD755BPhSxVV0q5du+oeAhERERERERERkULatm2Ltm3bYu7cubh37x7Onz+P8+fP4+LFizAwMECPHj2wceNGufpSKCuZnZ2NoKAgHDp0SOb+pk2bYty4cSgoKEBYWJjwcs/KVKNGDSxZsgS5ubmIiIgQtp86dQqjRo1C586d4eTkBF9fX9y7d0/YLxaLERISgoyMDKGki6+vLwDgyZMnmDNnDjw8PGBrawtvb2/s27cPBQUFco0pMjISWlpaCA4ORr169XD27NkSbYpKW+zevRs9evRAx44d8fbtW2HsAwcOhI2NDZydnTFlyhS8ePFC6viEhARMnjwZdnZ26N27N06dOiW1v3jplbKuteh6p02bBkdHR9jZ2cHPzw/Pnj2T6q+goAD79+9H37590aFDB7i5uWHWrFlIS0srs/+AgACp5DAApKamlig3UlY8Tpw4gf79+8PGxgZdu3bFpk2bhHr4pZHnHhaVHDl9+jSCg4Ph5OQEd3d3rFmzBnl5eVL9/fDDD+jTpw9sbGwwdOhQqblUHl1dXXh5eSEyMlLYlpKSgh9//LFEbIocO3YMXl5e6NChAzw9PbF9+/YS8+/OnTsYPHgwbGxs4OPjg2vXrsnsKyYmBmPHjoWdnR0cHR0xb948JCcnlznm90uvFN3H6OhoDBw4EHZ2dhg6dCgePHggdVxaWhrmz58Pe3t7uLi4YOPGjdi3bx9EIlGZ5yMiIiIiIiIiIqpMHTt2xBdffIGrV6/i8OHDGDJkCG7fvi338Qo9UR4eHo6TJ0/iu+++K7Ndjx49sG7dOrRu3RqjR49W5BRyadmyJRo0aICYmBhhW3x8PAYOHAhLS0vk5OTg3LlzGDNmDM6cOQNra2sMGzYMCQkJiIyMRFhYGIDCl5ACwKtXr2BtbY3+/fujZs2aiI2NhVgsRkZGBmbMmFHueCIjI9GpUyc0bdoUffv2RXh4ONLS0mBiYiLV7vvvv0ezZs2waNEiaGtrw8jICHv27MG6deswdOhQzJkzB7m5ubh16xZSUlLQuHFj4dj58+dj+PDhmDBhAo4fP46AgADY2NigRYsWJcZT1rU+f/4cI0eORKtWrbB69WpoaWlh586dGD9+PKKioqCvrw8AWL58OcLDwzFu3Di4ubnhn3/+wdWrV5GRkVFm/4qQFY/9+/dj3bp1GDduHAICAvDkyRMhUT5//vxS+1LkHm7evBk9e/bE5s2bERMTA7FYDEtLS4waNQoAEBsbi1mzZqFbt24IDAxEfHw8Pv/8c4VK6vj4+GDChAl49+4datWqhfPnz6NBgwawt7cv0fbQoUNYsWIFfH190b17d8TExCAkJARpaWlCOZ2kpCRMmjQJIpEImzdvRmpqKpYtW4aMjAy0bdtW6CsmJga+vr7w8PDApk2bkJmZic2bN8Pf3x/h4eFyj7/onCtWrICfnx9MTEywYcMGzJgxAxcvXoSenh4AIDAwELdu3cKCBQvQuHFjHD9+HA8fPlToPERERERERERERJXJ0dERjo6OWLRokdzHKJQoP3/+PHr37l1uuZOmTZvCy8sL586dq5JEOQA0bNgQr1+/Fj4XT4YWFBTAzc0N9+7dw8mTJzF37lxYWFjAwsKiRDkXAHBxcYGLiwsAQCKRwNHREVlZWTh8+HC5ifJ79+4hLi4OEyZMAFCYID106BAuXLiAoUOHSrXNzc3F7t27YWRkBKDwadyQkBCMGDFC6mneTz75pMR5xowZgzFjxgAA7O3tce3aNVy4cAH+/v4l2pZ1rSEhIahVqxb279+PGjVqACgsFdKzZ09ERERgzJgx+Ouvv3D06FHMmTNH6gWhXl5eUueQ1b8i3o9Heno6tm7dismTJ2Pu3LkACsvt6OnpYfXq1Zg0aRLq1Kkjsy9F7mHHjh2xePFiof/o6GhcuHBBSJTv2rULDRs2xLZt24Sa4jVq1FBoYXXq1Al169bF999/j2HDhuHcuXMynybPz8/Htm3b0K9fP2FM7u7uyM3Nxb59++Dn54c6deogLCwMWlpa2L17t/AFjIWFBcaPHy/V34YNG9ChQweEhIRAS0sLANC6dWvhCXQPDw+5r+Hdu3c4fPgwWrVqBQAwNDTE2LFj8csvv6BTp074448/cPHiRaxZswYDBw4EAHTt2hV9+/aV+xyySCQSAEBmZuYH9UNlK4ov41z1VCHWRf/OVjauV+VQhTmkKVQh1lyvHzdVmEOaQhVizfWqPj7kxW9UNlVYq0DVrVeg+q+Nysb1Lb+Peb2mp6fjv//9L549ewZtbW1YWVnB1dUVRkZGQn5MHgolyn///Xf0799frrYODg5y13+pCIlEInWhT548wcaNGxETEyNVZiIuLq7cvrKzsxEaGoqzZ8/i5cuXUiVj/vnnH9SsWbPUYyMjI6Gnp4c+ffoAAOzs7NC0aVOcPXu2RKLc2dlZ6mbHxMQgMzOzRDtZ3N3dhb8bGRmhUaNGSEhIKPe49924cQPe3t7Q0dERyo2YmpqiXbt2QlmNW7duQSKRyDWuDyErHhkZGejTp49UKRRXV1dkZWXh8ePH6Ny5s8y+FLmHxWMJAC1atMCtW7eEz7/88gs8PT2lXrzZp08fhRLlWlpa8Pb2xrlz5+Du7o47d+5g6dKlJdr9+eefePPmjTB/inh7eyM0NBT37t2Dh4cHfvnlFzg7O0v9loKLiwtq164tfM7MzMSdO3ewcOFCqVI1VlZWaNiwIe7fv69Qorx+/fpCkhz4Xx38xMREAMD9+/cBAD179hTaaGtro0ePHti/f7/c53lf0b2XZ+3Sh2Oclac6Y+3o6Fgl/XK9KhfjrDxcr/ShGGfl4XpVZ+ZKO1NsbKzSzqWpqns+V9V6Bar/2j5uVb/Oub4VV91zWtH1GhkZiX//+99IT0+HlpaWkDM2MTHBl19+KTzcKQ+FEuW5ublCyYXy6OnpKVSqQlEJCQmwsrICUPitwcSJE2FmZoaAgAA0atQINWrUwOLFi5GdnV1uX+vWrUNERASmT5+ODh06wMTEBJcuXcKOHTuQnZ1daqK8oKAA3333HTp37gxtbW2kpqYCKEwcHjx4EImJiWjQoIHQvm7dulLHF9Xkrl+/frljfL+MS0Xj++bNG4SFhQklU97vs2hcurq6JcZb2d7v/82bNwCAQYMGyWz/8uXLUvtS5B6WF8ukpKQSYzM2NhaewJdXv379EBYWhgMHDqBVq1YQiUTCHCny7t07ACVjUfS5aH9SUhKaNWtW4hxmZmbC31NTU5Gfn49Vq1Zh1apVJdqWFT9ZTE1NpT4XzY+iNZWUlAQ9Pb0S8Sw+porQ1dVFXl4erKysYGho+EF9UekyMzMRFxfHOCuBOsea61U51HkOqRp1jjXXq3Ko8xxSNeoca67XQi9uvy6/USUpXs6SKpc6r9Ui6nxtVS3ux6pf51zf8vsY1+tPP/2EL774Ao6Ojpg6dSpSUlIwf/58hIWF4dy5cwgMDESdOnXkfnBUoUR5/fr18fjxY7naPn78WK4EcEU8fvwYiYmJQkL17t27SEhIQGhoKNq0aSO0S0tLg4WFRbn9RUVFYcSIEfDz8xO2lfaixOJu3bqFpKQkJCUlwcnJqcT+7777TijJAqDEo/5FTwO/evVKrnFWhlq1asHDw0NmSZyiZHLt2rWRl5eH5ORkhZPl+vr6JV7iWpTsfd/78ahVqxaAwvIwsuLRpEmTUs9b0Xsoi7m5eYmXX6anp8v1pUtxHTp0QNOmTXHw4EHMnj1bZpuiOZCSkiK1vej8RTGRNab3jzMxMYGWlhY+++wzmeV7SitbU1Hm5ubIzc0tUY///WtRVNG8MDQ0rNJfj6NCjLPyqGOsuV6Vi3FWHnWMNdercjHOyqOOseZ6VT7Gueqp83xW52tTB7w3ivuY5vSuXbvQvHlz7Nu3D7q6uvjxxx8BAK1atcKyZcvw8uVL7Nq1q2oS5a6urjh9+jQ+++yzMhOoycnJOH36tFRN68qSnZ2N5cuXQ19fH8OGDQMAZGVlAYDU0+537tzBixcvpEpHlPYUdnZ2ttSx+fn5OHfuXLljOXv2LIyMjLB9+3Zoa2tL7fvqq69w9uxZqUT5++zt7WFoaIhvv/0WHTt2LPd8iijtWl1cXPD48WO0a9dOqrRIcV26dIGWlha+/fZbqcSzPP1bWFggISFBqtzJjRs35BpzUTwSEhLQq1cvuY4pUtF7KEvHjh1x5coVBAYGCjGKioqqUF9TpkzB5cuXSy1ZZG1tDTMzM0RFRUld8/nz56GnpyfMi44dO+Lo0aNSSekff/xR+K0EoPAffzs7O/z555+wsbGp0HgV0aFDBwDApUuXhF9jKSgowJUrV6r83EREREREREREpNl++eUXTJw4Ebq6slPcHh4eWL9+vdz9KZQonzJlCs6cOYNx48Zh5cqVsLW1lTnAopInkydPVqT7EgoKCnD37l0AhcX3f//9d4SHh+P58+dYvXq18ISxnZ0djIyMsGzZMvj5+SExMRFisViq7AlQWIs6Ly8PYWFhsLe3h7GxMZo3bw5XV1dERESgZcuWqFOnDo4cOVJuWZPs7GxcvHgRvXv3Fl4iWdyQIUOwcuVK/Pnnn2jevLnMPkxMTDB9+nSsX78eEokEPXv2REFBAaKjo9GvX78PSnaWdq2zZs3C0KFDMWnSJAwfPhz16tXD69evcfv2bXTq1Ak+Pj6wtrbGyJEjsWXLFrx79w4uLi7IysrC1atXMXPmTDRo0KDU/nv37o2tW7fiyy+/xPDhw/H48WN88803co3Z1NQUs2bNwrp165CQkIDOnTtDR0cHz58/x6VLlyAWi0v91Y+K3MPS+Pn5YejQoZg+fTpGjRqF+Ph47N27V+HSKwAwdOjQMmu96+jowN/fHytWrICZmRk8PDxw9+5d7N69G+PGjROeAh83bhyOHDmCKVOmYMqUKUhNTYVYLJaqUQ4ACxcuxLhx4/D555+jX79+MDU1RUJCAm7evInBgwfD2dlZ4WsoTatWrdCrVy+sWLECmZmZaNSoEY4fP46srCyFXpRARERERERERESkqJycnDJLACuao1IoUd60aVNs3rwZc+fOxciRI9G0aVO0bt0aNWvWxD///IPHjx/j2bNnMDAwwMaNG2FpaalI9yVkZWVhxIgRAAqflm3SpAlcXFwQEhKCFi1aCO3q1auHLVu2YO3atfD394eVlRWWLVuGPXv2SPXXo0cPjB49Grt27UJycjKcnJxw6NAhLFmyBEuXLsXy5cthaGiIQYMGoVevXli8eHGpY7t69SrS0tJKLQjv4+ODtWvX4uzZs6WW3QAKv3wwMzPDgQMHcOLECdSsWRP29vYfXB+8tGtt1qwZIiIisHnzZixbtgwZGRkwNzeHk5MTRCKRcHxQUBCaNGmCiIgIhIWFoXbt2nBychKeEi+t/5YtW2L16tXYvn07/P394ejoiPXr12PAgAFyjXvixIlo0KAB9u/fj8OHD0NXVxeWlpbo3r17mfXxK3IPS9OuXTts2bIF69evx4wZM9CqVSts2rQJkyZNUrgvefj6+kJXVxcHDhzA0aNHYW5ujhkzZmDq1KlCm/r162P37t1YsWIFZs+eDUtLSwQFBWHTpk1SfTk4OODIkSMQi8UIDAxEbm4uLCws0KVLF5k1zj/UV199heDgYKxduxb6+voYNGgQWrVqha+//rrSz0VERERERERERFSkQYMG+Pvvv0tsl0gkSElJwdGjR2WWyy6NlkQikSg6iPj4eOzevRtXr15FYmKisL1+/fro3r07pkyZgqZNmyraLRGpgTFjxkBbWxuHDh1S+Nj79+9DIpEgNzcXbdu2/WhqYn2MMjIyEBsbyzgrgbrGmutVedR1DqkidY0116vyqOscUkXqGmuu1/85c/WZ0s71afcPe8iPSqeuaxUoXK85OTlqeW3K8u2lql/nQ3pyfcvrY1yvX375JWJjY3Hy5EkAhWWKJ06cCFdXV9y7dw/a2tr4+uuv0bJlS7n6U+iJ8iJNmjTBsmXLABS+5LCoHrWxsXFFuiOij9SFCxfw8uVLtG7dGpmZmYiMjMTPP/+Mbdu2VffQiIiIiIiIiIhIjf3rX//CiRMnkJKSAjMzM+jq6sLExATx8fHo27cvpk6dikaNGsndX4US5cUZGxszQU6koYyMjHD69GnExcUhNzcXzZs3x7p16/DJJ59U99CIiIiIiIiIiEiNtWvXDu3atRM+Ozk54fbt2xXu74MT5USkubp27YquXbtW9zCIiIiIiIiIiIg+CBPlRERERERERKRSqrpuePFavERUPeStH/4x1s6mj5N2dQ+AiIiIiIiIiIiIiKg6MVFORERERERERERERBqNiXIiIiIiIiIiIiIi0mhMlBMRERERERERERGRRlOpRLlYLIZIJIJIJEKbNm3g6OiI/v37Izg4GE+ePKlQn9HR0di5c2eljTE+Pl4Y4/Xr10vsP378uLCfKldl38sPkZqaCpFIhBMnTpTZ7s2bN/jqq6/Qu3dv2NjYwMXFBaNGjcKBAweENvHx8RCLxUhMTKzUMapSvIiIiIiIiIiIiFSZSiXKAcDAwADh4eE4duwYtm7disGDB+PmzZsYMGAATp8+rXB/t2/fRmhoaKWP08jICN99912J7ZGRkXwDbxWpqntZVfLy8jBu3DhcvHgREyZMwJ49e7B48WK0a9cOV65cEdq9ePECISEhePXqVaWe/2OLFxERERERERERUXXRre4BvE9bWxt2dnbCZzc3N4wePRp+fn5YtGgRHBwc0LRp0+ob4P/Xs2dPXLx4EcuWLUONGjUAAK9evcJPP/0EHx8fnDlzpppHSNXt9u3b+O2333D48GE4OTkJ2/v164eCgoIK9ZmVlQUDA4PKGiIRERERERERERFBBZ8ol6VGjRpYsmQJcnNzERERIWw/deoURo0ahc6dO8PJyQm+vr64d++esF8sFiMkJAQZGRlCORRfX18AwJMnTzBnzhx4eHjA1tYW3t7e2Ldvn9wJzG7dukFLSwvXrl0Ttn333XewtLRE+/btS7Rfv349+vfvD3t7e3Tt2hVz584t8QSxr68vPvvsM0RFRcHLywv29vYYO3Ysnj17pnBfEokEISEhcHNzg729PWbNmoWbN29CJBIhOjpaqt3evXvh5eWFDh06oGfPnlJlQYriaG9vj0ePHmHEiBHo2LEjBg0ahEePHiE7OxtLly6Fk5MTunXrVuJYAIiJicHYsWNhZ2cHR0dHzJs3D8nJycL+onI2p0+fRnBwMJycnODu7o41a9YgLy9PGENp91KWmJgYTJ06Fe7u7rCzs8OAAQNw6tQpqTbR0dEQiUS4ceMG5s2bB3t7e/To0QO7d+8u0d/x48fh6ekJW1tbjBs3Dk+fPi313EXevXsHADA3Ny+xT1tbWxjD2LFjAQBDhw6VKttTNL6rV69i1qxZcHBwwOzZswF82NwHCuf/tGnT4OjoCDs7O/j5+ZWYZ2lpaZg/fz7s7e3h4uKCjRs3Yt++fcL4cnNz4ebmhk2bNpW4vs8//xxDhw4tN0ZERERERERERESqQOWeKC9Ny5Yt0aBBA8TExAjb4uPjMXDgQFhaWiInJwfnzp3DmDFjcObMGVhbW2PYsGFISEhAZGQkwsLCAADGxsYACp/+tra2Rv/+/VGzZk3ExsZCLBYjIyMDM2bMKHc8+vr66NWrFyIjI9G7d28AhWVXfHx8ZLZPTk7GZ599hvr16yMlJQX79++Hr68vzp07B13d/92G2NhYpKSkYP78+cjPz8fq1auxYMEChIeHK9TXoUOHEBISgsmTJ6NLly64desWFi9eXGJcK1euREREBKZOnQpbW1vcuXMH69evR40aNTBq1CihXW5uLr744guMHz8e9erVw/r16zFjxgw4ODigbt262Lx5My5duoRVq1ahY8eOcHBwAFCYsPb19YWHhwc2bdqEzMxMbN68Gf7+/lLXBACbN29Gz549sXnzZsTExEAsFsPS0hKjRo0q817K8vfff8PBwQGjRo2Cvr4+7ty5g8WLF0MikWDQoEFSbZcuXYoBAwZg27Zt+OGHH7B+/XqIRCJ069YNAHDlyhUsWbIEgwcPhre3Nx4+fCgkrMvStm1baGtrY/HixZg+fTocHR2hr68v1aZ9+/YICgpCcHAwVq1ahebNm5foZ8mSJfj000+xbds2IcH+IXP/+fPnGDlyJFq1aoXVq1dDS0sLO3fuxPjx4xEVFSWMMTAwELdu3cKCBQvQuHFjHD9+HA8fPhTGpaenh0GDBuHUqVOYPXu2MLa3b9/i0qVLWLRoUbkxIiIiIiIiIiIiUgUfTaIcABo2bIjXr18Ln4sntAsKCuDm5oZ79+7h5MmTmDt3LiwsLGBhYVGinAsAuLi4wMXFBUDhU9WOjo7IysrC4cOH5UqUA4CPjw/8/f3xzz//IDk5Gffv38e6deuknjIvsmrVKuHv+fn5sLe3R7du3XDr1i24u7sL+9LS0nDq1CmYmZkBADIyMhAYGIiEhARYWFjI1Vd+fj527dqFwYMHY/78+QAAd3d3vHnzBt98841w7LNnz3D48GEsW7YMI0aMAAC4uroiKysL27Ztw4gRI4TkZ25uLubPnw8PDw8h3kXJ9cDAQABAly5dEBUVhaioKCFRvmHDBnTo0AEhISHQ0tICALRu3Ro+Pj64du2a0B8AdOzYUUjmu7m5ITo6GhcuXMCoUaPKvJey9OvXT/i7RCKBk5MTEhMTER4eXiJR3rt3b8ycORNA4by4evUqLly4ICTKd+zYgU6dOglx79q1K7Kzs7F9+/Yyx2BlZYWAgACsW7cO48ePh56eHjp27Ii+ffti1KhR0NXVhbGxMVq2bAkAaNWqFWxsbEr04+npiQULFkht+5C5HxISglq1amH//v1C2SAHBwf07NkTERERGDNmDP744w9cvHgRa9aswcCBA4Xr7tu3r1Rfw4YNw549e/Cf//xHuJdnz56FtrZ2qV8alUcikQAAMjMzK3Q8yacovoxz1VOFWFfVuzO4XpVDFeaQplCFWHO9ftxUYQ5pClWINdfrx00V5pAmUJU4V+W75Kr72jSBqswjdacqca7Odz9+VIlyiUQiJFuBwvIRGzduRExMjFQpj7i4uHL7ys7ORmhoKM6ePYuXL18iNzdX2PfPP/+gZs2a5fbRpUsX1KxZEz/88ANevHiB9u3bw9raWmai/Nq1a9ixYwceP36M9PR0qbEWT5S3adNGSJIDEJKoxRPl5fWVkJCApKQkeHp6So2hZ8+eUonymzdvAihMFBeVOAEKk+W7d+/Gy5cv0bhxYwCFpUKKvlgACpPARW2L6OjowNLSEgkJCQAKF9adO3ewcOFC5OfnSx3bsGFD3L9/XypRXjwOANCiRQvcunWrRCzl8e7dO4jFYly6dAmJiYnC+WvXrl2ibfHzamlpoUWLFsI15Ofn4+HDhyUS1V5eXuUmygFg3Lhx8Pb2xuXLl3H79m38+OOPWLFiBb7//nuEhYUJX0SUpXv37iW2fcjcv3HjBry9vaGjoyPcd1NTU7Rr1w4PHjwAANy/fx9A4Zwpoq2tjR49emD//v3CtmbNmqFz58749ttvhXt54sQJeHl5lfnEf1mKxiTPtdCHY5yVpzpj7ejoWCX9cr0qF+OsPFyv9KEYZ+XheqUPxTgrR3XHuarWK1D916ZJGGvlqO44V+V6Lc9HlShPSEgQErTp6emYOHEizMzMEBAQgEaNGqFGjRpYvHgxsrOzy+1r3bp1iIiIwPTp09GhQweYmJjg0qVL2LFjB7Kzs+VKlOvo6KBv3744d+4cXrx4gSFDhshsd+/ePfj7+6Nnz56YMmUK6tatCy0tLQwfPrzEWE1NTaU+6+npAYDQTp6+kpKSAEAq4Q4AdevWlfr85s0bSCQSdOnSRea4iyfKDQwMpMqGFI3LxMSkxHiLxpGamor8/HysWrVK6in44v0XJ6uvnJwcmWMrT0BAAGJiYjB9+nS0bNkSxsbGOHr0KM6fP1+irazzpqWlAQBSUlKQl5dXIpb16tWTeyzm5uYYMWIERowYgdzcXAQFBeHEiRO4cuWKVCK6NO/ftw+d+2/evEFYWJhQkqW4ovualJQEPT29ErF5Pw4AMHz4cAQEBCAlJQWvXr3Co0ePEBAQUO44SqOrq4u8vDxYWVnB0NCwwv1Q2TIzMxEXF8c4K4E6x5rrVTnUeQ6pGnWONdercqjzHFI16hxrrlflUOc5pEo0Ic7qfG2qQhPmkSpgnD+iRPnjx4+RmJgolM24e/cuEhISEBoaijZt2gjt0tLShCevyxIVFYURI0bAz89P2CbrSfDy9OvXD2PGjAEAeHt7y2zzww8/wNjYGJs3bxaeIH7x4oXC55K3r6KXR6akpEhtL/7kMQDUqlULWlpaOHLkiJAgLc7a2rpCYyxiYmICLS0tfPbZZ/jkk09K7K9Tp84H9V+a7OxsXL16FQEBAVIvsDxy5IjCfZmZmUFXV7dELIuXAFKEnp4exo8fjxMnTuDJkydyJcqL/xYF8OFzv1atWvDw8MDo0aNL7Cv6gsjc3By5ublIS0uTSpa/Hweg8DcSli9fjjNnziA+Ph6Wlpbo3LlzueMoTdH1GhoaVuuv22gKxll51DHWXK/KxTgrjzrGmutVuRhn5VHHWHO9KhfjrBzqHGd1vjZVw1grhybH+aNIlGdnZ2P58uXQ19fHsGHDAABZWVkAIJXgvXPnDl68eIFWrVoJ20p7Kjk7O1vq2Pz8fJw7d07hsdnb28PHxwd169YtNUmZlZUFPT09qYTn2bNnFT6XvH1ZWFjA3Nwcly5dkkpQ//DDD1LtikqpvH37tkSZlspgZGQEOzs7/PnnnzJrbytK3ifMc3JyUFBQIHV/09PTcfnyZYXPqaOjg3bt2uHixYsYP368sP3ChQvlHvv27VsYGxtLvawV+N+vsBR9ofH+bw2U50PnvouLCx4/fox27dpBR0dH5jk6dOgAALh06ZJQo7ygoABXrlwp0VZfXx8DBgxAREQEXr9+jfHjx5dI7hMREREREREREakylUuUFxQU4O7duwAKX2T5+++/Izw8HM+fP8fq1avRpEkTAICdnR2MjIywbNky+Pn5ITExEWKxGA0aNJDqr0WLFsjLy0NYWBjs7e1hbGyM5s2bw9XVFREREWjZsiXq1KmDI0eOVKjMh5aWFtatW1dmGzc3N4SFhWH58uXo1asXYmJicPr0aYXPJW9fOjo68PPzw1dffYV69erB2dkZ0dHR+PHHHwFAeBLd2toaY8aMwcKFCzFp0iTY2toiNzcXcXFxiI6OlqsGd3kWLlyIcePG4fPPP0e/fv1gamqKhIQE3Lx5E4MHD4azs7PcfZV2L99nYmICGxsb7N69W3gifNeuXTA2Npb5RHR5pk6dCn9/fwQGBsLb2xsPHz6U6/7dunUL69evx6BBg9CxY0fo6uoiNjYWoaGhaNSoEXr16gWg8Ne0dHR08O2330JXVxc6OjplfrHwoXN/1qxZGDp0KCZNmoThw4ejXr16eP36NW7fvo1OnTrBx8cHrVq1Qq9evbBixQpkZmaiUaNGOH78OLKysmQmwYcPH46wsDDo6Ohg8ODBCkaYiIiIiIiIiIioeqlcojwrKwsjRowAUPhEcpMmTeDi4oKQkBC0aNFCaFevXj1s2bIFa9euhb+/P6ysrLBs2TLs2bNHqr8ePXpg9OjR2LVrF5KTk+Hk5IRDhw5hyZIlWLp0KZYvXw5DQ0MMGjQIvXr1wuLFiyv9mjw8PDB//nwcPnwYJ06cgIODA0JDQ+Hl5VVlffn6+iI1NRVHjhzBoUOH4OLiggULFmDOnDlSpTQWL14Ma2trhIeHY9u2bahZsyasra3Rp0+fD75uAHBwcMCRI0cgFosRGBiI3NxcWFhYoEuXLmjWrJlCfZV2L2XZsGEDgoKCEBAQgNq1a8PX1xcZGRnYt2+fwtfQs2dPLFu2DDt37sS5c+dga2uLzZs3C7/dUBpbW1t4eXnh0qVLCAsLQ3Z2NiwsLNC/f3/4+fkJL7s0MzNDUFAQ9uzZgzNnziAvLw+//fZbqf1+6Nxv1qwZIiIisHnzZixbtgwZGRkwNzeHk5MTRCKRcPxXX32F4OBgrF27Fvr6+hg0aBBatWqFr7/+usSYWrZsCSsrK1haWpZI2BMREREREREREak6LYlEIqnuQZBybN68Gfv370d0dDQMDAyqezj0ERozZgy0tbVLfEHx7Nkz9O7dG1u2bKnQF0BF7t+/D4lEgtzcXLRt21Zja2IpQ0ZGBmJjYxlnJVDXWHO9Ko+6ziFVpK6x5npVHnWdQ6pIXWPN9ao86jqHVI06x/n+/fvIyclRy2tTNeo8j1QJ46yCT5RT5Xjy5AnOnDkDe3t76Onp4fbt29i7dy9GjRrFJDnJ5cKFC3j58iVat26NzMxMREZG4ueff8a2bduENm/evMFff/2Fbdu2oVGjRnK9nJSIiIiIiIiIiEjVMFGupgwMDBATE4OjR4/in3/+QYMGDTBp0iTMnDmzuodGHwkjIyOcPn0acXFxyM3NRfPmzbFu3TqpF8ReuXIFX375JZo1a4Z169aVeHEpERERERERERHRx4BZLTXVuHFjHDx4sLqHQR+xrl27omvXrmW2GTx4MF/eSUREREREREREHz3t6h4AEREREREREREREVF1YqKciIiIiIiIiIiIiDQaE+VEREREREREREREpNGYKCciIiIiIiIiIiIijaaxiXKxWAyRSASRSIQ2bdrA0dER/fv3R3BwMJ48eVKhPqOjo7Fz585KHinw5MkTzJs3D25ubujQoQM++eQTrF27FqmpqZV+riLR0dEQiUS4f/++sE0kEmHv3r3C5xMnTuDs2bNVNobK9sMPP0AkEiE+Pr7ctu/HvGfPnli9ejXevn1b9QOtoPj4eIjFYiQmJkptl3UviYiIiIiIiIiI6H80NlEOAAYGBggPD8exY8ewdetWDB48GDdv3sSAAQNw+vRphfu7ffs2QkNDK3WMP/30E4YOHYo//vgDCxcuxL59+zB27FicPHkSI0eOREpKSqWeryzh4eHo37+/8PnkyZOIjIxU2vmVRVbMx40bh9OnT2PEiBFISkqq7iHK9OLFC4SEhODVq1dS29u3b4/w8HC0aNGimkZGRERERERERESk2nSrewDVSVtbG3Z2dsJnNzc3jB49Gn5+fli0aBEcHBzQtGnTahtfVlYW5s6di0aNGuHo0aMwMjICAHTu3Blubm4YOHAgVqxYgY0bNyplPMVjpa7kiXlwcDDEYrHSxmNgYPBBfRgbG2vEvSMiIiIiIiIiIqoojX6iXJYaNWpgyZIlyM3NRUREhLD91KlTGDVqFDp37gwnJyf4+vri3r17wn6xWIyQkBBkZGQIJV18fX0BFJbxmDNnDjw8PGBrawtvb2/s27cPBQUFZY4lKioKr169wtSpU4WEbZEWLVpgwIABOH/+vFBq48SJExCJRCWeMh8wYAACAgKEzzExMZg6dSrc3d1hZ2eHAQMG4NSpU+XGpnjpFV9fX9y+fRtXr14VrlcsFuPQoUOwtbVFenq61LFPnjyBSCTCtWvXSu2/vBgDhXG2t7fHb7/9hlGjRsHW1hY+Pj74z3/+I9UuNzcXK1euROfOneHo6Igvv/wS//zzT7nXKE/ML168iBcvXgD4X1mTa9euYcaMGbCzs4O7u7vMEjxPnjzBtGnT4OjoCDs7O/j5+eHZs2dSbUQiEXbt2oV169bBzc0NLi4uAMq/Z9HR0Rg7diwAYOjQocI9KT7G4qVXsrOzsWrVKri7u8PGxka4ruICAgLg4+OD6OhoDBw4EHZ2dhg6dCgePHgg1e6bb75Bv3790LFjRzg7O2PUqFEl7hsREREREREREZEq0+gnykvTsmVLNGjQADExMcK2+Ph4DBw4EJaWlsjJycG5c+cwZswYnDlzBtbW1hg2bBgSEhIQGRmJsLAwAIVP8gLAq1evYG1tjf79+6NmzZqIjY2FWCxGRkYGZsyYUeo4bt++DQDo0aOHzP2enp6IiIjATz/9BB8fH7mv7++//4aDgwNGjRoFfX193LlzB4sXL4ZEIsGgQYPk6mPp0qVYsGABDAwM8MUXXwAALCwsYGhoiHXr1iEyMhIjR44U2n/zzTdo0KAB3N3dS+2zvBgXyc3Nxfz58zF27Fj4+/tj9+7dmDVrFi5fvow6deoAADZu3IijR49i5syZaNeuHc6dO4cNGzaUe13yxvz//u//0LhxY2H7kiVL0K9fP4jFYty8eRObNm1CrVq1MGrUKADA8+fPMXLkSLRq1QqrV6+GlpYWdu7cifHjxyMqKgr6+vpCXwcPHoStrS1WrlyJvLw8AOXfs/bt2yMoKAjBwcFYtWoVmjdvXuZ1zp8/H//5z3/w+eefo3nz5jh9+jRmzpyJbdu2oWfPnkK7pKQkrFixAn5+fjAxMcGGDRswY8YMXLx4EXp6evjpp5+waNEiTJw4ER4eHsjKysK9e/eQlpZWbqyJiIiIiIiIiIhUBRPlpWjYsCFev34tfC6e0C4oKICbmxvu3buHkydPYu7cubCwsICFhUWJci4A4OLiIjwZLJFI4OjoiKysLBw+fLjMRHliYiJMTU2FhPv7GjVqBABISEhQ6Nr69esn/F0ikcDJyQmJiYkIDw+XO1HesmVLGBsbw8jIqMT1enl54dtvvxUS5Xl5eThz5gyGDh0KHR2dUvssL8ZFihLlHh4eAABra2v07NkT169fx4ABA/D27VscOXIEU6ZMwWeffQYA6Nq1K/71r3+VeNHl+yoa8y5dughfGHTt2hXJycnYsWMHRowYAW1tbYSEhKBWrVrYv38/atSoAQBwcHBAz549ERERgTFjxgh91apVCyEhIdDS0hK2lXfPjI2N0bJlSwBAq1atYGNjU+o1/vrrr/j++++xbNky4R5169YNL168KJEof/fuHQ4fPoxWrVoBAAwNDTF27Fj88ssv6NSpE+7du4fatWsL1w4A3bt3L/Xc8pBIJACAzMzMD+qHylYUX8a56qlCrN//DZnKwvWqHKowhzSFKsSa6/XjpgpzSFOoQqy5Xj9uqjCHNIGqxLmq1itQ/demCVRlHqk7VYlzVa7X8jBRXgqJRCKVqHzy5Ak2btyImJgYJCcnC9vj4uLK7Ss7OxuhoaE4e/YsXr58idzcXGHfP//8g5o1a37QWIuPUx7v3r2DWCzGpUuXkJiYiPz8fABA7dq1P2gcRYYPH45//etfePz4MVq1aoVr164hOTkZQ4YMKfM4eWOsra0tfPEAAE2aNIGBgYGQBP/999+RlZWFXr16SR3Xu3dv/PTTTx94dbK9fy4vLy+cPn0aCQkJaNSoEW7cuAFvb2/o6OgIT4mbmpqiXbt2JUqZdOvWrcQ9rcx79n//938AgD59+kht79u3L1atWoWMjAzhH6X69esLSXIAQjK+KNbt2rXD27dvERAQgP79+8PBwQGGhoYKj6m4ovjIs7bowzHOylOdsXZ0dKySfrlelYtxVh6uV/pQjLPycL3Sh2KclaO641xV6xWo/mvTJIy1clR3nKtyvZaHifJSJCQkwMrKCgCQnp6OiRMnwszMDAEBAWjUqBFq1KiBxYsXIzs7u9y+1q1bh4iICEyfPh0dOnSAiYkJLl26hB07diA7O7vURLmFhQX++9//Ij09XeYTzn///TcAoEGDBgpdW0BAAGJiYjB9+nThyfCjR4/i/PnzCvVTGicnJ1hbW+Obb75BYGAgvv32Wzg5OcHS0rLUYxSJsYGBgVSpEgDQ09MT2iUlJQEA6tatK9WmXr165Y69QYMGSE1NLTfmFhYWUtvNzMxknispKQmNGjXCmzdvEBYWJpTleX/sxb0/bqBy79m7d++gp6dXIsler149SCQSpKWlCYlyU1NTmWMtirWLiwvWrl2LgwcPYtKkSahRowa8vLzw5ZdfVviLF11dXeTl5cHKyuqDk+5UuszMTMTFxTHOSqDOseZ6VQ51nkOqRp1jzfWqHOo8h1SNOsea61U51HkOqRJNiLM6X5uq0IR5pAoYZybKZXr8+DESExOFMiR3795FQkICQkND0aZNG6FdWlpaiYSpLFFRURgxYgT8/PyEbWW91LKIk5MTvvnmG1y7dk2q9EaRq1evQltbW/impaikR/En1gEgNTVV+Ht2djauXr2KgIAA4WWjAHDkyJFyx6OIYcOGYc+ePZgwYQKuXbuGlStXltn+Q2NcnLm5OQAgOTlZ6kuE4qV0StO5c2d8++23ZcZcS0sLnTp1ktr+/gtUi85VNJZatWrBw8MDo0ePLtHn+1+UvP80eWXfs1q1aiE3Nxfv3r1DrVq1pMaspaUFExMThfobMGAABgwYgJSUFFy6dAmrVq2Crq4uvvrqqwqNr+j6DQ0Nq/XXbTQF46w86hhrrlflYpyVRx1jzfWqXIyz8qhjrLlelYtxVg51jrM6X5uqYayVQ5PjrF3dA1A12dnZWL58OfT19TFs2DAAQFZWFgDpJ3/v3LmDFy9eSB2rp6eHnJwcmX0WPzY/Px/nzp0rdyx9+vRBgwYNsGPHjhL1gf766y+cPHkS3bp1Q8OGDQH878nyP//8U2j35MkTvHz5Uvick5ODgoICqfGkp6fj8uXL5Y7nfcWf4n7foEGDkJaWhvnz58PAwKBEmY/3yRtjebRu3RoGBga4ePGi1Pbvv/++3GP79OmD+vXrlxnz3r17C7XKi7x/rgsXLqB+/fpCkt/FxQWPHz9Gu3btYGNjI/WnvBdvynvP3n/auzRFX6xERUVJbY+KikK7du0q/I+hmZkZhg0bBjc3N6k5SEREREREREREpOo0+onygoIC3L17FwCQkZGB33//HeHh4Xj+/DlWr16NJk2aAADs7OxgZGSEZcuWwc/PD4mJiRCLxSVKnrRo0QJ5eXkICwuDvb09jI2N0bx5c7i6uiIiIgItW7ZEnTp1cOTIEZkJ9fcZGBhgw4YN8PPzw+jRozFhwgQ0bNgQv/76K3bu3IlatWph+fLlQntbW1s0bNgQX331FebNm4f09HTs2rVLqgSGiYkJbGxssHv3bpiZmUFXVxe7du2CsbFxiaeiy9O8eXOcOnUKly9fhrm5OerXry/ExMzMDD179hSepjcwMCizL3ljLI/atWtj5MiR2L17NwwMDNCuXTucO3cOz549K/dYAwMDbNy4sdSYN2zYEEuWLClx3K1bt7BmzRq4ubnhxo0bOH36NIKCgqCtXfhd1KxZszB06FBMmjQJw4cPR7169fD69Wvcvn0bnTp1go+PT6ljkveeWVlZQUdHB99++y10dXWho6Mj86Webdq0Qe/evbF69WpkZWXB2toaZ86cQUxMDLZv3y5PiAVbt27F27dv0blzZ9StWxe///47/vOf/2D8+PEK9UNERERERERERFSdNDpRnpWVhREjRgAofKNqkyZN4OLigpCQELRo0UJoV69ePWzZsgVr166Fv78/rKyssGzZMuzZs0eqvx49emD06NHYtWsXkpOT4eTkhEOHDmHJkiVYunQpli9fDkNDQwwaNAi9evXC4sWLyx1jUfmVbdu2YfXq1UhJSYFEIoG9vT22bdsmVc9aT08PISEh+Pe//43Zs2fD0tISX375JVavXi3V54YNGxAUFISAgADUrl0bvr6+yMjIwL59+xSK35QpU/Ds2TN88cUXSE1NxYwZMzBz5kxhf69evRAVFYWhQ4eW25e8MZbXvHnzkJ+fjz179qCgoAC9evXCvHnzsHDhwnKPLYr59u3bsXr1aqSmpqJ+/fro378/pk6dKrP2dnBwMMLDw3H06FHUrFkTs2fPxpgxY4T9zZo1Q0REBDZv3oxly5YhIyMD5ubmcHJygkgkKndM8twzMzMzBAUFYc+ePThz5gzy8vLw22+/yexv3bp12LhxI3bv3o23b9+iefPm2Lp1Kzw9PcsdS3E2NjYICwvD+fPnkZ6eDgsLC0yaNAnTpk1TqB8iIiIiIiIiIqLqpCWRSCTVPQhSTFBQEL777jscOXIErVu3ru7hlGrhwoWIjY3F2bNnq3soVSY6Ohpjx47FN998I/PpbVLM/fv3IZFIkJubi7Zt22psTSxlyMjIQGxsLOOsBOoaa65X5VHXOaSK1DXWXK/Ko65zSBWpa6y5XpVHXeeQqlHnON+/fx85OTlqeW2qRp3nkSphnFmj/KO0ePFitGzZEn5+fnj16lV1D6eE3377DadOncJ3332HsWPHVvdwiIiIiIiIiIiIiMqk0aVXPlb6+vo4duxYdQ+jVNOmTUNKSgoGDhyIIUOGVPdwiIiIiIiIiIiIiMrERDlVusuXL1f3EJTG2dm51DrgRERERERERERE9HFg6RUiIiIiIiIiIiIi0mhMlBMRERERERERERGRRmOinIiIiIiIiIiIiIg0GhPlRERERERERERERKTRmCgnIiIiIiIiIiIiIo3GRLmGu3TpEiZOnIjOnTujQ4cO8PT0RFBQEP766y+hjUgkwt69e5U2ptjYWIhEIkRHRyvtnLKsXLkSnp6eZbY5ceIERCKR8Mfe3h59+vRBYGAg7t27V6HzxsbGQiwWIzMzs0LHK/t+ERERERERERERfeyYKNdg69evh7+/P4yNjbF8+XLs378f06dPxx9//IE5c+ZU9/A+Knv27EF4eDi2b9+OsWPH4vfff8fw4cOxa9cuhfuKjY1FSEhIhRPlREREREREREREpBjd6h4AVY9r165h9+7d8Pf3x+zZs4XtTk5OGDJkCK5cuVKNo/v4tG/fHmZmZgAAFxcXjBw5El988QU2btwIBwcHdOrUqZpHSERERERERERERKXhE+Uaat++fahXrx78/f1l7u/Ro0eZxx87dgxeXl5CuZbt27ejoKBA2C8Wi2Fvb1/iuE6dOkEsFktt2759O9zc3GBvb48ZM2YgOTlZrmtYv349+vfvD3t7e3Tt2hVz587Fq1evpNr4+vris88+Q1RUFLy8vGBvb4+xY8fi2bNnUu0SExMxdepU2NraomvXrti9e7dcYyiNtrY2Fi1aBH19fRw9elTYfvXqVUyYMAEuLi5wcHDAsGHDcP36dWH/iRMnEBgYCKAw4S4SiYTyL69evUJgYCB69uyJjh07onfv3ti4cSNycnJKnD8/Px9r165Fly5dYG9vj4CAAKSnpwv7MzIyEBwcDC8vL9ja2gold9LS0qT6uXTpEgYPHgx7e3t06tQJgwcPxrVr16TanDhxAv3794eNjQ26du2KTZs2IT8//4PiR0REREREREREpEx8olwD5eXl4c6dO+jduzf09PQUPv7QoUNYsWIFfH190b17d8TExCAkJARpaWn44osvFOrr8OHD2LJlCyZOnAhXV1fcvHkTixYtkuvY5ORkfPbZZ6hfvz5SUlKwf/9++Pr64ty5c9DV/d/Ujo2NRUpKCubPn4/8/HysXr0aCxYsQHh4uNDG398fiYmJ+Pe//w0TExPs3r0bL1++lOpHUbVr10b79u0RExMjbIuPj0ePHj0wceJEaGtr4/r16/Dz80NYWBicnZ3RvXt3TJs2DTt27MCePXtgYmICfX19AMCbN29Qu3ZtBAYGwtTUFHFxcRCLxUhKSsKqVaukzn3o0CG0b98ea9asQXx8PNavX4/s7Gxs2rQJAJCVlYX8/HzMmTMHZmZmePnyJXbu3Al/f38cOnQIAPDs2TPMnj0b/fr1w7x581BQUIBff/0V7969E86zf/9+rFu3DuPGjUNAQACePHkiJMrnz59f4dgREREREREREREpExPlGujt27fIyclBo0aNFD42Pz8f27ZtQ79+/bB48WIAgLu7O3Jzc7Fv3z74+fmhTp06cvcVGhqKAQMGCAn2rl27Ijk5GadPny73+OLJ4fz8fNjb26Nbt264desW3N3dhX1paWk4deqUUBolIyMDgYGBSEhIgIWFBa5fv44HDx7gwIEDcHFxAQA4OzvDw8MDtWvXlutaStOwYUM8fPhQ+Pyvf/1L+HtBQQGcnZ3xxx9/4Pjx43B2doaZmRksLS0BSJdzAQpf0ln8iwgHBwcYGhoiICAAQUFBMDQ0FPbp6+tj27Zt0NHRAQDUqFEDixcvxowZM9CiRQuYmZlh2bJlQvu8vDw0adIEo0ePxl9//QVra2s8evQIubm5WLJkCYyNjQEU3p8i6enp2Lp1KyZPnoy5c+cCANzc3KCnp4fVq1dj0qRJcs+F4iQSCQCwRnsVK4ov41z1VCHWRkZGVdIv16tyqMIc0hSqEGuu14+bKswhTaEKseZ6/bipwhzSBKoS56par0D1X5smUJV5pO5UJc5VuV7Lw0S5BtPS0lL4mD///BNv3rxBnz59pLZ7e3sjNDQU9+7dg4eHh1x9JSQk4NWrV+jVq5fUdi8vL7kS5deuXcOOHTvw+PFjqbIicXFxUonyNm3aSCWcW7ZsKZzfwsIC9+7dg4mJiZAkBwATExO4urri0aNHcl1LaSQSiVScExISsGnTJty8eRNJSUnCD8Ht27eXq6+wsDAcP34c8fHxyM7OFvY9f/4crVu3Fj736NFDSJIDQJ8+fbBo0SLcv38fLVq0AACcOnUKBw4cwNOnT5GRkSG0jYuLg7W1NUQiEXR0dDB//nwMHz4cTk5OMDExEdrFxMQgIyMDffr0QV5enrDd1dUVWVlZePz4MTp37qxIuABA6CsuLk7hY0lxjLPyVGesHR0dq6RfrlflYpyVh+uVPhTjrDxcr/ShGGflqO44V9V6Bar/2jQJY60c1R3nqlyv5WGiXAPVrl0bNWrUwN9//63wsUVlN+rWrSu1vehz8bIc5UlKSgIAqSQ2ANSrV6/cY+/duwd/f3/07NkTU6ZMQd26daGlpYXhw4dLJZABwNTUVOpzUbmZonavXr0qMQag5DVWREJCgnA9BQUFmDZtGtLS0jBr1iw0a9YMhoaG2Lp1K16+fFluX2FhYVizZg0mT54MZ2dnmJqa4v79+wgODi5xze+P3djYGDVq1BBquF+8eBFffPEFRowYgTlz5qB27dpISkrC9OnThb6sra2xc+dOhIaGYsaMGdDW1oa7uzuCgoLQqFEjvHnzBgAwaNAgmeOV55pk0dXVRV5eHqysrKSekqfKlZmZibi4OMZZCdQ51lyvyqHOc0jVqHOsuV6VQ53nkKpR51hzvSqHOs8hVaIJcVbna1MVmjCPVAHjzES5RtLV1YWDgwNu3bqFvLw8hepwF5UiSUlJkdpe9ALOWrVqASgs9ZGbmyvVJjc3V+rJZXNzc5l9vX79utxx/PDDDzA2NsbmzZuhrV34TtoXL17IfR3FFdU4f5+8LxUtzZs3b/DgwQN4eXkBAJ4+fYpHjx5h27Zt+OSTT4R2WVlZcvUXFRUFT09PzJs3T9j25MkTmW3fH3t6ejqys7NRv359oa+2bdsiODhYaHP79u0S/XTr1g3dunVDeno6rl+/jlWrViEwMBBhYWHCvQ4JCYGFhUWJY5s0aSLXdb2v6Al8Q0PDav11G03BOCuPOsaa61W5GGflUcdYc70qF+OsPOoYa65X5WKclUOd46zO16ZqGGvl0OQ4a1f3AKh6TJgwAUlJSdi5c6fM/deuXZO53draGmZmZoiKipLafv78eejp6aFjx44AgAYNGiA3NxfPnj0T2ty6dQv5+fnCZwsLC5ibm+PixYtSfV24cKHc8WdlZUFPT0+qrMnZs2fLPU4WGxsbpKWl4ccffxS2paWl4ebNmxXqDyh8evyrr75Cbm4uxowZA+B/T7AXf4HqixcvpF72WXx/Tk6O1Paiay6utGu+cuWKVKyjoqKgpaUFGxsbhfsCCp9I9/b2Rr9+/YTkvL29PQwNDZGQkAAbG5sSfypSn5yIiIiIiIiIiKg68IlyDeXh4YHJkydDLBbjjz/+QL9+/VCnTh3Ex8fj22+/RVpamsxa4zo6OvD398eKFStgZmYGDw8P3L17F7t378a4ceOE5Gi3bt1gZGSExYsXY8qUKUhISMDBgwdRo0YNqb78/PywcuVK1K1bF25ubrhx4waio6PLHb+bmxvCwsKwfPly9OrVCzExMXLVNZelW7duaN++PRYsWID58+fDxMQEu3btEl5gKY+HDx/CxMQEWVlZ+Ouvv/Dtt9/i4cOHWLBgAezt7QEAzZs3h4WFBTZs2ICCggJkZGRg69atwlPeRYpqiH/99df45JNPYGBgAJFIBFdXVxw8eBCHDx+GlZUVzpw5g6dPn8ocT05ODqZPn45Ro0YhPj4e69evh5eXl9C3q6srgoODsW3bNtjb2+PatWtSXxQAwLFjx3D37l107doV5ubmiI+Px5kzZ+Dm5gagsKTNrFmzsG7dOiQkJKBz587Q0dHB8+fPcenSJYjFYo39VR0iIiIiIiIiIvq4MFGuwYqSuF9//TW+/PJLZGZmon79+nB3d8ekSZNKPc7X1xe6uro4cOAAjh49CnNzc8yYMQNTp04V2tSpUwdbt27FmjVrMH36dLRt2xZr166Fr69vib5SU1Nx5MgRHD16FC4uLlixYgUmT55c5tg9PDwwf/58HD58GCdOnICDgwNCQ0OFMieK0NLSwvbt27F06VIEBQXB1NQUvr6+eP36NS5duiRXH0XjNTIyQv369eHg4ICgoCDhCXsA0NfXh1gsRnBwMGbPno2GDRti2rRpuHXrFh48eCC0a9euHWbOnImIiAjs2bMHDRs2xOXLlzF9+nS8efMGW7duBVD40tPFixdLxb2Ir68vUlJSsHDhQuTk5KBXr14ICgoS9o8cORLx8fE4fPgw9u7dC3d3d2zYsAHDhw8X2ohEIly5cgWrVq3C27dvYW5ujn79+mH27NlCm4kTJ6JBgwbYv38/Dh8+DF1dXVhaWqJ79+4lnlgnIiIiIiIiIiJSVVoSiURS3YMgIgKA+/fvQyKRIDc3F23bttXYmljKkJGRgdjYWMZZCdQ11lyvyqOuc0gVqWusuV6VR13nkCpS11hzvSqPus4hVaPOcb5//z5ycnLU8tpUjTrPI1XCOLNGORERERERERERERFpOCbKiYiIiIiIiIiIiEijMVFORERERERERERERBqNiXIiIiIiIiIiIiIi0mhMlBMRERERERERERGRRmOinIiIiIiIiIiIiIg0GhPlRERERERERERERKTRmCgnIiIiIiIiIiIiIo3GRDkRERERERERERERaTQmyomIiIiIiIiIiIhIozFRTkREREREREREREQajYlyIiIiIiIiIiIiItJoWhKJRFLdgyAiAoA7d+6g6J8kPT09aGlpVfOI1JdEIkFubi7jrASqEGt9fX2IRKJK7ZPrVXlUYQ5pClWINdfrx00V5pCmUIVYc71+3FRhDmkCVYlzVa7X6r42TaAq80jdqUqcq2K9yku3Ws5KRCRD0T/Eenp61TwS9aelpQV9ff3qHoZGUNdYc70qj7rOIVWkrrHmelUedZ1DqkhdY831qjzqOodUjTrHuWi9MnFb9dR5HqkSxplPlBMRERERERERERGRhmONciIiIiIiIiIiIiLSaEyUExEREREREREREZFGY6KciIiIiIiIiIiIiDQaE+VEREREREREREREpNGYKCciIiIiIiIiIiIijcZEORERERERERERERFpNCbKiYiIiIiIiIiIiEijMVFORERERERERERERBqNiXIiIiIiIiIiIiIi0mhMlBMRERERERERERGRRmOinIiIiIiIiIiIiIg0GhPlRERERERERERERKTRmCgnIiIiIiIiIiIiIo3GRDkRERERERERERERaTQmyomIiIiIiIiIiIhIozFRTkREREREREREREQajYlyIiIiIiIiIiIiItJoTJQTERERERERERERkUZjopyIiIiIiIiIiIiINBoT5URERERERERERESk0ZgoJyIiIiIiIiIiIiKNxkQ5EREREREREREREWk0JsqJiIiIiIiIiIiISKMxUU5EREREREREREREGo2JciIiIiIiIiIiIiLSaEyUExEREREREREREZFGY6KciIiIiIiIiIiIiDQaE+VEREREREREREREpNGYKCciIiIiIiIiIiIijcZEORERERERERERERFpNCbKiYiIiIiIiIiIiEijMVFORERERERERERERBqNiXIiIiIiIiIiIiIi0mhMlBMRERERERERERGRRmOinIhUxm+//YbffvutuodBRHLgeiX6eHC9En08uF6JPh5cr0Tqh4lyIlIZOTk5yM7Oxv/93/8hOzu7uoej1hhn5VHXWHO9Kg/jrDzqGmuuV+VhnJVHXWPN9ao8jLNyqHOcc3JykJ6erpbXpmrUeR6pEsaZiXIiUlH5+fnVPQS1VhRfxrnqaUKs1fnaVIEmzCFVoQmxVudrUwWaMIdUhSbEWp2vTRVowhxSBZoQZ3W+NlWhCfNIFTDOTJQTERERERERERERkYZjopyIiIiIiIiIiIiINBoT5URERERERERERESk0ZgoJyIiIiIiIiIiIiKNxkQ5EREREREREREREWk0JsqJiIiIiIiIiIiISKMxUU5EREREREREREREGo2JciIiIiIiIiIiIiLSaLrVPQAiIlUStP/PD+4jeELzShgJkWYray2Wtca8hsyUub1Wq96lHnN8dX/5B0ZEchu55Idy29QwtZCrL1tLY7naWdSqvOeAEt4VlNtm7girSjsfUUWcufqs3DbXf88tc39iSmqZ+11FZmXuv/lbSpn7tXX0ytwPALVq1ihz/z/ZZa/HAfaG5Rxf2h5z/P6f19AvJzMypKdl2Q0AHPzuaZn7x3o3K3P/+f8+L3N/X/em5Y6hPMo4B1XckSjZ6zk1UyJze0aO7O16OrL7NzXUqtC4ZDHSl91XcrrsMZV27tF9Sl9bERffj4c5/rzxGsN6yT4m6obs+d3HTfa8Ltn//5R2jtKcvCy7r0GeivVD1Y9PlBMRERERERERERGRRmOinIiIiIiIiIiIiIg0GhPlRERERERERERERKTRmCgvg1gshkgkgkgkQps2beDo6Ij+/fsjODgYT548qVCf0dHR2LlzZ6WO848//sCsWbPQrVs32NjYoFu3bvjss89w7do1hfoRiUTYu3dvpY5NHcTGxkIkEiE6OrrMdkVzRSQSoUOHDujVqxdWrFiBt2/fKmegShYdHQ2RSIT79+9X91CIiIiIiIiIiIg+CF/mWQ4DAwOEhYUBAP755x/8/vvvCA8Px/Hjx7Fy5UoMGDBAof5u376Nffv2YerUqZUyvmfPnmHYsGEQiUQICAiAmZkZXrx4gWvXruH27dvw8PCQu6/w8HA0atSoUsalqXx9feHj44Ps7Gzcvn0bO3fuRFxcHPbs2VPdQyMiIiIiIiIiIqJSMFFeDm1tbdjZ2Qmf3dzcMHr0aPj5+WHRokVwcHBA06bV92bob7/9FgCwf/9+GBr+703fQ4YMQUFB2W8Gf1/x66SKadiwoRBHZ2dnvHr1CsePH8erV69Qv3796h2cnPLz81FQUAA9vfLfTk9ERERERERERKQOWHqlAmrUqIElS5YgNzcXERERwvZTp05h1KhR6Ny5M5ycnODr64t79+4J+8ViMUJCQpCRkSGU6PD19QUAPHnyBHPmzIGHhwdsbW3h7e2Nffv2lZvsTk1NhbGxsVSSvIi2tvTtffLkCWbMmIHOnTvD1tYWn376KSIjI4X9skqvXL16FcOGDUPHjh3RpUsXLF26FBkZGcL+ovIbN27cwLx582Bvb48ePXpg9+7dJcYTExODiRMnwsHBAfb29hg2bBhu3Lgh7M/JycHGjRvRo0cPdOjQAX379sXZs2fLvP6iMU6YMAEuLi5wcHDAsGHDcP36dak2J06cgEgkwqNHjzB58mTY2dmhd+/eOHXqVIn+tm/fDjc3N9jb22PGjBlITk4udwyladu2LQDg5cuXCl3n48ePMWXKFDg7O8PW1hZeXl4lYhoTE4OxY8fCzs4Ojo6OmDdvXomxrl+/Hv3794e9vT26du2KuXPn4tWrV1JtfH198dlnn+HkyZPw8vKCjY0Nfv31VwCFsR05ciRsbW2FOf3o0SOp41NTU8u990RERERERERERKqMT5RXUMuWLdGgQQPExMQI2+Lj4zFw4EBYWloiJycH586dw5gxY3DmzBlYW1tj2LBhSEhIQGRkpFDOxdjYGADw6tUrWFtbo3///qhZsyZiY2MhFouRkZGBGTNmlDqO9u3b48iRIwgKCsKoUaMgEolKJMgBIC4uDiNGjEDDhg2xaNEimJub4/fff8fff/9dat9RUVGYM2cOBg8ejJkzZyIpKQkbNmxAamoqNm3aJNV26dKlGDBgALZt24YffvgB69evh0gkQrdu3QAA//d//4dx48bBzs4OK1asgKmpKR48eCB1/tmzZ+POnTuYPn06WrRogWvXrmHBggUwNTUts4RMfHw8evTogYkTJ0JbWxvXr1+Hn58fwsLC4OzsLNV2/vz5GD58OCZMmIDjx48jICAANjY2aNGiBQDg8OHD2LJlCyZOnAhXV1fcvHkTixYtKvXc5fn777+hra0tVdJGnuucOnUq6tWrh5UrV8LY2BjPnj1DQkKC0EdMTAx8fX3h4eGBTZs2ITMzE5s3b4a/vz/Cw8OFdsnJyfjss89Qv359pKSkYP/+/fD19cW5c+egq/u/5f/gwQO8ePECs2fPhqmpKRo2bIjvvvsOc+fORc+ePbFhwwbo6enhzp07SExMRLt27YRjy7v3REREREREREREqo6J8g/QsGFDvH79WvhcPKFdUFAANzc33Lt3DydPnsTcuXNhYWEBCwuLEuVcAMDFxQUuLi4AAIlEAkdHR2RlZeHw4cNlJsoHDRqEH3/8EeHh4QgPD0fNmjXRpUsXDBkyBD179hTaicVi6Onp4ejRo0Jy3tXVtdR+JRIJ1q5dC29vb6xcuVLYbm5uDj8/P/j7+6NVq1bC9t69e2PmzJnCtVy9ehUXLlwQkqXr1q1Ds2bNEBYWBh0dHQCAu7u7cPytW7dw+fJl7N27V9ju5uaGpKQkiMXiMhPl//rXv4S/FxQUwNnZGX/88QeOHz9eIlE+ZswYjBkzBgBgb2+Pa9eu4cKFC/D390d+fj5CQ0MxYMAAfPHFFwCArl27Ijk5GadPny71/MUVFBQgLy8POTk5iI6OxtGjRzFixAiYm5vLfZ0pKSmIj4/HokWL4OnpCQDo0qWL1Hk2bNiADh06ICQkBFpaWgCA1q1bw8fHB9euXRPitWrVKuGY/Px82Nvbo1u3brh165ZU/N+9e4dvvvkGDRs2BFB4/9esWQM3Nzds27ZNaCfrPpR37xUlkUgAAJmZmRU6XhUU/60LVVUU3485zh8LVYi1kZFRlfRbXeu1steYqq9ZVZhDmkIVYq1u61Xdvf/vhyrMIU2hCrHmetUclfGzwof2oYyfV6riHKqwVoGqW69A9V/bx6wic07RY5RxjqruR1k0Yb2Wh4nyDyCRSIQkJVBY2mTjxo2IiYmRKoERFxdXbl/Z2dkIDQ3F2bNn8fLlS+Tm5gr7/vnnH9SsWVPmcTo6OtiwYQOmTp2KK1eu4Oeff8aNGzdw6dIl+Pv7Y/bs2QAKE7ReXl5Ckrw8f/31F168eIEvv/wSeXl5wvbOnTtDW1sbDx48kEqUF0+6amlpoUWLFsIT0JmZmfjll18wd+5cIUn+vhs3bqB27dro0qWL1PlcXV3x73//G/n5+aUem5CQgE2bNuHmzZtISkoSfrBs3759ibbFx2lkZIRGjRoJ40xISMCrV6/Qq1cvqWO8vLzkTpSvX78e69evFz47Ojpi8eLFCl1nnTp10LhxY2zcuBHv3r2Di4sLLCwshLaZmZm4c+cOFi5ciPz8fGG7lZUVGjZsiPv37wsJ7WvXrmHHjh14/Pgx0tPThbZxcXFSsWjdurWQJAeAP//8EwkJCcIXBmUp695XRFFc5Fk3VaPOB/cQGxtbCeNQjuqLs+apzlg7OjpWSb9Vu15LX4uVvcY+ljXL9ao8XK8kr9L+/WCclYfr1bxKxkDS5PtZod4H9lH2O60q5+cVZZxDtur+d7Gq1itQmdemeeu57DknOx6lHyN7fpfevvR4K74WFB2ralPn9VoeJso/QEJCAqysrAAA6enpmDhxIszMzBAQEIBGjRqhRo0aWLx4MbKzs8vta926dYiIiMD06dPRoUMHmJiY4NKlS9ixYweys7NLTZQXadWqFVq1agU/Pz+kpKRg0qRJ2LVrF8aNG4fatWvj7du3Cr1M8s2bNwCA6dOny9xfvOY2AJiYmEh91tPTQ1paGoDCGtYFBQVlnv/Nmzd4+/atzOQ2ACQlJUkli4sUFBRg2rRpSEtLw6xZs9CsWTMYGhpi69atJcZY2jhzcnKEcwCAmZmZVJt69cr+gae4sWPH4tNPP0VmZibOnDmDiIgIbNmyBfPmzVPoOvfu3YtNmzYhODgYGRkZaN++PQIDA+Hk5ITU1FTk5+dj1apVUk+MFym67nv37sHf3x89e/bElClTULduXWhpaWH48OEl5uT71/j27VsAkGvOlHXvK0JXVxd5eXmwsrKSWXu/qp2+V/Ekf5Gi2vSqLDMzE3FxcdUWZ02izrGuyvVa1lqs7DWm6mtWneeQqlHnWFfL/6+nbirnPNXo/X8/1HkOqRp1jrUi6/XF7ddl7qfKIc/PCg8Tkz6oj1d3yn43VmX8vKKMc7xPnddqkcq6tt+SNG89lzXn/rwhOx6lHZMUI3t+l9a+tP7LG5csz24pNlZVpQnrtTxMlFfQ48ePkZiYiEGDBgEA7t69i4SEBISGhqJNmzZCu7S0NJkJ3vdFRUVhxIgR8PPzE7Zdu3atQmMzMzPD4MGDsWLFCjx9+hS1a9dG7dq1S7zEsSy1a9cGAAQFBaFjx44l9iuSdDcxMYG2tnaZ569VqxbMzMywa9cumfvfT14Xefr0KR49eoRt27bhk08+EbZnZWXJPb4iReVRUlJSpLYXL69THgsLC9jY2AAofPr+9evX2L9/P0aPHo2GDRvKfZ3W1tbYunUrcnNzERMTg40bN2Lq1Km4fv06TExMoKWlhc8++0zqmovUqVP4FOYPP/wAY2NjbN68Wahb/+LFC5nnLf6bEcD/7r8ic6ayFI3F0NCwWn/d5kN8TOP+mOP8sVHHWFfXeq3sc30s90Ud55CqUsdYq8P/r6qotFgyzsqjjrHmelU9lXEfyu+j7CR25cwFZZxDNnWez+p8bVWtInEr/RjZ87tyz1E9/SibJs/pkm99pHJlZ2dj+fLl0NfXx7BhwwD8LzGrp6cntLtz506JxGTxJ5jf77P4sfn5+Th37ly5YyktiVv0axJFTwq7uLjgwoULUuU3ytK8eXNYWFjg+fPnsLGxKfGnQYMGcvUDFP7DYGdnh9OnT0uVCinO1dUVKSkp0NPTk3k+fX19mccVPRldPHYvXryQesmqvCwsLGBubo6LFy9Kbb9w4YLCfRVZuHAhCgoKsHfvXgCKX6eenh46d+4MPz8/pKen49WrV0I8//zzT5l9NGnSBEDhnNTT05NKgp89e1aucRfd/xMnTlT42omIiIiIiIiIiD4WfKK8HAUFBbh79y6AwiL8v//+O8LDw/H8+XOsXr1aSEra2dnByMgIy5Ytg5+fHxITEyEWi0sklFu0aIG8vDyEhYXB3t4exsbGaN68OVxdXREREYGWLVuiTp06OHLkiMyE+vu2b9+O2NhY+Pj4oGXLlsjOzsaNGzdw5MgRfPLJJ2jcuDGAwheNXr16FaNHj8bkyZNhbm6OJ0+eIDMzE1OmTCnRr5aWFgICAjB//nxkZGSge/fuMDQ0xN9//41r165hzpw5sLa2ljuO8+bNw/jx4zF+/HiMHj0atWrVwsOHD1GnTh0MHToUbm5u6NGjByZPnozJkydDJBIhMzMTf/zxB54+fSr1QtHiihK6GzZsQEFBATIyMrB161aFnngvoqOjAz8/P6xcuRJ169aFm5sbbty4gejoaIX7Kj4+b29vfPPNN5g+fbpc1/nrr79izZo18Pb2RtOmTZGeno7Q0FA0btwYlpaWAAoT8OPGjcPnn3+Ofv36wdTUFAkJCbh58yYGDx4MZ2dnuLm5ISwsDMuXL0evXr0QExMjd611LS0tfPHFF5g7dy5mzpyJAQMGQF9fH3fv3oWNjQ169OhR4ZgQERERERERERGpGibKy5GVlYURI0YAKHwyukmTJnBxcUFISAhatGghtKtXrx62bNmCtWvXwt/fH1ZWVli2bBn27Nkj1V+PHj0wevRo7Nq1C8nJyXBycsKhQ4ewZMkSLF26FMuXL4ehoSEGDRqEXr16Sb0IUpZPP/0U2dnZOHToEBITE6Gjo4PGjRtj4cKFGD16tNDOysoKx44dw4YNG7Bs2TLk5+fDyspKqtTL+/r27QtTU1Ps3LlTeBK5cePG6Nq1q0J1uwGgU6dOOHjwIDZv3ozAwEBoa2ujVatW+Pzzz4U2W7duxa5du3D06FG8ePECJiYmaNWqFQYPHlxqv/r6+hCLxQgODsbs2bPRsGFDTJs2Dbdu3cKDBw8UGiMA+Pr6IjU1FUeOHMHRo0fh4uKCFStWYPLkyQr3VcTf3x/fffcdDh8+jJkzZ5Z7nebm5qhXrx5CQ0ORmJgIExMTdOrUCevWrRNeaOrg4IAjR45ALBYjMDAQubm5sLCwQJcuXdCsWTMAgIeHB+bPn4/Dhw/jxIkTcHBwQGhoKLy8vOQat7e3NwwMDLBz507MnTsXNWrUQLt27Uq87JSIiIiIiIiIiOhjpyWRSCTVPQgiIgC4f/8+JBIJcnNz0bZt22qpiRW0/88P7iN4QvNKGEnVysjIQGxsbLXFWZOoa6yrer2WtRbLWmNeQ2bK3F6rVe9Sjzm+ur/8A6sG6jqHVJG6xrq6/n8dueSHctvUMC3/XT4AYGtpLFc7i1qVV1ky4V1BuW3mjrCS+qyuc0gVqWusFV2vZ64+K7fP67/nlrk/MSW1zP2uItnviypy87eUMvdr6+iVuR8AatWsUeb+f7LLXo8D7Mt+6dw/2WWfX7+cRwiH9LQsuwGAg989LXP/WO9mZe4//9/nZe7v69603DGURxnneJ+6rlWgcL3m5ORU2rUdiZK9nlMzZaftMnJkb9fTkd2/qaGW7B0VYKQvu6/kdNljKu3co/uUvrYiLsqOx7Beso+JuiF7fvdxkz2vS+u/rHOU5uRl2X0N8lSsn+qmzutVXqxRTkREREREREREREQajYlyIiIiIiIiIiIiItJoTJQTERERERERERERkUbjyzyJiIr5GOqLE2mCiq7FC9+KK3kkRPS+jeFxcrX71K11uW3Kqk1aEaXVd63KcxJVp0+7lz+fP+3+Yef49lLZ6+pQgH2Z+8uri10ZKlJbu7Jr8ZZXg7w8VVEfvDrOQRVnZiK7jndp20tTWk3uskReV2yd+nSr+rlUvE548fVaGkWvW9E65GX52GqRU+n4RDkRERERERERERERaTQmyomIiIiIiIiIiIhIozFRTkREREREREREREQajYlyIiIiIiIiIiIiItJoGpEoF4vFsLcv+wUjVa1Tp04Qi5X3gjFPT08EBweX2SY6OhoikQj3799X0qg+zIABAxAQEFBmm4CAAIhEIgwfPrzEPolEAg8PD4hEIqXeC00hFotx586d6h4GERERERERERGRwjQiUU6ytW/fHuHh4WjRokV1D6VSGRkZ4ZdffsHz59Jvbf7555+RnJwMfX39ahqZegsJCUFMTEx1D4OIiIiIiIiIiEhhTJR/ZLKysiqtL2NjY9jZ2cHIyKjS+lQFjRs3Rps2bfDdd99JbY+MjIS7uztq1KhRTSMjIiIiIiIiIiIiVcRE+f+XkJCA+fPnw9nZGR07dsSYMWPw4MEDqTanTp3CqFGj0LlzZzg5OcHX1xf37t0r0dcPP/yAPn36wMbGBkOHDpXZBgCuXr2KYcOGoWPHjujSpQuWLl2KjIwMYX9RaZSrV69i1qxZcHBwwOzZswEAly5dwuDBg2Fvb49OnTph8ODBuHbtWolzfP311+jRowccHR3h7++PlJSUEv0XL70iEomwa9curF27Fl26dIG9vT0CAgKQnp5eZvxevXqFwMBA9OzZEx07dkTv3r2xceNG5OTkSLUTiUTYvXs3xGIxXF1d4ezsjMDAQKnrBoA7d+5g8ODBsLGxgY+Pj8xrK0u/fv0QGRkpfM7Ly8OFCxfg4+NTom1MTAymTp0Kd3d32NnZYcCAATh16pRUm6JY3bhxA/PmzYO9vT169OiB3bt3K9wXADx+/BhjxoyBjY0NevfujTNnzsDf3x++vr5S7Z48eYJp06bB0dERdnZ28PPzw7Nnz6TaFN2zTZs2wcXFBZ06dcLatWshkUjw448/YsCAAbC3t8e4cePw8uVLqWNzcnKwceNG9OjRAx06dEDfvn1x9uxZqTYBAQHw8fFBdHQ0Bg4cCDs7OwwdOlRqfYhEIgDA2rVrIRKJIBKJEB0dXeK6iYiIiIiIiIiIVJFudQ9AFbx79w6jR4+GkZERlixZAhMTExw6dAjjxo3D999/j7p16wIA4uPjMXDgQFhaWiInJwfnzp3DmDFjcObMGVhbWwMAYmNjMWvWLHTr1g2BgYGIj4/H559/XiJhHBUVhTlz5mDw4MGYOXMmkpKSsGHDBqSmpmLTpk1SbZcsWYJPP/0U27Ztg7a2Np49e4bZs2ejX79+mDdvHgoKCvDrr7/i3bt3UsddvnwZT58+RVBQEN68eYNVq1Zh+fLlJfp/36FDh9C+fXusWbMG8fHxWL9+PbKzs8s87s2bN6hduzYCAwNhamqKuLg4iMViJCUlYdWqVVJtv/76azg6OmL16tWIi4vD2rVrUbduXcyfPx8AkJSUhEmTJkEkEmHz5s1ITU3FsmXLkJGRgbZt25Y59iL9+vXDxo0b8ccff6Bly5a4ceMGsrOz4enpiX//+99Sbf/++284ODhg1KhR0NfXx507d7B48WJIJBIMGjRIqu3SpUsxYMAAbNu2DT/88APWr18PkUiEbt26yd1XVlYWJk6cCFNTU6xbtw4AsG3bNqSmpsLS0lI41/PnzzFy5Ei0atUKq1evhpaWFnbu3Inx48cjKipKqoTM119/jc6dO2Pt2rX45ZdfIBaLUVBQgBs3bmDatGnQ09PDihUrsGjRIuzbt084bvbs2bhz5w6mT5+OFi1a4Nq1a1iwYAFMTU3h4eEhtEtKSsKKFSvg5+cHExMTbNiwATNmzMDFixehp6eH8PBwjBgxAr6+vsKXES1btpTrXhEREREREREREVU3JsoBhIWFITU1FREREUJS3MXFBV5eXti7dy8WLlwIAJgxY4ZwTEFBAdzc3HDv3j2cPHkSc+fOBQDs2rULDRs2xLZt26CjowMAqFGjBhYtWiQcK5FIsHbtWnh7e2PlypXCdnNzc/j5+cHf3x+tWrUStnt6emLBggXC56ioKOTm5mLJkiUwNjYGAHTt2rXEdUkkEuzYsUNIqL548QKhoaEoKCiAtnbpv0ygr69fYvyLFy/GjBkzSq1nLhKJ8MUXXwifHRwcYGhoiICAAAQFBcHQ0FDqOjds2AAA6NatGx49eoQLFy4IifKwsDBoaWlh9+7dMDExAQBYWFhg/PjxpY75fY0bN4adnR0iIyPx+eefIzIyEp6enjLLzPTr10/4u0QigZOTExITExEeHl4iUd67d2/MnDkTQOEcuXr1Ki5cuCAkyuXp69tvv0VycjKOHj2KJk2aAAA6dOiA3r17SyXKQ0JCUKtWLezfv18oF+Pg4ICePXsiIiICY8aMEdrWr19fSLp37doVly9fxoEDB3Du3DnhniUmJmL58uVITU2Fqakpbt26hcuXL2Pv3r1wd3cHALi5uSEpKQlisVgqUf7u3TscPnxYmJeGhoYYO3YsfvnlF3Tq1Al2dnYAgIYNGwp/ryiJRAIAyMzM/KB+qGxF8WWcq54qxLqqSmxxvSqHKswhTaEKsda09fr+bxV+7OdUhTmkKVQh1pq2XktTHeu4MsagCnNIE6hKnKuy5Gx1X9v7lLEmlb3uVWUeqTtViXN1lohmohzAjRs34OzsjFq1aiEvLw8AoK2tDScn2knotwABAABJREFUJ6myJE+ePMHGjRsRExOD5ORkYXtcXJzw919++QWenp5CkhkA+vTpI5Uo/+uvv/DixQt8+eWXwvkAoHPnztDW1saDBw+kEuXdu3eXGq9IJIKOjg7mz5+P4cOHw8nJSUgoF+fk5CT11HGLFi2Qm5uL5ORkmJublxqPHj16yBz//fv3S02USyQShIWF4fjx44iPj0d2draw7/nz52jdurXw2dXVVerYFi1a4Ny5c8LnX375Bc7OzlLX5OLigtq1a5c6Zll8fHxw8OBBTJ06FZcuXcL69etltnv37h3EYjEuXbqExMRE5OfnA4DM8xUllAFAS0sLLVq0QEJCgkJ9PXjwAK1btxaS5ADQpEkTtGnTRupcN27cgLe3N3R0dIR5Ympqinbt2pUoC/R+TK2trfH69Wup+2VlZQWgsMyQqakpbty4gdq1a6NLly5S89DV1RX//ve/kZ+fL8yD+vXrS83JoqfFExMTS8ToQxWNpfi6oqrDOCtPdcba0dGxSvrlelUuxll5uF7LU7cS+igUGxtbaX0VKv1n3Ko7Z0lcr8rD9aoMZa+r8tdU/cobSoXHUDrVibN6q+44V9V6BSrz2ipnrVRsPSh2bmX8XypLdc8jTVHdca7K9VoeJspRWDbk7t27aN++fYl9RU/4pqenY+LEiTAzM0NAQAAaNWokPGldPCmclJQkPJVexNjYWOoFkm/evAEATJ8+XeZ43q8j/X5/1tbW2LlzJ0JDQzFjxgxoa2vD3d0dQUFBaNSokdDO1NRU6riipHnx8cpS2vhfvXpV6jFhYWFYs2YNJk+eDGdnZ5iamuL+/fsIDg4ucb73x6WnpydVmiYpKQnNmjUrcQ4zM7Myx/2+Pn364KuvvsKWLVugp6cn86l7oLAGd0xMDKZPn46WLVvC2NgYR48exfnz50u0ff8LCT09PaSlpSnU16tXr2Rei5mZmVSs3rx5g7CwMISFhZVoq6enJ/VZVkxlbQP+d//fvHmDt2/fypz3QOF9sLCwKLX/4n1VJl1dXeTl5cHKykrqNxGocmVmZiIuLo5xVgJ1jjXXq3Ko8xxSNeoc68pcr9f+KP1nQkXJW1ZPXr8lvVb6OYtT5zmkatQ51qr2/2vcj2Wvq/LW1Ks7yWXurwwVWdfqPIdUiSbEubKuLSmmctZKRdbDy58VO3dV/l8qiybMI1XAODNRDgCoVasWunbtKrwos7ii5PLdu3eRkJCA0NBQqSd/09LShGQiUFhWpPjT5kBhkr14QrHo6eKgoCB07NixxDnr15f+Jk9LS6tEm27duqFbt25IT0/H9evXsWrVKgQGBspMqiqqtPG/P67ioqKi4OnpiXnz5gnbnjx5UqHzy4ohAKkXkcqjXr166NKlCw4cOIChQ4eWSC4DhYneq1evIiAgQOpFmkeOHFF43PL2Vb9+fZnfvqakpKBmzZrC51q1asHDwwOjR48u0bZ4u4qqVasWzMzMsGvXLpn7Ff1iorIUzXdDQ8Nq/XUbTcE4K486xprrVbkYZ+VRx1ir6nqtjrEo45yqFmd1po6xVtX1Wpryx1j1ifIPidPHEuePnTrHufKurXLWSsXGoti5q+teqvM8UiWaHGcmylFYauLMmTNo0aJFqRMhKysLgPSTvHfu3MGLFy+kSlJ07NgRV65cQWBgoFC2IioqSqqv5s2bw8LCAs+fP5eqM10RxsbG8Pb2xr179xAZGflBfRWRNX4tLS3Y2NiUekxWVlaJRPTZs2crdP6OHTvi6NGjSEtLE57g/vHHH/H27VuF+/L19YWBgQGGDRsmc39OTg4KCgqkxp6eno7Lly8rfC55++rQoQNOnTqF58+fo2nTpgAKXxT766+/Sv16iYuLCx4/fox27dpJlcKpLK6urtizZw/09PRKlH2pCD09vSp5wpyIiIiIiIiIiKiqaUyiPD8/v0TCGihMyo4fPx5nz57Fv/71L4wdOxaNGjVCSkoKfvnlFzRo0ADjx4+HnZ0djIyMsGzZMvj5+SExMRFisRgNGjSQ6s/Pzw9Dhw7F9OnTMWrUKMTHx2Pv3r1SpVe0tLQQEBCA+fPnIyMjA927d4ehoSH+/vtvXLt2DXPmzIG1tXWp13Ls2DHcvXsXXbt2hbm5OeLj43HmzBm4ublVSqxycnKkxr9+/Xp4eXmVWp8cKEy6Hjx4EIcPH4aVlRXOnDmDp0+fVuj848aNw5EjRzBlyhRMmTIFqampEIvFCtcoBwrrrffo0aPU/SYmJrCxscHu3bthZmYGXV1d7Nq1C8bGxgo/wS5vX0OGDMHOnTsxdepU4cWgISEhqFevntRvD8yaNQtDhw7FpEmTMHz4cNSrVw+vX7/G7du30alTJ/j4+CgYDWlubm7o0aMHJk+ejMmTJ0MkEiEzMxN//PEHnj59KvWiWXk0b94cly5dQqdOnWBoaAhra2vhZbNERERERERERESqTGMS5dnZ2TJLq6xduxYDBgxAeHg4Nm/ejPXr1+Pt27eoW7cubG1t0atXLwCFZTy2bNmCtWvXwt/fH1ZWVli2bBn27Nkj1V+7du2wZcsWrF+/HjNmzECrVq2wadMmTJo0Sapd3759YWpqip07dwpPXjdu3Bhdu3ZFvXr1yrwWkUiEK1euYNWqVXj79i3Mzc3Rr18/mddXEb6+vkhJScHChQuRk5ODXr16ISgoqMxjpk+fjjdv3mDr1q0AAC8vLyxevBhTp05V+Pz169fH7t27sWLFCsyePRuWlpYICgrCpk2bKnQ95dmwYQOCgoIQEBCA2rVrw9fXFxkZGdi3b1+V9GVgYIB9+/Zh6dKlmD9/Pho0aAB/f3+cOnVKqgZ6s2bNEBERgc2bN2PZsmXIyMiAubk5nJycIBKJKuXat27dil27duHo0aN48eIFTExM0KpVKwwePFjhvoKCgvDVV19hypQpyMrKwsGDB+Hs7Fwp4yQiIiIiIiIiIqpKWhKJRFLdgyDVIRKJsHDhwhKJfapab9++xSeffILx48djxowZ1T2canP//n1IJBLk5uaibdu2GlsTSxkyMjIQGxvLOCuBusaa61V51HUOqSJ1jXVlr9eN4XFytbOopV1um9F9LD9oLO87EvVM6ecsTl3nkCpS11ir4v+v314qe10N6Vn2mjr/3+eVORyZ+ro3VfgYdZ1Dqkad43z//n3k5ORU2rVF3aictdLHTfH1EHldsXP7dFP8HB9CneeRKmGcNeiJciJVsmvXLtSrVw+NGzdGUlIS9u3bh/z8fAwZMqS6h0ZERERERERERKRxmCgnqgba2trYsWMHEhMToaOjA1tbW4SFhaFhw4bVPTQiIiIiIiIiIiKNw0Q5Sfntt9+qewgaoegFmkRERERERERERFT9mCgnIirGa8jMD+7jwrfiShgJkWYray2WtcaGB5yVuf346v4fPCYiTbBg15Ny29y7sFmuvsZNWVBum8qsdw7IV39cnjrm8vZFVN22RMSV2+ZS1Ddl7tfS1S9zf9brx2XuP/XTxDL3d2pRp8z9ANDErOw1/k/2h71arfQ66fXx6k4y+rprZi1eUi0X7mXL3P7s8U8yt+elv5K5fdPGP2Rur9mkU6nnNmrQTub2Hh3qy9wedu6pzO3J6bLXarsmOjK3J74tKHVMdU3e/3ehPl7+nAyfbqq3XkurL1+RevFUveT7iZOIiIiIiIiIiIiISE0xUU5EREREREREREREGo2JciIiIiIiIiIiIiLSaEyU00dPLBZDJBJBJBKhTZs2cHR0RP/+/REcHIwnT8qvsylLdHQ0du7cWckjLXTgwAGIRCJ8+eWXVdJ/cZ6enggODq7UPgMCAuDj41OpfRIREREREREREVUnJspJLRgYGCA8PBzHjh3D1q1bMXjwYNy8eRMDBgzA6dOnFe7v9u3bCA0NrYKRAmfOnAEAXLx4ETk5OVVyjqrk7++P9evXV/cwiIiIiIiIiIiIKg0T5aQWtLW1YWdnBzs7O7i5uWHChAk4ffo0HB0dsWjRIjx/XtpbzpXrr7/+wsOHD+Hq6orU1FRcvXq1uoekMEtLS7Rp06a6h0FERERERERERFRpmCgntVWjRg0sWbIEubm5iIiIELafOnUKo0aNQufOneHk5ARfX1/cu3dP2C8WixESEoKMjAyhpIuvry8A4MmTJ5gzZw48PDxga2sLb29v7Nu3DwUFBXKNKTIyElpaWggODka9evVw9uzZEm2KyqV8/fXX6NGjBxwdHeHv74+UlBShTUZGBoKDg+Hl5QVbW1t4enoiKCgIaWlppZ778uXLEIlEiIuLk9r+7t07dOzYEV9//TUA4PHjx5gyZQqcnZ1ha2sLLy8v7N69W2j/fumV1NRULF68GF27doWNjQ08PDwwZ84cueJBRERERERERESkCnSrewBEVally5Zo0KABYmJihG3x8fEYOHAgLC0tkZOTg3PnzmHMmDE4c+YMrK2tMWzYMCQkJCAyMhJhYWEAAGNjYwDAq1evYG1tjf79+6NmzZqIjY2FWCxGRkYGZsyYUe54IiMj0alTJzRt2hR9/x97dx5XY/r/D/zVqlJJRLJmO7YoSdpESZYauyyTXUy2sdfYPsKIxlqW7NEgzVhDxm4+DGa+MgyNMUbIKKlQWrT9/ujX/XF06pzTejq9no+Hx8O57+t+39f9Ptd1xrzPfa67b1+EhYUhJSUFenp6Yu0uXbqEZ8+eYenSpUhOTsbq1auxYsUKbNiwAQCQkZGBnJwczJ49G4aGhnj16hW2b98Ob29vHDhwQOK5HR0dUb9+ffz444+YO3euWJ8AwN3dHQAwdepU1K1bF6tWrYKuri6eP3+OuLi4Iq9p9erV+PnnnzF37lw0bNgQCQkJuHbtmtRcEBERERERERERKQoWyknpNWjQAG/evBFef1rQzs3NhZ2dHe7du4djx45hzpw5MDY2hrGxsbCcy6dsbGxgY2MDAMjLy4OlpSUyMjIQGhoqtVB+7949xMTEYPz48QAANzc3HDhwAOfOncPQoUPF2ubl5WHbtm3Q1NQEALx8+RLBwcHIzc2FqqoqDA0NsXz5cqF9dnY2GjVqhFGjRuHp06cwNTUtdH41NTUMHjwYP/74I77++muoqakBAH788Ue4uLhAX18fSUlJiI2NxaJFi+Dk5AQA6NatW7HXdf/+fbi5uWHQoEHCtv79+xd7THHy8vIAAOnp6SWOUdnS0tIquwtSFeS3Kue5qlCEXOvo6JRL3MqaryWZY1VhXhZFEcZQdaEIuVa2+VrRynKulySWIoyh6kIRcs35qjhKO/er8r8TFJ0izFWg/OYrUPnXVt1UpflalfoKVI/5Kg0L5aT08vLyoKKiIrx+8uQJ1q9fj6ioKCQmJgrbP1+SRJLMzEwEBwfj1KlTePXqFbKysoR9Hz58QM2aNYs8NiIiAhoaGujTpw8AwNzcHI0bN8apU6cKFcqtrKyEIjkAtGjRAllZWUhMTISRkRGA/CVk9u3bh2fPnol9+MbExEgslAPA0KFDsX37dvz888/o0aMH/vzzTzx48ADz588HANSuXRsNGzbE+vXr8e7dO9jY2MDY2LjYnLRr1w7Hjh2DkZERHBwc0Lp162LbS5OdnS1cR1UVHR1d2V2QWVXOc1VTmbm2tLQsl7iVNV9LMseq0rwsCudrxane89WgXM5fUWSb60ZlGEsyzteKU73nKwDUKZc+VDXS52u9Uh5PpVXZn4vlNV+Bsry2WmUUR7kp5nyV/BmjmH2VTpnnqzQslJPSi4uLQ7NmzQAAqampmDBhAgwNDeHj4wMTExPUqFEDixcvRmZmptRYAQEBCA8Px7Rp09ChQwfo6enh4sWL2LZtGzIzM4sslOfm5uLMmTPo2rUrVFVV8f79ewCAs7Mz9u/fj/j4eNSvX19or6+vL3Z8QdG8oI/nz5/HwoUL4eHhgdmzZ8PAwAAJCQmYNm1asdfRqFEj2NnZ4YcffkCPHj3w448/olGjRsJd4yoqKti9ezc2bNgAPz8/pKWloX379vD19YWVlZXEmEuWLEGtWrWwd+9erF27Fg0aNICXlxdGjRolNZ+SqKurIzs7G82aNYO2tnaJYlS2tm3bVnYXpEpPT0dMTEyVznNVocy5rqz5Wvwc+7cExyg2ZR5DikaZcy3zfP2/VxXXqXIgy1x/lPBGahtZY31OmceQolHmXMvz39f//vO6gnql2KTN19d3EovdX5X/naDolHmuFiiza7st+d+xJE4R52tClOTPGEXsa3Gqw3yVhoVyUmqPHz9GfHy8sCzI3bt3ERcXh+DgYLRp00Zol5KSIvXOaQCIjIyEh4cHvLy8hG1Xr16VetzNmzeRkJCAhIQEiQXnM2fOCEuyyCIyMhJt27aFn5+fsO327dsyHTts2DDMmzcP8fHxOHXqFDw9PcXuuDc1NcXmzZuRlZWFqKgorF+/HlOnTsW1a9ckfhGgp6eHRYsWYdGiRXj06BH279+P5cuXo3Xr1ujSpYvM11SgoC/a2tqV+nOb0qhK/a7Kea5qlDHXlTVfS3IuZci9Mo4hRaWMuVaG/77KoiyvrTSxlD3PikQZc11d5mtZkp6n4gvlzHP5U+bxrMzXpogUM9eSP2MUs6/SVecxrVrZHSAqL5mZmVixYgU0NTUxbNgwAPkPwQQADQ0Nod2dO3fw8uVLsWM1NDTw8eNHiTE/PTYnJwenT5+W2pdTp05BR0cH+/btw/79+8X+tGnTBqdOnZLr2jIyMsT6UXAOWTg7O0NfXx9z587Fu3fvMHjwYIntNDQ00LVrV3h5eSE1NRWvX0u/W0UkEsHX1xdA/hI3REREREREREREVQHvKCelkJubi7t37wLIf1jCX3/9hbCwMLx48QL+/v5o1KgRgPx1wXV0dLB8+XJ4eXkhPj4egYGBYsueAPlrgmdnZyMkJAQWFhbQ1dVF8+bNYWtri/DwcLRs2RK1a9fGwYMHJRbUP5WZmYnz58+jd+/ewoNAPzVkyBCsWrUK//zzD5o3by7T9dra2sLPzw9btmyBhYUFrl69il9++UWmYzU0NDBw4EDs3r0b9vb2aNCggbDvzz//xJo1a9CvXz80btwYqampCA4ORsOGDdGkSROJ8UaMGAEXFxe0atUKampqOH78ODQ0NEp0NzkREREREREREVFlYKGclEJGRgY8PDwA5P+0pVGjRrCxsUFQUBBatGghtKtbty42bdqEtWvXwtvbG82aNcPy5cuxa9cusXg9e/bEqFGjsGPHDiQmJsLKygoHDhzAkiVLsGzZMqxYsQLa2toYNGgQXFxcsHjx4iL7duXKFaSkpGDgwIES97u5uWHt2rU4deoUZs2aJdP1jhgxArGxsQgNDRUK3uvWrcPw4cNlOt7FxQW7d+/GkCFDxLYbGRmhbt26CA4ORnx8PPT09NClSxcEBARATU1NYqzOnTvj+PHjiI2NhaqqKlq3bo3t27eL5Z2IiIiIiIiIiEiRsVBOVd6MGTMwY8YMmdt3794d3bt3F9vm6Ogo9lpdXR3Lli3DsmXLxLbXrVsXW7ZsKRSzYGkXSVxdXfHo0aMi9xsaGuKPP/4QXl+6dKlQm169eonFUFNTw8KFC7Fw4UKxdp+fR1IsALh27RoMDAzQq1cvse116tRBQEBAkX0FAH9/f7HXCxYswIIFC4o9hoiIiIiIiIiISJGxUE5Ujfzzzz94+vQpQkNDMWrUKGhqalZ2l4iIiIiIiIiIiCodC+VE1ciyZctw9+5dODg4YMqUKZXdHSIiIiIiIiIiohI5duyY3McMGjSoyH0slBNVIwcOHKjsLhAREREREREREZXaN998g7y8PKioqBTaJ2l7Xl4eC+VERLKydptd2V0gIgDnfgwsct/Svf8Uua+NqH15dIeo2gjwkuFh3F5Fz89Pzd/xRGqbBrUkPyy8pNzHr5baRkPfRKZYo/qMLW13iMrdrGHNpLZJzRxSqnPEJmcVu/8LC+1i979MypV6jo/Zxe/XUCtcAJFHVnZesftPXnle7P4vejQp1fmJZOHcvobE7W5fjZQrTvh5yeNZT6foeZSUInmO6BdxTHpmEe2NJLfvY9e4yHPLIi0tDdHR0Wjbtq3cx0Zef1EufSqvWCS/mTNnFnr24P379/Gf//wHe/fuhZ6eHgDg559/xsaNG4uNxUI5EREREREREREREVU5jRo1Qvv24jdMvX//HgAgEolQu3ZtAPnP7ZNGtey7R0RERERERERERERUdbBQTkRERERERERERETVGgvlRERERERERERERFStlbhQ/vr167LsBymJwMBAiEQiiEQitGnTBpaWlnB3d4efnx+ePJH+QCdJbt26he3bt5dZH2NjY4U+fv6nT58+ZXaeT926dQsikQj3798vl/ilJRKJsHv37sruBhERERERERERUamoqamhRo0aUFERf8Ds568/V+KHefbo0QPdunXDF198gd69e0NHR6ekoUjJaGlpISQkBADw4cMH/PXXXwgLC8ORI0ewatUqDBgwQK54t2/fxp49ezB16tQy7eecOXNgbW0ttk1LS6tMz1Ggffv2CAsLQ4sWLcolfmmFhYXBxMSksrtBREREREREREQkk9WrV6Nz586Ftnft2hW///672DYHBweEhYUVG6/EhfKZM2ciIiICPj4+WL58OZydnfHFF1/A3t4eqqpc0aU6U1VVhbm5ufDazs4Oo0aNgpeXFxYtWoTOnTujcePGldfB/69p06Zi/SxPurq6FXYueWRkZEBLS0sh+0ZERERERERERFQUKysrAMDLly+LbdewYUMYGBjAwMCg2HYlrmhPnToVEREROHr0KEaMGIHbt2/Dy8sLDg4O+PbbbxV2iQmqHDVq1MCSJUuQlZWF8PBwYfvx48cxcuRIdO3aFVZWVvD09MS9e/eE/YGBgQgKCkJaWpqwPIqnpycA4MmTJ5g9ezYcHR3RqVMn9OvXD3v27EFubm6Z9NnHxwdubm64desWBg4cCHNzcwwdOhR//PGHWLuUlBTMmzcPFhYWsLGxwfr167Fnzx6IRCKhjaSlV0QiEXbu3InAwEDY2trC2toavr6+SEtLE4sfFxeHefPmwdraGh07dsTo0aML9QEAjh49Cnd3d5iZmcHBwQEbNmxATk6O2H6RSISoqCiMHz8e5ubmWLt2rdCXT5de8fT0xJQpUxAZGQlXV1dYWFhgzJgxeP78eaG+TZkyBZ06dYKjoyP27duHVatWwcnJqQQZJyIiIiIiIiIikk2vXr3g7Ows9Y+sSnxHeYF27dqhXbt2WLBgAW7evIlTp07h6NGjOHDgAExNTfHFF1/giy++4LIOhJYtW6J+/fqIiooStsXGxmLgwIFo0qQJPn78iNOnT2P06NE4efIkTE1NMWzYMMTFxSEiIkJYzkVXVxdA/jr5pqamcHd3R82aNREdHY3AwECkpaVh+vTpUvuTm5uL7OxssW2qqqpiv4hISEjAypUr4eXlBT09Paxbtw7Tp0/H+fPnoaGhAQDw9fXFzZs3MX/+fDRs2BBHjhzBgwcPZMrJ999/D0tLS/j7+yMmJgZr165FnTp1MG/ePADAu3fvMGrUKOjo6GDJkiXQ09PDgQMHMHbsWPz000+oU6cOAGDv3r0ICAjA2LFj4ePjgydPngiF8oJYBebOnQsPDw9MmTIF2traRfYtOjoaSUlJmDdvHnJycuDv74/58+cLP1PJy8uDt7c33rx5g+XLl0NPTw+7d+/Gv//+y1+VEBERERERERFRuVq4cCHy8vLEtuXk5CAuLg6nT59GgwYN4O7uLnO8UhfKC6ioqMDS0hLv379HfHw8rl+/jmfPniEoKAibN29Gr169sHjxYtSrV6+sTklVUIMGDfDmzRvh9acF7dzcXNjZ2eHevXs4duwY5syZA2NjYxgbGxdazgUAbGxsYGNjAyC/aGtpaYmMjAyEhobKVCifPXt2oW1Dhw7FqlWrhNfv3r1DaGgoWrVqBQDQ1tbGmDFj8Pvvv6NLly74+++/cf78eaxZswYDBw4EkL/mUd++fWXKh5GREdatWwcA6N69Ox4+fIhz584Jxe2QkBC8f/8e4eHhQlHcxsYGrq6u2L17NxYsWIDU1FRs3rwZkyZNwpw5cwDkL3ejoaEBf39/TJw4EbVr1xbOOWLECHh5eUntW0pKCo4fPw5DQ0MAQFpaGnx9fREXFwdjY2Ncu3YNDx48wPfff48uXboAALp16wZHR0fo6+vLdP2SFHzApaenlzhGZfv8VwGKqCC/VTnPVYUi5Lq8niNSleZrVZiXRVGEMVRdKEKuOV8VR0k+NxRhDFUXipBrzlfFUd7/na/K/46obIowV4Hym69A+V+bMoy/0l5DeYwjZchrWauK83XcuHFF7vv6668xZMgQ1KxZU+Z4ZVIoL7iT/KeffkJqaipat26NhQsXwt3dHWpqajh69CiCg4OxYMEC7Nu3ryxOSVVUXl6e2BNmnzx5gvXr1yMqKgqJiYnC9piYGKmxMjMzERwcjFOnTuHVq1fIysoS9n348EHqRJg3bx66desmtq2gKFygXr16QpEcyL8rHgDi4+MBQFhK5dOfcaiqqqJnz57Yu3ev1GuwtbUVe92iRQucPn1aeH39+nVYW1ujVq1awt3vqqqqsLKyEs4dFRWFtLQ09OnTR+wOeVtbW2RkZODx48fo2rWrsL1Hjx5S+wUAbdq0EctHwbUXFMrv378PfX19oUgOADVr1oSNjY3Md9RLUnANsoyB8lFbehMpoqOjy6AfFaPy8lz9VGauLS0tyyVu5c1X+edpVZqXReF8rTicr2XFoALPVfZK87nB+VpxOF9lYSi9SRUnfb4alXN8kqayPxfLa74CZXltkm8slX/8lW68l4eymkMly3VZ5bX6UJb5qquri+HDh2Pnzp0YPny4TMeUuFD+559/4uTJkzh9+jRev36NunXrYujQoRg4cKDY2swAMHHiRNSoUQNr1qwp6elIScTFxaFZs2YAgNTUVEyYMAGGhobw8fGBiYkJatSogcWLFyMzM1NqrICAAISHh2PatGno0KED9PT0cPHiRWzbtg2ZmZlSC+WNGzeGmZlZsW0+vzO6YLmVgv4lJCRAQ0MDenp6Yu0+L7jLE//jx4/C6+TkZNy9exft27cvdGyTJk2ENgAwaNAgied49eqV2Ou6deuWuG/A/6799evXEq9T1msvirq6OrKzs9GsWbNil4YpLyfuxZU6Rtu2bcugJ+UrPT0dMTExlZbn6kSZc11Z87Uk87QqzMuiKPMYUjTKnOtKma//90p6GwVWks8NZR5DikaZc13W8zUyOr4MeqXYpM3Xl7ffFLu/tPGpaMo8VwuU1bW9+i1R4nZ5x98/10s33stDaedQacZRQlTZ5LU6UMb5qqKigrg42f//scSF8oEDB0JLSwvOzs4YOHAg7Ozsil2XuGXLloWWzqDq5fHjx4iPjxcKunfv3kVcXByCg4PRpk0boV1KSgqMjY2lxouMjISHh4fYMiJXr14t+44Xw8jICFlZWUhJSRErliclJZVJ/Fq1asHBwQGzZs0qtE9TU1NoAwBBQUES89aoUaMy6cvn6tWrJ/E6S3vtBb840NbWLtefx5WnqtTvqpznqkYZc12V5qui908WVSHPykIZc12V5quiKE2emOeKo4y55nyVX3nnie9D6SnzeC67a5Nc0FWGvJXVNZQs18qb1/KiDPP1/fv3+O2337Bz585CN3QXp8SF8m+//Raurq4yr/PSrVu3QstcUPWRmZmJFStWQFNTE8OGDQMAZGRkAPjfncoAcOfOHbx8+VJsuZPP77L+NOanx+bk5IgtW1IROnToAAC4ePGisEZ5bm4uLl++XCbxbW1tcfLkSbRo0aLIDykLCwtoa2sjLi4OLi4uZXJeWZiZmeH9+/f49ddfYWVlBSB/yZtffvmlVGuUExERERERERERSdO2bdtCD/MsoKKiAmNjYyxbtkzmeCUqlKenpyM0NBSZmZkYOXJkSUKQEsvNzcXdu3cB5D8c4a+//kJYWBhevHgBf39/4Q5nc3Nz6OjoYPny5fDy8kJ8fDwCAwNRv359sXgtWrRAdnY2QkJCYGFhAV1dXTRv3hy2trYIDw9Hy5YtUbt2bRw8eFBiQb0oz549E/pZQEVFBZ06dZI5RqtWreDi4oKVK1ciPT0dJiYmOHLkCDIyMsTWYi+pcePG4dSpU/jyyy8xZswYmJiYICkpCb///jvq16+PcePGQV9fHzNnzkRAQADi4uLQtWtXqKmp4cWLF7h48SICAwPL5Scz3bt3R/v27TF37lzMmTMH+vr62LVrF2rWrFkm105ERERERERERFSU6dOnFyqUq6iooFatWmjcuDHs7e2hpqYmc7wSFcq1tbURGxvLYhhJlJGRAQ8PDwD5P2Vp1KgRbGxsEBQUhBYtWgjt6tati02bNmHt2rXw9vZGs2bNsHz5cuzatUssXs+ePTFq1Cjs2LEDiYmJsLKywoEDB7BkyRIsW7YMK1asgLa2NgYNGgQXFxcsXrxYpn6uX7++0DY1NTU8fPhQruv99ttv4efnh7Vr10JTUxODBg1Cq1at8P3338sVR5LatWsjLCwMGzduxHfffYe3b9+iTp066NSpk9jd4xMmTED9+vWxd+9ehIaGQl1dHU2aNEGPHj3E7rovSyoqKti6dSuWLl2KpUuXQl9fH2PGjMHTp0/5UAwiIiIiIiIiIipX06ZNK9N4JV56xcHBAf/9738xYsSIsuwPVXEzZszAjBkzZG7fvXt3dO/eXWybo6Oj2Gt1dXUsW7as0E8l6tatiy1bthSKWbC0S1EaNWqER48eSe2bv79/oW36+vqFjtXX18d3330ntm306NFi665bW1sXOk5SH8aNG4dx48aJbTMyMsKqVauk9rd///7o379/kfsHDx6MwYMHS9z3eV8OHDhQqE3btm0LtTM2NsaOHTuE1x8/fkT//v3RpUsXqf0lIiIiIiIiIiIqL7///jsOHz6M1atXy9S+xIVyb29vzJo1C/Pnz4eHhwcaN26MGjVqFGpnYGBQ0lMQVQnnzp3Dq1ev0Lp1a6SnpyMiIgK//fabxCK+sgkLC0Nubi5MTU3x/v17HDp0CC9fvpR4tz4REREREREREVFZyc3NxU8//YQHDx4gPT290DIsMTExuH79OrS1taGiooLu3bsXukH3UyUulBfcufr3338jIiKiyHZcgoGUnY6ODk6cOIGYmBhkZWWhefPmCAgIQK9evSq7a+WuRo0a2LFjB16+fAkAaNOmDYKDg2FmZlbJPSMiIiIiIiIiImW2evVqHDhwoNjlwVVUVHDo0CEA+Uscl0uhfNq0aVyjnAj5yxA5ODhUdjcqxcCBAzFw4MDK7gYREREREREREVUzZ86cwYABA7BkyRLo6uoW2h8REYF58+bhzz//lCleiQvl8qxDTURUVfiNb17qGEv3/lPpfSgtZbgGotIqbh4owhgvqn+K0DdSbgcjn8vUrkEtNalt5ng0kynW/B1PZGo30mO01Daj+jSRKdb6sBipbWTtP1F5OfvfF1LbmDWSPhdLc3xf+8bF7t9/5pnUc2hKqUxolO4S8EWPwvM+LS0N0dHRaNu2LS7cflO6ExBVgohrkue/no7km1qzc4qOVdR/G4s6xzAX+doXtb04bt2L/2yRVR87+eNEXpfc35LEovKVnJwMe3t7iUVyAIWWYpFGtSw6BQAZGRnIyMgoq3BERERERERERERERBLl5eVBTa34b1PlWRGlxHeUA8C///6LwMBAXL16FcnJyQD+t9bL9OnT0bBhw9KEJyIiIiIiIiIiIiIqRNqzMd3d3eHu7i5zvBIXyp88eYJRo0YhJSUFtra2aNGiBQDgn3/+wYkTJ3D58mUcPHgQzZvz579EREREREREREREVHZevnwpUztZb+YucaF83bp1UFVVxbFjxyASicT2/fXXXxg3bhzWrVuHLVu2lPQUSikwMBBBQUGoV68erl69ClVV8dVvRowYgaioKAwaNAj+/v5lem4fHx/88ccfiIiIKLadSCTCggULMHHixDI9f2n7pQicnJzQo0cPLF26tLK7UmYGDBiAtm3byjTesrKyYG9vj3fv3uHSpUswMTER23/r1i2MGTMGP/zwA8zMzMqry0REREREREREVM316tVLpnXIy/1hnr/++ivGjx9fqEgOAK1bt8bo0aOxb9++koZXahoaGkhOTsavv/4Ka2trYfvLly9x9+5d6OjoVGLvgLCwsEIFUMoXFBQEfX39yu5Gpfnvf/+Lt2/fAsh/crCXl1fldoiIiIiIiIiIiKqlhQsXFiqUZ2Rk4OXLlzh37hzq16+PIUOGyByvxIXy7OxsaGlpFblfW1sb2dnZJQ2v1DQ0NGBjY4PTp0+LFcpPnz6NVq1aFbrLvKKZm5tX6vkrUl5eHrKysqCpqSlT+3bt2pVzjxRbREQE9PX10bhxY5w6dYqFciIiIiIiIiIiqhTjxo0rct+8efMwfPhwueqsJa7Itm3bFuHh4UhJSSm0LzU1FT/88EO1LyoWx83NDefOnUNWVpawLSIiAm5uboXaPnnyBLNnz4ajoyM6deqEfv36Yc+ePcjNzRVr9/HjR2zYsAHOzs7o0KEDunfvDh8fn0Lxbt26hYEDB8Lc3BxDhw7FH3/8IbZfJBJh9+7dwmtPT09MmTIFkZGRcHV1hYWFBcaMGYPnz58XOv/69evRs2dPdOjQAX379sWpU6dKlB9JOfjqq69gaWkJc3NzeHl5FTr/nj17MGTIEFhaWsLGxgZTpkzB06dPxdr4+PjAzc0NV69exRdffAEzMzNcunQJgYGBsLCwwKNHjzBy5Eh06tQJbm5u+Pnnn8WOd3Jygp+fX6F40nKakpKCefPmwcLCAjY2Nli/fj327Nkj8RcZn3r9+jV8fX3h7OyMjh07onfv3li/fj0+fvwo1k4kEmHnzp0IDAyEra0trK2t4evri7S0NLF2d+7cweDBg2FmZibkQVZpaWm4dOkSXF1dMXjwYPz111949OiRzMcTERERERERERFVBAMDAwwfPhyhoaEyH1PiO8pnzJiByZMno2/fvhg8eDCaNWsGAHj69CmOHTuGt2/fKtU6zmWtZ8+eWLRoEa5fv44ePXrg77//xqNHj7BlyxacOXNGrO3r169hamoKd3d31KxZE9HR0QgMDERaWhqmT58utJsxYwZu3ryJKVOmwNzcHElJSfjpp5/EYiUkJGDlypXw8vKCnp4e1q1bh+nTp+P8+fPQ0NAosr/R0dFISkrCvHnzkJOTA39/f8yfPx9hYWFCm1mzZuHOnTuYNm0aWrRogatXr2L+/PnQ19eHo6NjiXP14sULjBgxAq1atYK/vz9UVFSwfft2jBs3DpGRkcLd4HFxcfjyyy9hYmKC1NRUHD58GCNGjMC5c+dgYGAgls+VK1fiq6++QoMGDWBiYoLHjx8jKysL8+bNw5gxY+Dt7Y2dO3di5syZuHTpEmrXrl1k/2TJqa+vL27evIn58+ejYcOGOHLkCB48eCD12pOTk2FgYABfX1/o6+sjJiYGgYGBSEhIwOrVq8Xafv/997C0tIS/vz9iYmKwdu1a1KlTB/PmzRP6OXHiRIhEImzcuBHv37/H8uXLkZaWhrZt20rty4ULF5CWlgY3Nze0atUKq1evRkREhNRiPxERERERERERUUXT1NTEv//+i5ycHKipqUltX+JCuY2NDXbs2IG1a9dix44dYvvatm2LgIAAdOvWraThlZ62tjacnJxw+vRp9OjRAxEREbCwsEDjxo0LtbWxsYGNjQ2A/KVCLC0tkZGRgdDQUKFQfv36dVy5cgXr1q0Tuyv98zvU3717h9DQULRq1Urox5gxY/D777+jS5cuRfY3JSUFx48fh6GhIYD8u4t9fX0RFxcHY2Nj3Lx5E5cuXcLu3bthb28PALCzs0NCQgICAwNLVSgPCgpCrVq1sHfvXtSoUQMA0LlzZzg7OyM8PByjR48GAHzzzTfCMTk5ObCzs4ONjQ3OnTsHDw8PsRzs3LkTnTp1EjtPQaG8oK+mpqZwdnbGtWvXMGDAgCL7Jy2nf//9N86fP481a9Zg4MCBAAAHBwf07dtX6rWLRCIsXLhQeN25c2doa2vDx8cHS5cuhba2trDPyMgI69atAwB0794dDx8+xLlz54RCeUhICFRUVLBz507o6ekBAIyNjYv9mcqnIiIiUL9+fXTt2hWqqqro1q0bIiIiMGfOHKioqMgUQxYFa0ulp6eXWcyq5vNfApSHgvyWV54r4hqqivLOtSzK69kXVWm+lvWYrMgxLu8Y4vwrOc7XilcZ47Usz/l5LEUYQ9WFIuS6us3XoijCf3dK0gd5xpAiXGNVpQhzFSi/+QqU/7VVxPiT9xwV3SdFGUfK/lmgKHkuy/nau3dvtG/fHrm5ueVbKAcAW1tbHD9+HAkJCfj3338BACYmJjAyMipN2GrDzc0Nc+fORUZGBs6cOQNPT0+J7TIzMxEcHIxTp07h1atXYsu1fPjwATVr1sQvv/wCbW1t9O/fv9hz1qtXTyjoAkDLli0BAPHx8cUe16ZNG6FI/ulxBYXy69evw8DAAN26dRNbm97W1hb/+c9/ZP7mRpLr16+jX79+UFNTE2Lr6+ujXbt2Ykuc3L17F5s2bcLDhw+FB04CQExMjFg8AwODQkVyAFBVVRW+kACARo0aQUtLS2pupOX0/v37AABnZ2exc/Xs2RN79+4tNnZeXh5CQkJw5MgRxMbGIjMzU9j34sULtG7dWnhta2srdmyLFi1w+vRp4fXvv/8Oa2troUgO5H8J8+nd9kVJSkrC9evX4enpKazt5O7ujoULF+L//u//iv2SRV4F7/Hn71vVUvQvEGQRHR1dRv2Qrug8V51rqCoqc0xbWlqWS9zKm6/yj8+Sjcmiz1MZY7xwniX3j/Ov9DhfpSm7f+vLPl4NKuGcdUocq2r/O6Zq4XytVy59kIf0OVVXAfpQtPw8F/+5xv+2ll5lfy6W13wFyvLaJM/nosdf2c1/ec9RWX2quHEk73UrF0Wer8eOHYOVlRUaNWpUbIy8vDzcuHEDx48fx4ULFxAVFSXTuUtVKC9gZGTE4ngJ2NvbQ0NDA5s2bUJsbGyRdxgHBAQgPDwc06ZNQ4cOHaCnp4eLFy9i27ZtyMzMRM2aNfH27VsYGRlJvbNXX19f7HXB0iCfFmBLclxycjLevn2L9u3bSzw+ISEBxsbGxZ6jKMnJyQgJCUFISEihfQX9+PfffzFhwgR06NABy5cvR7169aChoYEpU6YUura6dSX/Q01LS6vQQz01NDRKnZuEhARoaGiIFagBiH3xUJSQkBCsWbMGkyZNgrW1NfT19XH//n34+fkV6pekfny6lnlCQgKaNm1a6Byy9OPs2bPIzs6Go6Mj3r9/DwCwtraGpqYmTp06VaaFcnV1dWRnZ6NZs2Zid8xXJSfuxZXqeFmWwimt9PR0xMTEFJnnqnANVYW0XFdllTVfSzI+SzImiztPRY7xosZQUf3j/Cs5zlfZPEp4U0a9kmO8/t+rCj/n1b9fyx1LmceQolHmXMszX1/fSaygXhVN2px6EJ9Q6X2Q5NMx9PL+hzKPT/mUea4WKKtre/Wb5Plc1Pgrqn1JyHuOiu5TRY+jhCj5rltZVIX56uvrizVr1hRZKH/8+DGOHz+OU6dOISEhAXXq1MGwYcNkjl/iQvnx48eltqlRowaMjY3Rvn37QgVIyi9k9u7dG/v27YONjU2RBdzIyEh4eHjAy8tL2Pb5QxgNDAyQkJCAvLy8Ml0GQ1a1atWCoaFhoWV4CshSjC0utqOjI0aNGlVoX82aNQEAP//8M9LS0hAUFCQUjLOzs/Hu3btCx1R0foyMjJCVlYWUlBSxYnlSUpLUYyMjI+Hk5IS5c+cK2548eVLifiQmFv6wl6UfERERACQ/TTgyMhKLFy8udo17eRS8P9ra2uX68zhFVpHXXV55rq7vXXGUcUxXpfla1v2rjOuVNc+K/l5UBVVhTMtLUedrZfSlLM9ZVCxFy7MyU8ZcyzdfK79Qrgj5L00f8otBxRfKFeEaqzplnKsFyu7aJM/nomOX3fyX9xyV1aeKG0fyXrdyUeT52rBhQ/zwww+wsbFBvXr5d/4nJibi1KlTOHnyJB4+fIiaNWuiV69ecHd3h52dnVx1wBIXyn18fIQTFayhVuDT7SoqKtDV1YWXlxcmT55c0tMprWHDhiExMRHDhw8vsk1mZqZYETInJ0dsSQ0gf9mNnTt34uzZs+jXr1+59bcotra22LVrFzQ0NNCmTZsyjW1jY4PHjx+jXbt2RS7fkpGRARUVFair/29IF9wFXdk6dOgAALh48aKwRnlubi4uX74s9diMjIxCBehTp06VqB8dO3bEoUOHxAr2v/zyi9gyNZK8fPkSUVFRGDFiRKGxFR0djdWrV+Pnn3+Gk5NTifpFREREREREREQkjY+PDxYsWAAnJyf06NEDWVlZuH79OoD8lTsmTpyIXr16Cc84lFep7ij38fGBgYEBRo8ejSZNmgAAnj17hu+//x4pKSlYsmQJEhMTceDAAaxfvx41a9aUeFdwddaxY0ds3bq12Da2trYIDw9Hy5YtUbt2bRw8eFBsSY2CNo6Ojvjmm2/w/PlzdOrUCW/fvsW5c+ewcePGcryCfHZ2dujZsycmTZqESZMmQSQSIT09HX///TeePXuGVatWFXt8amoqIiMjC223trbGzJkzMXToUEycOBHDhw9H3bp18ebNG9y+fRtdunSBm5ub8OBYX19fjBgxAo8fP8bevXsLLUdSGVq1agUXFxesXLkS6enpMDExwZEjR4TifnFsbW2xf/9+hIaGolmzZjh58iSePXtWon6MHTsWBw8exOTJkzF58mS8f/8egYGBUtcoL7ibfNKkSYUeNmtpaYkdO3YgIiKChXIiIiIiIiIiIio3Li4uuHDhAsLCwnDixAk8f/4cANCrVy+MHTu21EsDl7hQHhISgjp16mD37t1i20UiEVxcXDBp0iT88MMPWLVqFZycnDBq1CgcOnSIhfISWLJkCZYtW4YVK1ZAW1sbgwYNgouLCxYvXizWLjAwEEFBQQgLC0NQUBDq1KkDOzu7Cuvn5s2bsWPHDhw6dAgvX76Enp4eWrVqhcGDB0s99tWrV5g1a1ah7d9//z26dOmC8PBwbNy4EcuXL0daWhqMjIxgZWUFkUgEIH/crV69GkFBQZgyZQratm2LTZs24euvvy7ryyyRb7/9Fn5+fli7di00NTUxaNAgtGrVCt9//32xx02bNg3JycnYvHkzAMDV1RWLFy/G1KlT5e5DvXr1sHPnTqxcuRKzZs1CkyZNsHTpUmzYsKHY4yIiItC5c+dCRXIgf/1ENzc3HDlyBB8+FP9zRSIiIiIiIiIiotKoU6cOvL294e3tjd9//x0nT57EmTNncP78eZiYmKB///5wd3dH69at5Y6tkvf5uikysrKywuzZs4ssfH///ffYtGkTbt++DQDYu3cvNmzYgHv37pXkdERKZ/To0VBVVcWBAwcquysK4/79+8jLy0NWVhbatm2rsGtiSbN07z+lOt5vfPMy6knR0tLSEB0dXWSeq8I1VBXScl1VVeZ8Lcn4LMmYLO48FTnGixpDRfWP86/kOF9lczDyuUzt4t7lSm0zx6OZTLHm75DtGS0WTaQ/M2VUnyYyxVofFiO1zef9V9YxpIiUNdfyztez/30hNWZWdon+l19mX/Qofk7tPyP9F7E1axT/S1sNyStwytwHST4dQxduF/+Q4pLEp3zKOleB/Pn68ePHMru2iGuS57Nb98I3rRXXXr2I+ZKdU/S55T2HvO1L4tNzVPQ4irwu+Tr62Em+bmVRledrTk4Ofv75Z5w4cQKXL19GRkYGRCIR3N3d4e7ujvr168sUp8R3lOfm5uLp06dF7n/69Clyc//3j2NNTc0Srw9DVNWdO3cOr169QuvWrZGeno6IiAj89ttv2LJlS2V3jYiIiIiIiIiIqMpSU1NDjx490KNHD6SmpuLcuXM4ceIE1q9fj/Xr1+Phw4cyxSlxodzJyQmHDh1C06ZNMWzYMKEInpmZiSNHjuDw4cPo27ev0D4qKkpYx5youtHR0cGJEycQExODrKwsNG/eHAEBAejVq1dld42IiIiIiIiIiEgp6OrqYsiQIRgyZAji4+Nx4sQJmY8tcaF80aJFePHiBVauXIk1a9bAyMgIAJCQkICsrCx07NgRixYtApBfPNfS0sL48eNLejqiKs3BwQEODg6V3Q0iIiIiIiIiIiKl8+bNG/z888/o16+fcEN3/fr14eXlJXOMEhfKDQwMcOjQIZw/fx4///wz/v33XwCAvb097O3t0atXL6iqqgIAatSogZUrV5b0VEREVYoyrA+sDNdAyquixqeizwNF7x8pL1nX+JZ1LXNZBHi1KLNYspJ1/XSiytTXXvp6uSevFD8XNdSLXx/8ZVLxzxv48WLx8Y30VYvdD0i/DlnWYi8NrkFOiqCo5wkUtV52YorkuamvLXlOp38s+tzyrkVeVJ+KWh+9KMWt+V34HPWQEJWIPnblv3a2sq9FrqweP34MX19fODg4lHj57xIXygFARUUFvXv3Ru/evUsThoiIiIiIiIiIiIio0pSqUA4A8fHx+PXXX5GYmAhXV1cYGxsjNzcX79+/h56eHtTU5Pw6iYiIiIiIiIiIiIioApW4UJ6Xlwd/f398//33yM7OhoqKClq3bg1jY2N8+PABTk5OmDlzJsaNG1eG3SUiIiIiIiIiIiIiEqeiUvxyYtJIXyysCLt27cL+/fsxYcIE7N27F3l5/1tLSU9PD71798ZPP/1Uqs5RxQoMDIRIJBL+mJmZoW/fvti5cydyc8XXvhKJRNi9e3cl9TRfdHQ0RCIRbt26JXH/H3/8AZFIVOTTbXNycmBra4sFCxYgNjYWIpEIkZGR5dnlIjk5OcHPz0947ePjAzc3tzI/T2BgIO7cuVNouyK8n0RERERERERERCVhY2OD6Oho1K1bt8QxSnxHeXh4OAYOHIg5c+YgOTm50H6RSIRr166VuGNUObS0tBASEgIAyMjIwK1bt7Bu3Trk5eXJ9ZRYRdChQweYmpri9OnTGDBgQKH9v/zyCxITE+Hu7o569eohLCwMzZo1q/iOSuDt7Y20tLQyjxsUFAQdHR107txZbHtYWBhMTEzK/HxERERERERERERVQYkL5a9evYKFhUWR+7W1tZGamlrS8FRJVFVVYW5uLrzu1q0b/vrrL/z0009VrlAOAG5ubti+fTuSk5NRu3ZtsX0RERGoU6cObG1toaamJnbdla1Jk4p96roiXTsREREREREREZE0vr6+ch9TsJy4JCVeeqVOnTp49epVkfsfPHiABg0alDQ8KZCaNWsiOztb7uOuXLmC8ePHw8bGBp07d8awYcMK/crg6NGjEIlEePjwISZNmgRzc3P07t0bx48fLxRv69atsLOzg4WFBaZPn47ExESpfXB3d0dWVlahJVUyMzNx/vx59OvXD2pqahKXXrl48SIGDx4MCwsLdOnSBYMHD8bVq1eF/ZKWK9m3bx9EIpHwOi0tDX5+fnB1dUWnTp3g5OSEpUuXIiUlpdh+f770ipOTk9iyOAV/fHx8AACvX7+Gr68vnJ2d0bFjR/Tu3Rvr16/Hx48fxfoLAGvXrhWOL1i2RtK1HD58GK6urujQoQOcnJywdetWsSV45HnviIiIiIiIiIiIytKvv/6K27dvy/2nKCW+o9zFxQWHDx/G4MGDoaurC+B/C6b/97//xbFjxzBx4sSShqdKVFAUL1h65aeffsKUKVPkjhMbG4uePXtiwoQJUFVVxbVr1+Dl5YWQkBBYW1uLtZ03bx6GDx+O8ePH48iRI/Dx8YGZmRlatGgBAAgNDcWmTZswYcIE2Nra4saNG1i0aJHUPjRt2hRmZmaIiIjAyJEjhe1XrlxBamoq3N3dJR73/PlzzJo1C/3798fcuXORm5uLP//8E+/evZMrBxkZGcjJycHs2bNhaGiIV69eYfv27fD29saBAwdkjhMUFCRW9H706BGWLVsGU1NTAEBycjIMDAzg6+sLfX19xMTEIDAwEAkJCVi9ejWA/OVVPDw84OnpKRThW7ZsKfF8Bw4cwMqVK+Hp6YkePXogKioKQUFBSElJwcKFC8XaSnvviIiIiIiIiIiIytqFCxfKNF6JC+UzZ87ErVu3MGDAAHTp0gUqKirYuXMnNm3ahLt376Jt27aYOnVqWfaVKkBaWhrat28vtq1fv34lWnblyy+/FP6em5sLa2tr/P333zhy5EihQvno0aMxevRoAICFhQWuXr2Kc+fOwdvbGzk5OQgODsaAAQOEIq2DgwMSExOLfFDnp9zd3bF69Wq8evVK+JXD6dOn0aRJE3Tq1EniMQ8fPkRWVhaWLFkifBHk4OAgdw4MDQ2xfPly4XV2djYaNWqEUaNG4enTp0KhW5p27doJf09KSsKcOXNgZ2eHyZMnA8i/I/zTAnbnzp2hra0NHx8fLF26FNra2sLyKg0aNCh2qZWcnBxs2bIF/fv3x+LFiwEA9vb2yMrKwp49e+Dl5SW2jE1x711JFDwYOD09vUTHk2wK8ss8lz9FyLWOjk65xOV8rRiKMIaqC0XIdXWbr+XxTJbKpAhjqLpQhFxXt/lankr7WVCS4xVhDFUHipLn8pqvQOVfW3kq7/9OlyS+sv3bQZFUh/kqTYkL5Xp6ejhy5Aj27NmDc+fOoUaNGvj111/RpEkTTJs2DZMmTYKWllZZ9pUqgJaWFkJDQwEAHz9+xIMHD7B582YsXrxYuDNZVnFxcdiwYQNu3LiBhIQE4R98nxfigfxCbAEdHR2YmJggLi5OiPP69Wu4uLiIHePq6ipTobxfv35Ys2YNzpw5g4kTJyI1NRVXrlwp9hcPIpEIampqwt3SVlZW0NPTk+m6P3f8+HHs27cPz549E/tAj4mJkblQXiArKwuzZs2Cmpoa1q9fD1XV/NWT8vLyEBISgiNHjiA2NhaZmZnCMS9evEDr1q1lPsc///yD5ORk9OnTR2x7v379EBwcjHv37sHR0VHYXtx7VxIFv2iIiYkpcQySHfNccSoz15aWluUSl/O1YjHPFYfztawYSW0RHR1dAf2oeJyvFYfzVRbS52Jlk/5ZUK+UxxeN87ViVHaey2u+Aso9V4ueW8XPydLHL/ocyvpvB0VSFefrr7/+irNnz+L58+dQVVVF06ZN4erqii5dusgVp8SFciC/qOrt7V3iO0dJ8aiqqsLMzEx4bWlpiZycHPj7+2P8+PEyF1xzc3Px1VdfISUlBTNnzkTTpk2hra2NzZs3S1zb/vMitIaGhrDUSEJCAoD8u7M/VbduXZn6YmRkBGtra0RERGDixIk4f/48MjMzi1x2BQBMTU2xfft2BAcHY/r06VBVVYW9vT2WLl0KExMTmc4LAOfPn8fChQvh4eGB2bNnw8DAAAkJCZg2bZpYMVtWq1atwh9//IEjR46gVq1awvaQkBCsWbMGkyZNgrW1NfT19XH//n34+fnJfZ6C5WXq1Kkjtr3g9efLzxT33pWEuro6srOz0axZM2hra5c4DhUvPT0dMTExzHMFUOZcc75WDGUeQ4pGmXNdGfP1UcIbqW3atm1bAT2pOMo8hhSNMue6rOfry9vS52Jlk/ZZ8PpO8c+nKslniTKPIUVSHfJcVtf2/KbizdWi5lZClPRnxpUmfnHnULZ/OyiSqjpfly1bhrCwMKirq8PAwABv3ryBvr4+Dhw4gKFDh2LFihXCcuHSlLhQPmbMGHz11VewsbGRuP/mzZvYunUr9u/fX9JTkIJo3rw5AODvv/+WuVD+7NkzPHz4EFu2bEGvXr2E7RkZGXKf38go/1vVpKQkse1v3sj+HxF3d3f4+vrin3/+QUREBNq3by9cV1G6d++O7t27IzU1FdeuXcPq1avh6+uLkJAQAICmpiaysrLEjnn//r3Y68jISLRt2xZ+fn7CtuIeGlCcw4cP4/Dhw9i8eTNatWpV6DxOTk6YO3eusO3JkyclOo+BgQGAwvkueHjqpwX68lDw4aWtrV2pP7epLpjniqOMueZ8rVjMc8VRxlwr6nxVpL6UJUXLszJTxlwr6nwtT9Kvs/iiXGnyVJ3yXJmUOc/KfG1FX1fZFMqLz5vkcyhrrhVJVRrT4eHhOHLkCKZMmYJp06bhzp07GDduHCIjI3Hz5k0sWLAALVu2xLhx42SKp1rSjty+fbvYQmVSUhJ+/fXXkoYnBfL48WMAEFuXWpqCu5g1NDSEbS9fvkRUVJTc5zc2NoaRkRHOnz8vtv3cuXMyx+jduzdq1KiBkJAQ3Lx5s9i7yT+nq6uLfv36oX///mLFZ2Nj40LF6Bs3boi9zsjIEMsBAJw6dUrmcxf47bffsHLlSkydOhW9e/cutF/W82hoaEi9w9zU1BSGhoaIjIwU23727FloaGigY8eOcvefiIiIiIiIiIioLIWFhcHBwQGzZ8+GpqamsOyziooK+vXrhyFDhuCHH36QOV6pll4p7rb1Z8+eoWbNmqUJT5UgNzcXd+/eBZC/HvaDBw+wbds2tGzZUq51fZo3bw5jY2OsW7cOubm5SEtLw+bNm1GvnvzrWKmpqcHLywurVq1CnTp1YGdnh+vXr+PWrVsyx9DV1UWPHj0QFhYmTJbiHD58GHfv3oWDgwOMjIwQGxuLkydPws7OTmjj6uqKkJAQmJmZwdTUFCdPnkR8fLxYHFtbW/j5+WHLli3Cgy5/+eUXua4/NTUVM2bMQNOmTeHo6Ci8P0D+cjRNmjSBra0t9u/fj9DQUDRr1gwnT57Es2fPCsVq3rw5Ll68iC5dukBbWxumpqbCw0oLqKmpwdvbGytXroShoaFwzp07d2Ls2LFyfWFCRERERERERERUHp48eVLszbDt27fHsWPHZI4nV6H82LFjYsG3bduGI0eOFGqXkpKCR48eoXv37vKEJwWQkZEBDw8PAPlr4xkbG+OLL77A9OnTC92xXBxNTU0EBgbCz88Ps2bNQoMGDfDVV1/h5s2b+OOPP+Tul6enJ96/f4+DBw/i0KFDsLGxwcqVKzFp0iSZY7i7u+PcuXOwtrZG/fr1i20rEolw+fJlrF69Gm/fvoWRkRH69++PWbNmCW28vb2RmJiILVu2QEVFBR4eHhgzZgz8/f2FNiNGjEBsbCxCQ0Oxe/du2NvbY926dRg+fLjM/X779i2SkpKQlJSEESNGiO0bNGgQ/P39MW3aNCQnJ2Pz5s0A8ov4ixcvxtSpU8XaL126FN9++y0mT56MjIwM7N+/H9bW1oXO6enpCXV1dezbtw+HDh2CkZERpk+fXigeERERERERERFRZVBTU4O6etHl7djY2ELP1iuOXIXy9PR0JCcnC68/fPgAVdXCq7fo6OhgxIgRmDZtmjzhqZLNmDEDM2bMkKnto0ePpLbp2LFjoZ83DBw4UOz14MGDMXjw4ELHnjhxQuy1iooKpk+fjunTp8vdjwIuLi5Ftm/UqJHYPgsLCwQHBxcbT0dHB6tXry60ffz48cLf1dTUsHDhQixcuLDYfl+6dEns9afF9s/7JknNmjUl9uXz47p06YKjR49KbQcAI0eOxMiRI4s8p6zvHRERERERERERUVlr2LAhYmJiCm3Py8vD9evXceDAAbmWX5arUD5q1CiMGjUKAODk5IRFixbB2dlZnhBERERERERERERERKXi6OiI48ePw8fHB2pqagDyb7bt1asX0tLS0LFjR8ybN0/meCVeo/zzO2CJiIiIiIiIiIiIiCrCmDFj0KFDB6SlpUFPTw+GhoZwcnJCnTp1YG1tjT59+ggFdFmU6mGeBVJTU5Gamorc3NxC+0xMTMriFEREREREREREREREAIC6deuid+/ewmuRSIQtW7aUOF6pCuUHDx7Evn378OLFiyLbREdHl+YUREREREREREREREQyS01NxcWLF3H27Fls375dpmNKXCg/dOgQ/Pz8YG9vjyFDhmDDhg0YN24catSogaNHj6Ju3brw9PQsaXgiIiIiIqrCRvVpUtldICIAX/So+nOxr33jyu4CUbkb5FR15mofu/Kfk5+eIy0tDdHR0Wjbtm25n5eqtvT0dFy+fBlnzpzBtWvXkJWVhYYNG8p8fIkL5aGhobC3t8euXbuQnJyMDRs2wNHRETY2Npg0aRKGDBmCt2/fljQ8EREREREREREREVGRPn78iKtXr+LMmTO4cuUKMjIy0KBBA3z55Zfo168fOnToIHOsEhfKnz9/jlGjRgEANDQ0AABZWVkAAD09PQwdOhQHDx7EhAkTSnoKIiIiIiIiIiIiIqJC5s+fj0uXLiEtLQ316tXD8OHD0bdvX5ibm5coXokL5Xp6esjJyQEA6OrqQltbG3FxccL+mjVr4s2bNyUNT0REREREREREREQkUUREBHR0dLB8+XIMGzYMKioqpYqnWtIDW7VqhT///FN43alTJxw6dAjx8fF49eoVwsLC0KxZsxJ3LDAwECKRCCKRCG3atIGlpSXc3d3h5+eHJ0+elCjmrVu3ZF68XV779u2DSCTCN998U6Zxjx49CpFIhKSkpGLbeXp6YsqUKcLr8rzWioiviLp06YLAwMBi23h6ekIkEmH27NmF9qWmpqJjx44QiUQ4evRomfRpwIAB8PHxkeuY2NhYiEQiREZGCtucnJzg5+dXJn0iIiIiIiIiIiIqb7a2tsjMzMTSpUsxaNAgBAcH48WLFyWOV+JC+RdffIHHjx/j48ePAIAZM2bgyZMn6NGjB5ycnPD06VN8/fXXJe4YAGhpaSEsLAyHDx/G5s2bMXjwYNy4cQMDBgzAiRMn5I53+/ZtBAcHl6pPRTl58iQA4Pz580JOKtKyZcuwcOFC4XV5XmtFxK/KdHR0cPnyZaSlpYltv3DhAtTU1CqpV8ULCgriMklERERERERERFRl7N69G9euXcPy5cuhr6+PzZs3w8XFBUOGDMGuXbvw77//yhWvxIXyIUOGIDw8HJqamgAAS0tLnD59Gj4+Pli0aBFOnjyJHj16lDR8fudUVWFubg5zc3PY2dlh/PjxOHHiBCwtLbFo0aJSfUNQlp4+fYoHDx7A1tYW79+/x5UrV6Qe8/HjR+Tm5pZZH1q2bInmzZuXWbyKVtb5qEydO3eGlpYWLl26JLY9IiICvXr1qqReFa9du3Zo1KhRZXeDiIiIiIiIiIhIZoaGhvDw8MD+/ftx5coVLF68GJqamli/fj2cnJzg4eEhcyy5CuUFt7IfOHBA4v7GjRtj7NixyM3NRUhIiPBwz7JUo0YNLFmyBFlZWQgPDxe2Hz9+HCNHjkTXrl1hZWUFT09P3Lt3T9gfGBiIoKAgpKWlCUu6eHp6AgCePHmC2bNnw9HREZ06dUK/fv2wZ88emQu3ERERUFFRgZ+fH+rWrYtTp04ValOwtMXOnTvRs2dPdOzYEW/fvhX6PnDgQJiZmcHa2hqTJ0/Gy5cvxY6Pi4vDpEmTYG5ujt69e+P48eNi+z9deqW4ay243q+++gqWlpYwNzeHl5cXnj9/LhYvNzcXe/fuRd++fdGhQwfY2dlh5syZSElJKTa+j48P3NzcxGK9f/++0HIjxeXj6NGjcHd3h5mZGRwcHLBhwwZhPfyiyPIeFiw5cuLECfj5+cHKygr29vZYs2YNsrOzxeJduHABffr0gZmZGYYOHSo2lqRRV1eHq6srIiIihG1JSUn45ZdfCuWmwOHDh+Hq6ooOHTrAyckJW7duLTT+7ty5g8GDB8PMzAxubm64evWqxFhRUVEYM2YMzM3NYWlpiblz5yIxMbHYPn++9ErB+3jr1i0MHDgQ5ubmGDp0KP744w+x41JSUjBv3jxYWFjAxsYG69evx549eyASiYo9HxERERERERERUVkyMjLCl19+iUOHDuHSpUvw8fGRWlP8lFwP8wwLC8OxY8dw5syZYtv17NkTAQEBaN26NUaNGiXPKWTSsmVL1K9fH1FRUcK22NhYDBw4EE2aNMHHjx9x+vRpjB49GidPnoSpqSmGDRuGuLg4REREICQkBED+Q0gB4PXr1zA1NYW7uztq1qyJ6OhoBAYGIi0tDdOnT5fan4iICHTp0gWNGzdG3759ERYWhpSUFOjp6Ym1++mnn9C0aVMsWrQIqqqq0NHRwa5duxAQEIChQ4di9uzZyMrKws2bN5GUlISGDRsKx86bNw/Dhw/H+PHjceTIEfj4+MDMzAwtWrQo1J/irvXFixcYMWIEWrVqBX9/f6ioqGD79u0YN24cIiMjhV8IrFixAmFhYRg7dizs7Ozw4cMHXLlyBWlpacXGl4ekfOzduxcBAQEYO3YsfHx88OTJE6FQPm/evCJjyfMebty4Ec7Ozti4cSOioqIQGBiIJk2aYOTIkQCA6OhozJw5E927d4evry9iY2Px9ddfy7WkjpubG8aPH493796hVq1aOHv2LOrXrw8LC4tCbQ8cOICVK1fC09MTPXr0QFRUFIKCgpCSkiIsp5OQkICJEydCJBJh48aNeP/+PZYvX460tDS0bdtWiBUVFQVPT084Ojpiw4YNSE9Px8aNG+Ht7Y2wsDCZ+19wzpUrV8LLywt6enpYt24dpk+fjvPnz0NDQwMA4Ovri5s3b2L+/Plo2LAhjhw5ggcPHsh1HiIiIiIiIiIiorJkbGyMcePGYdy4cTIfI1eh/OzZs+jduzcaN25cbLvGjRvD1dUVp0+fLpdCOQA0aNAAb968EV5/WgzNzc2FnZ0d7t27h2PHjmHOnDkwNjaGsbGxsJzLp2xsbGBjYwMAyMvLg6WlJTIyMhAaGiq1UH7v3j3ExMRg/PjxAPILpAcOHMC5c+cwdOhQsbZZWVnYuXMndHR0AOTfjRsUFAQPDw+xu3klLc8xevRojB49GgBgYWGBq1ev4ty5c/D29i7UtrhrDQoKQq1atbB3717UqFEDQP5SIc7OzggPD8fo0aPx9OlTHDp0CLNnzxZ7QKirq6vYOSTFl8fn+UhNTcXmzZsxadIkzJkzBwBgZ2cHDQ0N+Pv7Y+LEiahdu7bEWPK8hx07dsTixYuF+Ldu3cK5c+eEQvmOHTvQoEEDbNmyRVhTvEaNGli0aJHM19alSxfUqVMHP/30E4YNG4bTp09LvJs8JycHW7ZsQf/+/YU+2dvbIysrC3v27IGXlxdq166NkJAQqKioYOfOncIXMAUT/lPr1q1Dhw4dEBQUJDzpt3Xr1sId6I6OjjJfw7t37xAaGopWrVoBALS1tTFmzBj8/vvv6NKlC/7++2+cP38ea9aswcCBAwEADg4O6Nu3r8znkCQvLw8AkJ6eXqo4VLyC/DLP5U8Rcl3wOVvWOF8rhiKMoepCEXLN+Vq1KcIYqi4UIdecr1WbIoyh6kBR8lxe8xWo/GurDhRlHCk7RclzSebrixcv8NNPP+H58+dQVVVF06ZN0bt3b5iYmMgVR65C+V9//QV3d3eZ2nbu3Bnr16+XqzPyyMvLEwqBQP7SG+vXr0dUVJTYMhMxMTFSY2VmZiI4OBinTp3Cq1evxJaM+fDhA2rWrFnksREREdDQ0ECfPn0AAObm5mjcuDFOnTpVqFBubW0t9mZHRUUhPT29UDtJ7O3thb/r6OjAxMQEcXFxUo/73PXr19GvXz+oqakJy43o6+ujXbt2wrIaN2/eRF5enkz9Kg1J+UhLS0OfPn3ElkKxtbVFRkYGHj9+jK5du0qMJc97+GkuAaBFixa4efOm8Pr333+Hk5OT2IM3+/TpI1ehXEVFBf369cPp06dhb2+PO3fuYNmyZYXa/fPPP0hOThbGT4F+/fohODgY9+7dg6OjI37//XdYW1uL/UrBxsYGBgYGwuv09HTcuXMHCxYsEPtZSbNmzdCgQQPcv39frkJ5vXr1hCI5kP9LDgCIj48HANy/fx8A4OzsLLRRVVVFz549sXfvXpnP87mC916WuUulxzxXnMrMtaWlZbnE5XytWMxzxeF8pdJinisO5yuVFvNcMSo7z+U1X4HKv7bqhLmuGJWdZ3nn644dO7Bp0ybk5uZCRUUFubm5UFVVRUBAAKZPn46vvvpK5lhyFcqzsrKEJRek0dDQkGupCnnFxcWhWbNmAPLvRJ4wYQIMDQ3h4+MDExMT1KhRA4sXL0ZmZqbUWAEBAQgPD8e0adPQoUMH6Onp4eLFi9i2bRsyMzOLLJTn5ubizJkz6Nq1K1RVVfH+/XsA+YXD/fv3Iz4+HvXr1xfa16lTR+z4gjW569WrJ7WPny/jUtL8JicnIyQkRFgy5fOYBf1SV1cv1N+y9nn85ORkAMCgQYMktn/16lWRseR5D6XlMiEhoVDfdHV1hTvwZdW/f3+EhIRg3759aNWqFUQikTBGCrx79w5A4VwUvC7Yn5CQgKZNmxY6h6GhofD39+/fIycnB6tXr8bq1asLtS0uf5Lo6+uLvS4YHwVzKiEhARoaGoXy+WmfSkJdXR3Z2dlo1qwZtLW1SxWLipaeno6YmBjmuQIoc645XyuGMo8hRaPMueZ8rRjKPIYUjTLnmvO1YijzGFIk1SHPynxtiqI6jCNFUBXzfOHCBWzYsAFubm6YPn06Xr58iQkTJuDcuXM4e/YsNmzYABMTEwwYMECmeHIVyuvVq4fHjx/L1Pbx48cyFYBL4vHjx4iPjxcKqnfv3kVcXByCg4PRpk0boV1KSgqMjY2lxouMjISHhwe8vLyEbUU9KPFTN2/eREJCAhISEmBlZVVo/5kzZ4QlWQCI3QEPQLgb+PXr1zL1syzUqlULjo6OEpfEKSgmGxgYIDs7G4mJiXIXyzU1NQs9xLWg2Pu5z/NRq1YtAPnLw0jKR6NGjYo8b0nfQ0mMjIwKPfwyNTVVpi9dPtWhQwc0btwY+/fvx6xZsyS2KRgDSUlJYtsLzl+QE0l9+vw4PT09qKioYMqUKRKX7ylq2ZqSMjIyQlZWVqH1+D+/FnkVjAttbe1y/Xkc5WOeK44y5prztWIxzxVHGXPN+VqxmOeKo4y55nytWMxzxVDmPCvztSka5rpiVKU879u3D+bm5ggICAAA/PvvvwDyb3j18vLCo0ePEBISUj6FcltbW5w4cQJTpkwptoCamJiIEydOiK1pXVYyMzOxYsUKaGpqYtiwYQCAjIwMABC72/3OnTt4+fKl2NIRRd2FnZmZKXZsTk4OTp8+LbUvp06dgo6ODrZu3QpVVVWxfd9++y1OnTolVij/nIWFBbS1tfHjjz+iY8eOUs8nj6Ku1cbGBo8fP0a7du3Elhb5VLdu3aCiooIff/xRrPAsS3xjY2PExcWJLXdy/fp1mfpckI+4uDi4uLjIdEyBkr6HknTs2BGXL1+Gr6+vkKPIyMgSxZo8eTIuXbpU5JJFpqamMDQ0RGRkpNg1nz17FhoaGsK46NixIw4dOiRWlP7ll1+EXyUA+UvymJub459//oGZmVmJ+iuPDh06AAAuXrworFGem5uLy5cvl/u5iYiIiIiIiIioenv48CGmTZtW5H4rKytcuHBB5nhyFconT56MkydPYuzYsVi1ahU6depUqM3vv/8uLHkyadIkecIXkpubi7t37wIA0tLS8NdffyEsLAwvXryAv7+/cIexubk5dHR0sHz5cnh5eSE+Ph6BgYFiy54A+WtRZ2dnIyQkBBYWFtDV1UXz5s1ha2uL8PBwtGzZErVr18bBgwelLmuSmZmJ8+fPo3fv3sJDJD81ZMgQrFq1Cv/88w+aN28uMYaenh6mTZuG7777Dnl5eXB2dkZubi5u3bqF/v37l6rYWdS1zpw5E0OHDsXEiRMxfPhw1K1bF2/evMHt27fRpUsXuLm5wdTUFCNGjMCmTZvw7t072NjYICMjA1euXMGMGTNQv379IuP37t0bmzdvxjfffIPhw4fj8ePH+OGHH2Tqs76+PmbOnImAgADExcWha9euUFNTw4sXL3Dx4kUEBgYW+dOPkryHRfHy8sLQoUMxbdo0jBw5ErGxsdi9e7fcS68AwNChQ4td611NTQ3e3t5YuXIlDA0N4ejoiLt372Lnzp0YO3ascBf42LFjcfDgQUyePBmTJ0/G+/fvERgYKLZGOQAsWLAAY8eOxddff43+/ftDX18fcXFxuHHjBgYPHgxra2u5r6EorVq1gouLC1auXIn09HSYmJjgyJEjyMjIKPRrASIiIiIiIiIiorJW3LMlk5KSoKWlJXMsuQrljRs3xsaNGzFnzhyMGDECjRs3RuvWrVGzZk18+PABjx8/xvPnz6GlpYX169ejSZMm8oQvJCMjAx4eHgDy75Zt1KgRbGxsEBQUhBYtWgjt6tati02bNmHt2rXw9vZGs2bNsHz5cuzatUssXs+ePTFq1Cjs2LEDiYmJsLKywoEDB7BkyRIsW7YMK1asgLa2NgYNGgQXFxcsXry4yL5duXIFKSkpwp20n3Nzc8PatWtx6tSpIpfdAPK/fDA0NMS+fftw9OhR1KxZExYWFqVeH7yoa23atCnCw8OxceNGLF++HGlpaTAyMoKVlRVEIpFw/NKlS9GoUSOEh4cjJCQEBgYGsLKyEgZfUfFbtmwJf39/bN26Fd7e3rC0tMR3330n808cJkyYgPr162Pv3r0IDQ2Furo6mjRpgh49ehS7Pn5J3sOitGvXDps2bcJ3332H6dOno1WrVtiwYQMmTpwodyxZeHp6Ql1dHfv27cOhQ4dgZGSE6dOnY+rUqUKbevXqYefOnVi5ciVmzZqFJk2aYOnSpdiwYYNYrM6dO+PgwYMIDAyEr68vsrKyYGxsjG7duklc47y0vv32W/j5+WHt2rXQ1NTEoEGD0KpVK3z//fdlfi4iIiIiIiIiIqICDRo0wIsXLwptz8vLw99//40DBw7Azs5O5ngqeXl5efJ2IjY2Fjt37sSVK1cQHx8vbK9Xrx569OiByZMno3HjxvKGJSIlMHr0aKiqquLAgQNyH3v//n3k5eUhKysLbdu2rTJrYlVFaWlpiI6OZp4rgLLmmvO14ijrGFJEypprzteKo6xjSBEpa645XyuOso4hRaPMeb5//z4+fvyolNemaJR5HCmSqpjnlStX4ueff8a5c+cA5C9TPGHCBDRv3hzPnj2DsbExQkNDZX42pFx3lBdo1KgRli9fDiD/IYcF61Hr6uqWJBwRVVHnzp3Dq1ev0Lp1a6SnpyMiIgK//fYbtmzZUtldIyIiIiIiIiIiJebp6YmGDRvi7du3MDAwQM2aNSESiVCnTh0MGDAAo0aNkqteXaJC+ad0dXVZICeqpnR0dHDixAnExMQgKysLzZs3R0BAAHr16lXZXSMiIiIiIiIiIiXWtGlTjB8/XnjdsWNHHD9+vMTxSl0oJ6Lqy8HBAQ4ODpXdDSIiIiIiIiIiolJhoZyIiIiIiBTawcjnMrUb1adJOfeEqOrZf+ZZsfsNdFSK3f9Fj+Ln1dn/Fn6Imrz62iv2M85kuUZFvwZSPhHXJI9Lt+7yj8XI65Jj9bGTHEve9kTlxdfXV+5j8vLy4O/vL3EfC+VEREREREREREREVKX8+uuvyMvLk+uY4tqzUE5EREREREREREREVcqFCxfKNJ5qmUYjIiIiIiIiIiIiIqpiFKpQHhgYCJFIBJFIhDZt2sDS0hLu7u7w8/PDkydPShTz1q1b2L59e5n1MTY2VujjtWvXCu0/cuSIsJ/KVlm/l6Xx/v17iEQiHD16tNh2ycnJ+Pbbb9G7d2+YmZnBxsYGI0eOxL59+4Q2sbGxCAwMRHx8fJn2UZHyRUREREREREREpMgUqlAOAFpaWggLC8Phw4exefNmDB48GDdu3MCAAQNw4sQJuePdvn0bwcHBZd5PHR0dnDlzptD2iIgI6OjolPn5qPzey/KSnZ2NsWPH4vz58xg/fjx27dqFxYsXo127drh8+bLQ7uXLlwgKCsLr16/L9PxVLV9ERERERERERESVReHWKFdVVYW5ubnw2s7ODqNGjYKXlxcWLVqEzp07o3Hjyn+KrrOzM86fP4/ly5ejRo0aAIDXr1/j119/hZubG06ePFnJPaTKdvv2bTx69AihoaGwsrIStvfv3x+5ubklipmRkQEtLa2y6iIRERERERERERFBAe8ol6RGjRpYsmQJsrKyEB4eLmw/fvw4Ro4cia5du8LKygqenp64d++esD8wMBBBQUFIS0sTlkPx9PQEADx58gSzZ8+Go6MjOnXqhH79+mHPnj0yFzC7d+8OFRUVXL16Vdh25swZNGnSBO3bty/U/rvvvoO7uzssLCzg4OCAOXPmFLqD2NPTE1OmTEFkZCRcXV1hYWGBMWPG4Pnz53LHysvLQ1BQEOzs7GBhYYGZM2fixo0bEIlEuHXrlli73bt3w9XVFR06dICzs7PYsiAFebSwsMDDhw/h4eGBjh07YtCgQXj48CEyMzOxbNkyWFlZoXv37oWOBYCoqCiMGTMG5ubmsLS0xNy5c5GYmCjsL1jO5sSJE/Dz84OVlRXs7e2xZs0aZGdnC30o6r2UJCoqClOnToW9vT3Mzc0xYMAAHD9+XKzNrVu3IBKJcP36dcydOxcWFhbo2bMndu7cWSjekSNH4OTkhE6dOmHs2LF49uxZkecu8O7dOwCAkZFRoX2qqqpCH8aMGQMAGDp0qNiyPQX9u3LlCmbOnInOnTtj1qxZAEo39oH88f/VV1/B0tIS5ubm8PLyKjTOUlJSMG/ePFhYWMDGxgbr16/Hnj17hP5lZWXBzs4OGzZsKHR9X3/9NYYOHSo1R0RERERERERERIpA4e4oL0rLli1Rv359REVFCdtiY2MxcOBANGnSBB8/fsTp06cxevRonDx5Eqamphg2bBji4uIQERGBkJAQAICuri6A/Lu/TU1N4e7ujpo1ayI6OhqBgYFIS0vD9OnTpfZHU1MTLi4uiIiIQO/evQHkL7vi5uYmsX1iYiKmTJmCevXqISkpCXv37oWnpydOnz4NdfX/vQ3R0dFISkrCvHnzkJOTA39/f8yfPx9hYWFyxTpw4ACCgoIwadIkdOvWDTdv3sTixYsL9WvVqlUIDw/H1KlT0alTJ9y5cwffffcdatSogZEjRwrtsrKysHDhQowbNw5169bFd999h+nTp6Nz586oU6cONm7ciIsXL2L16tXo2LEjOnfuDCC/YO3p6QlHR0ds2LAB6enp2LhxI7y9vcWuCQA2btwIZ2dnbNy4EVFRUQgMDESTJk0wcuTIYt9LSf7991907twZI0eOhKamJu7cuYPFixcjLy8PgwYNEmu7bNkyDBgwAFu2bMGFCxfw3XffQSQSoXv37gCAy5cvY8mSJRg8eDD69euHBw8eCAXr4rRt2xaqqqpYvHgxpk2bBktLS2hqaoq1ad++PZYuXQo/Pz+sXr0azZs3LxRnyZIl+OKLL7BlyxahwF6asf/ixQuMGDECrVq1gr+/P1RUVLB9+3aMGzcOkZGRQh99fX1x8+ZNzJ8/Hw0bNsSRI0fw4MEDoV8aGhoYNGgQjh8/jlmzZgl9e/v2LS5evIhFixZJzREREREREREREZEiqDKFcgBo0KAB3rx5I7z+tKCdm5sLOzs73Lt3D8eOHcOcOXNgbGwMY2PjQsu5AICNjQ1sbGwA5N9VbWlpiYyMDISGhspUKAcANzc3eHt748OHD0hMTMT9+/cREBAgdpd5gdWrVwt/z8nJgYWFBbp3746bN2/C3t5e2JeSkoLjx4/D0NAQAJCWlgZfX1/ExcXB2NhYplg5OTnYsWMHBg8ejHnz5gEA7O3tkZycjB9++EE49vnz5wgNDcXy5cvh4eEBALC1tUVGRga2bNkCDw8PofiZlZWFefPmwdHRUch3QXHd19cXANCtWzdERkYiMjJSKJSvW7cOHTp0QFBQEFRUVAAArVu3hpubG65evSrEA4COHTsKxXw7OzvcunUL586dw8iRI4t9LyXp37+/8Pe8vDxYWVkhPj4eYWFhhQrlvXv3xowZMwDkj4srV67g3LlzQqF827Zt6NKli5B3BwcHZGZmYuvWrcX2oVmzZvDx8UFAQADGjRsHDQ0NdOzYEX379sXIkSOhrq4OXV1dtGzZEgDQqlUrmJmZFYrj5OSE+fPni20rzdgPCgpCrVq1sHfvXmHZoM6dO8PZ2Rnh4eEYPXo0/v77b5w/fx5r1qzBwIEDhevu27evWKxhw4Zh165d+Pnnn4X38tSpU1BVVS3ySyNp8vLyAADp6eklOp5kU5Bf5rn8KUKuy+vZGZyvFUMRxlB1oQi55nwtnbS0tEo9vyKMoepCEXJdXeZrRcyrypi7ZT2GKvvzR1EpwlwFym++ApV/bZ8ry7Eob6zymgeKMo6UnaLkuTKf/VilCuV5eXlCsRXIXz5i/fr1iIqKElvKIyYmRmqszMxMBAcH49SpU3j16hWysrKEfR8+fEDNmjWlxujWrRtq1qyJCxcu4OXLl2jfvj1MTU0lFsqvXr2Kbdu24fHjx0hNTRXr66eF8jZt2ghFcgBCEfXTQrm0WHFxcUhISICTk5NYH5ydncUK5Tdu3ACQXyguWOIEyC+W79y5E69evULDhg0B5C8VUvDFApBfBC5oW0BNTQ1NmjRBXFwcgPyJdefOHSxYsAA5OTlixzZo0AD3798XK5R/mgcAaNGiBW7evFkol7J49+4dAgMDcfHiRcTHxwvnNzAwKNT20/OqqKigRYsWwjXk5OTgwYMHhQrVrq6uUgvlADB27Fj069cPly5dwu3bt/HLL79g5cqV+OmnnxASEiJ8EVGcHj16FNpWmrF//fp19OvXD2pqasL7rq+vj3bt2uGPP/4AANy/fx9A/pgpoKqqip49e2Lv3r3CtqZNm6Jr16748ccfhffy6NGjcHV1LfaO/+IU9EmWa6HSY54rTmXm2tLSslzicr5WLOa54nC+KqLCS9lJEh0dXc79kE3VzXPVw/kqi7qlOlr6vKpXqviynaP8yJZn6deoKJ8/iqqyPxfLa74ClXltksdlycaivLHK8tyyq+xxVF1Udp7Lc75KU6UK5XFxcUKBNjU1FRMmTIChoSF8fHxgYmKCGjVqYPHixcjMzJQaKyAgAOHh4Zg2bRo6dOgAPT09XLx4Edu2bUNmZqZMhXI1NTX07dsXp0+fxsuXLzFkyBCJ7e7duwdvb284Oztj8uTJqFOnDlRUVDB8+PBCfdXX1xd7raGhAQBCO1liJSQkAIBYwR0A6tSpI/Y6OTkZeXl56Natm8R+f1oo19LSEls2pKBfenp6hfpb0I/3798jJycHq1evFrsL/tP4n5IU6+PHjxL7Jo2Pjw+ioqIwbdo0tGzZErq6ujh06BDOnj1bqK2k86akpAAAkpKSkJ2dXSiXdevK/o9NIyMjeHh4wMPDA1lZWVi6dCmOHj2Ky5cvixWii/L5+1basZ+cnIyQkBBhSZZPFbyvCQkJ0NDQKJSbz/MAAMOHD4ePjw+SkpLw+vVrPHz4ED4+PlL7URR1dXVkZ2ejWbNm0NbWLnEcKl56ejpiYmKY5wqgzLnmfK0YyjyGFI0y57qqz9dHCW+kN0L+0neVSZnHkKJR5lyX9Xx9EJ9QquOlzavXdxKL3V8W5ygP8owhWa6xsj9/FJUyz9UClXVtr36TPC5LMhYTouSLJW/70qoO40gRMM9VqFD++PFjxMfHC8tm3L17F3FxcQgODkabNm2EdikpKcKd18WJjIyEh4cHvLy8hG2S7gSXpn///hg9ejQAoF+/fhLbXLhwAbq6uti4caNwB/HLly/lPpessQoeHpmUlCS2/dM7jwGgVq1aUFFRwcGDB4UC6adMTU1L1McCenp6UFFRwZQpU9CrV69C+2vXrl2q+EXJzMzElStX4OPjI/YAy4MHD8ody9DQEOrq6oVy+ekSQPLQ0NDAuHHjcPToUTx58kSmQvmnv6IASj/2a9WqBUdHR4waNarQvoIviIyMjJCVlYWUlBSxYvnneQDyf5GwYsUKnDx5ErGxsWjSpAm6du0qtR9FKbhebW3tSv25TXXBPFccZcw152vFYp4rjjLmurrMV0W5NmXPsyJRxlwr2nyV3ofSF8or8zply7P0a1SE90qRKcp4Lg+Vd22Sx2XJ+iJvrLI8t+yUeRwpkuqc5ypRKM/MzMSKFSugqamJYcOGAQAyMjIAQKzAe+fOHbx8+RKtWrUSthV1V3JmZqbYsTk5OTh9+rTcfbOwsICbmxvq1KlTZJEyIyMDGhoaYgXPU6dOyX0uWWMZGxvDyMgIFy9eFCtQX7hwQaxdwVIqb9++LbRMS1nQ0dGBubk5/vnnH4lrb8tL1jvMP378iNzcXLH3NzU1FZcuXZL7nGpqamjXrh3Onz+PcePGCdvPnTsn9di3b99CV1dX7GGtwP9+wlLwhcbnvxqQprRj38bGBo8fP0a7du2gpqYm8RwdOnQAAFy8eFFYozw3NxeXL18u1FZTUxMDBgxAeHg43rx5g3HjxhUq7hMRERERERERESkyhSuU5+bm4u7duwDyHwLw119/ISwsDC9evIC/vz8aNWoEADA3N4eOjg6WL18OLy8vxMfHIzAwEPXr1xeL16JFC2RnZyMkJAQWFhbQ1dVF8+bNYWtri/DwcLRs2RK1a9fGwYMHS7TMh4qKCgICAoptY2dnh5CQEKxYsQIuLi6IiorCiRMn5D6XrLHU1NTg5eWFb7/9FnXr1oW1tTVu3bqFX375BQCEO9FNTU0xevRoLFiwABMnTkSnTp2QlZWFmJgY3Lp1S6Y1uKVZsGABxo4di6+//hr9+/eHvr4+4uLicOPGDQwePBjW1tYyxyrqvfycnp4ezMzMsHPnTuGO8B07dkBXV1fiHdHSTJ06Fd7e3vD19UW/fv3w4MEDmd6/mzdv4rvvvsOgQYPQsWNHqKurIzo6GsHBwTAxMYGLiwuA/J9pqamp4ccff4S6ujrU1NSK/WKhtGN/5syZGDp0KCZOnIjhw4ejbt26ePPmDW7fvo0uXbrAzc0NrVq1gouLC1auXIn09HSYmJjgyJEjyMjIkFgEHz58OEJCQqCmpobBgwfLmWEiIiIiIiIiIqLKpXCF8oyMDHh4eADIvyO5UaNGsLGxQVBQEFq0aCG0q1u3LjZt2oS1a9fC29sbzZo1w/Lly7Fr1y6xeD179sSoUaOwY8cOJCYmwsrKCgcOHMCSJUuwbNkyrFixAtra2hg0aBBcXFywePHiMr8mR0dHzJs3D6GhoTh69Cg6d+6M4OBguLq6llssT09PvH//HgcPHsSBAwdgY2OD+fPnY/bs2WJLaSxevBimpqYICwvDli1bULNmTZiamqJPnz6lvm4A6Ny5Mw4ePIjAwED4+voiKysLxsbG6NatG5o2bSpXrKLeS0nWrVuHpUuXwsfHBwYGBvD09ERaWhr27Nkj9zU4Oztj+fLl2L59O06fPo1OnTph48aNwq8bitKpUye4urri4sWLCAkJQWZmJoyNjeHu7g4vLy/hYZeGhoZYunQpdu3ahZMnTyI7OxuPHj0qMm5px37Tpk0RHh6OjRs3Yvny5UhLS4ORkRGsrKwgEomE47/99lv4+flh7dq10NTUxKBBg9CqVSt8//33hfrUsmVLNGvWDE2aNClUsCciIiIiIiIiIlJ0Knl5eXmV3QmqGBs3bsTevXtx69YtaGlpVXZ3qAoaPXo0VFVVC31B8fz5c/Tu3RubNm0q0RdABe7fv4+8vDxkZWWhbdu21XZNrIqQlpaG6Oho5rkCKGuuOV8rjrKOIUWkrLlWhvl6MPK5TO1G9WlSzj0pnrKOIUWkrLkuj/m6/8yzYvcb6BS/bOIXPYqfV2f/+0LuPn2ur33jUseQlzxjSJZrrIxrqAqUda4C+fP148ePlXZtEdckj0u37vKPxcjrkmP1sZMcS972paXM40iRMM8KeEc5lY0nT57g5MmTsLCwgIaGBm7fvo3du3dj5MiRLJKTTM6dO4dXr16hdevWSE9PR0REBH777Tds2bJFaJOcnIynT59iy5YtMDExkenhpERERERERERERIqGhXIlpaWlhaioKBw6dAgfPnxA/fr1MXHiRMyYMaOyu0ZVhI6ODk6cOIGYmBhkZWWhefPmCAgIEHtA7OXLl/HNN9+gadOmCAgIKPTgUiIiIiIiIiIioqqAVS0l1bBhQ+zfv7+yu0FVmIODAxwcHIptM3jwYD68k4iIiIiIiIiIqjzVyu4AEREREREREREREVFl4h3lRERERESk0Cr7IZ1EVdmYfk3LNX51eIhldbhGqnzyPiAzPTNPrvhFPfwTANTV5ApVbg/tJKpsvKOciIiIiIiIiIiIiKo1FsqJiIiIiIiIiIiIqFpjoZyIiIiIiIiIiIiIqrVqWygPDAyESCSCSCRCmzZtYGlpCXd3d/j5+eHJkyclinnr1i1s3769jHsKPHnyBHPnzoWdnR06dOiAXr16Ye3atXj//n2Zn6vArVu3IBKJcP/+fWGbSCTC7t27hddHjx7FqVOnyq0PZe3ChQsQiUSIjY2V2vbznDs7O8Pf3x9v374t/46WUGxsLAIDAxEfHy+2XdJ7SURERERERERERP9TbQvlAKClpYWwsDAcPnwYmzdvxuDBg3Hjxg0MGDAAJ06ckDve7du3ERwcXKZ9/PXXXzF06FD8/fffWLBgAfbs2YMxY8bg2LFjGDFiBJKSksr0fMUJCwuDu7u78PrYsWOIiIiosPNXFEk5Hzt2LE6cOAEPDw8kJCRUdhclevnyJYKCgvD69Wux7e3bt0dYWBhatGhRST0jIiIiIiIiIiJSbOqV3YHKpKqqCnNzc+G1nZ0dRo0aBS8vLyxatAidO3dG48aV9yTfjIwMzJkzByYmJjh06BB0dHQAAF27doWdnR0GDhyIlStXYv369RXSn09zpaxkybmfnx8CAwMrrD9aWlqliqGrq1st3jsiIiIiIiIiIqKSqtZ3lEtSo0YNLFmyBFlZWQgPDxe2Hz9+HCNHjkTXrl1hZWUFT09P3Lt3T9gfGBiIoKAgpKWlCUu6eHp6AshfxmP27NlwdHREp06d0K9fP+zZswe5ubnF9iUyMhKvX7/G1KlThYJtgRYtWmDAgAE4e/assNTG0aNHIRKJCt1lPmDAAPj4+Aivo6KiMHXqVNjb28Pc3BwDBgzA8ePHpebm06VXPD09cfv2bVy5ckW43sDAQBw4cACdOnVCamqq2LFPnjyBSCTC1atXi4wvLcdAfp4tLCzw6NEjjBw5Ep06dYKbmxt+/vlnsXZZWVlYtWoVunbtCktLS3zzzTf48OGD1GuUJefnz5/Hy5cvAfxvWZOrV69i+vTpMDc3h729vcQleJ48eYKvvvoKlpaWMDc3h5eXF54/fy7WRiQSYceOHQgICICdnR1sbGwASH/Pbt26hTFjxgAAhg4dKrwnn/bx06VXMjMzsXr1atjb28PMzEy4rk/5+PjAzc0Nt27dwsCBA2Fubo6hQ4fijz/+EGv3ww8/oH///ujYsSOsra0xcuTIQu8bERERERERERGRIqvWd5QXpWXLlqhfvz6ioqKEbbGxsRg4cCCaNGmCjx8/4vTp0xg9ejROnjwJU1NTDBs2DHFxcYiIiEBISAiA/Dt5AeD169cwNTWFu7s7atasiejoaAQGBiItLQ3Tp08vsh+3b98GAPTs2VPificnJ4SHh+PXX3+Fm5ubzNf377//onPnzhg5ciQ0NTVx584dLF68GHl5eRg0aJBMMZYtW4b58+dDS0sLCxcuBAAYGxtDW1sbAQEBiIiIwIgRI4T2P/zwA+rXrw97e/siY0rLcYGsrCzMmzcPY8aMgbe3N3bu3ImZM2fi0qVLqF27NgBg/fr1OHToEGbMmIF27drh9OnTWLdundTrkjXn//d//4eGDRsK25csWYL+/fsjMDAQN27cwIYNG1CrVi2MHDkSAPDixQuMGDECrVq1gr+/P1RUVLB9+3aMGzcOkZGR0NTUFGLt378fnTp1wqpVq5CdnQ1A+nvWvn17LF26FH5+fli9ejWaN29e7HXOmzcPP//8M77++ms0b94cJ06cwIwZM7BlyxY4OzsL7RISErBy5Up4eXlBT08P69atw/Tp03H+/HloaGjg119/xaJFizBhwgQ4OjoiIyMD9+7dQ0pKitRcExERERERERERKQoWyovQoEEDvHnzRnj9aUE7NzcXdnZ2uHfvHo4dO4Y5c+bA2NgYxsbGhZZzAQAbGxvhzuC8vDxYWloiIyMDoaGhxRbK4+Pjoa+vLxTcP2diYgIAiIuLk+va+vfvL/w9Ly8PVlZWiI+PR1hYmMyF8pYtW0JXVxc6OjqFrtfV1RU//vijUCjPzs7GyZMnMXToUKipqRUZU1qOCxQUyh0dHQEApqamcHZ2xrVr1zBgwAC8ffsWBw8exOTJkzFlyhQAgIODA7788stCD7r8XElz3q1bN+ELAwcHByQmJmLbtm3w8PCAqqoqgoKCUKtWLezduxc1atQAAHTu3BnOzs4IDw/H6NGjhVi1atVCUFAQVFRUhG3S3jNdXV20bNkSANCqVSuYmZkVeY1//vknfvrpJyxfvlx4j7p3746XL18WKpS/e/cOoaGhaNWqFQBAW1sbY8aMwe+//44uXbrg3r17MDAwEK4dAHr06FHkuWWRl5cHAEhPTy9VHCpeQX6Z5/KnCLn+/BcyZYXztWIowhiqLhQh15yvVZsijKHqQhFyzflatSnCGKoOFCXP5TVfgfK/trS0tHJtX1GxSkNRxpGyU5Q8l+d8lYaF8iLk5eWJFSqfPHmC9evXIyoqComJicL2mJgYqbEyMzMRHByMU6dO4dWrV8jKyhL2ffjwATVr1ixVXz/tpyzevXuHwMBAXLx4EfHx8cjJyQEAGBgYlKofBYYPH44vv/wSjx8/RqtWrXD16lUkJiZiyJAhxR4na45VVVWFLx4AoFGjRtDS0hKK4H/99RcyMjLg4uIidlzv3r3x66+/lvLqJPv8XK6urjhx4gTi4uJgYmKC69evo1+/flBTUxPuEtfX10e7du0KLWXSvXv3Qu9pWb5n//d//wcA6NOnj9j2vn37YvXq1UhLSxM+lOrVqycUyQEIxfiCXLdr1w5v376Fj48P3N3d0blzZ2hra8vdp08V5EeWuUWlxzxXnMrMtaWlZbnE5XytWMxzxeF8pdJinisO5yuVFvNcMSo7z+U1X4GyvLZ6ErdGR0cX0d5IzvaS4xen6FiVo7LHUXVR2Xkuz/kqDQvlRYiLi0OzZs0AAKmpqZgwYQIMDQ3h4+MDExMT1KhRA4sXL0ZmZqbUWAEBAQgPD8e0adPQoUMH6Onp4eLFi9i2bRsyMzOLLJQbGxvjv//9L1JTUyXe4fzvv/8CAOrXry/Xtfn4+CAqKgrTpk0T7gw/dOgQzp49K1ecolhZWcHU1BQ//PADfH198eOPP8LKygpNmjQp8hh5cqylpSW2VAkAaGhoCO0SEhIAAHXq1BFrU7duXal9r1+/Pt6/fy8158bGxmLbDQ0NJZ4rISEBJiYmSE5ORkhIiLAsz+d9/9Tn/QbK9j179+4dNDQ0ChXZ69ati7y8PKSkpAiFcn19fYl9Lci1jY0N1q5di/3792PixImoUaMGXF1d8c0335T4ixd1dXVkZ2ejWbNmpS66U9HS09MRExPDPFcAZc4152vFUOYxpGiUOdecrxVDmceQolHmXHO+VgxlHkOKpDrkuayuLSEqUeL2tm3bStz+z/U3ErcX1f7Vb5LjF6eoWBWtOowjRcA8s1Au0ePHjxEfHy8sQ3L37l3ExcUhODgYbdq0EdqlpKQUKphKEhkZCQ8PD3h5eQnbinuoZQErKyv88MMPuHr1qtjSGwWuXLkCVVVV4ZuWgiU9Pr1jHQDev38v/D0zMxNXrlyBj4+P8LBRADh48KDU/shj2LBh2LVrF8aPH4+rV69i1apVxbYvbY4/ZWSU/61qYmKi2JcIny6lU5SuXbvixx9/LDbnKioq6NKli9j2zx+gWnCugr7UqlULjo6OGDVqVKGYn39R8vnd5GX9ntWqVQtZWVl49+4datWqJdZnFRUV6OnpyRVvwIABGDBgAJKSknDx4kWsXr0a6urq+Pbbb0vUv4Lr19bWrtSf21QXzHPFUcZcc75WLOa54ihjrjlfKxbzXHGUMdecrxWLea4Yypznsrs2yYVseWMX3V7+QrmivWfKPI4USXXOs2pld0DRZGZmYsWKFdDU1MSwYcMAABkZGQDE7/y9c+cOXr58KXashoYGPn78KDHmp8fm5OTg9OnTUvvSp08f1K9fH9u2bSu0PtDTp09x7NgxdO/eHQ0aNADwvzvL//nnH6HdkydP8OrVK+H1x48fkZubK9af1NRUXLp0SWp/PvfpXdyfGzRoEFJSUjBv3jxoaWkVWubjc7LmWBatW7eGlpYWzp8/L7b9p59+knpsnz59UK9evWJz3rt3b2Gt8gKfn+vcuXOoV6+eUOS3sbHB48eP0a5dO5iZmYn9kfbgTVnfs8/v9i5KwRcrkZGRYtsjIyPRrl27En8YGhoaYtiwYbCzsxMbg0RERERERERERIquWt9Rnpubi7t37wLIf0DBX3/9hbCwMLx48QL+/v5o1KgRAMDc3Bw6OjpYvnw5vLy8EB8fj8DAwEJLnrRo0QLZ2dkICQmBhYUFdHV10bx5c9ja2iI8PBwtW7ZE7dq1cfDgQYkF9c9paWlh3bp18PLywqhRozB+/Hg0aNAAf/75J7Zv345atWphxYoVQvtOnTqhQYMG+PbbbzF37lykpqZix44dYktg6OnpwczMDDt37oShoSHU1dWxY8cO6OrqFrorWprmzZvj+PHjuHTpEoyMjFCvXj0hJ4aGhnB2dhbuptfS0io2lqw5loWBgQFGjBiBnTt3QktLC+3atcPp06fx/PlzqcdqaWlh/fr1Rea8QYMGWLJkSaHjbt68iTVr1sDOzg7Xr1/HiRMnsHTpUqiq5n8XNXPmTAwdOhQTJ07E8OHDUbduXbx58wa3b99Gly5d4ObmVmSfZH3PmjVrBjU1Nfz4449QV1eHmpqaxId6tmnTBr1794a/vz8yMjJgamqKkydPIioqClu3bpUlxYLNmzfj7du36Nq1K+rUqYO//voLP//8M8aNGydXHCIiIiIiIiIiospUrQvlGRkZ8PDwAJD/c5JGjRrBxsYGQUFBaNGihdCubt262LRpE9auXQtvb280a9YMy5cvx65du8Ti9ezZE6NGjcKOHTuQmJgIKysrHDhwAEuWLMGyZcuwYsUKaGtrY9CgQXBxccHixYul9rFg+ZUtW7bA398fSUlJyMvLg4WFBbZs2SK2nrWGhgaCgoLwn//8B7NmzUKTJk3wzTffwN/fXyzmunXrsHTpUvj4+MDAwACenp5IS0vDnj175Mrf5MmT8fz5cyxcuBDv37/H9OnTMWPGDGG/i4sLIiMjMXToUKmxZM2xrObOnYucnBzs2rULubm5cHFxwdy5c7FgwQKpxxbkfOvWrfD398f79+9Rr149uLu7Y+rUqRLX3vbz80NYWBgOHTqEmjVrYtasWRg9erSwv2nTpggPD8fGjRuxfPlypKWlwcjICFZWVhCJRFL7JMt7ZmhoiKVLl2LXrl04efIksrOz8ejRI4nxAgICsH79euzcuRNv375F8+bNsXnzZjg5OUnty6fMzMwQEhKCs2fPIjU1FcbGxpg4cSK++uorueIQERERERERERFVJpW8vLy8yu4EyWfp0qU4c+YMDh48iNatW1d2d4q0YMECREdH49SpU5XdlXJz69YtjBkzBj/88IPEu7dJPvfv30deXh6ysrLQtm3barsmVkVIS0tDdHQ081wBlDXXnK8VR1nHkCJS1lxzvlYcZR1DikhZc835WnGUdQwpGmXO8/379/Hx48cyu7bI6y8kbu9j11ji9vDzkn81P8ylicTtEdckxwcAdTXJ24s6d0VT5nGkSJhnrlFeJS1evBgtW7aEl5cXXr9+XdndKeTRo0c4fvw4zpw5gzFjxlR2d4iIiIiIiIiIiIiKVa2XXqmqNDU1cfjw4cruRpG++uorJCUlYeDAgRgyZEhld4eIiIiIiIiIiIioWCyUU5m7dOlSZXehwlhbWxe5DjgRERERERERERFVDSyUExERERERERERVSJ51wMvai3yorh1V4z1xokUGdcoJyIiIiIiIiIiIqJqjYVyIiIiIiIiIiIiIqrWWCgnIiIiIiIiIiIiomqNhfJq7uLFi5gwYQK6du2KDh06wMnJCUuXLsXTp0+FNiKRCLt3766wPkVHR0MkEuHWrVsVdk5JVq1aBScnp2LbHD16FCKRSPhjYWGBPn36wNfXF/fu3SvReaOjoxEYGIj09PQSHV/R7xcREREREREREVFVx0J5Nfbdd9/B29sburq6WLFiBfbu3Ytp06bh77//xuzZsyu7e1XKrl27EBYWhq1bt2LMmDH466+/MHz4cOzYsUPuWNHR0QgKCipxoZyIiIiIiIiIiIjko17ZHaDKcfXqVezcuRPe3t6YNWuWsN3KygpDhgzB5cuXK7F3VU/79u1haGgIALCxscGIESOwcOFCrF+/Hp07d0aXLl0quYdERERERERERERUFN5RXk3t2bMHdevWhbe3t8T9PXv2LPb4w4cPw9XVVViuZevWrcjNzRX2BwYGwsLCotBxXbp0QWBgoNi2rVu3ws7ODhYWFpg+fToSExNluobvvvsO7u7usLCwgIODA+bMmYPXr1+LtfH09MSUKVMQGRkJV1dXWFhYYMyYMXj+/LlYu/j4eEydOhWdOnWCg4MDdu7cKVMfiqKqqopFixZBU1MThw4dErZfuXIF48ePh42NDTp37oxhw4bh2rVrwv6jR4/C19cXQH7BXSQSCcu/vH79Gr6+vnB2dkbHjh3Ru3dvrF+/Hh8/fix0/pycHKxduxbdunWDhYUFfHx8kJqaKuxPS0uDn58fXF1d0alTJ2HJnZSUFLE4Fy9exODBg2FhYYEuXbpg8ODBuHr1qlibo0ePwt3dHWZmZnBwcMCGDRuQk5NTqvwRERERERERERFVJN5RXg1lZ2fjzp076N27NzQ0NOQ+/sCBA1i5ciU8PT3Ro0cPREVFISgoCCkpKVi4cKFcsUJDQ7Fp0yZMmDABtra2uHHjBhYtWiTTsYmJiZgyZQrq1auHpKQk7N27F56enjh9+jTU1f83tKOjo5GUlIR58+YhJycH/v7+mD9/PsLCwoQ23t7eiI+Px3/+8x/o6elh586dePXqlVgceRkYGKB9+/aIiooStsXGxqJnz56YMGECVFVVce3aNXh5eSEkJATW1tbo0aMHvvrqK2zbtg27du2Cnp4eNDU1AQDJyckwMDCAr68v9PX1ERMTg8DAQCQkJGD16tVi5z5w4ADat2+PNWvWIDY2Ft999x0yMzOxYcMGAEBGRgZycnIwe/ZsGBoa4tWrV9i+fTu8vb1x4MABAMDz588xa9Ys9O/fH3PnzkVubi7+/PNPvHv3TjjP3r17ERAQgLFjx8LHxwdPnjwRCuXz5s0rce6IiIiIiIiIiIgqEgvl1dDbt2/x8eNHmJiYyH1sTk4OtmzZgv79+2Px4sUAAHt7e2RlZWHPnj3w8vJC7dq1ZY4VHByMAQMGCAV2BwcHJCYm4sSJE1KP/7Q4nJOTAwsLC3Tv3h03b96Evb29sC8lJQXHjx8XlkZJS0uDr68v4uLiYGxsjGvXruGPP/7Avn37YGNjAwCwtraGo6MjDAwMZLqWojRo0AAPHjwQXn/55ZfC33Nzc2FtbY2///4bR44cgbW1NQwNDdGkSRMA4su5APkP6fz0i4jOnTtDW1sbPj4+WLp0KbS1tYV9mpqa2LJlC9TU1AAANWrUwOLFizF9+nS0aNEChoaGWL58udA+OzsbjRo1wqhRo/D06VOYmpri4cOHyMrKwpIlS6Crqwsg//0pkJqais2bN2PSpEmYM2cOAMDOzg4aGhrw9/fHxIkTZR4Ln8rLywMArtFezgryyzyXP0XItY6OTrnE5XytGIowhqoLRcg152vVpghjqLpQhFxzvlZtijCGqgNFyXN5zVeg8q+tOlCUcaTsFCXP5TlfpWGhvBpTUVGR+5h//vkHycnJ6NOnj9j2fv36ITg4GPfu3YOjo6NMseLi4vD69Wu4uLiIbXd1dZWpUH716lVs27YNjx8/FltWJCYmRqxQ3qZNG7GCc8uWLYXzGxsb4969e9DT0xOK5ACgp6cHW1tbPHz4UKZrKUpeXp5YnuPi4rBhwwbcuHEDCQkJwj+C27dvL1OskJAQHDlyBLGxscjMzBT2vXjxAq1btxZe9+zZUyiSA0CfPn2waNEi3L9/Hy1atAAAHD9+HPv27cOzZ8+QlpYmtI2JiYGpqSlEIhHU1NQwb948DB8+HFZWVtDT0xPaRUVFIS0tDX369EF2draw3dbWFhkZGXj8+DG6du0qT7oAQIgVExMj97EkP+a54lRmri0tLcslLudrxWKeKw7nK5UW81xxOF+ptJjnilHZeS6v+QpU/rVVJ8x1xajsPJfnfJWGhfJqyMDAADVq1MC///4r97EFy27UqVNHbHvB60+X5ZAmISEBAMSK2ABQt25dqcfeu3cP3t7ecHZ2xuTJk1GnTh2oqKhg+PDhYgVkANDX1xd7XbDcTEG7169fF+oDUPgaSyIuLk64ntzcXHz11VdISUnBzJkz0bRpU2hra2Pz5s149eqV1FghISFYs2YNJk2aBGtra+jr6+P+/fvw8/MrdM2f911XVxc1atQQ1nA/f/48Fi5cCA8PD8yePRsGBgZISEjAtGnThFimpqbYvn07goODMX36dKiqqsLe3h5Lly6FiYkJkpOTAQCDBg2S2F9ZrkkSdXV1ZGdno1mzZmJ3yVPZSk9PR0xMDPNcAZQ515yvFUOZx5CiUeZcc75WDGUeQ4pGmXPN+VoxlHkMKZLqkGdlvjZFUR3GkSJgnlkor5bU1dXRuXNn3Lx5E9nZ2XKtw12wFElSUpLY9oIHcNaqVQtA/lIfWVlZYm2ysrLE7lw2MjKSGOvNmzdS+3HhwgXo6upi48aNUFXNfybty5cvZb6OTxWscf45WR8qWpTk5GT88ccfcHV1BQA8e/YMDx8+xJYtW9CrVy+hXUZGhkzxIiMj4eTkhLlz5wrbnjx5IrHt531PTU1FZmYm6tWrJ8Rq27Yt/Pz8hDa3b98uFKd79+7o3r07UlNTce3aNaxevRq+vr4ICQkR3uugoCAYGxsXOrZRo0YyXdfnCu7A19bWrtSf21QXzHPFUcZcc75WLOa54ihjrjlfKxbzXHGUMdecrxWLea4YypxnZb42RcNcV4zqnGfVyu4AVY7x48cjISEB27dvl7j/6tWrErebmprC0NAQkZGRYtvPnj0LDQ0NdOzYEQBQv359ZGVl4fnz50KbmzdvIicnR3htbGwMIyMjnD9/XizWuXPnpPY/IyMDGhoaYsuanDp1SupxkpiZmSElJQW//PKLsC0lJQU3btwoUTwg/+7xb7/9FllZWRg9ejSA/93B/ukDVF++fCn2sM9P93/8+FFse8E1f6qoa758+bJYriMjI6GiogIzMzO5YwH5d6T369cP/fv3F4rzFhYW0NbWRlxcHMzMzAr9Kcn65ERERERERERERJWBd5RXU46Ojpg0aRICAwPx999/o3///qhduzZiY2Px448/IiUlReJa42pqavD29sbKlSthaGgIR0dH3L17Fzt37sTYsWOF4mj37t2ho6ODxYsXY/LkyYiLi8P+/ftRo0YNsVheXl5YtWoV6tSpAzs7O1y/fh23bt2S2n87OzuEhIRgxYoVcHFxQVRUlEzrmkvSvXt3tG/fHvPnz8e8efOgp6eHHTt2CA+wlMWDBw+gp6eHjIwMPH36FD/++CMePHiA+fPnw8LCAgDQvHlzGBsbY926dcjNzUVaWho2b94s3OVdoGAN8e+//x69evWClpYWRCIRbG1tsX//foSGhqJZs2Y4efIknj17JrE/Hz9+xLRp0zBy5EjExsbiu+++g6urqxDb1tYWfn5+2LJlCywsLHD16lWxLwoA4PDhw7h79y4cHBxgZGSE2NhYnDx5EnZ2dgDyl7SZOXMmAgICEBcXh65du0JNTQ0vXrzAxYsXERgYWG1/qkNERERERERERFULC+XVWEER9/vvv8c333yD9PR01KtXD/b29pg4cWKRx3l6ekJdXR379u3DoUOHYGRkhOnTp2Pq1KlCm9q1a2Pz5s1Ys2YNpk2bhrZt22Lt2rXw9PQsFOv9+/c4ePAgDh06BBsbG6xcuRKTJk0qtu+Ojo6YN28eQkNDcfToUXTu3BnBwcHCMifyUFFRwdatW7Fs2TIsXboU+vr68PT0xJs3b3Dx4kWZYhT0V0dHB/Xq1UPnzp2xdOlS4Q57ANDU1ERgYCD8/Pwwa9YsNGjQAF999RVu3ryJP/74Q2jXrl07zJgxA+Hh4di1axcaNGiAS5cuYdq0aUhOTsbmzZsB5D/0dPHixWJ5L+Dp6YmkpCQsWLAAHz9+hIuLC5YuXSrsHzFiBGJjYxEaGordu3fD3t4e69atw/Dhw4U2IpEIly9fxurVq/H27VsYGRmhf//+mDVrltBmwoQJqF+/Pvbu3YvQ0FCoq6ujSZMm6NGjR6E71omIiIiIiIiIiBSVSl5eXl5ld4KICADu37+PvLw8ZGVloW3bttV2TayKkJaWhujoaOa5AihrrjlfK46yjiFFpKy55nytOMo6hhSRsuaa87XiKOsYUjTKnOf79+/j48ePSnltikaZx5EiYZ65RjkRERERERERERERVXMslBMRERERERERERFRtcZCORERERERERERERFVayyUExEREREREREREVG1xkI5EREREREREREREVVrLJQTERERERERERERUbXGQjkRERERERERERERVWsslBMRERERERERERFRtcZCORERERERERERERFVayyUExEREREREREREVG1xkI5EREREREREREREVVrLJQTERERERERERERUbXGQjkRERERERERERERVWsqeXl5eZXdCSIiALhz5w4KPpI0NDSgoqJSyT1SXnl5ecjKymKeK4Ai5FpTUxMikahMY3K+VhxFGEPVhSLkmvO1alOEMVRdKEKuOV+rNkUYQ9WBouS5POdrZV9bdaAo40jZKUqey2O+ykq9Us5KRCRBwQexhoZGJfdE+amoqEBTU7Oyu1EtKGuuOV8rjrKOIUWkrLnmfK04yjqGFJGy5przteIo6xhSNMqc54L5ysJt+VPmcaRImGfeUU5ERERERERERERE1RzXKCciIiIiIiIiIiKiao2FciIiIiIiIiIiIiKq1lgoJyIiIiIiIiIiIqJqjYVyIiIiIiIiIiIiIqrWWCgnIiIiIiIiIiIiomqNhXIiIiIiIiIiIiIiqtZYKCciIiIiIiIiIiKiao2FciIiIiIiIiIiIiKq1lgoJyIiIiIiIiIiIqJqjYVyIiIiIiIiIiIiIqrWWCgnIiIiIiIiIiIiomqNhXIiIiIiIiIiIiIiqtZYKCciIiIiIqL/x959h0VxrX8A/9IFARFBsaGouDYURERAxF4x9n5RY0GDLdbAtV2Jxl7BxK5YgyY2MMEYWxJr7hVbJMYSVIwgAgpIZ/n9wY+V2Z2F3aUK38/z5HmyZ86cOXPmvLvry+wZIiIiokqNiXIiIiIiIiIiIiIiqtSYKCciIiIiIiIiIiKiSo2JciIiIiIiIiIiIiKq1JgoJyIiIiIiIiIiIqJKjYlyIiIiIiIiIiIiIqrUmCgnIiIiIiIiIiIiokqNiXIiIiIiIiIiIiIiqtSYKCciIiIiIiIiIiKiSo2JciIiIiIiIiIiIiKq1JgoJyIiIiIiIiIiIqJKjYlyIiIiIiIiIiIiIqrUmCgnIiIiIiIiIiIiokqNiXIiIiIiIiIiIiIiqtSYKCciIiIiIiIiIiKiSo2JciIiIiIiIiIiIiKq1JgoJyIiIiIiIiIiIqJKjYlyIiIiIiIiIiIiIqrUmCgnIiIiIiIiIiIiokqNiXIiIiIiIiIiIiIiqtSYKCciIiIiIiIiIiKiSo2JciIqNx4+fIiHDx+WdTeISAWMV6KPB+OV6OPBeCX6eDBeiSoeJsqJqNzIyMhAeno6/ve//yE9Pb2su1OhcZxLT0Uda8Zr6eE4l56KOtaM19LDcS49FXWsGa+lh+NcOiryOGdkZCA5OblCnlt5U5HnUXnCcWainIjKqezs7LLuQoWWN74c55JXGca6Ip9beVAZ5lB5URnGuiKfW3lQGeZQeVEZxroin1t5UBnmUHlQGca5Ip9beVEZ5lF5wHFmopyIiIiIiIiIiIiIKjkmyomIiIiIiIiIiIioUmOinIiIiIiIiIiIiIgqNSbKiYiIiIiIiIiIiKhSY6KciIiIiIiIiIiIiCo1JsqJiIiIiIiIiIiIqFJjopyIiIiIiIiIiIiIKjUmyomIiIiIiIiIiIioUmOinIiIiIiIiIiIiIgqNd2y7gARUVkat/Z+qRwn+vr2UjlOaTv7fUBZd6HYlcycMAAuPBWUBM1vVQLHqTgOhz0vk+NGv5OW+jHnjGio0X7zdzzR+JhrvRtrvC9VbicvKsamgZ6WaF0dJbfkpGXmiJYnpyqWZUvF60YliMfq49cZinV/CxSta+MxQ7S8lqn4P5GeRr9TKJNmZ4rWbXIvWry8po5ouYmh4hjGJ4ufe70aqt/r1NO1vmj5mV9fiJb3cxevr46froq3rawvyuqr0wYV7ty1gsc5K1v4On/oJaUK5+Lb98LXFibC+fswWq6xfLLlQvfKqQ2y/zdr1k+wrUp1a8Hr5Bf/E7w2sW73ob9S4TGTo4R1JXYdZf//T0L+NxsD6MvFQ0MLfcHrZ3Ef3lfa1K8i2KYrF44xicITNND9MDa2VsLKGVnCffXyvT3Iv6/GvBW2W81IuD1/ffn3Xj25t7QeLh/iSH5e5N8GACGXhdtz8OHaf+IhvD6FtUXqkx//PG/fK34GZot/ZOB5nPjn5YMnkQplVlbi1+yfyAei5ckvriv2Iz1JtG71FgNEy7NS4hXKGjRtK1o3NlHx31QA0L6RsUKZfCzmeZsi/v5U31xPoayhhfjn7WuRto2riH8XqmEs3kaWsgsmQtn3LF2RrxTqftaq87mqThvf/Sz+b7mh3a1Fy8sT3lFORERERERERERERJUaE+VEREREREREREREVKkxUU5ERERERERERERElRoT5QUICAiARCKBRCJBs2bN4OjoiP79+8Pf3x9Pnmi2LuiNGzewbdu2Yu3n48ePMXPmTHTq1Al2dnbo1KkTpkyZgsuXL6vVjkQiwe7du4u1bxVBREQEJBIJbty4UWC9vLkikUjQqlUr9OjRA8uXL8fbt29Lp6Ol7MaNG5BIJLh3715Zd4WIiIiIiIiIiKhI+DDPQlSpUgVBQUEAgPfv3+Ovv/5CcHAwjh49ihUrVmDAAPEHEihz8+ZN7NmzB1OnTi2W/j1//hzDhg2DRCKBr68vzM3N8fLlS1y+fBk3b96Eh4eHym0FBwejTp06xdKvysrLywuenp5IT0/HzZs3sW3bNkRGRmLXrl1l3TUiIiIiIiIiIiJSgonyQmhra8Pe3l722s3NDaNHj4a3tzcWLlyItm3bon79snua8/fffw8A2Lt3LwwNDWXlQ4YMgVQq/pRfZfKfJ2mmdu3asnF0dnbG69evcfToUbx+/Ro1a9Ys286pKDs7G1KpFHp6ik99JiIiIiIiIiIiqoi49IoGDAwMsHjxYmRmZuLYsWOy8pMnT2LUqFFo3749nJyc4OXlhbt378q2BwQEIDAwECkpKbIlOry8vAAAT548wezZs+Hh4YE2bdqgb9++2LNnT6HJ7sTERBgbGwuS5Hm0tYWX98mTJ5g+fTrat2+PNm3a4JNPPkFoaKhsu9jSK5cuXcKwYcPQunVrdOjQAUuXLkVKSopse97yG1euXMHcuXPh4OCALl26YOfOnQr9CQ8Px4QJE9C2bVs4ODhg2LBhuHLlimx7RkYGNmzYgC5duqBVq1bo06cPQkJCCjz/vD5++umncHFxQdu2bTFs2DD88ssvgjrHjx+HRCLBgwcPMGnSJNjb26Nnz544efKkQntff/013Nzc4ODggOnTpyMuLq7QPijTvHlzAMCrV6/UOs9Hjx5h8uTJcHZ2Rps2bdCrVy+FMQ0PD8fYsWNhb28PR0dHzJ07V6Gv69atQ//+/eHg4AB3d3fMmTMHr1+/FtTx8vLClClTcOLECfTq1Qt2dnb4888/AeSO7ciRI9GmTRvZnH7w4IFg/8TExEKvPRERERERERERUXnGO8o11KRJE9SqVQvh4eGysqioKAwcOBDW1tbIyMjAmTNnMGbMGJw+fRo2NjYYNmwYoqOjERoaKlvOxdjYGADw+vVr2NjYoH///qhatSoiIiIQEBCAlJQUTJ8+XWk/WrZsicOHD2PJkiUYNWoUJBKJQoIcACIjIzFixAjUrl0bCxcuhKWlJf766y/8888/StsOCwvD7NmzMXjwYMyYMQOxsbFYv349EhMTsXHjRkHdpUuXYsCAAdi6dSt+/vlnrFu3DhKJBJ06dQIA/O9//8O4ceNgb2+P5cuXw9TUFPfv3xccf9asWbh16xamTZuGxo0b4/Lly5g/fz5MTU0LXEImKioKXbp0wYQJE6CtrY1ffvkF3t7eCAoKgrOzs6DuvHnzMHz4cHz66ac4evQofH19YWdnh8aNGwMADh48iM2bN2PChAlwdXXF1atXsXDhQqXHLsw///wDbW1twZI2qpzn1KlTYWFhgRUrVsDY2BjPnz9HdHS0rI3w8HB4eXnBw8MDGzduRGpqKjZt2gQfHx8EBwfL6sXFxWHKlCmoWbMm4uPjsXfvXnh5eeHMmTPQ1f0Q/vfv38fLly8xa9YsmJqaonbt2vjhhx8wZ84cdOvWDevXr4eenh5u3bqFmJgYtGjRQrZvYdeeiIiIiIiIiIiovGOivAhq166NN2/eyF7nT2hLpVK4ubnh7t27OHHiBObMmQMrKytYWVkpLOcCAC4uLnBxcQEA5OTkwNHREWlpaTh48GCBifJBgwbh2rVrCA4ORnBwMKpWrYoOHTpgyJAh6Natm6xeQEAA9PT0cOTIEVly3tXVVWm7OTk5WLNmDfr27YsVK1bIyi0tLeHt7Q0fHx/Y2trKynv27IkZM2bIzuXSpUs4e/asLFm6du1aNGjQAEFBQdDR0QEAdOzYUbb/9evXceHCBezevVtW7ubmhtjYWAQEBBSYKP/Xv/4l+3+pVApnZ2c8fvwYR48eVUiUjxkzBmPGjAEAODg44PLlyzh79ix8fHyQnZ2N7du3Y8CAAfjiiy8AAO7u7oiLi8OpU6eUHj8/qVSKrKwsZGRk4MaNGzhy5AhGjBgBS0tLlc8zPj4eUVFRWLhwIbp27QoA6NChg+A469evR6tWrRAYGAgtLS0AQNOmTeHp6YnLly/LxmvlypWyfbKzs+Hg4IBOnTrh+vXrgvF/9+4dvvvuO9SuXRtA7vVfvXo13NzcsHXrVlk9setQ2LVXV05ODgAgNTVVo/2pdOX/hQmppzTHzsjIqETaZbwWj8LmQt74Fuc4M3bFlcRYq4vxSsVJ3VgvyfeG4mg7fxuMVyLNFBSL6sRpYXXztpeHWAVKLl6Bsj83ojxF/dzXJF6L830jT0nGa2GYKC+CnJwcWZISyF3aZMOGDQgPDxcsgREZGVloW+np6di+fTtCQkLw6tUrZGZmyra9f/8eVatWFd1PR0cH69evx9SpU3Hx4kX897//xZUrV3D+/Hn4+Phg1qxZAHITtL169ZIlyQvz999/4+XLl/j3v/+NrKwsWXn79u2hra2N+/fvCxLl+ZOuWlpaaNy4sewO6NTUVNy5cwdz5syRJcnlXblyBWZmZujQoYPgeK6urvjPf/6D7OxspftGR0dj48aNuHr1KmJjY2VfLFu2bKlQN38/jYyMUKdOHVk/o6Oj8fr1a/To0UOwT69evVROlK9btw7r1q2TvXZ0dMSiRYvUOs/q1aujbt262LBhA969ewcXFxdYWVnJ6qampuLWrVtYsGABsrOzZeUNGzZE7dq1ce/ePVlC+/Lly/jmm2/w6NEjJCcny+pGRkYKxqJp06ayJDkAPH36FNHR0bI/GBSkoGuvibxxUSVuiodBKR2nYoqIiCjrLpSA0pkTpTl2jo6OJdJuycarZQm0WT6pOhcUx9msxI9ZWZXeZ5Ci8h+vlSc2KwLlsS7+7JzieW9Qt23Vn+Mj1gbjVVUfx/OSqOQJ46hmAdsUt6tTV357WcYqUHLxCpTkuTFuST3F9bkvPqfVaUP8+6Kq3zNKMl4Lw0R5EURHR6Nhw4YAgOTkZEyYMAHm5ubw9fVFnTp1YGBggEWLFiE9Pb3QttauXYtjx45h2rRpaNWqFUxMTHD+/Hl88803SE9PV5ooz2NrawtbW1t4e3sjPj4eEydOxI4dOzBu3DiYmZnh7du3aj1MMiEhAQAwbdo00e3519wGABMTE8FrPT09JCUlAchdw1oqlRZ4/ISEBLx9+1Y0uQ0AsbGxgmRxHqlUis8++wxJSUmYOXMmGjRoAENDQ2zZskWhj8r6mZGRITsGAJibmwvqWFhYKO23vLFjx+KTTz5BamoqTp8+jWPHjmHz5s2YO3euWue5e/dubNy4Ef7+/khJSUHLli3h5+cHJycnJCYmIjs7GytXrhTcMZ4n77zv3r0LHx8fdOvWDZMnT0aNGjWgpaWF4cOHK8xJ+XN8+/YtAKg0Zwq69prQ1dVFVlYWGjZsKLr2frG78LTkj1GB5a3DX6GU0pyoCGNXkvH6MPZN4ZUqiMLmQmpqKiIjIxXH+X+Kn3PFdczKSulYVwDFFa8vblSe2KwIlMV69P/En8FTHO8Nv91Wr21l9Qtrg/Gqnit3NH/uElUs+eNIfl7Ix+k/vyufN/J1lbVVkWM1T0mdW0HjTyRG3c9a+foFxas6n+9/XxX/vvgx/BuEiXINPXr0CDExMRg0aBAA4Pbt24iOjsb27dvRrFkzWb2kpCTRBK+8sLAwjBgxAt7e3rKyy5cva9Q3c3NzDB48GMuXL8ezZ89gZmYGMzMzhYc4FsTMzAwAsGTJErRu3VphuzpJdxMTE2hraxd4/GrVqsHc3Bw7duwQ3S6fvM7z7NkzPHjwAFu3bkX37t1l5WlpaSr3L0/e8ijx8fGC8vzL6xTGysoKdnZ2AHLvvn/z5g327t2L0aNHo3bt2iqfp42NDbZs2YLMzEyEh4djw4YNmDp1Kn755ReYmJhAS0sLU6ZMEZxznurVqwMAfv75ZxgbG2PTpk2ydetfvnwpetz8v4wAPlx/deZMccnri6GhYZn+3IZUw2ukuYowdozX4qHq2BXnOPN6FawizmnGa+Wk/FqL/2O3eOaGum2rnggSa6MizumSiVcm3CiXcE7FFbBNcbs6deW3V8RYzVNy58a4JfUU1+e++Jwu+neHj+E9QPGpj1So9PR0fPnll9DX18ewYcMAfEjM6unpyerdunVLITGZ/w5m+Tbz75udnY0zZ84U2hdlSdy8n0nk3Sns4uKCs2fPCpbfKEijRo1gZWWFFy9ewM7OTuG/WrVqqdQOkBsI9vb2OHXqlGCpkPxcXV0RHx8PPT090ePp6+uL7pd3Z3T+sXv58qXgIauqsrKygqWlJc6dOycoP3v2rNpt5VmwYAGkUil2794NQP3z1NPTQ/v27eHt7Y3k5GS8fv1aNp5Pnz4VbaNevXoAcueknp6eIAkeEhKiUr/zrv/x48c1PnciIiIiIiIiIqKPBe8oL4RUKsXt27cB5C46/9dffyE4OBgvXrzAqlWrZElJe3t7GBkZYdmyZfD29kZMTAwCAgIUEsqNGzdGVlYWgoKC4ODgAGNjYzRq1Aiurq44duwYmjRpgurVq+Pw4cOiCXV5X3/9NSIiIuDp6YkmTZogPT0dV65cweHDh9G9e3fUrVsXQO6DRi9duoTRo0dj0qRJsLS0xJMnT5CamorJkycrtKulpQVfX1/MmzcPKSkp6Ny5MwwNDfHPP//g8uXLmD17NmxsbFQex7lz52L8+PEYP348Ro8ejWrVquGPP/5A9erVMXToULi5uaFLly6YNGkSJk2aBIlEgtTUVDx+/BjPnj0TPFA0v7yE7vr16yGVSpGSkoItW7aodcd7Hh0dHXh7e2PFihWoUaMG3NzccOXKFdy4cUPttvL3r2/fvvjuu+8wbdo0lc7zzz//xOrVq9G3b1/Ur18fycnJ2L59O+rWrQtra2sAuQn4cePG4fPPP0e/fv1gamqK6OhoXL16FYMHD4azszPc3NwQFBSEL7/8Ej169EB4eLjKa61raWnhiy++wJw5czBjxgwMGDAA+vr6uH37Nuzs7NClSxeNx4SIiIiIiIiIiKi8YaK8EGlpaRgxYgSA3Duj69WrBxcXFwQGBqJx48ayehYWFti8eTPWrFkDHx8fNGzYEMuWLcOuXbsE7XXp0gWjR4/Gjh07EBcXBycnJxw4cACLFy/G0qVL8eWXX8LQ0BCDBg1Cjx49BA+CFPPJJ58gPT0dBw4cQExMDHR0dFC3bl0sWLAAo0ePltVr2LAhvv32W6xfvx7Lli1DdnY2GjZsKFjqRV6fPn1gamqKbdu2ye5Erlu3Ltzd3dVatxsA2rVrh/3792PTpk3w8/ODtrY2bG1t8fnnn8vqbNmyBTt27MCRI0fw8uVLmJiYwNbWFoMHD1barr6+PgICAuDv749Zs2ahdu3a+Oyzz3D9+nXcv39frT4CgJeXFxITE3H48GEcOXIELi4uWL58OSZNmqR2W3l8fHzwww8/4ODBg5gxY0ah52lpaQkLCwts374dMTExMDExQbt27bB27VrZA03btm2Lw4cPIyAgAH5+fsjMzISVlRU6dOiABg0aAAA8PDwwb948HDx4EMePH0fbtm2xfft29OrVS6V+9+3bF1WqVMG2bdswZ84cGBgYoEWLFgoPOyUiIiIiIiIiIvrYMVFegBkzZmDGjBkq1+/UqRM6deokKPPw8BC81tXVxdKlS7F06VJBuYWFBbZu3arQZt7SLsrY29vD3t5epf41adIE33zzjdLtDx8+VChzc3ODm5ub0n2cnZ1F9/v6668Vytq2bYv9+/crbUtfXx/Tp0/H9OnTldYR07p1a3z33XeCsoEDBwpeDx48WDThLn+HtZaWlmgfxM5RnrI6jRo1woMHD2SvCzvPGjVqYO3atYUez87OTula53kmT56s8IsB+X4eOHBA6f5du3ZF165dRbepc+2JiIiIiIiIiIjKM65RTkRERERERERERESVGhPlRERERERERERERFSpMVFORERERERERERERJWaVk5OTk5Zd4KICADu3buHnJwcZGZmonnz5jAyMirrLlVYKSkpiIiI4DiXgoo61hU1XufveKLRfmu9GxdeSUMVdQ6VRxV1rIszXn+6+kKhrKdrfdG6P/6mWBcA+nQUry/Wdkq6ev9UyZYqlukouTXIyEBLtDwzW7y+tkh1qZLuidUFlJ+72Fgpa7ufu3gblYHYHAGUz8GPUXHG67lrH8arh0t9pdsAxdjJHzepcnFooKdkgv+/5LQP9TOyhNvMqgr3TXj/oW5iivA4FibCunpyT1jT0/mwPSNLuK+WXBfz91n+XOUzIvLxK99Wfro6yrcBgDTfsbQLuU0x/zWSn+slNcfl54H8PCkOFfWzFciN14yMjBI7tx9+FX/PE5tLvd3Er92hsOei5Ub6imVVq4hP9j9fin8wWpmpfu+tss9WsX7o6oj3Q/49II9U5LNf7PtAQW0o+9wWIxaPJfn5FHZFvG1l17yobVtWeV0h41VVvKOciIiIiIiIiIiIiCo1JsqJiIiIiIiIiIiIqFJjopyIiIiIiIiIiIiIKrVKkSgPCAiAg4NDmfahXbt2CAgIKLXjde3aFf7+/gXWuXHjBiQSCe7du1dKvSqaAQMGwNfXt8A6vr6+kEgkGD58uMK2nJwceHh4QCKRlOq1qCwCAgJw69atsu4GERERERERERGR2ipFopzEtWzZEsHBwWjcuOQeQFYWjIyMcOfOHbx4IXwowX//+1/ExcVBX1/kaRFUZIGBgQgPDy/rbhAREREREREREamNifKPTFpaWrG1ZWxsDHt7+wr3JNu6deuiWbNm+OGHHwTloaGh6NixIwwMDMqoZ0RERERERERERFQeMVH+/6KjozFv3jw4OzujdevWGDNmDO7fvy+oc/LkSYwaNQrt27eHk5MTvLy8cPfuXYW2fv75Z/Tu3Rt2dnYYOnSoaB0AuHTpEoYNG4bWrVujQ4cOWLp0KVJSUmTb85ZGuXTpEmbOnIm2bdti1qxZAIDz589j8ODBcHBwQLt27TB48GBcvnxZ4RiHDh1Cly5d4OjoCB8fH8THxyu0n3/pFYlEgh07dmDNmjXo0KEDHBwc4Ovri+Tk5ALH7/Xr1/Dz80O3bt3QunVr9OzZExs2bEBGRoagnkQiwc6dOxEQEABXV1c4OzvDz89PcN4AcOvWLQwePBh2dnbw9PQUPbeC9OvXD6GhobLXWVlZOHv2LDw9PRXqhoeHY+rUqejYsSPs7e0xYMAAnDx5UlAnb6yuXLmCuXPnwsHBAV26dMHOnTvVbgsAHj16hDFjxsDOzg49e/bE6dOn4ePjAy8vL0G9J0+e4LPPPoOjoyPs7e3h7e2N58+fC+rkXbONGzfCxcUF7dq1w5o1a5CTk4Nr165hwIABcHBwwLhx4/Dq1SvBvhkZGdiwYQO6dOmCVq1aoU+fPggJCRHU8fX1haenJ27cuIGBAwfC3t4eQ4cOFcSHRCIBAKxZswYSiQQSiQQ3btxQOG8iIiIiIiIiIqLySLesO1AevHv3DqNHj4aRkREWL14MExMTHDhwAOPGjcNPP/2EGjVqAACioqIwcOBAWFtbIyMjA2fOnMGYMWNw+vRp2NjYAAAiIiIwc+ZMdOrUCX5+foiKisLnn3+ukDAOCwvD7NmzMXjwYMyYMQOxsbFYv349EhMTsXHjRkHdxYsX45NPPsHWrVuhra2N58+fY9asWejXrx/mzp0LqVSKP//8E+/evRPsd+HCBTx79gxLlixBQkICVq5ciS+//FKhfXkHDhxAy5YtsXr1akRFRWHdunVIT08vcL+EhASYmZnBz88PpqamiIyMREBAAGJjY7Fy5UpB3UOHDsHR0RGrVq1CZGQk1qxZgxo1amDevHkAgNjYWEycOBESiQSbNm1CYmIili1bhpSUFDRv3rzAvufp168fNmzYgMePH6NJkya4cuUK0tPT0bVrV/znP/8R1P3nn3/Qtm1bjBo1Cvr6+rh16xYWLVqEnJwcDBo0SFB36dKlGDBgALZu3Yqff/4Z69atg0QiQadOnVRuKy0tDRMmTICpqSnWrl0LANi6dSsSExNhbW0tO9aLFy8wcuRI2NraYtWqVdDS0sK2bdswfvx4hIWFCZaQOXToENq3b481a9bgzp07CAgIgFQqxZUrV/DZZ59BT08Py5cvx8KFC7Fnzx7ZfrNmzcKtW7cwbdo0NG7cGJcvX8b8+fNhamoKDw8PWb3Y2FgsX74c3t7eMDExwfr16zF9+nScO3cOenp6CA4OxogRI+Dl5SX7Y0STJk1UulZERERERERERERljYlyAEFBQUhMTMSxY8dkSXEXFxf06tULu3fvxoIFCwAA06dPl+0jlUrh5uaGu3fv4sSJE5gzZw4AYMeOHahduza2bt0KHR0dAICBgQEWLlwo2zcnJwdr1qxB3759sWLFClm5paUlvL294ePjA1tbW1l5165dMX/+fNnrsLAwZGZmYvHixTA2NgYAuLu7K5xXTk4OvvnmG1lC9eXLl9i+fTukUim0tZX/mEBfX1+h/4sWLcL06dOVrmcukUjwxRdfyF63bdsWhoaG8PX1xZIlS2BoaCg4z/Xr1wMAOnXqhAcPHuDs2bOyRHlQUBC0tLSwc+dOmJiYAACsrKwwfvx4pX2WV7duXdjb2yM0NBSff/45QkND0bVrV9FlZvr16yf7/5ycHDg5OSEmJgbBwcEKifKePXtixowZAHLnyKVLl3D27FlZolyVtr7//nvExcXhyJEjqFevHgCgVatW6NmzpyBRHhgYiGrVqmHv3r2y5WLatm2Lbt264dixYxgzZoysbs2aNWVJd3d3d1y4cAH79u3DmTNnZNcsJiYGX375JRITE2Fqaorr16/jwoUL2L17Nzp27AgAcHNzQ2xsLAICAgSJ8nfv3uHgwYOyeWloaIixY8fizp07aNeuHezt7QEAtWvXlv2/pnJycgAAqampRWqHCpY3vhznklcexrqklthivH4g/8uo4lQe5lBlUR7G+mOMV3Xnf0nGS3lXHOdemcdPmbIak48pXjlvyr+CrlFpXb+SOE55+GwFSi5egbI/N4AxXprUGeuSvC6l8e+PslKWS0QzUQ7gypUrcHZ2RrVq1ZCVlQUA0NbWhpOTk2BZkidPnmDDhg0IDw9HXFycrDwyMlL2/3fu3EHXrl1lSWYA6N27tyBR/vfff+Ply5f497//LTseALRv3x7a2tq4f/++IFHeuXNnQX8lEgl0dHQwb948DB8+HE5OTrKEcn5OTk6Cu44bN26MzMxMxMXFwdLSUul4dOnSRbT/9+7dU5ooz8nJQVBQEI4ePYqoqCikp6fLtr148QJNmzaVvXZ1dRXs27hxY5w5c0b2+s6dO3B2dhack4uLC8zMzJT2WYynpyf279+PqVOn4vz581i3bp1ovXfv3iEgIADnz59HTEwMsrOzAUD0eHkJZQDQ0tJC48aNER0drVZb9+/fR9OmTWVJcgCoV68emjVrJjjWlStX0LdvX+jo6MjmiampKVq0aKGwLJD8mNrY2ODNmzeC69WwYUMAucsMmZqa4sqVKzAzM0OHDh0E89DV1RX/+c9/kJ2dLZsHNWvWFMzJvLvFY2JiFMaoqPL6kj+uqORwnEtPWY61o6NjibRbMePVTKO9IiIiircbIirWOJdvjNeC1FQoUT7/FetqUr8iKY5zL433m/JL3TlVsj6meFUco4ofbx8b4TWqWcC24lRaxyn77zElFa9ASZ5bcXw2KM/9kGbEx7okP59Kv+2KHK+FYaIcucuG3L59Gy1btlTYlneHb3JyMiZMmABzc3P4+vqiTp06sjut8yeFY2NjZXel5zE2NhY8QDIhIQEAMG3aNNH+yK8jLd+ejY0Ntm3bhu3bt2P69OnQ1tZGx44dsWTJEtSpU0dWz9TUVLBfXtI8f3/FKOv/69evle4TFBSE1atXY9KkSXB2doapqSnu3bsHf39/hePJ90tPT0+wNE1sbCwaNGigcAxzc/MC+y2vd+/e+Oqrr7B582bo6emJ3nUP5K7BHR4ejmnTpqFJkyYwNjbGkSNH8OOPPyrUlf+DhJ6eHpKSktRq6/Xr16LnYm5uLhirhIQEBAUFISgoSKGunp6e4LXYmIqVAR+uf0JCAt6+fSs674Hc62BlZaW0/fxtFSddXV1kZWWhYcOGgl8iUPFKTU1FZGQkx7kUVOSxrpDx+r9XhdcRoerSYJqoyHOovKnIY11c8frb7TiFMmXz//UtxboF1Rdru6JRd6zUaaMyUDZHKtqYFFe8XrnzYbzkxyj/Niof8l8j+bleUnNcfh6UxHEq8mdrnpI6t5j/Ff2z4c/YN8XVHfp/YmNdkp9PseGl33ZFjtfCMFEOoFq1anB3d5c9KDO/vOTy7du3ER0dje3btwvu/E1KSpIlE4HcZUXy320O5CbZ8ycU8+4uXrJkCVq3bq1wzJo1hX/R0dLSUqjTqVMndOrUCcnJyfjll1+wcuVK+Pn5iSZV1aWs//L9yi8sLAxdu3bF3LlzZWVPnjzR6PhiYwhA8CBSVVhYWKBDhw7Yt28fhg4dqpBcBnITvZcuXYKvr6/gQZqHDx9Wu9+qtlWzZk3Rv/zFx8ejatWqstfVqlWDh4cHRo8erVA3fz1NVatWDebm5tixY4fodnX/MFFc8ua7oaFhmf7cprLgOJeeijjWjNcPSuP8Oc6lpyKOdfHFq+J3NOXtif/jS936FUlxnHtFm5vqUXdOfZxKIl4V26n48faxEV6juAK2FafSOk7F/GzNU3Lnxs+G8kh8rEvy86n0267I8VoYJsqRu9TE6dOn0bhxY6UTIS0tDYDwTt5bt27h5cuXgiUpWrdujYsXL8LPz0+2bEVYWJigrUaNGsHKygovXrwQrDOtCWNjY/Tt2xd3795FaGhokdrKI9Z/LS0t2NnZKd0nLS1NIREdEhKi0fFbt26NI0eOICkpSXYH97Vr1/D27Vu12/Ly8kKVKlUwbNgw0e0ZGRmQSqWCvicnJ+PChQtqH0vVtlq1aoWTJ0/ixYsXqF+/PoDcB8X++eefgp+XuLi44NGjR2jRooVgKZzi4urqil27dkFPT09h2RdN6Onplcgd5kRERERERERERCWt0iTKs7OzFRLWQG5Sdvz48QgJCcG//vUvjB07FnXq1EF8fDzu3LmDWrVqYfz48bC3t4eRkRGWLVsGb29vxMTEICAgALVq1RK05+3tjaFDh2LatGkYNWoUoqKisHv3bsHSK1paWvD19cW8efOQkpKCzp07w9DQEP/88w8uX76M2bNnw8bGRum5fPvtt7h9+zbc3d1haWmJqKgonD59Gm5ubsUyVhkZGYL+r1u3Dr169VK6PjmQm3Tdv38/Dh48iIYNG+L06dN49uyZRscfN24cDh8+jMmTJ2Py5MlITExEQECA2muUA7nrrXfp0kXpdhMTE9jZ2WHnzp0wNzeHrq4uduzYAWNjY7XvYFe1rSFDhmDbtm2YOnWq7MGggYGBsLCwEPx6YObMmRg6dCgmTpyI4cOHw8LCAm/evMHNmzfRrl07eHp6qjkaQm5ubujSpQsmTZqESZMmQSKRIDU1FY8fP8azZ88ED5pVRaNGjXD+/Hm0a9cOhoaGsLGxkT1sloiIiIiIiIiIqDyrNIny9PR00aVV1qxZgwEDBiA4OBibNm3CunXr8PbtW9SoUQNt2rRBjx49AOQu47F582asWbMGPj4+aNiwIZYtW4Zdu3YJ2mvRogU2b96MdevWYfr06bC1tcXGjRsxceJEQb0+ffrA1NQU27Ztk915XbduXbi7u8PCwqLAc5FIJLh48SJWrlyJt2/fwtLSEv369RM9P014eXkhPj4eCxYsQEZGBnr06IElS5YUuM+0adOQkJCALVu2AAB69eqFRYsWYerUqWofv2bNmti5cyeWL1+OWbNmwdraGkuWLMHGjRs1Op/CrF+/HkuWLIGvry/MzMzg5eWFlJQU7Nmzp0TaqlKlCvbs2YOlS5di3rx5qFWrFnx8fHDy5EnBGugNGjTAsWPHsGnTJixbtgwpKSmwtLSEk5MTJBJJsZz7li1bsGPHDhw5cgQvX76EiYkJbG1tMXjwYLXbWrJkCb766itMnjwZaWlp2L9/P5ydnYuln0RERERERERERCVJKycnJ6esO0Hlh0QiwYIFCxQS+1Sy3r59i+7du2P8+PGYPn16WXenzNy7dw85OTnIzMxE8+bNK+2aWKUhJSUFERERHOdSUFHHuqLG6/wdmj1fY6238l9dFVVFnUPlUUUd6+KM15+uvlAo6+laX7Tuj78p1gWAPh3F64u1nZKu3j9VsqWKZTra4nWNDBSfAwQAmdni9bVFqkuVdE+sLqD83MXGSlnb/dzF26gMxOYIoHwOfoyKM17PXfswXj1c6ivdBijGTv64SZWLQwM9JRP8/yWnfaifkSXcZlZVuG/C+w91E1OEx7EwEdbVk7vNT0/nw/aMLOG+8o/5yt9n+XOVz4jIx6/II8NkdAtZIVOa71jaSt6L8uS/RvJzvaTmuPw8kJ8nxaGifrYCufGakZFRYuf2w6/i73lic6m3m/i1OxT2XLTcSF+xrGoV8cn+50vxD0Yrs0ImdT7KPlvF+qGrI94P+feAPFKRz36x7wMFtaHsc1uMWDyW5OdT2BXxtpVd86K2bVnldYWMV1VVmjvKicqTHTt2wMLCAnXr1kVsbCz27NmD7OxsDBkypKy7RkREREREREREVOkwUU5UBrS1tfHNN98gJiYGOjo6aNOmDYKCglC7du2y7hoREREREREREVGlw0Q5CTx8+LCsu1Ap5D1Ak4iIiIiIiIiIiMqe6gsKERERERERERERERFVQLyjnIiIiMqdw0oePKSK0b2tNd7XwVpPo/02BEdqfEyraqrct2CJh7FvBCVFOU8iTYk9lOqMkoeNZWaJP41yX+gz0fKLEUkKZZamhqJ1qyp5EKe+yAPA6lYXjzE9XfE2ImPFnwBmayXSjpKHhXm0rSH6ECxlD/sSe+Bonwr0gMrikn/+5X9AIBXu+/PCz9Uq+sL5L//g3JfxHyb3+wzhNvkHZ9pYCp9oGf4sU/b/dasLUw4v4oX9qpLvYzcqXvi0P31d4b7yD87UzRc3BnIf3+lyDxHNyffETvlzT8+Ue1ip3PuL/MM+1dFLjYft5X+wpvx7bUk9dLM4H95ZGg8GrWzk52YesQfqBn4fKVpXW8nTaK8+zlAoM68q/nTaaobin6N/vlJ8Qmdtlb7XfmAg8lmsrS1+3plZ4uciH/+A8gftltSDcUvyodLF8dBOVdvO/Wx9XWLH+xjwjnIiIiIiIiIiIiIiqtSYKCciIiIiIiIiIiKiSo2JciIiIiIiIiIiIiKq1Jgop49eQEAAJBIJJBIJmjVrBkdHR/Tv3x/+/v548uSJRm3euHED27ZtK+ae5tq3bx8kEgn+/e9/l0j7+XXt2hX+/v7F2qavry88PT2LtU0iIiIiIiIiIqKyxEQ5VQhVqlRBcHAwvv32W2zZsgWDBw/G1atXMWDAAJw6dUrt9m7evInt27eXQE+B06dPAwDOnTuHjAzFB2iUdz4+Pli3bl1Zd4OIiIiIiIiIiKjYMFFOFYK2tjbs7e1hb28PNzc3fPrppzh16hQcHR2xcOFCvHjxovBGSsHff/+NP/74A66urkhMTMSlS5fKuktqs7a2RrNmzcq6G0RERERERERERMWGiXKqsAwMDLB48WJkZmbi2LFjsvKTJ09i1KhRaN++PZycnODl5YW7d+/KtgcEBCAwMBApKSmyJV28vLwAAE+ePMHs2bPh4eGBNm3aoG/fvtizZw+kUqlKfQoNDYWWlhb8/f1hYWGBkJAQhTp5y6UcOnQIXbp0gaOjI3x8fBAfHy+rk5KSAn9/f/Tq1Qtt2rRB165dsWTJEiQlJSk99oULFyCRSBAZGSkof/fuHVq3bo1Dhw4BAB49eoTJkyfD2dkZbdq0Qa9evbBz505ZffmlVxITE7Fo0SK4u7vDzs4OHh4emD17tkrjQUREREREREREVB7olnUHiEpSkyZNUKtWLYSHh8vKoqKiMHDgQFhbWyMjIwNnzpzBmDFjcPr0adjY2GDYsGGIjo5GaGgogoKCAADGxsYAgNevX8PGxgb9+/dH1apVERERgYCAAKSkpGD69OmF9ic0NBTt2rVD/fr10adPHwQHByMpKQkmJiaCehcuXMCzZ8+wZMkSJCQkYOXKlfjyyy+xceNGAEBaWhqys7Mxe/ZsmJub49WrV9i2bRt8fHxw4MAB0WN7eHigVq1a+P777zF37lxBnwCgf//+AICpU6fCwsICK1asgLGxMZ4/f47o6Gil57Ry5Ur8+uuvmDt3LurWrYvY2Fj88ssvhY4FERERERERERFRecFEOVV4tWvXxps3b2Sv8ye0pVIp3NzccPfuXZw4cQJz5syBlZUVrKysZMu55Ofi4gIXFxcAQE5ODhwdHZGWloaDBw8Wmii/e/cuIiMj8emnnwIAPD09ceDAAZw9exZDhw4V1M3JycE333wDfX19AMDLly+xfft2SKVSaGtrw9zcHMuWLZPVz8rKQr169TB69Gj8/fffsLGxUTi+jo4OBg8ejO+//x6ff/45dHR0AADff/89evToAVNTU8THxyMqKgoLFy5E165dAQAdOnQo8Lzu3bsHT09PDBo0SFbWr1+/AvcpSE5ODgAgNTVV4zaocHnjy3EueeVhrI2MjEqk3fIarykpKWXdhVJRWc6zNDFeqbgUxzgzxgvGeKWPgaZxXNh+H8P7Q14fy0OsAiUXr0DZnxup7mOInbJUGeK1MEyUU4WXk5MDLS0t2esnT55gw4YNCA8PR1xcnKxcfkkSMenp6di+fTtCQkLw6tUrZGZmyra9f/8eVatWVbpvaGgo9PT00Lt3bwCAvb096tevj5CQEIVEuZOTkyxJDgCNGzdGZmYm4uLiYGlpCSB3CZl9+/bh2bNngjf7yMhI0UQ5AAwdOhTbtm3Dr7/+is6dO+PPP//EH3/8gfnz5wMAqlevjrp162LDhg149+4dXFxcYGVlVeCYtGjRAidOnIClpSXc3d3RtGnTAusXJisrS3YeVPI4zqWnLMfa0dGxRNot2Xi11HjPiIiIMjluaSvaeVJBGK/qqlkCbX7clI+z6mPFGFcN47UgjM2ypl4cf7heivsJr2X5fH8ouI9l/e+OkopXoCTP7eP5XvqxKJ+xU/5U5HgtDBPlVOFFR0ejYcOGAIDk5GRMmDAB5ubm8PX1RZ06dWBgYIBFixYhPT290LbWrl2LY8eOYdq0aWjVqhVMTExw/vx5fPPNN0hPT1eaKJdKpfjhhx/Qvn17aGtrIzExEQDQrVs37N+/HzExMahVq5asvqmpqWD/vKR5Xh/PnTuHL774AiNGjMDs2bNhZmaG2NhYTJs2rcDzqFevHtzc3PDdd9+hc+fO+P7771GvXj3ZXeNaWlrYvXs3Nm7cCH9/f6SkpKBly5bw8/ODk5OTaJuLFy9GtWrVsHfvXqxZswa1a9eGt7c3Ro8eXeh4itHV1UVWVhYaNmwIQ0NDjdqgwqWmpiIyMpLjXAoq8liXZLw+jH1TeCUlmjdvXibHLW1FOU8Sx3jVTPT/4gqvVMkoG+ffbqs+VozxgjFeC3flDmOzrKkTx/mvl/x+8teyPL4/KOtjRY7VPCV1bs+vfzzfSz8W5TF2ypPKEK+FYaKcKrRHjx4hJiZGtizI7du3ER0dje3bt6NZs2ayeklJSYXeOQ0AYWFhGDFiBLy9vWVlly9fLnS/69evIzY2FrGxsaIJ5x9++EG2JIsqwsLC0Lx5c/j7+8vKbt68qdK+w4YNw7x58xATE4OQkBB4eXkJ7ri3sbHBli1bkJmZifDwcGzYsAFTp07FL7/8IvqHABMTEyxcuBALFy7Ew4cPsX//fixbtgxNmzZFu3btVD6nPHl9MTQ0LNOf21QWHOfSUxHHurzGa3nqS0mqLOdZFsrbnC4OJRuvTMbJUz7Oqo9VRZuDJYXxWhDGZllT7/p9uF6K+wmvZfmc8wX3sSLGap6KfG4VDa+TairznNYu6w4QlZT09HR8+eWX0NfXx7BhwwDkPgQTAPT09GT1bt26hZcvXwr21dPTQ0ZGhmib+ffNzs7GmTNnCu1LSEgIjIyMsG/fPuzfv1/wX7NmzRASEqLWuaWlpQn6kXcMVXTr1g2mpqaYO3cu3r17h8GDB4vW09PTQ/v27eHt7Y3k5GS8fv260LYlEgn8/PwA5C5xQ0RERERERERE9DHgHeVUIUilUty+fRtA7sMZ/vrrLwQHB+PFixdYtWoV6tWrByB3XXAjIyMsW7YM3t7eiImJQUBAgGDZEyB3TfCsrCwEBQXBwcEBxsbGaNSoEVxdXXHs2DE0adIE1atXx+HDh0UT6vmlp6fj3Llz6Nmzp+xBoPkNGTIEK1aswNOnT9GoUSOVztfV1RX+/v7YunUrHBwccPnyZVy7dk2lffX09DBw4EDs3r0bHTt2RO3atWXb/vzzT6xevRp9+/ZF/fr1kZycjO3bt6Nu3bqwtrYWbW/kyJHo0aMHbG1toaOjg5MnT0JPT0+ju8mJiIiIiIiIiIjKAhPlVCGkpaVhxIgRAHJ/SlOvXj24uLggMDAQjRs3ltWzsLDA5s2bsWbNGvj4+KBhw4ZYtmwZdu3aJWivS5cuGD16NHbs2IG4uDg4OTnhwIEDWLx4MZYuXYovv/wShoaGGDRoEHr06IFFixYp7dulS5eQlJSEgQMHim739PTEmjVrEBISglmzZql0viNHjkRUVBQOHjwoS3ivX78ew4cPV2n/Hj16YPfu3RgyZIig3NLSEhYWFti+fTtiYmJgYmKCdu3aYe3atdDR0RFtq23btjh58iSioqKgra2Npk2bYtu2bYJxJyIiIiIiIiIiKs+YKKeP3owZMzBjxgyV63fq1AmdOnUSlHl4eAhe6+rqYunSpVi6dKmg3MLCAlu3blVoM29pFzG9evXCw4cPlW43NzfH/fv3Za8vXLigUKd79+6CNnR0dPDFF1/giy++ENSTP45YWwDwyy+/wMzMDN27dxeU16hRA2vXrlXaVwBYtWqV4PWCBQuwYMGCAvchIiIiIiIiIiIqz5goJ6pEnj59ir///hsHDx7E6NGjoa+vX9ZdIiIiIiIiIiIiKnNMlBNVIkuXLsXt27fh7u6OKVOmlHV3iIiIiIiIiIiIygUmyokqkQMHDpR1F4iIiIiIiIiIiModJsqJiCqYcWvvF14JAGAAXHhapGMFzW9VpP2JlBnd27qsu6CWs0fXa7xv616fa7TfaI2PCBwOe67xvh/btSHN/HT1hcp19cSf941/4nNEy6saaImW16pmqFBWzVBbtG5qpnjbt8J/U+yHbQfRukkpqaLlVmZVRcufvVEsMzYQ79+bxFjR8vfposUwM1Ickz0hz0Tr1qshfkwxPV3ri5afviz+HlBFT/zaKGuHyrceLh+um3xMa8ld6uxs4eta1T7MM6lcuL1OlApeS4UvUd3ow77v04Ubs+Tq3nn6IbB09KoIthkbmAiPm5QleG1h/OHNR1vuhGKThXWdbD4sOfnqrfCE5N/DapoKX79M+FDf1FB4nCp6wrrP44Qn+Pb9h1gzkds3R25cM7M/FJz59UWBdQu6nvmvu1jdosTzuWsf2pI/jnwfqeiqVhF/TxZTzVC8bsJ78Qsj9n6vpyPexrO4TNHy16/+ViiLr9tItK6yttMypQplZkbiXyyM9MXbsK6hWD9dyfeEAz+If7YairRtpOT7SnKaYttmVcXryr9/ysoVTxu6Sr5PFcdnsLLvdfx8V6T6tywiIiIiIiIiIiIiogqIiXIiIiIiIiIiIiIiqtSYKCciIiIiIiIiIiKiSo2JcipWAQEBkEgkkEgkaNasGRwdHdG/f3/4+/vjyZMnGrV548YNbNu2rdj6GBUVJeuj/H+9e/cutuPkd+PGDUgkEty7d69E2i8qiUSC3bt3l3U3iIiIiIiIiIiIyoTGD/N8/fo1atasWZx9oQqiSpUqCAoKAgC8f/8ef/31F4KDg3H06FGsWLECAwYMUKu9mzdvYs+ePZg6dWqx9nPOnDlwdnYWlFWpUkVJ7aJp2bIlgoOD0bhx4xJpv6iCg4NRp06dsu4GERERERERERFRmdA4Ud65c2d06NABn3zyCXr27AkjI6Pi7Bd9xLS1tWFvby977ebmhtGjR8Pb2xsLFy5E27ZtUb9+2T9Zt0GDBoJ+liRjY+NSO5Y60tLSUKVKlXLZNyIiIiIiIiIiotKi8dIrM2fOxOvXr+Hr6ws3NzfMmzcPv/zyC6RSaXH2jyoIAwMDLF68GJmZmTh27Jis/OTJkxg1ahTat28PJycneHl54e7du7LtAQEBCAwMREpKimx5FC8vLwDAkydPMHv2bHh4eKBNmzbo27cv9uzZU2xz0NfXF56enrhx4wYGDhwIe3t7DB06FPfv3xfUS0pKwrx58+Dg4AAXFxds2LABe/bsgUQikdURW3pFIpFg586dCAgIgKurK5ydneHn54eUlBRB+9HR0Zg3bx6cnZ3RunVrjBkzRqEPAHD8+HH0798fdnZ2cHd3x8aNG5GdnS3YLpFIEB4ejk8//RT29vZYs2aNrC/5l17x8vLClClTEBYWhl69esHBwQFjx47F8+fPFfo2ZcoUtGnTBh4eHti3bx9WrFiBrl27ajDiREREREREREREZUPjO8qnTp2KqVOn4sGDBwgJCcGZM2cQGhqKGjVqoF+/frKEHVGeJk2aoFatWggPD5eVRUVFYeDAgbC2tkZGRgbOnDmDMWPG4PTp07CxscGwYcMQHR2N0NBQ2XIuxsbGAHKX/7GxsUH//v1RtWpVREREICAgACkpKZg+fXqh/ZFKpcjKyhKUaWtrQ1v7w9+PYmNjsXz5cnh7e8PExATr16/H9OnTce7cOejp6QEA/Pz8cP36dcyfPx9169bF0aNH8ccff6g0JocOHYKjoyNWrVqFyMhIrFmzBjVq1MC8efMAAO/evcPo0aNhZGSExYsXw8TEBAcOHMC4cePw008/oUaNGgCAvXv3Yu3atRg3bhx8fX3x5MkTWaI8r608c+fOxYgRIzBlyhQYGhoq7VtERATi4+Mxb948ZGdnY9WqVZg/fz6Cg4MBADk5OfDx8cGbN2+wbNkymJiYYPfu3fjnn38EY0hERERERERERFTeaZwoz9OiRQu0aNECCxYswPXr1xESEoLjx4/jwIEDsLGxwSeffIJPPvmE6x8TAKB27dp48+aN7HX+hLZUKoWbmxvu3r2LEydOYM6cObCysoKVlZXCci4A4OLiAhcXFwC5SVtHR0ekpaXh4MGDKiXKZ8+erVA2dOhQrFixQvb63bt3OHjwIGxtbQEAhoaGGDt2LO7cuYN27drh8ePHOHfuHFavXo2BAwcCANzd3dGnTx+VxsPS0hLr168HAHTq1AkPHjzA2bNnZcntoKAgJCYm4tixY7KkuIuLC3r16oXdu3djwYIFSE5OxpYtWzBp0iTMmTMHQO5yN3p6eli1ahUmTpyI6tWry445cuRIeHt7F9q3pKQknDx5Eubm5gCAlJQU+Pn5ITo6GlZWVvjll1/wxx9/4NChQ2jXrh0AoEOHDvDw8ICpqalK5y8mJycHAJCamqpxG1R65H8BQYry5nJZzumSWh6N8Vq2yir+KnLcM16poimueC2Pcc94JRJXWLyW1vtC3vbyEKtAycUrUPbnRhVbSX4Gy7ddGeK1MEVOlOfR0tKCo6MjEhMTERMTgytXruDZs2cIDAzEli1b0L17dyxatIgPAK3kcnJyoKWlJXv95MkTbNiwAeHh4YiLi5OVR0ZGFtpWeno6tm/fjpCQELx69QqZmZmybe/fv0fVqlUL3H/evHno0KGDoCwvKZynZs2asiQ5kHtXPADExMQAgGwplW7dusnqaGtro0uXLti7d2+h5+Dq6ip43bhxY5w5c0b2+sqVK3B2dka1atVkd79ra2vDyclJduzw8HCkpKSgd+/egjvkXV1dkZaWhkePHqF9+/ay8s6dOxfaLwBo1qyZYDzyzj0vUX7v3j2YmprKkuQAULVqVbi4uKh8R72YvHNQZQ6QMgaldqSIiIhSO9bHrizntKOjY4m0WzHj1bKsO6CyosWf5udZGeKe8QoA/M5eESiPV/XeA8pz3DNeVcWYriwU47VmIdvV8aEtdY9T1t8XSypegZI8N8YtFddnsPhcUtZ2RY7XwhRLojzvTvKffvoJycnJaNq0Kb744gv0798fOjo6OH78OLZv344FCxZg3759xXFI+khFR0ejYcOGAIDk5GRMmDAB5ubm8PX1RZ06dWBgYIBFixYhPT290LbWrl2LY8eOYdq0aWjVqhVMTExw/vx5fPPNN0hPTy80UV6/fv1ClweSvzM6b7mVvP7FxsZCT08PJiYmgnryCXd12s/IyJC9TkhIwO3bt9GyZUuFfa2trWV1AGDQoEGix3j16pXgtYWFhcZ9Az6c++vXr0XPU9VzV0ZXVxdZWVlo2LBhgUvDUAEuPC21QzVv3rzUjvWxSk1NRWRkZIWc0xUxXh/Gvim8UjlRlPgrynlW5LhnvH7w2+24QutQ+acsXl/+rt57QHmMe8arehjTlYd8vMpf+6LE85U7H9pS9TgVOVbzlNS5MW4JKJ7PYGVzSb7tyhCvhdE4Uf7nn3/i9OnTOHPmDF6/fg0LCwsMHToUAwcOFDzEEAAmTpwIAwMDrF69usgdpo/Xo0ePEBMTI0vo3r59G9HR0di+fTuaNWsmq5eUlAQrK6tC2wsLC8OIESMEy4hcvny5+DteAEtLS2RmZiIpKUmQLI+Pjy+W9qtVqwZ3d3fMmjVLYZu+vr6sDgAEBgaKjlu9evWKpS/yatasKXqeRT33vF8cGBoalunPbUg1vEaqq4hzmvFatspqzCvDta6Ic1r9eOU/ziuC4prH5TkeGK+qYkxXFopzJq6Q7er40Ja6x6mIsZqn5M6NcUvF9RksPpeUtV2R47UwGifKBw4ciCpVqqBbt24YOHAg3NzcCnyAX5MmTRTWmKbKIz09HV9++SX09fUxbNgwAEBaWhqAD3cqA8CtW7fw8uVLwXIn8ndZ528z/77Z2dmCZUtKQ6tWrQAA58+fl61RLpVKcfHixWJp39XVFadPn0bjxo2Vvkk5ODjA0NAQ0dHR6NGjR7EcVxV2dnZITEzE77//DicnJwC5S95cu3atSGuUExERERERERERlTaNE+VfffUVevXqVejyFnk6dOigsB40VUxSqRS3b98GkPtggL/++gvBwcF48eIFVq1aJbvD2d7eHkZGRli2bBm8vb0RExODgIAA1KpVS9Be48aNkZWVhaCgIDg4OMDY2BiNGjWCq6srjh07hiZNmqB69eo4fPiwaEJdmWfPnsn6mUdLSwtt2rRRuQ1bW1v06NEDy5cvR2pqKurUqYOjR48iLS1NsBa7psaPH4+QkBD861//wtixY1GnTh3Ex8fjzp07qFWrFsaPHw9TU1PMnDkTa9euRXR0NNq3bw8dHR28ePEC58+fR0BAQIn8ZKZTp05o2bIl5s6dizlz5sDU1BS7du1C1apVi+XciYiIiIiIiIiISotGifLU1FQcPHgQ6enpGDVqVHH3iT5yaWlpGDFiBIDcn3HUq1cPLi4uCAwMROPGjWX1LCwssHnzZqxZswY+Pj5o2LAhli1bhl27dgna69KlC0aPHo0dO3YgLi4OTk5OOHDgABYvXoylS5fiyy+/hKGhIQYNGoQePXpg0aJFKvVzw4YNCmU6Ojp48OCBWuf71Vdfwd/fH2vWrIG+vj4GDRoEW1tbHDp0SK12xFSvXh3BwcHYtGkT1q1bh7dv36JGjRpo06aN4O7xCRMmoFatWti7dy8OHjwIXV1dWFtbo3PnzoK77ouTlpYWvv76ayxZsgRLliyBqakpxo4di7///rtcP/CJiIiIiIiIiIhInkaJckNDQ0RFRfGuUVIwY8YMzJgxQ+X6nTp1QqdOnQRlHh4egte6urpYunQpli5dKii3sLDA1q1bFdrMW9pFmXr16uHhw4eF9m3VqlUKZaampgr7mpqaYt26dYKyMWPGCNZdd3Z2VthPrA/jx4/H+PHjBWWWlpZYsWJFof3t168f+vXrp3T74MGDMXjwYNFt8n05cOCAQp3mzZsr1LOyssKOHTtkrzMyMtCvXz+0a9eu0P4SERERERERERFp4rfffsPq1auxaNEiODs7C7ZdvXoVu3fvxj///IP69etj6tSpaNu2baFtarz0iru7O3777TeMHDlS0yaIKoSzZ8/i1atXaNq0KVJTUxEaGor//ve/okn8iiY4OBhSqRQ2NjZITEzEkSNH8PLlS9G79YmIiIiIiIiIiIpDWFgYUlNTFZLk9+/fh7e3N6pUqYKWLVvijz/+wLhx4/Ddd99BIpEU2KbGiXIfHx/MmjUL8+fPx4gRI1C/fn0YGBgo1DMzM9P0EEQfBSMjI5w6dQqRkZHIzMxEo0aNsHbtWnTv3r2su1biDAwMsGPHDrx8+RIA0KxZM2zfvh12dnZl3DMiIiIiIiIiIqqowsPDBcsS59mxYwdycnJw5MgR2NraIjk5GUOGDMHWrVuxZcuWAtvUOFGet8TD48ePERoaqrQe1yqmis7d3R3u7u5l3Y0yMXDgQAwcOLCsu0FERERERERERJVIdHQ0GjVqJCjLysrCr7/+Cjs7O9ja2gIAjI2NMWjQIBw9erTQNjVOlE+bNo1rlBMRERERERERERFRqcrMzERGRoag7M8//0RaWprCs/MsLCzw5s2bQtvUOFGuzgMbiYjKq3Fr75d1F4pd0PxWhdZJSUlBREQEmjdvDiMjo1LoFVHFdvb7AI33rYjvQ/Tx6+lav8Ta/vG3F6Ll3Vvpq9xGZrZ4eYs6Hgpliak5onXrW1QRbztLvP7b94rlyvrxPl28vIGltvgGEVWUDEdxXJtPPKyL3EZx+emq4nwoyflXWcmP6blrwnE3NNCSe/3h/7OlwrZMDXUEr3WFL1HD5ENbmVnCbcnpwjhqWddK9v9hd94KthnJ9aldDWFQvM+XG3n8OlOwrYG5nvC4aR/+/12q8ITepghf62rrKt1uqC+MYfmxMdAV9jkr33uE2HtIfvn3NDQQ1s2Se6+RyjWln++4Jy8+F2xLEw6NIObUvfcxp+BToGKmznth2BXxz1ZAKlpav4bih4y2ko+oZ7HKJoqNQomejnhd86rijce/VyxLyRDvc2aWeNv3UhTrm1QRP15DSx3R8h/vpSqUebYxFK0rJj1TvDw2Ufxc9ES6UcNEvM8//Cp+beXfewsi/56hCWX96Otedp/ZtWvXxq1btzBmzBhZ2blz55CTkwMXFxdB3ejoaJWWB9c4US4vLS33k6dKFfEvnEREREREREREREREReXh4YFDhw6hWbNm6Ny5M+7fv4+goCCYm5srPODz9u3baNKkSaFtFilR/s8//yAgIACXL19GQkICAKB69erw8PDA9OnTUbdu3aI0T0REREREREREREQkMHXqVFy8eBHr16/H+vXroaWlBV1dXaxYsQK6uh9S3vHx8Xjx4gW8vLwKbVPjRPmTJ08wevRoJCUlwdXVFY0bNwYAPH36FKdOncLFixdx+PBhhUXViYiIiIiIiIiIiIg0ZW5ujhMnTuD48eN4+vQpLC0t0bt3b1mOOn+9s2fPqtSm6ovkyVm/fj20tbVx4sQJ7Nq1C35+fvDz88POnTtx8uRJaGtrY/369Zo2X2EFBARAIpHA3d0dUqniWkUjR46ERCKBr69vsR/b19cXnp6ehdaTSCTYvXt3sR9fGVX7VR507doV/v7+Zd2NYjVgwACV51tmZiacnZ3RrFkz/PPPPwrbb9y4AYlEgnv37hV3N4mIiIiIiIiIiGSMjY3RpUsX1K9fH69fv8bBgwexb98+0ZyVKjS+o/z333/Hp59+ColEorCtadOmGDNmDPbt26dp8xWanp4eEhIS8PvvvwvWzHn58iVu375d5g/WCw4ORp06dcq0D+VVYGAgTE1Ny7obZea3337D27dvAQChoaHw9vYu2w4REREREREREVGltGPHDmzevBlSqRRaWlqQSqXQ1tbG2rVrMX36dHz22WdqtafxHeVZWVkFPrjT0NAQWVlZSrdXZnp6eujUqRPOnDkjKD9z5gxsbW1hbV22T6G3t7dHzZo1y7QPpSUnJwcZGRmFV/x/LVq0QL169UqwR+VbaGgoTE1N0bJlS4SEhJR1d4iIiIiIiIiIqBL6+eefsXHjRvTt2xdhYWHYtWsXAODs2bOYNWsWtmzZglOnTqnVpsaJ8ubNm+PYsWNISkpS2JacnIzvvvsOLVq00LT5Cs/T0xNnz55FZmamrCw0NFR0CZInT55g9uzZ8PDwQJs2bdC3b1/s2bNHYemWjIwMbNy4Ed26dUOrVq3QqVMn0SU1bty4gYEDB8Le3h5Dhw7F/fv3Bdvll17x8vLClClTEBYWhl69esHBwQFjx47F8+fPFY6/YcMGdOnSBa1atUKfPn2KLZn65MkTfPbZZ3B0dIS9vT28vb0Vjr9nzx4MGTIEjo6OcHFxwZQpU/D3338L6uQt83L58mV88sknsLOzw4ULFxAQEAAHBwc8fPgQo0aNQps2beDp6Ylff/1VsL/80it57RU2pklJSZg3bx4cHBzg4uKCDRs2YM+ePaK/yMjv9evX8PPzQ7du3dC6dWv07NkTGzZsUEjuSyQS7Ny5EwEBAXB1dYWzszP8/PyQkpIiqHfr1i0MHjwYdnZ2snFQVUpKCi5cuIBevXph8ODB+Ouvv/Dw4UOV9yciIiIiIiIiIioO+/btg729PdauXYsGDRpAS0sLQO5yLN7e3ujbty+CgoLUalPjRPmMGTPw4sUL9OnTBxs2bMDx48dx/PhxrF+/Hr1798bz588xY8YMTZuv8Lp06YKMjAxcuXIFAPD48WM8fPgQffv2Vaj7+vVr2NjYYOnSpdixYweGDx+OrVu34uuvvxbUmzFjBvbt24chQ4Zgx44dWLBggUKiNDY2FsuXL8fEiROxadMmpKenY/r06YKEvZiIiAjs3r0b8+bNw8qVK/H8+XPMnz9fUGfWrFkIDg7Gp59+iu3bt8Pd3R3z589XKxkr5sWLFxg5ciTevXuHVatWYd26dYiPj8f48eMFCePo6Gj861//wtdff43ly5dDKpVi5MiRsqVC8rx+/RrLly/H+PHjsXPnTjRv3hxA7vrb8+bNw+DBgxEYGAhzc3PMnDkTCQkJBfZPlTH18/PDpUuXMH/+fKxatQpPnjzB/v37Cz33hIQEmJmZwc/PD7t27cKkSZNw4sQJLF26VKHuoUOHEBkZiVWrVmHatGkICQkRzJHY2FhMnDgR+vr62LRpEyZOnIhly5YhJiam0H4AuX+pS0lJgaenJ/r06QNdXV2EhoaqtC8REREREREREVFxefDgAbp37650u5OTE548eaJWmxqvUe7i4oIdO3ZgzZo12LFjh2Bb8+bNsXbtWnTo0EHT5is8Q0NDdO3aFWfOnEHnzp0RGhoKBwcH1K9fX6Gui4sLXFxcAOQuFeLo6Ii0tDQcPHgQ06dPBwBcuXIFly5dwvr16wV3pcvfof7u3TscPHgQtra2sn6MHTsWd+7cQbt27ZT2NykpCSdPnoS5uTmA3LuL/fz8EB0dDSsrK1y/fh0XLlzA7t270bFjRwCAm5sbYmNjERAQAA8PD43HKjAwENWqVcPevXthYGAAAGjbti26deuGY8eOYcyYMQCAf//737J9srOz4ebmBhcXF5w9exYjRowQjMHOnTvRpk0bwXHyEuV5fbWxsUG3bt3wyy+/YMCAAUr7V9iYPn78GOfOncPq1asxcOBAAIC7uzv69OlT6LlLJBJ88cUXstdt27aFoaEhfH19sWTJEhgaGsq2WVpayh6g26lTJzx48ABnz57FvHnzAABBQUHQ0tLCzp07YWJiAgCwsrLC+PHjC+0HkPuLh1q1aqF9+/bQ1tZGhw4dEBoaijlz5sj+alcccnJyAACpqanF1mZlI/8HMjF548txLnnlYaxL6tkXjNcPVIm7inDMsjxuaWC80segIsdgno/luwzjlSqzwuI0b3t5iFWg5OIVKPtzI9KUfBxrEq8l8b1EnXitWrWq0m3x8fEFLhsuRuNEOQC4urri5MmTiI2NlT1NtE6dOrC0tCxKs5WGp6cn5s6di7S0NPzwww/w8vISrZeeno7t27cjJCQEr169Etyp/P79e1StWhXXrl2DoaEh+vXrV+Axa9asKUvoAkCTJk0AoNC7ips1ayZLkuffLy9RfuXKFZiZmaFDhw6CteldXV3xn//8B9nZ2dDR0SnwGMpcuXIFffv2hY6OjqxtU1NTtGjRQrDEye3bt7F582Y8ePBAcBd5ZGSkoD0zMzOFJDkAaGtry/4gAQD16tVDlSpVCh2bwsb03r17AIBu3boJjtWlSxfs3bu3wLZzcnIQFBSEo0ePIioqCunp6bJtL168QNOmTWWvXV1dBfs2btxYsA7+nTt34OzsLEuSA7l/hDEzMyuwD0Dum8uVK1fg5eUFbe3cH6L0798fX3zxBf73v/8V+EcWdeVdY/nrVnIMSuk4pSciIkLluqU3zlSWY+3o6Fgi7ZZ+vJYGzb7DqBN3ijR7HyraMTX/rla0434cGK8lrXI8C6ekVLwYVJwPH8t3mY87XhmHVDSKcVqzwO1l/flTUvEKlP255WJMk/qUfd6Kz2nxOVYS30tUjdfatWvjxYsXCuU5OTl4/PgxDhw4ADc3N7WOXaREeR5LS0smxzXQsWNH6OnpYfPmzYiKilJ6h/HatWtx7NgxTJs2Da1atYKJiQnOnz+Pb775Bunp6ahatSrevn0LS0vLQu/sNTU1FbzW09MDAEECVpP9EhIS8PbtW7Rs2VJ0/9jYWFhZWRV4DGUSEhIQFBQkuq5QXj/++ecfTJgwAa1atcKyZctQs2ZN6OnpYcqUKQrnZmFhIXqcKlWqQF9fX6H9oo5NbGws9PT0BAlqAII/PCgTFBSE1atXY9KkSXB2doapqSnu3bsHf39/hX6J9SP/0jSxsbFo0KCBwjFU6cePP/6IrKwseHh4IDExEQDg7OwMfX19hISEFGuiXFdXF1lZWWjYsKHgjvkSc+FpyR+jlOUtJ1SQ1NRUREZGlt44V2IVeaxLPV5LwcPYNxrtp0rcKaXh+1BRjqnpeRb1uOUd47V0vL4VV6bH/9hVtBj87bbifKjs32VKI16v3GEcUtHIx6l8LOdtr8ixmqc8nFtsOGOa1CcfxwXFa8z/xOdYWX4vcXFxwc8//yxYGlpLSwteXl549uwZrKyssGDBArXa1DhRfvLkyULrGBgYwMrKCi1btlRIQFJuIrNnz57Yt28fXFxclCZww8LCMGLECHh7e8vK5Nf9NjMzQ2xsLHJycop1GQxVVatWDebm5grL8ORRJRlbUNseHh4YPXq0wra8n1j8+uuvSElJQWBgoCxhnJWVhXfv3insU9rjY2lpiczMTCQlJQmS5fHx8YXuGxYWhq5du2Lu3LmyMnXXV8rfj7g4xTc2VfqRtxa52DItYWFhWLRokewPBEWVd30MDQ1L9OdxFZk648ZxLj0VcawZrx+UxfmX1ZhXhmtdEed0+YpX/mO+KMr++hU3xflQ2b/LlE68Mg6paBTnZlyB2ytirOYpH+fGmCb1KZu34nNafI6V5dz38vJC3bp18fbtW5iZmaFq1aqQSCSoUaMGBgwYgNGjR8PY2FitNjVOlPv6+so+wPPWUMuTv1xLS0v2tNHJkydrergKa9iwYYiLi8Pw4cOV1klPTxckIbOzswVLagC5y27s3LkTP/74o+gDQUuaq6srdu3aBT09PTRr1qxY23ZxccGjR4/QokULpcu3pKWlQUtLC7q6H6Z03l3QZa1Vq1YAgPPnz8vWKJdKpbh48WKh+6alpSkkoENCQjTqR+vWrXHkyBFBwv7atWsKDzuV9/LlS4SHh2PkyJEKcysiIgIrV67Er7/+iq5du2rULyIiIiIiIiIiInU0aNAAn376qex169atVbqxuyBFuqPc19cXZmZmGDNmDKytrQEAz549w6FDh5CUlITFixcjLi4OBw4cwIYNG1C1alXRu4Irs9atW+Prr78usI6rqyuOHTuGJk2aoHr16jh8+LBgSY28Oh4eHvj3v/+N58+fo02bNnj79i3Onj2LTZs2leAZ5HJzc0OXLl0wadIkTJo0CRKJBKmpqXj8+DGePXuGFStWFLh/cnIywsLCFMqdnZ0xc+ZMDB06FBMnTsTw4cNhYWGBN2/e4ObNm2jXrh08PT1lD4718/PDyJEj8ejRI+zdu1dhOZKyYGtrix49emD58uVITU1FnTp1cPToUVlyvyCurq7Yv38/Dh48iIYNG+L06dN49uyZRv0YN24cDh8+jMmTJ2Py5MlITExEQEBAoWuU591NPmnSJIWHzTo6OmLHjh0IDQ1lopyIiIiIiIiIiD5aGifKg4KCUKNGDezevVtQLpFI0KNHD0yaNAnfffcdVqxYga5du2L06NE4cuQIE+UaWLx4MZYuXYovv/wShoaGGDRoEHr06IFFixYJ6gUEBCAwMBDBwcEIDAxEjRo11F60vii2bNmCHTt24MiRI3j58iVMTExga2uLwYMHF7rvq1evMGvWLIXyQ4cOoV27djh27Bg2bdqEZcuWISUlBZaWlnBycoJEIgGQO+9WrlyJwMBATJkyBc2bN8fmzZvx+eefF/dpauSrr76Cv78/1qxZA319fQwaNAi2trY4dOhQgftNmzYNCQkJ2LJlCwCgV69eWLRoEaZOnap2H2rWrImdO3di+fLlmDVrFqytrbFkyRJs3LixwP1CQ0PRtm1bhSQ5kLt+oqenJ44ePYr379+r3SciIiIiIiIiIiJ1eXl5qVTvwIEDKreplSO/boqKnJycMHv2bKWJ70OHDmHz5s24efMmAGDv3r3YuHEj7t69q8nhiCqcMWPGQFtbW62Areju3buHnJwcZGZmonnz5qWy1tW4tfdL/BilLWh+q0LrpKSkICIiotTGuTKrqGNdFvFaGg6HPddov9G9rTU+pqbvQ6rEujKanidQtHMt7xivpePH316Ilutoq95GZrZ4+bv3iv+0SUwV/+dOfQvxA2Zmidd/K9K2sn5kS8XLG1iqfpLpmeL9GNilYsXgT1cV50NPV8UbNOQxXovm3DXxOBQjP58z5Va31JVbHTMjXwzJ101OF85rfd0Pv7ANu/NWsK21tfDXwRbGwl/jvs/3I+vHrzMF2+pWE94TaGTwYd93qcITepsifN28tnDfV+8+bK9tJoxhbbkfCCenCV9Xr/qhQmGPysq/2dBAuC1L7r1Gfszzj2NahnCM04RDA3Nj1fskL3/2SD5O5WM5b3tFjVUgN14zMjLKxbmFXRGP6dh34h9IxlUUL762ko+oZ7HibTySizsAqKIn3oh5VfHy+PeKbUuVpCn15AOuACZVxI/X0FJ8Od8LEakKZZ5txB/QKvbZb6gv3rfYRPGx0xPpRg0TNb4IQfF9oCBSJZnf3m7COC4oXn/4VXyO9XUv/DO7pHzyyScKy4FLpVJER0fLbrKtVq2aWksYa3xHuVQqxd9//610+99//w2p9MOE0NfXh4GBgdL6RBXZ2bNn8erVKzRt2hSpqakIDQ3Ff//7X2zdurWsu0ZERERERERERPRROX36tNJtISEh2Lhxo9rLUWucKO/atSuOHDmCBg0aYNiwYbIkeHp6Oo4ePYpvv/0Wffr0kdUPDw+XrWNOVNkYGRnh1KlTiIyMRGZmJho1aoS1a9eie/fuZd01IiIiIiIiIiKiCqN///64f/8+/vOf/6i1koPGifKFCxfixYsXWL58OVavXg1LS0sAQGxsLDIzM9G6dWssXLgQQG7yvEqVKoInkRJVJu7u7nB3dy/rbhAREREREREREVV4NjY2CA4OVmsfjRPlZmZmOHLkCM6dO4dff/0V//zzDwCgY8eO6NixI7p37w7t/1/gyMDAAMuXL9f0UEREJaYoa/wSUcVTFutvF/Y+VBJre1bkdcap/OvTsehrWYqtaw0AFqaKa4SKlQGqrYOtroq8Fm9JKYnrQIrk1yTv4SIc97NK1jcGgLgk4fqvFibCmJJf+zY28UNBTbn4s9AXrsGblf2h7pD2ZoJt+dfdBoBUubW388d2/Rr6gm1JcssNW+arm5kt7IP8mt5Dugk/I09e/PBcj6oi6zqrSn7MKxrGcuk5dUnxWTMv48UXoW5US3zd66cximtn1zUXn991qou3Ub+G4vLKytY519MRbzsxRbHfpkbidWsZvS6xz1dLU82el1GZlOVa5JrIzMzE2bNnYWpqWnjlfDROlAOAlpYWevbsiZ49exalGSIiIiIiIiIiIiIilfj5+YmWv3v3Dnfv3kVcXBzmzp2rVptFSpQDQExMDH7//XfExcWhV69esLKyglQqRWJiIkxMTKCjo8ZjWImIiIiIiIiIiIiICvD7778jJ0f4qwQtLS2YmprC0dERQ4cOVXsZZI0T5Tk5OVi1ahUOHTqErKwsaGlpoWnTprCyssL79+/RtWtXzJw5E+PHj9f0EEREREREREREREREAj///HOxt6lk9aDC7dq1C/v378eECROwd+9eQQbfxMQEPXv2xE8//VQsnaTSERAQAIlEIvvPzs4Offr0wc6dOyGVCtevkkgk2L17dxn1NFdERAQkEglu3Lghuv3+/fuQSCQ4deqU6Pbs7Gy4urpiwYIFiIqKgkQiQVhYWEl2WamuXbvC399f9trX1xeenp7FfpyAgADcunVLobw8XE8iIiIiIiIiIqKyovEd5ceOHcPAgQMxZ84cJCQkKGyXSCT45ZdfitQ5Kn1VqlRBUFAQACAtLQ03btzA+vXrkZOTA29v7zLunXpatWoFGxsbnDlzBgMGDFDYfu3aNcTFxaF///6oWbMmgoOD0bBhw9LvqAgfHx+kpKQUe7uBgYEwMjJC27ZtBeXBwcGoU6dOsR+PiIiIiIiIiIiouL18+VKlenXr1hXUz3stRuNE+atXr+Dg4KB0u6GhIZKTkzVtnsqItrY27O3tZa87dOiAv/76Cz/99NNHlygHAE9PT2zbtg0JCQmoXr26YFtoaChq1KgBV1dX6OjoCM67rFlbWxdeqRiVp3MnIiIiIiIiIiIqSPfu3RXWKBfz559/IiMjQ1b/zz//VFpX40R5jRo18OrVK6Xb//jjD9SuXVvT5qkcqVq1KrKystTe79KlSwgKCsKff/6J9PR0NG7cGDNmzECnTp1kdY4fPw4/Pz+cOHECGzZswH//+1/UrFkTPj4+GDhwoKC9r7/+GocOHUJKSgrc3NwwcuTIQvvQv39/BAQEICwsDKNGjZKVp6en49y5cxg0aBB0dHQQFRWFbt26YfPmzejduzcA4Pz589i6dSv+/vtv6OjowNraGrNmzYKHhweA3F9NLFiwABMnTpS1u2/fPqxcuRIPHz4EAKSkpGDdunW4cuUKoqOjUaNGDXTs2BHz58+HiYmJ0n77+vri/v37CA0NBZC7NIvYX8oGDRqEVatW4fXr19i4cSNu3ryJ2NhYWFlZoXfv3pg+fTr09fVl/QWANWvWYM2aNQCA/fv3w9nZWfRcvv32W+zduxcvX75EzZo1MXToUEydOhXa2tpqXzsiIiIiIiIiIqLisnLlSpXr6unpqVRf40R5jx498O2332Lw4MEwNjYGkPtkUQD47bffcOLECUHSjT4eeUnxvKVXfvrpJ0yZMkXtdqKiotClSxdMmDAB2tra+OWXX+Dt7Y2goCA4OzsL6s6bNw/Dhw/Hp59+iqNHj8LX1xd2dnZo3LgxAODgwYPYvHkzJkyYAFdXV1y9ehULFy4stA8NGjSAnZ0dQkNDBYnyS5cuITk5Gf379xfd7/nz55g1axb69euHuXPnQiqV4s8//8S7d+/UGoO0tDRkZ2dj9uzZMDc3x6tXr7Bt2zb4+PjgwIEDKrcTGBiIjIwM2euHDx9i6dKlsLGxAQAkJCTAzMwMfn5+MDU1RWRkJAICAhAbGyt7IwgODsaIESPg5eUlW/+8SZMmosc7cOAAli9fDi8vL3Tu3Bnh4eEIDAxEUlISvvjiC0Hdwq4dERERERERERFRcVLnJk0tLS2V6mucKJ85cyZu3LiBAQMGoF27dtDS0sLOnTuxefNm3L59G82bN8fUqVM1bZ7KSEpKClq2bCko69u3r0bLrvzrX/+S/b9UKoWzszMeP36Mo0ePKiTKx4wZgzFjxgAAHBwccPnyZZw9exY+Pj7Izs7G9u3bMWDAAFmS1t3dHXFxcUof1Jlf//79sXLlSrx69Ur2K4czZ87A2toabdq0Ed3nwYMHyMzMxOLFi2V/CHJ3d1d7DMzNzbFs2TLZ66ysLNSrVw+jR4/G33//LUt0F6ZFixay/4+Pj8ecOXPg5uaGyZMnA8i9Wzx/Artt27YwNDSEr68vlixZAkNDQ9nyKrVr1y5wqZXs7Gxs3boV/fr1w6JFiwAAHTt2RGZmJvbs2QNvb2/BMjYFXTtN5P1sJjU1VaP9STV548txLnnlYayNjIxKpF3Ga+koD3OosigPY814LT0l8TyY8jCHKovyMNYfc7yWxPyvKEpqbCrrmJeHWAVKLl6Bsj+3yqQ0x7oyxuzHHK+xsbH4559/AAB16tSBpaWlRsfWOFFuYmKCo0ePYs+ePTh79iwMDAzw+++/w9raGtOmTcOkSZNQpUoVTZunMlKlShUcPHgQAJCRkYE//vgDW7ZswaJFi9T6SQMAREdHY+PGjbh69SpiY2NlX/jkE/FAbiI2j5GREerUqYPo6GhZO69fv0aPHj0E+/Tq1UulRHnfvn2xevVq/PDDD5g4cSKSk5Nx6dKlAn/xIJFIoKOjI7tb2snJqcClUgpy8uRJ7Nu3D8+ePRO80UZGRqqcKM+TmZmJWbNmQUdHBxs2bJAtg5KTk4OgoCAcPXoUUVFRSE9Pl+3z4sULNG3aVOVjPH36FAkJCbIlaPL07dsX27dvx927d2XLzwAFXztN5P2iITIyUuM2SHUc59JTlmPt6OhYIu0yXksXx7n0MF4/BjWL3EJEREQx9ENcxRnn8o/xqiphzCjO/6LHVEWhODaaJVsKb7dyKev3xZKKV6Aszq145uTHqOTGWvE9sDLH7McUr9euXcOaNWsUrlezZs2wYMECuLq6qnVsjRPlQG5S1cfHR+M7R6n80dbWhp2dney1o6MjsrOzsWrVKnz66acqJ1ylUik+++wzJCUlYebMmWjQoAEMDQ2xZcsW0bXt5ZPQenp6sqVGYmNjAeTenZ2fhYWFSn2xtLSEs7MzQkNDMXHiRJw7dw7p6elKl10BABsbG2zbtg3bt2/H9OnToa2tjY4dO2LJkiWoU6eOSscFgHPnzuGLL77AiBEjMHv2bJiZmSE2NhbTpk0TJLNVtWLFCty/fx9Hjx5FtWrVZOVBQUFYvXo1Jk2aBGdnZ5iamuLevXvw9/dX+zh5y8vUqFFDUJ73Wn75mYKunSZ0dXWRlZWFhg0bwtDQUON2qGCpqamIjIzkOJeCijzWjNfSUZHnUHlTkce6osXrb7fjitxG8+bNi6EnQhV5DpU3FXmsSyJer9wRxoz8/P81vOgxVVHIj82LG29KpN3KoiLHap7SPreom8UzJz9GJTXWYt8rKmPMfmzxev36dUyePBlmZmaYPHmy7GbUyMhIHD9+HN7e3ti5cydcXFxUblPjRPnYsWPx2WefKT3Y9evX8fXXX2P//v2aHoLKiUaNGgEAHj9+rHKi/NmzZ3jw4AG2bt2K7t27y8rT0tLUPn7ezyXi4+MF5W/eqP7h0L9/f/j5+eHp06cIDQ1Fy5YtZeelTKdOndCpUyckJyfjl19+wcqVK+Hn54egoCAAgL6+PjIzMwX7JCYmCl6HhYWhefPm8Pf3l5XdvHlT5X7n9+233+Lbb7/Fli1bYGtrq3Ccrl27Yu7cubKyJ0+eaHQcMzMzAIrjHReX+8GRP0FfEvKedWBoaFiiP4+jXBzn0lMRx5rxWro4zqWnIo51xYvXoif1SnIcKs44l38VcaxLJl6FMaPYLhPleUpqPlW0eaquihireSryuZU3JTfWiu+BlfmafixzOiAgANbW1jh69Khs2eQ8kydPxvDhw3rDdh8AAQAASURBVBEQEKBWolxb087cvHmzwERlfHw8fv/9d02bp3Lk0aNHACBYl7oweXcx6+npycpevnyJ8PBwtY9vZWUFS0tLnDt3TlB+9uxZldvo2bMnDAwMEBQUhOvXrxd4N7k8Y2Nj9O3bF/369RMkn62srBSS0VevXhW8TktLE4wBAISEhKh87Dz//e9/sXz5ckydOhU9e/ZU2K7qcfT09Aq9w9zGxgbm5uYICwsTlP/444/Q09ND69at1e4/ERERERERERFRcXnw4AGGDh2qkCQHclc/GDZsmNpL6BRp6ZW8v3SLefbsGapWrVqU5qkMSKVS3L59G0Dueth//PEHvvnmGzRp0gTt2rVTuZ1GjRrBysoK69evh1QqRUpKCrZs2YKaNdVf+05HRwfe3t5YsWIFatSoATc3N1y5cgU3btxQuQ1jY2N07twZwcHB0NLSQt++fQus/+233+L27dtwd3eHpaUloqKicPr0abi5ucnq9OrVC0FBQbCzs4ONjQ1Onz6NmJgYQTuurq7w9/fH1q1bZQ+6vHbtmlrnn5ycjBkzZqBBgwbw8PCQXR8gdzkaa2truLq6Yv/+/Th48CAaNmyI06dP49mzZwptNWrUCOfPn0e7du1gaGgIGxsbhTcUHR0d+Pj4YPny5TA3N5cdc+fOnRg3bpxafzAhIiIiIiIiIiIqbrq6ugU+eDQtLQ26uuqlvtWqfeLECZw4cUL2+ptvvsHRo0cV6iUlJeHhw4fo1KmTWp2hspeWloYRI0YAyJ1wVlZW+OSTTzB9+nSFO5YLoq+vj4CAAPj7+2PWrFmoXbs2PvvsM1y/fh33799Xu19eXl5ITEzE4cOHceTIEbi4uGD58uWYNGmSym30798fZ8+ehbOzM2rVqlVgXYlEgosXL2LlypV4+/YtLC0t0a9fP8yaNUtWx8fHB3Fxcdi6dSu0tLQwYsQIjB07FqtWrZLVGTlyJKKionDw4EHs3r0bHTt2xPr16zF8+HCV+/327VvEx8cjPj4eI0eOFGwbNGgQVq1ahWnTpiEhIQFbtmwBkJvEX7RoEaZOnSqov2TJEnz11VeYPHky0tLSsH//fjg7Oysc08vLC7q6uti3bx+OHDkCS0tLTJ8+XaE9IiIiIiIiIiKi0ta+fXscOnQIPXr0UFgq+uHDhzhw4ACcnJzUalMrJycnR9XKeUlKIHc5DisrK4UH+QG56/i0bNkS06ZNU3ggIBGRMvfu3UNOTg4yMzPRvHnzj2JNrI9VSkoKIiIiOM6loKKONeO19FTUOVQeVdSxrojx+tPVF0Vuo6dr/WLoiVBFnUPlUUUd65KK13PXhDHTw0U4/89eUR5Tb5KEKQMLE+Evy6VyGYWX8VLZ/9c0FdbV1ha+zsr+sLP8D9b1dYUFqRk5SrdL5dIaSXI3GFrm60dmtnBbmly7Q7pZC16fvPhc9v9Vqyj/VX1h5Me8sqiosQrkxmtGRkapn9upS88Vyl7Gi6f2GtUSX3H5aYxUoayuufj8zswS74eOSNPaShZ41tMRbzsxRbHfpkbidWsZvS6xsRb7XlES3xPKu48tXp8+fYrRo0cjMTERbm5usmcRPnnyBFevXoWJiQkOHz6Mxo0bq9ymWneUjx49GqNHjwYAdO3aFQsXLkS3bt3UaYKIiIiIiIiIiIiISGONGjXC999/j8DAQFy+fBm//fYbgNxnLA4YMAAzZsxAnTp11GpT4zXKL1y4oOmuREREREREREREREQaq1u3LlauXAkgdzlpAKhSpYrG7RXpYZ55kpOTkZycDKlU8acb6mbuiYiIiIiIiIiIiIhUVZQEeZ4iJcoPHz6Mffv24cUL5euZRUREFOUQRERERFTMDocprm2pqtG9rQuvRATgzK/i/0bQ0xGvL7YWqLK1yLMV788BACQkK651aqDkefTFsc75m0SxNWEtEZ0SByBOYYuyc1d2PmL6dBRfM7W8rK+qbFwr41qvHxP59cHzX68f5GI5Q269Yj25rEL+dcmT04XbqlcVvs6/vrmu3Prl6ZnC+DLQk1sbXSr+/wBQRS7u5dclz09XLi7l57CRwYfjyj/hTX7cCiK/Trw6CltTPv+60EVZC72wtew1rUvFS2y97/o1xCdjbzfx63LiguJ3QbH1wgHla4YnpymWGSjJMqami7edma1Yni0VP15MSk3E/E/881VVytZQV4f8eyKg+D6Sp7Q/+8KUPG9CW8l71cf+2RwYGFhonZycHMyYMUPlNjVOlB85cgT+/v7o2LEjhgwZgo0bN2L8+PEwMDDA8ePHYWFhAS8vL02bJyIiIiIiIiIiIiJSsHXrVqXbcvL9ZbNUEuUHDx5Ex44dsWvXLiQkJGDjxo3w8PCAi4sLJk2ahCFDhuDt27eaNk9EREREREREREREpODatWsKZenp6Xjx4gWCg4MRERGBzZs3q9Wmxj86eP78Obp06QIA0NPL/V1TZmYmAMDExARDhw7F4cOHNW2eiIiIiIiIiIiIiEiBmZmZwn+1atVCu3btsHbtWjRs2BBff/21Wm1qnCg3MTFBdnbuQl/GxsYwNDREdHS0bHvVqlXx5s0bTZsnIiIiIiIiIiIiIlJbx44d8euvv6q1j8aJcltbW/z555+y123atMGRI0cQExODV69eITg4GA0bNtS0eQQEBEAikUAikaBZs2ZwdHRE//794e/vjydPnmjU5o0bN7Bt2zaN+1SQffv2QSKR4N///nextnv8+HFIJBLEx8cXWM/LywtTpkyRvS7Jcy2N9sujdu3aISAgoMA6Xl5ekEgkmD17tsK25ORktG7dGhKJBMePHy+WPg0YMAC+vr5q7RMVFQWJRIKwsDBZWdeuXeHv718sfSIiIiIiIiIiIipLOTk5qFevHlJTU1XeR+NE+SeffIJHjx4hIyMDQO7C6E+ePEHnzp3RtWtX/P333/j88881bR4AUKVKFQQHB+Pbb7/Fli1bMHjwYFy9ehUDBgzAqVOn1G7v5s2b2L59e5H6pMzp06cBAOfOnZONSWlaunQpvvjiC9nrkjzX0mj/Y2ZkZISLFy8iJSVFUP7zzz9DR0fJo5DLWGBgICZMmFDW3SAiIiIiIiIiIiqyUaNG4fjx4zA0NFR5H40f5jlkyBAMGTJE9trR0RFnzpzBhQsXoKOjAzc3N9jY2GjaPABAW1sb9vb2stdubm4YPXo0vL29sXDhQrRt2xb169cv0jGKw99//40//vgDrq6uuHr1Ki5duoSePXsWuE9GRgZ0dXWhra3x3yoEmjRpUiztlJXiHo+y1LZtW/zxxx+4cOECPD09ZeWhoaHo3r277I8q5UmLFi3KugtERERERERERERlRq2sZHp6OpYsWYIDBw6Ibq9fvz7GjRsHqVSKoKAg2cM9i5OBgQEWL16MzMxMHDt2TFZ+8uRJjBo1Cu3bt4eTkxO8vLxw9+5d2faAgAAEBgYiJSVFtqSLl5cXAODJkyeYPXs2PDw80KZNG/Tt2xd79uyBVCpVqU+hoaHQ0tKCv78/LCwsEBISolAnb2mLnTt3okuXLmjdujXevn0r6/vAgQNhZ2cHZ2dnTJ48GS9fvhTsHx0djUmTJsHe3h49e/bEyZMnBdvzL71S0Lnmne9nn30GR0dH2Nvbw9vbG8+fPxe0J5VKsXfvXvTp0wetWrWCm5sbZs6ciaSkpALb9/X1FSSHASAxMVFhuZGCxuP48ePo378/7Ozs4O7ujo0bN8rWw1dGlWuYt+TIqVOn4O/vDycnJ3Ts2BGrV69GVlaWoL2ff/4ZvXv3hp2dHYYOHSqYS4XR1dVFr169EBoaKiuLj4/HtWvXFMYmz7fffotevXqhVatW6Nq1K77++muF+Xfr1i0MHjwYdnZ28PT0xOXLl0XbCg8Px9ixY2Fvbw9HR0fMnTsXcXFxBfZZfumVvOt448YNDBw4EPb29hg6dCju378v2C8pKQnz5s2Dg4MDXFxcsGHDBuzZswcSiaTA4xEREREREREREZUnat1RHhwcjBMnTuCHH34osF6XLl2wdu1aNG3aFKNHjy5SB8U0adIEtWrVQnh4uKwsKioKAwcOhLW1NTIyMnDmzBmMGTMGp0+fho2NDYYNG4bo6GiEhoYiKCgIQO5DSAHg9evXsLGxQf/+/VG1alVEREQgICAAKSkpmD59eqH9CQ0NRbt27VC/fn306dMHwcHBSEpKgomJiaDeTz/9hAYNGmDhwoXQ1taGkZERdu3ahbVr12Lo0KGYPXs2MjMzcf36dcTHx6Nu3bqyfefNm4fhw4fj008/xdGjR+Hr6ws7Ozs0btxYoT8FneuLFy8wcuRI2NraYtWqVdDS0sK2bdswfvx4hIWFQV9fHwDw5ZdfIjg4GOPGjYObmxvev3+PS5cuISUlpcD21SE2Hnv37sXatWsxbtw4+Pr64smTJ7JE+bx585S2pc413LRpE7p164ZNmzYhPDwcAQEBsLa2xqhRowAAERERmDlzJjp16gQ/Pz9ERUXh888/V2tJHU9PT3z66ad49+4dqlWrhh9//BG1atWCg4ODQt0DBw5g+fLl8PLyQufOnREeHo7AwEAkJSXJltOJjY3FxIkTIZFIsGnTJiQmJmLZsmVISUlB8+bNZW2Fh4fDy8sLHh4e2LhxI1JTU7Fp0yb4+PggODhY5f7nHXP58uXw9vaGiYkJ1q9fj+nTp+PcuXPQ09MDAPj5+eH69euYP38+6tati6NHj+KPP/5Q6zhERERERERERERlTa1E+Y8//oiePXsWutxJ/fr10atXL5w5c6ZEEuUAULt2bbx580b2On8yVCqVws3NDXfv3sWJEycwZ84cWFlZwcrKSmE5FwBwcXGBi4sLgNyF3h0dHZGWloaDBw8Wmii/e/cuIiMj8emnnwLITZAeOHAAZ8+exdChQwV1MzMzsXPnThgZGQHIvRs3MDAQI0aMENzN2717d4XjjBkzBmPGjAEAODg44PLlyzh79ix8fHwU6hZ0roGBgahWrRr27t0LAwMDALlLhXTr1g3Hjh3DmDFj8Pfff+PIkSOYPXu24AGhvXr1EhxDrH11yI9HcnIytmzZgkmTJmHOnDkAcpfb0dPTw6pVqzBx4kRUr15dtC11rmHr1q2xaNEiWfs3btzA2bNnZYnyHTt2oHbt2ti6datsTXEDAwMsXLhQ5XNr164datSogZ9++gnDhg3DmTNnRO8mz87OxtatW9GvXz9Znzp27IjMzEzs2bMH3t7eqF69OoKCgqClpYWdO3fK/gBjZWWF8ePHC9pbv349WrVqhcDAQGhpaQEAmjZtKrsD3cPDQ+VzePfuHQ4ePAhbW1sAgKGhIcaOHYs7d+6gXbt2ePz4Mc6dO4fVq1dj4MCBAAB3d3f06dNH5WOIycnJAQC1HrZA6ssbX45zySsPY533PlvcGK+lozzMofzkn8FRkZSHsa7M8VqR51ZxUmecytOYFndfGK9FU9j1KE9zh8ou7ovjuOUhVoGSi1eg7M+tIIxlzRXH2JX38ZfvX2WI18KolSj/66+/0L9/f5Xqtm3bFhs2bNCoU6rIycmRJQKB3KU3NmzYgPDwcMEyE5GRkYW2lZ6eju3btyMkJASvXr0SLBnz/v17VK1aVem+oaGh0NPTQ+/evQEA9vb2qF+/PkJCQhQS5c7OzoKLHR4ejtTUVIV6Yjp27Cj7fyMjI9SpUwfR0dGF7ifvypUr6Nu3L3R0dGTLjZiamqJFixayZTWuX7+OnJwclfpVFGLjkZKSgt69ewuWQnF1dUVaWhoePXqE9u3bi7alzjXMP5YA0LhxY1y/fl32+s6dO+jatavgwZu9e/dWK1GupaWFvn374syZM+jYsSNu3bqFpUuXKtR7+vQpEhISZPMnT9++fbF9+3bcvXsXHh4euHPnDpydnQW/UnBxcYGZmZnsdWpqKm7duoUFCxYIlqpp2LAhateujXv37qmVKK9Zs6YsSQ58WAc/JiYGAHDv3j0AQLdu3WR1tLW10aVLF+zdu1fl48jLu/aqxC4VHce59JTlWDs6OpZIu4zX0lW842yp8Z4RERHF2I/yifFaXGqqVVt8bqnXRmWgPAYVx6ps4lX8mpVUXxivqhJeF8XrUdB2xmFZK+x6FVxXHYXNE03rlv33xZKKV6Aszk31mFR+XTT/LlhZqPN5q34bJaU4vntV7HgtjFqJ8szMTNmSC4XR09NTa6kKdUVHR6Nhw4YAcu9EnjBhAszNzeHr64s6derAwMAAixYtQnp6eqFtrV27FseOHcO0adPQqlUrmJiY4Pz58/jmm2+Qnp6uNFEulUrxww8/oH379tDW1kZiYiKA3MTh/v37ERMTg1q1asnq16hRQ7B/3prcNWsWPpHll3HRdHwTEhIQFBQkWzJFvs28funq6ir0t7jJt5+QkAAAGDRokGj9V69eKW1LnWtY2FjGxsYq9M3Y2Fh2B76q+vXrh6CgIOzbtw+2traQSCSyOZLn3bt3ABTHIu913vbY2Fg0aNBA4Rjm5uay/09MTER2djZWrlyJlStXKtQtaPzEmJqaCl7nzY+8mIqNjYWenp7CeObvkyZ0dXWRlZWFhg0bqvVkYlJPamoqIiMjOc6loCKPNeO1dJTEHHoY+6bwSkrkX/KromG8Fq/o/xX8jBR5YnPrt9vqtVEZKItBsbEqi3hVds2Kuy+MV/VcuSO8LvLXQ/665d8eo2YsU/GTv16/hiu/JkWJtcLmiSZ1K3Ks5intc1MnJpVdl+fXNf8uWFkoG7viGP+SElvAe4MY+f5VhngtjFqJ8po1a+LRo0cq1X306JFKCWBNPHr0CDExMbKE6u3btxEdHY3t27ejWbNmsnpJSUmwsrIqtL2wsDCMGDEC3t7esjJlD0rM7/r164iNjUVsbCycnJwUtv/www+yJVkACO6AByC7G/j169cq9bM4VKtWDR4eHqJL4uQlk83MzJCVlYW4uDi1k+X6+voKD3HNS/bKkx+PatWqAchdHkZsPOrVq6f0uJpeQzGWlpYKD79MTk5W6Y8u+bVq1Qr169fH/v37MWvWLNE6eXMgPj5eUJ53/LwxEeuT/H4mJibQ0tLClClTRJfvUbZsjaYsLS2RmZmpsB6//LmoK29eGBoalunPbSoLjnPpqYhjzXgtXeVlnMtDH0paeRnr4lQ28areP9bE+8UEnTzl109xrMpmHotfs5LqC+NVVcLrothuQdsZh2WtsOtVcF11FDZPNK1bMWM1T+mfm+oxWVHHvDSo83mrfhslpTi+e1XseC2MWolyV1dXnDp1ClOmTCkwgRoXF4dTp04J1rQuLunp6fjyyy+hr6+PYcOGAQDS0tIAQHC3+61bt/Dy5UvB0hHK7sJOT08X7JudnY0zZ84U2peQkBAYGRnh66+/hra2tmDbV199hZCQEEGiXJ6DgwMMDQ3x/fffo3Xr1oUeTx3KztXFxQWPHj1CixYtBEuL5NehQwdoaWnh+++/FySeVWnfysoK0dHRguVOrly5olKf88YjOjoaPXr0UGmfPJpeQzGtW7fGxYsX4efnJxujsLAwjdqaPHkyLly4oHTJIhsbG5ibmyMsLExwzj/++CP09PRk86J169Y4cuSIICl97do12a8SgNw3OHt7ezx9+hR2dnYa9VcdrVq1AgCcP39etka5VCrFxYsXS/zYRERERERERERExUmtRPnkyZNx+vRpjBs3DitWrECbNm0U6ty5c0e25MmkSZOK1DmpVIrbt28DyF1g/q+//kJwcDBevHiBVatWye4wtre3h5GREZYtWwZvb2/ExMQgICBAsOwJkLsWdVZWFoKCguDg4ABjY2M0atQIrq6uOHbsGJo0aYLq1avj8OHDhS5rkp6ejnPnzqFnz56yh0jmN2TIEKxYsQJPnz5Fo0aNRNswMTHBtGnTsG7dOuTk5KBbt26QSqW4ceMG+vXrV6Rkp7JznTlzJoYOHYqJEydi+PDhsLCwwJs3b3Dz5k20a9cOnp6esLGxwciRI7F582a8e/cOLi4uSEtLw6VLlzBjxgzUqlVLafs9e/bEli1b8O9//xvDhw/Ho0eP8N1336nUZ1NTU8ycORNr165FdHQ02rdvDx0dHbx48QLnz59HQECA0p9+aHINlfH29sbQoUMxbdo0jBo1ClFRUdi9e7faS68AwNChQwtc611HRwc+Pj5Yvnw5zM3N4eHhgdu3b2Pnzp0YN26c7C7wcePG4fDhw5g8eTImT56MxMREBAQECNYoB4AFCxZg3Lhx+Pzzz9GvXz+YmpoiOjoaV69exeDBg+Hs7Kz2OShja2uLHj16YPny5UhNTUWdOnVw9OhRpKWlKfxagIiIiIiIiIiIqDxTK1Fev359bNq0CXPmzMHIkSNRv359NG3aFFWrVsX79+/x6NEjPH/+HFWqVMGGDRtgbW1dpM6lpaVhxIgRAHLvlq1Xrx5cXFwQGBiIxo0by+pZWFhg8+bNWLNmDXx8fNCwYUMsW7YMu3btErTXpUsXjB49Gjt27EBcXBycnJxw4MABLF68GEuXLsWXX34JQ0NDDBo0CD169MCiRYuU9u3SpUtISkqS3Ukrz9PTE2vWrEFISIjSZTeA3D8+mJubY9++fTh+/DiqVq0KBweHIq8PruxcGzRogGPHjmHTpk1YtmwZUlJSYGlpCScnJ0gkEtn+S5YsQb169XDs2DEEBQXBzMwMTk5OsrvElbXfpEkTrFq1Cl9//TV8fHzg6OiIdevWYcCAASr1e8KECahVqxb27t2LgwcPQldXF9bW1ujcuXOB6+Nrcg2VadGiBTZv3ox169Zh+vTpsLW1xcaNGzFx4kS121KFl5cXdHV1sW/fPhw5cgSWlpaYPn06pk6dKqtTs2ZN7Ny5E8uXL8esWbNgbW2NJUuWYOPGjYK22rZti8OHDyMgIAB+fn7IzMyElZUVOnToILrGeVF99dVX8Pf3x5o1a6Cvr49BgwbB1tYWhw4dKvZjERERERERERERlRStnJycHHV3ioqKws6dO3Hp0iXExMTIymvWrInOnTtj8uTJqF+/frF2lIg+DmPGjIG2tjYOHDig9r737t1DTk4OMjMz0bx580q7JlZpSElJQUREBMe5FFTUsWa8lp6SmEOHw55rvO/o3kW7EaI8Y7wWrzO/vhAt1xNf/Q89XRX//fDTVfE2sqXibSQkK/7TxkDJvRYmhkX/BdybRPF/SlWrKt62snNXdj5i+nQU/3eW2FiJjWlJU3bNirsvjFf1nLsmvC49XITXQ/665b9eP8jFsvx81ZO7/S4j80NcJMs95qm6XGxkZn+oq6st3JaRJYwvPV3hdmm+fmRLhXXl+1hFX3m8Z2UL9zUsoK680vohrfz1OntFeE3yrwQrX1cdhc0TTepW1FgFcuM1IyOj1M9NPiYB5Z8j/T3Er8uJC4rfBZPTxD/TTI3EJ3pymmKZgZLbcbOyxcszsxWPaVZVW6QmoCNerBZtNdro7SY+dmLjr6vGd5uSFHZF/DNYW8l7lXz/KnK8qkqtO8rz1KtXD8uWLQOQ+5DDvPWojY2Ni7VzRFS+nT17Fq9evULTpk2RmpqK0NBQ/Pe//8XWrVvLumtEREREREREREQq0yhRnp+xsTET5ESVlJGREU6dOoXIyEhkZmaiUaNGWLt2Lbp3717WXSMiIiIiIiIiIlJZkRPlRFR5ubu7w93dvay7QUREREREREREVCRMlBMREREV0ce25ndFXmecyj9l63WKrW2tbM1VZet0q9puQf0oisLW9lTWF3XOR5myWI9cTHnpBwnJrx8tv760/Frb+bfLr0GuK7eksPy+hgZa+f6/4H7p6ihf5Luvu+qfVeqsrZ2nJNbi1aQfmuilZO3kolKnvyV1blS4vu6qP9NDmapVFGNPrAwoH+/rKSkp+O12nOi20u6f2PiXF8rWVSfVFcNS+EREREREREREREREHy8myomIiIiIiIiIiIioUmOinIiIiIiIiIiIiIgqtXKVKA8ICIBEIoFEIkGzZs3g6OiI/v37w9/fH0+ePNGozRs3bmDbtm3F1seoqChZH3/55ReF7UePHpVtp+JV3NeyKBITEyGRSHD8+PEC6yUkJOCrr75Cz549YWdnBxcXF4waNQr79u2T1YmKikJAQABiYmKKtY/labyIiIiIiIiIiIjKs3KVKAeAKlWqIDg4GN9++y22bNmCwYMH4+rVqxgwYABOnTqldns3b97E9u3bi72fRkZG+OGHHxTKQ0NDi+1BICRUUteypGRlZWHcuHE4d+4cPv30U+zatQuLFi1CixYtcPHiRVm9ly9fIjAwEK9fvy7W439s40VERERERERERFRWdAuvUrq0tbVhb28ve+3m5obRo0fD29sbCxcuRNu2bVG/ftk/xbVbt244d+4cli1bBgOD3Ed5v379Gr///js8PT1x+vTpMu4hlbWbN2/i4cOHOHjwIJycnGTl/fr1g1Qq1ajNtLQ0VKlSpbi6SERERERERERERCiHd5SLMTAwwOLFi5GZmYljx47Jyk+ePIlRo0ahffv2cHJygpeXF+7evSvbHhAQgMDAQKSkpMiWQ/Hy8gIAPHnyBLNnz4aHhwfatGmDvn37Ys+ePSonMDt16gQtLS1cvnxZVvbDDz/A2toaLVu2VKi/bt069O/fHw4ODnB3d8ecOXMU7iD28vLClClTEBYWhl69esHBwQFjx47F8+fP1W4rJycHgYGBcHNzg4ODA2bOnImrV69CIpHgxo0bgnq7d+9Gr1690KpVK3Tr1k2wLEjeODo4OODBgwcYMWIEWrdujUGDBuHBgwdIT0/H0qVL4eTkhE6dOinsCwDh4eEYO3Ys7O3t4ejoiLlz5yIuLk62PW85m1OnTsHf3x9OTk7o2LEjVq9ejaysLFkflF1LMeHh4Zg6dSo6duwIe3t7DBgwACdPnhTUuXHjBiQSCa5cuYK5c+fCwcEBXbp0wc6dOxXaO3r0KLp27Yo2bdpg3LhxePbsmdJj53n37h0AwNLSUmGbtra2rA9jx44FAAwdOlSwbE9e/y5duoSZM2eibdu2mDVrFoCizX0gd/5/9tlncHR0hL29Pby9vRXmWVJSEubNmwcHBwe4uLhgw4YN2LNnj6x/mZmZcHNzw8aNGxXO7/PPP8fQoUMLHSMiIiIiIiIiIqLyoNzdUa5MkyZNUKtWLYSHh8vKoqKiMHDgQFhbWyMjIwNnzpzBmDFjcPr0adjY2GDYsGGIjo5GaGgogoKCAADGxsYAcu/+trGxQf/+/VG1alVEREQgICAAKSkpmD59eqH90dfXR48ePRAaGoqePXsCyF12xdPTU7R+XFwcpkyZgpo1ayI+Ph579+6Fl5cXzpw5A13dD5chIiIC8fHxmDdvHrKzs7Fq1SrMnz8fwcHBarV14MABBAYGYtKkSejQoQOuX7+ORYsWKfRrxYoVOHbsGKZOnYo2bdrg1q1bWLduHQwMDDBq1ChZvczMTHzxxRcYP348LCwssG7dOkyfPh1t27ZFjRo1sGnTJpw/fx4rV65E69at0bZtWwC5CWsvLy94eHhg48aNSE1NxaZNm+Dj4yM4JwDYtGkTunXrhk2bNiE8PBwBAQGwtrbGqFGjCryWYv755x+0bdsWo0aNgr6+Pm7duoVFixYhJycHgwYNEtRdunQpBgwYgK1bt+Lnn3/GunXrIJFI0KlTJwDAxYsXsXjxYgwePBh9+/bFH3/8IUtYF6R58+bQ1tbGokWLMG3aNDg6OkJfX19Qp2XLlliyZAn8/f2xcuVKNGrUSKGdxYsX45NPPsHWrVtlCfaizP0XL15g5MiRsLW1xapVq6ClpYVt27Zh/PjxCAsLk/XRz88P169fx/z581G3bl0cPXoUf/zxh6xfenp6GDRoEE6ePIlZs2bJ+vb27VucP38eCxcuLHSMiIiIiIiIiIiIyoOPJlEOALVr18abN29kr/MntKVSKdzc3HD37l2cOHECc+bMgZWVFaysrBSWcwEAFxcXuLi4AMi9q9rR0RFpaWk4ePCgSolyAPD09ISPjw/ev3+PuLg43Lt3D2vXrhXcZZ5n5cqVsv/Pzs6Gg4MDOnXqhOvXr6Njx46ybUlJSTh58iTMzc0BACkpKfDz80N0dDSsrKxUauv/2Lv3uJzv/3/gj46KSlLkMEq4nFJJUknSyBzmMGefMIdYmCFWQ+Y05kwZOS7HpTGULTMmPgzbR4ZpThOKDgrVOh9+f/Tr+na5zh2vrutxv912u+16v1+v1/v1fl7v53Xl2bvXu6ioCLt27cKIESPg7+8PAOjVqxdev36N77//Xtj32bNnOHToEJYvX44xY8YAAFxdXZGbm4vt27djzJgxwuJnQUEB/P394eHhIYx3WXE9MDAQANCzZ09ER0cjOjpaWCjfuHEjunTpgpCQEGhpaQEA2rdvj8GDByMmJkY4HgB07dpVWMx3c3PD9evXcfbsWYwbN07meynJoEGDhP9fUlICJycnJCcnIzw8XKxQ3r9/f8yZMwdA6XVx8eJFnD17Vlgo37FjB7p37y6Mu7u7O/Ly8vDNN9/InIOVlRUCAgKwfv16TJ48GXp6eujatSs++OADjBs3Drq6ujAyMkLbtm0BAO3atYOtra3YOH379sXChQtFtlXm2g8JCUHDhg2xf/9+4bJB3bp1g5eXFyIiIjBhwgQ8evQI586dw9dff41hw4YJz/uDDz4QGWvUqFHYs2cPLl++LHwvIyMjoa2tLfWXRvKUlJQAAHJycirUnxRTFl/GufqpQqyr69kZzNeqkZ2dLXO/KlxDmkIVYq0J+Srvmq9LY7yrotdQdcxF3TFf1V9l8kKRvjVxDTG3VSNXgerLV6D2z00WVf2+VJasGKvC/NSFJuSrPHWqUF5SUiIstgKly0ds2rQJsbGxIkt5xMfHyx0rLy8PoaGhiIyMxMuXL1FQUCDc9++//6JBgwZyx+jZsycaNGiAX375BYmJiejcuTOsra0lFspjYmKwY8cOPHz4EFlZWSJzLV8o79Chg7BIDkBYRC1fKJc3VlJSElJTU9G3b1+ROXh5eYkUyq9evQqgtFBctsQJUFos3717N16+fIkWLVoAKF0qpOwXC0BpEbisbRkdHR20atUKSUlJAEoT6+bNm1i0aBGKiopE+jZr1gx37twRKZSXjwMA2NjY4Nq1a2KxVMTbt28RHByM8+fPIzk5WXh8U1NTsbblj6ulpQUbGxvhORQVFeGvv/4SK1R7e3vLLZQDwKRJkzBw4EBcuHABN27cwG+//YZVq1bh559/RlhYmPAXEbL06dNHbFtlrv0rV65g4MCB0NHREb7vJiYm6NSpE+7evQsAuHPnDoDSa6aMtrY2PD09sX//fuG21q1bo0ePHjh+/LjwvTxx4gS8vb1l3vEvS9mcFDkXqjzGuebUZqwdHR2rZVzma3niy2wpKi4uTqF2jHPNYb5WlSYSt0q/5iW3V26MqphH5UmPc83PRd0xXytK8XyrLZXJc2X6Vm2cKz4PdVfbP8dUV74CtX9upVTlO7c68Tu0ptT2NV2d+SpPnSqUJyUlCQu0WVlZmDJlCszMzBAQEIDmzZujXr16WLJkCfLy8uSOtX79ekRERGDWrFno0qULjI2Ncf78eezYsQN5eXkKFcp1dHTwwQcf4MyZM0hMTMRHH30ksd3t27fh5+cHLy8vTJ8+HY0bN4aWlhZGjx4tNlcTExOR13p6egAgbKfIWKmpqQAgUnAHgMaNG4u8fv36NUpKStCzZ0+J8y5fKDcwMBBZNqRsXsbGxmLzLZtHRkYGioqKsGbNGpG74MuPX56ksfLz8yXOTZ6AgADExsZi1qxZaNu2LYyMjHD06FH89NNPYm0lHTczMxMAkJ6ejsLCQrFYmpubKzwXCwsLjBkzBmPGjEFBQQGCgoJw4sQJ/PrrryKFaGnefd8qe+2/fv0aYWFhwiVZyit7X1NTU6GnpycWm3fjAACjR49GQEAA0tPTkZKSgnv37iEgIEDuPKTR1dVFYWEhrKysYGhoWOFxSLacnBzEx8czzjVAnWPNfP0/91NfyW8kRceOHWXuV+drSNWoc6xrI1+T/pcmcbu0a/6/tyS3V2YMZcZVZgxFybuGanIu6o75WjlX/lQ832qLMnnx7vko0rc6rqGKzEPdqXOullGFc1P2+6W6vnOrS05ODtLuS75zXBXmpy40IV/lqTOF8ocPHyI5OVm4bMatW7eQlJSE0NBQdOjQQdguMzNTeOe1LNHR0RgzZgx8fX2F2yTdCS7PoEGDMGHCBADAwIEDJbb55ZdfYGRkhC1btgjvIE5MTFT6WIqOVfbwyPT0dJHt5e88BoCGDRtCS0sLR44cERZIy7O2tq7QHMsYGxtDS0sLM2bMwPvvvy+2v1GjRpUaX5q8vDxcvHgRAQEBIg+wPHLkiNJjmZmZQVdXVyyW5ZcAUoaenh4mT56MEydO4PHjxwoVysv/FQVQ+Wu/YcOG8PDwwPjx48X2lf2CyMLCAgUFBcjMzBQplr8bB6D0LxJWrlyJ06dPIyEhAa1atUKPHj3kzkOasvM1NDSs1T+30RSMc81Rx1gzX6uGorFjnGuOOsa6dvJV8j/CpR9f8X+0K3cOys6j8qTHuebnou6YrxWl+oXyyuS5Mn2rNs4Vn4e6U8dcLaMa56Yq37nVSXKhXHXmpz5U45quHXWiUJ6Xl4eVK1dCX18fo0aNAgDk5uYCgEiB9+bNm0hMTES7du2E26TdlZyXlyfSt6ioCGfOnFF6bg4ODhg8eDAaN24stUiZm5sLPT09kYJnZGSk0sdSdCxLS0tYWFjg/PnzIgXqX375RaRd2VIqb968EVumpSrUr18f9vb2+OeffySuva0sRe8wz8/PR3Fxscj7m5WVhQsXLih9TB0dHXTq1Annzp3D5MmThdvPnj0rt++bN29gZGQk8rBW4P/+hKXsFxrv/tWAPJW99l1cXPDw4UN06tQJOjo6Eo/RpUsXAMD58+eFa5QXFxfj119/FWurr6+PoUOHIiIiAq9evcLkyZPFivtERERERERERESqTOUK5cXFxbh16xaA0gX5Hzx4gPDwcDx//hxr165Fy5YtAQD29vaoX78+li9fDl9fXyQnJyM4OBhNmzYVGc/GxgaFhYUICwuDg4MDjIyM0KZNG7i6uiIiIgJt27ZFo0aNcOTIkQot86GlpYX169fLbOPm5oawsDCsXLkS/fr1Q2xsLE6dOqX0sRQdS0dHB76+vvjqq69gbm4OZ2dnXL9+Hb/99hsACO9Et7a2xoQJE7Bo0SJMnToVdnZ2KCgoQHx8PK5fv67QGtzyLFq0CJMmTcJnn32GQYMGwcTEBElJSbh69SpGjBgBZ2dnhceS9l6+y9jYGLa2tti9e7fwjvBdu3bByMhI4h3R8sycORN+fn4IDAzEwIED8ddffyn0/l27dg0bNmzA8OHD0bVrV+jq6iIuLg6hoaFo3rw5+vXrB6D0z7R0dHRw/Phx6OrqQkdHR+YvFip77X/66acYOXIkpk6ditGjR8Pc3ByvXr3CjRs30L17dwwePBjt2rVDv379sGrVKuTk5KB58+Y4duwYcnNzJRbBR48ejbCwMOjo6GDEiBFKRpiIiIiIiIiIiKh2qVyhPDc3F2PGjAFQekdyy5Yt4eLigpCQENjY2AjbmZubY+vWrVi3bh38/PxgZWWF5cuXY8+ePSLjeXp6Yvz48di1axfS0tLg5OSEgwcPYunSpVi2bBlWrlwJQ0NDDB8+HP369cOSJUuq/Jw8PDzg7++PQ4cO4cSJE+jWrRtCQ0Ph7e1dbWP5+PggIyMDR44cwcGDB+Hi4oKFCxdi3rx5IktpLFmyBNbW1ggPD8f27dvRoEEDWFtbY8CAAZU+bwDo1q0bjhw5guDgYAQGBqKgoACWlpbo2bMnWrdurdRY0t5LSTZu3IigoCAEBATA1NQUPj4+yM7Oxr59+5Q+By8vLyxfvhw7d+7EmTNnYGdnhy1btgj/ukEaOzs7eHt74/z58wgLC0NeXh4sLS0xZMgQ+Pr6Ch92aWZmhqCgIOzZswenT59GYWEh7t+/L3Xcyl77rVu3RkREBLZs2YLly5cjOzsbFhYWcHJygkAgEPb/6quvsGLFCqxbtw76+voYPnw42rVrh8OHD4vNqW3btrCyskKrVq3ECvZERERERERERESqTqukpKSktidBNWPLli3Yv38/rl+/DgMDg9qeDtVBEyZMgLa2ttgvKJ49e4b+/ftj69atFfoFUJk7d+6gpKQEBQUF6Nixo8auiVUTsrOzERcXxzjXAHWNNfNV1JHoZxXuO35AK5n71fUaUkXqGuvaytczl59L3D7I/T2J23++Kt6+qFjy2B/0kjyGouMCQH9XxcdQlLxrqCbnou6Yr5Vz7jfJ16Ii3q0gVNeqi/1cFM+Ld89Hkb7VcQ1VZB7qTl1zFSjN1/z8fJU4N2W/X6S1V2aMmpSdnS31AaSqMD91oc75qiiVu6Ocqsbjx49x+vRpODg4QE9PDzdu3MDevXsxbtw4FslJIWfPnsXLly/Rvn175OTkICoqCn/88Qe2b98ubPP69Ws8efIE27dvR/PmzRV6OCkREREREREREZGqYaFcTRkYGCA2NhZHjx7Fv//+i6ZNm2Lq1KmYM2dObU+N6oj69evj1KlTiI+PR0FBAdq0aYP169eLPCD2119/xRdffIHWrVtj/fr1Yg8uJSIiIiIiIiIiqgtY1VJTLVq0wIEDB2p7GlSHubu7w93dXWabESNG8OGdRERERERERERU57FQTkRERFRJ8tYZJ9JE2lLWLVZmXVQdbeXGkLROqSqtXapKcyHNUpk1yd99VoC0vCwja11uWfMolvJMAml9K7qGeVWuG15da5JzrXP1Jen7S9p3Q/QVyfkywK3y33WS2ivz/VydVGUepJnkfMUREREREREREREREak3FsqJiIiIiIiIiIiISKOxUE5EREREREREREREGo2FciIiIiIiIiIiIiLSaBpbKA8ODoZAIIBAIECHDh3g6OiIIUOGYMWKFXj8+HGFxrx+/Tp27txZxTMFHj9+jAULFsDNzQ1dunTB+++/j3Xr1iEjI6PKj1Xm+vXrEAgEuHPnjnCbQCDA3r17ha9PnDiByMjIaptDVfvll18gEAiQkJAgt+27Mffy8sLatWvx5s2b6p9oBSUkJCA4OBjJycki2yW9l0RERERERERERPR/NLZQDgAGBgYIDw/Hd999h23btmHEiBG4evUqhg4dilOnTik93o0bNxAaGlqlc/z9998xcuRIPHr0CIsWLcK+ffswceJE/PDDDxg7dizS09Or9HiyhIeHY8iQIcLXP/zwA6Kiomrs+DVFUswnTZqEU6dOYcyYMUhNTa3tKUqUmJiIkJAQpKSkiGzv3LkzwsPDYWNjU0szIyIiIiIiIiIiUm26tT2B2qStrQ17e3vhazc3N4wfPx6+vr5YvHgxunXrhvfee6/W5pebm4v58+ejefPmOHr0KOrXrw8A6NGjB9zc3DBs2DCsWrUKmzZtqpH5lI+VulIk5itWrEBwcHCNzcfAwKBSYxgZGWnEe0dERERERERERFRRGn1HuST16tXD0qVLUVBQgIiICOH2kydPYty4cejRowecnJzg4+OD27dvC/cHBwcjJCQE2dnZwiVdfHx8AJQu4zFv3jx4eHjAzs4OAwcOxL59+1BcXCxzLtHR0UhJScHMmTOFBdsyNjY2GDp0KH766SfhUhsnTpyAQCAQu8t86NChCAgIEL6OjY3FzJkz0atXL9jb22Po0KE4efKk3NiUX3rFx8cHN27cwMWLF4XnGxwcjIMHD8LOzg5ZWVkifR8/fgyBQICYmBip48uLMVAaZwcHB9y/fx/jxo2DnZ0dBg8ejMuXL4u0KygowOrVq9GjRw84Ojriiy++wL///iv3HBWJ+blz55CYmAjg/5Y1iYmJwezZs2Fvb49evXpJXILn8ePH+OSTT+Do6Ah7e3v4+vri2bNnIm0EAgF27dqF9evXw83NDS4uLgDkv2fXr1/HxIkTAQAjR44Uvifl51h+6ZW8vDysWbMGvXr1gq2trfC8ygsICMDgwYNx/fp1DBs2DPb29hg5ciTu3r0r0u7777/HoEGD0LVrVzg7O2PcuHFi7xsREREREREREZEq0+g7yqVp27YtmjZtitjYWOG2hIQEDBs2DK1atUJ+fj7OnDmDCRMm4PTp07C2tsaoUaOQlJSEqKgohIWFASi9kxcAUlJSYG1tjSFDhqBBgwaIi4tDcHAwsrOzMXv2bKnzuHHjBgDA09NT4v6+ffsiIiICv//+OwYPHqzw+b148QLdunXDuHHjoK+vj5s3b2LJkiUoKSnB8OHDFRpj2bJlWLhwIQwMDPD5558DACwtLWFoaIj169cjKioKY8eOFbb//vvv0bRpU/Tq1UvqmPJiXKagoAD+/v6YOHEi/Pz8sHv3bnz66ae4cOECGjVqBADYtGkTjh49ijlz5qBTp044c+YMNm7cKPe8FI35//73P7Ro0UK4fenSpRg0aBCCg4Nx9epVbN68GQ0bNsS4ceMAAM+fP8fYsWPRrl07rF27FlpaWti5cycmT56M6Oho6OvrC8c6cOAA7OzssHr1ahQWFgKQ/5517twZQUFBWLFiBdasWYM2bdrIPE9/f39cvnwZn332Gdq0aYNTp05hzpw52L59O7y8vITtUlNTsWrVKvj6+sLY2BgbN27E7Nmzce7cOejp6eH333/H4sWLMWXKFHh4eCA3Nxe3b99GZmam3FgTERERERERERGpChbKpWjWrBlevXolfF2+oF1cXAw3Nzfcvn0bP/zwA+bPnw9LS0tYWlqKLecCAC4uLsI7g0tKSuDo6Ijc3FwcOnRIZqE8OTkZJiYmwoL7u5o3bw4ASEpKUurcBg0aJPz/kpISODk5ITk5GeHh4QoXytu2bQsjIyPUr19f7Hy9vb1x/PhxYaG8sLAQp0+fxsiRI6GjoyN1THkxLlNWKPfw8AAAWFtbw8vLC5cuXcLQoUPx5s0bHDlyBNOnT8eMGTMAAO7u7vjPf/4j9qDLd1U05j179hT+wsDd3R1paWnYsWMHxowZA21tbYSEhKBhw4bYv38/6tWrBwDo1q0bvLy8EBERgQkTJgjHatiwIUJCQqClpSXcJu89MzIyQtu2bQEA7dq1g62trdRz/Pvvv/Hzzz9j+fLlwveod+/eSExMFCuUv337FocOHUK7du0AAIaGhpg4cSL+/PNPdO/eHbdv34apqanw3AGgT58+Uo+tiJKSEgBATk5OpcYh2criyzhXP1WI9bt/IVNVmK81QxWuIU2hCrFmvlZOdnZ2rR5fFa4hTaEKsWa+SlZVeShvHFn7FdlXHddQdX0G1fZnW2WoQq4C1ZevQOXPTdn3t6avB1W//lR9fnWJJuSrPCyUS1FSUiJSqHz8+DE2bdqE2NhYpKWlCbfHx8fLHSsvLw+hoaGIjIzEy5cvUVBQINz377//okGDBpWaa/l5KuLt27cIDg7G+fPnkZycjKKiIgCAqalppeZRZvTo0fjPf/6Dhw8fol27doiJiUFaWho++ugjmf0UjbG2trbwFw8A0LJlSxgYGAiL4A8ePEBubi769esn0q9///74/fffK3l2kr17LG9vb5w6dQpJSUlo3rw5rly5goEDB0JHR0d4l7iJiQk6deoktpRJ7969xd7TqnzP/ve//wEABgwYILL9gw8+wJo1a5CdnS38UGrSpImwSA5AWIwvi3WnTp3w5s0bBAQEYMiQIejWrRsMDQ2VnlN5ZfFRJLeo8hjnmlObsXZ0dKyWcZmvNYtxrjnM16rSpAaPVSouLq7GjykJ87XmMF8VVXP5KDsPFZ+H+DhNZOyXtU90/7v7KhdnecdVtXFrT21/LlZXvgLKnpt4Dkh/fyXnS/VdDzV9POXmIY065IeqUed8lYeFcimSkpJgZWUFAMjKysKUKVNgZmaGgIAANG/eHPXq1cOSJUuQl5cnd6z169cjIiICs2bNQpcuXWBsbIzz589jx44dyMvLk1oot7S0xH//+19kZWVJvMP5xYsXAICmTZsqdW4BAQGIjY3FrFmzhHeGHz16FD/99JNS40jj5OQEa2trfP/99wgMDMTx48fh5OSEVq1aSe2jTIwNDAxElioBAD09PWG71NRUAEDjxo1F2pibm8ude9OmTZGRkSE35paWliLbzczMJB4rNTUVzZs3x+vXrxEWFiZclufduZf37ryBqn3P3r59Cz09PbEiu7m5OUpKSpCZmSkslJuYmEica1msXVxcsG7dOhw4cABTp05FvXr14O3tjS+++KLCv3jR1dVFYWEhrKysKl10J+lycnIQHx/PONcAdY4187VmqPM1pGrUOda1ka8pN9PkN6piHTt2rPFjlqfO15CqUedYV0e+Xvmz5vJRVh4qM493x3m3b/n9sva9u79sX1VcQ/KOW1HVNW5tUOdcLaPMuf33lngOSHt/U2Ml50t1XQ+S5ladx1N2HtLU5fxQNZqQr/KwUC7Bw4cPkZycLFyG5NatW0hKSkJoaCg6dOggbJeZmSlWMJUkOjoaY8aMga+vr3CbrIdalnFycsL333+PmJgYkaU3yly8eBHa2trC37SULelR/o51AMjIyBD+f15eHi5evIiAgADhw0YB4MiRI3Lno4xRo0Zhz549+PjjjxETE4PVq1fLbF/ZGJdnYWEBAEhLSxP5JUL5pXSk6dGjB44fPy4z5lpaWujevbvI9ncfoFp2rLK5NGzYEB4eHhg/frzYmO/+ouTdu8mr+j1r2LAhCgoK8PbtWzRs2FBkzlpaWjA2NlZqvKFDh2Lo0KFIT0/H+fPnsWbNGujq6uKrr76q0PzKzt/Q0LBW/9xGUzDONUcdY818rVmMc81Rx1jXTr7WfKFcVd43dbyGVJU6xrp68rXm8lH2nBWfh/g4aTL2y9onuv/dfZWLs7zjVlR1jVt71DFXyyh3buI5IL2v5HypvjjW9PGUm4c06npd1SZ1zld5tGt7AqomLy8PK1euhL6+PkaNGgUAyM3NBSB65+/NmzeRmJgo0ldPTw/5+fkSxyzft6ioCGfOnJE7lwEDBqBp06bYsWOH2PpAT548wQ8//IDevXujWbNmAP7vzvJ//vlH2O7x48d4+fKl8HV+fj6Ki4tF5pOVlYULFy7Inc+7yt/F/a7hw4cjMzMT/v7+MDAwEFvm412KxlgR7du3h4GBAc6dOyey/eeff5bbd8CAAWjSpInMmPfv31+4VnmZd4919uxZNGnSRFjkd3FxwcOHD9GpUyfY2tqK/CfvwZuKvmfv3u0tTdkvVqKjo0W2R0dHo1OnThX+MDQzM8OoUaPg5uYmcg0SERERERERERGpOo2+o7y4uBi3bt0CULr4/4MHDxAeHo7nz59j7dq1aNmyJQDA3t4e9evXx/Lly+Hr64vk5GQEBweLLXliY2ODwsJChIWFwcHBAUZGRmjTpg1cXV0RERGBtm3bolGjRjhy5IjEgvq7DAwMsHHjRvj6+mL8+PH4+OOP0axZM/z999/YuXMnGjZsiJUrVwrb29nZoVmzZvjqq6+wYMECZGVlYdeuXSJLYBgbG8PW1ha7d++GmZkZdHV1sWvXLhgZGYndFS1PmzZtcPLkSVy4cAEWFhZo0qSJMCZmZmbw8vIS3k1vYGAgcyxFY6wIU1NTjB07Frt374aBgQE6deqEM2fO4NmzZ3L7GhgYYNOmTVJj3qxZMyxdulSs37Vr1/D111/Dzc0NV65cwalTpxAUFARt7dLfRX366acYOXIkpk6ditGjR8Pc3ByvXr3CjRs30L17dwwePFjqnBR9z6ysrKCjo4Pjx49DV1cXOjo6Eh/q2aFDB/Tv3x9r165Fbm4urK2tcfr0acTGxuKbb75RJMRC27Ztw5s3b9CjRw80btwYDx48wOXLlzF58mSlxiEiIiIiIiIiIqpNGl0oz83NxZgxYwCU/qlGy5Yt4eLigpCQENjY2AjbmZubY+vWrVi3bh38/PxgZWWF5cuXY8+ePSLjeXp6Yvz48di1axfS0tLg5OSEgwcPYunSpVi2bBlWrlwJQ0NDDB8+HP369cOSJUvkzrFs+ZXt27dj7dq1SE9PR0lJCRwcHLB9+3aR9az19PQQEhKCL7/8EnPnzkWrVq3wxRdfYO3atSJjbty4EUFBQQgICICpqSl8fHyQnZ2Nffv2KRW/6dOn49mzZ/j888+RkZGB2bNnY86cOcL9/fr1Q3R0NEaOHCl3LEVjrKgFCxagqKgIe/bsQXFxMfr164cFCxZg0aJFcvuWxfybb77B2rVrkZGRgSZNmmDIkCGYOXOmxLW3V6xYgfDwcBw9ehQNGjTA3LlzMWHCBOH+1q1bIyIiAlu2bMHy5cuRnZ0NCwsLODk5QSAQyJ2TIu+ZmZkZgoKCsGfPHpw+fRqFhYW4f/++xPHWr1+PTZs2Yffu3Xjz5g3atGmDbdu2oW/fvnLnUp6trS3CwsLw008/ISsrC5aWlpg6dSo++eQTpcYhIiIiIiIiIiKqTVolJSUltT0JUk5QUBB+/PFHHDlyBO3bt6/t6Ui1aNEixMXFITIysranUm2uX7+OiRMn4vvvv5d49zYp586dOygpKUFBQQE6duyosWti1YTs7GzExcUxzjVAXWPNfK056noNqSJ1jXVt5etP/30ucbtONS7+2N/1veobXAHqeg2pInWNdXXl67nfJOejIoqKRV/Ly+F+LtLzUNY8it85jreb6Djv9i1/HFn73t1ftq8qriF5x62o6hq3NqhrrgKl+Zqfn6/Uuf18VTwHpH13RV+RnC8D3KrnepA0N6Dmv1ulzUOa2v7uVyfqnK+K4hrlddCSJUvQtm1b+Pr6IiUlpbanI+b+/fs4efIkfvzxR0ycOLG2p0NEREREREREREQkk0YvvVJX6evr47vvvqvtaUj1ySefID09HcOGDcNHH31U29MhIiIiIiIiIiIikomFcqpyFy5cqO0p1BhnZ2ep64ATERERERERERFR3cBCORERERERVbkPeqn2mqFVsRar5DGaVHBGRNWntta4VmatbXnrqFfmHKrr/OvauFT7lPmOqa61yAHl1kqvruNJO6akbVw7m2oK1ygnIiIiIiIiIiIiIo3GQjkRERERERERERERaTQWyomIiIiIiIiIiIhIo7FQruHOnz+PKVOmoEePHujSpQv69u2LoKAgPHnyRNhGIBBg7969NTanuLg4CAQCXL9+vcaOKcnq1avRt29fmW1OnDgBgUAg/M/BwQEDBgxAYGAgbt++XaHjxsXFITg4GDk5ORXqX9PvFxERERERERERUV3HQrkG27BhA/z8/GBkZISVK1di//79mDVrFh49eoR58+bV9vTqlD179iA8PBzffPMNJk6ciAcPHmD06NHYtWuX0mPFxcUhJCSkwoVyIiIiIiIiIiIiUo5ubU+AakdMTAx2794NPz8/zJ07V7jdyckJH330EX799ddanF3d07lzZ5iZmQEAXFxcMHbsWHz++efYtGkTunXrhu7du9fyDImIiIiIiIiIiEga3lGuofbt2wdzc3P4+flJ3O/p6Smz/3fffQdvb2/hci3ffPMNiouLhfuDg4Ph4OAg1q979+4IDg4W2fbNN9/Azc0NDg4OmD17NtLS0hQ6hw0bNmDIkCFwcHCAu7s75s+fj5SUFJE2Pj4+mDFjBqKjo+Ht7Q0HBwdMnDgRz549E2mXnJyMmTNnws7ODu7u7ti9e7dCc5BGW1sbixcvhr6+Po4ePSrcfvHiRXz88cdwcXFBt27dMGrUKFy6dEm4/8SJEwgMDARQWnAXCATC5V9SUlIQGBgILy8vdO3aFf3798emTZuQn58vdvyioiKsW7cOPXv2hIODAwICApCVlSXcn52djRUrVsDb2xt2dnbCJXcyMzNFxjl//jxGjBgBBwcHdO/eHSNGjEBMTIxImxMnTmDIkCGwtbWFu7s7Nm/ejKKiokrFj4iIiIiIiIiIqCbxjnINVFhYiJs3b6J///7Q09NTuv/BgwexatUq+Pj4oE+fPoiNjUVISAgyMzPx+eefKzXWoUOHsHXrVkyZMgWurq64evUqFi9erFDftLQ0zJgxA02aNEF6ejr2798PHx8fnDlzBrq6/3dpx8XFIT09Hf7+/igqKsLatWuxcOFChIeHC9v4+fkhOTkZX375JYyNjbF79268fPlSZBxlmZqaonPnzoiNjRVuS0hIgKenJ6ZMmQJtbW1cunQJvr6+CAsLg7OzM/r06YNPPvkEO3bswJ49e2BsbAx9fX0AwOvXr2FqaorAwECYmJggPj4ewcHBSE1NxZo1a0SOffDgQXTu3Blff/01EhISsGHDBuTl5WHz5s0AgNzcXBQVFWHevHkwMzPDy5cvsXPnTvj5+eHgwYMAgGfPnmHu3LkYNGgQFixYgOLiYvz99994+/at8Dj79+/H+vXrMWnSJAQEBODx48fCQrm/v3+FY0dERERERERERFSTWCjXQG/evEF+fj6aN2+udN+ioiJs374dgwYNwpIlSwAAvXr1QkFBAfbt2wdfX180atRI4bFCQ0MxdOhQYYHd3d0daWlpOHXqlNz+5YvDRUVFcHBwQO/evXHt2jX06tVLuC8zMxMnT54ULo2SnZ2NwMBAJCUlwdLSEpcuXcLdu3fx7bffwsXFBQDg7OwMDw8PmJqaKnQu0jRr1gx//fWX8PV//vMf4f8XFxfD2dkZjx49wrFjx+Ds7AwzMzO0atUKgOhyLkDpQzrL/yKiW7duMDQ0REBAAIKCgmBoaCjcp6+vj+3bt0NHRwcAUK9ePSxZsgSzZ8+GjY0NzMzMsHz5cmH7wsJCtGzZEuPHj8eTJ09gbW2Ne/fuoaCgAEuXLoWRkRGA0venTFZWFrZt24Zp06Zh/vz5AAA3Nzfo6elh7dq1mDp1qsLXQnklJSUAwDXaq1lZfBnn6qcKsa5fv361jMt8rRmqcA1pClWINfO19mVnZ1d6DMa5+jFf6yZl8qsyuahIX1W4hjSBqsS5uvIVqP1zqypV8f1XXcdUletI3alKnKszX+VhoVyDaWlpKd3nn3/+wevXrzFgwACR7QMHDkRoaChu374NDw8PhcZKSkpCSkoK+vXrJ7Ld29tboUJ5TEwMduzYgYcPH4osKxIfHy9SKO/QoYNIwblt27bC41taWuL27dswNjYWFskBwNjYGK6urrh3755C5yJNSUmJSJyTkpKwefNmXL16FampqcIfgjt37qzQWGFhYTh27BgSEhKQl5cn3Pf8+XO0b99e+NrT01NYJAeAAQMGYPHixbhz5w5sbGwAACdPnsS3336Lp0+finw5xcfHw9raGgKBADo6OvD398fo0aPh5OQEY2NjYbvY2FhkZ2djwIABKCwsFG53dXVFbm4uHj58iB49eigTLgAQjhUfH690X1Ie41xzajPWjo6O1TIu87VmMc41h/mqKZpI3BoXF1fpMRjnmsN8VXWiOSI7v5RpW3V91SPOqq+241xd+QrU/rlVjPj3l3I5V/njVeSYdTPWdU9tx7k681UeFso1kKmpKerVq4cXL14o3bds2Y3GjRuLbC97XX5ZDnlSU1MBQKSIDQDm5uZy+96+fRt+fn7w8vLC9OnT0bhxY2hpaWH06NEiBWQAMDExEXldttxMWbuUlBSxOQDi51gRSUlJwvMpLi7GJ598gszMTHz66ado3bo1DA0NsW3bNrx8+VLuWGFhYfj6668xbdo0ODs7w8TEBHfu3MGKFSvEzvnduRsZGaFevXrCNdzPnTuHzz//HGPGjMG8efNgamqK1NRUzJo1SziWtbU1du7cidDQUMyePRva2tro1asXgoKC0Lx5c7x+/RoAMHz4cInzVeScJNHV1UVhYSGsrKxE7pKnqpWTk4P4+HjGuQaoc6yZrzVDna8hVaPOsWa+ivvvLcnPxenYsWOlx2Ccqx/ztW648qdojsjKL2XaVkVfdb6GVIkmxLkunpuk7y9lcq4qjqfMMTXhOlIFjDML5RpJV1cX3bp1w7Vr11BYWKjUOtxlS5Gkp6eLbC97AGfDhg0BlC71UVBQINKmoKBA5M5lCwsLiWO9evVK7jx++eUXGBkZYcuWLdDWLn0mbWJiosLnUV7ZGufvUvShotK8fv0ad+/ehbe3NwDg6dOnuHfvHrZv3473339f2C43N1eh8aKjo9G3b18sWLBAuO3x48cS274796ysLOTl5aFJkybCsTp27IgVK1YI29y4cUNsnN69e6N3797IysrCpUuXsGbNGgQGBiIsLEz4XoeEhMDS0lKsb8uWLRU6r3eV3YFvaGhYq39uoykY55qjjrFmvtYsxrnmqGOsma+SSP5ZT7n4SB6Dca456hhr9cpX0RyRfT7KtK26vuoRZ9WnznGum+cm/v1VvedQFd+5dTXWdY8mx1m7tidAtePjjz9Gamoqdu7cKXF/TEyMxO3W1tYwMzNDdHS0yPaffvoJenp66Nq1KwCgadOmKCgowLNnz4Rtrl27hqKiIuFrS0tLWFhY4Ny5cyJjnT17Vu78c3NzoaenJ7KsSWRkpNx+ktja2iIzMxO//fabcFtmZiauXr1aofGA0rvHv/rqKxQUFGDChAkA/u8O9vIPUE1MTBR52Gf5/fn5+SLby865PGnn/Ouvv4rEOjo6GlpaWrC1tVV6LKD0jvSBAwdi0KBBwuK8g4MDDA0NkZSUBFtbW7H/KrI+ORERERERERERUW3gHeUaysPDA9OmTUNwcDAePXqEQYMGoVGjRkhISMDx48eRmZkpca1xHR0d+Pn5YdWqVTAzM4OHhwdu3bqF3bt3Y9KkScLiaO/evVG/fn0sWbIE06dPR1JSEg4cOIB69eqJjOXr64vVq1ejcePGcHNzw5UrV3D9+nW583dzc0NYWBhWrlyJfv36ITY2VqF1zSXp3bs3OnfujIULF8Lf3x/GxsbYtWuX8AGWivjrr79gbGyM3NxcPHnyBMePH8dff/2FhQsXwsHBAQDQpk0bWFpaYuPGjSguLkZ2dja2bdsmvMu7TNka4ocPH8b7778PAwMDCAQCuLq64sCBAzh06BCsrKxw+vRpPH36VOJ88vPzMWvWLIwbNw4JCQnYsGEDvL29hWO7urpixYoV2L59OxwcHBATEyPyiwIA+O6773Dr1i24u7vDwsICCQkJOH36NNzc3ACULmnz6aefYv369UhKSkKPHj2go6OD58+f4/z58wgODtbYP9UhIiIiIiIiIqK6hYVyDVZWxD18+DC++OIL5OTkoEmTJujVqxemTp0qtZ+Pjw90dXXx7bff4ujRo7CwsMDs2bMxc+ZMYZtGjRph27Zt+PrrrzFr1ix07NgR69atg4+Pj9hYGRkZOHLkCI4ePQoXFxesWrUK06ZNkzl3Dw8P+Pv749ChQzhx4gS6deuG0NBQ4TInytDS0sI333yDZcuWISgoCCYmJvDx8cGrV69w/vx5hcYom2/9+vXRpEkTdOvWDUFBQcI77AFAX18fwcHBWLFiBebOnYtmzZrhk08+wbVr13D37l1hu06dOmHOnDmIiIjAnj170KxZM1y4cAGzZs3C69evsW3bNgClDz1dsmSJSNzL+Pj4ID09HYsWLUJ+fj769euHoKAg4f6xY8ciISEBhw4dwt69e9GrVy9s3LgRo0ePFrYRCAT49ddfsWbNGrx58wYWFhYYNGgQ5s6dK2wzZcoUNG3aFPv378ehQ4egq6uLVq1aoU+fPmJ3rBMREREREREREakqrZKSkpLangQREQDcuXMHJSUlKCgoQMeOHTV2TayakJ2djbi4OMa5BqhrrJmvNUddryFVpK6xZr5K9vPV5xK393d9r9Jj9LJvzDhXM+Zr3XDuN9Ec6eciPb+UaVsVfdX1GlI16hznO3fuID8/v06em6TvL2W+/6rieMocU52vI1XCOHONciIiIiIiIiIiIiLScCyUExEREREREREREZFGY6GciIiIiIiIiIiIiDQaH+ZJREREREQapyrWYn13jLK1PYHGlR6bSB0os864Mm2rsi+RJqrO9chV4XhEFcU7yomIiIiIiIiIiIhIo7FQTkREREREREREREQajYVyIiIiIiIiIiIiItJoLJQTERERERERERERkUZjoZyIiIiIiIiIiIiINBoL5URERERERERERESk0VgoJyIiIiIiIiIiIiKNxkI5EREREREREREREWk0FsqJiIiIiIiIiIiISKNplZSUlNT2JIiIAODmzZso+0jS09ODlpZWLc9IfZWUlKCgoIBxrgGqEGt9fX0IBIIqHZP5WnNU4RrSFKoQa+Zr3aYK15CmUIVYM1/rNlW4hjSBqsS5OvO1ts9NE6jKdaTuVCXO1ZGvitKtlaMSEUlQ9kGsp6dXyzNRf1paWtDX16/taWgEdY0187XmqOs1pIrUNdbM15qjrteQKlLXWDNfa466XkOqRp3jXJavLNxWP3W+jlQJ48w7yomIiIiIiIiIiIhIw3GNciIiIiIiIiIiIiLSaCyUExEREREREREREZFGY6GciIiIiIiIiIiIiDQaC+VEREREREREREREpNFYKCciIiIiIiIiIiIijcZCORERERERERERERFpNBbKiYiIiIiIiIiIiEijsVBORERERERERERERBqNhXIiIiIiIiIiIiIi0mgslBMRERERERERERGRRmOhnIiIiIiIiIiIiIg0GgvlRERERERERERERKTRWCgnIiIiIiIiIiIiIo3GQjkRERERERERERERaTQWyomIiIiIiIiIiIhIo7FQTkREREREREREREQajYVyIiIiIiIiIiIiItJoLJQTERERERERERERkUZjoZyIiIiIiIiIiIiINBoL5URERERERERERESk0VgoJyIiIiIiIiIiIiKNxkI5EREREREREREREWk0FsqJiIiIiIiIiIiISKOxUE5EREREREREREREGo2FciIiIiIiIiIiIiLSaCyUExEREREREREREZFGY6GciIiIiIiIiIiIiDQaC+VEREREREREREREpNFYKCciIiIiIiIiIiIijcZCORERERERERERERFpNBbKiYiIiIiIiIiIiEijsVBORERERERERERERBqNhXIiIiIiIiIiIiIi0mgslBORyrh//z7u379f29MgIgUwX4nqDuYrUd3BfCWqO5ivROqHhXIiUhn5+fnIy8vD//73P+Tl5dX2dNQa41xz1DXWzNeawzjXHHWNNfO15jDONUddY818rTmMc81Q5zjn5+cjKytLLc9N1ajzdaRKGGcWyolIRRUVFdX2FNRaWXwZ5+qnCbFW53NTBZpwDakKTYi1Op+bKtCEa0hVaEKs1fncVIEmXEOqQBPirM7npio04TpSBYwzC+VEREREREREREREpOFYKCciIiIiIiIiIiIijcZCORERERERERERERFpNBbKiYiIiIiIiIiIiEijsVBORERERERERERERBqNhXIiIiIiIiIiIiIi0mgslBMRERERERERERGRRmOhnIiIiIiIiIiIiIg0mm5tT4CIqLptCo9Xus/8MVZKtR8xN0yp9r1cPZRqr+x8qG5S9lrVlOsiaP8/lR7j6avsSvVPuhZa6TmcPR5c6TGIVMHJX5/J3D/Ms5XUfYF7Hlfq2M9epkjdp2tgLLNv7psEqft6O3aU2dfEQPa8jA21yr2ywPPrr2R3KCczp0Tmfp+BrRUeizTL2avPxbZ5u76nULuUN8USx4x9ViC2zaaJnti2//75RGJ/M4sWYtvyCsWP9fxyiNi2pj2mSRzT0NBQbFv2v1li21paNBTbZmIo+d7A7PyyvGuEk7eTAAAtTHXE2t17mS+xv5OVvtg2CaeJRg20xDdKYVhPsbY5eeKfGbI+d9+l6HVTWaLHaYLLt9IApFXLsdTVsXOSv2+P/vCT1D71GttI3P763impfRq07C51378Jf0jc3rBdf6l9pMl+ESt1X/8PJ0rc/ubf8te7Gc7cTRa+uht3V2KfkV52Uo9zN6FQ4na7VtLLo7riHw0AgLRM6d/fLcyq9r5kbzfJeXPqovSfyYb2kfy5cPaK+GdAGXeHxspNDMCBH59K3D6xjv78wjvKiYiIiIiIiIiIiEijsVBORERERERERERERBqNhXIiIiIiIiIiIiIi0mgslBMRERERERERERGRRmOhXIbg4GAIBAIIBAJ06NABjo6OGDJkCFasWIHHjyv2IKLr169j586dVTrPR48e4dNPP0Xv3r1ha2uL3r17Y8aMGYiJiVFqHIFAgL1791bp3NRBXFwcBAIBrl+/LrNd2bUiEAjQpUsX9OvXD6tWrcKbN29qZqI17Pr16xAIBLhz505tT4WIiIiIiIiIiKhSpD/WlQAABgYGCAsLAwD8+++/ePDgAcLDw3Hs2DGsXr0aQ4cOVWq8GzduYN++fZg5c2aVzO/Zs2cYNWoUBAIBAgICYGZmhsTERMTExODGjRvw8PBQeKzw8HA0b968SualqXx8fDB48GDk5eXhxo0b2LlzJ+Lj47Fnz57anhoRERERERERERFJwUK5HNra2rC3txe+dnNzw/jx4+Hr64vFixejW7dueO+992ptfsePHwcA7N+/H4aGhsLtH330EYqLi5Uaq/x5UsU0a9ZMGEdnZ2ekpKTg2LFjSElJQZMmTWp3cgoqKipCcXEx9PT0ansqRERERERERERENYJLr1RAvXr1sHTpUhQUFCAiIkK4/eTJkxg3bhx69OgBJycn+Pj44Pbt28L9wcHBCAkJQXZ2tnCJDh8fHwDA48ePMW/ePHh4eMDOzg4DBw7Evn375Ba7MzIyYGRkJFIkL6OtLfr2Pn78GLNnz0aPHj1gZ2eHDz/8EFFRUcL9kpZeuXjxIkaNGoWuXbuiZ8+eWLZsGbKzs4X7y5bfuHLlChYsWAAHBwd4enpi9+7dYvOJjY3FlClT0K1bNzg4OGDUqFG4cuWKcH9+fj42bdoET09PdOnSBR988AEiIyNlnn/ZHD/++GO4uLigW7duGDVqFC5duiTS5sSJExAIBLh37x6mTZsGe3t79O/fHydPnhQb75tvvoGbmxscHBwwe/ZspKWlyZ2DNB07dgQAvHz5UqnzfPjwIaZPnw5nZ2fY2dnB29tbLKaxsbGYOHEi7O3t4ejoiAULFojNdcOGDRgyZAgcHBzg7u6O+fPnIyUlRaSNj48PZsyYgR9++AHe3t6wtbXF33//DaA0tmPHjoWdnZ3wmr53755I/4yMDLnvPRERERERERERkSrjHeUV1LZtWzRt2hSxsbHCbQkJCRg2bBhatWqF/Px8nDlzBhMmTMDp06dhbW2NUaNGISkpCVFRUcLlXIyMjAAAKSkpsLa2xpAhQ9CgQQPExcUhODgY2dnZmD17ttR5dO7cGUeOHEFQUBDGjRsHgUAgViAHgPj4eIwZMwbNmjXD4sWLYWFhgQcPHuDFixdSx46Ojsa8efMwYsQIzJkzB6mpqdi4cSMyMjKwefNmkbbLli3D0KFDsX37dvzyyy/YsGEDBAIBevfuDQD43//+h0mTJsHe3h6rVq2CiYkJ7t69K3L8uXPn4ubNm5g1axZsbGwQExODhQsXwsTEROYSMgkJCfD09MSUKVOgra2NS5cuwdfXF2FhYXB2dhZp6+/vj9GjR+Pjjz/GsWPHEBAQAFtbW9jY2AAADh06hK1bt2LKlClwdXXF1atXsXjxYqnHlufFixfQ1tYWWdJGkfOcOXMmzM3NsXr1ahgZGeHZs2dISkoSjhEbGwsfHx94eHhg8+bNyMnJwZYtW+Dn54fw8HBhu7S0NMyYMQNNmjRBeno69u/fDx8fH5w5cwa6uv+X/nfv3kViYiLmzp0LExMTNGvWDD/++CPmz58PLy8vbNy4EXp6erh58yaSk5PRqVMnYV957z0REREREREREZGqY6G8Epo1a4ZXr14JX5cvaBcXF8PNzQ23b9/GDz/8gPnz58PS0hKWlpZiy7kAgIuLC1xcXAAAJSUlcHR0RG5uLg4dOiSzUD58+HD89ttvCA8PR3h4OBo0aICePXvio48+gpeXl7BdcHAw9PT0cPToUWFx3tXVVeq4JSUlWLduHQYOHIjVq1cLt1tYWMDX1xd+fn5o166dcHv//v0xZ84c4blcvHgRZ8+eFRZL169fj9atWyMsLAw6OjoAgF69egn7X7t2DRcuXMDevXuF293c3JCamorg4GCZhfL//Oc/wv8vLi6Gs7MzHj16hGPHjokVyidMmIAJEyYAABwcHBATE4OzZ8/Cz88PRUVFCA0NxdChQ/H5558DANzd3ZGWloZTp05JPX55xcXFKCwsRH5+Pq5fv46jR49izJgxsLCwUPg809PTkZCQgMWLF6Nv374AgJ49e4ocZ+PGjejSpQtCQkKgpaUFAGjfvj0GDx6MmJgYYbzWrFkj7FNUVAQHBwf07t0b165dE4n/27dv8f3336NZs2YASt//r7/+Gm5ubti+fbuwnaT3Qd57r6ySkhIAQE5OToX6V5XyfzmhCqp6PmXxre04a4LqjLWi10X9+vWr/NiA6uRrXVHRPGa+1hxViLU65KuqfYfWdYynZMxXyXi9aKbKvu81ed3U1jVaXfkK8Gc0qn4VyZuK9KnK79bK5Hp15qs8LJRXQklJibBICZQubbJp0ybExsaKLIERHx8vd6y8vDyEhoYiMjISL1++REFBgXDfv//+iwYNGkjsp6Ojg40bN2LmzJn49ddf8ccff+DKlSs4f/48/Pz8MHfuXAClBVpvb29hkVyeJ0+eIDExEV988QUKCwuF23v06AFtbW3cvXtXpFBevuiqpaUFGxsb4R3QOTk5+PPPPzF//nxhkfxdV65cgampKXr27ClyPFdXV3z55ZcoKiqS2jcpKQmbN2/G1atXkZqaKvzBsnPnzmJty8+zfv36aN68uXCeSUlJSElJQb9+/UT6eHt7K1wo37BhAzZs2CB87ejoiCVLlih1no0aNUKLFi2wadMmvH37Fi4uLrC0tBS2zcnJwc2bN7Fo0SIUFRUJt1tZWaFZs2a4c+eOsKAdExODHTt24OHDh8jKyhK2jY+PF4lF+/bthUVyAPjnn3+QlJQk/IWBLLLe+4ooi4sieaO4xkr3iIuLq8LjV151zadq40yyKBZr5a5VRa8LR0dHpcZVVPXkqySNqnn8mlHZPGa+1pzajHXdyFcLmXtlX+umVXB8zaJqP5OoGs3OV/FnIEm+XurGs5Ko4pT7nFD0uqksydddbX2mVVe+AtX5OST7+5Y0h/S8kX6NSO8j/Tuh7FpW7po2V/L48lVnvsrDQnklJCUlwcrKCgCQlZWFKVOmwMzMDAEBAWjevDnq1auHJUuWIC8vT+5Y69evR0REBGbNmoUuXbrA2NgY58+fx44dO5CXlye1UF6mXbt2aNeuHXx9fZGeno6pU6di165dmDRpEkxNTfHmzRulHib5+vVrAMCsWbMk7i+/5jYAGBsbi7zW09NDZmYmgNI1rIuLi2Ue//Xr13jz5o3E4jYApKamihSLyxQXF+OTTz5BZmYmPv30U7Ru3RqGhobYtm2b2BylzTM/P194DAAwMzMTaWNuLjnpJZk4cSI+/PBD5OTk4PTp04iIiMDWrVuxYMECpc5z79692Lx5M1asWIHs7Gx07twZgYGBcHJyQkZGBoqKirBmzRqRO8bLlJ337du34efnBy8vL0yfPh2NGzeGlpYWRo8eLXZNvnuOb968AQCFrhlZ731F6OrqorCwEFZWVhLX3q+ImEcp8hu9o2x9ecXdVfoYylB+PrLl5OQgPj6+SuNMkikTa2Wv1aq+LpRVHfkqyanbFf/lmyqp6PvFfK056hzrqszX59dfydwv81qPFf/5jGSr7c96VcV8BS7fEn+WkqTrRVI7Ui/KfE4oet1UlrTrTh0/06rrc+jxFdnft6Q5pOVNwg3p14i0PpdjpX8nWFlZKf3d+ldyqlLHV3UslFfQw4cPkZycjOHDhwMAbt26haSkJISGhqJDhw7CdpmZmRILvO+Kjo7GmDFj4OvrK9wWExNTobmZmZlhxIgRWLVqFZ4+fQpTU1OYmpqKPcRRFlNTUwBAUFAQunbtKrZfmaK7sbExtLW1ZR6/YcOGMDMzw65duyTuf7d4Xebp06e4d+8etm/fjvfff1+4PTc3V+H5lSlbHiU9PV1ke/nldeSxtLSEra0tgNK771+9eoX9+/dj/PjxaNasmcLnaW1tjW3btqGgoACxsbHYtGkTZs6ciUuXLsHY2BhaWlqYMWOGyDmXadSo9M7LX375BUZGRtiyZYtw3frExESJxy3/lxHA/73/ylwzVaVsLoaGhrX65za1eWxJqms+tR1nTVIdsa7t905V8rWuqGyMGOeao46xrsl8VbfY1TbGUzbNzlfxYofk9iyUqzvlckDR66ayJF936pavgHp+DpFqqcj1Jb2P9O+EsuJ4VVzTdTUnxJ/6SHLl5eVh5cqV0NfXx6hRowD8X2FWT09P2O7mzZtihcnydzC/O2b5vkVFRThz5ozcuUgr4pb9mUTZncIuLi44e/asyPIbsrRp0waWlpZ4/vw5bG1txf5r2rSpQuMApclhb2+PU6dOiSwVUp6rqyvS09Ohp6cn8Xj6+voS+5XdGV0+domJiSIPWVWUpaUlLCwscO7cOZHtZ8+eVXqsMosWLUJxcTH27t0LQPnz1NPTQ48ePeDr64usrCykpKQI4/nPP/9IHKNly5YASq9JPT09kSJ4ZGSkQvMue/9PnDhR4XMnIiIiIiIiIiKqK3hHuRzFxcW4desWgNKF6B88eIDw8HA8f/4ca9euFRYl7e3tUb9+fSxfvhy+vr5ITk5GcHCwWEHZxsYGhYWFCAsLg4ODA4yMjNCmTRu4uroiIiICbdu2RaNGjXDkyBGJBfV3ffPNN4iLi8PgwYPRtm1b5OXl4cqVKzhy5Ajef/99tGjRAkDpg0YvXryI8ePHY9q0abCwsMDjx4+Rk5OD6dOni42rpaWFgIAA+Pv7Izs7G3369IGhoSFevHiBmJgYzJs3D9bW1grHccGCBZg8eTImT56M8ePHo2HDhvjrr7/QqFEjjBw5Em5ubvD09MS0adMwbdo0CAQC5OTk4NGjR3j69KnIA0XLKyvobty4EcXFxcjOzsa2bduUuuO9jI6ODnx9fbF69Wo0btwYbm5uuHLlCq5fv670WOXnN3DgQHz//feYNWuWQuf5999/4+uvv8bAgQPx3nvvISsrC6GhoWjRogVatWoFoLQAP2nSJHz22WcYNGgQTExMkJSUhKtXr2LEiBFwdnaGm5sbwsLCsHLlSvTr1w+xsbEKr7WupaWFzz//HPPnz8ecOXMwdOhQ6Ovr49atW7C1tYWnp2eFY0JERERERERERKRqWCiXIzc3F2PGjAFQemd0y5Yt4eLigpCQENjY2AjbmZubY+vWrVi3bh38/PxgZWWF5cuXY8+ePSLjeXp6Yvz48di1axfS0tLg5OSEgwcPYunSpVi2bBlWrlwJQ0NDDB8+HP369RN5EKQkH374IfLy8nDw4EEkJydDR0cHLVq0wKJFizB+/HhhOysrK3z33XfYuHEjli9fjqKiIlhZWYks9fKuDz74ACYmJti5c6fwTuQWLVrA3d1dqXW7AaB79+44cOAAtmzZgsDAQGhra6Ndu3b47LPPhG22bduGXbt24ejRo0hMTISxsTHatWuHESNGSB1XX18fwcHBWLFiBebOnYtmzZrhk08+wbVr13D3rvJrRvv4+CAjIwNHjhzB0aNH4eLiglWrVmHatGlKj1XGz88PP/74Iw4dOoQ5c+bIPU8LCwuYm5sjNDQUycnJMDY2Rvfu3bF+/XrhA027deuGI0eOIDg4GIGBgSgoKIClpSV69uyJ1q1bAwA8PDzg7++PQ4cO4cSJE+jWrRtCQ0Ph7e2t0LwHDhwIAwMD7Ny5E/Pnz0e9evXQqVMnsYedEhERERERERER1XUslMswZ84czJkzR+H2vXv3Ru/evUW2eXh4iLzW1dXFsmXLsGzZMpHt5ubm2L59u9iYZUu7SGNvbw97e3uF5te2bVvs2LFD6v779++LbXNzc4Obm5vUPs7OzhL7ffPNN2LbunXrhgMHDkgdS19fH7Nnz8bs2bOltpGka9eu+P7770W2DRs2TOT1iBEjJBbc373DWktLS+IcJJ3ju6S1adOmDe7duyd8Le88GzdujPXr18s9nq2trdS1zstMnz5d7C8G3p3nwYMHpfbv27cv+vbtK3GfMu89ERERERERERGRKuMa5URERERERERERESk0VgoJyIiIiIiIiIiIiKNxkI5EREREREREREREWk0rlFORGpv/hiraj/Gia2Tqv0YpP5q4lqti1Z83KbSY+yLfFqp/lMWBld6DkQEONvoydwf96JI5v4Bjs2l7kt4XSyzb8funaTuG+bZSmZfeU7++qzaxiaSxtv1vQq3+/Hyc4ltC4tLxLZZW4jfX1fP0Vpi//r6WmLbLvydI7Zt1CR/sW3GBuJ9AaBIQmobGxqKbcvOE597/XqSx9SX8FGkaDylOXtVPKaVHbM61NScyo6TnZ2NuLg4dOzYEfXr16+RY9clpy5K//7QkXJrq4Nzf6l9zI0ld3rVdr7SfQAgK89D4nabJtL7SMrZUnZS+9ST8uNBi0ZSu8Cxjb3E7YPcpV/jDa9I/uzzdlO9XFXE0D7K/4wh61yzs7OVHm/iwNZK91FlvKOciIiIiIiIiIiIiDQaC+VEREREREREREREpNFYKCciIiIiIiIiIiIijaYRhfLg4GA4ODjU6hy6d++O4OCaW9+0b9++WLFihcw2169fh0AgwJ07d2poVpUzdOhQBAQEyGwTEBAAgUCA0aNHi+0rKSmBh4cHBAJBjb4XmiI4OBg3b96s7WkQEREREREREREpTSMK5SRZ586dER4eDhsbm9qeSpWqX78+/vzzTzx/LvqQhj/++ANpaWnQ19evpZmpt5CQEMTGxtb2NIiIiIiIiIiIiJTGQnkdk5ubW2VjGRkZwd7eXu2ePN2iRQt06NABP/74o8j2qKgo9OrVC/Xq1aulmREREREREREREZEqYqH8/0tKSoK/vz+cnZ3RtWtXTJgwAXfv3hVpc/LkSYwbNw49evSAk5MTfHx8cPv2bbGxfvnlFwwYMAC2trYYOXKkxDYAcPHiRYwaNQpdu3ZFz549sWzZMmRnZwv3ly2NcvHiRXz66afo1q0b5s6dCwA4f/48RowYAQcHB3Tv3h0jRoxATEyM2DEOHz4MT09PODo6ws/PD+np6WLjl196RSAQYNeuXVi3bh169uwJBwcHBAQEICsrS2b8UlJSEBgYCC8vL3Tt2hX9+/fHpk2bkJ+fL9JOIBBg9+7dCA4OhqurK5ydnREYGChy3gBw8+ZNjBgxAra2thg8eLDEc5Nl0KBBiIqKEr4uLCzE2bNnMXjwYLG2sbGxmDlzJnr16gV7e3sMHToUJ0+eFGlTFqsrV65gwYIFcHBwgKenJ3bv3q30WADw8OFDTJgwAba2tujfvz9Onz4NPz8/+Pj4iLR7/PgxPvnkEzg6OsLe3h6+vr549uyZSJuy92zz5s1wcXFB9+7dsW7dOpSUlOC3337D0KFD4eDggEmTJuHly5ciffPz87Fp0yZ4enqiS5cu+OCDDxAZGSnSJiAgAIMHD8b169cxbNgw2NvbY+TIkSL5IRAIAADr1q2DQCCAQCDA9evXxc6biIiIiIiIiIhIFenW9gRUwdu3bzF+/HjUr18fS5cuhbGxMQ4ePIhJkybh559/RuPGjQEACQkJGDZsGFq1aoX8/HycOXMGEyZMwOnTp2FtbQ0AiIuLw6efforevXsjMDAQCQkJ+Oyzz8QKxtHR0Zg3bx5GjBiBOXPmIDU1FRs3bkRGRgY2b94s0nbp0qX48MMPsX37dmhra+PZs2eYO3cuBg0ahAULFqC4uBh///033r59K9LvwoULePr0KYKCgvD69WusWbMGK1euFBv/XQcPHkTnzp3x9ddfIyEhARs2bEBeXp7Mfq9fv4apqSkCAwNhYmKC+Ph4BAcHIzU1FWvWrBFpe/jwYTg6OmLt2rWIj4/HunXr0LhxY/j7+wMAUlNTMXXqVAgEAmzZsgUZGRlYvnw5srOz0bFjR5lzLzNo0CBs2rQJjx49Qtu2bXHlyhXk5eWhb9+++PLLL0XavnjxAt26dcO4ceOgr6+PmzdvYsmSJSgpKcHw4cNF2i5btgxDhw7F9u3b8csvv2DDhg0QCATo3bu3wmPl5uZiypQpMDExwfr16wEA27dvR0ZGBlq1aiU81vPnzzF27Fi0a9cOa9euhZaWFnbu3InJkycjOjpaZAmZw4cPo0ePHli3bh3+/PNPBAcHo7i4GFeuXMEnn3wCPT09rFq1CosXL8a+ffuE/ebOnYubN29i1qxZsLGxQUxMDBYuXAgTExN4eHgI26WmpmLVqlXw9fWFsbExNm7ciNmzZ+PcuXPQ09NDeHg4xowZAx8fH+EvI9q2bavQe0VERERERERERFTbWCgHEBYWhoyMDERERAiL4i4uLvD29sbevXuxaNEiAMDs2bOFfYqLi+Hm5obbt2/jhx9+wPz58wEAu3btQrNmzbB9+3bo6OgAAOrVq4fFixcL+5aUlGDdunUYOHAgVq9eLdxuYWEBX19f+Pn5oV27dsLtffv2xcKFC4Wvo6OjUVBQgKVLl8LIyAgA4O7uLnZeJSUl2LFjh7CgmpiYiNDQUBQXF0NbW/ofE+jr64vNf8mSJZg9e7bU9cwFAgE+//xz4etu3brB0NAQAQEBCAoKgqGhoch5bty4EQDQu3dv3Lt3D2fPnhUWysPCwqClpYXdu3fD2NgYAGBpaYnJkydLnfO7WrRoAXt7e0RFReGzzz5DVFQU+vbtK3GZmUGDBgn/v6SkBE5OTkhOTkZ4eLhYobx///6YM2cOgNJr5OLFizh79qywUK7IWMePH0daWhqOHj2Kli1bAgC6dOmC/v37ixTKQ0JC0LBhQ+zfv1+4XEy3bt3g5eWFiIgITJgwQdi2SZMmwqK7u7s7Lly4gG+//RZnzpwRvmfJyclYuXIlMjIyYGJigmvXruHChQvYu3cvevXqBQBwc3NDamoqgoODRQrlb9++xaFDh4TXpaGhISZOnIg///wT3bt3h729PQCgWbNmwv+vqJKSEgBATk5OpcYh2criyzhXP1WIdXUtsaVJ+fruXz7VJFW4hjSFKsRaHfK1NvOloqpzznUxHnUB85WqWnXkKvNfNXIVqL58BWr/3EhURfKOuVpKE/JVHhbKAVy5cgXOzs5o2LAhCgsLAQDa2tpwcnISWZbk8ePH2LRpE2JjY5GWlibcHh8fL/z/P//8E3379hUWmQFgwIABIoXyJ0+eIDExEV988YXweADQo0cPaGtr4+7duyKF8j59+ojMVyAQQEdHB/7+/hg9ejScnJyEBeXynJycRO46trGxQUFBAdLS0mBhYSE1Hp6enhLnf+fOHamF8pKSEoSFheHYsWNISEhAXl6ecN/z58/Rvn174WtXV1eRvjY2Njhz5ozw9Z9//glnZ2eRc3JxcYGpqanUOUsyePBgHDhwADNnzsT58+exYcMGie3evn2L4OBgnD9/HsnJySgqKgIAiccrKygDgJaWFmxsbJCUlKTUWHfv3kX79u2FRXIAaNmyJTp06CByrCtXrmDgwIHQ0dERXicmJibo1KmT2LJA78bU2toar169Enm/rKysAJQuM2RiYoIrV67A1NQUPXv2FLkOXV1d8eWXX6KoqEh4HTRp0kTkmiy7Wzw5OVksRpVVNpfyeUXVh3GuObUZa0dHx2oZt27lq3mlesfFxVXRPCqubsRZPTBf5ZH+cyQgL19k960tlc9x6eelCp8f6oz5WlFNqnHsuqnyuSoeU+b//6ntn2OqK1+Byp6ban4v1mWy807yZx9zVZQ656s8LJSjdNmQW7duoXPnzmL7yu7wzcrKwpQpU2BmZoaAgAA0b95ceKd1+aJwamqq8K70MkZGRiIPkHz9+jUAYNasWRLn8+460u+OZ21tjZ07dyI0NBSzZ8+GtrY2evXqhaCgIDRv3lzYzsTERKRfWdG8/HwlkTb/lJQUqX3CwsLw9ddfY9q0aXB2doaJiQnu3LmDFStWiB3v3Xnp6emJLE2TmpqK1q1bix3DzMxM5rzfNWDAAHz11VfYunUr9PT0JN51D5SuwR0bG4tZs2ahbdu2MDIywtGjR/HTTz+JtX33FxJ6enrIzMxUaqyUlBSJ52JmZiYSq9evXyMsLAxhYWFibfX09EReS4qppG3A/73/r1+/xps3byRe90Dp+2BpaSl1/PJjVSVdXV0UFhbCyspK5C8RqGrl5OQgPj6eca4B6hzrupSvf75IrVR/RZf+qg7qfA2pGnWOdVXm6/Prr2Tul5Uv8vrWlsrmuKzzqs3PD3XGfK2c5P+lyW+kYSqbq5dviceU+a/euVqmMueWcEM1vxfrMll5dzlW8mcfc7WUJuSrPCyUA2jYsCHc3d2FD8osr6y4fOvWLSQlJSE0NFTkzt/MzExhMREoXVak/N3mQGmRvXxBsezu4qCgIHTt2lXsmE2aiP6GS0tLS6xN79690bt3b2RlZeHSpUtYs2YNAgMDJRZVlSVt/u/Oq7zo6Gj07dsXCxYsEG57/PhxhY4vKYYARB5Eqghzc3P07NkT3377LUaOHClWXAZKC70XL15EQECAyIM0jxw5ovS8FR2rSZMmEn9bmZ6ejgYNGghfN2zYEB4eHhg/frxY2/LtKqphw4YwMzPDrl27JO5X9hcTVaXsejc0NKzVP7fRFIxzzVHHWGtSvqrC+WlCnFWFOsa6JvO1LsauOudcF+NRlzBfK4qF8ndVPtbiMVW3a7My1DFXy6jzudVFst8LyZ99fP9EafI1zUI5SpeaOH36NGxsbKReCLm5uQBE7+S9efMmEhMTRZak6Nq1K3799VcEBgYKl62Ijo4WGatNmzawtLTE8+fPRdaZrggjIyMMHDgQt2/fRlRUVKXGKiNp/lpaWrC1tZXaJzc3V6wQHRkZWaHjd+3aFUePHkVmZqbwDu7ffvsNb968UXosHx8fGBgYYNSoURL35+fno7i4WGTuWVlZuHDhgtLHUnSsLl264OTJk3j+/Dnee+89AKUPiv37779F/rzExcUFDx8+RKdOnUSWwqkqrq6u2LNnD/T09MSWfakIPT29arnDnIiIiIiIiIiIqLppTKG8qKhIrGANlBZlJ0+ejMjISPznP//BxIkT0bx5c6Snp+PPP/9E06ZNMXnyZNjb26N+/fpYvnw5fH19kZycjODgYDRt2lRkPF9fX4wcORKzZs3CuHHjkJCQgL1794osvaKlpYWAgAD4+/sjOzsbffr0gaGhIV68eIGYmBjMmzcP1tbWUs/lu+++w61bt+Du7g4LCwskJCTg9OnTcHNzq5JY5efni8x/w4YN8Pb2lro+OVBadD1w4AAOHToEKysrnD59Gk+fPq3Q8SdNmoQjR45g+vTpmD59OjIyMhAcHKz0GuVA6Xrrnp6eUvcbGxvD1tYWu3fvhpmZGXR1dbFr1y4YGRkpfQe7omN99NFH2LlzJ2bOnCl8MGhISAjMzc1F/nrg008/xciRIzF16lSMHj0a5ubmePXqFW7cuIHu3btj8ODBSkZDlJubGzw9PTFt2jRMmzYNAoEAOTk5ePToEZ4+fSryoFlFtGnTBufPn0f37t1haGgIa2tr4cNmiYiIiIiIiIiIVJnGFMrz8vIkLq2ybt06DB06FOHh4diyZQs2bNiAN2/eoHHjxrCzs0O/fv0AlC7jsXXrVqxbtw5+fn6wsrLC8uXLsWfPHpHxOnXqhK1bt2LDhg2YPXs22rVrh82bN2Pq1Kki7T744AOYmJhg586dwjuvW7RoAXd3d5iby37gmEAgwK+//oo1a9bgzZs3sLCwwKBBgySeX0X4+PggPT0dixYtQn5+Pvr164egoCCZfWbNmoXXr19j27ZtAABvb28sWbIEM2fOVPr4TZo0we7du7Fq1SrMnTsXrVq1QlBQEDZv3lyh85Fn48aNCAoKQkBAAExNTeHj44Ps7Gzs27evWsYyMDDAvn37sGzZMvj7+6Np06bw8/PDyZMnRdZAb926NSIiIrBlyxYsX74c2dnZsLCwgJOTEwQCQZWc+7Zt27Br1y4cPXoUiYmJMDY2Rrt27TBixAilxwoKCsJXX32F6dOnIzc3FwcOHICzs3OVzJOIiIiIiIiIiKg6aZWUlJTU9iRIdQgEAixatEissE/V682bN3j//fcxefJkzJ49u7anU2vu3LmDkpISFBQUoGPHjhq7JlZNyM7ORlxcHONcA9Q11nUtX/dFVuyvnMpMGSL+kOmaoq7XkCpS11hXdb6e/PWZzP3DPFtVuG/ciyKZ+1s20pa6L+F1scy+HZtLX8pO1pwVIeu8Kjs2ScZ8rZwfLz+XuD36Tq7YtgG2BmLbXkjJt/r64s/XuvB3jti2nm3ExzQ2EO8LAEUSDmVsKN42O0+8tFG/nuQx9cUfXwVv1/cktlXU2aviMa3smOpAXXMVKM3X/Pz8Sp3bqYvSvz8KpXwl/pUg/bvS3Fjy9+SrTOnfkdL6AECWhLwCAJsm0vtIyll56knISUB6DADAQMLnDQAMcpeed2evSP7s83ZjrgLqna+K0pg7yolUya5du2Bubo4WLVogNTUV+/btQ1FRET766KPanhoREREREREREZHGYaGcqBZoa2tjx44dSE5Oho6ODuzs7BAWFoZmzZrV9tSIiIiIiIiIiIg0DgvlJOL+/fu1PQWNUPYATSIiIiIiIiIiIqp9LJQTEUng/dEcpdqfPR6sVPtN4fFKtZ8/xkqp9kSqpLLrg586/UOl5zD0w+GV6l/ZcwBqd51zoqqUmSP7EUcTVv4mdZ+ugbHUfQCQdC1U5n5z+/FS97Vq1kRm37Dvz0nd98MfrrL7Luwic7+smFRmTXciZUXGiK+/+yxNfMHgK3ckr9OrpS2+lv+Bn/8V21aYnS6xf0lxgdg2WzsXsW33kwrFtrm1k7xIcdIb8fwqKhbf1ljCOssDpaxVLG2N9ndJWncckLz2eL74qRPJpasjeZ1tAEiTsq64rvRHbuBhiuQLMStX+mLfRgb1pO4zN5I8v7RM6d97BvqStzesL/1cpTGS8uwCoGLriqvyWuTS1k8HVHve6kb66vtERERERERERERERBqAhXIiIiIiIiIiIiIi0mgslBMRERERERERERGRRmOhnIiIiIiIiIiIiIg0GgvlVOcFBwdDIBBAIBCgQ4cOcHR0xJAhQ7BixQo8fvy4QmNev34dO3furOKZlvr2228hEAjwxRdfVMv45fXt2xcrVqyo0jEDAgIwePDgKh2TiIiIiIiIiIioNrFQTmrBwMAA4eHh+O6777Bt2zaMGDECV69exdChQ3Hq1Cmlx7tx4wZCQ0OrYabA6dOnAQDnzp1Dfn5+tRyjOvn5+WHDhg21PQ0iIiIiIiIiIqIqw0I5qQVtbW3Y29vD3t4ebm5u+Pjjj3Hq1Ck4Ojpi8eLFeP78eW1PEQDw5MkT/PXXX3B1dUVGRgYuXrxY21NSWqtWrdChQ4fangYREREREREREVGVYaGc1Fa9evWwdOlSFBQUICIiQrj95MmTGDduHHr06AEnJyf4+Pjg9u3bwv3BwcEICQlBdna2cEkXHx8fAMDjx48xb948eHh4wM7ODgMHDsS+fftQXFys0JyioqKgpaWFFStWwNzcHJGRkWJtypZLOXz4MDw9PeHo6Ag/Pz+kp6cL22RnZ2PFihXw9vaGnZ0d+vbti6CgIGRmZko99oULFyAQCBAfHy+y/e3bt+jatSsOHz4MAHj48CGmT58OZ2dn2NnZwdvbG7t37xa2f3fplYyMDCxZsgTu7u6wtbWFh4cH5s2bp1A8iIiIiIiIiIiIVIFubU+AqDq1bdsWTZs2RWxsrHBbQkIChg0bhlatWiE/Px9nzpzBhAkTcPr0aVhbW2PUqFFISkpCVFQUwsLCAABGRkYAgJSUFFhbW2PIkCFo0KAB4uLiEBwcjOzsbMyePVvufKKiotC9e3e89957+OCDDxAeHo7MzEwYGxuLtLtw4QKePn2KoKAgvH79GmvWrMHKlSuxefNmAEBubi6Kioowb948mJmZ4eXLl9i5cyf8/Pxw8OBBicf28PBA06ZNcfz4cSxYsEBkTgAwZMgQAMDMmTNhbm6O1atXw8jICM+ePUNSUpLUc1qzZg0uX76MBQsWoEWLFkhNTcWlS5fkxoKIiIiIiIiIiEhVsFBOaq9Zs2Z49eqV8HX5gnZxcTHc3Nxw+/Zt/PDDD5g/fz4sLS1haWkpXM6lPBcXF7i4uAAASkpK4OjoiNzcXBw6dEhuofz27duIj4/Hxx9/DAAYPHgwDh48iLNnz2LkyJEibUtKSrBjxw7o6+sDABITExEaGori4mJoa2vDzMwMy5cvF7YvLCxEy5YtMX78eDx58gTW1tZix9fR0cGIESNw/PhxfPbZZ9DR0QEAHD9+HP369YOJiQnS09ORkJCAxYsXo2/fvgCAnj17yjyvO3fuYPDgwRg+fLhw26BBg2T2kaWkpAQAkJOTU+ExakN2dnadGr8svnUtznWRKsS6fv361TJuXc3X2lLRPFaFa0hTqEKsma+qqTq/56v7Zwh1xXwlRSiTX9XRlvmtGrkKVF++ArV/bppKk/Krps5VE/JVHhbKSe2VlJRAS0tL+Prx48fYtGkTYmNjkZaWJtz+7pIkkuTl5SE0NBSRkZF4+fIlCgoKhPv+/fdfNGjQQGrfqKgo6OnpYcCAAQAAe3t7vPfee4iMjBQrlDs5OQmL5ABgY2ODgoICpKWlwcLCAkDpEjLffvstnj59KvKhGR8fL7FQDgAjR47Ezp07cfnyZfTp0wd///03/vrrLyxcuBAA0KhRI7Ro0QKbNm3C27dv4eLiAktLS5kx6dSpE3744QdYWFjA3d0d7du3l9lensLCQuF51CVxcXFK9mhczeMrpq7FuS6rzVg7OjpWy7iK56t5tRy/rqlsHjNfa45m56siNC+n5edvxWNSXd/xmoL5+q4mVTqXuk56fonHSXJbyfFUtC3z+//U9s8x1ZWvQGXPjTlbUeqXX9KvhZo+V3XOV3lYKCe1l5SUBCsrKwBAVlYWpkyZAjMzMwQEBKB58+aoV68elixZgry8PLljrV+/HhEREZg1axa6dOkCY2NjnD9/Hjt27EBeXp7UQnlxcTF+/PFH9OjRA9ra2sjIyAAAeHl54cCBA0hOTkbTpk2F7U1MTET6lxXNy+Z47tw5fP755xgzZgzmzZsHU1NTpKamYtasWTLPo2XLlnBzc8P333+PPn364Pjx42jZsqXwrnEtLS3s3bsXmzdvxooVK5CdnY3OnTsjMDAQTk5OEsdcunQpGjZsiP3792PdunVo1qwZfH19MX78eLnxlERXVxeFhYWwsrKCoaFhhcaoDR07dlSqfcyjlGodX56cnBzEx8fXuTjXReoca0Xz9c8XqTU4K9VV0TxW52tI1ahzrKvy+/VesubltLz8rUxMqvo7XlMwXyV78Xua/EYaRFp+Jf9PPE6S2l6+JTmektpKij3zW71ztUxlzi1JwrVIilG3/LocK/1aqKlz1YR8lYeFclJrDx8+RHJysnBZkFu3biEpKQmhoaHo0KGDsF1mZqbcO6cBIDo6GmPGjIGvr69wW0xMjNx+165dQ2pqKlJTUyUWnH/88UfhkiyKiI6ORseOHbFixQrhths3bijUd9SoUfD390dycjIiIyPh4+Mjcse9tbU1tm3bhoKCAsTGxmLTpk2YOXMmLl26JPEXAcbGxli8eDEWL16M+/fv48CBA1i+fDnat2+P7t27K3xOZcrmYmhoWKt/bqOs6p5rdY1f1+Jcl6ljrOtqvtaWysaIca456hhr5mvlVGfM+H5Ujjpe05XLVxbdypMeP/E4SW4rOZ6KtlW3a7My1DFXy1Tu3JizFaV+15P0a6Gmz1Wd81Ue7dqeAFF1ycvLw8qVK6Gvr49Ro0YBKH0IJgDo6ekJ2928eROJiYkiffX09JCfny9xzPJ9i4qKcObMGblziYyMRP369fHtt9/iwIEDIv916NABkZGRSp1bbm6uyDzKjqEILy8vmJiYYMGCBXj79i1GjBghsZ2enh569OgBX19fZGVlISVF/h3QAoEAgYGBAEqXuCEiIiIiIiIiIqoLeEc5qYXi4mLcunULQOlDDh48eIDw8HA8f/4ca9euRcuWLQGUrgtev359LF++HL6+vkhOTkZwcLDIsidA6ZrghYWFCAsLg4ODA4yMjNCmTRu4uroiIiICbdu2RaNGjXDkyBGJBfXy8vLycO7cOfTv31/4INDyPvroI6xevRr//PMP2rRpo9D5urq6YsWKFdi+fTscHBwQExOD3377TaG+enp6GDZsGPbu3YtevXqhWbNmwn1///03vv76awwcOBDvvfcesrKyEBoaihYtWqBVq1YSxxs7diz69euHdu3aQUdHBydPnoSenl6F7iYnIiIiIiIiIiKqDSyUk1rIzc3FmDFjAJT+SUrLli3h4uKCkJAQ2NjYCNuZm5tj69atWLduHfz8/GBlZYXly5djz549IuN5enpi/Pjx2LVrF9LS0uDk5ISDBw9i6dKlWLZsGVauXAlDQ0MMHz4c/fr1w5IlS6TO7eLFi8jMzMSwYcMk7h88eDDWrVuHyMhIzJ07V6HzHTt2LBISEnDo0CFhwXvjxo0YPXq0Qv379euHvXv34qOPPhLZbmFhAXNzc4SGhiI5ORnGxsbo3r071q9fDx0dHYljdevWDSdPnkRCQgK0tbXRvn177Ny5UyTuREREREREREREqoyFcqrz5syZgzlz5ijcvnfv3ujdu7fINg8PD5HXurq6WLZsGZYtWyay3dzcHNu3bxcbs2xpF0m8vb1x//59qfvNzMxw9+5d4esLFy6ItXn//fdFxtDR0cHnn3+Ozz//XKTdu8eRNBYAXLp0Caampnj//fdFtjdu3Bjr16+XOlcAWLt2rcjrRYsWYdGiRTL7EBERERERERERqTIWyok0yD///IMnT57g0KFDGD9+PPT19Wt7SkRERERERERERLWOhXIiDbJs2TLcunUL7u7umDFjRm1Ph4iIiIiIiIiISCWwUE6kQQ4ePFjbUyAiIiIiIiIiIlI5LJQTkdrbFB6vdJ+zx4OVaj9p/V35jcqxa2WkVHuiuiz+VVGl+tu5fljpOURfuV2p/gPculZ6DkH7/6lE70Y4dTsJKz5uU+l5EMlz8MenMvf7DGwtc39GrvR9917ky+xr2bPif/HW0kz2P20yOnpK3ffq5WOZfU/+aiJzv7GhltR9wzxbyewrz8lfn1Xb2KR+hni8J7btx8vPxbbpdBVvBwBP0wrFtknKrYvXb0megHh3PEnNFtv26naE2Lbs/P9IHLKernh+JT5/JLatfdv2YtuevZL8eaarI75NUpwGukuO09mr4m2z80rEtkXGiLeT9B4RKSO3QPxaK/MmW0ISAsjLeiW1z/+ypH9/lhRLHk9LW3ofOxtLidt/e5wntU/HZpKXhTWqJ/37Vdq/8zu3lJDg/19hseTtg6TkOgCcvSKexwDg7aZ8H1n9ZI1XlWTNzd2hcY3MQZVp1/YEiIiIiIiIiIiIiIhqEwvlRERERERERERERKTRWCgnIiIiIiIiIiIiIo3GNcqpSgUHByMkJAQAoKWlhQYNGqB58+ZwcnLChAkTYGNjo/SY169fR2xsLGbOnFklc0xISICXl5fEfdbW1oiOjq6S45R3/fp1TJw4Ed9//z1sbW2rfPzKEggEWLRoEaZOnVrbUyEiIiIiIiIiIpLrhx9+ULrP8OHDpe6rcKE8JSUFTZo0qWh3UmMGBgYICwsDAPz777948OABwsPDcezYMaxevRpDhw5VarwbN25g3759VVYoLzN//nw4OzuLbDMwMKjSY5Tp3LkzwsPDK/SLgpoQHh6O5s2b1/Y0iIiIiIiIiIiIFPLFF1+gpKQEWlrSH/wKACUl//cQ3GoplPfp0wc9e/bEhx9+iP79+6N+/foVHYrUjLa2Nuzt7YWv3dzcMH78ePj6+mLx4sXo1q0b3nuv9p/03bp1a5F5VicjI6MaO5YycnNzYWBgoJJzIyIiIiIiIiIikub777+X2yYxMRFRUVG4dOkScnNzZbat8Brln376KVJSUhAQEAA3Nzf4+/vj0qVLKC4uruiQpMbq1auHpUuXoqCgABEREcLtJ0+exLhx49CjRw84OTnBx8cHt2/fFu4vW8olOzsbAoEAAoEAPj4+AIDHjx9j3rx58PDwgJ2dHQYOHIh9+/ZV2TUYEBCAwYMH4/r16xg2bBjs7e0xcuRI3L17V6RdZmYm/P394eDgABcXF2zatAn79u2DQCAQtrl+/ToEAgHu3Lkj3CYQCLB7924EBwfD1dUVzs7OCAwMRHZ2tsj4SUlJ8Pf3h7OzM7p27YoJEyaIzQEATpw4gSFDhsDW1hbu7u7YvHkzioqKRPYLBALExsbi448/hr29PdatWyecy969e4VtfXx8MGPGDERHR8Pb2xsODg6YOHEinj17Jja3GTNmwM7ODh4eHvj222+xevVq9O3btwIRJyIiIiIiIiIiUkznzp0l/tesWTPcunULq1atwmeffYY//vgDI0aMwJEjR2SOV+E7ymfOnImZM2fi3r17iIyMxJkzZxAVFYXGjRtj0KBBwoIdUZm2bduiadOmiI2NFW5LSEjAsGHD0KpVK+Tn5+PMmTOYMGECTp8+DWtra4waNQpJSUmIiooSLudiZGQEoHT5H2trawwZMgQNGjRAXFwcgoODkZ2djdmzZ8udT3FxMQoLC0W2aWtrQ1v7/35/lJqailWrVsHX1xfGxsbYuHEjZs+ejXPnzkFPTw8AEBgYiGvXrmHhwoVo0aIFjh07hr/++kuhmBw+fBiOjo5Yu3Yt4uPjsW7dOjRu3Bj+/v4AgLdv32L8+PGoX78+li5dCmNjYxw8eBCTJk3Czz//jMaNGwMA9u/fj/Xr12PSpEkICAjA48ePhYXysrHKLFiwAGPGjMGMGTNgaGgodW5xcXFIT0+Hv78/ioqKsHbtWixcuBDh4eEASv9sxc/PD69evcLy5cthbGyMvXv34sWLFyIxJCIiIiIiIiIiqk7Z2dk4d+4coqKi8Ntvv8HAwADvv/8+/Pz84ObmplCtqtIP8+zUqRM6deqERYsW4dq1a4iMjMSJEydw8OBBWFtb48MPP8SHH37I9Y8JANCsWTO8evVK+Lp8Qbu4uBhubm64ffs2fvjhB8yfPx+WlpawtLQUW84FAFxcXODi4gKgtGjr6OiI3NxcHDp0SKFC+bx588S2jRw5EqtXrxa+fvv2LQ4dOoR27doBAAwNDTFx4kT8+eef6N69Ox49eoRz587h66+/xrBhwwAA7u7u+OCDDxSKh4WFBTZu3AgA6N27N+7du4ezZ88Ki9thYWHIyMhARESEsCju4uICb29v7N27F4sWLUJWVha2bduGadOmYf78+QBKl7vR09PD2rVrMXXqVDRq1Eh4zLFjx8LX11fu3DIzM3Hy5EmYmZkBKP3ACQwMRFJSEiwtLXHp0iX89ddfOHz4MLp37w4A6NmzJzw8PGBiYqLQ+UtStm5UTk5OhceoCu/e2V/bqno+ZfGt7ThrAlWIdXUtj6Yq+apJVO2zSd0wXxXD61A51RkvTX4vmK9UE6ojxzQtb1UhV4Hqy1eg9s+NKq8ieVnRXFblz4C6nK8nT57El19+ieLiYnh4eGDjxo3w9PSEvr6+UuNUulBeRktLC46OjsjIyEBycjKuXLmCp0+fIiQkBNu2bcP777+PJUuW8AGgGu7dBfYfP36MTZs2ITY2FmlpacLt8fHxcsfKy8tDaGgoIiMj8fLlSxQUFAj3/fvvv2jQoIHM/v7+/ujZs6fItrKicJkmTZoIi+RA6V3xAJCcnAwAwqVUvLy8hG20tbXh6emJ/fv3yz0HV1dXkdc2NjY4c+aM8PWVK1fg7OyMhg0bCu9+19bWhpOTk/DYsbGxyM7OxoABA0TukHd1dUVubi4ePnyIHj16CLf36dNH7rwAoEOHDiLxKDv3skL5nTt3YGJiIiySA0CDBg3g4uKi8B31kpSdgyLXgOIaK90jLi5OyR71lD6GMpSfj2KqNs4kS23G2tHRsVrGVTxfG8nZT4qqrs8CEqXZ+QoA5jL3yr8OZffXNJXPW4tqHLvuY74qgv8GryjpOVbxmGpq3tb2vzuqK1+Byp4b81MVyM5Lye9RRfrI71cTpM+t7Fqui/mamJiIvLw82NnZwcvLC7169VK6SA5UUaG87E7yn3/+GVlZWWjfvj0+//xzDBkyBDo6Ojhx4gRCQ0OxaNEifPvtt1VxSKqjkpKSYGVlBQDIysrClClTYGZmhoCAADRv3hz16tXDkiVLkJeXJ3es9evXIyIiArNmzUKXLl1gbGyM8+fPY8eOHcjLy5NbKH/vvffkLg/07p3RZcutlM0vNTUVenp6MDY2Fmn3bsFdmfHz8/OFr1+/fo1bt26hc+fOYn1btWolbANIf2rvy5cvRV6bmyv2j1d5556SkiLxPBU9d2l0dXVRWFgIKysrmUvDKCPmUYrSfTp27Khchwv/KH0MZSg9HzlycnIQHx9fpXEmydQ51orm66nbSTU4K/VW1Z8FJIr5WupecqrM/fKuw2tPZffXNJXN2+fXX0ndp8mfCcxXxSX/L01+I5JIWo5dvlXxmGpa3qpzrpapzLklMT9Vgqy8vBwr+T2qSB95/WqCrLlZWVnV2XydPn06bGxsEBUVhaCgIHz55Zfo06cPBg8eDA8PD2FNS54KF8r//vtvnD59GmfOnEFKSgrMzc0xcuRIDBs2TOQhhgAwdepU1KtXD19//XVFD0dq4OHDh0hOThYWdG/duoWkpCSEhoaiQ4cOwnaZmZmwtLSUO150dDTGjBkjsoxITExM1U9cBgsLCxQUFCAzM1OkWJ6enl4l4zds2BDu7u6YO3eu2L6y34w1bNgQABASEiIxbi1btqySubyrSZMmEs+zsude9hcHhoaG1frncfLU5rElqa751HacNYk6xlpV8lWTMM41Qx2v6arMV3WLTXWrznjxvWC+KoaFuIqSHv+Kx1TdrldFqWOulqncuTE/VYHs90/ye1SRPvL71QTpcysrjtfFfNXX18eAAQMwYMAAZGVl4ezZs4iMjMTcuXNhZGQEb29vDB48WGTFBUkqXCgfNmwYDAwM4OXlhWHDhsldFL1t27Zia0yT5sjLy8PKlSuhr6+PUaNGAQByc3MBQOS3Ojdv3kRiYqLIcifv3mVdfszyfYuKikSWLakJXbp0AQCcP39euEZ5cXExfv311yoZ39XVFadPn4aNjY3UDykHBwcYGhoiKSkJ/fr1q5LjKsLW1hYZGRn4/fff4eTkBKB0yZvffvutUmuUExERERERERERVYSRkRE++ugjfPTRR0hNTcWZM2cQFRWFY8eOoWnTpjJvsq1wofyrr76Ct7e33OUtyvTs2VNsPWhST8XFxbh16xaA0ocUPHjwAOHh4Xj+/DnWrl0rvMPZ3t4e9evXx/Lly+Hr64vk5GQEBwejadOmIuPZ2NigsLAQYWFhcHBwgJGREdq0aQNXV1dERESgbdu2aNSoEY4cOSKxoC7N06dPhfMso6WlBTs7O4XHaNeuHfr164dVq1YhJycHzZs3x7Fjx5CbmyuyFntFTZ48GZGRkfjPf/6DiRMnonnz5khPT8eff/6Jpk2bYvLkyTAxMcGnn36K9evXIykpCT169ICOjg6eP3+O8+fPIzg4uFr+ZKZ3797o3LkzFixYgPnz58PExAR79uxBgwYNquTciYiIiIiIiIiIpPHy8hI+BFsWLS0t1KtXT/jMQWkqVCjPycnBoUOHkJeXh3HjxlVkCFJjubm5GDNmDIDSPylp2bIlXFxcEBISAhsbG2E7c3NzbN26FevWrYOfnx+srKywfPly7NmzR2Q8T09PjB8/Hrt27UJaWhqcnJxw8OBBLF26FMuWLcPKlSthaGiI4cOHo1+/fliyZIlC89y0aZPYNh0dHdy7d0+p8/3qq6+wYsUKrFu3Dvr6+hg+fDjatWuHw4cPKzWOJI0aNUJ4eDi2bNmCDRs24M2bN2jcuDHs7OxE7h6fMmUKmjZtiv379+PQoUPQ1dVFq1at0KdPH4XXYVKWlpYWvvnmGwQFBSEoKAgmJiaYOHEinjx5ogIPpyAiIiIiIiIiInXWvn17hQrlAERWr5CmQoVyQ0NDJCQk8K5REjNnzhzMmTNH4fa9e/dG7969RbZ5eHiIvNbV1cWyZcuwbNkyke3m5ubYvn272JhlS7tI07JlS9y/f1/u3NauXSu2zcTERKyviYkJNmzYILJtwoQJIuuuOzs7i/WTNIfJkydj8uTJItssLCywevVqufMdNGgQBg0aJHX/iBEjMGLECIn73p3LwYMHxdp07NhRrJ2lpSV27dolfJ2fn49Bgwahe/fucudLRERERERERERUUTt27KjS8Sq89Iq7uzv++9//YuzYsVU5H6I65+zZs3j58iXat2+PnJwcREVF4Y8//pBYxFc34eHhKC4uhrW1NTIyMnD06FEkJiZKvFufiIiIiIiIiIhIVVW4UO7n54e5c+di4cKFGDNmDN577z3Uq1dPrJ2pqWll5kek8urXr49Tp04hPj4eBQUFaNOmDdavX4/333+/tqdW7erVq4ddu3YhMTERANChQweEhobC1ta2lmdGRERERERERETqrGPHjgotvfL3338jPz8fdnZ2KCkpwd9//y2xXYUL5WVLPDx69AhRUVFS23GtYlJ37u7ucHd3r+1p1Iphw4Zh2LBhtT0NIiIiIiIiIiLSMLNnz1Z4jXJdXV257StcKJ81axbXKCeiOmH+GKtqP0bYwi7VfgyiusrKXKe2p4AVHw+p7SlUWHZ2NuLi4tCxY8dKj7Uv8mmlx5gypHWlx6htsuNgjj9fpModQx3iII3PwMqdW4tG0v+NkJEj558fjSr8zxPce5Evc39LGWN3al65/Ep8rdg/0CQ5+eszmfuHebaq8Nik3n68/Fxs20D398S26Uj4Gp45XHKeR8aIjynJQHd7idvNjbXFtuUXiufHtYYTxba910jyzwupWcVi27p36SC2TUf80LBuKmEjgCfJ4mNKmuf+KMnfF40aiH/O6UqYvqQ5SaPo+ynpM4OfE3XXIAnvsTxHoqV/b9i1kvxd92+e9OM0qCf9ezstU/L3W1aejEKjlB/9OzbTl9qnRSPJyZL4WjxX5cnKlT63IinDnb0i/bOvUPkpwNtN+fe1psiaW2kcmuBybBqANCX6KHecqjRr1iyF22pra8ttX+GfRJV5YCMRERERERERERERUXUoLi5Geno6AMDMzAza2kr8tvL/q/gtG+/Izc0FABgYGFTVkEREREREREREREREEt2/fx/BwcH473//K1KfdnNzw9y5c9G+fXuFx6pUofzFixcIDg5GTEwMXr9+DQBo1KgRPDw8MHv2bLRo0aIywxMRERERERERERERifnjjz8wdepUaGlpwcvLC1ZWVgCAp0+f4pdffsGVK1ewZ88edO/eXaHxlL8H/f97/Pgxhg8fjlOnTqFTp06YOHEiJk6ciM6dO+PUqVP46KOP8M8//1R0eLUVHBwMgUAAd3d3FBeLL3Q0duxYCAQCBAQEVPmxAwICMHjwYLntBAIB9u7dW+XHl0bReamCvn37YsWKFbU9jSo1dOhQha+3goICODs7o0OHDnjx4oXY/uvXr0MgEODOnTtVPU0iIiIiIiIiIiKhtWvXonHjxoiOjsbGjRsxZ84czJkzBxs2bMDZs2dhZmaGNWvWKDxehe8o37hxI7S1tfHDDz9AIBCI7Hvw4AEmT56MjRs3Yvv27RU9hNrS09PD69ev8fvvv8PZ2Vm4PTExEbdu3UL9+vVrcXZAeHg4mjdvXqtzUFUhISEwMTGp7WnUmv/+97948+YNACAqKgq+vr61OyEiIiIiIiIiItJIDx48wJw5c2BpaSm2r2nTphg/fjy2bdum8HgVvqP8999/h4+Pj1iRHADat2+PCRMm4MaNGxUdXq3p6emhd+/eOHPmjMj2M2fOoF27dmjVqnafWG1vb48mTZrU6hxqSklJCfLz8xVu36lTJ7Rs2bIaZ6TaoqKiYGJigs6dOyMyMrK2p0NERERERERERBrK0tIShYWFUvcXFhZKLKJLU+FCeWFhocwHdxoaGsqcqKYbPHgwzp49i4KCAuG2qKgoiUuQPH78GPPmzYOHhwfs7OwwcOBA7Nu3T2zplvz8fGzevBleXl7o0qULevfuLXFJjevXr2PYsGGwt7fHyJEjcffuXZH97y694uPjgxkzZiA6Ohre3t5wcHDAxIkT8ezZM7Hjb9q0CZ6enujSpQs++OCDKiumPn78GJ988gkcHR1hb28PX19fsePv27cPH330ERwdHeHi4oIZM2bgyZMnIm3KlnmJiYnBhx9+CFtbW1y4cAHBwcFwcHDA/fv3MW7cONjZ2WHw4MG4fPmySP93l14pG09eTDMzM+Hv7w8HBwe4uLhg06ZN2Ldvn8RfNJWXkpKCwMBAeHl5oWvXrujfvz82bdokVtwXCATYvXs3goOD4erqCmdnZwQGBiI7O1uk3c2bNzFixAjY2toK46Co7OxsXLhwAd7e3hgxYgQePHiA+/fvK9yfiIiIiIiIiIioqkybNg2HDx/G48ePxfb9888/OHToEKZOnarweBVeeqVjx46IiIjAqFGjYGxsLLIvKysL33//PTp16lTR4dWep6cnFi9ejCtXrqBPnz549OgR7t+/j+3bt+PHH38UaZuSkgJra2sMGTIEDRo0QFxcHIKDg5GdnY3Zs2cL282ZMwfXrl3DjBkzYG9vj/T0dPz8888iY6WmpmLVqlXw9fWFsbExNm7ciNmzZ+PcuXPQ09OTOt+4uDikp6fD398fRUVFWLt2LRYuXIjw8HBhm7lz5+LmzZuYNWsWbGxsEBMTg4ULF8LExAQeHh4VjtXz588xduxYtGvXDmvXroWWlhZ27tyJyZMnIzo6Gvr6+gCApKQk/Oc//0Hz5s2RlZWF7777DmPHjsXZs2dhamoqEs9Vq1bhk08+QbNmzdC8eXM8fPgQBQUF8Pf3x8SJE+Hn54fdu3fj008/xYULF9CoUSOp81MkpoGBgbh27RoWLlyIFi1a4NixY/jrr7/knvvr169hamqKwMBAmJiYID4+HsHBwUhNTRVbY+nw4cNwdHTE2rVrER8fj3Xr1qFx48bw9/cXznPq1KkQCATYsmULMjIysHz5cmRnZ6Njx45y5/LLL78gOzsbgwcPRrt27bBmzRpERUXJLfYTERERERERERFVtZSUFJibm+PDDz+Em5sb2rRpAwB48uQJ/vvf/6Jt27ZITU1FSEiIsE9JSQnmzJkjcbwKF8rnzJmD6dOn44MPPsCIESOETxV98uQJfvjhB7x58wZBQUEVHV7tGRoaom/fvjhz5gz69OmDqKgoODg44L333hNr6+LiAhcXFwClb6ajoyNyc3Nx6NAhYaH8ypUruHjxIjZu3ChyV/q7d6i/ffsWhw4dQrt27YTzmDhxIv7880+ZT4DNzMzEyZMnYWZmBqD07uLAwEAkJSXB0tIS165dw4ULF7B371706tULAODm5obU1FQEBwdXqlAeEhKChg0bYv/+/ahXrx4AoFu3bvDy8kJERAQmTJgAAPjiiy+EfYqKiuDm5gYXFxecPXsWY8aMEYnB7t27YWdnJ3KcskJ52Vytra3h5eWFS5cuYejQoVLnJy+mjx49wrlz5/D1119j2LBhAAB3d3d88MEHcs9dIBDg888/F77u1q0bDA0NERAQgKCgIBgaGgr3WVhYYOPGjQCA3r174969ezh79qywUB4WFgYtLS3s3r1b+MstS0tLTJ48We48gNK/eGjatCl69OgBbW1t9OzZE1FRUZg/fz60tLQUGkMRJSUlAICcnJwqG5PElcWXca5+qhDr6nr2RV3K13f/wqYuUYVrqLy6HMuqVF1xYL6qn8peK8w5yVThs1EV81XR64XXVe1RJvbq8H6qQq4C1ZevQO2fG9UOVc67qlaRc61MfJTJ1/LPxrx8+bLYyhAPHjzAgwcPRLZVS6HcxcUFu3btwrp167Br1y6RfR07dsT69evRs2fPig6vEQYPHowFCxYgNzcXP/74I3x8fCS2y8vLQ2hoKCIjI/Hy5UuR5Vr+/fdfNGjQAL/99hsMDQ0xaNAgmcds0qSJsKALAG3btgUAJCcny+zXoUMHYZG8fL+yQvmVK1dgamqKnj17iiy54+rqii+//BJFRUXQ0dGReQxprly5goEDB0JHR0c4tomJCTp16iSyxMmtW7ewdetW3Lt3T/jASQCIj48XGc/U1FSsSA4A2trawl9IAEDLli1hYGAgNzbyYnrnzh0AgJeXl8ixPD09sX//fpljl5SUICwsDMeOHUNCQgLy8vKE+54/f4727dsLX7u6uor0tbGxEVkH/88//4Szs7PIX4C4uLiI3G0vTXp6Oq5cuQIfHx9oa5eu2DRkyBB8/vnn+N///ifzlyzKKnuP333fqHowzjWnNmPt6OhYLeMqnq/m1XJ8ZcTFxdX2FCqtaq6hyr8X6hBLVY5D7edrVbCogWPUHfKvFdnxUo+cqz6a/f0q/lwpydeLou0kt6WqpUzsJbcV/8yoC58Ttf3vjurKV6A2zo3fs6qgLuSd8iR/B8g+14r0kU2ZfK3q96HChXKgtDB38uRJpKam4sWLFwCA5s2bw8KCSauIXr16QU9PD1u3bkVCQoLUO4zXr1+PiIgIzJo1C126dIGxsTHOnz+PHTt2IC8vDw0aNMCbN29gYWEh985eExMTkddlS4OUL8BWpN/r16/x5s0bdO7cWWL/1NRUpRbPL+/169cICwtDWFiY2L6yebx48QJTpkxBly5dsHz5cjRp0gR6enqYMWOG2LmZm0v+x7GBgYFwGZfy41c2NqmpqdDT0xNboqj8Lx6kCQsLw9dff41p06bB2dkZJiYmuHPnDlasWCE2L0nzKL+WeWpqKlq3bi12DEXm8dNPP6GwsBAeHh7IyMgAADg7O0NfXx+RkZFVWijX1dVFYWEhrKysRO6Yp6qVk5OD+Ph4xrkGqHOsFc3XP1+k1uCsJFNkiSlVVZXXUFW8F3U5lmU0MQ41+f36/Pqrah2/rpF3rciLV1271moKv1+B5P+liW2TdL1cvqVYOwB48bt4W6pa0mKv6Psp6TNDlT8n1DlXy9T0ud1P5fesKlDlvKuoy7GSvwNknWtF+qiyShXKy1hYWLA4XgF6enro378/vv32W7i4uEgt4EZHR2PMmDHw9fUVbnv3IYympqZITU1FSUlJlS6DoaiGDRvCzMxM7K8LyihSjJU1toeHB8aPHy+2r0GDBgBK/7wiOzsbISEhwoJxYWEh3r59K9anpuNjYWGBgoICZGZmihTL09PT5faNjo5G3759sWDBAuE2SQ8oUHQeaWniH2CKzCMqKgoAJC7TEh0djSVLlshc414ZZe+PoaFhtf55HJVinGuOOsa6LuWrqs9PEaoSZ1WYgyqoa3GoS/mqbiobb75fsqnjNa14vor/bC+5vaLtJLelqqVM7BW9tutCDqhjrpZR53Mj6dTzPZf8HaDsd5H8PtXj3r17WLNmjXAJ54qocKH85MmTctvUq1cPlpaW6Ny5s9idulRq1KhRSEtLw+jRo6W2ycvLEylCFhUViSypAZTe3b9792789NNPGDhwYLXNVxpXV1fs2bMHenp66NChQ5WO7eLigocPH6JTp05Sl2/Jzc2FlpYWdHX/75Iuuwu6tnXp0gUAcP78eeEa5cXFxfj111/l9s3NzRUrQEdGRlZoHl27dsXRo0dFCva//fabyDI1kiQmJiI2NhZjx44Vu7bi4uKwZs0aXL58GX379q3QvIiIiIiIiIiIiCrj7du3+P3330WWrFZWhQvlAQEBwt90lz1spEz57VpaWjAyMoKvry+mT59e4Ymqq65du+Kbb76R2cbV1RURERFo27YtGjVqhCNHjogsqVHWxsPDA1988QWePXsGOzs7vHnzBmfPnsWWLVuq8QxKubm5wdPTE9OmTcO0adMgEAiQk5ODR48e4enTp1i9erXM/llZWYiOjhbb7uzsjE8//RQjR47E1KlTMXr0aJibm+PVq1e4ceMGunfvjsGDBwvXww8MDMTYsWPx8OFD7N+/X2w5ktrQrl079OvXD6tWrUJOTg6aN2+OY8eOCYv7sri6uuLAgQM4dOgQrKyscPr0aTx9+rRC85g0aRKOHDmC6dOnY/r06cjIyEBwcLDcNcrL7iafNm2a2MNmHR0dsWvXLkRFRbFQTkREREREREREdVal7igPCAiAqakpJkyYgFatWgEAnj59isOHDyMzMxNLly5FWloaDh48iE2bNqFBgwYSl88g2ZYuXYply5Zh5cqVMDQ0xPDhw9GvXz8sWbJEpF1wcDBCQkIQHh6OkJAQNG7cGG5ubjU2z23btmHXrl04evQoEhMTYWxsjHbt2mHEiBFy+758+RJz584V23748GF0794dERER2LJlC5YvX47s7GxYWFjAyckJAoEAACAQCIR/XjFjxgx07NgRW7duxWeffVbVp1khX331FVasWIF169ZBX18fw4cPR7t27XD48GGZ/WbNmoXXr19j27ZtAABvb28sWbIEM2fOVHoOTZo0we7du7Fq1SrMnTsXrVq1QlBQEDZv3iyzX1RUFLp16yZWJAdK108cPHgwjh07hn///VfpOREREREREREREVWFyi63rFXy7u3gCgoMDERKSgr27t0rtq+kpATTpk2DpaUlVq9ejeLiYowfPx7//vtvhZeNIFI3EyZMgLa2Ng4ePFjbU1EZd+7cQUlJCQoKCtCxY0c1XfNLNWRnZyMuLo5xrgHqGmtl8nVfZMX+EqYqTRki/jDjuqIqr6GqeC/qcizLaFocavr79eSvz6Tui3tRVG3HTXgte8m9lo2k3yNkYqgts2+LRrL/0ZX4Wvo/qWaNkH2tyIoXAAzzbCVzv6bi9yvw4+XnYtsGuovf3HL2qng7b1fxdgAQGSPeVpJXmcUSt5sbi+dSfqF4flx7LP5n8e81krzMZmqW+LFMDMSPoyMhjTu/J3nMJ8niY1qaiuf5638l53ajBuJtiySExFBfvJ2k9whQ/P2U9Jmhyp8T6pqrQGm+5ufn1/i5HYmW/r1RT8qjw/7Nk/491aCe9O+4tEzJ/bJkjKcrOe1katFI8vdw4mvJnzUAUCjlR4rW5tK/0yXlKQA0rC89BoVS+gySkst12dkrkr8DvN2kn2tF+lSXx48fY8+ePVi8eDGMjIwqNIbsnwhl+OWXX+Dl5SVxn5aWFvr27Ytz586VHkRbG97e3hVeMoKorjt79iy+/fZbXL16FefPn8e8efPwxx9/YNKkSbU9NSIiIiIiIiIiojrNxsYGa9asqXCR/P+1d+dxNeX/H8BfrYpKImJsZblCaRGTKHtUxjJ23+wasg1jTI3tJ4x9GWU3CF8kO5Gxm7HO9yvDGDMMGjIihUrbrc7vjx6dr+ve6t6We2+31/PxmMdX537O+3zO+553fN/33M8BSrD0Sm5uLp48eVLg60+ePEFu7v8+djE2NkalSpWKeziicq1y5co4evQoYmNjIZVKYWdnh+XLl6Nr166anhoREREREREREVGFV+xGeefOnbF37140aNAAAwYMEJvgmZmZ2L9/P/bt24eePXuK42NiYsR1zIkqmg4dOqBDhw6angYREREREREREZFOsLe3R3FWFf/jjz8Ubi92o3zWrFl49uwZFi5ciKVLl8La2hoAkJCQAKlUCkdHR8yaNQtAXvPcxMQEo0aNKu7hiIiIiIiIiIiIiIgAAJMmTSpWo7wgxX6YJ5D30M4zZ87gp59+wj///AMA+OSTT9C+fXt07doV+vrFXgKdiCogPsxTfXT5oTraRldzzXpVH129hrSRruaa9ao+unoNaSNdzXVZ1Kuih3mmF/Agvkz5Z2zidar82Bpmih98p+jBnZUVPCgwTcHxLaso7h8oGqvogXzVLeSPk5qu+Dw/71JfZ68hbaPLeS7rh3kevaj4oZ1/vij4AZcFKahmi1LdXPF+in5X5CvoIZvZuYU8AFRf8XEKejgpkFfHihT0cEmg4AdMFmcfXaTL9aqsYt9RDuQ9tLN79+7o3r17ac2HiIiIiIiIiIiIiEhpubm5SEpKgr6+PqysrIoVo0SNcgB4+fIlfvnlFyQmJsLb2xs2NjbIzc1FcnIyzM3NYWBgUNJDEBERERERERERERHJSExMxIoVK/Djjz/i/fv3AAALCwt069YN06dPR/Xq1ZWOVexGuSAIWLJkCf79738jOzsbenp6aNq0KWxsbPD+/Xt07twZU6ZMwciRI4t7CCIiIiIiIiIiIiIiOUlJSRg4cCASEhLQvXt3GBgY4NixY+jZsycuXLiAn3/+Gfv370etWrWUilfsRcS3bt2KnTt3YvTo0di+fbvMwunm5ubo3r07fvzxx+KGJw0IDQ2FRCIR/3NwcEDPnj2xZcsW5ObKroElkUjwww8/aGimee7fvw+JRIIbN24ofP23336DRCLB0aNHFb6ek5ODdu3aYebMmYiLi4NEIkF0dHRZTrlAnTt3RkhIiPhzUFAQ/Pz8Sv04oaGhuHXrltx2bXg/iYiIiIiIiIiIlLV+/XokJSXh4MGDWLFiBfr06QNBEPDll1/ixIkTMDAwwJo1a5SOV+w7yiMjI9GnTx9Mnz4db968kXtdIpHg8uXLxQ1PGmJiYoLw8HAAQEZGBm7cuIGVK1dCEAQEBARoeHaqadmyJWxtbREVFYXevXvLvX7t2jUkJiaiV69eqFmzJiIiItCwYUP1T1SBwMBApKWllXrcsLAwVK5cGS4uLjLbIyIiUKdOnVI/HhERERERERERUVk4f/48BgwYgCZNmsi9ZmFhgYEDB2LXrl1Kxyt2o/zFixdwdnYu8HVTU1OkpqYWNzxpiL6+PpycnMSfP/30Uzx48AA//vhjuWuUA4Cfnx82btyIN2/eoFq1ajKvnThxAtWrV0e7du1gYGAgc96aVr++4qc3lxVtOnciIiIiIiIiIqKiJCQkoHHjxgW+Xq1aNSQnJysdr9hLr1SvXh0vXrwo8PV79+6hdu3axQ1PWqRKlSrIzs5Web+LFy9i1KhRcHd3h4uLCwYMGCD3LYNDhw5BIpHg999/x9ixY+Hk5ITu3bvjyJEjcvHWr18PDw8PODs7Y9KkSUhMTCxyDr169YJUKpVbUiUzMxNnzpyBj48PDAwMFC69cu7cOfTr1w/Ozs5o3bo1+vXrh0uXLomvK1quZMeOHZBIJOLPaWlpCAkJgbe3N1q1aoXOnTtj7ty5SElJKXTeHy+90rlzZ5llcfL/CwoKAgC8evUKwcHB6NKlCxwdHdG9e3esWrUKWVlZMvMFgGXLlon75y9bo+hc9u3bB29vb7Rs2RKdO3fG+vXrZZbgUeW9IyIiIiIiIiIiKk2WlpZ4+/Ztga9fvnwZDRo0UDpese8o79atG/bt24d+/frBzMwMAKCnpwcA+Pnnn3H48GGMGTOmuOFJg/Kb4vlLr/z444/44osvVI4TFxeHTp06YfTo0dDX18fly5cREBCA8PBwtG3bVmbsjBkzMHDgQIwaNQr79+9HUFAQHBwc0KhRIwDA7t278f3332P06NFo164drl69ilmzZhU5hwYNGsDBwQEnTpzAkCFDxO0XL15EamoqevXqpXC/p0+fYurUqfD19cVXX32F3Nxc/PHHH3j37p1KOcjIyEBOTg6mTZsGKysrvHjxAhs3bkRgYKBKX/0ICwuTaXr/+eefmDdvHmxtbQEAb968gaWlJYKDg2FhYYHY2FiEhoYiISEBixcvBpC3vMqgQYPg7+8vNuEL+tRt165dWLhwIfz9/dGxY0fExMQgLCwMKSkp+Oabb2TGFvXeERERERERERERlbYmTZrg3r17ctsvXLiAK1eu4Ny5c1i0aJHS8YrdKJ8yZQpu3LiB3r17o3Xr1tDT08OWLVvw/fff4/bt27C3t8f48eOLG540JC0tDS1atJDZ5uPjU6xlV/71r3+Jf87NzUXbtm3x119/Yf/+/XKN8mHDhmHYsGEAAGdnZ1y6dAmnT59GYGAgcnJysGnTJvTu3Vts0nbo0AGJiYkFPqjzQ7169cLixYvx4sUL8VsOUVFRqF+/Plq1aqVwn99//x1SqRRz5swRPwjq0KGDyjmwsrLC/PnzxZ+zs7NRt25dDB06FE+ePBEb3UVp3ry5+OekpCRMnz4dHh4eGDduHIC8O8I/bGC7uLjA1NQUQUFBmDt3LkxNTcXlVWrXrl3oUis5OTlYt24dfH19MXv2bABA+/btIZVKsW3bNgQEBMgsY1PYe1cc+Q8GTk9PL9b+pJz8/DLPZU8bcl25cuUyict6VQ9tuIYqCm3INeu1fNOGa6ii0IZcs17Lr7S0NK24hioCbclzWdUroPlzq6iK81w3de1TXpXHevXx8cHy5cuRlJQEKysrAHk3cs+aNQvm5ub49ttv8fnnnysdr9iNcnNzc+zfvx/btm3D6dOnUalSJfzyyy+oX78+Jk6ciLFjx8LExKS44UlDTExMsHv3bgBAVlYW7t27h7Vr12L27NnincnKio+Px+rVq3H16lUkJCSI/+D7uBEP5DVi81WuXBl16tRBfHy8GOfVq1fo1q2bzD7e3t5KNcp9fHywdOlSnDx5EmPGjEFqaiouXrxY6DceJBIJDAwMxLul3dzcYG5urtR5f+zIkSPYsWMH/v77b5lfsLGxsUo3yvNJpVJMnToVBgYGWLVqFfT181ZPEgQB4eHh2L9/P+Li4pCZmSnu8+zZMzRt2lTpYzx+/Bhv3rxBjx49ZLb7+Phg06ZNuHPnDry8vMTthb13xZH/jYbY2NhixyDlMc/qo8lcu7q6lklc1qt6Mc/qw3qlkmKe1Yf1qoyapRRHd9y/f1/8M+tVPTSd57KqV6Asz826jOLqhg/rWFbBv/NKdx/dVZ7qtX///ujfv7/4s0QiQWhoKKpXr44WLVrA2NhYpWMXu1EO5DVVAwMDi33nKGkffX19ODg4iD+7uroiJycHS5YswahRo5RuuObm5mLChAlISUnBlClT0KBBA5iammLt2rUK17b/uAltZGQkLjWSkJAAAOInQ/lq1Kih1Fysra3Rtm1bnDhxAmPGjMGZM2eQmZlZ4LIrAGBra4uNGzdi06ZNmDRpEvT19dG+fXvMnTsXderUUeq4AHDmzBl88803GDRoEKZNmwZLS0skJCRg4sSJMs1sZS1atAi//fYb9u/fj6pVq4rbw8PDsXTpUowdOxZt27aFhYUF7t69i5CQEJWPk7+8TPXq1WW25//88fIzhb13xWFoaIjs7Gw0bNgQpqamxY5DhUtPT0dsbCzzrAa6nGvWq3ro8jWkbXQ516xX9dDla0jb6HKuS7tef7pd9LOdKhp7e3udvoa0SUXIc1mdW9zN16UeU5fY29sr3P5TTMG/80pzH12kC/VqZWWFrl27ymxLTEyU63EVpNiN8uHDh2PChAlwd3dX+Pr169exfv167Ny5s7iHIC1hZ2cHAPjrr7+UbpT//fff+P3337Fu3TqZCzQjI0Pl41tb532KmpSUJLP99Wvl/9Lo1asXgoOD8fjxY5w4cQItWrQQz6sgnp6e8PT0RGpqKi5fvozFixcjODgY4eHhAABjY2NIpVKZfT5+km50dDTs7e0REhIibrt586bS8/7Qvn37sG/fPqxduxZNmjSRO07nzp3x1VdfidsePXpUrONYWloCkM93/sNTP2zQl4X8Zx2YmpqW6dfjKA/zrD66mGvWq3oxz+qji7lmvaoX86w+upjr0q9XNso/9mFedfEa0ka6nGddPjdtVnDOC/6dV7r76C5duKbfvHmDM2fO4OTJk/jll18UrmOuiH5xD3jz5s1CG5VJSUn45ZdfihuetMjDhw8BQGZd6qLk38VsZGQkbnv+/DliYmJUPr6NjQ2sra1x5swZme2nT59WOkb37t1RqVIlhIeH4/r164XeTf4xMzMz+Pj4wNfXV6b5bGNjI9eMvnr1qszPGRkZMjkAgOPHjyt97Hz/+c9/sHDhQowfPx7du3eXe13Z4xgZGRV5h7mtrS2srKwQHR0ts/3UqVMwMjKCo6OjyvMnIiIiIiIiIiIqKykpKTh8+DDGjh2LDh06YN68eUhMTFRpJZQSLb2S/0m3In///TeqVKlSkvCkAbm5ubh9+zaAvPWw7927hw0bNqBx48Zo3bq10nHs7OxgY2ODlStXIjc3F2lpaVi7di1q1lR9fTwDAwMEBARg0aJFqF69Ojw8PHDlyhXcuHFD6RhmZmbo2LEjIiIioKenBx8fn0LH79u3D7dv30aHDh1gbW2NuLg4HDt2DB4eHuIYb29vhIeHw8HBAba2tjh27BhevnwpE6ddu3YICQnBunXrxAddXrt2TaXzT01NxeTJk9GgQQN4eXmJ7w+Q95WS+vXro127dti5cyd2796Nhg0b4tixY/j777/lYtnZ2eHcuXNo3bo1TE1NYWtrKz6sNJ+BgQECAwOxcOFCWFlZicfcsmULRowYodIHJkRERERERERERGXh/fv3OH/+PE6ePImff/5ZXLosICAAPj4+aNy4sUrxVGqUHz58GIcPHxZ/3rBhA/bv3y83LiUlBX/++Sc8PT1VmgxpXkZGBgYNGgQgb208GxsbfPbZZ5g0aZLcHcuFMTY2RmhoKEJCQjB16lTUrl0bEyZMwPXr1/Hbb7+pPC9/f38kJydjz5492Lt3L9zd3bFw4UKMHTtW6Ri9evXC6dOn0bZtW9SqVavQsRKJBBcuXMDixYvx9u1bWFtbw9fXF1OnThXHBAYGIjExEevWrYOenh4GDRqE4cOHY8mSJeKYwYMHIy4uDrt378YPP/yA9u3bY+XKlRg4cKDS83779i2SkpKQlJSEwYMHy7zWt29fLFmyBBMnTsSbN2+wdu1aAHlN/NmzZ2P8+PEy4+fOnYvvvvsO48aNQ0ZGBnbu3Im2bdvKHdPf3x+GhobYsWMH9u7dC2tra0yaNEkuHhERERERERERkSZ4eHggIyMD9evXx+jRo9GzZ080a9as2PH0BEEQlB2c36QE8pbjsLGxkXuQH5C3fk+LFi0wceJEpRdLJyK6e/cuBEGAVCqFvb19uV8TS5ulpaXh/v37zLMa6GquWa/qo6vXkDbS1VyzXtVHV68hbaSruS6Lej199ZnctvRMxW2ATKn8ttep8mNrmCn+dnlWtvzYypXkx6YpOL5lFcUrwyoam5MrP666hfxxUtMVn+fnXerr7DWkbXQ5z3fv3kVWVlaZndvRi08Vbv/zhYICKEJBNVuU6uaK91P0uyJfdk4B23MLbj8a6is+TqVC7tf8vEt9hdtPX5H/nZfP26Neqe2ji8pjvTZv3hympqYYOnQofH19S9QkB1S8o3zo0KEYOnQoAKBz586YNWsWunTpUqIJEBERERERERERERGpYvny5YiKikJ4eDi2bNmChg0bokePHvD19UWTJk1UjlfsNcrPnz9f3F2JiIiIiIiIiIiIiIrN19cXvr6+SE1NxdmzZxEVFYWtW7di48aNaNy4sdg0t7W1VSpeiR7mmS81NRWpqanIzZX/+kedOnVK4xBERERERERERERERDLMzMzQp08f9OnTB2/fvsWZM2dw8uRJbNy4EWFhYfjjjz+UilOiRvmePXuwY8cOPHtW8Fo+9+/fL8khiIiIiDRu2/G/Sxwj9nUBCzYqpRqO3olHyCi7Es+DiIgqFu928uvrKlq3HACMFXQILBWsbZxVwPrEitYjN1Cw9LiZqfIxFa1RbKBgPWNF51lWFOVP2Tyrc55UvvXuqPoa3OrSu6Pq13FJ1gFXZe3s4qwpXpHWIa8ILC0tMWDAAAwYMABJSUk4deqU0vsqflqGEvbu3YuQkBDUr18fX375JQRBwIgRIxAQEIAaNWqgWbNmWLRoUXHDExEREREREREREREVi5WVFYYNG6b0+GI3ynfv3o327dtj69atGDhwIADAy8sL06ZNw8mTJ/H+/Xu8ffu2uOGJiIiIiIiIiIiIiAqUkZGBsLAw+Pn5wdHREU5OTujduzfWr1+PjIwMlWIVu1H+9OlTdOrUCQBgZJT3XSipNO+7Uubm5ujfvz/27NlT3PBERERERERERERERAqlp6dj6NChWLduHczNzeHm5obMzEzUqlVLvLk7JSVF6XjFbpSbm5sjJydvrU0zMzOYmpoiPj5efL1KlSp4/fp1ccMjNDQUEokEEokEzZo1g6urK3r16oWQkBA8evSoWDFv3LiBjRs3FntOhdmxYwckEgm+/fbbUo176NAhSCQSJCUlFTrO398fX3zxhfhzWZ6rOuJro9atWyM0NLTQMf7+/pBIJJg2bZrca6mpqXB0dIREIsGhQ4dKZU69e/dGUFCQSvvExcVBIpEgOjpa3Na5c2eEhISUypyIiIiIiIiIiIjK2saNG/HgwQNs2bIFe/fuxbhx4yAIApYuXYqjR48iISEBa9euVTpesRvlTZo0kXliaKtWrbB37168fPkSL168QEREBBo2bFjc8AAAExMTREREYN++fVi7di369euHq1evonfv3jh69KjK8W7evIlNmzaVaE4FOXbsGADgzJkzyMrKKpNjFGbevHn45ptvxJ/L8lzVEb88q1y5Mi5cuIC0tDSZ7WfPnoWBgYGGZlW4sLAwjB49WtPTICIiIiIiIiIiUkp0dDT69u2L9u3bAwAEQRBfq1evHgYPHowzZ84oHa/YjfLPPvsMDx8+FJvCkydPxqNHj9CxY0d07twZT548wZdfflnc8HmT09eHk5MTnJyc4OHhgVGjRuHo0aNwdXXFrFmz8OyZ5p/0CwBPnjzBvXv30K5dOyQnJ+PixYtF7pOVlYXc3NxSm0Pjxo1hZ2dXavHUrbTzoUkuLi4wMTHB+fPnZbafOHECXbt21dCsCte8eXPUrVtX09MgIiIiIiIiIiJSyosXL9CiRYsCX69duzYSExOVjlfsRvnnn3+OyMhIGBsbAwBcXV0RFRWFoKAgzJo1C8eOHUPHjh2LG75AlSpVwpw5cyCVShEZGSluP3LkCIYMGYI2bdrAzc0N/v7+uHPnjvh6aGgowsLCkJaWJi7p4u/vDwB49OgRpk2bBi8vL7Rq1Qo+Pj7Ytm2b0o3bEydOQE9PDyEhIahRowaOHz8uNyZ/aYstW7agU6dOcHR0FB92euTIEfTp0wcODg5o27Ytxo0bh+fPn8vsHx8fj7Fjx8LJyQndu3fHkSNHZF7/cOmVws41/3wnTJgAV1dXODk5ISAgAE+fPpWJl5ubi+3bt6Nnz55o2bIlPDw8MGXKFKSkpBQaPygoCH5+fjKxkpOT5ZYbKSwfhw4dQq9eveDg4IAOHTpg9erV4jI/BVHmPcxfcuTo0aMICQmBm5sb2rdvj6VLlyI7O1sm3tmzZ9GjRw84ODigf//+MtdSUQwNDeHt7Y0TJ06I25KSknDt2jW53OTbt28fvL290bJlS3Tu3Bnr16+Xu/5u3bqFfv36wcHBAX5+frh06ZLCWDExMRg+fDicnJzg6uqKr776qshfCh8vvZL/Pt64cQN9+vSBk5MT+vfvj99++01mv5SUFMyYMQPOzs5wd3fHqlWrsG3bNkgkkkKPR0REREREREREVBJmZmZIT08v8PWYmBjUrl1b6XiGqhw8MzMTixYtQpMmTWQar/nq1auHESNGYOfOnQgPD8esWbPEB32WpsaNG6NWrVqIiYkRt8XFxaFPnz6oX78+srKyEBUVhWHDhuHYsWOwtbXFgAEDEB8fjxMnTiA8PBxAXjIB4NWrV7C1tUWvXr1QpUoV3L9/H6GhoUhLS8OkSZOKnM+JEyfQunVr1KtXDz179kRERARSUlJgbm4uM+7HH39EgwYNMGvWLOjr66Ny5crYunUrli9fjv79+2PatGmQSqW4fv06kpKS8Mknn4j7zpgxAwMHDsSoUaOwf/9+BAUFwcHBAY0aNZKbT2Hn+uzZMwwePBhNmjTBkiVLoKenh40bN2LkyJGIjo4WP/hYsGABIiIiMGLECHh4eOD9+/e4ePEi0tLSCo2vCkX52L59O5YvX44RI0YgKCgIjx49EhvlM2bMKDCWKu/hmjVr0KVLF6xZswYxMTEIDQ1F/fr1MWTIEADA/fv3MWXKFHh6eiI4OBhxcXH48ssvVVpSx8/PD6NGjcK7d+9QtWpVnDp1CrVq1YKzs7Pc2F27dmHhwoXw9/dHx44dERMTg7CwMKSkpIjL6SQkJGDMmDGQSCRYs2YNkpOTMX/+fKSlpcHe3l6MFRMTA39/f3h5eWH16tVIT0/HmjVrEBgYiIiICKXnn3/MhQsXIiAgAObm5li5ciUmTZqEM2fOiHUdHByM69ev4+uvv8Ynn3yC/fv34969eyodh4iIiIiIiIiISFUNGzbEn3/+Kbf9jz/+wLVr13DkyBFMnz5d6XgqNcojIiJw+PBhnDx5stBxnTp1wvLly9G0aVMMHTpUlUMorXbt2jIPC/2wGZqbmwsPDw/cuXMHhw8fxvTp02FjYwMbGxtxOZcPubu7w93dHUDeWjaurq7IyMjA7t27i2yU37lzB7GxsRg1ahSAvAbprl27cPr0afTv319mrFQqxZYtW1C5cmUAeXfjhoWFYdCgQTJ38ypanmPYsGEYNmwYAMDZ2RmXLl3C6dOnERgYKDe2sHMNCwtD1apVsX37dlSqVAlA3lIhXbp0QWRkJIYNG4YnT55g7969mDZtmswDQr29vWWOoSi+Kj7OR2pqKtauXYuxY8eKF7GHhweMjIywZMkSjBkzBtWqVVMYS5X30NHREbNnzxbj37hxA6dPnxYb5Zs3b0bt2rWxbt06cU3xSpUqYdasWUqfW+vWrVG9enX8+OOPGDBgAKKiohTeTZ6Tk4N169bB19dXnFP79u0hlUqxbds2BAQEoFq1aggPD4eenh62bNkifgBjY2ODkSNHysRbuXIlWrZsibCwMOjp6QEAmjZtKt6B7uXlpfQ5vHv3Drt370aTJk0AAKamphg+fDh+/fVXtG7dGn/99RfOnDmDpUuXok+fPgCADh06oGfPnkofQ5H89aQK+0SQSi4/v8xz2dOGXOf/ni1trFf1+/j5F1S6WK9UUtpwDVUU2pBr1qt2UfXvyNK+hpQ9fkX7u1wbahUou3oFNH9umlDa13FR8bTlOtJ12pJnVeq1e/fuWLt2LWbNmiX2y/T09DBq1CgYGhpi+PDhGDt2rNLxVGqUnzp1Ct27d0e9evUKHVevXj14e3sjKiqqzBrlgiCIjUAgb+mNVatWISYmRmaZidjY2CJjZWZmYtOmTTh+/DhevHgBqVQqvvb+/XtUqVKlwH1PnDgBIyMj9OjRAwDg5OSEevXq4fjx43KN8rZt28q82TExMUhPT5cbp0j+ovRA3gVTp04dxMfHF7nfx65cuQIfHx8YGBiIy41YWFigefPm4rIa169fhyAISs2rJBTlIy0tDT169JBZCqVdu3bIyMjAw4cP0aZNG4WxVHkPP8wlADRq1AjXr18Xf/7111/RuXNnmQdv9ujRQ6VGuZ6eHnx8fBAVFYX27dvj1q1bmDdvnty4x48f482bN+L1k8/HxwebNm3CnTt34OXlhV9//RVt27aV+ZaCu7s7LC0txZ/T09Nx69YtzJw5U2apmoYNG6J27dq4e/euSo3ymjVrik1yIO+bHADw8uVLAMDdu3cBAF26dBHH6Ovro1OnTti+fbvSx/lY/nuvTO1SyTHP6qPJXLu6upZJ3IpVrzU0PQEAed96orLHeqWSYp7Vh/VaXDXLMLZmFPfvyOLlWT5/io+v7Djdp+nfi2VVr4Amzk3z9Vu867jgeSsbT9PXUUWh6TyrUq/+/v7o06eP2F+0tbVFUFAQqlevDjc3N9SqVUulY6vUKH/w4AF69eql1FgXFxesWrVKpcmoIj4+Hg0bNgSQdyfy6NGjYWVlhaCgINSpUweVKlXC7NmzkZmZWWSs5cuXIzIyEhMnTkTLli1hbm6Oc+fOYcOGDcjMzCywUZ6bm4uTJ0+iTZs20NfXR3JyMoC8xuHOnTvx8uVLmTekevXqMvvnr8lds2bRv+Q+XsbFyMhIpaVA8r158wbh4eHikikfx8yfl6Ghodx8S9vH8d+8eQMA6Nu3r8LxL168KDCWKu9hUblMSEiQm5uZmZl4B76yfH19ER4ejh07dqBJkyaQSCTiNZLv3bt3AORzkf9z/usJCQlo0KCB3DGsrKzEPycnJyMnJweLFy/G4sWL5cYWlj9FLCwsZH7Ovz7yayohIQFGRkZy+fxwTsVhaGiI7OxsNGzYEKampiWKRQVLT09HbGws86wGupzrilSvv/6ToOkpAIDMcltU+livVFK6fA1pG13OtTrq9afbyj/YrLxQ9e/IklxDivKn6PjKjtNlulyr+dR9bj/FaL5+i3MdFzbvouJVhOtIG5THPBsYGMjcRGpjY4MRI0YUO55KjXKpVKr0muPFbeQq4+HDh3j58qXYUL19+zbi4+OxadMmNGvWTByXkpICGxubIuNFR0dj0KBBCAgIELcV9KDED12/fh0JCQlISEiAm5ub3OsnT54Ul2QBIHMHPADxjXz16pVS8ywNVatWhZeXl8I7/fObyZaWlsjOzkZiYqLKzXJjY2OZu7mB/zV7P/ZxPqpWrQogb3kYRfmoW7dugcct7nuoiLW1tdzDL1NTU5X60OVDLVu2RL169bBz505MnTpV4Zj8ayApKUlme/7x83OiaE4f72dubg49PT188cUXCpfvKWjZmuKytraGVCqVW4//43NRVf51YWpqWqZfj6M8zLP66GKuWa/qxzyrhy5e06xX9WKe1UcXc62eetV8o620FTdXxcuzfP4Ux1B2nO7TxVrNp/5z03z9Fu98C563svF0+TrSJhU5zyo1ymvWrImHDx8qNfbhw4dK3SmtqszMTCxYsADGxsYYMGAAACAjIwMAZJr4t27dwvPnz2WWjiioeZ+ZmSmzb05ODqKiooqcy/Hjx1G5cmWsX78e+vr6Mq999913OH78uEyj/GPOzs4wNTXFwYMH4ejoWOTxVFHQubq7u+Phw4do3ry5zNIiH/r000+hp6eHgwcPyjSelYlvY2OD+Ph4meVOrly5otSc8/MRHx+Pbt26KbVPvuK+h4o4OjriwoULCA4OFnMUHR1drFjjxo3D+fPnC/wmhq2tLaysrBAdHS1zzqdOnYKRkZF4XTg6OmLv3r0yTelr166J30oA8v5icXJywuPHj+Hg4FCs+aqiZcuWAIBz586Ja5Tn5ubiwoULZX5sIiIiIiIiIiKq2Ozt7cVne6jijz/+ULhdpUZ5u3btcPToUXzxxReF3mmcmJiIo0ePyjz8sThyc3Nx+/ZtAHkL+z948AARERF49uwZlixZIt5h7OTkhMqVK2P+/PkICAjAy5cvERoaKrcOTaNGjZCdnY3w8HA4OzvDzMwMdnZ2aNeuHSIjI9G4cWNUq1YNe/bsKfJu+MzMTJw5cwbdu3cXHyL5oc8//xyLFi3C48ePYWdnpzCGubk5Jk6ciBUrVkAQBHTp0gW5ubm4ceMGfH19S9TsLOhcp0yZgv79+2PMmDEYOHAgatSogdevX+PmzZto3bo1/Pz8YGtri8GDB+P777/Hu3fv4O7ujoyMDFy8eBGTJ09GrVq1Coyfv4j+t99+i4EDB+Lhw4c4cOCAUnO2sLDAlClTsHz5csTHx6NNmzYwMDDAs2fPcO7cOYSGhhb41Y/ivIcFCQgIQP/+/TFx4kQMGTIEcXFx+OGHH1ReegUA+vfvX+ha7wYGBggMDMTChQthZWUFLy8v3L59G1u2bMGIESPEu8BHjBiBPXv2YNy4cRg3bhySk5MRGhoq8/USAJg5cyZGjBiBL7/8Er6+vrCwsEB8fDyuXr2Kfv36oW3btiqfQ0GaNGmCbt26YeHChUhPT0edOnWwf/9+ZGRkyH1bgIiIiIiIiIiIqDRNmjSpWI3ygqjUKB83bhyOHTuGESNGYNGiRWjVqpXcmF9//VVcG1yVp4oqkpGRgUGDBgHIu1u2bt26cHd3R1hYGBo1aiSOq1GjBr7//nssW7YMgYGBaNiwIebPn4+tW7fKxOvUqROGDh2KzZs3IzExEW5ubti1axfmzJmDefPmYcGCBTA1NUXfvn3RrVs3zJ49u8C5Xbx4ESkpKeKdtB/z8/PDsmXLcPz48QKX3QDycmplZYUdO3bg0KFDqFKlCpydnUu8PnhB59qgQQNERkZizZo1mD9/PtLS0mBtbQ03NzdIJBJx/7lz56Ju3bqIjIxEeHg4LC0t4ebmJt4lXlD8xo0bY8mSJVi/fj0CAwPh6uqKFStWoHfv3krNe/To0ahVqxa2b9+O3bt3w9DQEPXr10fHjh0LXfanOO9hQZo3b47vv/8eK1aswKRJk9CkSROsXr0aY8aMUTmWMvz9/WFoaIgdO3Zg7969sLa2xqRJkzB+/HhxTM2aNbFlyxYsXLgQU6dORf369TF37lysXr1aJpaLiwv27NmD0NBQBAcHQyqVwsbGBp9++qnCNc5L6rvvvkNISAiWLVsGY2Nj9O3bF02aNMG///3vUj8WERERERERERFRvokTJ5ZqPD1Bxbb7xYsXMX36dKSnp6NevXpo2rQpqlSpgvfv3+Phw4d4+vQpTExMsHLlSnTu3LlUJ0tE2m/YsGHQ19fHrl27VN737t27EAQBUqkU9vb2FXZNLHVIS0vD/fv3mWc10NVcV7R63Xb87xLHiH2dU+IYIaMUf0uNSgfrlUpKV68hbaSruVZXvZ6++kzh9hwFf1UpWrEzSyq/rSAG+gq2qRAzJ1e+ZWGgL/8N1l5e9ZSfFEp2DSnKn3c7+eMrO06X6WqtAnn1mpWVpfZzO31Fcf2qk7eH6tdxYfMuKp4uX0fahHlW8Y5yAOjYsSOOHTuGLVu24OLFizh79qz4Ws2aNTFgwACMGzcO9epVrF/+RBXR6dOn8eLFCzRt2hTp6ek4ceIE/vOf/2DdunWanhoREREREREREZHSVG6UA0DdunUxf/58AEBqaqr44EYzM7NSnRwRabfKlSvj6NGjiI2NhVQqhZ2dHZYvX46uXbtqempERERERERERERKK1aj/ENmZmZskBNVUB06dECHDh00PQ0iIiIiIiIiIqISUbBaGBERERERERERERFRxVHiO8qJiIiIdN3oXg00duwPH6pDRAU7cuFpoa/36VS/TPYl0naKHiiZnin/gEwAyFTyIZ3mpvIP0wSArGz5uL285OtH0ZyMjRQfy7td8euvoIeWlvSBmsruX9Ee3EmqK+wBl+/SFNdpaobi7QBQ3VxxbRbG0ED1fYqjOA8AJVI33lFORERERERERERERBUaG+VEREREREREREREVKGxUU5EREREREREREREFZpWNcpDQ0MhkUggkUjQrFkzuLq6olevXggJCcGjR4+KFfPGjRvYuHFjqc0xLi5OnOPly5flXt+/f7/4OpWu0n4vSyI5ORkSiQSHDh0qdNybN2/w3XffoXv37nBwcIC7uzuGDBmCHTt2iGPi4uIQGhqKly9fluoctSlfRERERERERERE2kyrGuUAYGJigoiICOzbtw9r165Fv379cPXqVfTu3RtHjx5VOd7NmzexadOmUp9n5cqVcfLkSbntJ06cQOXKlUv9eFR272VZyc7OxogRI3DmzBmMGjUKW7duxezZs9G8eXNcuHBBHPf8+XOEhYXh1atXpXr88pYvIiIiIiIiIiIiTTHU9AQ+pq+vDycnJ/FnDw8PDB06FAEBAZg1axZcXFxQr57mn5TbpUsXnDlzBvPnz0elSpUAAK9evcIvv/wCPz8/HDt2TMMzJE27efMm/vzzT+zevRtubm7idl9fX+Tm5hYrZkZGBkxMTEprikRERERERERERAQtvKNckUqVKmHOnDmQSqWIjIwUtx85cgRDhgxBmzZt4ObmBn9/f9y5c0d8PTQ0FGFhYUhLSxOXQ/H39wcAPHr0CNOmTYOXlxdatWoFHx8fbNu2TekGpqenJ/T09HDp0iVx28mTJ1G/fn20aNFCbvyKFSvQq1cvODs7o0OHDpg+fbrcHcT+/v744osvEB0dDW9vbzg7O2P48OF4+vSpyrEEQUBYWBg8PDzg7OyMKVOm4OrVq5BIJLhx44bMuB9++AHe3t5o2bIlunTpIrMsSH4enZ2d8fvvv2PQoEFwdHRE37598fvvvyMzMxPz5s2Dm5sbPD095fYFgJiYGAwfPhxOTk5wdXXFV199hcTERPH1/OVsjh49ipCQELi5uaF9+/ZYunQpsrOzxTkU9F4qEhMTg/Hjx6N9+/ZwcnJC7969ceTIEZkxN27cgEQiwZUrV/DVV1/B2dkZnTp1wpYtW+Ti7d+/H507d0arVq0wYsQI/P333wUeO9+7d+8AANbW1nKv6evri3MYPnw4AKB///4yy/bkz+/ixYuYMmUKXFxcMHXqVAAlu/aBvOt/woQJcHV1hZOTEwICAuSus5SUFMyYMQPOzs5wd3fHqlWrsG3bNnF+UqkUHh4eWL16tdz5ffnll+jfv3+ROSIiIiIiIiIiItIGWndHeUEaN26MWrVqISYmRtwWFxeHPn36oH79+sjKykJUVBSGDRuGY8eOwdbWFgMGDEB8fDxOnDiB8PBwAICZmRmAvLu/bW1t0atXL1SpUgX3799HaGgo0tLSMGnSpCLnY2xsjG7duuHEiRPo3r07gLxlV/z8/BSOT0xMxBdffIGaNWsiKSkJ27dvh7+/P6KiomBo+L+34f79+0hKSsKMGTOQk5ODJUuW4Ouvv0ZERIRKsXbt2oWwsDCMHTsWn376Ka5fv47Zs2fLzWvRokWIjIzE+PHj0apVK9y6dQsrVqxApUqVMGTIEHGcVCrFN998g5EjR6JGjRpYsWIFJk2aBBcXF1SvXh1r1qzBuXPnsHjxYjg6OsLFxQVAXsPa398fXl5eWL16NdLT07FmzRoEBgbKnBMArFmzBl26dMGaNWsQExOD0NBQ1K9fH0OGDCn0vVTkn3/+gYuLC4YMGQJjY2PcunULs2fPhiAI6Nu3r8zYefPmoXfv3li3bh3Onj2LFStWQCKRwNPTEwBw4cIFzJkzB/369YOPjw/u3bsnNqwLY29vD319fcyePRsTJ06Eq6srjI2NZca0aNECc+fORUhICBYvXgw7Ozu5OHPmzMFnn32GdevWiQ32klz7z549w+DBg9GkSRMsWbIEenp62LhxI0aOHIno6GhxjsHBwbh+/Tq+/vprfPLJJ9i/fz/u3bsnzsvIyAh9+/bFkSNHMHXqVHFub9++xblz5zBr1qwic0RERERERERERKQNyk2jHABq166N169fiz9/2NDOzc2Fh4cH7ty5g8OHD2P69OmwsbGBjY2N3HIuAODu7g53d3cAeXdVu7q6IiMjA7t371aqUQ4Afn5+CAwMxPv375GYmIi7d+9i+fLlMneZ51u8eLH455ycHDg7O8PT0xPXr19H+/btxddSUlJw5MgRWFlZAQDS0tIQHByM+Ph42NjYKBUrJycHmzdvRr9+/TBjxgwAQPv27fHmzRscOHBA3Pfp06fYvXs35s+fj0GDBgEA2rVrh4yMDKxbtw6DBg0Sm59SqRQzZsyAl5eXmO/85npwcDAA4NNPP0V0dDSio6PFRvnKlSvRsmVLhIWFQU9PDwDQtGlT+Pn54dKlS2I8AHB0dBSb+R4eHrhx4wZOnz6NIUOGFPpeKuLr6yv+WRAEuLm54eXLl4iIiJBrlHfv3h2TJ08GkHddXLx4EadPnxYb5Rs2bEDr1q3FvHfo0AGZmZlYv359oXNo2LAhgoKCsHz5cowcORJGRkZwdHREz549MWTIEBgaGsLMzAyNGzcGADRp0gQODg5ycTp37oyvv/5aZltJrv2wsDBUrVoV27dvF5cNcnFxQZcuXRAZGYlhw4bhr7/+wpkzZ7B06VL06dNHPO+ePXvKxBowYAC2bt2Kn376SXwvjx8/Dn19/QI/NCqKIAgAgPT09GLtT8rJzy/zXPa0Iddl9ewM1qt6aMM1VFFoQ65Zr2UnLS2tzPfVhmuootCGXLNelVOS2iuN/QuKqQ3XUEWgLXkuy2fJafrcNKEs6rIw2nId6TptybMmn/1YrhrlgiCIzVYgb/mIVatWISYmRmYpj9jY2CJjZWZmYtOmTTh+/DhevHgBqVQqvvb+/XtUqVKlyBiffvopqlSpgrNnz+L58+do0aIFbG1tFTbKL126hA0bNuDhw4dITU2VmeuHjfJmzZqJTXIAYhP1w0Z5UbHi4+ORkJCAzp07y8yhS5cuMo3yq1evAshrFOcvcQLkNcu3bNmCFy9e4JNPPgGQt1RI/gcLQF4TOH9sPgMDA9SvXx/x8fEA8grr1q1bmDlzJnJycmT2rV27Nu7evSvTKP8wDwDQqFEjXL9+XS6Xynj37h1CQ0Nx7tw5vHz5Ujy+paWl3NgPj6unp4dGjRqJ55CTk4N79+7JNaq9vb2LbJQDwIgRI+Dj44Pz58/j5s2buHbtGhYuXIgff/wR4eHh4gcRhenYsaPctpJc+1euXIGPjw8MDAzE993CwgLNmzfHb7/9BgC4e/cugLxrJp++vj46deqE7du3i9saNGiANm3a4ODBg+J7eejQIXh7exd6x39h8uekzLlQyTHP6qPJXLu6upZJXNarejHP6sN6La/kl7v70P3798toX3m6nWftwnpVRs1SilM8iutH+TmpWn/KHOfDmKxX9dB0nsuqXoGSnptm67O4SlaXxafp66ii0HSey7Jei1KuGuXx8fFigzY1NRWjR4+GlZUVgoKCUKdOHVSqVAmzZ89GZmZmkbGWL1+OyMhITJw4ES1btoS5uTnOnTuHDRs2IDMzU6lGuYGBAXr27ImoqCg8f/4cn3/+ucJxd+7cQWBgILp06YJx48ahevXq0NPTw8CBA+XmamFhIfOzkZERAIjjlImVkJAAADINdwCoXr26zM9v3ryBIAj49NNPFc77w0a5iYmJzLIh+fMyNzeXm2/+PJKTk5GTk4PFixfL3AX/YfwPKYqVlZWlcG5FCQoKQkxMDCZOnIjGjRvDzMwMe/fuxalTp+TGKjpuSkoKACApKQnZ2dlyuaxRo4bSc7G2tsagQYMwaNAgSKVSzJ07F4cOHcKFCxdkGtEF+fh9K+m1/+bNG4SHh4tLsnwo/31NSEiAkZGRXG4+zgMADBw4EEFBQUhKSsKrV6/w+++/IygoqMh5FMTQ0BDZ2dlo2LAhTE1Nix2HCpeeno7Y2FjmWQ10OdesV/XQ5WtI2+hyritCvT678brQ1+3t7ctk3w/p8jWkbXQ516Vdrz/dTix6UBlSVD+qzEnZ+lOkoOPY29vr9DWkTSpCnktybj/FaLY+i6skdVkcFeE60gbMczlqlD98+BAvX74Ul824ffs24uPjsWnTJjRr1kwcl5KSIt55XZjo6GgMGjQIAQEB4jZFd4IXxdfXF8OGDQMA+Pj4KBxz9uxZmJmZYc2aNeIdxM+fP1f5WMrGyn94ZFJSksz2D+88BoCqVatCT08Pe/bsERukH7K1tS3WHPOZm5tDT08PX3zxBbp27Sr3erVq1UoUvyCZmZm4ePEigoKCZB5guWfPHpVjWVlZwdDQUC6XHy4BpAojIyOMHDkShw4dwqNHj5RqlH/4LQqg5Nd+1apV4eXlhaFDh8q9lv8BkbW1NaRSKVJSUmSa5R/nAcj7RsKCBQtw7NgxxMXFoX79+mjTpk2R8yhI/vmamppq9Os2FQXzrD66mGvWq3oxz+qji7lmvZbsa7yq7luR86xuupjr0q9XzTbiFJ+D8nMqWQ4UH+fDmLp4DWkjXc5zyc6tfDbKNfVe6vJ1pE0qcp7LRaM8MzMTCxYsgLGxMQYMGAAAyMjIAACZBu+tW7fw/PlzNGnSRNxW0F3JmZmZMvvm5OQgKipK5bk5OzvDz88P1atXL7BJmZGRASMjI5mG5/Hjx1U+lrKxbGxsYG1tjXPnzsk0qM+ePSszLn8plbdv38ot01IaKleuDCcnJzx+/Fjh2tuqUvYO86ysLOTm5sq8v6mpqTh//rzKxzQwMEDz5s1x5swZjBw5Utx++vTpIvd9+/YtzMzMZB7WCvzvKyz5H2h8/K2BopT02nd3d8fDhw/RvHlzGBgYKDxGy5YtAQDnzp0T1yjPzc3FhQsX5MYaGxujd+/eiIyMxOvXrzFy5Ei55j4REREREREREZE207pGeW5uLm7fvg0g7+EADx48QEREBJ49e4YlS5agbt26AAAnJydUrlwZ8+fPR0BAAF6+fInQ0FDUqlVLJl6jRo2QnZ2N8PBwODs7w8zMDHZ2dmjXrh0iIyPRuHFjVKtWDXv27CnWMh96enpYvnx5oWM8PDwQHh6OBQsWoFu3boiJicHRo0dVPpaysQwMDBAQEIDvvvsONWrUQNu2bXHjxg1cu3YNAMQ70W1tbTFs2DDMnDkTY8aMQatWrSCVShEbG4sbN24otQZ3UWbOnIkRI0bgyy+/hK+vLywsLBAfH4+rV6+iX79+aNu2rdKxCnovP2Zubg4HBwds2bJFvCN88+bNMDMzU3hHdFHGjx+PwMBABAcHw8fHB/fu3VPq/bt+/TpWrFiBvn37wtHREYaGhrh//z42bdqEOnXqoFu3bgDyvqZlYGCAgwcPwtDQEAYGBoV+sFDSa3/KlCno378/xowZg4EDB6JGjRp4/fo1bt68idatW8PPzw9NmjRBt27dsHDhQqSnp6NOnTrYv38/MjIyFDbBBw4ciPDwcBgYGKBfv34qZpiIiIiIiIiIiEiztK5RnpGRgUGDBgHIuyO5bt26cHd3R1hYGBo1aiSOq1GjBr7//nssW7YMgYGBaNiwIebPn4+tW7fKxOvUqROGDh2KzZs3IzExEW5ubti1axfmzJmDefPmYcGCBTA1NUXfvn3RrVs3zJ49u9TPycvLCzNmzMDu3btx6NAhuLi4YNOmTfD29i6zWP7+/khOTsaePXuwa9cuuLu74+uvv8a0adNkltKYPXs2bG1tERERgXXr1qFKlSqwtbVFjx49SnzeAODi4oI9e/YgNDQUwcHBkEqlsLGxwaeffooGDRqoFKug91KRlStXYu7cuQgKCoKlpSX8/f2RlpaGbdu2qXwOXbp0wfz587Fx40ZERUWhVatWWLNmjfjthoK0atUK3t7eOHfuHMLDw5GZmQkbGxv06tULAQEB4sMuraysMHfuXGzduhXHjh1DdnY2/vzzzwLjlvTab9CgASIjI7FmzRrMnz8faWlpsLa2hpubGyQSibj/d999h5CQECxbtgzGxsbo27cvmjRpgn//+99yc2rcuDEaNmyI+vXryzXsiYiIiIiIiIiItJ2eIAiCpidB6rFmzRps374dN27cgImJiaanQ+XQsGHDoK+vL/cBxdOnT9G9e3d8//33xfoAKN/du3chCAKkUins7e0r7JpY6pCWlob79+8zz2qgq7lmvaqPrl5D2khXc11R6vXIhaeFvt6nU/0y2fdDunoNaSNdzXVZ1Ovpq8/ktqVnKm4DZEqVi2luqnipxaxs+biK6kfRnAri3a6e0mOVPY53u3o6ew1pG13O8927d5GVlVWiczt9peBaeJemuE5TMwpu41U3V30ZVEMD1ffx7VD8uiwOXb6OtAnzrIV3lFPpePToEY4dOwZnZ2cYGRnh5s2b+OGHHzBkyBA2yUkpp0+fxosXL9C0aVOkp6fjxIkT+M9//oN169aJY968eYMnT55g3bp1qFOnjlIPJyUiIiIiIiIiItI2bJTrKBMTE8TExGDv3r14//49atWqhTFjxmDy5MmanhqVE5UrV8bRo0cRGxsLqVQKOzs7LF++XOYBsRcuXMC3336LBg0aYPny5XIPLiUiIiIiIiIiIioP2NXSUZ988gl27typ6WlQOdahQwd06NCh0DH9+vXjwzuJiIiIiIiIiKjcY6OciIiIiIhKzbpDfxf42sR+hT/MvbB9i9pf2XXES3tfIm2Xk6P8WEVrj/soWIv45E+K11VWtpZKsu64KtR1HKLi8vYo3Wu0oDXPi3ucwtZQJ9JF+pqeABERERERERERERGRJrFRTkREREREREREREQVGhvlRERERERERERERFShVdhGeWhoKCQSCSQSCZo1awZXV1f06tULISEhePToUbFi3rhxAxs3bizlmQKPHj3CV199BQ8PD7Rs2RJdu3bFsmXLkJycXOrHynfjxg1IJBLcvXtX3CaRSPDDDz+IPx86dAjHjx8vszmUtrNnz0IikSAuLq7IsR/nvEuXLliyZAnevn1b9hMtpri4OISGhuLly5cy2xW9l0RERERERERERPQ/FbZRDgAmJiaIiIjAvn37sHbtWvTr1w9Xr15F7969cfToUZXj3bx5E5s2bSrVOf7yyy/o378//vrrL8ycORPbtm3D8OHDcfjwYQwePBhJSUmlerzCREREoFevXuLPhw8fxokTJ9R2fHVRlPMRI0bg6NGjGDRoEBISEjQ9RYWeP3+OsLAwvHr1SmZ7ixYtEBERgUaNGmloZkRERERERERERNrNUNMT0CR9fX04OTmJP3t4eGDo0KEICAjArFmz4OLignr1NPeU7IyMDEyfPh116tTB3r17UblyZQBAmzZt4OHhgT59+mDhwoVYtWqVWubzYa50lTI5DwkJQWhoqNrmY2JiUqIYZmZmFeK9IyIiIiIiIiIiKq4KfUe5IpUqVcKcOXMglUoRGRkpbj9y5AiGDBmCNm3awM3NDf7+/rhz5474emhoKMLCwpCWliYu6eLv7w8gbxmPadOmwcvLC61atYKPjw+2bduG3NzcQucSHR2NV69eYfz48WLDNl+jRo3Qu3dvnDp1Slxq49ChQ5BIJHJ3mffu3RtBQUHizzExMRg/fjzat28PJycn9O7dG0eOHCkyNx8uveLv74+bN2/i4sWL4vmGhoZi165daNWqFVJTU2X2ffToESQSCS5dulRg/KJyDOTl2dnZGX/++SeGDBmCVq1awc/PDz/99JPMOKlUikWLFqFNmzZwdXXFt99+i/fv3xd5jsrk/MyZM3j+/DmA/y1rcunSJUyaNAlOTk5o3769wiV4Hj16hAkTJsDV1RVOTk4ICAjA06dPZcZIJBJs3rwZy5cvh4eHB9zd3QEU/Z7duHEDw4cPBwD0799ffE8+nOOHS69kZmZi8eLFaN++PRwcHMTz+lBQUBD8/Pxw48YN9OnTB05OTujfvz9+++03mXEHDhyAr68vHB0d0bZtWwwZMkTufSMiIiIiIiIiItJmFfqO8oI0btwYtWrVQkxMjLgtLi4Offr0Qf369ZGVlYWoqCgMGzYMx44dg62tLQYMGID4+HicOHEC4eHhAPLu5AWAV69ewdbWFr169UKVKlVw//59hIaGIi0tDZMmTSpwHjdv3gQAdOrUSeHrnTt3RmRkJH755Rf4+fkpfX7//PMPXFxcMGTIEBgbG+PWrVuYPXs2BEFA3759lYoxb948fP311zAxMcE333wDALCxsYGpqSmWL1+OEydOYPDgweL4AwcOoFatWmjfvn2BMYvKcT6pVIoZM2Zg+PDhCAwMxJYtWzBlyhScP38e1apVAwCsWrUKe/fuxeTJk9G8eXNERUVh5cqVRZ6Xsjn/73//i08++UTcPmfOHPj6+iI0NBRXr17F6tWrUbVqVQwZMgQA8OzZMwwePBhNmjTBkiVLoKenh40bN2LkyJGIjo6GsbGxGGvnzp1o1aoVFi1ahOzsbABFv2ctWrTA3LlzERISgsWLF8POzq7Q85wxYwZ++uknfPnll7Czs8PRo0cxefJkrFu3Dl26dBHHJSQkYOHChQgICIC5uTlWrlyJSZMm4cyZMzAyMsIvv/yCWbNmYfTo0fDy8kJGRgbu3LmDlJSUInNNRERERERERESkLdgoL0Dt2rXx+vVr8ecPG9q5ubnw8PDAnTt3cPjwYUyfPh02NjawsbGRW84FANzd3cU7gwVBgKurKzIyMrB79+5CG+UvX76EhYWF2HD/WJ06dQAA8fHxKp2br6+v+GdBEODm5oaXL18iIiJC6UZ548aNYWZmhsqVK8udr7e3Nw4ePCg2yrOzs3Hs2DH0798fBgYGBcYsKsf58hvlXl5eAABbW1t06dIFly9fRu/evfH27Vvs2bMH48aNwxdffAEA6NChA/71r3/JPejyY8XN+aeffip+YNChQwckJiZiw4YNGDRoEPT19REWFoaqVati+/btqFSpEgDAxcUFXbp0QWRkJIYNGybGqlq1KsLCwqCnpyduK+o9MzMzQ+PGjQEATZo0gYODQ4Hn+Mcff+DHH3/E/PnzxffI09MTz58/l2uUv3v3Drt370aTJk0AAKamphg+fDh+/fVXtG7dGnfu3IGlpaV47gDQsWPHAo+tDEEQAADp6eklikOFy88v81z2tCHXH39DprSwXtVDG66hikIbcq3r9ZqWlqbR/cuaNlxDFYU25FrX6zWfKnWn7TX6IW24hioCbclzWdUroPlzU0Zp16a6a11briNdpy15Lst6LQob5QUQBEGmUfno0SOsWrUKMTExSExMFLfHxsYWGSszMxObNm3C8ePH8eLFC0ilUvG19+/fo0qVKiWa64fzVMa7d+8QGhqKc+fO4eXLl8jJyQEAWFpalmge+QYOHIh//etfePjwIZo0aYJLly4hMTERn3/+eaH7KZtjfX198YMHAKhbty5MTEzEJviDBw+QkZGBbt26yezXvXt3/PLLLyU8O8U+Ppa3tzeOHj2K+Ph41KlTB1euXIGPjw8MDAzEu8QtLCzQvHlzuaVMPD095d7T0nzP/vvf/wIAevToIbO9Z8+eWLx4MdLS0sRfSjVr1hSb5ADEZnx+rps3b463b98iKCgIvXr1gouLC0xNTVWe04fy86NMbVHJMc/qo8lcu7q6lklc1qt6Mc/qw3otqRoFvnL//v1i76vc/tqB9ao+rFdl1CzR3orrTnHM8lKjH2K9qoem81xW9Qpo/txklXZtaleta1eudZem81yW9VoUNsoLEB8fj4YNGwIAUlNTMXr0aFhZWSEoKAh16tRBpUqVMHv2bGRmZhYZa/ny5YiMjMTEiRPRsmVLmJub49y5c9iwYQMyMzMLbJTb2Njg559/RmpqqsI7nP/55x8AQK1atVQ6t6CgIMTExGDixInineF79+7FqVOnVIpTEDc3N9ja2uLAgQMIDg7GwYMH4ebmhvr16xe4jyo5NjExkVmqBACMjIzEcQkJCQCA6tWry4ypUaPw/+MF5OUyOTm5yJzb2NjIbLeyslJ4rISEBNSpUwdv3rxBeHi4uCzPx3P/0MfzBkr3PXv37h2MjIzkmuw1atSAIAhISUkRG+UWFhYK55qfa3d3dyxbtgw7d+7EmDFjUKlSJXh7e+Pbb78t9gcvhoaGyM7ORsOGDUvcdKeCpaenIzY2lnlWA13ONetVPXT5GtI2upxrddbr9b8TCnzN3t6+2Psqs7+m6fI1pG10OdelXa8v/5tY9KBCKKq7gmJqe41+SJevIW1SEfKsTef2U0zp1mZpxyuuinAdaQPmmY1yhR4+fIiXL1+Ky5Dcvn0b8fHx2LRpE5o1ayaOS0lJkWuYKhIdHY1BgwYhICBA3FbYQy3zubm54cCBA7h06ZLM0hv5Ll68CH19ffGTlvwlPT68Yx0AkpOTxT9nZmbi4sWLCAoKEh82CgB79uwpcj6qGDBgALZu3YpRo0bh0qVLWLRoUaHjS5rjD1lbWwMAEhMTZT5E+HApnYK0adMGBw8eLDTnenp6aN26tcz2jx+gmn+s/LlUrVoVXl5eGDp0qFzMjz8o+fhu8tJ+z6pWrQqpVIp3796hatWqMnPW09ODubm5SvF69+6N3r17IykpCefOncPixYthaGiI7777rljzyz9/U1NTjX7dpqJgntVHF3PNelUv5ll9dDHX2lKvJT12eXlfNJ3nikQXc1369VqyRrniOSiOWR7fC128hrSRLudZu86ttGtTu2pdu3KtuypynvU1PQFtk5mZiQULFsDY2BgDBgwAAGRkZACQvfP31q1beP78ucy+RkZGyMrKUhjzw31zcnIQFRVV5Fx69OiBWrVqYcOGDXLrAz158gSHDx+Gp6cnateuDeB/d5Y/fvxYHPfo0SO8ePFC/DkrKwu5ubky80lNTcX58+eLnM/HPryL+2N9+/ZFSkoKZsyYARMTE7llPj6mbI6V0bRpU5iYmODMmTMy23/88cci9+3Rowdq1qxZaM67d+8urlWe7+NjnT59GjVr1hSb/O7u7nj48CGaN28OBwcHmf+KevCmsu/Zx3d7FyT/g5Xo6GiZ7dHR0WjevHmxfxlaWVlhwIAB8PDwkLkGiYiIiIiIiIiItF2FvqM8NzcXt2/fBpD3IIIHDx4gIiICz549w5IlS1C3bl0AgJOTEypXroz58+cjICAAL1++RGhoqNySJ40aNUJ2djbCw8Ph7OwMMzMz2NnZoV27doiMjETjxo1RrVo17NmzR2FD/WMmJiZYuXIlAgICMHToUIwaNQq1a9fGH3/8gY0bN6Jq1apYsGCBOL5Vq1aoXbs2vvvuO3z11VdITU3F5s2bZZbAMDc3h4ODA7Zs2QIrKysYGhpi8+bNMDMzk7sruih2dnY4cuQIzp8/D2tra9SsWVPMiZWVFbp06SLeTW9iYlJoLGVzrAxLS0sMHjwYW7ZsgYmJCZo3b46oqCg8ffq0yH1NTEywatWqAnNeu3ZtzJkzR26/69evY+nSpfDw8MCVK1dw9OhRzJ07F/r6eZ9FTZkyBf3798eYMWMwcOBA1KhRA69fv8bNmzfRunVr+Pn5FTgnZd+zhg0bwsDAAAcPHoShoSEMDAwUPtSzWbNm6N69O5YsWYKMjAzY2tri2LFjiImJwfr165VJsWjt2rV4+/Yt2rRpg+rVq+PBgwf46aefMHLkSJXiEBERERERERERaVKFbpRnZGRg0KBBAPK+NlK3bl24u7sjLCwMjRo1EsfVqFED33//PZYtW4bAwEA0bNgQ8+fPx9atW2XiderUCUOHDsXmzZuRmJgINzc37Nq1C3PmzMG8efOwYMECmJqaom/fvujWrRtmz55d5Bzzl19Zt24dlixZgqSkJAiCAGdnZ6xbt05mPWsjIyOEhYXh//7v/zB16lTUr18f3377LZYsWSITc+XKlZg7dy6CgoJgaWkJf39/pKWlYdu2bSrlb9y4cXj69Cm++eYbJCcnY9KkSZg8ebL4erdu3RAdHY3+/fsXGUvZHCvrq6++Qk5ODrZu3Yrc3Fx069YNX331FWbOnFnkvvk5X79+PZYsWYLk5GTUrFkTvXr1wvjx4xWuvR0SEoKIiAjs3bsXVapUwdSpUzFs2DDx9QYNGiAyMhJr1qzB/PnzkZaWBmtra7i5uUEikRQ5J2XeMysrK8ydOxdbt27FsWPHkJ2djT///FNhvOXLl2PVqlXYsmUL3r59Czs7O6xduxadO3cuci4fcnBwQHh4OE6dOoXU1FTY2NhgzJgxmDBhgkpxiIiIiIiIiIiINElPEARB05Mg1cydOxcnT57Enj170LRpU01Pp0AzZ87E/fv3cfz4cU1PpczcuHEDw4cPx4EDBxTevU2quXv3LgRBgFQqhb29fYVdE0sd0tLScP/+feZZDXQ116xX9dHVa0gb6Wqu1V2v6w79XeBrE/s1KPa+yuyvabp6DWkjXc11WdTryZ+eyW3LylbcBjA21JPb5tOhnlIxCxqrrXT1GtI2upznu3fvIisrS6vO7fQVxbXp7VG82izteMWly9eRNmGeuUZ5uTR79mw0btwYAQEBePXqlaanI+fPP//EkSNHcPLkSQwfPlzT0yEiIiIiIiIiIiIqVIVeeqW8MjY2xr59+zQ9jQJNmDABSUlJ6NOnDz7//HNNT4eIiIiIiIiIiIioUGyUU6k7f/68pqegNm3bti1wHXAiIiIiIiIiIiIqH7j0ChERERERERERERFVaLyjnIiIiIiISk1JHrip7Q/rJCqPyuIBm+XpoZ1EVPBDOYHCH8xZ0GvFjVcQbXloKBHvKCciIiIiIiIiIiKiCo2NciIiIiIiIiIiIiKq0NgoJyIiIiIiIiIiIqIKjY3yCu7cuXMYPXo02rRpg5YtW6Jz586YO3cunjx5Io6RSCT44Ycf1Dan+/fvQyKR4MaNG2o7piKLFi1C586dCx1z6NAhSCQS8T9nZ2f06NEDwcHBuHPnTrGOe//+fYSGhiI9Pb1Y+6v7/SIiIiIiIiIiIirv2CivwFasWIHAwECYmZlhwYIF2L59OyZOnIi//voL06ZN0/T0ypWtW7ciIiIC69evx/Dhw/HgwQMMHDgQmzdvVjnW/fv3ERYWVuxGOREREREREREREanGUNMTIM24dOkStmzZgsDAQEydOlXc7ubmhs8//xwXLlzQ4OzKnxYtWsDKygoA4O7ujsGDB+Obb77BqlWr4OLigtatW2t4hkRERERERERERFQQ3lFeQW3btg01atRAYGCgwtc7depU6P779u2Dt7e3uFzL+vXrkZubK74eGhoKZ2dnuf1at26N0NBQmW3r16+Hh4cHnJ2dMWnSJCQmJip1DitWrECvXr3g7OyMDh06YPr06Xj16pXMGH9/f3zxxReIjo6Gt7c3nJ2dMXz4cDx9+lRm3MuXLzF+/Hi0atUKHTp0wJYtW5SaQ0H09fUxa9YsGBsbY+/eveL2ixcvYtSoUXB3d4eLiwsGDBiAy5cvi68fOnQIwcHBAPIa7hKJRFz+5dWrVwgODkaXLl3g6OiI7t27Y9WqVcjKypI7fk5ODpYtW4ZPP/0Uzs7OCAoKQmpqqvh6WloaQkJC4O3tjVatWolL7qSkpMjEOXfuHPr16wdnZ2e0bt0a/fr1w6VLl2TGHDp0CL169YKDgwM6dOiA1atXIycnp0T5IyIiIiIiIiIiUifeUV4BZWdn49atW+jevTuMjIxU3n/Xrl1YuHAh/P390bFjR8TExCAsLAwpKSn45ptvVIq1e/dufP/99xg9ejTatWuHq1evYtasWUrtm5iYiC+++AI1a9ZEUlIStm/fDn9/f0RFRcHQ8H+X9v3795GUlIQZM2YgJycHS5Yswddff42IiAhxTGBgIF6+fIn/+7//g7m5ObZs2YIXL17IxFGVpaUlWrRogZiYGHFbXFwcOnXqhNGjR0NfXx+XL19GQEAAwsPD0bZtW3Ts2BETJkzAhg0bsHXrVpibm8PY2BgA8ObNG1haWiI4OBgWFhaIjY1FaGgoEhISsHjxYplj79q1Cy1atMDSpUsRFxeHFStWIDPvPRBmAAAdZElEQVQzE6tXrwYAZGRkICcnB9OmTYOVlRVevHiBjRs3IjAwELt27QIAPH36FFOnToWvry+++uor5Obm4o8//sC7d+/E42zfvh3Lly/HiBEjEBQUhEePHomN8hkzZhQ7d0REREREREREROrERnkF9PbtW2RlZaFOnToq75uTk4N169bB19cXs2fPBgC0b98eUqkU27ZtQ0BAAKpVq6Z0rE2bNqF3795ig71Dhw5ITEzE0aNHi9z/w+ZwTk4OnJ2d4enpievXr6N9+/biaykpKThy5Ii4NEpaWhqCg4MRHx8PGxsbXL58Gb/99ht27NgBd3d3AEDbtm3h5eUFS0tLpc6lILVr18a9e/fEn//1r3+Jf87NzUXbtm3x119/Yf/+/Wjbti2srKxQv359ALLLuQB5D+n88IMIFxcXmJqaIigoCHPnzoWpqan4mrGxMdatWwcDAwMAQKVKlTB79mxMmjQJjRo1gpWVFebPny+Oz87ORt26dTF06FA8efIEtra2+P333yGVSjFnzhyYmZkByHt/8qWmpmLt2rUYO3Yspk+fDgDw8PCAkZERlixZgjFjxih9LXxIEAQA4BrtZSw/v8xz2dOGXFeuXLlM4rJe1UMbrqGKQhtyzXot37ThGqootCHXrNfyTRuuoYpAW/JcVvUKaP7cSiotLU1r4+XH0pbrSNdpS57Lsl6LwkZ5Baanp6fyPo8fP8abN2/Qo0cPme0+Pj7YtGkT7ty5Ay8vL6VixcfH49WrV+jWrZvMdm9vb6Ua5ZcuXcKGDRvw8OFDmWVFYmNjZRrlzZo1k2k4N27cWDy+jY0N7ty5A3Nzc7FJDgDm5uZo164dfv/9d6XOpSCCIMjkOT4+HqtXr8bVq1eRkJAg/iO4RYsWSsUKDw/H/v37ERcXh8zMTPG1Z8+eoWnTpuLPnTp1EpvkANCjRw/MmjULd+/eRaNGjQAAR44cwY4dO/D333/L/EUWGxsLW1tbSCQSGBgYYMaMGRg4cCDc3Nxgbm4ujouJiUFaWhp69OiB7OxscXu7du2QkZGBhw8fok2bNqqkCwDEWLGxsSrvS6pjntVHk7l2dXUtk7isV/VintWH9UolxTyrD+uVSop5Vg9N57ms6hXQ/LnJqqnyHvfv3y/V45RmvI9jaVeudZem81yW9VoUNsorIEtLS1SqVAn//POPyvvmL7tRvXp1me35P3+4LEdREhISAECmiQ0ANWrUKHLfO3fuIDAwEF26dMG4ceNQvXp16OnpYeDAgTINZACwsLCQ+Tl/uZn8ca9evZKbAyB/jsURHx8vnk9ubi4mTJiAlJQUTJkyBQ0aNICpqSnWrl2LFy9eFBkrPDwcS5cuxdixY9G2bVtYWFjg7t27CAkJkTvnj+duZmaGSpUqiWu4nzlzBt988w0GDRqEadOmwdLSEgkJCZg4caIYy9bWFhs3bsSmTZswadIk6Ovro3379pg7dy7q1KmDN2/eAAD69u2rcL7KnJMihoaGyM7ORsOGDWXukqfSlZ6ejtjYWOZZDXQ516xX9dDla0jb6HKuWa/qocvXkLbR5VyzXtVDl68hbVIR8qxN5/ZTjHLPfPuQvb19qR6nNOPlx6oI15E2YJ7ZKK+QDA0N4eLiguvXryM7O1uldbjzlyJJSkqS2Z7/AM6qVasCyFvqQyqVyoyRSqUydy5bW1srjPX69esi53H27FmYmZlhzZo10NfPeybt8+fPlT6PD+Wvcf4xZR8qWpA3b97gt99+g7e3NwDg77//xu+//45169aha9eu4riMjAyl4kVHR6Nz58746quvxG2PHj1SOPbjuaempiIzMxM1a9YUY9nb2yMkJEQcc/PmTbk4np6e8PT0RGpqKi5fvozFixcjODgY4eHh4nsdFhYGGxsbuX3r1q2r1Hl9LP8OfFNTU41+3aaiYJ7VRxdzzXpVL+ZZfXQx16xX9WKe1UcXc816VS/mWT10Oc/adW6q9zGKN/eCj1Oa8T6OpV251l0VOc/6mp4AacaoUaOQkJCAjRs3Knz90qVLCrfb2trCysoK0dHRMttPnToFIyMjODo6AgBq1aoFqVSKp0+fimOuX7+OnJwc8WcbGxtYW1vjzJkzMrFOnz5d5PwzMjJgZGQks6zJ8ePHi9xPEQcHB6SkpODatWvitpSUFFy9erVY8YC8u8e/++47SKVSDBs2DMD/7mD/8AGqz58/l3nY54evZ2VlyWzPP+cPFXTOFy5ckMl1dHQ09PT04ODgoHIsIO+OdB8fH/j6+orNeWdnZ5iamiI+Ph4ODg5y/xVnfXIiIiIiIiIiIiJN4B3lFZSXlxfGjh2L0NBQ/PXXX/D19UW1atUQFxeHgwcPIiUlReFa4wYGBggMDMTChQthZWUFLy8v3L59G1u2bMGIESPE5qinpycqV66M2bNnY9y4cYiPj8fOnTtRqVIlmVgBAQFYtGgRqlevDg8PD1y5cgU3btwocv4eHh4IDw/HggUL0K1bN8TExCi1rrkinp6eaNGiBb7++mvMmDED5ubm2Lx5s/gAS2Xcu3cP5ubmyMjIwJMnT3Dw4EHcu3cPX3/9NZydnQEAdnZ2sLGxwcqVK5Gbm4u0tDSsXbtWvMs7X/4a4v/+97/RtWtXmJiYQCKRoF27dti5cyd2796Nhg0b4tixY/j7778VzicrKwsTJ07EkCFDEBcXhxUrVsDb21uM3a5dO4SEhGDdunVwdnbGpUuXZD4oAIB9+/bh9u3b6NChA6ytrREXF4djx47Bw8MDQN6SNlOmTMHy5csRHx+PNm3awMDAAM+ePcO5c+cQGhpaYb+qQ0RERERERERE5Qsb5RVYfhP33//+N7799lukp6ejZs2aaN++PcaMGVPgfv7+/jA0NMSOHTuwd+9eWFtbY9KkSRg/frw4plq1ali7di2WLl2KiRMnwt7eHsuWLYO/v79crOTkZOzZswd79+6Fu7s7Fi5ciLFjxxY6dy8vL8yYMQO7d+/GoUOH4OLigk2bNonLnKhCT08P69evx7x58zB37lxYWFjA398fr1+/xrlz55SKkT/fypUro2bNmnBxccHcuXPFO+wBwNjYGKGhoQgJCcHUqVNRu3ZtTJgwAdevX8dvv/0mjmvevDkmT56MyMhIbN26FbVr18b58+cxceJEvHnzBmvXrgWQ99DT2bNny+Q9n7+/P5KSkjBz5kxkZWWhW7dumDt3rvj64MGDERcXh927d+OHH35A+/btsXLlSgwcOFAcI5FIcOHCBSxevBhv376FtbU1fH19MXXqVHHM6NGjUatWLWzfvh27d++GoaEh6tevj44dO8rdsU5ERERERERERKSt9ARBEDQ9CSIiALh79y4EQYBUKoW9vX2FXRNLHdLS0nD//n3mWQ10NdesV/XR1WtIG+lqrlmv6qOr15A20tVcs17VR1evIW2jy3m+e/cusrKytOrcTl95pvI+3h71SvU4pRkvP5YuX0fahHnmGuVEREREREREREREVMGxUU5EREREREREREREFRob5URERERERERERERUofFhnkREREREREREROVccdYH14bjqGveREXhHeVEREREREREREREVKGxUU5EREREREREREREFRob5URERERERERERERUobFRTkREREREREREREQVGhvlRERERERERERERFShsVFORERERERERERERBUaG+VEREREREREREREVKGxUU5EREREREREREREFZqeIAiCpidBRAQAt27dQv6vJCMjI+jp6Wl4RrpLEARIpVLmWQ20IdfGxsaQSCSlGpP1qj7acA1VFNqQa9Zr+aYN11BFoQ25Zr2Wb9pwDVUE2pLnsqxXTZ9bRaAt15Gu05Y8l0W9KstQI0clIlIg/xexkZGRhmei+/T09GBsbKzpaVQIuppr1qv66Oo1pI10NdesV/XR1WtIG+lqrlmv6qOr15C20eU859crG7dlT5evI23CPPOOciIiIiIiIiIiIiKq4LhGORERERERERERERFVaGyUExEREREREREREVGFxkY5EREREREREREREVVobJQTERERERERERERUYXGRjkRERERERERERERVWhslBMRERERERERERFRhcZGORERERERERERERFVaGyUExEREREREREREVGFxkY5EREREREREREREVVobJQTERERERERERERUYXGRjkRERERERERERERVWhslBORVnj06BFGjRoFJycneHh4YNmyZcjKytL0tNTq1KlTmDBhAjw9PeHk5ITevXvjwIEDEARBZlxkZCS8vb3h4OCAzz77DBcuXJCLlZKSgm+//RZt2rSBs7MzpkyZglevXsmNu3XrFgYNGgRHR0d06tQJmzdvljueIAjYvHkzOnbsCEdHRwwaNAi3b9+Wi/Xy5UtMnjwZzs7OaNOmDWbNmoXU1NSSJUVN3r9/D09PT0gkEty9e1fmNeZbHuuV9aoprFXVsV5Zr5rCelUd65X1qimsV9WxXlmvmsJ6LWMCEZGGvX37VvDw8BCGDRsmXL58WYiMjBRcXV2F+fPna3pqajVw4EBh2rRpQlRUlHD16lVhxYoVQrNmzYTQ0FBxzIkTJwSJRCKsXr1auHbtmjBnzhyhefPmQkxMjEys0aNHC56enkJUVJRw9uxZwc/PT/jss88EqVQqjomNjRWcnJyEiRMnClevXhW2b98utGjRQti6datMrE2bNgktWrQQtm/fLly9elWYOHGi4OzsLDx9+lQck5WVJfj5+Ql+fn7CuXPnhKioKMHT01MICAgom2SVsmXLlgnt2rUTmjZtKty5c0fcznzLY73mYb1qBmtVNazXPKxXzWC9qob1mof1qhmsV9WwXvOwXjWD9Vq22CgnIo3buHGj4OTkJLx580bctm/fPsHe3l6Ij4/X3MTULDExUW7b7NmzBRcXFyEnJ0cQBEHo3r27MH36dJkxgwYNEsaOHSv+fOvWLaFp06bCTz/9JG579OiRIJFIhKioKHHbnDlzhE6dOgmZmZnitpUrVwqtW7cWt2VkZAguLi7CypUrxTGZmZlCp06dhHnz5onbjh8/LkgkEuHRo0fitp9++klo2rSp8Ouvv6qaCrX666+/BCcnJ2Hv3r1y/9hgvuWxXvOwXtWPtao61mse1qv6sV5Vx3rNw3pVP9ar6liveViv6sd6LXtceoWINO7y5ctwd3eHpaWluK1nz57Izc3FlStXNDcxNbOyspLbZm9vj9TUVKSlpeHZs2eIjY1Fz549Zcb4+Pjg2rVr4lf9Ll++DAsLC3h4eIhj7OzsYG9vj8uXL4vbLl++jC5dusDY2FgmVnJyMmJiYgDkfd0qNTVV5pjGxsbo1q2bXCyJRAI7Oztxm4eHBywtLXHp0qXipkQtFi5ciMGDB8PW1lZmO/OtGOs1D+tV/VirqmO95mG9qh/rVXWs1zysV/VjvaqO9ZqH9ap+rNeyx0Y5EWnc48ePZX5pAoCFhQWsra3x+PFjDc1KO/z3v/9FrVq1YGZmJubi478UGzVqBKlUimfPngHIy6etrS309PRkxtnZ2Ykx0tLS8OLFC7m829nZQU9PTxyX/78fj2vUqBH++ecfZGRkiOM+HqOnpwdbW1utfg+jo6Px4MEDTJw4Ue415lsx1mvBWK9lh7VaPKzXgrFeyw7rtXhYrwVjvZYd1mvxsF4LxnotO6xX9WCjnIg0Ljk5GRYWFnLbq1atinfv3mlgRtrhP//5D06ePInRo0cDgJiLj3OV/3P+68nJyTA3N5eL92E+U1JSFMYyNjaGqampTCxjY2NUqlRJ7piCIKh0TG2Tnp6OJUuWYNq0aTAzM5N7nflWjPWqGOu17LBWi4/1qhjrteywXouP9aoY67XssF6Lj/WqGOu17LBe1YeNciIiLRQfH49p06ahbdu2GD58uKano5M2bNiA6tWr4/PPP9f0VKicY72WLdYqlSbWa9livVJpYr2WLdYrlSbWa9livaoPG+VEpHEWFhbip5YfevfuHapWraqBGWlWcnIyxo0bB0tLS4SGhkJfP+9XdX4uPs5VcnKyzOsWFhZITU2Vi/thPvM/0f04VlZWFtLT02ViZWVlITMzU+6Yenp6Kh1Tmzx//hzbtm3DlClTkJKSguTkZKSlpQHI+6rZ+/fvme8CsF5lsV7LFmu1ZFivslivZYv1WjKsV1ms17LFei0Z1qss1mvZYr2qFxvlRKRxH66HlS8lJQUJCQlya1npuoyMDHzxxRdISUnB1q1bZb6ilJ+Lj3P1+PFjGBkZoV69euK4J0+eQBAEmXFPnjwRY1SuXBm1a9eWi5W/X/64/P998uSJ3DHr1KkDExMTcdzHsQRBkDmmNomLi4NUKkVAQADc3Nzg5uaG8ePHAwCGDx+OUaNGMd8FYL3+D+u17LFWS4b1+j+s17LHei0Z1uv/sF7LHuu1ZFiv/8N6LXusV/Vio5yINM7T0xNXr14VP+0E8h5Uoa+vL/M0Zl2XnZ2NL7/8Eo8fP8bWrVtRq1Ytmdfr1auHhg0bIjo6Wmb7yZMn4e7uLj6R2tPTE+/evcO1a9fEMU+ePMHvv/8OT09PcZunpyfOnTsHqVQqE8vCwgLOzs4AABcXF5iZmeHUqVPiGKlUih9//FEu1h9//IHY2Fhx27Vr1/D27Vt4eXmVICtlw97eHjt37pT5Lzg4GAAwf/58zJs3j/kuAOs1D+tVPVirJcN6zcN6VQ/Wa8mwXvOwXtWD9VoyrNc8rFf1YL2qmUBEpGFv374VPDw8hH/961/CTz/9JBw4cEBo3bq1MH/+fE1PTa1mz54tNG3aVNi2bZsQExMj819mZqYgCIJw/PhxQSKRCN9//71w/fp1Ye7cuULz5s2FW7duycQaPXq04OXlJZw8eVI4d+6c4OfnJ3z22WeCVCoVx8TGxgpOTk7C5MmThatXrwo7duwQWrRoIWzdulUm1qZNm4SWLVsKO3bsEK5evSpMnjxZcHZ2Fp4+fSqOycrKEvz8/AQ/Pz/h/PnzQlRUlODl5SUEBASUYcZK1/Xr14WmTZsKd+7cEbcx3/JYr3lYr5rDWlUe6zUP61VzWK/KY73mYb1qDutVeazXPKxXzWG9lh02yolIK/z111/CiBEjBEdHR8Hd3V1YsmSJ+JdrRdGpUyehadOmCv979uyZOG7//v1Ct27dhBYtWoh/2XwsOTlZCA4OFlq3bi04OTkJkyZNEuLj4+XG/fe//xUGDBggtGzZUvD09BQ2bdok5ObmyozJzc0VNm7cKHh6egotW7YUBgwYIPeXrSAIQnx8vDBp0iTByclJaN26tRAcHCykpKSUQmbUQ9E/NgSB+VaE9cp61STWqmpYr6xXTWK9qob1ynrVJNaralivrFdNYr2WHT1B+GhhGiIiIiIiIiIiIiKiCoRrlBMRERERERERERFRhcZGORERERERERERERFVaGyUExEREREREREREVGFxkY5EREREREREREREVVobJQTERERERERERERUYXGRjkRERERERERERERVWhslBMRERERERERERFRhcZGORERERERERERERFVaGyUExFRkfz9/eHv76/paRCRElivROUH65Wo/GC9EpUfrFcqLkNNT4CIiMrGn3/+iXXr1uHu3bt4/fo1LC0t0bhxY3Tu3Jn/aCDSMqxXovKD9UpUfrBeicoP1itpAzbKiYh00K1btzB8+HDUqVMHAwYMgLW1NV68eIFff/0VO3fu5D80iLQI65Wo/GC9EpUfrFei8oP1StqCjXIiIh20ceNGmJub48CBA7CwsJB5LTExUUOzypOdnY3c3FwYGxtrdB5E2oL1SlR+sF6Jyg/WK1H5wXolbcE1yomIdNDTp0/RuHFjuX9kAED16tXFPx88eBDDhw+Hu7s7WrZsCR8fH+zZs6fI+FlZWfj+++/Rr18/uLq6wsnJCUOHDsX169dlxsXFxUEikeCHH37Ajh070LVrVzg4OODOnTtwcnLCwoUL5WLHx8fD3t4emzZtKsaZE5U/rFei8oP1SlR+sF6Jyg/WK2kL3lFORKSDPvnkE8TExODBgwdo2rRpgeP27t2LJk2aoHPnzjA0NMSFCxcwf/58CIKAYcOGFbhfamoqIiMj4efnhwEDBuD9+/c4cOAAxo4di8jISNjb28uMP3ToEDIzMzFw4EAYGxujTp066Nq1K06dOoXg4GAYGBiIY0+cOAFBENCrV6+SJ4KoHGC9EpUfrFei8oP1SlR+sF5JW+gJgiBoehJERFS6rly5gnHjxgEAHB0d4erqCnd3d7Rt2xZGRkbiuIyMDJiYmMjsO2bMGPz99984e/asuC1/Tbhdu3YBAHJycpCTkyPz9bPk5GT07NkTXl5e+O677wDkfSLfpUsXmJmZ4cyZM7CyshLH//zzzxgzZgy2bNkCT09Pcftnn32GqlWrisci0nWsV6Lyg/VKVH6wXonKD9YraQsuvUJEpIM8PDywb98+dO7cGX/88Qe2bt2KMWPGwNPTE+fOnRPHffiPjJSUFCQlJaFNmzZ49uwZUlJSCoxvYGAg/iMjNzcXb9++RXZ2Nlq2bInff/9dbnz37t1l/pEBAO3atUPNmjVx/PhxcduDBw/w559/4rPPPiv2uROVN6xXovKD9UpUfrBeicoP1itpCy69QkSkoxwdHREWFoasrCz88ccfOHv2LHbs2IGpU6fiyJEjaNy4Mf773/8iNDQUt2/fRnp6usz+KSkpMDc3LzD+4cOHsW3bNjx58gRSqVTcXrduXbmxirbp6+ujV69e2Lt3L9LT02Fqaorjx4+jUqVK6NGjRwnOnKj8Yb0SlR+sV6Lyg/VKVH6wXkkbsFFORKTjjI2N4ejoCEdHRzRs2BDBwcGIjo7GZ599hpEjR8LOzg5BQUGoXbs2jIyMcOnSJezYsQO5ubkFxjx69CiCgoLQtWtXjBkzBtWrV4eBgQE2bdqEZ8+eyY3/+Otx+fr06YMffvgBZ8+ehZ+fH06cOIGOHTsW+g8cIl3GeiUqP1ivROUH65Wo/GC9kiaxUU5EVIG0bNkSAPDq1SucP38eWVlZ2LBhA+rUqSOOuXHjRpFxTp8+jXr16iEsLAx6enri9rVr16o0n6ZNm6J58+Y4fvw4bGxs8M8//2D27NkqxSDSVaxXovKD9UpUfrBeicoP1iupG9coJyLSQdevX4eiZzVfunQJAGBnZyc+qfvDcSkpKTh48GCR8RXt++uvv+L27dsqz7V37964cuUKwsPDYWlpKfNgFKKKgPVKVH6wXonKD9YrUfnBeiVtwTvKiYh00MKFC5Geno5u3brBzs4OUqkUt27dwqlTp/DJJ5+gX79+eP36NYyMjDB+/HgMHjwY79+/R2RkJKpXr46EhIRC43fs2BE//vgjJk6ciI4dOyIuLg779u1D48aNkZaWptJc/fz8sHz5cpw5cwZDhgyReao5UUXAeiUqP1ivROUH65Wo/GC9krZgo5yISAfNnDkT0dHRuHTpEiIiIiCVSlGnTh0MHToUEyZMgIWFBSwsLLB27VqsWbMGS5cuRY0aNTBkyBBYWVnh22+/LTR+/j9UIiIi8PPPP6Nx48ZYvnw5oqOjcfPmTZXmWqNGDXh4eODSpUvo3bt3SU6bqFxivRKVH6xXovKD9UpUfrBeSVvoCYq+20BERKRGEydOxIMHD3DmzBlNT4WIisB6JSo/WK9E5Qfrlaj8YL3qLq5RTkREGvXq1St+Gk9UTrBeicoP1itR+cF6JSo/WK+6jUuvEBGRRjx79gy3bt3CgQMHYGhoiEGDBml6SkRUANYrUfnBeiUqP1ivROUH67Vi4B3lRESkEb/88gtmzpyJuLg4LFmyBNbW1pqeEhEVgPVKVH6wXonKD9YrUfnBeq0YuEY5EREREREREREREVVovKOciIiIiIiIiIiIiCo0NsqJiIiIiIiIiIiIqEJjo5yIiIiIiIiIiIiIKjQ2yomIiIiIiIiIiIioQmOjnIiIiIiIiIiIiIgqNDbKiYiIiIiIiIiIiKhCY6OciIiIiIiIiIiIiCo0NsqJiIiIiIiIiIiIqEJjo5yIiIiIiIiIiIiIKrT/B8oNkb+KWVyHAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":["fig=plt.figure(figsize=(20,15))\n","b=sns.displot(data=df_u,x='salary',y='job_category',col='work_year')\n","b.set_titles((\"{col_name}\"))\n","b.set_axis_labels(\"Salary\",\"Job Category\")\n","b.tight_layout()\n","b.fig.subplots_adjust(top=.8)\n","b.fig.suptitle(\"Salary Records by Year and Category\")\n","plt.savefig('/content/SalaryRecordsByYearAndCategory')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"rc553rZwUiXR","executionInfo":{"status":"ok","timestamp":1714702849475,"user_tz":300,"elapsed":3918,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"fb71d69a-8680-4bc3-ca9d-fd4bfe3cbfca"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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Level\")\n","f.tight_layout()\n","f.fig.subplots_adjust(top=.8)\n","f.fig.suptitle(\"Salary Records by Year and Experience\")\n","plt.savefig('/content/SalaryRecordsByYearAndExp')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"id":"Vg1UCCHbC5n4","executionInfo":{"status":"ok","timestamp":1714531758405,"user_tz":300,"elapsed":2972,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"870569ce-edfc-43b1-ce19-8472768904db"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"NiGy0QCVLN9o"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"5o7pBWsD_p-r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sns.countplot(data=df_u,x='work_year',hue='job_category',stat='count',palette='viridis')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"id":"B3OcQzLG_zaL","executionInfo":{"status":"ok","timestamp":1714702926666,"user_tz":300,"elapsed":875,"user":{"displayName":"Jenny 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3DkqVqyo0rPl6enJ0aNHGTlyJB4eHhgaGnLo0CGePHlS4CNG3ta9e3d27tzJ5cuXmTJlSqG3EwRBKA0ieRKEN3zzzTcYGxuza9culixZgqGhIX369GHSpElqCZGhoSFLly5l4cKFhISEYGJiwty5c+nTp0+R2uzduzeVKlVi06ZNbNiwgXLlymFhYcGQIUOAnGdRbdy4kYULFxIQEIC2tjYdOnRg4MCBasNbHTt2xMPDg6NHj3L48GGys7Pp0qULAPPnz6dJkybs27ePNWvWoKGhQfXq1fniiy+ws7OT6pgyZQrly5dn//79XLx4ERsbGwIDAxkwYIDKjRQmJibs27ePFStWsGvXLlJTU1EoFGzcuJG2bdsW6Rg0adIES0tL7t27J+6yEwShzJNliyeblag///wTQOXRCW9KSUkhPDwcc3Nz8fC/MmD//v3Mnj2bs2fPYmZmVtrhlFnx8fHY29szYcIEvv766/fSRvfu3TE0NCQoKOi91C8UT0pKCv+9co2AhYfUHpJZt2FN/EJmllJkglCyCrp+v0nMeRL+1aKjo5HJZO/0RPlPTUpKitoyZULTokWL99Lmn3/+ya1bt1QmxAuCIJRVYthO+Fd68eIFJ06cYN++fdjY2LyXJyTHxsbm+4JeTU1NjIyMSrzdd3Xs2DF++OEHWrduja6uLlevXiU0NJRWrVrRrFmzEm3rn3/+4caNG2zduhVTU1Pc3NxKtH5BEIT3QSRPwr/SvXv3WL58OdbW1ixYsOC9tNGrVy+1B2u+qUWLFuzcufO9tP0uFAoFGhoabNmyhdevX1OpUiUGDRokPcSyJJ04cYL169djbm7O6tWr1Z6yLgiCUBaJ5En4V3JwcOCPP/54r22sWLFC7fEDbzIwMHiv7RdX48aN2b59+wdp65tvvuGbb775IG0JgiCUFJE8CcJ7UtJDXIIgCELZICaMC4IgCIIgFIFIngRBEARBEIpAJE+CIAiCIAhFIJInQRAEQRCEIhDJkyAIgiAIQhGI5EkQBEEQBKEIRPJUhmRlZn1Ubfv5+aFQKFAoFDRo0IBmzZrRtWtX5s+fz71794oVx+XLl9m4cWOxts3L3bt3GTduHK1bt8bKyorWrVszatQozp49W6R6FAoFgYGBJRrbp+DWrVsoFAouX76cbznld0WhUNCkSRM6dOjAwoULefXq1YcJ9AO7fPkyCoVCel+WIAifDvGcpzJEriFnufdWHt+P/KDt1rQwY9rSYcXatnz58tJ7z16/fs0///xDcHAwISEhLFq0iG7duhWpvitXrrB161ZGjx5drHje9ujRI3r37o1CocDb2xtjY2MiIiI4e/YsV65coU2bNoWuKzg4mGrVqpVIXP9WHh4euLu7k5qaypUrV9i4cSMPHjxgy5YtpR2aIAhCoYnkqYx5fD+Se7cel3YYhSaXy7GxsZE+Ozs7M2DAADw9PZk1axZ2dnbUrFmz1OL7/vvvAdi2bZvK++u+/PJLsrKK1tv25n4KxVO1alXpODo4OBAVFUVISAhRUVFUrly5dIMrpMzMTLKystDU1CztUARBKCVi2E4ocdra2syZM4f09HT2798vLT906BD9+/enRYsW2Nvb4+HhwfXr16X1fn5++Pv7k5SUJA3veHh4ADnvops4cSJt2rShadOmuLm5sXXr1gIToPj4ePT09HJ98a9crvr1v3fvHmPHjqVFixY0bdqUL774gtDQUGl9bsN2Z86coXfv3lhbW9OyZUvmzZtHUlKStF45dHP+/HkmT56Mra0trq6ubN68WS2esLAwhg0bhp2dHba2tvTu3Zvz589L69PS0li9ejWurq40adKEzp07c+TIkXz3Xxnj0KFDcXR0xM7Ojt69e3Pu3DmVMgcPHkShUHDz5k1GjBiBjY0NHTt25NChQ2r1bdiwAWdnZ2xtbRk7diwxMTEFxpCXhg0bAvDs2bMi7eedO3cYOXIkDg4ONG3alE6dOqkd07CwMAYNGoSNjQ3NmjVj8uTJarGuXLmSrl27Ymtri4uLC5MmTSIqKkqljIeHB6NGjeKHH36gU6dOWFlZ8ffffwM5x7Zfv340bdpU+k7fvHlTZfv4+PgCz70gCB8X0fMkvBf16tWjSpUqhIWFScuePHlC9+7dqVWrFmlpaRw9epSBAwdy+PBhzM3N6d27N5GRkYSGhkpDgXp6egBERUVhbm5O165dqVChArdu3cLPz4+kpCTGjh2bZxyNGzdmz549zJ07l/79+6NQKNSSJoAHDx7Qt29fqlatyqxZszA1NeWff/7h6dOnedZ9/PhxJk6cSM+ePfnmm2+Ijo5m1apVxMfHs2bNGpWy8+bNo1u3bqxfv55ffvmFlStXolAoaN26NQD//e9/GTx4MDY2NixcuBADAwP++usvlfbHjx/P1atXGTNmDHXr1uXs2bNMnToVAwODfIcfnzx5gqurK8OGDUMul3Pu3Dk8PT0JCgrCwcFBpeyUKVPo06cPQ4cOJSQkBG9vb6ysrKhbty4Au3btYu3atQwbNgwnJycuXLjArFmz8my7IE+fPkUul6sMhxZmP0ePHo2JiQmLFi1CT0+PR48eERn5v+HusLAwPDw8aNOmDWvWrCE5ORlfX1+8vLwIDg6WysXExDBq1CgqV65MbGws27Ztw8PDg6NHj1Ku3P/+e/zrr7+IiIhg/PjxGBgYULVqVY4dO8akSZNo3749q1atQlNTk6tXr/L8+XMaNWokbVvQuRcE4eMjkifhvalatSovXryQPr+Z5GRlZeHs7Mz169f54YcfmDRpEmZmZpiZmakNBQI4Ojri6OgIQHZ2Ns2aNSMlJYVdu3blmzz16NGDixcvEhwcTHBwMBUqVKBly5Z8+eWXtG/fXirn5+eHpqYme/fulRI2JyenPOvNzs5m+fLluLm5sWjRImm5qakpnp6eeHl5YWlpKS3v2LGj9AJcR0dHzpw5w4kTJ6QL6IoVK6hduzZBQUFoaGgA0KpVK2n7S5cucfr0aQIDA6Xlzs7OREdH4+fnl2/y9NVXX0l/z8rKwsHBgbt37xISEqKWPA0cOJCBAwcCYGtry9mzZzlx4gReXl5kZmYSEBBAt27dmD59OgAuLi7ExMTw448/5tn+m7KyssjIyCAtLY3Lly+zd+9e+vbti6mpaaH3MzY2lidPnjBr1izatWsHQMuWLVXaWbVqFU2aNMHf3x+ZTAZA/fr1cXd35+zZs9LxWrJkibRNZmYmtra2tG7dmkuXLqkc/7i4OA4cOEDVqlWBnPO/bNkynJ2dWb9+vVQut/NQ0LkXBOHjI4bthPcmOztbunBBzrDYmDFjcHJyomHDhjRu3Jjw8HAePHhQYF2pqamsW7eODh06YGVlRePGjVmzZg3R0dG8fv06z+00NDRYtWoVoaGhTJ48mebNm3P+/Hm8vLxYu3atVO7SpUt06tRJSpwKEh4eTkREBJ07dyYjI0P606JFC+RyOX/99ZdK+TcvxDKZjLp160o9JcnJyfzxxx90795dSpzedv78eYyMjGjZsqVKe05OTty6dYvMzMw8Y42MjGT69Om4uLjQqFEjGjduzP/93/8RHh6uVvbNOHV1dalWrZoUZ2RkJFFRUXTo0EFlm06dOhVwtP5n5cqVNG7cGFtbW0aPHo1CoWD27NlF2s+KFStSvXp1Vq9ezQ8//KDS4wQ5x/Pq1at8/vnnZGZmSnXUqVOHqlWrqtz9dvbsWfr160ezZs1o1KiRlNC8/Z2sX7++lDgB3L9/n8jISL788ssC9zm/cy8IwsdJ9DwJ701kZCR16tQBIDExkWHDhmFsbIy3tzfVqlVDW1ub2bNnk5qaWmBdK1asYP/+/YwZM4YmTZqgr6/PqVOn+O6770hNTaVChQr5bm9paYmlpSWenp7ExsYyfPhwNm3axODBgzEyMuLVq1dFmrD88uVLAMaMGZPr+jfn8ADo6+urfNbU1CQhIQHImROTlZWVb/svX77k1atXNG7cONf10dHRmJmZqS3Pysri66+/JiEhgXHjxlG7dm10dHRYt26dWox5xZmWlia1AWBsbKxSxsTEJM+43zZo0CC++OILkpOTOXz4MPv372ft2rVMnjy5SPsZGBjImjVrmD9/PklJSTRu3JgZM2Zgb29PfHw8mZmZLFmyRKVnSUm539evX8fLy4v27dszcuRIKlWqhEwmo0+fPmrfybf3Ufl4hcJ8Z/I794IgfJxE8iS8F3fu3OH58+f06NEDgGvXrhEZGUlAQAANGjSQyiUkJOR60X/b8ePH6du3L56entKyoj6nScnY2JiePXuycOFCHj58iJGREUZGRmoThfNjZGQEwNy5c7G2tlZbX5RETF9fH7lcnm/7hoaGGBsbs2nTplzXv53QKD18+JCbN2+yfv16PvvsM2l5SkpKoeNTUg6txcbGqix/c2i2IGZmZlhZWQHQokULXrx4wbZt2xgwYABVq1Yt9H6am5uzbt060tPTCQsLY/Xq1YwePZpz586hr6+PTCZj1KhRKvusVLFiRQB++eUX9PT08PX1lebBRURE5Nrumz2o8L/zX5TvjCAInw4xbCeUuNTUVBYsWICWlha9e/cG/nexfvP27qtXr6pdrN7s6Xi7zje3zczM5OjRowXGkteFXTkso+xRcHR05MSJEyQmJhZYJ4CFhQVmZmY8fvwYKysrtT9VqlQpVD2QMzxmY2PDjz/+mOfwm5OTE7GxsWhqaubanpaWVq7bKXtQ3jx2ERERKhP5C8vMzAxTU1NOnjypsvzEiRNFrktp2rRpZGVlSXcxFnU/NTU1adGiBZ6eniQmJhIVFSUdz/v37+daR40aNYCc76SmpqZKYlSYuxfhf+f/4MGDxd53QRA+XqLnqYypaVFwL0xZajMrK4tr164BkJSUJD0k8/HjxyxdulS6UNnY2KCrq4uPjw+enp48f/4cPz8/tSSjbt26ZGRkEBQUhK2tLXp6elhYWODk5MT+/fupV68eFStWZM+ePbkmWW/bsGEDt27dwt3dnXr16pGamsr58+fZs2cPn332GdWrVwdyJrOfOXOGAQMGMGLECExNTbl37x7JycmMHDlSrV6ZTIa3tzdTpkwhKSmJtm3boqOjw9OnTzl79iwTJ07E3Ny80Mdx8uTJDBkyhCFDhjBgwAAMDQ25ceMGFStWpFevXjg7O+Pq6sqIESMYMWIECoWC5ORk7t69y8OHD1Umrb9JeZFftWoVWVlZJCUlsW7dumI9U0lDQwNPT08WLVpEpUqVcHZ25vz58wU+WTw/FhYWuLm5ceDAAcaMGVOo/fz7779ZtmwZbm5u1KxZk8TERAICAqhevTq1atUCcpKywYMHM2HCBLp06YKBgQGRkZFcuHCBnj174uDggLOzM0FBQSxYsIAOHToQFhZW6InvMpmM6dOnM2nSJL755hu6deuGlpYW165dw8rKCldX12IfE0EQyj6RPJUhWZlZxX7Sd0m0LdcoekdkSkoKffv2BXJ6UGrUqIGjoyP+/v7S7e2Q08Ozdu1ali9fjpeXF3Xq1MHHx0ftydKurq4MGDCATZs2ERMTg729PTt37mTOnDnMmzePBQsWoKOjQ48ePejQoYPKZOPcfPHFF6SmprJz506eP3+OhoYG1atXZ9q0aQwYMEAqV6dOHfbt28eqVavw8fEhMzOTOnXqqAwTvq1z584YGBiwceNGqceievXquLi4FGkeEEDz5s3ZsWMHvr6+zJgxA7lcjqWlJRMmTJDKrFu3jk2bNrF3714iIiLQ19fH0tKSnj175lmvlpYWfn5+zJ8/n/Hjx1O1alW+/vprLl26pDapvTA8PDyIj49nz5497N27F0dHRxYuXMiIESOKXJeSl5cXx44dY9euXXzzzTcF7qepqSkmJiYEBATw/Plz9PX1ad68OStWrJAm3NvZ2bFnzx78/PyYMWMG6enpmJmZ0bJlS2rXrg3k3Bk3ZcoUdu3axcGDB7GzsyMgIKDQE+Dd3NwoX748GzduZNKkSWhra9OoUSO1CfWCIHx6ZNnZ2dmlHcSnRHknj3Jex9tSUlIIDw/H3Nyc8uXLf8jQBEEQiiwlJYX/XrlGwMJDvHgWp7KubsOa+IXMLKXIBKFkFXT9fpOY8yQIgiAIglAEInkSBEEQBEEoApE8CYIgCIIgFIFIngRBEARBEIpAJE+CIAiCIAhFIJInQRAEQRCEIhDJkyAIgiAIQhGI5EkQBEEQBKEIRPIkCIIgCIJQBCJ5EorNz88PhUKBQqGgQYMGNGvWjK5duzJ//nzu3btXrDovX77Mxo0bSzjSHNu3b0ehUDBz5vt/InK7du2YP39+idbp7e2Nu7t7idYpCIIgFJ1InsqQrMysj67t8uXLExwczL59+1i3bh09e/bkwoULdOvWrdAvWX3TlStXCAgIKFYsBTl8+DAAJ0+eLNRLhcsaLy8vVq5cWdphCIIg/OuJFwOXIXINOSuW7efx4+gP2m7NmqZMnd67WNvK5XJsbGykz87OzgwYMABPT09mzZqFnZ0dNWvWLKFIiy88PJwbN27g5OTEhQsXOHPmDB07diztsIqkVq1apR2CIAiCgEieypzHj6O5d/dZaYfxTrS1tZkzZw5dunRh//79TJo0CYBDhw4RHBzMvXv3yM7OpkGDBkydOhVra2sgZxjQ398fAIVCAUCLFi3YuXMn9+7dw9/fn6tXr/Lq1SuqV69Or169GDJkCHJ5wR2ooaGhyGQy5s+fT79+/Thy5Iha8tSuXTvatm1L3bp12bJlC/Hx8Tg4OLBw4UKMjY0BSEpKYuXKlZw/f57IyEgqVapEq1atmDp1Kvr6+rm2ffr0ab7++mtOnDhBnTp1pOVxcXG4uLgwffp0Bg4cyJ07d1i+fDnXr18nJSUFMzMzevXqxciRI4GcYbu//vqL0NBQAOLj41m+fDlnz57l1atXGBsbY2dnx5o1awp7qgRBEIRiEMmT8F7Uq1ePKlWqEBYWJi178uQJ3bt3p1atWqSlpXH06FEGDhzI4cOHMTc3p3fv3kRGRhIaGkpQUBAAenp6AERFRWFubk7Xrl2pUKECt27dws/Pj6SkJMaOHVtgPKGhoTRv3pyaNWvSuXNngoODSUhIUEt4Tp8+zcOHD5k7dy4vX75kyZIlLFiwQEpIUlJSyMzMZOLEiRgbG/Ps2TM2btyIl5cXO3fuzLXtNm3aUKVKFb7//nsmT56sEhNA165dARg9ejQmJiYsWrQIPT09Hj16RGRkZJ77tGTJEn777TcmT55M9erViY6O5ty5cwUeC0EQBOHdiORJeG+qVq3KixcvpM9vJjlZWVk4Oztz/fp1fvjhByZNmoSZmRlmZmZqQ4EAjo6OODo6ApCdnU2zZs1ISUlh165dBSZP169f58GDBwwdOhQAd3d3du7cyYkTJ+jVq5dK2ezsbL777ju0tLQAiIiIICAggKysLORyOcbGxvj4+EjlMzIyqFGjBgMGDCA8PBxzc3O19jU0NOjZsyfff/89EyZMQENDA4Dvv/+eDh06YGBgQGxsLE+ePGHWrFm0a9cOgJYtW+a7X3/++Sfu7u706NFDWtalS5d8txEEQRDenUiehPcmOzsbmUwmfb537x6rV68mLCyMmJgYafmDBw8KrCs1NZWAgACOHDnCs2fPSE9Pl9a9fv2aChUq5LltaGgompqafP755wDY2NhQs2ZNjhw5opY82dvbS4kTQN26dUlPTycmJgZTU1MgZ/hx+/btPHz4kKSkJJX9yC15AujVqxcbN27kt99+o23btvz999/cuHGDqVOnAlCxYkWqV6/O6tWriYuLw9HRETMzs3yPSaNGjfjhhx8wNTXFxcWF+vXr51teEARBKBnibjvhvYmMjMTExASAxMREhg0bxtOnT/H29mb37t0cOHCABg0akJqaWmBdK1asIDAwkN69e7Np0yYOHDjA119/DZDv9llZWRw7dowWLVogl8uJj48nPj6e9u3bc+XKFZ4/f65S3sDAQOWzMpFStnHy5EmmT5+OtbU1vr6+hISEsH79+gLjqFGjBs7Ozhw4cADI6XWqUaOG1Lskk8kIDAzEwsKC+fPn06ZNG3r27Ml//vOfPOucM2cOX3zxBdu2baNr1660bduWPXv25FleEARBKBmi50l4L+7cucPz58+lIaVr164RGRlJQEAADRo0kMolJCQU2MMCcPz4cfr27Yunp6e07OzZswVud+nSJaKjo4mOjsbe3l5t/bFjx6ThvMI4fvw4DRs2VHmG05UrVwq1be/evZkyZQrPnz/nyJEjeHh4qPTMmZubs27dOtLT0wkLC2P16tWMHj2ac+fO5dqzpq+vz6xZs5g1axa3b99mx44d+Pj4UL9+fZo3b17ofRIEQRCKRvQ8CSUuNTWVBQsWoKWlRe/eOY9ASElJAUBTU1Mqd/XqVSIiIlS21dTUzPUZTKmpqSrbZmZmcvTo0QJjOXLkCLq6umzfvp0dO3ao/GnQoAFHjhwp0r6lpKSoxKFsozDat2+PgYEBkydPJi4ujp49e+ZaTlNTkxYtWuDp6UliYiJRUVEF1q1QKJgxYwZAsR9QKgiCIBSO6HkS3klWVhbXrl0Dcm7j/+effwgODubx48csXbqUGjVqADnzjHR1dfHx8cHT05Pnz5/j5+dHlSpVVOqrW7cuGRkZBAUFYWtri56eHhYWFjg5ObF//37q1atHxYoV2bNnT4EPukxNTeXkyZN07NhRmmz+pi+//JJFixZx//59LCwsCrW/Tk5OzJ8/n/Xr12Nra8vZs2e5ePFiobbV1NSke/fuBAYG0qpVK6pWrSqt+/vvv1m2bBlubm7UrFmTxMREAgICqF69ep7Pd+rXrx8dOnTA0tISDQ0NDh06hKampuh1EgRBeM9E8lTG1Kxp+lG1mZKSQt++fQHQ1dWlRo0aODo64u/vT926daVyJiYmrF27luXLl+Pl5UWdOnXw8fFhy5YtKvW5uroyYMAANm3aRExMDPb29uzcuZM5c+Ywb948FixYgI6ODj169KBDhw7Mnj07z9jOnDlDQkIC3bt3z3W9u7s7y5cv58iRI4wfP75Q+9uvXz+ePHnCrl27pCRo1apV9OnTp1Dbd+jQgcDAQL788kuV5aamppiYmBAQEMDz58/R19enefPmrFixQro77212dnYcOnSIJ0+eIJfLqV+/Phs3blQ57oIgCELJk2VnZ2eXdhCfkj///BMAKyurXNenpKRIt7SXL19eZV1WZhZyjdIZSS3Ntv9N1q5dy549e/jtt99U7uoThLIqJSWF/165RsDCQ7x4Fqeyrm7DmviFvP93RQrCh1DQ9ftNouepDCnN5EUkTu/X/fv3CQ8PZ9euXQwYMEAkToIgCB8xkTwJwgcwb948rl27houLC6NGjSrtcARBEIR3IJInQfgA8np1iyAIgvDxEWM1giAIgiAIRSCSJ0EQBEEQhCIQyZMgCIIgCEIRiORJEARBEAShCMpU8vTTTz/x9ddf07p1a2xsbOjWrRsHDhzg7UdR7d+/n06dOmFlZcUXX3zBr7/+qlZXQkICM2fOpEWLFtja2jJu3LhcX3Nx9epV+vbti7W1Na6urmzatEmtPUEQBEEQBKUylTxt374dHR0dvL29+e6772jdujVz5syR3loPcPToUebMmUPnzp3ZvHkzNjY2jB07VnpFiNKECRM4f/483377LStXriQ8PJyRI0eSkZEhlXn48CHDhw/H1NSUgIAABg8ezLp169i6deuH2mVBEARBED4yZepRBd999x3GxsbSZ0dHR169esW2bdvw8vJCLpezbt06unTpwoQJEwBo2bIl//zzD+vXr2fz5s0AhIWF8X//93/S6zMg5431bm5u/Pzzz7i5uQEQGBhIxYoVWb16NVpaWjg6OhIbG8vGjRvx8PAQDzIUBEEQBEFNmep5ejNxUmrYsCGJiYkkJSXx+PFjHjx4QOfOnVXKuLm5cfHiRelFsefOncPAwABnZ2epjIWFBQ0bNuTcuXPSsnPnztG+fXuVJMnNzY34+HjCwsJKevc+OX5+figUChQKBQ0aNKBZs2Z07dqV+fPnc+/evWLVefnyZTZu3FhiMT558kSK8e0/n3/+eYm186bLly+jUCikR/2XNQqFgsDAwNIOQxAE4aNVpnqecvPf//6XKlWqoKenx3//+18gpxfpTXXr1iU9PZ3Hjx9Tt25d7t+/j7m5OTKZTKWchYUF9+/fByApKYlnz55hYWGhVkYmk3H//n0cHBze456py8zKQkNeOvlscdsuX748QUFBALx+/Zp//vmH4OBgQkJCWLRoEd26dStSfVeuXGHr1q2MHj26yLHkZ9KkSWrn8+13C5aUxo0bExwcXGZf0BscHEy1atVKOwxBEISPVplOnn7//XeOHTvG9OnTAYiLy3kppYGBgUo55Wfl+vj4ePT19dXqMzQ05K+//gJyJpTnVpeWlhY6OjpSXcWRnZ1NUlJSrutSU1PJysoiMzOTzMxMlXUaGhosXv8Dj56+KHbbxVGrmgkzx/RQi6cgWVlZyGQylZcotmzZkr59+zJ69GhmzZpF06ZNqVmzZpHqzM7OLnIs+dUHULNmzVxf9lhS7bxJR0dHaut91F9cKSkplC9fvkzGJpRNb/8AzUtycrK40Ub46GVnZxf6O19mk6fIyEgmTpyIg4MDgwYNKu1wiiQ9PZ1bt27lub5cuXKkpqaqLJPL5ejo6PDo6QvuPoh83yHmKi0tTUo2CkM5+T4lJUVt3dSpU+nVqxf79u3jm2++ASA0NJSDBw9y//59srOzqV+/PuPHj6dJkyYAbNy4kU2bNgHQqFEjAJo1a8bmzZsJDw8nICCAP/74g7i4OKpWrUr37t0ZOHAg8nx6zJTHOT09Pdc4lebNm8fNmzeZPn06q1at4uHDh9StW5cZM2ZIsUBO0r106VLOnj2LtrY23bt3x8jIiDVr1nD16lUgJ+n39PRk165d0rZ2dnaMGzeOlJQUDhw4QFZWFi4uLnh7e6OjoyPV//z5c9atW8fFixdJTk6mUaNGTJ48WSUGgMOHD7Nr1y4ePXqEoaEhXbt25euvv0ZDQ0Na/+2337Jt2zY2btzIH3/8wRdffIG3tzd2dnZMmDBB+nc1cuRIdHR0+OKLL1i/fj1RUVE0btyYOXPmqCS+z58/Z9GiRfz+++8YGhoycOBAnj17xpkzZzh69Giex1b4eMnl8kJdTMLDw0lOTv4AEQnC+1XYuc5lMnmKj49n5MiRGBkZ4efnJ10cDQ0NgZwLmKmpqUr5N9cbGBgQGamegMTFxUlllD1Tyh4opbS0NJKTk6VyxaGpqUm9evVyXZeamsrTp0/R1tZ+b8NGxVXUCfLlyuV8fXLbj0aNGlGlShVu3LghrY+KiqJ79+7UrFmT9PR0jh07xogRIzh06BB16tShX79+xMTEcPToUbZt2wZAhQoVKF++PK9evaJu3bp069aNChUq8Pfff+Pv709aWhpjxozJM0ZtbW0gp1dPGa+SXC6XvlsaGhrExMSwcuVKRowYgb6+PmvWrGHKlCmcOHECTU1NAKZNm8bly5eZMmUKVatW5cCBA/z0008qx0F5HLW0tFSOTUhICM2aNWPJkiU8ePCAlStXUqVKFSZNmgTkfD+HDx+Orq4us2bNQk9Pj927dzN69Gh++uknKlWqBOTclbpq1SoGDRqEs7Mz9+7dY+3atcjlcqkuZbyzZ8+md+/ejB49mvLly0vxlCtXTvq7XC7nzp077Ny5k8mTJ5OZmcmyZcuYO3cue/fuBXJ+kU2ePJmYmBi+/fZb9PT02Lp1K0+fPkUul5e577JQMmQymTSXND/m5uai50n46N29e7fQZctc8pSSksKoUaNISEggODhYZfhNOT/p/v37KnOV7t+/j6ampvQr2cLCgosXL6p1wYWHh1O/fn0AdHV1qVq1qjQH6s0y2dnZanOhikImk6Grq5vrOuUFW0NDQ+olKCuKGo/yV2le21WtWpUXL15I65U9UIDU8/Lnn3/y448/MmnSJKpXr07VqlWRy+XY2dmp1NWqVSvpzsns7Gzs7e1JTU1l165djBs3Lt8YASZPnqy2rlevXixatAjIOWdxcXHs2rULS0tLICdxGzRoEH/99RfNmzfn7t27/PLLLyxbtozu3bsD0LZtW+kGBuV+KttUnmelypUrs3r1aunz33//zc8//8zUqVMB2LVrFwkJCRw4cEBKlFq1akWnTp3Yvn0706ZNIzExEX9/f0aMGCElSi4uLmhra7N06VJGjBhBxYoVpRj69euHp6dnrsdFGZtMJiMhIYFDhw5JN22kpKQwY8YMoqOjMTMz4+zZs9y8eZPdu3fTvHlzAJycnGjTpg0GBgZl7rssfFhv9p4KwseqsEN2UMaSp4yMDCZMmMD9+/fZvXs3VapUUVlfs2ZN6tSpw/Hjx/nss8+k5ceOHcPR0VH6xd+6dWs2bNjAxYsXcXJyAnKSops3bzJixAhpu9atW3Pq1CmmTp0q/VI/duwYBgYG2Nravu/d/eS9nbzeu3eP1atXExYWRkxMjLT8wYMHBdaVmppKQEAAR44c4dmzZ6Snp0vrXr9+TYUKFfLdfsqUKbRs2VJl2dt3d1auXFlKnACp9/D58+cA0t1z7du3l8rI5XJcXV2lnrL8KL+LSnXr1lUZ7jp//jwODg4YGhpKQ6JyuRx7e3up7bCwMJKSkvj8889Vnlnm5ORESkoKd+7coUWLFtLytm3bFhgXQIMGDVSOh3LfIyMjMTMz488//8TAwEBKnCAnuXR0dOTGjRuFakMQBOFTUaaSJx8fH3799Ve8vb1JTExUefBlo0aN0NLS4ptvvmHKlCnUqlULBwcHjh07xvXr19m1a5dU1tbWllatWjFz5kymT5+OtrY2a9asQaFQ0LFjR6nc8OHDOXLkCJMnT6Z///78888/BAYGMnHiRPGMpxIQGRlJnTp1AEhMTGTYsGEYGxvj7e1NtWrV0NbWZvbs2Wrzv3KzYsUK9u/fz5gxY2jSpAn6+vqcOnWK7777jtTU1AKTp7wmjL/p7ZsHlAm1Mr7o6Gg0NTXVbkbI7REbha3/zSGRly9fcu3aNRo3bqy2ba1ataQyAD169Mi1jWfPnql8NjExKXZs8L99j4qKynU/C7vvgiAIn5IylTydP38egKVLl6qtO3XqFDVq1MDd3Z3k5GQ2b97Mpk2bMDc3x9/fX62nyNfXlyVLljB37lwyMjJo1aoVs2fPVpn3Urt2bQIDA1m6dCmenp4YGxszbtw4hg0b9n539F/gzp07PH/+XLrIX7t2jcjISAICAmjQoIFULiEhATMzswLrO378OH379lUZgjp79mzJB54PU1NT0tPTSUhIUEmgYmNjS6R+Q0NDXFxcGD9+vNo6ZTKvnIvn7++f63GrUaNGicTytsqVK+e6nyW174IgCB+TMpU8nT59ulDlevfuTe/evfMto6+vz+LFi1m8eHG+5ezs7AgJCSl0jELBUlNTWbBgAVpaWtJ5Ut7ppuzRgJz3CkZERKgMlb3dG/NmnW9um5mZ+cHv8FLeFXjq1ClpzlNWVlau71YsDicnJw4fPkzdunXznDNna2uLjo4OkZGRdOjQoUTaLQwrKyvi4+P5z3/+g729PZAzXHrx4kW1XitBEIRPXZlKnoScZy59TG1mZWVJw6tJSUnSQzIfP37M0qVLpZ4QGxsbdHV18fHxwdPTk+fPn+Pn56c2r61u3bpkZGQQFBSEra0tenp6WFhY4OTkxP79+6lXrx4VK1Zkz549hboLSOnhw4dq7z+UyWQ0bdq00HVYWlrSoUMHFi5cSHJyMtWqVSMkJISUlJQiTTTMy5AhQzhy5AhfffUVgwYNolq1asTGxvLHH39QpUoVhgwZgoGBAePGjWPFihVERkbSokULNDQ0ePz4MadOncLPz++9TN5t3bo1jRs3ZvLkyUyaNAkDAwO2bNlChQoVSmTfBUEQPiYieSpDMrOymDkm97ksH6Lt4jxhPCUlhb59+wI5dzDWqFEDR0dH/P39VZ6wbWJiwtq1a1m+fDleXl7UqVMHHx8ftmzZolKfq6srAwYMYNOmTcTExGBvb8/OnTuZM2cO8+bNY8GCBejo6NCjRw86dOjA7NmzCxXnm3e5KWloaHDz5s0i7e/ixYuZP38+y5cvR0tLix49emBpacnu3buLVE9uKlasSHBwML6+vqxcuZJXr15RqVIlmjZtqtLLNGzYMKpUqcK2bdvYtWsX5cqVo1atWrRt21ald64kyWQyNmzYwNy5c5k7dy4GBgYMGjSI8PDwfJ9pJgiC8CmSZYuHc5Qo5V1ReU1OTklJITw8HHNzc/FsnE+E8kGdO3fuLO1QPqi0tDS6dOlC8+bNWbJkSWmHI7wnKSkp/PfKNQIWHuLFM9U3L9RtWBO/kJmlFJkglKyCrt9vEj1PglAEJ06c4NmzZ9SvX5/k5GRCQ0P5/fffWb9+fWmH9t4FBweTlZWFubk58fHx7N27l4iIiFx79QRBED5lInkShCLQ1dXlxx9/5MGDB6Snp2NhYcGKFStUnjv2qdLW1mbTpk1EREQAOc+GCggIKNSvNEEQhE+JSJ4EoQhcXFxwcXEp7TBKRffu3aW7DAVBEP7Nij5DWBAEQRAE4V9MJE+CIAiCIAhFIJInQRAEQRCEIhDJkyAIgiAIQhGI5EkQBEEQBKEIRPIkCIIgCIJQBCJ5EgRBEARBKAKRPJUhmVlZH1Xbfn5+KBQKXFxcyMpl+379+qFQKPD29i6JEFV4e3vj7u5eYDmFQkFgYGCJt5+XwsZVFrRr14758+eXdhglqlu3boX+vqWnp+Pg4ECDBg14+vSp2vrLly+jUCikVzYIgiAoiYdkliEacjnfbv+BB5EvPmi7dcxM+HZI8V5IrKmpycuXL/nPf/6Dg4ODtDwiIoJr166hq6tbUmEWS3BwMNWqVSvVGMoqf39/DAwMSjuMUvN///d/vHr1CoDQ0FA8PT1LNyBBED4aInkqYx5EvuCfJ5GlHUahaWpq4ujoyNGjR1WSp6NHj2JpaYlcXrqdmzY2NqXa/oeUnZ1Neno6WlpahSrfqFGj9xxR2RYaGoqBgQE1a9bkyJEjInkSBKHQxLCd8M7c3d05ceIE6enp0rLQ0NBch6/u3bvHxIkTadOmDU2bNsXNzY2tW7eqDfulpaWxZs0a2rdvT5MmTWjdunWuwzGXL1+me/fu2NjY0KtXL/766y+V9W8P23l4eDBq1CiOHz9Op06dsLW1ZdCgQTx69Eit/dWrV+Pq6kqTJk3o3LkzR44cKdbxye0YfP311zRr1gwbGxs8PT3V2t+6dStffvklzZo1w9HRkVGjRhEeHq5SRjlEePbsWb744gusrKw4ffo0fn5+2Nracvv2bfr370/Tpk1xd3fnt99+U9n+7WE7ZX0FHdOEhASmTJmCra0tjo6OrF69mq1bt6JQKPLd76ioKGbMmEH79u2xtramY8eOrF69mrS0NJVyCoWCzZs34+fnh5OTEw4ODsyYMYOkpCSVclevXqVnz55YWVlJx6GwkpKSOH36NJ06daJnz578888/3L59u9DbC4Lw7yaSJ+Gdubq6kpaWxvnz5wG4e/cut2/fxs3NTa1sVFQU5ubmzJs3j02bNtGnTx/Wr1/Phg0bVMp98803bN++nS+//JJNmzYxbdo0tYtndHQ0CxcuZPjw4fj6+pKamsrYsWNVkrjc3Lp1i8DAQKZMmcKSJUt49OgRU6dOVSkzfvx4goODGTp0KAEBAbi4uDB16tQiXaBz8/jxY/r160dcXBxLly5l5cqVxMbGMmTIEJUkIjIykq+++ooNGzawcOFCsrKy6NevnzTMpBQVFcXChQsZMmQImzdvpmHDhkDOfJ4pU6bQs2dP/P39MTY2Zty4cbx8+TLf+ApzTGfMmMGZM2eYOnUqS5cu5d69e+zYsaPAfX/58iVGRkbMmDGDLVu2MGLECH744QfmzZunVnb37t08ePCApUuXMmbMGI4cOaLyHYmOjmb48OFoaWnh6+vL8OHD8fHx4fnz5wXGAfDLL7+QlJSEu7s7nTt3ply5coSGhhZqW0EQBDFsJ7wzHR0d2rVrx9GjR2nbti2hoaHY2tpSs2ZNtbKOjo44OjoCOcNMzZo1IyUlhV27djF27FgAzp8/z5kzZ1i1apVK79XbPVlxcXHs2rULS0tLKY5Bgwbxxx9/0Lx58zzjTUhI4NChQxgbGwM5vRAzZswgMjISMzMzLl26xOnTpwkMDKRVq1YAODs7Ex0djZ+fH23atCn2sfL398fQ0JBt27ahra0NgJ2dHe3bt2f//v0MHDgQgJkzZ0rbZGZm4uzsjKOjIydOnKBv374qx2Dz5s00bdpUpR1l8qSM1dzcnPbt23Pu3Dm6deuWZ3wFHdO7d+9y8uRJli1bJr0k2MXFhc6dOxe47wqFgunTp0uf7ezs0NHRwdvbm7lz56KjoyOtMzU1ZdWqVQC0bt2amzdvcuLECaZMmQJAUFAQMpmMzZs3o6+vD4CZmRlDhgwpMA7I6RmtUqUKLVq0QC6X07JlS0JDQ5k0aRIymaxQdQiC8O8lep6EEuHu7s6pU6dISUnh2LFjdOnSJddyqamprFu3jg4dOmBlZUXjxo1Zs2YN0dHRvH79GoCLFy+io6OTZx1KlStXli7yAPXq1QMosPehQYMGUuL05naRkTlzzc6fP4+RkREtW7YkIyND+uPk5MStW7fIzMws4Gjk7fz587Rr1w4NDQ2pXgMDAxo1aqQyPHbt2jWGDh2Kg4MDjRo1omnTpiQlJfHgwQOV+oyMjNQSJwC5XC4lqQA1atSgfPnyBR6bgo6p8s6z9u3bq7Tl6upa4L5nZ2ezfft23NzcsLa2pnHjxkyZMoWMjAweP36sUtbJyUnlc926daXzA/DHH3/g4OAgJU6Qk5gbGRkVGEdsbCznz5/Hzc1NmpPXtWtXnj59yn//+98CtxcEQRA9T0KJaNWqFZqamqxdu5YnT57k2ROxYsUK9u/fz5gxY2jSpAn6+vqcOnWK7777jtTUVCpUqMCrV68wNTUtsAfg7TvFNDU1gZwE7V22e/nyJa9evaJx48a5bh8dHY2ZmVm+beTl5cuXBAUFERQUpLZOGcfTp08ZNmwYTZo0wcfHh8qVK6OpqcmoUaPU9s3ExCTXdsqXL682cVxTU/Odj010dDSampoqSQugkozmJSgoiGXLljFixAgcHBwwMDDgzz//ZP78+Wpx5RbHm8Oa0dHR1K5dW62NwsTx008/kZGRQZs2bYiPjwfAwcEBLS0tjhw5km+vpSAIAojkSSghmpqadOzYke3bt+Po6JjnRf348eP07dtX5c6mt+cRGRkZER0dTXZ2dqkMoRgaGmJsbMymTZtyXV+YC3R+dbdp04YBAwaoratQoQIAv/32G0lJSSqPEsjIyCAuLk5tmw99fExNTUlPTychIUElgYqNjS1w2+PHj9OuXTsmT54sLbt3716x44iJiVFbXpg4lHObchviO378OLNnz5aSRkEQhNyI5EkoMb179yYmJoY+ffrkWSY1NVXlwpSZmcnRo0dVyjg5ObF582Z++umnXCedv29OTk5s2bIFTU1NGjRoUKJ1Ozo6cufOHRo1aoSGhkauZVJSUpDJZJQr979/nsrektLWpEkTAE6dOiXNecrKyuLXX38tcNuUlBS1pKS4dzBaW1uzd+9elSTu4sWLahPq3xYREUFYWBj9+vVT+27dunWLJUuW8Ntvv9GuXbtixSUIwr+DSJ7KmDpmuffYfAxtWltbq9019zYnJyf2799PvXr1qFixInv27FG7Vd3JyYk2bdowc+ZMHj16RNOmTXn16hUnTpzA19e3RGLNj7OzM66urowYMYIRI0agUChITk7m7t27PHz4kEWLFuW7fWJiIsePH1db7uDgwLhx4+jVqxfDhw+nT58+mJiY8OLFC65cuULz5s1xd3enZcuWQM5dbf369ePOnTts27atTDzQ0tLSkg4dOrBw4UKSk5OpVq0aISEhUsKXHycnJ3bs2MGuXbuoU6cOhw8f5uHDh8WKY/DgwezZs4eRI0cycuRI4uPj8fPzK3DOk7LXacSIEWo3NDRr1oxNmzYRGhoqkidBEPIlkqcyJDMrq9hP+i6JtjU+wAMt58yZw7x581iwYAE6Ojr06NGDDh06MHv2bJVyfn5++Pv7ExwcjL+/P5UqVcLZ2fm9x6e0bt06Nm3axN69e4mIiEBfXx9LS0t69uxZ4LbPnj1j/Pjxast3795N8+bN2b9/P76+vvj4+JCUlISpqSn29vbSc5IUCgVLlizB39+fUaNG0bBhQ9auXcuECRNKejeLZfHixcyfP5/ly5ejpaVFjx49sLS0ZPfu3fluN2bMGF6+fMm6desA6NSpE7Nnz2b06NFFjqFy5cps3ryZhQsXMn78eGrVqsXcuXNZs2ZNvtuFhoZiZ2eX652g5cqVw93dnZCQEOnmBUEQhNzIsrOzs0s7iE+J8m4kKyurXNenpKQQHh6Oubk55cuX/5ChCcJ7M3DgQORyOTt37iztUIQSlpKSwn+vXCNg4SFePFOdd1e3YU38QmbmsaUgfFwKun6/SfQ8CYJQJCdOnODZs2fUr1+f5ORkQkND+f3331m/fn1phyYIgvBBiORJEIQi0dXV5ccff+TBgwekp6djYWHBihUr+Oyzz0o7NEEQhA9CJE+CIBSJi4sLLi4upR2GIAhCqRFPGBcEQRAEQSgCkTwJgiAIgiAUgUieBEEQBEEQikAkT4IgCIIgCEUgkidBEARBEIQiEMmTIAiCIAhCEYjkSSg2Pz8/FAoFCoWCBg0a0KxZM7p27cr8+fO5d+9eseq8fPkyGzduLOFIc2zfvh2FQsHMmSX7ROSDBw+iUCiIjY3Nt5yHhwejRo2SPr/Pff0Q9ZdFzZs3x8/PL98yHh4eKBQKJk6cqLYuMTERa2trFAoFBw8eLJGYunXrhre3d5G2efLkCQqFQuUdie3atWP+/PklEtOHlJmV9U7rBaEsEs95KkM+1PvlSrLt8uXLExQUBMDr16/5559/CA4OJiQkhEWLFtGtW7ci1XflyhW2bt1arPedFeTw4cMAnDx5km+//RYtLa0SbyM/8+bNQ/7GMX6f+/oh6v+Y6erq8uuvv5KUlISurq60/JdffkFDQ6MUI8ubv79/mXg5dFFpyOUsXv8Dj56+UFtXq5oJM8eUzvs8BeFdiOSpDNGQy5kdcpDwaPX/ZN4nc1MTFvYp+IW3uZHL5djY2EifnZ2dGTBgAJ6ensyaNSvPl7B+aOHh4dy4cQMnJycuXLjAmTNn6NixY77bpKWlUa5cOZWE513Uq1evROopLSV9PEqTnZ0dN27c4PTp07i7u0vLQ0ND+eyzz6REuyxp1KhRaYdQbI+evuDug8jSDkMQSszH/7/gJyY8+gV/P438oH9KOlnT1tZmzpw5pKens3//fmn5oUOH6N+/Py1atMDe3h4PDw+uX78urffz88Pf35+kpCRpONDDwwOAe/fuMXHiRNq0aUPTpk1xc3Nj69atZBWyyz80NBSZTMb8+fMxMTHhyJEjamWUwyKbN2/G1dUVa2trXr16JcXevXt3rKyscHBwYOTIkURERKhsHxkZyYgRI7CxsaFjx44cOnRIZf2bw3b57atyf7/++muaNWuGjY0Nnp6ePHr0SKW+rKwstm3bRufOnWnSpAnOzs6MGzeOhISEfOv39vZWSRgA4uPj1Yaq8jseBw8epGvXrlhZWeHi4sKaNWvIzMzM9xwU5hwqh6t+/PFH5s+fj729Pa1atWLZsmVkZGSo1PfLL7/w+eefY2VlRa9evVS+SwUpV64cnTp1IjQ0VFoWGxvLxYsX1Y6N0r59++jUqRNNmjShXbt2bNiwQe37d/XqVXr27ImVlRXu7u6cPXs217rCwsIYNGgQNjY2NGvWjMmTJxMTE5NvzG8P2ynP4+XLl+nevTs2Njb06tWLv/76S2W7hIQEpkyZgq2tLY6OjqxevZqtW7eiUCjybU8QhLyJnifhvahXrx5VqlQhLCxMWvbkyRO6d+9OrVq1SEtL4+jRowwcOJDDhw9jbm5O7969iYyMJDQ0VBoK1NPTAyAqKgpzc3O6du1KhQoVuHXrFn5+fiQlJTF27NgC4wkNDaV58+bUrFmTzp07ExwcTEJCAvr6+irlfv75Z2rXrs2sWbOQy+Xo6uqyZcsWVqxYQa9evZg4cSLp6elcunSJ2NhYqlevLm07ZcoU+vTpw9ChQwkJCcHb2xsrKyvq1q2rFk9++/r48WP69euHpaUlS5cuRSaTsXHjRoYMGcLx48el4cYFCxYQHBzM4MGDcXZ25vXr15w5c4akpKR86y+K3I7Htm3bWLFiBYMHD8bb25t79+5JydOUKVPyrKso59DX15f27dvj6+tLWFgYfn5+1KpVi/79+wNw69Ytxo0bR+vWrZkxYwZPnjxhwoQJpKWlFXrf3N3dGTp0KHFxcRgaGvLTTz9RpUoVbG1t1cru3LmThQsX4uHhQdu2bQkLC8Pf35+EhASmT58OQHR0NMOHD0ehUODr60t8fDw+Pj4kJSXRsGFDqa6wsDA8PDxo06YNa9asITk5GV9fX7y8vAgODi50/Mo2Fy5ciKenJ/r6+qxatYqxY8dy8uRJNDU1AZgxYwaXLl1i6tSpVK9enZCQEG7cuFGkdgRBUCWSJ+G9qVq1Ki9e/K9X680LZFZWFs7Ozly/fp0ffviBSZMmYWZmhpmZmdpQIICjoyOOjo4AZGdn06xZM1JSUti1a1eBydP169d58OABQ4cOBXIumjt37uTEiRP06tVLpWx6ejqbN2+W5sEkJCTg7+9P3759VX715/YS3IEDBzJw4EAAbG1tOXv2LCdOnMDLy0utbH776u/vj6GhIdu2bUNbWxvIGWZq3749+/fvZ+DAgYSHh7N3714mTpyoMgm9U6dOKm3kVn9RvH08EhMTWbduHSNGjGDSpElAzlCtpqYmS5cuZfjw4VSsWDHXuopyDq2trZk9e7ZU/+XLlzlx4oSUPG3atImqVauyfv16aY6StrY2s2bNKvS+NW/enEqVKvHzzz/Tu3dvjh49mmuvU2ZmJuvXr6dLly5STK1atSI9PZ2tW7fi6elJxYoVCQoKQiaTsXnzZikpNzMzY8iQISr1rVq1iiZNmuDv749MJgOgfv36Uk9VmzZtCr0PcXFx7Nq1C0tLSwB0dHQYNGgQf/zxB82bN+fu3bucPHmSZcuW0b17dyDn3YSdO3cudBuCIKgTw3bCe5OdnS1dHCBn2GbMmDE4OTnRsGFDGjduTHh4OA8ePCiwrtTUVNatW0eHDh2wsrKicePGrFmzhujoaF6/fp3vtqGhoWhqavL5558DYGNjQ82aNXMdunNwcFCZQBwWFkZycrJakpWbVq1aSX/X1dWlWrVqREYWfZ7H+fPnadeuHRoaGmRkZJCRkYGBgQGNGjWShmQuXbpEdnZ2oeJ6F7kdj6SkJD7//HMptoyMDJycnEhJSeHOnTt51lWUc/jmsQSoW7euyrH8448/cHV1VZncrTy/hSWTyXBzc+Po0aM8e/aMq1ev0qVLF7Vy9+/f5+XLl2r1u7m5kZ6eLg0X/vHHHzg4OKj0Zjo6OmJkZCR9Tk5O5urVq3z++edkZmZKx69OnTpUrVqVP//8s0j7ULlyZSlxgv/Nq3v+/DmAVF/79u2lMnK5HFdX1yK1IwiCKtHzJLw3kZGR1KlTB8jpsRg2bBjGxsZ4e3tTrVo1tLW1mT17NqmpqQXWtWLFCvbv38+YMWNo0qQJ+vr6nDp1iu+++47U1FQqVKiQ63ZZWVkcO3aMFi1aIJfLiY+PB3IuJjt27OD58+dUqVJFKl+pUiWV7ZVzfCpXrlxgjG8PAWpqahZpGEnp5cuXBAUFScNtb9epjKtcuXJq8Za0t+t/+fIlAD165H6H1LNnz/KsqyjnsKBjGR0drRabnp6e1FNXWF26dCEoKIjt27djaWmJQqGQviNKcXFxgPqxUH5Wro+OjqZ27dpqbRgbG0t/j4+PJzMzkyVLlrBkyRK1svkdv9y8ffed8vuh/DcVHR2Npqam2vF8MyZBEIpOJE/Ce3Hnzh2eP38uXWSvXbtGZGQkAQEBNGjQQCqXkJCAmZlZgfUdP36cvn374unpKS3LazLumy5dukR0dDTR0dHY29urrT927Jg0nAeo9JQBUq9BVFRUoeIsCYaGhrRp04YBAwaorVMmGEZGRmRkZBATE1PkBEpLS4v09HSVZcoE4G1vHw9DQ0MgZ2gxt+NRo0aNPNst7jnMjampqdoE68TExEIl4m9q0qQJNWvWZMeOHYwfPz7XMsrvwNvP8VK2rzwmucX09nb6+vrIZDJGjRqV69BvXkOexWVqakp6erra/L6CnkkmCEL+RPIklLjU1FQWLFiAlpYWvXv3BiAlJQX43y9jyLkzKSIiQmXYIa/emtTUVJVtMzMzOXr0aIGxHDlyBF1dXTZs2KB2i/3ixYs5cuSISvL0NltbW3R0dPj++++xtrYusL2iyGtfHR0duXPnDo0aNcrzmUMtW7ZEJpPx/fffqyQjhanfzMyMyMhIXr9+LSVj58+fL1TMyuMRGRlJhw4dCrWNUnHPYW6sra359ddfmTFjhnSM3nygZFGMHDmS06dP07Vr11zXm5ubY2xszPHjx1X2+aeffkJTU1P6XlhbW7N3716VROXixYtS7yXkDOfa2Nhw//59rKysihVvUTRp0gSAU6dOSXOesrKy+PXXX99724LwKRPJk/BOsrKyuHbtGgBJSUnSQzIfP37M0qVLpZ4IGxsbdHV18fHxwdPTk+fPn+Pn56cyZAY5c1syMjIICgrC1tYWPT09LCwscHJyYv/+/dSrV4+KFSuyZ8+eAofEUlNTOXnyJB07dpQmKr/pyy+/ZNGiRdy/fx8LC4tc69DX12fMmDGsXLmS7Oxs2rdvT1ZWFpcvX6ZLly7vdAHMa1/HjRtHr169GD58OH369MHExIQXL15w5coVmjdvjru7O+bm5vTr14+1a9cSFxeHo6MjKSkpnDlzhm+++YYqVarkWX/Hjh1Zt24dM2fOpE+fPty5c4cDBw4UKmYDAwPGjRvHihUriIyMpEWLFmhoaPD48WNOnTqFn58fOjo6uW5bnHOYF09PT3r16sWYMWPo378/T548ITAwsMjDdgC9evXKd+6YhoYGXl5eLFy4EGNjY9q0acO1a9fYvHkzgwcPlnqLBg8ezJ49exg5ciQjR44kPj4ePz8/lTlPANOmTWPw4MFMmDCBLl26YGBgQGRkJBcuXKBnz544ODgUeR/yYmlpSYcOHVi4cCHJyclUq1aNkJAQUlJS1HoVBUEoPJE8lTHmpiYfVZspKSn07dsXyPlVXaNGDRwdHfH391e5Rd/ExIS1a9eyfPlyvLy8qFOnDj4+PmzZskWlPldXVwYMGMCmTZuIiYnB3t6enTt3MmfOHObNm8eCBQvQ0dGhR48edOjQQbr7KTdnzpwhISFB+sX9Nnd3d5YvX86RI0fyHLKBnJ4JY2Njtm/fzsGDB6lQoQK2trbvPN8or32tXbs2+/fvx9fXV7rV3dTUFHt7e5Vn88ydO5caNWqwf/9+goKCMDIywt7eXupNyqv+evXqsXTpUjZs2ICXlxfNmjVj5cqVhX4a/LBhw6hSpQrbtm1j165dlCtXjlq1atG2bVuVnqW3Fecc5qVRo0asXbuWlStXMnbsWCwtLVmzZg3Dhw8vcl2F4eHhQbly5di+fTt79+7F1NSUsWPHqjy9vXLlymzevJmFCxcyfvx4atWqxdy5c1mzZo1KXXZ2duzZswc/Pz9mzJhBeno6ZmZmtGzZMtc5U+9q8eLFzJ8/n+XLl6OlpUWPHj2wtLRk9+7dJd6WIPxbyLKzs7NLO4hPifLulrx6JFJSUggPD8fc3Jzy5currPsYX88iCMLHZ+DAgcjlcnbu3Flg2ZSUFP575RoBCw/x4pnq3Li6DWviF1LwuyJHz9qc6xPG69UxY+OikYUPXBDeo4Ku328SPU9lSGkmLyJxEoRP04kTJ3j27Bn169cnOTmZ0NBQfv/9d9avX1/aoQnCR0skT4IgCJ8wXV1dfvzxRx48eEB6ejoWFhasWLEi17v9BEEoHJE8CYIgfMJcXFxwcXEp7TAE4ZMixmoEQRAEQRCKQCRPgiAIgiAIRSCSJ0EQBEEQhCIQyZMgCIIgCEIRiORJEARBEAShCETyJAiCIAiCUAQieRIEQRAEQSiCMpU8PXz4kLlz59KtWzcaNWqEu7u7WhkPDw8UCoXan3v37qmUS0hIYObMmbRo0QJbW1vGjRtHVFSUWn1Xr16lb9++WFtb4+rqyqZNmxBvrCkcPz8/bG1tSzWG5s2b4+fn98Haa9euHfPnz8+3zOXLl1EoFNKj/su6bt264e3tnW8Zb29vFAoFffr0UVuXnZ1NmzZtUCgUH/Rc/Fv4+flx9erV0g5DEIQ3lKmHZN65c4ezZ8/StGlTsrKy8kxi7OzsmD59usqyGjVqqHyeMGECd+/e5dtvv0VbWxtfX19GjhzJ999/T7lyObv98OFDhg8fjrOzMxMmTOD27dusXLkSDQ2N9/aC0fyId9t9Gho3bkxwcLDKi5E/Bbq6uvzxxx88fvyYmjVrSst///13YmJi0NLSKsXoPl3+/v7o6upiZ2dX2qEIgvD/lankqV27dtIrA7y9vfnrr79yLWdgYICNjU2e9YSFhfF///d/BAYG0qpVKwDMzc1xc3Pj559/xs3NDYDAwEAqVqzI6tWr0dLSwtHRkdjYWDZu3IiHh8cHvxhoyOXMOHqA+zEvPmi7FpVMWNKl1wdts6xJSUlRe1Fzcenp6eX7/fxYVa9eHQ0NDY4dO8aoUaOk5aGhobRq1Yrff/+9FKMTBEH4cMpUV4O8hHo+zp07h4GBAc7OztIyCwsLGjZsyLlz51TKtW/fXiVJcnNzIz4+nrCwsBKJpajux7zg76hnH/TP+0zWIiMjmTJlCg4ODlhbWzNw4EC1pPjQoUP079+fFi1aYG9vj4eHB9evX1er65dffuHzzz/HysqKXr165VoG4MyZM/Tu3Rtra2tatmzJvHnzSEpKktYrh9XOnDnDuHHjsLOzY/z48QCcOnWKnj17YmtrS/PmzenZsydnz55Va2P37t24urrSrFkzvLy8iI2NVav/zWE7hULBpk2bWL58OS1btsTW1hZvb28SExPzPX5RUVHMmDGD9u3bY21tTceOHVm9ejVpaWkq5RQKBZs3b8bPzw8nJyccHByYMWOGyn5DzjB1z549sbKywt3dPdd9y0+XLl0IDQ2VPmdkZHDixIlch9jDwsIYPXo0rVq1wsbGhm7dunHo0CGVMspjdf78eSZPnoytrS2urq5s3ry5yHVBTu/1wIEDsbKyomPHjhw+fBgvLy88PDxUyt27d4+vv/6aZs2aYWNjg6enJ48ePVIpozxna9aswdHRkebNm7N8+XKys7O5ePEi3bp1w9bWlsGDB/Ps2TOVbdPS0li9ejWurq40adKEzp07c+TIEZUy3t7euLu7c/nyZbp3746NjQ29evVS+fehUCgAWL58uTRF4fLly2r7LQjCh1Wmep4K68qVK9jY2JCZmUnTpk0ZP3489vb20vr79+9jbm6OTCZT2c7CwoL79+8DkJSUxLNnz7CwsFArI5PJuH//Pg4ODsWKLzs7W+2ipZSamkpWVhaZmZlkZmaqrNPQ0ChWeyXl7XgKohxazWu7uLg4+vfvj66uLrNmzUJPT4/du3czePBgfvrpJypVqgTA48eP+eKLL6hZsybp6ekcO3aMgQMHcujQIerUqQPArVu3GDduHC4uLkybNo2IiAgmTJhAWlqadDwh5w3ykydPpkePHowZM4bo6GjWrFlDXFwcq1atkuIGmDNnDl27dmXdunVoaGgQHh7OuHHj6NKlCxMnTiQrK4vbt2/z8uVLqf7s7GxOnTrFgwcPmD17Ni9fvmTZsmXMnz9frf434wLYuXMnjRo1YvHixURERLB69WpSUlKk7XITExODgYEB06ZNw9DQkAcPHrB+/XqeP3/O4sWLVcru2rWLZs2asXjxYh48eMDKlSsxNjZm0qRJAERHRzN8+HDq16/P6tWriY+P59tvvyU5OZkGDRrke/6zs7PJzs7m888/Z/Xq1dy+fZt69epx7tw5UlJSaNOmjdo+P3nyBFtbW/r06YO2tjZhYWHMmjWLzMxMunfvrnKs5s2bJ52L06dPs3LlSiwtLaV3shWmrpSUFIYNG4a+vj7Lli0DYMOGDSQkJFCzZk0prsePH9OvXz/q1avH4sWLkclkbNq0icGDB3Ps2DGVH1O7d+/G3t6epUuXcv36dfz9/cnIyODixYuMGjUKTU1NFi9ezMyZM9myZYu03bhx47h69SpeXl7UrVuXc+fOMXXqVPT09GjdurV0TKOjo1m4cCEjRoxAX1+fNWvWMGbMGE6cOIGmpiZ79+6lf//+DBw4UEpQ69atW+R/q8X19v+heUlOTs51moVMJkNHR6fY2wvCh5SdnV3o7/xHlzzZ29vTrVs36tSpQ1RUFIGBgQwdOpSdO3dKk5fj4+PR19dX29bQ0FD6VZeQkADkDAG+SUtLCx0dHeLi4oodY3p6Ordu3cpzfbly5UhNTVVZJpfLC/WfzPukTEQKKyMjA8i5aOVm69atxMfHs2PHDoyNjQGwtbWle/fubN68mQkTJgAwbNgwaZusrCzs7Oy4fv06+/fv55tvvgEgICAAMzMzVqxYISWZcrmc+fPnk5GRQUpKCtnZ2SxfvpyOHTsya9YsqU5DQ0PGjRvHsGHDqFu3rtRr07p1a8aMGSOV++WXX8jIyGDKlClUqFAByJmQ/uY+KpOIVatWSRfZhw8fsnXrVpKSkpDL5VL9aWlpKsdGU1NTLf4FCxYwYsQIzM3Ncz2GtWrVYty4cdLnhg0bUq5cOebNm8fUqVNVvjMmJiYsWLAAyPl38tdff3H8+HG8vLwA2LZtGzKZjLVr10r/PoyNjRk9ejSZmZl5nkfISayzs7OpVKkS1tbWUo/OkSNHaNOmDXK5nOzsbOlcQM4wvFJ2djZNmjQhIiKCffv28fnnn0vHCMDV1ZURI0YAOd+RM2fO8NNPP0k/igpTV0hICDExMWzdupVq1aoBYGlpSbdu3ahevboUl5+fHwYGBqxfvx5tbW0AGjVqRNeuXdm3b5/KpHgTExN8fHyAnO/CqVOn2LFjB/v375d+eA0aNIjly5cTHR2Nvr4+//nPf/j1119Zv349jo6OQM48zcjISPz8/GjRooV0TOPi4ti8ebM0P27KlCl4enry+++/Y2trK/U8mZqaSn+HvP/NlTS5XF6oi0l4eDjJyclqy3V0dGjUqFGxtxeED62w03U+uuTpzQsJQNu2bXF3d2fDhg1qXf2lRVNTk3r16uW6LjU1ladPn6KtrV1ic2xKSlHneCkn3ue1H1euXMHBwYHKlSurbNOiRQv+/vtvabt79+7h6+vLtWvXiImJkcpGRERIZW7cuIGrq6uU1AC4u7szf/58ypUrR/ny5QkPD+fZs2fMmDFDig3A2dkZuVzOnTt3aNy4sbSf7dq1U4m9SZMmaGhoMGfOHHr37k3z5s3VknCZTIa9vb1K0q1QKMjIyOD169eYmppK9WtpaanUn1f8//zzDw0bNsz1GGZnZ7Nz505CQkKIiIhQSbpfvHiBpaWlyn6+2V79+vU5ceKEtOzmzZu0aNECU1NTqUzr1q0xNDREQ0Mj3++jhoYGMpmM8uXL4+7uzs6dO/Hy8uLs2bMsX76c8uXLI5PJpHMBOT2P/v7+nD59mqioKKm3xMjISCqjPFatW7dWab9u3bq8ePGiSHX9/fffWFpaqvQmW1hYoFAokMvlUrlLly7h5uamci5MTU1p2LChyvcyt2NqYWFBbGysSkKgPAevXr3C1NSU//znPxgaGkq9ZkouLi74+PigqamJhoYGGhoaVK5cmcaNG0tllN+Dly9fqrT75nH9kGQymdoQcW7Mzc3z7HkqjLy2F4QP6e7du4Uu+9ElT2/T1dWlTZs2nDhxQlpmYGBAZGSkWtm4uDgMDQ0BpIuisgdKKS0tjeTkZKlccchkMnR1dXNdJ5fLkcvl0n+eZUlR41H+Ks1ru1evXvHHH39gbW2ttq5WrVpoaGiQmJjIyJEjMTY2xtvbm2rVqqGtrc3s2bNJS0uT6o6OjsbExESlLUNDQ7S1taXjqewtfDvBVnr+/DkaGhrS3DpTU1OV+urWrcvGjRsJCAhg3LhxyOVyWrVqxdy5c6WeDJlMJiUbSsqLWkZGhkr9yriU8or/xYsXeR7D7du3s3z5ckaMGIGDgwMGBgb8+eefzJ8/n/T0dLX63vyspaWldgxr166t1lalSpXyPY/K/VaWcXNzY+nSpfj7+6OpqUmbNm1UetOUf581axZhYWGMGTOGevXqoaenx969e/npp59UykNOEvR27AkJCUWq68WLF1SqVCnX/UtNTZWWv3r1ih07drBjxw61/dTS0irwmBoYGKgsU/ZeKc9HXFwccXFxuX7vAWJjYzEzM0Mmk6nVpfwuvX1u3/4ulTXv2mte2r3uggCFT/bhE0iecmNhYcHFixfVxi/Dw8OpX78+kJN0Va1aVZoD9WaZ7OxstblQQtEpf30rJ2O/SdnjcO3aNSIjIwkICKBBgwbS+oSEBMzMzKTPpqamKr1SAImJiSo9MUZGRgDMnTs31wvXmz1gkPs/lNatW9O6dWsSExM5d+4cS5YsYcaMGQQFBRVij/OXV/xvx/Wm48eP065dOyZPniwte/uZZoWV2zEEVCa7F4aJiQktW7Zk+/bt9OrVC01NTbUyqampnDlzBm9vb5XJ2nv27Cly3IWtq3LlyrkOl8fGxqr0MhkaGtKmTRsGDBigVvbNcsVlaGiIsbExmzZtynW9cghbEISPV5m62644kpKSOHPmDFZWVtKy1q1bExcXx8WLF6Vl4eHh3Lx5U5qsqSx36tQp0tPTpWXHjh3DwMCg1B/++ClwcnLi3r171K1bFysrK5U/yvkbyrkbb16Ar169SkREhEpd1tbW/PrrryoTZY8fP65SxsLCAjMzMx4/fqzWnpWVFVWqVCl07Hp6eri5udGlS5diJytvyy1+mUym8t19W0pKilpy8vZdW4VlbW3N5cuXVXpbL168yKtXr4pcl4eHB66urvTu3TvX9cr5c2/GnpiYyOnTp4vcVmHratKkCbdv3+bx48fSsidPnvD333+rlHN0dOTOnTs0atRI7TtSEj+anJyciI2NRVNTM9fvYVGHxzU1NdXmSAqCULqK3fN06NAhmjdvrvZwSqUnT57w+++/S3fCFEZycrJ063RERASJiYnSBbJFixbcv3+fLVu20KFDB6pXr05UVBTbtm0jOjqatWvXSvXY2trSqlUrZs6cyfTp09HW1mbNmjUoFAo6duwolRs+fDhHjhxh8uTJ9O/fn3/++YfAwEAmTpxYag/8s6hk8lG1mZmZqZbEQM6FesiQIRw5coSvvvqKQYMGUa1aNWJjY/njjz+oUqUKQ4YMwcbGBl1dXXx8fPD09OT58+f4+fmpJTqenp706tWLMWPG0L9/f548eUJgYKA0ZAI5PUne3t5MmTKFpKQk2rZti46ODk+fPuXs2bNMnDgxz4nZAPv27ePatWu4uLhgamrKkydPOHz4sMojL95FWlqaSvwrV66kU6dO+T5M08nJiR07drBr1y7q1KnD4cOHefjwYbHaHzx4MHv27GHkyJGMHDmS+Ph4/Pz8pB67onB1dcXV1TXP9fr6+lhZWbF582aMjY0pV64cmzZtQk9Pr8g9XYWt68svv2Tjxo2MHj1autHA398fExMTlV7GcePG0atXL4YPH06fPn0wMTHhxYsXXLlyhebNm+f62IWicHZ2libAjxgxAoVCQXJyMnfv3uXhw4csWrSoSPVZWFhw6tQpmjdvjo6ODubm5ujp6b1TjIIgvJtiJ08zZsxg+fLleSZP169fZ8aMGUVKnmJiYtSGeJSfd+zYgZmZGenp6axZs4ZXr16ho6ODra0tPj4+asM0vr6+LFmyhLlz55KRkUGrVq2YPXu2ykTi2rVrExgYyNKlS/H09MTY2Fi6K6s0ZGZlldrDKov7hPHU1NRch+WWL19Ot27dCA4OxtfXl5UrV/Lq1SsqVapE06ZN6dChA5AzBLR27VqWL1+Ol5cXderUwcfHR+W2b8i5G2rt2rWsXLmSsWPHYmlpyZo1a9SeBN+5c2cMDAzYuHGj1ENTvXp1XFxcMDHJP0lUKBT8+uuvLFmyRJr826VLl1z3rzg8PDyIjY1l2rRppKWl0aFDB+bOnZvvNmPGjOHly5esW7cOgE6dOjF79mxGjx5d5PYrV67M5s2bWbhwIePHj6dWrVrMnTuXNWvWFGt/CrJq1Srmzp2Lt7c3RkZGeHh4kJSUxNatW99LXeXLl2fr1q3MmzePKVOmUKVKFby8vDh06JDKxP/atWuzf/9+fH198fHxISkpCVNTU+zt7VXuaHsX69atY9OmTezdu5eIiAj09fWxtLSkZ8+eRa5r7ty5LF68mJEjR5KSksKOHTuK/RgVQRBKhiy7mLc4NGjQgBUrVtC1a9dc1x84cIBvv/02z6eEf6qUD0bMaygmJSWF8PBwzM3Ny9zddsL7o1AomDZtWqm89uff7NWrV3z22WcMGTKEsWPHlnY4H6WUlBT+e+UaAQsP8eKZ6iNc6jasiV/IzALrGD1rM3cfqN/EU6+OGRsXjSyxWAXhXRR0/X5TkXqe/v77b5X5A7///nuuD2uLj49n3759+Q6RCIIglLRNmzZhYmJC9erViY6OZuvWrWRmZvLll1+WdmiCIHxCipQ8/fLLL/j7+wM580uCg4MJDg7OtayBgYH0lF9BEIQPQS6X891330mPpWjatClBQUFUrVq1tEMTBOETUqTkqU+fPrRt25bs7Gx69+7NuHHjVO5eg/89jr9WrVoq84sE4d/s9u3bpR3Cv4JykrYgCML7VKTspnLlytIzaXbs2EHdunWl95MJgiAIgiD8GxS7a0j5fiZBEARBEIR/k3caV/vtt984cOAAjx8/Jj4+Xu3dRDKZjF9++eWdAhQEQRAEQShLip08bdmyhVWrVklvWS+p56MIgiAIgiCUZcVOnnbs2EHLli3ZtGlTru+2EgRBEARB+BQV+9128fHxdOrUSSROgiAIgiD8qxQ7ebKysiI8PLwkYxE+Mn5+figUChQKBQ0aNKBZs2Z07dqV+fPnF/tlupcvX2bjxo0lFuOTJ0+kGM+dO6e2PiQkRFovlKySPpfvIj4+HoVCwcGDB/Mt9/LlSxYvXkzHjh2xsrLC0dGR/v37s337dqnMkydP8PPz4/nz5yUaY1k6XoIg5K/YydO3337LyZMni/2Gd0FdZlbWR9d2+fLlCQ4OZt++faxbt46ePXty4cIFunXrxo8//ljk+q5cuUJAQECxYsmPrq4ux44dU1seGhqKrq5uibcnvL9z+b5kZGQwePBgTp48ydChQ9myZQuzZ8+mUaNG/Prrr1K5iIgI/P39iYqKKtH2P7bjJQj/ZsWe8zRhwgQyMjKYNm0a3377LWZmZsjferGsTCbj8OHD7xzkv4WGXI7PuRAevor+oO3WNjJlXus+xdpWLpdjY2MjfXZ2dmbAgAF4enoya9Ys7OzsqFmzZglFWnzt27fn5MmT+Pj4oK2tDUBUVBT/+c9/cHd3F99TgStXrnD79m127dqFvb29tLxLly5kFfPHRUpKiniHpSB8gord82RkZETt2rVp3rw5jRo1wtjYGCMjI5U/hoaGJRnrv8LDV9H8E/v0g/4p6WRNW1ubOXPmkJ6ezv79+6Xlhw4don///rRo0QJ7e3s8PDy4fv26tN7Pzw9/f3+SkpKkoTQPDw8A7t27x8SJE2nTpg1NmzbFzc2NrVu3Fvqi1rp1a2QyGWfPnpWWHTt2jFq1atG4cWO18itXrqRr167Y2tri4uLCpEmT1HoaPDw8GDVqFMePH6dTp07Y2toyaNAgHj16VOS6srOz8ff3x9nZGVtbW8aNG8eFCxdQKBRcvnxZpVxgYCCdOnWiSZMmtG/fXmVISXkcbW1tuXnzJn379sXa2poePXpw8+ZNUlNTmTdvHvb29rRu3VptW4CwsDAGDRqEjY0NzZo1Y/LkycTExEjrlUOhP/74I/Pnz8fe3p5WrVqxbNkyMjIypBjyOpe5CQsLY/To0bRq1QobGxu6devGoUOHVMpcvnwZhULB+fPnmTx5Mra2tri6urJ582a1+kJCQmjXrh1NmzZl8ODBPHz4MM+2leLicl56a2pqqrZO+cPw8uXLDBo0CIBevXqpDPkq4ztz5gzjxo3Dzs6O8ePHA+/23Yec7//XX39Ns2bNsLGxwdPTU+17lpCQwJQpU7C1tcXR0ZHVq1ezdetWKb709HScnZ1Zs2aN2v5NmDCBXr16FXiMBEHIUeyep507d5ZkHMInpl69elSpUoWwsDBp2ZMnT+jevTu1atUiLS2No0ePMnDgQA4fPoy5uTm9e/cmMjKS0NBQgoKCANDT0wNyeonMzc3p2rUrFSpU4NatW/j5+ZGUlMTYsWMLjEdLS4sOHToQGhpKx44dgZwhO3d391zLx8TEMGrUKCpXrkxsbCzbtm3Dw8ODo0ePqrx26NatW8TGxjJlyhQyMzNZunQpU6dOVXnnY2Hq2rlzJ/7+/owYMYKWLVty6dIlZs+erRbXokWL2L9/P6NHj6Zp06ZcvXqVlStXoq2tTf/+/aVy6enpTJ8+nSFDhmBiYsLKlSsZO3YsdnZ2VKpUCV9fX06dOsWSJUuwtrbGzs4OyEliPDw8aNOmDWvWrCE5ORlfX1+8vLzU3mPp6+tL+/bt8fX1JSwsDD8/P2rVqkX//v3zPZe5efr0KXZ2dvTv3x8tLS2uXr3K7Nmzyc7OpkePHipl582bR7du3Vi/fj2//PILK1euRKFQSK+K+vXXX5kzZw49e/bEzc2NGzduSElMfho2bIhcLmf27NmMGTOGZs2aoaWlpVKmcePGzJ07l/nz57NkyRIsLCzU6pkzZw5ffPEF69evl5Kud/nuP378mH79+mFpacnSpUuRyWRs3LiRIUOGcPz4cSnGGTNmcOnSJaZOnUr16tUJCQnhxo0bUlyampr06NGDQ4cOMX78eCm2V69ecerUKWbNmlXgMRIEIYd4+Zzw3lStWpUXL15In99McrKysnB2dub69ev88MMPTJo0CTMzM2n4982hQABHR0ccHR2BnN6XZs2akZKSwq5duwqVPAG4u7vj5eXF69eviYmJ4c8//2TFihUqvVFKS5Yskf6emZmJra0trVu35tKlS7Rq1Upal5CQwKFDhzA2NgYgKSmJGTNmEBkZiZmZWaHqyszMZNOmTfTs2ZMpU6YA0KpVK16+fMmBAwekbR89esSuXbvw8fGhb9++ADg5OZGSksL69evp27evdEFMT09nypQptGnTRjreyoRrxowZALRs2ZLjx49z/PhxKXlatWoVTZo0wd/fH5lMBkD9+vVxd3fn7NmzUn0A1tbWUoLn7OzM5cuXOXHiBP3798/3XOamS5cu0t+zs7Oxt7fn+fPnBAcHqyVPHTt25JtvvgFyvhdnzpzhxIkTUvL03Xff0bx5c+m4u7i4kJqayoYNG/KNoU6dOnh7e7NixQqGDBmCpqYm1tbWdO7cmf79+1OuXDn09PSoV68eAJaWllhZWanV065dO6ZOnaqy7F2++/7+/hgaGrJt2zZpyNnOzo727duzf/9+Bg4cyN27dzl58iTLli2je/fu0n537txZpa7evXuzZcsWfvvtN+lcHjlyBLlcnucPCUEQ1BU7efrPf/5TqHJvzh0Q/l2ys7OlCzDkDD2sXr2asLAwlWGgBw8eFFhXamoqAQEBHDlyhGfPnpGeni6te/36NRUqVCiwjpYtW1KhQgV++eUXIiIiaNy4Mebm5rkmT2fPnuW7777jzp07JCYmqsT6ZvLUoEEDKXECpAvrm8lTQXVFRkYSHR1Nu3btVGJo3769SvJ04cIFICd5UA6PQU4CtXnzZp49e0b16tWBnGEmZbIJOYmBsqyShoYGtWrVIjIyEoDk5GSuXr3KtGnTyMzMVNm2atWq/PnnnyrJ05vHAaBu3bpcunRJ7VgWRlxcHH5+fpw6dYrnz59L7RsZGamVfbNdmUxG3bp1pX3IzMzkxo0baslLp06dCkyeAAYPHoybmxunT5/mypUrXLx4kYULF/Lzzz8TFBSkNq8zN23btlVb9i7f/fPnz+Pm5oaGhoZ03g0MDGjUqBF//fUXAH/++SeQ851RksvluLq6sm3bNmlZ7dq1adGiBd9//710Lg8ePEinTp3y7RkUBEFVsZMnDw8PlQtjXm7dulXcJoSPXGRkpHTRTkxMZNiwYRgbG+Pt7U21atXQ1tZm9uzZpKamFljXihUr2L9/P2PGjKFJkybo6+tz6tQpvvvuO1JTUwuVPGloaNC5c2eOHj1KREQEX375Za7lrl+/jpeXF+3bt2fkyJFUqlQJmUxGnz591GI1MDBQ+ax87pmyXGHqio7OmXP2ZhIGqL10++XLl2RnZ9OyZctc434zeSpfvrzKkJMyLn19fbV4lXHEx8eTmZnJkiVLVHrL3qz/TbnVlZaWlmtsBfH29iYsLIwxY8ZQr1499PT02Lt3Lz/99JNa2dzaTUhIACA2NpaMjAy1Y2liYlLoWExNTenbty99+/YlPT2duXPncvDgQX799VeV5CQvb5+3d/3uv3z5kqCgIGk4703K8xodHY2mpqbasXn7OAD06dMHb29vYmNjiYqK4ubNm3h7excYhyAI//NOTxh/W2ZmJhEREYSEhJCVlcXkyZPfKTjh43Xnzh2eP38uDblcu3aNyMhIAgICaNCggVQuISFB6qHJz/Hjx+nbty+enp7Sstx6jArSpUsXBg4cCICbm1uuZX755Rf09PTw9fWVehoiIiKK3FZh61JOUI6NjVVZ/mYPBYChoSEymYw9e/bk+nBac3PzYsWopK+vj0wmY9SoUXz22Wdq6ytWrPhO9eclNTWVM2fO4O3trTJJes+ePUWuy9jYmHLlyqkdyzeHj4tCU1OTIUOGcPDgQe7du1eo5OntH5Xv+t03NDSkTZs2DBgwQG2d8keDqakp6enpJCQkqCRQbx8HyOm5XLBgAYcPH+bJkyfUqlVLvOhdEIqo2MlTfv/YevbsyYABA7hy5YrK0IHw75CamsqCBQvQ0tKid+/eQM4t24DKRf/q1atERERgaWkpLcur9yI1NVVl28zMTI4ePVrk2GxtbXF3d6dSpUp5XrhSUlLQ1NRUuQgW93lmhanLzMwMU1NTTp06pZK0vP1SbeW/pVevXqkN8ZUEXV1dbGxsuH//fq5zeYqqsD1RaWlpZGVlqZzfxMRETp8+XeQ2NTQ0aNSoESdPnmTIkCHS8hMnThS47atXr9DT01O5IQD+N7SmTHLf7l0syLt+9x0dHblz5w6NGjVCQ0Mj1zaaNGkCwKlTp6Q5T1lZWSrPp1LS0tKiW7du7N+/nxcvXjBkyJBCjSIIgvA/72XCuFwup0uXLgQEBBTqLhfh45WVlcW1a9eAnMnS//zzD8HBwTx+/JilS5dSo0YNAGxsbNDV1cXHxwdPT0+eP3+On58fVapUUamvbt26ZGRkEBQUhK2tLXp6elhYWODk5MT+/fupV68eFStWZM+ePcUaIpLJZKxYsSLfMs7OzgQFBbFgwQI6dOhAWFhYsR74Wdi6NDQ08PT0ZPHixZiYmODg4MDly5e5ePEi8L/b5M3NzRk4cCDTpk1j+PDhNG3alPT0dB48eMDly5cLNaenINOmTWPw4MFMmDCBLl26YGBgQGRkJBcuXKBnz544ODgUuq68zuXb9PX1sbKyYvPmzVLP0aZNm9DT08u156Qgo0ePxsvLixkzZkh32xXm/F26dImVK1fSo0cPrK2tKVeuHLdu3SIgIIBq1arRoUMHIGcOmIaGBt9//z3lypVDQ0Mj32TzXb/748aNo1evXgwfPpw+ffpgYmLCixcvuHLlCs2bN8fd3R1LS0s6dOjAwoULSU5Oplq1aoSEhJCSkpJrYtSnTx+CgoLQ0NCgZ8+eRTzCgiC8t7vt4uLipHkIQuHVNlJ/xkxZbjMlJUW680tXV5caNWrg6OiIv78/devWlcqZmJiwdu1ali9fjpeXF3Xq1MHHx4ctW7ao1Ofq6sqAAQPYtGkTMTEx2Nvbs3PnTubMmcO8efNYsGABOjo69OjRgw4dOuR6O/+7atOmDVOmTGHXrl0cPHgQOzs7AgIC6NSp03ury8PDg/j4ePbs2cPOnTtxdHRk6tSpTJw4UWUYZvbs2ZibmxMcHMz69eupUKEC5ubmfP755++835BzF9eePXvw8/NjxowZpKenY2ZmRsuWLaldu3aR6srrXOZm1apVzJ07F29vb4yMjPDw8CApKYmtW7cWeR/at2+Pj48PGzdu5OjRozRt2hRfX1+pFzQvTZs2pVOnTpw6dYqgoCBSU1MxMzOja9eueHp6ShOqjY2NmTt3Llu2bOHw4cNkZGRw+/btPOt91+9+7dq12b9/P76+vvj4+JCUlISpqSn29vYqrxVavHgx8+fPZ/ny5WhpadGjRw8sLS3ZvXu3Wkz16tWjTp061KpVSy2JEwShYLLs7Ozs4mz49OnTXJfHx8fz+++/s2rVKuzs7AgMDHynAD82yrte8volmpKSQnh4OObm5mpPHs7MykKjEHfzvA+l2baQO19fX7Zt28bly5fFU6qFYhk4cCByuVwtaX306BEdO3Zk7dq1Bf4oSElJ4b9XrhGw8BAvnsWprKvbsCZ+ITMLjGP0rM3cfRCptrxeHTM2LhpZiD0RhPevoOv3m4rd89SuXbs8x8mzs7OxsbHBx8enuNX/K5Vm8iISp9J17949Dh8+jK2tLZqamly5coXAwED69+8vEiehUE6cOMGzZ8+oX78+ycnJhIaG8vvvv7N+/XqpzMuXLwkPD2f9+vVUq1atUBPgBUFQV+zkafHixWrJk0wmw8DAgFq1aknPuxEEoWDly5cnLCyMvXv38vr1a6pUqcLw4cOlh0EKQkF0dXX58ccfefDgAenp6VhYWLBixQqVmxB+/fVXZs6cSe3atVmxYoXa5HhBEAqn2P9yxCRDQSg51atXz/XxH4JQWC4uLri4uORbpmfPnuL/bkEoASXys+Pu3bvSs2uqV68uep0EQRAEQfhkvVPy9Msvv7B06VK1h/7VqFEDb29vMZ4uCIIgCMInp9jJ09mzZxk3bhzVqlVj4sSJ0m3p9+7dIyQkhG+++YaNGzdKL+sUBEEQBEH4FBQ7edqwYQMKhYLdu3ejq6srLW/fvj1fffUVAwYMYP369SJ5EgRBEAThk1Ls+9Nv375N9+7dVRInJV1dXXr06JHvg+MEQRAEQRA+RsVOnrS1tYmLi8tzfVxcHNra2sWtXhAEQRAEoUwqdvLk4ODAjh07CAsLU1v3xx9/SK+YEARBEARB+JQUO3maOnUq2traDBgwgL59++Lt7Y23tzd9+/alX79+aGtrM2XKlJKMVShj/Pz8UCgU0h8rKys6d+7M5s2bycrKUimrUChK/VU9t27dQqFQcPny5VzX//XXXygUijxfIpuZmYmTkxPTpk3jyZMnKBQKjh8//j5DzlO7du2YP3++9Nnb2xt3d/cSb8fPz4+rV6+qLS8L51MQBKG0FHvCeM2aNTl8+DABAQGcO3eOY8eOAVCtWjUGDRqEp6cnlSpVKrFA/w2ysrOQy0rnNSnFbbt8+fIEBQUBOe/Aunz5MqtWrSI7OxtPT8+SDvO9atKkCebm5hw9epRu3bqprb948SIxMTF07dqVypUrExwcTJ06dT58oLnw8vIiKSmpxOv19/dHV1cXOzs7leXBwcFUq1atxNsTBEH4GBQ7ecrIyEBbW5uZM2cyc6b6iyETExPJyMgQj/8vArlMTsAfu3iW+PyDtltVrwqjmn5VrG3lcjk2NjbS55YtW/LPP//w888/f3TJE4C7uzsbN27k5cuXVKxYUWVdaGgolSpVwsnJCQ0NDZX9Lm21atX6oO2VpX0XBEH40IrdzbFw4UL69euX5/r+/fuzdOnS4lb/r/Us8TkP4yM+6J+STtYqVKhARkZGkbc7c+YMQ4cOxdHRETs7O3r37s25c+dUyhw8eBCFQsHNmzcZMWIENjY2dOzYkUOHDqnVt2HDBpydnbG1tWXs2LHExMQUGEPXrl1JT09XG45LTU3l5MmTuLm5oaGhkeuw3alTp+jZsye2trY0b96cnj17cvbsWWl9bkNd27dvR6FQSJ+TkpKYP38+nTp1omnTprRr1465c+eSkJCQb9xvD9u1a9dOZUhV+cfb2xuAqKgoZsyYQfv27bG2tqZjx46sXr2atLQ0lXgBli9fLm2vHPLMbV/27dtHp06daNKkCe3atWPDhg0qw7dFOXeCIKjLysx6p/VCySl2t9Bvv/1G9+7d81zfqVMnDh8+XNzqhY+IMlFSDtv9/PPPjBo1qsj1PHnyBFdXV4YNG4ZcLufcuXN4enoSFBSEg4ODStkpU6bQp08fhg4dSkhICN7e3lhZWUkPa921axdr165l2LBhODk5ceHCBWbNmlVgDLVr18bKyorQ0FD69+8vLT9z5gyJiYl07do11+0ePXrE+PHj6dKlC5MnTyYrK4u///473ztSc5OSkkJmZiYTJ07E2NiYZ8+esXHjRry8vNi5c2eh6/H391dJhG7fvs28efMwNzcH4OXLlxgZGTFjxgwMDAx48OABfn5+REdHs2TJEiBnaK5v3754eHhIiVler17auXMnCxcuxMPDg7Zt2xIWFoa/vz8JCQlMnz5dpWxB504QhNzJNeQs997K4/uRautqWpgxbemwUojq36nYyVNUVBRVqlTJc33lypV5/vzDDj8JH15SUhKNGzdWWebm5lasIbuvvvrf0GFWVhYODg7cvXuXkJAQteRp4MCBDBw4EABbW1vOnj3LiRMn8PLyIjMzk4CAALp16yZduF1cXIiJiclzMvibunbtypIlS3j27BlVq1YF4OjRo9SqVYumTZvmus3NmzdJT09nzpw56OnpSW0WlbGxMT4+PtLnjIwMatSowYABAwgPD5eSn4I0atRI+ntsbCyTJk3C2dmZkSNHAjk9R28mNXZ2dujo6ODt7c3cuXPR0dGRhuaqVq2a7zBdZmYm69evp0uXLsyePRuAVq1akZ6eztatW/H09FQZAs3v3An/LhUNK5CZlYWGPO9BkILW/9s8vh/JvVuPSzuMf71iJ09GRkaEh4fnuf7evXvSRUT4dJUvX55du3YBkJaWxo0bN1i3bh2zZ8+WejAKKzIykjVr1nDhwgWio6PJzs4GUEvOIOfirKSrq0u1atWIjIyU6omKiqJDhw4q23Tq1KlQyZObmxvLli3j2LFjDB8+nMTERM6cOcPw4cPz3EahUKChoSH1qtjb26Ovr1+o/X7boUOH2L59Ow8fPlSZBP7gwYNCJ09K6enpjB8/Hg0NDVavXo38/1+EsrOzCQoKIiQkhCdPnpCamipt8/jxY+rXr1/oNu7fv8/Lly/5/PPPVZa7ubkREBDA9evXadOmjbQ8v3Mn/LvoVSiPhlzOt9t/4EHkC7X1dcxM+HZIj1KITBDyV+zkycXFhX379tG1a1eVX7kAN27cICQkRO0/U+HTI5fLsbKykj43a9aMzMxMli5dytChQwt9Ec7KyuLrr78mISGBcePGUbt2bXR0dFi3bh3Pnj1TK/92YqKpqSkNU0VHRwM5vThvMjExKVQspqamODg4EBoayvDhwzl58iSpqal5DtkBmJubs3HjRgICAhg7dixyuZxWrVoxd+7cIt2VdvLkSaZPn07fvn2ZOHEiRkZGREdHM2bMGJUEp7AWLVrEX3/9RUhICIaGhtLyoKAgli1bxogRI3BwcMDAwIA///yT+fPnF7kd5dDk23fXKj+/PXSZ37kT/p0eRL7gnycigRY+HsVOnsaPH89vv/1G7969adeunTQX4s6dO/z6668YGxszfvz4EgtU+HhYWFgAcPfu3UInTw8fPuTmzZusX7+ezz77TFqekpJS5PZNTU2BnOGqN714of7LNi9du3ZlxowZ3L9/n9DQUBo3biztV15at25N69atSUxM5Ny5cyxZsoQZM2ZIj3LQ0tIiPT1dZZv4+HiVz8ePH6dhw4Yqz3C6cuVKoeN+0759+9i3bx/r1q3D0tJSrZ127doxefJkadm9e/eK1Y6RkRGgfryVE/TfTNoEQRA+BcUeSK5SpQrff/897u7uXLx4ke+++47vvvuOS5cu0bVrVw4cOICZmVlJxip8JO7cuQOgdqt/fpS9HZqamtKyiIiIXJ9gXxAzMzNMTU05efKkyvITJ04Uuo6OHTuira1NUFCQ9J0uLD09Pdzc3OjSpYtKQmJmZqaWoFy4cEHlc0pKisoxADhy5Eih21b6/fffWbhwIaNHj6Zjx45q6wvbjqamZoE9Uebm5hgbG6vdofjTTz+hqamJtbV1keMXBEEoy97pIUyVK1dm2bJlZGdnS786jY2NkclkJRKcUPZlZWVx7do1IGd+zY0bN/juu++oV68ezZs3L3Q9FhYWmJmZsWrVKrKyskhKSmLdunVUrly5yDFpaGjg6enJokWLqFSpEs7Ozpw/fz7PJ4vnRk9P7/+1d+dxUVX//8BfM8MumygChgq44JILiBKypaYWaKS5J1oiaIjmVqIhuYuaVgIloqZGmrthktUXS/uk5Yb50VxCUEHEBWSTbWDm9we/uR/GYRsFhuX1fDx65Nxz7rnve48wb8859168+uqr2Lt3L0QiEby8vKqs/9133+HSpUtwd3eHubk5UlNTERsbC1dXV6HOsGHDsHPnTvTs2RO2traIjY1VualiwIABWL58OSIjI4XF1GfOnFHr/PPy8jBr1ix06NABnp6eQv8AZT+f7du3x4ABA7Br1y7ExMTAxsYGsbGxuHPnjkpbdnZ2iI+Ph5OTE/T19WFra6uyllEikSAwMBArV66EmZmZcMzo6GhMmTJFrSSaiKgxqJUnWIpEIj5NvJZYGVZ+B2NDPGZhYSHGjRsHANDS0oKlpSXefPNNBAUFqYxsVEVHRwfh4eFYvnw5PvjgA1hZWeH999/Hn3/+iStXrqgdl6+vL3JycrB7927s2bMHLi4uWLlyJaZNm1bjNkaMGIGffvoJzs7OVd5ZCpQtGP/111+xZs0aZGVlwdzcHN7e3kpT14GBgcjIyEBkZCREIhHGjRuHyZMnKz0Pbfz48UhNTUVMTAy2bdsGNzc3bNiwAWPHjq1x3FlZWcjMzERmZqbKs9hGjhyJsLAwzJw5E0+ePMGmTZsAlCV2ISEhmDFjhlL90NBQrF69Gv7+/igsLMSuXbtU7nwEyq63lpYWduzYgT179sDc3BxBQUEq7VHT0rKVMWSlMoglvBuOmheRXHFLE9WK//73vwCgtIi6vMLCQuGWcz09PaWyxvh6FiJq2goLC3Hh7CVErTyCx/eVF/97ejlh4Vo/rF+7Hykpj1T27evUGVPeHYIZH0cj8bbqgvCBA3rg45mj8G5YdIULxrtYW2JHsH/tnUwTMGvs6gofVdCxWzuE71N92wfVXHXf3+Xx3SkNiCaTFyZORPS8UlIe4Vai6l2x1tY1u8OVqLHhNyYRERGRGpg8EREREamByRMRERGRGpg8EREREamByRMRERGRGpg8EREREamByRMRERGRGpg8EREREamByRMRERGRGpg8NSAyuaxRHjs+Ph5Tp05F//798fLLL2PQoEEIDQ1FcnKyUMfe3h7btm2rjVBr5Nq1a7C3t1frZcB1YdWqVRg0aFCVdQ4dOgR7e3vhPwcHB7z++utYtGgRLl++/FzHvXbtGsLDw1FQUPBc+9d3fxERNSZ8PUsDIhaJcfTGl8jIT6vX47YyaIsR9oHPte+nn36K6OhoDBs2DCtWrICZmRnu3r2LgwcPYu7cuThy5EjtBtuEbd26FUZGRigoKEBycjIOHjyIsWPHYt68eQgICFCrrWvXriEiIgLvvPMO9PX16yhiIqLmqUElT3fu3MG2bdvw999/499//4WdnR1++OEHlXr79+/H1q1bkZaWBltbW8ydOxcDBw5UqpObm4s1a9bg//7v/yCVSuHu7o6QkBC0adNGqd7Fixexdu1aXLt2Da1atcKECRPg7+8PkUhUp+damYz8NDx4elsjx1bXyZMnER0djcDAQHzwwQfC9n79+uHtt9/Gr7/+qsHoGp8ePXrAzMwMAODi4oLx48dj4cKF2LhxIxwdHeHk5KThCImICGhg03b//vsvTp48iQ4dOqBjx44V1jl27BiWLFmCN954A9HR0ejTpw+CgoJw6dIlpXpz5szBH3/8gaVLl+LTTz9FcnIy/P39UVJSItS5c+cO/Pz8YG5ujqioKEyZMgWbNm3C9u3b6/I0m4zt27ejdevWCAyseNTq2YT2Wd999x2GDRsmTPV9+eWXkMn+N30YHh4OBwcHlf2cnJwQHh6utO3LL7+Eq6srHBwcEBQUhIyMjBqdw6effooRI0bAwcEB7u7umDdvHh4+fKhUx9fXF9OnT8fx48cxbNgwODg4YPLkybh7965SvQcPHmDGjBno3bs33N3dER0dXaMYKiMWi/Hxxx9DR0cHe/bsEbb/9ttveO+99+Di4gJHR0eMGTMGp06dEsoPHTqERYsWAShLwuzt7YWpw4cPH2LRokUYPHgwevXqhaFDh2Ljxo0oLi5WOX5paSnWrVuHV155BQ4ODggODkZeXp5Qnp+fj+XLl2PYsGHo3bu3MF2bm5ur1E58fDxGjRoFBwcHODk5YdSoUTh58qRSnUOHDmHEiBHo2bMn3N3d8dlnn6G0tPSFrh8RUV1pUCNPgwYNwmuvvQYACA4OxpUrV1TqbNq0Cd7e3pgzZw4A4JVXXsHNmzcRGRkpfFklJCTgP//5D7Zt2wY3NzcAgK2tLby8vPDzzz/Dy8sLALBt2za0bNkSGzduhI6ODlxcXJCZmYnNmzfD19cXOjo69XDWjVNJSQkuXryIoUOHQltbW+39v/nmG6xcuRK+vr549dVXkZCQgIiICOTm5mLhwoVqtRUTE4MvvvgCU6dOxYABA3D69Gl8/PHHNdo3IyMD06dPR5s2bZCZmYmvv/4avr6+OHbsGLS0/vfjce3aNWRmZmLBggUoLS1FWFgYPvzwQ+zdu1eoExgYiAcPHmDp0qUwMjJCdHQ07t+/r9SOukxNTdGjRw8kJCQI21JTUzFw4EBMnToVYrEYp06dQkBAAHbu3AlnZ2e8+uqreP/99/HVV18JU4GKv8tPnjyBqakpFi1aBGNjY9y+fRvh4eF49OgR1qxZo3Tsb775Bj169MDatWuRmpqKTz/9FEVFRfjss88AAIWFhSgtLcXcuXNhZmaG+/fvY/PmzQgMDMQ333wDALh79y4++OADeHt7Y/78+ZDJZLh+/Tqys7OF43z99ddYv349pkyZguDgYNy6dUtInhYsWPDc146IqK40qORJLK56ICwlJQW3b9/Ghx9+qLTdy8sL69atQ3FxMXR0dHDq1CkYGxvD1dVVqGNnZ4du3brh1KlTQvJ06tQpDBkyRClJ8vLyQlRUFBISEuDs7FyLZ9e0ZGVlobi4GG3btlV739LSUkRGRsLb2xshISEAADc3N0ilUmzfvh0BAQFo2bJljduKioqCj4+PkHS5u7sjIyMD33//fbX7l08YSktL4eDgAA8PD/z5559C4g2UTQMfOXJEmFbLz8/HokWLkJ6eDktLS5w6dQpXrlzBjh074OLiAgBwdnaGp6cnTE1Na3QulbGyssLVq1eFz5MmTRL+LJPJ4OzsjMTEROzbtw/Ozs4wMzND+/btAShPBQJlC8HLJ6eOjo7Q19dHcHAwQkNDldZH6ejoIDIyEhKJBACgq6uLkJAQBAUFoWPHjjAzM8OyZcuE+iUlJbC2tsbEiRORnJwMW1tb/PPPP5BKpViyZAkMDQ0BlPWPQl5eHjZt2oRp06Zh3rx5AABXV1doa2sjLCwMfn5+Nf67QERUXxpU8lSdpKQkAGWjSOV17NgRUqkUKSkp6NixI5KSkmBra6uybsnOzk5oIz8/H/fv34ednZ1KHZFIhKSkpOdOnuRyOfLz8yssKyoqgkwmQ2lpqcq0hOJLSlPUmSZR1JXL5TXeT3He//77L548eYKhQ4cq7Tts2DBERUXh0qVL8PDwgEwmq7R9RVv37t3Dw4cPMXjwYKV6Q4YMwffffy/Uq8ypU6ewefNmJCYmKk1JJSUlCUmQXC5H165dYWJiIrSl+DuYlpYGc3NzXLp0CUZGRujfv79Qx8DAAC4uLvjnn3+qjEExVVlZrDKZDCKRSChLT0/HF198gTNnzuDRo0eQy+UAyhIlRZ3K2pTL5fjmm2+wb98+3Lt3D0VFRULZnTt30LlzZ+Hzq6++CuB/fT1kyBB8/PHH+Pvvv2FjYwMAiI2NxY4dO3Dnzh2lO/uSkpLQvn17dO7cGRKJBPPnz8eYMWPg5OQEIyMjod6FCxeQn5+PIUOGKMXi7OyMwsJC3LhxA/369av02lHd0tTaz2cVFBQIf8+bK5FIVKObP3itnp9cLq/x3/lGlTwphvqNjY2Vtis+K8pzcnKUfkErmJiYCFOBinUZz7alo6MDfX19pWkFdUmlUly7dq3Sci0tLaUvCqBs1E3Td0UVFxcrrTmqip6eHnR1dZGamorCwsIa7VNSUoLCwkI8fvwYAGBkZKS0r2Jk4vHjxygsLBTWpz3bvlwuF9q6d++esG/5eor+Ly4urjS+q1evIigoCJ6enpgyZQpatmwJkUiEKVOm4OnTp8J+MpkMBgYGSu0ofjnl5uaisLAQ6enpMDU1VTmWqakp5HJ5lddIKpUK51lRvfv376NVq1YoLCyETCZDYGAg8vLyMGPGDLRr1w76+vr46quvkJ6eLuxfWZsxMTH4/PPPMWXKFDg5OcHY2BhXr15FWFiYcC4KxsbGSp+1tLSgq6uL+/fvo7CwECdOnEBwcDBGjRqFwMBAmJiY4PHjx5g/fz7y8vJQWFgIS0tLfP7559i+fTtmz54NkUiEAQMGYOHChbCyshLWl40ePbrCa3P37l307Nmz0mtHdUssFjeIBCo5Ofm5H7vRVOjr66N79+7V1uO1ejE1Xa7TqJKnxkJbWxudOnWqsKyoqAhpaWnQ1dWFnp5ePUdWNXXXeDk4OODcuXPQ0tKq0boeLS0t6OnpwdzcHEDZlE35a6AY+WndujX09PTQokULlJSUKNWRSqUoKCgQ2nrppZcqbEuRHOvo6FR6nU+dOgVDQ0N88cUXwpSxIhlTtA+UfYFIJBKldhTXStG+paUlsrKyVI6VlZUFkUhUZV8r1ozp6elVuP8///yDoUOHQk9PD7dv38b169cRHh6OwYMHK12X8seprM0TJ05g4MCBSlPfKSkpFV6rnJwclf4pKiqClZUV9PT08Ouvv6Jr165YuXKlUOfcuXPC8RX7Dh48GIMHD0ZeXh5+//13rF27FsuXL8fXX3+N1q1bAyhby2hpaalybaytrRvcz0lzIhKJKryZoL7Z2to2+9GUmiaxvFbPLzExscZ1G1XyZGJiAqDsi1HxBQyU/ZIvX25sbIz09HSV/bOzs4U6ipGJZ+8MKi4uRkFBgVDveYhEIhgYGFRYJhaLhS9jTU/TPUvdeKZOnYqAgABER0cjKChIpfzkyZPw9PQUPivOu1OnTjAzM8PPP/+MYcOGCeU///wztLW10adPH0gkElhZWUEqleLevXvCGp7Tp0+jtLRUaOull16Cubk54uPjldr65ZdflI5ZkeLiYmhra0NLS0v4xRQXF6eyn0gkgkgkUmpH8WdFvd69eyM3Nxdnz54Vpvtyc3Nx5swZmJqaVnltFYnbs7HKZDKEhYVBKpVi0qRJkEgkwoiSrq6uUPfevXtISEiAjY2N0vokoGy0r3ybRUVF0NHRUdp27NixCo//22+/YfHixcK2X375BSKRCL1794ZEIlGrLaDs53P48OG4cuUKfvjhB0gkEvTt2xf6+vp4+PChUv8RlafpUfnGhNfq+akzytqokifF+qSkpCSltUpJSUnQ1tZGu3bthHpnzpxRmb9MTk5Gly5dAJStR7GyshLWQJWvI5fLVdZC1ZdWBuovwNbUMT09PTFt2jSEh4cjMTER3t7eaNmyJVJTU3Hw4EHk5uYqJU8KEokEgYGBWLlyJczMzODp6YlLly4hOjpamD4DAA8PDxgYGCAkJAT+/v5IT0/Hrl27hMRA0VZAQABWrVqFVq1awdXVFX/88UeNnizu6uqKnTt3YsWKFRgyZAgSEhJqtMi8Ih4eHujRowc+/PBDLFiwAEZGRtiyZYswFVkTV69eFaYyFQ/JvHr1Kj788EPhkQ12dnawtLTEhg0bIJPJkJ+fj02bNqk8v0zxqI9vv/0Wr732GvT09GBvb48BAwZg165diImJgY2NDWJjY3Hnzp0K4ykuLsbMmTMxYcIE4W67YcOGCW0PGDAAy5cvR2RkJBwcHHDy5EmcOXNGqY3vvvsOly5dgru7O8zNzZGamorY2FjhZg5jY2PMnj0b69evR3p6Ovr37w+JRIKUlBTEx8cjPDycXwZE1OA0quSpXbt2sLGxwfHjx4VHGgBlowUuLi7CVIqHhwe+/PJLnDlzBgMGDABQlhT9888/mDZtmrCfh4cH4uPj8eGHHwrTHHFxcTA2Nq7w+UJ1TSaXPfeTvmvj2GKR+o/9Unyxf/vtt1i8eDEKCgrQpk0buLm5wc/Pr9L9fH19oaWlhR07dmDPnj0wNzdHUFAQZsyYIdRp2bIlNm3ahLVr12LmzJno1q0b1q1bB19fX5W2cnJysHv3buzZswcuLi5YuXKlUl9XxNPTEwsWLEBMTAwOHToER0dHREVFPdcIiEgkwpdffolPPvkEoaGhMDY2hq+vLx4/foz4+PgataGI18DAAG3atIGjoyNCQ0PRq1cvoY6Ojg7Cw8OxfPlyfPDBB7CyssL777+PP//8U+nRHt27d8esWbOEB8paWVnhxIkTmDlzJp48eYJNmzYBKFukHxISonTdFXx9fZGZmYmPPvoIxcXFGDJkCEJDQ4Xy8ePHIzU1FTExMcJjQTZs2ICxY8cKdezt7fHrr79izZo1yMrKgrm5Oby9vZUeqjp16lRYWFjg66+/RkxMDLS0tNC+fXu8+uqrz/UYDCKiuiaSN6DJ0YKCAuHhed9++y1SUlIQHBwMAOjfvz/MzMzwww8/YMGCBQgMDISzszPi4uJw4MABxMTEKCU8fn5+uHXrFhYuXAhdXV189tlnEIvFOHjwoLA+586dO3jrrbfg7u6OCRMm4ObNm1i/fj3mzp1b5Rd/Vf773/8CQKWLXBWjCra2tlzLQUQNXmFhIS6cvYSolUfw+L7yjTSeXk5YuNYPs4O+xK3E+yr7er7aEx8Fj8WMj6OReFt1KcXAAT3w8cxReDcsGjdTVcu7WFtiR7B/7Z1MEzBr7Grcupaisr1jt3YI37dYAxE1HdV9f5fXoEaeMjIylP5FCkD4vGvXLjg7O2P48OEoKChAdHQ0tmzZAltbW0RERKiMFH3++edYs2YNQkNDUVJSAjc3N4SEhCgtbO7QoQO2bduGsLAwBAQEwMzMDLNnz8bUqVPr/mSJiIioUWpQyZO1tTVu3LhRbb0xY8ZgzJgxVdYxMjLC6tWrsXr16irrOTo6Yt++fWrFSURERM1Xg3q3HREREVFDx+SJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJnlt4eDjs7e1hb2+Prl27om/fvhgxYgSWL1+OW7duPVebf/31FzZv3lzLkQK3bt3C/Pnz4erqipdffhmvvfYa1q1bJ7xUui789ddfsLe3F55aC5S9rmTbtm3C50OHDuHo0aN1FkNt+7//+z/Y29sjNTW12rrPXvPBgwcjLCwMWVlZdR/oc0pNTUV4eDgePHigtL2iviSi5ovJUwMil5c2umPr6elh7969+O6777Bp0yaMGjUKp0+fho+Pz3O9ZPfs2bOIiop6rlgqc+7cOYwePRqJiYn46KOPsH37dkyePBmHDx/G+PHjkZmZWavHq8revXsxYsQI4fPhw4fxww8/1Nvx60tF13zKlCn4/vvvMW7cODx69EjTIVbo3r17iIiIwMOHD5W29+jRA3v37hVeikxEzVuDesJ4cycSSXAxcRnyCip+y31dMdTvAMdOnzzXvmKxGH369BE+u7q6YuLEiQgICMDHH38MR0dHtGvXrpYiVV9hYSHmzZuHtm3bYs+ePTAwMABQ9q5EV1dXvPXWW1i5ciU2btxYL/GUv1ZNVU2u+fLlyxEeHl5v8bzoeyQNDQ2bRd8RUc1w5KmBySu4g+z8m/X6X20na7q6uliyZAmkUin2798vbD9y5AgmTJiA/v37o1+/fvD19cXly5eF8vDwcERERCA/P1+YDvT19QVQNgU0d+5ceHp6onfv3vDy8sL27dshk8mqjOX48eN4+PAhZsyYIXyJK3Ts2BE+Pj748ccfhWmaQ4cOwd7eXmU0ysfHR3hJNQAkJCRgxowZcHNzQ58+feDj44MjR45Ue23KT9v5+vri7Nmz+O2334TzDQ8PxzfffIPevXsjLy9Pad9bt27B3t5eeHl2Raq7xkDZdXZwcMCNGzcwYcIE9O7dG8OHD8fvv/+uVE8qlWLVqlXo378/+vbti8WLF+Pp06fVnmNNrvkvv/yCe/fuAfjflNjJkycRFBSEPn36wM3NrcLp21u3buH9999H37590adPHwQEBODu3btKdezt7bFlyxasX78erq6ucHFxAVB9n/3111+YPHkyAGD06NFCn5SPsfy0XVFREdasWQM3Nzf07NlTOK/ygoODMXz4cPz1119466230KdPH4wePRpXrlxRqnfgwAF4e3ujV69ecHZ2xoQJE1T6jYgaDo48UZ3o1KkTLCwskJCQIGxLTU3FW2+9hfbt26O4uBjHjh3DO++8g9jYWNja2mLMmDFIT0/HDz/8gJ07dwIo+xc/ADx8+BC2trYYMWIEWrRogWvXriE8PBz5+fkICgqqNI6zZ88CAAYOHFhh+aBBg7B//36cO3cOw4cPr/H5paWlwdHRERMmTICOjg4uXryIkJAQyOVyjBw5skZtfPLJJ/jwww+hp6eHhQsXAgAsLS2hr6+P9evX44cffsD48eOF+gcOHICFhQXc3NwqbbO6a6wglUqxYMECTJ48GYGBgYiOjsbs2bNx4sQJtGzZEgCwceNG7NmzB7NmzUL37t1x7NgxbNiwodrzquk1v3DhAl566SVh+5IlS+Dt7Y3w8HCcPn0an332GUxMTDBhwgQAQEpKCsaPH4/OnTsjLCwMIpEImzdvxrvvvovjx49DR0dHaGvXrl3o3bs3Vq1ahZKSEgDV91mPHj0QGhqK5cuXY82aNbCzs6vyPBcsWIDff/8dc+bMgZ2dHb7//nvMmjULkZGRGDx4sFDv0aNHWLlyJQICAmBkZIQNGzYgKCgIv/zyC7S1tXHu3Dl8/PHHmDp1Kjw9PVFYWIjLly8jNze32mtNRJrB5InqjJWVFR4/fix8Lp/kyGQyuLq64vLlyzh8+DDmzZsHS0tLWFpaqkwFAoCLi4swgiCXy9G3b18UFhYiJiamyuTpwYMHMDY2FpKwZ7Vt2xYAkJ6erta5eXt7C3+Wy+Xo168fHjx4gL1799Y4eerUqRMMDQ1hYGCgcr7Dhg3DwYMHheSppKQEsbGxGD16NCQSSaVtVneNFRTJk6enJwDA1tYWgwcPxqlTp+Dj44OsrCzs3r0b/v7+mD59OgDA3d0dkyZNUllM/aznveavvPKKkES6u7sjIyMDX331FcaNGwexWIyIiAiYmJjg66+/hq6uLoCyF3sPHjwY+/fvxzvvvCO0ZWJigoiICIhEImFbdX1maGiITp06AQA6d+6Mnj17VnqO169fx88//4xly5YJfeTh4YF79+6pJE/Z2dmIiYlB586dAQD6+vqYPHky/v77bzg5OeHy5cswNTUVzh0AXn311UqPTUSax+SJ6oxcLlf68rp16xY2btyIhIQEZGRkCNtv375dbVtFRUWIiorC0aNHcf/+fUilUqHs6dOnaNGixQvFWj7OmsjOzkZ4eDji4+Px4MEDlJaWLbg3NTV9oTgUxo4di0mTJuHff/9F586dcfLkSWRkZODtt9+ucr+aXmOxWCwkowBgbW0NPT09ITG6efMmCgsLMWTIEKX9hg4dinPnzr3g2VXs2WMNGzYM33//PdLT09G2bVv88ccf8PLygkQiEUaTjI2N0b17d5VpMA8PD5U+rc0+u3DhAgDg9ddfV9r+xhtvYM2aNcjPzxemLNu0aSMkTgCEBE1xrbt3746srCwEBwdjxIgRcHR0hL6+vtoxEVH9YfJEdSY9PR02NjYAgLy8PEydOhVmZmYIDg5G27Ztoauri5CQEBQVFVXb1vr167F//37MnDkTL7/8MoyMjBAfH4+vvvoKRUVFlSZPlpaW+M9//oO8vLwKR0LS0tIAABYWFmqdW3BwMBISEjBz5kxhBGnPnj348ccf1WqnMv369YOtrS0OHDiARYsW4eDBg+jXrx/at29f6T7qXGM9PT2laS4A0NbWFuop7oZr1aqVUp3WrVtXG7uFhQVycnKqveaWlpZK283MzCo81qNHj9C2bVs8efIEO3fuFKZ0n429vGfjBmq3z7Kzs6Gtra2SeLVu3RpyuRy5ublC8mRsbFxhrIpr7eLignXr1mHXrl3w8/ODrq4uhg0bhsWLF9daMk5EtYvJE9WJf//9Fw8ePBCmsC5duoT09HRERUWha9euQr3c3FyVL9GKHD9+HOPGjUNAQICwraqF0wr9+vXDgQMHcPLkSaVpG4XffvsNYrEYffv2BQBhOqj8yBYApedBFRUV4bfffkNwcLCwoB0Adu/eXW086hgzZgy2bt2K9957DydPnsSqVauqrP+i17g8c3NzAEBGRoZSYll+GrYy/fv3x8GDB6u85iKRCE5OTkrbn12krziWIhYTExN4enpi4sSJKm0+mzw/O+pU231mYmICqVSK7OxsmJiYKMUsEolgZGSkVns+Pj7w8fFBZmYm4uPjsWbNGmhpaWH16tXPFR8R1S3ebUe1rqioCCtWrICOjg7GjBkDoOx2cUB5hODixYvCHVcK2traKC4urrDN8vuWlpbi2LFj1cby+uuvw8LCAl999RUKCgqUypKTk3H48GF4eHjAysoKwP9GoJKSkoR6t27dwv3794XPxcXFkMlkSvHk5eXhxIkT1cbzrPKjPc8aOXIkcnNzsWDBAujp6alMET2rpte4Jrp06QI9PT2Vu8d+/vnnavd9/fXX0aZNmyqv+dChQ4W1TwrPHuunn35CmzZthMTPxcUF//77L7p3746ePXsq/Vfd4u6a9tmzo0KVUSTbx48fV9p+/PhxdO/eXeUuw5oyMzPDmDFj4OrqqvR3kIgaFo480QuRyWS4dOkSACA/Px83b97E3r17kZKSgrCwMFhbWwMoe76RgYEBli1bhoCAADx48ADh4eEq02UdO3ZESUkJdu7cCQcHBxgaGsLOzg4DBgzA/v370alTJ7Rs2RK7d++uMMl6lp6eHjZs2ICAgABMnDgR7733HqysrHD9+nVs3rwZJiYmWLFihVC/d+/esLKywurVqzF//nzk5eVhy5YtStMnRkZG6NmzJ6Kjo2FmZgYtLS1s2bIFhoaGaj9w087ODkeOHMGJEydgbm6ONm3aCNfEzMwMgwcPFkbdqntWUU2vcU2Ymppi/PjxiI6Ohp6ennC33bOPBaiInp4eNm7cWOk1t7KywpIlS1T2+/PPP7F27Vq4urrijz/+wPfff4/Q0FCIxWX/xps9ezZGjx4NPz8/jB07Fq1bt8bjx49x9uxZODk5VXm3ZE37zMbGBhKJBAcPHoSWlhYkEkmFC8e7du2KoUOHIiwsDIWFhbC1tUVsbCwSEhLw5Zdf1uQSCzZt2oSsrCz0798frVq1ws2bN/H777/j3XffVasdIqo/TJ4aGEP9Do3qmIWFhRg3bhwAwMDAANbW1nBxcUFERITS05hbt26NL774AuvWrUNgYCBsbGywbNkybN26Vam9gQMHYuLEidiyZQsyMjLQr18/fPPNN1iyZAk++eQTrFixAvr6+hg5ciSGDBmCkJCQamNUTN1FRkYiLCwMmZmZkMvlcHBwQGRkpNL6GG1tbURERGDp0qX44IMP0L59eyxevBhhYWFKbW7YsAGhoaEIDg6GqakpfH19kZ+fj+3bt6t1/fz9/XH37l0sXLgQOTk5CAoKwqxZs4TyIUOG4Pjx4xg9enS1bdX0GtfU/PnzUVpaiq1bt0Imk2HIkCGYP38+Pvroo2r3VVzzL7/8EmFhYcjJyUGbNm0wYsQIzJgxo8K1PMuXL8fevXuxZ88etGjRAh988IHSHXQdOnTA/v378fnnn2PZsmXIz8+Hubk5+vXrJzyPqSo16TMzMzOEhoZi69atiI2NRUlJCW7cuFFhe+vXr8fGjRsRHR2NrKws2NnZYdOmTRg0aFC1sZTXs2dP7Ny5Ez/++CPy8vJgaWkJPz8/vP/++2q1Q0T1RySXy+WaDqIpUTxEr7LbnAsLC5GcnAxbW1uVkQS5vBQiUeW3odclTR5bE0JDQxEXF4fdu3ejS5cumg6nUh999BGuXbvWqN5/py7FwykPHDhQ5eMBSDMKCwtx4ewlRK08gsf3s5XKPL2csHCtH2YHfYlbifdV9vV8tSc+Ch6LGR9HI/G26uNABg7ogY9njsK7YdG4mapa3sXaEjuC/WvvZJqAWWNX49a1FJXtHbu1Q/i+xRqIqOmo7vu7PK55akA0mbw0p8QJAEJCQtCpUycEBASovMesIbhx4waOHDmCuLg44anXRETUMHDajpolHR0dfPfdd5oOo1Lvv/8+MjMz8dZbb1X7bCciIqpfTJ6IGqDnuXOvsXJ2dq50XRERUUPEaTsiIiIiNTB5IiIiIlIDkycN4U2ORNQYyOVyyCGHXMbfWUQKTJ7qmeIJxvn5+RqOhIioevn5+SgtkSE3u6D6ykTNBBeM1zOJRAJTU1Ph9ngDAwOV93AREWmaXC5Hfn4+Hj58iJt/p6C4UFr9TkTNBJMnDVC8q6shPl+IiKg8U1NTXD7D9+wRlcfkSQNEIhGsrKzQpk0bSKX81xwRNUza2tqQSJrXA3SJaoLJkwZJJBL+YiIiImpkuGCciIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIioGSiVyV6onP5HS9MBEBERUd2TiMVYHXkYd9Meq5S1b9sai2eO1EBUjROTJyIiombibtpjJN5O13QYjR6n7YiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjUwOSJiIiISA1MnoiIiIjU0OiSp0OHDsHe3l7lv08//VSp3v79+zFs2DD07NkTb775Jn799VeVtnJzc7F48WL0798fDg4OmD17Nh4+fFhfp0JERESNUKN9MfDWrVthZGQkfLawsBD+fOzYMSxZsgQzZszAK6+8gri4OAQFBeHbb79Fnz59hHpz5sxBYmIili5dCl1dXXz++efw9/fHwYMHoaXVaC8NERER1aFGmyH06NEDZmZmFZZt2rQJ3t7emDNnDgDglVdewc2bNxEZGYno6GgAQEJCAv7zn/9g27ZtcHNzAwDY2trCy8sLP//8M7y8vOrlPIiIiKhxaXTTdtVJSUnB7du38cYbbyht9/LywpkzZ1BcXAwAOHXqFIyNjeHq6irUsbOzQ7du3XDq1Kl6jZmIiIgaj0abPA0fPhzdunXD4MGDERUVhdLSUgBAUlISgLJRpPI6duwIqVSKlJQUoZ6trS1EIpFSPTs7O6ENIiKixqBlK2PISmWaDqPZaHTTdubm5pg1axZ69+4NkUiEEydO4PPPP8eDBw8QGhqK7OxsAICxsbHSforPivKcnBylNVMKJiYmuHLlygvFKJfLkZ+f/0JtEBFpmkgkgr6+vqbDQEFBAeRyuabD0Kjq+qKFsT7EEjHWr92PlJRHKuV9nTpjyrtDqj1OYWFhlde6KfeDXC5XGVCpTKNLntzd3eHu7i58dnNzg66uLnbu3IkZM2ZoMLL/kUqluHbtmqbDICJ6Ifr6+ujevbumw0BycjIKCgo0HYZG1bQvUlIe4VbifZXt1tatq9yvpUkLlMpk0NPTq7ROSUkp/vnnKqRSafUBN1I6Ojo1qtfokqeKvPHGG9i+fTuuXbsGExMTAGWPITA3Nxfq5OTkAIBQbmxsjPT0dJW2srOzhTrPS1tbG506dXqhNoiINK2m/wqva7a2tk16xKMm6rovDFvoQSIWY+mOw7id/lil3MayNZa+OxKdO3dusn2RmJhY47pNInkqz87ODkDZmibFnxWftbW10a5dO6HemTNnVIbpkpOT0aVLlxeKQSQSwcDA4IXaICKiMg1h6rC5uJ3+GDdTVQcWFJpyX6iToDbaBePlxcXFQSKRoHv37mjXrh1sbGxw/PhxlTouLi7CkJyHhweys7Nx5swZoU5ycjL++ecfeHh41Gv8RERE1Hg0upEnPz8/ODs7w97eHgAQHx+Pffv2YfLkycI03axZs7BgwQK0b98ezs7OiIuLw+XLlxETEyO04+DgADc3NyxevBgLFy6Erq4uPvvsM9jb22Po0KEaOTciIiJq+Bpd8mRra4uDBw8iPT0dMpkMNjY2WLx4MXx9fYU6w4cPR0FBAaKjo7FlyxbY2toiIiICDg4OSm19/vnnWLNmDUJDQ1FSUgI3NzeEhITw6eJERERUqUaXJYSEhNSo3pgxYzBmzJgq6xgZGWH16tVYvXp1bYRGREREzUCTWPNEREREVF+YPBERERGpgckTERERkRqYPBERERGpgckTETUppbKqX45aXTkRUXUa3d12RNT0yUplEEsq/7ddVeUSsRirIw/jbprqKybat22NxTNH1lqcRNQ8MXkiogZHLBFjXfB2pCSpviainZ0lPgqbWuX+d9MeI/F25a+YoKahVCaDRFx5kl1dOdHzYvJERA1SSlI6bl1L0XQY1IBJxGIsOnYASRmqo4x2rVpjjfdoDURFzQGTJyIiarSSMh7j+sP7mg6DmhmOZxIRERGpgckTERERkRqYPBERERGpgckTERERkRqYPBERERGpgckTERERkRqYPBERERGpgckTERE1SzJ51e85rK6cmi8+JJOIiJolsUiMqL9jcD/vgUqZlaEFpveepIGoqDFg8kRERM3W/bwHuJNzT9NhUCPDaTsiIiIiNTB5IiIiIlIDkyciIiIiNTB5IiIiIlIDkyciIiIiNTB5IiIiIlIDkyciIiIiNTB5IiIiIlIDkyciIiIiNTB5IiIiIlIDkycialRatjKGrJQvbCUizeG77YioUWlhrA+xRIz1a/cjJeWRUllfp86Y8u4QDUVGRM0FkyciapRSUh7hVuJ9pW3W1q01FA3VBTPjFiiVySARc5KEGhYmT0RE1CAZ6etBIhYjZN8hJD96rFQ2oHMnzBw6SEORUXPH5ImIiBq05EePcT0tXWmbTetWGoqGiAvGiYiIqBaUyqq+kaO68saEI09ERET0wiRiMRYdO4CkjMcqZXatWmON92gNRFU3mDwRERFRrUjKeIzrD+9XX7ECMrkMYlHFE2JVlWkCkyciIiLSOLFIjKi/Y3A/74HSditDC0zvPUlDUVWMyRMRERE1CPfzHuBOzj1Nh1GthjMGRkRERNQIMHkiIiIiUgOTJyIiIiI1MHkiIiIiUgOTJyIiIiI1MHkiIiIiUgOTJyJqNlqatGhWr5AgorrB5zwRUbNh2EIPErEYS3ccxu101VdI2Fi2xtJ3R2ogMiJqTJg8EVGzczv9MW6mpms6DCJqpDhtR0RERKQGJk9ERERUJTPj6tcLNiectiMiIqIqGemXrRcM2XcIyY9U1wsO6NwJM4cO0kBkmsHkiYiIiGok+dFjXE9TXS9o07qVBqLRHE7bEREREamByRMRkRr4nCgi4rQdEZEaJGIxFh07gKQM1XUfdq1aY433aA1ERUT1ickTEZGakjIe4/rD+5oOg6rQqoUhSmUySMScYKHax+SJiIiaHGPdsrvDlp3ahztZj1TKna07Y7rjUA1ERk0Bkyciov9P8SwbjlY0HXeyHuFmZprK9vYmrTUQDTUVzT55unXrFlauXImEhAS0aNECPj4+mDNnDnR0dDQdGhHVMz7LhtQhl5dCJJI8d3lz0tSmUZt18pSdnY0pU6bAxsYG4eHhePDgAcLCwlBYWIjQ0FBNh0f1SFYqg1hS+Q91deXV/VJoSr80mgM+y4ZqQiSS4GLiMuQV3FEpM9TvAMdOn2ggqoapqU2jNuvk6bvvvsPTp08REREBU1NTAEBpaSmWLVuG6dOnw8LCQrMBUr0RS8RYF7wdKUmqX5jdHTpi+kdjqtxfIhZjdeRh3E1THa1o37Y1Fs8cWWuxElHDkVdwB9n5NzUdRqPRVKZRm3XydOrUKbi4uAiJEwC88cYb+OSTT/DHH39g1KhRdXr8Fx3toNqVkpSOW9dSVLZb21pALBFj/dr9SElR/RdTX6fOmPLuENxNe4zE26rJV0uT6tfRcGSqaajJ1MSL9jWniohU1ffPhUgul8trrbVGxsXFBW+//TYWLFigtN3d3R0+Pj4q22vi4sWLkMvl0NbWrrauSCRCXm4+ZCWqD9XT0taCgaGe2sdXVzPufiUikQjZmbkoKSlVKdPV04GhsQGys56ipLSCcl1tGBrqIysnv+JyHW0YtdBDbkEhSktV+1pbS4IWerovfA5NqS+ftz9q2hdP8vIrbFtPRxtGBnp48vQppBX0lZ62Noz19ZCZ/xQlsgr219KGsZ4+8ooLUVLBwzK1JRK00K6+r6vqS5FIBGlpHuTykgrKtKAtMazVvwv18bPxPP1R0754Ulh5uZGOPnKL8yos1xJLYKRT9bUUiUQolj6BrIK+EIu0oKPdsln0BVC3/aEt0YKhdguVfZ71oj8XUqkUIpEIjo6O1R6rWY885eTkwNjYWGW7iYkJsrOzn6tNkUik9P/qGBoZPNdxaktN42wOTMyMqi43rfqH19S46r400q/bZLip9eWL9Ed1fdHSsJryFlX3tZlB1eWGOi/W19X1pbbE8IX2V1dd/2y8SH9U1xct9aouN9J5sWupo93yhfZXV0PuC6Du+6MqL/pzIRKJatxfzTp5qgsODg6aDoGIiIjqULNeZGFsbIzc3FyV7dnZ2TAxMdFARERERNTQNevkyc7ODklJSUrbcnNz8ejRI9jZ2WkoKiIiImrImnXy5OHhgdOnTyMnJ0fYdvz4cYjFYri6umowMiIiImqomvXddtnZ2fD29oatrS2mT58uPCRzxIgRfEgmERERVahZJ09A2etZVqxYofR6lrlz5/L1LERERFShZp88EREREamjWa95IiIiIlIXkyciIiIiNTB5IiIiIlIDkyciIiIiNTB5IiIiIlIDkyciIiIiNfDFwE3Qjz/+iNjYWFy9ehU5OTno0KEDfH198fbbbyu9MXr//v3YunUr0tLSYGtri7lz52LgwIFC+eXLl7Fnzx6cP38eDx8+hIWFBYYNG4b3338fBgbKb96+ePEi1q5di2vXrqFVq1aYMGEC/P39a/2N4o1NfffFf//7X+zevRuXLl1CcnIyPD09ERUVVa/n3FDVd1989913+Pnnn3Hjxg0UFBSgU6dOCAgIwGuvvVav590Q1XdfHD58GHv27MHt27dRUFCAtm3b4s0334S/vz+f6QfNfGcopKen44033kB+fj7OnDkDMzOzOj/f2sDkqQnasWMHXnrpJQQHB6Nly5Y4ffo0lixZgvT0dAQFBQEAjh07hiVLlmDGjBl45ZVXEBcXh6CgIHz77bfo06cPgLIfqDt37mDatGmwsbFBYmIiNm3ahL///hu7du0Sjnfnzh34+fnB1dUVc+bMwY0bN/Dpp59CIpHAz89PE5egwajvvrh48SLOnz+PXr16oaioSBOn3GDVd19s3rwZbm5umDBhAgwMDHD8+HHMnDkTYWFhGDlypCYuQYNR332RnZ0Nd3d3BAQEwNDQEJcvX0ZERATS09OxYsUKTVyCBqW++6O8sLAwGBgYID8/v75Ot3bIqcnJyMhQ2RYSEiJ3dHSUl5aWyuVyuXzo0KHyefPmKdUZN26cfNq0aVW2ExsbK+/SpYv8v//9r7BtyZIl8oEDB8qLioqEbRs2bJA7OTkpbWuO6rsvFG3K5XL5pEmT5AEBAS98Dk1FffdFRfXee+89+fDhw5/7HJqK+u6LimzcuFHeq1cveUlJyfOcQpOiqf44ffq0vH///vJt27bJu3TpUuH+DRXXPDVBFQ17duvWDXl5ecjPz0dKSgpu376NN954Q6mOl5cXzpw5g+Li4krb6d69OwDg4cOHwrZTp05h8ODBSsPfXl5eyMnJQUJCQq2cU2NV330hFvNHujL13ReVHa98neaqvvuiIqampigpKYFMJnve02gyNNEfUqkUK1aswKxZs2BqalpLZ1J/+Ju2mbhw4QIsLCxgaGiIpKQkAICtra1SnY4dO0IqlSIlJaXKdgDAzs4OAJCfn4/79+8LnxXs7OwgEomEY9H/1FVfkPrquy8uXLjA/qpEffRFSUkJCgoKcP78eezcuRMTJkyAtrZ2LZ5F01HX/bFr1y5IJBJMmDChliOvH1zz1AycP38ecXFxWLhwIYCy+X8AMDY2Vqqn+Kwof1ZmZibCw8MxePBg2NjYAAByc3MrbEtHRwf6+vqVttVc1WVfkHrquy+OHj2KhIQEREZG1kL0TUt99EVJSQl69OghfB45ciQWL15cW6fQpNR1fzx48ACRkZGIjIyERCKpgzOoe0yemrj09HTMnTsXzs7OmDx58nO3I5VKMW/ePADA0qVLaym65oV90XDUd19cv34dn3zyCUaNGsW77Z5RX32hpaWFAwcOoKioCFeuXMFXX32FRYsWYe3atc99zKaoPvpj3bp1cHV1hYuLy4uEqlFMnpqwnJwc+Pv7w9TUFOHh4cJ6GBMTEwBlo0bm5uZK9cuXK8jlcixevBiXL1/G7t270aZNG6HMyMhIaKu84uJiFBQUqLTVXNVHX1DN1Hdf3Lt3D/7+/ujVqxeWL19eF6fUaNV3X/Ts2RMA4OTkBGtra8ycOROTJk0Stjd39dEfCQkJ+Omnn7Bv3z5h/4KCAgDA06dPoa+vD319/bo7yVrC5KmJKiwsxPTp05Gbm4u9e/cKSQ7wv7nnpKQkpXnopKQkaGtro127dkptrV27Fj/++COio6PRtWtXpTIDAwNYWVmprG1KTk6GXC7n+g7UX19Q9eq7LzIzM+Hn54dWrVohIiKC62vK0fTPxcsvvwwAuHv3LpMn1F9/JCcnQyqVVvi4jtdeew1eXl747LPPavPU6gQXjDdBJSUlmDNnDpKSkrB161ZYWFgolbdr1w42NjY4fvy40va4uDi4uLgo3TW3ZcsW7NixA2FhYZUOsXp4eCA+Ph5SqVSpLWNjYzg4ONTimTU+9d0XVLn67ounT5/C398fUqkUW7ZsgaGhYe2fVCPVEH4uFAuZn/3ib47qsz/c3d2xa9cupf/8/f0BAJGRkZg5c2YdnGHt48hTE7Rs2TL8+uuvCA4ORl5eHi5duiSUde/eHTo6Opg1axYWLFiA9u3bw9nZGXFxcbh8+TJiYmKEukePHsWGDRvw5ptvwtraWqmd9u3bC7el+vn54ejRo5g/fz4mTJiAmzdvYtu2bZg7d26zf3pvffdFZmYmzp49K/z56dOnwi88T0/PRjEcXlfquy9mzZqF69evY9WqVUhLS0NaWppQT/FQweaqvvvinXfewZAhQ2BnZwexWIy///4b27dvh7u7O3r16lVfp91g1Wd/mJubK039AWVT2wDg6OjYaJ4wLpLL5XJNB0G1a9CgQcJfxmfFx8fD2toaQNmj9qOjo4VH7c+bN0/pUfvBwcE4fPhwhe2sWbMGo0aNEj5fvHgRYWFhuHbtGszMzPDOO+/w9Syo/77466+/Kl3kWf54zVF994W9vX2lsdy4ceN5T6NJqO++WLNmDX7//XekpaVBS0sL1tbWeOuttzBx4sRm/w88QDPfGeUdOnQIixYtalSvZ2HyRERERKQGrnkiIiIiUgOTJyIiIiI1MHkiIiIiUgOTJyIiIiI1MHkiIiIiUgOTJyIiIiI1MHkiIiIiUgOTJyIiIiI1MHkiomZv0KBBmD59uqbDIKJGgskTERERkRqYPBERERGpgckTETVbBQUFmg6hzuTn52s6BKImi8kTETUI169fh729PeLj44VtV65cgb29PUaOHKlUd9q0aRgzZozw+dtvv4W3tzdefvlluLm5YdmyZcjJyVHax9fXF8OHD8eVK1fwzjvvoHfv3ti4cWOl8Rw+fBjdu3fH2rVraxT/pk2b0KNHD2RmZqqULVmyBE5OTigqKhK2nTx5EhMnTkSfPn3g4OCAgIAA/PvvvyrXJDg4GIMHD0bPnj3h6uqKRYsW4cmTJ0r1wsPDYW9vj8TERMyfPx/9+vXDxIkTaxQ3EamPyRMRNQhdunSBsbExzp8/L2w7f/48xGIxrl+/jry8PACATCZDQkICnJycAJQlDsuXL0ebNm0QHByMYcOGYe/evZg6dSqkUqnSMbKysuDv749u3bph8eLFcHZ2rjCWvXv3YtGiRfD398fChQtrFL+Pjw9KSkoQFxentL24uBg//fQThg4dCl1dXQDAkSNHMH36dBgYGGDBggUIDAxEYmIiJk6ciNTUVGHf06dPIyUlBaNGjcKSJUvg5eWFuLg4BAQEQC6Xq8TwwQcfoKCgAHPnzlVKLomodmlpOgAiIgAQi8VwdHRUSp4uXLiA1157DfHx8bh48SI8PDyERMrJyQmZmZmIioqCm5sboqOjIRaX/XvQzs4Oy5cvR2xsLN5++22hvUePHmHZsmUYP358pXHs2rULq1evxuzZsxEYGFjj+Dt06AAHBwfExsZi0qRJwvaTJ08iOzsbPj4+AICnT59i1apVGDNmDFasWCHUGzlyJF5//XVERUUJ2ydOnIipU6cqHadPnz6YN28eLly4ICSQCl27dsWGDRtqHDMRPR+OPBFRg9G3b1/8888/wnqdCxcuwMPDA127dsWFCxcAlI1GiUQi9O3bF6dPn4ZUKsXkyZOFxAkAxowZA0NDQ5w8eVKpfR0dHYwaNarS40dHR2PVqlXCaJC6fHx88Pfff+Pu3bvCtqNHj8LKygr9+/cHUDaalJOTA29vb2RmZgr/icVi9O7dG3/99Zewr56envDnoqIiZGZmonfv3gCAq1evqhy/qqSQiGoPR56IqMFwcnJCSUkJLl26BEtLS2RkZMDJyQmJiYnCiNT58+fRqVMnmJqaIi0tDUDZSFN5Ojo6aNeuHe7du6e03cLCAjo6OhUe++zZs/jtt9/g7++PadOmPVf8Xl5eWL16NWJjYxEUFITc3Fz8+uuvePfddyESiQAAt2/fBgBMmTKlwjYMDQ2FP2dlZSEiIgJxcXHIyMhQqpebm6uyr7W19XPFTUTqYfJERA3Gyy+/DF1dXZw7dw5t27ZFq1atYGtrCycnJ+zevRvFxcXCVN7zKD+S86zOnTsjJycH33//PcaNG4d27dqp3b6JiQkGDhyIo0ePIigoCMePH0dxcTHefPNNoY5irdK6detgbm6u0oZEIhH+PGfOHCQkJMDPzw/dunWDgYEBZDIZpk2bVuGaJ8WaKiKqW0yeiKjB0NHRQa9evXD+/Hm0bdtWWNPTt29fFBcXIzY2Fo8fP0a/fv0AAG3btgUAJCUlKSU7xcXFSE1NxYABA2p87JYtW2LTpk2YOHEi3n33XezevRsWFhZqn4OPjw8CAwNx+fJlHD16FN27d0fnzp2FckWcrVq1qjK+7OxsnDlzBrNmzUJQUJCwXTFyRUSawzVPRNSg9O3bF5cvX8Zff/2Fvn37AgDMzMzQsWNHREdHA4CQVA0YMADa2tr45ptvlEZiDhw4gNzcXHh6eqp1bEtLS3z99dcoKirC1KlTVR4JUBMeHh5o2bIltm7dinPnzimNOgGAu7s7DA0NERUVpXI3IADhUQflR6DK27lzp9oxEVHt4sgTETUoTk5O2Lx5M+7fv690N5mTkxP27t2Ll156CZaWlgDKkqrp06cjIiIC06ZNw6BBg5CcnIzdu3ejZ8+eKolLTXTo0AHbtm3D5MmT4efnh127dimtQ6qOtrY2vL29ERMTA4lEAm9vb6VyQ0NDLF26FB999BFGjRoFLy8vmJmZIS0tDSdPnoSjoyNCQ0NhaGiIfv36YevWrZBKpbCwsMAff/yh9CgDItIMjjwRUYPi4OAAiUSCFi1aoGvXrsJ2RSL17O35s2bNQmhoKNLS0rBmzRr8+OOPGDt2LLZv3w5tbe3nisHe3h7R0dG4ffs2ZsyYgcLCQrX2VzyWwMXFBW3atFEpHzFiBHbs2IE2bdpg27ZtWLVqFeLi4tCtWzeluwE3bNgANzc37N69Gxs3boSWlpYw+kZEmiOSV7TqkIiIntv169fh4+ODtWvX4q233tJ0OERUyzjyRERUy/bt2wcDAwMMHTpU06EQUR3gmiciompkZWVVuLhbQSKRwMzMDCdOnEBiYiL27duHd955BwYGBvUYJRHVF07bERFVw9fXF2fPnq20/KWXXsKJEycwaNAgPH78GG5ubli3bp1aC82JqPFg8kREVI0rV64gJyen0nJdXV3hsQpE1PQxeSIiIiJSAxeMExEREamByRMRERGRGpg8EREREamByRMRERGRGpg8EREREamByRMRERGRGpg8EREREanh/wFVC04gjJfzeAAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":["fig=plt.figure(figsize=(10,5))\n","jobcategorycount=sns.countplot(data=df_u,x='work_year',hue='job_category',palette='viridis')\n","jobcategorycount.set(xlabel='Year',ylabel='Number Reported',title='Count of Recorded Job Category by Year')\n","sns.move_legend(jobcategorycount,'upper left',bbox_to_anchor=(1,1),title='Job Categories')\n","plt.savefig('/content/CountCategoryByYear')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":365},"id":"f1yXfNGjA_k3","executionInfo":{"status":"ok","timestamp":1714702941908,"user_tz":300,"elapsed":1084,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"581d1803-7d52-4b48-8e02-198b87f157c2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["fig=plt.figure(figsize=(7,5))\n","exp=sns.countplot(data=df_u,x='work_year',hue='experience_level',palette='viridis')\n","exp.set(xlabel='Year',ylabel='Number Reported',title='Count of Recorded Experience Level by Year')\n","sns.move_legend(exp,'upper left',bbox_to_anchor=(1,1),title='Experience Level')\n","plt.savefig('/content/countexp')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":496},"id":"diSVXaN__zW0","executionInfo":{"status":"ok","timestamp":1714703230169,"user_tz":300,"elapsed":1243,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"eca8c8d4-a0bd-4b01-948d-fd5e7143533e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["|"],"metadata":{"id":"RiPLVVgcNIQh","colab":{"base_uri":"https://localhost:8080/","height":106},"executionInfo":{"status":"error","timestamp":1714615275002,"user_tz":300,"elapsed":110,"user":{"displayName":"Jenny O","userId":"07622373657144372939"}},"outputId":"a82ccb4e-0497-4da2-9136-685199318443"},"execution_count":null,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"invalid syntax (, line 1)","traceback":["\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m |\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"veGTlZII_Mg4"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/05. Jupyter Notebook _ Code - P3 b/05. Jupyter Notebook _ Code - P3 new file mode 100644 index 0000000..daea9e9 --- /dev/null +++ b/05. Jupyter Notebook _ Code - P3 @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"rI9YOJXtoB_R"},"source":["## Loading Data"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"RYJCMFmqvvsJ","executionInfo":{"status":"ok","timestamp":1716216732169,"user_tz":300,"elapsed":2250,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["import pandas as pd\n","\n","# Load the dataset\n","file_path = r'/content/Student_Stress_Levels.csv'\n","data_df = pd.read_csv(file_path)"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":726},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1716216734139,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"wDbtGk_R8Ef1","outputId":"2a5680e9-efa2-468c-b937-c1b93cd7ce8d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem mental_health_history depression headache \\\n","0 $1,4) ($20) 0 11 2 \n","1 15 8 1 15 5 \n","2 12 18 1 14 2 \n","3 16 12 1 15 4 \n","4 16 28 0 7 2 \n","5 20 13 1 21 3 \n","6 4 26 0 6 1 \n","7 17 3 1 22 4 \n","8 13 22 1 12 3 \n","9 6 8 0 27 4 \n","10 17 $12 1 25 4 \n","11 17 15 1 ($2,2) 3 \n","12 5 28 0 8 1 \n","13 9 23 1 24 4 \n","14 2 28 0 3 1 \n","15 11 21 0 14 3 \n","16 6 28 0 1 1 \n","17 7 25 0 3 1 \n","18 11 ($23) 0 12 3 \n","19 21 1 1 25 4 \n","\n"," blood_pressure sleep_quality breathing_problem noise_level \\\n","0 1 2 4 2 \n","1 3 1 4 3 \n","2 1 2 2 2 \n","3 3 1 3 4 \n","4 3 5 1 3 \n","5 3 1 4 3 \n","6 2 4 1 1 \n","7 3 1 5 3 \n","8 1 2 4 3 \n","9 3 1 2 0 \n","10 3 1 3 4 \n","11 3 1 5 5 \n","12 2 4 2 2 \n","13 3 1 0 1 \n","14 2 4 2 1 \n","15 1 2 4 2 \n","16 2 4 2 1 \n","17 2 4 2 2 \n","18 1 2 2 3 \n","19 3 1 4 4 \n","\n"," living_conditions ... basic_needs academic_performance study_load \\\n","0 3 ... 2 3 2 \n","1 1 ... 2 1 4 \n","2 2 ... 2 2 3 \n","3 2 ... 2 2 4 \n","4 2 ... 3 4 3 \n","5 2 ... 1 2 5 \n","6 4 ... 4 5 1 \n","7 1 ... 1 1 3 \n","8 3 ... 3 3 3 \n","9 5 ... 2 2 2 \n","10 2 ... 1 1 3 \n","11 2 ... 1 1 3 \n","12 3 ... 5 5 2 \n","13 2 ... 3 1 2 \n","14 3 ... 4 4 2 \n","15 2 ... 2 3 3 \n","16 4 ... 4 5 1 \n","17 4 ... 4 4 2 \n","18 2 ... 3 2 3 \n","19 1 ... 1 1 5 \n","\n"," teacher_student_relationship future_career_concerns social_support \\\n","0 3 3 2 \n","1 1 5 1 \n","2 3 2 2 \n","3 1 4 1 \n","4 1 2 1 \n","5 2 5 1 \n","6 4 1 3 \n","7 2 4 1 \n","8 2 3 3 \n","9 1 5 1 \n","10 1 4 1 \n","11 1 4 1 \n","12 4 1 3 \n","13 3 3 0 \n","14 5 1 3 \n","15 3 3 2 \n","16 5 1 3 \n","17 5 1 3 \n","18 2 2 3 \n","19 2 5 1 \n","\n"," peer_pressure extracurricular_activities bullying stress_level \n","0 3 3 2 1 \n","1 4 5 5 2 \n","2 3 2 2 1 \n","3 4 4 5 2 \n","4 5 0 5 1 \n","5 4 4 5 2 \n","6 2 2 1 0 \n","7 4 4 5 2 \n","8 3 2 2 1 \n","9 5 3 4 1 \n","10 4 4 5 2 \n","11 5 5 4 2 \n","12 1 1 1 0 \n","13 1 0 1 2 \n","14 1 2 1 0 \n","15 3 2 2 1 \n","16 2 2 1 0 \n","17 1 1 1 0 \n","18 3 2 3 1 \n","19 4 4 5 2 \n","\n","[20 rows x 21 columns]"],"text/html":["\n","
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anxiety_levelself_esteemmental_health_historydepressionheadacheblood_pressuresleep_qualitybreathing_problemnoise_levelliving_conditions...basic_needsacademic_performancestudy_loadteacher_student_relationshipfuture_career_concernssocial_supportpeer_pressureextracurricular_activitiesbullyingstress_level
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"data_df"}},"metadata":{},"execution_count":2}],"source":["# Display the first few rows of the DataFrame\n","data_df.head((20))"]},{"cell_type":"markdown","metadata":{"id":"SMhIdWsYBzvy"},"source":["## Dataset info"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":168,"status":"ok","timestamp":1716216737518,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"3GcuZkdM_WLx","outputId":"93958bc1-318d-420d-da37-acd8aef1ff74"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Data Info:\n","\n","RangeIndex: 1100 entries, 0 to 1099\n","Data columns (total 21 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 anxiety_level 1100 non-null object\n"," 1 self_esteem 1100 non-null object\n"," 2 mental_health_history 1100 non-null int64 \n"," 3 depression 1100 non-null object\n"," 4 headache 1100 non-null int64 \n"," 5 blood_pressure 1100 non-null int64 \n"," 6 sleep_quality 1100 non-null int64 \n"," 7 breathing_problem 1100 non-null int64 \n"," 8 noise_level 1100 non-null int64 \n"," 9 living_conditions 1100 non-null int64 \n"," 10 safety 1100 non-null int64 \n"," 11 basic_needs 1100 non-null int64 \n"," 12 academic_performance 1100 non-null int64 \n"," 13 study_load 1100 non-null int64 \n"," 14 teacher_student_relationship 1100 non-null int64 \n"," 15 future_career_concerns 1100 non-null int64 \n"," 16 social_support 1100 non-null int64 \n"," 17 peer_pressure 1100 non-null int64 \n"," 18 extracurricular_activities 1100 non-null int64 \n"," 19 bullying 1100 non-null int64 \n"," 20 stress_level 1100 non-null int64 \n","dtypes: int64(18), object(3)\n","memory usage: 180.6+ KB\n","None\n"]}],"source":["# Display information about the dataset\n","print(\"\\nData Info:\")\n","print(data_df.info())"]},{"cell_type":"markdown","metadata":{"id":"P8lPrKvMBn9C"},"source":["## Checking Missing Value"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":224,"status":"ok","timestamp":1716216739137,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"JhAyasvYA7ZZ","outputId":"4fda898d-5527-4f22-df3d-2dbaa527b8b4"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Missing values in each column:\n","anxiety_level 0\n","self_esteem 0\n","mental_health_history 0\n","depression 0\n","headache 0\n","blood_pressure 0\n","sleep_quality 0\n","breathing_problem 0\n","noise_level 0\n","living_conditions 0\n","safety 0\n","basic_needs 0\n","academic_performance 0\n","study_load 0\n","teacher_student_relationship 0\n","future_career_concerns 0\n","social_support 0\n","peer_pressure 0\n","extracurricular_activities 0\n","bullying 0\n","stress_level 0\n","dtype: int64\n"]}],"source":["# Checking for missing values\n","print(\"\\nMissing values in each column:\")\n","print(data_df.isnull().sum())"]},{"cell_type":"markdown","metadata":{"id":"4eU0mEQmBiXe"},"source":["## Summary Statistic for Numerical Variables"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":320},"executionInfo":{"elapsed":237,"status":"ok","timestamp":1716216741382,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"jeQApM_1BBXD","outputId":"3fc68d4d-7f19-4078-c6b8-337869547d7d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" mental_health_history headache blood_pressure sleep_quality \\\n","count 1100.000000 1100.000000 1100.000000 1100.000000 \n","mean 0.492727 2.508182 2.181818 2.660000 \n","std 0.500175 1.409356 0.833575 1.548383 \n","min 0.000000 0.000000 1.000000 0.000000 \n","25% 0.000000 1.000000 1.000000 1.000000 \n","50% 0.000000 3.000000 2.000000 2.500000 \n","75% 1.000000 3.000000 3.000000 4.000000 \n","max 1.000000 5.000000 3.000000 5.000000 \n","\n"," breathing_problem noise_level living_conditions safety \\\n","count 1100.000000 1100.000000 1100.000000 1100.000000 \n","mean 2.753636 2.649091 2.518182 2.737273 \n","std 1.400713 1.328127 1.119208 1.406171 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 2.000000 2.000000 2.000000 2.000000 \n","50% 3.000000 3.000000 2.000000 2.000000 \n","75% 4.000000 3.000000 3.000000 4.000000 \n","max 5.000000 5.000000 5.000000 5.000000 \n","\n"," basic_needs academic_performance study_load \\\n","count 1100.000000 1100.000000 1100.000000 \n","mean 2.772727 2.772727 2.621818 \n","std 1.433761 1.414594 1.315781 \n","min 0.000000 0.000000 0.000000 \n","25% 2.000000 2.000000 2.000000 \n","50% 3.000000 2.000000 2.000000 \n","75% 4.000000 4.000000 3.000000 \n","max 5.000000 5.000000 5.000000 \n","\n"," teacher_student_relationship future_career_concerns social_support \\\n","count 1100.000000 1100.000000 1100.000000 \n","mean 2.648182 2.649091 1.881818 \n","std 1.384579 1.529375 1.047826 \n","min 0.000000 0.000000 0.000000 \n","25% 2.000000 1.000000 1.000000 \n","50% 2.000000 2.000000 2.000000 \n","75% 4.000000 4.000000 3.000000 \n","max 5.000000 5.000000 3.000000 \n","\n"," peer_pressure extracurricular_activities bullying stress_level \n","count 1100.000000 1100.000000 1100.000000 1100.000000 \n","mean 2.734545 2.767273 2.617273 0.996364 \n","std 1.425265 1.417562 1.530958 0.821673 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 2.000000 2.000000 1.000000 0.000000 \n","50% 2.000000 2.500000 3.000000 1.000000 \n","75% 4.000000 4.000000 4.000000 2.000000 \n","max 5.000000 5.000000 5.000000 2.000000 "],"text/html":["\n","
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mental_health_historyheadacheblood_pressuresleep_qualitybreathing_problemnoise_levelliving_conditionssafetybasic_needsacademic_performancestudy_loadteacher_student_relationshipfuture_career_concernssocial_supportpeer_pressureextracurricular_activitiesbullyingstress_level
count1100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.000000
mean0.4927272.5081822.1818182.6600002.7536362.6490912.5181822.7372732.7727272.7727272.6218182.6481822.6490911.8818182.7345452.7672732.6172730.996364
std0.5001751.4093560.8335751.5483831.4007131.3281271.1192081.4061711.4337611.4145941.3157811.3845791.5293751.0478261.4252651.4175621.5309580.821673
min0.0000000.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%0.0000001.0000001.0000001.0000002.0000002.0000002.0000002.0000002.0000002.0000002.0000002.0000001.0000001.0000002.0000002.0000001.0000000.000000
50%0.0000003.0000002.0000002.5000003.0000003.0000002.0000002.0000003.0000002.0000002.0000002.0000002.0000002.0000002.0000002.5000003.0000001.000000
75%1.0000003.0000003.0000004.0000004.0000003.0000003.0000004.0000004.0000004.0000003.0000004.0000004.0000003.0000004.0000004.0000004.0000002.000000
max1.0000005.0000003.0000005.0000005.0000005.0000005.0000005.0000005.0000005.0000005.0000005.0000005.0000003.0000005.0000005.0000005.0000002.000000
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1100.0,\n 2.1818181818181817,\n 3.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sleep_quality\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.0681128767385,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.66,\n 2.5,\n 1100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"breathing_problem\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 387.99482706574184,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.7536363636363634,\n 3.0,\n 1100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"noise_level\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.0539392699523,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1100.0,\n 2.649090909090909,\n 3.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"living_conditions\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.1216204260495,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1100.0,\n 2.518181818181818,\n 3.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"safety\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.04588935099054,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1100.0,\n 2.7372727272727273,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"basic_needs\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 387.99218049940083,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.772727272727273,\n 3.0,\n 1100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"academic_performance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.04367396356093,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1100.0,\n 2.772727272727273,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"study_load\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.1063945709441,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1100.0,\n 2.6218181818181816,\n 3.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"teacher_student_relationship\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.0514797343733,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1100.0,\n 2.648181818181818,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"future_career_concerns\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.09490013687764,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.649090909090909,\n 2.0,\n 1100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"social_support\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.3075359543629,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1100.0,\n 1.8818181818181818,\n 2.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"peer_pressure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.0450551585041,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1100.0,\n 2.7345454545454544,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"extracurricular_activities\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.01849982822415,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.767272727272727,\n 2.5,\n 1100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bullying\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.0459606008991,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.617272727272727,\n 3.0,\n 1100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stress_level\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 388.56510710117675,\n \"min\": 0.0,\n \"max\": 1100.0,\n \"num_unique_values\": 6,\n \"samples\": [\n 1100.0,\n 0.9963636363636363,\n 2.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":5}],"source":["# Calculate the summary statistics\n","summary_Stats = data_df.describe()\n","\n","# Print Summary Statistics\n","summary_Stats"]},{"cell_type":"markdown","metadata":{"id":"ETPjbxNqCfGL"},"source":["## Observations:\n","\n","#### Dataset Overview\n","\n","- **Entries:** 1100\n","- **Features:** 21 (3 categorical, 18 numerical)\n","\n","#### Data Completeness\n","\n","- **Missing Values:** None across all features.\n","\n","#### Data Types\n","\n","- **Categorical:** `anxiety_level`, `self_esteem`, `depression`\n","- **Numerical:** The rest of the columns.\n","\n","#### Summary Statistics (Numerical Features)\n","\n","- **Range:** Values for most features range from 0 to 5, with exceptions like `blood_pressure` which caps at 3.\n","- **Central Tendency:**\n"," - **Mean:** The average values across features like `mental_health_history` (0.49), `headache` (2.51), and `blood_pressure` (2.18) indicate a moderate distribution of these conditions among the subjects.\n"," - **Median (50%):** The median values also reflect a moderate level for most features, such as `sleep_quality` at 2.5 and `safety` at 2, suggesting a balanced middle point in data distribution.\n","- **Variability:**\n"," - **Standard Deviation:** Features show varying levels of spread, with `headache` having a standard deviation of 1.41 and `social_support` at 1.05, indicating differences in variability.\n"," - **Interquartile Range (25% to 75%):** For `sleep_quality`, the values spread from 1 to 4, which signifies a wide variation in sleep quality among individuals.\n","\n","## Key Insights\n","\n","- The dataset is complete with no missing entries.\n","- Most features have a balanced distribution of data with a central tendency around the mid-point of their respective ranges.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"CmgBz18WEI1s"},"source":["## checking for the unique values for categorical variables"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":175,"status":"ok","timestamp":1716216744873,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"dZyj6pIQEGI6","outputId":"d5f18983-74f3-4020-bc5f-9ade8e6cc6cb"},"outputs":[{"output_type":"stream","name":"stdout","text":["Unique values in anxiety_level:\n","anxiety_level\n","13 67\n","10 63\n","19 61\n","21 61\n","9 60\n","12 56\n","18 55\n","11 54\n","6 52\n","15 51\n","7 51\n","14 50\n","3 48\n","2 46\n","17 46\n","1 46\n","16 45\n","8 44\n","5 40\n","20 40\n","4 33\n","0 29\n","$1,7 1\n","$1,4) 1\n","Name: count, dtype: int64\n","Unique values in self_esteem:\n","self_esteem\n","25 83\n","30 60\n","27 59\n","26 58\n","28 53\n","29 52\n","15 50\n","23 44\n","17 40\n","21 37\n","11 37\n","16 37\n","20 35\n","13 32\n","6 31\n","24 30\n","7 28\n","1 28\n","19 28\n","22 27\n","8 26\n","3 25\n","9 25\n","0 24\n","18 24\n","12 22\n","10 21\n","2 21\n","14 21\n","5 19\n","4 19\n","$21 1\n","($23) 1\n","$12 1\n","($20) 1\n","Name: count, dtype: int64\n","Unique values in depression:\n","depression\n","10 66\n","13 63\n","12 63\n","14 57\n","8 49\n","3 47\n","11 46\n","9 46\n","0 44\n","5 43\n","7 42\n","18 41\n","6 36\n","27 36\n","1 36\n","2 36\n","4 35\n","22 35\n","24 34\n","20 33\n","26 32\n","17 30\n","19 29\n","21 28\n","23 27\n","25 27\n","15 21\n","16 16\n","($2,2) 1\n","($2,6 1\n","Name: count, dtype: int64\n"]}],"source":["# Names of categorical columns\n","categorical_cols = ['anxiety_level', 'self_esteem', 'depression']\n","\n","# Checking the unique values and their counts for categorical variables\n","for col in categorical_cols:\n"," print(f\"Unique values in {col}:\")\n"," print(data_df[col].value_counts())"]},{"cell_type":"markdown","metadata":{"id":"Dps54ASEGZsa"},"source":["## Observations:\n","The presence of formatting errors and non-standard entries in categorical data points to the need for a thorough data cleaning process to ensure the accuracy and reliability of subsequent analyses."]},{"cell_type":"markdown","metadata":{"id":"No_aiaYtGwdK"},"source":["## Cleaning Data"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":726},"executionInfo":{"elapsed":190,"status":"ok","timestamp":1716216746761,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"6NdMn0ROGGt2","outputId":"7a0e3adc-7008-413c-c024-9b00765885bf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem mental_health_history depression headache \\\n","0 14 20 0 11 2 \n","1 15 8 1 15 5 \n","2 12 18 1 14 2 \n","3 16 12 1 15 4 \n","4 16 28 0 7 2 \n","5 20 13 1 21 3 \n","6 4 26 0 6 1 \n","7 17 3 1 22 4 \n","8 13 22 1 12 3 \n","9 6 8 0 27 4 \n","10 17 12 1 25 4 \n","11 17 15 1 22 3 \n","12 5 28 0 8 1 \n","13 9 23 1 24 4 \n","14 2 28 0 3 1 \n","15 11 21 0 14 3 \n","16 6 28 0 1 1 \n","17 7 25 0 3 1 \n","18 11 23 0 12 3 \n","19 21 1 1 25 4 \n","\n"," blood_pressure sleep_quality breathing_problem noise_level \\\n","0 1 2 4 2 \n","1 3 1 4 3 \n","2 1 2 2 2 \n","3 3 1 3 4 \n","4 3 5 1 3 \n","5 3 1 4 3 \n","6 2 4 1 1 \n","7 3 1 5 3 \n","8 1 2 4 3 \n","9 3 1 2 0 \n","10 3 1 3 4 \n","11 3 1 5 5 \n","12 2 4 2 2 \n","13 3 1 0 1 \n","14 2 4 2 1 \n","15 1 2 4 2 \n","16 2 4 2 1 \n","17 2 4 2 2 \n","18 1 2 2 3 \n","19 3 1 4 4 \n","\n"," living_conditions ... basic_needs academic_performance study_load \\\n","0 3 ... 2 3 2 \n","1 1 ... 2 1 4 \n","2 2 ... 2 2 3 \n","3 2 ... 2 2 4 \n","4 2 ... 3 4 3 \n","5 2 ... 1 2 5 \n","6 4 ... 4 5 1 \n","7 1 ... 1 1 3 \n","8 3 ... 3 3 3 \n","9 5 ... 2 2 2 \n","10 2 ... 1 1 3 \n","11 2 ... 1 1 3 \n","12 3 ... 5 5 2 \n","13 2 ... 3 1 2 \n","14 3 ... 4 4 2 \n","15 2 ... 2 3 3 \n","16 4 ... 4 5 1 \n","17 4 ... 4 4 2 \n","18 2 ... 3 2 3 \n","19 1 ... 1 1 5 \n","\n"," teacher_student_relationship future_career_concerns social_support \\\n","0 3 3 2 \n","1 1 5 1 \n","2 3 2 2 \n","3 1 4 1 \n","4 1 2 1 \n","5 2 5 1 \n","6 4 1 3 \n","7 2 4 1 \n","8 2 3 3 \n","9 1 5 1 \n","10 1 4 1 \n","11 1 4 1 \n","12 4 1 3 \n","13 3 3 0 \n","14 5 1 3 \n","15 3 3 2 \n","16 5 1 3 \n","17 5 1 3 \n","18 2 2 3 \n","19 2 5 1 \n","\n"," peer_pressure extracurricular_activities bullying stress_level \n","0 3 3 2 1 \n","1 4 5 5 2 \n","2 3 2 2 1 \n","3 4 4 5 2 \n","4 5 0 5 1 \n","5 4 4 5 2 \n","6 2 2 1 0 \n","7 4 4 5 2 \n","8 3 2 2 1 \n","9 5 3 4 1 \n","10 4 4 5 2 \n","11 5 5 4 2 \n","12 1 1 1 0 \n","13 1 0 1 2 \n","14 1 2 1 0 \n","15 3 2 2 1 \n","16 2 2 1 0 \n","17 1 1 1 0 \n","18 3 2 3 1 \n","19 4 4 5 2 \n","\n","[20 rows x 21 columns]"],"text/html":["\n","
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anxiety_levelself_esteemmental_health_historydepressionheadacheblood_pressuresleep_qualitybreathing_problemnoise_levelliving_conditions...basic_needsacademic_performancestudy_loadteacher_student_relationshipfuture_career_concernssocial_supportpeer_pressureextracurricular_activitiesbullyingstress_level
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20 rows × 21 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"stress_level_data"}},"metadata":{},"execution_count":7}],"source":["\n","# Define the list of columns to clean and convert to integers.\n","categorical_cols = ['anxiety_level', 'self_esteem', 'depression']\n","\n","# Define the characters to remove.\n","chars_to_remove = ['$', '(', ')', ',']\n","\n","# Define a function to clean and convert the data.\n","def clean_and_convert(data_df, column):\n"," # Remove each character from chars_to_remove.\n"," for char in chars_to_remove:\n"," data_df[column] = data_df[column].str.replace(char, '', regex=False)\n"," # Convert column to numeric, setting errors='coerce' will convert non-convertible values to NaN.\n"," data_df[column] = pd.to_numeric(data_df[column], errors='coerce')\n"," return data_df\n","\n","# Apply the cleaning and conversion function to each categorical column.\n","for col in categorical_cols:\n"," stress_level_data = clean_and_convert(data_df, col)\n","\n","# Display the first few rows of the cleaned and converted data to verify.\n","stress_level_data.head((20))\n"]},{"cell_type":"markdown","metadata":{"id":"UYJLYk6GHn4r"},"source":["## checking for the unique values to observe if the errors have been cleaned"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":280,"status":"ok","timestamp":1716216751615,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"oc2GMk3-IAUE","outputId":"24aff9db-142d-4e51-8647-816a330e5f37"},"outputs":[{"output_type":"stream","name":"stdout","text":["Unique values in anxiety_level:\n","anxiety_level\n","13 67\n","10 63\n","19 61\n","21 61\n","9 60\n","12 56\n","18 55\n","11 54\n","6 52\n","15 51\n","7 51\n","14 51\n","3 48\n","17 47\n","1 46\n","2 46\n","16 45\n","8 44\n","5 40\n","20 40\n","4 33\n","0 29\n","Name: count, dtype: int64\n","Unique values in self_esteem:\n","self_esteem\n","25 83\n","30 60\n","27 59\n","26 58\n","28 53\n","29 52\n","15 50\n","23 45\n","17 40\n","21 38\n","16 37\n","11 37\n","20 36\n","13 32\n","6 31\n","24 30\n","7 28\n","19 28\n","1 28\n","22 27\n","8 26\n","9 25\n","3 25\n","18 24\n","0 24\n","12 23\n","10 21\n","14 21\n","2 21\n","4 19\n","5 19\n","Name: count, dtype: int64\n","Unique values in depression:\n","depression\n","10 66\n","13 63\n","12 63\n","14 57\n","8 49\n","3 47\n","11 46\n","9 46\n","0 44\n","5 43\n","7 42\n","18 41\n","6 36\n","22 36\n","1 36\n","27 36\n","2 36\n","4 35\n","24 34\n","26 33\n","20 33\n","17 30\n","19 29\n","21 28\n","23 27\n","25 27\n","15 21\n","16 16\n","Name: count, dtype: int64\n"]}],"source":["# Names of categorical columns\n","categorical_cols = ['anxiety_level', 'self_esteem', 'depression']\n","\n","# Checking the unique values and their counts for categorical variables\n","for col in categorical_cols:\n"," print(f\"Unique values in {col}:\")\n"," print(stress_level_data[col].value_counts())"]},{"cell_type":"markdown","metadata":{"id":"H2h7SREiLK8d"},"source":["## Checking the Dataset description to ensure the data type is interger"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1716216753621,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"INFyNubnLh_3","outputId":"e9a0a0f3-51f5-456f-8d08-f1b343894874"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","stress_level_data Info:\n","\n","RangeIndex: 1100 entries, 0 to 1099\n","Data columns (total 21 columns):\n"," # Column Non-Null Count Dtype\n","--- ------ -------------- -----\n"," 0 anxiety_level 1100 non-null int64\n"," 1 self_esteem 1100 non-null int64\n"," 2 mental_health_history 1100 non-null int64\n"," 3 depression 1100 non-null int64\n"," 4 headache 1100 non-null int64\n"," 5 blood_pressure 1100 non-null int64\n"," 6 sleep_quality 1100 non-null int64\n"," 7 breathing_problem 1100 non-null int64\n"," 8 noise_level 1100 non-null int64\n"," 9 living_conditions 1100 non-null int64\n"," 10 safety 1100 non-null int64\n"," 11 basic_needs 1100 non-null int64\n"," 12 academic_performance 1100 non-null int64\n"," 13 study_load 1100 non-null int64\n"," 14 teacher_student_relationship 1100 non-null int64\n"," 15 future_career_concerns 1100 non-null int64\n"," 16 social_support 1100 non-null int64\n"," 17 peer_pressure 1100 non-null int64\n"," 18 extracurricular_activities 1100 non-null int64\n"," 19 bullying 1100 non-null int64\n"," 20 stress_level 1100 non-null int64\n","dtypes: int64(21)\n","memory usage: 180.6 KB\n","None\n"]}],"source":["# Display information about the dataset\n","print(\"\\nstress_level_data Info:\")\n","print(stress_level_data.info())"]},{"cell_type":"markdown","metadata":{"id":"v94oa6dUVRXp"},"source":["## Observation:\n","- The unwanted characters have been removed.\n","\n","- All data types are now int64"]},{"cell_type":"markdown","metadata":{"id":"SrRos6MkysPG"},"source":["# DATA SCIENCE"]},{"cell_type":"markdown","metadata":{"id":"QAjRlbw_nUyw"},"source":["#EXPLORATORY DATA ANALYSIS"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"4d47r9jm21-m","executionInfo":{"status":"ok","timestamp":1716216769463,"user_tz":300,"elapsed":1710,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"pK-fb7Zmyr32","executionInfo":{"status":"ok","timestamp":1716216774436,"user_tz":300,"elapsed":326,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["df=stress_level_data"]},{"cell_type":"markdown","metadata":{"id":"0Y_wvYuI2mf1"},"source":["## Visualize and Understand the current data"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":7639,"status":"ok","timestamp":1716216789479,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"EPJpWM662mLi","outputId":"0094fb7e-b5dc-4c57-ce0b-08bfe2f2e066"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["df.hist(bins=5,figsize=(20,20));"]},{"cell_type":"markdown","metadata":{"id":"oPJA79luYTp1"},"source":["#### Look at the intial correlation based on the dataset with no altercations."]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":757},"executionInfo":{"elapsed":110,"status":"ok","timestamp":1716216841738,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"N0kfKdrqzTfP","outputId":"29690bcb-f8db-4d9c-b8f6-3f70402310a1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem \\\n","anxiety_level 1.000000 -0.672745 \n","self_esteem -0.672745 1.000000 \n","mental_health_history 0.634450 -0.603502 \n","depression 0.694340 -0.699602 \n","headache 0.632738 -0.626058 \n","blood_pressure 0.330867 -0.514692 \n","sleep_quality -0.710292 0.662693 \n","breathing_problem 0.561654 -0.510514 \n","noise_level 0.608624 -0.571169 \n","living_conditions -0.568434 0.550535 \n","safety -0.651220 0.643981 \n","basic_needs -0.637079 0.631212 \n","academic_performance -0.649601 0.639045 \n","study_load 0.586064 -0.575112 \n","teacher_student_relationship -0.663176 0.652934 \n","future_career_concerns 0.717016 -0.712520 \n","social_support -0.569748 0.679211 \n","peer_pressure 0.642910 -0.607118 \n","extracurricular_activities 0.641022 -0.641202 \n","bullying 0.709982 -0.640737 \n","stress_level 0.736795 -0.756195 \n","\n"," mental_health_history depression headache \\\n","anxiety_level 0.634450 0.694340 0.632738 \n","self_esteem -0.603502 -0.699602 -0.626058 \n","mental_health_history 1.000000 0.615882 0.604826 \n","depression 0.615882 1.000000 0.657700 \n","headache 0.604826 0.657700 1.000000 \n","blood_pressure 0.295617 0.436084 0.361986 \n","sleep_quality -0.614146 -0.693161 -0.638771 \n","breathing_problem 0.464347 0.522540 0.461719 \n","noise_level 0.515290 0.566250 0.543557 \n","living_conditions -0.508525 -0.530351 -0.532825 \n","safety -0.546731 -0.625857 -0.589136 \n","basic_needs -0.601196 -0.608776 -0.623199 \n","academic_performance -0.586193 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anxiety_levelself_esteemmental_health_historydepressionheadacheblood_pressuresleep_qualitybreathing_problemnoise_levelliving_conditions...basic_needsacademic_performancestudy_loadteacher_student_relationshipfuture_career_concernssocial_supportpeer_pressureextracurricular_activitiesbullyingstress_level
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self_esteem-0.6727451.000000-0.603502-0.699602-0.626058-0.5146920.662693-0.510514-0.5711690.550535...0.6312120.639045-0.5751120.652934-0.7125200.679211-0.607118-0.641202-0.640737-0.756195
mental_health_history0.634450-0.6035021.0000000.6158820.6048260.295617-0.6141460.4643470.515290-0.508525...-0.601196-0.5861930.532267-0.5877280.625909-0.4825600.5806020.5545760.6243660.648644
depression0.694340-0.6996020.6158821.0000000.6577000.436084-0.6931610.5225400.566250-0.530351...-0.608776-0.6331740.602498-0.6738530.706561-0.6179720.6355440.6485510.6657900.734379
headache0.632738-0.6260580.6048260.6577001.0000000.361986-0.6387710.4617190.543557-0.532825...-0.623199-0.6220590.542890-0.6259280.679307-0.5729880.6225810.5825620.6097750.713484
blood_pressure0.330867-0.5146920.2956170.4360840.3619861.000000-0.3003230.1623080.352744-0.274686...-0.280590-0.2627850.348964-0.3521230.434087-0.7525310.4013920.4262540.3704400.394200
sleep_quality-0.7102920.662693-0.614146-0.693161-0.638771-0.3003231.000000-0.541687-0.5766450.535462...0.6209550.671326-0.5517750.677569-0.6821300.554553-0.649098-0.623092-0.699427-0.749068
breathing_problem0.561654-0.5105140.4643470.5225400.4617190.162308-0.5416871.0000000.459235-0.448997...-0.508172-0.5072510.428791-0.4988950.545345-0.3651730.4927290.5168840.5763410.573984
noise_level0.608624-0.5711690.5152900.5662500.5435570.352744-0.5766450.4592351.000000-0.452362...-0.572327-0.5137300.493625-0.5387580.575439-0.4920940.5838170.5636140.5854580.663371
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safety-0.6512200.643981-0.546731-0.625857-0.589136-0.2883540.657686-0.519348-0.5366300.563571...0.6247740.642846-0.4939030.663328-0.6581060.614988-0.556945-0.580304-0.645673-0.709602
basic_needs-0.6370790.631212-0.601196-0.608776-0.623199-0.2805900.620955-0.508172-0.5723270.503275...1.0000000.639387-0.5134590.649519-0.6393480.584141-0.587037-0.506426-0.644886-0.708968
academic_performance-0.6496010.639045-0.586193-0.633174-0.622059-0.2627850.671326-0.507251-0.5137300.507221...0.6393871.000000-0.5204170.669469-0.6438050.567501-0.562948-0.588612-0.666229-0.720922
study_load0.586064-0.5751120.5322670.6024980.5428900.348964-0.5517750.4287910.493625-0.437732...-0.513459-0.5204171.000000-0.5141230.576078-0.4733120.5441890.5435430.5866690.634156
teacher_student_relationship-0.6631760.652934-0.587728-0.673853-0.625928-0.3521230.677569-0.498895-0.5387580.549332...0.6495190.669469-0.5141231.000000-0.6702550.681288-0.587770-0.582311-0.655960-0.680163
future_career_concerns0.717016-0.7125200.6259090.7065610.6793070.434087-0.6821300.5453450.575439-0.565071...-0.639348-0.6438050.576078-0.6702551.000000-0.6027920.6668730.6665650.7112780.742619
social_support-0.5697480.679211-0.482560-0.617972-0.572988-0.7525310.554553-0.365173-0.4920940.466594...0.5841410.567501-0.4733120.681288-0.6027921.000000-0.490172-0.530047-0.567078-0.632497
peer_pressure0.642910-0.6071180.5806020.6355440.6225810.401392-0.6490980.4927290.583817-0.501795...-0.587037-0.5629480.544189-0.5877700.666873-0.4901721.0000000.6183710.6610580.690684
extracurricular_activities0.641022-0.6412020.5545760.6485510.5825620.426254-0.6230920.5168840.563614-0.515794...-0.506426-0.5886120.543543-0.5823110.666565-0.5300470.6183711.0000000.6519790.692977
bullying0.709982-0.6407370.6243660.6657900.6097750.370440-0.6994270.5763410.585458-0.551139...-0.644886-0.6662290.586669-0.6559600.711278-0.5670780.6610580.6519791.0000000.751162
stress_level0.736795-0.7561950.6486440.7343790.7134840.394200-0.7490680.5739840.663371-0.581723...-0.708968-0.7209220.634156-0.6801630.742619-0.6324970.6906840.6929770.7511621.000000
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe"}},"metadata":{},"execution_count":14}],"source":["df.corr()"]},{"cell_type":"markdown","metadata":{"id":"XlWIA8U-YjPI"},"source":["#### in researching the dataset, I found the basis of this idea and really liked the visualizaion of the correlation\n"," - Credit: https://www.kaggle.com/code/xkevnx/student-stress-factors-analysis/notebook"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":545},"executionInfo":{"elapsed":650,"status":"ok","timestamp":1716216845126,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"liXP5FfJ1Uib","outputId":"259b6f99-7c53-43c8-9dd3-6165b466d2fd"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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JykpKRrny5Yti5aWlnpK04MHD9LVX758eYB0W9NmV1bzfKljx44kJiayatUqdu/eTYcOHTTOe3p6YmpqyvTp0zP87pp79+5lO0dfX1/u379P37590z0rgL179/L3338D0LRpU+7cucOGDRvU51NSUli0aBHGxsbUrVs32/d/KbvP6lXa2tooFAqNkYHIyEj1jluvyurvWqNGjdDT02PhwoUa+fz66688evRIvWPb+2ratCkA8+fP1yh/OWLxoe7zug4dOpCamsqUKVPSnUtJSVE/o5cjH68+g6SkJJYsWZLuOiMjowynr73snP/zzz/qstTUVH7++eds5fvff/+xfPnydOeePXvG06dPgZz9exbicycjOUIIAQwZMoSEhARat25NyZIlSUpK4tixY+oRilcXFnt4eLB//37mzp2Lvb09RYsWpWrVqpnWXbx4caZOnYqPjw+RkZG0atUKExMTIiIi2Lp1K/3792fEiBEcPHgQLy8v2rdvj6urKykpKaxZswZtbW3atm0LwOTJk/nnn39o1qwZjo6OxMTEsGTJEgoXLqzx/S2ZuXr1qnpE6FUVKlSgWbNmWcrzpYoVK+Ls7MzYsWNJTEzUmKoGL9bQLF26lG7dulGxYkU6deqEtbU1UVFR7Nixg5o1a7J48eK35vyqjh07EhISwrRp0zh37hydO3fG0dGR+/fvs3v3bg4cOKCe8tW/f39++uknevbsyZkzZ3BycmLTpk0EBgYyf/78LG9gkJGs/kwz0qxZM+bOnUuTJk3o0qULMTEx/Pjjjzg7O6dbE5PV3zVra2t8fHzw9fWlSZMmfPXVV4SFhbFkyRIqV678wb5ks1y5cvTo0YOff/5ZPTXs5MmTrFq1ilatWlG/fv0Pcp/X1a1blwEDBuDn50dQUBBffPEFurq6hIeHs3HjRhYsWEC7du2oUaMG5ubm9OjRA29vbxQKBWvWrMnwAwwPDw82bNjAsGHDqFy5MsbGxrRo0YLSpUtTrVo1fHx8ePDgARYWFqxfvz7DTnVmunXrxh9//MHAgQM5dOgQNWvWJDU1lcuXL/PHH3+wZ88eKlWq9N5/z0KIN/jo+7kJIUQetGvXLlXv3r1VJUuWVBkbG6v09PRUzs7OqiFDhqju3r2rEXv58mVVnTp1VAYGBipAvcXvy+2GM9sOdvPmzapatWqpjIyMVEZGRqqSJUuqBg8erAoLC1OpVCrV9evXVb1791YVL15cpa+vr7KwsFDVr19ftX//fnUdBw4cULVs2VJlb2+v0tPTU9nb26s6d+6sunLlylvb6OjomOE2vYCqT58+Wc7zVWPHjlUBKmdn50zve+jQIZWnp6eqQIECKn19fVXx4sVVPXv2VJ0+fVodk5UtpF/18jkULFhQpaOjo7K2tla1aNFCtX37do24u3fvqnr16qWysrJS6enpqcqWLavy9/fXiHm5hfQPP/yQ7j7v+zNVqTLeQvrXX39Vubi4qJRKpapkyZIqf3//DJ9BZr9rmW1zvHjxYlXJkiVVurq6KhsbG9U333yjevjwoUZM3bp1VaVLl07XlozyzEhycrLK19dXVbRoUZWurq7KwcFB5ePjo7E988v6srOFdFZif/75Z5WHh4fKwMBAZWJioipbtqxq1KhRqtu3b6tjAgMDVdWqVVMZGBio7O3t1dvB89r2z/Hx8aouXbqozMzMVIBG269du6Zq1KiRSqlUqmxsbFTff/+9at++fRluIZ3Rs1SpXmwLPnPmTFXp0qVVSqVSZW5urvLw8FD5+vqqHj16pFKp3u/vWQjxZgqVKourDIUQQgghhBDiEyBrcoQQQgghhBD5inRyhBBCCCGEEPmKdHKEEEIIIYQQ+Yp0coQQQgghhBD5inRyhBBCCCGEEPmKdHKEEEIIIYQQ+Yp0coQQQgghhBD5ik5uJyDE52iHboncTkEIIYQQ2dQsOSzX7p2T7x1ys105RUZyhBBCCCGEEPmKjOQIIYQQQgiRxyl0FbmdwidFRnKEEEIIIYQQ+YqM5AghhBBCCJHHaenISE52yEiOyBX16tVj6NCh73x9ZGQkCoWCoKAgAAICAlAoFMTFxb3T9UIIIYQQIv+QkRzxWXJwcCA6OhorK6vcTkUIkYc5ftOFYsP6oLS15vH5y1wcOoVHp0IyjK22fzWWdaumK4/ZGcCplgMAcP/VD4fubTTP7znCqeZ9P3zyQoh8RaErYxPZIZ0c8VnS1tbG1tY2t9MQQuRhdu2/xO0HHy4MnkjcyWCKeveg6o5fCSjdhKR7D9LFn2k/BC09XfVrXUszap/ZTvTm3RpxMbv/4XxfH/Xr1MSknGuEECLfkOlq2SNdQpFrUlJS8PLyokCBAlhZWTF+/HhUKhUACoWCbdu2acSbmZmxcuXKt9b79OlTTE1N2bRpk0b5tm3bMDIy4smTJ5lOdztw4ACVKlXC0NCQGjVqEBamuW/81KlTKViwICYmJvTt25cxY8ZQvnz5d30EQog8rOjQXtz89Q9urdpCfOg1QgZNJDXhOQ4922YYn/zwEYl3Y9WHVaOapCY8J3qTZicnLTFJIy4l7vHHaI4QQnxWpJMjcs2qVavQ0dHh5MmTLFiwgLlz5/LLL7+8d71GRkZ06tQJf39/jXJ/f3/atWuHiYlJpteOHTuWOXPmcPr0aXR0dOjdu7f63Nq1a5k2bRozZ87kzJkzFClShKVLl753vkKIvEehq0uBiqWJPXDsf4UqFbEHj2FWrUKW6nDo1ZboP3aQmvBMo9yybhUa/XeMuhd2U2bxJHQtzD5g5kKI/Eqhq8ixIz+S6Woi1zg4ODBv3jwUCgUlSpQgJCSEefPm0a9fv/euu2/fvtSoUYPo6Gjs7OyIiYlh586d7N+//43XTZs2jbp16wIwZswYmjVrxvPnz9HX12fRokX06dOHXr16ATBhwgT27t1LfHz8e+crhMhb9KzM0dLRITHmvkZ54t37GJUo9tbrC1Qui2mZEpzvP1aj/N6eI9zZuo9nkbcwLOZAiSnDqPL3cgJrdYS0tA/aBiGE+JzJSI7INdWqVUOh+N+nB9WrVyc8PJzU1NT3rrtKlSqULl2aVatWAfDbb7/h6OhInTp13nidu7u7+r/t7OwAiImJASAsLIwqVaqku8/bJCYm8vjxY40jWSVvZoTIzxx6teNxSFi6TQqi/9hJzN8HeXLhCnf/PMCpVgMwq+yOZd23/1sihPi8aekocuzIj6STI/IkhUKhXp/zUnJycrbq6Nu3r3oNj7+/P7169dLoVGVEV/d/i4Zfxqa956erfn5+FChQQOP4Iy39omUhRN6RFPuQtJQUlAUtNcqVNpYk3ol947XahgbYd2jGTf9Nb4wDeBZxi8R7DzBydnyvfIUQQmiSTo7INSdOnNB4/e+//+Li4oK2tjbW1tZER0erz4WHh5OQkJCt+r/++mtu3LjBwoULuXTpEj169HivfEuUKMGpU6c0yl5/nREfHx8ePXqkcXTQsnivXIQQOUuVnMyjsxexalD9f4UKBZb1qxP377k3XmvXrglaSj3+W/vnW++jX8gGPUsznkffe9+UhRD5nKzJyR5ZkyNyTVRUFMOGDWPAgAGcPXuWRYsWMWfOHAAaNGjA4sWLqV69OqmpqYwePVpjlCUrzM3NadOmDSNHjuSLL76gcOHC75XvkCFD6NevH5UqVaJGjRps2LCB8+fPU6zYm+fnK5VKlEqlRpmuQj5fECKvi5jvT7kVM4k7c4FHp87j5N0DHSMDbq7aAkA5/5k8/+8uYePmalzn0Ksdd7fvJ/lBnEa5tpEhLuO9uLN1D4l3YjEs5oDbjJE8vXqD2L1HPlazhBDisyCdHJFrunfvzrNnz6hSpQra2tp8++239O/fH4A5c+bQq1cvateujb29PQsWLODMmTPZvkefPn1Yt26dxi5p76pr165cv36dESNG8Pz5czp06EDPnj05efLke9cthMh7ojfuQs/aAteJ3i++DDQ4lJPN+5L0/5sRGDjYoXptOquRa1EsalXiRJNe6epTpaZiWtaVwt1aoWtmwvPbMcTuDyRs4gLSkrI3HVcI8fnJr2tncopC9frCByHykTVr1vDdd99x+/Zt9PT0Pnj9jRs3xtbWljVr1mTruh26JT54LkIIIYTIWc2Sw94elEP+KZO17evfRZ0Lb56G+ymSkRyRLyUkJBAdHc2MGTMYMGDAB+ngJCQksGzZMjw9PdHW1ub3339n//797Nu37wNkLIQQQgghPhRZGCDypVmzZlGyZElsbW3x8fH5IHUqFAp27txJnTp18PDw4K+//mLz5s00atTog9QvhBBCCJEZLW1Fjh35kUxXEyIXyHQ1IYQQ4tOTm9PVjparmGN11wo+m2N15xaZriaEEEIIIUQep9DKnyMuOUWmqwkhhBBCCCHyFRnJEUIIIYQQIo9TaMvYRHZIJ0eIXJBfv11YCCGEECIvkE6OEEIIIYQQeVx+3QUtp0gnRwghhBBCiDxONh7IHpncJ4QQQgghhMhXZCRHCCGEEEKIPE6mq2VPvhrJUalU9O/fHwsLCxQKBUFBQbmdkhBCCCGEEOIjy1ednN27d7Ny5Ur+/vtvoqOjKVOmzFuvUSgUbNu2LeeTE0II8clxHNCFeqEH8HwQTI3DGyhQqWymsVV3r6ZpwuV0R6UtyzKML7NwEk0TLuM0uHsOZS+EyE8U2oocO/KjfDVd7dq1a9jZ2VGjRo2Pfu+kpCT09PQ++n3fJCdzSk5ORldXN0fqzi0qlYrU1FR0dPLVn4UQ4h3Ztf2SkjPGcNF7EnGngnHy6kGV7b9wuPyXJN17kC7+bOchKPT+9++inoUZtU5sI3rLnnSxNl81wqxKOZ7fvpujbRBCiM9VvhnJ6dmzJ0OGDCEqKgqFQoGTkxNOTk7Mnz9fI658+fJMmjQJACcnJwBat26tvuZlXa1atdK4bujQodSrV0/9ul69enh5eTF06FCsrKzw9PQE4MKFC3z55ZcYGxtjY2NDt27diI2NzVIb0tLSmDVrFs7OziiVSooUKcK0adPU50ePHo2rqyuGhoYUK1aM8ePHk5ycrD4/adIkypcvzy+//ELRokXR19cHIC4ujr59+2JtbY2pqSkNGjQgODhY497bt2+nYsWK6OvrU6xYMXx9fUlJSVGfVygULF26lK+++gojIyONvDJz8eJFmjdvjqmpKSYmJtSuXZtr166p2zp58mQKFy6MUqmkfPny7N69W31tZGQkCoWCLVu2UL9+fQwNDSlXrhzHjx/XuEdgYCD16tXD0NAQc3NzPD09efjwofoefn5+FC1aFAMDA8qVK8emTZvU1wYEBKBQKNi1axceHh4olUqOHj1KvXr18Pb2ZtSoUVhYWGBra6v+nYEXnaFJkyZRpEgRlEol9vb2eHt7v/V5CCE+LUW9e3LTfyO31mwh/vI1LgyZSOqz5xTu3jbD+OSHj0i6G6s+rBrUIDXhOXe27NaIU9oXpNSccQT1GklackqGdQkhxOsUWlo5duRH+aZVCxYsUL9pjo6O5tSpU2+95mWMv79/lq951apVq9DT0yMwMJBly5YRFxdHgwYNqFChAqdPn2b37t3cvXuXDh06ZKk+Hx8fZsyYwfjx47l06RLr1q3DxsZGfd7ExISVK1dy6dIlFixYwPLly5k3b55GHVevXmXz5s1s2bJFvSapffv2xMTEsGvXLs6cOUPFihVp2LAhDx68+CTyyJEjdO/enW+//ZZLly7x008/sXLlynQdmUmTJtG6dWtCQkLo3bv3G9vy33//UadOHZRKJQcPHuTMmTP07t1b3XFasGABc+bMYfbs2Zw/fx5PT0+++uorwsPDNeoZO3YsI0aMICgoCFdXVzp37qyuIygoiIYNG1KqVCmOHz/O0aNHadGiBampqQD4+fmxevVqli1bxsWLF/nuu+/4+uuvOXz4sMY9xowZw4wZMwgNDcXd3R148bM1MjLixIkTzJo1i8mTJ7Nv3z4ANm/ezLx58/jpp58IDw9n27ZtlC2b+RQWIcSnR6Gri2mF0tw/dOx/hSoVsQePY161fJbqcOjRjuhNO0lNePZKxQrK/TKLiHm/Eh969cMmLYQQQi3fzMspUKAAJiYmaGtrY2trm6VrrK2tATAzM8vyNa9ycXFh1qxZ6tdTp06lQoUKTJ8+XV22YsUKHBwcuHLlCq6urpnW9eTJExYsWMDixYvp0aMHAMWLF6dWrVrqmHHjxqn/28nJiREjRrB+/XpGjRqlLk9KSmL16tXqth09epSTJ08SExODUqkEYPbs2Wzbto1NmzbRv39/fH19GTNmjPq+xYoVY8qUKYwaNYqJEyeq6+7SpQu9evXK0rP58ccfKVCgAOvXr1dPa3u1/bNnz2b06NF06tQJgJkzZ3Lo0CHmz5/Pjz/+qI4bMWIEzZo1A8DX15fSpUtz9epVSpYsyaxZs6hUqRJLlixRx5cuXRqAxMREpk+fzv79+6levbq6XUePHuWnn36ibt266msmT55M48aNNfJ3d3dXt93FxYXFixdz4MABGjduTFRUFLa2tjRq1AhdXV2KFClClSpVsvRchBCfBj0rc7R0dEi8e1+jPDEmFuMSRd96fYFKZTEp48r5QWM1yosP74cqJZXIJWs+aL5CiPxPvicne/JNJyc3eHh4aLwODg7m0KFDGBsbp4u9du3aGzs5oaGhJCYm0rBhw0xjNmzYwMKFC7l27Rrx8fGkpKRgamqqEePo6Kju4LzMKT4+HktLS424Z8+eqaeOBQcHExgYqDFyk5qayvPnz0lISMDQ0BCASpUqZZrb64KCgqhdu3aG63YeP37M7du3qVmzpkZ5zZo1002jezmyAmBnZwdATEwMJUuWJCgoiPbt22d4/6tXr5KQkJCu85KUlESFChU0yjJq16v3fXnvmJgY4MXI2Pz58ylWrBhNmjShadOmtGjRItO1PImJiSQmJmqUJavS0FXkm4FUIcRrHHq043FIGI9Oh6jLTCuUxmlwN47WyHi6mxBCvIlsIZ09+bqTo6WlhUql0ih7dQ3L+15nZGSk8To+Pp4WLVowc+bMdLEv36BnxsDA4I3njx8/TteuXfH19cXT01M9SjJnzpy35mRnZ0dAQEC6Os3MzNQxvr6+tGnTJl3My3U9GdX9Jm9rT1a92klSKF78caelpb31HvHx8QDs2LGDQoUKaZx7OaL1Ukbter1zplAo1Pd1cHAgLCyM/fv3s2/fPgYNGsQPP/zA4cOHM+zU+fn54evrq1HWRceSrrpWmeYvhMhdSbEPSUtJQWmj+QGRsqAViXffvM5S29AAu3ZNCZ+yUKPcooYHetaW1A87qC7T0tHBbcZonLx6EOCW+YdcQgghsidfd3Ksra2Jjo5Wv378+DEREREaMbq6uuo1HK9ed+HCBY2yoKCgt+4mVrFiRTZv3oyTk1O2d+hycXHBwMCAAwcO0Ldv33Tnjx07hqOjI2PH/m/qw40bN95ab8WKFblz5w46OjrqjRUyigkLC8PZ2TlbOb+Ju7s7q1atynAXNlNTU+zt7QkMDNSYNhYYGJitaV/u7u4cOHAgXQcCoFSpUiiVSqKiojTu8aEYGBjQokULWrRoweDBgylZsiQhISFUrFgxXayPjw/Dhg3TKDtkk/VRMSHEx6dKTubxuYtY1qvO3b8OvChUKLCsX40by9a+8VrbNk3QUurx3/q/NMr/+/1PYg9pbp5S5c9f+G/ddm6t2fpB8xdC5D8yXS178nUnp0GDBqxcuZIWLVpgZmbGhAkT0NbW1ohxcnLiwIED1KxZE6VSibm5OQ0aNOCHH35g9erVVK9end9++40LFy6km+b0usGDB7N8+XI6d+6s3pnr6tWrrF+/nl9++SXdvV+lr6/P6NGjGTVqFHp6etSsWZN79+5x8eJF+vTpg4uLC1FRUaxfv57KlSuzY8cOtm59+/8UGzVqRPXq1WnVqhWzZs3C1dWV27dvs2PHDlq3bk2lSpWYMGECzZs3p0iRIrRr1w4tLS2Cg4O5cOECU6dOzdrDfo2XlxeLFi2iU6dO+Pj4UKBAAf7991+qVKlCiRIlGDlyJBMnTqR48eKUL18ef39/goKCWLv2zW8eXuXj40PZsmUZNGgQAwcORE9Pj0OHDtG+fXusrKwYMWIE3333HWlpadSqVYtHjx4RGBiIqampev3Ru1i5ciWpqalUrVoVQ0NDfvvtNwwMDHB0dMwwXqlUphs9kqlqQuR9EQtX4r58Bo/OXiDu9HmKevVAx9CAW2u2AOC+fAaJt2MImzhX4zqHHm25+9d+kh/EaZQnP4hLV5aWnELi3Viehmt+ACeEEOL95Ot3Wj4+PtStW5fmzZvTrFkzWrVqRfHixTVi5syZw759+3BwcFB3Yjw9PRk/fjyjRo2icuXKPHnyhO7d3/5lbS9HJ1JTU/niiy8oW7YsQ4cOxczMDK0sbM83fvx4hg8fzoQJE3Bzc6Njx47qdSBfffUV3333HV5eXpQvX55jx44xfvz4t9apUCjYuXMnderUoVevXri6utKpUydu3Lih3rnN09OTv//+m71791K5cmWqVavGvHnzMn3TnhWWlpYcPHiQ+Ph46tati4eHB8uXL1eP6nh7ezNs2DCGDx9O2bJl2b17N3/++ScuLi5Zvoerqyt79+4lODiYKlWqUL16dbZv364eRZsyZQrjx4/Hz88PNzc3mjRpwo4dOyha9O2Lht/EzMyM5cuXU7NmTdzd3dm/fz9//fVXunVPQohPW/TmXVz+fhau44dQ699tmLqX5GSrfiTFvNiMwMDBHqWttcY1Ri5FsahZiVurNudGykKIfEy2kM4eher1xSdCiBy307BkbqcghBBCiGxqmnA51+59vmm9HKvbfWdAjtWdW/L1dDUhhBBCCCHyA1mTkz35c3wqD4qKisLY2DjTIyoqKrdTzJaBAwdm2paBAwfmdnpCCCGEEOIzJtPVPpKUlBQiIyMzPf8uO7LlppiYGB4/fpzhOVNTUwoWLPiRM/q0yHQ1IYQQ4tOTm9PVLrZskGN1l95+8O1Br/jxxx/54YcfuHPnDuXKlWPRokVv3CE3Li6OsWPHsmXLFh48eICjoyPz58+nadOm75t6pj6dd9WfOB0dnQ+6RXNuK1iwoHRkhBBCCCE+krwyXW3Dhg0MGzaMZcuWUbVqVebPn4+npydhYWEZvjdMSkqicePGFCxYkE2bNlGoUCFu3Lih/r7GnCKdHCGEEEIIIUSWzJ07l379+tGrVy8Ali1bxo4dO1ixYgVjxoxJF79ixQoePHjAsWPH1LvsZvbdjR+SrMkRQgghhBAij8vJLaQTExN5/PixxpGYmJguh6SkJM6cOUOjRo3UZVpaWjRq1Ijjx4+niwf4888/qV69OoMHD8bGxoYyZcowffp0UlNTc+xZgYzkCJErtHTyxpCzEEIIIYSfnx++vr4aZRMnTmTSpEkaZbGxsaSmpqq/a/ElGxsbLl/OeL3S9evXOXjwIF27dmXnzp1cvXqVQYMGkZyczMSJEz9oO14lnRwhhBBCCCHyuJxck+Pj48OwYcM0ypRK5QepOy0tjYIFC/Lzzz+jra2Nh4cH//33Hz/88IN0coQQQgghhBA5Q6lUZqlTY2Vlhba2Nnfv3tUov3v3Lra2thleY2dnh66uLtra2uoyNzc37ty5Q1JSEnp6eu+XfCZkTY4QQgghhBB5nEJLkWNHVunp6eHh4cGBAwfUZWlpaRw4cIDq1atneE3NmjW5evUqaWlp6rIrV65gZ2eXYx0ckE6OEEIIIYQQIouGDRvG8uXLWbVqFaGhoXzzzTc8ffpUvdta9+7d8fHxUcd/8803PHjwgG+//ZYrV66wY8cOpk+fzuDBg3M0T+nkiA8iMjIShUJBUFBQruahUCjYtm3bR72nk5MT8+fP/6j3FEIIIcTnJS+M5AB07NiR2bNnM2HCBMqXL09QUBC7d+9Wb0YQFRVFdHS0Ot7BwYE9e/Zw6tQp3N3d8fb25ttvv81wu+kPSdbkiA/CwcGB6OhorKyssnxNvXr1KF++vHQQhBB5VpF+XSjq3Rs9GyueXLhM6MhpPDoTkmFslR2rsKid/hu/Y/Yc5mz7genKS82bSJE+nQgd48eNJas/eO5CiPxFoZV3xia8vLzw8vLK8FxAQEC6surVq/Pvv//mcFaapJMjPghtbe1MF5wJIcSnyLbNl5ScPpqLQycRd/o8ToO6U2nLco54NCUp9kG6+HNfe6P4/y+6A9CzMKPGsa3c3bo7XWzB5o0wq1yO57fvpjsnhBDi/eWdLqHIUbt376ZWrVqYmZlhaWlJ8+bNuXbtGvC/qWZbtmyhfv36GBoaUq5cOY0vderduzfu7u7qL4ZKSkqiQoUKdO/eXaOOV6erXbhwgS+//BJjY2NsbGzo1q0bsbGxAPTs2ZPDhw+zYMECFAoFCoWCiIgInJ2dmT17tkbuQUFBKBQKrl69mu1237x5kw4dOmBmZoaFhQUtW7YkMjISgL1796Kvr09cXJzGNd9++y0NGjRQvz569Ci1a9fGwMAABwcHvL29efr0abZzEUJ8Wpy8enBz1Ub+W7uVp2HXuDh0EqnPnlOoW5sM45MfPiIpJlZ9WDaoQVrCc+5s26MRp7QrSKkfxnK+7yhUySkfoylCiHxAS1uRY0d+JJ2cz8TTp08ZNmwYp0+f5sCBA2hpadG6dWuNnS7Gjh3LiBEjCAoKwtXVlc6dO5OS8uJ/wAsXLuTp06fq+ZNjx44lLi6OxYsXZ3i/uLg4GjRoQIUKFTh9+jS7d+/m7t27dOjQAYAFCxZQvXp1+vXrR3R0NNHR0RQpUoTevXvj7++vUZe/vz916tTB2dk5W21OTk7G09MTExMTjhw5QmBgIMbGxjRp0oSkpCQaNmyImZkZmzdvVl+TmprKhg0b6Nq1KwDXrl2jSZMmtG3blvPnz7NhwwaOHj2a6RCtECJ/UOjqYlq+NPcPvfIN3ioV9wOOY1alfJbqKNytLdGbd5Ka8OyVihW4/zyTiIUriL+c/Q9uhBBCZI1MV/tMtG3bVuP1ihUrsLa25tKlSxgbGwMwYsQImjVrBoCvry+lS5fm6tWrlCxZEmNjY3777Tfq1q2LiYkJ8+fP59ChQ5iammZ4v8WLF1OhQgWmT5+ucU8HBweuXLmCq6srenp6GBoaakxz69mzJxMmTODkyZNUqVKF5ORk1q1bl250Jys2bNhAWloav/zyCwrFi08p/P39MTMzIyAggC+++IJOnTqxbt06+vTpA8CBAweIi4tTPy8/Pz+6du3K0KFDAXBxcWHhwoXUrVuXpUuXoq+vn+28hBB5n56lGVo6OiTdu69RnhhzHyPXom+9voBHWUxKu3LBa5xGebHv+qJKTeXG0jUfNF8hRP6Xk18Gmh/JSM5nIjw8nM6dO1OsWDFMTU1xcnICXuyA8ZK7u7v6v+3s7ACIiYlRl1WvXp0RI0YwZcoUhg8fTq1atTK9X3BwMIcOHcLY2Fh9lCxZEkA9TS4j9vb2NGvWjBUrVgDw119/kZiYSPv27bPd5uDgYK5evYqJiYk6BwsLC54/f67OoWvXrgQEBHD79m0A1q5dS7NmzTAzM1PXsXLlSo12eHp6kpaWRkRERJbySExM5PHjxxpHkirt7RcKIT5Zhbu15cmFMI1NCkzLl8Lxm26EDPR5w5VCCCE+BBnJ+Uy0aNECR0dHli9fjr29PWlpaZQpU4akpCR1jO4rC2Zfjny8Op0tLS2NwMBAtLW137o+Jj4+nhYtWjBz5sx05152oDLTt29funXrxrx58/D396djx44YGhpmqZ2v5+Dh4cHatWvTnbO2tgagcuXKFC9enPXr1/PNN9+wdetWVq5cqVHHgAED8Pb2TldHkSJFspSHn58fvr6+GmVd9Sz5WmmdjdYIIT6mpPtxpKWkoGdtqVGuLGhJ4t3YN16rbWiAbdumXJ2+SKPcvEYl9KwtqXvpoLpMS0eHktNG4fRNdw6XbfThGiCEyHfy0u5qnwLp5HwG7t+/T1hYGMuXL6d27drAi8X02fXDDz9w+fJlDh8+jKenJ/7+/uovfnpdxYoV2bx5M05OTujoZPxrpqenR2pqarrypk2bYmRkxNKlS9m9ezf//PNPtnN9mcOGDRsoWLBgptPq4MVoztq1aylcuDBaWlrqKXsv67h06VK21wO9ysfHh2HDhmmUBRSq/M71CSFynio5mcdBF7GsV42YHf//zd4KBZZ1q3Hj5/QfnLzKtpUnWko9bm/4S6P89vo/Ndf4AJW2Luf2+j/577ctHzR/IYT43EmX8DNgbm6OpaUlP//8M1evXuXgwYPp3nS/zblz55gwYQK//PILNWvWZO7cuXz77bdcv349w/jBgwfz4MEDOnfuzKlTp7h27Rp79uyhV69e6o6Nk5MTJ06cIDIyktjYWPWokba2Nj179sTHxwcXFxeqV6/+Tu3u2rUrVlZWtGzZkiNHjhAREUFAQADe3t7cunVLI+7s2bNMmzaNdu3aoVQq1edGjx7NsWPH8PLyIigoiPDwcLZv356tjQeUSiWmpqYah55C/vSEyOsiF6+icI/22HdpiZFrMUrPm4i2oQH//bYVgLI/zcB14nfprivUvS0xOw6Q/CBOozz5QRzxoeEahyo5hcSYWJ5ejfwILRJCfMryypeBfirkndZnQEtLi/Xr13PmzBnKlCnDd999xw8//JDl658/f87XX39Nz549adGiBQD9+/enfv36dOvWLcPRGHt7ewIDA0lNTeWLL76gbNmyDB06FDMzM7T+f7h1xIgRaGtrU6pUKaytrTXWB/Xp04ekpKRMR4qywtDQkH/++YciRYrQpk0b3Nzc6NOnD8+fP9cY2XF2dqZKlSqcP39evavaS+7u7hw+fJgrV65Qu3ZtKlSowIQJE7C3t3/nvIQQn4Y7W3YRNm4WLt97UzNwKybuJTndtr96MwKDwnYobTWnnRo5O2FRoxK3Vm/OqEohhHhn0snJHoVKpVLldhJCvO7IkSM0bNiQmzdvYmNjk9vpfHC7Td1yOwUhhBBCZFOTx6G5du8b/VvlWN2OP2/Lsbpzi6zJEXlKYmIi9+7dY9KkSbRv3z5fdnCEEEIIIbJLNh7IHnlaIk/5/fffcXR0JC4ujlmzZmmcW7t2rcZWzq8epUuXzqWMhRBCCCFEXiPT1cQn48mTJ9y9ezfDc7q6ujg6On7kjN6dTFcTQgghPj25OV3t5qC2bw96Rw5L8t86QpmuJj4ZJiYmmJiY5HYaQgghhBAij5NOjhBCCCGEEHmcrMnJHnlaQgghhBBCiHxFRnKEEEIIIYTI6xT58/tscoqM5AghhBBCCCHyFRnJEUIIIYQQIo9TaMlITnZIJ0cIIYQQQog8TjYeyB55WkIIIYQQQoh8RTo54p3Vq1ePoUOH5nYa7ywgIACFQkFcXFxupyKEEEII8UYKLUWOHfmRTFcTn60aNWoQHR1NgQIFcjsVIUQeVaRfF4p690bPxoonFy4TOnIaj86EZBhbZccqLGpXSVces+cwZ9sPBMDZZzC2bZuiX8gWVVIyj4IuET5lPo9On8/RdgghxOdGOjkiz0pKSkJPTy/H6tfT08PW1jbH6hdCfNps23xJyemjuTh0EnGnz+M0qDuVtizniEdTkmIfpIs/97U3Cl1d9Ws9CzNqHNvK3a271WVPr0YSOmIqCZE30dbXx3FwDypt/YV/ynuSfP/hR2mXEOLTJGtyskeelsiSp0+f0r17d4yNjbGzs2POnDka5xMTExkxYgSFChXCyMiIqlWrEhAQoD6/cuVKzMzM2LZtGy4uLujr6+Pp6cnNmzfVMZMmTaJ8+fL88ssvFC1aFH19fQDi4uLo27cv1tbWmJqa0qBBA4KDg9XXBQcHU79+fUxMTDA1NcXDw4PTp08DcOPGDVq0aIG5uTlGRkaULl2anTt3AhlPV9u8eTOlS5dGqVTi5OSUrp1OTk5Mnz6d3r17Y2JiQpEiRfj5558/yDMWQuQtTl49uLlqI/+t3crTsGtcHDqJ1GfPKdStTYbxyQ8fkRQTqz4sG9QgLeE5d7btUcdEb9zB/YDjPIu8Rfzlq1z+fga6BUwwKVPiYzVLCCE+C9LJEVkycuRIDh8+zPbt29m7dy8BAQGcPXtWfd7Ly4vjx4+zfv16zp8/T/v27WnSpAnh4eHqmISEBKZNm8bq1asJDAwkLi6OTp06adzn6tWrbN68mS1bthAUFARA+/btiYmJYdeuXZw5c4aKFSvSsGFDHjx48Ulq165dKVy4MKdOneLMmTOMGTMG3f//NHXw4MEkJibyzz//EBISwsyZMzE2Ns6wjWfOnKFDhw506tSJkJAQJk2axPjx41m5cqVG3Jw5c6hUqRLnzp1j0KBBfPPNN4SFhb3vIxZC5CEKXV1My5fm/qHj/ytUqbgfcByzKuWzVEfhbm2J3ryT1IRnmd7DoWcHkuMe8yTk8gfIWgiRn8manOyR6WrireLj4/n111/57bffaNiwIQCrVq2icOHCAERFReHv709UVBT29vYAjBgxgt27d+Pv78/06dMBSE5OZvHixVStWlVdh5ubGydPnqRKlRfz2JOSkli9ejXW1tYAHD16lJMnTxITE4NSqQRg9uzZbNu2jU2bNtG/f3+ioqIYOXIkJUuWBMDFxUWde1RUFG3btqVs2bIAFCtWLNN2zp07l4YNGzJ+/HgAXF1duXTpEj/88AM9e/ZUxzVt2pRBgwYBMHr0aObNm8ehQ4coUUI+iRUiv9CzNENLR4eke/c1yhNj7mPkWvSt1xfwKItJaVcueI1Ld866ST3KrZiNtqEBiXfucapVH5IfxH2o1IUQQiAjOSILrl27RlJSkrpzAmBhYaF+Ux8SEkJqaiqurq4YGxurj8OHD3Pt2jX1NTo6OlSuXFn9umTJkpiZmREaGqouc3R0VHdw4MVUtPj4eCwtLTXqjoiIUNc9bNgw+vbtS6NGjZgxY4bGPb29vZk6dSo1a9Zk4sSJnD+f+eLe0NBQatasqVFWs2ZNwsPDSU1NVZe5u7ur/1uhUGBra0tMTEym9SYmJvL48WONI0mVlmm8EOLTV7hbW55cCMtwk4IH/5zgWK02/Nu4C7H7j1J+5Tz0rCxyIUshxKdERnKyRzo54r3Fx8ejra3NmTNnCAoKUh+hoaEsWLAgW3UZGRmlq9vOzk6j3qCgIMLCwhg5ciTwYi3PxYsXadasGQcPHqRUqVJs3boVgL59+3L9+nW6detGSEgIlSpVYtGiRe/VXt1XFhbDi45OWlrmnRY/Pz8KFCigcfyRdD/TeCFE7ku6H0daSgp61pYa5cqCliTejX3jtdqGBti2bcqtNZszPJ+a8IyE61E8OhXMBa9xqFJTKdy97QfLXQiRT2lp5dyRD+XPVokPqnjx4ujq6nLixAl12cOHD7ly5QoAFSpUIDU1lZiYGJydnTWOV3cvS0lJUW8IABAWFkZcXBxubm6Z3rtixYrcuXMHHR2ddHVbWVmp41xdXfnuu+/Yu3cvbdq0wd/fX33OwcGBgQMHsmXLFoYPH87y5cszvJebmxuBgYEaZYGBgbi6uqKtrZ3Fp5Wej48Pjx490jg66Fm+/UIhRK5RJSfzOOgilvWq/a9QocCybjXiTga98VrbVp5oKfW4veGvLN1LoaVAS5lzO0kKIcTnSNbkiLcyNjamT58+jBw5EktLSwoWLMjYsWPR+v+ev6urK127dqV79+7MmTOHChUqcO/ePQ4cOIC7uzvNmjUDXoyADBkyhIULF6Kjo4OXlxfVqlVTr8fJSKNGjahevTqtWrVi1qxZuLq6cvv2bXbs2EHr1q0pXbo0I0eOpF27dhQtWpRbt25x6tQp2rZ98ano0KFD+fLLL3F1deXhw4ccOnQo007V8OHDqVy5MlOmTKFjx44cP36cxYsXs2TJkvd6fkqlUr2e6CU9hXy+IEReF7l4FWWX+fHo3AUenQ7BaVB3tA0N+O+3FyPFZX+aQeLtu1zxnadxXaHubYnZcSDdOhttQwOKjRhAzK5DJN65h56lGUX6dUFpZ8OdrXsQQog3USjy57SynCKdHJElP/zwA/Hx8bRo0QITExOGDx/Oo0eP1Of9/f2ZOnUqw4cP57///sPKyopq1arRvHlzdYyhoSGjR4+mS5cu/Pfff9SuXZtff/31jfdVKBTs3LmTsWPH0qtXL+7du4etrS116tTBxsYGbW1t7t+/T/fu3bl79y5WVla0adMGX19fAFJTUxk8eDC3bt3C1NSUJk2aMG/evAzvVbFiRf744w8mTJjAlClTsLOzY/LkyRqbDgghPh93tuxCz8ocl++9UdpY8TgklNNt+6s3IzAobAevTVU1cnbCokYlTrXsk64+VWoqRq7FqNClFXqW5iQ9iOPR2RBONPma+MtXP0qbhBDic6FQqVSq3E5C5H8rV65k6NChGt9J8znbbZr5FD0hhBBC5E1NHoe+PSiHxE5I/+HJh2I1+c0fOn+KZM6MEEIIIYQQIl+R6WpCCCGEEELkcfl1q+ecIiM54qPo2bOnTFUTQgghhBAfhYzkCCGEEEIIkdfl0++zySnSyRFCCCGEECKPk+lq2SNdQiGEEEIIIUS+IiM5QgghhBBC5HEK+SLxbJFOjhC5QM9CN7dTEEIIIYTIt6STI4QQQgghRF4na3KyRca9hBBCCCGEEPmKjOQIIYQQQgiRxylkC+lskacl3lu9evUYOnToR7+vk5MT8+fP/2D15VY7hBBCCCHEhyUjOUIIIUQmCnXrSJEBPdGztiI+9ApXJvrxJPhChrEV1v+KebXK6cpjD/7D+d5eAFh7NqRQ1/aYlC2FrrkZJ5u2J/5SWI62QQiRP8j35GSPdHKEEEKIDBRs7onLuJGEjZvCo3MhOPT+mvKrl/Fvg69Ivv8gXXzIgO/Q0vvfzom6ZmZU3rWRmJ171WXahgbEnT7H3R17cZs56WM0QwiRX8gW0tkiT0t8EGlpaYwaNQoLCwtsbW2ZNGmS+lxcXBx9+/bF2toaU1NTGjRoQHBwsPr8tWvXaNmyJTY2NhgbG1O5cmX279+vUX9MTAwtWrTAwMCAokWLsnbt2nQ5zJ07l7Jly2JkZISDgwODBg0iPj5eIyYwMJB69ephaGiIubk5np6ePHz4MEvtyEpbhBD5h0Pf7txev5nojdtJuHqdsLFTSHv2DPsOrTKMT3n0mKR799WHee3qpD17TsyOfeqYO1v/JnLhTzwM/PcjtUIIIT5P0skRH8SqVaswMjLixIkTzJo1i8mTJ7Nv34v/sbdv356YmBh27drFmTNnqFixIg0bNuTBgxefhMbHx9O0aVMOHDjAuXPnaNKkCS1atCAqKkpdf8+ePbl58yaHDh1i06ZNLFmyhJiYGI0ctLS0WLhwIRcvXmTVqlUcPHiQUaNGqc8HBQXRsGFDSpUqxfHjxzl69CgtWrQgNTU1S+3ISluEEPmDQlcHkzJuPHi1M6JS8SDwBKYVy2WpDvsOrbn7127Snj3LoSyFEJ8ThZYix478SKFSqVS5nYT4tNWrV4/U1FSOHDmiLqtSpQoNGjSgefPmNGvWjJiYGJRKpfq8s7Mzo0aNon///hnWWaZMGQYOHIiXlxdXrlyhRIkSnDx5ksqVX8x3v3z5Mm5ubsybNy/TzQI2bdrEwIEDiY2NBaBLly5ERUVx9OjRbLdjxowZHD169J3akpGDTu5ZjhVCfHx6Ba2pdfIAp9t8zeOz59Xlxcd8h1m1Spxp1fWN15uUK0Pl7es41bJLhmt49AvbU+PoblmTI8QnpkHk+bcH5ZDHc4fmWN2mw+bnWN25RUZyxAfh7q75pt3Ozo6YmBiCg4OJj4/H0tISY2Nj9REREcG1a9eAFyM5I0aMwM3NDTMzM4yNjQkNDVWP5ISGhqKjo4OHh4e6/pIlS2JmZqZxz/3799OwYUMKFSqEiYkJ3bp14/79+yQkJAD/G8l5l3YAWWpLRhITE3n8+LHGkaRKe2MeQohPm33H1sSHXsl0kwIhhMg2La2cO7Lpxx9/xMnJCX19fapWrcrJkyezdN369etRKBS0atUq2/fMLtl4QHwQurq6Gq8VCgVpaWnEx8djZ2dHQEBAumtedlJGjBjBvn37mD17Ns7OzhgYGNCuXTuSkpKyfP/IyEiaN2/ON998w7Rp07CwsODo0aP06dOHpKQkDA0NMTAweOd2AFlqS0b8/Pzw9fXVKOteoCA9zGze3jAhRK5IfviQtJQU9KwsNcr1rC1Juhf7xmu1DAywad6E6/OW5GSKQgiRKzZs2MCwYcNYtmwZVatWZf78+Xh6ehIWFkbBggUzvS4yMpIRI0ZQu3btj5KnjOSIHFWxYkXu3LmDjo4Ozs7OGoeVlRXwYjOAnj170rp1a8qWLYutrS2RkZHqOkqWLElKSgpnzpxRl4WFhREXF6d+febMGdLS0pgzZw7VqlXD1dWV27dva+Ti7u7OgQMHcrQtGfHx8eHRo0caR+cC1u+chxAi56mSU3hyIRTzGlX/V6hQYF6jKo/PvnmzkYLNGqNQ6nFn6985nKUQ4nOiUChy7MiOuXPn0q9fP3r16kWpUqVYtmwZhoaGrFixItNrUlNT6dq1K76+vhQrVux9H0WWSCdH5KhGjRpRvXp1WrVqxd69e4mMjOTYsWOMHTuW06dPA+Di4sKWLVsICgoiODiYLl26qEdPAEqUKEGTJk0YMGAAJ06c4MyZM/Tt21djZMbZ2Znk5GQWLVrE9evXWbNmDcuWLdPIxcfHh1OnTjFo0CDOnz/P5cuXWbp0qXrNzodoS0aUSiWmpqYah55sAylEnnfzl9XYd26LbduvMCxelBLTxqFtaMDtjdsAcJszjWKjvNNdZ9+hDbF7D5IS9yjdOZ0CphiXKoGR84v/yRsWc8K4VAn0rC3TxQohRF6TlJTEmTNnaNSokbpMS0uLRo0acfz48Uyvmzx5MgULFqRPnz4fI80XeX20O4nPkkKhYOfOndSpU4devXrh6upKp06duHHjBjY2L6ZrzZ07F3Nzc2rUqEGLFi3w9PSkYsWKGvX4+/tjb29P3bp1adOmDf3799cYEi1Xrhxz585l5syZlClThrVr1+Ln56dRh6urK3v37iU4OJgqVapQvXp1tm/fjo5O1mZtZqUtQoj8I+bvPVydNodi3w2iys6NGJcqSXCPb0iOfbGbon4hW5QFNUdlDYs5YValIrc3bM2wTqvG9aiycyPlVr6YylZm8Q9U2bmRQl075GxjhBCfvhxck5PR+uHExMR0KcTGxpKamprufY+NjQ137tzJMO2jR4/y66+/snz58hx5LJmR3dWEyAWyu5oQQgjx6cnN3dXifxz19qB3NPueYbr1wxMnTkz3fYG3b9+mUKFCHDt2jOrVq6vLR40axeHDhzlx4oRG/JMnT3B3d2fJkiV8+eWXwIuvBYmLi2Pbtm050paXZOMBIYQQQgghPmM+Pj4MGzZMo+zVr8t4ycrKCm1tbe7evatRfvfuXWxtbdPFX7t2jcjISFq0aKEue7kkQUdHh7CwMIoXL/4hmpCOdHKEEEIIIYTI63JwPa9SqcywU/M6PT09PDw8OHDggHob6LS0NA4cOICXl1e6+JIlSxISEqJRNm7cOJ48ecKCBQtwcHD4IPlnRDo5QgghhBBCiCwZNmwYPXr0oFKlSlSpUoX58+fz9OlTevXqBUD37t0pVKgQfn5+6OvrU6ZMGY3rX37txuvlH5p0coQQQgghhMjrtLK31XNO6dixI/fu3WPChAncuXOH8uXLs3v3bvVmBFFRUWi9wxeMfmiy8YAQuUA2HhBCCCE+Pbm68cAynxyr23ig39uDPjEykiOEEEIIIUQep5Dv2MsWeVpCCCGEEEKIfEVGcoTIBTrG2rmdghBCCCE+JXlkTc6nQjo5QgghhBBC5HGKPLCY/1MiT0sIIYQQQgiRr8hIjhBCCCGEEHmdQqarZYeM5ORx9erVY+jQoR+svkmTJlG+fPkPVt/HlN3cV65cqf7CqTdRKBRs27btnfMSQgghhBB5S77v5AQEBKBQKIiLi8vtVN7Jli1bmDJlSm6n8dFl1PEYMWIEBw4cyHIdHTt25MqVK+rXmXWSoqOj+fLLL981VSFEPmbXqQNV9uyg1pl/Kb9uNSZlSr8xXtvEGOexY6h6aC+1zp6g0t/bMK9d63/nDQ0pNnoEVfbupObp45T7bSXGZUrldDOEEPmBllbOHfmQTFf7f0lJSejp6eXKvZOTk9HV1c0wHwsLi1zJ6U0yyvdjMDY2xtjYOMvxBgYGGBgYvDXO1tb2fdISQuRT1k2+oPio4YRPnsaT8xco1K0LZX5awukWrUh+8DBdvEJHB/fly0h68IDQYSNJvBuDvr09KU+eqGNcJk/AyNmZMJ9xJMbcw6ZFU9yXL+N0y7Ykxdz7mM0TQoh87ZPouqWlpeHn50fRokUxMDCgXLlybNq0CZVKRaNGjfD09ESlUgHw4MEDChcuzIQJE4iMjKR+/foAmJubo1Ao6NmzJ/BiGpiXlxdDhw7FysoKT09PAObOnUvZsmUxMjLCwcGBQYMGER8fr5FPYGAg9erVw9DQEHNzczw9PXn48MX/8JycnJg/f75GfPny5Zk0aZL6tUKhYOnSpXz11VcYGRkxbdo09SjDL7/8QtGiRdHX11fn+ep0tcTEREaPHo2DgwNKpRJnZ2d+/fVXIOPpWdu2bUPxhjmcp06donHjxlhZWVGgQAHq1q3L2bNnNWIyyvdNUlNT6dOnj/rnVaJECRYsWJAubsWKFZQuXRqlUomdnR1eXl7qZwjQunVrFAqF+vWrIzF79+5FX18/3Qjdt99+S4MGDdI9j5UrV+Lr60twcDAKhQKFQsHKlSvV7Xt11OjmzZt06NABMzMzLCwsaNmyJZGRkerzAQEBVKlSBSMjI8zMzKhZsyY3btx44zMRQnx6CnX/muhNW7i77U8Srl8nfPI00p4/x7Z1qwzjbdu0QqeAKZe8h/H4XDCJt6N5dPoMT8NejChrKZVYN2pIxNz5PDpzluc3b3JjyU88i7qJfcf2H7FlQohPkkKRc0c+9El0cvz8/Fi9ejXLli3j4sWLfPfdd3z99df8888/rFq1ilOnTrFw4UIABg4cSKFChZgwYQIODg5s3rwZgLCwMKKjozXebK9atQo9PT0CAwNZtmwZAFpaWixcuJCLFy+yatUqDh48yKhRo9TXBAUF0bBhQ0qVKsXx48c5evQoLVq0IDU1NVttmjRpEq1btyYkJITevXsDcPXqVTZv3syWLVsICgrK8Lru3bvz+++/s3DhQkJDQ/npp5+yNbrxuidPntCjRw+OHj3Kv//+i4uLC02bNuXJK588ZpZvZtLS0ihcuDAbN27k0qVLTJgwge+//54//vhDHbN06VIGDx5M//79CQkJ4c8//8TZ2Rl40fEC8Pf3Jzo6Wv36VQ0bNsTMzEz984UXnasNGzbQtWvXdPEdO3Zk+PDhlC5dmujoaKKjo+nYsWO6uOTkZDw9PTExMeHIkSMEBgZibGxMkyZNSEpKIiUlhVatWlG3bl3Onz/P8ePH6d+//xs7kkKIT49CRweTUm7E/Xvif4UqFXH/nsCknHuG11jWq8vj4PM4jx1DtcP78di6EYd+vdVTQRTa2ih0dEhLTNK4Li0xEdOKFXKsLUII8TnK89PVEhMTmT59Ovv376d69eoAFCtWjKNHj/LTTz+xbt06fvrpJ7p3786dO3fYuXMn586dQ0fnRdNeTvcqWLBgulEOFxcXZs2apVH26qiJk5MTU6dOZeDAgSxZsgSAWbNmUalSJfVrgNKl3zxHOyNdunShV69eGmVJSUmsXr0aa2vrDK+5cuUKf/zxB/v27aNRo0bqZ/E+Xo56vPTzzz9jZmbG4cOHad68+RvzzYyuri6+vr7q10WLFuX48eP88ccfdOjQAYCpU6cyfPhwvv32W3Vc5cqVAdTtNzMzy3Qqmba2Np06dWLdunX06dMHgAMHDhAXF0fbtm3TxRsYGGBsbIyOjs4bp6dt2LCBtLQ0fvnlF3XHxd/fHzMzMwICAqhUqRKPHj2iefPmFC9eHAA3N7csPRchxKdD19wchY4OSfcfaJQn3b9PgaJOGV6jX7gQZoUqE7NjFxe+GYJBEQecx/mg0NEhaunPpCYk8CgomCID+5FwPYKk+/cp2LQJpuXceRZ18yO0SgjxKZPvycmePN/JuXr1KgkJCTRu3FijPCkpiQoVXnzy1b59e7Zu3cqMGTNYunQpLi4uWarbw8MjXdn+/fvx8/Pj8uXLPH78mJSUFJ4/f05CQgKGhoYEBQXRvv37TyuoVKlSujJHR8dMOzjwYhRJW1ubunXrvvf9X7p79y7jxo0jICCAmJgYUlNTSUhIICoq6q35vsmPP/7IihUriIqK4tmzZyQlJamnmsXExHD79m0aNmz4Xrl37dqVatWqcfv2bezt7Vm7di3NmjXL0o5qmQkODubq1auYmJholD9//pxr167xxRdf0LNnTzw9PWncuDGNGjWiQ4cO2NnZZVpnYmIiiYmJGmVJaWnoyT9WQuQvWlokPXjAlUlTIC2N+Euh6BUsSOFe3Yla+jMAYT7jcJ08iWqH9qJKSeFJ6GVidu3GpJR8WCKEeAuFvG/Ijjz/tF6uh9mxYwdBQUHq49KlS2zatAmAhIQEzpw5g7a2NuHh4Vmu28jISON1ZGQkzZs3x93dnc2bN3PmzBl+/PFH4EWnCnjrQnYtLS31+qCXkpOT33rvzMpe9aHu/aoePXoQFBTEggULOHbsGEFBQVhaWqrbm9XcXrV+/XpGjBhBnz592Lt3L0FBQfTq1SvLzzCrKleuTPHixVm/fj3Pnj1j69atGU5Vy474+Hg8PDw0fteCgoK4cuUKXbp0AV6M7Bw/fpwaNWqwYcMGXF1d+ffffzOt08/PjwIFCmgca2PvvleeQoiclfzwIaqUFPQsNTd/0bO0JCn2fobXJN2L5VlkFKSlqcsSrkegtLZG8f+zC57fvMX5Xn05Wrk6Jxp9SVDnbih0dHh267+ca4wQQnyG8nwnp1SpUiiVSqKionB2dtY4HBwcABg+fDhaWlrs2rWLhQsXcvDgQfX1L3dMy8qamTNnzpCWlsacOXOoVq0arq6u3L59WyPG3d39jdsYW1tbEx0drX79+PFjIiIistXmzJQtW5a0tDQOHz6c6b2fPHnC06dP1WWZre15KTAwEG9vb5o2bareBCA2Nva98gwMDKRGjRoMGjSIChUq4OzszLVr19TnTUxMcHJyeuNz1NXVzdLPrGvXrqxdu5a//voLLS0tmjVrlmmsnp7eW+usWLEi4eHhFCxYMN3vW4ECBdRxFSpUwMfHh2PHjlGmTBnWrVuXaZ0+Pj48evRI4+hqZfPWtgkhco8qJYUnl0Ixq1r1f4UKBWZVq/Ak+HyG1zwOCsKgiIPGIl4DpyIkxtxDlZKiEZv27DlJsbHomJpgUaMG9w8G5EQzhBD5iZYi5458KM93ckxMTBgxYgTfffcdq1at4tq1a5w9e5ZFixaxatUqduzYwYoVK1i7di2NGzdm5MiR9OjRQ73bmaOjIwqFgr///pt79+6l2yntVc7OziQnJ7No0SKuX7/OmjVr1BsSvOTj48OpU6cYNGgQ58+f5/LlyyxdulTdMWjQoAFr1qzhyJEjhISE0KNHD7S1tT/Is3BycqJHjx707t2bbdu2ERERQUBAgHpBf9WqVTE0NOT777/n2rVrrFu3Tr2DWGZcXFxYs2YNoaGhnDhxgq5du773SIuLiwunT59mz549XLlyhfHjx6fbPGDSpEnMmTOHhQsXEh4erv6ZvtrWAwcOcOfOHfXPMiNdu3bl7NmzTJs2jXbt2qFUKjONdXJyIiIigqCgIGJjY9NNIXtZn5WVFS1btuTIkSPqZ+zt7c2tW7eIiIjAx8eH48ePc+PGDfbu3Ut4ePgb1+UolUpMTU01DpmqJkTe99/q37Br1xqbr1pgUKwoLuO/R8vAgDvbtgNQYvoUnIYOUcdHb9iITgFTio8ZhYFjESzq1KJIvz7cXr9BHWNeozrmNWugX8ges+pVcV+xnISICO5u+/Ojt08IIfKzT+Kd1pQpUxg/fjx+fn64ubnRpEkTduzYgZOTE3369GHSpElUrFgRAF9fX2xsbBg4cCAAhQoVwtfXlzFjxmBjY6Pepjgj5cqVY+7cucycOZMyZcqwdu1a/Pz8NGJcXV3Zu3cvwcHBVKlSherVq7N9+3b1Rgc+Pj7UrVuX5s2b06xZM1q1aqVeoP4hLF26lHbt2jFo0CBKlixJv3791CM3FhYW/Pbbb+zcuZOyZcvy+++/a2xdnZFff/2Vhw8fUrFiRbp164a3tzcFCxZ8rxwHDBhAmzZt6NixI1WrVuX+/fsMGjRII6ZHjx7Mnz+fJUuWULp0aZo3b64x1XDOnDns27cPBwcH9dqrjDg7O1OlShXOnz//1qlqbdu2pUmTJtSvXx9ra2t+//33dDGGhob8888/FClShDZt2uDm5kafPn14/vw5pqamGBoacvnyZdq2bYurqyv9+/dn8ODBDBgwIJtPSQiR193bvZfrs+fh6PUNHpvWY1SyBBcGDib5/zcjUNrZomdlpY5PvHOXkAGDMSlTCo8tf1DcZxT//baOm7/4q2O0TYxxHjeGSn9tpcT0KTw6e46QAYPTjfQIIcTrFAqtHDvyI4Xq9UUcQogc908Z2S5WCCGE+NTUuXAu1+79/PeZOVa3fufROVZ3bsnzu6sJIYQQQgjx2cuna2dySv4cnxI5auDAgRgbG2d4vJwmKIQQQgghRG6RkRyRbZMnT2bEiBEZnjM1Nf3I2QghhBBCfAby6dqZnCKdHJFtBQsWfO/NCYQQQgghRDYoZLpadkiXUAghhBBCCJGvyEiOEEIIIYQQeZ18x162yNMSQgghhBBC5CsykiNELtDSlnm1QgghhMgG2XggW+RpCSGEEEIIIfIVGckRQgghhBAir5MvA80WGckRQgghhBBC5CsykiOEEEIIIUReJ2tyskU6OUIIIUQm7Dp2oFCP7uhZWfL0yhWuzZhF/IWLmcZrmxjj6OWFVcP66BQoQGJ0NNdnzebh0cAX5w0NKTJ4EJYN6qNrYc7Ty2Fcn/UD8RcvfawmCSHEZ0E6OeK9JCUloaen90HrVKlUpKamoqOTd34982JOQoicZeX5BUVHDOPq1Ok8CQmhUNeulFn6I2datib5wcN08QodHcosW0rygweEjhhFUkwMSjs7Up88Ucc4T5qAoXNxrowdT9K9exRs1pQyPy3lbJt2JMXc+5jNE0J8ahSyJic7ZNwrH6lXrx5eXl54eXlRoEABrKysGD9+PCqVCoDExERGjBhBoUKFMDIyomrVqgQEBGjUcfToUWrXro2BgQEODg54e3vz9OlT9XknJyemTJlC9+7dMTU1pX///m/MKTIyEoVCwfr166lRowb6+vqUKVOGw4cPq2MCAgJQKBTs2rULDw8PlEolR48eJS0tDT8/P4oWLYqBgQHlypVj06ZN6usePnxI165dsba2xsDAABcXF/z9/YEXnS8vLy/s7OzQ19fH0dERPz8/jZyCgoLUdcXFxaFQKNTP411zEkLkH4W6deXOlq3EbP+TZ9cjuDp1GqnPn2PTqmWG8TatW6JTwJTQ74bzJCiYxNvRPD5zlqdXwgHQUiqxatiAyHkLeHz2LM9v3iRq2U88v3kL2/btP2bThBCfIi2tnDvyofzZqs/YqlWr0NHR4eTJkyxYsIC5c+fyyy+/AODl5cXx48dZv34958+fp3379jRp0oTw8Bf/A7527RpNmjShbdu2nD9/ng0bNnD06FG8vLw07jF79mzKlSvHuXPnGD9+fJbyGjlyJMOHD+fcuXNUr16dFi1acP/+fY2YMWPGMGPGDEJDQ3F3d8fPz4/Vq1ezbNkyLl68yHfffcfXX3+t7iCNHz+eS5cusWvXLkJDQ1m6dClWVlYALFy4kD///JM//viDsLAw1q5di5OTU7afZ3ZzEkLkDwodHYzd3Ij798T/ClUq4v49gYm7e4bXWNSty5PzIRT3GUOVg/uosPkPCvfprX4DodDWRqGjQ1piksZ1qYnPKVChfE41RQghPksy9yafcXBwYN68eSgUCkqUKEFISAjz5s3D09MTf39/oqKisLe3B2DEiBHs3r0bf39/pk+fjp+fH127dmXo0KEAuLi4sHDhQurWrcvSpUvR19cHoEGDBgwfPjxbeXl5edG2bVsAli5dyu7du/n1118ZNWqUOmby5Mk0btwYeDHqNH36dPbv30/16tUBKFasGEePHuWnn36ibt26REVFUaFCBSpVqgSg0YmJiorCxcWFWrVqoVAocHR0zP7DfIechBD5g665GQodHZLvP9AoT77/AMOiThleo1+4EPr2lYnZuYuLg70xKOJA8e/HoNDR4eZPP5OakMDjoGCK9O9LWMR1ku4/wPrLJpi6u/Ps5s2P0CohxCdNpqtli3Ry8plq1aqheOWPoHr16syZM4eQkBBSU1NxdXXViE9MTMTS0hKA4OBgzp8/z9q1a9XnVSoVaWlpRERE4ObmBqDuVGTHy04BgI6ODpUqVSI0NFQj5tV6r169SkJCgrqD8VJSUhIVKlQA4JtvvqFt27acPXuWL774glatWlGjRg0AevbsSePGjSlRogRNmjShefPmfPHFF9nOO7s5ZSQxMZHExETNa9LS0Munw8NCfK4UWlokPXjA1clTIS2Np6Gh6BW0pnCP7tz86WcArowdj4vvRKrs34sqJYX4y5e5t3sPxv//76sQQogPQzo5n4n4+Hi0tbU5c+YM2traGueMjY3VMQMGDMDb2zvd9UWKFFH/t5GRUY7k+Gq98fHxAOzYsYNChQppxCmVSgC+/PJLbty4wc6dO9m3bx8NGzZk8ODBzJ49m4oVKxIREcGuXbvYv38/HTp0oFGjRmzatAmt/+9cvFyrBJCcnPxBcsqIn58fvr6+GmW9CtrS29Yu02uEELkr+WEcqpQUdC0tNMp1LS1Iir2f4TVJ92JRpaRAWpq67Nn1CPSsrVHo6KBKSeH5rVuE9OmHloE+2kbGJMfGUmLWDJ7fupWj7RFC5AOyhXS2SCcnnzlx4oTG63///RcXFxcqVKhAamoqMTEx1K5dO8NrK1asyKVLl3B2dv7gef3777/UqVMHgJSUFM6cOZNurc+rSpUqhVKpJCoq6o3TwKytrenRowc9evSgdu3ajBw5ktmzZwNgampKx44d6dixI+3ataNJkyY8ePAAa2trAKKjo9UjMK9uQvC+Ob3Ox8eHYcOGaZSdrlkny9cLIT4+VUoK8aGhmFWtwoNDAS8KFQrMqlYhev2GDK95HBSM9ZdNXkwp+f8PUQwcHUmMufei8/OKtGfPSXv2HG0TE8yrVydi/oKcbI4QQnx2pJOTz0RFRTFs2DAGDBjA2bNnWbRoEXPmzMHV1ZWuXbvSvXt35syZQ4UKFbh37x4HDhzA3d2dZs2aMXr0aKpVq4aXlxd9+/bFyMiIS5cusW/fPhYvXvxeef3444+4uLjg5ubGvHnzePjwIb1798403sTEhBEjRvDdd9+RlpZGrVq1ePToEYGBgZiamtKjRw8mTJiAh4cHpUuXJjExkb///ls9pW7u3LnY2dlRoUIFtLS02LhxI7a2tpiZmaGlpUW1atWYMWMGRYsWJSYmhnHjxr21DVnJKSNKpTLdSI9MVRMi7/tvzVpcp/gSf/ESTy5cxP7rLmgbGHB3258AuE6dTGJMDDcWvvj3MfqPjdh16kCx0SO5/ft6DIoUoXDf3txet15dp1mN6oCCZzciMXBwwOm7oSRERhKz/c/caKIQ4lMi7x2yRTo5+Uz37t159uwZVapUQVtbm2+//Va9zbO/vz9Tp05l+PDh/Pfff1hZWVGtWjWaN28OgLu7O4cPH2bs2LHUrl0blUpF8eLF6dix43vnNWPGDGbMmEFQUBDOzs78+eef6p3QMjNlyhSsra3x8/Pj+vXrmJmZUbFiRb7//nsA9PT08PHxITIyEgMDA2rXrs369S/eTJiYmDBr1izCw8PR1tamcuXK7Ny5Uz1VbcWKFfTp0wcPDw9KlCjBrFmzsrRm5205CSHyj9g9e9E1N6fIoG9efBloWBgXBnmR/ODFZgRKW1tUr0xNS7p7l4vfeFF05HAqbtxAYkwMt9f+zi3/leoYHWNjHL29UNrYkPLoEbEHDnJj0Y/pRnqEEEK8H4Xq1YUJ4pNWr149ypcvz/z583M7FbXIyEiKFi3KuXPnKF++fG6nk2ccLVcxt1MQQgghRDbVCj6ba/d+vufXHKtb37NPjtWdW2QkRwghhBBCiLxONh7IFnla4r1Mnz4dY2PjDI8vv/wyt9MTQgghhBCfIRnJyUcCAgI++j0HDhxIhw4dMjxnYGBAoUKFkBmRQgghhBDvSb4MNFukkyPei4WFBRYWFm8PFEIIIYQQ4iORTo4QQgghhBB5nWwhnS3ytIQQQgghhBD5iozkCCGEEEIIkcepZE1OtkgnR4hcoGuom9spCCGEEELkW9LJEUIIIYQQIq+T78nJFunkCCGEEEIIkddJJydb5GkJIYQQQgghsuzHH3/EyckJfX19qlatysmTJzONXb58ObVr18bc3Bxzc3MaNWr0xvgPRTo5QgghhBBC5HEqhSLHjuzYsGEDw4YNY+LEiZw9e5Zy5crh6elJTExMhvEBAQF07tyZQ4cOcfz4cRwcHPjiiy/477//PsRjyZR0ckSOmjRpEuXLl/8o91IoFGzbtu2j3OslJycn5s+f/1HvKYQQQgiRW+bOnUu/fv3o1asXpUqVYtmyZRgaGrJixYoM49euXcugQYMoX748JUuW5JdffiEtLY0DBw7kaJ6yJkfkqBEjRjBkyJDcTkMIId6JTdt22HXtiq6FJQlXw4mcO4enly5lGq9tbIzDwG8wr1sPHVNTEu/c4cb8eTw6fkwdo2ttTZFBgylQvQba+kqe37rF9alTeHr58sdokhDiU5WDa3ISExNJTEzUKFMqlSiVSo2ypKQkzpw5g4+Pj7pMS0uLRo0acfz48SzdKyEhgeTkZCwsLN4/8TeQkRyRo4yNjbG0tMztNIQQItssGjaiiPe33Pr1Vy707EFC+FVKzluAjrl5hvEKHR1KLliEnq0d4WN9CO7YgYgZ00m+d08do21iQumffkaVkkrYsKGc79yJqIULSXny5GM1Swgh0vHz86NAgQIah5+fX7q42NhYUlNTsbGx0Si3sbHhzp07WbrX6NGjsbe3p1GjRh8k98xIJ0e8Ub169fD29mbUqFFYWFhga2vLpEmT1OejoqJo2bIlxsbGmJqa0qFDB+7evas+//p0tYCAAKpUqYKRkRFmZmbUrFmTGzduqM9v376dihUroq+vT7FixfD19SUlJeWdcr958yYdOnTAzMwMCwsLWrZsSWRkJAB79+5FX1+fuLg4jWu+/fZbGjRooH599OhRateujYGBAQ4ODnh7e/P06dN3ykcI8Wmx69yZmD+3E7vjb55FRhAxawZpic+xbt4iw3jrFi3QMTUlfPRI4s+fJ+lONE/OnSPharg6xv7rbiTejeH6tCk8vXSJxOhoHp08QWIOz00XQuQDCkWOHT4+Pjx69EjjeHW05kOZMWMG69evZ+vWrejr63/w+l8lnRzxVqtWrcLIyIgTJ04wa9YsJk+ezL59+0hLS6Nly5Y8ePCAw4cPs2/fPq5fv07Hjh0zrCclJYVWrVpRt25dzp8/z/Hjx+nfvz+K/1/wduTIEbp37863337LpUuX+Omnn1i5ciXTpk3Lds7Jycl4enpiYmLCkSNHCAwMxNjYmCZNmpCUlETDhg0xMzNj8+bN6mtSU1PZsGEDXbt2BeDatWs0adKEtm3bcv78eTZs2MDRo0fx8vJ6h6cohPiUKHR0MCpRksenXtkBSKXi0alTmJQpm+E15rXqEH8hBKcRo6i4Yxdlf1uHfY8eoPW//9Wa167D08uhOE+bTsUduyizajXWX7XM6eYIIcQbKZVKTE1NNY7Xp6oBWFlZoa2trfGBNsDdu3extbV94z1mz57NjBkz2Lt3L+7u7h80/4zImhzxVu7u7kycOBEAFxcXFi9erF4sFhISQkREBA4ODgCsXr2a0qVLc+rUKSpXrqxRz+PHj3n06BHNmzenePHiALi5uanP+/r6MmbMGHr06AFAsWLFmDJlCqNGjVLfP6s2bNhAWloav/zyi7oT5e/vj5mZGQEBAXzxxRd06tSJdevW0adPHwAOHDhAXFwcbdu2BV4M3Xbt2pWhQ4eq275w4ULq1q3L0qVLs/wJREbzXJPS0tDTks8YhMirdMzMUOjokPzggUZ58oMHGDg6ZniNspA9ph4exO7dw+Vh36FfuDBOI0eh0NbhvxW/voixt8emdRui1//O7VUrMXIrhdOwYahSkonduTPH2yWE+ITlgfcNenp6eHh4cODAAVq1agWg3kTgTR8Cz5o1i2nTprFnzx4qVar0UXKVTo54q9d723Z2dsTExBAaGoqDg4O6gwNQqlQpzMzMCA0NTdfJsbCwoGfPnnh6etK4cWMaNWpEhw4dsLOzAyA4OJjAwECNkZvU1FSeP39OQkIChoaGWc45ODiYq1evYmJiolH+/Plzrl27BkDXrl2pVq0at2/fxt7enrVr19KsWTPMzMzUdZw/f561a9eqr1epVKSlpREREaHRQXsTPz8/fH19Ncr6FLKnn0PhLLdHCPEJUGiR/PAhETP8IC2NhLDL6FlbY9f1a3UnBy0tnl4O5daypQAkXLmCYbFiFGzVRjo5Qog3yu5Wzzll2LBh9OjRg0qVKlGlShXmz5/P06dP6dWrFwDdu3enUKFC6jU9M2fOZMKECaxbtw4nJyf12h1jY2OMjY1zLE/p5Ii30tXV1XitUChIS0t7p7r8/f3x9vZm9+7dbNiwgXHjxrFv3z6qVatGfHw8vr6+tGnTJt112Z23GR8fj4eHh0YH5SVra2sAKleuTPHixVm/fj3ffPMNW7duZeXKlRp1DBgwAG9v73R1FClSJMu5+Pj4MGzYMI2y4MYNs3y9EOLjS4mLQ5WSgu5ru//oWliQfP9Bhtck349FlZICr/z7+CwyEj0rKxQ6OqhSUkiOjeVZRITGdc8iI7GoX//DN0IIIXJAx44duXfvHhMmTODOnTuUL1+e3bt3qzcjiIqKQuuVUaelS5eSlJREu3btNOqZOHGixjrvD006OeKdubm5cfPmTW7evKkezbl06RJxcXGUKlUq0+sqVKhAhQoV8PHxoXr16qxbt45q1apRsWJFwsLCcHZ2fu/cKlasyIYNGyhYsCCmpqaZxnXt2pW1a9dSuHBhtLS0aNasmUYdly5deu98MtqCUaaqCZG3qVJSeBp2GdNKlXn4zz8vChUKClSqzJ1NGzO85sn581h98cWLhbwqFQD6RYqQdO/ei84P8CTkPPpFNKe76RcpQmIWdyUSQnzGcnAL6ezy8vLKdHpaQECAxuuXmz59bHnnaYlPTqNGjShbtixdu3bl7NmznDx5ku7du1O3bt0M51tGRETg4+PD8ePHuXHjBnv37iU8PFw97WvChAmsXr0aX19fLl68SGhoKOvXr2fcuHHZzq1r165YWVnRsmVLjhw5QkREBAEBAXh7e3Pr1i2NuLNnzzJt2jTatWun0RkZPXo0x44dw8vLi6CgIMLDw9m+fbtsPCDEZyL6998p+FVLrJo2Rd/RCadRo9HS1+fe338DUGzCRBy+GaSOj9myGR3TAjh+Nwx9BwfMatSkUI+e3N28SR1zZ/3vGJcpg32PHigLF8byiy8o2LIVdzdtSnd/IYQQ705GcsQ7UygUbN++nSFDhlCnTh20tLRo0qQJixYtyjDe0NCQy5cvs2rVKu7fv4+dnR2DBw9mwIABAHh6evL3338zefJkZs6cia6uLiVLlqRv377Zzs3Q0JB//vmH0aNH06ZNG548eUKhQoVo2LChxsiOs7MzVapU4eTJk8yfP1+jDnd3dw4fPszYsWOpXbs2KpWK4sWLZ7p7nBAif3lwYD+65mYU7tsfXUtLEsKvcPm7oaQ8fDFdTWljozE1LSkmhstDvXH89jsKrllL0r173PljPbfXrFHHPA0NJXzMKBy+GUShXn1IjL7NjfnzuL93z0dvnxDi06LKQyM5nwKFSvX/Y+pCiI/mRPWquZ2CEEIIIbKp6vETuXbv+H//zLG6jat9lWN15xYZyRFCCCGEECKvyyO7q30qZNxLfBLWrl2r3mrw9aN06dK5nZ4QQgghhMhDZCRHfBK++uorqlbNeIrX61tcCyGEEELkN7ImJ3ukkyM+CSYmJum+2FMIIYQQ4rMh09WyRbqEQgghhBBCiHxFRnKEEEIIIYTI62S6WrZIJ0eIXKCjL396QgghhBA5Rd5pCSGEEEIIkcepZE1Otsi4lxBCCCGEECJfkZEcIYQQQggh8jpZk5Mt8rSEEEIIIYQQ+Yp0cvIwJycn5s+fn6VYhULBtm3bcjSfyMhIFAoFQUFBb4yrV68eQ4cOfef7vK0tAQEBKBQK4uLi3vkeQgghhBCfEhWKHDvyI5mu9hEoFAq2bt1Kq1atcjuVDyogIID69evz8OFDzMzMPtp9a9SoQXR0NAUKFHhrbG7lKITIH6xbtcGmYxd0LSx4du0qUQvnkXA5NNN4bSNj7Pv2x7x2XbRNTEm6e4ebPy7k8YnjANj16I19zz4a1zyPusHFHl1ytB1CiE+fSqarZYt0csQnR09PD1tb2496T5VKRWpqKjo68icjxOfCvH5DCn8zhKh5P/A09BIF23XAZdZcLnbvTEoGI8kKHR1cZs8nJe4h1yaNI/nePfRsbUmNj9eIexZxnSvDv1W/VqWm5nRThBDis/NZdQnr1avHkCFDGDp0KObm5tjY2LB8+XKePn1Kr169MDExwdnZmV27dqmvuXDhAl9++SXGxsbY2NjQrVs3YmNjNer09vZm1KhRWFhYYGtry6RJk9TnnZycAGjdujUKhUL9+tq1a7Rs2RIbGxuMjY2pXLky+/fvf6/2xcbG0rp1awwNDXFxceHPP//UOP+2tuzevZtatWphZmaGpaUlzZs359q1axneKzIykvr16wNgbm6OQqGgZ8+e6vNpaWmZPpP3bcvr09Vu3LhBixYtMDc3x8jIiNKlS7Nz58435piYmIi3tzcFCxZEX1+fWrVqcerUqXT32LVrFx4eHiiVSn777Te0tLQ4ffq0Rq7z58/H0dGRtLS0bLVRCJG32bTvSOyOv7i/eyfPb0QSNfcH0p4nYvll8wzjLb9sjo6JKVfHjeHphRCS7t4hPjiIZ9euasSpUlNJefhAfaQ+fvQxmiOE+NQptHLuyIfyZ6veYNWqVVhZWXHy5EmGDBnCN998Q/v27alRowZnz57liy++oFu3biQkJBAXF0eDBg2oUKECp0+fZvfu3dy9e5cOHTqkq9PIyIgTJ04wa9YsJk+ezL59+wDUb5z9/f2Jjo5Wv46Pj6dp06YcOHCAc+fO0aRJE1q0aEFUVNQ7t83X15cOHTpw/vx5mjZtSteuXXnw4AFAltry9OlThg0bxunTpzlw4ABaWlq0bt06wzfvDg4ObN68GYCwsDCio6NZsGBBlp7J+7bldYMHDyYxMZF//vmHkJAQZs6cibGx8RtzHDVqFJs3b2bVqlWcPXsWZ2dnPD09091jzJgxzJgxg9DQUL766isaNWqEv7+/Roy/vz89e/ZES+uz+3MSIt9S6Ohg6FqCx2f+9+EHKhVPzp7GuHSZDK8xq1GL+EsXKDJ0OO6b/6LUijXYdu0Or/3boCxUmLIbt1Nm7R84jZ2IbkGbnGyKEEJ8lhQqlUqV20l8LPXq1SM1NZUjR44AkJqaSoECBWjTpg2rV68G4M6dO9jZ2XH8+HH279/PkSNH2LNnj7qOW7du4eDgQFhYGK6urunqBKhSpQoNGjRgxowZQNbX5JQpU4aBAwfi5eUFvBgFGjp0aJYW8SsUCsaNG8eUKVOAFx0WY2Njdu3aRZMmTZg6depb2/K62NhYrK2tCQkJoUyZMkRGRlK0aFHOnTtH+fLlM13vkpVn8j5tef2+7u7utG3blokTJ6arK6Mcnz59irm5OStXrqRLlxfz4JOTk9XPe+TIkerrtm3bRsuWLdX1/fHHHwwcOJDo6GiUSiVnz56lUqVKXL9+XT1K97rExEQSExM1yi628ERPOkVC5Fm6lla4b9rO5cH9eXrporq80IBBmJQrz+VB/dNdU3rVOvRsbXmwfy/3tm9FWagwRb4dTsyWjUSvfvHhiGmVamgZGJB4MwpdS0vsuvdG18qaS727kfYs4aO1TwjxbjwOBebavR8GH86xus3L1c2xunPLZ/cuy93dXf3f2traWFpaUrZsWXWZjc2LT9RiYmIIDg7m0KFDGBsbq4+SJUsCaEzjerVOADs7O2JiYt6YR3x8PCNGjMDNzQ0zMzOMjY0JDQ19r5GcV/MwMjLC1NRUnUdW2hIeHk7nzp0pVqwYpqam6jft75LTuzyTrLbldd7e3kydOpWaNWsyceJEzp8//8a6r127RnJyMjVr1lSX6erqUqVKFUJDNRcUV6pUSeN1q1at0NbWZuvWrQCsXLmS+vXrZ9rBAfDz86NAgQIah/+NW2/MUQjxCVIoSHn4kBtzZpFwJYyHhw4QvXYV1l+1Uoc8PvkvcYcP8ez6NR6fOsnVMSPQMTbGvH6D3MtbCCHyoc9uFbWurq7Ga4VCoVGmULzYRi8tLY34+HhatGjBzJkz09VjZ2f3xjrftj5jxIgR7Nu3j9mzZ+Ps7IyBgQHt2rUjKSkp223KSh5ZaUuLFi1wdHRk+fLl2Nvbk5aWRpkyZd4pp3d5Ju96fd++ffH09GTHjh3s3bsXPz8/5syZw5AhQ7Kd9+uMjIw0Xuvp6dG9e3f8/f1p06YN69at05imlxEfHx+GDRumUXaxhed75yaEyDkpj+JQpaagY26hUa5rbkFyJlNnkx/cR5WSAq/8W/X8xg10La1Q6Oi8OPea1KfxPL91E6V94Q/bACFEviO7q2XPZ9fJyY6KFSuyefNmnJyc3mtXLV1dXVJf2z0nMDCQnj170rp1a+BFJyQyMvJ90n2jt7Xl/v37hIWFsXz5cmrXrg3A0aNH31innp4eQLq25QYHBwcGDhzIwIED8fHxYfny5QwZMiTDHIsXL46enh6BgYE4OjoCL6arnTp1KktTA/v27UuZMmVYsmQJKSkptGnT5o3xSqUSpVKpUSZT1YTI21QpKSRcCcO0YiUeBf7/1FuFApOKHsRs3ZzhNfEXQrBo2BgUCvj/meD6Dg4kxcZm2MEB0NI3QGlfiOR9u3OkHUII8bmSd1pvMHjwYB48eEDnzp05deoU165dY8+ePfTq1Stbb+ydnJw4cOAAd+7c4eHDhwC4uLiwZcsWgoKCCA4OpkuXLjm6O9fb2mJubo6lpSU///wzV69e5eDBg+lGH17n6OiIQqHg77//5t69e8S/tk3qxzJ06FD27NlDREQEZ8+e5dChQ7i5uWWao5GREd988w0jR45k9+7dXLp0iX79+pGQkECfPn3ecjdwc3OjWrVqjB49ms6dO2NgYJDTTRRC5IK7Gzdg1bwFFp5fol/EkSLfjUBLX5/7u3cA4OQzDvu+A9Xx97ZvRcfEFAevoSgLO2BarTq2Xbpzb9v/OkWFBg7GuFx59GxsMSpdhuJT/FClpfLwwPvtrimE+AwoFDl35EPSyXkDe3t7AgMDSU1N5YsvvqBs2bIMHToUMzOzbO2kNWfOHPbt24eDgwMVKlQAYO7cuZibm1OjRg1atGiBp6cnFStWzKmmvLUtWlparF+/njNnzlCmTBm+++47fvjhhzfWWahQIXx9fRkzZgw2NjbqDRM+ttTUVAYPHoybmxtNmjTB1dWVJUuWvDHHGTNm0LZtW7p160bFihW5evUqe/bswdzcPEv37NOnD0lJSfTu3TvH2iWEyF0PDx3g1tIfse/ZF7flKzF0diF89HBS/v/DKr2CNuhaWqrjk+/FED7qOwxLulHq11UU8fqOmC0bufP7b+oYPeuCFB3nS+nVv1Ns4hRSHj/i8uABpDyK+9jNE0J8YlQKrRw78qPPanc1IT6UKVOmsHHjxrducpCZM/Vrvj1ICCGEEHlKbu6udv/CsRyr27JMjRyrO7fImhwhsuHl2qnFixczderU3E5HCCGEEJ8JFflzWllOyZ/jU/nM2rVrNbZ+fvUoXbp0bqeXLZ96W7y8vPDw8KBevXoyVU0IIYQQIo+S6WqfgCdPnnD37t0Mz+nq6qp3CPsU5Ke2vA+ZriaEEEJ8enJzutq9iydyrG7r0lVzrO7cItPVPgEmJiaYmJjkdhofRH5qixBCCCGEyJukkyOEEEIIIURel0+3es4psiZHCCGEEEIIka/ISI4QuUBbVz5fEEIIIUTWqWRsIlukkyOEEEIIIUQep5LpatkiXUIhhBBCCCFEviIjOUIIIYQQQuRxKoWMTWSHPC0hhBBCCCFEviKdHJFnrFy5EjMzsw9SV0BAAAqFgri4uA9SX27fRwghhBCfNxWKHDvyI5muJt5Lz549iYuLY9u2bbmdihBCfHBWLVpTsH1ndCwseHb9Gv/9OJ+EsNBM47WNjLHt1Q+zmnXRNjEhKeYu/y1dyJNT/wJg2bwVVs1boWdjC8DzGxHcWbuSJ6dy7pvMhRDicySdHCGEECIDZnUbYD/Ai1sL5/D08iWs27Sn2PQ5XO7ThZQMRm8VOjoUnzGX5Lg4IqeMJ/n+PXQL2pL69Ik6Jjk2htu/LiPxv1soFArMGzeh6CQ/rgzqzfMbkR+vcUKIT46syckeeVoiSzZt2kTZsmUxMDDA0tKSRo0aMXLkSFatWsX27dtRKBQoFAoCAgIynMIVFBSEQqEgMjJSXbZy5UqKFCmCoaEhrVu35v79++pzkZGRaGlpcfr0aY085s+fj6OjI2lpadluw+bNmyldujRKpRInJyfmzJmjcX7NmjVUqlQJExMTbG1t6dKlCzExMRoxO3fuxNXVFQMDA+rXr6/RHiFE/mLdtiP3d/3Fg707SYyK5NaC2aQlPsfCs1mG8RaezdA2MSVikg9PL4WQdPcOT0OCeH79mjrm8b/HeHLqX5Ju3yLxv5vcWbmctGfPMHQr/bGaJYQQnwXp5Ii3io6OpnPnzvTu3ZvQ0FACAgJo06YNEydOpEOHDjRp0oTo6Giio6OpUaNGluo8ceIEffr0wcvLi6CgIOrXr8/UqVPV552cnGjUqBH+/v4a1/n7+9OzZ0+0tLL3q3vmzBk6dOhAp06dCAkJYdKkSYwfP56VK1eqY5KTk5kyZQrBwcFs27aNyMhIevbsqT5/8+ZN2rRpQ4sWLQgKCqJv376MGTMmW3kIIT4NCh0dDF1ciT935n+FKhXx505jlEmHpED1mjwNvUjhIcMovWE7JX5eRcFO3SCzf6+0tDCr1xAtfX2eXrqYA60QQuQnKoUix478SKaribeKjo4mJSWFNm3a4OjoCEDZsmUBMDAwIDExEVtb22zVuWDBApo0acKoUaMAcHV15dixY+zevVsd07dvXwYOHMjcuXNRKpWcPXuWkJAQtm/fnu02zJ07l4YNGzJ+/Hj1/S5dusQPP/yg7sj07t1bHV+sWDEWLlxI5cqViY+Px9jYmKVLl1K8eHH1CFCJEiUICQlh5syZ2c5HCJG3aZsWQKGtQ/LDBxrlyQ8fonRwzPAaPTt7jMvb8vDgPq6PG4nSvjCFhwxDoaPN3d9WquP0nYrhsmApWnp6pD17RoTvWBKjInOwNUII8fmRkRzxVuXKlaNhw4aULVuW9u3bs3z5ch4+fPhedYaGhlK1alWNsurVq2u8btWqFdra2mzduhV4Mb2tfv36ODk5vdP9atasqVFWs2ZNwsPDSU1NBV6M9rRo0YIiRYpgYmJC3bp1AYiKispyzhlJTEzk8ePHGkfSO0y3E0LkcQotUuLiuDn/B56FXyHu8EHu/r4Gq2atNMISb0UR9k1vrngPIPbv7TiOHIuyiFOupCyE+HTI7mrZI50c8Vba2trs27ePXbt2UapUKRYtWkSJEiWIiIjIMP7lVDKVSqUuS05OzvZ99fT06N69O/7+/iQlJbFu3TqN0ZYP6enTp3h6emJqasratWs5deqUunOVlJT0XnX7+flRoEABjWNFxM0PkbYQIoekPn6EKjUFXXMLjXJdc3NSHtzP8JqUB/dJvHUTXvkQ43lUJLqWlih0/jdxQpWSQtLt/3gWfoXoFT/x7PpVrFu3y5mGCCHyDZVCK8eO/Ch/tkp8cAqFgpo1a+Lr68u5c+fQ09Nj69at6OnpqUdCXrK2tgZeTHN7KSgoSCPGzc2NEyc0t0z9999/0923b9++7N+/nyVLlqinzL0LNzc3AgMDNcoCAwNxdXVFW1uby5cvc//+fWbMmEHt2rUpWbJkuk0H3NzcOHny5Ftzfp2Pjw+PHj3SOHoXdXindgghPg5VSgoJ4VcwLu/xv0KFAuPyHjwNzXj9zNOLISjtC8Er89uVhRxIvh+LKiUl85tpKdDS1ftQqQshhEA6OSILTpw4wfTp0zl9+jRRUVFs2bKFe/fu4ebmhpOTE+fPnycsLIzY2FiSk5NxdnbGwcGBSZMmER4ezo4dO9LtZObt7c3u3buZPXs24eHhLF68WGM9zktubm5Uq1aN0aNH07lzZwwMDN6pDcOHD+fAgQNMmTKFK1eusGrVKhYvXsyIESMAKFKkCHp6eixatIjr16/z559/MmXKFI06Bg4cSHh4OCNHjiQsLIx169ZpbFyQGaVSiampqcahl82NE4QQH9+9zRuwbNoc88ZNUDo4Uth7OFr6BjzYsxOAIiPHYtd7gDo+9u9taJuYUuibb1EWcsC0SnVsOncj9s8t6hi73gMwKlsOPRtb9J2KYdd7AMbuFXhwcO9Hb58Q4tMi09WyR95pibcyNTXln3/+oWnTpri6ujJu3DjmzJnDl19+Sb9+/ShRogSVKlXC2tqawMBAdHV1+f3337l8+TLu7u7MnDlTY+c0gGrVqrF8+XIWLFhAuXLl2Lt3L+PGjcvw/n369CEpKem9pqpVrFiRP/74g/Xr11OmTBkmTJjA5MmT1ZsOWFtbs3LlSjZu3EipUqWYMWMGs2fP1qijSJEibN68mW3btlGuXDmWLVvG9OnT3zknIUTeFnf4ILd/XoJd9z6UWLoCg+IuXB87gpS4F2sS9QraoGthqY5PvhfDte+HY1iiJCV+8qfQoG+5t3UTdzesVcfomJnhOHIsJX9dS/FZ8zF0Lcm174cTf/Z0uvsLIYR4dwrVqwsnhMiDpkyZwsaNGzl//nxup/LBBH1RO7dTEEIIIUQ2ld97JNfufeNqWI7V7ehcIsfqzi0ykiPyrPj4eC5cuMDixYsZMmRIbqcjhBBCCCE+EdLJEXmWl5cXHh4e1KtXL91UtYEDB2JsbJzhMXDgwFzKWAghhBAiZ+SlNTk//vgjTk5O6OvrU7Vq1XQbM71u48aNlCxZEn19fcqWLcvOnTvf9TFkmUxXE5+kmJgYHj9+nOE5U1NTChYs+JEzyh6ZriaEEEJ8enJzulrk1Ss5VreTs2uWYzds2ED37t1ZtmwZVatWZf78+WzcuJGwsLAM338dO3aMOnXq4OfnR/PmzVm3bh0zZ87k7NmzlClT5kM2Q4N0coTIBdLJEUIIIT49udnJibh2NcfqLlrcOcuxVatWpXLlyixevBiAtLQ0HBwcGDJkCGPGjEkX37FjR54+fcrff/+tLqtWrRr/1959x9d0/w8cf93smz1kkmEkEQQhdjW2oFqjZlqbKqkZylftVtDE7lAjVLXaWjVTo2IEMSpKRUSIKIkYiQiR/fsjP1dvk0iCSMT7+Xicx8M55/P5nPc5SO77fsapW7cu33777YsHXwAZriaEEEIIIUQZV5LD1dLS0khOTlbb0tLS8sSQnp7O6dOnadOmjeqYhoYGbdq04dixY/nGfezYMbXyAO3bty+w/MsiSY4QQgghhBBvMH9/f0xMTNQ2f3//POXu3LlDVlYW1tbWasetra2Jj4/Pt+34+PhilX9ZtEq0dSFEvoxsjEs7BCGEEEK8RnIUJffSzsmTJzNu3Di1Y7q6uiV2vVdBkhwhhBBCCCHeYLq6ukVKaipUqICmpia3bt1SO37r1i1sbGzyrWNjY1Os8i+LDFcTQgghhBCijMvJUZTYVlQ6OjrUr1+f/fv3q45lZ2ezf/9+mjRpkm+dJk2aqJUH2Lt3b4HlXxbpyRFCCCGEEEIUybhx4+jfvz+enp40bNiQRYsW8fDhQwYOHAhAv379qFixompOz+jRo/Hy8iIwMJBOnTqxYcMGTp06xXfffVeicUqSI4QQQgghRBmXU0YGYPXq1Yvbt28zbdo04uPjqVu3LsHBwarFBWJjY9HQeBpr06ZN+fHHH/nss8/43//+h7OzM1u3bi3Rd+SAvCfnpWjRogV169Zl0aJFpR0KULR4FAoFW7ZsoUuXLq8srpIyYMAAkpKS2Lp1a4FlytrfUXS/TqUdghBCCCGKqer3O0vt2lHR10qsbeeqjiXWdmmRnpzXWEhICC1btiQxMRFTU9Ni1Y2Li8PMzKxkAhNCiHLCuHUnTDt2R9PEjPTrV7mz7lvSruT/1nGjt9pgNWys2rHs9HSuDumq2i/oA9LdDatI2rX55QUuhCh3cii51dXKI0lySkF6ejo6OjqlGkNJr2jxMpSF5ySEeHMZNGpOhb5Dub1mGY+jIzFt3wXbCbO5PnEYWQ/u51sn69FDrn/60dMD/xksEfPJB2r7+rXrYzl4NCknj770+IUQ5YskOcVTNgb3lQOZmZn4+vpiYmJChQoVmDp1Kk9GAjo5OTF79mz69euHsbExw4YNA+DIkSM0b94cpVKJvb09o0aN4uHDh6o2161bh6enJ0ZGRtjY2NC3b18SEhIAiImJoWXLlgCYmZmhUCgYMGCAqm52djYTJ07E3NwcGxsbZsyYoRavQqFQDe+KiYlBoVCwefNmWrZsib6+PnXq1MnzJtoVK1Zgb2+Pvr4+Xbt2ZcGCBUXuQZoxYwZ169Zl+fLlqjZ69uzJ/ftPPygMGDCALl268MUXX2BnZ4erqysA586do1WrViiVSiwsLBg2bBgpKSl5rjFz5kwsLS0xNjZm+PDhpKenFxhPWloafn5+VKxYEQMDAxo1akRISIjq/Jo1azA1NWXHjh24urqir6/P+++/z6NHj1i7di1OTk6YmZkxatQosrKyivQMhBCvF1PvriSHBPPg8D4ybl7n9ppl5KQ9xsirXcGVcnLIup/4dEtOUjutdu5+Igb1GpMa8ReZt0v2pXhCCPGmkSTnJVm7di1aWlqcOHGCxYsXs2DBAlauXKk6HxAQQJ06dThz5gxTp04lOjoab29vunfvzl9//cXPP//MkSNH8PX1VdXJyMhg9uzZnD17lq1btxITE6NKZOzt7dm0aRMAkZGRxMXFsXjxYrV4DAwMCAsLY/78+cyaNYu9e/c+8x6mTJmCn58f4eHhuLi40KdPHzIzMwEIDQ1l+PDhjB49mvDwcNq2bcsXX3xRrGd0+fJlfvnlF7Zv305wcDBnzpxhxIgRamX2799PZGQke/fuZceOHTx8+JD27dtjZmbGyZMn+fXXX9m3b5/ac3pSLyIigpCQEH766Sc2b97MzJkzC4zF19eXY8eOsWHDBv766y969OiBt7c3UVFRqjKPHj1iyZIlbNiwgeDgYEJCQujatSu7du1i165drFu3juXLl7Nx48ZiPQchxGtAUwtdp2o8+jv86bGcHFIvhKNXrXqB1TT0lDgsCMJx4RpsxkxFu6JDwZcwNkW/TgMeHNrzEgMXQpRXOShKbCuPZLjaS2Jvb8/ChQtRKBS4urpy7tw5Fi5cyNChQwFo1aoV48ePV5UfMmQIPj4+jBkzBgBnZ2eWLFmCl5cX33zzDXp6egwaNEhVvkqVKixZsoQGDRqQkpKCoaEh5ubmAFhZWeXpUalduzbTp09Xtb1s2TL2799P27ZtC7wHPz8/OnXKnRA/c+ZMatasyeXLl6levTpLly6lQ4cO+Pn5AeDi4sLRo0fZsWNHkZ/R48eP+f7776lYsSIAS5cupVOnTgQGBqqGzxkYGLBy5UrVMLUVK1ao6hkYGACwbNkyOnfuzLx581Qreejo6LB69Wr09fWpWbMms2bNYsKECcyePVtthQ/IXfUjKCiI2NhY7OzsVPceHBxMUFAQc+bMAXKTzG+++YaqVasC8P7777Nu3Tpu3bqFoaEhNWrUoGXLlhw4cIBevXoV+TkIIco+TSNjFJqaeXpiMu8nobS1z7dOevw/JKxcRPr1GDSU+ph27EbFqQFcn/wxWYl385Q3eqs12Y9TeXhKhqoJIcTLJj05L0njxo1RKJ5mwk2aNCEqKko1lMnT01Ot/NmzZ1mzZg2GhoaqrX379mRnZ3P16lUATp8+TefOnXFwcMDIyAgvLy8g90N6YWrXrq22b2trqxrqVpQ6tra2AKo6kZGRNGzYUK38f/cL4+DgoEpwIPcZZWdnExkZqTrm7u6uNg8nIiKCOnXqqBIcgGbNmuWpV6dOHfT19dXaTklJ4fr163niOHfuHFlZWbi4uKg9/4MHDxIdHa0qp6+vr0pwAKytrXFycsLQ0FDtWGHPNS0tjeTkZLUtTYa4CVHupF2+SEroH6THXuFx5Hnil3xB9oP7GLfqkG95o7fbknIshJyMjFccqRDidSQ9OcUjPTmvyL8/pAOkpKTw0UcfMWrUqDxlHRwcVMO02rdvz/r167G0tCQ2Npb27ds/c67JE9ra2mr7CoWC7OzsItd5krAVVudl++9zKgkpKSloampy+vRpNDU11c79O4HJ7xk+z3P19/fPM3Tuk9rVGF3H5XnCF0K8AlkPksnJykLT2FTtuJaJKVn3E4vYSBZp166gbWWX55SeS0107Oy59dW8lxCtEEKI/5Ik5yUJCwtT2z9+/DjOzs55PkQ/Ua9ePS5cuEC1atXyPX/u3Dnu3r3L3LlzsbfPHRpx6tQptTJPejxexcR3V1dXTp48qXbsv/uFiY2N5ebNm6ohYsePH0dDQ0O1wEB+3NzcWLNmDQ8fPlQlQKGhoXnqnT17ltTUVJRKpaptQ0ND1bP7Nw8PD7KyskhISKB58+bFuofnMXnyZMaNG6d27J+Pe5b4dYUQLyArk7SYy+jXrMujP4/nHlMoUNaoy/19RRymq9BAp5Ijj/46leeUkVc7Hl+NIv361ZcYtBCiPMvJKZ89LiVFhqu9JLGxsYwbN47IyEh++uknli5dyujRowss/+mnn3L06FF8fX0JDw8nKiqK3377TTWh3sHBAR0dHZYuXcqVK1fYtm0bs2fPVmvD0dERhULBjh07uH37dr4rjr0sn3zyCbt27WLBggVERUWxfPlydu/erTZErzB6enr079+fs2fPcvjwYUaNGkXPnj2fuZy1j4+Pqt758+c5cOAAn3zyCR9++KFqPg7kLjc9ePBgLly4wK5du5g+fTq+vr555uNA7nwiHx8f+vXrx+bNm7l69SonTpzA39+fnTtf/ku+dHV1MTY2Vtt0C0h+hRBlR1LwFoy82mP0Vmu07eyp0H8kCl09HhzKXcTFatg4zHv0V5U3e68PyloeaFnaoONYFavhfmhVsCI55He1dhV6SgwbvsWD/xwXQgjx8kiS85L069eP1NRUGjZsyMiRIxk9erRqqej81K5dm4MHD3Lp0iWaN2+Oh4cH06ZNU/VyWFpasmbNGn799Vdq1KjB3LlzCQgIUGujYsWKzJw5k0mTJmFtbZ1nxbGXqVmzZnz77bcsWLCAOnXqEBwczNixY9HT0ytyG9WqVaNbt2507NiRdu3aUbt2bb7++utn1tHX1+f333/n3r17NGjQgPfff5/WrVuzbNkytXKtW7fG2dmZt99+m169evHuu+/mWTb734KCgujXrx/jx4/H1dWVLl26cPLkSRwcCl4JSQjxZnkYdpi7G1Zh1u0D7GcvRdexCnFfTlMtRqBlYYmmqbmqvIaBIZaDRuEw91tsx89EQ6nPjdl+ZNxUnxto2Dh3fmXK8YOv7F6EEK8/mZNTPIqcnP+8qUyIIho6dCgXL17k8OHDhZadMWMGW7duJTw8vOQDew1E9+tU2iEIIYQQopiqfv/yR3wU1fnLJfc+rVrVyv5L4otL5uSIIgsICKBt27YYGBiwe/du1q5dW2hPjBBCCCGEEK+aJDmiyE6cOMH8+fN58OCB6r09Q4YMAaBmzZpcu3Yt33rLly9/lWEKIYQQQpQ75XVYWUmR4Wripbh27RoZBbzrwdraGiMjo1ccUdkmw9WEEEKI109pDlc7d/lWibXtXs268EKvGenJES+Fo6NjaYcghBBCCFFuyRLSxSOrqwkhhBBCCCHKFenJEUIIIYQQoozLljk5xSJJjhClwLpRzdIOQQghhBCi3JIkRwghhBBCiDJOVlcrHklyhBBCCCGEKONk4YHikYUHhBBCCCGEEOWK9OQIIYQQQghRxslwteKRnpzXTIsWLRgzZkyB552cnFi0aFGJx6FQKNi6dWuJX0cIIYQQQojikp4cIYQQogC/nL3C939e5u6jNJwrGDPRqza1bMzyLfvH5ZusPnWJ60kPyczOwcHUgA88qtHJzV5V5u6jxywJvcDx2AQepGVSz86CiS3ccTA1fFW3JIR4TcmcnOKRJEeUOTk5OWRlZaGlVXb+eZbFmIQQJWvPpRssOPw3/2tVm1rWZvwYfgXf346x+cPWmOvr5ilvrKfDoAYuVDYzQktDg8Mx8czcdwYzfV2aOlqRk5PD+B0n0NLQYME7jTDQ0WL9mWg+3nKUjR+0QqktP1+EEOJlkeFqr6HMzEx8fX0xMTGhQoUKTJ06lZycnHzLxsbG8t5772FoaIixsTE9e/bk1q1bamW++eYbqlatio6ODq6urqxbt07tfFRUFG+//TZ6enrUqFGDvXv3FjnWmJgYFAoFGzZsoGnTpujp6VGrVi0OHjyoKhMSEoJCoWD37t3Ur18fXV1djhw5QnZ2Nv7+/lSuXBmlUkmdOnXYuHGjql5iYiI+Pj5YWlqiVCpxdnYmKCgIgPT0dHx9fbG1tUVPTw9HR0f8/f3VYgoPD1e1lZSUhEKhICQk5IViEkKUHz+cuUzXWo68W8ORKhbG/K9VHfS0NPntwrV8y3tWqkCrqnZUNjfC3tSAvnWrUq2CMeE37wIQm/SQc/GJTG5Zm5rWZjiZGTG5ZR3SMrMIjrzxKm9NCPEaykFRYlt5JF8bvYbWrl3L4MGDOXHiBKdOnWLYsGE4ODgwdOhQtXLZ2dmqBOfgwYNkZmYycuRIevXqpfowv2XLFkaPHs2iRYto06YNO3bsYODAgVSqVImWLVuSnZ1Nt27dsLa2JiwsjPv37z9zTlBBJkyYwKJFi6hRowYLFiygc+fOXL16FQsLC1WZSZMmERAQQJUqVTAzM8Pf358ffviBb7/9FmdnZw4dOsQHH3yApaUlXl5eTJ06lQsXLrB7924qVKjA5cuXSU1NBWDJkiVs27aNX375BQcHB65fv87169eLHXdxYxJClA8ZWdlcTLjPQE8X1TENhYKG9paci0sstH5OTg4n/7nDtcQURjWrAUB6VjYAOpqaam3qaGoQHneXrrUcX/JdCCHEm0uSnNeQvb09CxcuRKFQ4Orqyrlz51i4cGGeJGf//v2cO3eOq1evYm+fOyb8+++/p2bNmpw8eZIGDRoQEBDAgAEDGDFiBADjxo3j+PHjBAQE0LJlS/bt28fFixf5/fffsbOzA2DOnDl06NChWDH7+vrSvXt3ILfnKDg4mFWrVjFx4kRVmVmzZtG2bVsA0tLSmDNnDvv27aNJkyYAVKlShSNHjrB8+XK8vLyIjY3Fw8MDT09PIHfRhSdiY2NxdnbmrbfeQqFQ4Oj4fB8eihuTEKJ8SEpNIysnB4v/DEuz0NclJvFBgfUepGXQYfXvpGdlo6lQMKlFbRo7WAHgZGaIjZGSZUcvMKVVHZTaucPVbqU85s7DxyV6P0KI15/MySkeSXJeQ40bN0ahePoPvUmTJgQGBpKVlaVWLiIiAnt7e1WCA1CjRg1MTU2JiIigQYMGREREMGzYMLV6zZo1Y/HixWptPElwnlyvuP5dR0tLC09PTyIiItTKPElWAC5fvsyjR49UCcYT6enpeHh4APDxxx/TvXt3/vzzT9q1a0eXLl1o2rQpAAMGDKBt27a4urri7e3NO++8Q7t27Yodd3Fjyk9aWhppaWlqxzIyMtGV8fdClDsGOlr81KcFjzKyOHH9NgsOn6eiiQGelSqgralBQKeGzNp3hpbf7Ubz/3uGmjlakf+AYyGEeCq7tAN4zcinLFFmGBgYqP6ckpICwM6dO6lYsaJaOV3d3G9WO3TowLVr19i1axd79+6ldevWjBw5koCAAOrVq8fVq1fZvXs3+/bto2fPnrRp04aNGzeioZE7Fe3f85gyMjJeSkz58ff3Z+bMmWrHJndoyv86NSuwjhCidJkqddFUKLj7SP0LiruP0qigr1dgPQ2FAvv/XynN1dKEq/ceEHTqEp6VKgDgZmXKT31b8iAtg8ysbMz0den380FqWJmW2L0IIcSbSBYeeA2FhYWp7R8/fhxnZ2c0/zXOG8DNzS3PXJQLFy6QlJREjRo1VGVCQ0PV6oWGhqqdv379OnFxcWrXK65/18nMzOT06dO4ubkVWL5GjRro6uoSGxtLtWrV1LZ/90xZWlrSv39/fvjhBxYtWsR3332nOmdsbEyvXr1YsWIFP//8M5s2beLevXtYWloCqN3TvxcheNGY/mvy5Mncv39fbRvfrlGh1xNClB5tTQ2qW5lw8vpt1bHsnBxOXr+Nu23+S0jnJ4fc+T3/ZaSrjZm+LrFJKUQkJOFVxfZlhC2EKMdychQltpVH0pPzGoqNjWXcuHF89NFH/PnnnyxdupTAwMA85dq0aYO7uzs+Pj4sWrSIzMxMRowYgZeXl2oY1oQJE+jZsyceHh60adOG7du3s3nzZvbt26dqw8XFhf79+/Pll1+SnJzMlClTih3zV199hbOzM25ubixcuJDExEQGDRpUYHkjIyP8/PwYO3Ys2dnZvPXWW9y/f5/Q0FCMjY3p378/06ZNo379+tSsWZO0tDR27NihSpwWLFiAra0tHh4eaGho8Ouvv2JjY4OpqSkaGho0btyYuXPnUrlyZRISEvjss88KvYeixJQfXV3dPD09KTJUTYgy7wOPakzf+ydu1qb/v4R0NKmZWbxbwwGAaXtOY2mg5JP/X1hg9clL1LA2pZKJARlZ2RyJucXOi9eZ3KKOqs29UTcwU+piY6Tk8p1kAg6do0UVW5o4WpXKPQohRHkln7ReQ/369SM1NZWGDRuiqanJ6NGj88yrAVAoFPz222988sknvP3222hoaODt7c3SpUtVZbp06cLixYsJCAhg9OjRVK5cmaCgIFq0aAGAhoYGW7ZsYfDgwTRs2BAnJyeWLFmCt7d3sWKeO3cuc+fOJTw8nGrVqrFt2zYqVKjwzDqzZ8/G0tISf39/rly5gqmpKfXq1eN///sfADo6OkyePJmYmBiUSiXNmzdnw4YNQG5CMn/+fKKiotDU1KRBgwbs2rVLNVRt9erVDB48mPr16+Pq6sr8+fOLNGensJiEEOVHO5eKJKam8e3xi9x9mIaLpTFL32uMxf8PV4t/kKo2P/JxZhZzD/xFQkoqulqaOJkZ8nm7+rRzeTq89c7Dxyw8fD532JuBHp2q2zO0oesrvzchxOunvC71XFIUOQW9YEWIlyAmJobKlStz5swZ6tatW9rhlBkpX00svJAQQgghyhTDkfNL7dpHIwpe2fFFNXUzKrG2S4v05AghhBBCCFHGlde5MyVFFh4QL2TOnDkYGhrmuxX3XTpCCCGEEEK8DNKTI17I8OHD6dmzZ77nlEolFStWREZECiGEEEK8GJmTUzyS5IgXYm5ujrm5eWmHIYQQQghRrmXLd8bFIsPVhBBCCCGEEOWK9OQIIYQQQghRxslwteKRJEeIUjDy1rjSDkEIIYQQxbS2tAMQRSZJjhBCCCGEEGWcLCFdPDInRwghhBBCCFGuSE+OEEIIIYQQZZy8kaN4pCdHCCGEEEIIUa5IklMGtGjRgjFjxgDg5OTEokWLnqtuebZmzRpMTU1V+zNmzKBu3brPrBMTE4NCoSA8PLxEYxNCCCGEKGnZKEpsK49kuFoZc/LkSQwMDIpcfvPmzWhra5dgRGWTn58fn3zyiWp/wIABJCUlsXXrVtUxe3t74uLiqFChQilEKIQoT7q2MqRFfSX6ehpExaazdnsyt+5lFVi+S0tDurY0VDt283Ymk5feUe2bGGrQq50RNavqoNRVEHcni+2HUjh1Ia3E7kMI8fqShQeKR5KcMsbS0rJY5c3NzUsokrLN0NAQQ0PDZ5bR1NTExsbmFUUkhCivOr5lQNtG+qzYcp87iVl0a2WIXz8z/rfsDhmZBdf751YG89cmqvaz/vO68mHdTNDXU7D4xyQePMqmSW09RvY0Zfq3d4mNf0bDQgghCiXD1cqYfw9X69u3L7169VI7n5GRQYUKFfj++++BvMPVnJycmDNnDoMGDcLIyAgHBwe+++47tTaOHj1K3bp10dPTw9PTk61btxZrWNfff//NO++8g7GxMUZGRjRv3pzo6GgAsrOzmTVrFpUqVUJXV5e6desSHBysqvtkCNnmzZtp2bIl+vr61KlTh2PHjqldY82aNTg4OKCvr0/Xrl25e/eu2vl/D1ebMWMGa9eu5bfffkOhUKBQKAgJCcl3uNrBgwdp2LAhurq62NraMmnSJDIzn36YaNGiBaNGjWLixImYm5tjY2PDjBkzVOdzcnKYMWMGDg4O6OrqYmdnx6hRo4r03IQQr6f2TfTZfiiFMxfTuH4rk+8238fUSJN61fWeWS8rG+6nZKu2lEfqSU41e232hj3iyo0Mbidmse3gQx49zqGy3ZvXOy+EKFxOTslt5ZEkOWWYj48P27dvJyUlRXXs999/59GjR3Tt2rXAeoGBgXh6enLmzBlGjBjBxx9/TGRkJADJycl07twZd3d3/vzzT2bPns2nn35a5Jhu3LjB22+/ja6uLn/88QenT59m0KBBqkRh8eLFBAYGEhAQwF9//UX79u159913iYqKUmtnypQp+Pn5ER4ejouLC3369FG1ERYWxuDBg/H19SU8PJyWLVvy+eefFxiTn58fPXv2xNvbm7i4OOLi4mjatGm+sXfs2JEGDRpw9uxZvvnmG1atWpWn7bVr12JgYEBYWBjz589n1qxZ7N27F4BNmzaxcOFCli9fTlRUFFu3bsXd3b3Iz08I8XqxNNPE1EiTv6PTVcdS03K4ciODavbPTkZsLDRZ5GfJl2Mq8FF3E8xN1H/lXr6eQaNaehgoFSgU0KiWHtpaEBGTXkCLQgghikqSnDKsffv2GBgYsGXLFtWxH3/8kXfffRcjI6MC63Xs2JERI0ZQrVo1Pv30UypUqMCBAwdU9RUKBStWrKBGjRp06NCBCRMmFDmmr776ChMTEzZs2ICnpycuLi4MHDgQV1dXAAICAvj000/p3bs3rq6uzJs3j7p16+ZZTMHPz49OnTrh4uLCzJkzuXbtGpcvXwZyEyVvb28mTpyIi4sLo0aNon379gXGZGhoiFKpRFdXFxsbG2xsbNDR0clT7uuvv8be3p5ly5ZRvXp1unTpwsyZMwkMDCQ7O1tVrnbt2kyfPh1nZ2f69euHp6cn+/fvByA2NhYbGxvatGmDg4MDDRs2ZOjQoUV+fkKI14uJYe6vyfsp2WrHk1OyVOfyc+WfdFZsuU/gukTW7kjG0kyTKYMt0NN5Oqb+q1+S0NRU8PVka1ZOs2bAu8Ys+SmJhGfM9RFCvLlyUJTYVlLu3buHj48PxsbGmJqaMnjwYLUv7/Mr/8knn+Dq6opSqcTBwYFRo0Zx//79Yl9bkpwyTEtLi549e7J+/XoAHj58yG+//YaPj88z69WuXVv1Z4VCgY2NDQkJCQBERkZSu3Zt9PSeDrNo2LBhkWMKDw+nefPm+S52kJyczM2bN2nWrJna8WbNmhEREVFgjLa2tgCqGCMiImjUqJFa+SZNmhQ5xoJERETQpEkTFIqn/5mbNWtGSkoK//zzT76xPYnvSWw9evQgNTWVKlWqMHToULZs2aI23C0/aWlpJCcnq21ZmTKxWIiyqEltPZZPsVJtmprP185fUemc/Dt3eNv5y+ks+CERfT0FDWs9/dnbrZUh+noK5q25x4xv7/L70YeM6GlKJSuZLiuEKB98fHz4+++/2bt3Lzt27ODQoUMMGzaswPI3b97k5s2bBAQEcP78edasWUNwcDCDBw8u9rXlJ2kZ5+Pjg5eXFwkJCezduxelUom3t/cz6/w3AVEoFGo9FS9CqVS+lHb+HeOTpONlxfiinvX87O3tiYyMZN++fezdu5cRI0bw5ZdfcvDgwQJXufP392fmzJlqx2q/PZ66Xn4lcwNCiOd25mIa0f88nQOorZn788nEUEOtN8fYUJPYuIwit/vocQ7xd7OwNs/NmqzMNGnb2ID/Lb3Djdu5X5Rcv5WJi6MOrRvps3Z78su4HSFEOZL9ms2diYiIIDg4mJMnT+Lp6QnA0qVL6dixIwEBAdjZ2eWpU6tWLTZt2qTar1q1Kl988QUffPABmZmZaGkVPXWRnpwyrmnTptjb2/Pzzz+zfv16evTo8UJLRru6unLu3DnS0p72JJw8ebLI9WvXrs3hw4fJyMj7y93Y2Bg7OztCQ0PVjoeGhlKjRo0iX8PNzY2wsDC1Y8ePH39mHR0dHbKynj3Ew83NjWPHjpHzrxl2oaGhGBkZUalSpSLHp1Qq6dy5M0uWLCEkJIRjx45x7ty5AstPnjyZ+/fvq23uzT4psLwQovQ8Ts8h4V6WartxO5OkB1nUqPJ0CKyeroIqFbW5fL3oSY6ujgIrM02SHuQmSjra///lzn9m/GbngIasEiuEKAeOHTuGqampKsEBaNOmDRoaGnk+5z3L/fv3MTY2LlaCA5LkvBb69u3Lt99+y969ewsdqlaUtrKzsxk2bBgRERH8/vvvBAQEAKgN4yqIr68vycnJ9O7dm1OnThEVFcW6detUCxtMmDCBefPm8fPPPxMZGcmkSZMIDw9n9OjRRY5x1KhRBAcHExAQQFRUFMuWLVNboS0/Tk5O/PXXX0RGRnLnzp18k7ARI0Zw/fp1PvnkEy5evMhvv/3G9OnTGTduHBoaRfuvsGbNGlatWsX58+e5cuUKP/zwA0qlEkdHxwLr6OrqYmxsrLZpaukW6XpCiNL3+7FHvOtliIerLpWstBjWzYSkB1n8efGxqszEAWa0aaiv2u/d3ghXJ20qmGpSzV6bUX1Myc6B4+dSAYi7k0n83UwGvmtClYraWJlp4t1Un5pVdDgd8ThPDEIIkZOjKLEtv6H1//5C/HnEx8djZWWldkxLSwtzc3Pi4+OL1MadO3eYPXv2M4e4FUSSnNeAj48PFy5coGLFinnmuxSXsbEx27dvJzw8nLp16zJlyhSmTZsGoDZPpyAWFhb88ccfpKSk4OXlRf369VmxYoWqd2nUqFGMGzeO8ePH4+7uTnBwMNu2bcPZ2bnIMTZu3JgVK1awePFi6tSpw549e/jss8+eWWfo0KG4urri6emJpaVlnt4kgIoVK7Jr1y5OnDhBnTp1GD58OIMHDy607X8zNTVlxYoVNGvWjNq1a7Nv3z62b9+OhYVFkdsQQrxedh15yN6wRwx415jpH+UuHhCwLlHtHTlWZloYGjz9lWpmrMHH75syd1QFRvY0JeVRNrO/u8uD/19GOisbFqxLJPlhNmN8TPl8pAXN6ipZseU+f0XJ6mpCiLxKcglpf39/TExM1DZ/f/9845g0aZLqlR0FbRcvXnzh+01OTqZTp07UqFFD7XUeRaXIySmvq2OLolq/fj0DBw7k/v37L23OjXi2/tOK9g2GEEIIIcqOtbNK7yXju/4s+hDZ4mpdMztPz42uri66unlHnty+fTvP+wv/q0qVKvzwww+MHz+exMSnL0XOzMxET0+PX3/99ZmvQ3nw4AHt27dHX1+fHTt2FOmL+P+ShQfeQN9//z1VqlShYsWKnD17lk8//ZSePXtKgiOEEEIIUUZll+BSzwUlNPmxtLTE0tKy0HJNmjQhKSmJ06dPU79+fQD++OMPsrOz86yi+2/Jycm0b98eXV1dtm3b9lwJDshwtTdSfHw8H3zwAW5ubowdO5YePXrw3XffATB8+HAMDQ3z3YYPH17KkQshhBBCiNeBm5sb3t7eDB06lBMnThAaGoqvry+9e/dWrax248YNqlevzokTJ4DcBKddu3Y8fPiQVatWkZycTHx8PPHx8YUuMPVfMlxNqElISCA5Of+lS42NjfNMIBPPR4arCSGEEK+f0hyutv30s9/L9yI61y+ZwV337t3D19eX7du3o6GhQffu3VmyZAmGhoYAxMTEULlyZQ4cOECLFi0ICQmhZcuW+bZ19epVnJycinxtGa4m1FhZWUkiI4QQQgghXpi5uTk//vhjgeednJzUXu3RokULXlb/iyQ5QgghhBBClHE5OfISreKQOTlCCCGEEEKIckV6coQoBY8epJZ2CEIIIYR4jWTLLPpikZ4cIYQQQgghRLkiPTlCCCGEEEKUcbIecvFIkiOEEEIIIUQZl1OCLwMtj2S4mhBCCCGEEKJckSRHPNOMGTOoW7duseooFAq2bt1aIvEIIYQQQryJsnNKbiuPZLiaeCY/Pz8++eST0g6jTHryVt7ExERMTU1LOxwhRAnq5W1K6yZGGOhpcDEmjRW/3iH+zrPfPm5uoonPO+Z4uCnR1VYQfyeTrzbc5sr1dAB6tDelmYcBFqZaZGblcOWfdH7amcjl2LRXcUtCCFGuSZIjnsnQ0BBDQ8PSDqPMycjIKO0QhBCvyHutTOjwtjHLfrxDwt0Mencw47PhNoyde4OMzPy/AjVQajB7lC1/Rz1mznfxJKdkY2OpzcNH2aoycbczWLX5LrfuZqKjreAdLxOmDrfhky+uk/wwO992hRBvLll4oHhkuFo5sXHjRtzd3VEqlVhYWNCmTRsePnxIdnY2s2bNolKlSujq6lK3bl2Cg4PV6v7zzz/06dMHc3NzDAwM8PT0JCwsDMg7XO3kyZO0bduWChUqYGJigpeXF3/++edzxZyeno6vry+2trbo6enh6OiIv78/ADExMSgUCsLDw1Xlk5KSUCgUhISEALk9KQqFgp07d1K7dm309PRo3Lgx58+fV9VZs2YNpqambN26FWdnZ/T09Gjfvj3Xr19Xi+Wbb76hatWq6Ojo4Orqyrp169TOKxQKvvnmG959910MDAwYOnQoLVu2BMDMzAyFQsGAAQOe6zkIIcq2Tl7GbNqTxKnzj4iNy2DZj7cxM9akgbt+gXW6tDbhblIWX2+4w+XYdBLuZfJXZCq37j7t/Tny50POXXpMwt1M/onPYO3Wu+grNXCw03kVtyWEEOWaJDnlQFxcHH369GHQoEFEREQQEhJCt27dyMnJYfHixQQGBhIQEMBff/1F+/bteffdd4mKigIgJSUFLy8vbty4wbZt2zh79iwTJ04kOzv/bxEfPHhA//79OXLkCMePH8fZ2ZmOHTvy4MGDYse9ZMkStm3bxi+//EJkZCTr16/Hycmp2O1MmDCBwMBATp48iaWlJZ07d1braXn06BFffPEF33//PaGhoSQlJdG7d2/V+S1btjB69GjGjx/P+fPn+eijjxg4cCAHDhxQu86MGTPo2rUr586dY+bMmWzatAmAyMhI4uLiWLx4cbFjF0KUbVYWWpgZa3Hu0mPVsUePc7h8LQ1XJ90C63nW1Cf6ehrj+luxcpYD88fb0bqxUYHltTShTRMjHqZmce1m+ku9ByFE+ZCTU3JbeSTD1cqBuLg4MjMz6datG46OjgC4u7sDEBAQwKeffqr6UD9v3jwOHDjAokWL+Oqrr/jxxx+5ffs2J0+exNzcHIBq1aoVeK1WrVqp7X/33XeYmppy8OBB3nnnnWLFHRsbi7OzM2+99RYKhUIVe3FNnz6dtm3bArB27VoqVarEli1b6NmzJ5A7tGzZsmU0atRIVcbNzY0TJ07QsGFDAgICGDBgACNGjABg3LhxHD9+nICAAFVvDUDfvn0ZOHCgav/q1asAWFlZyZwcIcopUyNNAJJSstSOJ6Vkqc7lx8pCi3ZNjdgRkszmfUlUc9BlUFdzMrNyOHgyRVWuXg0lY/tZoaOtICk5i9nfxPNAhqoJIcQLk56ccqBOnTq0bt0ad3d3evTowYoVK0hMTCQ5OZmbN2/SrFkztfLNmjUjIiICgPDwcDw8PFQJTmFu3brF0KFDcXZ2xsTEBGNjY1JSUoiNjS123AMGDCA8PBxXV1dGjRrFnj17it0GQJMmTVR/Njc3x9XVVXV/AFpaWjRo0EC1X716dUxNTVVlIiIinvmMnvD09Hyu+NLS0khOTlbbsjJlYrEQZdFb9QxYN9dRtWlpPt97KTQUCq7+k85PuxKJuZHOvmMP2Hf8Ae2aqvfm/H35MRMCbvDZkjjCL6Yyrr8Vxobyq1kIkVd2jqLEtvJIfpKWA5qamuzdu5fdu3dTo0YNli5diqurq6qn4VmUSmWxrtW/f3/Cw8NZvHgxR48eJTw8HAsLC9LTiz+8ol69ely9epXZs2eTmppKz549ef/99wHQ0Mj9p5nzrz7U0p7sb2Bg8Fz1/P39MTExUdsunvzmJUcnhHgZTv39iAkBN1Rb8sPcHhxTQ/VeG1NDTZIeZOXXBACJyVn8c0v9Z9aNWxlUMFUfQJGWnkP8nUyirqXxzc93yMqGVo0KHtYmhHhzyXC14pEkp5xQKBQ0a9aMmTNncubMGXR0dNi/fz92dnaEhoaqlQ0NDaVGjRoA1K5dm/DwcO7du1ek64SGhjJq1Cg6duxIzZo10dXV5c6dO88dt7GxMb169WLFihX8/PPPbNq0iXv37mFpaQnkDsV74t+LEPzb8ePHVX9OTEzk0qVLuLm5qY5lZmZy6tQp1X5kZCRJSUmqMm5ubs98RgXR0cmdHJyVVfAHHYDJkydz//59ta16g4+fWUcIUToep+UmHU+2f+IzSEzOpJaLnqqMUldBNUddImMK7pGNvPoYOytttWO2VtrcTnz2stMKBWhrlc9vVYUQ4lWSOTnlQFhYGPv376ddu3ZYWVkRFhbG7du3cXNzY8KECUyfPp2qVatSt25dgoKCCA8PZ/369QD06dOHOXPm0KVLF/z9/bG1teXMmTPY2dmpDQN7wtnZmXXr1uHp6UlycjITJkwodm/QEwsWLMDW1hYPDw80NDT49ddfsbGxwdTUFA0NDRo3bszcuXOpXLkyCQkJfPbZZ/m2M2vWLCwsLLC2tmbKlClUqFCBLl26qM5ra2vzySefsGTJErS0tPD19aVx48Y0bNgQyF24oGfPnnh4eNCmTRu2b9/O5s2b2bdv3zPjd3R0RKFQsGPHDjp27IhSqcx3uW1dXV10ddUnKGtq3S3m0xJClJadB5Pp3taU+NuZJNzLoFcHMxKTszh57pGqzLSPbThx7iHBR3IXYdlx8D6fj7ajaxsTjoU/pJqDLm0aG7H8l9wvhXR1FHRrY8qpvx+RmJyFsYEG7d8yxtxEk2NnH5bKfQohyrby2uNSUiTJKQeMjY05dOgQixYtIjk5GUdHRwIDA+nQoQPt27fn/v37jB8/noSEBGrUqMG2bdtwdnYGcnsj9uzZw/jx4+nYsSOZmZnUqFGDr776Kt9rrVq1imHDhlGvXj3s7e2ZM2cOfn5+zxW3kZER8+fPJyoqCk1NTRo0aMCuXbtUQ9VWr17N4MGDqV+/Pq6ursyfP5927drlaWfu3LmMHj2aqKgo6taty/bt21W9LAD6+vp8+umn9O3blxs3btC8eXNWrVqlOt+lSxcWL15MQEAAo0ePpnLlygQFBdGiRYtnxl+xYkVmzpzJpEmTGDhwIP369WPNmjXP9SyEEGXXb3/cR09HwUc9LdBXanDxahpfLI9Xe0eOdQUtjAyeDmmLvp7Ol6tv4dPJnPfbmZJwL5M1W+9x5M/cBCY7Gypaa9OigRVGhpo8eJhFdGwa05bG8U+8vIdLCCFelCInR/JC8XoKCQmhZcuWJCYmFri62Zo1axgzZgxJSUmvNLbC9Bhb+HwpIYQQQpQtvy6sXGrX/uFwyX1k/6B5+RsmK3NyhBBCCCGEEOWKJDmixMyZMwdDQ8N8tw4dOpR2eEIIIYQQr42cHEWJbeWRDFcTJebevXsFrtqmVCqpWLHiK46o7JDhakIIIcTrpzSHq607VHJtf/h2ybVdWmThAVFizM3Ni/ySUSGEEEIIUTDpligeSXKEEEIIIYQo47IlySkWmZMjhBBCCCGEKFekJ0cIIYQQQogyToarFY8kOUKUgof3U0o7BCGEEEKIckuSHCGEEEIIIco46ckpHpmTI4QQQgghhChXpCdHCCGEEEKIMk5WVyueMt+TM2PGDOrWrVvaYZSYmJgYFAoF4eHhpR3Kc3lZfz+v+u85JCQEhUJBUlJSmYlJCCGEEEK8HMVKclq0aMGYMWNKKJTXx5o1azA1NS3tMJ5JoVCwdevW0g4jX/nF5ufnx/79+0snoAKUxZiEEK/eB12s+GFBdbZ8W5Mv/CpjZ6VTaB0LUy38hlZiwxI3tnxbk69nOePspFSd19PV4GMfO74PyG3328+d6dhCXp4shChYTk7JbeXRGzlcLScnh6ysLLS03sjbL5L09HR0dAr/Rf6yGBoaYmho+MquVxRlMSYhxKv1focKvNumAgtWXif+TgYfdrVm9vjKDJ9yiYzM/D8ZGOprEPC/qvx1MYVpC2O4/yATO2tdHjzMUpUZ2tuWOtUN+HLFdW7dSadeLUNGflCRu0kZhIU/eFW3J4R4jWRnl3YEr5ci9+QMGDCAgwcPsnjxYhQKBQqFgpiYGM6fP0+HDh0wNDTE2tqaDz/8kDt37qjqBQcH89Zbb2FqaoqFhQXvvPMO0dHRam3/888/9OnTB3NzcwwMDPD09CQsLEytzLp163BycsLExITevXvz4MHTXwLZ2dn4+/tTuXJllEolderUYePGjarzT4Ym7d69m/r166Orq8uRI0eeeb9nz56lZcuWGBkZYWxsTP369Tl16hQhISEMHDiQ+/fvq57DjBkzgPx7KExNTVmzZo1q/8SJE3h4eKCnp4enpydnzpzJc+3CnmmLFi0YNWoUEydOxNzcHBsbG1UMAE5OTgB07doVhUKh2n+WJ0OzVq5cSeXKldHT0wMgKSmJIUOGYGlpibGxMa1ateLs2bMFtnPy5Enatm1LhQoVMDExwcvLiz///LPQ2P47NCw7O5tZs2ZRqVIldHV1qVu3LsHBwarzT4b5bd68mZYtW6Kvr0+dOnU4duyYqsy1a9fo3LkzZmZmGBgYULNmTXbt2qUW7+nTp/H09ERfX5+mTZsSGRmZ55k8MWDAALp06cLMmTNVz2P48OGkp6cX+nyFEK+nLm0rsGF7AsfDHxDzz2MCV17HwlSLJvWMC6zzfkdLbt/LYOHqG1y6msqtOxmc+TuF+NtPf1a4VdVn/9EkzkU+JOFuBsEHE7ly/TGulfVfxW0JIUS5V+QkZ/HixTRp0oShQ4cSFxdHXFwcRkZGtGrVCg8PD06dOkVwcDC3bt2iZ8+eqnoPHz5k3LhxnDp1iv3796OhoUHXrl3J/v90NCUlBS8vL27cuMG2bds4e/YsEydOVJ0HiI6OZuvWrezYsYMdO3Zw8OBB5s6dqzrv7+/P999/z7fffsvff//N2LFj+eCDDzh48KDaPUyaNIm5c+cSERFB7dq1n3m/Pj4+VKpUiZMnT3L69GkmTZqEtrY2TZs2ZdGiRRgbG6ueg5+fX5GeYUpKCu+88w41atTg9OnTzJgxI0/dpKSkQp8pwNq1azEwMCAsLIz58+cza9Ys9u7dC+QmGgBBQUHExcWp9gtz+fJlNm3axObNm1VzhHr06EFCQgK7d+/m9OnT1KtXj9atW3Pv3r1823jw4AH9+/fnyJEjHD9+HGdnZzp27KhKSosa2+LFiwkMDCQgIIC//vqL9u3b8+677xIVFaVWbsqUKfj5+REeHo6Liwt9+vQhMzMTgJEjR5KWlsahQ4c4d+4c8+bNy9MzM2XKFAIDAzl16hRaWloMGjTomc9o//79REREEBISwk8//cTmzZuZOXPmsx+sEOK1ZGOpjbmpNuEXnr7X6lFqNpFXHuFWteBkpHFdY6JiUpn8sQM/LnJj6fRqtH/bTK1MRPQjGtU1wsI0d0RB7eoGVLTR4c+/pRdHCJE/Ga5WPEUer2ViYoKOjg76+vrY2NgA8Pnnn+Ph4cGcOXNU5VavXo29vT2XLl3CxcWF7t27q7WzevVqLC0tuXDhArVq1eLHH3/k9u3bnDx5EnPz3PHI1apVU6uTnZ3NmjVrMDIyAuDDDz9k//79fPHFF6SlpTFnzhz27dtHkyZNAKhSpQpHjhxh+fLleHl5qdqZNWsWbdu2LdL9xsbGMmHCBKpXrw6As7Oz2rNQKBSq51BUP/74I9nZ2axatQo9PT1q1qzJP//8w8cff6wqs2zZskKfKUDt2rWZPn26KrZly5axf/9+2rZti6WlJZDbi1ScGNPT0/n+++9V9Y8cOcKJEydISEhAV1cXgICAALZu3crGjRsZNmxYnjZatWqltv/dd99hamrKwYMHeeedd4ocW0BAAJ9++im9e/cGYN68eRw4cIBFixbx1Vdfqcr5+fnRqVMnAGbOnEnNmjW5fPky1atXJzY2lu7du+Pu7g7k/rv4ry+++EL1b2TSpEl06tSJx48fq3qy/ktHR4fVq1ejr69PzZo1mTVrFhMmTGD27NloaJT5dTyEEMVgZqwNQGJyptrxpORMzEwK/vVpY6lDp5bmbPn9Dj/vTMClspLhfe3IzMxh/9EkAL5Zf5NR/SuyboEbmZk55OTksHjtDc5felRi9yOEEG+SF5qUcvbsWQ4cOJDvvIXo6GhcXFyIiopi2rRphIWFcefOHVUPTWxsLLVq1SI8PBwPDw9VgpMfJycnVYIDYGtrS0JCApDb+/Do0aM8yUt6ejoeHh5qxzw9PYt8b+PGjWPIkCGsW7eONm3a0KNHD6pWrVrk+vl50oP07w/QTxKzJ4ryTIE8PVH/fibPy9HRUZWEPIklJSUFCwsLtXKpqal5hhw+cevWLT777DNCQkJISEggKyuLR48eERsbW+Q4kpOTuXnzJs2aNVM73qxZszxD5f79HGxtbQFISEigevXqjBo1io8//pg9e/bQpk0bunfvnue5FVTfwcEh39jq1KmDvv7Tb3CbNGlCSkoK169fx9HRMd86aWlppKWlqR3LykpHU/PVzXkSQhSuRWNTPulnp9qfvujac7WjUEBUTCprN98C4ErsYxwr6tGxhYUqyXm3tQXVq+ozY3EMCXczqOViwIgP7LiXlEH4hYcvfC9CiPKnvPa4lJQXSnJSUlLo3Lkz8+bNy3PuyQfGzp074+joyIoVK7CzsyM7O5tatWqp5jEolco8df9LW1tbbV+hUKgNdwPYuXMnFStWVCv3pPfhCQMDgyLeWe58jL59+7Jz5052797N9OnT2bBhA127di2wjkKhIOc//wIzMjKKfE0o2jOFZz+T5/Xf55OSkoKtrS0hISF5yha0ulz//v25e/cuixcvxtHREV1dXZo0aVJi81b+/RwUCgWA6jkMGTKE9u3bs3PnTvbs2YO/vz+BgYF88sknRar/svj7++cZ0lat7nCcPUa81OsIIV5MWHgykVee9qRoa+X+TDAz1iLx/tPeHFNjLa7EPi6wncSkTK7fVP9i4/rNNJrVNwFAR1tB/+7WfL4slpN/5Q5Pi/nnMVUd9OjW3lKSHCGEeAmKleTo6OiQlfV0dZh69eqxadMmnJyc8l2p7O7du0RGRrJixQqaN28OkGfCf+3atVm5ciX37t17Zm9OQWrUqIGuri6xsbFqQ9NeBhcXF1xcXBg7dix9+vQhKCiIrl275nkOT1haWhIXF6faj4qK4tGjp78w3dzcWLdundpwqOPHj6u1UdgzLSptbe18YyyOevXqER8fj5aWVpEWLwAIDQ3l66+/pmPHjgBcv35dbdGEosRmbGyMnZ0doaGhan+noaGhNGzYsFj3YG9vz/Dhwxk+fDiTJ09mxYoVaklOcZ09e5bU1FRVcn78+HEMDQ2xt7cvsM7kyZMZN26c2rEen0QVUFoIUVpSH2eT+lj9C5l7SRnUqWHIleu5SY1STwPXKvrsPJD/vESAC5cfUdFG/Uu2ija6JNzNbVtTU4G2lkaeL8WysnPQULyMOxFClEfyMtDiKdYkAicnJ8LCwoiJieHOnTuMHDmSe/fu0adPH06ePEl0dDS///47AwcOJCsrCzMzMywsLPjuu++4fPkyf/zxR54Pe3369MHGxoYuXboQGhrKlStX2LRpk9oqWc9iZGSEn58fY8eOZe3atURHR/Pnn3+ydOlS1q5dW5zbU0lNTcXX15eQkBCuXbtGaGgoJ0+exM3NTfUcUlJS2L9/P3fu3FElMq1atWLZsmWcOXOGU6dOMXz4cLWegr59+6JQKBg6dCgXLlxg165dBAQEqF27sGdaVE5OTuzfv5/4+HgSExOf6zm0adOGJk2a0KVLF/bs2UNMTAxHjx5lypQpnDp1Kt86zs7OrFu3joiICMLCwvDx8cnTW1eU2CZMmMC8efP4+eefiYyMZNKkSYSHhzN69Ogixz9mzBh+//13rl69yp9//smBAwdUf4fPKz09ncGDB6v+/qZPn46vr+8z5+Po6upibGystslQNSFeD1v33qH3O1Y0qmuEU0Vd/IZU4m5SJsf+TFaVmeNXmXdaPR3Wu2XPHapX0adnJ0tsrXRo0ciEDl7m7PgjNzFKfZzNXxdTGNTDFndXA6wraNOmmSmtm5px9F/tCiGEeH7FSnL8/PzQ1NSkRo0aWFpakp6eTmhoKFlZWbRr1w53d3fGjBmDqakpGhoaaGhosGHDBk6fPk2tWrUYO3YsX375pVqbOjo67NmzBysrKzp27Ii7uztz585FU1OzyHHNnj2bqVOn4u/vj5ubG97e3uzcuZPKlSsX5/ZUNDU1uXv3Lv369cPFxYWePXvSoUMH1ZCjpk2bMnz4cHr16oWlpSXz588HIDAwEHt7e5o3b07fvn3x8/NTm79haGjI9u3bOXfuHB4eHkyZMiXPsLQnPRgFPdOiCgwMZO/evdjb2+eZm1RUCoWCXbt28fbbbzNw4EBcXFzo3bs3165dw9raOt86q1atIjExkXr16vHhhx8yatQorKysih3bqFGjGDduHOPHj8fd3Z3g4GC2bdumtgBEYbKyshg5cqTq34SLiwtff/110R9APlq3bo2zszNvv/02vXr14t1331VbvlsIUb5s3H2H7fvv8En/iiyaVg09PU2mLbiq9o4cWysdTIye/s6Kiknl86+u0aKRKd/MdqZ3ZyuW/3STkONJqjLzvr1OVMwjJgyz59vPXejR0ZLvN99iV0jBPURCiDdbTk5OiW3lkSKnvN6ZEC/ZgAEDSEpKyvMupOfRcdC5Fw9ICCGEEK/UrtXupXbtpTtL7iP7J53K31hZWfNWCCGEEEIIUa68sUlOzZo1MTQ0zHdbv359aYf30r1p9yuEEEIIUZ5kZ5fcVh690BLSr7Ndu3YVuLxzQfNNXmdv2v2WhDVr1pR2CEIIIYQQogje2CSnoBc3lldv2v0KIYQQQpQnMou+eN7Y4WpCCCGEEEKI8umN7ckRQgghhBDidSEvAy0e6ckRQgghhBBClCvSkyNEKchISy/tEIQQQgjxGpE5OcUjSY4QQgghhBBlXE6JjleTl4EKIYQQQgghRKHu3buHj48PxsbGmJqaMnjwYFJSUopUNycnhw4dOqBQKNi6dWuxry1JjhBCCCGEEGVcdk7JbSXFx8eHv//+m71797Jjxw4OHTrEsGHDilR30aJFKBTP38MkSc4brkWLFowZM6ZE2o6JiUGhUBAeHl4i7b8qTk5OLFq0qLTDEEIIIYR4bURERBAcHMzKlStp1KgRb731FkuXLmXDhg3cvHnzmXXDw8MJDAxk9erVz319mZMjSoy9vT1xcXFUqFChtEMRQogX0r+7LR1aWmJooMnfl1JYsjqWG7fSCiy/blEtbCx18xzftjeBpWuuA9CxZQVaNTWnWmV9DJSadBkazsNHWSV2D0KI11tJLjyQlpZGWpr6zzRdXV10dfP+HCuqY8eOYWpqiqenp+pYmzZt0NDQICwsjK5du+Zb79GjR/Tt25evvvoKGxub576+9OSIEqOpqYmNjQ1aWpJLCyFeX73esaZLeysWB13jk2kXeZyWjf8kZ7S1Cx5G4Tv1Ij1HnFVtE+dcAuBgWKKqjK6uBif/us9Pv8WV+D0IIcSz+Pv7Y2Jiorb5+/u/UJvx8fFYWVmpHdPS0sLc3Jz4+PgC640dO5amTZvy3nvvvdD1JckRZGZm4uvri4mJCRUqVGDq1Knk/P/XBevWrcPT0xMjIyNsbGzo27cvCQkJqrqJiYn4+PhgaWmJUqnE2dmZoKAgIP/han///TfvvPMOxsbGGBkZ0bx5c6KjowuNccCAAXTp0oWAgABsbW2xsLBg5MiRZGRkqMqkpaXh5+dHxYoVMTAwoFGjRoSEhKi1c+TIEZo3b45SqcTe3p5Ro0bx8OFD1fmEhAQ6d+6MUqmkcuXKrF+/Xq1+Tk4OM2bMwMHBAV1dXezs7Bg1alSRn7UQ4vXT1dua9VvjOXb6PlevpzLvm6tYmGrTrL5pgXXuP8gk8f7TrbGHCTfiH/NXxNMJt1uCE/h5+y0iLj8ssB0hhHgiOzunxLbJkydz//59tW3y5Mn5xjFp0iQUCsUzt4sXLz7XPW7bto0//vjjpUwTkK/YBWvXrmXw4MGcOHGCU6dOMWzYMBwcHBg6dCgZGRnMnj0bV1dXEhISGDduHAMGDGDXrl0ATJ06lQsXLrB7924qVKjA5cuXSU1Nzfc6N27c4O2336ZFixb88ccfGBsbExoaSmZmZpHiPHDgALa2thw4cIDLly/Tq1cv6taty9ChQwHw9fXlwoULbNiwATs7O7Zs2YK3tzfnzp3D2dmZ6OhovL29+fzzz1m9ejW3b9/G19cXX19fVWI2YMAAbt68yYEDB9DW1mbUqFFqSd2mTZtYuHAhGzZsoGbNmsTHx3P27NkXefxCiDLMxlIHCzNtzvydrDr2KDWbi9EPqeFsQMjxxGfUzqWlqaD1WxZs2nWrJEMVQojnVpyhaePHj2fAgAHPLFOlShVsbGzUPkNB7hfr9+7dK3AY2h9//EF0dDSmpqZqx7t3707z5s3zfHn9LJLkCOzt7Vm4cCEKhQJXV1fOnTvHwoULGTp0KIMGDVKVq1KlCkuWLKFBgwakpKRgaGhIbGwsHh4eqvGWTk5OBV7nq6++wsTEhA0bNqCtrQ2Ai4tLkeM0MzNj2bJlaGpqUr16dTp16sT+/fsZOnQosbGxBAUFERsbi52dHQB+fn4EBwcTFBTEnDlz8Pf3x8fHR7XQgrOzM0uWLMHLy4tvvvmG2NhYdu/ezYkTJ2jQoAEAq1atws3NTRVDbGwsNjY2tGnTBm1tbRwcHGjYsGGR70EI8XoxN839WZV4P0PteOL9DMz+/1xhmnqaYqivyZ5Dd196fEKIN0dZeRmopaUllpaWhZZr0qQJSUlJnD59mvr16wO5SUx2djaNGjXKt86kSZMYMmSI2jF3d3cWLlxI586dixWnJDmCxo0bqy3R16RJEwIDA8nKyiI8PJwZM2Zw9uxZEhMTyc7OBnI/7NeoUYOPP/6Y7t278+eff9KuXTu6dOlC06ZN871OeHg4zZs3VyU4xVWzZk00NTVV+7a2tpw7dw6Ac+fOkZWVlSdpSktLw8LCAoCzZ8/y119/qQ1By8nJITs7m6tXr3Lp0iW0tLRU/xEBqlevrvZtQo8ePVi0aBFVqlTB29ubjh070rlz52fOO8pvMl92VjoamjrFfwhCiBLVqqk5YwY7qPY/+/LyC7fZoYUFJ87e525SRuGFhRCiAGUlySkqNzc3vL29GTp0KN9++y0ZGRn4+vrSu3dv1RfSN27coHXr1nz//fc0bNgQGxubfHt5HBwcqFy5crGuL0mOKNDjx49p37497du3Z/369VhaWhIbG0v79u1JT08HoEOHDly7do1du3axd+9eWrduzciRIwkICMjTnlKpfKF4/pscKRQKVdKVkpKCpqYmp0+fVkuEAAwNDVVlPvroo3zn0Dg4OHDp0qVCY7C3tycyMpJ9+/axd+9eRowYwZdffsnBgwcLTN78/f2ZOXOm2rHKtYZStfZHhV5PCPFqHfsziYvRT+fIaGvlfgFkZqLNvaSnQ2vNTLSJvvao0PasKujgUcuYmYsKn3sohBDlzfr16/H19aV169ZoaGjQvXt3lixZojqfkZFBZGQkjx4V/vO0uCTJEYSFhantHz9+HGdnZy5evMjdu3eZO3cu9vb2AJw6dSpPfUtLS/r370///v1p3rw5EyZMyDfJqV27NmvXriUjI+O5e3MK4uHhQVZWFgkJCTRv3jzfMvXq1ePChQtUq1Yt3/PVq1cnMzOT06dPq4arRUZGkpSUpFZOqVTSuXNnOnfuzMiRI6levTrnzp2jXr16+bY7efJkxo0bp3as67C/i3mHQohXIfVxNqmP1Xte7yZm4FHTiOhrufMN9ZUaVK9qwPZ9twttr/3bFiTdzyTszP0SiVcI8ebIft26cgBzc3N+/PHHAs87OTmpFrsqSGHnCyKrqwliY2MZN24ckZGR/PTTTyxdupTRo0fj4OCAjo4OS5cu5cqVK2zbto3Zs2er1Z02bRq//fYbly9f5u+//2bHjh1qc1j+zdfXl+TkZHr37s2pU6eIiopi3bp1REZGvvA9uLi44OPjQ79+/di8eTNXr17lxIkT+Pv7s3PnTgA+/fRTjh49iq+vL+Hh4URFRfHbb7/h6+sLgKurK97e3nz00UeEhYVx+vRphgwZotYDtWbNGlatWsX58+e5cuUKP/zwA0qlEkdHxwJj09XVxdjYWG2ToWpCvD62BN+ibxdbmtQzwclej4nDK3M3KYPQ00mqMvMnO/NeW/Ux6goFtPeyYO/hu/x/p7MaMxMtqjoqqWidO9m3sr2Sqo5KjAw08xYWQghRLNKTI+jXrx+pqak0bNgQTU1NRo8ezbBhw1AoFKxZs4b//e9/LFmyhHr16hEQEMC7776rqqujo8PkyZOJiYlBqVTSvHlzNmzYkO91LCws+OOPP5gwYQJeXl5oampSt25dmjVr9lLuIygoiM8//5zx48dz48YNKlSoQOPGjXnnnXeA3J6kgwcPMmXKFJo3b05OTg5Vq1alV69eam0MGTIELy8vrK2t+fzzz5k6darqvKmpKXPnzmXcuHFkZWXh7u7O9u3bVfN+hBDlz887bqGnq8GYwY4Y6mty/lIKk+dFkZHx9NtFW2tdjI3Uf6XWq2WEdQVdgg/eybfdd1pb0q+7nWp/4TRXAL5cHiOLFAgh8sjJ58sSUTBFzvP2AQkhnltbn9OlHYIQQgghimnv+vqFFyohs9YX7ZUbz2OaT/nr9yh/dySEEEIIIUQ5I/0SxSNzckSZYGhoWOB2+PDh0g5PCCGEEEK8RqQnR5QJ4eHhBZ6rWLHiqwtECCGEEKIMym8BE1EwSXJEmVDQss5CCCGEEEKGqxWXDFcTQgghhBBClCvSkyOEEEIIIUQZly0dOcUiPTlCCCGEEEKIckV6coQoBTnydYwQQgghikE+OxSP9OQIIYQQQgghyhXpyRFCCCGEEKKMk8XVikd6coQQQgghhBDliiQ54o0QHx9P27ZtMTAwwNTUtLTDEUIIIYQoluzsnBLbyiMZribeCAsXLiQuLo7w8HBMTEyKVGfGjBls3bqV8PDwkg1OCFHm9X/fjo6tKmBooMXfkSksXn2NG/FpBZb/YYk7Npa6eY7/tieBpUGxAHRqVYFWzSyo5qSPgb4m7w0+w8NHWSV2D0II8SaRJEe8EaKjo6lfvz7Ozs6lHYoQ4jXTq7MNXb2tmP9NDHG30xjYw465k1wYNOE8GRn5fwM6ckoEGv8aK1HZXsn8Ka4cOp6oOqarq8HJs/c5efY+Q/pUKunbEEK85nJkUk6xyHA18drYuHEj7u7uKJVKLCwsaNOmDQ8fPuTkyZO0bduWChUqYGJigpeXF3/++aeqnpOTE5s2beL7779HoVAwYMAAAJKSkhgyZAiWlpYYGxvTqlUrzp49C8CaNWuYOXMmZ8+eRaFQoFAoWLNmDYMGDeKdd95RiysjIwMrKytWrVr1yp6FEOLV6dbBivVb4jh6OomrsanM+zoGCzNtmnmaFljn/oNMEu8/3RrVM+VG/GPORjxQldm8O4EN2+KJiHr4Cu5CCPG6y8kuua08kp4c8VqIi4ujT58+zJ8/n65du/LgwQMOHz5MTk4ODx48oH///ixdupScnBwCAwPp2LEjUVFRGBkZcfLkSfr164exsTGLFy9GqVQC0KNHD5RKJbt378bExITly5fTunVrLl26RK9evTh//jzBwcHs27cPABMTE1xcXHj77beJi4vD1tYWgB07dvDo0SN69epVas9HCFEybK10sDDT4c/zyapjD1OziIh+SA1nQ0KOJT6jdi4tTQVt3jJn485bJRmqEEKIf5EkR7wW4uLiyMzMpFu3bjg6OgLg7u4OQKtWrdTKfvfdd5iamnLw4EHeeecdLC0t0dXVRalUYmNjA8CRI0c4ceIECQkJ6OrmjpsPCAhg69atbNy4kWHDhmFoaIiWlpaqDkDTpk1xdXVl3bp1TJw4EYCgoCB69OiBoaFhiT8HIcSrZWaiDUDi/Uy140n3MzA31S5SG80amGKor8WeQ3dfenxCiDdHtgxXKxYZriZeC3Xq1KF169a4u7vTo0cPVqxYQWJi7jeot27dYujQoTg7O2NiYoKxsTEpKSnExsYW2N7Zs2dJSUnBwsICQ0ND1Xb16lWio6OfGcuQIUMICgpSXXv37t0MGjSowPJpaWkkJyerbdlZ6c/xFIQQJa1VM3O2B3moNi0txQu32aFFBU6E3+duYsZLiFAIIURRSE+OeC1oamqyd+9ejh49yp49e1i6dClTpkwhLCyMjz/+mLt377J48WIcHR3R1dWlSZMmpKcXnEikpKRga2tLSEhInnOFLTHdr18/Jk2axLFjxzh69CiVK1emefPmBZb39/dn5syZascq1xxKFfdhz7yOEOLVO3Y6iYuXn86R0dbOTXLMTLS4l/Q0STE10SY65lGh7VlV0MHD3ZiZC5795YkQQhRGFh4oHklyxGtDoVDQrFkzmjVrxrRp03B0dGTLli2Ehoby9ddf07FjRwCuX7/OnTt3ntlWvXr1iI+PR0tLCycnp3zL6OjokJWVdzlXCwsLunTpQlBQEMeOHWPgwIHPvNbkyZMZN26c2rEuQ84/s44QonSkPs4m9bH60tB3E9PxqGVM9LVUAPSVGrhVNWD73oRC2/P2qkDS/QyOn0kqiXCFEEIUQJIc8VoICwtj//79tGvXDisrK8LCwrh9+zZubm44Ozuzbt06PD09SU5OZsKECarFBQrSpk0bmjRpQpcuXZg/fz4uLi7cvHmTnTt30rVrVzw9PXFycuLq1auEh4dTqVIljIyMVPN3hgwZwjvvvENWVhb9+/d/5rV0dXVV9Z7Q0NR5sQcihHhlNu9OwKeLLTfiHxOfkM6AHnbcTcwg9FSSqsz8KS6Enkzktz23VccUCmjvZcHeQ3fJzmf1IjMTLcxNtbGzyf35UNleSerjLBLupPPgobwvRwihrry+tLOkSJIjXgvGxsYcOnSIRYsWkZycjKOjI4GBgXTo0AEbGxuGDRtGvXr1sLe3Z86cOfj5+T2zPYVCwa5du5gyZQoDBw7k9u3b2NjY8Pbbb2NtbQ1A9+7d2bx5My1btiQpKYmgoCDV8tNt2rTB1taWmjVrYmdnV9K3L4QoRT9vj0dPV4OxQ5ww1NfkfGQKk+ZeUntHjp21LiZG6gsR1KtljLWlLrtD8u9Z7tzGin7vP/35sWhGdQDmf3NVFikQQogXpMiRAX5CFFtKSgoVK1YkKCiIbt26Fbt+mz6nSiAqIYQQQpSkfT95ltq1xyxNKbG2F31S/laIlZ4cIYohOzubO3fuEBgYiKmpKe+++25phySEEEKIN0CODFcrFklyhCiG2NhYKleuTKVKlVizZg1aWvJfSAghhBCirJFPaEIUg5OTkyzhKIQQQohXTl4GWjzyMlAhhBBCCCFEuSI9OUIIIYQQQpRxMieneKQnRwghhBBCCFGuSE+OEEIIIYQQZZz05BSPJDlClIKsLHmbuRBCCCFESZEkRwghhBBCiDJOOnKKR5IcIYQQQgghyjgZrlY8svCAEEIIIYQQolyRnhwhhBBCCCHKOHkZefFIT84LcnJyYtGiRSV+HYVCwdatW0v8Oi9q69atVKtWDU1NTcaMGVPa4QghhBBCiDeQ9OS8JuLi4jAzMyvtMAr10UcfMXDgQEaNGoWRkVFphyOEEC/FwJ4V6dTaCkMDLc5ffMDClVe5EZ9WYPmfltXFxko3z/Gtv99i8aoYjAw0GdCzEp51TLCuoEtScgahJxNZveEfHqbK6otCiLyyZU5OsUiS85qwsbEp7RCeKSMjg7S0NBISEmjfvj12dnbP3VZ6ejo6OjovMTohhHh+vd+zpVsHG+Z+dYW4hMcM6mXP/CnVGTDuLzIy8v/QMXzyeTQ0FKr9yg5KAqe6EXLsLgAW5jpUMNfh23WxXPsnFesKuowd6oSFmQ4zFkS9kvsSQojyrFwNVwsODuatt97C1NQUCwsL3nnnHaKjo1Xn//nnH/r06YO5uTkGBgZ4enoSFhYGQHR0NO+99x7W1tYYGhrSoEED9u3bp9Z+QkICnTt3RqlUUrlyZdavX58nhqSkJIYMGYKlpSXGxsa0atWKs2fPqs7PmDGDunXrsnr1ahwcHDA0NGTEiBFkZWUxf/58bGxssLKy4osvvlBr97/D1Z51L8/y5PrLly/H3t4efX19evbsyf3799XKrVy5Ejc3N/T09KhevTpff/216lxMTAwKhYKff/4ZLy8v9PT0WL9+varnplWrVigUCkJCQgDYtGkTNWvWRFdXFycnJwIDA9Wu5eTkxOzZs+nXrx/GxsYMGzaMNWvWYGpqyo4dO3B1dUVfX5/333+fR48esXbtWpycnDAzM2PUqFFq75xZt24dnp6eGBkZYWNjQ9++fUlISFCdDwkJQaFQsH//fjw9PdHX16dp06ZERkaqxbR9+3YaNGiAnp4eFSpUoGvXrqpzaWlp+Pn5UbFiRQwMDGjUqJHqXoUQ5c/7HW1Yt/kGoacSuRKbiv+yaCqY6fBWg4J71+8/yCTxfoZqa1LPlBvxjzl74QEAMddTmR4YxbHTSdy8lcaZv5NZteEfmtQ3RaNc/WYWQrwsOTk5JbaVR+XqR+nDhw8ZN24cp06dYv/+/WhoaNC1a1eys7NJSUnBy8uLGzdusG3bNs6ePcvEiRPJzs4GICUlhY4dO7J//37OnDmDt7c3nTt3JjY2VtX+gAEDuH79OgcOHGDjxo18/fXXah+gAXr06EFCQgK7d+/m9OnT1KtXj9atW3Pv3j1VmejoaHbv3k1wcDA//fQTq1atolOnTvzzzz8cPHiQefPm8dlnnxWYtBR2L4W5fPkyv/zyC9u3byc4OJgzZ84wYsQI1fn169czbdo0vvjiCyIiIpgzZw5Tp05l7dq1au1MmjSJ0aNHExERQcuWLVWJwqZNm4iLi6Np06acPn2anj170rt3b86dO8eMGTOYOnUqa9asUWsrICCAOnXqcObMGaZOnQrAo0ePWLJkCRs2bCA4OJiQkBC6du3Krl272LVrF+vWrWP58uVs3LhR1U5GRgazZ8/m7NmzbN26lZiYGAYMGJDnGUyZMoXAwEBOnTqFlpYWgwYNUp3buXMnXbt2pWPHjpw5c4b9+/fTsGFD1XlfX1+OHTvGhg0b+Ouvv+jRowfe3t5ERcm3r0KUN7ZWuliY6XD6r2TVsYepWURcTqGmS9GG5GppKmjbvAK7D9x+ZjkDfU0epWZRxB/lQgghnqFcDVfr3r272v7q1auxtLTkwoULHD16lNu3b3Py5EnMzc0BqFatmqpsnTp1qFOnjmp/9uzZbNmyhW3btuHr68ulS5fYvXs3J06coEGDBgCsWrUKNzc3VZ0jR45w4sQJEhIS0NXNHYsdEBDA1q1b2bhxI8OGDQMgOzub1atXY2RkRI0aNVQJwq5du9DQ0MDV1ZV58+Zx4MABGjVqlOc+f/zxx2feS2EeP37M999/T8WKFQFYunQpnTp1IjAwEBsbG6ZPn05gYCDdunUDoHLlyly4cIHly5fTv39/VTtjxoxRlYHcXiwAc3Nz1fC6BQsW0Lp1a1Xi4uLiwoULF/jyyy/Vko9WrVoxfvx41f7hw4fJyMjgm2++oWrVqgC8//77rFu3jlu3bmFoaKh6dgcOHKBXr14AaslKlSpVWLJkCQ0aNCAlJQVDQ0PVuS+++AIvLy8gN1nr1KkTjx8/Rk9Pjy+++ILevXszc+ZMVfkn/zZiY2MJCgoiNjZWNSTPz8+P4OBggoKCmDNnTp7nnZaWRlqa+tj97Kx0NDRlSJ4QZZ25qTYAifcz1I4n3s9QnSvMWw3NMDTQIjik4CTH2EiLD7tXZMe+hALLCCHebPKenOIpVz05UVFR9OnThypVqmBsbIyTkxOQ+8E0PDwcDw8PVVLwXykpKfj5+eHm5oapqSmGhoZERESoenIiIiLQ0tKifv36qjrVq1fH1NRUtX/27FlSUlKwsLDA0NBQtV29elVt2JyTk5PapHxra2tq1KiBxr/GKFhbW+fpJXqisHspjIODgyrBAWjSpAnZ2dlERkby8OFDoqOjGTx4sNo9fP7552r3AODp6VnotSIiImjWrJnasWbNmhEVFaU2zCy/tvT19VUJDuQ+EycnJ7Vk5b/P6fTp03Tu3BkHBweMjIxUicy/e+QAateurfqzra0tgKqd8PBwWrdune/9nDt3jqysLFxcXNSez8GDB/M8nyf8/f0xMTFR265dXJtvWSFE6WrzlgW7vvdUbVqaisIrFaJjS0vCwpO4m5iR73l9pSZzJ7ly7Z9U1vx644WvJ4Qon3Kyc0psK4/KVU9O586dcXR0ZMWKFdjZ2ZGdnU2tWrVIT09HqVQ+s66fnx979+4lICCAatWqoVQqef/990lPTy/y9VNSUrC1tc13fsa/kyFtbfVv/xQKRb7HChp+Vti9vIiUlBQAVqxYkacXSVNTU23fwMDgpV03v7aK+5wePnxI+/btad++PevXr8fS0pLY2Fjat2+f5+/x3+0oFLkfYp6086znm5KSgqamJqdPn87zPP6dfP3b5MmTGTdunNqxzgPP5ltWCFG6Qk8lciEqRbWvo5375ZOZiTb3kp4mKWYm2lyOeVRoe9YVdKhX24TpAfkPZ1XqaTDvf648Ss1iasAlsrLK54cNIYR41cpNknP37l0iIyNZsWIFzZs3B3KHjz1Ru3ZtVq5cyb179/LtAQkNDWXAgAGqCeYpKSnExMSozlevXp3MzExOnz6tGq4WGRmpGqIFUK9ePeLj49HS0lL1IpWEwu6lMLGxsdy8eVM13Or48eOqYXLW1tbY2dlx5coVfHx8XjhWNzc3QkND1Y6Fhobi4uKSJ0l4URcvXuTu3bvMnTsXe3t7AE6dOlXsdmrXrs3+/fsZOHBgnnMeHh5kZWWRkJCg+ndWGF1dXdXwxSdkqJoQZVPq42xSH6sPL72bmE49d2Oir+UmNfpKTdyqGfLbnluFtufd0pKk+xkc+zMxzzl9pSbzp7iSkZHDlPmXClypTQghALLL6QIBJaXcDFczMzPDwsKC7777jsuXL/PHH3+ofXvep08fbGxs6NKlC6GhoVy5coVNmzZx7NgxAJydndm8eTPh4eGcPXuWvn37qvWkuLq64u3tzUcffURYWBinT59myJAhat/6t2nThiZNmtClSxf27NlDTEwMR48eZcqUKc/1Ybsghd1LYfT09Ojfvz9nz57l8OHDjBo1ip49e6rm0cycORN/f3+WLFnCpUuXOHfuHEFBQSxYsKDYsY4fP579+/cze/ZsLl26xNq1a1m2bBl+fn7FbqswDg4O6OjosHTpUq5cucK2bduYPXt2sduZPn06P/30E9OnTyciIoJz584xb948IHdOkY+PD/369WPz5s1cvXqVEydO4O/vz86dO1/2LQkhyoCNu+L5sFtFmtY3pbK9ksm+VbiTmM6Rk08Tl8Cp1enS3lqtnkIB3i0s+f3gnTyLCegrNflySnX0dDX58tsr6Cs1MTPRxsxEG40XHyEnhBBvvHKT5GhoaLBhwwZOnz5NrVq1GDt2LF9++aXqvI6ODnv27MHKyoqOHTvi7u7O3LlzVb0JCxYswMzMjKZNm9K5c2fat29PvXr11K4RFBSEnZ0dXl5edOvWjWHDhmFlZaU6r1Ao2LVrF2+//TYDBw7ExcWF3r17c+3aNayt1X/5vYjC7qUw1apVo1u3bnTs2JF27dpRu3ZttSWihwwZwsqVKwkKCsLd3R0vLy/WrFlD5cqVix1rvXr1+OWXX9iwYQO1atVi2rRpzJo1K98Vz16UpaUla9as4ddff6VGjRrMnTuXgICAYrfTokULfv31V7Zt20bdunVp1aoVJ06cUJ0PCgqiX79+jB8/HldXV7p06cLJkydxcHB4mbcjhCgjNvwWx5bgeMZ/VJlv/Wuh1NXk0zmRaj0vdtZ6mBirD46o726CjaVuvquqOVfWp4aLIVUd9Vm/tC6bV9RTbZYVpKdXCJGXzMkpHkVOeV0cW+RrxowZbN26lfDw8NIO5Y3Wsmfh7zQSQgghRNly4Je8q96+Kv2nxZdY22tnle2Xzj+PctOTI4QQQgghRHn1Or4M9N69e/j4+GBsbIypqSmDBw9WLXL1LMeOHaNVq1YYGBhgbGzM22+/TWpqarGuLUlOOVOzZk21pY3/va1fv760wxNCCCGEEG8IHx8f/v77b/bu3cuOHTs4dOiQ6r2RBTl27Bje3t60a9eOEydOcPLkSXx9fdVetVIUMlytnLl27RoZGfm/i8Ha2lrt/Tyi9MhwNSGEEOL1U5rD1T6YcrPE2v7hC7uX3mZERAQ1atTg5MmTqvchBgcH07FjR/755x/VKr//1bhxY9q2bftci0f9W7lZQlrkcnR0LO0QhBBCCCHES1aSCwSkpaWRlqa+fH5+r8AojmPHjmFqaqr2wvc2bdqgoaFBWFiY6rUt/5aQkEBYWBg+Pj40bdqU6OhoqlevzhdffMFbb71VrOvLcDUhhBBCCCHeYP7+/piYmKht/v7+L9RmfHy82irEAFpaWpibmxMfn/8iCleuXAFyF8oaOnQowcHB1KtXj9atWxMVlf9LlQsiSY4QQgghhBBlXEkuPDB58mTu37+vtk2ePDnfOCZNmoRCoXjmdvHixee6xyfvqPzoo48YOHAgHh4eLFy4EFdXV1avXl2stmS4mhClIDMtvbRDEEIIIYQAijc0bfz48YW+77BKlSrY2NiQkJCgdjwzM5N79+6pXkD/X7a2tgDUqFFD7bibmxuxsbFFiu8JSXKEEEIIIYQo43L+v5ejtFlaWmJpaVlouSZNmpCUlMTp06epX78+AH/88QfZ2dk0apT/Ag5OTk7Y2dkRGRmpdvzSpUt06NChWHHKcDUhhBBCCCHES+Xm5oa3tzdDhw7lxIkThIaG4uvrS+/evVUrq924cYPq1atz4sQJABQKBRMmTGDJkiVs3LiRy5cvM3XqVC5evMjgwYOLdX3pyRFCCCGEEKKMyy7B1dVKyvr16/H19aV169ZoaGjQvXt3lixZojqfkZFBZGQkjx49Uh0bM2YMjx8/ZuzYsdy7d486deqwd+9eqlatWqxry3tyhCgFzd87XNohCCGEEKKYDv/WvNSu3cvvWom1/XNA+XsFiQxXe8MNGDCALl26lHYYpeK/996iRQvGjBlTavEIIYQQQhSkJFdXK49kuJoQ/2/z5s1oa2ur9p2cnBgzZowkPkIIBvd1pHNbGwwNNDl3MZnAby7zT9zjAsv/8l0DbK318hzfvOsmC5dHA7Dkc3c83E3Vzm8NjiPwm8svNXYhRPlQki8DLY8kyRHi/5mbm5d2CEKIMqhvt0p072THnMWRxN16zGAfJwJn1OJD39OkZ+T/oWOYXzga/xorUdnRgEWz3DkQeket3Lbf41j149MhKI/TysbqSUII8bqT4WpviI0bN+Lu7o5SqcTCwoI2bdrw8OHDPOWys7Px9/encuXKKJVK6tSpw8aNG9XKnD9/ng4dOmBoaIi1tTUffvghd+48/cXdokULfH198fX1xcTEhAoVKjB16tQid4cmJCTQuXNnlEollStXZv369Tg5ObFo0SIAYmJiUCgUhIeHq+okJSWhUCgICQkBICsri8GDB6vuw9XVlcWLFz/zuv8ertaiRQuuXbvG2LFjVS+2evjwIcbGxnmex9atWzEwMODBgwdFuj8hxOulZ+eKfP9rLEdO3CP62iO+WBSJhbkuzRtXKLBOUnIG95Kebk09zfknLpXw8/fVyj1Oy1Yr9yg1q6RvRwjxmsrJzimxrTySJOcNEBcXR58+fRg0aBARERGEhITQrVu3fJMOf39/vv/+e7799lv+/vtvxo4dywcffMDBgweB3GSiVatWeHh4cOrUKYKDg7l16xY9e/ZUa2ft2rVoaWlx4sQJFi9ezIIFC1i5cmWR4h0wYADXr1/nwIEDbNy4ka+//jrPy6QKk52dTaVKlfj111+5cOEC06ZN43//+x+//PJLkepv3ryZSpUqMWvWLOLi4oiLi8PAwIDevXsTFBSkVjYoKIj3338fIyOjYsUohCj7bK31sDDX4dTZJNWxh4+y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Level Correlation of Features')\n","plt.show()"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"executionInfo":{"elapsed":336,"status":"ok","timestamp":1716216847523,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"jTpncqzKEV6D","outputId":"f3327711-99b9-4200-eab4-c411019b3fd8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem mental_health_history depression \\\n","count 1100.000000 1100.000000 1100.000000 1100.000000 \n","mean 11.063636 17.777273 0.492727 12.555455 \n","std 6.117558 8.944599 0.500175 7.727008 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 6.000000 11.000000 0.000000 6.000000 \n","50% 11.000000 19.000000 0.000000 12.000000 \n","75% 16.000000 26.000000 1.000000 19.000000 \n","max 21.000000 30.000000 1.000000 27.000000 \n","\n"," headache blood_pressure sleep_quality breathing_problem \\\n","count 1100.000000 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8 rows × 21 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe"}},"metadata":{},"execution_count":16}],"source":["df.describe()"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":216,"status":"ok","timestamp":1716216848751,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"JE-35YkzlfaB","outputId":"864acbda-d10a-44e8-cf0b-8022ad0344d2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['anxiety_level', 'self_esteem', 'mental_health_history', 'depression',\n"," 'headache', 'blood_pressure', 'sleep_quality', 'breathing_problem',\n"," 'noise_level', 'living_conditions', 'safety', 'basic_needs',\n"," 'academic_performance', 'study_load', 'teacher_student_relationship',\n"," 'future_career_concerns', 'social_support', 'peer_pressure',\n"," 'extracurricular_activities', 'bullying', 'stress_level'],\n"," dtype='object')"]},"metadata":{},"execution_count":17}],"source":["df.columns"]},{"cell_type":"markdown","metadata":{"id":"9L3XVy2bY9Pc"},"source":["## Scaling - Standardizing\n","- Most of the columns were done in a 0-5 rating scale\n","- By scaling the data, we are able to see a better correlation between each feature.\n"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"zx3RJsf2LVUy","executionInfo":{"status":"ok","timestamp":1716216850993,"user_tz":300,"elapsed":2,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["cols=['anxiety_level', 'self_esteem', 'mental_health_history', 'depression',\n"," 'headache', 'blood_pressure', 'sleep_quality', 'breathing_problem',\n"," 'noise_level', 'living_conditions', 'safety', 'basic_needs',\n"," 'academic_performance', 'study_load', 'teacher_student_relationship',\n"," 'future_career_concerns', 'social_support', 'peer_pressure',\n"," 'extracurricular_activities', 'bullying', 'stress_level']\n","cols_norm=['blood_pressure']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_eJwxExRIgjG"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":19,"metadata":{"id":"Dd9_DBHrjk8F","executionInfo":{"status":"ok","timestamp":1716216853276,"user_tz":300,"elapsed":317,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["from sklearn.preprocessing import StandardScaler\n","sc=StandardScaler()\n","scaled=sc.fit_transform(df[cols].iloc[:,range(0,21)].values)\n","df_scaled=pd.DataFrame(sc.fit_transform(scaled),columns=cols)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"acn8z1x8O02x"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":167,"status":"ok","timestamp":1716216856658,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"aplsM5vsGMm0","outputId":"a3688c28-9b38-45e0-f9cd-c3d34263b011"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem mental_health_history depression headache \\\n","0 0.487122 0.695889 0.000000 0.382739 0.069589 \n","1 0.567758 0.302804 0.037851 0.567758 0.189253 \n","2 0.439057 0.658586 0.036588 0.512233 0.073176 \n","3 0.569976 0.427482 0.035624 0.534353 0.142494 \n","4 0.452911 0.792594 0.000000 0.198148 0.056614 \n","... ... ... ... ... ... \n","1095 0.414873 0.641167 0.000000 0.528020 0.113147 \n","1096 0.470438 0.627250 0.000000 0.418167 0.000000 \n","1097 0.138178 0.898155 0.000000 0.103633 0.034544 \n","1098 0.676014 0.000000 0.032191 0.611632 0.160956 \n","1099 0.663039 0.221013 0.036835 0.552532 0.110506 \n","\n"," blood_pressure sleep_quality breathing_problem noise_level \\\n","0 0.034794 0.069589 0.139178 0.069589 \n","1 0.113552 0.037851 0.151402 0.113552 \n","2 0.036588 0.073176 0.073176 0.073176 \n","3 0.106871 0.035624 0.106871 0.142494 \n","4 0.084921 0.141535 0.028307 0.084921 \n","... ... ... ... ... \n","1095 0.037716 0.113147 0.075431 0.075431 \n","1096 0.156813 0.000000 0.000000 0.000000 \n","1097 0.069089 0.172722 0.069089 0.069089 \n","1098 0.096573 0.032191 0.128765 0.096573 \n","1099 0.110506 0.000000 0.110506 0.110506 \n","\n"," living_conditions ... basic_needs academic_performance study_load \\\n","0 0.104383 ... 0.069589 0.104383 0.069589 \n","1 0.037851 ... 0.075701 0.037851 0.151402 \n","2 0.073176 ... 0.073176 0.073176 0.109764 \n","3 0.071247 ... 0.071247 0.071247 0.142494 \n","4 0.056614 ... 0.084921 0.113228 0.084921 \n","... ... ... ... ... ... \n","1095 0.075431 ... 0.113147 0.075431 0.075431 \n","1096 0.052271 ... 0.209083 0.000000 0.052271 \n","1097 0.103633 ... 0.138178 0.172722 0.034544 \n","1098 0.032191 ... 0.032191 0.064382 0.160956 \n","1099 0.000000 ... 0.110506 0.110506 0.147342 \n","\n"," teacher_student_relationship future_career_concerns social_support \\\n","0 0.104383 0.104383 0.069589 \n","1 0.037851 0.189253 0.037851 \n","2 0.109764 0.073176 0.073176 \n","3 0.035624 0.142494 0.035624 \n","4 0.028307 0.056614 0.028307 \n","... ... ... ... \n","1095 0.075431 0.113147 0.113147 \n","1096 0.052271 0.052271 0.052271 \n","1097 0.138178 0.034544 0.103633 \n","1098 0.032191 0.128765 0.032191 \n","1099 0.110506 0.110506 0.036835 \n","\n"," peer_pressure extracurricular_activities bullying stress_level \n","0 0.104383 0.104383 0.069589 0.034794 \n","1 0.151402 0.189253 0.189253 0.075701 \n","2 0.109764 0.073176 0.073176 0.036588 \n","3 0.142494 0.142494 0.178118 0.071247 \n","4 0.141535 0.000000 0.141535 0.028307 \n","... ... ... ... ... \n","1095 0.075431 0.113147 0.113147 0.037716 \n","1096 0.156813 0.209083 0.156813 0.104542 \n","1097 0.034544 0.069089 0.034544 0.000000 \n","1098 0.128765 0.128765 0.128765 0.064382 \n","1099 0.184177 0.036835 0.147342 0.073671 \n","\n","[1100 rows x 21 columns]"],"text/html":["\n","
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1100 rows × 21 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_norm"}},"metadata":{},"execution_count":20}],"source":["from sklearn.preprocessing import Normalizer\n","\n","data_norm = Normalizer()\n","Normalize = data_norm.fit_transform(df[cols].iloc[:,range(0,21)].values)\n","df_norm=pd.DataFrame(data_norm.fit_transform(Normalize),columns=cols)\n","df_norm\n"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":134,"status":"ok","timestamp":1716216859915,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"UcXyYmIoGuuZ","outputId":"d9f97fac-5e35-4cfd-ac4e-39090b57d57a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem mental_health_history depression headache \\\n","0 0.666667 0.666667 -2.0 -0.370370 -0.4 \n","1 0.857143 -0.933333 2.0 0.222222 2.0 \n","2 0.285714 0.400000 2.0 0.074074 -0.4 \n","3 1.047619 -0.400000 2.0 0.222222 1.2 \n","4 1.047619 1.733333 -2.0 -0.962963 -0.4 \n","... ... ... ... ... ... \n","1095 0.095238 0.266667 -2.0 0.074074 0.4 \n","1096 -0.285714 -0.400000 -2.0 -0.814815 -2.0 \n","1097 -1.238095 1.466667 -2.0 -1.555556 -1.2 \n","1098 2.000000 -2.000000 2.0 0.814815 2.0 \n","1099 1.428571 -1.200000 2.0 0.222222 0.4 \n","\n"," blood_pressure sleep_quality breathing_problem noise_level \\\n","0 -2.0 -0.4 1.2 -0.4 \n","1 2.0 -1.2 1.2 0.4 \n","2 -2.0 -0.4 -0.4 -0.4 \n","3 2.0 -1.2 0.4 1.2 \n","4 2.0 2.0 -1.2 0.4 \n","... ... ... ... ... \n","1095 -2.0 0.4 -0.4 -0.4 \n","1096 2.0 -2.0 -2.0 -2.0 \n","1097 0.0 2.0 -0.4 -0.4 \n","1098 2.0 -1.2 1.2 0.4 \n","1099 2.0 -2.0 0.4 0.4 \n","\n"," living_conditions ... basic_needs academic_performance study_load \\\n","0 0.4 ... -0.4 0.4 -0.4 \n","1 -1.2 ... -0.4 -1.2 1.2 \n","2 -0.4 ... -0.4 -0.4 0.4 \n","3 -0.4 ... -0.4 -0.4 1.2 \n","4 -0.4 ... 0.4 1.2 0.4 \n","... ... ... ... ... ... \n","1095 -0.4 ... 0.4 -0.4 -0.4 \n","1096 -1.2 ... 1.2 -2.0 -1.2 \n","1097 0.4 ... 1.2 2.0 -1.2 \n","1098 -1.2 ... -1.2 -0.4 2.0 \n","1099 -2.0 ... 0.4 0.4 1.2 \n","\n"," teacher_student_relationship future_career_concerns social_support \\\n","0 0.4 0.4 0.666667 \n","1 -1.2 2.0 -0.666667 \n","2 0.4 -0.4 0.666667 \n","3 -1.2 1.2 -0.666667 \n","4 -1.2 -0.4 -0.666667 \n","... ... ... ... \n","1095 -0.4 0.4 2.000000 \n","1096 -1.2 -1.2 -0.666667 \n","1097 1.2 -1.2 2.000000 \n","1098 -1.2 1.2 -0.666667 \n","1099 0.4 0.4 -0.666667 \n","\n"," peer_pressure extracurricular_activities bullying stress_level \n","0 0.4 0.4 -0.4 0.0 \n","1 1.2 2.0 2.0 2.0 \n","2 0.4 -0.4 -0.4 0.0 \n","3 1.2 1.2 2.0 2.0 \n","4 2.0 -2.0 2.0 0.0 \n","... ... ... ... ... \n","1095 -0.4 0.4 0.4 0.0 \n","1096 0.4 1.2 0.4 2.0 \n","1097 -1.2 -0.4 -1.2 -2.0 \n","1098 1.2 1.2 1.2 2.0 \n","1099 2.0 -1.2 1.2 2.0 \n","\n","[1100 rows x 21 columns]"],"text/html":["\n","
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anxiety_levelself_esteemmental_health_historydepressionheadacheblood_pressuresleep_qualitybreathing_problemnoise_levelliving_conditions...basic_needsacademic_performancestudy_loadteacher_student_relationshipfuture_career_concernssocial_supportpeer_pressureextracurricular_activitiesbullyingstress_level
00.4802080.248612-0.985559-0.201393-0.360741-1.418416-0.4264450.890211-0.4889490.430695...-0.5391960.160736-0.4728000.2542130.2295500.1128390.1863340.164249-0.4033770.004428
10.643746-1.0935901.0146530.3165081.7688590.981981-1.0725740.8902110.264334-1.357096...-0.539196-1.2537411.047901-1.1909271.537869-0.8419520.8882771.5757631.5570711.222011
20.1531310.0249121.0146530.187033-0.360741-1.418416-0.426445-0.538282-0.488949-0.463200...-0.539196-0.5465020.2875510.254213-0.4246090.1128390.186334-0.541508-0.4033770.004428
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40.8072841.143414-0.985559-0.719293-0.3607410.9819811.511942-1.2525290.264334-0.463200...0.1585870.8679740.287551-1.190927-0.424609-0.8419521.590220-1.9530231.5570710.004428
..................................................................
1095-0.010407-0.086938-0.9855590.1870330.349125-1.4184160.219684-0.538282-0.488949-0.463200...0.158587-0.546502-0.472800-0.4683570.2295501.067629-0.5156090.1642490.2501060.004428
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1100 rows × 21 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_scaled"}},"metadata":{},"execution_count":22}],"source":["df_scaled"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":17522,"status":"ok","timestamp":1716216895894,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"Y6Nxys-mdHkV","outputId":"44f2c5f7-4300-47f5-f495-dfea8a6132a3"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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embOnIk+ffoY/J7PnTuHCxcuYNasWe0u7+rqiieffFKcdnR0xPjx4w3uLw4cOIA77rgDDz/8sJjm5OSEhQsXdri8H330EcaMGdOi5wQA8VP2xr7L1Js1axZuu+02cfr+++8HALEOFRUVKC0tRVxcnMFzdnh4uMG9tykY+75Wb/78+QbvApqXXV/evLw8mxuT2dIYGLFhQ4YMMZgeNGgQ7O3tW3xLXu+7775r9fvt+s+cfPfdd+LfQ4YMaTG4Umv5vLy84OrqapCvK9+Ib14nOzs7DB48uEWd7rjjjhYDR3399dc4f/48br/9doM/d999N4Ab3xoEblz8Jk6ciD/96U+Qy+WIjY3Fzp07DYIkK1asgKurK8aPH48hQ4YgPj7e4FNbndH8hd/PP/+M6upqvPfeey3KrA/66MtMRF2TkZGBc+fOwdfXF+PHj0dycvItXwp9/fXXqKmpgaenZ4vf57Vr18Tf5tdffw0ACAkJaZEvPz+/xW/Y3t4ed911l0Ga/hrV1rW7Of01uPn1UiKRtFh3R5SXl2PevHnw8PAQxwh44IEHAMCk3yGdO3cufv/9d/GTEmVlZSgpKcGcOXNMtg2insKYe72u3l9s3boVo0ePhpOTE/r374/bb78d+/bt69Dv3snJqcWnAW677TajvyU/cODAFssCEJf/7rvvYG9v36KugwcPNrqMzTXft8CN63Hza3GfPn1afC7hu+++g7e3t0FjHqDlvXJ726qvr8fPP//cavk6+v9La8fAzc0NPj4+Lca+c3Nz43f+iTrhVtcq/XWq+XVJoVDA3d29xXXhZu09e5r7udHY52jgxrP0sWPH8OOPPwK48b37qqoqgxepxt5HE5H1DBgwAKGhodi5c6eY9uGHH6JPnz7iZ1NvpbX7i+b3ft999x0GDRrUIl9n7t8uXbp0y8+P6rdnzLtMPWPuP4HW7+NMPTamse9r9doru7+/PxITE/GPf/wDAwYMQGRkJDIzMzm+SCs4xkgP0vxi05Pd3LJZT6fTYdSoUfj73//e6jL67+737dsXhYWFOHLkCPbt24cDBw7gww8/REhICPLz8+Hg4IDhw4ejrKwMubm5OHDgAD766CNs2LABq1atEgeoamt/t9Xyr3mZ9YGYJ598ss1xWW7+fjcRdd5jjz2G+++/H3v27EF+fj7eeOMNvP7669i9e7f4DdSb6XQ6eHp6Yvv27a2uT//ySf87/uc//2nwrWW9Pn2s+9+ssdeppqYmhIeH48qVK1ixYgWGDRsGFxcX/Pjjj5g3b16bves6IyAgAEFBQdi2bRvmzp2Lbdu2wdHR0aBFMxG1rrXfdFfuL7Zt24Z58+ZhxowZWL58OTw9PeHg4ID09HRcunTJ6HI5ODgYnbcjywttDGRuSVKptMUDtiV09P+XtvZhd963RLbGmN9TZ57J23v2NPdzo7HP0cCNwEhSUhJycnKwdOlS7Ny5E25ubpgyZYrB+oy5jyYi64qNjcX8+fNRWlqKwMBA7Ny5E6GhoeJ4GLfSE+4vbLkOxpT9b3/7G+bNm4ePP/4Y+fn5ePbZZ8VxC9sbI6U3YWDEhn399dcGLecuXrwInU5nMBDjzfz8/FBWVtYi/auvvhLn6/8+c+YMdDqdwYNga/kOHTqEa9euGfQaaW0bHanTzQRBwMWLF4260Rs0aBC++OILhIaGtntDam9vj9DQUISGhuLvf/87XnvtNfz1r3/FkSNHEBYWBgBwcXHBrFmzMGvWLFy/fh0zZ87E6tWrkZSUBCcnJ9x2222orq5use5btQa62e23345+/fqhqalJ3CYRmY+Xlxf+/Oc/489//jOqqqrwhz/8AatXr241MDJo0CAcPHgQEydObDUQe3M+APD09DTqd6zT6fDNN9+ILfAA4L///S8AtHntbk5/Df7666/FTxwAgFarxeXLlzFmzBgxTd9ypPm1qvl16uzZs/jvf/+LrVu3Yu7cuWK6SqUyqkzNtXcNnjt3LhITE1FRUYHs7GxER0cbdGMmohs6eq8HdOz+YteuXbjrrruwe/dug99t84E4rd34xs/PDzqdDpcvXzZotXfx4sVOr7P5PSdw43pszLXYz88PBw8exNWrVw16jTS/V25vW87Ozm2+IOzo/y9EZF3669TXX38ttvAFbgyoW11d3eK60Nytnj3N/dzYkedof39/jB8/Hh9++CESEhKwe/duzJgxA1Kp1GB9xtxHE5F1zZgxA08//bT4Oa3//ve/SEpKMtn6/fz8cOHCBQiCYHBt6cz926BBg3Du3Ll2t2fMu0xj3fzc3Zwx7z07cv9s7Pvajho1ahRGjRqFlStX4vjx45g4cSI2btyIV199tVPr64n4KS0blpmZaTD99ttvA0CrL/kAYOrUqTh58iSKiorEtLq6Orz33nu48847xW/kTZ06FWq12uBbg42NjXj77bfh6uoqflpl6tSpaGxsRFZWlpivqalJLEdnfPDBBwbfnd+1axcqKirarNPNHnvsMfz44494//33W8z7/fffUVdXBwC4cuVKi/mBgYEAAI1GAwD49ddfDeY7OjoiICAAgiBAq9UCuHFhrqmpwZkzZ8R8FRUV4udh2uPg4ICYmBh89NFHrV7g2/q0AhF1TFNTU4suo56envD29hZ/88099thjaGpqQlpaWot5jY2NYqAhMjISMpkMr732mnhtuFlrv+N33nlH/LcgCHjnnXcgkUgQGhpqVH3GjRuH22+/HRs3bsT169fF9C1btrQIgOhfrBUWFoppTU1NeO+99wzy6Vuc3NzCRBAErF+/3qgyNefi4gKgZUBG7/HHH4ednR2ee+45fPPNNwbfpyWi/+novR7QsfuL1n77J06cMLhXBABnZ2cAbf+mzS0yMhIAsGHDBoP0rtxz7t27V/wUDACcPHkSJ06cMOqec+rUqWhqajK4ngPA2rVrYWdn12IdRUVFBt////777/Hxxx8jIiKizRZ/nfn/hYisZ+rUqQCAdevWGaTre2FER0e3uWx7z57mfm409jlab9asWSguLsb//d//4ZdffmkxHoGx99FEZF3u7u6IjIzEzp07sWPHDjg6OmLGjBkmW39kZCR+/PFH/Otf/xLTGhoaWr3WtCcmJgZffPFFq+/b9Pexxr7LNJaXlxcCAwOxdetWg/cJKpWqxZgfrWnvmfhmxr6vNVZtbS0aGxsN0kaNGgV7e/s234H0VuwxYsMuX76Mhx9+GFOmTEFRURG2bduGJ554wqC18M3+8pe/4P/9v/+HqKgoPPvss/Dw8MDWrVtx+fJlfPTRR2JEddGiRXj33Xcxb948lJSU4M4778SuXbtw7NgxrFu3TmwZN23aNEycOBF/+ctf8O233yIgIAC7d+/u0jfrPDw8cN9992H+/PmorKzEunXrMHjwYKMGZ5ozZw527tyJZ555BkeOHMHEiRPR1NSEr776Cjt37kReXh7GjRuH1NRUFBYWIjo6Gn5+fqiqqsKGDRvg4+MjDnQUEREBhUKBiRMnQi6X48svv8Q777yD6Ohosf6xsbFYsWIFHnnkETz77LOor69HVlYW7r77bqMHv1uzZg2OHDmCCRMmYOHChQgICMCVK1fwn//8BwcPHmw1iENEHXP16lX4+Pjg0UcfxZgxY+Dq6oqDBw/i1KlT+Nvf/tbqMg888ACefvpppKeno7S0FBEREZBIJPj666+Rk5OD9evX49FHH4VMJkNWVhbmzJmDP/zhD4iNjcXtt9+O8vJy7Nu3DxMnTjR4cebk5IQDBw4gLi4OEyZMwKeffop9+/bhpZdeMvqzAhKJBK+++iqefvpphISEYNasWbh8+TI2b97cYoyRESNGIDg4GElJSbhy5Qo8PDywY8eOFjdJw4YNw6BBg/DCCy/gxx9/hEwmw0cffdTpb9AHBgbCwcEBr7/+OmpqaiCVShESEgJPT08AN1q0T5kyBTk5OXB3d7/lywKi3qyj93p6xt5fPPTQQ9i9ezceeeQRREdH4/Lly9i4cSMCAgJw7do1cX19+/ZFQEAAPvzwQ9x9993w8PDAyJEj2/3Ws6kEBQUhJiYG69atw6+//org4GAUFBSIPe4606Nl8ODBuO+++7B48WJoNBqsW7cO/fv3x4svvtjustOmTcODDz6Iv/71r/j2228xZswY5Ofn4+OPP8bSpUvFoLTeyJEjERkZiWeffRZSqVQM8Og/z9qajv7/QkTWNWbMGMTFxeG9995DdXU1HnjgAZw8eRJbt27FjBkz8OCDD7a5rDHPnuZ8bjT2OVrvsccewwsvvIAXXngBHh4eLXqxGHsfTUTWN2vWLDz55JPYsGEDIiMj4e7ubrJ1P/3003jnnXfw+OOP47nnnoOXlxe2b98OJycnAB27f1u+fDl27dqFP/7xj3jqqacQFBSEK1eu4F//+hc2btyIMWPGGP0usyPS09MRHR2N++67D0899RSuXLmCt99+GyNGjDC4V25NUFAQAOCvf/0rYmNjIZFIMG3aNDFgcjNj39ca6/Dhw0hISMAf//hH3H333WhsbMQ///lPMdBONxHI5rzyyisCAOHChQvCo48+KvTr10+47bbbhISEBOH3338X8/n5+QlxcXEGy166dEl49NFHBXd3d8HJyUkYP368kJub22IblZWVwvz584UBAwYIjo6OwqhRo4TNmze3yPfrr78Kc+bMEWQymeDm5ibMmTNH+PzzzwUAreZvy5EjRwQAwv/7f/9PSEpKEjw9PYW+ffsK0dHRwnfffWeQ94EHHhBGjBjR6nquX78uvP7668KIESMEqVQq3HbbbUJQUJCQkpIi1NTUCIIgCIcOHRKmT58ueHt7C46OjoK3t7fw+OOPC//973/F9bz77rvCpEmThP79+wtSqVQYNGiQsHz5cnEdevn5+cLIkSMFR0dHYejQocK2bdvE43MzAEJ8fHyrZa6srBTi4+MFX19fQSKRCAqFQggNDRXee+89o/cfEbVNo9EIy5cvF8aMGSP069dPcHFxEcaMGSNs2LBBzBMXFyf4+fm1WPa9994TgoKChL59+wr9+vUTRo0aJbz44ovCTz/9ZJDvyJEjQmRkpODm5iY4OTkJgwYNEubNmyecPn3aYBsuLi7CpUuXhIiICMHZ2VmQy+XCK6+8IjQ1NXW4Xhs2bBD8/f0FqVQqjBs3TigsLBQeeOAB4YEHHjDId+nSJSEsLEyQSqWCXC4XXnrpJUGlUgkAhCNHjoj5Lly4IISFhQmurq7CgAEDhIULFwpffPFFi+t5a9e41v6/ef/994W77rpLcHBwaLEtQRCEnTt3CgCERYsWdbjuRD2dsfd6Xb2/0Ol0wmuvvSb4+fkJUqlUGDt2rJCbm9vqNfH48eNCUFCQ4OjoKAAQXnnlFUEQ/ndta6sON7t5uZvz/Pzzzwb5Nm/eLAAQLl++LKbV1dUJ8fHxgoeHh+Dq6irMmDFDKCsrEwAIa9asudXuNHD58mUBgPDGG28If/vb3wRfX19BKpUK999/v/DFF18Y5G2rboIgCFevXhWWLVsmeHt7CxKJRBgyZIjwxhtvCDqdrkWd4+PjhW3btglDhgwR93Pza2JrdRaEjv3/0lxb98x+fn5CdHT0rXYTEd3E2GuVVqsVUlJSBH9/f0EikQi+vr5CUlKS0NDQYLBc8/s1Y589TfXc2Nr9ojHP0TebOHGiAED405/+1OZ2jLmPbq0sRNR1za9Pbf3Wamtrhb59+woAhG3btrWYr39Xd/N9S1v3F63dP37zzTdCdHS00LdvX+H2228Xnn/+eeGjjz4SAAjFxcUdqtOvv/4qJCQkCHfccYfg6Ogo+Pj4CHFxccIvv/wi5jHmXebN94LNNb9XFQRB+Oijj4Thw4cLUqlUCAgIEHbv3t1qXVtbNi0tTbjjjjsEe3t7g+PR2fe1+uORk5PTap30df3mm2+Ep556Shg0aJDg5OQkeHh4CA8++KBw8ODBlju2l7MTBBsYVYZ6vKNHj+LBBx9ETk4OW48QUY80b9487Nq1q92WJV0xefJkADeuqd3dxx9/jBkzZqCwsBD333+/tYtDRDaotLQUY8eOxbZt2zB79myjlvn222/h7++PN954Ay+88IKZS0hEREREN1u3bh2WLVuGH374AXfccYe1i0O9HMcYISIiIot7//33cdddd4mfMCQiupXff/+9Rdq6detgb2+PSZMmWaFERERERHQrze/fGhoa8O6772LIkCEMilC3wDFGyKyuX7/e7vdO3dzcLFQaIqLu7cqVKwYDqjfn4OBg9Fgk3dWOHTtw5swZ7Nu3D+vXr+/U2ABE1PtkZGSgpKQEDz74IPr06YNPP/0Un376KRYtWgRfX180NTW1OwCxq6urhUpLRGR5P//8M5qamtqc7+joCA8PDwuWiIh6u5kzZ2LgwIEIDAxETU0Ntm3bhq+++grbt28HcCNw0t44xR4eHnB0dLREcakXYmCEzOr48eO3HGgOADZv3ow777zTMgUiIurGZs6ciYKCgjbn+/n54dtvv7Vcgczg8ccfh6urKxYsWIA///nP1i4OEdmIe++9FyqVCmlpabh27RoGDhyI5ORk/PWvfwUAfP/99/D397/lOl555RXMmzfPAqUlIrK8e+65B999912b8x944AGb+NwqEfUckZGR+Mc//oHt27ejqakJAQEB2LFjB2bNmgUA+PDDDzF//vxbruPIkSPiJ6OJTI1jjJBZ/fbbbygpKbllnhEjRsDLy8tCJSIi6r5KSkrw22+/tTm/b9++mDhxogVLRERkGxoaGvDZZ5/dMs9dd92Fu+66y0IlIiKyrGPHjrX62UG92267DUFBQRYsERHRrVVUVOD8+fO3zBMUFITbbrvNQiWi3oaBESIiIiIiIiIiIiIi6jU4+DoREREREREREREREfUaNjnGiE6nw08//YR+/fpx0FaiHkoQBFy9ehXe3t6wt2cM91Z4TSTq+XhNNB6viUQ9H6+JxuM1kajn4zXReLwmEvV8Hbkm2mRg5KeffoKvr6+1i0FEFvD999/Dx8fH2sXo1nhNJOo9eE1sH6+JRL0Hr4nt4zWRqPfgNbF9vCYS9R7GXBNtMjDSr18/ADcqKJPJoNVqkZ+fj4iICEgkEiuXrmNYduux5fL3hrLX1tbC19dX/L1T25pfE3sLW/4dmBP3S9tsed/wmmg8Y66JtnwumALr33vr31Pqzmui8Tpyn9hTzo+O6G11Zn17Jl4TjWfuZ+eedM6xLt0T69K+jlwTbTIwou/uJpPJxMCIs7MzZDKZzZ0ULLv12HL5e1PZ2b21fc2vib2FLf8OzIn7pW09Yd/wmtg+Y66JPeFc6ArWv/fWv6fVndfE9nXkPrGnnR/G6G11Zn17Nl4T22fuZ+eedM6xLt0T62I8Y66J/PggERERERERERERERH1GgyMEBERERERERERERFRr8HACBEREREREREREZlFVlYWRo8eLX7CSqlU4tNPPxXnT548GXZ2dgZ/nnnmGYN1lJeXIzo6Gs7OzvD09MTy5cvR2Nho6aoQUQ9ik2OMEBERERERERERUffn4+ODNWvWYMiQIRAEAVu3bsX06dPx+eefY8SIEQCAhQsXIjU1VVzG2dlZ/HdTUxOio6OhUChw/PhxVFRUYO7cuZBIJHjttdcsXh8i6hkYGCEiIiIiIiIiIiKzmDZtmsH06tWrkZWVheLiYjEw4uzsDIVC0ery+fn5uHDhAg4ePAi5XI7AwECkpaVhxYoVSE5OhqOjo9nrQEQ9T4cCI8nJyUhJSTFIGzp0KL766isAQENDA55//nns2LEDGo0GkZGR2LBhA+RyuZi/vLwcixcvxpEjR+Dq6oq4uDikp6ejTx/GaIiIiIiIiIiIiHqqpqYm5OTkoK6uDkqlUkzfvn07tm3bBoVCgWnTpuHll18We40UFRVh1KhRBu8XIyMjsXjxYpw/fx5jx45tdVsajQYajUacrq2tBQBotVpotVqT102/TnOs29JYl+6JdTF+vcbocDRixIgROHjw4P9WcFNAY9myZdi3bx9ycnLg5uaGhIQEzJw5E8eOHQPArm9ERERERERERES9zdmzZ6FUKtHQ0ABXV1fs2bMHAQEBAIAnnngCfn5+8Pb2xpkzZ7BixQqUlZVh9+7dAAC1Wm0QFAEgTqvV6ja3mZ6e3qKBN3CjB8rNn+oyNZVKZbZ1Wxrr0j2xLm2rr683Om+HAyN9+vRptWtbTU0NNm3ahOzsbISEhAAANm/ejOHDh6O4uBjBwcHs+kZERERERERERNTLDB06FKWlpaipqcGuXbsQFxeHgoICBAQEYNGiRWK+UaNGwcvLC6Ghobh06RIGDRrU6W0mJSUhMTFRnK6trYWvry8iIiIgk8m6VJ/WaLVaqFQqhIeHQyKRmHz9lsS6dE+sS/v0PcOM0eHAyNdffw1vb284OTlBqVQiPT0dAwcORElJCbRaLcLCwsS8w4YNw8CBA1FUVITg4GCzdX2z5W5ELLv12HL5e0PZbbFuRERERERERNSSo6MjBg8eDAAICgrCqVOnsH79erz77rst8k6YMAEAcPHiRQwaNAgKhQInT540yFNZWQkAbY5LAgBSqRRSqbRFukQiMetLZXOv35JYl+6Jdbn1+ozVocDIhAkTsGXLFgwdOhQVFRVISUnB/fffj3PnzkGtVsPR0RHu7u4Gy8jlcrFbm7m7vtlyNyKW3Xpsufw9uewd6fpGRHTnX/a1m0fqICBjPDAyOQ+aJjsLlKp9366JtnYRiKgHauua2B2vgzfjNZE6y5j7gNZY8zfB8516O51OZ9AI+malpaUAAC8vLwCAUqnE6tWrUVVVBU9PTwA33inIZDLxc1z0P529Jt7M0tdHXhPJGjoUGImKihL/PXr0aEyYMAF+fn7YuXMn+vbta/LC6bXX9c2WuxGx7NZjy+XXl/3l0/bQ6LrfQ+2tfP7XEKP2e0e6vhERERERERFR95SUlISoqCgMHDgQV69eRXZ2No4ePYq8vDxcunQJ2dnZmDp1Kvr3748zZ85g2bJlmDRpEkaPHg0AiIiIQEBAAObMmYOMjAyo1WqsXLkS8fHxrfYIISIyRoc/pXUzd3d33H333bh48SLCw8Nx/fp1VFdXG/QaqaysFLu1mbvrmy13I2LZrceWy6/R2XXL1n63ot/X7e13Wz0mRERERERERPQ/VVVVmDt3LioqKuDm5obRo0cjLy8P4eHh+P7773Hw4EGsW7cOdXV18PX1RUxMDFauXCku7+DggNzcXCxevBhKpRIuLi6Ii4tDamqqFWtFRLauS4GRa9eu4dKlS5gzZw6CgoIgkUhw6NAhxMTEAADKyspQXl4OpVIJgF3fiIiIiIiIiIiIepNNmza1Oc/X1xcFBQXtrsPPzw/79+83ZbGIqJez70jmF154AQUFBfj2229x/PhxPPLII3BwcMDjjz8ONzc3LFiwAImJiThy5AhKSkowf/58KJVKBAcHAzDs+vbFF18gLy+PXd+IiIiIeqA1a9bAzs4OS5cuFdMaGhoQHx+P/v37w9XVFTExMWLvYb3y8nJER0fD2dkZnp6eWL58ORobGy1ceiIiIiIiIurJOhQY+eGHH/D4449j6NCheOyxx9C/f38UFxfj9ttvBwCsXbsWDz30EGJiYjBp0iQoFArs3r1bXF7f9c3BwQFKpRJPPvkk5s6dy65vRERERD3IqVOn8O6774rfhdZbtmwZPvnkE+Tk5KCgoAA//fQTZs6cKc5vampCdHQ0rl+/juPHj2Pr1q3YsmULVq1aZekqEBF1SVZWFkaPHg2ZTAaZTAalUolPP/1UnM9AMREREZF1dehTWjt27LjlfCcnJ2RmZiIzM7PNPOz6RkRERNRzXbt2DbNnz8b777+PV199VUyvqanBpk2bkJ2djZCQEADA5s2bMXz4cBQXFyM4OBj5+fm4cOECDh48CLlcjsDAQKSlpWHFihVITk6Go6OjtapFRNQhPj4+WLNmDYYMGQJBELB161ZMnz4dn3/+OUaMGIFly5Zh3759yMnJgZubGxISEjBz5kwcO3YMwP8CxQqFAsePH0dFRQXmzp0LiUSC1157zcq1IyIiIrJ9XRpjhIiIiIjoZvHx8YiOjkZYWJhBYKSkpARarRZhYWFi2rBhwzBw4EAUFRUhODgYRUVFGDVqFORyuZgnMjISixcvxvnz5zF27NgW29NoNNBoNOJ0bW0tAECr1UKr1bZaRn16W/N7Ota/59df6iC0nm4vGPzd3Rh7TGzh2E2bNs1gevXq1cjKykJxcTF8fHwYKCYiIiKyMgZGiIiIiMgkduzYgf/85z84depUi3lqtRqOjo5wd3c3SJfL5VCr1WKem4Mi+vn6ea1JT09HSkpKi/T8/Hw4OzvfsrwqleqW83s61r/n1j9j/K3np43TWaYgHWTslwXq6+vNXBLTampqQk5ODurq6qBUKs0WKCYiIiIi4zEwQkRERERd9v333+O5556DSqWCk5OTxbablJSExMREcbq2tha+vr6IiIiATCZrdRmtVguVSoXw8HBIJBJLFbXbYP17fv1HJue1mi61F5A2ToeXT9tDo7OzcKnady450qh8+p5h3d3Zs2ehVCrR0NAAV1dX7NmzBwEBASgtLTVLoBjoXC86PVvuTdVWL6l2l7NiLypr7GdbPsad0Vvq29PrR0RkLgyMEBEREVGXlZSUoKqqCn/4wx/EtKamJhQWFuKdd95BXl4erl+/jurqaoOXgZWVlVAoFAAAhUKBkydPGqxXPxixPk9zUqkUUqm0RbpEImn3pbcxeXoy1r/n1l/TdOugh0Zn124eazD2eNjKcRs6dChKS0tRU1ODXbt2IS4uDgUFBWbdZld60enZYm+q9npJtccavaisOfaqLR7jrujp9bW1XnRERN0FAyNERERE1GWhoaE4e/asQdr8+fMxbNgwrFixAr6+vpBIJDh06BBiYmIAAGVlZSgvL4dSqQQAKJVKrF69GlVVVfD09ARw42WGTCZDQECAZStERNRFjo6OGDx4MAAgKCgIp06dwvr16zFr1iyzBIqBzvWi07Pl3lRt9ZJqjzV7URnbQ8qUbPkYd0Zvqa+t9KIjIupuGBghIiIioi7r168fRo4caZDm4uKC/v37i+kLFixAYmIiPDw8IJPJsGTJEiiVSgQHBwMAIiIiEBAQgDlz5iAjIwNqtRorV65EfHx8q71CiIhsiU6ng0ajQVBQkNkCxV3pRdeZvN1FV3tAWaMXlTX3sS0e467o6fXtyXUjIjInBkaIiIiIyCLWrl0Le3t7xMTEQKPRIDIyEhs2bBDnOzg4IDc3F4sXL4ZSqYSLiwvi4uKQmppqxVITEXVcUlISoqKiMHDgQFy9ehXZ2dk4evQo8vLy4ObmxkAxERFRD3DnX/ZZdHtSBwEZ42/0lOxsQP/bNdEmLpXtsrd2AYiIbFVWVhZGjx4NmUwGmUwGpVKJTz/9VJw/efJk2NnZGfx55plnDNZRXl6O6OhoODs7w9PTE8uXL0djY6Olq0JEZBZHjx7FunXrxGknJydkZmbiypUrqKurw+7du1t8EsbPzw/79+9HfX09fv75Z7z55pvo04dteYjItlRVVWHu3LkYOnQoQkNDcerUKeTl5SE8PBzAjUDxQw89hJiYGEyaNAkKhQK7d+8Wl9cHih0cHKBUKvHkk09i7ty5DBQTERERmQifMomIOsnHxwdr1qzBkCFDIAgCtm7diunTp+Pzzz/HiBEjAAALFy40eIC9edDLpqYmREdHQ6FQ4Pjx46ioqMDcuXMhkUjw2muvWbw+RERERGQamzZtuuV8faA4MzOzzTz6QDERERERmR4DI0REnTRt2jSD6dWrVyMrKwvFxcViYMTZ2bnNATLz8/Nx4cIFHDx4EHK5HIGBgUhLS8OKFSuQnJwMR0dHs9eBiIiIiIiIiIiot+GntIiITKCpqQk7duxAXV2dOGgmAGzfvh0DBgzAyJEjkZSUhPr6enFeUVERRo0aBblcLqZFRkaitrYW58+ft2j5iYiIiIiIiIiIegv2GCEi6oKzZ89CqVSioaEBrq6u2LNnDwICAgAATzzxBPz8/ODt7Y0zZ85gxYoVKCsrE78frVarDYIiAMRptVrd5jY1Gg00Go04XVtbCwDQarXQarUmrV93pq9rb6qzMXrrfpE6CO3nsRcM/u4OjD1Ove14EhER9XSWHrAX4KC9REREN2NghIioC4YOHYrS0lLU1NRg165diIuLQ0FBAQICArBo0SIx36hRo+Dl5YXQ0FBcunQJgwYN6vQ209PTkZKS0iI9Pz/fYAyT3kKlUlm7CN1Sb9svGeONz5s2Tme+gnSQsd+Ov7m3GRERERERERF1DQMjRERd4OjoiMGDBwMAgoKCcOrUKaxfvx7vvvtui7wTJkwAAFy8eBGDBg2CQqHAyZMnDfJUVlYCQJvjkgBAUlISEhMTxena2lr4+voiIiICMpmsy3WyFVqtFiqVCuHh4ZBIJNYuTrfRW/fLyOS8dvNI7QWkjdPh5dP20Og610rS1M4lRxqVT98zjIiIiIiIiIi6joERIiIT0ul0Bp+5ullpaSkAwMvLCwCgVCqxevVqVFVVwdPTE8CNVv4ymUz8HFdrpFIppFJpi3SJRNKrXoTr9dZ6t6e37ZeOfA5Co7Pr9OcjTM3YY9SbjiURERERERGRuTEwQkTUSUlJSYiKisLAgQNx9epVZGdn4+jRo8jLy8OlS5eQnZ2NqVOnon///jhz5gyWLVuGSZMmYfTo0QCAiIgIBAQEYM6cOcjIyIBarcbKlSsRHx/fauCDiIiIiIiIiIiIuo6BESKiTqqqqsLcuXNRUVEBNzc3jB49Gnl5eQgPD8f333+PgwcPYt26dairq4Ovry9iYmKwcuVKcXkHBwfk5uZi8eLFUCqVcHFxQVxcHFJTU61YKyIiIiIiIiIiop6NgREiok7atGlTm/N8fX1RUFDQ7jr8/PyMHnyZiIiIiIiIiIiIus7e2gUgIiIiIiIiIiIiIiKyFAZGiIiIiIiIiIiIiIio12BghIiIiIiIiIiIiMwiKysLo0ePhkwmg0wmg1KpxKeffirOb2hoQHx8PPr37w9XV1fExMSgsrLSYB3l5eWIjo6Gs7MzPD09sXz5cjQ2Nlq6KkTUgzAwQkRERERERERERGbh4+ODNWvWoKSkBKdPn0ZISAimT5+O8+fPAwCWLVuGTz75BDk5OSgoKMBPP/2EmTNniss3NTUhOjoa169fx/Hjx7F161Zs2bIFq1atslaViKgH4ODrREREREREREREZBbTpk0zmF69ejWysrJQXFwMHx8fbNq0CdnZ2QgJCQEAbN68GcOHD0dxcTGCg4ORn5+PCxcu4ODBg5DL5QgMDERaWhpWrFiB5ORkODo6WqNaRGTjGBghIiIiIiIiIiIis2tqakJOTg7q6uqgVCpRUlICrVaLsLAwMc+wYcMwcOBAFBUVITg4GEVFRRg1ahTkcrmYJzIyEosXL8b58+cxduzYVrel0Wig0WjE6draWgCAVquFVqs1ed306zTHujtC6iB0fR32gsHf5mbOfWbO42KKfd2h7ZnguFj7/NQz13HpyPoYGCEiIiIiIiIiIiKzOXv2LJRKJRoaGuDq6oo9e/YgICAApaWlcHR0hLu7u0F+uVwOtVoNAFCr1QZBEf18/by2pKenIyUlpUV6fn4+nJ2du1ijtqlUKrOt2xgZ4023rrRxOtOt7Bb2799v9m2Y47iYcl93RFeOiyX2dUeY+rjU19cbnZeBESIisgl3/mWfwbTUQUDGeGBkch40TXZWKtWtfbsm2tpFICIiIiIisrqhQ4eitLQUNTU12LVrF+Li4lBQUGDWbSYlJSExMVGcrq2tha+vLyIiIiCTyUy+Pa1WC5VKhfDwcEgkEpOv31gjk/O6vA6pvYC0cTq8fNoeGp35n7fPJUeabd3mPC6m2NcdYYrjYs593RHmOi76nmHGYGCEiIiIiIiIiIiIzMbR0RGDBw8GAAQFBeHUqVNYv349Zs2ahevXr6O6utqg10hlZSUUCgUAQKFQ4OTJkwbrq6ysFOe1RSqVQiqVtkiXSCRmDVyYe/3tMWXDQY3OziINES2xv8xxXKzVSLMrx8Wa52ZrTH1cOrIue5NtlYiIiIiIiIiIiKgdOp0OGo0GQUFBkEgkOHTokDivrKwM5eXlUCqVAAClUomzZ8+iqqpKzKNSqSCTyRAQEGDxshNRz8AeI0RERERERERERGQWSUlJiIqKwsCBA3H16lVkZ2fj6NGjyMvLg5ubGxYsWIDExER4eHhAJpNhyZIlUCqVCA4OBgBEREQgICAAc+bMQUZGBtRqNVauXIn4+PhWe4QQERmDgREiIiIiIiIiIiIyi6qqKsydOxcVFRVwc3PD6NGjkZeXh/DwcADA2rVrYW9vj5iYGGg0GkRGRmLDhg3i8g4ODsjNzcXixYuhVCrh4uKCuLg4pKamWqtKRNQDMDBCREREREREREREZrFp06ZbzndyckJmZiYyMzPbzOPn54f9+/ebumhE1ItxjBEiIiIiIiIiIiIiIuo1GBghIiIiIiIiIiIiIqJeg4ERIiIiIiIiIiIiIiLqNRgYISIiIiIiIiIiIiKiXoOBESIiIiIiIiIiIiIi6jW6FBhZs2YN7OzssHTpUjGtoaEB8fHx6N+/P1xdXRETE4PKykqD5crLyxEdHQ1nZ2d4enpi+fLlaGxs7EpRiIiIiIiIiLqF9PR03HPPPejXrx88PT0xY8YMlJWVGeSZPHky7OzsDP4888wzBnn47ExERERkHp0OjJw6dQrvvvsuRo8ebZC+bNkyfPLJJ8jJyUFBQQF++uknzJw5U5zf1NSE6OhoXL9+HcePH8fWrVuxZcsWrFq1qvO1ICIiIiIiIuomCgoKEB8fj+LiYqhUKmi1WkRERKCurs4g38KFC1FRUSH+ycjIEOfx2ZmIiIjIfDoVGLl27Rpmz56N999/H7fddpuYXlNTg02bNuHvf/87QkJCEBQUhM2bN+P48eMoLi4GAOTn5+PChQvYtm0bAgMDERUVhbS0NGRmZuL69eumqRURkQVkZWVh9OjRkMlkkMlkUCqV+PTTT8X57EFHRERE1DsdOHAA8+bNw4gRIzBmzBhs2bIF5eXlKCkpMcjn7OwMhUIh/pHJZOI8PjsTERERmU+fziwUHx+P6OhohIWF4dVXXxXTS0pKoNVqERYWJqYNGzYMAwcORFFREYKDg1FUVIRRo0ZBLpeLeSIjI7F48WKcP38eY8eObbE9jUYDjUYjTtfW1gIAtFqt+Ec/bWtYduux5fLryyy1F6xcko4zdr/bwnHx8fHBmjVrMGTIEAiCgK1bt2L69On4/PPPMWLECCxbtgz79u1DTk4O3NzckJCQgJkzZ+LYsWMA/tcKUKFQ4Pjx46ioqMDcuXMhkUjw2muvWbl2RERERGQqNTU1AAAPDw+D9O3bt2Pbtm1QKBSYNm0aXn75ZTg7OwNAp56diYiIiMg4HQ6M7NixA//5z39w6tSpFvPUajUcHR3h7u5ukC6Xy6FWq8U8N9/Y6efr57UmPT0dKSkpLdLz8/PFm0YAUKlUHapLd8KyW48tlz9tnM7aRegw/f5ub7/X19dbojhdMm3aNIPp1atXIysrC8XFxfDx8cGmTZuQnZ2NkJAQAMDmzZsxfPhwFBcXIzg4WGwFePDgQcjlcgQGBiItLQ0rVqxAcnIyHB0drVEtIiIiIjIhnU6HpUuXYuLEiRg5cqSY/sQTT8DPzw/e3t44c+YMVqxYgbKyMuzevRtA556d22tUeCu23HBM6tC5BmP6hma22OCsM0xRX1s6P2z5nO6Inl4/IiJz6VBg5Pvvv8dzzz0HlUoFJycnc5WphaSkJCQmJorTtbW18PX1RUREBGQyGbRaLVQqFcLDwyGRSCxWLlNg2a3HlsuvL/vLp+2h0dlZuzgd8vlfQ4za7/qHOFvR1NSEnJwc1NXVQalUmq0HHRERERHZlvj4eJw7dw6fffaZQfqiRYvEf48aNQpeXl4IDQ3FpUuXMGjQoE5ty9hGhbdiiw3HMsZ3bXlbbHDWFV2p7/79+01YEsuwxXO6I2yhUSERUXfUocBISUkJqqqq8Ic//EFMa2pqQmFhId555x3k5eXh+vXrqK6uNug1UllZCYVCAQBQKBQ4efKkwXr139zX52lOKpVCKpW2SJdIJAYvVptP2xKW3XpsufwanR00TbYVGNHv6/b2u60ck7Nnz0KpVKKhoQGurq7Ys2cPAgICUFpaapYedEDXWgLasuYtAW2hhZ81jkdvaRnXnDEtRbvjOWPsceptx5OIqKdISEhAbm4uCgsL4ePjc8u8EyZMAABcvHgRgwYN6tSzc3uNCm/FlhuOjUzO69RyUnsBaeN0NtngrDNMUd9zyZEmLpX52PI53RG21qiQiKi76FBgJDQ0FGfPnjVImz9/PoYNG4YVK1bA19cXEokEhw4dQkxMDACgrKwM5eXlUCqVAAClUonVq1ejqqoKnp6eAG5E72UyGQICAkxRJyIiixk6dChKS0tRU1ODXbt2IS4uDgUFBWbdpilaAtqitloCducWftZsUdfTW8Y115GWot3pnDH2HGFLQCIi2yIIApYsWYI9e/bg6NGj8Pf3b3eZ0tJSAICXlxeAzj07G9uo8FZsseFYVxuL2WKDs67oSn1t7dwAbPOc7oieXDciInPqUGCkX79+Bt9EBQAXFxf0799fTF+wYAESExPh4eEBmUyGJUuWQKlUIjg4GAAQERGBgIAAzJkzBxkZGVCr1Vi5ciXi4+NbvYEjIurOHB0dMXjwYABAUFAQTp06hfXr12PWrFlm6UEHdK0loC1r3hLQFlr4WaNFXW9pGdecMS1Fu+M5Y+w5wpaA1BPc+Zd91i4CgBs9zDLG37huGPNi8Ns10RYoFfU08fHxyM7Oxscff4x+/fqJvYHd3NzQt29fXLp0CdnZ2Zg6dSr69++PM2fOYNmyZZg0aRJGjx4NgM/ORERERObU4cHX27N27VrY29sjJiYGGo0GkZGR2LBhgzjfwcEBubm5WLx4MZRKJVxcXBAXF4fU1FRTF4WIyOJ0Oh00Gg2CgoLM1oPOFC0BbVFbL6+6cws/ax6Pnn4+NNeRc6A7nTMdab1LRES2IysrCwAwefJkg/TNmzdj3rx5cHR0xMGDB7Fu3TrU1dXB19cXMTExWLlypZiXz85ERERE5tPlwMjRo0cNpp2cnJCZmYnMzMw2l/Hz87PJAbuIiG6WlJSEqKgoDBw4EFevXkV2djaOHj2KvLw8uLm5sQcdERERUS8lCLcez8rX19eoz6/y2ZmIiIjIPEzeY4SIqLeoqqrC3LlzUVFRATc3N4wePRp5eXkIDw8HwB50RERERERERERE3REDI0REnbRp06ZbzmcPOiIiIiIiIiIiou7H3toFICIiIiIiIiIiIiIishQGRoiIiIiIiIiIiIiIqNdgYISIiIiIiIiIiIiIiHoNBkaIiIiIiIiIiIiIiKjXYGCEiIiIiIiIiIiIiIh6DQZGiIiIiKjLsrKyMHr0aMhkMshkMiiVSnz66afi/IaGBsTHx6N///5wdXVFTEwMKisrDdZRXl6O6OhoODs7w9PTE8uXL0djY6Olq0JEREREJpSeno577rkH/fr1g6enJ2bMmIGysjKDPJMnT4adnZ3Bn2eeecYgD+8ViciUGBghIiIioi7z8fHBmjVrUFJSgtOnTyMkJATTp0/H+fPnAQDLli3DJ598gpycHBQUFOCnn37CzJkzxeWbmpoQHR2N69ev4/jx49i6dSu2bNmCVatWWatKRERERGQCBQUFiI+PR3FxMVQqFbRaLSIiIlBXV2eQb+HChaioqBD/ZGRkiPN4r0hEptbH2gUgIiIiIts3bdo0g+nVq1cjKysLxcXF8PHxwaZNm5CdnY2QkBAAwObNmzF8+HAUFxcjODgY+fn5uHDhAg4ePAi5XI7AwECkpaVhxYoVSE5OhqOjozWqRURERERddODAAYPpLVu2wNPTEyUlJZg0aZKY7uzsDIVC0eo6eK9IRKbGwAgRERERmVRTUxNycnJQV1cHpVKJkpISaLVahIWFiXmGDRuGgQMHoqioCMHBwSgqKsKoUaMgl8vFPJGRkVi8eDHOnz+PsWPHtrotjUYDjUYjTtfW1gIAtFottFptq8vo09ua39NZq/5SB8Gi22uL1F4w+Ls9tnietLWvO1p3SzN2X9viMSEiov+pqakBAHh4eBikb9++Hdu2bYNCocC0adPw8ssvw9nZGQA6fa9IRNQWBkaIiIiIyCTOnj0LpVKJhoYGuLq6Ys+ePQgICEBpaSkcHR3h7u5ukF8ul0OtVgMA1Gq1wYOufr5+XlvS09ORkpLSIj0/P198kG6LSqUyplo9lqXrnzHeoptrV9o4nVH59u/fb+aSmF57+9rYuluasfu6vr7ezCUhIiJz0el0WLp0KSZOnIiRI0eK6U888QT8/Pzg7e2NM2fOYMWKFSgrK8Pu3bsBdO5esTMNaLqiuzS+MUVjFEs3pjDnPjPncbF0wx9THBdrn5965jouHVkfAyNEREREZBJDhw5FaWkpampqsGvXLsTFxaGgoMCs20xKSkJiYqI4XVtbC19fX0REREAmk7W6jFarhUqlQnh4OCQSiVnL1x1Zq/4jk/Mstq1bkdoLSBunw8un7aHR2bWb/1xypAVKZVpt7euO1t3SjN3X+hdbRERke+Lj43Hu3Dl89tlnBumLFi0S/z1q1Ch4eXkhNDQUly5dwqBBgzq1ra40oOkKaze+MWVjFEs1prBEQxRzHBdrNfzpynHpbo1+TH1cOtKAhoERIiIiIjIJR0dHDB48GAAQFBSEU6dOYf369Zg1axauX7+O6upqg14jlZWV4nekFQoFTp48abC+yspKcV5bpFIppFJpi3SJRNLuS39j8vRklq6/pql7vYjX6OyMKpMtniPt1cvYuluasfvaFo+JLRmZnNctzw8isn0JCQnIzc1FYWEhfHx8bpl3woQJAICLFy9i0KBBnbpX7EwDmq7oLo1vTNEYxdKNKczZEMWcx8XSDX9McVy6S6Mfcx2XjjSgYWCEiIiIiMxCp9NBo9EgKCgIEokEhw4dQkxMDACgrKwM5eXlUCqVAAClUonVq1ejqqoKnp6eAG60HpLJZAgICLBaHYiIiIioawRBwJIlS7Bnzx4cPXoU/v7+7S5TWloKAPDy8gLQuXvFrjSg6QprN74xZXDbUo0pLLG/zHFcrNWQoCvHpbs1MDH1cenIuhgYISIiIqIuS0pKQlRUFAYOHIirV68iOzsbR48eRV5eHtzc3LBgwQIkJibCw8MDMpkMS5YsgVKpRHBwMAAgIiICAQEBmDNnDjIyMqBWq7Fy5UrEx8e3+kBLRERERLYhPj4e2dnZ+Pjjj9GvXz9xTBA3Nzf07dsXly5dQnZ2NqZOnYr+/fvjzJkzWLZsGSZNmoTRo0cD4L0iEZkeAyNERERE1GVVVVWYO3cuKioq4ObmhtGjRyMvLw/h4eEAgLVr18Le3h4xMTHQaDSIjIzEhg0bxOUdHByQm5uLxYsXQ6lUwsXFBXFxcUhNTbVWlYiIiIjIBLKysgAAkydPNkjfvHkz5s2bB0dHRxw8eBDr1q1DXV0dfH19ERMTg5UrV4p5ea9IRKbGwAgRERERddmmTZtuOd/JyQmZmZnIzMxsM4+fn1+3GwyQiIiIiLpGEIRbzvf19UVBQUG76+G9IhGZkr21C0BERERERERERERERGQpDIwQEREREREREREREVGvwcAIERERERERERERERH1GgyMEBERERERERERERFRr8HACBFRJ6Wnp+Oee+5Bv3794OnpiRkzZqCsrMwgz+TJk2FnZ2fw55lnnjHIU15ejujoaDg7O8PT0xPLly9HY2OjJatCRERERERERETUa/SxdgGIiGxVQUEB4uPjcc8996CxsREvvfQSIiIicOHCBbi4uIj5Fi5ciNTUVHHa2dlZ/HdTUxOio6OhUChw/PhxVFRUYO7cuZBIJHjttdcsWh8iIiIiIiIiIqLegIERIqJOOnDggMH0li1b4OnpiZKSEkyaNElMd3Z2hkKhaHUd+fn5uHDhAg4ePAi5XI7AwECkpaVhxYoVSE5OhqOjo1nrQERERERERERE1NvwU1pERCZSU1MDAPDw8DBI3759OwYMGICRI0ciKSkJ9fX14ryioiKMGjUKcrlcTIuMjERtbS3Onz9vmYITERERERERERH1IuwxQkRkAjqdDkuXLsXEiRMxcuRIMf2JJ56An58fvL29cebMGaxYsQJlZWXYvXs3AECtVhsERQCI02q1utVtaTQaaDQacbq2thYAoNVqodVqTVqv7kTqIBhO2wsGf3dH1jge+m325HOhNc3Pj1bzdMNzxtjj1NuOJxEREREREZE5MTBCRGQC8fHxOHfuHD777DOD9EWLFon/HjVqFLy8vBAaGopLly5h0KBBndpWeno6UlJSWqTn5+cbjF/S02SMbz09bZzOsgXpgP3791tt2yqVymrbtoa2zo/WdKdzxthz5OaeZkRE1P2lp6dj9+7d+Oqrr9C3b1/ce++9eP311zF06FAxT0NDA55//nns2LEDGo0GkZGR2LBhg0GjmfLycixevBhHjhyBq6sr4uLikJ6ejj59+ChPRERE1BW8myIi6qKEhATk5uaisLAQPj4+t8w7YcIEAMDFixcxaNAgKBQKnDx50iBPZWUlALQ5LklSUhISExPF6draWvj6+iIiIgIymawrVenWRibnGUxL7QWkjdPh5dP20OjsrFSqWzuXHGnxbWq1WqhUKoSHh0MikVh8+9bS/PxoTXc8Z4w9R/Q9w4iIyDYUFBQgPj4e99xzDxobG/HSSy8hIiICFy5cgIuLCwBg2bJl2LdvH3JycuDm5oaEhATMnDkTx44dAwA0NTUhOjoaCoUCx48fR0VFBebOnQuJRILXXnvNmtUjIiIisnkMjBARdZIgCFiyZAn27NmDo0ePwt/fv91lSktLAQBeXl4AAKVSidWrV6Oqqgqenp4AbrT0l8lkCAgIaHUdUqkUUqm0RbpEIunRL8I1Ta2/yNbo7NqcZ23WPB49/XxoriPnQHc6Z4w9Rr3pWBIR9QQHDhwwmN6yZQs8PT1RUlKCSZMmoaamBps2bUJ2djZCQkIAAJs3b8bw4cNRXFyM4OBg5Ofn48KFCzh48CDkcjkCAwORlpaGFStWIDk5GY6OjtaoGhEREVGPwMAIEVEnxcfHIzs7Gx9//DH69esnjgni5uaGvn374tKlS8jOzsbUqVPRv39/nDlzBsuWLcOkSZMwevRoAEBERAQCAgIwZ84cZGRkQK1WY+XKlYiPj281+EFEREREtqempgYA4OHhAQAoKSmBVqtFWFiYmGfYsGEYOHAgioqKEBwcjKKiIowaNcrg01qRkZFYvHgxzp8/j7Fjx7bYTlfGotPP705jcZlbdxx/zJxMUV9bGvest4y919PrR0RkLgyMEBF1UlZWFgBg8uTJBumbN2/GvHnz4OjoiIMHD2LdunWoq6uDr68vYmJisHLlSjGvg4MDcnNzsXjxYiiVSri4uCAuLg6pqamWrAoRERERmYlOp8PSpUsxceJEjBw5EgCgVqvh6OgId3d3g7xyuVxsbKNWqw2CIvr5+nmtMcVYdN1pLC5L6W117kp9rTmGXmf19LH3OBYdEXXEnX/ZZ+0iAACkDgIyxt/4LLYxX3T4dk20ycvAwAgRUScJwq1bWvn6+qKgoKDd9fj5+dnkAwYRERERtS8+Ph7nzp3DZ599ZvZtdWUsOv04Zd1pLC5z647jj5mTKeprjTH0Oqu3jL3HseiIiDqHgREiIiIiIiIiM0hISEBubi4KCwvh4+MjpisUCly/fh3V1dUGvUYqKyuhUCjEPCdPnjRYX2VlpTivNaYYi647jcVlKb2tzl2pry0GGHr62Hs9uW5EROZkb+0CEBEREREREfUkgiAgISEBe/bsweHDh+Hv728wPygoCBKJBIcOHRLTysrKUF5eDqVSCQBQKpU4e/YsqqqqxDwqlQoymQwBAQGWqQgRERFRD8UeI0REREREREQmFB8fj+zsbHz88cfo16+fOCaIm5sb+vbtCzc3NyxYsACJiYnw8PCATCbDkiVLoFQqERwcDACIiIhAQEAA5syZg4yMDKjVaqxcuRLx8fGt9gohIiIiIuN1qMdIVlYWRo8eDZlMBplMBqVSiU8//VSc39DQgPj4ePTv3x+urq6IiYkRu/rqlZeXIzo6Gs7OzvD09MTy5cvR2NhomtoQERERERERWVlWVhZqamowefJkeHl5iX8+/PBDMc/atWvx0EMPISYmBpMmTYJCocDu3bvF+Q4ODsjNzYWDgwOUSiWefPJJzJ07F6mpqdaoEhEREVGP0qEeIz4+PlizZg2GDBkCQRCwdetWTJ8+HZ9//jlGjBiBZcuWYd++fcjJyYGbmxsSEhIwc+ZMHDt2DADQ1NSE6OhoKBQKHD9+HBUVFZg7dy4kEglee+01s1SQiIiIiIiIyJIEQWg3j5OTEzIzM5GZmdlmHj8/P+zfv9+URSMiIiIidDAwMm3aNIPp1atXIysrC8XFxfDx8cGmTZuQnZ2NkJAQAMDmzZsxfPhwFBcXIzg4GPn5+bhw4QIOHjwIuVyOwMBApKWlYcWKFUhOToajo6PpakZERERERERERERERNRMpwdfb2pqwo4dO1BXVwelUomSkhJotVqEhYWJeYYNG4aBAweiqKgIAFBUVIRRo0ZBLpeLeSIjI1FbW4vz5893oRpERERERERERERERETt6/Dg62fPnoVSqURDQwNcXV2xZ88eBAQEoLS0FI6OjnB3dzfIL5fLxYHm1Gq1QVBEP18/ry0ajQYajUacrq2tBQBotVrxj37a1rDs1mPL5deXWWrffhf97sbY/W6Lx4WIiIiIiIiIiIi6vw4HRoYOHYrS0lLU1NRg165diIuLQ0FBgTnKJkpPT0dKSkqL9Pz8fDg7O4vTKpXKrOUwJ5bdemy5/GnjdNYuQofp93d7+72+vt4SxSEiIiIiIiIiM0pPT8fu3bvx1VdfoW/fvrj33nvx+uuvY+jQoWKehoYGPP/889ixYwc0Gg0iIyOxYcMGgwbW5eXlWLx4MY4cOQJXV1fExcUhPT0dffp0+PUmEVHHAyOOjo4YPHgwACAoKAinTp3C+vXrMWvWLFy/fh3V1dUGvUYqKyuhUCgAAAqFAidPnjRYX2VlpTivLUlJSUhMTBSna2tr4evri4iICMhkMmi1WqhUKoSHh0MikbRYfmRyXkeraTFSewFp43R4+bQ9NDo7g3nnkiOtVCrjtLbfu/O+bu7mfV+yaoq1i9Mh+n3f2nnT3X3+15Bb/l719D3DiIiIiIiIiMh2FRQUID4+Hvfccw8aGxvx0ksvISIiAhcuXICLiwsAYNmyZdi3bx9ycnLg5uaGhIQEzJw5E8eOHQNw45P+0dHRUCgUOH78OCoqKjB37lxIJBK89tprZin3nX/Z16H8UgcBGeNvvBvTNNnWuxqi3qjLIVWdTgeNRoOgoCBIJBIcOnQIMTExAICysjKUl5dDqVQCAJRKJVavXo2qqip4enoCuNFqXCaTISAgoM1tSKVSSKXSFukSicTgxWrzaT1buBhpdHYtynmrl8bdyc373Rb2dXManZ3N7OvmWjtvujv9vm7r99o8HxERERERERHZrgMHDhhMb9myBZ6enigpKcGkSZNQU1ODTZs2ITs7GyEhIQCAzZs3Y/jw4SguLkZwcDDy8/Nx4cIFHDx4EHK5HIGBgUhLS8OKFSuQnJwMR0dHa1SNiGxYhwZfT0pKQmFhIb799lucPXsWSUlJOHr0KGbPng03NzcsWLAAiYmJOHLkCEpKSjB//nwolUoEBwcDACIiIhAQEIA5c+bgiy++QF5eHlauXIn4+PhWAx9ERERERERERETUc9TU1AAAPDw8AAAlJSXQarUICwsT8wwbNgwDBw5EUVERAKCoqAijRo0y+LRWZGQkamtrcf78eQuWnoh6ig71GKmqqsLcuXNRUVEBNzc3jB49Gnl5eQgPDwcArF27Fvb29oiJiTH4HqCeg4MDcnNzsXjxYiiVSri4uCAuLg6pqammrRURERERERERERF1KzqdDkuXLsXEiRMxcuRIAIBarYajo6PBp/kBQC6XQ61Wi3luDoro5+vntUaj0UCj0YjT+k92a7VaaLXadssqdRCMq5Q+v71g8Lcts3RdjDkeXV23ObbR0XOky9vrxeeYscevI8e5Q4GRTZs23XK+k5MTMjMzkZmZ2WYePz8/7N+/vyObJSIiIiIiIiIiIhsXHx+Pc+fO4bPPPjP7ttLT05GSktIiPT8/H87Ozu0unzG+c9tNG6fr3ILdkKXqYol3xSqVyuTr7Ow50lW98Rwz9hypr683ettdHmOEiIiIiIiIiIiI6FYSEhKQm5uLwsJC+Pj4iOkKhQLXr19HdXW1Qa+RyspKKBQKMc/JkycN1ldZWSnOa01SUhISExPF6draWvj6+iIiIgIymazd8o5MzjO6bsCNlu9p43R4+bQ9NDrbGg+2OUvX5VxypNnWrdVqoVKpEB4ebvLxbDt6jnRVbz7HjD1H9D3DjMHACBEREREREREREZmFIAhYsmQJ9uzZg6NHj8Lf399gflBQECQSCQ4dOoSYmBgAQFlZGcrLy6FUKgEASqUSq1evRlVVFTw9PQHc6AEgk8kQEBDQ6nalUmmrYxpLJBKjXpBrmjr34lmjs+v0st2Npepi6oBFW9sw9XasdZx74zlm7LHryDFmYISIiIiIiIiIiIjMIj4+HtnZ2fj444/Rr18/cUwQNzc39O3bF25ubliwYAESExPh4eEBmUyGJUuWQKlUIjg4GAAQERGBgIAAzJkzBxkZGVCr1Vi5ciXi4+NbDX4QEbWHgREiIiIiIiIiIiIyi6ysLADA5MmTDdI3b96MefPmAQDWrl0Le3t7xMTEQKPRIDIyEhs2bBDzOjg4IDc3F4sXL4ZSqYSLiwvi4uKQmppqqWoQUQ/DwAgRERERERERERGZhSAI7eZxcnJCZmYmMjMz28zj5+dnkUG6iah3sLd2AYiIiIiIiIiIiIiIiCyFgREiIiIiIiIiIiIiIuo1GBghIuqk9PR03HPPPejXrx88PT0xY8YMlJWVGeRpaGhAfHw8+vfvD1dXV8TExKCystIgT3l5OaKjo+Hs7AxPT08sX74cjY2NlqwKERERERERERFRr8HACBFRJxUUFCA+Ph7FxcVQqVTQarWIiIhAXV2dmGfZsmX45JNPkJOTg4KCAvz000+YOXOmOL+pqQnR0dG4fv06jh8/jq1bt2LLli1YtWqVNapERERERERERETU43HwdSKiTjpw4IDB9JYtW+Dp6YmSkhJMmjQJNTU12LRpE7KzsxESEgIA2Lx5M4YPH47i4mIEBwcjPz8fFy5cwMGDByGXyxEYGIi0tDSsWLECycnJcHR0tEbViIh6jTv/ss/aReiUb9dEW7sIRERERERENouBESIiE6mpqQEAeHh4AABKSkqg1WoRFhYm5hk2bBgGDhyIoqIiBAcHo6ioCKNGjYJcLhfzREZGYvHixTh//jzGjh3bYjsajQYajUacrq2tBQBotVpotVqz1K07kDoIhtP2gsHf3ZE1jod+mz35XGhN8/Oj1Tzd8Jwx9jj1tuNJREREREREZE4MjBARmYBOp8PSpUsxceJEjBw5EgCgVqvh6OgId3d3g7xyuRxqtVrMc3NQRD9fP6816enpSElJaZGen58PZ2fnrlal28oY33p62jidZQvSAfv377fatlUqldW2bQ1tnR+t6U7njLHnSH19vZlLQkRERERERNR7MDBCRGQC8fHxOHfuHD777DOzbyspKQmJiYnidG1tLXx9fREREQGZTGb27VvLyOQ8g2mpvYC0cTq8fNoeGp2dlUp1a+eSIy2+Ta1WC5VKhfDwcEgkEotv31qanx+t6Y7njLHniL5nGBERERERERF1HQMjRERdlJCQgNzcXBQWFsLHx0dMVygUuH79Oqqrqw16jVRWVkKhUIh5Tp48abC+yspKcV5rpFIppFJpi3SJRNKjX4Rrmlp/ka3R2bU5z9qseTx6+vnQXEfOge50zhh7jHrTsSQiIiIiIiIyN3trF4CIyFYJgoCEhATs2bMHhw8fhr+/v8H8oKAgSCQSHDp0SEwrKytDeXk5lEolAECpVOLs2bOoqqoS86hUKshkMgQEBFimIkRERERERERERL0Ie4wQEXVSfHw8srOz8fHHH6Nfv37imCBubm7o27cv3NzcsGDBAiQmJsLDwwMymQxLliyBUqlEcHAwACAiIgIBAQGYM2cOMjIyoFarsXLlSsTHx7faK4SIiIiIiIiIiIi6hoERIqJOysrKAgBMnjzZIH3z5s2YN28eAGDt2rWwt7dHTEwMNBoNIiMjsWHDBjGvg4MDcnNzsXjxYiiVSri4uCAuLg6pqamWqgYREREREREREVGvwk9pERF1kiAIrf7RB0UAwMnJCZmZmbhy5Qrq6uqwe/fuFmOH+Pn5Yf/+/aivr8fPP/+MN998E336MG5NRLYlPT0d99xzD/r16wdPT0/MmDEDZWVlBnkaGhoQHx+P/v37w9XVFTExMeK4Snrl5eWIjo6Gs7MzPD09sXz5cjQ2NlqyKkRERERERNTDMTBCRERERF1WUFCA+Ph4FBcXQ6VSQavVIiIiAnV1dWKeZcuW4ZNPPkFOTg4KCgrw008/YebMmeL8pqYmREdH4/r16zh+/Di2bt2KLVu2YNWqVdaoEhFRlxQWFmLatGnw9vaGnZ0d9u7dazB/3rx5sLOzM/gzZcoUgzxXrlzB7NmzIZPJ4O7ujgULFuDatWsWrAURERFRz8QmyURERGZy51/2WXybUgcBGeOBkcl50DTZdWod366JNnGpqDc4cOCAwfSWLVvg6emJkpISTJo0CTU1Ndi0aROys7MREhIC4ManB4cPH47i4mIEBwcjPz8fFy5cwMGDByGXyxEYGIi0tDSsWLECycnJcHR0tEbViIg6pa6uDmPGjMFTTz1lEAS+2ZQpU7B582ZxuvkYc7Nnz0ZFRYUYcJ4/fz4WLVqE7Oxss5adiIiIqKdjYISIiIiITK6mpgYA4OHhAQAoKSmBVqtFWFiYmGfYsGEYOHAgioqKEBwcjKKiIowaNQpyuVzMExkZicWLF+P8+fMYO3Zsi+1oNBpoNBpxura2FgCg1Wqh1WpbLZs+XavVQuogdLGm1tFW3TqybFfW0RndZV9L7QWDv9tj6f1kCm3t647W3dKM3de2ckyioqIQFRV1yzxSqbTFZ1b1vvzySxw4cACnTp3CuHHjAABvv/02pk6dijfffBPe3t4mLzMRERFRb8HACBERERGZlE6nw9KlSzFx4kSMHDkSAKBWq+Ho6Ah3d3eDvHK5HGq1Wsxzc1BEP18/rzXp6elISUlpkZ6fnw9nZ+dbllOlUiFjvFFV6nb279/f5XWoVCoTlMR43W1fp43TGZXPFPva0trb18bW3dKM3df19fVmLonlHD16FJ6enrjtttsQEhKCV199Ff379wcAFBUVwd3dXQyKAEBYWBjs7e1x4sQJPPLIIy3W15lgsZ5+fncNnJlDdw8Wmpop6msrgUnAeg0BLK2n14+IyFwYGCEiIiIik4qPj8e5c+fw2WefmX1bSUlJSExMFKdra2vh6+uLiIgIyGSyVpfRarVQqVQIDw/H2NWHzV5GcziXHNnpZW+uv0QiMWGpbm1kcp7FtnUrUnsBaeN0ePm0PTS69j852JV9bS1t7euO1t3SjN3X+pf9tm7KlCmYOXMm/P39cenSJbz00kuIiopCUVERHBwcoFar4enpabBMnz594OHhYZZgsV53DZyZU2+rc1fqa4vBYks3BLC0nhQsJiKyJAZGiIiIiMhkEhISkJubi8LCQvj4+IjpCoUC169fR3V1tUGvkcrKSvEzMgqFAidPnjRYX2VlpTivNVKptMU3+QFAIpG0+9JfIpF0eiweazNFQMOYfWRK3W1fa3R2RpXJkvvIVNqrl7F1tzRj97UtHpPWxMbGiv8eNWoURo8ejUGDBuHo0aMIDQ3t1Do7EyzW0wdNu2vgzBy6e7DQ1ExRX1sKFlurIYCl9ZRgMRGRpTEwQkRERERdJggClixZgj179uDo0aPw9/c3mB8UFASJRIJDhw4hJiYGAFBWVoby8nIolUoAgFKpxOrVq1FVVSW2klapVJDJZAgICLBshYiILOyuu+7CgAEDcPHiRYSGhkKhUKCqqsogT2NjI65cuWKWYLFedw2cmVNvq3NX6muLAQZLNwSwtJ5cNyIic2JghIiIiIi6LD4+HtnZ2fj444/Rr18/8TMvbm5u6Nu3L9zc3LBgwQIkJibCw8MDMpkMS5YsgVKpRHBwMAAgIiICAQEBmDNnDjIyMqBWq7Fy5UrEx8e3+qKPiKgn+eGHH/Drr7/Cy8sLwI1gcXV1NUpKShAUFAQAOHz4MHQ6HSZMmGDNohIRERHZPAZGiIiIiKjLsrKyAACTJ082SN+8eTPmzZsHAFi7di3s7e0RExMDjUaDyMhIbNiwQczr4OCA3NxcLF68GEqlEi4uLoiLi0NqaqqlqkFEZDLXrl3DxYsXxenLly+jtLQUHh4e8PDwQEpKCmJiYqBQKHDp0iW8+OKLGDx4MCIjb3yqaPjw4ZgyZQoWLlyIjRs3QqvVIiEhAbGxsfD29rZWtYiIiIh6BAZGiIiIiKjLBEFoN4+TkxMyMzORmZnZZh4/Pz+bHNiViKi506dP48EHHxSn9WN/xMXFISsrC2fOnMHWrVtRXV0Nb29vREREIC0tzaCH3Pbt25GQkIDQ0FAxsPzWW29ZvC5EREREPQ0DI0REREREREQmNnny5FsGjfPy8tpdh4eHB7Kzs01ZLCIiIiICYG/tAhAREREREREREVHPVVhYiGnTpsHb2xt2dnbYu3evwfx58+bBzs7O4M+UKVMM8ly5cgWzZ8+GTCaDu7s7FixYgGvXrlmwFkTUk7DHCBERERm48y/7rF0EIiIiIiLqQerq6jBmzBg89dRTmDlzZqt5pkyZgs2bN4vTN39aEABmz56NiooKqFQqaLVazJ8/H4sWLWLPOiLqFAZGiIiIiIiIiIiIyGyioqIQFRV1yzxSqRQKhaLVeV9++SUOHDiAU6dOYdy4cQCAt99+G1OnTsWbb74Jb29vk5eZiHo2BkaIiIiIiIiIiKhdttSzWOogIGM8MDI5D5omO2sXp8O+XRNt7SJY3NGjR+Hp6YnbbrsNISEhePXVV9G/f38AQFFREdzd3cWgCACEhYXB3t4eJ06cwCOPPNJifRqNBhqNRpyura0FAGi1Wmi12nbLI3Voe5yoVvPbCwZ/2zJL18WY49HVdZtjGx09R7q8vV58jhl7/DpynBkYISIiIiIiIiIiIquZMmUKZs6cCX9/f1y6dAkvvfQSoqKiUFRUBAcHB6jVanh6ehos06dPH3h4eECtVre6zvT0dKSkpLRIz8/Ph7Ozc7tlyhjfubqkjdN1bsFuyFJ12b9/v9m3oVKpTL7Ozp4jXdUbzzFjz5H6+nqjt83ACBEREREREREREVlNbGys+O9Ro0Zh9OjRGDRoEI4ePYrQ0NBOrTMpKQmJiYnidG1tLXx9fREREQGZTNbu8iOT8zq0Pam9gLRxOrx82h4ane31UrqZpetyLjnSbOvWarVQqVQIDw+HRCIx6bo7eo50VW8+x4w9R/Q9w4zBwAgRERERERERERF1G3fddRcGDBiAixcvIjQ0FAqFAlVVVQZ5GhsbceXKlTbHJZFKpS0GcAcAiURi1Avyzn6CTaOzs8nPt7XGUnUxdcCirW2YejvWOs698Rwz9th15BjbG52TiIiIiIiIiIiIyMx++OEH/Prrr/Dy8gIAKJVKVFdXo6SkRMxz+PBh6HQ6TJgwwVrFJCIb1qHASHp6Ou655x7069cPnp6emDFjBsrKygzyNDQ0ID4+Hv3794erqytiYmJQWVlpkKe8vBzR0dFwdnaGp6cnli9fjsbGxq7XhoiIiIiIiIiIiLqVa9euobS0FKWlpQCAy5cvo7S0FOXl5bh27RqWL1+O4uJifPvttzh06BCmT5+OwYMHIzLyxudzhg8fjilTpmDhwoU4efIkjh07hoSEBMTGxsLb29uKNSMiW9WhwEhBQQHi4+NRXFwMlUoFrVaLiIgI1NXViXmWLVuGTz75BDk5OSgoKMBPP/2EmTNnivObmpoQHR2N69ev4/jx49i6dSu2bNmCVatWma5WREQWUlhYiGnTpsHb2xt2dnbYu3evwfx58+bBzs7O4M+UKVMM8ly5cgWzZ8+GTCaDu7s7FixYgGvXrlmwFkRERERERETmc/r0aYwdOxZjx44FACQmJmLs2LFYtWoVHBwccObMGTz88MO4++67sWDBAgQFBeHf//63waewtm/fjmHDhiE0NBRTp07Ffffdh/fee89aVSIiG9ehMUYOHDhgML1lyxZ4enqipKQEkyZNQk1NDTZt2oTs7GyEhIQAADZv3ozhw4ejuLgYwcHByM/Px4ULF3Dw4EHI5XIEBgYiLS0NK1asQHJyMhwdHU1XOyIiM6urq8OYMWPw1FNPGQSBbzZlyhRs3rxZnG7+jdPZs2ejoqJCDDjPnz8fixYtQnZ2tlnLTkRERERERGQJkydPhiAIbc7Py2t/EGsPDw8+JxORyXRp8PWamhoANy5MAFBSUgKtVouwsDAxz7BhwzBw4EAUFRUhODgYRUVFGDVqFORyuZgnMjISixcvxvnz58XI8c00Gg00Go04rR9dXqvVin/0062ROrR94bU2qb1g8PfN2qpPd9Hafu/O+7q5m/d9d9/XzenL29p5092193ttnq+7i4qKQlRU1C3zSKXSNgeD+/LLL3HgwAGcOnUK48aNAwC8/fbbmDp1Kt588012CSYiIiIiIiIiIjKxTgdGdDodli5diokTJ2LkyJEAALVaDUdHR7i7uxvklcvlUKvVYp6bgyL6+fp5rUlPT0dKSkqL9Pz8fDg7O4vTKpWq1eUzxhtXJ2tKG6drkbZ//34rlKTjbt7vtrCvm0sbp7OZfd1ca+dNd6c/X9r6verV19dbojgWcfToUXh6euK2225DSEgIXn31VfTv3x8AUFRUBHd3dzEoAgBhYWGwt7fHiRMn8Mgjj7RYX3vB4p6qeeD1VoHl3oz7pW3dcd8Y+5vtyb9tIiIiIiIiIkvrdGAkPj4e586dw2effWbK8rQqKSkJiYmJ4nRtbS18fX0REREBmUwGrVYLlUqF8PBwSCSSFsuPTG6/O561SO0FpI3T4eXT9tDo7AzmnUuOtFKpjNPafu/O+7q5m/d9yaop7S/Qjej3fWvnTXf3+V9Dbvl71dO/7Ld1U6ZMwcyZM+Hv749Lly7hpZdeQlRUFIqKiuDg4AC1Wg1PT0+DZfr06QMPD48uB4t7mrYCr7YYILQE7pe2dad9Y2xgvicFi4mIiIiIiIisrVOBkYSEBOTm5qKwsBA+Pj5iukKhwPXr11FdXW3Qa6SyslL8jIxCocDJkycN1ldZWSnOa41UKm3xTX4AkEgkBi9Wm0/raZq6/4tjjc6uRTlv9dK4O7l5v9vCvm5Oo7OzmX3dXGvnTXen39dt/V6b57N1sbGx4r9HjRqF0aNHY9CgQTh69ChCQ0M7tc72gsU9VfPA660Cy70Z90vbuuO+MbYRRE8JFhMRERERERF1Bx0KjAiCgCVLlmDPnj04evQo/P39DeYHBQVBIpHg0KFDiImJAQCUlZWhvLwcSqUSAKBUKrF69WpUVVWJraRVKhVkMhkCAgJMUSciom7rrrvuwoABA3Dx4kWEhoZCoVCgqqrKIE9jYyOuXLnS5WBxT9NWENAWA4SWwP3Stu60b4z9zfbk3zYRERERERGRpXUoMBIfH4/s7Gx8/PHH6Nevn/iZFzc3N/Tt2xdubm5YsGABEhMT4eHhAZlMhiVLlkCpVCI4OBgAEBERgYCAAMyZMwcZGRlQq9VYuXIl4uPjW33RR0TUk/zwww/49ddf4eXlBeBGsLi6uholJSUICgoCABw+fBg6nQ4TJkywZlGJiIiIiIiIiIh6pA4FRrKysgAAkydPNkjfvHkz5s2bBwBYu3Yt7O3tERMTA41Gg8jISGzYsEHM6+DggNzcXCxevBhKpRIuLi6Ii4tDampq12pCRGQF165dw8WLF8Xpy5cvo7S0FB4eHvDw8EBKSgpiYmKgUChw6dIlvPjiixg8eDAiI298Pmf48OGYMmUKFi5ciI0bN0Kr1SIhIQGxsbHw9va2VrWIiIiIiIiIiIh6rA5/Sqs9Tk5OyMzMRGZmZpt5/Pz8jB5slIioOzt9+jQefPBBcVo/9kdcXByysrJw5swZbN26FdXV1fD29kZERATS0tIMesht374dCQkJCA0NFQPLb731lsXrQkRERERERERE1Bt0avB1IiK6YfLkybcMGufl5bU5T8/DwwPZ2dmmLBYRERERERERERG1wd7aBSAiIiIiIiIiIiIiIrIUBkaIiIiIiIiIiIiIiKjXYGCEiIiIiIiIiIiIiIh6DQZGiIiIiIiIiIiIiIio12BghIiIiIiIiIiIiIiIeg0GRoiIiIiIiIiIiIiIqNdgYISIiIiIiIiIiIiIiHqNPtYuABERERERUXd151/2WbsIZKMKCwvxxhtvoKSkBBUVFdizZw9mzJghzhcEAa+88gref/99VFdXY+LEicjKysKQIUPEPFeuXMGSJUvwySefwN7eHjExMVi/fj1cXV2tUCMiIiKinoM9RoiIiIiIiIhMrK6uDmPGjEFmZmar8zMyMvDWW29h48aNOHHiBFxcXBAZGYmGhgYxz+zZs3H+/HmoVCrk5uaisLAQixYtslQViIiIiHos9hghIiIiIiIiMrGoqChERUW1Ok8QBKxbtw4rV67E9OnTAQAffPAB5HI59u7di9jYWHz55Zc4cOAATp06hXHjxgEA3n77bUydOhVvvvkmvL29LVYXIiIiop6GgREiIiIiIiIiC7p8+TLUajXCwsLENDc3N0yYMAFFRUWIjY1FUVER3N3dxaAIAISFhcHe3h4nTpzAI4880mK9Go0GGo1GnK6trQUAaLVaaLXaW5ZJP19qL3SpbrZEX9feUmfW17a095vtaD4iIjLEwAgRERERERGRBanVagCAXC43SJfL5eI8tVoNT09Pg/l9+vSBh4eHmKe59PR0pKSktEjPz8+Hs7OzUWVLG6czKl9P0tvqzPrahv379xuVr76+3swlISLqmRgYISIiIiIiIuoBkpKSkJiYKE7X1tbC19cXERERkMlkt1xWq9VCpVLh5dP20OjszF3UbkFqLyBtnK7X1Jn1tS3nkiONyqfvGUZERB3DwAgRERERERGRBSkUCgBAZWUlvLy8xPTKykoEBgaKeaqqqgyWa2xsxJUrV8Tlm5NKpZBKpS3SJRIJJBKJUWXT6OygabK9l8hd0dvqzPraBmN/s8bmIyIiQ/bWLgARERERERFRb+Lv7w+FQoFDhw6JabW1tThx4gSUSiUAQKlUorq6GiUlJWKew4cPQ6fTYcKECRYvMxFRVxQWFmLatGnw9vaGnZ0d9u7dazBfEASsWrUKXl5e6Nu3L8LCwvD1118b5Lly5Qpmz54NmUwGd3d3LFiwANeuXbNgLYioJ2FghIiIiIiIiMjErl27htLSUpSWlgK4MeB6aWkpysvLYWdnh6VLl+LVV1/Fv/71L5w9exZz586Ft7c3ZsyYAQAYPnw4pkyZgoULF+LkyZM4duwYEhISEBsbC29vb+tVjIioE+rq6jBmzBhkZma2Oj8jIwNvvfUWNm7ciBMnTsDFxQWRkZFoaGgQ88yePRvnz5+HSqVCbm4uCgsLsWjRIktVgYh6GH5Ki4iIiIiIiMjETp8+jQcffFCc1o/9ERcXhy1btuDFF19EXV0dFi1ahOrqatx33304cOAAnJycxGW2b9+OhIQEhIaGwt7eHjExMXjrrbcsXhcioq6KiopCVFRUq/MEQcC6deuwcuVKTJ8+HQDwwQcfQC6XY+/evYiNjcWXX36JAwcO4NSpUxg3bhwA4O2338bUqVPx5ptvMmBMRB3GwAgRERERERGRiU2ePBmCILQ5387ODqmpqUhNTW0zj4eHB7Kzs81RPCKibuPy5ctQq9UICwsT09zc3DBhwgQUFRUhNjYWRUVFcHd3F4MiABAWFgZ7e3ucOHECjzzySIv1ajQaaDQacVo/UL1Wq4VWq223XFKHtq/hrea3Fwz+tmWWrosxx6Or6zbHNjp6jnR5e734HDP2+HXkODMwQkRERERERERERFahVqsBAHK53CBdLpeL89RqNTw9PQ3m9+nTBx4eHmKe5tLT05GSktIiPT8/H87Ozu2WK2O8UcVvIW2crnMLdkOWqsv+/fvNvg2VSmXydXb2HOmq3niOGXuO1NfXG71tBkaIiIiIiIiIiIioR0lKShI/Ywjc6DHi6+uLiIgIyGSydpcfmZzXoe1J7QWkjdPh5dP20OjsOlze7sTSdTmXHGm2dWu1WqhUKoSHh0MikZh03R09R7qqN59jxp4j+p5hxmBghIioCwoLC/HGG2+gpKQEFRUV2LNnjzhgJnDjW6mvvPIK3n//fVRXV2PixInIysrCkCFDxDxXrlzBkiVL8Mknn4jfjl6/fj1cXV2tUCMiIiIiIiIiy1EoFACAyspKeHl5iemVlZUIDAwU81RVVRks19jYiCtXrojLNyeVSiGVSlukSyQSo16Qa5o69+JZo7Pr9LLdjaXqYuqARVvbMPV2rHWce+M5Zuyx68gxtjc6JxERtVBXV4cxY8YgMzOz1fkZGRl46623sHHjRpw4cQIuLi6IjIxEQ0ODmGf27Nk4f/48VCoVcnNzUVhYiEWLFlmqCkRERERERERW4+/vD4VCgUOHDolptbW1OHHiBJRKJQBAqVSiuroaJSUlYp7Dhw9Dp9NhwoQJFi8zEdk+9hghIuqC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Meier","userId":"06365571306976914087"},"user_tz":300},"id":"wI9Bh3Kp9ihi","outputId":"8c6d1f08-f14a-43b6-d594-763120e150fb"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["\n","df_scaled_corr=df_scaled.corr()\n","df_scaled_corr=df_scaled_corr['stress_level'].sort_values(ascending=False)\n","df_scaled_corr=df_scaled_corr.drop('stress_level')\n","plt.figure(figsize=(8,6))\n","sns.heatmap(df_scaled_corr.to_frame(),annot=True,cmap='coolwarm')\n","plt.title('Stress Level Correlation of Features after Standard Scaling')\n","plt.show()"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":545},"executionInfo":{"elapsed":669,"status":"ok","timestamp":1716216909976,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"l2_RcNVzKXVj","outputId":"a131e01f-9570-49df-a8c6-71cccf87b0a3"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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y71796hfvz7Xrl3TGN6aHcHBwYSFhdGjRw/u37+vjvvJkyc0btyYgwcPpovz5fdGVpibmzNixAi2bdvGmTNnMsyT3dd0Rs9Dms6dO6s/XwFq1KgBwGeffaaxMECNGjVITEzk33//Vafl1Otp1apVfP/998yePZuGDRsCWb/+KSkp7N69m3bt2lG8eHF1mWXKlMHd3T1L5/fx8WHt2rVUrlyZ3bt3M3HiRNzc3KhSpQqXLl1S59u4cSOFChXSWHwnzYvD7N71Z1pWPqN27dpF0aJFadOmjTrNwMCAL7744o3OKd4/Ga4mxHtSrVo1Nm3aRGJiImfPnmXz5s3Mnz+fTp06ERwcTNmyZbNUTokSJTQeh4WFoSgKzs7OGeZPG3JRokQJRo0axbx581izZg1169alTZs2fPbZZ+p/0F27duWnn36if//+TJgwgcaNG9OhQwc6deqk8UXgTWQ1zrQ4/Pz82LZtGz169CAuLo4dO3YwcOBA9T++sLAwAPUXu5eZmZllK760/I8fP85S/hs3buDs7JzuupQpU0a9/0UvP2+v2peda5WRP//8k2+++Ybg4OB0c1neRFpdSpUqpZGup6dHyZIl09W1WLFi6c5laWnJuXPnXnseLS0tnJycNNJtbGywsLBId57s0NbWpkmTJhnuCwsLIzY2Nt1cgTQvTpS+cOECX3/9NX/99Ze6YZzmTb/8v0rRokXTTeoPCwvj0qVLmTaa0+J9m/dzUFAQU6ZM4ciRI+luisTGxmp8qc+qtPds7969M80TGxuLpaWl+vGr3jevMnz4cObPn4+3tzdbt25Ntz+7r+mMnoc0LzYE4L8bSnZ2dhmmvzjPKydeT8HBwQwaNIju3btr3DDI6vVPSEjg6dOnGX7+lCpVSn0z4nW6d+9O9+7defToEceOHWPFihWsXbuW1q1bc/78eQwMDAgPD6dUqVKvXSXuXX+mZeUz6saNGzg6OqbL9/Lnk8i7pJEjxHump6dHtWrVqFatGi4uLvTp04fff/+dKVOmZOn4F+9owfMJ0yqVip07d2a4ItaLcwHmzp2Lh4cHW7duZc+ePQwbNgxfX1+OHj2qnhx78OBB9u/fz/bt29m1axfr16+nUaNG7Nmz561W3MpOnDVr1sTBwYHffvuNHj168Mcff/D06VO6du2qUR48nxdgY2OTrrzsLq1aunRp4PlY9Jzw8vP2qn3ZuVYvO3ToEG3atKFevXp8//332Nraoquri7+/P2vXrn3zCmRDZq8TJQtzKuDNv7i8qdTUVKytrVmzZk2G+9MaEzExMdSvXx8zMzOmTp2Ko6MjBgYGnD59mvHjx2eptySzumU20Tyj101qairly5dn3rx5GR6T9uX6Td/P4eHhNG7cmNKlSzNv3jzs7OzQ09Njx44dzJ8//417edOO+/bbbzNdWvrl1/ar3jevktab4+3tnWlvTna8Ko7MruPr3gfv4vX0socPH9KxY0dcXFzSLZme1ev/8uIVb8vMzIymTZvStGlTdHV1WblyJceOHaN+/fpZOj4nPtPe9jNKfBikkSNELqpatSoAUVFR6rTsfsFzdHREURRKlCiBi4vLa/OXL1+e8uXL8/XXX/P3339Tp04dli5dyjfffAM8X3KzcePGNG7cmHnz5jFjxgwmTpzI/v37M70TnhNxdunShQULFvDo0SPWr1+Pg4MDNWvW1CgPnk9yfZu40ri4uFCqVCm2bt3KggULXjtR3N7ennPnzpGamqpxVzxtiIm9vf0bx5Lda/WijRs3YmBgwO7duzV+58bf3z9d3qy+1tLqEhoaSsmSJdXpiYmJXL9+/Z1c/7TzpKamEhYWpu4RA7hz5w4xMTFvdU1fxdHRkX379lGnTp1XfpkNDAzk/v37bNq0iXr16qnTr1+/ni5vZtc2rZfi5RW8stNL5ejoyNmzZ2ncuPFrn8M3eT//8ccfJCQksG3bNo1eiqwOAc0sprT3rJmZ2Tt7zbzKiBEj8PPzw8fHJ91CHe/rNf0q2Xk9ZUVqaio9e/YkJiaGffv2pVviOqvX38rKCkNDQ3XPz4tCQ0PfKLY0VatWZeXKler/eY6Ojhw7doykpKRMe6iz85n2Ltnb23Px4kUURdF4TV+9ejVHzyveHZmTI8R7sH///gzvEKV1+784ZMLY2DjdF6BX6dChA9ra2vj4+KQ7h6Io3L9/H3g+5yQ5OVljf/ny5dHS0lLfuXt5eVNAfcfvbe/uZTXONF27diUhIYGVK1eya9cuunTporHf3d0dMzMzZsyYkeFv19y9ezfbMfr4+HD//n369++f7lrB8193//PPPwH49NNPuX37NuvXr1fvT05OZtGiRZiYmGT5LmVGsnutXqStrY1KpdLoGYiIiFCvuPWirL7WmjRpgp6eHgsXLtSI5+effyY2NvaNVjfKyKeffgqAn5+fRnpaj8W7Os/LunTpQkpKCtOmTUu3Lzk5WX2N0u7+vngNEhMT+f7779MdZ2xsnOFwo7QvmgcPHlSnpaSk8OOPP2Yr3n///Zdly5al2/f06VOePHkCvPn7OaN6xsbGZvlLZdpv2bz82nJzc8PR0ZE5c+ZozLdL8ybv2VdJ683ZunUrwcHBGvve12v6VbLzesoKHx8fdu/eza+//prhML+sXn9tbW3c3d3ZsmULkZGR6v2XLl1i9+7dr40jPj6eI0eOZLhv586dwH//8zp27Mi9e/dYvHhxurxp1yU7n2nvkru7O//++6/GvNlnz55l+L4TeZP05AjxHgwdOpT4+Hjat29P6dKlSUxM5O+//1b3ULw4odXNzY19+/Yxb948ihQpQokSJdQTWTPi6OjIN998g5eXFxEREbRr1w5TU1OuX7/O5s2bGTBgAGPGjOGvv/7C09OTzp074+LiQnJyMqtXr0ZbW5uOHTsCMHXqVA4ePEjLli2xt7cnOjqa77//nmLFimXpNyquXr2q7hF6UeXKlWnZsmWW4kxTpUoVnJycmDhxIgkJCRpD1eD53cglS5bw+eefU6VKFbp164aVlRWRkZFs376dOnXqZPiP81W6du1KSEgI06dP58yZM3Tv3h17e3vu37/Prl27CAgIUA+PGDBgAD/88AMeHh6cOnUKBwcHNmzYQFBQEH5+fllewCAjWX1OM9KyZUvmzZtH8+bN6dGjB9HR0Xz33Xc4OTmlmxOT1dealZUVXl5e+Pj40Lx5c9q0aUNoaCjff/891apVe2c/slmxYkV69+7Njz/+qB7Kc/z4cVauXEm7du3UE6jftfr16zNw4EB8fX0JDg6mWbNm6OrqEhYWxu+//86CBQvo1KkTtWvXxtLSkt69ezNs2DBUKhWrV6/O8AaGm5sb69evZ9SoUVSrVg0TExNat26Nq6srNWvWxMvLiwcPHlCgQAHWrVuXYaM6M59//jm//fYbgwYNYv/+/dSpU4eUlBQuX77Mb7/9xu7du6lateobv5+bNWuGnp4erVu3ZuDAgcTFxbFs2TKsra01ep0z4+joiIWFBUuXLsXU1BRjY2Nq1KhBiRIl+Omnn2jRogWurq706dOHokWL8u+//7J//37MzMz4448/snwdsiJtbs7Zs2c1fkj0fb2mXyU7r6fXCQkJYdq0adSrV4/o6Gh++eUXjf2fffYZWlpaWb7+Pj4+7Nq1i7p16zJ48GD1DRxXV9fXzq2Lj4+ndu3a1KxZk+bNm2NnZ0dMTAxbtmzh0KFDtGvXjsqVKwPQq1cvVq1axahRozh+/Dh169blyZMn7Nu3j8GDB9O2bdtsfaa9SwMHDmTx4sV0796d4cOHY2try5o1a9Q/Lvq+h9WKN/De1nET4iO2c+dOpW/fvkrp0qUVExMTRU9PT3FyclKGDh2q3LlzRyPv5cuXlXr16imGhoYKoF4OM21J1cyWg924caPyySefKMbGxoqxsbFSunRpZciQIUpoaKiiKIpy7do1pW/fvoqjo6NiYGCgFChQQGnYsKGyb98+dRkBAQFK27ZtlSJFiih6enpKkSJFlO7duytXrlx5bR3t7e0zXKYXUPr165flOF80ceJEBVCcnJwyPe/+/fsVd3d3xdzcXDEwMFAcHR0VDw8P5eTJk+o8WVlC+kVp18Ha2lrR0dFRrKyslNatWytbt27VyHfnzh2lT58+SqFChRQ9PT2lfPny6ZbNTVtm9ttvv013nrd9ThUl46Vlf/75Z8XZ2VnR19dXSpcurfj7+2d4DTJ7rWW2zPHixYuV0qVLK7q6ukrhwoWVL7/8Unn48KFGnvr16yuurq7p6pJRnBlJSkpSfHx8lBIlSii6urqKnZ2d4uXlpbGcblp52VlCOit5f/zxR8XNzU0xNDRUTE1NlfLlyyvjxo1Tbt26pc4TFBSk1KxZUzE0NFSKFCmiXg6el5aUjYuLU3r06KFYWFgogEbdw8PDlSZNmij6+vpK4cKFla+++krZu3dvhkvdZnQtFeX5ErqzZs1SXF1dFX19fcXS0lJxc3NTfHx8lNjYWEVR3u79vG3bNqVChQqKgYGB4uDgoMyaNUu93O+Lr4vMru3WrVuVsmXLKjo6OumWkz5z5ozSoUMHpWDBgoq+vr5ib2+vdOnSRQkICFDned1742UvLiH9srSyMorzbV7Tmb23M4sl7X114sQJdVpWX0+vW0I67ZyZbS/KyvVXFEU5cOCA4ubmpujp6SklS5ZUli5dmqXP0qSkJGXZsmVKu3btFHt7e0VfX18xMjJSKleurHz77bdKQkKCRv74+Hhl4sSJ6ve8jY2N0qlTJyU8PFydJ6ufaVldQjqrn1HXrl1TWrZsqRgaGipWVlbK6NGjlY0bNyqAcvTo0VdeB5H7VIois6yEEEIIIYR4HT8/P0aOHMk///xD0aJFczsc8QrSyBFCCCGEEOIlT58+1ViM5NmzZ1SuXJmUlBSuXLmSi5GJrJA5OUIIIYQQQrykQ4cOFC9enEqVKhEbG8svv/zC5cuXM11uXuQt0sgRQgghhBDiJe7u7vz000+sWbOGlJQUypYty7p169IthCPyJhmuJoQQQgghhMhX5HdyhBBCCCGEEPmKNHKEEEIIIYQQ+Yo0coQQQgghhBD5iiw8IEQu2K5bKrdDEEIIIUQ2tUwKzbVz5+R3h9ysV06RnhwhhBBCCCFEviI9OUIIIYQQQuRxKl1VbofwQZGeHCGEEEIIIUS+Ij05QgghhBBC5HFaOtKTkx3SkyNyRYMGDRgxYsQbHx8REYFKpSI4OBiAwMBAVCoVMTExb3S8EEIIIYTIP6SRIz5KdnZ2REVFUa5cudwORQiRh9l/2YOGYQE0f3yO2kG/YV6t/Cvz65ib4rpwMo0jD9E8LoT6F3Zh1byeer+2iTFl535Fw6t/0fzRWWof/BXzqq8uUwghAFS6Wjm25UcyXE18lLS1tbGxscntMIQQeZht5xaU+daL80OmEHP8LCWG9abG9p8JdG1O4t0H6fKrdHWpscufxOj7nO42nGf/3sGweBGSYh+p81T44RtMXZ056zGOZ1HRFO3Rhhq7/DlQ4VMSbkW/z+oJIT4wMlwte/Jn0018EJKTk/H09MTc3JxChQoxadIkFEUBQKVSsWXLFo38FhYWrFix4rXlPnnyBDMzMzZs2KCRvmXLFoyNjXn8+HGmw90CAgKoWrUqRkZG1K5dm9BQzXXjv/nmG6ytrTE1NaV///5MmDCBSpUqveklEELkYSVG9OHmz7/xz8pNxF0KJ2TwFFLin2Hn0THD/HZ9OqJrac7JjkN4+Pdpnt74lweHTvD43PPPES0DfWw6NOOy17c8OHyS+PBIwqYtJj78BvYDe7zPqgkhRL4njRyRa1auXImOjg7Hjx9nwYIFzJs3j59++umtyzU2NqZbt274+/trpPv7+9OpUydMTU0zPXbixInMnTuXkydPoqOjQ9++fdX71qxZw/Tp05k1axanTp2iePHiLFmy5K3jFULkPSpdXcyruHIv4O//EhWFe3/9jUXNyhkeU7hVI2KOBVNu0WSa/BNEvTN/4Dh+IGg9/1er0tFBS0eHlGcJGselPE2gQJ0qOVYXIUT+oNJV5diWH8lwNZFr7OzsmD9/PiqVilKlShESEsL8+fP54osv3rrs/v37U7t2baKiorC1tSU6OpodO3awb9++Vx43ffp06tevD8CECRNo2bIlz549w8DAgEWLFtGvXz/69OkDwOTJk9mzZw9xcXFvHa8QIm/RK2SJlo4OCdH3NdIT7tzHuFTJDI8xKmGHYcOa3Pr1D463GYCxY3HKLZqClq4OYd98R0rcEx4eOY3zxMHEXb5Gwp17FO3WCsualXhyNfJ9VEsIIT4a0pMjck3NmjVRqf67e1CrVi3CwsJISUl567KrV6+Oq6srK1euBOCXX37B3t6eevXqvfK4ChUqqP+2tbUFIDr6+Tj50NBQqlevnu48r5OQkMCjR480tiQlNVv1EUJ8ALRUJEbf59ygSTw6fYGo33dydeZSig/ops4S7DEOVCqaRB6ixZMQHDw/59b67ZAqnwlCiFfT0lHl2JYfSSNH5EkqlUo9PydNUlJStsro37+/eg6Pv78/ffr00WhUZURXV1cjBoDUt/zy4evri7m5ucb2W2r6SctCiLwj8d5DUpOT0bcuqJGuX7ggCbfvZXhMwu27PAmL0GiwxF26hoGtNar/f7bEX7vJ0cafs8u8En+VaEBQ7c6odHSIv34zp6oihBAfJWnkiFxz7NgxjcdHjx7F2dkZbW1trKysiIqKUu8LCwsjPj4+W+V/9tln3Lhxg4ULF3Lx4kV69+79VvGWKlWKEydOaKS9/DgjXl5exMbGamxdtAq8VSxCiJylJCURe/oChRrV+i9RpaJgw1rEHD2T4TEP/z6NkWNxeOFmirGLA89uRaO8dJMmJf4pCbfvomNhhlWzT7j9R0CO1EMIkX/InJzskTk5ItdERkYyatQoBg4cyOnTp1m0aBFz584FoFGjRixevJhatWqRkpLC+PHjNXpZssLS0pIOHTowduxYmjVrRrFixd4q3qFDh/LFF19QtWpVateuzfr16zl37hwlS2Y8Pj+Nvr4++vr6Gmm6Krm/IERed93Pn4rLZxFz6jyxJ87hMKw3OsaG3Fy5CYCK/rN49u8dQr+eB8CNH37FfvBnuM6fSMR3v2DsZI/T+IFELF6tLrNQ009QqVTEXbmOsWNxSs8aR1zoNf5ZsSlX6iiEEPmVNHJErunVqxdPnz6levXqaGtrM3z4cAYMGADA3Llz6dOnD3Xr1qVIkSIsWLCAU6dOZfsc/fr1Y+3atRqrpL2pnj17cu3aNcaMGcOzZ8/o0qULHh4eHD9+/K3LFkLkPVG/70TPqgAuU4ahb2PFo7OXON6qP4n/X4zA0M4W5YWhac/+uc3xlv0oO8eLuqe38ezfO1xftIrwb5ep8+iam1Lqm1EYFLMh6UEMtzfvIXTSfJTk5PdePyHEhyW/zp3JKSrl5YkPQuQjq1evZuTIkdy6dQs9Pb13Xn7Tpk2xsbFh9erVr8/8gu26pd55LEIIIYTIWS2TQl+fKYccLJfx8vXvQr3zGQ/D/ZBJT47Il+Lj44mKimLmzJkMHDjwnTRw4uPjWbp0Ke7u7mhra/Prr7+yb98+9u7d+w4iFkIIIYQQ74pMDBD50uzZsyldujQ2NjZ4eXm9kzJVKhU7duygXr16uLm58ccff7Bx40aaNGnyTsoXQgghhMiMlrYqx7b8SIarCZELZLiaEEII8eHJzeFqhytWybGyPzl7OsfKzi0yXE0IIYQQQog8TqWVP3tccooMVxNCCCGEEELkK9KTI4QQQgghRB6n0pa+ieyQRo4QuSC//rqwEEIIIUReII0cIYQQQggh8rj8ugpaTpFGjhBCCCGEEHmcLDyQPTK4TwghhBBCCJGvSCNHCCGEEEKIPC4v/Rjod999h4ODAwYGBtSoUYPjx4+/Mn9MTAxDhgzB1tYWfX19XFxc2LFjx5teiiyRRo54aw0aNGDEiBHv/bwODg74+fm9s/Jyqx5CCCGEEB+K9evXM2rUKKZMmcLp06epWLEi7u7uREdHZ5g/MTGRpk2bEhERwYYNGwgNDWXZsmUULVo0R+OUOTlCCCFEJuwH9qDEiH7oFy7E45DLXBj9DbEnQzLNr2NuSinvERRu2xRdSwueRd7i4rgZ3N198HkGLS2cv/akaLc26BcuxLOoaP79ZTNXZy55TzUSQnyoVHlk4YF58+bxxRdf0KdPHwCWLl3K9u3bWb58ORMmTEiXf/ny5Tx48IC///4bXV1d4PmN6pwmPTlCCCFEBmw7tqD0zAlcnfEdQbU78CgklOpbf0LPqkCG+VW6ulT/czmG9kU502M4Byu2IGTIJJ7duqPO4zj6C+z7d+fCqGkcrNyS0K/nUnJkf+y//Px9VUsIIdJJSEjg0aNHGltCQkK6fImJiZw6dYomTZqo07S0tGjSpAlHjhzJsOxt27ZRq1YthgwZQuHChSlXrhwzZswgJSUlx+oD0sgR70hqairjxo2jQIEC2NjY4O3trd4XExND//79sbKywszMjEaNGnH27Fn1/vDwcNq2bUvhwoUxMTGhWrVq7Nu3T6P86OhoWrdujaGhISVKlGDNmjXpYpg3bx7ly5fH2NgYOzs7Bg8eTFxcnEaeoKAgGjRogJGREZaWlri7u/Pw4cMs1SMrdRFC5B8lhnlw0/93/lm9ibjL4ZwfOoWUp88o1qtjhvntendA19KcU108eXj0DE8j/+XB4RM8DglV57GsWZk72wO4u+sATyP/5faW3dwLCMKiavn3VS0hxAdKpaWVY5uvry/m5uYam6+vb7oY7t27R0pKCoULF9ZIL1y4MLdv384w7mvXrrFhwwZSUlLYsWMHkyZNYu7cuXzzzTc5cp3SSCNHvBMrV67E2NiYY8eOMXv2bKZOncrevXsB6Ny5M9HR0ezcuZNTp05RpUoVGjduzIMHDwCIi4vj008/JSAggDNnztC8eXNat25NZGSkunwPDw9u3rzJ/v372bBhA99//326sZ9aWlosXLiQCxcusHLlSv766y/GjRun3h8cHEzjxo0pW7YsR44c4fDhw7Ru3VrjTsKr6pGVuggh8geVri5mlV25v//v/xIVhXt/HcGyRqUMj7Fu2YiYY8G4+k2m8fXD1D2xDcexA0Hrv3+1D4+eoWCDWhg7OQBgWr4UlrWqcHfPwRysjRBCvJqXlxexsbEam5eX1zspOzU1FWtra3788Ufc3Nzo2rUrEydOZOnSpe+k/MzInBzxTlSoUIEpU6YA4OzszOLFiwkICMDQ0JDjx48THR2Nvr4+AHPmzGHLli1s2LCBAQMGULFiRSpWrKgua9q0aWzevJlt27bh6enJlStX2LlzJ8ePH6datWoA/Pzzz5QpU0YjhhcXDXBwcOCbb75h0KBBfP/99wDMnj2bqlWrqh8DuLq6ZqkeTZs25fDhw6+tixAif9ArZImWjg4Jd+5rpCdE38OkVIkMjzFysMOwfk1urf+DEx0GYlyyOK5+U1Dp6nB1xncAhM/5ER1TY+oF70BJSUGlrc0Vbz9urf8zx+skhPiw5eTv5Ojr66u/27xKoUKF0NbW5s6dOxrpd+7cwcbGJsNjbG1t0dXVRVtbW51WpkwZbt++TWJiInp6em8XfCakkSPeiQoVKmg8trW1JTo6mrNnzxIXF0fBggU19j99+pTw8HDgeU+Ot7c327dvJyoqiuTkZJ4+faruybl06RI6Ojq4ubmpjy9dujQWFhYaZe7btw9fX18uX77Mo0ePSE5O5tmzZ8THx2NkZERwcDCdO3d+o3oAWapLRhISEtKNa01SUtFVSUeqEPmJSkuLxLv3CRkyGVJTeXTmAgZFClNiZF91I8e2YwuKdGtNsMcY4i5dxbRCacrO/ur5AgRrtuRuBYQQedqbLPX8runp6eHm5kZAQADt2rUDnvfUBAQE4OnpmeExderUYe3ataSmpqL1/57tK1euYGtrm2MNHJBGjnhH0lbLSKNSqUhNTSUuLg5bW1sCAwPTHZPWSBkzZgx79+5lzpw5ODk5YWhoSKdOnUhMTMzy+SMiImjVqhVffvkl06dPp0CBAhw+fJh+/fqRmJiIkZERhoaGb1wPIEt1yYivry8+Pj4aaT10CtJTt9DrKyaEyBWJ9x6SmpyMfmHNmxr61oVIuHMvw2Oe3b6LkpQE///MAIgLDcfAxhqVri5KUhKlZ4zl2txlRG14/vsQjy9cwbB4ERzHDJBGjhDigzBq1Ch69+5N1apVqV69On5+fjx58kS92lqvXr0oWrSoek7Pl19+yeLFixk+fDhDhw4lLCyMGTNmMGzYsByNUxo5IkdVqVKF27dvo6Ojk+lygUFBQXh4eNC+fXvgeWMiIiJCvb906dIkJydz6tQp9XC10NBQYmJi1HlOnTpFamoqc+fOVd8l+O233zTOU6FCBQICAtI1ON5lXTLi5eXFqFGjNNL2F676RjEIId4PJSmJR2cuULBBLe78EfA8UaWiYMOa3FiafuETgIdHT1OkSytQqUBRADB2cuBZVPTzxg+gbWiI8kIjCICUVFRa0rMrhHi1nByulh1du3bl7t27TJ48mdu3b1OpUiV27dqlXowgMjJS/V0MwM7Ojt27dzNy5EgqVKhA0aJFGT58OOPHj8/ROKWRI3JUkyZNqFWrFu3atWP27Nm4uLhw69Yttm/fTvv27alatSrOzs5s2rSJ1q1bo1KpmDRpkrr3BKBUqVI0b96cgQMHsmTJEnR0dBgxYoRGz4yTkxNJSUksWrSI1q1bExQUlG5Cm5eXF+XLl2fw4MEMGjQIPT099u/fT+fOnSlU6PW9KlmpS0YyGucqQ9WEyPuuL1xBhWUziT19npiT5yjh2RsdI0P+Wb0JgArLZpJwK5rQKfMAiPzxV+wH9qTsnIncWPILRk72OI4dSMSS1eoyo3fsx3HcIJ7ejCLu4lXMKpXBYagH/6zamCt1FEKIN+Hp6Znp8LSMRrzUqlWLo0eP5nBUmuSblshRKpWKHTt2UK9ePfr06YOLiwvdunXjxo0b6hb/vHnzsLS0pHbt2rRu3Rp3d3eqVKmiUY6/vz9FihShfv36dOjQgQEDBmBtba3eX7FiRebNm8esWbMoV64ca9asSbf0oYuLC3v27OHs2bNUr16dWrVqsXXrVnR0stbWz0pdhBD5R9TGnVz+ajYuk4byydEtmFUozfF2X5AY/XwxAkO7IujbWKnzP/v3Nifa9MfcrRyfHN9K2TkTifh+NeFzlqnzXBj9Dbc376ac32TqndlO6RnjuLl8PVemLnzv9RNCfFhycgnp/EilKP/vUxdCvDc7jErndghCCCGEyKZP4y/n2rnPfdogx8qusCMwx8rOLTJcTQghhBBCiDwur8zJ+VDkz/4pIYQQQgghxEdLenKEEEIIIYTI4/LC7+R8SKSRI4QQQgghRB4nw9WyR4arCSGEEEIIIfIV6ckRQgghhBAij8uvSz3nFGnkCJELdE3lrSeEEEIIkVPkm5YQQgghhBB5nMzJyR7p9xJCCCGEEELkK9KTI4QQQgghRB4nPTnZIz05QgghhBBCiHwlXzVyFEVhwIABFChQAJVKRXBwcG6HJIQQQgghxFtTaalybMuP8tVwtV27drFixQoCAwMpWbIkhQoVeu0xKpWKzZs3065du5wPUAghxAelWJ9uOAzug551IeIuhnL5qxk8OnM+0/w6ZqY4eQ3DumUTdC3MefrPLa5MmsW9gEMAfHJiN4bFi6Y77ubyX7nsNT3H6iGE+PDJEtLZk68aOeHh4dja2lK7du33fu7ExET09PTe+3lfJSdjSkpKQldXN0fKzi2KopCSkoKOTr56Wwgh3lDhts0p5TOOS+OmEnv6HMUHfE6VdT8QVKc1SfcepMuv0tWhym/LSLz3gLP9RpFw+w6GxYqQFPtYnedY824aX1RMyjjj9vtP3Pljz3upkxBCfCzyTZPQw8ODoUOHEhkZiUqlwsHBAQcHB/z8/DTyVapUCW9vbwAcHBwAaN++vfqYtLJe7tkZMWIEDRo0UD9u0KABnp6ejBgxgkKFCuHu7g7A+fPnadGiBSYmJhQuXJjPP/+ce/fuZakOqampzJ49GycnJ/T19SlevDjTp/93Z2/8+PG4uLhgZGREyZIlmTRpEklJSer93t7eVKpUiZ9++okSJUpgYGAAQExMDP3798fKygozMzMaNWrE2bNnNc69detWqlSpgoGBASVLlsTHx4fk5GT1fpVKxZIlS2jTpg3GxsYacWXmwoULtGrVCjMzM0xNTalbty7h4eHquk6dOpVixYqhr69PpUqV2LVrl/rYiIgIVCoVmzZtomHDhhgZGVGxYkWOHDmicY6goCAaNGiAkZERlpaWuLu78/DhQ/U5fH19KVGiBIaGhlSsWJENGzaojw0MDESlUrFz507c3NzQ19fn8OHDNGjQgGHDhjFu3DgKFCiAjY2N+jUDzxtD3t7eFC9eHH19fYoUKcKwYcNeez2EEB8W+0G9+OeXDdxat4UnV65xaexUUp4+o2j39hnmL9q9A7qW5pz1GEbsiTM8u3mLh0dOEncxVJ0n6f5DEu/eV2+FmtYn/nokD/8+8b6qJYT4QGlpq3Jsy4/yTSNnwYIF6i/NUVFRnDjx+n8YaXn8/f2zfMyLVq5ciZ6eHkFBQSxdupSYmBgaNWpE5cqVOXnyJLt27eLOnTt06dIlS+V5eXkxc+ZMJk2axMWLF1m7di2FCxdW7zc1NWXFihVcvHiRBQsWsGzZMubPn69RxtWrV9m4cSObNm1Sz0nq3Lkz0dHR7Ny5k1OnTlGlShUaN27MgwfP70QeOnSIXr16MXz4cC5evMgPP/zAihUr0jVkvL29ad++PSEhIfTt2/eVdfn333+pV68e+vr6/PXXX5w6dYq+ffuqG04LFixg7ty5zJkzh3PnzuHu7k6bNm0ICwvTKGfixImMGTOG4OBgXFxc6N69u7qM4OBgGjduTNmyZTly5AiHDx+mdevWpKSkAODr68uqVatYunQpFy5cYOTIkXz22WccOHBA4xwTJkxg5syZXLp0iQoVKgDPn1tjY2OOHTvG7NmzmTp1Knv37gVg48aNzJ8/nx9++IGwsDC2bNlC+fLlX/3kCiE+KCpdHUwrlOXBoaP/JSoKDw4exbxqxQyPsXJvQOzJs5SeOZF65w9Q68BmHIZ/AZkMMVHp6mDbsRX//ro5J6oghBAftXwzLsfc3BxTU1O0tbWxsbHJ0jFWVlYAWFhYZPmYFzk7OzN79mz142+++YbKlSszY8YMddry5cuxs7PjypUruLi4ZFrW48ePWbBgAYsXL6Z3794AODo68sknn6jzfP311+q/HRwcGDNmDOvWrWPcuHHq9MTERFatWqWu2+HDhzl+/DjR0dHo6+sDMGfOHLZs2cKGDRsYMGAAPj4+TJgwQX3ekiVLMm3aNMaNG8eUKVPUZffo0YM+ffpk6dp89913mJubs27dOvWwthfrP2fOHMaPH0+3bt0AmDVrFvv378fPz4/vvvtOnW/MmDG0bNkSAB8fH1xdXbl69SqlS5dm9uzZVK1ale+//16d39XVFYCEhARmzJjBvn37qFWrlrpehw8f5ocffqB+/frqY6ZOnUrTpk014q9QoYK67s7OzixevJiAgACaNm1KZGQkNjY2NGnSBF1dXYoXL0716tWzdF2EEB8GvQKWaOnokHj3vkZ64t37GDuXyPAYQ/tiWH5SlNubtnOmx5cYlShO6Zlfo6Wjw7W5S9Llt27RGB1zU6LWbcmJKggh8pn8ukBATsk3jZzc4ObmpvH47Nmz7N+/HxMTk3R5w8PDX9nIuXTpEgkJCTRu3DjTPOvXr2fhwoWEh4cTFxdHcnIyZmZmGnns7e3VDZy0mOLi4ihYsKBGvqdPn6qHjp09e5agoCCNnpuUlBSePXtGfHw8RkZGAFStWjXT2F4WHBxM3bp1M5y38+jRI27dukWdOnU00uvUqZNuGF1azwqAra0tANHR0ZQuXZrg4GA6d+6c4fmvXr1KfHx8usZLYmIilStX1kjLqF4vnjft3NHR0cDznjE/Pz9KlixJ8+bN+fTTT2ndunWmc3kSEhJISEjQjENJRU+VbzpShRAAWlok3nvAxdHekJrK43MX0bexxmFInwwbOUV6dOD+X4dJuHP3/ccqhBD5XL5u5GhpaaEoikbai3NY3vY4Y2NjjcdxcXG0bt2aWbNmpcub9gU9M4aGhq/cf+TIEXr27ImPjw/u7u7qXpK5c+e+NiZbW1sCAwPTlWlhYaHO4+PjQ4cOHdLlSZvXk1HZr/K6+mTVi40kler5HYzU1NTXniMuLg6A7du3U7So5kpGaT1aaTKq18uNM5VKpT6vnZ0doaGh7Nu3j7179zJ48GC+/fZbDhw4kGGjztfXFx8fH420z4ys6GVinWn8QojclfjgIanJyehZad4g0rMqSEJ0xvMsE+/cJTU5Gf7/WQHwJOwa+oWtUOnqoCT9N8/RoJgtBevV5GzfETkSvxAi/5HV1bInX18tKysroqKi1I8fPXrE9evXNfLo6uqq53BkdhyQpd/cqVKlChcuXMDBwQEnJyeN7XUNBGdnZwwNDQkICMhw/99//429vT0TJ06katWqODs7c+PGjSzFdPv2bXR0dNLFlLbEdpUqVQgNDU2338nJCa03fENVqFCBQ4cOZdg4NDMzo0iRIgQFBWmkBwUFUbZs2WydI7PrVbZsWfT19YmMjExXJzs7u+xVJgOGhoa0bt2ahQsXEhgYyJEjRwgJCckwr5eXF7GxsRpbN+PXL28uhMg9SlIyj89dpEDdGv8lqlQUqFuD2JNnMzwm5kQwRg7FQfXfkBIjRwcSbkdrNHAAinRrT+K9B9zbezBH4hdCiI9dvm7kNGrUiNWrV3Po0CFCQkLo3bs32traGnkcHBwICAjg9u3b6lW5GjVqxMmTJ1m1ahVhYWFMmTKF8+cz/12ENEOGDOHBgwd0796dEydOEB4ezu7du+nTp0+6htTLDAwMGD9+POPGjWPVqlWEh4dz9OhRfv75Z+B5IygyMpJ169YRHh7OwoUL2bz59ZNVmzRpQq1atWjXrh179uwhIiKCv//+m4kTJ3Ly5EkAJk+ezKpVq/Dx8eHChQtcunSJdevWacwByi5PT08ePXpEt27dOHnyJGFhYaxevZrQ0OerDI0dO5ZZs2axfv16QkNDmTBhAsHBwQwfPjzL5/Dy8uLEiRMMHjyYc+fOcfnyZZYsWcK9e/cwNTVlzJgxjBw5kpUrVxIeHs7p06dZtGgRK1eufON6AaxYsYKff/6Z8+fPc+3aNX755RcMDQ2xt7fPML++vj5mZmYamwxVEyLvu7F0FUV7dsK2SxuMnUtSZvYktI0MufX/OTSui2bgNHGEOv/NFevRtTSn1PQJGJW0p1CTepQY/gU3/ddpFqxSUaRbO279thXlNf8bhBAijfwYaPbk6+FqXl5eXL9+nVatWmFubs60adPS9eTMnTuXUaNGsWzZMooWLUpERATu7u5MmjSJcePG8ezZM/r27UuvXr0yvVOfJq13Yvz48TRr1oyEhATs7e1p3rx5lnpEJk2ahI6ODpMnT+bWrVvY2toyaNAgANq0acPIkSPx9PQkISGBli1bMmnSJI2ljTOiUqnYsWMHEydOpE+fPty9excbGxvq1aunXrnN3d2dP//8k6lTpzJr1ix0dXUpXbo0/fv3f23MmSlYsCB//fUXY8eOpX79+mhra1OpUiX1PJxhw4YRGxvL6NGjiY6OpmzZsmzbtg1nZ+csn8PFxYU9e/bw1VdfUb16dQwNDalRowbdu3cHYNq0aVhZWeHr68u1a9ewsLCgSpUqfPXVV29cL3g+zG/mzJmMGjWKlJQUypcvzx9//JFu3pMQ4sN2Z+su9Apa4jjOE33rQjy+cJnT3QepFyMwKGqrMTQt4dZtTncbiMvUcdTcv4mE29FELvuFiEU/a5RboF4tDO2KcGutrKomhMi6/NoYySkq5eXJJ0KIHLe3cLncDkEIIYQQ2dT0zutH9uSUGwPa5VjZ9j9uybGyc0u+7skRQgghhBAiP5CFB7JHrtZ7EhkZiYmJSaZbZGRkboeYLYMGDcq0LmlD7IQQQgghhMgN0pPznhQpUuSVK7QVKVLk/QXzDkydOpUxY8ZkuO/l3+4RQgghhBBvR+bkZI80ct6TtCWc8wtra2usreV3XoQQQgghRN4jjRwhhBBCCCHyOJmTkz1ytYQQQgghhBD5ivTkCJELdMy0X59JCCGEECKNSubkZIf05AghhBBCCCHyFenJEUIIIYQQIo+T1dWyRxo5QgghhBBC5HGy8ED2yNUSQgghhBBC5CvSyMnjGjRowIgRI95Zed7e3lSqVOmdlfc+ZTf2FStWYGFh8dp8KpWKLVu2vHFcQgghhBA5TaWlyrEtP8r3jZzAwEBUKhUxMTG5Hcob2bRpE9OmTcvtMN67jBoeY8aMISAgIMtldO3alStXrqgfZ9ZIioqKokWLFm8aqhAiHyvasys19++g3vnjuG34BdMK5V6ZX8fUFOcpXtQO2kf9CyeosWcbBep/ot7vMHQQDcPOamzVd23J4VoIIcTHR+bk/F9iYiJ6enq5cu6kpCR0dXUzjKdAgQK5EtOrZBTv+2BiYoKJiUmW8xsaGmJoaPjafDY2Nm8TlhAin7L+1B2nr8YQOvkbHp0Nwa53TyouX8KxZm1JevAgXX6Vrg4VVywl6cEDLgwdQ8KdaAyK2pL06LFGvrgrVznbe4D6sZKSkuN1EUJ8+GROTvZ8EFcrNTUVX19fSpQogaGhIRUrVmTDhg0oikKTJk1wd3dHURQAHjx4QLFixZg8eTIRERE0bNgQAEtLS1QqFR4eHsDzYWCenp6MGDGCQoUK4e7uDsC8efMoX748xsbG2NnZMXjwYOLi4jTiCQoKokGDBhgZGWFpaYm7uzsPHz4EwMHBAT8/P438lSpVwtvbW/1YpVKxZMkS2rRpg7GxMdOnT1f3Mvz000+UKFECAwMDdZwvDldLSEhg/Pjx2NnZoa+vj5OTEz///DOQ8fCsLVu2oHrFuuonTpygadOmFCpUCHNzc+rXr8/p06c18mQU76ukpKTQr18/9fNVqlQpFixYkC7f8uXLcXV1RV9fH1tbWzw9PdXXEKB9+/aoVCr14xd7Yvbs2YOBgUG6Hrrhw4fTqFGjdNdjxYoV+Pj4cPbsWVQqFSqVihUrVqjr92Kv0c2bN+nSpQsWFhYUKFCAtm3bEhERod4fGBhI9erVMTY2xsLCgjp16nDjxo1XXhMhxIfHru/n3Fq/idsbtxJ/9Rqhk78h9ekzbDu1yzC/baf26FqYE/LlSGJPB/Ps31vEHD/Fk8tXNPIpKckk3ruv3pIexuR8ZYQQ4iPzQTRyfH19WbVqFUuXLuXChQuMHDmSzz77jIMHD7Jy5UpOnDjBwoULARg0aBBFixZl8uTJ2NnZsXHjRgBCQ0OJiorS+LK9cuVK9PT0CAoKYunSpQBoaWmxcOFCLly4wMqVK/nrr78YN26c+pjg4GAaN25M2bJlOXLkCIcPH6Z169akZPNOnLe3N+3btyckJIS+ffsCcPXqVTZu3MimTZsIDg7O8LhevXrx66+/snDhQi5dusQPP/yQrd6Nlz1+/JjevXtz+PBhjh49irOzM59++imPH2veecwo3sykpqZSrFgxfv/9dy5evMjkyZP56quv+O2339R5lixZwpAhQxgwYAAhISFs27YNJycn4HnDC8Df35+oqCj14xc1btwYCwsL9fMLzxtX69evp2fPnunyd+3aldGjR+Pq6kpUVBRRUVF07do1Xb6kpCTc3d0xNTXl0KFDBAUFYWJiQvPmzUlMTCQ5OZl27dpRv359zp07x5EjRxgwYMArG5JCiA+PSlcHE9cyPPz76H+JisKDv49iVrlChscUalSf2DPncJniRZ0jf1Ft+0bsB/WDl+6+GtnbU/vwXmr+tZ0yc2egbyu9yUKI15M5OdmT54erJSQkMGPGDPbt20etWrUAKFmyJIcPH+aHH35g7dq1/PDDD/Tq1Yvbt2+zY8cOzpw5g47O86qlDfeytrZO18vh7OzM7NmzNdJe7DVxcHDgm2++YdCgQXz//fcAzJ49m6pVq6ofA7i6uma7Xj169KBPnz4aaYmJiaxatQorK6sMj7ly5Qq//fYbe/fupUmTJupr8TbSej3S/Pjjj1hYWHDgwAFatWr1yngzo6uri4+Pj/pxiRIlOHLkCL/99htdunQB4JtvvmH06NEMHz5cna9atWoA6vpbWFhkOpRMW1ubbt26sXbtWvr16wdAQEAAMTExdOzYMV1+Q0NDTExM0NHReeXwtPXr15OamspPP/2kbrj4+/tjYWFBYGAgVatWJTY2llatWuHo6AhAmTJlsnRdhBAfDl1LS7R0dEi8d18jPen+fYwdS2R4jIFdMSxqFeHOth2c7T8EI/viuHh/hUpHh4jFPwDw6GwIl8ZPIv56BPpWVjgMHUiVX/053rIjKU/ic7xeQgjxscjzjZyrV68SHx9P06ZNNdITExOpXLkyAJ07d2bz5s3MnDmTJUuW4OzsnKWy3dzc0qXt27cPX19fLl++zKNHj0hOTubZs2fEx8djZGREcHAwnTt3fut6Va1aNV2avb19pg0ceN6LpK2tTf369d/6/Gnu3LnD119/TWBgINHR0aSkpBAfH09kZORr432V7777juXLlxMZGcnTp09JTExUDzWLjo7m1q1bNG7c+K1i79mzJzVr1uTWrVsUKVKENWvW0LJlyyytqJaZs2fPcvXqVUxNTTXSnz17Rnh4OM2aNcPDwwN3d3eaNm1KkyZN6NKlC7a2tpmWmZCQQEJCgkZaopKKnuqD6EgVQmSRSkuLpPsPCP16KqSmEnfhEvqFrbHr31vdyHlwMEid/0loGI/OhlDrwE6sW7gTtWFzboUuhPgA5Ncel5yS579lpc2H2b59O8HBwert4sWLbNiwAYD4+HhOnTqFtrY2YWFhWS7b2NhY43FERAStWrWiQoUKbNy4kVOnTvHdd98BzxtVwGsnsmtpaannB6VJSkp67bkzS3vRuzr3i3r37k1wcDALFizg77//Jjg4mIIFC6rrm9XYXrRu3TrGjBlDv3792LNnD8HBwfTp0yfL1zCrqlWrhqOjI+vWrePp06ds3rw5w6Fq2REXF4ebm5vGay04OJgrV67Qo0cP4HnPzpEjR6hduzbr16/HxcWFo0ePZlqmr68v5ubmGtvaB9FvFacQImclPXxIanIyeoUKaqTrFixIwt17GR6TePcu8ddvQGqqOu1J+DX0ra1Q6WZ8TzH58WPir9/A0N7u3QUvhMiftLRybsuH8nytypYti76+PpGRkTg5OWlsdnbP/ymMHj0aLS0tdu7cycKFC/nrr7/Ux6etmJaVOTOnTp0iNTWVuXPnUrNmTVxcXLh165ZGngoVKrxyGWMrKyuioqLUjx89esT169ezVefMlC9fntTUVA4cOJDpuR8/fsyTJ0/UaZnN7UkTFBTEsGHD+PTTT9WLANy7l/E/8KwKCgqidu3aDB48mMqVK+Pk5ER4eLh6v6mpKQ4ODq+8jrq6ull6znr27MmaNWv4448/0NLSomXLlpnm1dPTe22ZVapUISwsDGtr63SvN3Nzc3W+ypUr4+Xlxd9//025cuVYu3ZtpmV6eXkRGxursfUoYP3augkhco+SlEzchUtY1qrxX6JKhWXtGjw6cy7DY2JPBT9vrLwwR8+ohD0Jd6JRkpIzPEbbyBDD4naZNpyEEEK8mTzfyDE1NWXMmDGMHDmSlStXEh4ezunTp1m0aBErV65k+/btLF++nDVr1tC0aVPGjh1L79691aud2dvbo1Kp+PPPP7l79266ldJe5OTkRFJSEosWLeLatWusXr1avSBBGi8vL06cOMHgwYM5d+4cly9fZsmSJeqGQaNGjVi9ejWHDh0iJCSE3r17o62t/U6uhYODA71796Zv375s2bKF69evExgYqJ7QX6NGDYyMjPjqq68IDw9n7dq16hXEMuPs7Mzq1au5dOkSx44do2fPnm/d0+Ls7MzJkyfZvXs3V65cYdKkSekWD/D29mbu3LksXLiQsLAw9XP6Yl0DAgK4ffu2+rnMSM+ePTl9+jTTp0+nU6dO6OvrZ5rXwcGB69evExwczL1799INIUsrr1ChQrRt25ZDhw6pr/GwYcP4559/uH79Ol5eXhw5coQbN26wZ88ewsLCXjkvR19fHzMzM41NhqoJkffdXL4a264dsGnfGiPHErhM/RptQ0OiNm4BoMzsbyg5epg6/79rf0PXwhznr8dj6GBPwQZ1sR/Un3/XrFfncRw/CovqbhgULYJZ5YqU+34+SmoK0X/ufN/VE0J8YNJWh82JLT/6IL5pTZs2jUmTJuHr60uZMmVo3rw527dvx8HBgX79+uHt7U2VKlUA8PHxoXDhwgwaNAiAokWL4uPjw4QJEyhcuLB6meKMVKxYkXnz5jFr1izKlSvHmjVr8PX11cjj4uLCnj17OHv2LNWrV6dWrVps3bpVvdCBl5cX9evXp1WrVrRs2ZJ27dqpJ6i/C0uWLKFTp04MHjyY0qVL88UXX6h7bgoUKMAvv/zCjh07KF++PL/++qvG0tUZ+fnnn3n48CFVqlTh888/Z9iwYVhbv10vw8CBA+nQoQNdu3alRo0a3L9/n8GDB2vk6d27N35+fnz//fe4urrSqlUrjaGGc+fOZe/evdjZ2annXmXEycmJ6tWrc+7cudcOVevYsSPNmzenYcOGWFlZ8euvv6bLY2RkxMGDBylevDgdOnSgTJky9OvXj2fPnmFmZoaRkRGXL1+mY8eOuLi4MGDAAIYMGcLAgQOzeZWEEHld9I7dhM+cR4nhg6m27TdMy5TiXL/BJN1//hs5+kVs0LMupM6fcPsOZ/t8iWkFV6r9+TvOk8bzz8o13PhhuTqPvk1hys6bSY09W3Fd+C1JD2M51flzkh5kfjNHCCFE9qmUlydxCCFy3H7nirkdghBCCCGyqWHY2Vw7973J/XKs7EJTf86xsnPLB9GTI4QQQgghhBBZJY0ckW2DBg3CxMQkwy1tmKAQQgghhHh35MdAsyfP/06OyHumTp3KmDFjMtxnZmb2nqMRQgghhBBCkzRyRLZZW1u/9eIEQgghhBAiG/Lp79nkFGnkCCGEEEIIkcfl12FlOUWahEIIIYQQQoh8RRo5QgghhBBC5HEqlVaObdn13Xff4eDggIGBATVq1OD48eNZOm7dunWoVCratWuX7XNmlwxXEyIXaOlIl7MQQgghPjzr169n1KhRLF26lBo1auDn54e7uzuhoaGvnLMdERHBmDFjqFu37nuJU3pyhBBCCCGEyOu0VDm3ZcO8efP44osv6NOnD2XLlmXp0qUYGRmxfPnyTI9JSUmhZ8+e+Pj4ULJkybe9ElkijRwhhBBCCCE+YgkJCTx69EhjS0hISJcvMTGRU6dO0aRJE3WalpYWTZo04ciRI5mWP3XqVKytrenXr1+OxJ8RaeQIIYQQQgiRx6m0tHJs8/X1xdzcXGPz9fVNF8O9e/dISUmhcOHCGumFCxfm9u3bGcZ9+PBhfv75Z5YtW5Yj1yUz0sgR70RERAQqlYrg4OBcjUOlUrFly5b3ek4HBwf8/Pze6zmFEEIIId4VLy8vYmNjNTYvL6+3Lvfx48d8/vnnLFu2jEKFCr2DSLNOFh4Q74SdnR1RUVHZegE3aNCASpUqSQNBCJFnFenRFbu+vdErVJC4y1e4On0Wj0POZ5pf29SUEiM8KdS0Ebrm5jy7FUW477c8OHgYAPshg3DwHKRxTPy165xo2T5H6yGE+PDl5O/k6Ovro6+v/9p8hQoVQltbmzt37mik37lzBxsbm3T5w8PDiYiIoHXr1uq01NRUAHR0dAgNDcXR0fEto8+YNHLEO6GtrZ3hi1sIIT5UVi2a4Th+NFe8p/P4XAhFe/Wk/LLvOfFpW5IePEyXX6WrQ4Wfl5L04AEXh48l4U40BkVtSX70WCPfk7CrnO07UP1YSU7J8boIIfKBN1jq+V3T09PDzc2NgIAA9TLQqampBAQE4OnpmS5/6dKlCQkJ0Uj7+uuvefz4MQsWLMDOzi7HYs39qyXei127dvHJJ59gYWFBwYIFadWqFeHh4cB/Q802bdpEw4YNMTIyomLFihoTyPr27UuFChXUk9ASExOpXLkyvXr10ijjxeFq58+fp0WLFpiYmFC4cGE+//xz7t27B4CHhwcHDhxgwYIFqFQqVCoV169fx8nJiTlz5mjEHhwcjEql4urVq9mu982bN+nSpQsWFhYUKFCAtm3bEhERAcCePXswMDAgJiZG45jhw4fTqFEj9ePDhw9Tt25dDA0NsbOzY9iwYTx58iTbsQghPizFen9O1O+buLN5K/Hh1wjz/obUZ8+w6dAuw/w2Hdqha27GBc+RPDoTTMKtW8SeOMWT0Csa+ZTkFJLu3VdvyS99BgkhRF42atQoli1bxsqVK7l06RJffvklT548oU+fPgD06tVLPdTNwMCAcuXKaWwWFhaYmppSrlw59PT0cixOaeR8JJ48ecKoUaM4efIkAQEBaGlp0b59e3WXIcDEiRMZM2YMwcHBuLi40L17d5KTkwFYuHAhT548YcKECeq8MTExLF68OMPzxcTE0KhRIypXrszJkyfZtWsXd+7coUuXLgAsWLCAWrVq8cUXXxAVFUVUVBTFixenb9+++Pv7a5Tl7+9PvXr1cHJyyladk5KScHd3x9TUlEOHDhEUFISJiQnNmzcnMTGRxo0bY2FhwcaNG9XHpKSksH79enr27Ak872Zt3rw5HTt25Ny5c6xfv57Dhw9neLdCCJF/qHR1MHUtw8Mjx/5LVBQeHjmGWaUKGR5TsFEDHgWfw2mSF7UOBVB12waKD+gHWpr/ag3ti1PzwB6q7/mT0rNnoG8rveBCiNdTaalybMuOrl27MmfOHCZPnkylSpUIDg5m165d6sUIIiMjiYqKyolLkC0yXO0j0bFjR43Hy5cvx8rKiosXL2JiYgLAmDFjaNmyJQA+Pj64urpy9epVSpcujYmJCb/88gv169fH1NQUPz8/9u/fj5mZWYbnW7x4MZUrV2bGjBka57Szs+PKlSu4uLigp6eHkZGRxjA3Dw8PJk+ezPHjx6levTpJSUmsXbs2Xe9OVqxfv57U1FR++uknVKrnb2B/f38sLCwIDAykWbNmdOvWjbVr16qXNAwICCAmJkZ9vXx9fenZsycjRowAwNnZmYULF1K/fn2WLFmCgYFBtuMSQuR9uhaWqHR0SLp/XyM96f59jEo4ZHiMYbGiGNSoxp0/dxAy0BNDezucJ3+FSkeHG9//AMDjcyFc/moyT69HoGdVCPshg6j0y3JOtu5ESnx8TldLCCHeCU9Pz0xv+AYGBr7y2BUrVrz7gDIgjZyPRFhYGJMnT+bYsWPcu3dP3YMTGRlJ2bJlAahQ4b+7k7a2tgBER0dTunRpAGrVqsWYMWOYNm0a48eP55NPPsn0fGfPnmX//v3qBtSLwsPDcXFxyfC4IkWK0LJlS5YvX0716tX5448/SEhIoHPnztmu89mzZ7l69SqmpqYa6c+ePVMP1evZsyc1a9bk1q1bFClShDVr1tCyZUssLCzUZZw7d441a9aoj1cUhdTUVK5fv06ZMmVeG0dCQkK6teYTU1PR05KOVCHyFS0tEu8/4MrkaZCaStzFS+hbW1OsX291I+fBoSB19idXwnh07jw1A3Zg1aIZtzduyaXAhRAfBPnekC3SyPlItG7dGnt7e5YtW0aRIkVITU2lXLlyJCYmqvPo6uqq/07r+XhxOFtqaipBQUFoa2u/dn5MXFwcrVu3ZtasWen2pTWgMtO/f38+//xz5s+fj7+/P127dsXIyChL9Xw5Bjc3N40GShorKysAqlWrhqOjI+vWrePLL79k8+bNGncY4uLiGDhwIMOGDUtXRvHixbMUh6+vLz4+PhppvQsWpo+VDFERIq9KinmIkpyMbsGCGum6BQuS+P+5hS9LvHsXJTkZXvjcjL92HX0rK1S6OihJyemOSXn8mPiISAyL59zkWyGE+BhJI+cjcP/+fUJDQ1m2bBl169YFnk+mz65vv/2Wy5cvc+DAAdzd3fH391dPMntZlSpV2LhxIw4ODujoZPwy09PTIyUl/apCn376KcbGxixZsoRdu3Zx8ODBbMeaFsP69euxtrbOdFgdPO/NWbNmDcWKFUNLS0s9ZC+tjIsXL2Z7PtCLvLy8GDVqlEbasWqZ94IJIXKfkpTM4wuXsKxZnfsB+58nqlRY1qzOv2vWZXjMo9NnsW7VAlQqUBQADB3sSYiOzrCBA6BlZIihXTGit2XccBJCiDRpN6BF1ki/10fA0tKSggUL8uOPP3L16lX++uuvdF+6X+fMmTNMnjyZn376iTp16jBv3jyGDx/OtWvXMsw/ZMgQHjx4QPfu3Tlx4gTh4eHs3r2bPn36qBs2Dg4OHDt2jIiICI0hdNra2nh4eODl5YWzszO1atV6o3r37NmTQoUK0bZtWw4dOsT169cJDAxk2LBh/PPPPxr5Tp8+zfTp0+nUqZPGOvHjx4/n77//xtPTk+DgYMLCwti6dWu2Fh7Q19fHzMxMY5OhakLkff+sXI1t5w4Ubtsao5IlcJ4yES1DQ25v3gpAqZnTKDFyqDr/rXW/oWNuhtNX4zB0KE6B+nUpPqAft9b+ps5TcuxIzKu5oV+kCGaVKlJu0XyU1BSit+967/UTQoj8TL5pfQS0tLRYt24dp06doly5cowcOZJvv/02y8c/e/aMzz77DA8PD/WPOQ0YMICGDRvy+eefZ9gbU6RIEYKCgkhJSaFZs2aUL1+eESNGYGFhgdb/v+CPGTMGbW1typYti5WVFZGRkerj+/XrR2JiYqY9RVlhZGTEwYMHKV68OB06dKBMmTL069ePZ8+eafTsODk5Ub16dc6dO6deVS1NhQoVOHDgAFeuXKFu3bpUrlyZyZMnU6RIkTeOSwjxYbi7cw/hs+fhMOxL3Davx6R0KUIGDCbp/gMADGxt0fv/0FeAhNt3CPliMKblXam65XecvhrHv6vXErlsuTqPvk1hyszxpfrOLZSdP5ukmBjOdOtF0sP0v7sjhBAatLRybsuHVIry/z51IfKQQ4cO0bhxY27evKlekjA/OVCmUm6HIIQQQohsqn8pONfOHffduBwr22TI7BwrO7fInByRpyQkJHD37l28vb3p3LlzvmzgCCGEEEKInJU/+6fEB+vXX3/F3t6emJgYZs/WvKuwZs0aTExMMtxcXV1zKWIhhBBCiPdApZVzWz4kPTkiT/Hw8MDDwyPDfW3atKFGjRoZ7ntx+WshhBBCCPFxk0aO+GCYmpqm+2FPIYQQQoiPgpYsIZ0d+bN/SgghhBBCCPHRkp4cIYQQQggh8jhVPp07k1PkagkhhBBCCCHyFenJESIXqLRlXK0QQgghskHm5GSLNHKEEEIIIYTI41RaMgArO+RqCSGEEEIIIfIV6ckRQgghhBAir1PJcLXskJ4c8cYaNGjAiBEjcjuMNxYYGIhKpSImJia3QxFCCCGEEO+QNHLER6t27dpERUVhbm6e26EIIfIo225dqL57O5+cOkqltaswLef6yvzapiY4TZxAjf17+OT0Mar+uQXLup9kmNeuXx/qnT9DyfFjciJ0IUR+o6WVc1s+JMPVRJ6VmJiInp5ejpWvp6eHjY1NjpUvhPiwWTVvhuO40YRNnc7jc+cp+nkPyv3wPSdbtyPpwcN0+VU6OlRYtpTEBw+4NGosCXeiMShShOTHj9PlNSlXFtvOHYkLvfI+qiKEEB+d/Nl0E+/ckydP6NWrFyYmJtja2jJ37lyN/QkJCYwZM4aiRYtibGxMjRo1CAwMVO9fsWIFFhYWbNmyBWdnZwwMDHB3d+fmzZvqPN7e3lSqVImffvqJEiVKYGBgAEBMTAz9+/fHysoKMzMzGjVqxNmzZ9XHnT17loYNG2JqaoqZmRlubm6cPHkSgBs3btC6dWssLS0xNjbG1dWVHTt2ABkPV9u4cSOurq7o6+vj4OCQrp4ODg7MmDGDvn37YmpqSvHixfnxxx/fyTUWQuQtRXt9RtSGTdzZso34a9cImzqd1GfPsGnfLsP8Nh3aoWNuxsVho3h05iwJt6KIPXmKJy81ZLQMDSk9cwZXvKeR/OjRe6iJECJfUKlybsuHpJEjsmTs2LEcOHCArVu3smfPHgIDAzl9+rR6v6enJ0eOHGHdunWcO3eOzp0707x5c8LCwtR54uPjmT59OqtWrSIoKIiYmBi6deumcZ6rV6+yceNGNm3aRHBwMACdO3cmOjqanTt3curUKapUqULjxo158OABAD179qRYsWKcOHGCU6dOMWHCBHR1dQEYMmQICQkJHDx4kJCQEGbNmoWJiUmGdTx16hRdunShW7duhISE4O3tzaRJk1ixYoVGvrlz51K1alXOnDnD4MGD+fLLLwkNDX3bSyyEyENUOjqYli1DzNFj/yUqCjFHj2FasUKGxxRsUJ9HZ8/hNHECNQ/sw23z79h90TfdUBDnr714cPCQZtlCCCHeKRmuJl4rLi6On3/+mV9++YXGjRsDsHLlSooVKwZAZGQk/v7+REZGUqRIEQDGjBnDrl278Pf3Z8aMGQAkJSWxePFiatSooS6jTJkyHD9+nOrVqwPPh6itWrUKKysrAA4fPszx48eJjo5GX18fgDlz5rBlyxY2bNjAgAEDiIyMZOzYsZQuXRoAZ2dndeyRkZF07NiR8uXLA1CyZMlM6zlv3jwaN27MpEmTAHBxceHixYt8++23eHh4qPN9+umnDB48GIDx48czf/589u/fT6lSpd70Egsh8hhdS0tUOjok3n+gkZ54/z7mJRwyPMagWFEsilYjevtOzn85FMPidjh97YVKR4fIJc97fK1auGNSpjSnu32W01UQQuQz8js52SONHPFa4eHhJCYmqhsnAAUKFFB/qQ8JCSElJQUXFxeN4xISEihYsKD6sY6ODtWqVVM/Ll26NBYWFly6dEndyLG3t1c3cOD5ULS4uDiNcgCePn1KeHg4AKNGjaJ///6sXr2aJk2a0LlzZxwdHQEYNmwYX375JXv27KFJkyZ07NiRChUyvgt76dIl2rZtq5FWp04d/Pz8SElJQVtbG0DjeJVKhY2NDdHR0Zlev4SEBBISEjTSElNT0ZMPKyHyFy0tEh884Ir3NEhNJe7iJfSsrSnWpxeRS35E36YwjhPGEvLFlyiJibkdrRDiQ6OS7w3ZIY0c8dbi4uLQ1tbm1KlT6oZAmsyGhmXG2Ng4Xdm2trYa83vSWFhYAM/n8vTo0YPt27ezc+dOpkyZwrp162jfvj39+/fH3d2d7du3s2fPHnx9fZk7dy5Dhw7NVlwvShsKl0alUpGampppfl9fX3x8fDTSPKwK08fa9o1jEELkrKSHD1GSk9ErWEAjXa9gQRLv3c/wmMS791CSk+GFz4P4a9fRt7JCpaODSdky6BUsSJXf1qr3q3R0MHerQtHuXTlUpYbGsUIIId6cNAnFazk6OqKrq8uxY/+NH3/48CFXrjyfTFu5cmVSUlKIjo7GyclJY3tx9bLk5GT1ggAAoaGhxMTEUKZMmUzPXaVKFW7fvo2Ojk66sgsVKqTO5+LiwsiRI9mzZw8dOnTA399fvc/Ozo5BgwaxadMmRo8ezbJlyzI8V5kyZQgKCtJICwoKwsXFJV3jLTu8vLyIjY3V2HoWKvzG5Qkhcp6SnMzji5eweKEHG5UKixrVeXz2XIbHPAoOxrC4ncYkXkOH4iRE30VJTibm6HFOtuvEqU7d1Nvj8xeI3r6DU526SQNHCPFqWqqc2/Ih6ckRr2ViYkK/fv0YO3YsBQsWxNramokTJ6L1/+FWLi4u9OzZk169ejF37lwqV67M3bt3CQgIoEKFCrRs2RJ43gMydOhQFi5ciI6ODp6entSsWVM9VC0jTZo0oVatWrRr147Zs2fj4uLCrVu32L59O+3bt8fV1ZWxY8fSqVMnSpQowT///MOJEyfo2LEjACNGjKBFixa4uLjw8OFD9u/fn2mjavTo0VSrVo1p06bRtWtXjhw5wuLFi/n+++/f6vrp6+ur5xOlkaFqQuR9/676hVLTpxJ34SKPzp+n2Gc90DI05PaWrQCUmjGNhOhoIvwWARC1/neKdO+K44Rx3Fr7K4b2xSn+RT/+XfMrACnx8cRfDdc4R8rTpyTFxKZLF0II8XakkSOy5NtvvyUuLo7WrVtjamrK6NGjiY2NVe/39/fnm2++YfTo0fz7778UKlSImjVr0qpVK3UeIyMjxo8fT48ePfj333+pW7cuP//88yvPq1Kp2LFjBxMnTqRPnz7cvXsXGxsb6tWrR+HChdHW1ub+/fv06tWLO3fuUKhQITp06KAeHpaSksKQIUP4559/MDMzo3nz5syfPz/Dc1WpUoXffvuNyZMnM23aNGxtbZk6darGogNCiI/H3V170LW0xN7zS/QKFSTucijnBw0h6f+LEejb2qC80PuScPsOIQOH4DhuNG6bfiMhOpp/f1nLzZ9X5FINhBD5iUrm5GSLSlEUJbeDEPnfihUrGDFihMZv0nzMDparnNshCCGEECKb6p0/k2vnfvbrrBwr26D7+BwrO7dIT44QQgghhBB5XT6dO5NTpN9LCCGEEEIIka9II0e8Fx4eHjJUTQghhBDiTam0cm7Lh2S4mhBCCCGEEHmdSoarZUf+bLoJIYQQQgghPlrSkyOEEEIIIUReJ7+xly1ytYQQQgghhBD5ivTkCJELjK2McjsEIYQQQnxI8ukCATlFrpYQQgghhBAiX5GeHCGEEEIIIfI6+THQbJGeHCGEEEIIIUS+Ij05QgghhBBC5HUyJydb5GqJHOXt7U2lSpXey7lUKhVbtmx5L+dK4+DggJ+f33s9pxAid1m160C5XzdQefdflP7+R4xKl3llfm1jE+yGj6LChq1U3r0f11W/Ylaj1nuKVgghPk7SkyNy1JgxYxg6dGhuhyGEEO+EZcPGFPtyKJHzv+XJpYtYd+qC8+x5XOjVneSYmHT5VTo6OM/xIznmIeHeX5N09y56NjakxMW9/+CFEB82lczJyQ5p5IgcZWJigomJSW6HIYQQ70Thzl25t/0P7u/aAUDkvG8xr1Gbgi1acefXX9LlL9iiFTqmZlz2HAgpKQAk3rn9XmMWQuQT8mOg2SJXS7xSgwYNGDZsGOPGjaNAgQLY2Njg7e2t3h8ZGUnbtm0xMTHBzMyMLl26cOfOHfX+l4erBQYGUr16dYyNjbGwsKBOnTrcuHFDvX/r1q1UqVIFAwMDSpYsiY+PD8nJyW8U+82bN+nSpQsWFhYUKFCAtm3bEhERAcCePXswMDAg5qU7r8OHD6dRo0bqx4cPH6Zu3boYGhpiZ2fHsGHDePLkyRvFI4T4sKl0dDByKcWjUyf+S1QUHp8+iYlruQyPsaj9CXEXz1N8xGgqbPyDsstXY9Ozl3xZEUKIHCafsuK1Vq5cibGxMceOHWP27NlMnTqVvXv3kpqaStu2bXnw4AEHDhxg7969XLt2ja5du2ZYTnJyMu3ataN+/fqcO3eOI0eOMGDAAFT/7349dOgQvXr1Yvjw4Vy8eJEffviBFStWMH369GzHnJSUhLu7O6amphw6dIigoCBMTExo3rw5iYmJNG7cGAsLCzZu3Kg+JiUlhfXr19OzZ08AwsPDad68OR07duTcuXOsX7+ew4cP4+np+QZXUQjxodMxt0ClrUPywwca6UkPH6BboECGx+gXKYJl/QaotLS46jWGqNUrKNy5G7af9X4fIQsh8hOVKue2fEiGq4nXqlChAlOmTAHA2dmZxYsXExAQAEBISAjXr1/Hzs4OgFWrVuHq6sqJEyeoVq2aRjmPHj0iNjaWVq1a4ejoCECZMv9N2PXx8WHChAn07v38n3/JkiWZNm0a48aNU58/q9avX09qaio//fSTuhHl7++PhYUFgYGBNGvWjG7durF27Vr69esHQEBAADExMXTs2BEAX19fevbsyYgRI9R1X7hwIfXr12fJkiUYGBhkKZaEhAQSEhI00hJTU9GTO7lC5H8qFckPH3Jj7mxITSX+Sii6hQph07UHUav8czs6IYTIt+RblnitChUqaDy2tbUlOjqaS5cuYWdnp27gAJQtWxYLCwsuXbqUrpwCBQrg4eGBu7s7rVu3ZsGCBURFRan3nz17lqlTp6rn8ZiYmPDFF18QFRVFfHx8tmI+e/YsV69exdTUVF1WgQIFePbsGeHh4QD07NmTwMBAbt26BcCaNWto2bIlFhYW6jJWrFihEY+7uzupqalcv349y7H4+vpibm6usfnf+Cdb9RFC5L7k2BiUlGR0LDV7bXQtC5D04EGGxyQ9uM+zf25Caqo67dmNG+gWLIRKR+4zCiGyQaWVc1s+lD9rJd4pXV1djccqlYrUF/5hZ4e/vz9Hjhyhdu3arF+/HhcXF44ePQpAXFwcPj4+BAcHq7eQkBDCwsKy3GuSJi4uDjc3N42ygoODuXLlCj169ACgWrVqODo6sm7dOp4+fcrmzZvVQ9XSyhg4cKDG8WfPniUsLEzdE5UVXl5exMbGamx97Itlqz5CiNynJCcTfyUUsypV/0tUqTCt4kbchfMZHhN3PgT9osU0hoMY2NmReO8eyhvONxRCCPF6chtJvLEyZcpw8+ZNbt68qe7NuXjxIjExMZQtWzbT4ypXrkzlypXx8vKiVq1arF27lpo1a1KlShVCQ0NxcnJ669iqVKnC+vXrsba2xszMLNN8PXv2ZM2aNRQrVgwtLS1atmypUcbFixffOh59fX309fU10mSomhAfpju/r8dhwkSeXLlM/P+XkNYyMOD+ru0AOHh9TeLde9z6aSkAd7duxrpdR+w8RxC9eQP6xYph06MX0Zt+z81qCCE+RPLdIVvkaok31qRJE8qXL0/Pnj05ffo0x48fp1evXtSvX5+qVaumy3/9+nW8vLw4cuQIN27cYM+ePYSFhann5UyePJlVq1bh4+PDhQsXuHTpEuvWrePrr7/Odmw9e/akUKFCtG3blkOHDnH9+nUCAwMZNmwY//zzj0a+06dPM336dDp16qTRGBk/fjx///03np6eBAcHExYWxtatW2XhASE+Yg/3B/DPku8o4tGfMstWYOTkTNj40SQ/fAiAnnVhdAsWVOdPuhtN2LiRGJUuQ9mfV1LccyTRm37ndgbLTQshhHh3pCdHvDGVSsXWrVsZOnQo9erVQ0tLi+bNm7No0aIM8xsZGXH58mVWrlzJ/fv3sbW1ZciQIQwcOBAAd3d3/vzzT6ZOncqsWbPQ1dWldOnS9O/fP9uxGRkZcfDgQcaPH0+HDh14/PgxRYsWpXHjxho9O05OTlSvXp3jx4/j5+enUUaFChU4cOAAEydOpG7duiiKgqOjY6arxwkhPg53t2zk7paNGe67MjL9jx8/uXiB0CEDcjosIUR+l09XQcspKkVRlNwOQoiPzamGdXI7BCGEEEJkk9v+oFw797M9Obcio0GzPjlWdm6R4WpCCCGEEEKIfEUaOeKDsGbNGo2lnF/cXF1dczs8IYQQQoiclYd+DPS7777DwcEBAwMDatSowfHjxzPNu2zZMurWrYulpSWWlpY0adLklfnfFZmTIz4Ibdq0oUaNGhnue3mJayGEEEIIkTPWr1/PqFGjWLp0KTVq1MDPzw93d3dCQ0OxtrZOlz8wMJDu3btTu3ZtDAwMmDVrFs2aNePChQsULVo0x+KUOTlC5AKZkyOEEEJ8eHJ1Tk7Aqhwr26BxryznrVGjBtWqVWPx4sUApKamYmdnx9ChQ5kwYcJrj09JScHS0pLFixfTq1fWz5tdMlxNCCGEEEKIj1hCQgKPHj3S2BISEtLlS0xM5NSpUzRp0kSdpqWlRZMmTThy5EiWzhUfH09SUhIFChR4Z/FnRBo5QgghhBBC5HGKSpVjm6+vL+bm5hqbr69vuhju3btHSkoKhQsX1kgvXLgwt2/fzlI9xo8fT5EiRTQaSjlB5uQIIYQQQgjxEfPy8mLUqFEaaS/+QPq7MnPmTNatW0dgYCAGBgbvvPwXSSNHCCGEEEKIvE6VcwOw9PX1s9SoKVSoENra2ty5c0cj/c6dO9jY2Lzy2Dlz5jBz5kz27dtHhQoV3irerJDhakIIIYQQQuR1Kq2c27JIT08PNzc3AgIC1GmpqakEBARQq1atTI+bPXs206ZNY9euXVStWvWtLkNWSU+OEEIIIYQQIktGjRpF7969qVq1KtWrV8fPz48nT57Qp08fAHr16kXRokXVc3pmzZrF5MmTWbt2LQ4ODuq5O2m/d5hTpJEjhBBCCCFEHqe8wY925oSuXbty9+5dJk+ezO3bt6lUqRK7du1SL0YQGRmJltZ/vUNLliwhMTGRTp06aZQzZcoUvL29cyxO+Z0cIXKB/E6OEEII8eHJzd/JiT+wLsfKNqrfLcfKzi3SkyOEEEJkg1W7DhTu2gPdAgV4Gn6VyIXzib98KdP82sYmFOk/AMu69dE2NSPxzm1ufreQR8ey9psSQggB5OjCA/mRNHLEW0lMTERPT++dlqkoCikpKejo5J2XZ16MSQjx/lk2bEyxL4cSOf9bnly6iHWnLjjPnseFXt1JjolJl1+lo4PzHD+SYx4S7v01SXfvomdjQ0pc3PsPXgghPiLSJMxHGjRogKenJ56enpibm1OoUCEmTZpE2ojEhIQExowZQ9GiRTE2NqZGjRoEBgZqlHH48GHq1q2LoaEhdnZ2DBs2jCdPnqj3Ozg4MG3aNHr16oWZmRkDBgx4ZUwRERGoVCrWrVtH7dq1MTAwoFy5chw4cECdJzAwEJVKxc6dO3Fzc0NfX5/Dhw+TmpqKr68vJUqUwNDQkIoVK7Jhwwb1cQ8fPqRnz55YWVlhaGiIs7Mz/v7+wPPGl6enJ7a2thgYGGBvb6+eAJcWU3BwsLqsmJgYVCqV+nq8aUxCiPytcOeu3Nv+B/d37eDZjQgi531L6rMECrZolWH+gi1aoWNqxtWvJ/DkfAiJd24TdzaYp+FX33PkQogPnkqVc1s+JI2cfGblypXo6Ohw/PhxFixYwLx58/jpp58A8PT05MiRI6xbt45z587RuXNnmjdvTlhYGADh4eE0b96cjh07cu7cOdavX8/hw4fx9PTUOMecOXOoWLEiZ86cYdKkSVmKa+zYsYwePZozZ85Qq1YtWrduzf379zXyTJgwgZkzZ3Lp0iUqVKiAr68vq1atYunSpVy4cIGRI0fy2WefqRtIkyZN4uLFi+zcuZNLly6xZMkSChUqBMDChQvZtm0bv/32G6GhoaxZswYHB4dsX8/sxiSEyL9UOjoYuZTi0akT/yUqCo9Pn8TEtVyGx1jU/oS4i+cpPmI0FTb+Qdnlq7Hp2Qu05N+vEELkJBl7k8/Y2dkxf/58VCoVpUqVIiQkhPnz5+Pu7o6/vz+RkZEUKVIEgDFjxrBr1y78/f2ZMWMGvr6+9OzZkxEjRgDg7OzMwoULqV+/PkuWLFH/Mm2jRo0YPXp0tuLy9PSkY8eOwPNVNnbt2sXPP//MuHHj1HmmTp1K06ZNgee9TjNmzGDfvn3qdddLlizJ4cOH+eGHH6hfvz6RkZFUrlxZvd76i42YyMhInJ2d+eSTT1CpVNjb22f/Yr5BTBlJSEggISFBIy0xNRU9+ZIjxAdFx9wClbYOyQ8faKQnPXyAQfHiGR6jX6QIpjZVeLBvD1e9xqBftBjFh49Gpa1N1Cr/9xG2ECK/kO8N2SKNnHymZs2aqF7odqxVqxZz584lJCSElJQUXFxcNPInJCRQsGBBAM6ePcu5c+dYs2aNer+iKKSmpnL9+nXKlCkD8EY/4vTiD0Tp6OhQtWpVLl3SnKj7YrlXr14lPj5e3cBIk5iYSOXKlQH48ssv6dixI6dPn6ZZs2a0a9eO2rVrA+Dh4UHTpk0pVaoUzZs3p1WrVjRr1izbcWc3poz4+vri4+OjkfaFfTEGlsj4S5EQIh9RqUh++JAbc2dDairxV0LRLVQIm649pJEjhMiWvLKE9IdCGjkfibi4OLS1tTl16hTa2toa+9J+iCkuLo6BAwcybNiwdMcXf+EupbGxcY7E+GK5cf+flLt9+3aKFi2qkU9fXx+AFi1acOPGDXbs2MHevXtp3LgxQ4YMYc6cOVSpUoXr16+zc+dO9u3bR5cuXWjSpAkbNmxQr93+4urpSUlJ7ySmjHh5eTFq1CiNtAut3TPNL4TIm5JjY1BSktGxLKCRrmtZgKQHDzI8JunBfZTkZEhNVac9u3ED3YKFUOnoPN8nhBDinZNGTj5z7NgxjcdHjx7F2dmZypUrk5KSQnR0NHXr1s3w2CpVqnDx4kWcnJzeeVxHjx6lXr16ACQnJ3Pq1Kl0c31eVLZsWfT19YmMjMx0GBiAlZUVvXv3pnfv3tStW5exY8cyZ84cAMzMzOjatStdu3alU6dONG/enAcPHmBlZQVAVFSUugfmxUUI3jaml+nr66drBMlQNSE+PEpyMvFXQjGrUpXYoEPPE1UqTKu4Eb15Y4bHxJ0PoUDjps8n9v7/xoqBnR2J9+5JA0cIkT2yhHS2SCMnn4mMjGTUqFEMHDiQ06dPs2jRIubOnYuLiws9e/akV69ezJ07l8qVK3P37l0CAgKoUKECLVu2ZPz48dSsWRNPT0/69++PsbExFy9eZO/evSxevPit4vruu+9wdnamTJkyzJ8/n4cPH9K3b99M85uamjJmzBhGjhxJamoqn3zyCbGxsQQFBWFmZkbv3r2ZPHkybm5uuLq6kpCQwJ9//qkeUjdv3jxsbW2pXLkyWlpa/P7779jY2GBhYYGWlhY1a9Zk5syZlChRgujoaL7++uvX1iErMQkh8rc7v6/HYcJEnly5TPz/l5DWMjDg/q7tADh4fU3i3Xvc+mkpAHe3bsa6XUfsPEcQvXkD+sWKYdOjF9Gbfs/NagghRL4njZx8plevXjx9+pTq1aujra3N8OHD1cs8+/v788033zB69Gj+/fdfChUqRM2aNWnV6vnSpxUqVODAgQNMnDiRunXroigKjo6OdO3a9a3jmjlzJjNnziQ4OBgnJye2bdumXgktM9OmTcPKygpfX1+uXbuGhYUFVapU4auvvgJAT08PLy8vIiIiMDQ0pG7duqxb9/zXgE1NTZk9ezZhYWFoa2tTrVo1duzYoR6qtnz5cvr164ebmxulSpVi9uzZWZqz87qYhBD528P9AeiYW1DEo///fww0jLDxo0l++BAAPevCKKkvDIW9G03YuJEUGzKcsj+vJOnuPaI3/c7tX3/JrSoIIT5QivTkZItKeXFigvigNWjQgEqVKuHn55fboahFRERQokQJzpw5Q6VKlXI7nDzjVMM6uR2CEEIIIbLJbX9Qrp077ui2HCvbpGabHCs7t0hPjhBCCCGEEHmdrK6WLdLvJd7KjBkzMDExyXBr0aJFbocnhBBCCCE+QjJcTbyVBw8e8CCTpVMNDQ3TLbUsnpPhakIIIcSHJzeHqz0+vj3Hyjat3jLHys4tMlxNvJUCBQpQoECB12cUQgghhBBvToarZYsMVxNCCCGEEELkK9KTI4QQQgghRF4nS0hnizRyhMgFhhYGuR2CEEIIIUS+JY0cIYQQQggh8jhF5uRki/R7CSGEEEIIIfIV6ckRQgghhBAir5M5OdkiV0sIIYQQQgiRr0gjR+QZK1aswMLC4p2UFRgYiEqlIiYm5p2Ul9vnEUIIIcTHTUGVY1t+JMPVxFvx8PAgJiaGLVu25HYoQgjxzlm2aEvBdl3QsShAQkQ4UT8t4llYaIZ5zRu6U3TYOI201MRELndtoZGmV6w4hT//AiPXCqi0tUm4eYObs31IvhedY/UQQnz4FBmuli3SyBFCCCEyYFanAYX7DCJqqR9Pr1ymYOsO2E+exVVPD1JiYzI8JuVJHFc9Pf5LUDT369rY4jBjATH7dnJ33UpSnj5B384BJSkxx+ohhBAfI2kSiizZsGED5cuXx9DQkIIFC9KkSRPGjh3LypUr2bp1KyqVCpVKRWBgYIZDuIKDg1GpVERERKjTVqxYQfHixTEyMqJ9+/bcv39fvS8iIgItLS1OnjypEYefnx/29vakpqZmuw4bN27E1dUVfX19HBwcmDt3rsb+1atXU7VqVUxNTbGxsaFHjx5ER2veWd2xYwcuLi4YGhrSsGFDjfoIIfKXgm06EbN3B7F/7SbxnxtELfUjNSEBi8bNX3lcSszD/7bYhxr7rHv0I+7UMaJX/ciz61dJuh1F3IkjmTaahBBCTaWVc1s+lD9rJd6pqKgounfvTt++fbl06RKBgYF06NCBKVOm0KVLF5o3b05UVBRRUVHUrl07S2UeO3aMfv364enpSXBwMA0bNuSbb75R73dwcKBJkyb4+/trHOfv74+HhwdaWtl76Z46dYouXbrQrVs3QkJC8Pb2ZtKkSaxYsUKdJykpiWnTpnH27Fm2bNlCREQEHh4e6v03b96kQ4cOtG7dmuDgYPr378+ECROyFYcQ4gOho4OBowtPzp7+L01ReHLuNEalymZ6mJaBIU4/rMV52a/YeU1F387+v50qFSZVa5B46x+KT56Jy4oNlJi1GNPqdXKwIkII8XGS4WritaKiokhOTqZDhw7Y2z//h12+fHkADA0NSUhIwMbGJltlLliwgObNmzNu3PPx6y4uLvz999/s2rVLnad///4MGjSIefPmoa+vz+nTpwkJCWHr1q3ZrsO8efNo3LgxkyZNUp/v4sWLfPvtt+qGTN++fdX5S5YsycKFC6lWrRpxcXGYmJiwZMkSHB0d1T1ApUqVIiQkhFmzZr3y3AkJCSQkJGikJaakoqct9xiEyKt0TM1RaWuT/FJPTHLMQ/SL2mV4TOKtm9xa/C3PIq6hbWxMwbZdcPBdSPjwfiTfv4e2uQXahkYU6tCN6LX+3Fm1DJMq1Sg23psbk0cTf+Hc+6iaEOIDJT8Gmj3yLUu8VsWKFWncuDHly5enc+fOLFu2jIcPH77+wFe4dOkSNWrU0EirVauWxuN27dqhra3N5s2bgefD2xo2bIiDg8Mbna9OHc27pXXq1CEsLIyUlBTgeW9P69atKV68OKamptSvXx+AyMjILMecEV9fX8zNzTW2ZVcisl0HIUTe9jT0IrGBe0mICCf+wjluzppCyqNYLJu1AkD1/yEhj4//zYM/NpIQEc79TeuIO3kUS/fWuRm6EELkO9LIEa+lra3N3r172blzJ2XLlmXRokWUKlWK69evZ5g/bSiZovw34zYpKSnb59XT06NXr174+/uTmJjI2rVrNXpb3qUnT57g7u6OmZkZa9as4cSJE+rGVWLi200I9vLyIjY2VmP7wsXhHUQthMgpyY9jUVJS0DG31EjXsbAkOeZB1gpJSeHZ9avo2Rb9r8zkZBJu3tDIlvBPJLqFrN9J3EKI/EtRaeXYlh/lz1qJd06lUlGnTh18fHw4c+YMenp6bN68GT09PXVPSBorKyvg+TC3NMHBwRp5ypQpw7FjxzTSjh49mu68/fv3Z9++fXz//ffqIXNvokyZMgQFBWmkBQUF4eLigra2NpcvX+b+/fvMnDmTunXrUrp06XSLDpQpU4bjx4+/NuaX6evrY2ZmprHJUDUh8rjkZJ6FX8G4QuX/0lQqjMtXJj70YtbK0NJCv3gJkh8+UJf59Gooei8Nd9MrUoyku3feUeBCCCFAGjkiC44dO8aMGTM4efIkkZGRbNq0ibt371KmTBkcHBw4d+4coaGh3Lt3j6SkJJycnLCzs8Pb25uwsDC2b9+ebiWzYcOGsWvXLubMmUNYWBiLFy/WmI+TpkyZMtSsWZPx48fTvXt3DA0N36gOo0ePJiAggGnTpnHlyhVWrlzJ4sWLGTNmDADFixdHT0+PRYsWce3aNbZt28a0adM0yhg0aBBhYWGMHTuW0NBQ1q5dq7FwgRAif7m/bQMWTVti3rAZesWKYztwBFoGBsQE7AagyLDxWH/WT52/UJfPMa7ohm5hWwxKOlN0hBe6VoV5uHfHf2VuWY95nQZYNP0UXZsiWLZoi2m1WjzYte29108I8YFRqXJuy4ekkSNey8zMjIMHD/Lpp5/i4uLC119/zdy5c2nRogVffPEFpUqVomrVqlhZWREUFISuri6//vorly9fpkKFCsyaNUtj5TSAmjVrsmzZMhYsWEDFihXZs2cPX3/9dYbn79evH4mJiW81VK1KlSr89ttvrFu3jnLlyjF58mSmTp2qXnTAysqKFStW8Pvvv1O2bFlmzpzJnDlzNMooXrw4GzduZMuWLVSsWJGlS5cyY8aMN45JCJG3PQoK5M6KpVh186DkvB8wKOFI5NQJ6mWhda2s0bEsqM6vbWyC7eDROC5aTvGvZ6BlaESE1zAS//lveNrjY0FE/eBHwXZdcfT7Ccumn3JztjdPL51/7/UTQnxYZLha9qiUFydOCJEHTZs2jd9//51z5/LPykMX2zfO7RCEEEIIkU1lNwfk2rnvn/87x8ouWC5rPwHyIZElpEWeFRcXR0REBIsXL07XEySEEEII8TFRyJ/DynJK/uyfEvmCp6cnbm5uNGjQIN1QtUGDBmFiYpLhNmjQoFyKWAghhBBC5AUyXE18kKKjo3n06FGG+8zMzLC2ztvLscpwNSGEEOLDk5vD1e5eOPb6TG/IyrXG6zN9YGS4mvggWVtb5/mGjBBCCCGEyB3SyBFCCCGEECKvy6dLPecUmZMjhBBCCCGEyFekJ0eIXKDSkvsLQgghhMg6RfomskUaOUIIIYQQQuRxigxXyxZpEgohhBBCCCHyFenJEUIIIYQQIo9TVNI3kR1ytYQQQgghhBD5ijRy8jAHBwf8/PyylFelUrFly5YcjSciIgKVSkVwcPAr8zVo0IARI0a88XleV5fAwEBUKhUxMTFvfA4hhBBCiA+JgirHtvxIhqu9ByqVis2bN9OuXbvcDuWdCgwMpGHDhjx8+BALC4v3dt7atWsTFRWFubn5a/PmVoxCiPzBsnkbCrTtjI5FARIiwrn983c8uxqaYV7zhs0o4jlWIy01MZHQ7i3Vj209x2LRsJlGnrgzJ7j5zVfvPnghhPiISSNHfHD09PSwsbF5r+dUFIWUlBR0dOQtI8THwrR2faw9BnL7h4U8DbtEgVYdKD7Jl/ChfUl5FJPhMSlPnhA+rM9/CYqSLk/c6ePc+m7Of1mSkt516EKIfEjm5GTPR3W1GjRowNChQxkxYgSWlpYULlyYZcuW8eTJE/r06YOpqSlOTk7s3LlTfcz58+dp0aIFJiYmFC5cmM8//5x79+5plDls2DDGjRtHgQIFsLGxwdvbW73fwcEBgPbt26NSqdSPw8PDadu2LYULF8bExIRq1aqxb9++t6rfvXv3aN++PUZGRjg7O7Nt2zaN/a+ry65du/jkk0+wsLCgYMGCtGrVivDw8AzPFRERQcOGDQGwtLREpVLh4eGh3p+amprpNXnburw8XO3GjRu0bt0aS0tLjI2NcXV1ZceOHa+MMSEhgWHDhmFtbY2BgQGffPIJJ06cSHeOnTt34ubmhr6+Pr/88gtaWlqcPHlSI1Y/Pz/s7e1JTU3NVh2FEHlbwdYdidm3k9j9u0n8J5LbPywgNSEBi8burzhKISXm4X9bbEz6HMlJGnlSn8TlWB2EEOJj9VE1cgBWrlxJoUKFOH78OEOHDuXLL7+kc+fO1K5dm9OnT9OsWTM+//xz4uPjiYmJoVGjRlSuXJmTJ0+ya9cu7ty5Q5cuXdKVaWxszLFjx5g9ezZTp05l7969AOovzv7+/kRFRakfx8XF8emnnxIQEMCZM2do3rw5rVu3JjIy8o3r5uPjQ5cuXTh37hyffvopPXv25MGDBwBZqsuTJ08YNWoUJ0+eJCAgAC0tLdq3b5/hl3c7Ozs2btwIQGhoKFFRUSxYsCBL1+Rt6/KyIUOGkJCQwMGDBwkJCWHWrFmYmJi8MsZx48axceNGVq5cyenTp3FycsLd3T3dOSZMmMDMmTO5dOkSbf7H3n3H13T/Dxx/3aybcbOFGJEYSQRBiF17JJTWaClam2qlZijVmq2gSc0OpRVUv9patWtUlCBmgkqDEDFCEBEZMu69vz/yc/U2Q4JIxPv5eJxHe8/5fD7nfQ6S+z6fcd54g/bt27NixQq9MitWrGDgwIEYyAs+hSg9jIwwreZGyumTj/dptaScPomZW808qxmYmlH9u5+ovnQNlT6egYmTc44y5rXq4vrjr1Rd9COOw0dhqLIsiisQQpQyWoWiyLbSSKHV5tKXXkq1bt0atVrNgQMHAFCr1VhbW9OjRw9WrVoFwM2bNylfvjyHDx9mz549HDhwgD/++EPXxrVr13ByciIqKgo3N7ccbQI0atSItm3bMmfOHKDgc3Jq167NiBEj8PPzA7J7gcaMGVOgSfwKhYJPP/2UWbNmAdkJi0qlYseOHfj6+vL5558/8Vr+686dOzg4OHDmzBlq165NTEwMVapU4dSpU9SrVy/P+S4FuSfPci3/PW+dOnXo2bMn06ZNy9FWbjGmpKRga2tLcHAwffv2BSAzM1N3vydMmKCrt2nTJt58801de7/++isjRowgLi4OpVLJyZMn8fb25tKlS7peuoKI7NmhwGWFEC+eka09rsvXEjN5FGnnI3X7y743FPOadYiZPCpHHTM3D0zKV+ThlcsYmltg9+ZbmHvU4dKYoWQlZPeaWzVvjSY9ncz4OIwdK1C272A0D9OI+WQ0SG+wECWex/qCP7B93q6fP1NkbVd08yyytovLK/fouU6dOrr/NzQ0xN7eHk/Px3+w5cqVAyA+Pp6IiAj27duHSqXSbTVq1ADQG8b17zYBypcvT3x8fL5xJCcn4+/vj4eHBzY2NqhUKiIjI5+pJ+ffcVhYWGBlZaWLoyDXcuHCBfr06UPVqlWxsrLSfWl/mpie5p4U9Fr+a9SoUXz++ec0b96cadOmcfr06Xzbjo6OJjMzk+bNm+v2GRsb06hRIyIjI/XKent7633u1q0bhoaGbNy4EYDg4GDatGmTb4KTnp5OUlKS3pahli8zQpQ2aecjub9/D+kx0aSeO821eTNQJyVi2/HxwgNJoSEkHz9MemwMyUcPcTXgU8xca2Beq24xRi6EeBnI6mqF88olOcbGxnqfFQqF3j7F/3fZaTQakpOT6dq1K+Hh4XrbhQsXaNmyZb5tPml+hr+/Pxs3bmT27NkcOHCA8PBwPD09ycjIeK7X9iiOglxL165dSUhIYNmyZYSFhREWFgbwVDE9zT152vpDhw7l0qVLvPfee5w5cwZvb28WL15c6JhzY2FhoffZxMSE/v37s2LFCjIyMvj5558ZPHhwvm0EBARgbW2tt30fdfm5xCeEKBpZD+6jVasxtLHV229obUtW4r2CNaJW8/ByNMblK+ZZJPPWTbLuJ2LiWOFZwhVCvAK0CoMi20qj0nlVz0n9+vX5+++/cXFxoXr16nrbf7/85sfY2Bi1Wq23LzQ0lIEDB9K9e3c8PT1xdHQkJibmOV/BY0+6lrt37xIVFcWnn35Ku3bt8PDw4N69/H+Rm5iYAOS4tuLg5OTEiBEj2LBhA+PHj2fZsmVA7jFWq1YNExMTQkNDdfsyMzM5duwYNWvmPdb+kaFDh7Jnzx6++eYbsrKy6NGjR77lJ0+ezP379/W24e5VnuYyhRAvSlYWD6PPY+Hp9XifQoFFHS/Szp8rWBsGBiidXci6dzfPIkZ2ZTC0tCLrXu5zDoUQQjwdSXLyMXLkSBISEujTpw/Hjh0jOjqaP/74g0GDBhXqi72Liwt79+7l5s2busTB1dWVDRs2EB4eTkREBH379i3S1bmedC22trbY29vz/fffc/HiRf7880/GjRuXb5vOzs4oFAq2bt3K7du3SU4unhWCxowZwx9//MHly5c5efIk+/btw8PDI88YLSws+OCDD5gwYQI7d+7k3LlzDBs2jNTUVIYMGfLE83l4eNCkSRM+/vhj+vTpg5mZWb7llUolVlZWepuJofzTE6Kku7tlPTbtO2PdugMmFSvjOHwUBkpTEv/MnttY/qOJOPR73JNb5u13sajbAONyjphWqU6F0ZMwLlOOxD3ZK3YqTE0p238Ypq4eGDuUw9zTi0qTZpBx8wYp4cdzjUEIIR4pScPVvv76a1xcXDA1NaVx48YcPXo03/K//fYbNWrUwNTUFE9PT7Zv3/60t6HA5JtWPipUqEBoaChqtZqOHTvi6enJmDFjsLGxKdRKWkFBQezevRsnJye8vLKfCn711VfY2trSrFkzunbtio+PD/Xr1y+qS3nitRgYGLB27VpOnDhB7dq1GTt2LF9++WW+bVasWJEZM2YwadIkypUrp1sw4UVTq9WMHDkSDw8PfH19cXNz45tvvsk3xjlz5tCzZ0/ee+896tevz8WLF/njjz+wtbXN71Q6Q4YMISMj44lD1YQQL68Hh/YTv+p7HN4ZQJWgbzF1qUbs55/oloU2LlMWI1t7XXkDCxXlPxhL1YU/4DTlCwzNzImZMpqMa/8/r1GjQelcFafJM6i2eAXlPxzHw0sXuPLpWLRZ8q4cIcTL4ZdffmHcuHFMmzaNkydPUrduXXx8fPKcO33o0CH69OnDkCFDOHXqFN26daNbt26cPXu2SON8pVZXE+J5mTVrFr/99tsTFznIi6yuJoQQQrx8inN1tSsXo4qsbefq7gUu27hxYxo2bMiSJUuA7HnsTk5OfPTRR0yaNClH+d69e5OSksLWrVt1+5o0aUK9evX47rvvnj34PEhPjhCFkJyczNmzZ1myZAkfffRRcYcjhBBCCPHMclsJNj09PUe5jIwMTpw4Qfv27XX7DAwMaN++PYcPH8617cOHD+uVB/Dx8cmz/PMiSc5LYM2aNXpLP/97q1WrVnGHVygv+7X4+fnRoEEDWrduLUPVhBBCCPHCFOWcnNxWgg0ICMgRw507d1Cr1bpXrjxSrlw5bt68mWvcN2/eLFT558WoSFsXz8Ubb7xB48aNcz3236WWS7qX/VqCg4MJDg4u7jCEEEIIIZ6byZMn51hwSqlUFlM0z4ckOS8BS0tLLC0tizuM56I0XYsQQgghxItSlO+zUSqVBUpqypQpg6GhIbdu3dLbf+vWLRwdHXOt4+joWKjyz4sMVxNCCCGEEKKEKwlLSJuYmNCgQQP27t2r26fRaNi7dy9NmzbNtU7Tpk31ygPs3r07z/LPi/TkCCGEEEIIIQpk3LhxDBgwAG9vbxo1asSCBQtISUlh0KBBAPTv35+KFSvq5vSMHj2aVq1aERQUxOuvv87atWs5fvw433//fZHGKUmOEMXAzNa8uEMQQgghxEtEqyj8SzuLQu/evbl9+zZTp07l5s2b1KtXj507d+oWF4iNjdV7n2SzZs34+eef+fTTT/nkk09wdXVl06ZN1K5du0jjlPfkCFEMYoa+WdwhCCGEEKKQXJb/Xmznjr50qcjarla1apG1XVykJ0cIIYQQQogSTqstGT05LwtZeEAIIYQQQghRqkhPjhBCCCGEECWcVvomCkXu1nPQunVrxowZU9xh6BQkHoVCwaZNm15IPEVt4MCBdOvWLd8yJe3PSAghhBBCFB3pyXmJhYSE0KZNG+7du4eNjU2h6sbFxWFra1s0gQkhRClh2aYz1j7dMLS2JeNqDHf/9z0Zly/kWlbVrC1lBo/W26fNzODKB2/rPpvXb4JlK19MnKthqLLixowxZFy9XKTXIIQoHQrzPhshSU6xyMjIwMTEpFhjKOq3zD4PJeE+CSFeXeYNX8Ou12Du/vQt6ZfOY9W+K+XGTOf6px+ieXA/1zqa1BSuf/qh7rMW/QVMFSamPLwQScrxUMoM8CvS+IUQpYskOYUjw9Wek6ysLPz8/LC2tqZMmTJ89tlnPFqd28XFhVmzZtG/f3+srKwYPnw4AAcPHqRFixaYmZnh5OTEqFGjSElJ0bW5evVqvL29sbS0xNHRkb59+xIfHw9ATEwMbdq0AcDW1haFQsHAgQN1dTUaDRMnTsTOzg5HR0emT5+uF++/h6vFxMSgUCjYsGEDbdq0wdzcnLp163L48GG9OsuWLcPJyQlzc3O6d+/OV199VeAepOnTp1OvXj2WLl2qa6NXr17cv//4i8KjYWdffPEFFSpUwN3dHYAzZ87Qtm1bzMzMsLe3Z/jw4SQnJ+c4x4wZM3BwcMDKyooRI0aQkZGRZzzp6en4+/tTsWJFLCwsaNy4MSEhIbrjwcHB2NjYsHXrVtzd3TE3N+ett94iNTWVlStX4uLigq2tLaNGjUKtVhfoHgghXi7WHd7kwYFdJIfuJTPuKnd/+hZtRjqWr7XPp5YWdVKibtMk6SdDKUdCuL/1Fx6eiyja4IUQ4hUnSc5zsnLlSoyMjDh69CgLFy7kq6++Yvny5brjgYGB1K1bl1OnTvHZZ58RHR2Nr68vPXv25PTp0/zyyy8cPHgQP7/HT/YyMzOZNWsWERERbNq0iZiYGF0i4+TkxPr16wGIiooiLi6OhQsX6sVjYWFBWFgY8+bNY+bMmezevTvfa5gyZQr+/v6Eh4fj5uZGnz59yMrKAiA0NJQRI0YwevRowsPD6dChA1988UWh7tHFixf59ddf2bJlCzt37uTUqVN8+OGHemX27t1LVFQUu3fvZuvWraSkpODj44OtrS3Hjh3jt99+Y8+ePXr36VG9yMhIQkJC+N///seGDRuYMWNGnrH4+flx+PBh1q5dy+nTp3n77bfx9fXlwoXHw1BSU1NZtGgRa9euZefOnYSEhNC9e3e2b9/O9u3bWb16NUuXLmXdunWFug9CiJeAoREmztX0kxGtloeRESiruudZTaE0o9LcZVSa9wNlR36CcQWnFxCsEOJVoEVRZFtpJMPVnhMnJyfmz5+PQqHA3d2dM2fOMH/+fIYNGwZA27ZtGT9+vK780KFD6devn24yvKurK4sWLaJVq1Z8++23mJqaMnjwYF35qlWrsmjRIho2bEhycjIqlQo7OzsAypYtm6NHpU6dOkybNk3X9pIlS9i7dy8dOnTI8xr8/f15/fXXgexekVq1anHx4kVq1KjB4sWL6dSpE/7+/gC4ublx6NAhtm7dWuB79PDhQ1atWkXFihUBWLx4Ma+//jpBQUG64XMWFhYsX75cN0xt2bJlunoWFhYALFmyhK5duzJ37lzd23VNTEz48ccfMTc3p1atWsycOZMJEyYwa9YsvbfuQvabeFesWEFsbCwVKlTQXfvOnTtZsWIFs2fPBrKTzG+//ZZq1aoB8NZbb7F69Wpu3bqFSqWiZs2atGnThn379tG7d+8C3wchRMlnqLJCYWiIOilRb786KRFjx0q51sm8eZ07wYvJvBaDwswca59ulJ80l+vTPkJ97+4LiFoIIcQj0pPznDRp0gSF4nEm3LRpUy5cuKAbyuTt7a1XPiIiguDgYFQqlW7z8fFBo9Fw+XL2JNQTJ07QtWtXKleujKWlJa1atQKyv6Q/SZ06dfQ+ly9fXjfUrSB1ypcvD6CrExUVRaNGjfTK//fzk1SuXFmX4ED2PdJoNERFRen2eXp66s3DiYyMpG7duroEB6B58+Y56tWtWxdzc3O9tpOTk7l69WqOOM6cOYNarcbNzU3v/u/fv5/o6GhdOXNzc12CA1CuXDlcXFxQqVR6+550X9PT00lKStLb0mWImxClTvqlKFIO7yPj6mXSz/9N/DdzUCcnYdnSp7hDE0KUAtKTUzjSk/OC/PtLOkBycjLvv/8+o0aNylG2cuXKumFaPj4+rFmzBgcHB2JjY/Hx8cl3rskjxsbGep8VCgUajabAdR4lbE+q87z99z4VheTkZAwNDTlx4gSGhoZ6x/6dwOR2D5/mvgYEBOQYOjfay40x9Ws8TfhCiBdAnZyEVq3G0MpGb7+hlQ3q+/cK2IiajNhLGJUt//wDFEIIkS9Jcp6TsLAwvc9HjhzB1dU1x5foR+rXr8+5c+eoXr16rsfPnDnD3bt3mTNnDk5O2WO6jx8/rlfmUY/Hi5j47u7uzrFjx/T2/ffzk8TGxnLjxg3dELEjR45gYGCgW2AgNx4eHgQHB5OSkqJLgEJDQ3PUi4iIIC0tDTMzM13bKpVKd+/+zcvLC7VaTXx8PC1atCjUNTyNyZMnM27cOL19caP7Fvl5hRDPQJ1FxpVoTD3qkBr+/z/fFQpMa9Thwb7tBWtDYYBJRWdSz5woujiFEK8MrbZ09rgUFRmu9pzExsYybtw4oqKi+N///sfixYsZPXp0nuU//vhjDh06hJ+fH+Hh4Vy4cIHff/9dN6G+cuXKmJiYsHjxYi5dusTmzZuZNWuWXhvOzs4oFAq2bt3K7du3c11x7Hn56KOP2L59O1999RUXLlxg6dKl7NixQ2+I3pOYmpoyYMAAIiIiOHDgAKNGjaJXr175Lmfdr18/Xb2zZ8+yb98+PvroI9577z3dfBzIXm56yJAhnDt3ju3btzNt2jT8/PxyzMeB7PlE/fr1o3///mzYsIHLly9z9OhRAgIC2LZtW+FuTAEolUqsrKz0NmUeya8QouS4v/t3LFt2xKJZG4zLV8L+3REolKY8CN0DQJnBY7Dp8Z6uvHWX3pjWrIdRmXKYVK5KmaFjMbR3IPnA40VfDCxUmDhV0S1IYORYEROnKjl6jIQQQjwb6cl5Tvr3709aWhqNGjXC0NCQ0aNH65aKzk2dOnXYv38/U6ZMoUWLFmi1WqpVq6abwO7g4EBwcDCffPIJixYton79+gQGBvLGG2/o2qhYsSIzZsxg0qRJDBo0iP79+xMcHFwk19e8eXO+++47ZsyYwaeffoqPjw9jx45lyZIlBW6jevXq9OjRg86dO5OQkECXLl345ptv8q1jbm7OH3/8wejRo2nYsCHm5ub07NmTr776Sq9cu3btcHV1pWXLlqSnp9OnT58cy2b/24oVK/j8888ZP348169fp0yZMjRp0oQuXboU+HqEEKVb6rGDJKissH2zL4ZWtmRcvcytBTN0y0Ib2ZcB7ePhqgYWKsoMGImhlS2a1GTSr0RzM+BjMuMezw00r9tI74WhZd+fAEDi5v+RuHntC7oyIcTLqLTOnSkqCu2jl7kIUUjDhg3jn3/+4cCBA08sO336dDZt2kR4eHjRB/YSiBn6ZnGHIIQQQohCcln+e7Gd++zFm0XWdu3qJf8l8YUlPTmiwAIDA+nQoQMWFhbs2LGDlStXPrEnRgghhBBCiBdNkhxRYEePHmXevHk8ePBA996eoUOHAlCrVi2uXLmSa72lS5e+yDCFEEIIIUodGa5WODJcTTwXV65cITMzM9dj5cqVw9LS8gVHVLLJcDUhhBDi5VOcw9XOXLxVZG17Vi/35EIvGenJEc+Fs7NzcYcghBBCCFFqyRLShSNLSAshhBBCCCFKFenJEUIIIYQQooTTyJycQpEkR4hiYFnJobhDEEIIIYQotSTJEUIIIYQQooST1dUKR5IcIYQQQgghSjhZeKBwZOEBIYQQQgghRKkiPTlCCCGEEEKUcDJcrXCkJ+cl07p1a8aMGZPncRcXFxYsWFDkcSgUCjZt2lTk5xFCCCGEEKKwpCdHCCGEyIOyYRvMmvtgoLIm6+ZVUnf8j6zrl59Yz6R2Qyzfep+Mf07xYO3X2TsNDDFv2w1jV08MbR3QpqeReekcKXvWo31wv4ivRAjxspM5OYUjPTmixNFqtWRlZRV3GHpKYkxCiKJlUqshFj69SAvZwv2lM1Hfuorlu2NQWFjmW8/Axh7zjm+TeeW83n6FsQmG5Z1J+2sriUtn8uCXbzCwd8Sqz0dFeRlCCPFKkiTnJZSVlYWfnx/W1taUKVOGzz77DK1Wm2vZ2NhY3nzzTVQqFVZWVvTq1Ytbt27plfn222+pVq0aJiYmuLu7s3r1ar3jFy5coGXLlpiamlKzZk12795d4FhjYmJQKBSsXbuWZs2aYWpqSu3atdm/f7+uTEhICAqFgh07dtCgQQOUSiUHDx5Eo9EQEBBAlSpVMDMzo27duqxbt05X7969e/Tr1w8HBwfMzMxwdXVlxYoVAGRkZODn50f58uUxNTXF2dmZgIAAvZjCw8N1bSUmJqJQKAgJCXmmmIQQpYdp0w6knzxAengo6ttxpGz9CTIzUHq9lnclhQJVj2Gk7duM+t5tvUPa9DQerP6KjL+Po7l7i6xrl0jZ/jNGFVwwsLYr4qsRQrzstCiKbCuNZLjaS2jlypUMGTKEo0ePcvz4cYYPH07lypUZNmyYXjmNRqNLcPbv309WVhYjR46kd+/eui/zGzduZPTo0SxYsID27duzdetWBg0aRKVKlWjTpg0ajYYePXpQrlw5wsLCuH//fr5zgvIyYcIEFixYQM2aNfnqq6/o2rUrly9fxt7eXldm0qRJBAYGUrVqVWxtbQkICOCnn37iu+++w9XVlb/++ot3330XBwcHWrVqxWeffca5c+fYsWMHZcqU4eLFi6SlpQGwaNEiNm/ezK+//krlypW5evUqV69eLXTchY1JCFFKGBpiVMGZtIPbH+/Tasm4FIlxpao8zKOaWauuaFOSSD91ECNn1yeexsDUDK1Wg/Zh6vOJWwghBCBJzkvJycmJ+fPno1AocHd358yZM8yfPz9HkrN3717OnDnD5cuXcXJyAmDVqlXUqlWLY8eO0bBhQwIDAxk4cCAffvghAOPGjePIkSMEBgbSpk0b9uzZwz///MMff/xBhQoVAJg9ezadOnUqVMx+fn707NkTyO452rlzJz/88AMTJ07UlZk5cyYdOnQAID09ndmzZ7Nnzx6aNm0KQNWqVTl48CBLly6lVatWxMbG4uXlhbe3N5C96MIjsbGxuLq68tprr6FQKHB2di5UvE8bkxCidFCYq1AYGKJNTtLbr01JQlHGMdc6RpWro6z/Gve/m1mwkxgZYd7+LTLOHEWbnlfaJIQQ2WROTuHIcLWXUJMmTVAoHv9Fb9q0KRcuXECtVuuVi4yMxMnJSZfgANSsWRMbGxsiIyN1ZZo3b65Xr3nz5nrHnZycdAnOo/MV1r/rGBkZ4e3trTvHI4+SFYCLFy+SmppKhw4dUKlUum3VqlVER0cD8MEHH7B27Vrq1avHxIkTOXTokK7+wIEDCQ8Px93dnVGjRrFr165Cx/w0MeUmPT2dpKQkvS09S51neSHES8hEiar7EFI2r0Kbmvzk8gaGWL49AhSQsu2noo9PCPHS0xThVhpJT44oMSwsLHT/n5yc/SVh27ZtVKxYUa+cUqkEoFOnTly5coXt27eze/du2rVrx8iRIwkMDKR+/fpcvnyZHTt2sGfPHnr16kX79u1Zt24dBgbZuf2/5zFlZmY+l5hyExAQwIwZM/T2TWjlxcet6+dZRwhRvLSpyWg1ahQqK739CgsrtMk5V0IztCuLoa0Dln3/tYjA/z+Mspu6lMTFn6J5NEfHwBDLt9/HwNqepJWB0osjhBBFQJKcl1BYWJje5yNHjuDq6oqhoaHefg8PD91clEe9OefOnSMxMZGaNWvqyoSGhjJgwABdvdDQUL3jV69eJS4ujvLly+vOV1hHjhyhZcuWQPbCCSdOnMDPzy/P8jVr1kSpVBIbG5vvMDAHBwcGDBjAgAEDaNGiBRMmTCAwMBAAKysrevfuTe/evXnrrbfw9fUlISEBBwcHAOLi4vDy8gLQW4TgWWP6r8mTJzNu3Di9fcnzRhe4vhCiGKjVZN24gnEVDzL/Cc/ep1BgXLUGD4/uy1n8ThyJ30zV22fetjsKE1NSdv4PTVJC9s5HCY59OZKCv0SbllLEFyKEKC1kuFrhSJLzEoqNjWXcuHG8//77nDx5ksWLFxMUFJSjXPv27fH09KRfv34sWLCArKwsPvzwQ1q1aqUbhjVhwgR69eqFl5cX7du3Z8uWLWzYsIE9e/bo2nBzc2PAgAF8+eWXJCUlMWXKlELH/PXXX+Pq6oqHhwfz58/n3r17DB48OM/ylpaW+Pv7M3bsWDQaDa+99hr3798nNDQUKysrBgwYwNSpU2nQoAG1atUiPT2drVu34uHhAcBXX31F+fLl8fLywsDAgN9++w1HR0dsbGwwMDCgSZMmzJkzhypVqhAfH8+nn376xGsoSEy5USqVOXp6Mo0Mcy0rhCg5Hh7ejar7YNQ3rpB1/TKmTdqjMFaSfioUAFX3wWiSEknduwGyslDH39Crr3mYigE83m9giGWvERiWd+bBz4vAwEDXU6RNSwG1DGMVQojnRZKcl1D//v1JS0ujUaNGGBoaMnr0aIYPH56jnEKh4Pfff+ejjz6iZcuWGBgY4Ovry+LFi3VlunXrxsKFCwkMDGT06NFUqVKFFStW0Lp1awAMDAzYuHEjQ4YMoVGjRri4uLBo0SJ8fX0LFfOcOXOYM2cO4eHhVK9enc2bN1OmTJl868yaNQsHBwcCAgK4dOkSNjY21K9fn08++QQAExMTJk+eTExMDGZmZrRo0YK1a9cC2QnJvHnzuHDhAoaGhjRs2JDt27frhqr9+OOPDBkyhAYNGuDu7s68efPo2LHjE6/jSTEJIUqPjL+PkWqhwqzNmxiorMi6eZUHPy1Am5K9GIGBtX2ey/fnxsDKBpMa2b3HNh9M1zt2P/hLsmKinlvsQojSp7Qu9VxUFNrC/IQWopBiYmKoUqUKp06dol69esUdTolxd/rQ4g5BCCGEEIVkP315sZ37UOSDImu7mUf+Lzl+GUlPjhBCCCGEECWczMkpHFlCWjyT2bNn6y2n/O+tsO/SEUIIIYQQ4nmQnhzxTEaMGEGvXr1yPWZmZkbFihULNWZdCCGEEELkJHNyCkeSHPFM7OzssLOzK+4whBBCCCFKNY08My4UGa4mhBBCCCGEKFWkJ0cIIYQQQogSToarFY4kOUIUg+B6y4o7BCGEEEIU0vjiDkAUmCQ5QgghhBBClHCyhHThyJwcIYQQQgghRKkiPTlCCCGEEEKUcPJGjsKRnhwhhBBCCCFEqSJJTgnQunVrxowZA4CLiwsLFix4qrqlWXBwMDY2NrrP06dPp169evnWiYmJQaFQEB4eXqSxCSGEEEIUNQ2KIttKIxmuVsIcO3YMCwuLApffsGEDxsbGRRhRyeTv789HH32k+zxw4EASExPZtGmTbp+TkxNxcXGUKVOmGCIUQpQWWq2WE7sXE3n0NzLSknB0qc9r3adhXcYlzzrnDv+Pc0f+x4N71wGwLVed+u1GUrlGSwAeJFzjf3Pb51q3fb8FVK3j+9yvQwjxcpOFBwpHkpwSxsHBoVDl7ezsiiiSkk2lUqFSqfItY2hoiKOj4wuKSAhRWkXsX87Z0NW07jUHS7tKHN+1kO0/DOXtcdswMlbmWsfCuhyNOo3HuowzWq2W8yc2sWvVSHqM2oCdoysWNuV599MDenUiw37l9P4fcHJv8SIuSwghSjUZrlbC/Hu4Wt++fendu7fe8czMTMqUKcOqVauAnMPVXFxcmD17NoMHD8bS0pLKlSvz/fff67Vx6NAh6tWrh6mpKd7e3mzatKlQw7r+/vtvunTpgpWVFZaWlrRo0YLo6GgANBoNM2fOpFKlSiiVSurVq8fOnTt1dR8NIduwYQNt2rTB3NycunXrcvjwYb1zBAcHU7lyZczNzenevTt3797VO/7v4WrTp09n5cqV/P777ygUChQKBSEhIbkOV9u/fz+NGjVCqVRSvnx5Jk2aRFZWlu5469atGTVqFBMnTsTOzg5HR0emT5+uO67Vapk+fTqVK1dGqVRSoUIFRo0aVaD7JoR4+Wi1Ws4cXIVX2xG41GqHfXl32vSaS2pSPDF/78mznnPNtlSu0QrrMi7YOFShke9YjE3MiY+NAMDAwBBzSwe9LebvPVSt0wljZcF784UQrw6ttui20kiSnBKsX79+bNmyheTkZN2+P/74g9TUVLp3755nvaCgILy9vTl16hQffvghH3zwAVFRUQAkJSXRtWtXPD09OXnyJLNmzeLjjz8ucEzXr1+nZcuWKJVK/vzzT06cOMHgwYN1icLChQsJCgoiMDCQ06dP4+PjwxtvvMGFCxf02pkyZQr+/v6Eh4fj5uZGnz59dG2EhYUxZMgQ/Pz8CA8Pp02bNnz++ed5xuTv70+vXr3w9fUlLi6OuLg4mjVrlmvsnTt3pmHDhkRERPDtt9/yww8/5Gh75cqVWFhYEBYWxrx585g5cya7d+8GYP369cyfP5+lS5dy4cIFNm3ahKenZ4HvnxDi5fIg4RppD25T0fXxzxQTM0vKOtUhPja8QG1oNGouhm8jMyOVcs71ci1z+9pZ7t6IxL1hz+cQtRBCCBmuVoL5+PhgYWHBxo0bee+99wD4+eefeeONN7C0tMyzXufOnfnwww8B+Pjjj5k/fz779u3D3d2dn3/+GYVCwbJlyzA1NaVmzZpcv36dYcOGFSimr7/+Gmtra9auXaubC+Tm5qY7HhgYyMcff8w777wDwNy5c9m3bx8LFizg66+/1pXz9/fn9ddfB2DGjBnUqlWLixcvUqNGDRYuXIivry8TJ07UtX/o0CG9HqF/U6lUmJmZkZ6enu/wtG+++QYnJyeWLFmCQqGgRo0a3Lhxg48//pipU6diYJCd89epU4dp06YB4OrqypIlS9i7dy8dOnQgNjYWR0dH2rdvj7GxMZUrV6ZRo0YFundCiJdP6oPbAJir7PX2m6nKkPrgTr51E+Ki2PRNH9RZ6RibmNOx/xJsy1XPtWzUsfXYlK2Go0v95xO4EKLU0b6ECwQkJCTw0UcfsWXLFgwMDOjZsycLFy7Mc8pBQkIC06ZNY9euXcTGxuLg4EC3bt2YNWsW1tbWhTq39OSUYEZGRvTq1Ys1a9YAkJKSwu+//06/fv3yrVenTh3d/ysUChwdHYmPjwcgKiqKOnXqYGpqqitTmC/p4eHhtGjRItfFDpKSkrhx4wbNmzfX29+8eXMiIyPzjLF8+fIAuhgjIyNp3LixXvmmTZsWOMa8REZG0rRpUxSKxz8kmjdvTnJyMteuXcs1tkfxPYrt7bffJi0tjapVqzJs2DA2btyoN9wtN+np6SQlJeltWZnpz3w9Qojn78KpLfz4WX3dptHk/+87P9YOVeg5eiPdRv5CzSbvEPLrJO7dupijXFbmQy6Gb6WG9OIIIUqZfv368ffff7N79262bt3KX3/9xfDhw/Msf+PGDW7cuEFgYCBnz54lODiYnTt3MmTIkEKfW3pySrh+/frRqlUr4uPj2b17N2ZmZvj65r/qzn8TEIVCgUajeS7xmJmZPZd2/h3jo6TjecX4rPK7f05OTkRFRbFnzx52797Nhx9+yJdffsn+/fvzXOUuICCAGTNm6O3r0HsqPu9ML5L4hRBPz7lmG8o6PX7Qoc7KACA1+S7mVmV1+9OS72BfwSPftgyNTLAu4wyAQ6Xa3L52ljMHV9Gy50y9cpfO/EFW5kNc63d7TlchhCiNNC/Z3JnIyEh27tzJsWPH8Pb2BmDx4sV07tyZwMBAKlSokKNO7dq1Wb9+ve5ztWrV+OKLL3j33XfJysrCyKjgqYv05JRwzZo1w8nJiV9++YU1a9bw9ttvP9OS0e7u7pw5c4b09Mc9CceOHStw/Tp16nDgwAEyMzNzHLOysqJChQqEhobq7Q8NDaVmzZoFPoeHhwdhYWF6+44cOZJvHRMTE9Rq9RPbPXz4MNp/zbALDQ3F0tKSSpUqFTg+MzMzunbtyqJFiwgJCeHw4cOcOXMmz/KTJ0/m/v37elu7npMLfD4hxItjolRhXcZZt9mWq46ZpQM3Lj5eHCXjYTLxV09TtnK9QrWt1WrQqDNy7I86tg5njzaYqV7N1TKFEMUvt1En//6u+DQOHz6MjY2NLsEBaN++PQYGBjm+5+Xn/v37WFlZFSrBAUlyXgp9+/blu+++Y/fu3U8cqlaQtjQaDcOHDycyMpI//viDwMBAAL1hXHnx8/MjKSmJd955h+PHj3PhwgVWr16tW9hgwoQJzJ07l19++YWoqCgmTZpEeHg4o0ePLnCMo0aNYufOnQQGBnLhwgWWLFmS53ycR1xcXDh9+jRRUVHcuXMn1yTsww8/5OrVq3z00Uf8888//P7770ybNo1x48bp5uM8SXBwMD/88ANnz57l0qVL/PTTT5iZmeHs7JxnHaVSiZWVld6W17KzQoiSRaFQ4Plaf07++R0x5/4kIS6Kfb98jLlVWVxqPX7PzdbvB3L20E+6z0d3BBF36RgPEq6REBfF0R1B3Lh0lOr1uuq1f//OFeIuH6dGo7df2DUJIV5OWq2iyLaAgACsra31toCAgGeK9+bNm5QtW1Zvn5GREXZ2dty8ebNAbdy5c4dZs2blO8QtL5LkvAT69evHuXPnqFixYo75LoVlZWXFli1bCA8Pp169ekyZMoWpU6cC6M3TyYu9vT1//vknycnJtGrVigYNGrBs2TJd79KoUaMYN24c48ePx9PTk507d7J582ZcXV0LHGOTJk1YtmwZCxcupG7duuzatYtPP/003zrDhg3D3d0db29vHBwccvQmAVSsWJHt27dz9OhR6taty4gRIxgyZMgT2/43Gxsbli1bRvPmzalTpw579uxhy5Yt2NvbP7myEOKlVLfVUGo3e5cD66eyccnbZGWk0mnwMr2HFUkJsTxMuaf7nJacwL5fP+aXwE5sXTaI29fO0nnwciq56f8Mjzq+HgsrRyq5PtvPdiFE6VeUS0jnNupk8uTcR51MmjRJ98qOvLZ//vnnma83KSmJ119/nZo1a+q9zqOgFFptaV0dWxTUmjVrGDRoEPfv339uc25E/oI2yT87IYQQ4mUzvlvxrXC2/WTOUSrPS+f6BZ8Kcfv27RzvL/yvqlWr8tNPPzF+/Hju3Xv8ACgrKwtTU1N+++23fF+H8uDBA3x8fDA3N2fr1q0FehD/X7LwwCto1apVVK1alYoVKxIREcHHH39Mr169JMERQgghhCihNCVkCWkHBwccHByeWK5p06YkJiZy4sQJGjRoAMCff/6JRqPJsYruvyUlJeHj44NSqWTz5s1PleCADFd7Jd28eZN3330XDw8Pxo4dy9tvv833338PwIgRI1CpVLluI0aMKObIhRBCCCHEy8DDwwNfX1+GDRvG0aNHCQ0Nxc/Pj3feeUe3str169epUaMGR48eBbITnI4dO5KSksIPP/xAUlISN2/e5ObNm09cYOq/ZLia0BMfH09SUlKux6ysrHJMIBNPR4arCSGEEC+f4hyutuXE07+360m6NiiawV0JCQn4+fnpvQx00aJFupeBxsTEUKVKFfbt20fr1q0JCQmhTZs2ubZ1+fJlXFxcCnxuSXKEKAaS5AghhBAvH0lyXh6l74qEEEIIIYQoZbTakjEn52Uhc3KEEEIIIYQQpYr05AhRDK5dSynuEIQQQghRaKpiO7NGRroXivTkCCGEEEIIIUoV6ckRQgghhBCihJOlwgpHkhwhhBBCCCFKOG0JeRnoy0KGqwkhhBBCCCFKFUlyXnEDBw6kW7duxR1Gsfjvtbdu3ZoxY8YUWzxCCCGEEHnRaItuK41kuJoQ/2/Dhg0YGxvrPru4uDBmzBhJfIQQ+DYyoWktI0yVCmLi1PwWks6d+wX7ZtCuvjFdminZH57BpoMZuv32VgreaK6kagVDjAzhnytZrP8rg+S0UvqNQwghXiDpyRHi/9nZ2WFpaVncYQghSpi29Y1pWdeY30LSWfBbGumZMOINM4wMn1zXqawBTWsbc/2OWm+/iRGMeNMMgG82pbFofRqGhgqGdjGVUfdCiFxptUW3lUaS5Lwi1q1bh6enJ2ZmZtjb29O+fXtSUnK+q0Wj0RAQEECVKlUwMzOjbt26rFu3Tq/M2bNn6dSpEyqVinLlyvHee+9x584d3fHWrVvj5+eHn58f1tbWlClThs8++wxtAf8VxcfH07VrV8zMzKhSpQpr1qzBxcWFBQsWABATE4NCoSA8PFxXJzExEYVCQUhICABqtZohQ4borsPd3Z2FCxfme95/D1dr3bo1V65cYezYsSgUChQKBSkpKVhZWeW4H5s2bcLCwoIHDx4U6PqEEC+XVnWN2XU8g7OX1cTd1fDznodYWSjwrJr/YAgTY3i3oym//plOWrr+sSrlDbGzVPDznofE3dXo2nUqa4BrpQJkT0IIIfIlSc4rIC4ujj59+jB48GAiIyMJCQmhR48euSYdAQEBrFq1iu+++46///6bsWPH8u6777J//34gO5lo27YtXl5eHD9+nJ07d3Lr1i169eql187KlSsxMjLi6NGjLFy4kK+++orly5cXKN6BAwdy9epV9u3bx7p16/jmm2+Ij48v1DVrNBoqVarEb7/9xrlz55g6dSqffPIJv/76a4Hqb9iwgUqVKjFz5kzi4uKIi4vDwsKCd955hxUrVuiVXbFiBW+99Zb0AglRCtlbKbCyMOD81cc9MQ8z4MotDS6O+f8KfauVksiYLM5fU+c4ZmQIWiDrX4cys7KfqFapIEmOECIn6ckpHJmT8wqIi4sjKyuLHj164OzsDICnp2eOcunp6cyePZs9e/bQtGlTAKpWrcrBgwdZunQprVq1YsmSJXh5eTF79mxdvR9//BEnJyfOnz+Pm5sbAE5OTsyfPx+FQoG7uztnzpxh/vz5DBs2LN9Yz58/z44dOzh69CgNGzYE4IcffsDDw6NQ12xsbMyMGTN0n6tUqcLhw4f59ddfcyRkubGzs8PQ0BBLS0scHR11+4cOHUqzZs2Ii4ujfPnyxMfHs337dvbs2VOo+IQQLwdL8+zBY8mp+t8CklM1umO58XI1oqKDAfN/Tcv1eMxNNRmZ0LWZCduOZKAAujQzwdBAgVU+7QohhCgYSXJeAXXr1qVdu3Z4enri4+NDx44deeutt7C1tdUrd/HiRVJTU+nQoYPe/oyMDLy8vACIiIhg3759qFSqHOeJjo7WJTlNmjRBoXj8i7pp06YEBQWhVqsxNMz7KWVkZCRGRkY0aNBAt69GjRrY2NgU+rq//vprfvzxR2JjY0lLSyMjI4N69eoVup1/a9SoEbVq1WLlypVMmjSJn376CWdnZ1q2bJlnnfT0dNLT9ceqZGVmYmSsfKZYhBDPX303I3q1fvxvc9nW3JOU/NioFHRvYcK3vz/U66n5t5SHsHLnQ95qraRFXWO0Wjh1Pour8epS+1RVCPFsNFp5AFIYkuS8AgwNDdm9ezeHDh1i165dLF68mClTphAWFqZXLjk5GYBt27ZRsWJFvWNKpVJXpmvXrsydOzfHecqXL19EV6DPwCB7iMi/h9tlZmbqlVm7di3+/v4EBQXRtGlTLC0t+fLLL3Nc89MYOnQoX3/9NZMmTWLFihUMGjRIL6H7r4CAAL1eJYDGnSbTtPMnzxyLEOL5+vtyFoG3HmcmRobZ/7ZV5gqS/tWbozI34Mad3DOYSg4GWJobML63mW6foYGCqhUMeK2OMRO+TUGrhairar5YnYqFKag12cPgZgwy526SpoiuTgjxMpMHIIUjSc4rQqFQ0Lx5c5o3b87UqVNxdnZm48aNemVq1qyJUqkkNjaWVq1a5dpO/fr1Wb9+PS4uLhgZ5f3X57/JxJEjR3B1dc23Fweye22ysrI4ceKEbrhaVFQUiYmJujIODg5A9jC8Rz1M/16EACA0NJRmzZrx4Ycf6vZFR0fne+7/MjExQa3O+SXm3XffZeLEiSxatIhz584xYMCAfNuZPHky48aN09s35YfMPEoLIYpTeiak6y0NrSUpRYNbJUNu3MlOPpTG4FzOgENnc/93fOGamrk/p+rt69NOSfw9DXtPZub4opLyMPu/1SsaojJXcPZy1vO6HCGEeGVJkvMKCAsLY+/evXTs2JGyZcsSFhbG7du38fDw4PTp07pylpaW+Pv7M3bsWDQaDa+99hr3798nNDQUKysrBgwYwMiRI1m2bBl9+vRh4sSJ2NnZcfHiRdauXcvy5ct1SUxsbCzjxo3j/fff5+TJkyxevJigoKAnxuru7o6vry/vv/8+3377LUZGRowZMwYzs8dPRM3MzGjSpAlz5syhSpUqxMfH8+mnn+q14+rqyqpVq/jjjz+oUqUKq1ev5tixY1SpUqXA983FxYW//vqLd955B6VSSZkyZQCwtbWlR48eTJgwgY4dO1KpUqV821EqlbqesEeMjJMLHIcQonjtj8ikg7cJtxM1JDzQ0qmxCUkpWs5cepyMfPCmKWcuqTl4JpP0TLiZoN8bk5EFKQ+1evsbeRhxK0FDcpoWF0dDurdUsj88k9uJ8rhWCJGT9OQUjqyu9gqwsrLir7/+onPnzri5ufHpp58SFBREp06dcpSdNWsWn332GQEBAXh4eODr68u2bdt0yUGFChUIDQ1FrVbTsWNHPD09GTNmDDY2NrphZAD9+/cnLS2NRo0aMXLkSEaPHs3w4cMLFO+KFSuoUKECrVq1okePHgwfPpyyZcvqlfnxxx/JysqiQYMGjBkzhs8//1zv+Pvvv0+PHj3o3bs3jRs35u7du3q9OgUxc+ZMYmJiqFatmq736JEhQ4aQkZHB4MGDC9WmEOLl8+fJTA6czqRXGyVj3zZDaQxLt6TpzbcpY22AhVnhxsuXtTFgcGdTJvUzp2MjE3Yfz2BzaMaTKwohhHgihbagLy8RooBat25NvXr1dO+1eR5cXFwYM2aM7j02xW316tWMHTuWGzduYGJiUuj6Y5dIT44QQgjxspnvl3PhpRflpwNF95X93Ralb1EDGa4mRCGkpqYSFxfHnDlzeP/9958qwRFCCCGEEEVLhquJF+rAgQOoVKo8t5Ju3rx51KhRA0dHRyZPnlzc4QghhBDiFaHVKopsK41kuJp4odLS0rh+/Xqex6tXr/4Coyk+MlxNCCGEePkU53C11X8VXdvv5f26v5eWDFcTL5SZmdkrk8gIIYQQQjwv0i1ROJLkCCGEEEIIUcJpJMkpFJmTI4QQQgghhChVpCdHCCGEEEKIEk6GqxWOJDlCFIO0lMziDkEIIYQQotSSJEcIIYQQQogSTnpyCkfm5AghhBBCCCFKFenJEUIIIYQQooST1dUKp8T35EyfPp169eoVdxhFJiYmBoVCQXh4eHGH8lSe15/Pi/5zDgkJQaFQkJiYWGJiEkIIIYQQz0ehenJat25NvXr1WLBgQRGF83IIDg5mzJgx+X5BLm4KhYKNGzfSrVu34g4lh9xi8/f356OPPiq+oHJREmMSQhSPrq+Z8lpdJWZKBdHXs/jfrlTi72nyLN+luSldXjPT23fzrprpy5MAMDdV0PU1UzxcjLGzMiA5TUv4+Qw2H0jjYUaRXooQ4iUlc3IK55UcrqbValGr1RgZvZKXXyAZGRmYmJi8sPOpVCpUKtULO19BlMSYhBAvXsfGSto0ULJyWyp37mt4o4UpH/VSMWN5ElnqvOtdv61m4S8PdJ/V/8qJbFQKrFUGrN+XRtxdNfZWBvT1McfG0oDvN6UU4dUIIV5Wmryfq4hcFHi42sCBA9m/fz8LFy5EoVCgUCiIiYnh7NmzdOrUCZVKRbly5Xjvvfe4c+eOrt7OnTt57bXXsLGxwd7eni5duhAdHa3X9rVr1+jTpw92dnZYWFjg7e1NWFiYXpnVq1fj4uKCtbU177zzDg8ePP7FodFoCAgIoEqVKpiZmVG3bl3WrVunO/5oaNKOHTto0KABSqWSgwcP5nu9ERERtGnTBktLS6ysrGjQoAHHjx8nJCSEQYMGcf/+fd19mD59OpDdQ7Fp0ya9dmxsbAgODtZ9Pnr0KF5eXpiamuLt7c2pU6dynPtJ97R169aMGjWKiRMnYmdnh6Ojoy4GABcXFwC6d++OQqHQfc7Po6FZy5cvp0qVKpiamgKQmJjI0KFDcXBwwMrKirZt2xIREZFnO8eOHaNDhw6UKVMGa2trWrVqxcmTJ58Y23+Hhmk0GmbOnEmlSpVQKpXUq1ePnTt36o4/Gua3YcMG2rRpg7m5OXXr1uXw4cO6MleuXKFr167Y2tpiYWFBrVq12L59u168J06cwNvbG3Nzc5o1a0ZUVFSOe/LIwIED6datGzNmzNDdjxEjRpCRIY9dhSjN2nmbsuPwQyIuZnL9tpoVW1OwURlQz80433oajZaklMdbStrjx7A37mj4flMKZ6IzuZOoISo2i9//SsOzmjEGiqK+IiGEKP0KnOQsXLiQpk2bMmzYMOLi4oiLi8PS0pK2bdvi5eXF8ePH2blzJ7du3aJXr166eikpKYwbN47jx4+zd+9eDAwM6N69O5r/T0eTk5Np1aoV169fZ/PmzURERDBx4kTdcYDo6Gg2bdrE1q1b2bp1K/v372fOnDm64wEBAaxatYrvvvuOv//+m7Fjx/Luu++yf/9+vWuYNGkSc+bMITIykjp16uR7vf369aNSpUocO3aMEydOMGnSJIyNjWnWrBkLFizAyspKdx/8/f0LdA+Tk5Pp0qULNWvW5MSJE0yfPj1H3cTExCfeU4CVK1diYWFBWFgY8+bNY+bMmezevRvITjQAVqxYQVxcnO7zk1y8eJH169ezYcMG3Ryht99+m/j4eHbs2MGJEyeoX78+7dq1IyEhIdc2Hjx4wIABAzh48CBHjhzB1dWVzp0765LSgsa2cOFCgoKCCAwM5PTp0/j4+PDGG29w4cIFvXJTpkzB39+f8PBw3Nzc6NOnD1lZWQCMHDmS9PR0/vrrL86cOcPcuXNz9MxMmTKFoKAgjh8/jpGREYMHD873Hu3du5fIyEhCQkL43//+x4YNG5gxY0b+N1YI8dIqY22AtcqAyJgs3b6HGXD5RhZVK+Q/GqCsrSFzPrRm1vtWDO5ijq1l/tmLmVLBwwytTC4WQuRKqy26rTQq8Hgta2trTExMMDc3x9HREYDPP/8cLy8vZs+erSv3448/4uTkxPnz53Fzc6Nnz5567fz44484ODhw7tw5ateuzc8//8zt27c5duwYdnZ2AFSvXl2vjkajITg4GEtLSwDee+899u7dyxdffEF6ejqzZ89mz549NG3aFICqVaty8OBBli5dSqtWrXTtzJw5kw4dOhToemNjY5kwYQI1atQAwNXVVe9eKBQK3X0oqJ9//hmNRsMPP/yAqakptWrV4tq1a3zwwQe6MkuWLHniPQWoU6cO06ZN08W2ZMkS9u7dS4cOHXBwcACye5EKE2NGRgarVq3S1T948CBHjx4lPj4epVIJQGBgIJs2bWLdunUMHz48Rxtt27bV+/z9999jY2PD/v376dKlS4FjCwwM5OOPP+add94BYO7cuezbt48FCxbw9ddf68r5+/vz+uuvAzBjxgxq1arFxYsXqVGjBrGxsfTs2RNPT08g++/Ff33xxRe6vyOTJk3i9ddf5+HDh7qerP8yMTHhxx9/xNzcnFq1ajFz5kwmTJjArFmzMDAo8et4CCEKyUqVnZgkpeiPE3mQqsXKIu9/85fjsli5PYVbCRqsVQpeb26Gfz9LZv6YRHounb8WZgo6NzPlYHj6c41fCCFeVc80KSUiIoJ9+/blOm8hOjoaNzc3Lly4wNSpUwkLC+POnTu6HprY2Fhq165NeHg4Xl5eugQnNy4uLroEB6B8+fLEx8cD2b0PqampOZKXjIwMvLy89PZ5e3sX+NrGjRvH0KFDWb16Ne3bt+ftt9+mWrVqBa6fm0c9SP/+Av0oMXukIPcUyNET9e978rScnZ11ScijWJKTk7G3t9crl5aWlmPI4SO3bt3i008/JSQkhPj4eNRqNampqcTGxhY4jqSkJG7cuEHz5s319jdv3jzHULl/34fy5csDEB8fT40aNRg1ahQffPABu3bton379vTs2TPHfcurfuXKlXONrW7dupibm+s+N23alOTkZK5evYqzs3OuddLT00lP1//ios5Kx9BImWt5IUTxaVTThL4+j/+Nf70u+ana+fvS456f67fh8o1kZn9gTYMaJhw6rZ/lmJqA31sq4u5q2BL68OkCF0KUeqW1x6WoPFOSk5ycTNeuXZk7d26OY4++MHbt2hVnZ2eWLVtGhQoV0Gg01K5dWzePwczMLEfd/zI21h/3rFAo9Ia7AWzbto2KFSvqlXvU+/CIhYVFAa8sez5G37592bZtGzt27GDatGmsXbuW7t2751lHoVCg/c/fwMzMzAKfEwp2TyH/e/K0/nt/kpOTKV++PCEhITnK2tjY5NrGgAEDuHv3LgsXLsTZ2RmlUknTpk2LbN7Kv++DQpH9xPXRfRg6dCg+Pj5s27aNXbt2ERAQQFBQkN6KafnVf14CAgJyDGlr0G4i3h0mPdfzCCGeXcTFDC7feJygPFqfxsrCgKSUx6sMWJoruBafz6oD/5GWruVWgpqyNvq9P0oT+KiXJQ8ztHy3IVkmFgshxHNSqCTHxMQEtfrxD/X69euzfv16XFxccl2p7O7du0RFRbFs2TJatGgBkGPCf506dVi+fDkJCQn59ubkpWbNmiiVSmJjY/WGpj0Pbm5uuLm5MXbsWPr06cOKFSvo3r17jvvwiIODA3FxcbrPFy5cIDU1VffZw8OD1atX6w2HOnLkiF4bT7qnBWVsbJxrjIVRv359bt68iZGRUYEWLwAIDQ3lm2++oXPnzgBcvXpVb9GEgsRmZWVFhQoVCA0N1fszDQ0NpVGjRoW6BicnJ0aMGMGIESOYPHkyy5Yte6ZloSMiIkhLS9Ml50eOHEGlUuHk5JRnncmTJzNu3Di9feMXp+ZRWghRnNIz4HaGfqZxP1lDDWcjXVJjagJVKhjxVyGGlimNwcHGgLCUxw/CTE1gVC9LstRavlmfnO9KbUIIIfP1CqdQkwhcXFwICwsjJiaGO3fuMHLkSBISEujTpw/Hjh0jOjqaP/74g0GDBqFWq7G1tcXe3p7vv/+eixcv8ueff+b4stenTx8cHR3p1q0boaGhXLp0ifXr1+utkpUfS0tL/P39GTt2LCtXriQ6OpqTJ0+yePFiVq5cWZjL00lLS8PPz4+QkBCuXLlCaGgox44dw8PDQ3cfkpOT2bt3L3fu3NElMm3btmXJkiWcOnWK48ePM2LECL2egr59+6JQKBg2bBjnzp1j+/btBAYG6p37Sfe0oFxcXNi7dy83b97k3r17T3Uf2rdvT9OmTenWrRu7du0iJiaGQ4cOMWXKFI4fP55rHVdXV1avXk1kZCRhYWH069cvR29dQWKbMGECc+fO5ZdffiEqKopJkyYRHh7O6NGjCxz/mDFj+OOPP7h8+TInT55k3759uj/Dp5WRkcGQIUN0f37Tpk3Dz88v3/k4SqUSKysrvU2Gqgnx8th7/CGdmplSp7oxFcoYMPB1CxKTNYSff9xTP6a3itb1H/+77tnGDFcnI+ytDKha0ZARPVRotHDsXHavtqkJjOptiYkxrNqRiplSgZVF9qaQ1dWEEOKZFSrJ8ff3x9DQkJo1a+Lg4EBGRgahoaGo1Wo6duyIp6cnY8aMwcbGBgMDAwwMDFi7di0nTpygdu3ajB07li+//FKvTRMTE3bt2kXZsmXp3Lkznp6ezJkzB0NDwwLHNWvWLD777DMCAgLw8PDA19eXbdu2UaVKlcJcno6hoSF3796lf//+uLm50atXLzp16qQbctSsWTNGjBhB7969cXBwYN68eQAEBQXh5OREixYt6Nu3L/7+/nrzN1QqFVu2bOHMmTN4eXkxZcqUHMPSHvVg5HVPCyooKIjdu3fj5OSUY25SQSkUCrZv307Lli0ZNGgQbm5uvPPOO1y5coVy5crlWueHH37g3r171K9fn/fee49Ro0ZRtmzZQsc2atQoxo0bx/jx4/H09GTnzp1s3rxZbwGIJ1Gr1YwcOVL3d8LNzY1vvvmm4DcgF+3atcPV1ZWWLVvSu3dv3njjDb3lu4UQpc+usHRCTqTTz8ecyQOsUJooWPyrfs+Lg60BKrPH2YmNpQFDulowfZgVw95UkZymZe7qByT//zLSlcsZUbWCEZXKGvH5+9bM87PRbXaWsoiJECInrVZbZFtppNCW1isT4jkbOHAgiYmJOd6F9DRGzH263jUhhBBCFJ/vPrYttnMv3lZ0X9k/er30dSHL4yIhhBBCCCFEqfLKJjm1atVCpVLluq1Zs6a4w3vuXrXrFUIIIYQoTTSaottKo2daQvpltn379jyXd85rvsnL7FW73qIQHBxc3CEIIYQQQogCeGWTnLxe3FhavWrXK4QQQghRmsgs+sJ5ZYerCSGEEEIIIUqnV7YnRwghhBBCiJeFvAy0cKQnRwghhBBCCFGqSE+OEMVAXk8lhBBCiMKQrw6FI0mOEEIIIYQQJZy2SMeryctAhRBCCCGEEKJEk54cIYQQQgghSjhZeKBwpCfnFde6dWvGjBlTJG3HxMSgUCgIDw8vkvZfFBcXFxYsWFDcYQghhBBCiAKSnhxRZJycnIiLi6NMmTLFHYoQQjyTri3MaFFXiZlSQfT1LH7+I4X4e5o8y3d5zYyur5np7bt5V820Zfd1n/v5mOPhYoy1yoD0TC3R17PYsC+VWwl5tyuEeHW9jAsPJCQk8NFHH7FlyxYMDAzo2bMnCxcuRKVSPbGuVqulc+fO7Ny5k40bN9KtW7dCnVuSHFFkDA0NcXR0LO4whBDimfg0NqVtAyXB21K4k6jhjZZmjOptyfRl98lS513v+u0sFqx9oPus/k/uEntTzdFzGSQkaTA3VdD1NTPG9Lbkk+/uv5RfZoQQ4r/69etHXFwcu3fvJjMzk0GDBjF8+HB+/vnnJ9ZdsGABCsXTL4ggw9UEWVlZ+Pn5YW1tTZkyZfjss890SxyvXr0ab29vLC0tcXR0pG/fvsTHx+vq3rt3j379+uHg4ICZmRmurq6sWLECyH242t9//02XLl2wsrLC0tKSFi1aEB0d/cQYBw4cSLdu3QgMDKR8+fLY29szcuRIMjMzdWXS09Px9/enYsWKWFhY0LhxY0JCQvTaOXjwIC1atMDMzAwnJydGjRpFSkqK7nh8fDxdu3bFzMyMKlWqsGbNGr36Wq2W6dOnU7lyZZRKJRUqVGDUqFEFvtdCiJdPu4ambD/0kIgLmVy/rWbF1hRsVAbUczPJt55GA0kpWt2WkqafuRyISOfC1Szu3tdw9Zaa3/9Kw87aEHtr+dUshMhJo9EW2VYUIiMj2blzJ8uXL6dx48a89tprLF68mLVr13Ljxo1864aHhxMUFMSPP/741OeXn6SClStXYmRkxNGjR1m4cCFfffUVy5cvByAzM5NZs2YRERHBpk2biImJYeDAgbq6n332GefOnWPHjh1ERkby7bff5jk87fr167Rs2RKlUsmff/7JiRMnGDx4MFlZWQWKc9++fURHR7Nv3z5WrlxJcHAwwcHBuuN+fn4cPnyYtWvXcvr0ad5++218fX25cOECANHR0fj6+tKzZ09Onz7NL7/8wsGDB/Hz89O1MXDgQK5evcq+fftYt24d33zzjV5St379eubPn8/SpUu5cOECmzZtwtPTs6C3WgjxkiljbYC1yoDImMcPVB6ma7l8I4uqFfMfDFHW1pC5I234fIQ1g7taYGuV969cE2NoVkfJ7UQ195JkuJoQ4sVKT08nKSlJb0tPT3+mNg8fPoyNjQ3e3t66fe3bt8fAwICwsLA866WmptK3b1++/vrrZxoRJMPVBE5OTsyfPx+FQoG7uztnzpxh/vz5DBs2jMGDB+vKVa1alUWLFtGwYUOSk5NRqVTExsbi5eWl+wvs4uKS53m+/vprrK2tWbt2LcbGxgC4ubkVOE5bW1uWLFmCoaEhNWrU4PXXX2fv3r0MGzaM2NhYVqxYQWxsLBUqVADA39+fnTt3smLFCmbPnk1AQAD9+vXTLbTg6urKokWLaNWqFd9++y2xsbHs2LGDo0eP0rBhQwB++OEHPDw8dDHExsbi6OhI+/btMTY2pnLlyjRq1KjA1yCEeLlYqbITk6QU/cQjKUWDtUXewygu38gieFsytxI0WKsM6NLclAn9LJnxw33SMx6Xa+WlpEcbc0xNFNy8q2bB2gc5hrUJIQQU7ZycgIAAZsyYobdv2rRpTJ8+/anbvHnzJmXLltXbZ2RkhJ2dHTdv3syz3tixY2nWrBlvvvnmU58bJMkRQJMmTfTGPDZt2pSgoCDUajXh4eFMnz6diIgI7t27h0aT/ds3NjaWmjVr8sEHH9CzZ09OnjxJx44d6datG82aNcv1POHh4bRo0UKX4BRWrVq1MDQ01H0uX748Z86cAeDMmTOo1eocSVN6ejr29vYAREREcPr0ab0haFqtFo1Gw+XLlzl//jxGRkY0aNBAd7xGjRrY2NjoPr/99tssWLCAqlWr4uvrS+fOnenatStGRnn/U0pPT8/xNESdlY6hkbLwN0EIUaQa1TShn6+F7vOS3x7kUzpvf1963PNz/baayzeyCPjAGu8aJoSefpzlhJ3LIDImE2uVAR0amTK8m4p5q5PynesjhHg1FWWSM3nyZMaNG6e3T6nM/XvKpEmTmDt3br7tRUZGPlUcmzdv5s8//+TUqVNPVf/fJMkReXr48CE+Pj74+PiwZs0aHBwciI2NxcfHh4yM7F/SnTp14sqVK2zfvp3du3fTrl07Ro4cSWBgYI72zMzMcuwrjP8mRwqFQpd0JScnY2hoyIkTJ/QSIUC3gkdycjLvv/9+rnNoKleuzPnz558Yg5OTE1FRUezZs4fdu3fz4Ycf8uWXX7J///48k7fcno7UbzcR7/YfP/F8QogXK+JiBpd/fDyE1sgo+wGQlYUBSSmPMw8rCwOuxhc8E0lL13LrngYHW/2fTw/TtTxM1xJ/T8Ol68nMH2OLl5sJxyIz8mhJCCGeP6VSmWdS81/jx4/Xm7qQm6pVq+Lo6Kg35B+y54EnJCTkOQztzz//JDo6Wu8BM0DPnj1p0aJFjrnW+ZEkR+QYF3nkyBFcXV35559/uHv3LnPmzMHJyQmA48eP56jv4ODAgAEDGDBgAC1atGDChAm5Jjl16tRh5cqVZGZmPnVvTl68vLxQq9XEx8fTokWLXMvUr1+fc+fOUb169VyP16hRg6ysLE6cOKEbrhYVFUViYqJeOTMzM7p27UrXrl0ZOXIkNWrU4MyZM9SvXz/XdnN7OjJuUUquZYUQxSs9A25n6I8Xu5+soYaLMdf+P6kxNYEqFYzYf6rg49WVxuBgY8CR5LzHoikU2Vs+HcNCiFeYpoQsu+jg4ICDg8MTyzVt2pTExEROnDihGyXz559/otFoaNy4ca51Jk2axNChQ/X2eXp6Mn/+fLp27VqoOOVHqSA2NpZx48bx/vvvc/LkSRYvXkxQUBCVK1fGxMSExYsXM2LECM6ePcusWbP06k6dOpUGDRpQq1Yt0tPT2bp1q94cln/z8/Nj8eLFvPPOO0yePBlra2uOHDlCo0aNcHd3f6ZrcHNzo1+/fvTv35+goCC8vLy4ffs2e/fupU6dOrz++ut8/PHHNGnSBD8/P4YOHYqFhQXnzp1j9+7dLFmyBHd3d3x9fXn//ff59ttvMTIyYsyYMXo9UMHBwajVaho3boy5uTk//fQTZmZmODs75xlbbk9HDI0KttiCEKL47T32kM7NTIlPUHPnvoY3W5iRmKwh/Pzj3pax71hy6nwGISezE5+ebcw4fTGThKTsOTldXzNDo4Vj57LrlLE2wNvDhHOXM3mQpsXW0gDfJqZkZGk5G52ZaxxCCPEy8fDwwNfXl2HDhvHdd9+RmZmJn58f77zzjm7+9PXr12nXrh2rVq2iUaNGODo65trLU7lyZapUqVKo80uSI+jfvz9paWk0atQIQ0NDRo8ezfDhw1EoFAQHB/PJJ5+waNEi6tevT2BgIG+88YauromJCZMnTyYmJgYzMzNatGjB2rVrcz2Pvb09f/75JxMmTKBVq1YYGhpSr149mjdv/lyuY8WKFXz++eeMHz+e69evU6ZMGZo0aUKXLl2A7J6k/fv3M2XKFFq0aIFWq6VatWr07t1br42hQ4fSqlUrypUrx+eff85nn32mO25jY8OcOXMYN24carUaT09PtmzZopv3I4Qoff4Ie4iJiYJ3fS0wN1Vw8VoWi355oDdvpoytASrzx6un2VoaMPQNFRZmCpJTtVy8lsmcVSkk//8y0plqLdWdjGjX0BRzUwVJKRouXM1i3uokHqSWjKe1QoiSRfsSLkqyZs0a/Pz8aNeune5loIsWLdIdz8zMJCoqitTU1Od+boVWW0L6voR4hbw/J6G4QxBCCCFEIS2dZFds5565puhGgUztV/r6PUrfFQkhhBBCCFHKSL9E4cjLQEWJoFKp8twOHDhQ3OEJIYQQQoiXiPTkiBIhPDw8z2MVK1Z8cYEIIYQQQpRAmpdwTk5xkiRHlAh5LesshBBCCCFkuFphyXA1IYQQQgghRKkiPTlCCCGEEEKUcBrpyCkU6ckRQgghhBBClCrSkyOEEEIIIUQJp5WunEKRnhwhhBBCCCFEqSI9OUIIIYQQQpRwsrha4UhPjhBCCCGEEKJUkZ4cIYQQQgghSjiNzMkpFElyRL6mT5/Opk2bCA8PL3AdhULBxo0b6datW5HFJYQQL1LXFma0qKvETKkg+noWP/+RQvy9vF8/3uU1M7q+Zqa37+ZdNdOW3dd97udjjoeLMdYqA9IztURfz2LDvlRuJchrzYUQ4llJkiPy5e/vz0cffVTcYZRIISEhtGnThnv37mFjY1Pc4QghiohPY1PaNlASvC2FO4ka3mhpxqjelkxfdp8sdd71rt/OYsHaB7rP6v/kLrE31Rw9l0FCkgZzUwVdXzNjTG9LPvnuvoy9F0LkoJUfDIUic3JEvlQqFfb29sUdRomTmZlZ3CEIIV6Qdg1N2X7oIREXMrl+W82KrSnYqAyo52aSbz2NBpJStLotJU3/C8qBiHQuXM3i7n0NV2+p+f2vNOysDbG3ll/NQoictJqi20oj+UlaSqxbtw5PT0/MzMywt7enffv2pKSkoNFomDlzJpUqVUKpVFKvXj127typV/fatWv06dMHOzs7LCws8Pb2JiwsDMgerlavXj1d2WPHjtGhQwfKlCmDtbU1rVq14uTJk08Vc0ZGBn5+fpQvXx5TU1OcnZ0JCAgAICYmBoVCoTdMLjExEYVCQUhICJDdk6JQKNi2bRt16tTB1NSUJk2acPbsWV2d4OBgbGxs2LRpE66urpiamuLj48PVq1f1Yvn222+pVq0aJiYmuLu7s3r1ar3jCoWCb7/9ljfeeAMLCwuGDRtGmzZtALC1tUWhUDBw4MCnug9CiJKrjLUB1ioDImMeP9h4mK7l8o0sqlbMfzBEWVtD5o604fMR1gzuaoGtVd6/ck2MoVkdJbcT1dxLKqXfOIQQ4gWSJKcUiIuLo0+fPgwePJjIyEhCQkLo0aMHWq2WhQsXEhQURGBgIKdPn8bHx4c33niDCxcuAJCcnEyrVq24fv06mzdvJiIigokTJ6LR5P5L9sGDBwwYMICDBw9y5MgRXF1d6dy5Mw8ePMi1fH4WLVrE5s2b+fXXX4mKimLNmjW4uLgUup0JEyYQFBTEsWPHcHBwoGvXrno9LampqXzxxResWrWK0NBQEhMTeeedd3THN27cyOjRoxk/fjxnz57l/fffZ9CgQezbt0/vPNOnT6d79+6cOXOGGTNmsH79egCioqKIi4tj4cKFhY5dCFGyWamyf00mpej/TExK0WBtociz3uUbWQRvS2bRrw/4+Y9UylgbMKGfJcr/dP608lKycJwti8fbUbuqMQvWPsgxrE0IIQA0Wm2RbaWRzMkpBeLi4sjKyqJHjx44OzsD4OnpCUBgYCAff/yx7kv93Llz2bdvHwsWLODrr7/m559/5vbt2xw7dgw7OzsAqlevnue52rZtq/f5+++/x8bGhv3799OlS5dCxR0bG4urqyuvvfYaCoVCF3thTZs2jQ4dOgCwcuVKKlWqxMaNG+nVqxeQPbRsyZIlNG7cWFfGw8ODo0eP0qhRIwIDAxk4cCAffvghAOPGjePIkSMEBgbqemsA+vbty6BBg3SfL1++DEDZsmXznZOTnp5Oenq63j51VjqGRsqnul4hRNFpVNOEfr4Wus9Lfiv8AxyAvy89ftBy/baayzeyCPjAGu8aJoSeztAdCzuXQWRMJtYqAzo0MmV4NxXzViflO9dHCCHEk0lPTilQt25d2rVrh6enJ2+//TbLli3j3r17JCUlcePGDZo3b65Xvnnz5kRGRgIQHh6Ol5eXLsF5klu3bjFs2DBcXV2xtrbGysqK5ORkYmNjCx33wIEDCQ8Px93dnVGjRrFr165CtwHQtGlT3f/b2dnh7u6uuz4AIyMjGjZsqPtco0YNbGxsdGUiIyPzvUePeHt7P1V8AQEBWFtb622nQhY8VVtCiKIVcTGDz3+8r9uS/38ejZWF/q9LKwsD7qcU/OlnWrqWW/c0ONga6u1/mK4l/p6GC1ezWLoxGUc7Q7yeMNdHCPFq0mq1RbaVRpLklAKGhobs3r2bHTt2ULNmTRYvXoy7u7uupyE/ZmZmTyzzbwMGDCA8PJyFCxdy6NAhwsPDsbe3JyMj48mV/6N+/fpcvnyZWbNmkZaWRq9evXjrrbcAMDDI/qv57394xT3Z38LC4smFcjF58mTu37+vt3m1HvN8gxNCPBfpGXA7UaPb4u6ouZ+soYaLsa6MqQlUqWDEpetZBW5XaQwONgbcT857LJpCkb0ZyRgLIYR4ZpLklBIKhYLmzZszY8YMTp06hYmJCXv37qVChQqEhobqlQ0NDaVmzZoA1KlTh/DwcBISEgp0ntDQUEaNGkXnzp2pVasWSqWSO3fuPHXcVlZW9O7dm2XLlvHLL7+wfv16EhIScHBwALKH4j2S17t6jhw5ovv/e/fucf78eTw8PHT7srKyOH78uO5zVFQUiYmJujIeHh753qO8mJhkP21Vq/MfV6JUKrGystLbZKiaEC+Pvcce0rmZKXWqG1PBwZBBXVQkJmsIP//44c7YdyxpXf/xv+uebcxwdTLC3tqAqhWNGNHDEo0Wjp3LrlPG2gDfJqZULmeIrVV2meHdVGRkaTkbLas3CiFy0mi0RbaVRvK8qBQICwtj7969dOzYkbJlyxIWFsbt27fx8PBgwoQJTJs2jWrVqlGvXj1WrFhBeHg4a9asAaBPnz7Mnj2bbt26ERAQQPny5Tl16hQVKlTQGwb2iKurK6tXr8bb25ukpCQmTJhQ6N6gR7766ivKly+Pl5cXBgYG/Pbbbzg6OmJjY4OBgQFNmjRhzpw5VKlShfj4eD799NNc25k5cyb29vaUK1eOKVOmUKZMGb0XkRobG/PRRx+xaNEijIyM8PPzo0mTJjRq1AjIXrigV69eeHl50b59e7Zs2cKGDRvYs2dPvvE7OzujUCjYunUrnTt3xszMDJVK9VT3QghRcv0R9hATEwXv+lpgbqrg4rUsFv3yQG/eTBlbA1Tmj58b2loaMPQNFRZmCpJTtVy8lsmcVSm64W+Zai3VnYxo19AUc1MFSSnZQ9bmrU7iQWrp/MIhhBAvkiQ5pYCVlRV//fUXCxYsICkpCWdnZ4KCgujUqRM+Pj7cv3+f8ePHEx8fT82aNdm8eTOurq5Adm/Erl27GD9+PJ07dyYrK4uaNWvy9ddf53quH374geHDh1O/fn2cnJyYPXs2/v7+TxW3paUl8+bN48KFCxgaGtKwYUO2b9+uG6r2448/MmTIEBo0aIC7uzvz5s2jY8eOOdqZM2cOo0eP5sKFC9SrV48tW7boelkAzM3N+fjjj+nbty/Xr1+nRYsW/PDDD7rj3bp1Y+HChQQGBjJ69GiqVKnCihUraN26db7xV6xYkRkzZjBp0iQGDRpE//79CQ4Ofqp7IYQo2bYcSGPLgbQ8j0/59r7e5+WbU/Jt736yliW/JT+X2IQQr4ZSOnWmyCi0pXW2kSj1QkJCaNOmDffu3ctzdbPg4GDGjBlDYmLiC43tSd6fU7DhgUIIIYQoOZZOKthCTUVh9MKnW+2xIBaOtiyytouLzMkRQgghhBBClCqS5IgiM3v2bFQqVa5bp06dijs8IYQQQoiXhrwMtHBkuJooMgkJCXmu2mZmZkbFihVfcEQlhwxXE0IIIV4+xTlc7aMFSUXW9uIxVkXWdnGRhQdEkbGzsyvwS0aFEEIIIUTetKV0qeeiIsPVhBBCCCGEEKWK9OQIIYQQQghRwklPTuFIkiNEMcjKVD+5kBBCCCGEeCqS5AghhBBCCFHCSUdO4UiSI4QQQgghRAknw9UKRxYeEEIIIYQQQpQq0pMjhBBCCCFECSevtiwc6cl5Ri4uLixYsKDIz6NQKNi0aVORn+dZbdq0ierVq2NoaMiYMWOKOxwhhBBCCPEKkp6cl0RcXBy2trbFHcYTvf/++wwaNIhRo0ZhaWlZ3OEIIcRz8WYrc1p6mWJuasDFq5ms3pFMfELeqyS+0dKcN1tZ6O2Lu5PFp9/e032e8J41NVxM9MqEnEhj9fbk5xu8EKJU0MicnEKRJOcl4ejoWNwh5CszM5P09HTi4+Px8fGhQoUKT91WRkYGJiYmTy4ohBAvQKdmZrRvZMYPvz/gTqKabq0tGNfXmk+/TSArn9Xgr8dnEfhTou6zRpOzzP6TaWwKSdF9zsh8joELIcQrrFQNV9u5cyevvfYaNjY22Nvb06VLF6Kjo3XHr127Rp8+fbCzs8PCwgJvb2/CwsIAiI6O5s0336RcuXKoVCoaNmzInj179NqPj4+na9eumJmZUaVKFdasWZMjhsTERIYOHYqDgwNWVla0bduWiIgI3fHp06dTr149fvzxRypXroxKpeLDDz9ErVYzb948HB0dKVu2LF988YVeu/8drpbfteTn0fmXLl2Kk5MT5ubm9OrVi/v37+uVW758OR4eHpiamlKjRg2++eYb3bGYmBgUCgW//PILrVq1wtTUlDVr1uh6btq2bYtCoSAkJASA9evXU6tWLZRKJS4uLgQFBemdy8XFhVmzZtG/f3+srKwYPnw4wcHB2NjYsHXrVtzd3TE3N+ett94iNTWVlStX4uLigq2tLaNGjUKtfvwtY/Xq1Xh7e2NpaYmjoyN9+/YlPj5edzwkJASFQsHevXvx9vbG3NycZs2aERUVpRfTli1baNiwIaamppQpU4bu3bvrjqWnp+Pv70/FihWxsLCgcePGumsVQpQ+7RuZsfVAKuHnM7gWr+aH3x9gY2lA/RrKfOupNZCUotVtyWk5n8JmZGr1yjzMkCe1QojcabXaIttKo1LVk5OSksK4ceOoU6cOycnJTJ06le7duxMeHk5qaiqtWrWiYsWKbN68GUdHR06ePInm/x+tJScn07lzZ7744guUSiWrVq2ia9euREVFUblyZQAGDhzIjRs32LdvH8bGxowaNUrvCzTA22+/jZmZGTt27MDa2pqlS5fSrl07zp8/j52dHZCdUO3YsYOdO3cSHR3NW2+9xaVLl3Bzc2P//v0cOnSIwYMH0759exo3bpzjOpOTk/O9lie5ePEiv/76K1u2bCEpKYkhQ4bw4Ycf6pK2NWvWMHXqVJYsWYKXlxenTp1i2LBhWFhYMGDAAF07kyZNIigoCC8vLwwMDIiKisLd3Z3169fTrFkz7OzsOHHiBL169WL69On07t2bQ4cO8eGHH2Jvb8/AgQN1bQUGBjJ16lSmTZsGwIEDB0hNTWXRokWsXbuWBw8e0KNHD7p3746NjQ3bt2/n0qVL9OzZk+bNm9O7d28gu0dp1qxZuLu7Ex8fz7hx4xg4cCDbt2/XuwdTpkwhKCgIBwcHRowYweDBgwkNDQVg27ZtdO/enSlTprBq1SoyMjL06vv5+XHu3DnWrl1LhQoV2LhxI76+vpw5cwZXV9cC/RkIIV4OZWwMsLE05NzlDN2+tHQtl65nUq2iEUf/Ts+zbjk7Q4LG2JGZBdHXMln/ZwoJSfo/p5vUNqWJpylJyRrCL2Sw9a8UMrKK7HKEEOKVodCW1vQNuHPnDg4ODpw5c4ZDhw7h7+9PTEyMLtl4ktq1azNixAj8/Pw4f/487u7uHD16lIYNGwLwzz//4OHhwfz58xkzZgwHDx7k9ddfJz4+HqXy8RO+6tWrM3HiRIYPH8706dP58ssvuXnzpq7nw9fXl6ioKKKjozEwyO5cq1GjBgMHDmTSpElAdk/Oxo0b6datG99//32hr+WR6dOn8/nnn3PlyhUqVqwIZPeAvf7661y/fh1HR0eqV6/OrFmz6NOnj67e559/zvbt2zl06BAxMTFUqVKFBQsWMHr0aF2ZxMREbG1t2bdvH61btwagX79+3L59m127dunKTZw4kW3btvH3338D2T05Xl5ebNy4UVcmODiYQYMGcfHiRapVqwbAiBEjWL16Nbdu3UKlUununYuLC999912u13v8+HEaNmzIgwcPUKlUhISE0KZNG/bs2UO7du0A2L59O6+//jppaWmYmprSrFkzqlatyk8//ZSjvdjYWKpWrUpsbKzekLz27dvTqFEjZs+enaNOeno66en6X4RGBSVhaJT/U2AhRPGrVsmITwbZMm7+Xe4nP05QRvS0RKuFpRse5FqvdjUTTE3g5l011ioD3mhpga2lAVOX3tP11rT0MuXufTWJyRoqlTXirXYWXL6RxTe/Jb2QaxNCFN4PnzkU27mHzLpdZG0X53UVlVLVk3PhwgWmTp1KWFgYd+7c0fVsxMbGEh4ejpeXV55JQXJyMtOnT2fbtm3ExcWRlZVFWloasbGxAERGRmJkZESDBg10dWrUqIGNjY3uc0REBMnJydjb2+u1nZaWpjdszsXFRW9Sfrly5TA0NNQlOI/2/beX6JEnXcuTVK5cWZfgADRt2hSNRkNUVBSWlpZER0czZMgQhg0bpiuTlZWFtbW1Xjve3t5PPFdkZCRvvvmm3r7mzZuzYMEC1Go1hoaGebZlbm6uS3Ag+564uLjoEpxH+/59n06cOMH06dOJiIjg3r17en8HatasqStXp04d3f+XL18eyB6OWLlyZcLDw/Wu/d/OnDmDWq3Gzc1Nb396enqOP/dHAgICmDFjht6+eq39qd92Yq7lhRDFp3FtJf1ff/zzeeH/7udTOm9nox/3/FyLV3Pp+n3mjbLDu6aSg+EPAfjr1ENdmevxau4na5jwng0OtgbcvlewnnkhxKtDXgZaOKUqyenatSvOzs4sW7aMChUqoNFoqF27NhkZGZiZmeVb19/fn927dxMYGEj16tUxMzPjrbfeIiMjI996/5acnEz58uVznZ/x72TI2NhY75hCoch1X17Dz550Lc8iOTl7VZ9ly5blGCr3KCF5xMJCf+WgZ5FbW4W9TykpKfj4+ODj48OaNWtwcHAgNjYWHx+fHH+O/25HoVAA6NrJ7/4mJydjaGjIiRMnctyPfydf/zZ58mTGjRunt29UkDypFaIkijifwYzrCbrPRkbZPx+sLBTc/9eiZ1YWBly9WfBxZWnpWm4lqClrZ5hnmUvXs1cdKGtrKEmOEEI8o1KT5Ny9e5eoqCiWLVtGixYtADh48KDueJ06dVi+fDkJCQm59oCEhoYycOBA3QTz5ORkYmJidMdr1KhBVlYWJ06c0A1Xi4qKIjExUVemfv363Lx5EyMjI1xcXJ7/RRbwWp4kNjaWGzdu6IZbHTlyBAMDA9zd3SlXrhwVKlTg0qVL9OvX75lj9fDw0M11eSQ0NBQ3N7ccScKz+ueff7h79y5z5szByckJyB6uVlh16tRh7969DBo0KMcxLy8v1Go18fHxur9nT6JUKvWGLwIYGuU9jl8IUXweZuSc/J/4QI1HFROu3koDwNREQdWKxoSceJhbE7lSGmcnL4dP5/1vv3K57F/J/x4WJ4QQj2hK7wyTIlFqVleztbXF3t6e77//nosXL/Lnn3/qPT3v06cPjo6OdOvWjdDQUC5dusT69es5fPgwAK6urmzYsIHw8HAiIiLo27evXk+Ku7s7vr6+vP/++4SFhXHixAmGDh2q99S/ffv2NG3alG7durFr1y5iYmI4dOgQU6ZMeaov23l50rU8iampKQMGDCAiIoIDBw4watQoevXqpVumesaMGQQEBLBo0SLOnz/PmTNnWLFiBV999VWhYx0/fjx79+5l1qxZnD9/npUrV7JkyRL8/f0L3daTVK5cGRMTExYvXsylS5fYvHkzs2bNKnQ706ZN43//+x/Tpk0jMjKSM2fOMHfuXADc3Nzo168f/fv3Z8OGDVy+fJmjR48SEBDAtm3bnvclCSFKgD1H0+jymjl13UyoWNaQod0sSXyg4eQ/jxMW/3etaettqvvcq70FbpWNsbc2oFolI0b2skajgbC/sxMjB1sDurQwx9nRCHtrA+q6mTDkTSuirmSv4CaEEOLZlJokx8DAgLVr13LixAlq167N2LFj+fLLL3XHTUxM2LVrF2XLlqVz5854enoyZ84cXW/CV199ha2tLc2aNaNr1674+PhQv359vXOsWLGCChUq0KpVK3r06MHw4cMpW7as7rhCoWD79u20bNmSQYMG4ebmxjvvvMOVK1coV67cc7vWJ13Lk1SvXp0ePXrQuXNnOnbsSJ06dfSWiB46dCjLly9nxYoVeHp60qpVK4KDg6lSpUqhY61fvz6//vora9eupXbt2kydOpWZM2fqraz2vDg4OBAcHMxvv/1GzZo1mTNnDoGBgYVup3Xr1vz2229s3ryZevXq0bZtW44ePao7vmLFCvr378/48eNxd3enW7duHDt2TLcKnxCidNlxKI29x9IY8Lolnw2xRWmsYP7P9/XekeNga4jK/PGvVFsrA97vYckXH9oxoqcVKWkavlhxj+TU7CexWWqoWcWYcf2s+eJDO3q3t+DEP+ksWitDWYUQudNqtEW2lUalenU1kdP06dPZtGkT4eHhxR3KK60oV0gRQgghRNEozlXIBky9WWRtr5xZsl86/zRKzZwcIYQQQgghSivplygcSXJKmVq1anHlypVcjy1duvQFRyOEEEIIIcSLJ0lOKbN9+3YyMzNzPVauXDksLS2ZPn36iw1KCCGEEEI8E00pnTtTVCTJKWWcnZ2LOwQhhBBCCPGcldYFAopKqVldTQghhBBCCCFAenKEEEIIIYQo8WThgcKRJEeIYqBWyxvNhRBCCCGKigxXE0IIIYQQooTTajRFthWVhIQE+vXrh5WVFTY2NgwZMoTk5OQn1jt8+DBt27bFwsICKysrWrZsSVpaWqHOLUmOEEIIIYQQ4rnr168ff//9N7t372br1q389ddfDB8+PN86hw8fxtfXl44dO3L06FGOHTuGn58fBgaFS1tkuJoQQgghhBAl3Mu2hHRkZCQ7d+7k2LFjeHt7A7B48WI6d+5MYGAgFSpUyLXe2LFjGTVqFJMmTdLtc3d3L/T5pSdHCCGEEEKIV1h6ejpJSUl6W3p6+jO1efjwYWxsbHQJDkD79u0xMDAgLCws1zrx8fGEhYVRtmxZmjVrRrly5WjVqhUHDx4s9PklyRGvhJs3b9KhQwcsLCywsbEp7nCEEEIIIQpFq9UW2RYQEIC1tbXeFhAQ8Ezx3rx5k7Jly+rtMzIyws7Ojps3b+Za59KlSwBMnz6dYcOGsXPnTurXr0+7du24cOFCoc4vw9XEK2H+/PnExcURHh6OtbV1gepMnz6dTZs2ER4eXrTBCSFKvO5tLGhV3wxzUwMuXM1g1dYH3EpQ51m+W2sLurVW6e2Lu5PF5CV39fZVq2RMz3YqqlU0RqPVEnszi8DV98jMKpLLEEK8xIryZaCTJ09m3LhxevuUSmWuZSdNmsTcuXPzbS8yMvKp4tD8/yII77//PoMGDQLAy8uLvXv38uOPPxYq8ZIkR7wSoqOjadCgAa6ursUdihDiJdO5uTkdGpuzbGMStxPV9Ghjwfj3bJjy9d18k5Fr8Vl8ueqe7rP6P19QqlUyZvy7Nmw7mMJP25PQaMDJ0Qh5FYYQ4kVTKpV5JjX/NX78eAYOHJhvmapVq+Lo6Eh8fLze/qysLBISEnB0dMy1Xvny5QGoWbOm3n4PDw9iY2MLFN8jMlxNvDTWrVuHp6cnZmZm2Nvb0759e1JSUjh27BgdOnSgTJkyWFtb06pVK06ePKmr5+Liwvr161m1ahUKhUL3DzMxMZGhQ4fi4OCAlZUVbdu2JSIiAoDg4GBmzJhBREQECoUChUJBcHAwgwcPpkuXLnpxZWZmUrZsWX744YcXdi+EEC9OxybmbP4rhVNR6Vy7lcWyjUnYWhpSv0b+Xwg0Gi33kzW6LTlVP3vp66tiT1gq2w6mcuO2mpt31Rz7O52svDuIhBCvMK1GW2RbYTg4OFCjRo18NxMTE5o2bUpiYiInTpzQ1f3zzz/RaDQ0btw417ZdXFyoUKECUVFRevvPnz+Ps7NzoeKUnhzxUoiLi6NPnz7MmzeP7t278+DBAw4cOIBWq+XBgwcMGDCAxYsXo9VqCQoKonPnzly4cAFLS0uOHTtG//79sbKyYuHChZiZmQHw9ttvY2Zmxo4dO7C2tmbp0qW0a9eO8+fP07t3b86ePcvOnTvZs2cPANbW1ri5udGyZUvi4uJ0Txu2bt1KamoqvXv3Lrb7I4QoGg62hthYGnLuUoZuX1q6luhrmVSrZELY2bwn5pazM2L++DJkZmmJvprJb3uTSbifPRTD0kJBtUomHD79kClDbClra0jcHTXr/0zmQmxmkV+XEEIUNQ8PD3x9fRk2bBjfffcdmZmZ+Pn58c477+hWVrt+/Trt2rVj1apVNGrUCIVCwYQJE5g2bRp169alXr16rFy5kn/++Yd169YV6vyS5IiXQlxcHFlZWfTo0UOXyXt6egLQtm1bvbLff/89NjY27N+/ny5duuDg4IBSqcTMzEzXPXrw4EGOHj1KfHy8rns2MDCQTZs2sW7dOoYPH45KpcLIyEivS7VZs2a4u7uzevVqJk6cCMCKFSt4++23Uan0x98/kp6enmOFEnVWOoZGBesWFkIUH2tV9oCH+8n6L8tLStHojuUm+lomyzfdJ+6uGhuVAW+2tuCTQXZ8+s1dHmZoKWub/eu3W2sVa3c9IPZmFs3rmjKxvy2ffnM33/k+QohXk0ZbdC/tLCpr1qzBz8+Pdu3aYWBgQM+ePVm0aJHueGZmJlFRUaSmpur2jRkzhocPHzJ27FgSEhKoW7cuu3fvplq1aoU6tyQ54qVQt25d2rVrh6enJz4+PnTs2JG33noLW1tbbt26xaeffkpISAjx8fGo1WpSU1PzHbsZERFBcnIy9vb2evvT0tKIjo7ON5ahQ4fy/fffM3HiRG7dusWOHTv4888/8ywfEBDAjBkz9K+n1XjqtZ5QgCsXQrxITT1NGdDVUvd5/prEp2rnzMXHPT/XbsGl65kEjilDo1pK/jr1EIUi+9i+E2kcDH8IQOzNZGpWNaGFlxnr9j75jeBCCFHS2dnZ8fPPP+d53MXFBW0uExEnTZqk956cpyFJjngpGBoasnv3bg4dOsSuXbtYvHgxU6ZMISwsjA8++IC7d++ycOFCnJ2dUSqVNG3alIyMjDzbS05Opnz58oSEhOQ49qQlpvv378+kSZM4fPgwhw4dokqVKrRo0SLP8rmtWDJyXmK+5xBCFI9TUelEX388XMzIMPu/1ioDvd4cKwsDYm8WfAm01Idabt5VU9Yu+9du4oPsnpobt/XbuHFbjb21TJcVQuRUlKurlUaS5IiXhkKhoHnz5jRv3pypU6fi7OzMxo0bCQ0N5ZtvvqFz584AXL16lTt37uTbVv369bl58yZGRka4uLjkWsbExAS1OueQEXt7e7p168aKFSs4fPiwbonDvOS2YomhUVq+dYQQxeNhhpaH/xkqlvhATc0qJrqkxlSpoFolY/YdT82tiVwpTRSUtTPk0Onstu8kariXpKa8vaFeOUd7Q05fzPsBjRBCiIKRJEe8FMLCwti7dy8dO3akbNmyhIWFcfv2bTw8PHB1dWX16tV4e3uTlJTEhAkTdIsL5KV9+/Y0bdqUbt26MW/ePNzc3Lhx4wbbtm2je/fueHt74+LiwuXLlwkPD6dSpUpYWlrqkpWhQ4fSpUsX1Go1AwYMeBG3QAhRTHYdSaVrSwtuJqi5c09Nj7YW3Hug5uQ/j+faTexvw4l/0tl7NPsBRu+OKsKj0rl7X42NpSHdWlug0UDYmYe6OjsOpdKttQWxt7KIvZnFa3VNKV/GiCW/3n/h1yiEKPmkJ6dwJMkRLwUrKyv++usvFixYQFJSEs7OzgQFBdGpUyccHR0ZPnw49evXx8nJidmzZ+Pv759vewqFgu3btzNlyhQGDRrE7du3cXR0pGXLlpQrVw6Anj17smHDBtq0aUNiYiIrVqzQLT/dvn17ypcvT61atXQrhAghSqftoakoTRQM6mqJuakB52MzCPopUe8dOWXtjLA0fzzMzc7KkBFvWaMyM+BBqoYLsZnMWp7Ag38tI73rSCrGRtDHxxKVmQGxtzL5cvU9bt+TRQeEEDnlNndF5E2hlTsmRKElJydTsWJFVqxYQY8ePQpdf+D0W0UQlRBCCCGKUvD0csV27jc/iHpyoaf0+7fuRdZ2cZGeHCEKQaPRcOfOHYKCgrCxseGNN94o7pCEEEII8QrQaF6+JaSLkyQ5QhRCbGwsVapUoVKlSgQHB2NkJP+EhBBCCCFKGvmGJkQh5LWeuxBCCCFEUZKFBwpHFuMXQgghhBBClCrSkyOEEEIIIUQJp9XKnJzCkJ4cIYQQQgghRKkiPTlCFIOLJ/4p7hCEEEIIUWjFt4S0zMkpHElyhBBCCCGEKOEkySkcGa4mhBBCCCGEKFWkJ0cIIYQQQogSTiMLDxSK9OQIIYQQQgghShVJcsQzi4mJQaFQEB4ertsXGhqKp6cnxsbGdOvWrdhiE0IIIYQoDbQabZFtpZEMVxNFYty4cdSrV48dO3agUqmK9FwDBw4kMTGRTZs2Fel5hBCvriH9XOja0RFLCyPORCYR+M0FrsWl5Vn+t+WNKV/ONMf+Dduu89V3FwGYMNIV77q2lLEzIfWhmrORSXy78hKx1/JuVwghRMFIkiOKRHR0NCNGjKBSpUrFHYoQQjyTfj2deKtLRb5Y8A9xtx4ytJ8LX8305N0Pj5GRmfsT0GHjTmLwr7ESVZ0tWPB5XfYdvK3bF3UxmV0h8dy6/RArS2MG93Fm/sw6vD00DI0MvRdC/IdWfjAUigxXEzrr1q3D09MTMzMz7O3tad++PSkpKQAsX74cDw8PTE1NqVGjBt98802ubTwaunb37l0GDx6MQqEgODj4iec+e/YsnTp1QqVSUa5cOd577z3u3LnzxNimT5/OypUr+f3331EoFCgUCkJCQgC4evUqvXr1wsbGBjs7O958801iYmL0zpvfdT26ll9//ZUWLVpgZmZGw4YNOX/+PMeOHcPb2xuVSkWnTp24ffs2QojS6e03KrLq1yscDLtLdEwKn8//B3s7JS2alMmzTmJSJgmJj7dmDe25diONU2fv68ps/iOOiL/vczM+nfPRySz7KYZyDqY4ls3ZAySEEKJwJMkRAMTFxdGnTx8GDx5MZGQkISEh9OjRA61Wy5o1a5g6dSpffPEFkZGRzJ49m88++4yVK1fmaMfJyYm4uDisrKxYsGABcXFx9O7dO99zJyYm0rZtW7y8vDh+/Dg7d+7k1q1b9OrV64mx+fv706tXL3x9fYmLiyMuLo5mzZqRmZmJj48PlpaWHDhwgNDQUFQqFb6+vmRkZAAU+LqmTZvGp59+ysmTJzEyMqJv375MnDiRhQsXcuDAAS5evMjUqVOf05+EEKIkqVDOlDJ2So6F39PtS0lVc+58ErVrWBWoDSMjBR3blGPbnpt5ljFVGtC5vSM3bqYRfyf9meMWQpQ+MiencGS4mgCyE4msrCx69OiBs7MzAJ6enkD2l/ygoCB69OgBQJUqVTh37hxLly5lwIABeu0YGhri6OiIQqHA2toaR0fHJ557yZIleHl5MXv2bN2+H3/8EScnJ86fP09ycnKesQGYmZmRnp6ud66ffvoJjUbD8uXLUSgUAKxYsQIbGxtCQkLo2LFjga/L398fHx8fAEaPHk2fPn3Yu3cvzZs3B2DIkCEF6q0SQrx87GxNALiXmKm3/15ihu7Yk7RsUgaVhRHb9+ZMcrp3rsAHA6tibmbIlWupjPnsNFlZpfMLhxBCvEiS5AgA6tatS7t27fD09MTHx4eOHTvy1ltvYWJiQnR0NEOGDGHYsGG68llZWVhbWz+Xc0dERLBv375cFyiIjo6mY8eOucZma2ubb5sXL17E0tJSb//Dhw+Jjo4mJSWlwNdVp04d3f+XK1cO0E+yypUrR3x8fJ6xpKenk56u/2RWo87AwLBgX5CEEC9Oh1ZlmTDSTfd54swzz9zm6x0cCTuRwN2EjBzHdoXc4tipe9jbmdCneyVmfVyTDyaeynOujxDi1aWV9+QUiiQ5Asjugdm9ezeHDh1i165dLF68mClTprBlyxYAli1bRuPGjXPUeR6Sk5Pp2rUrc+fOzXGsfPnyecYWFhZGlSpV8myzQYMGrFmzJscxBwcHkpOTgYJdl7Gxse7/H/UK/XefJp/JgAEBAcyYMUNvn5PrACq7D8qzjhCieBw8epdz54/rPpsYZ4/qtrUx5u69x0mKrY0JFy8lP7G9cg5KvOvaMiXg71yPp6SqSUlN41pcGn9HJbHjf81p2bQMLH/AtAAACqlJREFUe/6SeX5CCH2aUjqsrKhIkiN0FAoFzZs3p3nz5kydOhVnZ2dCQ0OpUKECly5dol+/fkVy3vr167N+/XpcXFwwMsr9r2RusW3cuJFx48ZhYmKCWq3O0eYvv/xC2bJlsbLKOW7e2tq6yK/rkcmTJzNu3Di9fb7vhBXpOYUQTyctTc31NP2fJ3cS0vGua8vFy9kLsZibGVLTzYpN2288sb3X2zty734Gh4/dfWJZBaBQgLGxTJcVQohnJUmOACAsLIy9e/fSsWNHypYtS1hYGLdv38bDw4MZM2YwatQorK2t8fX1JT09nePHj3Pv3r0cX96fxsiRI1m2bBl9+vRh4sSJ2NnZcfHiRdauXcvy5cs5fvx4nrEBuLi48McffxAVFYW9vT3W1tb069ePL7/8kjfffJOZM2dSqVIlrly5woYNG5g4cSKVKlUq8ut6RKlUolQq9fbJUDUhXh6/bb7OgN6VuXojLXsJ6XdduJuQzoEjj1eAXPB5Hf46fIcN2x4nPgoFdG7vyM4/b6H+T2dvhXKmtG3hwLFT90hMysTBXsm7bzmRnq7h8PGEF3VpQoiXiCwhXTiS5AgArKys+Ouvv1iwYAFJSUk4OzsTFBREp06dADA3N+fLL79kwoQJWFhY4OnpyZgxY57LuStUqEBoaCgff/wxHTt2JD09HWdnZ3x9fTEwMHhibMOGDSMkJARvb2+Sk5PZt28frVu35q+//uLjjz+mR48ePHjwgIoVK9KuXTtdz87QoUOL9LqEEKXDmvVXMTU1ZKKfGyoLI86cu8/4aWf05s1UdDTDxspYr553PVscy5qybXfOBQfSMzXUrWVNrzcqYakyIiExg4i/7zNi4ikS72fmKC+EEKJwFFqtVgb4CfGCvdZ1f3GHIIQQQohCOrilVbGdu1WPQ0XW9v4NzYqs7eIiA3+FEEIIIYQQpYokOaLIjRgxApVKles2YsSI4g5PCCGEEKLE02o1RbaVRjInRxS5mTNn4u/vn+ux3FY+E0IIIYQQ4llIkiOKXNmyZSlbtmxxhyGEEEII8dLSyntyCkWSHCGEEEIIIUo4WUK6cGROjhBCCCGEEKJUkSWkhRBCiOckPT2dgIAAJk+enOMlwEIIIV4cSXKEEEKI5yQpKQlra2vu378vC6sIIUQxkuFqQgghhBBCiFJFkhwhhBBCCCFEqSJJjhBCCCGEEKJUkSRHCCGEeE6USiXTpk2TRQeEEKKYycIDQgghhBBCiFJFenKEEEIIIYQQpYokOUIIIYQQQohSRZIcIYQQQgghRKkiSY4QQgjxArm4uLBgwYIXes7WrVszZsyYF3pOIYQoTpLkCCGEKDUGDhxIt27dijsMIYQQxUySHCGEEK+czMzM4g5BCCFEEZIkRwghxEtn3bp1eHp6YmZmhr29Pe3bt2fChAmsXLmS33//HYVCgUKhICQkhJiYGBQKBb/88gutWrXC1NSUNWvWALB8+XI8PDwwNTWlRo0afPPNN7pzZGRk4OfnR/ny5TE1NcXZ2ZmAgAAAtFot06dPp3LlyiiVSipUqMCoUaOe6loSExMZOnQoDg4OWFlZ0bZtWyIiIgA4f/48CoWCf/75R6/O/PnzqVatmu7z2bNn6dSpEyqVinLlyvHee+9x586dp4pHCCFKA6PiDkAIIYQojLi4OPr06cO8efPo3r07Dx484MCBA/Tv35/Y2FiSkpJYsWIFAHZ2dty4cQOASZMmERQUhJeXly7RmTp1KkuWLMHLy4tTp04xbNgwLCwsGDBgAIsWLWLz5s38+uuvVK5cmatXr3L16lUA1q9fz/z581m7di21/q+d+wtpeo3jOP6ef4pp6nCQc2CzKDNiRGNlYQTpkHUxKGn+aRcRWWCCiJBeCEUUUZoV1I0spAuVKMMgUlJL+0tGQREEWmoIlSQkxGINHJ2LOD/whOcUJ87B+XnBD37b83u+e57djM+e5/dbu5bJyUkjmPwqv9+P2Wymp6eHtLQ0WlpaKCwsZGRkhJycHNxuN+3t7Rw7dszo097ezu7du4HvIamgoICKigrOnj1LOBymvr6ekpIS7ty582++ahGReUshR0RE5pUPHz4wMzNDcXExDocDAKfTCYDZbCYSiWCz2X7oV1NTQ3FxsfH6yJEjNDc3G+8tX76cV69e0dLSwp49e5iYmGDVqlVs2bIFk8lkfBbAxMQENpsNj8dDYmIiy5YtY+PGjb88lwcPHvDkyRM+fvzI4sWLATh9+jTXr1+ns7OTAwcOEAgEuHDhghFyRkZGePbsGW1tbQBGSDtx4oRRt7W1laysLCMoiYgsNNquJiIi88q6desoLCzE6XTi9/sJBoNMT0//Yz+3222cf/nyhdHRUfbt28eSJUuM4/jx44yOjgLfH2Lw/PlzVq9eTXV1Nb29vUZ/v99POBxmxYoV7N+/n66uLmZmZn55Li9evCAUCmG1WmeNY3x83BhHWVkZb9++5fHjx8D3VRyXy0Vubq5RY2BgYFb/P9v+rCEistBoJUdEROaV+Ph4+vr6ePToEb29vZw/f56GhgaGhob+tl9ycrJxHgqFAAgGg+Tl5f1QH8DlcjE+Pk5PTw/9/f2UlJTg8Xjo7OwkKyuL4eFh+vv76evr4+DBgzQ1NXH37l0SExN/ei6hUIjMzEwGBwd/aLNYLADYbDYKCgro6Ohg06ZNdHR0UFlZOauGz+fj1KlTP9TIzMz86bGIiMQShRwREZl3TCYT+fn55Ofnc/jwYRwOB11dXSxatIhoNPqP/TMyMrDb7YyNjREIBOa8LjU1ldLSUkpLS9m1axder5dPnz6Rnp6O2WzG5/Ph8/moqqoiNzeXly9f4nK5fnoeLpeLyclJEhISyM7OnvO6QCBAXV0d5eXljI2NUVZWNqvGtWvXyM7OJiFBP+siIqCQIyIi88zQ0BC3b9+mqKiIpUuXMjQ0xNTUFGvWrOHr16/cunWL4eFhrFYraWlpc9Y5evQo1dXVpKWl4fV6iUQiPH36lOnpaWprazlz5gyZmZmsX7+euLg4rl69is1mw2KxcOnSJaLRKHl5eSQlJdHW1obZbJ51387P8Hg8bN68mR07dtDY2EhOTg7v37/n5s2b7Ny509hiV1xcTGVlJZWVlWzbtg273W7UqKqqIhgMUl5eTl1dHenp6bx584bLly9z8eJFY2VKRGQhUcgREZF5JTU1lXv37nHu3Dk+f/6Mw+GgubmZ7du343a7GRwcxO12EwqFGBgYmHOFpKKigqSkJJqamjh06BDJyck4nU5qamoASElJobGxkdevXxMfH8+GDRvo7u4mLi4Oi8XCyZMnqa2tJRqN4nQ6uXHjBlar9ZfmYjKZ6O7upqGhgb179zI1NYXNZmPr1q1kZGQY16WkpODz+bhy5Qqtra2zatjtdh4+fEh9fT1FRUVEIhEcDgder5e4ON16KyILk+nbt2/f/u9BiIiIiIiI/C76i0dERERERGKKQo6IiMhvcv/+/VmPcv7rISIi/w1tVxMREflNwuEw7969m7N95cqV/+FoREQWLoUcERERERGJKdquJiIiIiIiMUUhR0REREREYopCjoiIiIiIxBSFHBERERERiSkKOSIiIiIiElMUckREREREJKYo5IiIiIiISExRyBERERERkZjyB2DqecyZfEGQAAAAAElFTkSuQmCC\n"},"metadata":{}}],"source":["\n","df_norm_corr=df_norm.corr()\n","df_norm_corr=df_norm_corr['stress_level'].sort_values(ascending=False)\n","df_norm_corr=df_norm_corr.drop('stress_level')\n","plt.figure(figsize=(8,6))\n","sns.heatmap(df_norm_corr.to_frame(),annot=True,cmap='coolwarm')\n","plt.title('Stress Level Correlation of Features after Normalized Scaling')\n","plt.show()"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":545},"executionInfo":{"elapsed":1377,"status":"ok","timestamp":1716216913440,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"sJKr80mdQ-2J","outputId":"8e1bb1ed-c82f-44a0-f8a2-3911b3fa347f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["\n","df_minmax_corr=df_minmax.corr()\n","df_minmax_corr=df_minmax_corr['stress_level'].sort_values(ascending=False)\n","df_minmax_corr=df_minmax_corr.drop('stress_level')\n","plt.figure(figsize=(8,6))\n","sns.heatmap(df_minmax_corr.to_frame(),annot=True,cmap='coolwarm')\n","plt.title('Stress Level Correlation of Features after minmax Scaling')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cIib4EpuaftE"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"Ed0qgUJOWFa6"},"source":["## Data based on different Stress Level Amounts"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":153,"status":"ok","timestamp":1716216918858,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"2vHmwpiYa_N0","outputId":"81ab7aaf-63aa-4757-ab20-a53fbb2b842b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Unique values now in Standardized Scaling Stress Level [ 0.00442758 1.22201077 -1.21315562]\n","Unique values now in Standardized Scaling Mental Health History [-0.98555881 1.0146528 ]\n","Unique values now in Standardized Scaling Blood Pressure [-1.41841629 0.98198051 -0.21821789]\n"]}],"source":["print('Unique values now in Standardized Scaling Stress Level',df_scaled['stress_level'].unique())\n","print('Unique values now in Standardized Scaling Mental Health History',df_scaled['mental_health_history'].unique())\n","print('Unique values now in Standardized Scaling Blood Pressure',df_scaled['blood_pressure'].unique())"]},{"cell_type":"code","execution_count":29,"metadata":{"id":"USOJtZfpWNV6","executionInfo":{"status":"ok","timestamp":1716216920003,"user_tz":300,"elapsed":2,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["high_stress=df_scaled.loc[df_scaled['stress_level']>1]\n","some_stress=df_scaled.loc[(df_scaled['stress_level']>0)&(df_scaled['stress_level']<1)]\n","no_stress=df_scaled.loc[df_scaled['stress_level']<1]\n"]},{"cell_type":"code","execution_count":30,"metadata":{"id":"WEw7lCd8R2JJ","executionInfo":{"status":"ok","timestamp":1716216920898,"user_tz":300,"elapsed":2,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["mm_high_stress=df_minmax.loc[df_minmax['stress_level']>1]\n","mm_some_stress=df_minmax.loc[(df_minmax['stress_level']>=0)&(df_scaled['stress_level']<1)]\n","mm_no_stress=df_minmax.loc[df_scaled['stress_level']<1]"]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"executionInfo":{"elapsed":166,"status":"ok","timestamp":1716216921933,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"-2Ool63gWqKk","outputId":"152d5034-caf2-4966-f57a-1d4d686a1fe6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem mental_health_history depression \\\n","count 1100.000000 1100.000000 1100.000000 1100.000000 \n","mean 0.368818 0.595372 0.016636 0.413715 \n","std 0.193923 0.287024 0.017503 0.231995 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 0.208850 0.346908 0.000000 0.212121 \n","50% 0.406671 0.697734 0.000000 0.429681 \n","75% 0.520191 0.867775 0.032564 0.616495 \n","max 0.887412 0.936586 0.084215 0.910687 \n","\n"," headache blood_pressure sleep_quality breathing_problem \\\n","count 1100.000000 1100.000000 1100.000000 1100.000000 \n","mean 0.085220 0.074124 0.091818 0.093461 \n","std 0.050624 0.033184 0.057268 0.050210 \n","min 0.000000 0.030443 0.000000 0.000000 \n","25% 0.034214 0.040681 0.033168 0.062638 \n","50% 0.084265 0.069254 0.087918 0.084854 \n","75% 0.116916 0.092981 0.133780 0.133921 \n","max 0.422577 0.253546 0.404226 0.421076 \n","\n"," noise_level living_conditions ... basic_needs academic_performance \\\n","count 1100.000000 1100.000000 ... 1100.000000 1100.000000 \n","mean 0.089740 0.086223 ... 0.094540 0.094597 \n","std 0.047104 0.041966 ... 0.051401 0.049713 \n","min 0.000000 0.000000 ... 0.000000 0.000000 \n","25% 0.062676 0.060132 ... 0.059990 0.060071 \n","50% 0.086512 0.089146 ... 0.086347 0.086027 \n","75% 0.117714 0.114582 ... 0.131769 0.132110 \n","max 0.336861 0.308901 ... 0.359908 0.304290 \n","\n"," study_load teacher_student_relationship future_career_concerns \\\n","count 1100.000000 1100.000000 1100.000000 \n","mean 0.088578 0.090413 0.089651 \n","std 0.046101 0.048203 0.052697 \n","min 0.000000 0.000000 0.000000 \n","25% 0.062862 0.057771 0.034401 \n","50% 0.082990 0.079030 0.086146 \n","75% 0.115984 0.128515 0.127057 \n","max 0.404226 0.242536 0.384615 \n","\n"," social_support peer_pressure extracurricular_activities bullying \\\n","count 1100.000000 1100.000000 1100.000000 1100.000000 \n","mean 0.064001 0.091839 0.093612 0.088320 \n","std 0.036473 0.048255 0.049483 0.052311 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 0.030861 0.062363 0.062885 0.034085 \n","50% 0.069779 0.081541 0.087289 0.094333 \n","75% 0.098110 0.123886 0.126887 0.125801 \n","max 0.129701 0.337869 0.336861 0.338062 \n","\n"," stress_level \n","count 1100.000000 \n","mean 0.034006 \n","std 0.028911 \n","min 0.000000 \n","25% 0.000000 \n","50% 0.035817 \n","75% 0.058489 \n","max 0.169031 \n","\n","[8 rows x 21 columns]"],"text/html":["\n","
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count1100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.000000...1100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.000000
mean0.3688180.5953720.0166360.4137150.0852200.0741240.0918180.0934610.0897400.086223...0.0945400.0945970.0885780.0904130.0896510.0640010.0918390.0936120.0883200.034006
std0.1939230.2870240.0175030.2319950.0506240.0331840.0572680.0502100.0471040.041966...0.0514010.0497130.0461010.0482030.0526970.0364730.0482550.0494830.0523110.028911
min0.0000000.0000000.0000000.0000000.0000000.0304430.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%0.2088500.3469080.0000000.2121210.0342140.0406810.0331680.0626380.0626760.060132...0.0599900.0600710.0628620.0577710.0344010.0308610.0623630.0628850.0340850.000000
50%0.4066710.6977340.0000000.4296810.0842650.0692540.0879180.0848540.0865120.089146...0.0863470.0860270.0829900.0790300.0861460.0697790.0815410.0872890.0943330.035817
75%0.5201910.8677750.0325640.6164950.1169160.0929810.1337800.1339210.1177140.114582...0.1317690.1321100.1159840.1285150.1270570.0981100.1238860.1268870.1258010.058489
max0.8874120.9365860.0842150.9106870.4225770.2535460.4042260.4210760.3368610.308901...0.3599080.3042900.4042260.2425360.3846150.1297010.3378690.3368610.3380620.169031
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8 rows × 21 columns

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count1100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001.100000e+031100.0000001100.0000001100.000000...1100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001100.0000001.100000e+031100.0000001100.000000
mean0.1073590.370303-0.029091-0.1399330.0065450.3636361.280000e-010.2029090.1192730.014545...0.2181820.2181820.0974550.1185450.1192730.5090910.1876362.138182e-010.093818-0.007273
std1.1652491.1926132.0006981.1447421.1274851.6671491.238706e+001.1205711.0625010.895366...1.1470091.1316751.0526251.1076631.2235001.3971021.1402121.134050e+001.2247661.643345
min-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000e+00-2.000000-2.000000-2.000000...-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000e+00-2.000000-2.000000
25%-0.857143-0.533333-2.000000-1.111111-1.200000-2.000000-1.200000e+00-0.400000-0.400000-0.400000...-0.400000-0.400000-0.400000-0.400000-1.200000-0.666667-0.400000-4.000000e-01-1.200000-2.000000
50%0.0952380.533333-2.000000-0.2222220.4000000.0000002.220446e-160.4000000.400000-0.400000...0.400000-0.400000-0.400000-0.400000-0.4000000.666667-0.4000002.220446e-160.4000000.000000
75%1.0476191.4666672.0000000.8148150.4000002.0000001.200000e+001.2000000.4000000.400000...1.2000001.2000000.4000001.2000001.2000002.0000001.2000001.200000e+001.2000002.000000
max2.0000002.0000002.0000002.0000002.0000002.0000002.000000e+002.0000002.0000002.000000...2.0000002.0000002.0000002.0000002.0000002.0000002.0000002.000000e+002.0000002.000000
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8 rows × 21 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe"}},"metadata":{},"execution_count":32}],"source":["df_minmax.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fsAZvg_nRWUe"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"vYSpOg38WP4Y"},"source":["### Three different Levels:\n","- 0 = No Stress Level Reported\n","- 1 = Some Stress Level Reported\n","- 2 - High Stress Level Reported\n","\n","#### Singled out the different levels reported and then looked at the average for each column."]},{"cell_type":"markdown","metadata":{"id":"Lz6Goj0jW246"},"source":["### Stress Level 2"]},{"cell_type":"code","execution_count":33,"metadata":{"id":"zL5ERKAZx_h5","executionInfo":{"status":"ok","timestamp":1716216928305,"user_tz":300,"elapsed":167,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["H_stress_mean=high_stress.mean()\n","H_stress_min=high_stress.min()\n","H_stress_max=high_stress.max"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"elapsed":1940,"status":"ok","timestamp":1716216936361,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"tpI4bdP9WPqL","outputId":"4b0a739f-30ea-4435-e185-32070e922487"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["sns.boxenplot(high_stress,orient='y');"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"executionInfo":{"elapsed":372,"status":"ok","timestamp":1716216939051,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"GnCMrhZabwi4","outputId":"fdfc2cfd-aca8-4820-fea1-aefe6538ac32"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem mental_health_history depression \\\n","count 369.000000 369.000000 369.000000 369.000000 \n","mean 0.872877 -1.006292 0.797828 0.941777 \n","std 0.767377 0.679338 0.622683 0.772034 \n","min -1.809328 -1.988391 -0.985559 -1.625618 \n","25% 0.643746 -1.540990 1.014653 0.575458 \n","50% 1.134361 -1.093590 1.014653 1.093358 \n","75% 1.297899 -0.534339 1.014653 1.481783 \n","max 1.624976 1.367114 1.014653 1.870208 \n","\n"," headache blood_pressure sleep_quality breathing_problem \\\n","count 369.000000 3.690000e+02 369.000000 369.000000 \n","mean 0.889701 9.819805e-01 -0.876459 0.642451 \n","std 0.787605 2.223461e-16 0.660030 0.853594 \n","min -1.780475 9.819805e-01 -1.718703 -1.966776 \n","25% 0.349125 9.819805e-01 -1.072574 0.175964 \n","50% 1.058992 9.819805e-01 -1.072574 0.890211 \n","75% 1.768859 9.819805e-01 -1.072574 1.604458 \n","max 1.768859 9.819805e-01 1.511942 1.604458 \n","\n"," noise_level living_conditions ... basic_needs academic_performance \\\n","count 369.000000 369.000000 ... 369.000000 369.000000 \n","mean 0.862469 -0.705448 ... -0.768009 -0.786082 \n","std 0.842513 0.845074 ... 0.643862 0.634992 \n","min -1.995514 -2.250991 ... -1.934764 -1.960979 \n","25% 0.264334 -1.357096 ... -1.236980 -1.253741 \n","50% 1.017616 -0.463200 ... -0.539196 -0.546502 \n","75% 1.770899 -0.463200 ... -0.539196 -0.546502 \n","max 1.770899 2.218486 ... 1.554154 1.575213 \n","\n"," study_load teacher_student_relationship future_career_concerns \\\n","count 369.000000 369.000000 369.000000 \n","mean 0.808875 -0.730753 0.949303 \n","std 0.905172 0.567927 0.793368 \n","min -1.993501 -1.913497 -1.732927 \n","25% 0.287551 -1.190927 0.883709 \n","50% 1.047901 -0.468357 0.883709 \n","75% 1.808252 -0.468357 1.537869 \n","max 1.808252 0.976783 1.537869 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count369.000000369.000000369.000000369.000000369.0000003.690000e+02369.000000369.000000369.000000369.000000...369.000000369.000000369.000000369.000000369.000000369.000000369.000000369.000000369.0000003.690000e+02
mean0.872877-1.0062920.7978280.9417770.8897019.819805e-01-0.8764590.6424510.862469-0.705448...-0.768009-0.7860820.808875-0.7307530.949303-0.9118140.9396390.9465110.9390081.222011e+00
std0.7673770.6793380.6226830.7720340.7876052.223461e-160.6600300.8535940.8425130.845074...0.6438620.6349920.9051720.5679270.7933680.2489810.8636050.8321930.7863932.223461e-16
min-1.809328-1.988391-0.985559-1.625618-1.7804759.819805e-01-1.718703-1.966776-1.995514-2.250991...-1.934764-1.960979-1.993501-1.913497-1.732927-1.796742-1.919495-1.953023-1.7103431.222011e+00
25%0.643746-1.5409901.0146530.5754580.3491259.819805e-01-1.0725740.1759640.264334-1.357096...-1.236980-1.2537410.287551-1.1909270.883709-0.8419520.8882770.8700060.9035891.222011e+00
50%1.134361-1.0935901.0146531.0933581.0589929.819805e-01-1.0725740.8902111.017616-0.463200...-0.539196-0.5465021.047901-0.4683570.883709-0.8419520.8882770.8700060.9035891.222011e+00
75%1.297899-0.5343391.0146531.4817831.7688599.819805e-01-1.0725741.6044581.770899-0.463200...-0.539196-0.5465021.808252-0.4683571.537869-0.8419521.5902201.5757631.5570711.222011e+00
max1.6249761.3671141.0146531.8702081.7688599.819805e-011.5119421.6044581.7708992.218486...1.5541541.5752131.8082520.9767831.537869-0.8419521.5902201.5757631.5570711.222011e+00
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8 rows × 21 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe"}},"metadata":{},"execution_count":36}],"source":["high_stress.describe()"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1716216940299,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"WU6_yaoqf9Ur","outputId":"b7d6e286-f83d-4a3b-cedf-bca2cacc384e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["stress_level 1.222011\n","blood_pressure 0.981981\n","future_career_concerns 0.949303\n","extracurricular_activities 0.946511\n","depression 0.941777\n","peer_pressure 0.939639\n","bullying 0.939008\n","headache 0.889701\n","anxiety_level 0.872877\n","noise_level 0.862469\n","study_load 0.808875\n","mental_health_history 0.797828\n","breathing_problem 0.642451\n","living_conditions -0.705448\n","teacher_student_relationship -0.730753\n","safety -0.757853\n","basic_needs -0.768009\n","academic_performance -0.786082\n","sleep_quality -0.876459\n","social_support -0.911814\n","self_esteem -1.006292\n","dtype: float64"]},"metadata":{},"execution_count":37}],"source":["high_stress_mean=high_stress.mean()\n","high_stress_mean=high_stress_mean.sort_values(ascending = False)\n","high_stress_mean"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"executionInfo":{"elapsed":1153,"status":"ok","timestamp":1716216948153,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"kjP9duRgbkRp","outputId":"c744166e-5aff-4b01-ff02-a95a1bb8dfc0"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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Nq1atVJeF88bPHjwS+PctGkTGRkZeHl5acRpYWGBnZ2dct3z8hrJjpaWFo0bN1bGGxUVxZ07dxg/fjxqtZpjx44Bz2aza9WqlWU2rVu3bhozwpklGJnvk7t373Lw4EG8vLyU19vt27e5c+cO7u7uREdH8/fff2v0OXDgQI3vtYuLC+np6dy4cSNPY6patSrm5ubY2trSt29fqlSpwq5du5SZ0sjISKKjo+nZsyd37txRYnr48CEtWrTgt99+y1Ia9OL3e/jw4cD/vd/z+76vWLEi7u7ueRpPbmrUqKFcc3g2g1y1alWNn1O7d+/G2dlZY6nPkiVLKmUZL9O3b192796Nm5sb4eHhTJkyBRcXF+zs7Dh69Gi+4h0yZIjG87z+XMl83W3fvp2nT59m27eZmRkPHz7UKCksKHmN08TEhDZt2rB+/XqN/0atW7eORo0aYW1tDeT9ff06pk+fzv79+5kxY0aBzIJnR8pChHiPmJub07JlS9asWcOjR49IT0+nS5cu2baNjo7m/v37Su3fi56/4eaPP/7gq6++4uDBgyQlJWm0ezEhq1ChQpba1xIlSnDhwoVcY79x4wZaWlpUqVJFY7uFhQVmZmZ5ThCeV7FiRUaNGsW8efNYvXo1Li4ufPLJJ3z22WdKUhUTE4OWlhY1atTIta/8XIPn5fU6Z47Pzs5OY7+5ublGEpaTmJgYypUrp5GgZiciIoLJkydz7NixLMnk/fv3cyyDyBQdHc2FCxeUOtEXPT8eS0tLJTHL9OL3N3PcVatWzdJX9erV2bNnT5YbuCpWrJhrjJlxqtXqLNczU2bpRV5eIzlxcXFRyliOHDmCpaUljo6O1KlThyNHjtCqVSvCw8Px8vLKcmxmspAp83ucWYd69epV1Go1EydOZOLEidmePyEhgfLly+e5z5fZuHEjJiYm3Lp1i0WLFnH9+nWNP8aio6MBsi3tyXT//n2N1+uL179y5cpoaWkpJSf5fd/n5XufFy9eK3h2vZ6/Vjdu3MDZ2TlLuxdjzY27uzvu7u48evSIM2fOsG7dOr777jvat2/P5cuXc/y58LxixYplKSvJ688VV1dXOnfuTGBgIPPnz8fNzY0OHTrQs2dPpbxl6NChrF+/XlkysHXr1nh5eeHh4ZHnceYkP79nunXrxpYtWzh27BiNGzcmJiaGM2fOKKVmmf3l5X39qtatW8dXX31Fv379svxBU5AkuRbiPdOzZ08GDBjAv//+S5s2bXL8yzsjI4MyZcqwevXqbPdnJk+JiYm4urpiYmLC119/TeXKldHT0+Ps2bOMGzcuy0xVTnfmq/O4pFFBfxDI3Llz8fHxYevWrezduxc/Pz+l/jOnOsgX5fcaPC+v1/ltiImJoUWLFlSrVo158+ZhZWWFjo4OO3fuZP78+bmOI1NGRgatWrVi7Nix2e63t7cv6LCzyG11iEwZGRmoVCp27dqV7WvSyMhI+fpVXyMff/wxT58+5dixYxw5ckSZCXVxceHIkSNcvnyZW7duacyQZnrZ+yTze+Hv75/jTO2LSd7rvveaNm2q1Jh6enri4OCAt7c3Z86cQUtLS4lp9uzZOX5w0/PXNTs5vb/z+r7Py/c+L173WuWXgYEBLi4uuLi4ULp0aQIDA9m1a1euf6hk0tXVzfJftbz+XFGpVGzYsIHjx4/z66+/smfPHvr27cvcuXM5fvw4RkZGlClThsjISPbs2cOuXbvYtWsXwcHB9OrVi9DQ0Ncad35+/nl6emJgYMD69etp3Lgx69evR0tLi65du2r0l9f3dX7t27ePXr160a5dO7777rtX7icvJLkW4j3TsWNHBg0axPHjx3O9kaly5crs37+fJk2a5PoLKywsjDt37rBp0yaNm7KuX79eoHHb2NiQkZFBdHQ01atXV7b/999/JCYmvtY6tQ4ODjg4OPDVV19x9OhRmjRpwnfffcfUqVOpXLkyGRkZXLp0KceE4XWuQV6vc+b4oqOjNe6av3XrVp5mHitXrsyePXu4e/dujrPXv/76KykpKWzbtk1j5i67f6XmlOxUrlyZ5OTkly5PZWNjw6FDh3j06JHG7PXVq1eztINnN6q96PLly5QuXfqVlh2rXLkyarWaihUr5inhz+01khMnJyd0dHQ4cuQIR44cYcyYMcCzJHX58uUcOHBAeZ5fma+B4sWLv5GlwF7GyMiIyZMn06dPH9avX0/37t2VmwRNTEzyHFN0dLTGbPPVq1fJyMhQblItqPf9m/h0ThsbmyyvV8j6Gs6vzJuL4+PjgVeLPa8/VzI1atSIRo0aMW3aNNasWYO3tzdr166lf//+wLPyPU9PTzw9PcnIyGDo0KEsW7aMiRMn5mum/nXiNDQ0pH379vzyyy/MmzePdevW4eLiQrly5TT6y8/7Oq9OnDhBx44dadCgAevXr3/jy3pKzbUQ7xkjIyOWLl1KQEAAnp6eObbz8vIiPT2dKVOmZNmXlpamLDGWOTvw/IxOamoq3377bYHGnflBMM//CxBg3rx5ALRr1y7ffSYlJZGWlqaxzcHBAS0tLWWZrw4dOqClpcXXX3+dZeY2c8yvcw3yep1btmxJ8eLFWbx4scZ5XrweOencuTNqtZrAwMAs+3Ibx/379wkODs5yjKGhYbafzufl5cWxY8fYs2dPln2JiYnK9XZ3d+fp06caH5aRkZHBN998o3GMpaUldevWJTQ0VON8v//+O3v37n3lDwjq1KkT2traBAYGZpmNVKvV3LlzB8jbayQnenp6NGzYkJ9//pm4uDiNmevHjx+zaNEiKleujKWlZb7jL1OmDG5ubixbtkxJwp73Okvs5ZW3tzcVKlRQVpCoX78+lStXZs6cOSQnJ+cpphe/34sXLwZQVg4qqPd9Tq/X1+Hu7s6xY8eIjIxUtt29ezfHWdgXZf5x9aLMOvPMUqjMPz7zE39ef67cu3cvy+s/cxIh8/Wd+V7IpKWlpaxW8rL3QEHFmalbt278888/rFixgvPnz9OtWzeN/Xl9X+dHVFQU7dq1w9bWlu3btxfYf0dyIzPXQryH8vKvRldXVwYNGkRQUBCRkZG0bt2a4sWLEx0dzS+//MLChQvp0qULjRs3pkSJEvTu3Rs/Pz9UKhWrVq0q8H+f1qlTh969e/P9998rZRgnT54kNDSUDh060KxZs3z3efDgQXx9fenatSv29vakpaWxatUqtLW16dy5M/DsX+tffvmlcrNRp06d0NXV5dSpU5QrV46goKDXugZ5vc7m5ub4+/sTFBRE+/btadu2LefOnWPXrl15Wg6qWbNmfP755yxatIjo6Gg8PDzIyMjgyJEjNGvWDF9fX1q3bq3MUA0aNIjk5GSWL19OmTJlsiRw9evXZ+nSpUydOpUqVapQpkwZmjdvzpgxY9i2bRvt27dXli57+PAhFy9eZMOGDcTGxlK6dGk6dOiAk5MTo0eP5urVq1SrVo1t27Zx9+5dQHO2bvbs2bRp0wZnZ2f69eunLMVnamqqrPebX5UrV2bq1KlMmDCB2NhYOnTogLGxMdevX2fz5s0MHDgQf3//PL1GcuPi4sKMGTMwNTXFwcEBeJYYV61alStXrijrLb+Kb775ho8//hgHBwcGDBhApUqV+O+//zh27Bh//fUX58+ff+W+86J48eKMGDGCMWPGsHv3bjw8PFixYgVt2rShZs2a9OnTh/Lly/P3339z6NAhTExM+PXXXzX6uH79Op988gkeHh4cO3aMn376iZ49e1KnTh2g4N739evXZ//+/cybN49y5cpRsWJFPvroo9ca/9ixY/npp59o1aoVw4cPV5bis7a25u7duy+dcf7000+pWLEinp6eVK5cmYcPH7J//35+/fVXGjZsqEx+6OvrU6NGDdatW4e9vT0lS5akVq1a1KpVK8e+8/pzJTQ0lG+//ZaOHTtSuXJlHjx4wPLlyzExMVH+sOnfvz93796lefPmVKhQgRs3brB48WLq1q2r8d+EnJw+fTrb//C4ubnlOc5MmWt5+/v7Z/sezOv7Oq8ePHiAu7s79+7dY8yYMVluoK1cuXK2dfev7Y2sQSKEKDDPL8WXmxeX4sv0/fffq+vXr6/W19dXGxsbqx0cHNRjx45V//PPP0qbiIgIdaNGjdT6+vrqcuXKqceOHaves2ePGlAfOnRIaefq6qquWbNmlnPkdUmkp0+fqgMDA9UVK1ZUFy9eXG1lZaWeMGGCxlJkmf3lZSm+a9euqfv27auuXLmyWk9PT12yZEl1s2bN1Pv378/S9scff1TXq1dPraurqy5RooTa1dVVvW/fvnxfg5zGmpfrnJ6erg4MDFRbWlqq9fX11W5uburff/9dbWNj89Kl+NRqtTotLU09e/ZsdbVq1dQ6Ojpqc3NzdZs2bdRnzpxR2mzbtk1du3ZttZ6entrW1lY9c+ZM9Y8//phlabF///1X3a5dO7WxsbEa0FiW78GDB+oJEyaoq1SpotbR0VGXLl1a3bhxY/WcOXPUqampSrtbt26pe/bsqTY2NlabmpqqfXx81BEREWpAvXbtWo3Y9+/fr27SpIlaX19fbWJiovb09FRfunRJo03m8m63bt3KMvYXl+LLtHHjRvXHH3+sNjQ0VBsaGqqrVaumHjZsmPrKlStqtTp/r5Hs7NixQw2o27Rpo7G9f//+akD9ww8/aGzPXIpv9uzZWfoim+XYYmJi1L169VJbWFioixcvri5fvry6ffv26g0bNihtcvoZcOjQoSyvz+zkdl3v37+vNjU11fj+nzt3Tt2pUyd1qVKl1Lq6umobGxu1l5eX+sCBA1n6vHTpkrpLly5qY2NjdYkSJdS+vr7qx48fa5wjr+/7nH6GqdVq9eXLl9VNmzZV6+vrayxdmdNSfNn14+rqmmX5yXPnzqldXFzUurq66goVKqiDgoLUixYtUgPqf//9N9tYMv3888/q7t27qytXrqzW19dX6+npqWvUqKH+8ssv1UlJSRptjx49qq5fv75aR0dH43Xwsp91L/u5cvbsWXWPHj3U1tbWal1dXXWZMmXU7du3V58+fVrpY8OGDerWrVury5Qpo9bR0VFbW1urBw0apI6Pj891fGr1s9dsTo8pU6bkOc7neXt7K0tL5uRl7+vMa/ey3zuZ78ecHnn5ufsqVGr1G6ruF0II8cHZsmULHTt2JDw8nCZNmhR2OOINCQgIIDAwkFu3br25D+IoJCNHjmTZsmUkJycX2keri/eb1FwLIYR4JY8fP9Z4np6ezuLFizExMcHR0bGQohIi7158Dd+5c4dVq1bx8ccfS2ItXpnUXAshhHglw4cP5/Hjxzg7O5OSksKmTZs4evQo06dPfys3DQnxupydnXFzc6N69er8999//PDDDyQlJeW49rgQeSHJtRBCiFfSvHlz5s6dy/bt23ny5AlVqlRh8eLF+Pr6FnZoQuRJ27Zt2bBhA99//z0qlQpHR0d++OGHV1peUYhMUnMthBBCCCFEAZGaayGEEEIIIQqIJNdCCCGEEEIUEKm5FuIty8jI4J9//sHY2PiNfKSvEEIIIQqeWq3mwYMHlCtXDi2tnOenJbkW4i37559/sLKyKuwwhBBCCPEKbt68SYUKFXLcL8m1EG+ZsbEx8OzNaWJiUsjRCPHhavrVz4UdghDiDfhtao830m9SUhJWVlbK7/GcSHItxFuWWQpiYmIiybUQhUhbV9biFqIoetO/W19W0ik3NAohhBBCCFFAJLkWRYqtrS0LFix4q+d0c3Nj5MiRb/WcQgghhHg3SXIt8szHx4cOHToUdhhCCCGEEO8sSa5FgXv69GlhhyCEEEIIUSgkuRZZbNiwAQcHB/T19SlVqhQtW7ZkzJgxhIaGsnXrVlQqFSqVirCwMGJjY1GpVKxbtw5XV1f09PRYvXo1ACtWrKB69ero6elRrVo1vv32W+Ucqamp+Pr6YmlpiZ6eHjY2NgQFBQHP1pEMCAjA2toaXV1dypUrh5+f3yuNJTExkf79+2Nubo6JiQnNmzfn/PnzAPz555+oVCouX76sccz8+fOpXLmy8vz333+nTZs2GBkZUbZsWT7//HNu376d5xhSUlJISkrSeAghhBCiaJLkWmiIj4+nR48e9O3bl6ioKMLCwujUqROTJ0/Gy8sLDw8P4uPjiY+Pp3Hjxspx48ePZ8SIEURFReHu7s7q1auZNGkS06ZNIyoqiunTpzNx4kRCQ0MBWLRoEdu2bWP9+vVcuXKF1atXY2trC8DGjRuZP38+y5YtIzo6mi1btuDg4PBK4+natSsJCQns2rWLM2fO4OjoSIsWLbh79y729vY0aNBA+WMg0+rVq+nZsyfwLDlv3rw59erV4/Tp0+zevZv//vsPLy+vPMcQFBSEqamp8pA1roUQQoiiS5biExri4+NJS0ujU6dO2NjYACiJrb6+PikpKVhYWGQ5buTIkXTq1El5PnnyZObOnatsq1ixIpcuXWLZsmX07t2buLg47Ozs+Pjjj1GpVMq5AOLi4rCwsKBly5YUL14ca2trnJyc8j2W8PBwTp48SUJCArq6ugDMmTOHLVu2sGHDBgYOHIi3tzdLlixhypQpwLPZ7DNnzvDTTz8BsGTJEurVq8f06dOVfn/88UesrKz4888/sbe3f2kcEyZMYNSoUcrzzHUyhRBCCFH0yMy10FCnTh1atGiBg4MDXbt2Zfny5dy7d++lxzVo0ED5+uHDh8TExNCvXz+MjIyUx9SpU4mJiQGe3RwZGRlJ1apV8fPzY+/evcrxXbt25fHjx1SqVIkBAwawefNm0tLS8j2W8+fPk5ycTKlSpTTiuH79uhJH9+7diY2N5fjx48CzWWtHR0eqVaum9HHo0CGN4zP3ZfbxMrq6usqa1rK2tRBCCFG0ycy10KCtrc2+ffs4evQoe/fuZfHixXz55ZecOHEi1+MMDQ2Vr5OTkwFYvnw5H330UZb+ARwdHbl+/Tq7du1i//79eHl50bJlSzZs2ICVlRVXrlxh//797Nu3j6FDhzJ79mwOHz5M8eLF8zyW5ORkLC0tCQsLy7LPzMwMAAsLC5o3b86aNWto1KgRa9asYciQIRp9eHp6MnPmzCx9WFpa5jkWIYQQQnwYJLkWWahUKpo0aUKTJk2YNGkSNjY2bN68GR0dHdLT0196fNmyZSlXrhzXrl3D29s7x3YmJiZ069aNbt260aVLFzw8PLh79y4lS5ZEX18fT09PPD09GTZsGNWqVePixYs4OjrmeRyOjo78+++/FCtWTKnnzo63tzdjx46lR48eXLt2je7du2v0sXHjRmxtbSlWTN4uQgghhMidZAtCw4kTJzhw4ACtW7emTJkynDhxglu3blG9enWePHnCnj17uHLlCqVKlcLU1DTHfgIDA/Hz88PU1BQPDw9SUlI4ffo09+7dY9SoUcybNw9LS0vq1auHlpYWv/zyCxYWFpiZmRESEkJ6ejofffQRBgYG/PTTT+jr62vUZedFy5YtcXZ2pkOHDsyaNQt7e3v++ecfduzYQceOHZVSlk6dOjFkyBCGDBlCs2bNKFeunNLHsGHDWL58OT169GDs2LGULFmSq1evsnbtWlasWKHMxAshhBBCgCTX4gUmJib89ttvLFiwgKSkJGxsbJg7dy5t2rShQYMGhIWF0aBBA5KTkzl06FCOM8L9+/fHwMCA2bNnM2bMGAwNDXFwcFA+ydDY2JhZs2YRHR2NtrY2DRs2ZOfOnWhpaWFmZsaMGTMYNWoU6enpODg48Ouvv1KqVKl8jUWlUrFz506+/PJL+vTpw61bt7CwsKBp06aULVtWaWdsbIynpyfr16/nxx9/1OijXLlyREREMG7cOFq3bk1KSgo2NjZ4eHigpSW3LAghhBBCk0qtVqsLOwghPiRJSUmYmppy//59ublRiEJUf8zKwg5BCPEGnJnd6430m9ff3zJzLYQQ4oP0pn4BCyE+bPJ/bfHeOHLkiMaSeC8+hBBCCCEKm8xci/dGgwYNiIyMLOwwhBBCCCFyJMm1eG/o6+tTpUqVwg5DCFFESM21ENmTkqnXI2UhQgghhBBCFBBJrt8zbm5uynJ22bG1tWXBggVvPA6VSsWWLVve+HmEEEIIId4nklwLIYQQQghRQCS5Fu8ctVpNWlpaYYeh4V2MSQghhBDvHkmu30NpaWn4+vpiampK6dKlmThxIjl9FlBcXByffvopRkZGmJiY4OXlxX///afRZunSpVSuXBkdHR2qVq3KqlWrNPZHR0fTtGlT9PT0qFGjBvv27ctzrLGxsahUKtauXUvjxo3R09OjVq1aHD58WGkTFhaGSqVi165d1K9fH11dXcLDw8nIyCAoKIiKFSuir69PnTp12LBhg3LcvXv38Pb2xtzcHH19fezs7AgODgYgNTUVX19fLC0t0dPTw8bGhqCgII2Ynl95JDExEZVKRVhY2GvFJIQQQogPm6wW8h4KDQ2lX79+nDx5ktOnTzNw4ECsra0ZMGCARruMjAwlsT58+DBpaWkMGzaMbt26KUnk5s2bGTFiBAsWLKBly5Zs376dPn36UKFCBZo1a0ZGRgadOnWibNmynDhxgvv37+da852TMWPGsGDBAmrUqMG8efPw9PTk+vXrGh9pPn78eObMmUOlSpUoUaIEQUFB/PTTT3z33XfY2dnx22+/8dlnn2Fubo6rqysTJ07k0qVL7Nq1i9KlS3P16lUeP34MwKJFi9i2bRvr16/H2tqamzdvcvPmzXzHnd+YspOSkkJKSoryPCkpKd9xCCGEEOL9IMn1e8jKyor58+ejUqmoWrUqFy9eZP78+VmS6wMHDnDx4kWuX7+OlZUVACtXrqRmzZqcOnWKhg0bMmfOHHx8fBg6dCgAo0aN4vjx48yZM4dmzZqxf/9+Ll++zJ49eyhXrhwA06dPp02bNvmK2dfXl86dOwPPZsp3797NDz/8wNixY5U2X3/9Na1atQKeJaTTp09n//79ODs7A1CpUiXCw8NZtmwZrq6uxMXFUa9ePRo0aAA8u5kzU1xcHHZ2dnz88ceoVCpsbGzyFe+rxpSdoKAgAgMDX+n8QgghhHi/SFnIe6hRo0aoVCrlubOzM9HR0aSnp2u0i4qKwsrKSkmsAWrUqIGZmRlRUVFKmyZNmmgc16RJE439VlZWSmKdeb78ev6YYsWK0aBBA+UcmTKTZICrV6/y6NEjWrVqpfEpjCtXriQmJgaAIUOGsHbtWurWrcvYsWM5evSocryPjw+RkZFUrVoVPz8/9u7dm++YXyWm7EyYMIH79+8rj1eZQRdCCCHE+0FmrsU7w9DQUPk6OTkZgB07dlC+fHmNdrq6ugC0adOGGzdusHPnTvbt20eLFi0YNmwYc+bMwdHRkevXr7Nr1y7279+Pl5cXLVu2ZMOGDWhpPfub8vk69adPnxZITNnR1dXNdb8QQgghig6ZuX4PnThxQuP58ePHsbOzQ1tbW2N79erVs9QaX7p0icTERGrUqKG0iYiI0DguIiJCY//NmzeJj4/XOF9+PX9MWloaZ86coXr16jm2r1GjBrq6usTFxVGlShWNx/Mz8ebm5vTu3ZuffvqJBQsW8P333yv7TExM6NatG8uXL2fdunVs3LiRu3fvYm5uDqAxprx8rHpeYxJCCCHEh0tmrt9DcXFxjBo1ikGDBnH27FkWL17M3Llzs7Rr2bIlDg4OeHt7s2DBAtLS0hg6dCiurq5KucOYMWPw8vKiXr16tGzZkl9//ZVNmzaxf/9+pQ97e3t69+7N7NmzSUpK4ssvv8x3zN988w12dnZUr16d+fPnc+/ePfr27Ztje2NjY/z9/fniiy/IyMjg448/5v79+0RERGBiYkLv3r2ZNGkS9evXp2bNmqSkpLB9+3YlYZ83bx6WlpbUq1cPLS0tfvnlFywsLDAzM0NLS4tGjRoxY8YMKlasSEJCAl999dVLx5CXmIQQQgjxYZPk+j3Uq1cvHj9+jJOTE9ra2owYMYKBAwdmaadSqdi6dSvDhw+nadOmaGlp4eHhweLFi5U2HTp0YOHChcyZM4cRI0ZQsWJFgoODcXNzA0BLS4vNmzfTr18/nJycsLW1ZdGiRXh4eOQr5hkzZjBjxgwiIyOpUqUK27Zto3Tp0rkeM2XKFMzNzQkKCuLatWuYmZnh6OjI//73PwB0dHSYMGECsbGx6Ovr4+Liwtq1a4FnifCsWbOIjo5GW1ubhg0bsnPnTqUk5Mcff6Rfv37Ur1+fqlWrMmvWLFq3bv3ScbwsJiGEEEJ82FTqnBZIFqIAxMbGUrFiRc6dO0fdunULO5x3QlJSEqampty/fx8TE5PCDkeID1b9MSsLOwQh3klnZvcq7BDeSXn9/S0z10IIIT5IkkAIId4EuaFRvJbp06drLEv3/CO/a2ELIYQQQrzvpCxEvJa7d+9y9+7dbPfp6+tnWbJOSFmIEEII8T6SshDxVpQsWZKSJUsWdhhCCCGEEO8ESa6FEEJ8kOSGRvEhk3sO3hypuRZCCCGEEKKAFKnkWq1WM3DgQEqWLIlKpcrTp+4JIYQQQghRUIpUcr17925CQkLYvn078fHx1KpV66XHqFQqtmzZ8uaDE0IIIYQQRV6RqrmOiYnB0tKSxo0bv/Vzp6amoqOj89bPm5s3GdPTp08pXrz4G+m7sKjVatLT0ylWrEi9LYQQQgjxFhWZmWsfHx+GDx9OXFwcKpUKW1tbbG1tWbBggUa7unXrEhAQAICtrS0AHTt2VI7J7KtDhw4ax40cOVL5SHAANzc3fH19GTlyJKVLl8bd3R2A33//nTZt2mBkZETZsmX5/PPPuX37dp7GkJGRwaxZs6hSpQq6urpYW1szbdo0Zf+4ceOwt7fHwMCASpUqMXHiRJ4+farsDwgIoG7duqxYsYKKFSuip6cHQGJiIv3798fc3BwTExOaN2/O+fPnNc69detWHB0d0dPTo1KlSgQGBpKWlqbsV6lULF26lE8++QRDQ0ONuHLyxx9/0L59e0xMTDA2NsbFxYWYmBhlrF9//TUVKlRAV1eXunXrsnv3buXY2NhYVCoVmzZtolmzZhgYGFCnTh2OHTumcY6IiAjc3NwwMDCgRIkSuLu7c+/ePeUcQUFBVKxYEX19ferUqcOGDRuUY8PCwlCpVOzatYv69eujq6tLeHg4bm5u+Pn5MXbsWEqWLImFhYXymoFnSXhAQADW1tbo6upSrlw5/Pz8Xno9hBBCCFH0FZnkeuHChUqyFh8fz6lTp156TGab4ODgPB/zvNDQUHR0dIiIiOC7774jMTGR5s2bU69ePU6fPs3u3bv577//8PLyylN/EyZMYMaMGUycOJFLly6xZs0aypYtq+w3NjYmJCSES5cusXDhQpYvX878+fM1+rh69SobN25k06ZNSs15165dSUhIYNeuXZw5cwZHR0datGihrE995MgRevXqxYgRI7h06RLLli0jJCQkSwIdEBBAx44duXjxIn379s11LH///TdNmzZFV1eXgwcPcubMGfr27ask7AsXLmTu3LnMmTOHCxcu4O7uzieffEJ0dLRGP19++SX+/v5ERkZib29Pjx49lD4iIyNp0aIFNWrU4NixY4SHh+Pp6Ul6ejoAQUFBrFy5ku+++44//viDL774gs8++4zDhw9rnGP8+PHMmDGDqKgoateuDTz73hoaGnLixAlmzZrF119/zb59+wDYuHEj8+fPZ9myZURHR7NlyxYcHBxyvBYpKSkkJSVpPIQQQghRNBWZ/3+bmppibGyMtrY2FhYWeTrG3NwcADMzszwf8zw7OztmzZqlPJ86dSr16tVj+vTpyrYff/wRKysr/vzzT+zt7XPs68GDByxcuJAlS5bQu3dvACpXrszHH3+stPnqq6+Ur21tbfH392ft2rWMHTtW2Z6amsrKlSuVsYWHh3Py5EkSEhLQ1dUFYM6cOWzZsoUNGzYwcOBAAgMDGT9+vHLeSpUqMWXKFMaOHcvkyZOVvnv27EmfPn3ydG2++eYbTE1NWbt2rVI+8vz458yZw7hx4+jevTsAM2fO5NChQyxYsIBvvvlGaefv70+7du0ACAwMpGbNmly9epVq1aoxa9YsGjRowLfffqu0r1mzJvAsoZ0+fTr79+/H2dlZGVd4eDjLli3D1dVVOebrr7+mVatWGvHXrl1bGbudnR1LlizhwIEDtGrViri4OCwsLGjZsiXFixfH2toaJyenHK9FUFAQgYGBebpuQgghhHi/FZnkujDUr19f4/n58+c5dOgQRkZGWdrGxMTkmlxHRUWRkpJCixYtcmyzbt06Fi1aRExMDMnJyaSlpWX5hCAbGxslsc6MKTk5mVKlSmm0e/z4sVKicf78eSIiIjRmqtPT03ny5AmPHj3CwMAAgAYNGuQY24siIyNxcXHJti47KSmJf/75hyZNmmhsb9KkSZZylcyZZABLS0sAEhISqFatGpGRkXTt2jXb81+9epVHjx5lSZpTU1OpV6+exrbsxvX8eTPPnZCQADz7T8CCBQuoVKkSHh4etG3bFk9PzxxrtSdMmMCoUaM0xm9lZZVtWyGEEEK834p0cq2lpcWLn+7+fI3y6x5naGio8Tw5ORlPT09mzpyZpW1mYpgTfX39XPcfO3YMb29vAgMDcXd3V2aF586d+9KYLC0tCQsLy9KnmZmZ0iYwMJBOnTplaZNZt51d37l52Xjy6vnkXKVSAc9qqV92juTkZAB27NiR5SPYM2fwM2U3rhf/KFCpVMp5raysuHLlCvv372ffvn0MHTqU2bNnc/jw4Wz/mNDV1c1yTiGEEEIUTUU6uTY3Nyc+Pl55npSUxPXr1zXaFC9eXKnRff6433//XWNbZGTkS1fHcHR0ZOPGjdja2uZ7xQk7Ozv09fU5cOAA/fv3z7L/6NGj2NjY8OWXXyrbbty48dJ+HR0d+ffffylWrJhyw2Z2ba5cuUKVKlXyFXNuateuTWhoaLaripiYmFCuXDkiIiI0yjMiIiJyLa/I7hwHDhzItuSiRo0a6OrqEhcXp3GOgqKvr4+npyeenp4MGzaMatWqcfHiRRwdHQv8XEIIIYR4fxSZGxqz07x5c1atWsWRI0e4ePEivXv3RltbW6ONra0tBw4c4N9//1VWmWjevDmnT59m5cqVREdHM3ny5CzJdnaGDRvG3bt36dGjB6dOnSImJoY9e/bQp0+fLAn8i/T09Bg3bhxjx45l5cqVxMTEcPz4cX744QfgWfIdFxfH2rVriYmJYdGiRWzevPmlMbVs2RJnZ2c6dOjA3r17iY2N5ejRo3z55ZecPn0agEmTJrFy5UoCAwP5448/iIqKYu3atRo13vnl6+tLUlIS3bt35/Tp00RHR7Nq1SquXLkCwJgxY5g5cybr1q3jypUrjB8/nsjISEaMGJHnc0yYMIFTp04xdOhQLly4wOXLl1m6dCm3b9/G2NgYf39/vvjiC0JDQ4mJieHs2bMsXryY0NDQVx4XQEhICD/88AO///47165d46effkJfXx8bG5vX6lcIIYQQ778inVxPmDABV1dX2rdvT7t27ejQoQOVK1fWaDN37lz27duHlZWVUovr7u7OxIkTGTt2LA0bNuTBgwf06tXrpefLnI1NT0+ndevWODg4MHLkSMzMzNDSevmlnjhxIqNHj2bSpElUr16dbt26KXW+n3zyCV988QW+vr7UrVuXo0ePMnHixJf2qVKp2LlzJ02bNqVPnz7Y29vTvXt3bty4oaxE4u7uzvbt29m7dy8NGzakUaNGzJ8//7WSxVKlSnHw4EGSk5NxdXWlfv36LF++XJnF9vPzY9SoUYwePRoHBwd2797Ntm3bsLOzy/M57O3t2bt3L+fPn8fJyQlnZ2e2bt2q/NdgypQpTJw4kaCgIKpXr46Hhwc7duygYsWKrzwueFZOs3z5cpo0aULt2rXZv38/v/76a5a6diGEEEJ8eFTqF4uLhRBvVFJSEqampty/fz/LDalCiLen/piVhR2CEIXmzOyXTxoKTXn9/V2ka66FEEKInEhyIYR4E4p0Wci7JC4uDiMjoxwfcXFxhR1ivgwePDjHsQwePLiwwxNCCCGEKBRSFvKWpKWlERsbm+P+V1lhpDAlJCTk+EmDJiYmlClT5i1H9P6QshAhhBDi/SNlIe+YYsWKFehSd4WtTJkykkALId5rUnMtPlRSEvVmSVmIEEIIIYQQBUSSayGEEEIIIQqIJNfvODc3N0aOHFlg/QUEBFC3bt0C6+9tym/sISEhyke850alUrFly5ZXjksIIYQQIlORT67DwsJQqVQkJiYWdiivZNOmTUyZMqWww3jrskt4/f39OXDgQJ776NatG3/++afyPKfkPD4+njZt2rxqqEIIIYQQCrmh8f9LTU1FR0enUM799OlT5ZMLX4ynZMmShRJTbrKL923IXOovr/T19dHX139pOwsLi9cJSwghhBBC8V7MXGdkZBAUFETFihXR19enTp06bNiwAbVaTcuWLXF3dydzRcG7d+9SoUIFJk2aRGxsLM2aNQOgRIkSqFQqfHx8gGflFr6+vowcOZLSpUvj7u4OwLx583BwcMDQ0BArKyuGDh1KcnKyRjwRERG4ublhYGBAiRIlcHd35969e8CzJfUWLFig0b5u3boEBAQoz1UqFUuXLuWTTz7B0NCQadOmKbOqK1asoGLFiujp6SlxPl8WkpKSwrhx47CyskJXV5cqVarwww8/ANmXQWzZsgWVSpXjtT116hStWrWidOnSmJqa4urqytmzZzXaZBdvbtLT0+nXr5/y/apatSoLFy7M0u7HH3+kZs2a6OrqYmlpia+vr3INATp27IhKpVKePz/zvHfvXvT09LL8R2LEiBE0b948y/UICQkhMDCQ8+fPo1KpUKlUhISEKON7fpb85s2beHl5YWZmRsmSJfn00081llEMCwvDyckJQ0NDzMzMaNKkCTdu3MjxeqSkpJCUlKTxEEIIIUTR9F4k10FBQaxcuZLvvvuOP/74gy+++ILPPvuM3377jdDQUE6dOsWiRYuAZx9uUr58eSZNmoSVlRUbN24E4MqVK8THx2skeaGhoejo6BAREcF3330HgJaWFosWLeKPP/4gNDSUgwcPMnbsWOWYyMhIWrRoQY0aNTh27Bjh4eF4enqSnp6erzEFBATQsWNHLl68SN++fQG4evUqGzduZNOmTURGRmZ7XK9evfj5559ZtGgRUVFRLFu2LF+zuS968OABvXv3Jjw8nOPHj2NnZ0fbtm158ODBS+PNSUZGBhUqVOCXX37h0qVLTJo0if/973+sX79eabN06VKGDRvGwIEDuXjxItu2bVOWKjx16hQAwcHBxMfHK8+f16JFC8zMzJTvLzxL6tetW4e3t3eW9t26dWP06NHUrFmT+Ph44uPj6datW5Z2T58+xd3dHWNjY44cOUJERARGRkZ4eHiQmppKWloaHTp0wNXVlQsXLnDs2DEGDhyY6x8wQUFBmJqaKg8rK6tcr58QQggh3l/vfFlISkoK06dPZ//+/Tg7OwNQqVIlwsPDWbZsGWvWrGHZsmX06tWLf//9l507d3Lu3DnlA1kyyyrKlCmTZVbXzs6OWbNmaWx7fpbY1taWqVOnMnjwYL799lsAZs2aRYMGDZTnADVr1sz3uHr27EmfPn00tqWmprJy5UrMzc2zPebPP/9k/fr17Nu3j5YtWyrX4nVkzvJm+v777zEzM+Pw4cO0b98+13hzUrx4cQIDA5XnFStW5NixY6xfvx4vLy8Apk6dyujRoxkxYoTSrmHDhgDK+M3MzHIs2dDW1qZ79+6sWbOGfv36AXDgwAESExPp3Llzlvb6+voYGRlRrFixXMtA1q1bR0ZGBitWrFAS5uDgYMzMzAgLC6NBgwbcv3+f9u3bU7lyZQCqV6+e6/WYMGECo0aNUp4nJSVJgi2EEEIUUe98cn316lUePXpEq1atNLanpqZSr149ALp27crmzZuZMWMGS5cuxc7OLk99169fP8u2/fv3ExQUxOXLl0lKSiItLY0nT57w6NEjDAwMiIyMpGvXrq89rgYNGmTZZmNjk2NiDc9mzbW1tXF1dX3t82f677//+OqrrwgLCyMhIYH09HQePXqU5ePYs4s3N9988w0//vgjcXFxPH78mNTUVKWkIyEhgX/++YcWLVq8Vuze3t40atSIf/75h3LlyrF69WratWuXpxVCcnL+/HmuXr2KsbGxxvYnT54QExND69at8fHxwd3dnVatWtGyZUu8vLywtLTMsU9dXV10dXVfOSYhhBBCvD/e+eQ6s955x44dlC9fXmNfZsLy6NEjzpw5g7a2NtHR0Xnu29DQUON5bGws7du3Z8iQIUybNo2SJUsSHh5Ov379SE1NxcDA4KU3yGlpafHiJ8o/ffr0pefOadvzCurcz+vduzd37txh4cKF2NjYoKuri7OzM6mpqfmK7Xlr167F39+fuXPn4uzsjLGxMbNnz+bEiRN5GkdeNWzYkMqVK7N27VqGDBnC5s2blTrqV5WcnEz9+vVZvXp1ln2Zf/gEBwfj5+fH7t27WbduHV999RX79u2jUaNGr3VuIYQQQrz/3vnkukaNGujq6hIXF5fjjO3o0aPR0tJi165dtG3blnbt2inlDpkrgOSlJvrMmTNkZGQwd+5ctLSelaM/XycMULt2bQ4cOKBR9vA8c3Nz4uPjledJSUlcv3795QPNAwcHBzIyMjh8+LBSFvLiuR88eMDDhw+VZDin2u1MERERfPvtt7Rt2xZ4djPf7du3XyvOiIgIGjduzNChQ5VtMTExytfGxsbY2tpy4MAB5YbTFxUvXjxP3zNvb29Wr15NhQoV0NLSol27djm21dHReWmfjo6OrFu3jjJlymBiYpJju3r16lGvXj0mTJiAs7Mza9askeRaCCGEEO/+DY3Gxsb4+/vzxRdfEBoaSkxMDGfPnmXx4sWEhoayY8cOfvzxR1avXk2rVq0YM2YMvXv3VlbvsLGxQaVSsX37dm7dupVl5Y/nValShadPn7J48WKuXbvGqlWrlBsdM02YMIFTp04xdOhQLly4wOXLl1m6dKmSkDZv3pxVq1Zx5MgRLl68SO/evdHW1i6Qa2Fra0vv3r3p27cvW7Zs4fr164SFhSl/AHz00UcYGBjwv//9j5iYGNasWfPSmVw7OztWrVpFVFQUJ06cwNvb+7Vnlu3s7Dh9+jR79uzhzz//ZOLEiVluSgwICGDu3LksWrSI6Oho5Xv6/FgPHDjAv//+q3wvs+Pt7c3Zs2eZNm0aXbp0ybX8wtbWluvXrxMZGcnt27dJSUnJtr/SpUvz6aefcuTIEeUa+/n58ddff3H9+nUmTJjAsWPHuHHjBnv37iU6OvqldddCCCGE+DC888k1wJQpU5g4cSJBQUFUr14dDw8PduzYga2tLf369SMgIABHR0cAAgMDKVu2LIMHDwagfPnyBAYGMn78eMqWLass95adOnXqMG/ePGbOnEmtWrVYvXo1QUFBGm3s7e3Zu3cv58+fx8nJCWdnZ7Zu3arcQDlhwgRcXV1p37497dq1o0OHDsqNbwVh6dKldOnShaFDh1KtWjUGDBjAw4cPgWc3b/7000/s3LkTBwcHfv75Z40lALPzww8/cO/ePRwdHfn888/x8/OjTJkyrxXjoEGD6NSpE926deOjjz7izp07GrPY8KwcZcGCBXz77bfUrFmT9u3ba5T0zJ07l3379mFlZaXU1menSpUqODk5ceHChWxXCXle586d8fDwoFmzZpibm/Pzzz9naWNgYMBvv/2GtbU1nTp1onr16vTr148nT55gYmKCgYEBly9fpnPnztjb2zNw4ECGDRvGoEGD8nmVhBBCCFEUqdQvFukKId6opKQkTE1NuX//fq6lJ0IIIYR4d+T19/d7MXMthBBCCCHE+0CSa5FvgwcPVj6K/MVHZjmOEEIIIcSHSMpCRL4lJCTk+BHeJiYmr12zXdRJWYgQQgjx/snr7+93fik+8e4pU6aMJNBCiPde/TErCzsEId66M7N7FXYIRZ6UhQghhBBCCFFAJLkWr8zNzY2RI0cWdhivLCwsDJVKRWJiYmGHIoQQQogiQpJr8cFq3Lgx8fHxmJqaFnYoQgghhCgiJLkW76zU1NQ32r+Ojg4WFhaoVKo3eh4hhBBCfDgkuRZ58vDhQ3r16oWRkRGWlpbMnTtXY39KSgr+/v6UL18eQ0NDPvroI8LCwpT9ISEhmJmZsWXLFuzs7NDT08Pd3Z2bN28qbQICAqhbty4rVqygYsWK6OnpAZCYmEj//v0xNzfHxMSE5s2bc/78eeW48+fP06xZM4yNjTExMaF+/fqcPn0agBs3buDp6UmJEiUwNDSkZs2a7Ny5E8i+LGTjxo3UrFkTXV1dbG1ts4zT1taW6dOn07dvX4yNjbG2tub7778vkGsshBBCiPefJNciT8aMGcPhw4fZunUre/fuJSwsjLNnzyr7fX19OXbsGGvXruXChQt07doVDw8PjY80f/ToEdOmTWPlypVERESQmJhI9+7dNc5z9epVNm7cyKZNm4iMjASga9euJCQksGvXLs6cOYOjoyMtWrTg7t27AHh7e1OhQgVOnTrFmTNnGD9+PMWLFwdg2LBhpKSk8Ntvv3Hx4kVmzpyJkZFRtmM8c+YMXl5edO/enYsXLxIQEMDEiRMJCQnRaDd37lwaNGjAuXPnGDp0KEOGDOHKlSs5XruUlBSSkpI0HkIIIYQommQpPvFSycnJ/PDDD/z000+0aNECgNDQUCpUqABAXFwcwcHBxMXFUa5cOQD8/f3ZvXs3wcHBTJ8+HYCnT5+yZMkSPvroI6WP6tWrc/LkSZycnIBnpSArV67E3NwcgPDwcE6ePElCQgK6uroAzJkzhy1btrBhwwYGDhxIXFwcY8aMoVq1agDY2dkpscfFxdG5c2ccHBwAqFSpUo7jnDdvHi1atGDixIkA2Nvbc+nSJWbPno2Pj4/Srm3btgwdOhSAcePGMX/+fA4dOkTVqlWz7TcoKIjAwMA8XWshhBBCvN9k5lq8VExMDKmpqUpSDFCyZEklmbx48SLp6enY29trfFrj4cOHiYmJUY4pVqwYDRs2VJ5Xq1YNMzMzoqKilG02NjZKYg3PSj6Sk5MpVaqURt/Xr19X+h41ahT9+/enZcuWzJgxQ+Ocfn5+TJ06lSZNmjB58mQuXLiQ4zijoqJo0qSJxrYmTZoQHR1Nenq6sq127drK1yqVCgsLCxISEnLsd8KECdy/f195PF8KI4QQQoiiRWauxWtLTk5GW1ubM2fOoK2trbEvpxKMnBgaGmbp29LSUqN+O5OZmRnwrFa7Z8+e7Nixg127djF58mTWrl1Lx44d6d+/P+7u7uzYsYO9e/cSFBTE3LlzGT58eL7iel5myUkmlUpFRkZGju11dXWVWXchhBBCFG0ycy1eqnLlyhQvXpwTJ04o2+7du8eff/4JQL169UhPTychIYEqVapoPCwsLJRj0tLSlBsNAa5cuUJiYiLVq1fP8dyOjo78+++/FCtWLEvfpUuXVtrZ29vzxRdfsHfvXjp16kRwcLCyz8rKisGDB7Np0yZGjx7N8uXLsz1X9erViYiI0NgWERGBvb19lj8ahBBCCCGyI8m1eCkjIyP69evHmDFjOHjwIL///js+Pj5oaT17+djb2+Pt7U2vXr3YtGkT169f5+TJkwQFBbFjxw6ln+LFizN8+HBOnDjBmTNn8PHxoVGjRkq9dXZatmyJs7MzHTp0YO/evcTGxnL06FG+/PJLTp8+zePHj/H19SUsLIwbN24QERHBqVOnlIR95MiR7Nmzh+vXr3P27FkOHTqUYzI/evRoDhw4wJQpU/jzzz8JDQ1lyZIl+Pv7F+DVFEIIIURRJmUhIk9mz55NcnIynp6eGBsbM3r0aO7fv6/sDw4OZurUqYwePZq///6b0qVL06hRI9q3b6+0MTAwYNy4cfTs2ZO///4bFxcXfvjhh1zPq1Kp2LlzJ19++SV9+vTh1q1bWFhY0LRpU8qWLYu2tjZ37tyhV69e/Pfff5QuXZpOnTopNxCmp6czbNgw/vrrL0xMTPDw8GD+/PnZnsvR0ZH169czadIkpkyZgqWlJV9//bXGzYxCCCGEELlRqdVqdWEHIYq+kJAQRo4cKR81DiQlJWFqasr9+/cxMTEp7HCE+GDVH7OysEMQ4q07M7tXYYfw3srr72+ZuRZCCPFBkiRDCPEmSM21EEIIIYQQBUSSa/FW+Pj4SEmIEEIIIYo8KQsRQgjxQZKaa/EhknKoN09mroUQQgghhCggklwLIYQQQghRQCS5FkIIIYQQooBIci2EEEIIIUQBkeRavJbU1NQC71OtVpOWllbg/b6OdzEmIYQQQrx7JLkuQtzc3PD19cXX1xdTU1NKly7NxIkTyfwQzpSUFPz9/SlfvjyGhoZ89NFHhIWFafQRHh6Oi4sL+vr6WFlZ4efnx8OHD5X9tra2TJkyhV69emFiYsLAgQNzjSk2NhaVSsXatWtp3Lgxenp61KpVi8OHDyttwsLCUKlU7Nq1i/r166Orq0t4eDgZGRkEBQVRsWJF9PX1qVOnDhs2bFCOu3fvHt7e3pibm6Ovr4+dnR3BwcHAs6Tf19cXS0tL9PT0sLGxISgoSCOmyMhIpa/ExERUKpVyPV41puykpKSQlJSk8RBCCCFE0STJdRETGhpKsWLFOHnyJAsXLmTevHmsWLECAF9fX44dO8batWu5cOECXbt2xcPDg+joaABiYmLw8PCgc+fOXLhwgXXr1hEeHo6vr6/GOebMmUOdOnU4d+4cEydOzFNcY8aMYfTo0Zw7dw5nZ2c8PT25c+eORpvx48czY8YMoqKiqF27NkFBQaxcuZLvvvuOP/74gy+++ILPPvtMScwnTpzIpUuX2LVrF1FRUSxdupTSpUsDsGjRIrZt28b69eu5cuUKq1evxtbWNt/XM78xZScoKAhTU1PlYWVlle84hBBCCPF+UKkzpzXFe8/NzY2EhAT++OMPVCoV8Cw53LZtG7t376ZSpUrExcVRrlw55ZiWLVvi5OTE9OnT6d+/P9ra2ixbtkzZHx4ejqurKw8fPkRPTw9bW1vq1avH5s2b8xRTbGwsFStWZMaMGYwbNw6AtLQ0KlasyPDhwxk7dixhYWE0a9aMLVu28OmnnwLPZntLlizJ/v37cXZ2Vvrr378/jx49Ys2aNXzyySeULl2aH3/8Mct5/fz8+OOPP9i/f79yLV6M6dy5c9StWxd4NnNdokQJDh06hJub2yvHlJ2UlBRSUlKU50lJSVhZWXH//n1MTEzydB2FEAVP1rkWHyJZ5/rVJSUlYWpq+tLf3/IhMkVMo0aNNJJJZ2dn5s6dy8WLF0lPT8fe3l6jfUpKCqVKlQLg/PnzXLhwgdWrVyv71Wo1GRkZXL9+nerVqwPQoEGDfMf1fDJarFgxGjRoQFRUlEab5/u9evUqjx49olWrVhptUlNTqVevHgBDhgyhc+fOnD17ltatW9OhQwcaN24MPPtEyFatWlG1alU8PDxo3749rVu3znfc+Y0pO7q6uujq6ub73EIIIYR4/0hy/YFITk5GW1ubM2fOoK2trbHPyMhIaTNo0CD8/PyyHG9tba18bWho+EZifL7f5ORkAHbs2EH58uU12mUmqm3atOHGjRvs3LmTffv20aJFC4YNG8acOXNwdHTk+vXr7Nq1i/379+Pl5UXLli3ZsGEDWlrPqqGe/6fN06dPCyQmIYQQQnzYJLkuYk6cOKHx/Pjx49jZ2VGvXj3S09NJSEjAxcUl22MdHR25dOkSVapUKfC4jh8/TtOmTYFnZSFnzpzJUsv9vBo1aqCrq0tcXByurq45tjM3N6d379707t0bFxcXxowZw5w5cwAwMTGhW7dudOvWjS5duuDh4cHdu3cxNzcHID4+Xplxfv7mxteNSQghhBAfLkmui5i4uDhGjRrFoEGDOHv2LIsXL2bu3LnY29vj7e1Nr169mDt3LvXq1ePWrVscOHCA2rVr065dO8aNG0ejRo3w9fWlf//+GBoacunSJfbt28eSJUteK65vvvkGOzs7qlevzvz587l37x59+/bNsb2xsTH+/v588cUXZGRk8PHHH3P//n0iIiIwMTGhd+/eTJo0ifr161OzZk1SUlLYvn27Uroyb948LC0tqVevHlpaWvzyyy9YWFhgZmaGlpYWjRo1YsaMGVSsWJGEhAS++uqrl44hLzEJIYQQ4sMmyXUR06tXLx4/foyTkxPa2tqMGDFCWS4vODiYqVOnMnr0aP7++29Kly5No0aNaN++PQC1a9fm8OHDfPnll7i4uKBWq6lcuTLdunV77bhmzJjBjBkziIyMpEqVKmzbtk1Z2SMnU6ZMwdzcnKCgIK5du4aZmRmOjo7873//A0BHR4cJEyYQGxuLvr4+Li4urF27FniWCM+aNYvo6Gi0tbVp2LAhO3fuVEpCfvzxR/r160f9+vWpWrUqs2bNylNN9stiEkIIIcSHTVYLKULc3NyoW7cuCxYsKOxQFNmtzPGhy+vdxkKIN0tWCxEfIlkt5NXJaiFCCCFELiTJEEK8CfIhMuK1TJ8+HSMjo2wfbdq0KezwhBBCCCHeKikLEa/l7t273L17N9t9+vr6WZasE1IWIoQQQryPpCxEvBUlS5akZMmShR2GEELkm9Rciw+RlEO9eVIWIoQQQgghRAGR5FoUCjc3N0aOHPnKx8fGxqJSqZQPfwkLC0OlUpGYmPhKxwshhBBCFAQpCxEfJCsrK+Lj41+61rYQQgghRH5Ici0+SNra2lhYWBR2GEIIIYQoYqQsRBSatLQ0fH19MTU1pXTp0kycOJHMxWtUKhVbtmzRaG9mZkZISMhL+3348CEmJiZs2LBBY/uWLVswNDTkwYMHOZaVHDhwgAYNGmBgYEDjxo25cuWKRh9Tp06lTJkyGBsb079/f8aPHy8fjiOEEEIIhSTXotCEhoZSrFgxTp48ycKFC5k3bx4rVqx47X4NDQ3p3r07wcHBGtuDg4Pp0qULxsbGOR775ZdfMnfuXE6fPk2xYsXo27evsm/16tVMmzaNmTNncubMGaytrVm6dOlL40lJSSEpKUnjIYQQQoiiScpCRKGxsrJi/vz5qFQqqlatysWLF5k/fz4DBgx47b779+9P48aNiY+Px9LSkoSEBHbu3Mn+/ftzPW7atGm4uroCMH78eNq1a8eTJ0/Q09Nj8eLF9OvXjz59+gAwadIk9u7dS3Jycq59BgUFERgY+NpjEkIIIcS7T2auRaFp1KgRKpVKee7s7Ex0dDTp6emv3beTkxM1a9YkNDQUgJ9++gkbGxuaNm2a63G1a9dWvra0tAQgISEBgCtXruDk5JTlPC8zYcIE7t+/rzxu3ryZr7EIIYQQ4v0hybV4J6lUKl788NCnT5/mq4/+/fsrNdrBwcH06dNHI5nPTvHixTViAMjIyMjXeV+kq6uLiYmJxkMIIYQQRZMk16LQnDhxQuP58ePHsbOzQ1tbG3Nzc+Lj45V90dHRPHr0KF/9f/bZZ9y4cYNFixZx6dIlevfu/VrxVq1alVOnTmlse/G5EEIIIT5sUnMtCk1cXByjRo1i0KBBnD17lsWLFzN37lwAmjdvzpIlS3B2diY9PZ1x48ZpzCrnRYkSJejUqRNjxoyhdevWVKhQ4bXiHT58OAMGDKBBgwY0btyYdevWceHCBSpVqvRa/QohhBCi6JDkWhSaXr168fjxY5ycnNDW1mbEiBEMHDgQgLlz59KnTx9cXFwoV64cCxcu5MyZM/k+R79+/VizZo3Gqh+vytvbm2vXruHv78+TJ0/w8vLCx8eHkydPvnbfQgghhCgaVOoXC1uFKEJWrVrFF198wT///IOOjk6B99+qVSssLCxYtWpVno9JSkrC1NSU+/fvS/21EIWo/piVhR2CEG/dmdm9CjuE91Zef3/LzLUokh49ekR8fDwzZsxg0KBBBZJYP3r0iO+++w53d3e0tbX5+eef2b9/P/v27SuAiIUQb5skGUKIN0FuaBRF0qxZs6hWrRoWFhZMmDChQPpUqVTs3LmTpk2bUr9+fX799Vc2btxIy5YtC6R/IYQQQrz/pCxEiLdMykKEEEKI909ef3/LzLUQQgghhBAFRGquhRBCfJDkhkZRlMg9BO8OmbkWQgghhBCigEhyLV6bm5sbI0eOfOvntbW1ZcGCBQXWX2GNQwghhBBFhyTXQgghhBBCFBBJroUQQgghhCggklyLApGRkcHYsWMpWbIkFhYWBAQEKPsSExPp378/5ubmmJiY0Lx5c86fP6/sj4mJ4dNPP6Vs2bIYGRnRsGFD9u/fr9F/QkICnp6e6OvrU7FiRVavXp0lhnnz5uHg4IChoSFWVlYMHTqU5ORkjTYRERG4ublhYGBAiRIlcHd35969e3kaR17GIoQQQogPmyTXokCEhoZiaGjIiRMnmDVrFl9//bXyyYVdu3YlISGBXbt2cebMGRwdHWnRogV3794FIDk5mbZt23LgwAHOnTuHh4cHnp6exMXFKf37+Phw8+ZNDh06xIYNG/j2229JSEjQiEFLS4tFixbxxx9/EBoaysGDBxk7dqyyPzIykhYtWlCjRg2OHTtGeHg4np6epKen52kceRlLdlJSUkhKStJ4CCGEEKJokg+REa/Nzc2N9PR0jhw5omxzcnKiefPmtG/fnnbt2pGQkICurq6yv0qVKowdO5aBAwdm22etWrUYPHgwvr6+/Pnnn1StWpWTJ0/SsGFDAC5fvkz16tWZP39+jjchbtiwgcGDB3P79m0AevbsSVxcHOHh4fkex4wZMwgPD3+lsQQEBBAYGJhlu3yIjBCFS5biE0WJLMX35uX1Q2RknWtRIGrXrq3x3NLSkoSEBM6fP09ycjKlSpXS2P/48WNiYmKAZzPXAQEB7Nixg/j4eNLS0nj8+LEycx0VFUWxYsWoX7++cny1atUwMzPT6HP//v0EBQVx+fJlkpKSSEtL48mTJzx69AgDAwMiIyPp2rXrK40DyNNYsjNhwgRGjRqlPE9KSsLKyirXOIQQQgjxfpLkWhSI4sWLazxXqVRkZGSQnJyMpaUlYWFhWY7JTI79/f3Zt28fc+bMoUqVKujr69OlSxdSU1PzfP7Y2Fjat2/PkCFDmDZtGiVLliQ8PJx+/fqRmpqKgYEB+vr6rzwOIE9jyY6urq7GTLcQQgghii5JrsUb5ejoyL///kuxYsWwtbXNtk1ERAQ+Pj507NgReJbExsbGKvurVatGWloaZ86cUcpCrly5QmJiotLmzJkzZGRkMHfuXLS0nt1KsH79eo3z1K5dmwMHDmRbolFQYxFCCCHEh01uaBRvVMuWLXF2dqZDhw7s3buX2NhYjh49ypdffsnp06cBsLOzY9OmTURGRnL+/Hl69uypzBYDVK1aFQ8PDwYNGsSJEyc4c+YM/fv315iJrlKlCk+fPmXx4sVcu3aNVatW8d1332nEMmHCBE6dOsXQoUO5cOECly9fZunSpUpNdkGMRQghhBAfNkmuxRulUqnYuXMnTZs2pU+fPtjb29O9e3du3LhB2bJlgWdL6JUoUYLGjRvj6emJu7s7jo6OGv0EBwdTrlw5XF1d6dSpEwMHDqRMmTLK/jp16jBv3jxmzpxJrVq1WL16NUFBQRp92Nvbs3fvXs6fP4+TkxPOzs5s3bqVYsXy9g+cvIxFCCGEEB82WS1EiLcsr3cbCyHeLFktRBQlslrImyerhQghhBC5kGRECPEmSFmIEEIIIYQQBUSSayGEEEIIIQqIlIUIIYT4IEnNtXjfSWnTu0lmroUQQgghhCggklwLIYQQQghRQCS5FgUiNjYWlUpFZGRkocahUqnYsmXLWz2nra0tCxYseKvnFEIIIcS7SZJrUSCsrKyIj4+nVq1aeT7Gzc2NkSNHvrmghBBCCCHeMrmhURQIbW1tLCwsCjsMIYQQQohCJTPXH4jdu3fz8ccfY2ZmRqlSpWjfvj0xMTHA/5V0bNq0iWbNmmFgYECdOnU4duyYcnzfvn2pXbs2KSkpAKSmplKvXj169eql0cfzZSG///47bdq0wcjIiLJly/L5559z+/ZtAHx8fDh8+DALFy5EpVKhUqm4fv06VapUYc6cORqxR0ZGolKpuHr1ar7HffPmTby8vDAzM6NkyZJ8+umnxMbGArB371709PRITEzUOGbEiBE0b95ceR4eHo6Liwv6+vpYWVnh5+fHw4cP8xxDSkoKSUlJGg8hhBBCFE2SXH8gHj58yKhRozh9+jQHDhxAS0uLjh07kpGRobT58ssv8ff3JzIyEnt7e3r06EFaWhoAixYt4uHDh4wfP15pm5iYyJIlS7I9X2JiIs2bN6devXqcPn2a3bt3899//+Hl5QXAwoULcXZ2ZsCAAcTHxxMfH4+1tTV9+/YlODhYo6/g4GCaNm1KlSpV8jXmp0+f4u7ujrGxMUeOHCEiIgIjIyM8PDxITU2lRYsWmJmZsXHjRuWY9PR01q1bh7e3NwAxMTF4eHjQuXNnLly4wLp16wgPD8fX1zfPcQQFBWFqaqo8rKys8jUOIYQQQrw/pCzkA9G5c2eN5z/++CPm5uZcunQJIyMjAPz9/WnXrh0AgYGB1KxZk6tXr1KtWjWMjIz46aefcHV1xdjYmAULFnDo0CFMTEyyPd+SJUuoV68e06dP1zinlZUVf/75J/b29ujo6GBgYKBRTuLj48OkSZM4efIkTk5OPH36lDVr1mSZzc6LdevWkZGRwYoVK1CpVMCzRN3MzIywsDBat25N9+7dWbNmDf369QPgwIEDJCYmKtcrKCgIb29vpTbczs6ORYsW4erqytKlS9HT03tpHBMmTGDUqFHK86SkJEmwhRBCiCJKZq4/ENHR0fTo0YNKlSphYmKCra0tAHFxcUqb2rVrK19bWloCkJCQoGxzdnbG39+fKVOmMHr0aD7++OMcz3f+/HkOHTqEkZGR8qhWrRqAUo6SnXLlytGuXTt+/PFHAH799VdSUlLo2rVrvsd8/vx5rl69irGxsRJDyZIlefLkiRKDt7c3YWFh/PPPPwCsXr2adu3aYWZmpvQREhKiMQ53d3cyMjK4fv16nuLQ1dXFxMRE4yGEEEKIoklmrj8Qnp6e2NjYsHz5csqVK0dGRga1atUiNTVVaVO8eHHl68yZ3ufLRjIyMoiIiEBbW/ul9c/Jycl4enoyc+bMLPsyE/ec9O/fn88//5z58+cTHBxMt27dMDAwyNM4X4yhfv36rF69Oss+c3NzABo2bEjlypVZu3YtQ4YMYfPmzYSEhGj0MWjQIPz8/LL0YW1tne+YhBBCCFG0SXL9Abhz5w5Xrlxh+fLluLi4AM9u0suv2bNnc/nyZQ4fPoy7uzvBwcH06dMn27aOjo5s3LgRW1tbihXL/mWmo6NDenp6lu1t27bF0NCQpUuXsnv3bn777bd8x5oZw7p16yhTpkyus8Xe3t6sXr2aChUqoKWlpZTGZPZx6dKlfNd7CyGEEOLDJGUhH4ASJUpQqlQpvv/+e65evcrBgwc1aoDz4ty5c0yaNIkVK1bQpEkT5s2bx4gRI7h27Vq27YcNG8bdu3fp0aMHp06dIiYmhj179tCnTx8loba1teXEiRPExsZy+/ZtZZZcW1sbHx8fJkyYgJ2dHc7Ozq80bm9vb0qXLs2nn37KkSNHuH79OmFhYfj5+fHXX39ptDt79izTpk2jS5cu6OrqKvvGjRvH0aNH8fX1JTIykujoaLZu3ZqvGxqFEEII8eGQ5PoDoKWlxdq1azlz5gy1atXiiy++YPbs2Xk+/smTJ3z22Wf4+Pjg6ekJwMCBA2nWrBmff/55trPP5cqVIyIigvT0dFq3bo2DgwMjR47EzMwMLa1nLzt/f3+0tbWpUaMG5ubmGvXf/fr1IzU1NceZ8bwwMDDgt99+w9ramk6dOlG9enX69evHkydPNGayq1SpgpOTExcuXFBWCclUu3ZtDh8+zJ9//omLiwv16tVj0qRJlCtX7pXjEkIIIUTRpVKr1erCDkKIFx05coQWLVpw8+ZNypYtW9jhFKikpCRMTU25f/++3NwohBBCvCfy+vtbaq7FOyUlJYVbt24REBBA165di1xiLYQQQoiiTcpCxDvl559/xsbGhsTERGbNmqWxb/Xq1RpL4j3/qFmzZiFFLIQQQgjxf6QsRLw3Hjx4wH///ZftvuLFi2NjY/OWI3o1UhYihBBCvH+kLEQUOcbGxhgbGxd2GEKIIqL+mJWFHYIQ+XZmdq/CDkG8hJSFCCGEEEIIUUAkuRZvVEBAAHXr1n0r51KpVGzZsuWtnCuTra0tCxYseKvnFEIIIcS7S5Jr8Ub5+/tz4MCBwg5DCCGEEOKtkJpr8UZlruYhhBBCCPEhkJlrkSs3Nzf8/PwYO3YsJUuWxMLCgoCAAGV/XFwcn376KUZGRpiYmODl5aWxoseLZSFhYWE4OTlhaGiImZkZTZo04caNG8r+rVu34ujoiJ6eHpUqVSIwMJC0tLRXiv3mzZt4eXlhZmZGyZIl+fTTT4mNjQVg79696OnpkZiYqHHMiBEjaN68ufI8PDwcFxcX9PX1sbKyws/Pj4cPH75SPEIIIYQo+iS5Fi8VGhqKoaEhJ06cYNasWXz99dfs27ePjIwMPv30U+7evcvhw4fZt28f165do1u3btn2k5aWRocOHXB1deXChQscO3aMgQMHolKpgGefytirVy9GjBjBpUuXWLZsGSEhIUybNi3fMT99+hR3d3eMjY05cuQIERERGBkZ4eHhQWpqKi1atMDMzIyNGzcqx6Snp7Nu3TrlI9BjYmLw8PCgc+fOXLhwgXXr1hEeHo6vr2++YklJSSEpKUnjIYQQQoiiScpCxEvVrl2byZMnA2BnZ8eSJUuUOuqLFy9y/fp1rKysAFi5ciU1a9bk1KlTNGzYUKOfpKQk7t+/T/v27alcuTIA1atXV/YHBgYyfvx4evfuDUClSpWYMmUKY8eOVc6fV+vWrSMjI4MVK1YoyXtwcDBmZmaEhYXRunVrunfvzpo1a+jXrx8ABw4cIDExkc6dOwMQFBSEt7c3I0eOVMa+aNEiXF1dWbp0KXp6enmKJSgoiMDAwHzFL4QQQoj3k8xci5eqXbu2xnNLS0sSEhKIiorCyspKSawBatSogZmZGVFRUVn6KVmyJD4+Pri7u+Pp6cnChQuJj49X9p8/f56vv/5a45MXBwwYQHx8PI8ePcpXzOfPn+fq1asYGxsrfZUsWZInT54QExMDgLe3N2FhYfzzzz/As0+AbNeuHWZmZkofISEhGvG4u7uTkZHB9evX8xzLhAkTuH//vvK4efNmvsYihBBCiPeHzFyLlypevLjGc5VKRUZGxiv1FRwcjJ+fH7t372bdunV89dVX7Nu3j0aNGpGcnExgYCCdOnXKclxeZ4kzJScnU79+fVavXp1ln7m5OQANGzakcuXKrF27liFDhrB582ZCQkI0+hg0aBB+fn5Z+rC2ts5zLLq6uujq6uYrfiGEEEK8nyS5Fq+sevXq3Lx5k5s3byqz15cuXSIxMZEaNWrkeFy9evWoV68eEyZMwNnZmTVr1tCoUSMcHR25cuUKVapUee3YHB0dWbduHWXKlMn1I0q9vb1ZvXo1FSpUQEtLi3bt2mn0cenSpQKJRwghhBAfBikLEa+sZcuWODg44O3tzdmzZzl58iS9evXC1dWVBg0aZGl//fp1JkyYwLFjx7hx4wZ79+4lOjpaqbueNGkSK1euJDAwkD/++IOoqCjWrl3LV199le/YvL29KV26NJ9++ilHjhzh+vXrhIWF4efnx19//aXR7uzZs0ybNo0uXbpozDCPGzeOo0eP4uvrS2RkJNHR0WzdujXfNzQKIYQQ4sMhybV4ZSqViq1bt1KiRAmaNm1Ky5YtqVSpEuvWrcu2vYGBAZcvX6Zz587Y29szcOBAhg0bxqBBgwBwd3dn+/bt7N27l4YNG9KoUSPmz5+PjY1NvmMzMDDgt99+w9ramk6dOlG9enX69evHkydPNGayq1SpgpOTExcuXFBWCclUu3ZtDh8+zJ9//omLiwv16tVj0qRJlCtXLt/xCCGEEOLDoFKr1erCDkKID0lSUhKmpqbcv38/15IVIcSbVX/MysIOQYh8OzO7V2GH8MHK6+9vqbkWQgjxQZIkRQjxJkhZiHgvrF69WmNJvOcfNWvWLOzwhBBCCCEAmbkW74lPPvmEjz76KNt9Ly4VKIQQQghRWCS5Fu8FY2NjjI2NCzsMIUQRIjXX4l0iZUpFh5SFCCGEEEIIUUAkuRZCCCGEEKKASHIt3hkhISGYmZkVSF9hYWGoVCoSExMLpL/CPo8QQggh3g+SXIvX4uPjQ4cOHQo7DCGEEEKId4Ik10IIIYQQQhQQSa5FnmzYsAEHBwf09fUpVaoULVu2ZMyYMYSGhrJ161ZUKhUqlYqwsLBsSyUiIyNRqVTExsYq20JCQrC2tsbAwICOHTty584dZV9sbCxaWlqcPn1aI44FCxZgY2NDRkZGvsewceNGatasia6uLra2tsydO1dj/6pVq2jQoAHGxsZYWFjQs2dPEhISNNrs3LkTe3t79PX1adasmcZ4cpKSkkJSUpLGQwghhBBFkyTX4qXi4+Pp0aMHffv2JSoqirCwMDp16sTkyZPx8vLCw8OD+Ph44uPjady4cZ76PHHiBP369cPX15fIyEiaNWvG1KlTlf22tra0bNmS4OBgjeOCg4Px8fFBSyt/L90zZ87g5eVF9+7duXjxIgEBAUycOJGQkBClzdOnT5kyZQrnz59ny5YtxMbG4uPjo+y/efMmnTp1wtPTk8jISPr378/48eNfeu6goCBMTU2Vh5WVVb5iF0IIIcT7Q9a5Fi8VHx9PWloanTp1wsbGBgAHBwcA9PX1SUlJwcLCIl99Lly4EA8PD8aOHQuAvb09R48eZffu3Uqb/v37M3jwYObNm4euri5nz57l4sWLbN26Nd9jmDdvHi1atGDixInK+S5dusTs2bOVBLpv375K+0qVKrFo0SIaNmxIcnIyRkZGLF26lMqVKysz3lWrVuXixYvMnDkz13NPmDCBUaNGKc+TkpIkwRZCCCGKKJm5Fi9Vp04dWrRogYODA127dmX58uXcu3fvtfqMiorK8omLzs7OGs87dOiAtrY2mzdvBp6VkTRr1gxbW9tXOl+TJk00tjVp0oTo6GjS09OBZ7Pbnp6eWFtbY2xsjKurKwBxcXF5jjk7urq6mJiYaDyEEEIIUTRJci1eSltbm3379rFr1y5q1KjB4sWLqVq1KtevX8+2fWbJhlqtVrY9ffo03+fV0dGhV69eBAcHk5qaypo1azRmlwvSw4cPcXd3x8TEhNWrV3Pq1CklqU9NTX0j5xRCCCFE0SPJtcgTlUpFkyZNCAwM5Ny5c+jo6LB582Z0dHSUmd9M5ubmwLNykkyRkZEabapXr86JEyc0th0/fjzLefv378/+/fv59ttvldKUV1G9enUiIiI0tkVERGBvb4+2tjaXL1/mzp07zJgxAxcXF6pVq5blZsbq1atz8uTJl8YshBBCiA+XJNfipU6cOMH06dM5ffo0cXFxbNq0iVu3blG9enVsbW25cOECV65c4fbt2zx9+pQqVapgZWVFQEAA0dHR7NixI8vKHH5+fuzevZs5c+YQHR3NkiVLNOqtM1WvXp1GjRoxbtw4evTogb6+/iuNYfTo0Rw4cIApU6bw559/EhoaypIlS/D39wfA2toaHR0dFi9ezLVr19i2bRtTpkzR6GPw4MFER0czZswYrly5wpo1azRuiBRCCCGEkORavJSJiQm//fYbbdu2xd7enq+++oq5c+fSpk0bBgwYQNWqVWnQoAHm5uZERERQvHhxfv75Zy5fvkzt2rWZOXOmxkogAI0aNWL58uUsXLiQOnXqsHfvXr766qtsz9+vXz9SU1NfqyTE0dGR9evXs3btWmrVqsWkSZP4+uuvlZsZzc3NCQkJ4ZdffqFGjRrMmDGDOXPmaPRhbW3Nxo0b2bJlC3Xq1OG7775j+vTprxyTEEIIIYoelfr5wlgh3kFTpkzhl19+4cKFC4UdSoFISkrC1NSU+/fvy82NQhSi+mNWFnYIQijOzO5V2CGIl8jr729Zik+8s5KTk4mNjWXJkiVZZr6FEOJ1STIjhHgTpCxEvLN8fX2pX78+bm5uWUpCBg8ejJGRUbaPwYMHF1LEQgghhPjQSVmIeC8lJCTk+DHiJiYmlClT5i1HlHdSFiKEEEK8f6QsRBRpZcqUeacTaCHEu09qrkVhk9KkoknKQoQQQgghhCggkly/w2xtbVmwYEGe2qpUKrZs2fJG44mNjUWlUmX5QJgXubm5MXLkyFc+z8vGEhYWhkqlIjEx8ZXPIYQQQgjxJkhy/Ra8jcS3MBRWktu4cWPi4+MxNTV9aVtJxIUQQgjxNklyLd47Ojo6WFhYoFKp3to51Wo1aWlpb+18QgghhHg/fVDJtZubG8OHD2fkyJGUKFGCsmXLsnz5ch4+fEifPn0wNjamSpUq7Nq1Sznm999/p02bNhgZGVG2bFk+//xzbt++rdGnn58fY8eOpWTJklhYWBAQEKDst7W1BaBjx46oVCrleUxMDJ9++illy5bFyMiIhg0bsn///tca3+3bt+nYsSMGBgbY2dmxbds2jf0vG8vu3bv5+OOPMTMzo1SpUrRv356YmJhszxUbG0uzZs0AKFGiBCqVSvm0Q4CMjIwcr8nrjuXF2egbN27g6elJiRIlMDQ0pGbNmuzcuTPXGFNSUvDz86NMmTLo6enx8ccfc+rUqSzn2LVrF/Xr10dXV5effvoJLS0tTp8+rRHrggULsLGxISMjI19jFEIIIUTR80El1wChoaGULl2akydPMnz4cIYMGULXrl1p3LgxZ8+epXXr1nz++ec8evSIxMREmjdvTr169Th9+jS7d+/mv//+w8vLK0ufhoaGnDhxglmzZvH111+zb98+ACVhCw4OJj4+XnmenJxM27ZtOXDgAOfOncPDwwNPT0/i4uJeeWyBgYF4eXlx4cIF2rZti7e3N3fv3gXI01gePnzIqFGjOH36NAcOHEBLS4uOHTtmmzRaWVmxceNGAK5cuUJ8fDwLFy7M0zV53bG8aNiwYaSkpPDbb79x8eJFZs6ciZGRUa4xjh07lo0bNxIaGsrZs2epUqUK7u7uWc4xfvx4ZsyYQVRUFJ988gktW7YkODhYo01wcDA+Pj5oaWX/dkpJSSEpKUnjIYQQQoii6YNa59rNzY309HSOHDkCQHp6OqampnTq1ImVK58tyfTvv/9iaWnJsWPH2L9/P0eOHGHPnj1KH3/99RdWVlZcuXIFe3v7LH0CODk50bx5c2bMmAE8q7nevHkzHTp0yDW+WrVqMXjwYHx9fYFns94jR47M082BKpWKr776iilTpgDPEmUjIyN27dqFh4cHU6dOfelYXnT79m3Mzc25ePEitWrVIjY2looVK3Lu3Dnq1q1LWFgYzZo14969e5iZmeV4nbO7Jq8zlhfPW7t2bTp37szkyZOz9JVdjA8fPqREiRKEhITQs2dPAJ4+fapc7zFjxijHbdmyhU8//VTpb/369QwePJj4+Hh0dXU5e/YsDRo04Nq1a8p/JV4UEBBAYGBglu2yzrUQhUuW4hOFTZbie7/kdZ3rD27munbt2srX2tralCpVCgcHB2Vb2bJlgWcfUnL+/HkOHTqk8el/1apVA9Aol3i+TwBLS0sSEhJyjSM5ORl/f3+qV6+OmZkZRkZGREVFvdbM9fNxGBoaYmJiosSRl7FER0fTo0cPKlWqhImJiZIsvkpMr3JN8jqWF/n5+TF16lSaNGnC5MmTuXDhQq59x8TE8PTpU5o0aaJsK168OE5OTkRFRWm0bdCggcbzDh06oK2tzebNmwEICQmhWbNmOSbWABMmTOD+/fvK4+bNm7nGJ4QQQoj31wf3ITLFixfXeK5SqTS2Zd4kl5GRQXJyMp6ensycOTNLP5aWlrn2+bL6W39/f/bt28ecOXOoUqUK+vr6dOnShdTU1HyPKS9x5GUsnp6e2NjYsHz5csqVK0dGRga1atV6pZhe5Zq86vH9+/fH3d2dHTt2sHfvXoKCgpg7dy7Dhw/Pd9wvMjQ01Hiuo6NDr169CA4OplOnTqxZs0ajHCY7urq66OrqvnYsQgghhHj3fXDJdX44OjqyceNGbG1tKVbs1S9V8eLFSU9P19gWERGBj48PHTt2BJ4lv7Gxsa8Tbq5eNpY7d+5w5coVli9fjouLCwDh4eG59qmjowOQZWyFwcrKisGDBzN48GAmTJjA8uXLGT58eLYxVq5cGR0dHSIiIrCxsQGelYWcOnUqTyU4/fv3p1atWnz77bekpaXRqVOnNzImIYQQQrx/PriykPwYNmwYd+/epUePHpw6dYqYmBj27NlDnz598pVQ2tracuDAAf7991/u3bsHgJ2dHZs2bSIyMpLz58/Ts2fPN7raxMvGUqJECUqVKsX333/P1atXOXjwIKNGjcq1TxsbG1QqFdu3b+fWrVskJye/sfhzM3LkSPbs2cP169c5e/Yshw4donr16jnGaGhoyJAhQxgzZgy7d+/m0qVLDBgwgEePHtGvX7+Xnq969eo0atSIcePG0aNHD/T19d/0EIUQQgjxnpDkOhflypUjIiKC9PR0WrdujYODAyNHjsTMzCzHlSGyM3fuXPbt24eVlRX16tUDYN68eZQoUYLGjRvj6emJu7s7jo6Ob2ooLx2LlpYWa9eu5cyZM9SqVYsvvviC2bNn59pn+fLlCQwMZPz48ZQtW1a5EfNtS09PZ9iwYVSvXh0PDw/s7e359ttvc41xxowZdO7cmc8//xxHR0euXr3Knj17KFGiRJ7O2a9fP1JTU+nbt+8bG5cQQggh3j8f1GohQhSUKVOm8Msvv7z05sns5PVuYyHEmyWrhYjCJquFvF/y+vtbaq6FyIfM2vglS5YwderUwg5HCPEaJLERQrwJUhbyHli9erXGEnrPP2rWrFnY4eXL+z4WX19f6tevj5ubm5SECCGEECILKQt5Dzx48ID//vsv233FixdXVrx4HxSlsbwqKQsRQggh3j9SFlKEGBsbY2xsXNhhFIiiNBYhhBBCiBdJci2EEOKDJDc0CpDae1HwpOZaCCGEEEKIAiLJdQFwc3PL0yf7vS15iUelUrFly5a3Es+b5uPjQ4cOHXJt8659j4QQQghRNEly/R4LCwtDpVKRmJiY72Pj4+Np06ZNwQclhBBCCPEBk+S6EKSmphZ2CFhYWKCrq1vYYeTqXbhOQgghhBD5Icl1AUlLS8PX1xdTU1NKly7NxIkTyVzl0NbWlilTptCrVy9MTEwYOHAgAOHh4bi4uKCvr4+VlRV+fn48fPhQ6XPVqlU0aNAAY2NjLCws6NmzJwkJCQDExsbSrFkzAEqUKIFKpcLHx0c5NiMjg7Fjx1KyZEksLCwICAjQiPf5spDY2FhUKhWbNm2iWbNmGBgYUKdOHY4dO6ZxzPLly7GyssLAwICOHTsyb948zMzM8nR9AgICqFu3LsuWLVP68PLy4v79+0qbzPKOadOmUa5cOapWrQrAxYsXad68Ofr6+pQqVYqBAweSnJyc5RyBgYGYm5tjYmLC4MGDc03OU1JS8Pf3p3z58hgaGvLRRx8RFham7A8JCcHMzIzt27dTtWpVDAwM6NKlC48ePSI0NBRbW1tKlCiBn58f6enpeboGQgghhCj6JLkuIKGhoRQrVoyTJ0+ycOFC5s2bx4oVK5T9c+bMoU6dOpw7d46JEycSExODh4cHnTt35sKFC6xbt47w8HB8fX2VY54+fcqUKVM4f/48W7ZsITY2Vkmgrays2LhxIwBXrlwhPj6ehQsXasRjaGjIiRMnmDVrFl9//TX79u3LdQxffvkl/v7+REZGYm9vT48ePUhLSwMgIiKCwYMHM2LECCIjI2nVqhXTpk3L1zW6evUq69ev59dff2X37t2cO3eOoUOHarQ5cOAAV65cYd++fWzfvp2HDx/i7u5OiRIlOHXqFL/88gv79+/XuE6Zx0VFRREWFsbPP//Mpk2bCAwMzDEWX19fjh07xtq1a7lw4QJdu3bFw8OD6Ohopc2jR49YtGgRa9euZffu3YSFhdGxY0d27tzJzp07WbVqFcuWLWPDhg25jjslJYWkpCSNhxBCCCGKJvkQmQLg5uZGQkICf/zxByqVCoDx48ezbds2Ll26hK2tLfXq1WPz5s3KMf3790dbW5tly5Yp28LDw3F1deXhw4fo6ellOc/p06dp2LAhDx48wMjIiLCwMJo1a8a9e/c0ZpDd3NxIT0/nyJEjyjYnJyeaN2/OjBkzgGcz15s3b6ZDhw7ExsZSsWJFVqxYQb9+/QC4dOkSNWvWJCoqimrVqtG9e3eSk5PZvn270udnn33G9u3b81TzHRAQwNSpU7lx4wbly5cHYPfu3bRr146///4bCwsLfHx82L17N3Fxcejo6ADPZsvHjRvHzZs3MTQ0BGDnzp14enryzz//ULZsWXx8fPj111+5efMmBgYGAHz33XeMGTOG+/fvo6WlhZubG3Xr1mXBggXExcVRqVIl4uLiKFeunBJjy5YtcXJyYvr06YSEhNCnTx+uXr1K5cqVARg8eDCrVq3iv//+w8jICAAPDw9sbW357rvvch17dom+fIiMEIVLluITIEvxibzL64fIyMx1AWnUqJGSWAM4OzsTHR2tlAw0aNBAo/358+cJCQnR+Phvd3d3MjIyuH79OgBnzpzB09MTa2trjI2NcXV1BSAuLu6l8dSuXVvjuaWlpVJSkpdjLC0tAZRjrly5gpOTk0b7F5+/jLW1tZJYw7NrlJGRwZUrV5RtDg4OSmINEBUVRZ06dZTEGqBJkyZZjqtTp46SWGf2nZyczM2bN7PEcfHiRdLT07G3t9e4/ocPHyYmJkZpZ2BgoCTWAGXLlsXW1lZJrDO3vey6Tpgwgfv37yuP7GISQgghRNEgHyLzljyfHAIkJyczaNAg/Pz8srS1trZWyiHc3d1ZvXo15ubmxMXF4e7unqcb/YoXL67xXKVSkZGRkedjMv9QeNkxBe3F6/QmJCcno62tzZkzZ9DW1tbY93zinN01fJXrqqur+87fPCqEEEKIgiHJdQE5ceKExvPjx49jZ2eXJXnL5OjoyKVLl6hSpUq2+y9evMidO3eYMWMGVlZWwLOykOdlzvC+jRvqqlatyqlTpzS2vfj8ZeLi4vjnn3+UUozjx4+jpaWl3LiYnerVqxMSEsLDhw+VxDsiIiLLcefPn+fx48fo6+srfRsZGSnX7nn16tUjPT2dhIQEXFxc8jUGIYQQQojcSFlIAYmLi2PUqFFcuXKFn3/+mcWLFzNixIgc248bN46jR4/i6+tLZGQk0dHRbN26VblRz9raGh0dHRYvXsy1a9fYtm0bU6ZM0ejDxsYGlUrF9u3buXXrVrYraBSU4cOHs3PnTubNm0d0dDTLli1j165dGqUwL6Onp0fv3r05f/48R44cwc/PDy8vLywsLHI8xtvbWznu999/59ChQwwfPpzPP/+csmXLKu1SU1Pp168fly5dYufOnUyePBlfX1+0tLK+xO3t7fH29qZXr15s2rSJ69evc/LkSYKCgtixY0f+LowQQgghxHMkuS4gvXr14vHjxzg5OTFs2DBGjBihLLmXndq1a3P48GH+/PNPXFxcqFevHpMmTVJmdc3NzQkJCeGXX36hRo0azJgxgzlz5mj0Ub58eQIDAxk/fjxly5bNsoJGQWrSpAnfffcd8+bNo06dOuzevZsvvvgi2xsvc1KlShU6depE27Ztad26NbVr1+bbb7/N9RgDAwP27NnD3bt3adiwIV26dKFFixYsWbJEo12LFi2ws7OjadOmdOvWjU8++STL8oPPCw4OplevXowePZqqVavSoUMHTp06hbW1dZ7HI4QQQgjxIlktRLyyAQMGcPnyZY1VSXISEBDAli1biIyMfPOBvePyerexEOLNktVCBMhqISLv8vr7W2quRZ7NmTOHVq1aYWhoyK5duwgNDX3pzLMQQryrJKkSQrwJklyLPDt58iSzZs3iwYMHVKpUiUWLFtG/f38AatasyY0bN7I97vm1vIUQQgghijIpCxEF4saNGzx9+jTbfWXLlsXY2PgtR/TukrIQIYQQ4v0jZSHirbKxsSnsEMQHSupmxauSshAhxJsgq4UIIYQQQghRQCS5FkIIIYQQooBIcv0OcHNzY+TIkQDY2tqyYMGCVzq2KAsJCcHMzEx5HhAQQN26dXM9JjY2FpVKJcv/CSGEEOKtkZrrd8ypU6eUj/nOi02bNlG8ePE3GNG7yd/fn+HDhyvPfXx8SExMZMuWLco2Kysr4uPjKV26dCFEKIQQQogPkSTX7xhzc/N8tS9ZsuQbiuTdZmRkhJGRUa5ttLW1c/1odSGEEEKIgiZlIe+Y58tCevbsSbdu3TT2P336lNKlS7Ny5bMVEl4sC7G1tWX69On07dsXY2NjrK2t+f777zX6OHr0KHXr1kVPT48GDRqwZcuWfJVP/PHHH7Rv3x4TExOMjY1xcXEhJiYGgIyMDL7++msqVKiArq4udevWZffu3cqxmaUamzZtolmzZhgYGFCnTh2OHTumcY6QkBCsra0xMDCgY8eO3LlzR2P/82UhAQEBhIaGsnXrVlQqFSqVirCwsGzLQg4fPoyTkxO6urpYWloyfvx40tLSlP1ubm74+fkxduxYSpYsiYWFhcbHqKvVagICArC2tkZXV5dy5crh5+eX6/VKSUkhKSlJ4yGEEEKIokmS63eYt7c3v/76K8nJycq2PXv28OjRIzp27JjjcXPnzqVBgwacO3eOoUOHMmTIEK5cuQI8W6PR09MTBwcHzp49y5QpUxg3blyeY/r7779p2rQpurq6HDx4kDNnztC3b18lQV24cCFz585lzpw5XLhwAXd3dz755BOio6M1+vnyyy/x9/cnMjISe3t7evToofRx4sQJ+vXrh6+vL5GRkTRr1oypU6fmGJO/vz9eXl54eHgQHx9PfHw8jRs3zjb2tm3b0rBhQ86fP8/SpUv54YcfsvQdGhqKoaEhJ06cYNasWXz99dfs27cPgI0bNzJ//nyWLVtGdHQ0W7ZswcHBIddrFhQUhKmpqfKwsrJ6+YUWQgghxHtJykLeYe7u7hgaGrJ582Y+//xzANasWcMnn3yS64eytG3blqFDhwIwbtw45s+fz6FDh6hatSpr1qxBpVKxfPly9PT0qFGjBn///TcDBgzIU0zffPMNpqamrF27Vqn1tre3V/bPmTOHcePG0b17dwBmzpzJoUOHWLBgAd98843Szt/fn3bt2gEQGBhIzZo1uXr1KtWqVWPhwoV4eHgwduxYpf+jR49qzIA/z8jICH19fVJSUnItA/n222+xsrJiyZIlqFQqqlWrxj///MO4ceOYNGkSWlrP/tasXbs2kydPBsDOzo4lS5Zw4MABWrVqRVxcHBYWFrRs2ZLixYtjbW2Nk5NTrtdswoQJjBo1SnmelJQkCbYQQghRRMnM9TusWLFieHl5sXr1agAePnzI1q1b8fb2zvW42rVrK1+rVCosLCxISEgA4MqVK9SuXRs9PT2lzcuSw+dFRkbi4uKS7U2USUlJ/PPPPzRp0kRje5MmTYiKisoxRktLSwAlxqioKD766CON9s7OznmOMSdRUVE4OzujUqk0YktOTuavv/7KNrbM+DJj69q1K48fP6ZSpUoMGDCAzZs3a5SVZEdXVxcTExONhxBCCCGKJkmu33He3t4cOHCAhIQEtmzZgr6+Ph4eHrke82Liq1KpyMjIKJB49PX/X3v3Htfj/T9+/PFWStS7SEdSmUosdMDSh8wpmrbYmBhy9hkf0mJ8zHmTQ23M5jNzKMxnbE5jDkMTE1JRTi2JZFs0hyR8HOr9+8PP9fVeB2Ul1fN+u123m/d1va7X9XxdV+397LXndV0GZdLP0zE+SXbLKsa/q7jzZ2NjQ0pKCkuXLsXAwID333+fDh06FPnqdyGEEEJUL5Jcv+TatWuHjY0NGzZsYN26dfTp0+dvPXrPycmJU6dOcf/+fWVdXFxcifdv0aIFv/zyS6HJpFqtxtrampiYGK31MTExNGvWrMTHcHZ2JjY2Vmvd0aNHi91HT0+PvLy8Z/Z75MgRNBqNVmxGRkY0bNiwxPEZGBjg5+fH559/TnR0NEeOHOHUqVMl3l8IIYQQVZck15VA//79+eqrr9i7d+8zS0JK0ld+fj4jR44kOTmZn376ibCwMACtcomijB07lpycHPr160d8fDypqamsXbtWuWFy4sSJzJ8/nw0bNpCSksLkyZNJTExk/PjxJY5x3Lhx7N69m7CwMFJTU/niiy+KrLd+ws7OjpMnT5KSksK1a9cKTf7ff/99Ll++zL/+9S9+/fVXfvjhB2bMmEFwcLBSb/0skZGRrFy5ktOnT3PhwgW++eYbDAwMsLW1LfH4hBBCCFF1SXJdCQwYMICzZ8/SoEGDAvXMpaVWq9m+fTuJiYm0atWKqVOnMn36dACtOuyimJqa8vPPP5Obm4u3tzfu7u4sX75cmU0fN24cwcHBfPDBB7i4uLB79262bduGg4NDiWN87bXXWL58OYsXL6Zly5bs2bOHjz76qNh9RowYgZOTEx4eHpiZmRWYPQdo0KABO3fu5NixY7Rs2ZLRo0czbNiwZ/b9NBMTE5YvX46XlxctWrRg3759bN++HVNT0xL3IYQQQoiqS6V5+v+Ri2pp3bp1DBkyhFu3bpVZTbUoWk5ODsbGxty6dUtubhRCCCEqiZJ+f8uj+KqhNWvW0LhxYxo0aEBSUhIffvghffv2lcRaCCGEEOJvkrKQaujKlSu89957ODs7M2HCBPr06aO8xXH06NHKq8X/uowePbqCIxdCCCGEeLlJWYjQkpWVVeTrudVqNebm5i84oqpHykKEEEKIykfKQsRzMTc3lwRavLTcJ66p6BBEFZKwcFBFhyCEqIKkLEQIIYQQQogy8tIn1zNnzqRVq1YVHUa5SU9PR6VSkZiYWNGhPJeyuj4v+jpHR0ejUqnIzs5+aWISQgghROVXquS6Y8eOBAUFlVMolUdkZCQmJiYVHUaxVCoVW7duregwClVYbCEhIURFRVVMQEV4GWMSQgghxMutWtZcazQa8vLy0NWtlsMvkQcPHqCnp/fCjvfkiSQvk5cxJiGEEEK83Eo8cx0YGMiBAwdYvHgxKpUKlUpFeno6p0+fpkePHhgaGmJhYcHAgQO5du2ast/u3bv5xz/+gYmJCaampvTs2ZO0tDStvn/77TcCAgKoV68ederUwcPDg9jYWK02a9euxc7ODmNjY/r168ft27eVbfn5+YSGhmJvb4+BgQEtW7Zk48aNyvYnJQC7du3C3d0dfX19Dh06VOx4k5KSeP311zEyMkKtVuPu7k58fDzR0dHKC1eenIeZM2cChc/ImpiYEBkZqXw+duwYrq6u1KpVCw8PD06cOFHg2M86px07dmTcuHFMmjSJevXqYWlpqcQAj18FDtCrVy9UKpXyuThPSiBWrFiBvb298rbG7Oxshg8fjpmZGWq1mk6dOpGUlFRkP3FxcXTt2pX69etjbGyMt7c3x48ff2Zsfy3ByM/PZ/bs2TRs2BB9fX1atWql9Qr0J+U0mzdv5vXXX6d27dq0bNmSI0eOKG0uXbqEn58fdevWpU6dOjRv3pydO3dqxZuQkICHhwe1a9emXbt2ymvcC4spMDAQf39/Zs2apZyP0aNH8+DBg2eeXyGEEEJUDyVOrhcvXoynpycjRowgMzOTzMxMjIyM6NSpE66ursTHx7N7926uXr1K3759lf3u3LlDcHAw8fHxREVFUaNGDXr16kV+fj6A8hrt33//nW3btpGUlMSkSZOU7QBpaWls3bqVH3/8kR9//JEDBw4wb948ZXtoaChr1qzhq6++4syZM0yYMIH33nuPAwcOaI1h8uTJzJs3j+TkZFq0aFHseAcMGEDDhg2Ji4sjISGByZMnU7NmTdq1a8eiRYtQq9XKeQgJCSnROczNzaVnz540a9aMhIQEZs6cWWDf7OzsZ55TgNWrV1OnTh1iY2NZsGABs2fPZu/evcDjBBcgIiKCzMxM5fOznD9/nk2bNrF582alBrxPnz5kZWWxa9cuEhIScHNzo3Pnzty4caPQPm7fvs3gwYM5dOgQR48excHBAV9fX+WPoZLGtnjxYsLDwwkLC+PkyZP4+Pjw5ptvkpqaqtVu6tSphISEkJiYiKOjIwEBATx69AiAMWPGcP/+fQ4ePMipU6eYP39+gZnoqVOnEh4eTnx8PLq6ugwdOrTYcxQVFUVycjLR0dF8++23bN68mVmzZhW7z/3798nJydFahBBCCFE1lbguwtjYGD09PWrXro2lpSUAH3/8Ma6ursydO1dpt2rVKmxsbDh37hyOjo68/fbbWv2sWrUKMzMzzp49y6uvvsp///tf/vzzT+Li4qhXrx4ATZo00donPz+fyMhIjIyMABg4cCBRUVF88skn3L9/n7lz57Jv3z48PT0BaNy4MYcOHWLZsmV4e3sr/cyePZuuXbuWaLwZGRlMnDiRpk2bAuDg4KB1LlQqlXIeSuq///0v+fn5rFy5klq1atG8eXN+++03/vnPfyptvvjii2eeU4AWLVowY8YMJbYvvviCqKgounbtipmZGfB41rw0MT548IA1a9Yo+x86dIhjx46RlZWFvr4+AGFhYWzdupWNGzcycuTIAn106tRJ6/PXX3+NiYkJBw4coGfPniWOLSwsjA8//JB+/foBMH/+fPbv38+iRYv48ssvlXYhISG88cYbAMyaNYvmzZtz/vx5mjZtSkZGBm+//TYuLi7A45+Lv/rkk0+Un5HJkyfzxhtv8L///U+Zuf8rPT09Vq1aRe3atWnevDmzZ89m4sSJzJkzhxo1Cv9bNTQ09JkJuBBCCCGqhr/1tJCkpCT279+v9Ra/J8nok9KP1NRUAgICaNy4MWq1WikDyMjIACAxMRFXV1clsS6MnZ2dklgDWFlZkZWVBTyebb179y5du3bVimPNmjUFyk88PDxKPLbg4GCGDx9Oly5dmDdvXoG+nseTGfOnE7cnfxA8UZJzChSYeX/6nDwvW1tbJfl9Ektubi6mpqZa8Vy8eLHI83H16lVGjBiBg4MDxsbGqNVqcnNzletdEjk5Ofzxxx94eXlprffy8iI5OVlr3dPnwcrKCkA5D+PGjePjjz/Gy8uLGTNmcPLkyQLHKm7/wrRs2ZLatWsrnz09PcnNzeXy5ctF7jNlyhRu3bqlLMW1FUIIIUTl9rfu6MvNzcXPz4/58+cX2PYkUfHz88PW1pbly5djbW1Nfn4+r776qlKnamBg8Mzj1KxZU+uzSqXSKisB2LFjBw0aNNBq92S29Yk6deqUcGSP62379+/Pjh072LVrFzNmzGD9+vX06tWryH1UKhV/feHlw4cPS3xMKNk5heLPyfP66/nJzc3FysqK6OjoAm2LelrK4MGDuX79OosXL8bW1hZ9fX08PT3LrS756fOgUqkAlPMwfPhwfHx82LFjB3v27CE0NJTw8HD+9a9/lWj/sqKvr1/gZ1EIIYQQVVOpkms9PT3y8vKUz25ubmzatAk7O7tCn7xx/fp1UlJSWL58Oe3btwcocCNhixYtWLFiBTdu3Ch29roozZo1Q19fn4yMDK0SkLLg6OiIo6MjEyZMICAggIiICHr16lXgPDxhZmZGZmam8jk1NZW7d+8qn52dnVm7dq1W2cHRo0e1+njWOS2pmjVrFhpjabi5uXHlyhV0dXVLdFMkQExMDEuXLsXX1xeAy5cva92MWZLY1Go11tbWxMTEaF3TmJgY2rRpU6ox2NjYMHr0aEaPHs2UKVNYvny5VnJdWklJSdy7d0/5o/Do0aMYGhpiY2Pz3H0KIYQQouooVVmInZ0dsbGxpKenc+3aNcaMGcONGzcICAggLi6OtLQ0fvrpJ4YMGUJeXh5169bF1NSUr7/+mvPnz/Pzzz8THBys1WdAQACWlpb4+/sTExPDhQsX2LRpk9ZTH4pjZGRESEgIEyZMYPXq1aSlpXH8+HGWLFnC6tWrSzM8xb179xg7dizR0dFcunSJmJgY4uLicHZ2Vs5Dbm4uUVFRXLt2TUmgO3XqxBdffMGJEyeIj49n9OjRWjOj/fv3R6VSMWLECM6ePcvOnTsJCwvTOvazzmlJ2dnZERUVxZUrV7h58+ZznYcuXbrg6emJv78/e/bsIT09ncOHDzN16lTi4+ML3cfBwYG1a9eSnJxMbGwsAwYMKPB/J0oS28SJE5k/fz4bNmwgJSWFyZMnk5iYyPjx40scf1BQED/99BMXL17k+PHj7N+/X7mGz+vBgwcMGzZMuX4zZsxg7NixRdZbCyGEEKJ6KVVGEBISgo6ODs2aNcPMzIwHDx4QExNDXl4e3bp1w8XFhaCgIExMTKhRowY1atRg/fr1JCQk8OqrrzJhwgQWLlyo1aeenh579uzB3NwcX19fXFxcmDdvHjo6OiWOa86cOUybNo3Q0FCcnZ3p3r07O3bswN7evjTDU+jo6HD9+nUGDRqEo6Mjffv2pUePHspNae3atWP06NG8++67mJmZsWDBAgDCw8OxsbGhffv29O/fn5CQEK36XENDQ7Zv386pU6dwdXVl6tSpBco/nszYFnVOSyo8PJy9e/diY2ODq6vrc50HlUrFzp076dChA0OGDMHR0ZF+/fpx6dIlLCwsCt1n5cqV3Lx5Ezc3NwYOHMi4ceMwNzcvdWzjxo0jODiYDz74ABcXF3bv3s22bdu0bix9lry8PMaMGaP8TDg6OrJ06dKSn4BCdO7cGQcHBzp06MC7777Lm2++qfUYRCGEEEJUbyrNX4uEhRCFCgwMJDs7+2+/+TInJwdjY2Nu3bqFWq0um+CqCfeJayo6BFGFJCwcVNEhCCEqkZJ+f8srCoUQlYYkQ0IIIV521bZQtHnz5lqPl3t6WbduXUWHV+aq23iFEEIIISpCtS0LuXTpUpGPybOwsNB6rnZVUN3G+zKTshAhhBCi8pGykGewtbWt6BBeqOo2XlH5SX21KG9SZiSEKA/VtixECCGEEEKIsibJtRBCCCGEEGVEkmtRLVy5coWuXbtSp06dIl/dLoQQQgjxd0lyLaqFzz77jMzMTBITEzl37lyJ9pk5cyatWrUq38CEEEIIUaVU2xsaRfWSlpaGu7t7qd7wKIQQQghRWjJzLSqNjRs34uLigoGBAaampnTp0oU7d+4QFxdH165dqV+/PsbGxnh7e3P8+HFlPzs7OzZt2sSaNWtQqVQEBgYCkJ2dzfDhwzEzM0OtVtOpUyeSkpIAiIyMZNasWSQlJaFSqVCpVERGRjJ06FB69uypFdfDhw8xNzdn5cqVhcZ9//59cnJytBYhhBBCVE0ycy0qhczMTAICAliwYAG9evXi9u3b/PLLL2g0Gm7fvs3gwYNZsmQJGo2G8PBwfH19SU1NxcjIiLi4OAYNGoRarWbx4sUYGBgA0KdPHwwMDNi1axfGxsYsW7aMzp07c+7cOd59911Onz7N7t272bdvHwDGxsY4OjrSoUMHMjMzsbKyAuDHH3/k7t27vPvuu4XGHhoayqxZs17MiRJCCCFEhZLkWlQKmZmZPHr0iN69eyvP7HZxcQGgU6dOWm2//vprTExMOHDgAD179sTMzAx9fX0MDAywtLQE4NChQxw7doysrCz09fUBCAsLY+vWrWzcuJGRI0diaGiIrq6usg9Au3btcHJyYu3atUyaNAmAiIgI+vTpg6GhYaGxT5kyheDgYOVzTk4ONjY2ZXRmhBBCCPEykbIQUSm0bNmSzp074+LiQp8+fVi+fDk3b94E4OrVq4wYMQIHBweMjY1Rq9Xk5uaSkZFRZH9JSUnk5uZiamqq9Sr4ixcvkpaWVmwsw4cPJyIiQjn2rl27GDp0aJHt9fX1UavVWosQQgghqiaZuRaVgo6ODnv37uXw4cPs2bOHJUuWMHXqVGJjY/nnP//J9evXWbx4Mba2tujr6+Pp6cmDBw+K7C83NxcrKyuio6MLbHvWo/oGDRrE5MmTOXLkCIcPH8be3p727dv/zREKIYQQoiqQ5FpUGiqVCi8vL7y8vJg+fTq2trZs2bKFmJgYli5diq+vLwCXL1/m2rVrxfbl5ubGlStX0NXVxc7OrtA2enp65OXlFVhvamqKv78/ERERHDlyhCFDhvztsQkhhBCiapDkWlQKsbGxREVF0a1bN8zNzYmNjeXPP//E2dkZBwcH1q5di4eHBzk5OUycOFG5abEoXbp0wdPTE39/fxYsWICjoyN//PEHO3bsoFevXnh4eGBnZ8fFixdJTEykYcOGGBkZKfXZw4cPp2fPnuTl5TF48OAXcQqEEEIIUQlIzbWoFNRqNQcPHsTX1xdHR0c++ugjwsPD6dGjBytXruTmzZu4ubkxcOBAxo0bh7m5ebH9qVQqdu7cSYcOHRgyZAiOjo7069ePS5cuYWFhAcDbb79N9+7def311zEzM+Pbb79V9u/SpQtWVlb4+PhgbW1drmMXQgghROWh0mg0mooOQojKJjc3lwYNGhAREUHv3r1LtW9OTg7GxsbcunVLbm4shvvENRUdgqjiEhYOqugQhBCVSEm/v6UsRIhSyM/P59q1a4SHh2NiYsKbb75Z0SFVWZL4CCGEqIwkuRaiFDIyMrC3t6dhw4ZERkaiqyu/QkIIIYT4P5IZCFEKdnZ2SCWVEEIIIYoiybUQ4qUhddbiRZLSIyFEeZCnhQghhBBCCFFGJLmu5jp27EhQUFC59J2eno5KpSIxMbFc+n9R7OzsWLRoUUWHIYQQQohKQMpCRLmxsbEhMzOT+vXrV3QoQgghhBAvhCTXotzo6OhgaWlZ0WEIIYQQQrwwUhYiePToEWPHjsXY2Jj69eszbdo05YkYT14rbmRkhKWlJf379ycrK0vZ9+bNmwwYMAAzMzMMDAxwcHAgIiICKLws5MyZM/Ts2RO1Wo2RkRHt27cnLS3tmTEGBgbi7+9PWFgYVlZWmJqaMmbMGB4+fKi0uX//PiEhITRo0IA6derQtm1boqOjtfo5dOgQ7du3x8DAABsbG8aNG8edO3eU7VlZWfj5+WFgYIC9vT3r1q3T2l+j0TBz5kwaNWqEvr4+1tbWjBs3rsTnWgghhBBVmyTXgtWrV6Orq8uxY8dYvHgxn376KStWrADg4cOHzJkzh6SkJLZu3Up6ejqBgYHKvtOmTePs2bPs2rWL5ORk/vOf/xRZBvL777/ToUMH9PX1+fnnn0lISGDo0KE8evSoRHHu37+ftLQ09u/fz+rVq4mMjCQyMlLZPnbsWI4cOcL69es5efIkffr0oXv37qSmpgKQlpZG9+7defvttzl58iQbNmzg0KFDjB07VukjMDCQy5cvs3//fjZu3MjSpUu1/pjYtGkTn332GcuWLSM1NZWtW7fi4uJSbNz3798nJydHaxFCCCFE1SSvP6/mOnbsSFZWFmfOnEGlUgEwefJktm3bxtmzZwu0j4+Pp3Xr1ty+fRtDQ0PefPNN6tevz6pVqwq0TU9Px97enhMnTtCqVSv+/e9/s379elJSUqhZs2ap4gwMDCQ6Opq0tDR0dHQA6Nu3LzVq1GD9+vVkZGTQuHFjMjIysLa2Vvbr0qULbdq0Ye7cuQwfPhwdHR2WLVumbD906BDe3t7cuXOHjIwMnJycOHbsGK1btwbg119/xdnZmc8++4ygoCA+/fRTli1bxunTp0s8hpkzZzJr1qwC6+X15wXJo/jEiySP4hNClEZJX38uM9eC1157TUmsATw9PUlNTSUvL4+EhAT8/Pxo1KgRRkZGeHt7A4/fVAjwz3/+k/Xr19OqVSsmTZrE4cOHizxOYmIi7du3L3Vi/UTz5s2VxBrAyspKmVU+deoUeXl5ODo6YmhoqCwHDhxQyk6SkpKIjIzU2u7j40N+fj4XL14kOTkZXV1d3N3dlWM0bdoUExMT5XOfPn24d+8ejRs3ZsSIEWzZsuWZM+9Tpkzh1q1bynL58uXnGr8QQgghXn5yQ6Mo0v/+9z98fHzw8fFh3bp1mJmZkZGRgY+PDw8ePACgR48eXLp0iZ07d7J37146d+7MmDFjCAsLK9CfgYHB34rnr0m5SqUiPz8fgNzcXHR0dEhISNBKwAEMDQ2VNqNGjSq0RrpRo0acO3fumTHY2NiQkpLCvn372Lt3L++//z4LFy7kwIEDRf7RoK+vj76+fonGKIQQQojKTZJrQWxsrNbno0eP4uDgwK+//sr169eZN28eNjY2wOOykL8yMzNj8ODBDB48mPbt2zNx4sRCk+sWLVqwevVqHj58+Nyz10VxdXUlLy+PrKws2rdvX2gbNzc3zp49S5MmTQrd3rRpUx49ekRCQoJSFpKSkkJ2drZWOwMDA/z8/PDz82PMmDE0bdqUU6dO4ebmVqZjEkIIIUTlI2UhgoyMDIKDg0lJSeHbb79lyZIljB8/nkaNGqGnp8eSJUu4cOEC27ZtY86cOVr7Tp8+nR9++IHz589z5swZfvzxR5ydnQs9ztixY8nJyaFfv37Ex8eTmprK2rVrSUlJ+dtjcHR0ZMCAAQwaNIjNmzdz8eJFjh07RmhoKDt27ADgww8/5PDhw4wdO5bExERSU1P54YcflBsanZyc6N69O6NGjSI2NpaEhASGDx+uNeMeGRnJypUrOX36NBcuXOCbb77BwMAAW1vbvz0GIYQQQlR+klwLBg0axL1792jTpg1jxoxh/PjxjBw5EjMzMyIjI/n+++9p1qwZ8+bNKzAjraenx5QpU2jRogUdOnRAR0eH9evXF3ocU1NTfv75Z3Jzc/H29sbd3Z3ly5eX2Sx2REQEgwYN4oMPPsDJyQl/f3/i4uJo1KgR8Hjm/MCBA5w7d4727dvj6urK9OnTtW6AjIiIwNraGm9vb3r37s3IkSMxNzdXtpuYmLB8+XK8vLxo0aIF+/btY/v27ZiampbJGIQQQghRucnTQoR4wUp6t3F1JE8LES+SPC1ECFEaJf3+lpprIcRLQ5IdIYQQlZ2UhYiXwtOPx/vr8ssvv1R0eEIIIYQQJSIz1+Kl8PQr0v+qQYMGLy4QIYQQQoi/QZJr8VIo6vF4QgghhBCViSTXQogKJTcxiooiNf5CiPIgNddCCCGEEEKUEUmu/yY7OzsWLVpU7sdRqVRs3bq13I/zd23dupUmTZqgo6NDUFBQRYcjhBBCCPFCSVlIJZGZmUndunUrOoxnGjVqFEOGDGHcuHEYGRlVdDhCCCGEEC+UJNeVhKWlZUWHUKyHDx9y//59srKy8PHx0XrrYWk9ePAAPT29MoxOCCGEEOLFqFJlIbt37+Yf//gHJiYmmJqa0rNnT9LS0pTtv/32GwEBAdSrV486derg4eFBbGwsAGlpabz11ltYWFhgaGhI69at2bdvn1b/WVlZ+Pn5YWBggL29PevWrSsQQ3Z2NsOHD8fMzAy1Wk2nTp1ISkpSts+cOZNWrVqxatUqGjVqhKGhIe+//z55eXksWLAAS0tLzM3N+eSTT7T6/WtZSHFjKc6T4y9btgwbGxtq165N3759uXXrlla7FStW4OzsTK1atWjatClLly5VtqWnp6NSqdiwYQPe3t7UqlWLdevWKTPVnTp1QqVSER0dDcCmTZto3rw5+vr62NnZER4ernUsOzs75syZw6BBg1Cr1YwcOZLIyEhMTEz48ccfcXJyonbt2rzzzjvcvXuX1atXY2dnR926dRk3bhx5eXlKX2vXrsXDwwMjIyMsLS3p378/WVlZyvbo6GhUKhVRUVF4eHhQu3Zt2rVrR0pKilZM27dvp3Xr1tSqVYv69evTq1cvZdv9+/cJCQmhQYMG1KlTh7Zt2ypjFUIIIUT1VqWS6zt37hAcHEx8fDxRUVHUqFGDXr16kZ+fT25uLt7e3vz+++9s27aNpKQkJk2aRH5+PgC5ubn4+voSFRXFiRMn6N69O35+fmRkZCj9BwYGcvnyZfbv38/GjRtZunSpVuIG0KdPH7Kysti1axcJCQm4ubnRuXNnbty4obRJS0tj165d7N69m2+//ZaVK1fyxhtv8Ntvv3HgwAHmz5/PRx99VGSy/KyxPMv58+f57rvv2L59O7t37+bEiRO8//77yvZ169Yxffp0PvnkE5KTk5k7dy7Tpk1j9erVWv1MnjyZ8ePHk5yczOuvv64kqJs2bSIzM5N27dqRkJBA37596devH6dOnWLmzJlMmzaNyMhIrb7CwsJo2bIlJ06cYNq0aQDcvXuXzz//nPXr17N7926io6Pp1asXO3fuZOfOnaxdu5Zly5axceNGpZ+HDx8yZ84ckpKS2Lp1K+np6QQGBhY4B1OnTiU8PJz4+Hh0dXUZOnSosm3Hjh306tULX19fTpw4QVRUFG3atFG2jx07liNHjrB+/XpOnjxJnz596N69O6mpqYWe7/v375OTk6O1CCGEEKJqUmk0Gk1FB1Ferl27hpmZGadOneLw4cOEhISQnp5OvXr1SrT/q6++yujRoxk7diznzp3DycmJY8eO0bp1awB+/fVXnJ2d+eyzzwgKCuLQoUO88cYbZGVloa+vr/TTpEkTJk2axMiRI5k5cyYLFy7kypUrykxv9+7dSUlJIS0tjRo1Hv+907RpUwIDA5k8eTLweOZ6y5Yt+Pv78/XXX5d6LE/MnDmTjz/+mEuXLikvZ9m9ezdvvPEGv//+O5aWljRp0oQ5c+YQEBCg7Pfxxx+zc+dODh8+THp6Ovb29ixatIjx48crbbKzs6lbty779++nY8eOAAwYMIA///yTPXv2KO0mTZrEjh07OHPmDPB45trV1ZUtW7YobSIjIxkyZAjnz5/nlVdeAWD06NGsXbuWq1evYmhoqJw7Ozs7vvrqq0LHGx8fT+vWrbl9+zaGhoZER0fz+uuvs2/fPjp37gzAzp07eeONN7h37x61atWiXbt2NG7cmG+++aZAfxkZGTRu3JiMjAyt0pcuXbrQpk0b5s6dW+g5nzVrVoH1t27dQq1WFxp3dSKP4hMVRR7FJ4QojZycHIyNjZ/5/V2lZq5TU1MJCAigcePGqNVq7OzsgMcJUWJiIq6urkUmo7m5uYSEhODs7IyJiQmGhoYkJycrM9fJycno6uri7u6u7NO0aVNMTEyUz0lJSeTm5mJqaqr1+u6LFy9qlafY2dlp3exnYWFBs2bNlMT6ybq/zoo/8ayxPEujRo203nro6elJfn4+KSkp3Llzh7S0NIYNG6Y1ho8//lhrDAAeHh7PPFZycjJeXl5a67y8vEhNTdUq5yisr9q1ayuJNTw+J3Z2dkpi/WTd0+cpISEBPz8/GjVqhJGREd7e3gBa/wcCoEWLFsq/raysAJR+EhMTlcT7r06dOkVeXh6Ojo5a5+fAgQMFzs8TU6ZM4datW8py+fLlQtsJIYQQovKrUjc0+vn5YWtry/Lly7G2tiY/P59XX32VBw8eYGBgUOy+ISEh7N27l7CwMJo0aYKBgQHvvPMODx48KPHxc3NzsbKyKrT+9ukkvGbNmlrbVCpVoeuKKvN41lj+jtzcXACWL19O27Zttbbp6Ohofa5Tp06ZHbewvkp7nu7cuYOPjw8+Pj6sW7cOMzMzMjIy8PHxKXAdn+5HpVIBKP0Ud35zc3PR0dEhISGhwPl4Oul/mr6+vtb/yRBCCCFE1VVlkuvr16+TkpLC8uXLad++PQCHDh1Strdo0YIVK1Zw48aNQmd8Y2JiCAwMVG5cy83NJT09XdnetGlTHj16REJCglIWkpKSQnZ2ttLGzc2NK1euoKurq8yal4dnjeVZMjIy+OOPP5SyhqNHj1KjRg2cnJywsLDA2tqaCxcuMGDAgL8dq7OzMzExMVrrYmJicHR0LJCc/l2//vor169fZ968edjY2ACPy0JKq0WLFkRFRTFkyJAC21xdXcnLyyMrK0v5ORNCCCGEeKLKlIXUrVsXU1NTvv76a86fP8/PP/9McHCwsj0gIABLS0v8/f2JiYnhwoULbNq0iSNHjgDg4ODA5s2bSUxMJCkpif79+2vNHDs5OdG9e3dGjRpFbGwsCQkJDB8+XGuWs0uXLnh6euLv78+ePXtIT0/n8OHDTJ069bmSvKI8ayzPUqtWLQYPHkxSUhK//PIL48aNo2/fvsrj/mbNmkVoaCiff/45586d49SpU0RERPDpp5+WOtYPPviAqKgo5syZw7lz51i9ejVffPEFISEhpe7rWRo1aoSenh5LlizhwoULbNu2jTlz5pS6nxkzZvDtt98yY8YMkpOTOXXqFPPnzwfA0dGRAQMGMGjQIDZv3szFixc5duwYoaGh7Nixo6yHJIQQQohKpsok1zVq1GD9+vUkJCTw6quvMmHCBBYuXKhs19PTY8+ePZibm+Pr64uLiwvz5s1TZk8//fRT6tatS7t27fDz88PHxwc3NzetY0RERGBtbY23tze9e/dm5MiRmJubK9tVKhU7d+6kQ4cODBkyBEdHR/r168elS5ewsLAos7E+ayzP0qRJE3r37o2vry/dunWjRYsWWo/aGz58OCtWrCAiIgIXFxe8vb2JjIzE3t6+1LG6ubnx3XffsX79el599VWmT5/O7NmzC32Cx99lZmZGZGQk33//Pc2aNWPevHmEhYWVup+OHTvy/fffs23bNlq1akWnTp04duyYsj0iIoJBgwbxwQcf4OTkhL+/P3FxcTRq1KgshyOEEEKISqhKPy1EFDRz5ky2bt1KYmJiRYdSbZX0buPqQp4WIiqKPC1ECFEaJf3+rjI110KIykkSHCGEEFWJJNdVTPPmzbl06VKh25YtW/aCoxFCCCGEqF6kLKSKuXTpEg8fPix0m4WFhdbztUXFkLIQIYQQovKRspBqytbWtqJDENWE1EqLyk5KkoQQ5aHKPC1ECCGEEEKIiibJtRBCCCGEEGVEkutqLjAwEH9//4oOo0L8dewdO3YkKCiowuIRQgghROUnNddC/H+bN2+mZs2aymc7OzuCgoIk4RZCCCFEiUlyLcT/V69evYoOQQghhBCVnJSFVBMbN27ExcUFAwMDTE1N6dKlC3fu3CnQLj8/n9DQUOzt7TEwMKBly5Zs3LhRq83p06fp0aMHhoaGWFhYMHDgQK5du6Zs79ixI2PHjmXs2LEYGxtTv359pk2bRkmf+piVlYWfnx8GBgbY29uzbt067OzsWLRoEQDp6emoVCqtt0xmZ2ejUqmIjo4GIC8vj2HDhinjcHJyYvHixcUe9+mykI4dO3Lp0iUmTJiASqVCpVJx584d1Gp1gfOxdetW6tSpw+3btwvt9/79++Tk5GgtQgghhKiaJLmuBjIzMwkICGDo0KEkJycTHR1N7969C012Q0NDWbNmDV999RVnzpxhwoQJvPfeexw4cAB4nMR26tQJV1dX4uPj2b17N1evXqVv375a/axevRpdXV2OHTvG4sWL+fTTT1mxYkWJ4g0MDOTy5cvs37+fjRs3snTpUrKysko15vz8fBo2bMj333/P2bNnmT59Ov/+97/57rvvSrT/5s2badiwIbNnzyYzM5PMzEzq1KlDv379iIiI0GobERHBO++8U+QzxENDQzE2NlYWGxubUo1FCCGEEJWHlIVUA5mZmTx69IjevXsrz8F2cXEp0O7+/fvMnTuXffv24enpCUDjxo05dOgQy5Ytw9vbmy+++AJXV1fmzp2r7Ldq1SpsbGw4d+4cjo6OANjY2PDZZ5+hUqlwcnLi1KlTfPbZZ4wYMaLYWM+dO8euXbs4duwYrVu3BmDlypU4OzuXasw1a9Zk1qxZymd7e3uOHDnCd999V+APgcLUq1cPHR0djIyMsLS0VNYPHz6cdu3akZmZiZWVFVlZWezcuZN9+/YV2deUKVMIDg5WPufk5EiCLYQQQlRRMnNdDbRs2ZLOnTvj4uJCnz59WL58OTdv3izQ7vz589y9e5euXbtiaGioLGvWrCEtLQ2ApKQk9u/fr7W9adOmAEobgNdeew2VSqV89vT0JDU1lby8vGJjTU5ORldXF3d3d2Vd06ZNMTExKfW4v/zyS9zd3TEzM8PQ0JCvv/6ajIyMUvfztDZt2tC8eXNWr14NwDfffIOtrS0dOnQoch99fX3UarXWIoQQQoiqSWauqwEdHR327t3L4cOH2bNnD0uWLGHq1KnExsZqtcvNzQVgx44dNGjQQGubvr6+0sbPz4/58+cXOI6VlVU5jUBbjRqP/yZ8uqzlr698X79+PSEhIYSHh+Pp6YmRkRELFy4sMObnMXz4cL788ksmT55MREQEQ4YM0fpDQgghhBDVlyTX1YRKpcLLywsvLy+mT5+Ora0tW7Zs0WrTrFkz9PX1ycjIwNvbu9B+3Nzc2LRpE3Z2dujqFv3j89ck9ujRozg4OKCjo1NsnE2bNuXRo0ckJCQoZSEpKSlkZ2crbczMzIDH5S6urq4AWjc3AsTExNCuXTvef/99Zd3TM+sloaenV+hM+3vvvcekSZP4/PPPOXv2LIMHDy5Vv0IIIYSouqQspBqIjY1l7ty5xMfHk5GRwebNm/nzzz8L1DEbGRkREhLChAkTWL16NWlpaRw/fpwlS5YoZRBjxozhxo0bBAQEEBcXR1paGj/99BNDhgzRSkQzMjIIDg4mJSWFb7/9liVLljB+/Phnxurk5ET37t0ZNWoUsbGxJCQkMHz4cAwMDJQ2BgYGvPbaa8ybN4/k5GQOHDjARx99pNWPg4MD8fHx/PTTT5w7d45p06YRFxdXqvNmZ2fHwYMH+f3337WehlK3bl169+7NxIkT6datGw0bNixVv0IIIYSouiS5rgbUajUHDx7E19cXR0dHPvroI8LDw+nRo0eBtnPmzGHatGmEhobi7OxM9+7d2bFjB/b29gBYW1sTExNDXl4e3bp1w8XFhaCgIExMTJRyDYBBgwZx79492rRpw5gxYxg/fjwjR44sUbwRERFYW1vj7e1N7969GTlyJObm5lptVq1axaNHj3B3dycoKIiPP/5Ya/uoUaPo3bs37777Lm3btuX69etas9glMXv2bNLT03nllVeU2fInhg0bxoMHDxg6dGip+hRCCCFE1abSlPThw0KUUMeOHWnVqpXyXOqy8LK9LXHt2rVMmDCBP/74Az09vVLtm5OTg7GxMbdu3ZKbG4UQQohKoqTf31JzLUQp3L17l8zMTObNm8eoUaNKnVgLIYQQomqTshDxQv3yyy9aj/H76/KyW7BgAU2bNsXS0pIpU6ZUdDhCCCGEeMlIWYh4oe7du8fvv/9e5PYmTZq8wGgqhpSFCCGEEJWPlIWIl5KBgUG1SKCrAveJayo6BCHKVcLCQRUdghCiCpKyECGEEEIIIcqIJNeiWDNnzqRVq1al2kelUrF169ZyiUcIIYQQ4mUmybUoVkhICFFRURUdxkspOjoalUql9fZIIYQQQlRvUnMtilVZnuLxoj18+LCiQxBCCCHES0hmrquIjRs34uLigoGBAaampnTp0oU7d+6Qn5/P7NmzadiwIfr6+rRq1Yrdu3dr7fvbb78REBBAvXr1qFOnDh4eHsTGxgIFy0Li4uLo2rUr9evXx9jYGG9vb44fP/5cMT948ICxY8diZWVFrVq1sLW1JTQ0FID09HRUKhWJiYlK++zsbFQqFdHR0cD/zRzv2LGDFi1aUKtWLV577TVOnz6t7BMZGYmJiQlbt27FwcGBWrVq4ePjw+XLl7Vi+c9//sMrr7yCnp4eTk5OrF27Vmu7SqXiP//5D2+++SZ16tRhxIgRvP7668Dj16GrVCoCAwOf6zwIIYQQouqQ5LoKyMzMJCAggKFDh5KcnEx0dDS9e/dGo9GwePFiwsPDCQsL4+TJk/j4+PDmm2+SmpoKQG5uLt7e3vz+++9s27aNpKQkJk2aRH5+fqHHun37NoMHD+bQoUMcPXoUBwcHfH19uX37dqnj/vzzz9m2bRvfffcdKSkprFu3Djs7u1L3M3HiRMLDw4mLi8PMzAw/Pz+tmeW7d+/yySefsGbNGmJiYsjOzqZfv37K9i1btjB+/Hg++OADTp8+zahRoxgyZAj79+/XOs7MmTPp1asXp06dYtasWWzatAmAlJQUMjMzWbx4caHx3b9/n5ycHK1FCCGEEFWTlIVUAZmZmTx69IjevXtja2sLgIuLCwBhYWF8+OGHSjI5f/589u/fz6JFi/jyyy/573//y59//klcXBz16tUDin/WdKdOnbQ+f/3115iYmHDgwAF69uxZqrgzMjJwcHDgH//4ByqVSom9tGbMmEHXrl0BWL16NQ0bNmTLli307dsXeFzC8cUXX9C2bVuljbOzM8eOHaNNmzaEhYURGBjI+++/D0BwcDBHjx4lLCxMmZ0G6N+/P0OGDFE+X7x4EQBzc3NMTEyKjC80NJRZs2Y919iEEEIIUbnIzHUV0LJlSzp37oyLiwt9+vRh+fLl3Lx5k5ycHP744w+8vLy02nt5eZGcnAxAYmIirq6uSmL9LFevXmXEiBE4ODhgbGyMWq0mNzeXjIyMUscdGBhIYmIiTk5OjBs3jj179pS6DwBPT0/l3/Xq1cPJyUkZH4Curi6tW7dWPjdt2hQTExOlTXJycrHn6AkPD4/nim/KlCncunVLWf5akiKEEEKIqkOS6ypAR0eHvXv3smvXLpo1a8aSJUtwcnJSZlaLY2BgUKpjDR48mMTERBYvXszhw4dJTEzE1NSUBw8elDpuNzc3Ll68yJw5c7h37x59+/blnXfeAaBGjcc/mk+/QLSibyKsU6fOc+2nr6+PWq3WWoQQQghRNUlyXUWoVCq8vLyYNWsWJ06cQE9Pj6ioKKytrYmJidFqGxMTQ7NmzQBo0aIFiYmJ3Lhxo0THiYmJYdy4cfj6+tK8eXP09fW5du3ac8etVqt59913Wb58ORs2bGDTpk3cuHEDMzMz4HHJyxNP39z4tKNHjyr/vnnzJufOncPZ2VlZ9+jRI+Lj45XPKSkpZGdnK22cnZ2LPUdF0dPTAyAvL68EIxVCCCFEdSA111VAbGwsUVFRdOvWDXNzc2JjY/nzzz9xdnZm4sSJzJgxg1deeYVWrVoRERFBYmIi69atAyAgIIC5c+fi7+9PaGgoVlZWnDhxAmtra61yiyccHBxYu3YtHh4e5OTkMHHixFLPfj/x6aefYmVlhaurKzVq1OD777/H0tISExMTatSowWuvvca8efOwt7cnKyuLjz76qNB+Zs+ejampKRYWFkydOpX69evj7++vbK9Zsyb/+te/+Pzzz9HV1WXs2LG89tprtGnTBnh8Q2Tfvn1xdXWlS5cubN++nc2bN7Nv375i47e1tUWlUvHjjz/i6+uLgYGBPLZQCCGEqOZk5roKUKvVHDx4EF9fXxwdHfnoo48IDw+nR48ejBs3juDgYD744ANcXFzYvXs327Ztw8HBAXg8+7pnzx7Mzc3x9fXFxcWFefPmoaOjU+ixVq5cyc2bN3Fzc2PgwIGMGzcOc3Pz54rbyMiIBQsW4OHhQevWrUlPT2fnzp1KSciqVat49OgR7u7uBAUF8fHHHxfaz7x58xg/fjzu7u5cuXKF7du3K7PKALVr1+bDDz+kf//+eHl5YWhoyIYNG5Tt/v7+LF68mLCwMJo3b86yZcuIiIigY8eOxcbfoEEDZs2axeTJk7GwsGDs2LHPdR6EEEIIUXWoNE8XtQpRiURHR/P6669z8+bNIp/WERkZSVBQ0Ev1FsWcnByMjY25devWS11/7T5xTUWHIES5Slg4qKJDEEJUIiX9/payECFEoSTxEEIIIUpPykJEuZk7d67y+vS/Lj169Kjo8IQQQgghypyUhYhyc+PGjSKfQmJgYECDBg1ecEQvh8pSFiKEEEKI/yNlIaLC1atXr8QvpxFlQ+qkhSg5KX0SQpQHKQsRQgghhBCijEhyLYQQQgghRBmR5Fr8benp6ahUKq03KMbExODi4kLNmjW1XugihBBCCFGVSc21KBfBwcG0atWKXbt2lftbCwMDA8nOzmbr1q3lehwhhBBCiGeRmWtRLtLS0ujUqRMNGzYs8gUvQgghhBBVjSTXQrFx40ZcXFwwMDDA1NSULl26cOfOHQBWrFiBs7MztWrVomnTpixdurTQPp6UiFy/fp2hQ4eiUqmIjIx85rFPnz5Njx49MDQ0xMLCgoEDB3Lt2rVnxjZz5kxWr17NDz/8gEqlQqVSER0dDcDly5fp27cvJiYm1KtXj7feeov09HSt4xY3ridj+e6772jfvj0GBga0bt2ac+fOERcXh4eHh/LM7j///LPIsd2/f5+cnBytRQghhBBVkyTXAoDMzEwCAgIYOnQoycnJREdH07t3bzQaDevWrWP69Ol88sknJCcnM3fuXKZNm8bq1asL9GNjY0NmZiZqtZpFixaRmZnJu+++W+yxs7Oz6dSpE66ursTHx7N7926uXr1K3759nxlbSEgIffv2pXv37mRmZpKZmUm7du14+PAhPj4+GBkZ8csvvxATE4OhoSHdu3fnwYMHACUe14wZM/joo484fvw4urq69O/fn0mTJrF48WJ++eUXzp8/z/Tp04scX2hoKMbGxspiY2NT2ssjhBBCiEpCaq4F8DiBffToEb1798bW1hYAFxcX4HFyGR4eTu/evQGwt7fn7NmzLFu2jMGDB2v1o6Ojg6WlJSqVCmNjYywtLZ957C+++AJXV1fmzp2rrFu1ahU2NjacO3eO3NzcImODxy+kuX//vtaxvvnmG/Lz81mxYgUqlQqAiIgITExMiI6Oplu3biUeV0hICD4+PgCMHz+egIAAoqKi8PLyAmDYsGHFzs5PmTKF4OBg5XNOTo4k2EIIIUQVJcm1AKBly5Z07twZFxcXfHx86NatG++88w56enqkpaUxbNgwRowYobR/9OgRxsbGZXLspKQk9u/fX+iNj2lpaXTr1q3Q2OrWrVtsn+fPn8fIyEhr/f/+9z/S0tK4c+dOicfVokUL5d8WFhaAdnJvYWFBVlZWkbHo6+ujr69f5HYhhBBCVB2SXAvg8Yzz3r17OXz4MHv27GHJkiVMnTqV7du3A7B8+XLatm1bYJ+ykJubi5+fH/Pnzy+wzcrKqsjYYmNjsbe3L7JPd3d31q1bV2CbmZkZubm5QMnGVbNmTeXfT2bB/7ouPz+/hKMVQgghRFUmybVQqFQqvLy88PLyYvr06dja2hITE4O1tTUXLlxgwIAB5XJcNzc3Nm3ahJ2dHbq6hf9IFhbbli1bCA4ORk9Pj7y8vAJ9btiwAXNzc9RqdYH+jI2Ny31cQgghhKh+5IZGAUBsbCxz584lPj6ejIwMNm/ezJ9//omzszOzZs0iNDSUzz//nHPnznHq1CkiIiL49NNPy+TYY8aM4caNGwQEBBAXF0daWho//fQTQ4YMIS8vr9jYAOzs7Dh58iQpKSlcu3aNhw8fMmDAAOrXr89bb73FL7/8wsWLF4mOjmbcuHH89ttvAOU+LiGEEEJUPzJzLQBQq9UcPHiQRYsWkZOTg62tLeHh4fTo0QOA2rVrs3DhQiZOnEidOnVwcXEhKCioTI5tbW1NTEwMH374Id26deP+/fvY2trSvXt3atSo8czYRowYQXR0NB4eHuTm5rJ//346duzIwYMH+fDDD+nduze3b9+mQYMGdO7cWZnJHj58eLmOSwghhBDVj0qj0WgqOgghqpOcnByMjY25detWoSUrf4f7xDVl2p8QVVnCwkEVHYIQohIp6fe3zFwL8YI9+Xu2PF4ms3+af5n3KURVJS90EkKUxpP/ZjxrXlqSa1HuRo8ezTfffFPotvfee4+vvvrqBUdUsW7fvg0gz7oWQgghKqHbt28X+zhiKQsR5S4rK6vIGSK1Wo25ufkLjqhi5efn88cff2BkZKQ82q8sPXlJzeXLl8u87EQ8H7kmLx+5Ji8fuSYvH7km2jQaDbdv38ba2poaNYp+JojMXItyZ25uXu0S6OLUqFGDhg0blvtx1Gq1/MfwJSPX5OUj1+TlI9fk5SPX5P+U5AV68ig+IYQQQgghyogk10IIIYQQQpQRSa6FqGL09fWZMWMG+vr6FR2K+P/kmrx85Jq8fOSavHzkmjwfuaFRCCGEEEKIMiIz10IIIYQQQpQRSa6FEEIIIYQoI5JcCyGEEEIIUUYkuRZCCCGEEKKMSHIthBBCCCFEGZHkWohK7pNPPqFdu3bUrl0bExOTEu2j0WiYPn06VlZWGBgY0KVLF1JTU8s30Grkxo0bDBgwALVajYmJCcOGDSM3N7fYfTp27IhKpdJaRo8e/YIirpq+/PJL7OzsqFWrFm3btuXYsWPFtv/+++9p2rQptWrVwsXFhZ07d76gSKuP0lyTyMjIAr8TtWrVeoHRVn0HDx7Ez88Pa2trVCoVW7dufeY+0dHRuLm5oa+vT5MmTYiMjCz3OCsbSa6FqOQePHhAnz59+Oc//1nifRYsWMDnn3/OV199RWxsLHXq1MHHx4f//e9/5Rhp9TFgwADOnDnD3r17+fHHHzl48CAjR4585n4jRowgMzNTWRYsWPACoq2aNmzYQHBwMDNmzOD48eO0bNkSHx8fsrKyCm1/+PBhAgICGDZsGCdOnMDf3x9/f39Onz79giOvukp7TeDxa7ef/p24dOnSC4y46rtz5w4tW7bkyy+/LFH7ixcv8sYbb/D666+TmJhIUFAQw4cP56effirnSCsZjRCiSoiIiNAYGxs/s11+fr7G0tJSs3DhQmVddna2Rl9fX/Ptt9+WY4TVw9mzZzWAJi4uTlm3a9cujUql0vz+++9F7uft7a0ZP378C4iwemjTpo1mzJgxyue8vDyNtbW1JjQ0tND2ffv21bzxxhta69q2basZNWpUucZZnZT2mpT0v2mibACaLVu2FNtm0qRJmubNm2ute/fddzU+Pj7lGFnlIzPXQlQzFy9e5MqVK3Tp0kVZZ2xsTNu2bTly5EgFRlY1HDlyBBMTEzw8PJR1Xbp0oUaNGsTGxha777p166hfvz6vvvoqU6ZM4e7du+UdbpX04MEDEhIStH7Ga9SoQZcuXYr8GT9y5IhWewAfHx/5nSgjz3NNAHJzc7G1tcXGxoa33nqLM2fOvIhwRRHk96RkdCs6ACHEi3XlyhUALCwstNZbWFgo28Tzu3LlCubm5lrrdHV1qVevXrHnt3///tja2mJtbc3Jkyf58MMPSUlJYfPmzeUdcpVz7do18vLyCv0Z//XXXwvd58qVK/I7UY6e55o4OTmxatUqWrRowa1btwgLC6Ndu3acOXOGhg0bvoiwxV8U9XuSk5PDvXv3MDAwqKDIXi4ycy3ES2jy5MkFbuT561LUF5IoH+V9TUaOHImPjw8uLi4MGDCANWvWsGXLFtLS0spwFEJUHp6engwaNIhWrVrh7e3N5s2bMTMzY9myZRUdmhDFkplrIV5CH3zwAYGBgcW2ady48XP1bWlpCcDVq1exsrJS1l+9epVWrVo9V5/VQUmviaWlZYEbtB49esSNGzeUc18Sbdu2BeD8+fO88sorpY63Oqtfvz46OjpcvXpVa/3Vq1eLvAaWlpalai9K53muyV/VrFkTV1dXzp8/Xx4hihIo6vdErVbLrPVTJLkW4iVkZmaGmZlZufRtb2+PpaUlUVFRSjKdk5NDbGxsqZ44Ut2U9Jp4enqSnZ1NQkIC7u7uAPz888/k5+crCXNJJCYmAmj9ASRKRk9PD3d3d6KiovD39wcgPz+fqKgoxo4dW+g+np6eREVFERQUpKzbu3cvnp6eLyDiqu95rslf5eXlcerUKXx9fcsxUlEcT0/PAo+olN+TQlT0HZVCiL/n0qVLmhMnTmhmzZqlMTQ01Jw4cUJz4sQJze3bt5U2Tk5Oms2bNyuf582bpzExMdH88MMPmpMnT2reeustjb29vebevXsVMYQqp3v37hpXV1dNbGys5tChQxoHBwdNQECAsv23337TODk5aWJjYzUajUZz/vx5zezZszXx8fGaixcvan744QdN48aNNR06dKioIVR669ev1+jr62siIyM1Z8+e1YwcOVJjYmKiuXLlikaj0WgGDhyomTx5stI+JiZGo6urqwkLC9MkJydrZsyYoalZs6bm1KlTFTWEKqe012TWrFman376SZOWlqZJSEjQ9OvXT1OrVi3NmTNnKmoIVc7t27eV7wxA8+mnn2pOnDihuXTpkkaj0WgmT56sGThwoNL+woULmtq1a2smTpyoSU5O1nz55ZcaHR0dze7duytqCC8lSa6FqOQGDx6sAQos+/fvV9oAmoiICOVzfn6+Ztq0aRoLCwuNvr6+pnPnzpqUlJQXH3wVdf36dU1AQIDG0NBQo1arNUOGDNH6Y+fixYta1ygjI0PToUMHTb169TT6+vqaJk2aaCZOnKi5detWBY2galiyZImmUaNGGj09PU2bNm00R48eVbZ5e3trBg8erNX+u+++0zg6Omr09PQ0zZs31+zYseMFR1z1leaaBAUFKW0tLCw0vr6+muPHj1dA1FXX/v37C/3+eHIdBg8erPH29i6wT6tWrTR6enqaxo0ba323iMdUGo1GUyFT5kIIIYQQQlQx8rQQIYQQQgghyogk10IIIYQQQpQRSa6FEEIIIYQoI5JcCyGEEEIIUUYkuRZCCCGEEKKMSHIthBBCCCFEGZHkWgghhBBCiDIiybUQQgghhBBlRJJrIYQQQgghyogk10IIIYQQQpQRSa6FEEIIIYQoI/8P2zIw4ZcKEx8AAAAASUVORK5CYII=\n"},"metadata":{}}],"source":["sns.barplot(data=high_stress_mean,orient='y')\n","plt.title('Mean of scaled categories when Reporting Stress Level 2');"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"elapsed":936,"status":"ok","timestamp":1716216954410,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"gFWUgCqzS5ph","outputId":"0ee3b12b-8c34-4ecd-8c6c-b08730eb8bd3"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["sns.barplot(data=mm_high_stress.mean(),orient='y');"]},{"cell_type":"markdown","metadata":{"id":"ftv25SkqW7eR"},"source":["### Stress Level 1"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"elapsed":1404,"status":"ok","timestamp":1716216959880,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"MP3wZgqhbrMq","outputId":"094e1336-a463-4827-caca-b79787f8b3a3"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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0.175964 \n","75% 0.349125 -1.418416 0.219684 0.890211 \n","max 1.768859 0.981981 1.511942 1.604458 \n","\n"," noise_level living_conditions ... basic_needs academic_performance \\\n","count 358.000000 358.000000 ... 358.000000 358.000000 \n","mean -0.103891 -0.013756 ... -0.205898 -0.198810 \n","std 0.609152 0.716706 ... 0.597000 0.571942 \n","min -1.995514 -2.250991 ... -1.934764 -1.960979 \n","25% -0.488949 -0.463200 ... -0.539196 -0.546502 \n","50% 0.264334 -0.463200 ... 0.158587 -0.546502 \n","75% 0.264334 0.430695 ... 0.158587 0.160736 \n","max 1.770899 2.218486 ... 1.554154 1.575213 \n","\n"," study_load teacher_student_relationship future_career_concerns \\\n","count 358.000000 358.000000 358.000000 \n","mean -0.067138 -0.210008 -0.082911 \n","std 0.647756 0.543843 0.535599 \n","min -1.993501 -1.913497 -1.732927 \n","25% -0.472800 -0.468357 -0.424609 \n","50% -0.472800 -0.468357 -0.424609 \n","75% 0.287551 0.254213 0.229550 \n","max 1.808252 0.976783 1.537869 \n","\n"," social_support peer_pressure extracurricular_activities bullying \\\n","count 358.000000 358.000000 358.000000 358.000000 \n","mean 0.283528 -0.194049 -0.202429 -0.040128 \n","std 0.893387 0.576581 0.560741 0.566601 \n","min -1.796742 -1.919495 -1.953023 -1.710343 \n","25% 0.112839 -0.515609 -0.541508 -0.403377 \n","50% 0.112839 -0.515609 -0.541508 0.250106 \n","75% 1.067629 0.186334 0.164249 0.250106 \n","max 1.067629 1.590220 1.575763 1.557071 \n","\n"," stress_level \n","count 3.580000e+02 \n","mean 4.427575e-03 \n","std 8.685757e-19 \n","min 4.427575e-03 \n","25% 4.427575e-03 \n","50% 4.427575e-03 \n","75% 4.427575e-03 \n","max 4.427575e-03 \n","\n","[8 rows x 21 columns]"],"text/html":["\n","
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count358.000000358.000000358.000000358.000000358.000000358.000000358.000000358.000000358.000000358.000000...358.000000358.000000358.000000358.000000358.000000358.000000358.000000358.000000358.0000003.580000e+02
mean0.0599420.166131-0.007802-0.088192-0.033568-1.029525-0.0835280.124092-0.103891-0.013756...-0.205898-0.198810-0.067138-0.210008-0.0829110.283528-0.194049-0.202429-0.0401284.427575e-03
std0.4818910.5486331.0012550.4699540.5927900.8856910.5526370.8654180.6091520.716706...0.5970000.5719420.6477560.5438430.5355990.8933870.5765810.5607410.5666018.685757e-19
min-1.645790-1.764691-0.985559-1.625618-1.780475-1.418416-1.718703-1.966776-1.995514-2.250991...-1.934764-1.960979-1.993501-1.913497-1.732927-1.796742-1.919495-1.953023-1.7103434.427575e-03
25%-0.173945-0.086938-0.985559-0.330868-0.360741-1.418416-0.426445-0.538282-0.488949-0.463200...-0.539196-0.546502-0.472800-0.468357-0.4246090.112839-0.515609-0.541508-0.4033774.427575e-03
50%-0.0104070.248612-0.985559-0.071918-0.360741-1.418416-0.4264450.1759640.264334-0.463200...0.158587-0.546502-0.472800-0.468357-0.4246090.112839-0.515609-0.5415080.2501064.427575e-03
75%0.3166700.5841631.0146530.0575580.349125-1.4184160.2196840.8902110.2643340.430695...0.1585870.1607360.2875510.2542130.2295501.0676290.1863340.1642490.2501064.427575e-03
max1.6249761.3671141.0146531.8702081.7688590.9819811.5119421.6044581.7708992.218486...1.5541541.5752131.8082520.9767831.5378691.0676291.5902201.5757631.5570714.427575e-03
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8 rows × 21 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe"}},"metadata":{},"execution_count":41}],"source":["some_stress.describe()"]},{"cell_type":"code","execution_count":42,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":191,"status":"ok","timestamp":1716216963395,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"H_ILDoASsHsk","outputId":"ef498b1c-3b54-4c47-b88c-685c97204ba8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["social_support 0.283528\n","self_esteem 0.166131\n","breathing_problem 0.124092\n","anxiety_level 0.059942\n","stress_level 0.004428\n","mental_health_history -0.007802\n","living_conditions -0.013756\n","headache -0.033568\n","bullying -0.040128\n","study_load -0.067138\n","future_career_concerns -0.082911\n","sleep_quality -0.083528\n","depression -0.088192\n","noise_level -0.103891\n","peer_pressure -0.194049\n","academic_performance -0.198810\n","extracurricular_activities -0.202429\n","basic_needs -0.205898\n","teacher_student_relationship -0.210008\n","safety -0.228434\n","blood_pressure -1.029525\n","dtype: float64"]},"metadata":{},"execution_count":42}],"source":["some_stress_mean=some_stress.mean()\n","some_stress_mean=some_stress_mean.sort_values(ascending = False)\n","some_stress_mean"]},{"cell_type":"code","execution_count":43,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":305,"status":"ok","timestamp":1716216965667,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"LA8QWcZDWlng","outputId":"8c18a2b2-4947-49b6-8b49-4c5ca3d5c2bf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["social_support 0.905028\n","self_esteem 0.568343\n","breathing_problem 0.341899\n","anxiety_level 0.177175\n","bullying 0.044693\n","study_load 0.026816\n","sleep_quality 0.024581\n","future_career_concerns 0.017877\n","noise_level 0.008939\n","living_conditions 0.002235\n","stress_level 0.000000\n","academic_performance -0.006704\n","extracurricular_activities -0.015642\n","basic_needs -0.017877\n","headache -0.031285\n","peer_pressure -0.033520\n","mental_health_history -0.044693\n","safety -0.067039\n","teacher_student_relationship -0.113966\n","depression -0.240844\n","blood_pressure -1.351955\n","dtype: float64"]},"metadata":{},"execution_count":43}],"source":["mm_some_stress_mean=mm_some_stress.mean()\n","mm_some_stress_mean=mm_some_stress_mean.sort_values(ascending = False)\n","mm_some_stress_mean"]},{"cell_type":"code","execution_count":45,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"executionInfo":{"elapsed":1025,"status":"ok","timestamp":1716216971128,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"W4ustv7ox_Wq","outputId":"31df4375-b16f-4beb-a36a-8e7579fde262"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["sns.barplot(data=some_stress_mean,orient='y')\n","plt.title('Standardized scaled categories when Reporting Stress Level 1');"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"executionInfo":{"elapsed":2192,"status":"ok","timestamp":1716216976502,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"-PG2tSqzWumy","outputId":"0f4192a6-719d-4e69-eedf-686c40b72a4f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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iL/++ivdlJOM9OvXT2u9YcOG3Lt3j7i4uCzX0blzZwICAvjvv/84cOAA//33X7qx6Ovro6Pz8mMjKSmJe/fuqf+GPHfunFbZZs2a0bdvXyZNmkS7du0wMDBg2bJl2WzhS/v27SMxMZHBgwer5wfo3bs3ZmZmbN++Pdt1GhoaoqenR0BAQJr/6oaX/2599OgRo0ePTpUb+uo0VYaGhurPjx8/5u7duzRo0ABFUTh//ny6MWzevJnk5GTc3Ny4e/euulhZWVGuXDl1FO3MmTPExMTQr18/rZvDPDw8MDc3z7St/v7+JCcnM378eK3rl1E7Hj16xN27d2nYsCFPnjzhypUrmZ5nw4YNVKpUiYoVK2q1p0mTJgBqe1JSKPr37691/Ks3mcHL19iePXto06YNpUuXVrcXK1aMzp07c/To0VSv9d69e2ea3713714ePnyIu7u7Vpy6urp8/vnnapxZeY2kp2HDhmpa0KNHjwgODqZPnz4ULlxY3X7kyBEsLCxSjXw2bdpUa3SzatWqmJmZcf36deDlzEibNm3C1dUVRVG02uDi4kJsbGyq92P37t21XjspaSMpdWamadOmWFpaYmNjQ/v27TE2Nmbbtm1qas/9+/c5cOAAbm5u6mvn7t273Lt3DxcXF8LCwlLNjtKnTx+tkevvv/+efPnysWPHDiD773t9fX26d++epfZkxMTEhG+//VZd19PTo27dulrXateuXRQvXpzWrVur2wwMDOjdu3eWzvHll19y/PhxWrduTXBwMDNnzsTFxYXixYunStPJzOvfA1n9XEn57Ni9e3e66Yop/3nbunUrycnJ2YorM1mNU6PR0KFDB3bs2EF8fLx6/Lp16yhevLiaapPV9/WHQNJOhMgFlpaWNG3alDVr1vDkyROSkpJo3759tuspWbKk1nrKv4AfPHiAmZlZlupo2bIlpqamrFu3jqCgIOrUqUPZsmXTzGlNmUVkyZIlREREaOXZpfw7/FWzZ89m69atBAUFsWbNGq1cxuy4ceMGABUqVNDarqenR+nSpdX92aGvr8+MGTMYNmwYRYsWpV69enz11Vd07doVKysr4P/ns2b0b2GAqKgoxo8fz7Zt21J10l7NnXxdWFgYiqJQrly5NPendExS2vd6ufz582t1StMTHh6Ojo4OlStXzrDcpUuX+PHHHzlw4ECqTm1G7UgRFhZGSEgIlpaWae5PuZHsxo0b6OjoUKpUKa39r89Oc+fOHZ48eZLq9w4vc6WTk5O5efMmVapUUbe/Xmd6cQLqHwWvS3nvZOU1kp6GDRvy888/c+3aNcLDw9FoNNSvX1/tlPfu3ZsjR47g4OCQ6g+i19/X8PK9nfLaunPnDg8fPuSXX35JdyrN12/ay+izIit++uknypcvT2xsLL/99huHDx/Wmj3m2rVrKIrCuHHjGDduXLoxFS9eXF1//fVsYmJCsWLF1M+e7L7vixcv/k5mLilRokSqOaALFCiglcN/48YNypQpk6pcdmZYqlOnDps3byYxMZHg4GC2bNnCvHnzaN++PUFBQZm+X1O8/prP6udKqVKlGDp0KHPnzsXPz4+GDRvSunVrvv32W7Vj3rFjR1asWEGvXr0YPXo0zs7OtGvXjvbt26d63WZXVuNMiWP+/Pls27aNzp07Ex8fz44dO+jbt6/6O8jq+/pDIJ1vIXJJ586d6d27N//99x8tWrR4o9k50hvhU/4v7y8r9PX1adeuHb6+vly/fj3DGxSnTZvGuHHj6NGjB5MnT6ZgwYLo6OgwePDgNEdFzp8/r3YCLl68iLu7e5bjygmDBw/G1dUVf39/du/ezbhx4/D29ubAgQPUqFEjS3UkJSXx5Zdfcv/+fUaNGkXFihUxNjbm33//xcPDI8PRouTkZDQaDTt37kzzd5mSS50THj58iKOjI2ZmZkyaNIkyZcpgYGDAuXPnGDVqVJZGvZKTk7G3t2fu3Llp7s9KbvrbenX0Pj0pbVm9enWanehXZyF609dIymjc4cOHuX79OjVr1sTY2JiGDRuycOFC4uPjOX/+PFOnTk11bGbv65T4v/32W7p165Zm2dfvdXjbz4q6deuqs520adOGL774gs6dOxMaGoqJiYka0/Dhw3FxcUmzjvc99WdWfvdZ8S4+V7NDT0+POnXqUKdOHcqXL0/37t3ZsGGDevN4Zl5vd3Y+V+bMmYOHhwdbt25lz549eHp64u3tzYkTJyhRogSGhoYcPnyYgwcPsn37dnbt2sW6deto0qQJe/bseatZhLITZ7169bCzs2P9+vV07tyZP//8k6dPn2rdrJyd93Vu+3AiESKPadu2LX379uXEiROsW7cuV2Pp3Lkzv/32Gzo6OnTq1Cndchs3bqRx48b8+uuvWtsfPnxI4cKFtbY9fvyY7t27U7lyZRo0aMDMmTNp27YtderUyXZ8tra2wMsHHrw60puYmEhERARNmzbNdp0pypQpw7Bhwxg2bBhhYWFUr16dOXPm8Pvvv6v/+v/777/T7ThcvHiRq1ev4uvrS9euXdXtr85mk9G5FUWhVKlSlC9fPt1yKe0PCwvTGtV5/vw5ERERVKtWLdPzJCcnc/nyZapXr55mmYCAAO7du8fmzZtp1KiRuj0iIiJV2fSeDFemTBmCg4NxdnbO8Olxtra2JCcnExERoTXq9frsG5aWlhgZGREaGpqqjitXrqCjo/NGHfqU32uRIkWy9NrJ6DWSnpIlS1KyZEmOHDnC9evX1TSPRo0aMXToUDZs2EBSUpLWtc4qS0tLTE1NSUpKeqvX/pvS1dXF29ubxo0bs3jxYkaPHq2+L/Pnz5/lmMLCwmjcuLG6Hh8fT3R0NC1btgTe3fv+fTzJ0NbWlsuXL6Moilb9bzODDKD+gRMdHa1uy278Wf1cSWFvb4+9vT0//vgjx44dw8HBgZ9//pkpU6YAL6dkdHZ2xtnZmblz5zJt2jTGjh3LwYMH3/qzNztxurm5sWDBAuLi4li3bh12dnbUq1dPqz7I+vs6N0nOtxC5xMTEhKVLl+Ll5YWrq2uuxtK4cWMmT57M4sWLM/x3uq6ubqrRnw0bNqT5pLtRo0YRFRWFr68vc+fOxc7Ojm7duqWaci4rmjZtip6eHgsXLtQ6/6+//kpsbGyas2Rk5smTJ6keIlSmTBlMTU3VGJs1a4apqSne3t6pyqbEkTJi82pciqKwYMGCTGNo164durq6TJw4MdV1VRSFe/fuAS+/kC0tLfn5559JTExUy/j4+KSaci0tbdq0QUdHh0mTJqUawc6oHYmJiSxZsiRVfcbGxmmmobi5ufHvv/+yfPnyVPuePn2qzj6SMjL6et2vP9lPV1eXZs2asXXrVq00qNu3b6sPqnqTfyW7uLhgZmbGtGnTeP78ear9d+7cAbL2GslIw4YNOXDgAKdOnVI739WrV8fU1JTp06djaGhIrVq1sh2/rq4u33zzDZs2beLvv/9ON/73ycnJibp16zJ//nyePXtGkSJFcHJyYtmyZVodx4xi+uWXX7Su/9KlS3nx4oU6o8i7et8bGxtn6X2SHS4uLvz7779a+dnPnj1L87WfloMHD6Y5kp6S7/5qqk1248/q50pcXBwvXrzQ2m9vb4+Ojo76+r5//36q+lP+gH+Tz/I3iTNFx44dSUhIwNfXl127dqWaXz6r7+sPgYx8C5GL0vuXcU7T0dHhxx9/zLTcV199xaRJk+jevTsNGjTg4sWL+Pn5pco7PnDgAEuWLGHChAnqlIUrV67EycmJcePGpTlnbkYsLS0ZM2YMEydOpHnz5rRu3ZrQ0FCWLFlCnTp1tG6OyqqrV6/i7OyMm5sblStXJl++fGzZsoXbt2+ro/9mZmbMmzePXr16UadOHXUu3eDgYJ48eYKvry8VK1akTJkyDB8+nH///RczMzM2bdqUpVzaMmXKMGXKFMaMGUNkZCRt2rTB1NSUiIgItmzZQp8+fRg+fDj58+dnypQp9O3blyZNmtCxY0ciIiJYuXJllnK+y5Yty9ixY5k8eTINGzakXbt26Ovrc/r0aaytrfH29qZBgwYUKFCAbt264enpiUajYfXq1Wl2EGrVqsW6desYOnQoderUwcTEBFdXV7777jvWr19Pv379OHjwIA4ODiQlJXHlyhXWr1/P7t27qV27NrVq1eKbb75h/vz53Lt3T51q8OrVq4D2SN+UKVPUeYb79+9Pvnz5WLZsGQkJCdl+HaUwMzNj6dKlfPfdd9SsWZNOnTphaWlJVFQU27dvx8HBgcWLF2fpNZKRhg0b4ufnh0ajUdNQdHV1adCgAbt378bJyemNc5SnT5/OwYMH+fzzz+nduzeVK1fm/v37nDt3jn379qXZaXrXRowYQYcOHfDx8aFfv3789NNPfPHFF9jb29O7d29Kly7N7du3OX78OP/880+q5wEkJiaq1zfl/fzFF1+oNzG+q/d9rVq1WLp0KVOmTKFs2bIUKVIk3bzgrOrbty+LFy/G3d2dQYMGUaxYMfXJwJD5aPUPP/zAkydPaNu2LRUrViQxMZFjx46pI7qv3jhaq1Yt9u3bx9y5c7G2tqZUqVKppth8VVY/Vw4cOMDAgQPp0KED5cuX58WLF6xevVr94w5ePtn08OHDtGrVCltbW2JiYliyZAklSpRI9ZTmtFy7dk0dQX9VjRo1aNWqVZbiTFGzZk31sywhISHV/PhZfV9nR2xsrDookDK94uLFi7GwsMDCwiLVNKFZ9tbzpQghsuTVqQYzkp2pBl+f/un1KdXS8+pUg+lJb6rBYcOGKcWKFVMMDQ0VBwcH5fjx44qjo6M6lVdcXJxia2ur1KxZU3n+/LlWnUOGDFF0dHSU48ePZ3ju9CxevFipWLGikj9/fqVo0aLK999/rzx48ECrTFanGrx7964yYMAApWLFioqxsbFibm6ufP7558r69etTld22bZvSoEEDxdDQUDEzM1Pq1q2r/PHHH+r+y5cvK02bNlVMTEyUwoULK71791anh1u5cmWq2F63adMm5YsvvlCMjY0VY2NjpWLFisqAAQOU0NBQrXJLlixRSpUqpejr6yu1a9dWDh8+rHXtM/Pbb78pNWrUUPT19ZUCBQoojo6Oyt69e9X9gYGBSr169RRDQ0PF2tpanf6M16ZOi4+PVzp37qxYWFgogNa0g4mJicqMGTOUKlWqqOepVauWMnHiRCU2NlYt9/jxY2XAgAFKwYIF1WkoQ0NDFUCZPn26Vtznzp1TXFxcFBMTE8XIyEhp3LixcuzYMa0yGb2/0ntfHDx4UHFxcVHMzc0VAwMDpUyZMoqHh4dy5swZRVGy9xpJy6VLl9Rp/l41ZcoUBVDGjRuX6hhAGTBgQKrttra2qaabu337tjJgwADFxsZGyZ8/v2JlZaU4Ozsrv/zyi1YbSWM6uZT396uvz7RkdF2TkpKUMmXKKGXKlFGndwwPD1e6du2qWFlZKfnz51eKFy+ufPXVV8rGjRtT1Xno0CGlT58+SoECBRQTExOlS5cuyr1791KdJyvve0dHR6VKlSpptuG///5TWrVqpZiammpNzZneVINp1dOtW7dU02tev35dadWqlWJoaKhYWloqw4YNUzZt2qQAyokTJ9KMJcXOnTuVHj16KBUrVlRMTEwUPT09pWzZssoPP/yg3L59W6vslStXlEaNGimGhoYKoL4OMvusy+xz5fr160qPHj2UMmXKKAYGBkrBggWVxo0bK/v27VPr2L9/v/L1118r1tbWip6enmJtba24u7srV69ezbB9ivLyNQukufTs2TPLcb5q7NixCqCULVs23fNm9r5+9dplJuV9ktaS1nSrWaVRlPd0B4EQQgiRDUFBQdSoUYPff/+dLl265HY44j3x8fGhe/funD59Ws1x/lTMnz+fIUOG8M8//2jN7CLEqyTnWwghRI57+vRpqm3z589HR0fnjW5CFCKnvf4afvbsGcuWLaNcuXLS8RYZkpxvIYQQOW7mzJmcPXuWxo0bky9fPnbu3MnOnTvp06dPjkxJKMTbateuHSVLlqR69erExsby+++/c+XKFfz8/HI7NPGBk863EEKIHNegQQP27t3L5MmTiY+Pp2TJknh5eTF27NjcDk2ILHFxcWHFihX4+fmRlJRE5cqVWbt2baobAYV4neR8CyGEEEIIkUMk51sIIYQQQogcIp1vIYQQQgghcojkfAuRw5KTk7l16xampqbv5bHHQgghhHj3FEXh0aNHWFtbo6Pz5uPX0vkWIofdunVLZnMQQgghPlI3b96kRIkSb3y8dL6FyGGmpqbAyzevmZlZLkcjhMjrGv34R26HIMQ7dXiK+3upNy4uDhsbG/V7/E1J51uIHJaSamJmZiadbyFErtPVN8ztEIR4p973d+vbpozKDZdCCCGEEELkEOl8iwx5eXlRvXr1bB2j0Wjw9/d/L/EIIYQQQnzMpPMtMjR8+HD279+f22F8kAICAtBoNDx8+DC3QxFCCCHER0JyvkWGTExMMDExye0wPjjPnz/P7RCEEEII8RGSke9PxMaNG7G3t8fQ0JBChQrRtGlTHj9+THJyMpMmTaJEiRLo6+tTvXp1du3apXXsP//8g7u7OwULFsTY2JjatWtz8uRJIHXayenTp/nyyy8pXLgw5ubmODo6cu7cuTeKOTExkYEDB1KsWDEMDAywtbXF29sbgMjISDQaDUFBQWr5hw8fotFoCAgIAP7/yPP27dupWrUqBgYG1KtXj7///ls9xsfHBwsLC/z9/SlXrhwGBga4uLhw8+ZNrViWLl1KmTJl0NPTo0KFCqxevVprv0ajYenSpbRu3RpjY2N69+5N48aNAShQoAAajQYPD48025mQkEBcXJzWIoQQQoi8STrfn4Do6Gjc3d3p0aMHISEhBAQE0K5dOxRFYcGCBcyZM4fZs2dz4cIFXFxcaN26NWFhYQDEx8fj6OjIv//+y7Zt2wgODmbkyJEkJyenea5Hjx7RrVs3jh49yokTJyhXrhwtW7bk0aNH2Y574cKFbNu2jfXr1xMaGoqfnx92dnbZrmfEiBHMmTOH06dPY2lpiaurq9bI9JMnT5g6dSqrVq0iMDCQhw8f0qlTJ3X/li1bGDRoEMOGDePvv/+mb9++dO/enYMHD2qdx8vLi7Zt23Lx4kUmTpzIpk2bAAgNDSU6OpoFCxakGZ+3tzfm5ubqInN8CyGEEHmXpJ18AqKjo3nx4gXt2rXD1tYWAHt7ewBmz57NqFGj1M7mjBkzOHjwIPPnz+enn35izZo13Llzh9OnT1OwYEEAypYtm+65mjRporX+yy+/YGFhwaFDh/jqq6+yFXdUVBTlypXjiy++QKPRqLFn14QJE/jyyy8B8PX1pUSJEmzZsgU3NzfgZYrI4sWL+fzzz9UylSpV4tSpU9StW5fZs2fj4eFB//79ARg6dCgnTpxg9uzZ6ug2QOfOnenevbu6HhERAUCRIkWwsLBIN74xY8YwdOhQdT1lnlAhhBBC5D0y8v0JqFatGs7Oztjb29OhQweWL1/OgwcPiIuL49atWzg4OGiVd3BwICQkBICgoCBq1Kihdrwzc/v2bXr37k25cuUwNzfHzMyM+Ph4oqKish23h4cHQUFBVKhQAU9PT/bs2ZPtOgDq16+v/lywYEEqVKigtg8gX7581KlTR12vWLEiFhYWapmQkJAMr1GK2rVrv1F8+vr66pzeMre3EEIIkbdJ5/sToKury969e9m5cyeVK1dm0aJFVKhQQR2ZzYihYfYertCtWzeCgoJYsGABx44dIygoiEKFCpGYmJjtuGvWrElERASTJ0/m6dOnuLm50b59ewB0dF6+NBVFUcvn9k2OxsbGuXp+IYQQQnz8pPP9idBoNDg4ODBx4kTOnz+Pnp4e+/fvx9ramsDAQK2ygYGBVK5cGYCqVasSFBTE/fv3s3SewMBAPD09admyJVWqVEFfX5+7d+++cdxmZmZ07NiR5cuXs27dOjZt2sT9+/extLQEXqbUpHj15stXnThxQv35wYMHXL16lUqVKqnbXrx4wZkzZ9T10NBQHj58qJapVKlShtcoPXp6egAkJSVloaVCCCGEEJLz/Uk4efIk+/fvp1mzZhQpUoSTJ09y584dKlWqxIgRI5gwYQJlypShevXqrFy5kqCgIPz8/ABwd3dn2rRptGnTBm9vb4oVK8b58+extrbWSudIUa5cOVavXk3t2rWJi4tjxIgR2R49TzF37lyKFStGjRo10NHRYcOGDVhZWWFhYYGOjg716tVj+vTplCpVipiYGH788cc065k0aRKFChWiaNGijB07lsKFC9OmTRt1f/78+fnhhx9YuHAh+fLlY+DAgdSrV4+6desCL2/YdHNzo0aNGjRt2pQ///yTzZs3s2/fvgzjt7W1RaPR8Ndff9GyZUsMDQ1lWkYhhBBCZEhGvj8BZmZmHD58mJYtW1K+fHl+/PFH5syZQ4sWLfD09GTo0KEMGzYMe3t7du3axbZt2yhXrhzwcvR2z549FClShJYtW2Jvb8/06dPR1dVN81y//vorDx48oGbNmnz33Xd4enpSpEiRN4rb1NSUmTNnUrt2berUqUNkZCQ7duxQU05+++03Xrx4Qa1atRg8eDBTpkxJs57p06czaNAgatWqxX///ceff/6pjkoDGBkZMWrUKDp37oyDgwMmJiasW7dO3d+mTRsWLFjA7NmzqVKlCsuWLWPlypU4OTllGH/x4sWZOHEio0ePpmjRogwcOPCNroMQQggh8g6N8mpSrRAfkYCAABo3bsyDBw/SnW3Ex8eHwYMHf1BPoYyLi8Pc3JzY2Fi5+VIIketqjViV2yEI8U6dndX1vdT7rr6/Je1ECCGEyMPeV0dFCJE2STsR7820adPUx9O/vrRo0SK3wxNCCCGEyHGSdiLem/v376c7i4qhoSHFixfP4Yg+DJJ2IoQQQnx8JO1EfPAKFiyY5Yf3CCGEyB2S8/1pkPShj4eknQghhBBCCJFDpPMt3lpkZCQajUbrITiBgYHY29uTP39+rTm3hRBCCCHyMkk7Ee/F0KFDqV69Ojt37nzvD57x8PDg4cOH+Pv7v9fzCCGEEEK8LRn5Fu9FeHg4TZo0oUSJEunOwS2EEEIIkddI51uoNm7ciL29PYaGhhQqVIimTZvy+PFjAFasWEGlSpUwMDCgYsWKLFmyJM06UlJQ7t27R48ePdBoNPj4+GR67r///psWLVpgYmJC0aJF+e6777h7926msXl5eeHr68vWrVvRaDRoNBoCAgIAuHnzJm5ublhYWFCwYEG+/vprIiMjtc6bUbtS2rJ+/XoaNmyIoaEhderU4erVq5w+fZratWur0ybeuXMnexdbCCGEEHmSdL4FANHR0bi7u9OjRw9CQkIICAigXbt2KIqCn58f48ePZ+rUqYSEhDBt2jTGjRuHr69vqnpsbGyIjo7GzMyM+fPnEx0dTceOHTM898OHD2nSpAk1atTgzJkz7Nq1i9u3b+Pm5pZpbMOHD8fNzY3mzZsTHR1NdHQ0DRo04Pnz57i4uGBqasqRI0cIDAzExMSE5s2bk5iYCJDldk2YMIEff/yRc+fOkS9fPjp37szIkSNZsGABR44c4dq1a4wfPz7d9iUkJBAXF6e1CCGEECJvkpxvAbzs4L548YJ27dpha2sLgL29PfCy8zlnzhzatWsHQKlSpbh8+TLLli2jW7duWvXo6upiZWWFRqPB3NwcKyurTM+9ePFiatSowbRp09Rtv/32GzY2Nly9epX4+Ph0Y4OXc4YnJCRonev3338nOTmZFStWoNFoAFi5ciUWFhYEBATQrFmzLLdr+PDhuLi4ADBo0CDc3d3Zv38/Dg4OAPTs2TPD0X1vb28mTpyY6XUQQgghxKdPOt8CgGrVquHs7Iy9vT0uLi40a9aM9u3bo6enR3h4OD179qR3795q+RcvXmBubv5Ozh0cHMzBgwfTvDEzPDycZs2apRlbgQIFMqzz2rVrmJqaam1/9uwZ4eHhPH78OMvtqlq1qvpz0aJFAe3Of9GiRYmJiUk3ljFjxjB06FB1PS4uDhsbm3TLCyGEEOLTJZ1vAbwcsd67dy/Hjh1jz549LFq0iLFjx/Lnn38CsHz5cj7//PNUx7wL8fHxuLq6MmPGjFT7ihUrlm5sJ0+epFSpUunWWatWLfz8/FLts7S0JD4+Hshau/Lnz6/+nDKK/vq25OTkdNunr6+Pvr5+uvuFEEIIkXdI51uoNBoNDg4OODg4MH78eGxtbQkMDMTa2prr16/TpUuX93LemjVrsmnTJuzs7MiXL+2XZFqxbdmyhaFDh6Knp0dSUlKqOtetW0eRIkXSfASsubn5e2+XEEIIIcTr5IZLAcDJkyeZNm0aZ86cISoqis2bN3Pnzh0qVarExIkT8fb2ZuHChVy9epWLFy+ycuVK5s6d+07OPWDAAO7fv4+7uzunT58mPDyc3bt30717d5KSkjKMDcDOzo4LFy4QGhrK3bt3ef78OV26dKFw4cJ8/fXXHDlyhIiICAICAvD09OSff/4BeO/tEkIIIYR4nYx8CwDMzMw4fPgw8+fPJy4uDltbW+bMmUOLFi0AMDIyYtasWYwYMQJjY2Ps7e0ZPHjwOzm3tbU1gYGBjBo1imbNmpGQkICtrS3NmzdHR0cn09h69+5NQEAAtWvXJj4+noMHD+Lk5MThw4cZNWoU7dq149GjRxQvXhxnZ2d1JLxXr17vtV1CCCGEEK/TKIqi5HYQQuQlcXFxmJubExsbm2ZKjBBC5KRaI1bldgjiHTg7q2tuh/DJe1ff3zLyLYQQQuRh0mkTImdJzrd47/r164eJiUmaS79+/XI7PCGEEEKIHCNpJ+K9i4mJSfepjmZmZhQpUiSHI8pdknYihBBCfHwk7UR8NIoUKZLnOthCCCGEEGmRzrcQQgiRh8kNl6lJHrx4nyTnWwghhBBCiBwine93wMnJ6YOaGzor8Wg0Gvz9/XMknvfNw8ODNm3aZFjmQ/sdCSGEECJvks73RywgIACNRsPDhw+zfWx0dLT6kBohhBBCCJEzpPOdCxITE3M7BKysrNDX18/tMDL0IVwnIYQQQoh3STrf78iLFy8YOHAg5ubmFC5cmHHjxpEyi6OdnR2TJ0+ma9eumJmZ0adPHwCOHj1Kw4YNMTQ0xMbGBk9PTx4/fqzWuXr1amrXro2pqSlWVlZ07tyZmJgYACIjI2ncuDEABQoUQKPR4OHhoR6bnJzMyJEjKViwIFZWVnh5eWnF+2raSWRkJBqNhs2bN9O4cWOMjIyoVq0ax48f1zpm+fLl2NjYYGRkRNu2bZk7dy4WFhZZuj5eXl5Ur16dZcuWqXW4ubkRGxurlklJH5k6dSrW1tZUqFABgIsXL9KkSRMMDQ0pVKgQffr0IT4+PtU5Jk6ciKWlJWZmZvTr1y/DzntCQgLDhw+nePHiGBsb8/nnnxMQEKDu9/HxwcLCgr/++osKFSpgZGRE+/btefLkCb6+vtjZ2VGgQAE8PT1JSkrK0jUQQgghhJDO9zvi6+tLvnz5OHXqFAsWLGDu3LmsWLFC3T979myqVavG+fPnGTduHOHh4TRv3pxvvvmGCxcusG7dOo4ePcrAgQPVY54/f87kyZMJDg7G39+fyMhItYNtY2PDpk2bAAgNDSU6OpoFCxZoxWNsbMzJkyeZOXMmkyZNYu/evRm2YezYsQwfPpygoCDKly+Pu7s7L168ACAwMJB+/foxaNAggoKC+PLLL5k6dWq2rtG1a9dYv349f/75J7t27eL8+fP0799fq8z+/fsJDQ1l7969/PXXXzx+/BgXFxcKFCjA6dOn2bBhA/v27dO6TinHhYSEEBAQwB9//MHmzZuZOHFiurEMHDiQ48ePs3btWi5cuECHDh1o3rw5YWFhapknT56wcOFC1q5dy65duwgICKBt27bs2LGDHTt2sHr1apYtW8bGjRszbHdCQgJxcXFaixBCCCHyJnnIzjvg5ORETEwMly5dQqPRADB69Gi2bdvG5cuXsbOzo0aNGmzZskU9plevXujq6rJs2TJ129GjR3F0dOTx48cYGBikOs+ZM2eoU6cOjx49wsTEhICAABo3bsyDBw+0RqCdnJxISkriyJEj6ra6devSpEkTpk+fDrwc+d6yZQtt2rQhMjKSUqVKsWLFCnr27AnA5cuXqVKlCiEhIVSsWJFOnToRHx/PX3/9pdb57bff8tdff2Up59zLy4spU6Zw48YNihcvDsCuXbto1aoV//77L1ZWVnh4eLBr1y6ioqLQ09MDXo62jxo1ips3b2JsbAzAjh07cHV15datWxQtWhQPDw/+/PNPbt68iZGREQA///wzI0aMIDY2Fh0dHZycnKhevTrz588nKiqK0qVLExUVhbW1tRpj06ZNqVu3LtOmTcPHx4fu3btz7do1ypQpA7x8Uufq1au5ffs2JiYmADRv3hw7Ozt+/vnnDNue1h8C8pAdIcSHQKYaTE2mGhRpeVcP2ZGR73ekXr16ascboH79+oSFhakpCbVr19YqHxwcjI+Pj9aj1l1cXEhOTiYiIgKAs2fP4urqSsmSJTE1NcXR0RGAqKioTOOpWrWq1nqxYsXUlJWsHFOsWDEA9ZjQ0FDq1q2rVf719cyULFlS7XjDy2uUnJxMaGious3e3l7teAOEhIRQrVo1teMN4ODgkOq4atWqqR3vlLrj4+O5efNmqjguXrxIUlIS5cuX17r+hw4dIjw8XC1nZGSkdrwBihYtip2dndrxTtmW2XUdM2YMsbGx6pJWTEIIIYTIG+QhOznk1c4jQHx8PH379sXT0zNV2ZIlS6rpFi4uLvj5+WFpaUlUVBQuLi5ZuhExf/78WusajYbk5OQsH5Pyh0Rmx7xrr1+n9yE+Ph5dXV3Onj2Lrq6u1r5XO9ZpXcM3ua76+vof/M2tQgghhMgZ0vl+R06ePKm1fuLECcqVK5eqc5eiZs2aXL58mbJly6a5/+LFi9y7d4/p06djY2MDvEw7eVXKCHFO3PBXoUIFTp8+rbXt9fXMREVFcevWLTXV48SJE+jo6Kg3VqalUqVK+Pj48PjxY7VjHhgYmOq44OBgnj59iqGhoVq3iYmJeu1eVaNGDZKSkoiJiaFhw4bZaoMQQgghxNuQtJN3JCoqiqFDhxIaGsoff/zBokWLGDRoULrlR40axbFjxxg4cCBBQUGEhYWxdetW9UbCkiVLoqenx6JFi7h+/Trbtm1j8uTJWnXY2tqi0Wj466+/uHPnTpozgLwrP/zwAzt27GDu3LmEhYWxbNkydu7cqZVqkxkDAwO6detGcHAwR44cwdPTEzc3N6ysrNI9pkuXLupxf//9NwcPHuSHH37gu+++o2jRomq5xMREevbsyeXLl9mxYwcTJkxg4MCB6OikfomXL1+eLl260LVrVzZv3kxERASnTp3C29ub7du3Z+/CCCGEEEJkg3S+35GuXbvy9OlT6taty4ABAxg0aJA6pWBaqlatyqFDh7h69SoNGzakRo0ajB8/Xh0VtrS0xMfHhw0bNlC5cmWmT5/O7NmzteooXrw4EydOZPTo0RQtWjTVDCDvkoODAz///DNz586lWrVq7Nq1iyFDhqR5Y2h6ypYtS7t27WjZsiXNmjWjatWqLFmyJMNjjIyM2L17N/fv36dOnTq0b98eZ2dnFi9erFXO2dmZcuXK0ahRIzp27Ejr1q1TTa/4qpUrV9K1a1eGDRtGhQoVaNOmDadPn6ZkyZJZbo8QQgghRHbJbCfijfXu3ZsrV65ozaqSHi8vL/z9/QkKCnr/gX3g3tXd0kII8S7IbCepyWwnIi3v6vtbcr5Fls2ePZsvv/wSY2Njdu7cia+vb6Yj10IIIT5s0tEUImdJ51tk2alTp5g5cyaPHj2idOnSLFy4kF69egFQpUoVbty4keZxr85lLoQQQgiRl0naiXgnbty4wfPnz9PcV7RoUUxNTXM4og+XpJ0IIYQQHx9JOxEfFFtb29wOQQghxBv40HK+JQ1GfOpkthMhhBBCCCFyiHS+hRBCCCGEyCHS+RbvRGRkJBqNJtenEtRoNPj7++foOe3s7Jg/f36OnlMIIYQQHyfpfIt3wsbGhujoaD777LMsH+Pk5MTgwYPfX1BCCCGEEB8YueFSvBO6uroZPiZeCCGEEELIyHeesWvXLr744gssLCwoVKgQX331FeHh4cD/TxnZvHkzjRs3xsjIiGrVqnH8+HH1+B49elC1alUSEhIASExMpEaNGnTt2lWrjlfTTv7++29atGiBiYkJRYsW5bvvvuPu3bsAeHh4cOjQIRYsWIBGo0Gj0RAREUHZsmWZPXu2VuxBQUFoNBquXbuW7XbfvHkTNzc3LCwsKFiwIF9//TWRkZEA7NmzBwMDAx4+fKh1zKBBg2jSpIm6fvToURo2bIihoSE2NjZ4enry+PHjLMeQkJBAXFyc1iKEEEKIvEk633nE48ePGTp0KGfOnGH//v3o6OjQtm1bkpOT1TJjx45l+PDhBAUFUb58edzd3Xnx4gUACxcu5PHjx4wePVot+/DhQxYvXpzm+R4+fEiTJk2oUaMGZ86cYdeuXdy+fRs3NzcAFixYQP369enduzfR0dFER0dTsmRJevTowcqVK7XqWrlyJY0aNaJs2bLZavPz589xcXHB1NSUI0eOEBgYiImJCc2bNycxMRFnZ2csLCzYtGmTekxSUhLr1q2jS5cuAISHh9O8eXO++eYbLly4wLp16zh69CgDBw7Mchze3t6Ym5uri42NTbbaIYQQQohPh6Sd5BHffPON1vpvv/2GpaUlly9fxsTEBIDhw4fTqlUrACZOnEiVKlW4du0aFStWxMTEhN9//x1HR0dMTU2ZP38+Bw8eTHeS+cWLF1OjRg2mTZumdU4bGxuuXr1K+fLl0dPTw8jISCtdxcPDg/Hjx3Pq1Cnq1q3L8+fPWbNmTarR8KxYt24dycnJrFixAo1GA7zsyFtYWBAQEECzZs3o1KkTa9asoWfPngDs37+fhw8fqtfL29ubLl26qLnp5cqVY+HChTg6OrJ06VIMDAwyjWPMmDEMHTpUXY+Li5MOuBBCCJFHych3HhEWFoa7uzulS5fGzMwMOzs7AKKiotQyVatWVX8uVqwYADExMeq2+vXrM3z4cCZPnsywYcP44osv0j1fcHAwBw8exMTERF0qVqwIoKa7pMXa2ppWrVrx22+/AfDnn3+SkJBAhw4dst3m4OBgrl27hqmpqRpDwYIFefbsmRpDly5dCAgI4NatWwD4+fnRqlUrLCws1Dp8fHy02uHi4kJycjIRERFZikNfXx8zMzOtRQghhBB5k4x85xGurq7Y2tqyfPlyrK2tSU5O5rPPPiMxMVEtkz9/fvXnlJHiV9NSkpOTCQwMRFdXN9P86/j4eFxdXZkxY0aqfSkd+/T06tWL7777jnnz5rFy5Uo6duyIkZFRltr5egy1atXCz88v1T5LS0sA6tSpQ5kyZVi7di3ff/89W7ZswcfHR6uOvn374unpmaqOkiVLZjsmIYQQQuRt0vnOA+7du0doaCjLly+nYcOGwMubCLNr1qxZXLlyhUOHDuHi4sLKlSvp3r17mmVr1qzJpk2bsLOzI1++tF9menp6JCUlpdresmVLjI2NWbp0Kbt27eLw4cPZjjUlhnXr1lGkSJEMR5u7dOmCn58fJUqUQEdHR029Sanj8uXL2c43F0IIIYRIi6Sd5AEFChSgUKFC/PLLL1y7do0DBw5o5SBnxfnz5xk/fjwrVqzAwcGBuXPnMmjQIK5fv55m+QEDBnD//n3c3d05ffo04eHh7N69m+7du6sdbjs7O06ePElkZCR3795VR9l1dXXx8PBgzJgxlCtXjvr1679Ru7t06ULhwoX5+uuvOXLkCBEREQQEBODp6ck///yjVe7cuXNMnTqV9u3bo6+vr+4bNWoUx44dY+DAgQQFBREWFsbWrVuzdcOlEEIIIUQK6XznATo6Oqxdu5azZ8/y2WefMWTIEGbNmpXl4589e8a3336Lh4cHrq6uAPTp04fGjRvz3XffpTl6bW1tTWBgIElJSTRr1gx7e3sGDx6MhYUFOjovX3bDhw9HV1eXypUrY2lpqZV/3rNnTxITE9MdWc8KIyMjDh8+TMmSJWnXrh2VKlWiZ8+ePHv2TGskvGzZstStW5cLFy6os5ykqFq1KocOHeLq1as0bNiQGjVqMH78eKytrd84LiGEEELkXRpFUZTcDkKI1x05cgRnZ2du3rxJ0aJFczucdyouLg5zc3NiY2Pl5kshhBDiI/Guvr8l51t8UBISErhz5w5eXl506NDhk+t4CyGEECJvk7QT8UH5448/sLW15eHDh8ycOVNrn5+fn9aUf68uVapUyaWIhRBCCCGyTtJOxEfj0aNH3L59O819+fPnx9bWNocjejOSdiKEEEJ8fCTtROQ5pqammJqa5nYYQgjxSak1YlWmZc7O6poDkQiRN0jaiRBCCCGEEDlEOt8iVzg5OTF48OA3Pj4yMhKNRkNQUBAAAQEBaDQaHj58+EbHCyGEEELkBEk7EXmSjY0N0dHRFC5cOLdDEUIIIUQeIp1vkSfp6upiZWWV22EIIYQQIo+RtBORa168eMHAgQMxNzencOHCjBs3jpTJdzQaDf7+/lrlLSws8PHxybTex48fY2ZmxsaNG7W2+/v7Y2xszKNHj9JNW9m/fz+1a9fGyMiIBg0aEBoaqlXHlClTKFKkCKampvTq1YvRo0dTvXr1N70EQgghhMhjpPMtco2vry/58uXj1KlTLFiwgLlz57JixYq3rtfY2JhOnTqxcuVKre0rV66kffv2Gc6YMnbsWObMmcOZM2fIly8fPXr0UPf5+fkxdepUZsyYwdmzZylZsiRLly7NNJ6EhATi4uK0FiGEEELkTZJ2InKNjY0N8+bNQ6PRUKFCBS5evMi8efPo3bv3W9fdq1cvGjRoQHR0NMWKFSMmJoYdO3awb9++DI+bOnUqjo6OAIwePZpWrVrx7NkzDAwMWLRoET179qR79+4AjB8/nj179hAfH59hnd7e3kycOPGt2ySEEEKIj5+MfItcU69ePTQajbpev359wsLCSEpKeuu669atS5UqVfD19QXg999/x9bWlkaNGmV4XNWqVdWfixUrBkBMTAwAoaGh1K1bN9V5MjNmzBhiY2PV5ebNm9lqixBCCCE+HdL5Fh8kjUbD6w9fff78ebbq6NWrl5ojvnLlSrp3767V2U9L/vz5tWIASE5OztZ5X6evr4+ZmZnWIoQQQoi8STrfItecPHlSa/3EiROUK1cOXV1dLC0tiY6OVveFhYXx5MmTbNX/7bffcuPGDRYuXMjly5fp1q3bW8VboUIFTp8+rbXt9XUhhBBCiIxIzrfINVFRUQwdOpS+ffty7tw5Fi1axJw5cwBo0qQJixcvpn79+iQlJTFq1CitUemsKFCgAO3atWPEiBE0a9aMEiVKvFW8P/zwA71796Z27do0aNCAdevWceHCBUqXLv1W9QohhBAi75DOt8g1Xbt25enTp9StWxddXV0GDRpEnz59AJgzZw7du3enYcOGWFtbs2DBAs6ePZvtc/Ts2ZM1a9ZozVryprp06cL169cZPnw4z549w83NDQ8PD06dOvXWdQshhBAib9AoryfWCvEJWb16NUOGDOHWrVvo6em98/q//PJLrKysWL16dZaPiYuLw9zcnNjYWMn/FkLkulojVmVa5uysrjkQiRAftnf1/S0j3+KT9OTJE6Kjo5k+fTp9+/Z9Jx3vJ0+e8PPPP+Pi4oKuri5//PEH+/btY+/eve8gYiGEyB3SsRYiZ8kNl+KTNHPmTCpWrIiVlRVjxox5J3VqNBp27NhBo0aNqFWrFn/++SebNm2iadOm76R+IYQQQnz6JO1EiBwmaSdCCCHEx0fSToQQQgjx1tLL+ZZ0FCHeD0k7EUIIIYQQIodI51sIIYQQQogcIp1v8cHw8fHBwsLindQVEBCARqPh4cOH76S+3D6PEEIIIT4N0vkWb8XDw4M2bdrkdhhCCCGEEB8F6XwLIYQQQgiRQ6TzLbJk48aN2NvbY2hoSKFChWjatCkjRozA19eXrVu3otFo0Gg0BAQEpJmKERQUhEajITIyUt3m4+NDyZIlMTIyom3btty7d0/dFxkZiY6ODmfOnNGKY/78+dja2pKcnJztNmzatIkqVaqgr6+PnZ0dc+bM0dq/evVqateujampKVZWVnTu3JmYmBitMjt27KB8+fIYGhrSuHFjrfakJyEhgbi4OK1FCCGEEHmTdL5FpqKjo3F3d6dHjx6EhIQQEBBAu3btmDBhAm5ubjRv3pzo6Giio6Np0KBBluo8efIkPXv2ZODAgQQFBdG4cWOmTJmi7rezs6Np06asXLlS67iVK1fi4eGBjk72Xrpnz57Fzc2NTp06cfHiRby8vBg3bhw+Pj5qmefPnzN58mSCg4Px9/cnMjISDw8Pdf/Nmzdp164drq6uBAUF0atXL0aPHp3pub29vTE3N1cXGxubbMUuhBBCiE+HzPMtMhUdHc2LFy9o164dtra2ANjb2wNgaGhIQkICVlZW2apzwYIFNG/enJEjRwJQvnx5jh07xq5du9QyvXr1ol+/fsydOxd9fX3OnTvHxYsX2bp1a7bbMHfuXJydnRk3bpx6vsuXLzNr1iy1g92jRw+1fOnSpVm4cCF16tQhPj4eExMTli5dSpkyZdQR8woVKnDx4kVmzJiR4bnHjBnD0KFD1fW4uDjpgAshhBB5lIx8i0xVq1YNZ2dn7O3t6dChA8uXL+fBgwdvVWdISAiff/651rb69etrrbdp0wZdXV22bNkCvExTady4MXZ2dm90PgcHB61tDg4OhIWFkZSUBLwcHXd1daVkyZKYmpri6OgIQFRUVJZjTou+vj5mZmZaixBCCCHyJul8i0zp6uqyd+9edu7cSeXKlVm0aBEVKlQgIiIizfIpKSGKoqjbnj9/nu3z6unp0bVrV1auXEliYiJr1qzRGp1+lx4/foyLiwtmZmb4+flx+vRptdOfmJj4Xs4phBBCiLxHOt8iSzQaDQ4ODkycOJHz58+jp6fHli1b0NPTU0eOU1haWgIv01VSBAUFaZWpVKkSJ0+e1Np24sSJVOft1asX+/btY8mSJWrqy5uoVKkSgYGBWtsCAwMpX748urq6XLlyhXv37jF9+nQaNmxIxYoVU91sWalSJU6dOpVpzEIIIYQQ6ZHOt8jUyZMnmTZtGmfOnCEqKorNmzdz584dKlWqhJ2dHRcuXCA0NJS7d+/y/PlzypYti42NDV5eXoSFhbF9+/ZUM4t4enqya9cuZs+eTVhYGIsXL9bK905RqVIl6tWrx6hRo3B3d8fQ0PCN2jBs2DD279/P5MmTuXr1Kr6+vixevJjhw4cDULJkSfT09Fi0aBHXr19n27ZtTJ48WauOfv36ERYWxogRIwgNDWXNmjVaN2wKIYQQQmRGOt8iU2ZmZhw+fJiWLVtSvnx5fvzxR+bMmUOLFi3o3bs3FSpUoHbt2lhaWhIYGEj+/Pn5448/uHLlClWrVmXGjBlaM5kA1KtXj+XLl7NgwQKqVavGnj17+PHHH9M8f8+ePUlMTHyrlJOaNWuyfv161q5dy2effcb48eOZNGmSerOlpaUlPj4+bNiwgcqVKzN9+nRmz56tVUfJkiXZtGkT/v7+VKtWjZ9//plp06a9cUxCCCGEyHs0yquJuUJ8gCZPnsyGDRu4cOFCbofyTsTFxWFubk5sbKzcfCmEyHW1RqxKc/vZWV1zOBIhPmzv6vtbphoUH6z4+HgiIyNZvHhxqpFzIYQQ74Z0soXIWZJ2Ij5YAwcOpFatWjg5OaVKOenXrx8mJiZpLv369culiIUQQgghMiZpJ+KjFBMTk+5j2s3MzChSpEgOR5R1knYihBBCfHwk7UTkaUWKFPmgO9hCCPGxSCvnW1JRhHh/JO1ECCGEEEKIHCKd7zzOw8ODNm3a5HYYueL1tjs5OTF48OBci0cIIYQQnz5JOxHi/2zevJn8+fOr63Z2dgwePFg65EIIIYR4Z6TzLcT/KViwYG6HIIQQQohPnKSd5BEbN27E3t4eQ0NDChUqRNOmTXn8+HGqcsnJyXh7e1OqVCkMDQ2pVq0aGzdu1Crz999/06JFC0xMTChatCjfffcdd+/eVfc7OTkxcOBABg4ciLm5OYULF2bcuHFkdWKdmJgYXF1dMTQ0pFSpUvj5+WFnZ8f8+fMBiIyMRKPREBQUpB7z8OFDNBoNAQEBACQlJdGzZ0+1HRUqVGDBggUZnvfVtBMnJydu3LjBkCFD0Gg0aDQaHj9+jJmZWarr4e/vj7GxMY8ePcpS+4QQQgiRd0nnOw+Ijo7G3d2dHj16EBISQkBAAO3atUuzM+zt7c2qVav4+eefuXTpEkOGDOHbb7/l0KFDwMtObpMmTahRowZnzpxh165d3L59Gzc3N616fH19yZcvH6dOnWLBggXMnTuXFStWZCleDw8Pbt68ycGDB9m4cSNLliwhJiYmW21OTk6mRIkSbNiwgcuXLzN+/Hj+97//sX79+iwdv3nzZkqUKMGkSZOIjo4mOjoaY2NjOnXqxMqVK7XKrly5kvbt22NqappmXQkJCcTFxWktQgghhMibJO0kD4iOjubFixe0a9cOW1tbAOzt7VOVS0hIYNq0aezbt4/69esDULp0aY4ePcqyZctwdHRk8eLF1KhRg2nTpqnH/fbbb9jY2HD16lXKly8PgI2NDfPmzUOj0VChQgUuXrzIvHnz6N27d4axXr16lZ07d3Lq1Cnq1KkDwK+//kqlSpWy1eb8+fMzceJEdb1UqVIcP36c9evXp/pDIS0FCxZEV1cXU1NTrKys1O29evWiQYMGREdHU6xYMWJiYtixYwf79u1Lty5vb2+tWIQQQgiRd8nIdx5QrVo1nJ2dsbe3p0OHDixfvpwHDx6kKnft2jWePHnCl19+qfXEyFWrVhEeHg5AcHAwBw8e1NpfsWJFALUMQL169dBoNOp6/fr1CQsLIykpKcNYQ0JCyJcvH7Vq1VK3VaxYEQsLi2y3+6effqJWrVpYWlpiYmLCL7/8QlRUVLbreVXdunWpUqUKvr6+APz+++/Y2trSqFGjdI8ZM2YMsbGx6nLz5s23ikEIIYQQHy8Z+c4DdHV12bt3L8eOHWPPnj0sWrSIsWPHcvLkSa1y8fHxAGzfvp3ixYtr7dPX11fLuLq6MmPGjFTnKVas2HtqgTYdnZd/M76aNvP8+XOtMmvXrmX48OHMmTOH+vXrY2pqyqxZs1K1+U306tWLn376idGjR7Ny5Uq6d++u9YfG6/T19dXrJ4QQQoi8TTrfeYRGo8HBwQEHBwfGjx+Pra0tW7Zs0SpTuXJl9PX1iYqKwtHRMc16atasyaZNm7CzsyNfvvRfPq93ck+cOEG5cuXQ1dXNMM6KFSvy4sULzp49q6adhIaG8vDhQ7WMpaUl8DKdpkaNGgBaN18CBAYG0qBBA/r3769ue3VkPiv09PTSHKn/9ttvGTlyJAsXLuTy5ct069YtW/UKIYQQIu+StJM84OTJk0ybNo0zZ84QFRXF5s2buXPnTqo8alNTU4YPH86QIUPw9fUlPDycc+fOsWjRIjXNYsCAAdy/fx93d3dOnz5NeHg4u3fvpnv37lod1aioKIYOHUpoaCh//PEHixYtYtCgQZnGWqFCBZo3b07fvn05efIkZ8+epVevXhgaGqplDA0NqVevHtOnTyckJIRDhw7x448/atVTrlw5zpw5w+7du7l69Srjxo3j9OnT2bpudnZ2HD58mH///VdrNpcCBQrQrl07RowYQbNmzShRokS26hVCCCFE3iWd7zzAzMyMw4cP07JlS8qXL8+PP/7InDlzaNGiRaqykydPZty4cXh7e1OpUiWaN2/O9u3bKVWqFADW1tYEBgaSlJREs2bNsLe3Z/DgwVhYWKjpIABdu3bl6dOn1K1blwEDBjBo0CD69OmTpXhXrlyJtbU1jo6OtGvXjj59+lCkSBGtMr/99hsvXrygVq1aDB48mClTpmjt79u3L+3ataNjx458/vnn3Lt3T2sUPCsmTZpEZGQkZcqUUUfbU/Ts2ZPExER69OiRrTqFEEIIkbdplKxOvixEFjk5OVG9enV1Xu534UN72uTq1asZMmQIt27dQk9PL1vHxsXFYW5uTmxsLGZmZu8pQiGEyJpaI1al2nZ2VtdciESID9u7+v6WnG8hsuHJkydER0czffp0+vbtm+2OtxBCfGikoy1EzpK0E5Gjjhw5ojVN4evLh27mzJlUrFgRKysrxowZk9vhCCGEEOIjI2knIkc9ffqUf//9N939ZcuWzcFocoeknQghhBAfH0k7ER8lQ0PDPNHBFkIIIYRIi3S+hRBCiDzs9RsuJQdciPdLcr6FEEIIIYTIIZ9U51tRFPr06UPBggXRaDSpnnoohBBCCCFEbvqkOt+7du3Cx8eHv/76i+joaD777LNMj9FoNPj7+7//4IQQQgghRJ73SeV8h4eHU6xYMRo0aJDj505MTPzg5nx+nzE9f/6c/Pnzv5e6c4uiKCQlJZEv3yf1thBCCCHEB+STGfn28PDghx9+ICoqCo1Gg52dHXZ2dqmesli9enW8vLyAl09NBGjbtq16TEpdbdq00Tpu8ODBODk5qetOTk4MHDiQwYMHU7hwYVxcXAD4+++/adGiBSYmJhQtWpTvvvuOu3fvZqkNycnJzJw5k7Jly6Kvr0/JkiWZOnWqun/UqFGUL18eIyMjSpcuzbhx43j+/Lm638vLi+rVq7NixQpKlSqFgYEBAA8fPqRXr15YWlpiZmZGkyZNCA4O1jr31q1bqVmzJgYGBpQuXZqJEyfy4sULdb9Go2Hp0qW0bt0aY2NjrbjSc+nSJb766ivMzMwwNTWlYcOGhIeHq22dNGkSJUqUQF9fn+rVq7Nr1y712MjISDQaDZs3b6Zx48YYGRlRrVo1jh8/rnWOwMBAnJycMDIyokCBAri4uPDgwQP1HN7e3pQqVQpDQ0OqVavGxo0b1WMDAgLQaDTs3LmTWrVqoa+vz9GjR3FycsLT05ORI0dSsGBBrKys1NcMvOyke3l5UbJkSfT19bG2tsbT0zPT6yGEEEII8cl0vhcsWKB25qKjozl9+nSmx6SUWblyZZaPeZWvry96enoEBgby888/8/DhQ5o0aUKNGjU4c+YMu3bt4vbt27i5uWWpvjFjxjB9+nTGjRvH5cuXWbNmDUWLFlX3m5qa4uPjw+XLl1mwYAHLly9n3rx5WnVcu3aNTZs2sXnzZjXnvUOHDsTExLBz507Onj1LzZo1cXZ25v79+8DLB9907dqVQYMGcfnyZZYtW4aPj0+qDraXlxdt27bl4sWL9OjRI8O2/PvvvzRq1Ah9fX0OHDjA2bNn6dGjh9qhX7BgAXPmzGH27NlcuHABFxcXWrduTVhYmFY9Y8eOZfjw4QQFBVG+fHnc3d3VOoKCgnB2dqZy5cocP36co0eP4urqSlJSEgDe3t6sWrWKn3/+mUuXLjFkyBC+/fZbDh06pHWO0aNHM336dEJCQqhatSrw8ndrbGzMyZMnmTlzJpMmTWLv3r0AbNq0iXnz5rFs2TLCwsLw9/fH3t4+3WuRkJBAXFyc1iKEEEKIPEr5hMybN0+xtbVV121tbZV58+ZplalWrZoyYcIEdR1QtmzZolWmW7duytdff621bdCgQYqjo6O67ujoqNSoUUOrzOTJk5VmzZppbbt586YCKKGhoRnGHhcXp+jr6yvLly/PsNyrZs2apdSqVUtdnzBhgpI/f34lJiZG3XbkyBHFzMxMefbsmdaxZcqUUZYtW6YoiqI4Ozsr06ZN09q/evVqpVixYuo6oAwePDjLsY0ZM0YpVaqUkpiYmOZ+a2trZerUqVrb6tSpo/Tv319RFEWJiIhQAGXFihXq/kuXLimAEhISoiiKori7uysODg5p1v/s2TPFyMhIOXbsmNb2nj17Ku7u7oqiKMrBgwcVQPH399cq4+joqHzxxRepYhs1apSiKIoyZ84cpXz58um27XUTJkxQgFRLbGxslo4XQoj3qeZwX61FCJG22NjYd/L9Lcmtb6FWrVpa68HBwRw8eDDNx6SHh4dTvnz5dOsKCQkhISEBZ2fndMusW7eOhQsXEh4eTnx8PC9evEj1hCVbW1ssLS21YoqPj6dQoUJa5Z4+faqmgAQHBxMYGKg10p2UlMSzZ8948uQJRkZGANSuXTvd2F4XFBREw4YN08wLj4uL49atWzg4OGhtd3BwSJUOkzISDVCsWDEAYmJiqFixIkFBQXTo0CHN81+7do0nT57w5Zdfam1PTEykRo0aWtvSater5005d0xMDPDyPwnz58+ndOnSNG/enJYtW+Lq6ppurviYMWMYOnSoVvttbGzSLCuEEEKIT9sn3fnW0dFBURStba/mSL/tccbGxlrr8fHxuLq6MmPGjFRlUzqO6TE0NMxw//Hjx+nSpQsTJ07ExcUFc3Nz1q5dy5w5czKNqVixYgQEBKSq08LCQi0zceJE2rVrl6pMSt54WnVnJLP2ZNWrnXeNRgO8zOXO7Bzx8fEAbN++neLFi2vt09fX11pPq12v/9Gg0WjU89rY2BAaGsq+ffvYu3cv/fv3Z9asWRw6dCjNPzb09fVTnVMIIYQQedMn3fm2tLQkOjpaXY+LiyMiIkKrTP78+dUc4VeP+/vvv7W2BQUFZTq7R82aNdm0aRN2dnbZnjGjXLlyGBoasn//fnr16pVq/7Fjx7C1tWXs2LHqths3bmRab82aNfnvv//Ily+fekNpWmVCQ0Pf6WPfq1atiq+vb5qzopiZmWFtbU1gYCCOjo7q9sDAQOrWrZutc+zfv5+JEyem2le5cmX09fWJiorSOse7YmhoiKurK66urgwYMICKFSty8eJFatas+c7PJYQQQohPxydzw2VamjRpwurVqzly5AgXL16kW7du6OrqapWxs7Nj//79/Pfff+osGU2aNOHMmTOsWrWKsLAwJkyYkKoznpYBAwZw//593N3dOX36NOHh4ezevZvu3bun6uC/zsDAgFGjRjFy5EhWrVpFeHg4J06c4NdffwVeds6joqJYu3Yt4eHhLFy4kC1btmQaU9OmTalfvz5t2rRhz549REZGcuzYMcaOHcuZM2cAGD9+PKtWrWLixIlcunSJkJAQ1q5dy48//php/ekZOHAgcXFxdOrUiTNnzhAWFsbq1asJDQ0FYMSIEcyYMYN169YRGhrK6NGjCQoKYtCgQVk+x5gxYzh9+jT9+/fnwoULXLlyhaVLl3L37l1MTU0ZPnw4Q4YMwdfXl/DwcM6dO8eiRYvw9fV943YB+Pj48Ouvv/L3339z/fp1fv/9dwwNDbG1tX2reoUQQgjx6fukO99jxozB0dGRr776ilatWtGmTRvKlCmjVWbOnDns3bsXGxsbNRfYxcWFcePGMXLkSOrUqcOjR4/o2rVrpudLGc1NSkqiWbNm2NvbM3jwYCwsLNDRyfxSjxs3jmHDhjF+/HgqVapEx44d1Tzj1q1bM2TIEAYOHEj16tU5duwY48aNy7ROjUbDjh07aNSoEd27d6d8+fJ06tSJGzduqDOpuLi48Ndff7Fnzx7q1KlDvXr1mDdv3lt1JgsVKsSBAweIj4/H0dGRWrVqsXz5cnUU3NPTk6FDhzJs2DDs7e3ZtWsX27Zto1y5clk+R/ny5dmzZw/BwcHUrVuX+vXrs3XrVvW/DpMnT2bcuHF4e3tTqVIlmjdvzvbt2ylVqtQbtwtepussX74cBwcHqlatyr59+/jzzz9T5dULIYQQQrxOo7ye3CyEeK/i4uIwNzcnNjY21Q2zQgiR02qNWKW1fnZW5oNNQuRF7+r7+5PO+RZCCCFExqSzLUTO+qTTTj4kUVFRmJiYpLtERUXldojZ0q9fv3Tb0q9fv9wOTwghhBDigyRpJznkxYsXREZGprv/TWZIyU0xMTHpPqnRzMyMIkWK5HBEHw9JOxFCCCE+PpJ28pHJly/fO53KL7cVKVJEOthCCPEJSMn5lvQTIXKGpJ0IIYQQQgiRQ6TzLYQQQgghRA6Rzrd4r7y8vKhevXqOnEuj0eDv758j50phZ2fH/Pnzc/ScQgghhPh4SedbvFfDhw9n//79uR2GEEIIIcQHQW64FO9VyvSDQgghhBBCRr5FJpycnPD09GTkyJEULFgQKysrvLy81P1RUVF8/fXXmJiYYGZmhpubG7dv31b3v552EhAQQN26dTE2NsbCwgIHBwdu3Lih7t+6dSs1a9bEwMCA0qVLM3HiRF68ePFGsd+8eRM3NzcsLCwoWLAgX3/9tTrd4549ezAwMODhw4daxwwaNIgmTZqo60ePHqVhw4YYGhpiY2ODp6cnjx8/zlYcCQkJxMXFaS1CCCGEyJuk8y0y5evri7GxMSdPnmTmzJlMmjSJvXv3kpyczNdff839+/c5dOgQe/fu5fr163Ts2DHNel68eEGbNm1wdHTkwoULHD9+nD59+qDRaAA4cuQIXbt2ZdCgQVy+fJlly5bh4+PD1KlTsx3z8+fPcXFxwdTUlCNHjhAYGIiJiQnNmzcnMTERZ2dnLCws2LRpk3pMUlIS69ato0uXLgCEh4fTvHlzvvnmGy5cuMC6des4evQoAwcOzFYs3t7emJubq4uNjU222yOEEEKIT4QiRAYcHR2VL774QmtbnTp1lFGjRil79uxRdHV1laioKHXfpUuXFEA5deqUoiiKMmHCBKVatWqKoijKvXv3FEAJCAhI81zOzs7KtGnTtLatXr1aKVasWJZiBZQtW7aox1WoUEFJTk5W9yckJCiGhobK7t27FUVRlEGDBilNmjRR9+/evVvR19dXHjx4oCiKovTs2VPp06eP1jmOHDmi6OjoKE+fPlUURVFsbW2VefPmZRjXs2fPlNjYWHW5efOmAiixsbFZapcQQrxPNYf7KjWH++Z2GEJ88GJjY9/J97fkfItMVa1aVWu9WLFixMTEEBISgo2NjdZIbuXKlbGwsCAkJIQ6depoHVewYEE8PDxwcXHhyy+/pGnTpri5uVGsWDEAgoODCQwM1BrpTkpK4tmzZzx58gQjI6MsxxwcHMy1a9cwNTXV2v7s2TPCw8MB6NKlC/Xq1ePWrVtYW1vj5+dHq1atsLCwUOu4cOECfn5+6vGKopCcnExERASVKlXKUiz6+vro6+tnOXYhhBBCfLqk8y0ylT9/fq11jUZDcnLyG9W1cuVKPD092bVrF+vWrePHH39k79691KtXj/j4eCZOnEi7du1SHWdgYJCt88THx1OrVi2tjnMKS0tLAOrUqUOZMmVYu3Yt33//PVu2bMHHx0erjr59++Lp6ZmqjpIlS2YrHiGEEEIIkM63eAuVKlXi5s2b3Lx5Ux39vnz5Mg8fPqRy5crpHlejRg1q1KjBmDFjqF+/PmvWrKFevXrUrFmT0NBQypYt+9ax1axZk3Xr1lGkSBHMzMzSLdelSxf8/PwoUaIEOjo6tGrVSquOy5cvv5N4hBBCCCFAbrgUb6Fp06bY29vTpUsXzp07x6lTp+jatSuOjo7Url07VfmIiAjGjBnD8ePHuXHjBnv27CEsLExN3xg/fjyrVq1i4sSJXLp0iZCQENauXcuPP/6Y7di6dOlC4cKF+frrrzly5AgREREEBATg6enJP//8o1Xu3LlzTJ06lfbt22ulh4waNYpjx44xcOBAgoKCCAsLY+vWrdm+4VIIIYQQIoV0vsUb02g0bN26lQIFCtCoUSOaNm1K6dKlWbduXZrljYyMuHLlCt988w3ly5enT58+DBgwgL59+wLg4uLCX3/9xZ49e6hTpw716tVj3rx52NraZjs2IyMjDh8+TMmSJWnXrh2VKlWiZ8+ePHv2TGskvGzZstStW5cLFy6os5ykqFq1KocOHeLq1as0bNiQGjVqMH78eKytrbMdjxBCCCEEgEZRFCW3gxAiL4mLi8Pc3JzY2NgMU2KEEEII8eF4V9/fMvIthBBCCCFEDpHOt/go+Pn5qY+qf32pUqVKbocnhBBCCJElMtuJ+Ci0bt2azz//PM19r0+FKIQQQgjxoZLOt/gomJqapnpgjhBCiLdXa8Qqzs7qmtthCJFnSNqJEEIIIYQQOUQ63x8AJycnBg8eDICdnR3z589/o2M/ZT4+Pupj3wG8vLyoXr16hsdERkai0WgICgp6r7EJIYQQQmSVpJ18YE6fPo2xsXGWy2/evDlP5jwPHz6cH374QV338PDg4cOH+Pv7q9tsbGyIjo6mcOHCuRChEEIIIURq0vn+wFhaWmarfMGCBd9TJB+2lJlOMqKrq4uVlVUORSSEEEIIkTlJO/nAvJp20rlzZzp27Ki1//nz5xQuXJhVq1YBqdNO7OzsmDZtGj169MDU1JSSJUvyyy+/aNVx7NgxqlevjoGBAbVr18bf3z9b6RmXLl3iq6++wszMDFNTUxo2bEh4eDgAycnJTJo0iRIlSqCvr0/16tXZtWuXemxKKsjmzZtp3LgxRkZGVKtWjePHj2udw8fHh5IlS2JkZETbtm25d++e1v5X0068vLzw9fVl69ataDQaNBoNAQEBaaadHDp0iLp166Kvr0+xYsUYPXo0L168UPc7OTnh6enJyJEjKViwIFZWVnh5ean7FUXBy8uLkiVLoq+vj7W1NZ6enlm6bkIIIYQQ0vn+gHXp0oU///yT+Ph4ddvu3bt58uQJbdu2Tfe4OXPmULt2bc6fP0///v35/vvvCQ0NBV4+ncnV1RV7e3vOnTvH5MmTGTVqVJZj+vfff2nUqBH6+vocOHCAs2fP0qNHD7UDu2DBAubMmcPs2bO5cOECLi4utG7dmrCwMK16xo4dy/DhwwkKCqJ8+fK4u7urdZw8eZKePXsycOBAgoKCaNy4MVOmTEk3puHDh+Pm5kbz5s2Jjo4mOjqaBg0apBl7y5YtqVOnDsHBwSxdupRff/01Vd2+vr4YGxtz8uRJZs6cyaRJk9i7dy8AmzZtYt68eSxbtoywsDD8/f2xt7fP8JolJCQQFxentQghhBAij1JErnN0dFQGDRqkKIqi2NraKvPmzVMURVGeP3+uFC5cWFm1apVa1t3dXenYsWOax6Yc/+2336rrycnJSpEiRZSlS5cqiqIoS5cuVQoVKqQ8ffpULbN8+XIFUM6fP59prGPGjFFKlSqlJCYmprnf2tpamTp1qta2OnXqKP3791cURVEiIiIUQFmxYoW6/9KlSwqghISEqG1s2bKlVh0dO3ZUzM3N1fUJEyYo1apVU9e7deumfP3111rHpJwrpV3/+9//lAoVKijJyclqmZ9++kkxMTFRkpKSFEV5eT2/+OKLVPGPGjVKURRFmTNnjlK+fPl025+WCRMmKECqJTY2Nst1CCHE+1JzuG9uhyDERyE2NvadfH/LyPcHLF++fLi5ueHn5wfA48eP2bp1K126dMnwuKpVq6o/azQarKysiImJASA0NJSqVatiYGCglqlbt26WYwoKCqJhw4Zp3uQZFxfHrVu3cHBw0Nru4OBASEhIujEWK1YMQI0xJCQk1QN16tevn+UY0xMSEkL9+vXRaDRascXHx/PPP/+kGVtKfCmxdejQgadPn1K6dGl69+7Nli1btNJW0jJmzBhiY2PV5ebNm2/dFiGEEEJ8nKTz/YHr0qUL+/fvJyYmBn9/fwwNDWnevHmGx7zeMdZoNCQnJ7+TeAwNDd9JPa/GmNIZflcxvq2Mrp+NjQ2hoaEsWbIEQ0ND+vfvT6NGjXj+/Hm69enr62NmZqa1CCGEECJvks73B65BgwbY2Niwbt06/Pz86NChw1tNLVihQgUuXrxIQkKCuu306dNZPr5q1aocOXIkzc6mmZkZ1tbWBAYGam0PDAykcuXKWT5HpUqVOHnypNa2EydOZHiMnp4eSUlJmdZ7/PhxFEXRis3U1JQSJUpkOT5DQ0NcXV1ZuHAhAQEBHD9+nIsXL2b5eCGEEELkXdL5/gh07tyZn3/+mb1792aacpKVupKTk+nTpw8hISHs3r2b2bNnA2ilY6Rn4MCBxMXF0alTJ86cOUNYWBirV69Wb+gcMWIEM2bMYN26dYSGhjJ69GiCgoIYNGhQlmP09PRk165dzJ49m7CwMBYvXqw1Y0pa7OzsuHDhAqGhody9ezfNPw769+/PzZs3+eGHH7hy5Qpbt25lwoQJDB06FB2drL0VfHx8+PXXX/n777+5fv06v//+O4aGhtja2ma5fUIIIYTIu6Tz/RHo0qULly9fpnjx4qnyqbPLzMyMP//8k6CgIKpXr87YsWMZP348gFYeeHoKFSrEgQMHiI+Px9HRkVq1arF8+XJ1NN7T05OhQ4cybNgw7O3t2bVrF9u2baNcuXJZjrFevXosX76cBQsWUK1aNfbs2cOPP/6Y4TG9e/emQoUK1K5dG0tLy1Sj7wDFixdnx44dnDp1imrVqtGvXz969uyZad2vsrCwYPny5Tg4OFC1alX27dvHn3/+SaFChbJchxBCCCHyLo3y6v/gRZ7k5+dH9+7diY2NfWc53SJ9cXFxmJubExsbK/nfQohcV2vEKs7O6prbYQjxwXtX39/yhMs8aNWqVZQuXZrixYsTHBzMqFGjcHNzk463EELkQdLxFiJnSdpJHvTff//x7bffUqlSJYYMGUKHDh3Up2D269dPfXT760u/fv1yOXIhhBBCiI+bpJ0ILTExMek+gdHMzIwiRYrkcESfHkk7EUIIIT4+knYi3osiRYpIB1sIIYQQ4j2RtBMhhBBCCCFyiHS+hRBCCCGEyCHS+RafFDs7O+bPn5+j53RycmLw4ME5ek4hhBBCfJyk8y2yzMPDgzZt2uR2GEIIIYQQHy3pfIt3Lq1HuwshhBBCCOl8izRs3LgRe3t7DA0NKVSoEE2bNmXEiBH4+vqydetWNBoNGo2GgIAAIiMj0Wg0rFu3DkdHRwwMDPDz8wNgxYoVVKpUCQMDAypWrMiSJUvUcyQmJjJw4ECKFSuGgYEBtra2eHt7A6AoCl5eXpQsWRJ9fX2sra3x9PR8o7Y8fPiQXr16YWlpiZmZGU2aNCE4OBiAq1evotFouHLlitYx8+bNo0yZMur633//TYsWLTAxMaFo0aJ899133L17N8sxJCQkEBcXp7UIIYQQIm+SzrfQEh0djbu7Oz169CAkJISAgADatWvHhAkTcHNzo3nz5kRHRxMdHU2DBg3U40aPHs2gQYMICQnBxcUFPz8/xo8fz9SpUwkJCWHatGmMGzcOX19fABYuXMi2bdtYv349oaGh+Pn5YWdnB8CmTZuYN28ey5YtIywsDH9/f+zt7d+oPR06dCAmJoadO3dy9uxZatasibOzM/fv36d8+fLUrl1b/WMhhZ+fH507dwZedt6bNGlCjRo1OHPmDLt27eL27du4ubllOQZvb2/Mzc3VxcbG5o3aIoQQQohPgCLEK86ePasASmRkZKp93bp1U77++mutbREREQqgzJ8/X2t7mTJllDVr1mhtmzx5slK/fn1FURTlhx9+UJo0aaIkJyenOs+cOXOU8uXLK4mJidmO39bWVpk3b56iKIpy5MgRxczMTHn27Fmq2JYtW6YoiqLMmzdPKVOmjLovNDRUAZSQkBA15mbNmmkdf/PmTQVQQkNDFUVRFEdHR2XQoEHpxvTs2TMlNjZWXVKOj42NzXb7hBBCCJE7YmNj38n3t4x8Cy3VqlXD2dkZe3t7OnTowPLly3nw4EGmx9WuXVv9+fHjx4SHh9OzZ0+tx9NPmTKF8PBw4OXNm0FBQVSoUAFPT0/27NmjHt+hQweePn1K6dKl6d27N1u2bOHFixfZbktwcDDx8fEUKlRIK46IiAg1jk6dOhEZGcmJEyeAl6PeNWvWpGLFimodBw8e1Do+ZV9KHZnR19fHzMxMaxFCCCFE3iRPuBRadHV12bt3L8eOHWPPnj0sWrSIsWPHcvLkyQyPMzY2Vn+Oj48HYPny5Xz++eep6geoWbMmERER7Ny5k3379uHm5kbTpk3ZuHEjNjY2hIaGsm/fPvbu3Uv//v2ZNWsWhw4dIn/+/FluS3x8PMWKFSMgICDVPgsLCwCsrKxo0qQJa9asoV69eqxZs4bvv/9eqw5XV1dmzJiRqo5ixYplORYhhBBCCJDOt0iDRqPBwcEBBwcHxo8fj62tLVu2bEFPT4+kpKRMjy9atCjW1tZcv36dLl26pFvOzMyMjh070rFjR9q3b0/z5s25f/8+BQsWxNDQEFdXV1xdXRkwYAAVK1bk4sWL1KxZM8vtqFmzJv/99x/58uVT88nT0qVLF0aOHIm7uzvXr1+nU6dOWnVs2rQJOzs78uWTt4sQQggh3o70JoSWkydPsn//fpo1a0aRIkU4efIkd+7coVKlSjx79ozdu3cTGhpKoUKFMDc3T7eeiRMn4unpibm5Oc2bNychIYEzZ87w4MEDhg4dyty5cylWrBg1atRAR0eHDRs2YGVlhYWFBT4+PiQlJfH5559jZGTE77//jqGhIba2ttlqS9OmTalfvz5t2rRh5syZlC9fnlu3brF9+3batm2rpsq0a9eO77//nu+//57GjRtjbW2t1jFgwACWL1+Ou7s7I0eOpGDBgly7do21a9eyYsUKdSRfCCGEECIrpPMttJiZmXH48GHmz59PXFwctra2zJkzhxYtWlC7dm0CAgKoXbs28fHxHDx4MN0R5V69emFkZMSsWbMYMWIExsbG2Nvbq0+CNDU1ZebMmYSFhaGrq0udOnXYsWMHOjo6WFhYMH36dIYOHUpSUhL29vb8+eefFCpUKFtt0Wg07Nixg7Fjx9K9e3fu3LmDlZUVjRo1omjRomo5U1NTXF1dWb9+Pb/99ptWHdbW1gQGBjJq1CiaNWtGQkICtra2NG/eHB0duWVCCCGEENmjURRFye0ghMhL4uLiMDc3JzY2Vm6+FEIIIT4S7+r7W4buhBBCCCGEyCHS+RYfjSNHjmhN+ff6IoQQQgjxoZOcb/HRqF27NkFBQbkdhhBCCCHEG5POt/hoGBoaUrZs2dwOQwghPhm1Rqzi7KyuuR2GEHmKpJ0IIYQQQgiRQ6Tz/Zbs7OyYP3/+ez+PRqPB39//vZ/nbfn7+1O2bFl0dXXVaQWFEEIIIcRLknbykYiOjqZAgQK5HUam+vbtS/fu3fH09MTU1DS3wxFCCCGE+KBI5/sjYWVlldshZOj58+ckJCQQExODi4uL1lMisysxMRE9Pb13GJ0QQgghxIfhk0o72bVrF1988QUWFhYUKlSIr776ivDwcHX/P//8g7u7OwULFsTY2JjatWtz8uRJAMLDw/n6668pWrQoJiYm1KlTh3379mnVHxMTg6urK4aGhpQqVQo/P79UMTx8+JBevXphaWmJmZkZTZo0ITg4WN3v5eVF9erV+e233yhZsiQmJib079+fpKQkZs6ciZWVFUWKFGHq1Kla9b6edpJRWzKScv5ly5ZhY2ODkZERbm5uxMbGapVbsWIFlSpVwsDAgIoVK7JkyRJ1X2RkJBqNhnXr1uHo6IiBgQF+fn7qSHeTJk3QaDQEBAQAsGnTJqpUqYK+vj52dnbMmTNH61x2dnZMnjyZrl27YmZmRp8+ffDx8cHCwoK//vqLChUqYGRkRPv27Xny5Am+vr7Y2dlRoEABPD09SUpKUutavXo1tWvXxtTUFCsrKzp37kxMTIy6PyAgAI1Gw/79+6lduzZGRkY0aNCA0NBQrZj+/PNP6tSpg4GBAYULF6Zt27bqvoSEBIYPH07x4sUxNjbm888/V9sqhBBCCJGRT6rz/fjxY4YOHcqZM2fYv38/Ojo6tG3bluTkZOLj43F0dOTff/9l27ZtBAcHM3LkSJKTkwGIj4+nZcuW7N+/n/Pnz9O8eXNcXV2JiopS6/fw8ODmzZscPHiQjRs3smTJEq2OHUCHDh2IiYlh586dnD17lpo1a+Ls7Mz9+/fVMuHh4ezcuZNdu3bxxx9/8Ouvv9KqVSv++ecfDh06xIwZM/jxxx/T7Uxn1pbMXLt2jfXr1/Pnn3+ya9cuzp8/T//+/dX9fn5+jB8/nqlTpxISEsK0adMYN24cvr6+WvWMHj2aQYMGERISQuPGjdUO7KZNm4iOjqZBgwacPXsWNzc3OnXqxMWLF/Hy8mLcuHH4+Pho1TV79myqVavG+fPnGTduHABPnjxh4cKFrF27ll27dhEQEEDbtm3ZsWMHO3bsYPXq1SxbtoyNGzeq9Tx//pzJkycTHByMv78/kZGReHh4pLoGY8eOZc6cOZw5c4Z8+fLRo0cPdd/27dtp27YtLVu25Pz58+zfv5+6deuq+wcOHMjx48dZu3YtFy5coEOHDjRv3pywsLA0r3dCQgJxcXFaixBCCCHyKOUTdufOHQVQLl68qCxbtkwxNTVV7t27l+Xjq1SpoixatEhRFEUJDQ1VAOXUqVPq/pCQEAVQ5s2bpyiKohw5ckQxMzNTnj17plVPmTJllGXLlimKoigTJkxQjIyMlLi4OHW/i4uLYmdnpyQlJanbKlSooHh7e6vrgLJlyxZFUZQ3akuKCRMmKLq6uso///yjbtu5c6eio6OjREdHq/GuWbNG67jJkycr9evXVxRFUSIiIhRAmT9/vlaZBw8eKIBy8OBBdVvnzp2VL7/8UqvciBEjlMqVK6vrtra2Sps2bbTKrFy5UgGUa9euqdv69u2rGBkZKY8ePVK3ubi4KH379k23vadPn1YA9ZiDBw8qgLJv3z61zPbt2xVAefr0qaIoilK/fn2lS5cuadZ348YNRVdXV/n333+1tjs7OytjxoxJ85gJEyYoQKolNjY23biFECIn1Bzum9shCPHRiI2NfSff35/UyHdYWBju7u6ULl0aMzMz7OzsAIiKiiIoKIgaNWpQsGDBNI+Nj49n+PDhVKpUCQsLC0xMTAgJCVFHvkNCQsiXLx+1atVSj6lYsSIWFhbqenBwMPHx8RQqVEjryYsRERFa6S92dnZaNyMWLVqUypUro6Ojo7Xt9VH1FJm1JTMlS5akePHi6nr9+vVJTk4mNDSUx48fEx4eTs+ePbXaMGXKFK02wMuH3mQmJCQEBwcHrW0ODg6EhYVppYukVZeRkRFlypRR14sWLYqdnZ3W0yxfv05nz57F1dWVkiVLYmpqiqOjI4DWfzAAqlatqv5crFgxALWeoKAgnJ2d02zPxYsXSUpKonz58lrX59ChQ6muT4oxY8YQGxurLjdv3kyznBBCCCE+fZ/UDZeurq7Y2tqyfPlyrK2tSU5O5rPPPiMxMRFDQ8MMjx0+fDh79+5l9uzZlC1bFkNDQ9q3b09iYmKWzx8fH0+xYsXSzP99tZOeP39+rX0ajSbNbemlkWTWlrcRHx8PwPLly/n888+19unq6mqtGxsbv7PzplVXdq/T48ePcXFxwcXFBT8/PywtLYmKisLFxSXV7/HVejQaDYBaT0bXNz4+Hl1dXc6ePZvqeqT3iHt9fX309fXTrVMIIYQQeccn0/m+d+8eoaGhLF++nIYNGwJw9OhRdX/VqlVZsWIF9+/fT3PEODAwEA8PD/XGuvj4eCIjI9X9FStW5MWLF5w9e5Y6deoAEBoaysOHD9UyNWvW5L///iNfvnzqqPv7kFlbMhMVFcWtW7fUGUlOnDiBjo4OFSpUoGjRolhbW3P9+nW6dOny1rFWqlSJwMBArW2BgYGUL18+Vef1bV25coV79+4xffp0bGxsADhz5ky266latSr79++ne/fuqfbVqFGDpKQkYmJi1NeZEEIIIURWfTJpJwUKFKBQoUL88ssvXLt2jQMHDjB06FB1v7u7O1ZWVrRp04bAwECuX7/Opk2bOH78OADlypVj8+bNBAUFERwcTOfOnbVGnitUqEDz5s3p27cvJ0+e5OzZs/Tq1UtrlLRp06bUr1+fNm3asGfPHiIjIzl27Bhjx459o05gejJrS2YMDAzo1q0bwcHBHDlyBE9PT9zc3NTpDCdOnIi3tzcLFy7k6tWrXLx4kZUrVzJ37txsxzps2DD279/P5MmTuXr1Kr6+vixevJjhw4dnu67MlCxZEj09PRYtWsT169fZtm0bkydPznY9EyZM4I8//mDChAmEhIRw8eJFZsyYAUD58uXp0qULXbt2ZfPmzURERHDq1Cm8vb3Zvn37u26SEEIIIT4xn0znW0dHh7Vr13L27Fk+++wzhgwZwqxZs9T9enp67NmzhyJFitCyZUvs7e2ZPn26Ovo6d+5cChQoQIMGDXB1dcXFxYWaNWtqnWPlypVYW1vj6OhIu3bt6NOnD0WKFFH3azQaduzYQaNGjejevTvly5enU6dO3Lhxg6JFi76ztmbWlsyULVuWdu3a0bJlS5o1a0bVqlW1phLs1asXK1asYOXKldjb2+Po6IiPjw+lSpXKdqw1a9Zk/fr1rF27ls8++4zx48czadKkNGcgeVuWlpb4+PiwYcMGKleuzPTp05k9e3a263FycmLDhg1s27aN6tWr06RJE06dOqXuX7lyJV27dmXYsGFUqFCBNm3acPr0aUqWLPkumyOEEEKIT5BGURQlt4MQOcfLywt/f3+CgoJyO5Q8Ky4uDnNzc2JjYzEzM8vtcIQQeVitEas4O6trbochxEfhXX1/fzIj30IIIYTIHul4C5HzPpkbLsVLVapU4caNG2nuW7ZsWQ5HI4QQQgghXiVpJ5+YGzdu8Pz58zT3FS1aVGt+cZE7JO1ECCGE+Pi8q+9vGfn+xNja2uZ2CEIIIYQQIh3S+RZCCCHymFojVqk/S963EDlLbrgUQgghhBAih0jn+wPn5OTE4MGD31l9Xl5eVK9e/Z3Vl5OyG7uPjw8WFhaZltNoNPj7+79xXEIIIYQQWfXJd74DAgLQaDRaj4H/mGzevPmNntL4sUurQzx8+HD279+f5To6duzI1atX1fX0Ou/R0dG0aNHiTUMVQgghhMgyyfn+P4mJiejp6eXKuZ8/f07+/PnTjKdgwYK5ElNG0oo3J5iYmGBiYpLl8oaGhhgaGmZazsrK6m3CEkIIIYTIso9i5Ds5ORlvb29KlSqFoaEh1apVY+PGjSiKQtOmTXFxcSFlxsT79+9TokQJxo8fT2RkJI0bNwagQIECaDQa9bHmTk5ODBw4kMGDB1O4cGFcXFyAl4+Zt7e3x9jYGBsbG/r37098fLxWPIGBgTg5OWFkZESBAgVwcXHhwYMHANjZ2TF//nyt8tWrV8fLy0td12g0LF26lNatW2NsbMzUqVPVUdkVK1ZQqlQpDAwM1DhfTTtJSEhg1KhR2NjYoK+vT9myZfn111+BtNMs/P390Wg06V7b06dP8+WXX1K4cGHMzc1xdHTk3LlzWmXSijcjSUlJ9OzZU/19VahQgQULFqQq99tvv1GlShX09fUpVqwYAwcOVK8hQNu2bdFoNOr6qyPXe/bswcDAINV/NAYNGkSTJk1SXQ8fHx8mTpxIcHAwGo0GjUaDj4+P2r5XR9lv3ryJm5sbFhYWFCxYkK+//prIyEh1f0BAAHXr1sXY2BgLCwscHBzSnVtdCCGEEOJVH0Xn29vbm1WrVvHzzz9z6dIlhgwZwrfffsvhw4fx9fXl9OnTLFy4EIB+/fpRvHhxxo8fj42NDZs2bQIgNDSU6OhorU6gr68venp6BAYG8vPPPwOgo6PDwoULuXTpEr6+vhw4cICRI0eqxwQFBeHs7EzlypU5fvw4R48exdXVlaSkpGy1ycvLi7Zt23Lx4kV69OgBwLVr19i0aRObN29O9/HvXbt25Y8//mDhwoWEhISwbNmybI0Gv+7Ro0d069aNo0ePcuLECcqVK0fLli159OhRpvGmJzk5mRIlSrBhwwYuX77M+PHj+d///sf69evVMkuXLmXAgAH06dOHixcvsm3bNsqWLQu8/IMAYOXKlURHR6vrr3J2dsbCwkL9/cLLTv+6devo0qVLqvIdO3Zk2LBhVKlShejoaKKjo+nYsWOqcs+fP8fFxQVTU1OOHDlCYGAgJiYmNG/enMTERF68eEGbNm1wdHTkwoULHD9+nD59+mT4B05CQgJxcXFaixBCCCHypg8+7SQhIYFp06axb98+6tevD0Dp0qU5evQoy5YtY82aNSxbtoyuXbvy33//sWPHDs6fP0++fC+blpK2UaRIkVSjwuXKlWPmzJla214dZbazs2PKlCn069ePJUuWADBz5kxq166trsPLp0pmV+fOnenevbvWtsTERFatWoWlpWWax1y9epX169ezd+9emjZtql6Lt5EySpzil19+wcLCgkOHDvHVV19lGG968ufPz8SJE9X1UqVKcfz4cdavX4+bmxsAU6ZMYdiwYQwaNEgtV6dOHQC1/RYWFummhOjq6tKpUyfWrFlDz549Adi/fz8PHz7km2++SVXe0NAQExMT8uXLl2Gaybp160hOTmbFihVqh3rlypVYWFgQEBBA7dq1iY2N5auvvqJMmTIAVKpUKcPr4e3trXU9hBBCCJF3ffAj39euXePJkyd8+eWXas6viYkJq1atIjw8HIAOHTrQtm1bpk+fzuzZsylXrlyW6q5Vq1aqbfv27cPZ2ZnixYtjamrKd999x71793jy5Anw/0e+31bt2rVTbbO1tU23451ybl1dXRwdHd/6/Clu375N7969KVeuHObm5piZmREfH09UVFSm8Wbkp59+olatWlhaWmJiYsIvv/yi1hkTE8OtW7fe+jp26dKFgIAAbt26BYCfnx+tWrXK0gwn6QkODubatWuYmpqqr7WCBQvy7NkzwsPDKViwIB4eHri4uODq6sqCBQuIjo7OsM4xY8YQGxurLjdv3nzj+IQQQgjxcfvgR75T8q23b99O8eLFtfbp6+sD8OTJE86ePYuuri5hYWFZrtvY2FhrPTIykq+++orvv/+eqVOnUrBgQY4ePUrPnj1JTEzEyMgo0xv4dHR01PzzFGk97v31c6e37VXv6tyv6tatG/fu3WPBggXY2tqir69P/fr1SUxMzFZsr1q7di3Dhw9nzpw51K9fH1NTU2bNmsXJkyez1I6sqlOnDmXKlGHt2rV8//33bNmyRc3jflPx8fHUqlULPz+/VPtS/jBauXIlnp6e7Nq1i3Xr1vHjjz+yd+9e6tWrl2ad+vr66mtVCCGEEHnbB9/5rly5Mvr6+kRFRaU74jts2DB0dHTYuXMnLVu2pFWrVmo6RcoMJlnJyT579izJycnMmTMHHZ2X/xR4NU8ZoGrVquzfvz/dNAJLS0utkdC4uDgiIiIyb2gW2Nvbk5yczKFDh9S0k9fP/ejRIx4/fqx2ltPLHU8RGBjIkiVLaNmyJfDyZsO7d+++VZyBgYE0aNCA/v37q9tS/ksBYGpqip2dHfv371dviH1d/vz5s/Q769KlC35+fpQoUQIdHR1atWqVblk9Pb1M66xZsybr1q2jSJEimJmZpVuuRo0a1KhRgzFjxlC/fn3WrFmTbudbCCGEECLFB592YmpqyvDhwxkyZAi+vr6Eh4dz7tw5Fi1ahK+vL9u3b+e3337Dz8+PL7/8khEjRtCtWzd19hFbW1s0Gg1//fUXd+7cSTVzyavKli3L8+fPWbRoEdevX2f16tXqjZgpxowZw+nTp+nfvz8XLlzgypUrLF26VO2wNmnShNWrV3PkyBEuXrxIt27d0NXVfSfXws7Ojm7dutGjRw/8/f2JiIggICBA/QPh888/x8jIiP/973+Eh4ezZs2aTEeCy5Urx+rVqwkJCeHkyZN06dLlrUemy5Urx5kzZ9i9ezdXr15l3LhxqW6a9PLyYs6cOSxcuJCwsDD1d/pqW/fv389///2n/i7T0qVLF86dO8fUqVNp3759hiPMdnZ2REREEBQUxN27d0lISEizvsKFC/P1119z5MgR9Rp7enryzz//EBERwZgxYzh+/Dg3btxgz/9r787DsqjXx4+/H1AekdUFARUBExR3wA0NccFAk3LJ/aiYe6K5gMvXY24lqOCS7Wag5slKzTwqbiR2QEVFQVMyJBBPByUXQNRAYH5/+HMunwQEFxC4X9c11+U885nP3J8Z6Ln5dM/MgQMkJiY+se5bCCGEEAIqQPINsHTpUhYsWEBgYCBOTk54e3uzZ88e7OzsGDt2LIsWLcLFxQWAxYsXY2lpyaRJkwBo0KABixcvZu7cuVhaWqqPsytMmzZtWLVqFcuXL6dly5Zs2bKFwMBAnTaOjo4cOHCA+Ph4OnTogJubGz/++KN6g+e8efPw8PCgb9++vP766/Tr10+9Me95+PTTT3nrrbd45513aNasGePHj+fOnTvAg5tLv/76a/bu3UurVq345ptvdB5xWJgNGzZw69YtXFxcGDlyJNOmTaNevXrPFOPEiRMZMGAAQ4YMoWPHjty4cUNnFhwelLusWbOGTz75hBYtWtC3b1+dkqGQkBAOHjyIjY0Nzs7ORR6rSZMmdOjQgbNnzxb6lJNHDRw4EG9vb7p3746FhQXffPPNY21q1qzJzz//TKNGjRgwYABOTk6MHTuWv/76C1NTU2rWrMmvv/7KwIEDcXR0ZMKECUyZMoWJEyeW8iwJIYQQoirSKH8vEhZCvFBZWVmYmZmRmZlZbGmLEEK8KK4Bm9R/x64cVY6RCFFxPK/v75e+5lsIIYQQz5ck3EKUnwpRdiJeLpMmTdJ57OOjy8NyHyGEEEII8TgpOxGllp6eXuRbGk1NTZ+5Zryyk7ITIYQQouKRshNRburVqycJthBCVGBS8y1E+ZGyEyGEEEIIIcqIJN9CCCGEEEKUEUm+q7hu3boxffr0F9J3SkoKGo3miW/ZfNnZ2dmxZs2a8g5DCCGEEJWA1HyLF8bGxoa0tDTq1q1b3qEIIYQQQrwUJPkWL4y+vj5WVlblHYYQQgghxEtDyk4EeXl5+Pn5YWZmRt26dVmwYAEPn0C5efNm2rVrh4mJCVZWVgwfPpz09HR131u3bjFixAgsLCwwNDTEwcGB0NBQoPCyk/Pnz9O3b19MTU0xMTHB3d2dpKSkJ8bo6+tLv379CA4Oxtramjp16jBlyhTu37+vtsnJycHf358GDRpgZGREx44diYyM1OknKioKd3d3DA0NsbGxYdq0ady5c0fdnp6ejo+PD4aGhtjb27Nlyxad/RVFYdGiRTRq1AitVkv9+vWZNm1asbHn5OSQlZWlswghhBCiapLkW7Bx40aqVavGiRMnWLt2LatWreLLL78E4P79+yxdupT4+Hh27txJSkoKvr6+6r4LFizgwoULhIeHk5CQwKefflpkmckff/xB165d0Wq1/PTTT8TGxvL222+Tl5dXojgPHz5MUlIShw8fZuPGjYSFhREWFqZu9/Pz49ixY2zdupWzZ88yaNAgvL29SUxMBCApKQlvb28GDhzI2bNn+fbbb4mKisLPz0/tw9fXlytXrnD48GG2bdvGJ598ovPHxvbt21m9ejWff/45iYmJ7Ny5k1atWhUbd2BgIGZmZupiY2NTovEKIYQQovKRl+xUcd26dSM9PZ3z58+j0WgAmDt3Lrt27eLChQuPtT916hTt27fn9u3bGBsb88Ybb1C3bl2++uqrx9qmpKRgb2/PmTNnaNu2Lf/3f//H1q1buXjxItWrVy9VnL6+vkRGRpKUlIS+vj4AgwcPRk9Pj61bt5Kamkrjxo1JTU2lfv366n6enp506NCBZcuWMW7cOPT19fn888/V7VFRUXh4eHDnzh1SU1Np2rQpJ06coH379gD8+uuvODk5sXr1aqZPn86qVav4/PPP+eWXX0o8hpycHHJyctT1rKwsbGxs5CU7QohyI8/5FqL0ntdLdmTmW9CpUyc18QZwc3MjMTGR/Px8YmNj8fHxoVGjRpiYmODh4QFAamoqAJMnT2br1q20bduW2bNnc/To0SKPExcXh7u7e6kT74datGihJt4A1tbW6qz0uXPnyM/Px9HRUed190eOHFHLWuLj4wkLC9PZ7uXlRUFBAcnJySQkJFCtWjVcXV3VYzRr1gxzc3N1fdCgQdy7d4/GjRszfvx4fvjhhyfO3Gu1WkxNTXUWIYQQQlRNcsOlKNJff/2Fl5cXXl5ebNmyBQsLC1JTU/Hy8iI3NxeA3r17c/nyZfbu3cvBgwfp2bMnU6ZMITg4+LH+DA0NnymevyftGo2GgoICALKzs9HX1yc2NlYnQQcwNjZW20ycOLHQGu1GjRrx22+/PTEGGxsbLl68yKFDhzh48CDvvPMOK1eu5MiRI0/9R4UQQgghqg5JvgUxMTE668ePH8fBwYFff/2VGzduEBQUpNYpnzp16rH9LSwsGD16NKNHj8bd3Z2AgIBCk+/WrVuzceNG7t+//9wTVWdnZ/Lz80lPT8fd3b3QNi4uLly4cIEmTZoUur1Zs2bk5eURGxurlp1cvHiRjIwMnXaGhob4+Pjg4+PDlClTaNasGefOncPFxeW5jkkIIYQQlY+UnQhSU1OZOXMmFy9e5JtvvmHdunW8++67NGrUCAMDA9atW8fvv//Orl27WLp0qc6+7733Hj/++COXLl3i/Pnz7N69Gycnp0KP4+fnR1ZWFkOHDuXUqVMkJiayefNmLl68+MxjcHR0ZMSIEYwaNYodO3aQnJzMiRMnCAwMZM+ePQDMmTOHo0eP4ufnR1xcHImJifz444/qDZdNmzbF29ubiRMnEhMTQ2xsLOPGjdOZsQ8LC2PDhg388ssv/P7773z99dcYGhpia2v7zGMQQgghROUnybdg1KhR3Lt3jw4dOjBlyhTeffddJkyYgIWFBWFhYXz//fc0b96coKCgx2a0DQwMmDdvHq1bt6Zr167o6+uzdevWQo9Tp04dfvrpJ7Kzs/Hw8MDV1ZX169c/t1nw0NBQRo0axaxZs2jatCn9+vXj5MmTNGrUCHgw837kyBF+++033N3dcXZ25r333tO5QTM0NJT69evj4eHBgAEDmDBhAvXq1VO3m5ubs379erp06ULr1q05dOgQ//73v6lTp85zGYMQQgghKjd52okQZex53S0thBBCiLIjTzsRQgghhBCigpHkW7wUHn3839+X//znP+UdnhBCCCHEcyFPOxEvhUdfQf93DRo0KLtAhBBCCCFeIEm+xUuhqMf/CSGEeD4efavlo+QNl0KULSk7EUIIIYQQooxI8i2eWbdu3Zg+fXqZH9fOzo41a9Y8t/7KaxxCCCGEqDok+RZCCCGEEKKMSPIthBBCCCFEGZHkWzwXBQUFzJ49m9q1a2NlZcWiRYvUbRkZGYwbNw4LCwtMTU3p0aMH8fHx6vakpCTefPNNLC0tMTY2pn379hw6dEin//T0dHx8fDA0NMTe3p4tW7Y8FsOqVato1aoVRkZG2NjY8M4775Cdna3TJjo6mm7dulGzZk1q1aqFl5cXt27dKtE4SjIWIYQQQojiSPItnouNGzdiZGRETEwMK1asYMmSJRw8eBCAQYMGkZ6eTnh4OLGxsbi4uNCzZ09u3rwJQHZ2Nn369CEiIoIzZ87g7e2Nj48Pqampav++vr5cuXKFw4cPs23bNj755BPS09N1YtDT0+PDDz/k/PnzbNy4kZ9++onZs2er2+Pi4ujZsyfNmzfn2LFjREVF4ePjQ35+fonGUZKxFCYnJ4esrCydRQghhBBVk7xeXjyzbt26kZ+fr/MynA4dOtCjRw/69u3L66+/Tnp6OlqtVt3epEkTZs+ezYQJEwrts2XLlkyaNAk/Pz9+++03mjZtyokTJ2jfvj0Av/76K05OTqxevbrImyS3bdvGpEmTuH79OgDDhw8nNTWVqKioUo8jKCiIqKiopxrLokWLWLx48WOfy+vlhRBlSR41KMSzeV6vl5fnfIvnonXr1jrr1tbWpKenEx8fT3Z2NnXq1NHZfu/ePZKSkoAHM9+LFi1iz549pKWlkZeXx71799SZ74SEBKpVq4arq6u6f7NmzTA3N9fp89ChQwQGBvLrr7+SlZVFXl4ef/31F3fv3qVmzZrExcUxaNCgpxoHUKKxFGbevHnMnDlTXc/KysLGxqbYOIQQQghROUnyLZ6L6tWr66xrNBoKCgrIzs7G2tqayMjIx/Z5mDz7+/tz8OBBgoODadKkCYaGhrz11lvk5uaW+PgpKSn07duXyZMn88EHH1C7dm2ioqIYO3Ysubm51KxZE0NDw6ceB1CisRRGq9XqzJQLIYQQouqS5Fu8UC4uLly9epVq1aphZ2dXaJvo6Gh8fX3p378/8CDJTUlJUbc3a9aMvLw8YmNj1bKTixcvkpGRobaJjY2loKCAkJAQ9PQe3Mrw3Xff6RyndevWREREFFoC8rzGIoQQQghRHLnhUrxQnp6euLm50a9fPw4cOEBKSgpHjx5l/vz5nDp1CgAHBwd27NhBXFwc8fHxDB8+XJ1tBmjatCne3t5MnDiRmJgYYmNjGTdunM5MdpMmTbh//z7r1q3j999/Z/PmzXz22Wc6scybN4+TJ0/yzjvvcPbsWX799Vc+/fRTtSb8eYxFCCGEEKI4knyLF0qj0bB37166du3KmDFjcHR0ZOjQoVy+fBlLS0vgwSMCa9WqRefOnfHx8cHLywsXFxedfkJDQ6lfvz4eHh4MGDCACRMmUK9ePXV7mzZtWLVqFcuXL6dly5Zs2bKFwMBAnT4cHR05cOAA8fHxdOjQATc3N3788UeqVSvZ/wAqyViEEEIIIYojTzsRoow9r7ulhRCiNORpJ0I8G3naiRBCCCFKTJJsIV4OUnYihBBCCCFEGZHkWwghhBBCiDIiZSdCCCFEFSA130K8HGTmWwghhBBCiDIiybcQQgghhBBlRJJvIYQQQgghyogk30IIIYQQQpQRSb7FM8nNzX3ufSqKQl5e3nPv91m8jDEJIYQQouKR5LsS6datG35+fvj5+WFmZkbdunVZsGABD19impOTg7+/Pw0aNMDIyIiOHTsSGRmp00dUVBTu7u4YGhpiY2PDtGnTuHPnjrrdzs6OpUuXMmrUKExNTZkwYUKxMaWkpKDRaNi6dSudO3emRo0atGzZkiNHjqhtIiMj0Wg0hIeH4+rqilarJSoqioKCAgIDA7G3t8fQ0JA2bdqwbds2db9bt24xYsQILCwsMDQ0xMHBgdDQUODBHwV+fn5YW1tTo0YNbG1t1dfNP4wpLi5O7SsjIwONRqOej6eNqTA5OTlkZWXpLEIIIYSomiT5rmQ2btxItWrVOHHiBGvXrmXVqlV8+eWXAPj5+XHs2DG2bt3K2bNnGTRoEN7e3iQmJgKQlJSEt7c3AwcO5OzZs3z77bdERUXh5+enc4zg4GDatGnDmTNnWLBgQYniCggIYNasWZw5cwY3Nzd8fHy4ceOGTpu5c+cSFBREQkICrVu3JjAwkE2bNvHZZ59x/vx5ZsyYwT/+8Q81cV+wYAEXLlwgPDychIQEPv30U+rWrQvAhx9+yK5du/juu++4ePEiW7Zswc7OrtTns7QxFSYwMBAzMzN1sbGxKXUcQgghhKgkFFFpeHh4KE5OTkpBQYH62Zw5cxQnJyfl8uXLir6+vvLHH3/o7NOzZ09l3rx5iqIoytixY5UJEybobP/Pf/6j6OnpKffu3VMURVFsbW2Vfv36lTim5ORkBVCCgoLUz+7fv680bNhQWb58uaIoinL48GEFUHbu3Km2+euvv5SaNWsqR48e1elv7NixyrBhwxRFURQfHx9lzJgxhR536tSpSo8ePXTOxd9jOnPmjPrZrVu3FEA5fPjwM8VUmL/++kvJzMxUlytXriiAkpmZWeQ+QgjxvLn4byx0EUKUTGZm5nP5/paX7FQynTp1QqPRqOtubm6EhIRw7tw58vPzcXR01Gmfk5NDnTp1AIiPj+fs2bNs2bJF3a4oCgUFBSQnJ+Pk5ARAu3btSh2Xm5ub+u9q1arRrl07EhISdNo82u+lS5e4e/cuvXr10mmTm5uLs7MzAJMnT2bgwIGcPn2a1157jX79+tG5c2cAfH196dWrF02bNsXb25u+ffvy2muvlTru0sZUGK1Wi1arLfWxhRBCCFH5SPJdRWRnZ6Ovr09sbCz6+vo624yNjdU2EydOZNq0aY/t36hRI/XfRkZGLyTGR/vNzs4GYM+ePTRo0ECn3cNEtnfv3ly+fJm9e/dy8OBBevbsyZQpUwgODsbFxYXk5GTCw8M5dOgQgwcPxtPTk23btqGn96DaSvn/tfAA9+/ffy4xCSGEEEIUR5LvSiYmJkZn/fjx4zg4OODs7Ex+fj7p6em4u7sXuq+LiwsXLlygSZMmzz2u48eP07VrVwDy8vKIjY19rJb8Uc2bN0er1ZKamoqHh0eR7SwsLBg9ejSjR4/G3d2dgIAAgoODATA1NWXIkCEMGTKEt956C29vb27evImFhQUAaWlp6oz1ozdfPmtMQgghhBBFkeS7kklNTWXmzJlMnDiR06dPs27dOkJCQnB0dGTEiBGMGjWKkJAQnJ2d+fPPP4mIiKB169a8/vrrzJkzh06dOuHn58e4ceMwMjLiwoULHDx4kI8++uiZ4vr4449xcHDAycmJ1atXc+vWLd5+++0i25uYmODv78+MGTMoKCjg1VdfJTMzk+joaExNTRk9ejTvvfcerq6utGjRgpycHHbv3q2WxqxatQpra2ucnZ3R09Pj+++/x8rKCnNzc/T09OjUqRNBQUHY29uTnp7OP//5zyeOoSQxCSGEEEIUR5LvSmbUqFHcu3ePDh06oK+vz7vvvqs+DjA0NJT333+fWbNm8ccff1C3bl06depE3759AWjdujVHjhxh/vz5uLu7oygKr7zyCkOGDHnmuIKCgggKCiIuLo4mTZqwa9cu9ckkRVm6dCkWFhYEBgby+++/Y25ujouLC//3f/8HgIGBAfPmzSMlJQVDQ0Pc3d3ZunUr8CBRXrFiBYmJiejr69O+fXv27t2rlpx89dVXjB07FldXV5o2bcqKFStKVBP+pJiEEEIIIYqjUR4tfBUVWrdu3Wjbti1r1qwp71BUKSkp2Nvbc+bMGdq2bVve4bwUsrKyMDMzIzMzE1NT0/IORwhRRbgGbCr089iVo8o4EiEqpuf1/S0z30IIIUQVIEm2EC8HecmOeCbLli3D2Ni40KV3797lHZ4QQgghxEtFyk7EM7l58yY3b94sdJuhoeFjj+QTUnYihBBCVERSdiJeCrVr16Z27drlHYYQQohCFFXn/SgpRxGibEnZiRBCCCGEEGVEku+XmJ2dXYmfXKLRaNi5c+cLjSclJQWNRvPEF9J069aN6dOnP/VxnjSWyMhINBoNGRkZT30MIYQQQojyIMl3GSiLxLg8lFcS3LlzZ9LS0jAzM3tiW0nUhRBCCPEykeRbVDgGBgZYWVmh0WjK7JiKopCXl1dmxxNCCCFE5VSlku9u3boxdepUpk+fTq1atbC0tGT9+vXcuXOHMWPGYGJiQpMmTQgPD1f3+eWXX+jduzfGxsZYWloycuRIrl+/rtPntGnTmD17NrVr18bKyopFixap2+3s7ADo378/Go1GXU9KSuLNN9/E0tISY2Nj2rdvz6FDh55pfNevX6d///7UrFkTBwcHdu3apbP9SWPZt28fr776Kubm5tSpU4e+ffuSlJRU6LFSUlLo3r07ALVq1UKj0eDr66tuLygoKPKcPOtY/j6bffnyZXx8fKhVqxZGRka0aNGCvXv3FhtjTk4O06ZNo169etSoUYNXX32VkydPPnaM8PBwXF1d0Wq1fP311+jp6XHq1CmdWNesWYOtrS0FBQWlGqMQQgghqp4qlXwDbNy4kbp163LixAmmTp3K5MmTGTRoEJ07d+b06dO89tprjBw5krt375KRkUGPHj1wdnbm1KlT7Nu3j2vXrjF48ODH+jQyMiImJoYVK1awZMkSDh48CKAmdKGhoaSlpanr2dnZ9OnTh4iICM6cOYO3tzc+Pj6kpqY+9dgWL17M4MGDOXv2LH369GHEiBHqYwBLMpY7d+4wc+ZMTp06RUREBHp6evTv37/QpNLGxobt27cDcPHiRdLS0li7dm2JzsmzjuXvpkyZQk5ODj///DPnzp1j+fLlGBsbFxvj7Nmz2b59Oxs3buT06dM0adIELy+vx44xd+5cgoKCSEhI4I033sDT05PQ0FCdNqGhofj6+qqvrv+7nJwcsrKydBYhhBBCVFFKFeLh4aG8+uqr6npeXp5iZGSkjBw5Uv0sLS1NAZRjx44pS5cuVV577TWdPq5cuaIAysWLFwvtU1EUpX379sqcOXPUdUD54YcfnhhfixYtlHXr1qnrtra2yurVq0s0NkD55z//qa5nZ2crgBIeHq4oilKisfzdn3/+qQDKuXPnFEVRlOTkZAVQzpw5oyiKohw+fFgBlFu3bunsV5Jz8ixj+ftxW7VqpSxatKjQvgqLMTs7W6levbqyZcsW9bPc3Fylfv36yooVK3T227lzp05/3377rVKrVi3lr7/+UhRFUWJjYxWNRqMkJycXOZ6FCxcqwGNLZmZmic6HEEI8LRf/jU9chBAlk5mZ+Vy+v6vczHfr1q3Vf+vr61OnTh1atWqlfmZpaQlAeno68fHxHD58WOetjc2aNQPQKcd4tE8Aa2tr0tPTi40jOzsbf39/nJycMDc3x9jYmISEhGea+X40DiMjI0xNTdU4SjKWxMREhg0bRuPGjTE1NVVLZJ4mpqc5JyUdy99NmzaN999/ny5durBw4ULOnj1bbN9JSUncv3+fLl26qJ9Vr16dDh06kJCQoNO2Xbt2Ouv9+vVDX1+fH374AYCwsDC6d++unqvCzJs3j8zMTHW5cuVKsfEJIYQQovKqci/ZqV69us66RqPR+ezhTXwFBQVkZ2fj4+PD8uXLH+vH2tq62D6fVP/r7+/PwYMHCQ4OpkmTJhgaGvLWW2+Rm5tb6jGVJI6SjMXHxwdbW1vWr19P/fr1KSgooGXLlk8V09Ock6fdf9y4cXh5ebFnzx4OHDhAYGAgISEhTJ06tdRx/52RkZHOuoGBAaNGjSI0NJQBAwbwr3/9S6fcpjBarRatVvvMsQghhBCi4qtyyXdpuLi4sH37duzs7KhW7elPVfXq1cnPz9f5LDo6Gl9fX/r37w88SI5TUlKeJdxiPWksN27c4OLFi6xfvx53d3cAoqKiiu3TwMAA4LGxlQcbGxsmTZrEpEmTmDdvHuvXr2fq1KmFxvjKK69gYGBAdHQ0tra2ANy/f5+TJ0+W6Pnk48aNo2XLlnzyySfk5eUxYMCAFzImIYQQQlQ+Va7spDSmTJnCzZs3GTZsGCdPniQpKYn9+/czZsyYUiWcdnZ2REREcPXqVW7dugWAg4MDO3bsIC4ujvj4eIYPH/5Cn5bxpLHUqlWLOnXq8MUXX3Dp0iV++uknZs6cWWyftra2aDQadu/ezZ9//kl2dvYLi78406dPZ//+/SQnJ3P69GkOHz6Mk5NTkTEaGRkxefJkAgIC2LdvHxcuXGD8+PHcvXuXsWPHPvF4Tk5OdOrUiTlz5jBs2DAMDQ1f9BCFEEIIUUlI8l2M+vXrEx0dTX5+Pq+99hqtWrVi+vTpmJubF/lki8KEhIRw8OBBbGxscHZ2BmDVqlXUqlWLzp074+Pjg5eXFy4uLi9qKE8ci56eHlu3biU2NpaWLVsyY8YMVq5cWWyfDRo0YPHixcydOxdLS0v8/PxeWPzFyc/PZ8qUKTg5OeHt7Y2joyOffPJJsTEGBQUxcOBARo4ciYuLC5cuXWL//v3UqlWrRMccO3Ysubm5vP322y9sXEIIIYSofDSKoijlHYQQFc3SpUv5/vvvn3hzZ2GysrIwMzMjMzMTU1PTFxCdEEI84Bqw6YltYleOKoNIhKj4ntf3t9R8C1EKD2vzP/roI95///3yDkcIIYolibUQLx8pO6kAtmzZovOIwEeXFi1alHd4pVLRx+Ln54erqyvdunWTkhMhhBBClJqUnVQAt2/f5tq1a4Vuq169uvrEjoqgMo3laUnZiRBCCFHxSNlJFWJiYoKJiUl5h/FcVKaxCCGEEEKUliTfQgghRCVRkhss/07qwoUoW1LzLYQQQgghRBmR5FtUCVevXqVXr14YGRlhbm5e3uEIIYQQooqS5FtUCatXryYtLY24uDh+++23Eu2zaNEi2rZt+2IDE0IIIUSVIjXfokpISkrC1dUVBweH8g5FCCGEEFWYzHyLCmPbtm20atUKQ0ND6tSpg6enJ3fu3OHkyZP06tWLunXrYmZmhoeHB6dPn1b3s7OzY/v27WzatAmNRoOvry8AGRkZjBs3DgsLC0xNTenRowfx8fEAhIWFsXjxYuLj49FoNGg0GsLCwnj77bfp27evTlz379+nXr16bNiwoczOhRBCCCEqJpn5FhVCWloaw4YNY8WKFfTv35/bt2/zn//8B0VRuH37NqNHj2bdunUoikJISAh9+vQhMTERExMTTp48yahRozA1NWXt2rUYGhoCMGjQIAwNDQkPD8fMzIzPP/+cnj178ttvvzFkyBB++eUX9u3bx6FDhwAwMzPD0dGRrl27kpaWhrW1NQC7d+/m7t27DBkypNDYc3JyyMnJUdezsrJe8NkSQgghxMtKkm9RIaSlpZGXl8eAAQPUF/G0atUKgB49eui0/eKLLzA3N+fIkSP07dsXCwsLtFothoaGWFlZARAVFcWJEydIT09Hq9UCEBwczM6dO9m2bRsTJkzA2NiYatWqqfsAdO7cmaZNm7J582Zmz54NQGhoKIMGDcLY2LjQ2AMDA1m8ePHzPSFCCCGEqJCk7ERUCG3atKFnz560atWKQYMGsX79em7dugXAtWvXGD9+PA4ODpiZmWFqakp2djapqalF9hcfH092djZ16tTRecV9cnIySUlJxcYybtw4QkND1WOHh4cX+6r5efPmkZmZqS5Xrlx5ijMghBBCiMpAZr5FhaCvr8/Bgwc5evQoBw4cYN26dcyfP5+YmBgmT57MjRs3WLt2Lba2tmi1Wtzc3MjNzS2yv+zsbKytrYmMjHxs25MeRThq1Cjmzp3LsWPHOHr0KPb29ri7uxfZXqvVqrPrQgghhKjaJPkWFYZGo6FLly506dKF9957D1tbW3744Qeio6P55JNP6NOnDwBXrlzh+vXrxfbl4uLC1atXqVatGnZ2doW2MTAwID8//7HP69SpQ79+/QgNDeXYsWOMGTPmmccmhBBCiKpBkm9RIcTExBAREcFrr71GvXr1iImJ4c8//8TJyQkHBwc2b95Mu3btyMrKIiAgQL2psiienp64ubnRr18/VqxYgaOjI//73//Ys2cP/fv3p127dtjZ2ZGcnExcXBwNGzbExMREncEeN24cffv2JT8/n9GjR5fFKRBCCCFEJSA136JCMDU15eeff6ZPnz44Ojryz3/+k5CQEHr37s2GDRu4desWLi4ujBw5kmnTplGvXr1i+9NoNOzdu5euXbsyZswYHB0dGTp0KJcvX8bS0hKAgQMH4u3tTffu3bGwsOCbb75R9/f09MTa2hovLy/q16//QscuhBBCiMpDoyiKUt5BCFHRZGdn06BBA0JDQxkwYECp9s3KysLMzIzMzExMTU1fUIRCiKrINWBTqfeJXTnqBUQiROXzvL6/pexEiFIoKCjg+vXrhISEYG5uzhtvvFHeIQkhhEoSaSFefpJ8C1EKqamp2Nvb07BhQ8LCwqhWTX6FhBBCCFFykjkIUQp2dnZIpZYQQgghnpYk30IIIUQF8DT13CUhpSpClC152okQQgghhBBlRJJvIYQQQgghyshLn3wvWrSItm3blncYL0xKSgoajYa4uLjyDuWpPK/rU9bXOTIyEo1GQ0ZGxksTkxBCCCEqv1Il3926dWP69OkvKJSKIywsDHNz8/IOo1gajYadO3eWdxiFKiw2f39/IiIiyiegIryMMQkhhBCiYquSN1wqikJ+fr48Jq4Yubm5GBgYlNnxjI2NMTY2LrPjlcTLGJMQQgghKrYSz3z7+vpy5MgR1q5di0ajQaPRkJKSwi+//ELv3r0xNjbG0tKSkSNHcv36dXW/ffv28eqrr2Jubk6dOnXo27cvSUlJOn3/97//ZdiwYdSuXRsjIyPatWtHTEyMTpvNmzdjZ2eHmZkZQ4cO5fbt2+q2goICAgMDsbe3x9DQkDZt2rBt2zZ1+8MSg/DwcFxdXdFqtURFRRU73vj4eLp3746JiQmmpqa4urpy6tQpIiMjGTNmDJmZmep5WLRoEVD4jK65uTlhYWHq+okTJ3B2dqZGjRq0a9eOM2fOPHbsJ53Tbt26MW3aNGbPnk3t2rWxsrJSY4AHj8MD6N+/PxqNRl0vzsMSiy+//BJ7e3tq1KgBQEZGBuPGjcPCwgJTU1N69OhBfHx8kf2cPHmSXr16UbduXczMzPDw8OD06dNPjO3vJR4FBQUsWbKEhg0botVqadu2Lfv27VO3PyzX2bFjB927d6dmzZq0adOGY8eOqW0uX76Mj48PtWrVwsjIiBYtWrB3716deGNjY2nXrh01a9akc+fOXLx48bFz8pCvry/9+vVj8eLF6vmYNGkSubm5xZ7bnJwcsrKydBYhhBBCVE0lTr7Xrl2Lm5sb48ePJy0tjbS0NExMTOjRowfOzs6cOnWKffv2ce3aNQYPHqzud+fOHWbOnMmpU6eIiIhAT0+P/v37U1BQADx4TbeHhwd//PEHu3btIj4+ntmzZ6vbAZKSkti5cye7d+9m9+7dHDlyhKCgIHV7YGAgmzZt4rPPPuP8+fPMmDGDf/zjHxw5ckRnDHPnziUoKIiEhARat25d7HhHjBhBw4YNOXnyJLGxscydO5fq1avTuXNn1qxZg6mpqXoe/P39S3QOs7Oz6du3L82bNyc2NpZFixY9tm9GRsYTzynAxo0bMTIyIiYmhhUrVrBkyRIOHjwIPEiAAUJDQ0lLS1PXn+TSpUts376dHTt2qDXogwYNIj09nfDwcGJjY3FxcaFnz57cvHmz0D5u377N6NGjiYqK4vjx4zg4ONCnTx/1j6WSxrZ27VpCQkIIDg7m7NmzeHl58cYbb5CYmKjTbv78+fj7+xMXF4ejoyPDhg0jLy8PgClTppCTk8PPP//MuXPnWL58+WMz2fPnzyckJIRTp05RrVo13n777WLPUUREBAkJCURGRvLNN9+wY8cOFi9eXOw+gYGBmJmZqYuNjU2x7YUQQghReZW47sLMzAwDAwNq1qyJlZUVAO+//z7Ozs4sW7ZMbffVV19hY2PDb7/9hqOjIwMHDtTp56uvvsLCwoILFy7QsmVL/vWvf/Hnn39y8uRJateuDUCTJk109ikoKCAsLAwTExMARo4cSUREBB988AE5OTksW7aMQ4cO4ebmBkDjxo2Jiori888/x8PDQ+1nyZIl9OrVq0TjTU1NJSAggGbNmgHg4OCgcy40Go16HkrqX//6FwUFBWzYsIEaNWrQokUL/vvf/zJ58mS1zUcfffTEcwrQunVrFi5cqMb20UcfERERQa9evbCwsAAezLqXJsbc3Fw2bdqk7h8VFcWJEydIT09Hq9UCEBwczM6dO9m2bRsTJkx4rI8ePXrorH/xxReYm5tz5MgR+vbtW+LYgoODmTNnDkOHDgVg+fLlHD58mDVr1vDxxx+r7fz9/Xn99dcBWLx4MS1atODSpUs0a9aM1NRUBg4cSKtWrYAHPxd/98EHH6g/I3PnzuX111/nr7/+Umf+/87AwICvvvqKmjVr0qJFC5YsWUJAQABLly5FT6/wv2XnzZvHzJkz1fWsrCxJwIUQQogq6pmedhIfH8/hw4fV2lhjY2M1WX1YWpKYmMiwYcNo3LgxpqamaplBamoqAHFxcTg7O6uJd2Hs7OzUxBvA2tqa9PR04MFs7d27d+nVq5dOHJs2bXqsvKVdu3YlHtvMmTMZN24cnp6eBAUFPdbX03g44/5oYvfwD4aHSnJOgcdm7h89J0/L1tZWTY4fxpKdnU2dOnV04klOTi7yfFy7do3x48fj4OCAmZkZpqamZGdnq9e7JLKysvjf//5Hly5ddD7v0qULCQkJOp89eh6sra0B1PMwbdo03n//fbp06cLChQs5e/bsY8cqbv/CtGnThpo1a6rrbm5uZGdnc+XKlSL30Wq1mJqa6ixCCCGEqJqe6Y7D7OxsfHx8WL58+WPbHiYyPj4+2Nrasn79eurXr09BQQEtW7ZU62QNDQ2feJzq1avrrGs0Gp2yFYA9e/bQoEEDnXYPZ2sfMjIyKuHIHtT7Dh8+nD179hAeHs7ChQvZunUr/fv3L3IfjUbz2KvH79+/X+JjQsnOKRR/Tp7W389PdnY21tbWREZGPta2qKe9jB49mhs3brB27VpsbW3RarW4ubk9sS76aT16HjQaDYB6HsaNG4eXlxd79uzhwIEDBAYGEhISwtSpU0u0vxBCCCHE81aq5NvAwID8/Hx13cXFhe3bt2NnZ1fok0Nu3LjBxYsXWb9+Pe7u7gCP3ejYunVrvvzyS27evFns7HdRmjdvjlarJTU1VafE5HlwdHTE0dGRGTNmMGzYMEJDQ+nfv/9j5+EhCwsL0tLS1PXExETu3r2rrjs5ObF582adsobjx4/r9PGkc1pS1atXLzTG0nBxceHq1atUq1atRDdtAkRHR/PJJ5/Qp08fAK5cuaJzs2hJYjM1NaV+/fpER0frXNPo6Gg6dOhQqjHY2NgwadIkJk2axLx581i/fr1O8l1a8fHx3Lt3T/2j8fjx4xgbG0sZiRBCCCFKpFRlJ3Z2dsTExJCSksL169eZMmUKN2/eZNiwYZw8eZKkpCT279/PmDFjyM/Pp1atWtSpU4cvvviCS5cu8dNPP+nUvgIMGzYMKysr+vXrR3R0NL///jvbt2/XeWpFcUxMTPD392fGjBls3LiRpKQkTp8+zbp169i4cWNphqe6d+8efn5+REZGcvnyZaKjozl58iROTk7qecjOziYiIoLr16+rCXaPHj346KOPOHPmDKdOnWLSpEk6M6vDhw9Ho9Ewfvx4Lly4wN69ewkODtY59pPOaUnZ2dkRERHB1atXuXXr1lOdB09PT9zc3OjXrx8HDhwgJSWFo0ePMn/+fE6dOlXoPg4ODmzevJmEhARiYmIYMWLEY/93oySxBQQEsHz5cr799lsuXrzI3LlziYuL49133y1x/NOnT2f//v0kJydz+vRpDh8+rF7Dp5Wbm8vYsWPV67dw4UL8/PyKrPcWQgghhHhUqTIGf39/9PX1ad68ORYWFuTm5hIdHU1+fj6vvfYarVq1Yvr06Zibm6Onp4eenh5bt24lNjaWli1bMmPGDFauXKnTp4GBAQcOHKBevXr06dOHVq1aERQUhL6+fonjWrp0KQsWLCAwMBAnJye8vb3Zs2cP9vb2pRmeSl9fnxs3bjBq1CgcHR0ZPHgwvXv3Vp9q0blzZyZNmsSQIUOwsLBgxYoVAISEhGBjY4O7uzvDhw/H399fpz7Y2NiYf//735w7dw5nZ2fmz5//WHnJwxnfos5pSYWEhHDw4EFsbGxwdnZ+qvOg0WjYu3cvXbt2ZcyYMTg6OjJ06FAuX76MpaVlofts2LCBW7du4eLiwsiRI5k2bRr16tUrdWzTpk1j5syZzJo1i1atWrFv3z527dqlc+Prk+Tn5zNlyhT1Z8LR0ZFPPvmk5CegED179sTBwYGuXbsyZMgQ3njjDZ3HPAohhBBCFEej/L1IWQhRKF9fXzIyMp75zaFZWVmYmZmRmZkpN18KIYQQFcTz+v6W/1cuhBBCCCFEGamyyXeLFi10Hp/36LJly5byDu+5q2rjFUIIIYR4GVXZspPLly8X+RhAS0tLneeKVwZVbbwvMyk7EUIIISqe5/X9/UzP+a7IbG1tyzuEMlXVxiuEEGXFNWBTeYfwTGJXjirvEISoUqps2YkQQgghhBBlTZJv8dS6devG9OnTyzuMpxYZGYlGoyEjI6O8QxFCCCFEFSHJt6iyOnfuTFpaGmZmZuUdihBCCCGqCEm+xUsrNzf3hfZvYGCAlZUVGo3mhR5HCCGEEOIhSb5Fidy5c4dRo0ZhbGyMtbU1ISEhOttzcnLw9/enQYMGGBkZ0bFjRyIjI9XtYWFhmJubs3PnThwcHKhRowZeXl5cuXJFbbNo0SLatm3Ll19+ib29PTVq1AAgIyODcePGYWFhgampKT169CA+Pl7dLz4+nu7du2NiYoKpqSmurq6cOnUKePCUFx8fH2rVqoWRkREtWrRg7969QOFlJ9u3b6dFixZotVrs7OweG6ednR3Lli3j7bffxsTEhEaNGvHFF188l3MshBBCiMpPkm9RIgEBARw5coQff/yRAwcOEBkZyenTp9Xtfn5+HDt2jK1bt3L27FkGDRqEt7c3iYmJapu7d+/ywQcfsGnTJqKjo8nIyGDo0KE6x7l06RLbt29nx44dxMXFATBo0CDS09MJDw8nNjYWFxcXevbsyc2bNwEYMWIEDRs25OTJk8TGxjJ37lyqV68OwJQpU8jJyeHnn3/m3LlzLF++HGNj40LHGBsby+DBgxk6dCjnzp1j0aJFLFiwgLCwMJ12ISEhtGvXjjNnzvDOO+8wefJkLl68WOS5y8nJISsrS2cRQgghRNVUZR81KEouOzubDRs28PXXX9OzZ08ANm7cSMOGDQFITU0lNDSU1NRU6tevD4C/vz/79u0jNDSUZcuWAXD//n0++ugjOnbsqPbh5OTEiRMn6NChA/Cg1GTTpk1YWFgAEBUVxYkTJ0hPT0er1QIQHBzMzp072bZtGxMmTCA1NZWAgACaNWsGgIODgxp7amoqAwcOpFWrVgA0bty4yHGuWrWKnj17smDBAgAcHR25cOECK1euxNfXV23Xp08f3nnnHQDmzJnD6tWrOXz4ME2bNi2038DAQBYvXlyicy2EEEKIyk1mvsUTJSUlkZubqybNALVr11aTzXPnzpGfn4+jo6POmzOPHDlCUlKSuk+1atVo3769ut6sWTPMzc1JSEhQP7O1tVUTb3hQUpKdnU2dOnV0+k5OTlb7njlzJuPGjcPT05OgoCCdY06bNo3333+fLl26sHDhQs6ePVvkOBMSEujSpYvOZ126dCExMZH8/Hz1s9atW6v/1mg0WFlZkZ6eXmS/8+bNIzMzU10eLbURQgghRNUiM9/imWVnZ6Ovr09sbCz6+vo624oq8SiKkZHRY31bW1vr1I8/ZG5uDjyoFR8+fDh79uwhPDychQsXsnXrVvr378+4cePw8vJiz549HDhwgMDAQEJCQpg6dWqp4nrUw5KWhzQaDQUFBUW212q16qy9EEIIIao2mfkWT/TKK69QvXp1YmJi1M9u3brFb7/9BoCzszP5+fmkp6fTpEkTncXKykrdJy8vT70REuDixYtkZGTg5ORU5LFdXFy4evUq1apVe6zvunXrqu0cHR2ZMWMGBw4cYMCAAYSGhqrbbGxsmDRpEjt27GDWrFmsX7++0GM5OTkRHR2t81l0dDSOjo6P/VEhhBBCCPE0JPkWT2RsbMzYsWMJCAjgp59+4pdffsHX1xc9vQc/Po6OjowYMYJRo0axY8cOkpOTOXHiBIGBgezZs0ftp3r16kydOpWYmBhiY2Px9fWlU6dOar13YTw9PXFzc6Nfv34cOHCAlJQUjh49yvz58zl16hT37t3Dz8+PyMhILl++THR0NCdPnlQT+unTp7N//36Sk5M5ffo0hw8fLjLZnzVrFhERESxdupTffvuNjRs38tFHH+Hv7/8cz6YQQgghqjIpOxElsnLlSrKzs/Hx8cHExIRZs2aRmZmpbg8NDeX9999n1qxZ/PHHH9StW5dOnTrRt29ftU3NmjWZM2cOw4cP548//sDd3Z0NGzYUe1yNRsPevXuZP38+Y8aM4c8//8TKyoquXbtiaWmJvr4+N27cYNSoUVy7do26desyYMAA9QbH/Px8pkyZwn//+19MTU3x9vZm9erVhR7LxcWF7777jvfee4+lS5dibW3NkiVLdG62FEIIIYR4FhpFUZTyDkJUfmFhYUyfPl1e5Q5kZWVhZmZGZmYmpqam5R2OEOIZuQZsKu8QnknsylHlHYIQFcLz+v6WmW8hhBDiGUjyKoQoDan5FkIIIYQQooxI8i3KhK+vr5ScCCGEEKLKk7ITISqRil57KoQoe1I2I0TZkplvIYQQQgghyogk30IIIYQQQpQRSb4rmG7dujF9+vQit9vZ2bFmzZoXHodGo2Hnzp0v/DhCCCGEEJWJJN9CCCGEEEKUEUm+xUtHURTy8vLKOwwdL2NMQgghhKh4JPmugPLy8vDz88PMzIy6deuyYMECinpRaWpqKm+++SbGxsaYmpoyePBgrl27ptPm008/5ZVXXsHAwICmTZuyefNmne2JiYl07dqVGjVq0Lx5cw4ePFjiWFNSUtBoNGzdupXOnTtTo0YNWrZsyZEjR9Q2kZGRaDQawsPDcXV1RavVEhUVRUFBAYGBgdjb22NoaEibNm3Ytm2but+tW7cYMWIEFhYWGBoa4uDgQGhoKAC5ubn4+flhbW1NjRo1sLW1JTAwUCemuLg4ta+MjAw0Gg2RkZHPFFNhcnJyyMrK0lmEEEIIUTXJowYroI0bNzJ27FhOnDjBqVOnmDBhAo0aNWL8+PE67QoKCtTE+8iRI+Tl5TFlyhSGDBmiJpk//PAD7777LmvWrMHT05Pdu3czZswYGjZsSPfu3SkoKGDAgAFYWloSExNDZmZmsTXnRQkICGDNmjU0b96cVatW4ePjQ3JyMnXq1FHbzJ07l+DgYBo3bkytWrUIDAzk66+/5rPPPsPBwYGff/6Zf/zjH1hYWODh4cGCBQu4cOEC4eHh1K1bl0uXLnHv3j0APvzwQ3bt2sV3331Ho0aNuHLlCleuXCl13KWNqTCBgYEsXry41McWQgghROUjyXcFZGNjw+rVq9FoNDRt2pRz586xevXqx5LviIgIzp07R3JyMjY2NgBs2rSJFi1acPLkSdq3b09wcDC+vr688847AMycOZPjx48THBxM9+7dOXToEL/++iv79++nfv36ACxbtozevXuXKmY/Pz8GDhwIPJhp37dvHxs2bGD27NlqmyVLltCrVy/gwWzxsmXLOHToEG5ubgA0btyYqKgoPv/8czw8PEhNTcXZ2Zl27doBD242fSg1NRUHBwdeffVVNBoNtra2pYr3aWMqzLx585g5c6a6npWVpV4PIYQQQlQtUnZSAXXq1AmNRqOuu7m5kZiYSH5+vk67hIQEbGxsdBK95s2bY25uTkJCgtqmS5cuOvt16dJFZ7uNjY2aeD88Xmk9uk+1atVo166deoyHHibRAJcuXeLu3bv06tULY2Njddm0aRNJSUkATJ48ma1bt9K2bVtmz57N0aNH1f19fX2Ji4ujadOmTJs2jQMHDpQ65qeJqTBarRZTU1OdRQghhBBVk8x8i5eGkZGR+u/s7GwA9uzZQ4MGDXTaabVaAHr37s3ly5fZu3cvBw8epGfPnkyZMoXg4GBcXFxITk4mPDycQ4cOMXjwYDw9Pdm2bRt6eg/+5ny0Tv7+/fvPJSYhhBBCiOLIzHcFFBMTo7N+/PhxHBwc0NfX1/ncycnpsVrnCxcukJGRQfPmzdU20dHROvtFR0frbL9y5QppaWk6xyutR/fJy8sjNjYWJyenIts3b94crVZLamoqTZo00Vkencm3sLBg9OjRfP3116xZs4YvvvhC3WZqasqQIUNYv3493377Ldu3b+fmzZtYWFgA6Izp0ZsvnzUmIYQQQoiiyMx3BZSamsrMmTOZOHEip0+fZt26dYSEhDzWztPTk1atWjFixAjWrFlDXl4e77zzDh4eHmo5RUBAAIMHD8bZ2RlPT0/+/e9/s2PHDg4dOqT24ejoyOjRo1m5ciVZWVnMnz+/1DF//PHHODg44OTkxOrVq7l16xZvv/12ke1NTEzw9/dnxowZFBQU8Oqrr5KZmUl0dDSmpqaMHj2a9957D1dXV1q0aEFOTg67d+9WE/pVq1ZhbW2Ns7Mzenp6fP/991hZWWFubo6enh6dOnUiKCgIe3t70tPT+ec///nEMZQkJiGEEEKI4kjyXQGNGjWKe/fu0aFDB/T19Xn33XeZMGHCY+00Gg0//vgjU6dOpWvXrujp6eHt7c26devUNv369WPt2rUEBwfz7rvvYm9vT2hoKN26dQNAT0+PH374gbFjx9KhQwfs7Oz48MMP8fb2LlXMQUFBBAUFERcXR5MmTdi1axd169Ytdp+lS5diYWFBYGAgv//+O+bm5ri4uPB///d/ABgYGDBv3jxSUlIwNDTE3d2drVu3Ag8S5RUrVpCYmIi+vj7t27dn7969asnJV199xdixY3F1daVp06asWLGC11577YnjeFJMQgghhBDF0ShFPSBaiOcgJSUFe3t7zpw5Q9u2bcs7nJdCVlYWZmZmZGZmPvebL10DNj3X/oQQlV/sylHlHYIQFcLz+v6WmW8hytjDv3dfxMt2Di/o99z7FEJUbvLiLyFK5uHvyrPOW0vyLZ7JsmXLWLZsWaHb3N3d+fTTT8s4opff7du3AeQmTSGEEKICun37NmZmZk+9v5SdiGdy8+ZNbt68Weg2Q0PDxx7JJx68efR///sfJiYmOs9rFy/OwxcbXblyRZ6zXs7kWrxc5Hq8XOR6vFz+fj0UReH27dvUr19fvYfsacjMt3gmtWvXpnbt2uUdRoWip6dHw4YNyzuMKklecvTykGvxcpHr8XKR6/FyefR6PMuM90PynG8hhBBCCCHKiCTfQgghhBBClBFJvoUQlZ5Wq2XhwoVotdryDqXKk2vxcpHr8XKR6/FyeVHXQ264FEIIIYQQoozIzLcQQgghhBBlRJJvIYQQQgghyogk30IIIYQQQpQRSb6FEEIIIYQoI5J8CyGEEEIIUUYk+RZCVDoffPABnTt3pmbNmpibm5doH0VReO+997C2tsbQ0BBPT08SExNfbKBVxM2bNxkxYgSmpqaYm5szduxYsrOzi92nW7duaDQanWXSpEllFHHl8vHHH2NnZ0eNGjXo2LEjJ06cKLb9999/T7NmzahRowatWrVi7969ZRRp1VCa6xEWFvbY70GNGjXKMNrK7eeff8bHx4f69euj0WjYuXPnE/eJjIzExcUFrVZLkyZNCAsLK/VxJfkWQlQ6ubm5DBo0iMmTJ5d4nxUrVvDhhx/y2WefERMTg5GREV5eXvz1118vMNKqYcSIEZw/f56DBw+ye/dufv75ZyZMmPDE/caPH09aWpq6rFixogyirVy+/fZbZs6cycKFCzl9+jRt2rTBy8uL9PT0QtsfPXqUYcOGMXbsWM6cOUO/fv3o168fv/zySxlHXjmV9nrAg1ebP/p7cPny5TKMuHK7c+cObdq04eOPPy5R++TkZF5//XW6d+9OXFwc06dPZ9y4cezfv790B1aEEKKSCg0NVczMzJ7YrqCgQLGyslJWrlypfpaRkaFotVrlm2++eYERVn4XLlxQAOXkyZPqZ+Hh4YpGo1H++OOPIvfz8PBQ3n333TKIsHLr0KGDMmXKFHU9Pz9fqV+/vhIYGFho+8GDByuvv/66zmcdO3ZUJk6c+ELjrCpKez1K+t8w8ewA5Ycffii2zezZs5UWLVrofDZkyBDFy8urVMeSmW8hRJWXnJzM1atX8fT0VD8zMzOjY8eOHDt2rBwjq/iOHTuGubk57dq1Uz/z9PRET0+PmJiYYvfdsmULdevWpWXLlsybN4+7d+++6HArldzcXGJjY3V+rvX09PD09Czy5/rYsWM67QG8vLzk9+A5eJrrAZCdnY2trS02Nja8+eabnD9/vizCFYV4Xr8f1Z5nUEIIURFdvXoVAEtLS53PLS0t1W3i6Vy9epV69erpfFatWjVq165d7LkdPnw4tra21K9fn7NnzzJnzhwuXrzIjh07XnTIlcb169fJz88v9Of6119/LXSfq1evyu/BC/I016Np06Z89dVXtG7dmszMTIKDg+ncuTPnz5+nYcOGZRG2eERRvx9ZWVncu3cPQ0PDEvUjM99CiAph7ty5j9149PelqC8w8fy96OsxYcIEvLy8aNWqFSNGjGDTpk388MMPJCUlPcdRCPFyc3NzY9SoUbRt2xYPDw927NiBhYUFn3/+eXmHJp6BzHwLISqEWbNm4evrW2ybxo0bP1XfVlZWAFy7dg1ra2v182vXrtG2bdun6rOyK+n1sLKyeuxmsry8PG7evKme95Lo2LEjAJcuXeKVV14pdbxVUd26ddHX1+fatWs6n1+7dq3Ic29lZVWq9qLknuZ6/F316tVxdnbm0qVLLyJE8QRF/X6YmpqWeNYbJPkWQlQQFhYWWFhYvJC+7e3tsbKyIiIiQk22s7KyiImJKdUTU6qSkl4PNzc3MjIyiI2NxdXVFYCffvqJgoICNaEuibi4OACdP45E8QwMDHB1dSUiIoJ+/foBUFBQQEREBH5+foXu4+bmRkREBNOnT1c/O3jwIG5ubmUQceX2NNfj7/Lz8zl37hx9+vR5gZGKori5uT326M2n+v0o7d2gQgjxsrt8+bJy5swZZfHixYqxsbFy5swZ5cyZM8rt27fVNk2bNlV27NihrgcFBSnm5ubKjz/+qJw9e1Z58803FXt7e+XevXvlMYRKxdvbW3F2dlZiYmKUqKgoxcHBQRk2bJi6/b///a/StGlTJSYmRlEURbl06ZKyZMkS5dSpU0pycrLy448/Ko0bN1a6du1aXkOosLZu3apotVolLCxMuXDhgjJhwgTF3NxcuXr1qqIoijJy5Ehl7ty5avvo6GilWrVqSnBwsJKQkKAsXLhQqV69unLu3LnyGkKlUtrrsXjxYmX//v1KUlKSEhsbqwwdOlSpUaOGcv78+fIaQqVy+/Zt9fsBUFatWqWcOXNGuXz5sqIoijJ37lxl5MiRavvff/9dqVmzphIQEKAkJCQoH3/8saKvr6/s27evVMeV5FsIUemMHj1aAR5bDh8+rLYBlNDQUHW9oKBAWbBggWJpaalotVqlZ8+eysWLF8s++Eroxo0byrBhwxRjY2PF1NRUGTNmjM4fQsnJyTrXJzU1VenatatSu3ZtRavVKk2aNFECAgKUzMzMchpBxbZu3TqlUaNGioGBgdKhQwfl+PHj6jYPDw9l9OjROu2/++47xdHRUTEwMFBatGih7Nmzp4wjrtxKcz2mT5+utrW0tFT69OmjnD59uhyirpwOHz5c6HfFw2swevRoxcPD47F92rZtqxgYGCiNGzfW+R4pKY2iKMqzTcILIYQQQgghSkKediKEEEIIIUQZkeRbCCGEEEKIMiLJtxBCCCGEEGVEkm8hhBBCCCHKiCTfQgghhBBClBFJvoUQQgghhCgjknwLIYQQQghRRiT5FkIIIYQQooxI8i2EEEIIIUQZkeRbCCGEEEKIMiLJtxBCCCGEEGXk/wEhuZTikw+GbAAAAABJRU5ErkJggg==\n"},"metadata":{}}],"source":["sns.barplot(data=mm_some_stress_mean,orient='y')\n","plt.title('Min Max of scaled categories when Reporting Stress Level 1');"]},{"cell_type":"markdown","metadata":{"id":"bez0AAJRW-eb"},"source":["### Stress Level 0"]},{"cell_type":"code","execution_count":47,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"elapsed":3080,"status":"ok","timestamp":1716216981199,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"C_eFgnVylF1j","outputId":"ea44690a-8387-4239-b817-87b81396f712"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["sns.boxenplot(no_stress,orient='y');"]},{"cell_type":"code","execution_count":48,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"executionInfo":{"elapsed":216,"status":"ok","timestamp":1716216983387,"user":{"displayName":"Daniel 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-0.538282 \n","75% 0.349125 -0.218218 0.865813 0.890211 \n","max 1.768859 0.981981 1.511942 1.604458 \n","\n"," noise_level living_conditions ... basic_needs academic_performance \\\n","count 731.000000 731.000000 ... 731.000000 731.000000 \n","mean -0.435364 0.356102 ... 0.387682 0.396805 \n","std 0.763685 0.876528 ... 0.921527 0.912999 \n","min -1.995514 -2.250991 ... -1.934764 -1.960979 \n","25% -1.242232 -0.463200 ... -0.539196 -0.546502 \n","50% -0.488949 0.430695 ... 0.158587 0.160736 \n","75% 0.264334 1.324591 ... 0.856371 0.867974 \n","max 1.770899 2.218486 ... 1.554154 1.575213 \n","\n"," study_load teacher_student_relationship future_career_concerns \\\n","count 731.000000 731.000000 731.000000 \n","mean -0.408310 0.368876 -0.479197 \n","std 0.772103 0.968541 0.709984 \n","min -1.993501 -1.913497 -1.732927 \n","25% -1.233150 -0.468357 -1.078768 \n","50% -0.472800 0.254213 -0.424609 \n","75% 0.287551 0.976783 0.229550 \n","max 1.808252 1.699353 1.537869 \n","\n"," social_support peer_pressure extracurricular_activities bullying \\\n","count 731.000000 731.000000 731.000000 731.000000 \n","mean 0.460273 -0.474318 -0.477787 -0.474000 \n","std 0.918259 0.677712 0.690136 0.724167 \n","min -1.796742 -1.919495 -1.953023 -1.710343 \n","25% 0.112839 -1.217552 -1.247265 -1.056860 \n","50% 1.067629 -0.515609 -0.541508 -0.403377 \n","75% 1.067629 0.186334 0.164249 0.250106 \n","max 1.067629 1.590220 1.575763 1.557071 \n","\n"," stress_level \n","count 731.000000 \n","mean -0.616856 \n","std 0.609080 \n","min -1.213156 \n","25% -1.213156 \n","50% -1.213156 \n","75% 0.004428 \n","max 0.004428 \n","\n","[8 rows x 21 columns]"],"text/html":["\n","
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anxiety_levelself_esteemmental_health_historydepressionheadacheblood_pressuresleep_qualitybreathing_problemnoise_levelliving_conditions...basic_needsacademic_performancestudy_loadteacher_student_relationshipfuture_career_concernssocial_supportpeer_pressureextracurricular_activitiesbullyingstress_level
count731.000000731.000000731.000000731.000000731.000000731.000000731.000000731.000000731.000000731.000000...731.000000731.000000731.000000731.000000731.000000731.000000731.000000731.000000731.000000731.000000
mean-0.4406180.507964-0.402734-0.475398-0.449111-0.4956920.442426-0.324301-0.4353640.356102...0.3876820.396805-0.4083100.368876-0.4791970.460273-0.474318-0.477787-0.474000-0.616856
std0.7940110.7099020.9095160.7292030.7694420.8794180.8384100.9086220.7636850.876528...0.9215270.9129990.7721030.9685410.7099840.9182590.6777120.6901360.7241670.609080
min-1.809328-1.988391-0.985559-1.625618-1.780475-1.418416-1.718703-1.966776-1.995514-2.250991...-1.934764-1.960979-1.993501-1.913497-1.732927-1.796742-1.919495-1.953023-1.710343-1.213156
25%-1.1551750.024912-0.985559-0.978243-1.070608-1.418416-0.426445-1.252529-1.242232-0.463200...-0.539196-0.546502-1.233150-0.468357-1.0787680.112839-1.217552-1.247265-1.056860-1.213156
50%-0.3374840.696013-0.985559-0.460343-0.360741-0.2182180.219684-0.538282-0.4889490.430695...0.1585870.160736-0.4728000.254213-0.4246091.067629-0.515609-0.541508-0.403377-1.213156
75%0.1531311.0315641.014653-0.0719180.349125-0.2182180.8658130.8902110.2643341.324591...0.8563710.8679740.2875510.9767830.2295501.0676290.1863340.1642490.2501060.004428
max1.6249761.3671141.0146531.8702081.7688590.9819811.5119421.6044581.7708992.218486...1.5541541.5752131.8082521.6993531.5378691.0676291.5902201.5757631.5570710.004428
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe"}},"metadata":{},"execution_count":48}],"source":["no_stress.describe()"]},{"cell_type":"code","execution_count":49,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":196,"status":"ok","timestamp":1716216984984,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"0PQrCUSBTdgU","outputId":"7e75dc04-d23a-4ff0-8e12-76209b6be10a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["self_esteem 0.507964\n","social_support 0.460273\n","sleep_quality 0.442426\n","academic_performance 0.396805\n","basic_needs 0.387682\n","safety 0.382555\n","teacher_student_relationship 0.368876\n","living_conditions 0.356102\n","breathing_problem -0.324301\n","mental_health_history -0.402734\n","study_load -0.408310\n","noise_level -0.435364\n","anxiety_level -0.440618\n","headache -0.449111\n","bullying -0.474000\n","peer_pressure -0.474318\n","depression -0.475398\n","extracurricular_activities -0.477787\n","future_career_concerns -0.479197\n","blood_pressure -0.495692\n","stress_level -0.616856\n","dtype: float64"]},"metadata":{},"execution_count":49}],"source":["no_stress_mean=no_stress.mean()\n","no_stress_mean=no_stress_mean.sort_values(ascending = False)\n","no_stress_mean"]},{"cell_type":"code","execution_count":50,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":124,"status":"ok","timestamp":1716216986716,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"Jb3RwX1WVhrU","outputId":"815db14b-966e-4a74-c0e6-47143f75a4cb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["social_support 1.151847\n","self_esteem 0.975832\n","sleep_quality 0.675787\n","academic_performance 0.667031\n","basic_needs 0.662654\n","safety 0.619973\n","teacher_student_relationship 0.526949\n","living_conditions 0.333242\n","breathing_problem -0.160328\n","extracurricular_activities -0.327770\n","study_load -0.332148\n","noise_level -0.343092\n","peer_pressure -0.352941\n","anxiety_level -0.405837\n","blood_pressure -0.462380\n","future_career_concerns -0.466758\n","bullying -0.486457\n","headache -0.499590\n","depression -0.683893\n","mental_health_history -0.834473\n","stress_level -1.020520\n","dtype: float64"]},"metadata":{},"execution_count":50}],"source":["mm_no_stress_mean=mm_no_stress.mean()\n","mm_no_stress_mean=mm_no_stress_mean.sort_values(ascending = False)\n","mm_no_stress_mean"]},{"cell_type":"code","execution_count":51,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"executionInfo":{"elapsed":2139,"status":"ok","timestamp":1716216991979,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"VbSTAY7BTlmS","outputId":"5fc1b6e7-3690-4a43-bab9-8d7779784155"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["sns.barplot(data=no_stress_mean,orient='y')\n","plt.title('Standard Scaled No Stress Level Means');"]},{"cell_type":"code","execution_count":52,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"executionInfo":{"elapsed":1078,"status":"ok","timestamp":1716216997298,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"i1SnJjT4V5oW","outputId":"805680a8-0987-4ec7-d137-02d49262b2b1"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["sns.barplot(data=mm_no_stress_mean,orient='y')\n","plt.title('Min_Max Scaled No Stress Level Means');"]},{"cell_type":"code","execution_count":53,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"executionInfo":{"elapsed":1378,"status":"ok","timestamp":1716217002031,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"PN7SMAkrx_GG","outputId":"2ecdfa4f-3a5d-4bf2-ca90-9a64398a0168"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["sns.barplot(data=no_stress,orient='y',errorbar=None)\n","plt.title('Scaled Reporting when Stress Level 0');"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gIuWAZqkhE1W"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":54,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":161,"status":"ok","timestamp":1716217004667,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"RoAvbDW4mEef","outputId":"998607c4-dcd1-4cb6-a719-89fde8d0f8bd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem mental_health_history depression headache \\\n","0 0.487122 0.695889 0.000000 0.382739 0.069589 \n","1 0.567758 0.302804 0.037851 0.567758 0.189253 \n","2 0.439057 0.658586 0.036588 0.512233 0.073176 \n","3 0.569976 0.427482 0.035624 0.534353 0.142494 \n","4 0.452911 0.792594 0.000000 0.198148 0.056614 \n","... ... ... ... ... ... \n","1095 0.414873 0.641167 0.000000 0.528020 0.113147 \n","1096 0.470438 0.627250 0.000000 0.418167 0.000000 \n","1097 0.138178 0.898155 0.000000 0.103633 0.034544 \n","1098 0.676014 0.000000 0.032191 0.611632 0.160956 \n","1099 0.663039 0.221013 0.036835 0.552532 0.110506 \n","\n"," blood_pressure sleep_quality breathing_problem noise_level \\\n","0 0.034794 0.069589 0.139178 0.069589 \n","1 0.113552 0.037851 0.151402 0.113552 \n","2 0.036588 0.073176 0.073176 0.073176 \n","3 0.106871 0.035624 0.106871 0.142494 \n","4 0.084921 0.141535 0.028307 0.084921 \n","... ... ... ... ... \n","1095 0.037716 0.113147 0.075431 0.075431 \n","1096 0.156813 0.000000 0.000000 0.000000 \n","1097 0.069089 0.172722 0.069089 0.069089 \n","1098 0.096573 0.032191 0.128765 0.096573 \n","1099 0.110506 0.000000 0.110506 0.110506 \n","\n"," living_conditions ... basic_needs academic_performance study_load \\\n","0 0.104383 ... 0.069589 0.104383 0.069589 \n","1 0.037851 ... 0.075701 0.037851 0.151402 \n","2 0.073176 ... 0.073176 0.073176 0.109764 \n","3 0.071247 ... 0.071247 0.071247 0.142494 \n","4 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1100 rows × 21 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_norm"}},"metadata":{},"execution_count":54}],"source":["from sklearn.preprocessing import Normalizer\n","\n","data_norm = Normalizer()\n","Normalize = data_norm.fit_transform(df[cols].iloc[:,range(0,21)].values)\n","df_norm=pd.DataFrame(data_norm.fit_transform(Normalize),columns=cols)\n","df_norm"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XJyrkWkEIJg9"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"RDedsyotT8H4"},"source":["## Looking at how data looks when factors involved are the greatest based on correlation values (positive correlation would look at higher amounts, negative correlation would look at lower amounts)"]},{"cell_type":"code","execution_count":55,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":228,"status":"ok","timestamp":1716217007274,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"fKjJIbIimISJ","outputId":"db288d5d-beb0-4fe3-e577-63d7be7dcf11"},"outputs":[{"output_type":"stream","name":"stdout","text":["Pyschological Factors:\n"," anxiety_level self_esteem mental_health_history depression\n","count 1.100000e+03 1.100000e+03 1.100000e+03 1.100000e+03\n","mean 2.341561e-17 -3.229740e-18 2.260818e-17 3.068253e-17\n","std 1.000455e+00 1.000455e+00 1.000455e+00 1.000455e+00\n","min -1.809328e+00 -1.988391e+00 -9.855588e-01 -1.625618e+00\n","25% -8.280983e-01 -7.580391e-01 -9.855588e-01 -8.487679e-01\n","50% -1.040698e-02 1.367622e-01 -9.855588e-01 -7.191751e-02\n","75% 8.072843e-01 9.197134e-01 1.014653e+00 8.344079e-01\n","max 1.624976e+00 1.367114e+00 1.014653e+00 1.870208e+00\n","\n","\n","Physiological Factors:\n"," headache blood_pressure sleep_quality breathing_problem\n","count 1.100000e+03 1.100000e+03 1.100000e+03 1.100000e+03\n","mean -4.683123e-17 -4.844610e-17 -5.490558e-17 -3.391227e-17\n","std 1.000455e+00 1.000455e+00 1.000455e+00 1.000455e+00\n","min -1.780475e+00 -1.418416e+00 -1.718703e+00 -1.966776e+00\n","25% -1.070608e+00 -1.418416e+00 -1.072574e+00 -5.382823e-01\n","50% 3.491253e-01 -2.182179e-01 -1.033806e-01 1.759644e-01\n","75% 3.491253e-01 9.819805e-01 8.658128e-01 8.902111e-01\n","max 1.768859e+00 9.819805e-01 1.511942e+00 1.604458e+00\n","\n","\n","Enviromental Factors:\n"," noise_level living_conditions safety basic_needs\n","count 1.100000e+03 1.100000e+03 1.100000e+03 1.100000e+03\n","mean -4.037175e-17 1.130409e-17 -1.291896e-17 -2.099331e-17\n","std 1.000455e+00 1.000455e+00 1.000455e+00 1.000455e+00\n","min -1.995514e+00 -2.250991e+00 -1.947500e+00 -1.934764e+00\n","25% -4.889489e-01 -4.632004e-01 -5.245507e-01 -5.391964e-01\n","50% 2.643337e-01 -4.632004e-01 -5.245507e-01 1.585872e-01\n","75% 2.643337e-01 4.306951e-01 8.983982e-01 8.563708e-01\n","max 1.770899e+00 2.218486e+00 1.609873e+00 1.554154e+00\n","\n","\n","Academic Factors:\n"," academic_performance study_load teacher_student_relationship \\\n","count 1.100000e+03 1.100000e+03 1.100000e+03 \n","mean -8.881784e-18 -5.006097e-17 -8.074349e-18 \n","std 1.000455e+00 1.000455e+00 1.000455e+00 \n","min -1.960979e+00 -1.993501e+00 -1.913497e+00 \n","25% -5.465023e-01 -4.727998e-01 -4.683567e-01 \n","50% -5.465023e-01 -4.727998e-01 -4.683567e-01 \n","75% 8.679743e-01 2.875508e-01 9.767832e-01 \n","max 1.575213e+00 1.808252e+00 1.699353e+00 \n","\n"," future_career_concerns \n","count 1.100000e+03 \n","mean 2.745279e-17 \n","std 1.000455e+00 \n","min -1.732927e+00 \n","25% -1.078768e+00 \n","50% -4.246087e-01 \n","75% 8.837095e-01 \n","max 1.537869e+00 \n","\n","\n","Social Factors:\n"," social_support peer_pressure extracurricular_activities bullying\n","count 1.100000e+03 1.100000e+03 1.100000e+03 1.100000e+03\n","mean 9.689219e-18 1.453383e-17 1.776357e-17 1.130409e-17\n","std 1.000455e+00 1.000455e+00 1.000455e+00 1.000455e+00\n","min -1.796742e+00 -1.919495e+00 -1.953023e+00 -1.710343e+00\n","25% -8.419517e-01 -5.156090e-01 -5.415083e-01 -1.056860e+00\n","50% 1.128389e-01 -5.156090e-01 -1.886297e-01 2.501057e-01\n","75% 1.067629e+00 8.882770e-01 8.700062e-01 9.035886e-01\n","max 1.067629e+00 1.590220e+00 1.575763e+00 1.557071e+00\n"]}],"source":["Pyschological=['anxiety_level','self_esteem','mental_health_history','depression']\n","Physiological=['headache','blood_pressure','sleep_quality','breathing_problem']\n","Envriomental=['noise_level','living_conditions','safety','basic_needs']\n","Academic=['academic_performance','study_load','teacher_student_relationship','future_career_concerns']\n","Social=['social_support','peer_pressure','extracurricular_activities','bullying']\n","print('Pyschological Factors:\\n',df_scaled[Pyschological].describe())\n","print('\\n\\nPhysiological Factors:\\n',df_scaled[Physiological].describe())\n","print('\\n\\nEnviromental Factors:\\n',df_scaled[Envriomental].describe())\n","print('\\n\\nAcademic Factors:\\n',df_scaled[Academic].describe())\n","print('\\n\\nSocial Factors:\\n',df_scaled[Social].describe())\n","\n","\n"]},{"cell_type":"code","execution_count":56,"metadata":{"id":"P7opE8ZiCID8","executionInfo":{"status":"ok","timestamp":1716217010024,"user_tz":300,"elapsed":191,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["pysch=pd.DataFrame(df_scaled[Pyschological])\n","pysch['stress_level']=df_scaled['stress_level']\n","\n","physio=pd.DataFrame(df_scaled[Physiological])\n","physio['stress_level']=df_scaled['stress_level']\n","\n","enviro=pd.DataFrame(df_scaled[Envriomental])\n","enviro['stress_level']=df_scaled['stress_level']\n","\n","acad=pd.DataFrame(df_scaled[Academic])\n","acad['stress_level']=df_scaled['stress_level']\n","\n","social=pd.DataFrame(df_scaled[Social])\n","social['stress_level']=df_scaled['stress_level']\n"]},{"cell_type":"markdown","metadata":{"id":"H17Tm14sUEAD"},"source":["### Psychological"]},{"cell_type":"code","execution_count":57,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1716217011411,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"5s_TCPB4lB8p","outputId":"9fb445ee-191b-42e2-daed-7d6491fa4143"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 1100 entries, 0 to 1099\n","Data columns (total 5 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 anxiety_level 1100 non-null float64\n"," 1 self_esteem 1100 non-null float64\n"," 2 mental_health_history 1100 non-null float64\n"," 3 depression 1100 non-null float64\n"," 4 stress_level 1100 non-null float64\n","dtypes: float64(5)\n","memory usage: 43.1 KB\n"]}],"source":["pysch.info()"]},{"cell_type":"code","execution_count":58,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1716217012412,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"3LsOOVqfCIpT","outputId":"220e8cae-f959-48c1-c5ef-17769b383e2e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" anxiety_level self_esteem mental_health_history depression \\\n","count 1.100000e+03 1.100000e+03 1.100000e+03 1.100000e+03 \n","mean 2.341561e-17 -3.229740e-18 2.260818e-17 3.068253e-17 \n","std 1.000455e+00 1.000455e+00 1.000455e+00 1.000455e+00 \n","min -1.809328e+00 -1.988391e+00 -9.855588e-01 -1.625618e+00 \n","25% -8.280983e-01 -7.580391e-01 -9.855588e-01 -8.487679e-01 \n","50% -1.040698e-02 1.367622e-01 -9.855588e-01 -7.191751e-02 \n","75% 8.072843e-01 9.197134e-01 1.014653e+00 8.344079e-01 \n","max 1.624976e+00 1.367114e+00 1.014653e+00 1.870208e+00 \n","\n"," stress_level \n","count 1.100000e+03 \n","mean -2.260818e-17 \n","std 1.000455e+00 \n","min -1.213156e+00 \n","25% -1.213156e+00 \n","50% 4.427575e-03 \n","75% 1.222011e+00 \n","max 1.222011e+00 "],"text/html":["\n","
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anxiety_levelself_esteemmental_health_historydepressionstress_level
count1.100000e+031.100000e+031.100000e+031.100000e+031.100000e+03
mean2.341561e-17-3.229740e-182.260818e-173.068253e-17-2.260818e-17
std1.000455e+001.000455e+001.000455e+001.000455e+001.000455e+00
min-1.809328e+00-1.988391e+00-9.855588e-01-1.625618e+00-1.213156e+00
25%-8.280983e-01-7.580391e-01-9.855588e-01-8.487679e-01-1.213156e+00
50%-1.040698e-021.367622e-01-9.855588e-01-7.191751e-024.427575e-03
75%8.072843e-019.197134e-011.014653e+008.344079e-011.222011e+00
max1.624976e+001.367114e+001.014653e+001.870208e+001.222011e+00
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anxiety_level1.000000-0.6727450.6344500.6943400.736795
self_esteem-0.6727451.000000-0.603502-0.699602-0.756195
mental_health_history0.634450-0.6035021.0000000.6158820.648644
depression0.694340-0.6996020.6158821.0000000.734379
stress_level0.736795-0.7561950.6486440.7343791.000000
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\n"},"metadata":{}}],"source":["pysch_corr=pysch.corr()\n","pysch_corr=pysch_corr['stress_level'].sort_values(ascending=False)\n","pysch_corr=pysch_corr.drop('stress_level')\n","plt.figure(figsize=(8,6))\n","sns.heatmap(pysch_corr.to_frame(),annot=True,cmap='coolwarm')\n","plt.title('Stress Level Correlation of Only Psychological Factors')\n","plt.show()"]},{"cell_type":"code","execution_count":62,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537},"executionInfo":{"elapsed":716,"status":"ok","timestamp":1716217022004,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"36513o7zIshr","outputId":"f5fbf111-8949-424f-aeb4-6ee5d3b24e82"},"outputs":[{"output_type":"stream","name":"stderr","text":[":1: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(pysch_corr,orient='y',palette='Spectral');\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["sns.barplot(pysch_corr,orient='y',palette='Spectral');\n"]},{"cell_type":"markdown","metadata":{"id":"ynFv5DkeUYs0"},"source":["###Physiological"]},{"cell_type":"code","execution_count":63,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":633,"status":"ok","timestamp":1716217025265,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"CZb1bFERlHyY","outputId":"6f48e850-646b-40e2-f467-34c44af49de6"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 1100 entries, 0 to 1099\n","Data columns (total 5 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 headache 1100 non-null float64\n"," 1 blood_pressure 1100 non-null float64\n"," 2 sleep_quality 1100 non-null float64\n"," 3 breathing_problem 1100 non-null float64\n"," 4 stress_level 1100 non-null float64\n","dtypes: float64(5)\n","memory usage: 43.1 KB\n"]}],"source":["physio.info()"]},{"cell_type":"code","execution_count":64,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":169,"status":"ok","timestamp":1716217026398,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"aGkqr37IKFPz","outputId":"6f913ae8-5ed1-4c07-b81c-4109ee7989e2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" headache blood_pressure sleep_quality breathing_problem \\\n","count 1.100000e+03 1.100000e+03 1.100000e+03 1.100000e+03 \n","mean -4.683123e-17 -4.844610e-17 -5.490558e-17 -3.391227e-17 \n","std 1.000455e+00 1.000455e+00 1.000455e+00 1.000455e+00 \n","min -1.780475e+00 -1.418416e+00 -1.718703e+00 -1.966776e+00 \n","25% -1.070608e+00 -1.418416e+00 -1.072574e+00 -5.382823e-01 \n","50% 3.491253e-01 -2.182179e-01 -1.033806e-01 1.759644e-01 \n","75% 3.491253e-01 9.819805e-01 8.658128e-01 8.902111e-01 \n","max 1.768859e+00 9.819805e-01 1.511942e+00 1.604458e+00 \n","\n"," stress_level \n","count 1.100000e+03 \n","mean -2.260818e-17 \n","std 1.000455e+00 \n","min -1.213156e+00 \n","25% -1.213156e+00 \n","50% 4.427575e-03 \n","75% 1.222011e+00 \n","max 1.222011e+00 "],"text/html":["\n","
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headacheblood_pressuresleep_qualitybreathing_problemstress_level
count1.100000e+031.100000e+031.100000e+031.100000e+031.100000e+03
mean-4.683123e-17-4.844610e-17-5.490558e-17-3.391227e-17-2.260818e-17
std1.000455e+001.000455e+001.000455e+001.000455e+001.000455e+00
min-1.780475e+00-1.418416e+00-1.718703e+00-1.966776e+00-1.213156e+00
25%-1.070608e+00-1.418416e+00-1.072574e+00-5.382823e-01-1.213156e+00
50%3.491253e-01-2.182179e-01-1.033806e-011.759644e-014.427575e-03
75%3.491253e-019.819805e-018.658128e-018.902111e-011.222011e+00
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headacheblood_pressuresleep_qualitybreathing_problemstress_level
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blood_pressure0.3619861.000000-0.3003230.1623080.394200
sleep_quality-0.638771-0.3003231.000000-0.541687-0.749068
breathing_problem0.4617190.162308-0.5416871.0000000.573984
stress_level0.7134840.394200-0.7490680.5739841.000000
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\n"},"metadata":{}}],"source":["physio_corr=physio.corr()\n","physio_corr=physio_corr['stress_level'].sort_values(ascending=False)\n","physio_corr=physio_corr.drop('stress_level')\n","plt.figure(figsize=(8,6))\n","sns.heatmap(physio_corr.to_frame(),annot=True,cmap='coolwarm')\n","plt.title('Stress Level Correlation of Features when Physiological Factors the Greatest')\n","plt.show()"]},{"cell_type":"code","execution_count":67,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537},"executionInfo":{"elapsed":527,"status":"ok","timestamp":1716217035163,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"jYw-KU47KUec","outputId":"9fdabe81-d43b-40b0-e717-8a44bc62a396"},"outputs":[{"output_type":"stream","name":"stderr","text":[":1: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(physio_corr,orient='y',palette='Spectral');\n"]},{"output_type":"display_data","data":{"text/plain":["
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Environmental"]},{"cell_type":"code","execution_count":68,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1716217037846,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"oanJts0ClM0y","outputId":"ea680eb3-e794-4b11-f197-63095e0fa715"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 1100 entries, 0 to 1099\n","Data columns (total 5 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 noise_level 1100 non-null float64\n"," 1 living_conditions 1100 non-null float64\n"," 2 safety 1100 non-null float64\n"," 3 basic_needs 1100 non-null float64\n"," 4 stress_level 1100 non-null float64\n","dtypes: float64(5)\n","memory usage: 43.1 KB\n"]}],"source":["enviro.info()"]},{"cell_type":"code","execution_count":69,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":210,"status":"ok","timestamp":1716217039401,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"IqvHGppyQt-f","outputId":"9a46bd70-a571-4b42-9b5e-b58160c6b64c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" noise_level living_conditions safety basic_needs \\\n","count 1.100000e+03 1.100000e+03 1.100000e+03 1.100000e+03 \n","mean -4.037175e-17 1.130409e-17 -1.291896e-17 -2.099331e-17 \n","std 1.000455e+00 1.000455e+00 1.000455e+00 1.000455e+00 \n","min -1.995514e+00 -2.250991e+00 -1.947500e+00 -1.934764e+00 \n","25% -4.889489e-01 -4.632004e-01 -5.245507e-01 -5.391964e-01 \n","50% 2.643337e-01 -4.632004e-01 -5.245507e-01 1.585872e-01 \n","75% 2.643337e-01 4.306951e-01 8.983982e-01 8.563708e-01 \n","max 1.770899e+00 2.218486e+00 1.609873e+00 1.554154e+00 \n","\n"," stress_level \n","count 1.100000e+03 \n","mean -2.260818e-17 \n","std 1.000455e+00 \n","min -1.213156e+00 \n","25% -1.213156e+00 \n","50% 4.427575e-03 \n","75% 1.222011e+00 \n","max 1.222011e+00 "],"text/html":["\n","
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noise_levelliving_conditionssafetybasic_needsstress_level
count1.100000e+031.100000e+031.100000e+031.100000e+031.100000e+03
mean-4.037175e-171.130409e-17-1.291896e-17-2.099331e-17-2.260818e-17
std1.000455e+001.000455e+001.000455e+001.000455e+001.000455e+00
min-1.995514e+00-2.250991e+00-1.947500e+00-1.934764e+00-1.213156e+00
25%-4.889489e-01-4.632004e-01-5.245507e-01-5.391964e-01-1.213156e+00
50%2.643337e-01-4.632004e-01-5.245507e-011.585872e-014.427575e-03
75%2.643337e-014.306951e-018.983982e-018.563708e-011.222011e+00
max1.770899e+002.218486e+001.609873e+001.554154e+001.222011e+00
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noise_levelliving_conditionssafetybasic_needsstress_level
noise_level1.000000-0.452362-0.536630-0.5723270.663371
living_conditions-0.4523621.0000000.5635710.503275-0.581723
safety-0.5366300.5635711.0000000.624774-0.709602
basic_needs-0.5723270.5032750.6247741.000000-0.708968
stress_level0.663371-0.581723-0.709602-0.7089681.000000
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tmyZ7bwIoXtlFYBYsGCBxryrVq0SJiYm4tChQxrtCxYsEADEkSNHhBDv/n4+efKkRsX03LlzAoDo1KmTqFmzpnq+Nm3aaFRgs/YtMDBQqFQqdfuwYcOEqamp+hscXd67WeduwoQJGvNWq1ZN+Pn55bov2qrcWVP//v3V82W9Fz///HON5du1aycKFy6s/vnq1asCgJgzZ47GfF988YWwsbHRuD6QQ2X1zfe7Ltd9VlXn6NGj6rbdu3cLAMLKykrcuXNH3b5w4cJs11Tjxo2Fr6+vxrWqUqlE7dq1RdmyZdVteT2XQryqNmurLL18+VLj80KIV5+HSqVS43zqWlnVNkVHR+d5e8uWLRMAxIwZM7JtI2tfHz16lO38ZalatapwcXERjx8/VredPXtWmJiYiB49eqjbcjrfT548UVdBdfXll19q/V2WdQwLFy6s8W1Q1u+17du3q9vyeg1o065dOwFA4/wL8apS/ejRI/X0+jduWeetdOnSGu8PlUolypYtK4KCgjSusZSUFFGqVCnRpEkTjbY3RUZGZvtds3HjxmzXvBB5/8zJ67nJ6Zo3RDp1tGjSpAkiIyPRpk0bnD17Fj/++COCgoLg7u6O33//XZdV4X//+5/Gz1u2bIFKpULnzp0RHx+vntzc3FC2bFkcOHAAwH83E+zevRspKSla151Vrdu2bRtUKpVOceUmr3EqFAp06tQJO3fuxPPnz9XLr1+/Hu7u7qhbty4AYO/evXj69Cm6du2qsT5TU1PUrFlTvb68SkpKAoA8VVWBVx3d3dzc0LVrV3Wbubk5Bg8ejOfPn+Ovv/7SmL9Dhw4oUqSI1nW96znNiZWVlfr/T548QWJiIurVq4fTp0/nad/etG/fPqSlpWHo0KEafYz69u0LOzs77NixQ2N+Gxsbjb+8LSwsUKNGDdy6deut29m5cycAIDQ0VKN9+PDhAJBtO7qwtLTE3r17Nabp06cDADZu3Ah7e3s0adJE43j7+fnBxsZG43i/fmyzKjz16tVDSkoKrly58s7x5USpVKJXr14abRs3bkSFChXg7e2tEW+jRo0AQB3vu76fq1WrBhsbG/z9998AXlVQixcvjh49euD06dNISUmBEAKHDx9GvXr1si3fr18/jaHi6tWrh8zMTNy5cwfAu71333yP1KtXL9frKYunp2e2c793716tN79p287jx4/Vnw/lypVD1apVNarEmZmZ2LRpE1q3bq1xfeTkzW3oet1XrFgRAQEB6p9r1qwJAGjUqBFKliyZrT3rOCUkJGD//v3o3Lmz+tqNj4/H48ePERQUhOvXryMmJkZjW7mdy7dRKpXqz4vMzEw8fvwYNjY2KF++/Dt/FgHA2LFjs51LNze3PG9v8+bNcHZ21rixOUtuQxw+ePAAUVFR6Nmzp8a3Y5UrV0aTJk3U5/J1b57vrL72Bw8exJMnT3Ta99wEBwfD0dFR/XPW+/N9roHXZb0P3vymaMGCBShSpIh6yvo9/bqQkBCN90dUVBSuX7+Obt264fHjx+pYkpOT0bhxY/z999/qz63Xl0tPT8fjx49RpkwZODg45Olayutnjj7PjVR0vsXzo48+wpYtW5CWloazZ8/it99+w8yZM9GxY0dERUWhYsWKeVpPqVKlNH6+fv06hBAoW7as1vmzSv+lSpVCaGgoZsyYgTVr1qBevXpo06YNPv30U3UiGxwcjCVLlqBPnz4YOXIkGjdujPbt26Njx47v3RE6r3FmxTFr1iz8/vvv6NatG54/f46dO3eqv9rIWh8A9S/oN9nZ2ekUX9b8z549y9P8d+7cQdmyZbMdl6yvHN78MH/zvL3tNV2OlTZ//PEHJk6ciKioKKSmpqrb33Ws2ax9KV++vEa7hYUFSpcunW1fixcvnm1bjo6OOHfuXK7bMTExQZkyZTTa3dzc4ODgkKdfkDkxNTVFYGCg1teuX7+OxMREuLi4aH399ZsgL168iO+++w779+9Xf3BnSUxMfOf4cuLu7p7tJrLr16/j8uXLOf7xkxXvu76fTU1NERAQgEOHDgF4lazWq1cPdevWRWZmJo4dOwZXV1ckJCRoTVZfT5gAqH95Zn346/retbS0zLavjo6Oef5lYm1tneO5f9PbYs+KKzg4GN9++y1iYmLg7u6OgwcPIi4uDsHBwXnaxpvvd12v+zdjzPr8LlGihNb2rON048YNCCEwZsyYHO/UjouLg7u7e47bevNcvo1KpcLs2bPx888/Izo6WmM4o8KFC+e6fE58fX21ns+8bu/mzZsoX778O43UkNNnIfDqs3/37t3ZbqJ683wrlUpMmTIFw4cPh6urK2rVqoVWrVqhR48eGl1L3kVu5+tdroHXZRVznj9/rjGaRocOHdRdHIcPH6516Cptv+eAV0lsThITE+Ho6IgXL14gPDwcy5cvR0xMjLo7RdY8ucnrZ44+z41U3nk8EgsLC3z00Uf46KOPUK5cOfTq1QsbN25EWFhYnpZ/8y93lUoFhUKBP//8U+sd3K//BTR9+nT07NkT27Ztw549ezB48GCEh4fj2LFjKF68OKysrPD333/jwIED2LFjB3bt2oX169ejUaNG2LNnz3vdIa5LnLVq1YKnpyc2bNiAbt26Yfv27Xjx4oXGL4Osv7hWrVql9SLS9YMoa8zF8+fP67RcXr2t4vI+5/RNWX0K69evj59//hlFixaFubk5li9fjrVr1777Duggp+vk9Q+YtynoBzioVCq4uLhgzZo1Wl/PSpSePn2KBg0awM7ODhMmTICXlxcsLS1x+vRpjBgxIk/Vy5z2LadxCbVdNyqVCr6+vpgxY4bWZbKSlvd5P9etWxeTJk3Cy5cvcejQIYwePRoODg7w8fHBoUOH1H20tCWruZ1/Xd+7BTkyRV6u3eDgYIwaNQobN27E0KFDsWHDBtjb26NZs2Z52kZOnwV5ve5zijGvx/2rr75CUFCQ1nnfTJjf5708efJkjBkzBp9//jm+//57ODk5wcTEBEOHDs33b+6k2F5eaTvfQ4cORevWrbF161bs3r0bY8aMQXh4OPbv349q1aq987b0cQ28Luv35IULFzT6qpcoUUL9uePo6JhjH/HXZcUyderUHB/qkvW7btCgQVi+fDmGDh2KgIAA2Nvbq8fXzcu51eUzR1/nRir5Mniev78/gFdfLWTR9Re1l5cXhBAoVaoUypUrl+v8vr6+8PX1xXfffYejR4+iTp06WLBgASZOnAgAMDExQePGjdG4cWPMmDEDkydPxujRo3HgwIE8VyfyI87OnTtj9uzZSEpKwvr16+Hp6YlatWpprA8AXFxc3iuuLOXKlUP58uWxbds2zJ49O9cbYjw8PHDu3DmoVCqNKlXWV8EeHh7vHIuux+p1mzdvhqWlJXbv3q0xTury5cuzzZvXay1rX65evYrSpUur29PS0hAdHZ0vxz9rOyqVCtevX1dXqAHg4cOHePr06Xsd07fx8vLCvn37UKdOnbf+UXHw4EE8fvwYW7ZsQf369dXt0dHR2ebN6dhmVTqePn2q0a5L1djLywtnz55F48aNcz2H7/p+rlevHtLS0vDrr78iJiZGnZTWr19fnayWK1funW4syO/3bkErVaoUatSogfXr12PgwIHYsmUL2rZt+87jEhfUdZ/13jU3N8/X457TNbhp0yZ8/PHHWLp0qUb706dP4ezsnG/b13V7Xl5eOH78ONLT03P8liqnfXr9s/BNV65cgbOzc56HpvLy8sLw4cMxfPhwXL9+HVWrVsX06dOxevXqHJd53z/k3/caaNWqFX744QesWbMm242Vusr6HLCzs8s1lk2bNiEkJETddQt4NfzVm5+jOR0fXT9zcjs3H9ITMXX6TvzAgQNa/xLN6t/y+lcK1tbW2U7A27Rv3x6mpqYYP358tm0IIfD48WMAr/qaZGRkaLzu6+sLExMT9VfFCQkJ2daf9RfP618nv4u8xpklODgYqampWLlyJXbt2oXOnTtrvB4UFAQ7OztMnjxZ69injx490jnG8ePH4/Hjx+jTp0+2YwW8emrSH3/8AQBo0aIFYmNjNfquZWRkYM6cObCxsUGDBg103n4WXY/V60xNTaFQKDQqdbdv39b6pKq8XmuBgYGwsLDATz/9pBHP0qVLkZiYqB5h4H21aNECALI9+Smrgphf23lT586dkZmZie+//z7baxkZGepjlFW1eP0YpKWl4eeff862nLW1tdavp7I+NLP6gwKvqqqLFi3SKd6YmBgsXrw422svXrxAcnIygPd7P9esWRPm5uaYMmUKnJycUKlSJQCvkthjx47hr7/+0lpVzQt9vHcLWnBwMI4dO4Zly5YhPj4+z10AtCmo697FxQUNGzbEwoULNQokWd71uOf0OWJqaprt82vjxo1v7RP5PvK6vQ4dOiA+Ph5z587Nto6s5bPGMn5zv4oWLYqqVati5cqVGq9duHABe/bsUZ/Lt0lJScHLly812ry8vGBra5vr+zIrEdYlR3jd+14DderUQZMmTbBo0SJs27ZN6zx5/QbNz88PXl5emDZtmsb9Kdpi0XZu58yZk+0bqZyOT14/c/J6bnTN06SkU2V10KBBSElJQbt27eDt7Y20tDQcPXpUXTF8/QYKPz8/7Nu3DzNmzECxYsVQqlQpdUd5bby8vDBx4kSMGjUKt2/fRtu2bWFra4vo6Gj89ttv6NevH7766ivs378fAwcORKdOnVCuXDlkZGRg1apVMDU1RYcOHQAAEyZMwN9//42WLVvCw8MDcXFx+Pnnn1G8eHGtHabfdOPGDXWF9nXVqlVDy5Yt8xRnlurVq6NMmTIYPXo0UlNTs/0ysLOzw/z58/HZZ5+hevXq6NKlC4oUKYK7d+9ix44dqFOnjtYPo7cJDg7G+fPnMWnSJJw5cwZdu3ZVP8Fq165diIiIUH+V3q9fPyxcuBA9e/bEqVOn4OnpiU2bNuHIkSOYNWtWnm/U0iav51Sbli1bYsaMGWjWrBm6deuGuLg4zJs3D2XKlMnWZzSv11qRIkUwatQojB8/Hs2aNUObNm1w9epV/Pzzz/joo4/ybcD1KlWqICQkBIsWLVJ/5X7ixAmsXLkSbdu2xccff5wv23lTgwYN0L9/f4SHhyMqKgpNmzaFubk5rl+/jo0bN2L27Nno2LEjateuDUdHR4SEhGDw4MFQKBRYtWqV1g9nPz8/rF+/HqGhofjoo49gY2OD1q1bo1KlSqhVqxZGjRqFhIQEODk5Yd26dVr/OMrJZ599hg0bNuB///sfDhw4gDp16iAzMxNXrlzBhg0bsHv3bvj7+7/X+7lQoULw8/PDsWPH0Lp1a3UloX79+khOTkZycvI7J6v6eO++TWJiYo7Vqne9djt37oyvvvoKX331FZycnN6rUlmQ1/28efNQt25d+Pr6om/fvihdujQePnyIyMhI/Pvvvzh79qzO6/Tz88P8+fMxceJElClTBi4uLmjUqBFatWqFCRMmoFevXqhduzbOnz+PNWvWaHw7k5/yur0ePXrgl19+QWhoKE6cOIF69eohOTkZ+/btwxdffIFPPvkEVlZWqFixItavX49y5crByckJPj4+8PHxwdSpU9G8eXMEBASgd+/e6qGr7O3t8/RkqWvXrqFx48bo3LkzKlasCDMzM/z22294+PBhrkOf+fn5AXj1FK+goCCYmprq/KjZ970GVq9ejWbNmqFt27Zo3rw5AgMD4ejoqH6C1d9//53rEIXAq299lixZgubNm6NSpUro1asX3N3dERMTgwMHDsDOzk49VFarVq2watUq2Nvbo2LFioiMjMS+ffuy9X2uWrUqTE1NMWXKFCQmJkKpVKJRo0ZwcXHJ02dOXs9NTte8QdJl6IA///xTfP7558Lb21vY2NgICwsLUaZMGTFo0CDx8OFDjXmvXLki6tevL6ysrASQ/aEAOQ1Ds3nzZlG3bl1hbW0trK2thbe3t/jyyy/F1atXhRBC3Lp1S3z++efCy8tLPajuxx9/LPbt26deR0REhPjkk09EsWLFhIWFhShWrJjo2rWruHbtWq77qG2g3Kypd+/eeY7zdaNHjxYARJkyZXLc7oEDB0RQUJCwt7cXlpaWwsvLS/Ts2VP8888/6nny+lCAN4+Di4uLMDMzE0WKFBGtW7fONmjyw4cPRa9evYSzs7OwsLAQvr6+2YZfeX2w5je97zkVQvvQVUuXLlUPdu3t7S2WL1+u9RjkdK3lNLzS3Llzhbe3tzA3Nxeurq5iwIABOT4U4E3a4tQmPT1djB8/XpQqVUqYm5uLEiVKaB0cXdehq/Iy76JFi4Sfn5+wsrIStra2wtfXV3zzzTfi/v376nmOHDkiatWqpX7YQdYwdHhjuJTnz5+Lbt26CQcHBwFAY99v3rwpAgMDhVKpFK6uruLbb78Ve/fu1Tp0VU4D2qelpYkpU6aISpUqCaVSKRwdHYWfn58YP368SExMFEK83/tZCCG+/vprASDbAx3KlCkjAIibN29qtGddNydPntRo1zYsV1Z7bu/dnM5dXt/Tbxu66vXlc3ov5vReEEKIOnXqaB1yKgtyGLpK2/s9r9d9TkMEAhBffvmlRltOnz03b94UPXr0EG5ubsLc3Fy4u7uLVq1aiU2bNmXb77ycy9jYWNGyZUtha2sr8NqQcC9fvhTDhw8XRYsWFVZWVqJOnToiMjJSNGjQQGPYn/x6KEBetyfEq6GQRo8erT7ebm5uomPHjhrX9NGjR4Wfn5+wsLDIdi737dsn6tSpI6ysrISdnZ1o3bp1jg8FePN8x8fHiy+//FJ4e3sLa2trYW9vL2rWrCk2bNjw1v0XQoiMjAwxaNAgUaRIEaFQKNTX8Nt+z7wZuxB5uwbe5sWLF2LWrFkiICBA2NnZCTMzM+Hm5iZatWol1qxZo/HAjtzO25kzZ0T79u1F4cKFhVKpFB4eHqJz584iIiJCPc+TJ0/Uv2ttbGxEUFCQuHLlivDw8Mg2/OLixYtF6dKlhampabZrNbfPnLyem5yueUOkECKPtW4iIiIiogLGB9oSERERkcFiskpEREREBovJKhEREREZLCarRERERGSwmKwSERERkcFiskpEREREBovJKhEREREZLJ2eYEXZ7TAvn/tMREREZFBapl+VbNv6zB2k3C99YWWViIiIiAwWK6tEREREBUhhrpA6hA8KK6tEREREZLBYWSUiIiIqQCZmrKzqgpVVIiIiIjJYrKwSERERFSCFOWuFumCySkRERFSA2A1AN0ztiYiIiMhgsbJKREREVIA4dJVuWFklIiIiIoPFyioRERFRAWKfVd2wskpEREREBouVVSIiIqICxD6rumFllYiIiIgMFiurRERERAWIfVZ1w2SViIiIqAApTJms6oLdAIiIiIhkat68efD09ISlpSVq1qyJEydOvHX+p0+f4ssvv0TRokWhVCpRrlw57Ny5U68xsrJKREREVIBMDKSyun79eoSGhmLBggWoWbMmZs2ahaCgIFy9ehUuLi7Z5k9LS0OTJk3g4uKCTZs2wd3dHXfu3IGDg4Ne42SySkRERCRDM2bMQN++fdGrVy8AwIIFC7Bjxw4sW7YMI0eOzDb/smXLkJCQgKNHj8Lc3BwA4Onpqfc42Q2AiIiIqAApTBR6m1JTU5GUlKQxpaamZoshLS0Np06dQmBgoLrNxMQEgYGBiIyM1Br377//joCAAHz55ZdwdXWFj48PJk+ejMzMTL0dK4DJKhEREZHRCA8Ph729vcYUHh6ebb74+HhkZmbC1dVVo93V1RWxsbFa133r1i1s2rQJmZmZ2LlzJ8aMGYPp06dj4sSJetmXLOwGQERERFSAFKb6qxWOGjUKoaGhGm1KpTJf1q1SqeDi4oJFixbB1NQUfn5+iImJwdSpUxEWFpYv29DG6JPVn376Kc/zDh48WI+REBEREemXUqnMU3Lq7OwMU1NTPHz4UKP94cOHcHNz07pM0aJFYW5uDlNTU3VbhQoVEBsbi7S0NFhYWLxf8Dkw+mR15syZeZpPoVAwWSUiIiK9M4TRACwsLODn54eIiAi0bdsWwKvKaUREBAYOHKh1mTp16mDt2rVQqVQwMXlVHb527RqKFi2qt0QVkEGyGh0dLXUIRERERGoKE+mTVQAIDQ1FSEgI/P39UaNGDcyaNQvJycnq0QF69OgBd3d3dZ/XAQMGYO7cuRgyZAgGDRqE69evY/LkyXov9hl9sqpNWloaoqOj4eXlBTMzWR4CIiIikrng4GA8evQIY8eORWxsLKpWrYpdu3apb7q6e/euuoIKACVKlMDu3bsxbNgwVK5cGe7u7hgyZAhGjBih1zgVQgih1y0YkJSUFAwaNAgrV64E8Kp0Xbp0aQwaNAju7u5axxTLzQ7z8vkdJhEREelZy/Srkm37ZN1aelv3R4eP6W3dUpHV0FWjRo3C2bNncfDgQVhaWqrbAwMDsX79egkjIyIiIiJtZPUd+NatW7F+/XrUqlULCsV//UUqVaqEmzdvShgZERERyYXCAG6w+pDIqrL66NEjrc+6TU5O1kheiYiIiMgwyCpZ9ff3x44dO9Q/ZyWoS5YsQUBAgFRhERERkYwoTEz0NhkjWXUDmDx5Mpo3b45Lly4hIyMDs2fPxqVLl3D06FH89ddfUodHRERERG8wzhQ8B3Xr1kVUVBQyMjLg6+uLPXv2wMXFBZGRkfDz85M6PCIiIpIBhYlCb5MxklVlFQC8vLywePFiqcMgIiIimTKEJ1h9SGRVWQ0MDMSKFSuQlJQkdShERERElAeySlYrVaqEUaNGwc3NDZ06dcK2bduQnp4udVhEREQkI+wGoBtZJauzZ89GTEwMtm7dCmtra/To0QOurq7o168fb7AiIiIiMkCySlYBwMTEBE2bNsWKFSvw8OFDLFy4ECdOnECjRo2kDo2IiIhkgENX6UZ2N1hliY2Nxbp167B69WqcO3cONWrUkDokIiIiInqDrJLVpKQkbN68GWvXrsXBgwdRunRpdO/eHevXr4eXl5fU4REREZEMGGvfUn2RVbLq6uoKR0dHBAcHIzw8HP7+/lKHRERERERvIatk9ffff0fjxo1hYqR9OoiIiMjwcZxV3cgqa2vSpAlUKhX27duHhQsX4tmzZwCA+/fv4/nz5xJHR0RERHLAoat0I6vK6p07d9CsWTPcvXsXqampaNKkCWxtbTFlyhSkpqZiwYIFUodIRERERK+RVWV1yJAh8Pf3x5MnT2BlZaVub9euHSIiIiSMjIiIiOSCQ1fpRlaV1UOHDuHo0aOwsLDQaPf09ERMTIxEURERERFRTmSVrKpUKmRmZmZr//fff2FraytBRERERCQ3xtq3VF+Ms16cg6ZNm2LWrFnqnxUKBZ4/f46wsDC0aNFCusCIiIiISCtZVVanT5+OoKAgVKxYES9fvkS3bt1w/fp1ODs749dff5U6PCIiIpIBVlZ1I6tktXjx4jh79izWrVuHc+fO4fnz5+jduze6d++uccMVERERERkGWSWrAGBmZoZPP/1U6jCIiIhIplhZ1Y3RJ6u///57nudt06aNHiMhIil5DOiG0qG9oXQrgqRzV3Bx6PdIPHk+x/nN7G1R/vthcGvbBOZODnhxJwaXhk/Go11/q+dRFnNBhfCvUSSoHkwLWSH55h2c6/MtEk9dKIhdIqIPlLEOMaUvRp+stm3bNk/zKRQKrSMFENGHr2in5qgwdRQufBmGpyfOotTgENTcsRQHKzVD2qOEbPMrzM1Rc9dypMU9xukuQ/Ay5iGsShZDemKSeh4zBzvU/utXPP7rOE607ou0R09gXcYD6U8SC3LXiIiMntEnqyqVSuoQiEhipYb2wr2lG/Dvyi0AgPNfhMGleUOU6NkBN6cuzjZ/iV4dYO5oj6P1ukBkZAAAXtzRHIvZ6+u+ePlvLM71+Vbd9uL2v3rcCyIyFiam7AagC9ahtfD19cW9e/ekDoOI8oHC3Bz21SshPuLof41CIH7/UTjUqqZ1GddWjfD0eBR85oxF4L9HUP/MdniN6A+89tWda6tGeHrqAqr/OhuBMUdR9+RvKNG7k753h4hIdpisanH79m2kp6dLHQYR5QMLZ0eYmJkhNe6xRnvqw8dQujlrXaZQqRJwax8EhakpTrTph+uTf0bpYb1Q9tsB/81TugQ8+ndF8o3bONGyN+4s/BWVZn4H98/a6nN3iMgIKEwUepuMkdF3A8hPqampSE1N1WhLFyqYK5jzExkVEwXS4h7j3P/GACoVkk5fhKW7K0qH9sb1ifMAvPplk3jqAq6OmQkASIq6DNtKZeHRrwtiVm2VMHgiIuPCLEsH4eHhsLe315g2qLLfnEFEhiMt/glUGRlQuhTWaFe6FkZqbLzWZVJjHyH5+m3gtT7vzy/fgmVRFyjMzQEALx88wrPLNzWWe37lFqxKFMvfHSAio6MwMdHbZIyMc6/0ZNSoUUhMTNSYOps4SR0WEb2FSE9H4umLcG4U8F+jQoHCHwfg6bEzWpd5cvQ0CnmVBBT/faVmXc4TL+/HQfx/F6EnR0/DplwpjeWsy3rixV3NG7GIiOj9MFnVgVKphJ2dncbELgBEhi961nKU6N0Z7p+1hY13afjMGwczayvc+//RAaosn4LyE0PV899Z+CvMnRxQaeZoWJf1hEvzBigzoj/uzF/z3zp/WgmHmlXgNaI/CnmVRLEurVCyT2fcnr+2wPePiD4s7LOqG/ZZJSKj92Djn7Ao4oRyYYNfPRTg7GWcaNUHaf9/05VViaIQr33l//LfWJxo2RsVp41CvdO/42XMQ0TP+UVjmKvEf87jVMeBKD8pFGW/+xIvov/FpeGTcf/X7QW+f0T0YTHWpFJfFEIIIXUQUnj58iUsLS21vrZ27Vp88sknsLa2znU9O8zL53doREREpGct069Ktu07/drqbd0ei7bqbd1SkdV32CqVCt9//z3c3d1hY2ODW7duAQDGjBmDpUuXqufr1q1bnhJVIiIiIl3xBivdGOde5WDixIlYsWIFfvzxR1hYWKjbfXx8sGTJEgkjIyIiIiJtZJWs/vLLL1i0aBG6d+8OU1NTdXuVKlVw5coVCSMjIiIiueANVrqRVbIaExODMmXKZGtXqVR8YhURERGRAZLVaAAVK1bEoUOH4OHhodG+adMmVKum/RnhRERERPnJWPuW6ousktWxY8ciJCQEMTExUKlU2LJlC65evYpffvkFf/zxh9ThEREREdEbZJXaf/LJJ9i+fTv27dsHa2trjB07FpcvX8b27dvRpEkTqcMjIiIiOVAo9DcZIVlVVgGgXr162Lt3r9RhEBEREVEeyCpZvXfvHhQKBYoXLw4AOHHiBNauXYuKFSuiX79+EkdHREREcmCsd+3ri6y6AXTr1g0HDhwAAMTGxiIwMBAnTpzA6NGjMWHCBImjIyIiIjngQwF0Y5x7lYMLFy6gRo0aAIANGzbA19cXR48exZo1a7BixQppgyMiIiKibGTVDSA9PR1KpRIAsG/fPrRp0wYA4O3tjQcPHkgZGhEREckEuwHoRlaV1UqVKmHBggU4dOgQ9u7di2bNmgEA7t+/j8KFC0scHRERERG9SVbJ6pQpU7Bw4UI0bNgQXbt2RZUqVQAAv//+u7p7ABEREZE+sc+qbmTVDaBhw4aIj49HUlISHB0d1e39+vVDoUKFJIyMiIiIiLSRVbIKAKamphqJKgB4enpKEwwRERHJDvus6sbok9Xq1asjIiICjo6OqFatGhRvebrD6dOnCzAyIiIiIsqN0Sern3zyiXoEgLZt20obDBEREckeK6u6UQghhNRBfMh2mJeXOgQiIiLSUcv0q5JtO250T72t22XSCr2tWypGX1nV5tSpU7h8+TKAV8NZVatWTeKIiIiIiEgbWSWrcXFx6NKlCw4ePAgHBwcAwNOnT/Hxxx9j3bp1KFKkiLQBEhERkdF72/0zlJ1xDsiVg0GDBuHZs2e4ePEiEhISkJCQgAsXLiApKQmDBw+WOjwiIiKiAjVv3jx4enrC0tISNWvWxIkTJ/K03Lp166BQKArkfiBZJau7du3Czz//jAoVKqjbKlasiHnz5uHPP/+UMDIiIiKSC0N5KMD69esRGhqKsLAwnD59GlWqVEFQUBDi4uLeutzt27fx1VdfoV69eu9zGPJMVsmqSqWCubl5tnZzc3OoVCoJIiIiIiKSxowZM9C3b1/06tULFStWxIIFC1CoUCEsW7Ysx2UyMzPRvXt3jB8/HqVLly6QOGWVrDZq1AhDhgzB/fv31W0xMTEYNmwYGjduLGFkREREJBcKE4XeprxKS0vDqVOnEBgYqG4zMTFBYGAgIiMjc1xuwoQJcHFxQe/evd/rGOhCVjdYzZ07F23atIGnpydKlCgBALh79y58fX2xevVqiaMjIiIiej+pqalITU3VaFMqleox57PEx8cjMzMTrq6uGu2urq64cuWK1nUfPnwYS5cuRVRUVL7GnBtZJaslSpTA6dOnERERoR66qkKFChp/VRARERHplY59S3URHh6O8ePHa7SFhYVh3Lhx77XeZ8+e4bPPPsPixYvh7Oz8XuvSlaySVQDYv38/9u/fj7i4OKhUKpw5cwZr164FgLf20SAiIiLKD/p8gtWoEaMQGhqq0fZmVRUAnJ2dYWpqiocPH2q0P3z4EG5ubtnmv3nzJm7fvo3WrVur27Lu9zEzM8PVq1fh5eWVH7uQjayS1fHjx2PChAnw9/dH0aJFOc4ZERERGRVtX/lrY2FhAT8/P0RERKiHn1KpVIiIiMDAgQOzze/t7Y3z589rtH333Xd49uwZZs+ere5eqQ+ySlYXLFiAFStW4LPPPpM6FCIiIpIphcIw7m8PDQ1FSEgI/P39UaNGDcyaNQvJycno1asXAKBHjx5wd3dHeHg4LC0t4ePjo7F81gOW3mzPb7JKVtPS0lC7dm2pwyAiIiKSXHBwMB49eoSxY8ciNjYWVatWxa5du9Q3Xd29excmeuxfm1cKIYSQOoiCMmLECNjY2GDMmDH5ts4d5uXzbV1ERERUMFqmX5Vs20/Cv9Dbuh1H/ay3dUtFVpXVly9fYtGiRdi3bx8qV66c7QEBM2bMkCgyIiIiItJGVsnquXPnULVqVQDAhQsXNF7jzVZERERUEHR9LKrcySpZPXDggNQhEBEREZEOZJWsEhEREUlNn+OsGiMmq0REREQFyUCGrvpQ8GgRERERkcFiZZWIiIioALEbgG5YWSUiIiIig8XKKhEREVFB4tBVOuHRIiIiIiKDxcoqERERUQHig4h0w8oqERERERksVlaJiIiIChL7rOqEySoRERFRAeLQVbphak9EREREBouVVSIiIqKCxMet6oRHi4iIiIgMFiurRERERAWJfVZ1wsoqERERERksVlaJiIiICpCCfVZ1wqNFRERERAaLldX39MuQPVKHQERERDpqKeXG2WdVJ0xWiYiIiAqQgk+w0gmPFhEREREZLFknq5mZmYiKisKTJ0+kDoWIiIjkQqHQ32SEZJWsDh06FEuXLgXwKlFt0KABqlevjhIlSuDgwYPSBkdERERE2cgqWd20aROqVKkCANi+fTuio6Nx5coVDBs2DKNHj5Y4OiIiIpIFExP9TUbIOPcqB/Hx8XBzcwMA7Ny5E506dUK5cuXw+eef4/z58xJHR0RERERvklWy6urqikuXLiEzMxO7du1CkyZNAAApKSkwNTWVODoiIiKSBfZZ1Ymshq7q1asXOnfujKJFi0KhUCAwMBAAcPz4cXh7e0scHRERERG9SVbJ6rhx4+Dj44N79+6hU6dOUCqVAABTU1OMHDlS4uiIiIhIDjjOqm5klawCQMeOHbO1hYSESBAJERERyZKCyaouZJesRkREICIiAnFxcVCpVBqvLVu2TKKoiIiIiEgbWSWr48ePx4QJE+Dv76/ut0pERERUoEyYf+hCVsnqggULsGLFCnz22WdSh0JEREREeSCrZDUtLQ21a9eWOgwiIiKSMQX7rOpEVkerT58+WLt2rdRhEBEREVEeyaqy+vLlSyxatAj79u1D5cqVYW5urvH6jBkzJIqMiIiIZIN9VnUiq2T13LlzqFq1KgDgwoULGq/xZisiIiIiwyOrZPXAgQNSh0BERERyxz6rOpFVsvq6f//9FwBQvHhxiSMhIiIiWeG3uTqRVWqvUqkwYcIE2Nvbw8PDAx4eHnBwcMD333+f7QEBRERERCQ9WVVWR48ejaVLl+KHH35AnTp1AACHDx/GuHHj8PLlS0yaNEniCImIiMjomciqVvjeZJWsrly5EkuWLEGbNm3UbZUrV4a7uzu++OILJqtEREREBkZWyWpCQgK8vb2ztXt7eyMhIUGCiIiIiEh2eIOVTmR1tKpUqYK5c+dma587dy6qVKkiQURERERE9Dayqqz++OOPaNmyJfbt24eAgAAAQGRkJO7du4edO3dKHB0RERHJAh8KoBNZVVYbNGiAa9euoV27dnj69CmePn2K9u3b4+rVq6hXr57U4RERERHRG2RVWQWAYsWK8UYqIiIikg77rOrE6JPVc+fOwcfHByYmJjh37txb561cuXIBRUVEREREeWH0yWrVqlURGxsLFxcXVK1aFQqFAkKIbPMpFApkZmZKECERERHJCp9gpROjT1ajo6NRpEgR9f+JiIiIJMWHAujE6JNVDw8P9f/v3LmD2rVrw8xMc7czMjJw9OhRjXmJiIiISHqySu0//vhjrYP/JyYm4uOPP5YgIiIiIpIdhUJ/kxGSVbIqhIBCy4l8/PgxrK2tJYiIiIiIiN7G6LsBAED79u0BvLqJqmfPnlAqlerXMjMzce7cOdSuXVuq8IiIiEhOOHSVTmSRrNrb2wN4VVm1tbWFlZWV+jULCwvUqlULffv2lSo8IiIiIsqBLJLV5cuXAwA8PT3x1Vdf8St/IiIiko4BjQYwb948TJ06FbGxsahSpQrmzJmDGjVqaJ138eLF+OWXX3DhwgUAgJ+fHyZPnpzj/PnFcI5WAQgLC2OiSkRERARg/fr1CA0NRVhYGE6fPo0qVaogKCgIcXFxWuc/ePAgunbtigMHDiAyMhIlSpRA06ZNERMTo9c4FULbCPlGpHr16oiIiICjoyOqVaum9QarLKdPn9Z5/cFf3Xmf8IiIiEgC66dJN1zly91L9bZuy6DeeZ63Zs2a+OijjzB37lwAgEqlQokSJTBo0CCMHDky1+UzMzPh6OiIuXPnokePHu8cc26MvhvAJ598or6hqm3bttIGQ0RERKTHG6xSU1ORmpqq0aZUKjVuLgeAtLQ0nDp1CqNGjVK3mZiYIDAwEJGRkXnaVkpKCtLT0+Hk5PT+gb+F0SerYWFhWv9PREREZGzCw8Mxfvx4jbawsDCMGzdOoy0+Ph6ZmZlwdXXVaHd1dcWVK1fytK0RI0agWLFiCAwMfK+Yc2P0ySoRERGRQdHj4P2jRo1CaGioRtubVdX88MMPP2DdunU4ePAgLC0t8339rzP6ZNXR0fGt/VRfp+3pVkREREQfCm1f+Wvj7OwMU1NTPHz4UKP94cOHcHNze+uy06ZNww8//IB9+/ahcuXK7xVvXhh9sjpr1iz1/x8/foyJEyciKCgIAQEBAIDIyEjs3r0bY8aMkShCIiIikhUDGLrKwsICfn5+iIiIUN/To1KpEBERgYEDB+a43I8//ohJkyZh9+7d8Pf3L5BYjT5ZDQkJUf+/Q4cOmDBhgsZJGDx4MObOnYt9+/Zh2LBhUoRIREREVOBCQ0MREhICf39/1KhRA7NmzUJycjJ69eoFAOjRowfc3d0RHh4OAJgyZQrGjh2LtWvXwtPTE7GxsQAAGxsb2NjY6C1Oo09WX7d7925MmTIlW3uzZs3yNEQDERER0fsSeuyzqovg4GA8evQIY8eORWxsLKpWrYpdu3apb7q6e/cuTF6rAs+fPx9paWno2LGjxnq03cCVn2SVrBYuXBjbtm3D8OHDNdq3bduGwoULSxQVERERkTQGDhyY49f+Bw8e1Pj59u3b+g9IC1klq+PHj0efPn1w8OBB1KxZEwBw/Phx7Nq1C4sXL5Y4OiIiIpIFPY6zaoxklaz27NkTFSpUwE8//YQtW7YAACpUqIDDhw+rk1ciIiIivWKyqhNZJavAq0eLrVmzRuowiIiIiCgPZJesqlQq3LhxA3FxcVCpVBqv1a9fX6KoiIiISC4M5QarD4WsktVjx46hW7duuHPnDoQQGq8pFApkZmZKFBkRERERaSOrZPV///sf/P39sWPHDhQtWjTPT7YiIuPQKcgejWvawNrKBFejU7FkSwJi4zNynL9jU3t0auqg0RYTl47QH++rf7a3NcGnrRxRuawVLC0VeBCXji0RSThxPkVfu0FEHzr2WdWJrJLV69evY9OmTShTpozUoRBRAWvzsR2a17XDz+viEZeQgc5BDvi2rwuGT72P9JzzVdyLTcP3C/97HKHqjS9gvuziDGsrE/y4PA7PklWoW80awz5zxqhZD3D7frqe9oaISD5kldrXrFkTN27ckDoMIpJAi3q22LIvEf9cfIG7D9Ixb108HO3M8JFPobcul5kJJD5TqadnKZp93ct7KrHr8DPcvJeGuIQMbIlIRPILFUoXz/3Z3EQkUwqF/iYjJKvK6qBBgzB8+HDExsbC19cX5ubmGq9XrlxZosiISJ9cnMzgaGeG89dfqNtevBS4cTcVZT2UOBqV81f2bkXMMH+MO9IzBK7fScPanU/w+Ol/5dWrt1MRULUQTl9+gZSXKgRUKQRzcwUu3nyp130iIpILWSWrHTp0AAB8/vnn6jaFQgEhRJ5usEpNTUVqaqpGW2ZGKkzNWEEhMmQOtqYAXlVIX5f4PFP9mjY37qZi/rrHuP8oHY62pujQ1B7jv3TDV9Pu42Xqq5s0Z616hKGfFcGy70sgI1MgLU1g+opHePj4LX0LiEjeTGT1xfZ7k1WyGh0d/V7Lh4eHY/z48RptFQOGwKf2sPdaLxHlr7rVrNG3o5P65x+Wxr3TeqKu/FcdvfsgHdfvpmLe6OIIqGKNAyeeAwCCmzmgkJUJvl/wEM+SM/GRTyEM/awIwubF4l4s+6wSUXYcuko3skpWPTw83mv5UaNGITQ0VKPt87Gx77VOIsp//1xKwfUZ/30LYm726heDva0Jnj777xsUextT3L6fluf1prwUeBCfDrfCrz46XQuboVldOwyfeh//PnyVmN55kAjvUkoE1bHFks0J+bE7RESyJqtkFQBu3ryJWbNm4fLlywCAihUrYsiQIfDy8sp1WaVSCaVS8yt/U7MneomTiN7dy1SBl6maX8M/ScqAb1lL3Pn/O/StlAqUKanE3shneV6v0kIB18Jm+Pv/E14L81dJ8JvjNquE0d7nQET5gUNX6URWR2v37t2oWLEiTpw4gcqVK6Ny5co4fvw4KlWqhL1790odHhHp0c5Dz9CusT38KlqhhJs5vuzqjCdJGTh54b+bq77r74KgOrbqnz9t5YAKpZUo4miKch5KfNWzCFQq4MiZZADA/bh0PHiUjr4dC8OrhAVcC5uhVQNb+Ja11FgvERG9O1lVVkeOHIlhw4bhhx9+yNY+YsQINGnSRKLIiEjffj+QBKWFAv06FkYhKxNcjX6J8MVxGmOsuhY2h631f90HCtubYXB3Z9hamyLpeSauRqfiuzmxeJb86katTNWr/rDdWjjgm89dYKlU4GF8Bn5e91ijvysR0esEK6s6UYg3v78yYpaWljh//jzKli2r0X7t2jVUrlwZL1/q/ssl+Ks7+RUeERERFZD1097vPpb38fzY73pbt02tNnpbt1RkldoXKVIEUVFR2dqjoqLg4uJS8AERERGR/PChADqRVTeAvn37ol+/frh16xZq164NADhy5AimTJmS7S5/IiIiIpKerJLVMWPGwNbWFtOnT8eoUaMAAMWKFcO4ceMwePBgiaMjIiIiOWCfVd3IKllVKBQYNmwYhg0bhmfPXg1XY2trm8tSRERERPnISL+u1xdZJavR0dHIyMhA2bJlNZLU69evw9zcHJ6entIFR0RERETZyKoO3bNnTxw9ejRb+/Hjx9GzZ8+CD4iIiIjkR2Giv8kIGede5eDMmTOoU6dOtvZatWppHSWAiIiIiKQlq24ACoVC3Vf1dYmJicjMzNSyBBEREVH+EuyzqhNZVVbr16+P8PBwjcQ0MzMT4eHhqFu3roSREREREZE2sqqsTpkyBfXr10f58uVRr149AMChQ4eQlJSE/fv3SxwdERERyYKR9i3VF1kdrYoVK+LcuXPo3Lkz4uLi8OzZM/To0QNXrlyBj4+P1OERERER0RtkVVkFXj0EYPLkyW+d54svvsCECRPg7OxcQFERERGRXAiwz6ouZFVZzavVq1cjKSlJ6jCIiIjICAmFid4mY2Sce/WehBBSh0BEREREkGE3ACIiIiJJGWkFVF94tIiIiIjIYLGySkRERFSA+FAA3bCySkREREQGi5VVLT799FPY2dlJHQYREREZIWO9a19fZJWsnjt3Tmu7QqGApaUlSpYsCaVSifnz5xdwZERERESkjayS1apVq0Lxln4i5ubmCA4OxsKFC2FpaVmAkREREZFssM+qTmRVh/7tt99QtmxZLFq0CFFRUYiKisKiRYtQvnx5rF27FkuXLsX+/fvx3XffSR0qERERGSk+FEA3sqqsTpo0CbNnz0ZQUJC6zdfXF8WLF8eYMWNw4sQJWFtbY/jw4Zg2bZqEkRIRERERILNk9fz58/Dw8MjW7uHhgfPnzwN41VXgwYMHBR0aERERyYQAuwHowjjrxTnw9vbGDz/8gLS0NHVbeno6fvjhB3h7ewMAYmJi4OrqKlWIRERERPQaWVVW582bhzZt2qB48eKoXLkygFfV1szMTPzxxx8AgFu3buGLL76QMkwiIiIyYsbat1RfZJWs1q5dG9HR0VizZg2uXbsGAOjUqRO6desGW1tbAMBnn30mZYhERERE9BpZJasAYGtri//9739Sh0FERERyxaGrdCK7ZPX69es4cOAA4uLioFKpNF4bO3asRFERERERkTaySlYXL16MAQMGwNnZGW5ubhoPCFAoFExWiYiISO+EvO5vf2+ySlYnTpyISZMmYcSIEVKHQkRERDIl2A1AJ7JK7Z88eYJOnTpJHQYRERER5ZGsktVOnTphz549UodBREREMsbHrepGVt0AypQpgzFjxuDYsWPw9fWFubm5xuuDBw+WKDIiIiIi0kYhhBBSB1FQSpUqleNrCoUCt27d0nmdwV/deZ+QiIiISALrp2V//HpBeXAlSm/rLupdVW/rloqsKqvR0dFSh0BEREREOpBVskpEREQkNWPtW6ovRp+shoaG4vvvv4e1tTVCQ0PfOu+MGTMKKCoiIiIiygujT1bPnDmD9PR09f9zouCYZ0RERFQAOM6qbow+WT1w4IDW/xMRERGR4ZNVp4nVq1cjJSVF6jCIiIhIxgQUept0NW/ePHh6esLS0hI1a9bEiRMn3jr/xo0b4e3tDUtLS/j6+mLnzp3vehjyTFbJ6rBhw+Di4oJu3bph586dyMzMlDokIiIikhlDeSjA+vXrERoairCwMJw+fRpVqlRBUFAQ4uLitM5/9OhRdO3aFb1798aZM2fQtm1btG3bFhcuXMiPw5IjWY2zmpGRgV27duHXX3/Ftm3bUKhQIXTq1Andu3dH7dq132mdHGeViIjowyPlOKv3rl/S27pLlK2Y53lr1qyJjz76CHPnzgUAqFQqlChRAoMGDcLIkSOzzR8cHIzk5GT88ccf6rZatWqhatWqWLBgwfsHnwNZVVbNzMzQqlUrrFmzBnFxcZg5cyZu376Njz/+GF5eXlKHR0RERDKgz24AqampSEpK0phSU1OzxZCWloZTp04hMDBQ3WZiYoLAwEBERkZqjTsyMlJjfgAICgrKcf78Iqtk9XWFChVCUFAQmjdvjrJly+L27dtSh0RERET0XsLDw2Fvb68xhYeHZ5svPj4emZmZcHV11Wh3dXVFbGys1nXHxsbqNH9+MfrRAN6UkpKC3377DWvWrEFERARKlCiBrl27YtOmTVKHRkRERDKgz4cCjBo1Ktu48kqlUm/bKwiySla7dOmCP/74A4UKFULnzp0xZswYBAQESB0WERERUb5QKpV5Sk6dnZ1hamqKhw8farQ/fPgQbm5uWpdxc3PTaf78IqtuAKamptiwYQMePHiAuXPnMlElIiKiAmcIQ1dZWFjAz88PERER6jaVSoWIiIgc86OAgACN+QFg7969es+nZFVZXbNmjdQhEBERERmE0NBQhISEwN/fHzVq1MCsWbOQnJyMXr16AQB69OgBd3d3dZ/XIUOGoEGDBpg+fTpatmyJdevW4Z9//sGiRYv0GqfRJ6s//fQT+vXrB0tLS/z0009vnXfw4MEFFBURERHJlT77rOoiODgYjx49wtixYxEbG4uqVati165d6puo7t69CxOT/2KtXbs21q5di++++w7ffvstypYti61bt8LHx0evcRr9OKulSpXCP//8g8KFC6NUqVI5zqdQKHDr1i2d189xVomIiD48Uo6zeuvmTb2tu7QRDsVp9JXV6Ohorf8nIiIiIsNn9Mnqm8M35EShUGD69Ok6rz/m6m2dlyEiIiKpSVdZFYq83whFMkhWz5w5k6f5FLxwiIiIiAyO0SerBw4ckDoEIiIiIjUhWCDThWHcjkZEREREpIXRV1aJiIiIDIlgrVAnPFpEREREZLBYWSUiIiIqQLo8FpWYrBIREREVKCarumE3ACIiIiIyWKysEhERERUgVlZ1w8oqERERERksVlaJiIiIChArq7phZZWIiIiIDBYrq0REREQFiI9b1Q0rq0RERERksFhZJSIiIipA7LOqGyarRERERAWIyapu2A2AiIiIiAwWK6tEREREBYiVVd3IurKamZmJqKgoPHnyROpQiIiIiEgLWSWrQ4cOxdKlSwG8SlQbNGiA6tWro0SJEjh48KC0wREREZEsCKHQ22SMZJWsbtq0CVWqVAEAbN++HdHR0bhy5QqGDRuG0aNHSxwdEREREb1JVslqfHw83NzcAAA7d+5Ep06dUK5cOXz++ec4f/68xNERERGRHKig0NtkjGSVrLq6uuLSpUvIzMzErl270KRJEwBASkoKTE1NJY6OiIiIiN4kq9EAevXqhc6dO6No0aJQKBQIDAwEABw/fhze3t4SR0dERERywNEAdCOrZHXcuHHw8fHBvXv30KlTJyiVSgCAqakpRo4cKXF0REREJAfGeiOUvsgqWQWAjh07ZmsLCQmRIBIiIiIiyo3RJ6s//fRTnucdPHiwHiMhIiIiYjcAXRl9sjpz5kyNnx89eoSUlBQ4ODgAAJ4+fYpChQrBxcWFySoRERGRgTH60QCio6PV06RJk1C1alVcvnwZCQkJSEhIwOXLl1G9enV8//33UodKREREMsCHAuhGIYQQUgdRULy8vLBp0yZUq1ZNo/3UqVPo2LEjoqOjdV5n3dZ/5Vd4REREVEAOb28g2bb/uaq/x7z7l3fU27qlYvTdAF734MEDZGRkZGvPzMzEw4cPJYiIiIiI5IZ9VnVj9N0AXte4cWP0798fp0+fVredOnUKAwYMUI+5SkRERESGQ1bJ6rJly+Dm5gZ/f38olUoolUrUqFEDrq6uWLJkidThERERkQywz6puZNUNoEiRIti5cyeuXbuGK1euAAC8vb1Rrlw5iSMjIiIiuVBJHcAHRlbJahZPT08IIeDl5QUzM1keAiIiIqIPgqy6AaSkpKB3794oVKgQKlWqhLt37wIABg0ahB9++EHi6IiIiEgO2A1AN7JKVkeNGoWzZ8/i4MGDsLS0VLcHBgZi/fr1EkZGRERERNrI6jvwrVu3Yv369ahVqxYUiv/++qhUqRJu3rwpYWREREQkFxy6Sjeyqqw+evQILi4u2dqTk5M1klciIiIiMgyySlb9/f2xY8cO9c9ZCeqSJUsQEBAgVVhEREQkI+yzqhtZdQOYPHkymjdvjkuXLiEjIwOzZ8/GpUuXcPToUfz1Fx+bSkRERGRoZFVZrVu3LqKiopCRkQFfX1/s2bMHLi4uiIyMhJ+fn9ThERERkQwIKPQ2GSNZVVYBwMvLC4sXL5Y6DCIiIpIplZA6gg+LrCqrAHDz5k1899136NatG+Li4gAAf/75Jy5evChxZERERET0Jlklq3/99Rd8fX1x/PhxbN68Gc+fPwcAnD17FmFhYRJHR0RERHLAbgC6kVWyOnLkSEycOBF79+6FhYWFur1Ro0Y4duyYhJERERERkTay6rN6/vx5rF27Nlu7i4sL4uPjJYiIiIiI5MZYh5jSF1lVVh0cHPDgwYNs7WfOnIG7u7sEERERERHR28gqWe3SpQtGjBiB2NhYKBQKqFQqHDlyBF999RV69OghdXhEREQkA0LobzJGskpWJ0+eDG9vb5QoUQLPnz9HxYoVUb9+fdSuXRvfffed1OERERER0Rtk1WfVwsICixcvxpgxY3DhwgU8f/4c1apVQ9myZaUOjYiIiGRCZaR37euLrJLVLCVLlkTJkiWlDoOICljv7p5o3dQNttZmOH85CdN+vo5/H7zIcf6NS2qiqKtltvYtO2IwY8ENAECboKJo0sAF5bxsYF3IDM26HMbz5Ey97QMRffh4g5VuZJWsZmZmYsWKFYiIiEBcXBxUKpXG6/v375coMiLSt+4dSqBjK3dMmnUFDx6+RJ/unpgxwReffnESaenaO3r1DT0Nk9c6S5X2sMasiVVw4PAjdZtSaYLjpxNw/HQC/hdSWt+7QUQkO7JKVocMGYIVK1agZcuW8PHxgULBv2yI5KJTG3f8suEODh9/DACYOPMKfl9VG/VqOSPi0COtyzxNStf4+dOOhfHv/Rc4cyFR3bbx9xgAQDUfez1FTkTGxlhvhNIXWSWr69atw4YNG9CiRQupQyGiAlTM1RLOTkqcjHqibktOycSla0nw8bbLMVl9nZmZAk0/dsX6rf/qM1QiIoOUkJCAQYMGYfv27TAxMUGHDh0we/Zs2NjY5Dh/WFgY9uzZg7t376JIkSJo27Ytvv/+e9jb6/bHvaySVQsLC5QpU0bqMIiogDk5vnpi3ZOnmpXSJ0/T1K/lpn4tZ9hYm2FnRGy+x0dE8vIhPha1e/fuePDgAfbu3Yv09HT06tUL/fr10/qwJQC4f/8+7t+/j2nTpqFixYq4c+cO/ve//+H+/fvYtGmTTtuWVbI6fPhwzJ49G3Pnzn2nLgCpqalITU3VaFNlpsHENG+/7IioYDRp4IKvvyyn/vmbCeffe50tm7jh+KkEPE5Ie+91ERF9SC5fvoxdu3bh5MmT8Pf3BwDMmTMHLVq0wLRp01CsWLFsy/j4+GDz5s3qn728vDBp0iR8+umnyMjIgJlZ3lNQWSWrhw8fxoEDB/Dnn3+iUqVKMDc313h9y5Ytb10+PDwc48eP12grUTYEJcv3yvdYiejdHT7xGJeu/aP+2cL81V1Sjg7mePzkv2TT0cECN249z3V9rkWU8K/iiNHhF/M/WCKSHdUH1mc1MjISDg4O6kQVAAIDA2FiYoLjx4+jXbt2eVpPYmIi7OzsdEpUAZklqw4ODnk+oNqMGjUKoaGhGm3Nuhx/37CIKJ+9eJGJmBeaw0fFJ6TCv4ojbkQnAwAKWZmiYjk7bN15P9f1tQx0w5PENESefKyXeImI8ou2b4GVSiWUSuU7rzM2NhYuLi4abWZmZnByckJsbN66RsXHx+P7779Hv379dN6+rJLV5cuX52m+I0eOwN/fP9uJ1Xay2QWA6MOw8fcYhASXxL37L14NXfWpJx4npOLQsXj1PLMmVsbfkfHYsuO/BFahAFoEumHX/ofIVGVfr5ODOZwcLeBezAoAUNrDBikvMvDwUSqePc/Q+34R0YdHn+OshodPzvYtcFhYGMaNG5dt3pEjR2LKlClvXd/ly5ffO6akpCS0bNkSFStW1BpHbmSVrOZV8+bNERUVhdKlOWYikbFYs/keLC1N8c3AcrCxNsP5S4kYHnZeY4xVdzcrONhpdg/yr+oINxdL7NirvXrQtnkxfN7NU/3zz1OqAgAmzbqCPyMe5vt+ENGHT59DV2n7Fjinqurw4cPRs2fPt66vdOnScHNzQ1xcnEZ7RkYGEhIS4Obm9tblnz17hmbNmsHW1ha//fZbti6YeaEQgqN9vcnW1hZnz57NU7Jat/VfBRARERER5afD2xtItu2dp9Nzn+kdtaiuezKYm8uXL6NixYr4559/4OfnBwDYs2cPmjVrhn///VfrDVbAq4pqUFAQlEoldu7ciUKFCr3T9k1yn4WIiIiI8osKCr1N+lChQgU0a9YMffv2xYkTJ3DkyBEMHDgQXbp0USeqMTEx8Pb2xokTJwC8SlSbNm2K5ORkLF26FElJSYiNjUVsbCwyM3V7JDW7ARARERHRW61ZswYDBw5E48aN1Q8F+Omnn9Svp6en4+rVq0hJSQEAnD59GsePv7oJ/c0x7qOjo+Hp6ZnnbTNZJSIiIipAH2IHTCcnpxwfAAAAnp6eeL1nacOGDZFfPU3ZDUCLd3lgABERERHlP1ZWteA9Z0RERKQv+hy6yhjJKlmNjo5GRkYGypYtq9F+/fp1mJubq/tPPHv2TILoiIiIiOhNsuoG0LNnTxw9ejRb+/Hjx3MdZ4yIiIgoP6iE/iZjJKtk9cyZM6hTp0629lq1aiEqKqrgAyIiIiKit5JVNwCFQqH1K/7ExESdx/wiIiIiehe8NUY3sqqs1q9fH+Hh4RqJaWZmJsLDw1G3bl0JIyMiIiK5EFDobTJGsqqsTpkyBfXr10f58uVRr149AMChQ4eQlJSE/fv3SxwdEREREb1JVpXVihUr4ty5c+jcuTPi4uLw7Nkz9OjRA1euXIGPj4/U4REREZEM8AYr3ciqsgoAxYoVw+TJk6UOg4iIiIjywOiT1XPnzsHHxwcmJiY4d+7cW+etXLlyAUVFREREcsUbrHRj9Mlq1apVERsbCxcXF1StWhUKhULrE6oUCgVHBCAiIiIyMEafrEZHR6NIkSLq/xMRERFJiZVV3Rh9surh4aH1/0RERERk+GQ1GsDKlSuxY8cO9c/ffPMNHBwcULt2bdy5c0fCyIiIiEguVEKht8kYySpZnTx5MqysrAAAkZGRmDt3Ln788Uc4Oztj2LBhEkdHREREciCE/iZjZPTdAF537949lClTBgCwdetWdOzYEf369UOdOnXQsGFDaYMjIiIiomxkVVm1sbHB48ePAQB79uxBkyZNAACWlpZ48eKFlKERERGRTLCyqhtZVVabNGmCPn36oFq1arh27RpatGgBALh48SI8PT2lDY6IiIiIspFVZXXevHkICAjAo0ePsHnzZhQuXBgAcOrUKXTt2lXi6IiIiEgO+LhV3SiEthHyKc/qtv5L6hCIiIhIR4e3N5Bs26sP6S/1+rSe8Y0IIKtuAFlSUlJw9+5dpKWlabTzcatERESkb8JIh5jSF1klq48ePULPnj2xa9cura/zcatEREREhkVWfVaHDh2KxMREHD9+HFZWVti1axdWrlyJsmXL4vfff5c6PCIiIpIBjgagG1lVVvfv349t27bB398fJiYm8PDwQJMmTWBnZ4fw8HC0bNlS6hCJiIjIyBnrjVD6IqvKanJyMlxcXAAAjo6OePToEQDA19cXp0+fljI0IiIiItJCVslq+fLlcfXqVQBAlSpVsHDhQsTExGDBggUoWrSoxNERERGRHLAbgG5k1Q1gyJAhePDgAQAgLCwMzZo1w+rVq2FhYYGVK1dKHB0RERERvUlWyeqnn36q/n/16tVx584dXLlyBSVLloSzs7OEkREREZFcGGsFVF9k1Q0AAJYuXQofHx9YWlrC0dERPXr0wNatW6UOi4iIiIi0kFVldezYsZgxYwYGDRqEgIAAAEBkZCSGDRuGu3fvYsKECRJHSERERMaOowHoRlbJ6vz587F48WJ07dpV3damTRtUrlwZgwYNYrJKREREZGBklaymp6fD398/W7ufnx8yMjIkiIiIiIjkhn1WdSOrPqufffYZ5s+fn6190aJF6N69uwQRERERkdyoVPqbjJHRV1ZDQ0PV/1coFFiyZAn27NmDWrVqAQCOHz+Ou3fvokePHlKFSEREREQ5MPpk9cyZMxo/+/n5AQBu3rwJAHB2doazszMuXrxY4LERERGR/LAbgG6MPlk9cOCA1CEQERER0Tsy+mSViIiIyJCwsqobWd1gRUREREQfFlZWiYiIiAoQHwqgG1ZWiYiIiMhgsbJKREREVICEXjutKvS4bmkwWSUiIiIqQLzBSjfsBkBEREREBouVVSIiIqICZKyPRdUXVlaJiIiIyGCxskpERERUgNhnVTesrBIRERGRwWJllYiIiKgA8aEAumFllYiIiIgMFiurRERERAWIfVZ1w2SViIiIqAAJvfYDML4nWLEbABEREREZLFZWiYiIiAoQb7DSDSurRERERGSwWFklIiIiKkC8wUo3rKwSERERkcFiskpERERUgFQqobdJXxISEtC9e3fY2dnBwcEBvXv3xvPnz/O0rBACzZs3h0KhwNatW3XeNpNVIiIiInqr7t274+LFi9i7dy/++OMP/P333+jXr1+elp01axYUincfUot9VomIiIgK0IfWZ/Xy5cvYtWsXTp48CX9/fwDAnDlz0KJFC0ybNg3FihXLcdmoqChMnz4d//zzD4oWLfpO22dllYiIiKgACaG/KTU1FUlJSRpTamrqe8UbGRkJBwcHdaIKAIGBgTAxMcHx48dzXC4lJQXdunXDvHnz4Obm9s7bZ7JKREREZCTCw8Nhb2+vMYWHh7/XOmNjY+Hi4qLRZmZmBicnJ8TGxua43LBhw1C7dm188skn77V9dgMgIiIiKkAqPfYDGDVqFEJDQzXalEql1nlHjhyJKVOmvHV9ly9ffqc4fv/9d+zfvx9nzpx5p+Vfx2SViIiIyEgolcock9M3DR8+HD179nzrPKVLl4abmxvi4uI02jMyMpCQkJDj1/v79+/HzZs34eDgoNHeoUMH1KtXDwcPHsxTjACTVSIiIqICJVRSR/BKkSJFUKRIkVznCwgIwNOnT3Hq1Cn4+fkBeJWMqlQq1KxZU+syI0eORJ8+fTTafH19MXPmTLRu3VqnOJmsEhEREVGOKlSogGbNmqFv375YsGAB0tPTMXDgQHTp0kU9EkBMTAwaN26MX375BTVq1ICbm5vWqmvJkiVRqlQpnbbPG6yIiIiICpAQQm+TvqxZswbe3t5o3LgxWrRogbp162LRokXq19PT03H16lWkpKTk+7ZZWSUiIiKit3JycsLatWtzfN3T0zPXZPldk2kmq0REREQFSGUgfVY/FExWiYiIiAqQPr+uN0bss0pEREREBouVVSIiIqICpGJhVSesrBIRERGRwWJllYiIiKgACZZWdcLKKhEREREZLFZWiYiIiAoQBwPQDSurRERERGSwWFklIiIiKkAq9lnVCSurRERERGSwWFklIiIiKkB8gpVumKwSERERFSChkjqCDwu7ARARERGRwWJllYiIiKgAqdgNQCesrBIRERGRwWJllYiIiKgA8QYr3bCySkREREQGi5VVIiIiogLEhwLohpVVIiIiIjJYskpWly9fjpSUFKnDICIiIhkTQn+TMZJVsjpy5Ei4ubmhd+/eOHr0qNThEBERkQwJldDbZIxklazGxMRg5cqViI+PR8OGDeHt7Y0pU6YgNjZW6tCIiIiISAtZJatmZmZo164dtm3bhnv37qFv375Ys2YNSpYsiTZt2mDbtm1QqfgMNCIiItIflRB6m4yRrJLV17m6uqJu3boICAiAiYkJzp8/j5CQEHh5eeHgwYNSh0dEREREkGGy+vDhQ0ybNg2VKlVCw4YNkZSUhD/++APR0dGIiYlB586dERISInWYREREZKTYZ1U3skpWW7dujRIlSmDFihXo27cvYmJi8OuvvyIwMBAAYG1tjeHDh+PevXsSR0pEREREgMweCuDi4oK//voLAQEBOc5TpEgRREdHF2BUREREJCfGWgHVF1lVVhs0aIDq1atna09LS8Mvv/wCAFAoFPDw8Cjo0IiIiIhIC1klq7169UJiYmK29mfPnqFXr14SRERERERyoxL6m4yRrLoBCCGgUCiytf/777+wt7eXICIiIiKSG3YD0I0sktVq1apBoVBAoVCgcePGMDP7b7czMzMRHR2NZs2aSRghEREREWkji2S1bdu2AICoqCgEBQXBxsZG/ZqFhQU8PT3RoUMHiaIjIiIiORFGOni/vsgiWQ0LCwMAeHp6Ijg4GJaWlhJHRERERER5IYtkNUtISAiePn2K1atX4+bNm/j666/h5OSE06dPw9XVFe7u7lKHSER61Lu7J1o3dYOttRnOX07CtJ+v498HL3Kcf+OSmijqmv2P2y07YjBjwQ0AQJugomjSwAXlvGxgXcgMzbocxvPkTL3tAxF9+FTss6oTWSWr586dQ2BgIOzt7XH79m307dsXTk5O2LJlC+7evasevoqIjE/3DiXQsZU7Js26ggcPX6JPd0/MmOCLT784ibR07b84+oaehslrY6aU9rDGrIlVcODwI3WbUmmC46cTcPx0Av4XUlrfu0FEJDuyGrpq2LBh6NmzJ65fv67RFaBFixb4+++/JYyMiPStUxt3/LLhDg4ff4ybt5MxceYVFHZSol4t5xyXeZqUjoSn/021PyqMf++/wJkL/w2Bt/H3GKzedA8XryQVxG4QkREQQuhtMkaySlb/+ecf9O/fP1u7u7s7YmNjJYiIiApCMVdLODspcTLqibotOSUTl64lwcfbLk/rMDNToOnHrtixj58VREQFSVbdAJRKJZKSslc/rl27hiJFiuS6fGpqKlJTUzXaVJlpMDG1yLcYiSj/OTm+eo8+eZqu0f7kaZr6tdzUr+UMG2sz7IxgskpE74fjrOpGVpXVNm3aYMKECUhPf/ULS6FQ4O7duxgxYkSehq4KDw+Hvb29xvTvjTX6DpuIdNSkgQv2bKirnszMsj8MRFctm7jh+KkEPE5Iy4cIiUjOhErobTJGskpWp0+fjufPn8PFxQUvXrxAgwYNUKZMGdja2mLSpEm5Lj9q1CgkJiZqTMXLdC+AyIlIF4dPPEavIf+op8SkV3+gOjqYa8zn6GCBhCe5J5+uRZTwr+KI7Xse6CVeIiLKmay6Adjb22Pv3r04cuQIzp49i+fPn6N69eoIDAzM0/JKpRJKpVKjjV0AiAzPixeZiHmhOXxUfEIq/Ks44kZ0MgCgkJUpKpazw9ad93NdX8tANzxJTEPkycd6iZeI5EVlpDdC6YvRJ6tOTk64du0anJ2d8fnnn2P27NmoU6cO6tSpI3VoRFSANv4eg5Dgkrh3/8Wroas+9cTjhFQcOhavnmfWxMr4OzIeW3b8l8AqFECLQDfs2v8Qmars63VyMIeTowXci1kBAEp72CDlRQYePkrFs+cZet8vIiJjZ/TJalpaGpKSkuDs7IyVK1diypQpsLW1lTosIipgazbfg6WlKb4ZWA421mY4fykRw8POa4yx6u5mBQc7za4C/lUd4eZiiR17td9Y1bZ5MXzezVP9889TqgIAJs26gj8jHub7fhDRh89Y+5bqi0IY66Bc/69JkyZ4+PAh/Pz8sHLlSgQHB8PKykrrvMuWLdN5/XVb//W+IRIREVEBO7y9gWTbDhmrv1FFVk5w09u6pWL0ldXVq1dj5syZuHnzJhQKBRITE/Hy5UupwyIiIiKZMvI6Yb4z+mTV1dUVP/zwAwCgVKlSWLVqFQoXLixxVERERESUF0afrL4uOjpa6hCIiIhI5lTss6oTWSWrAJCcnIy//voLd+/eRVqa5viKgwcPligqIiIikgveYKUbWSWrZ86cQYsWLZCSkoLk5GQ4OTkhPj4ehQoVgouLC5NVIiIiIgMjqydYDRs2DK1bt8aTJ09gZWWFY8eO4c6dO/Dz88O0adOkDo+IiIhkQAiht8kYySpZjYqKwvDhw2FiYgJTU1OkpqaiRIkS+PHHH/Htt99KHR4RERERvUFWyaq5uTlMTF7tsouLC+7evQvg1WNY7927J2VoREREJBNCpdLbZIxk1We1WrVqOHnyJMqWLYsGDRpg7NixiI+Px6pVq+Dj4yN1eERERET0BllVVidPnoyiRYsCACZNmgRHR0cMGDAA8fHxWLhwocTRERERkRyoVEJvkzGSVWW1UqVK6s7HLi4uWLBgAX777TdUrFgRVatWlTY4IiIiIspGVpXVTz75BL/88gsA4OnTp6hVqxZmzJiBtm3bYv78+RJHR0RERHLwIY4GkJCQgO7du8POzg4ODg7o3bs3nj9/nutykZGRaNSoEaytrWFnZ4f69evjxYsXOm1bVsnq6dOnUa9ePQDApk2b4Orqijt37uCXX37BTz/9JHF0REREJAdCJfQ26Uv37t1x8eJF7N27F3/88Qf+/vtv9OvX763LREZGolmzZmjatClOnDiBkydPYuDAgeqb3fNKVt0AUlJSYGtrCwDYs2cP2rdvDxMTE9SqVQt37tyRODoiIiIiw3P58mXs2rULJ0+ehL+/PwBgzpw5aNGiBaZNm4ZixYppXW7YsGEYPHgwRo4cqW4rX768ztuXVWW1TJky2Lp1K+7du4fdu3ejadOmAIC4uDjY2dlJHB0RERHJgT4rq6mpqUhKStKYUlNT3yveyMhIODg4qBNVAAgMDISJiQmOHz+udZm4uDgcP34cLi4uqF27NlxdXdGgQQMcPnxY5+3LKlkdO3YsvvrqK3h6eqJmzZoICAgA8KrKWq1aNYmjIyIiIno/4eHhsLe315jCw8Pfa52xsbFwcXHRaDMzM4OTkxNiY2O1LnPr1i0AwLhx49C3b1/s2rUL1atXR+PGjXH9+nWdti+rbgAdO3ZE3bp18eDBA1SpUkXd3rhxY7Rr107CyIiIiEguVEJ/g/ePGjUKoaGhGm1KpVLrvCNHjsSUKVPeur7Lly+/Uxyq/39AQf/+/dGrVy8Ar8a7j4iIwLJly3RKoGWVrAKAm5sb3NzcNNpq1KghUTRERERE+UepVOaYnL5p+PDh6Nmz51vnKV26NNzc3BAXF6fRnpGRgYSEhGw5VZasce0rVqyo0V6hQgX1E0TzSnbJKhEREZGU9HnXvi6KFCmCIkWK5DpfQEAAnj59ilOnTsHPzw8AsH//fqhUKtSsWVPrMp6enihWrBiuXr2q0X7t2jU0b95cpzhl1WeViIiIiHRToUIFNGvWDH379sWJEydw5MgRDBw4EF26dFGPBBATEwNvb2+cOHECAKBQKPD111/jp59+wqZNm3Djxg2MGTMGV65cQe/evXXaPiurRERERAXIUCqrulizZg0GDhyIxo0bw8TEBB06dNAYoz49PR1Xr15FSkqKum3o0KF4+fIlhg0bhoSEBFSpUgV79+6Fl5eXTttWCH0+7kAG6rb+S+oQiIiISEeHtzeQbNufDLia+0zvaNt83ccxNXTsBkBEREREBovdAIiIiIgKUNawTpQ3rKwSERERkcFiZZWIiIioAH2IN1hJiZVVIiIiIjJYrKwSERERFSChx8etGiNWVomIiIjIYLGySkRERFSA2GdVN0xWiYiIiAoQk1XdsBsAERERERksVlaJiIiICpCKN1jphJVVIiIiIjJYrKwSERERFSD2WdUNK6tEREREZLBYWSUiIiIqQELFPqu6YGWViIiIiAwWK6tEREREBYh9VnXDyioRERERGSxWVomIiIgKkOA4qzphskpERERUgFTsBqATdgMgIiIiIoPFyioRERFRAeLQVbphZZWIiIiIDBYrq0REREQFiENX6YaVVSIiIiIyWKysEhERERUgDl2lG1ZWiYiIiMhgsbJKREREVIDYZ1U3TFaJiIiIChCHrtINuwEQERERkcFSCCFYiyYiekNqairCw8MxatQoKJVKqcMhIpItJqtERFokJSXB3t4eiYmJsLOzkzocIiLZYjcAIiIiIjJYTFaJiIiIyGAxWSUiIiIig8VklYhIC6VSibCwMN5cRUQkMd5gRUREREQGi5VVIiIiIjJYTFaJiIiIyGAxWSUiIiIig8VklYjoHXl6emLWrFkFus2GDRti6NChBbpNIiIpMVklIoPUs2dPtG3bVuowiIhIYkxWieiDlp6eLnUIRESkR0xWiUhSmzZtgq+vL6ysrFC4cGEEBgbi66+/xsqVK7Ft2zYoFAooFAocPHgQt2/fhkKhwPr169GgQQNYWlpizZo1AIAlS5agQoUKsLS0hLe3N37++Wf1NtLS0jBw4EAULVoUlpaW8PDwQHh4OABACIFx48ahZMmSUCqVKFasGAYPHvxO+/L06VP06dMHRYoUgZ2dHRo1aoSzZ88CAK5duwaFQoErV65oLDNz5kx4eXmpf75w4QKaN28OGxsbuLq64rPPPkN8fPw7xUNEZAzMpA6AiOTrwYMH6Nq1K3788Ue0a9cOz549w6FDh9CjRw/cvXsXSUlJWL58OQDAyckJ9+/fBwCMHDkS06dPR7Vq1dQJ69ixYzF37lxUq1YNZ86cQd++fWFtbY2QkBD89NNP+P3337FhwwaULFkS9+7dw7179wAAmzdvxsyZM7Fu3TpUqlQJsbGx6gRTV506dYKVlRX+/PNP2NvbY+HChWjcuDGuXbuGcuXKwd/fH2vWrMH333+vXmbNmjXo1q0bgFfJbqNGjdCnTx/MnDkTL168wIgRI9C5c2fs37//fQ41EdEHi8kqEUnmwYMHyMjIQPv27eHh4QEA8PX1BQBYWVkhNTUVbm5u2ZYbOnQo2rdvr/45LCwM06dPV7eVKlUKly5dwsKFCxESEoK7d++ibNmyqFu3LhQKhXpbAHD37l24ubkhMDAQ5ubmKFmyJGrUqKHzvhw+fBgnTpxAXFyc+qlX06ZNw9atW7Fp0yb069cP3bt3x9y5c9XJ6rVr13Dq1CmsXr0aANTJ9uTJk9XrXbZsGUqUKKFOeImI5IbdAIhIMlWqVEHjxo3h6+uLTp06YfHixXjy5Emuy/n7+6v/n5ycjJs3b6J3796wsbFRTxMnTsTNmzcBvLpZKyoqCuXLl8fgwYOxZ88e9fKdOnXCixcvULp0afTt2xe//fYbMjIydN6Xs2fP4vnz5yhcuLBGHNHR0eo4unTpgtu3b+PYsWMAXlVVq1evDm9vb/U6Dhw4oLF81mtZ6yAikhtWVolIMqampti7dy+OHj2KPXv2YM6cORg9ejSOHz/+1uWsra3V/3/+/DkAYPHixahZs2a29QNA9erVER0djT///BP79u1D586dERgYiE2bNqFEiRK4evUq9u3bh7179+KLL77A1KlT8ddff8Hc3DzP+/L8+XMULVoUBw8ezPaag4MDAMDNzQ2NGjXC2rVrUatWLaxduxYDBgzQWEfr1q0xZcqUbOsoWrRonmMhIjImTFaJSFIKhQJ16tRBnTp1MHbsWHh4eOC3336DhYUFMjMzc13e1dUVxYoVw61bt9C9e/cc57Ozs0NwcDCCg4PRsWNHNGvWDAkJCXBycoKVlRVat26N1q1b48svv4S3tzfOnz+P6tWr53k/qlevjtjYWJiZmcHT0zPH+bp3745vvvkGXbt2xa1bt9ClSxeNdWzevBmenp4wM+PHMxERwGSViCR0/PhxREREoGnTpnBxccHx48fx6NEjVKhQAS9fvsTu3btx9epVFC5cGPb29jmuZ/z48Rg8eDDs7e3RrFkzpKam4p9//sGTJ08QGhqKGTNmoGjRoqhWrRpMTEywceNGuLm5wcHBAStWrEBmZiZq1qyJQoUKYfXq1bCystLo15oXgYGBCAgIQNu2bfHjjz+iXLlyuH//Pnbs2IF27dqpuy60b98eAwYMwIABA/Dxxx+jWLFi6nV8+eWXWLx4Mbp27YpvvvkGTk5OuHHjBtatW4clS5aoK8VERHLCZJWIJGNnZ4e///4bs2bNQlJSEjw8PDB9+nQ0b94c/v7+OHjwIPz9/fH8+XMcOHAgx4plnz59UKhQIUydOhVff/01rK2t4evrq37Sk62tLX788Udcv34dpqam+Oijj7Bz506YmJjAwcEBP/zwA0JDQ5GZmQlfX19s374dhQsX1mlfFAoFdu7cidGjR6NXr1549OgR3NzcUL9+fbi6uqrns7W1RevWrbFhwwYsW7ZMYx3FihXDkSNHMGLECDRt2hSpqanw8PBAs2bNYGLCWwyISJ4UQgghdRBERERERNrwT3UiIiIiMlhMVomItDh06JDGEFJvTkREVDDYDYCISIsXL14gJiYmx9fLlClTgNEQEckXk1UiIiIiMljsBkBEREREBovJKhEREREZLCarRERERGSwmKwSERERkcFiskpEREREBovJKhEREREZLCarRERERGSwmKwSERERkcH6PxtjcKZmhiniAAAAAElFTkSuQmCC\n"},"metadata":{}}],"source":["enviro_corr=enviro.corr()\n","enviro_corr=enviro_corr['stress_level'].sort_values(ascending=False)\n","enviro_corr=enviro_corr.drop('stress_level')\n","plt.figure(figsize=(8,6))\n","sns.heatmap(enviro_corr.to_frame(),annot=True,cmap='coolwarm')\n","plt.title('Stress Level Correlation of Features when Enviromental Factors the Greatest')\n","plt.show()"]},{"cell_type":"code","execution_count":72,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537},"executionInfo":{"elapsed":908,"status":"ok","timestamp":1716217045941,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"AratWVc-QegR","outputId":"78bf9c8f-38fd-4843-ba2c-938a6c607b7e"},"outputs":[{"output_type":"stream","name":"stderr","text":[":1: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(enviro_corr,orient='y',palette='Spectral');\n"]},{"output_type":"display_data","data":{"text/plain":["
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Academic"]},{"cell_type":"code","execution_count":73,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":286,"status":"ok","timestamp":1716217048596,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"MlOkiqzxlRGd","outputId":"bacdb237-fc8d-4f6d-a633-a23c69c0bb96"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 1100 entries, 0 to 1099\n","Data columns (total 5 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 academic_performance 1100 non-null float64\n"," 1 study_load 1100 non-null float64\n"," 2 teacher_student_relationship 1100 non-null float64\n"," 3 future_career_concerns 1100 non-null float64\n"," 4 stress_level 1100 non-null float64\n","dtypes: float64(5)\n","memory usage: 43.1 KB\n"]}],"source":["acad.info()"]},{"cell_type":"code","execution_count":74,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":172,"status":"ok","timestamp":1716217050149,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"hFK277vNQtca","outputId":"6d033b44-013e-4775-8b2e-2e540c363186"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" academic_performance study_load teacher_student_relationship \\\n","count 1.100000e+03 1.100000e+03 1.100000e+03 \n","mean -8.881784e-18 -5.006097e-17 -8.074349e-18 \n","std 1.000455e+00 1.000455e+00 1.000455e+00 \n","min -1.960979e+00 -1.993501e+00 -1.913497e+00 \n","25% -5.465023e-01 -4.727998e-01 -4.683567e-01 \n","50% -5.465023e-01 -4.727998e-01 -4.683567e-01 \n","75% 8.679743e-01 2.875508e-01 9.767832e-01 \n","max 1.575213e+00 1.808252e+00 1.699353e+00 \n","\n"," future_career_concerns stress_level \n","count 1.100000e+03 1.100000e+03 \n","mean 2.745279e-17 -2.260818e-17 \n","std 1.000455e+00 1.000455e+00 \n","min -1.732927e+00 -1.213156e+00 \n","25% -1.078768e+00 -1.213156e+00 \n","50% -4.246087e-01 4.427575e-03 \n","75% 8.837095e-01 1.222011e+00 \n","max 1.537869e+00 1.222011e+00 "],"text/html":["\n","
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academic_performancestudy_loadteacher_student_relationshipfuture_career_concernsstress_level
count1.100000e+031.100000e+031.100000e+031.100000e+031.100000e+03
mean-8.881784e-18-5.006097e-17-8.074349e-182.745279e-17-2.260818e-17
std1.000455e+001.000455e+001.000455e+001.000455e+001.000455e+00
min-1.960979e+00-1.993501e+00-1.913497e+00-1.732927e+00-1.213156e+00
25%-5.465023e-01-4.727998e-01-4.683567e-01-1.078768e+00-1.213156e+00
50%-5.465023e-01-4.727998e-01-4.683567e-01-4.246087e-014.427575e-03
75%8.679743e-012.875508e-019.767832e-018.837095e-011.222011e+00
max1.575213e+001.808252e+001.699353e+001.537869e+001.222011e+00
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academic_performance1.000000-0.5204170.669469-0.643805-0.720922
study_load-0.5204171.000000-0.5141230.5760780.634156
teacher_student_relationship0.669469-0.5141231.000000-0.670255-0.680163
future_career_concerns-0.6438050.576078-0.6702551.0000000.742619
stress_level-0.7209220.634156-0.6801630.7426191.000000
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\n"},"metadata":{}}],"source":["acad_corr=acad.corr()\n","acad_corr=acad_corr['stress_level'].sort_values(ascending=False)\n","acad_corr=acad_corr.drop('stress_level')\n","plt.figure(figsize=(8,6))\n","sns.heatmap(acad_corr.to_frame(),annot=True,cmap='coolwarm')\n","plt.title('Stress Level Correlation of Features when Academic Factors the Greatest')\n","plt.show()"]},{"cell_type":"code","execution_count":77,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537},"executionInfo":{"elapsed":578,"status":"ok","timestamp":1716217057028,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"xjNjWORwSwL-","outputId":"35e2e149-604e-47a5-a084-78ab9728f0c9"},"outputs":[{"output_type":"stream","name":"stderr","text":[":1: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(acad_corr,orient='y',palette='Spectral');\n"]},{"output_type":"display_data","data":{"text/plain":["
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Social"]},{"cell_type":"code","execution_count":78,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1716217059588,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"iCTPMrPwlUUK","outputId":"ee0a52d8-e5d4-4d20-ce05-16366150393e"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 1100 entries, 0 to 1099\n","Data columns (total 5 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 social_support 1100 non-null float64\n"," 1 peer_pressure 1100 non-null float64\n"," 2 extracurricular_activities 1100 non-null float64\n"," 3 bullying 1100 non-null float64\n"," 4 stress_level 1100 non-null float64\n","dtypes: float64(5)\n","memory usage: 43.1 KB\n"]}],"source":["social.info()"]},{"cell_type":"code","execution_count":79,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":157,"status":"ok","timestamp":1716217060961,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"oa_5r3tjQtPP","outputId":"2e911e26-583d-425e-9961-24ddc6c87173"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" social_support peer_pressure extracurricular_activities \\\n","count 1.100000e+03 1.100000e+03 1.100000e+03 \n","mean 9.689219e-18 1.453383e-17 1.776357e-17 \n","std 1.000455e+00 1.000455e+00 1.000455e+00 \n","min -1.796742e+00 -1.919495e+00 -1.953023e+00 \n","25% -8.419517e-01 -5.156090e-01 -5.415083e-01 \n","50% 1.128389e-01 -5.156090e-01 -1.886297e-01 \n","75% 1.067629e+00 8.882770e-01 8.700062e-01 \n","max 1.067629e+00 1.590220e+00 1.575763e+00 \n","\n"," bullying stress_level \n","count 1.100000e+03 1.100000e+03 \n","mean 1.130409e-17 -2.260818e-17 \n","std 1.000455e+00 1.000455e+00 \n","min -1.710343e+00 -1.213156e+00 \n","25% -1.056860e+00 -1.213156e+00 \n","50% 2.501057e-01 4.427575e-03 \n","75% 9.035886e-01 1.222011e+00 \n","max 1.557071e+00 1.222011e+00 "],"text/html":["\n","
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social_supportpeer_pressureextracurricular_activitiesbullyingstress_level
count1.100000e+031.100000e+031.100000e+031.100000e+031.100000e+03
mean9.689219e-181.453383e-171.776357e-171.130409e-17-2.260818e-17
std1.000455e+001.000455e+001.000455e+001.000455e+001.000455e+00
min-1.796742e+00-1.919495e+00-1.953023e+00-1.710343e+00-1.213156e+00
25%-8.419517e-01-5.156090e-01-5.415083e-01-1.056860e+00-1.213156e+00
50%1.128389e-01-5.156090e-01-1.886297e-012.501057e-014.427575e-03
75%1.067629e+008.882770e-018.700062e-019.035886e-011.222011e+00
max1.067629e+001.590220e+001.575763e+001.557071e+001.222011e+00
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social_supportpeer_pressureextracurricular_activitiesbullyingstress_level
social_support1.000000-0.490172-0.530047-0.567078-0.632497
peer_pressure-0.4901721.0000000.6183710.6610580.690684
extracurricular_activities-0.5300470.6183711.0000000.6519790.692977
bullying-0.5670780.6610580.6519791.0000000.751162
stress_level-0.6324970.6906840.6929770.7511621.000000
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\n"},"metadata":{}}],"source":["social_corr=social.corr()\n","social_corr=social_corr['stress_level'].sort_values(ascending=False)\n","social_corr=social_corr.drop('stress_level')\n","plt.figure(figsize=(8,6))\n","sns.heatmap(social_corr.to_frame(),annot=True,cmap='coolwarm')\n","plt.title('Stress Level Correlation of only Social Factors')\n","plt.show()"]},{"cell_type":"code","execution_count":82,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537},"executionInfo":{"elapsed":668,"status":"ok","timestamp":1716217068512,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"3tD9WLPOSx_h","outputId":"61aee244-c1a5-4bef-8bc3-19d9532962e4"},"outputs":[{"output_type":"stream","name":"stderr","text":[":1: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.barplot(social_corr,orient='y',palette='Spectral');\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["sns.barplot(social_corr,orient='y',palette='Spectral');"]},{"cell_type":"code","execution_count":83,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":3539,"status":"ok","timestamp":1716217073672,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"ZZ8wblTv6IsU","outputId":"f0d09ea3-8c9b-46c6-d6bf-e216e0be1a06"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["fig, ax = plt.subplots(ncols=2,nrows=3,figsize=(16,14))\n","fig.subplots_adjust(wspace=0.4)\n","pysch_grp_corr=sns.heatmap(pysch_corr.to_frame(),ax=ax[0,0],annot=True,cmap='coolwarm')\n","ax[0,0].set_title('Pyschological Correlation')\n","\n","physio_grp_corr=sns.heatmap(physio_corr.to_frame(),ax=ax[0,1],annot=True,cmap='coolwarm')\n","ax[0,1].set_title('Physiological Correlation')\n","\n","enviro_grp_corr=sns.heatmap(enviro_corr.to_frame(),ax=ax[1,0],annot=True,cmap='coolwarm')\n","ax[1,0].set_title('Enviromental Correlation')\n","\n","acad_grp_corr=sns.heatmap(acad_corr.to_frame(),ax=ax[1,1],annot=True,cmap='coolwarm')\n","ax[1,1].set_title('Academic Correlation')\n","\n","social_grp_corr=sns.heatmap(social_corr.to_frame(),ax=ax[2,0],annot=True,cmap='coolwarm')\n","ax[2,0].set_title('Social Correlation')\n","\n","all_grp_corr=sns.heatmap(df_scaled_corr.to_frame(),ax=ax[2,1],annot=True,cmap='coolwarm')\n","ax[2,1].set_title('Scaled Correlation')\n","plt.title('Stress Level Correlation of All Factors')\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{"id":"z1VrEeP0i9iQ"},"source":["## Accounting for all exploratory options into dataset\n","\n","* Scaled Data\n"," * changes the distribution across many features\n","* Reviewed Data at different stress levels\n"," * At highest stress level, positive and negative stress levels values (min, max, mean) are farthest apart when compared to some stress and no stress levels.\n","* Created sub groups for surrounding features\n"," * Psychological - 3 of the 4 strongly impact a positive/increase in stress levels.\n"," * Physiological - 3 of the 4 strongly impact a positive/increase in stress levels\n"," * Environmental - 3 of the 4 strongly impact a negative/decrease in stess levels\n"," * Academic - 2 features negatively and 2 positively impact stress\n"," * Social - 3 of the 4 strongly impact a postive / increase in stress levels.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yv4hiPO5kApm"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"h4qc7dOdEV77"},"source":["# EDA Results and interpretation for: \n","##What are the key factors influencing stress levels amount students?\n","\n","* Contributing factors for ***increased stress levels*** in students in order of highest impact with exception to blood pressure which needs further analysis:\n"," * By individual factor:\n"," * **Future Career Concerns, Extracurricular Activities, Depression, Peer Pressure and Bullying** are consistently reported high when stress levels are also elevated.\n"," * **Self Esteem, ,Social Support, Sleep Quality, Academic Performance and Basic Needs** are the consistently reported low when stress levels are highest.\n","\n"," * By Sub Groups\n"," * The highest impacting factor for students to report high levels in stess is **Psychological factors** such as high anxiety level, low self_esteem issues, past mental health history and increased depression.\n"," * Very closely behing Psychological factors are **Physiologocial factors** such as increased Headaches, Higher Blood Pressure, and Breathing Problems with decreased Sleep Quality.\n"," * The third highest factor are the **Social factors** such as low levels of social support with increased peer pressure, extracurricular activities and bullying.\n"," * Next highest are **Academic factors** like low academic performance and low teacher-student relationships with increased study loads and future career concerns. \n"," * The lowest impact to stress levels seems to be the **Enviromental factors** such where noise levels have increased and there are less reported high levels of living conditions, safety and basic needs.\n","* Contributing factors for ***decreased stress levels*** in students in order of highest impact:\n"," * By Individual Factor:\n"," * **Self Esteem, Social Support, Sleep Quality, Academic Performance, Basic Needs, and Safety** are often reported as higher levels when stress levels are lowest.\n"," * Whereas **Anxiety, depression, bullying, future career concerns and headaches** are at the lowest correlation for contributing to a lesser stress level.\n"," * By Sub Groups\n"," * The highest impacting factor for students to report the lowest levels of stress are those in the **Physiologicial factors** report lower headaches, blood pressure and breathing problems with increased levels of sleep quality.\n"," * In the second highest impact for reporting lower levels of stress comes from the **Psychological factors** with reports of lower anxiety levels, higher self esteem levels, less mental health history and decreased depression.\n"," * Coming in third for decresased levels in stress are the **Enviromental factors** with reports of lower levels of noise and higher levels of living conditions, safety and basic needs.\n"," * The last two categorys of **Academic and Social Factors** show simliar results when academic factors have higher reported academic performance and teacher-student relationship levels,lower study loads and fewer future career concerns. Social factors include higher levels of social support, less peer pressure, fewer extracurricular activites and lower levels of bullying."]},{"cell_type":"markdown","metadata":{"id":"DJ3ko04ZDhpt"},"source":["# Preparing for modeling."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MBalWx9UDhM4"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":84,"metadata":{"id":"_CNHiZEKCdkx","executionInfo":{"status":"ok","timestamp":1716217083667,"user_tz":300,"elapsed":654,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["from sklearn import preprocessing\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.model_selection import train_test_split\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.svm import SVC\n","from sklearn import metrics\n","from sklearn.metrics import confusion_matrix, classification_report\n","from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n","from sklearn.metrics import accuracy_score\n","\n","import warnings\n","warnings.filterwarnings('ignore')\n","\n","import os\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{"id":"SqzwBOurX3JQ"},"source":["## Random Forest Classifier"]},{"cell_type":"markdown","metadata":{"id":"tVYUVwvnhcSN"},"source":["### Iteration 1)\n"," * Random Forest Classifier\n"," * Unscaled data\n"," * All Features"]},{"cell_type":"code","execution_count":85,"metadata":{"id":"mYXBg6uBWZZK","executionInfo":{"status":"ok","timestamp":1716217085997,"user_tz":300,"elapsed":119,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["rfc_X=df.drop(['stress_level'],axis=1)\n","rfc_y=df.stress_level\n","\n","rfc_X_train,rfc_X_test,rfc_y_train,rfc_y_test=train_test_split(rfc_X,rfc_y,test_size=.1,random_state=43)"]},{"cell_type":"code","execution_count":86,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":373,"status":"ok","timestamp":1716217087699,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"EcKkeOtgRWtV","outputId":"c4d1c3ea-a6bf-4436-b13b-9dd6a4fd0ec8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Non Scaled Random Forest Classification Training Score: 1.0\n","Non Scaled Random Forest Classification Testing Score: 0.8636363636363636\n","[[25 2 6]\n"," [ 1 33 1]\n"," [ 5 0 37]]\n"]}],"source":["rfc_model=RandomForestClassifier().fit(rfc_X_train,rfc_y_train)\n","rfc_y_pred=rfc_model.predict(rfc_X_test)\n","print('Non Scaled Random Forest Classification Training Score:',rfc_model.score(rfc_X_train,rfc_y_train))\n","print('Non Scaled Random Forest Classification Testing Score:',rfc_model.score(rfc_X_test,rfc_y_test))\n","print(confusion_matrix(rfc_y_test,rfc_y_pred))"]},{"cell_type":"code","execution_count":87,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":677},"executionInfo":{"elapsed":271,"status":"ok","timestamp":1716217090397,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"wMTMr7w0eYzF","outputId":"ea6003c4-1674-4315-92f7-79490063d7dc"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" 0\n","blood_pressure 0.134233\n","sleep_quality 0.088009\n","headache 0.072716\n","academic_performance 0.062386\n","teacher_student_relationship 0.061093\n","anxiety_level 0.058176\n","social_support 0.058140\n","depression 0.055449\n","safety 0.053888\n","extracurricular_activities 0.052910\n","bullying 0.050501\n","peer_pressure 0.050155\n","basic_needs 0.046876\n","future_career_concerns 0.044726\n","self_esteem 0.038379\n","noise_level 0.024182\n","study_load 0.019534\n","living_conditions 0.011821\n","breathing_problem 0.011384\n","mental_health_history 0.005441"],"text/html":["\n","
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0
blood_pressure0.134233
sleep_quality0.088009
headache0.072716
academic_performance0.062386
teacher_student_relationship0.061093
anxiety_level0.058176
social_support0.058140
depression0.055449
safety0.053888
extracurricular_activities0.052910
bullying0.050501
peer_pressure0.050155
basic_needs0.046876
future_career_concerns0.044726
self_esteem0.038379
noise_level0.024182
study_load0.019534
living_conditions0.011821
breathing_problem0.011384
mental_health_history0.005441
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"pd\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": 0,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.029218439588864893,\n \"min\": 0.0054408438280761865,\n \"max\": 0.1342325304988937,\n \"num_unique_values\": 20,\n \"samples\": [\n 0.1342325304988937,\n 0.0118213487109477,\n 0.024182246768814402\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":87}],"source":["pd.DataFrame(rfc_model.feature_importances_,index=rfc_X_train.columns).sort_values(by=0,ascending=False)"]},{"cell_type":"code","execution_count":88,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":172,"status":"ok","timestamp":1716217094596,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"dJS2me_oRdEu","outputId":"f815acdd-64a8-4829-bf29-25640e2945f9"},"outputs":[{"output_type":"stream","name":"stdout","text":[" precision recall f1-score support\n","\n"," 0 0.81 0.76 0.78 33\n"," 1 0.94 0.94 0.94 35\n"," 2 0.84 0.88 0.86 42\n","\n"," accuracy 0.86 110\n"," macro avg 0.86 0.86 0.86 110\n","weighted avg 0.86 0.86 0.86 110\n","\n"]}],"source":["print(classification_report(rfc_y_test,rfc_y_pred))"]},{"cell_type":"code","execution_count":89,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":147,"status":"ok","timestamp":1716217097498,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"5we0ArrQRqaP","outputId":"dd8b79dc-b212-47f9-9c00-9cdd15e5df04"},"outputs":[{"output_type":"stream","name":"stdout","text":["Random Forest Classifier No Scaled Data Accuracy Score: 0.8636363636363636\n"]}],"source":["rfc_accuracy=accuracy_score(rfc_y_test,rfc_y_pred)\n","print(\"Random Forest Classifier No Scaled Data Accuracy Score:\",rfc_accuracy)\n"]},{"cell_type":"markdown","metadata":{"id":"btNImQZug43s"},"source":["### Iteration 2)\n"," * Random Forest Classifier\n"," * Unscaled Data\n"," * Grid Search with syntax assistance from Daniel Meier"]},{"cell_type":"code","execution_count":90,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dUOvG3o7b7e6","outputId":"7600f71e-0cb8-40f9-8995-6db62bf7a887","executionInfo":{"status":"ok","timestamp":1716217272321,"user_tz":300,"elapsed":172132,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 648 candidates, totalling 3240 fits\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.919 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.914 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.3s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.5s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.3s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.838 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.6s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.914 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.823 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.3s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.3s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.904 total time= 0.3s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.3s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.3s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.3s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.2s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.4s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.2s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.2s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.909 total time= 0.2s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.2s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.2s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.914 total time= 0.2s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.2s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.929 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.2s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.2s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.828 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.914 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.914 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.2s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.4s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.3s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.2s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.5s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.4s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.6s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.3s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.2s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.3s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.3s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.2s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.2s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.2s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.2s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.2s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.2s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.2s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.3s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.4s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.2s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.828 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.914 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.914 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.914 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.914 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.2s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.2s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.2s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.2s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.2s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.2s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.2s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.2s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.2s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.914 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.914 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.3s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.3s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.2s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.2s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.2s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.914 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.2s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.2s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.2s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n"]}],"source":["rf_grid_search={'bootstrap':[True,False],\n"," 'max_depth':[2,6,10],\n"," 'n_estimators':[5,10,20,30],\n"," 'max_features':['sqrt','log2',None],\n"," 'min_samples_leaf':[2,4,6],\n"," 'min_samples_split':[2,4,6]\n"," }\n","rf_grid=GridSearchCV(rfc_model,param_grid=rf_grid_search,cv=5,refit=True,verbose=3,scoring='accuracy').fit(rfc_X_train,rfc_y_train)"]},{"cell_type":"code","execution_count":91,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PD5iEcs6dLqi","outputId":"c33565ad-b07a-4338-94b4-82f6515e5dea","executionInfo":{"status":"ok","timestamp":1716217285290,"user_tz":300,"elapsed":174,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Best hyperarameters: {'bootstrap': True, 'max_depth': 6, 'max_features': 'log2', 'min_samples_leaf': 2, 'min_samples_split': 6, 'n_estimators': 5}\n","Mean Accuracy: 0.895\n"]}],"source":["rf_best_params=rf_grid.best_params_\n","rf_best_estim=rf_grid.best_estimator_\n","rf_feat_importance=rf_best_estim.feature_importances_\n","rf_average_score=\"{:.3}\".format(rf_grid.best_score_)\n","print(f\"Best hyperarameters: {rf_best_params}\")\n","print(f\"Mean Accuracy: {rf_average_score}\")"]},{"cell_type":"code","execution_count":92,"metadata":{"id":"xyyisDhxd4RO","colab":{"base_uri":"https://localhost:8080/","height":677},"executionInfo":{"status":"ok","timestamp":1716217290140,"user_tz":300,"elapsed":178,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"7a7edb11-a6d5-4e76-c025-454702248de5"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Importance\n","academic_performance 0.239793\n","depression 0.239335\n","social_support 0.121686\n","blood_pressure 0.102990\n","peer_pressure 0.085484\n","anxiety_level 0.078727\n","bullying 0.022320\n","noise_level 0.015323\n","self_esteem 0.014190\n","future_career_concerns 0.014094\n","study_load 0.012096\n","extracurricular_activities 0.010304\n","living_conditions 0.009991\n","safety 0.008307\n","basic_needs 0.006967\n","sleep_quality 0.005700\n","headache 0.005232\n","teacher_student_relationship 0.003567\n","breathing_problem 0.002611\n","mental_health_history 0.001283"],"text/html":["\n","
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Importance
academic_performance0.239793
depression0.239335
social_support0.121686
blood_pressure0.102990
peer_pressure0.085484
anxiety_level0.078727
bullying0.022320
noise_level0.015323
self_esteem0.014190
future_career_concerns0.014094
study_load0.012096
extracurricular_activities0.010304
living_conditions0.009991
safety0.008307
basic_needs0.006967
sleep_quality0.005700
headache0.005232
teacher_student_relationship0.003567
breathing_problem0.002611
mental_health_history0.001283
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"feature_imp_df","summary":"{\n \"name\": \"feature_imp_df\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"Importance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07447554897983502,\n \"min\": 0.0012833584149463818,\n \"max\": 0.23979305724238617,\n \"num_unique_values\": 20,\n \"samples\": [\n 0.23979305724238617,\n 0.003567468290912466,\n 0.005699562937988627\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":92}],"source":["feat_imp_col_names=rfc_X.columns\n","feature_imp_df=pd.DataFrame(rf_feat_importance,index=feat_imp_col_names,columns=['Importance'])\n","feature_imp_df=feature_imp_df.sort_values(by='Importance',ascending=False)\n","feature_imp_df"]},{"cell_type":"code","execution_count":93,"metadata":{"id":"bOqStbH_d2qc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1716217305455,"user_tz":300,"elapsed":128,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"8d59f249-f022-4c65-fe6a-b6ab2c3414af"},"outputs":[{"output_type":"stream","name":"stdout","text":["No Scaling, Tuned Model\n","\n","0.9515\n","0.8909\n","\n"," precision recall f1-score support\n","\n"," 0 0.96 0.82 0.89 33\n"," 1 0.80 0.91 0.85 35\n"," 2 0.93 0.93 0.93 42\n","\n"," accuracy 0.89 110\n"," macro avg 0.90 0.89 0.89 110\n","weighted avg 0.90 0.89 0.89 110\n","\n"]}],"source":["rfc_model=rf_best_estim.fit(rfc_X_train,rfc_y_train)\n","\n","rfc_y_train_pred=rfc_model.predict(rfc_X_train)\n","rfc_y_pred=rfc_model.predict(rfc_X_test)\n","rfc_train_accuracy=accuracy_score(rfc_y_train,rfc_y_train_pred).round(4)\n","rfc_accuracy=accuracy_score(rfc_y_test,rfc_y_pred).round(4)\n","print('No Scaling, Tuned Model\\n')\n","print(rfc_train_accuracy)\n","print(rfc_accuracy)\n","print()\n","print(classification_report(rfc_y_test,rfc_y_pred))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ocNqpZu11WO2"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"aEUgECHhh9rB"},"source":["### Iteration 3)\n"," * Random Forest Classifier\n"," * Standardized Scaled Data"]},{"cell_type":"code","execution_count":94,"metadata":{"id":"3yOsnidEh8bw","executionInfo":{"status":"ok","timestamp":1716217316535,"user_tz":300,"elapsed":154,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["rfscale_X=df.drop(['stress_level'],axis=1)\n","rfscale_y=df.stress_level\n","\n","rfscale_X_train,rfscale_X_test,rfscale_y_train,rfscale_y_test=train_test_split(rfscale_X,rfscale_y,test_size=.1,random_state=43)\n","\n","sc = StandardScaler()\n","scaled_train_data = pd.DataFrame(sc.fit_transform(rfscale_X_train), columns = rfscale_X.columns)\n","scaled_test_data = pd.DataFrame(sc.transform(rfscale_X_test), columns = rfscale_X.columns)\n"]},{"cell_type":"code","execution_count":95,"metadata":{"id":"LjgXO5wEwq4z","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1716217317914,"user_tz":300,"elapsed":175,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"488391c4-14b3-4064-d3af-38f0b043ee25"},"outputs":[{"output_type":"stream","name":"stdout","text":["1.0\n","0.8909090909090909\n","[[28 1 4]\n"," [ 2 33 0]\n"," [ 2 3 37]]\n"]}],"source":["rfscale_model=RandomForestClassifier().fit(rfscale_X_train,rfscale_y_train)\n","rfscale_y_pred=rfscale_model.predict(rfscale_X_test)\n","print(rfscale_model.score(rfscale_X_train,rfscale_y_train))\n","print(rfscale_model.score(rfscale_X_test,rfscale_y_test))\n","print(confusion_matrix(rfscale_y_test,rfscale_y_pred))\n","\n"]},{"cell_type":"code","execution_count":96,"metadata":{"id":"q50s8UekwM1i","colab":{"base_uri":"https://localhost:8080/","height":677},"executionInfo":{"status":"ok","timestamp":1716217323698,"user_tz":300,"elapsed":179,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"6d3b5182-8ef9-419f-b017-c2bde61de28f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" 0\n","blood_pressure 0.107561\n","sleep_quality 0.096442\n","teacher_student_relationship 0.092478\n","anxiety_level 0.071901\n","extracurricular_activities 0.070979\n","depression 0.068497\n","safety 0.064523\n","headache 0.063609\n","social_support 0.062342\n","future_career_concerns 0.059891\n","peer_pressure 0.040894\n","bullying 0.040507\n","basic_needs 0.040145\n","self_esteem 0.034302\n","academic_performance 0.029969\n","living_conditions 0.015827\n","study_load 0.012825\n","noise_level 0.012232\n","breathing_problem 0.010159\n","mental_health_history 0.004917"],"text/html":["\n","
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0
blood_pressure0.107561
sleep_quality0.096442
teacher_student_relationship0.092478
anxiety_level0.071901
extracurricular_activities0.070979
depression0.068497
safety0.064523
headache0.063609
social_support0.062342
future_career_concerns0.059891
peer_pressure0.040894
bullying0.040507
basic_needs0.040145
self_esteem0.034302
academic_performance0.029969
living_conditions0.015827
study_load0.012825
noise_level0.012232
breathing_problem0.010159
mental_health_history0.004917
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"pd\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": 0,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.030518664861581304,\n \"min\": 0.004916585337590592,\n \"max\": 0.10756072108675069,\n \"num_unique_values\": 20,\n \"samples\": [\n 0.10756072108675069,\n 0.012231850927765839,\n 0.01582728683961223\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":96}],"source":["pd.DataFrame(rfscale_model.feature_importances_,index=rfscale_X_train.columns).sort_values(by=0,ascending=False)"]},{"cell_type":"code","execution_count":97,"metadata":{"id":"YBEJs3QnwMj1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1716217328239,"user_tz":300,"elapsed":132,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"06372a63-7d7c-4e0a-841a-f5116e95dd5f"},"outputs":[{"output_type":"stream","name":"stdout","text":[" precision recall f1-score support\n","\n"," 0 0.88 0.85 0.86 33\n"," 1 0.89 0.94 0.92 35\n"," 2 0.90 0.88 0.89 42\n","\n"," accuracy 0.89 110\n"," macro avg 0.89 0.89 0.89 110\n","weighted avg 0.89 0.89 0.89 110\n","\n"]}],"source":["print(classification_report(rfscale_y_test,rfscale_y_pred))"]},{"cell_type":"code","execution_count":98,"metadata":{"id":"CUXP504PxGvF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1716217333079,"user_tz":300,"elapsed":266,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"1ec5f39c-d6fd-425b-8d26-e55053f8d9c8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Random Forest Classifier on Scaled Data Accuracy: 0.8909090909090909\n"]}],"source":["rfscale_accuracy=accuracy_score(rfscale_y_test,rfscale_y_pred)\n","print(\"Random Forest Classifier on Scaled Data Accuracy:\", rfscale_accuracy)"]},{"cell_type":"markdown","metadata":{"id":"ReJi3e1_iMrM"},"source":["### Iteration 4)\n"," * Random Forest Classifier\n"," * Scaled Data\n"," * Grid Search"]},{"cell_type":"code","execution_count":99,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":148120,"status":"ok","timestamp":1716217490960,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"et453sFwiVN0","outputId":"195d5c4f-3b21-4ea0-efca-5def5107c69d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 648 candidates, totalling 3240 fits\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.909 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.919 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.914 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.919 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.914 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.823 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.2s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.919 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.833 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.914 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.909 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.929 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.818 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.919 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.924 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.914 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.2s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.2s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.2s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.914 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.838 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.904 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=2, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.823 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.914 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.919 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.914 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.909 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.828 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.828 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=6, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.833 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.843 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.833 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.909 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.843 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.823 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.838 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.838 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.823 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.899 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.919 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.899 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=10;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.859 total time= 0.2s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=30;, score=0.848 total time= 0.2s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.904 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=10;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.859 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.899 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=4, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=5;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.869 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=10;, score=0.848 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=20;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.869 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=6, n_estimators=30;, score=0.848 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.859 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.874 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.884 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=2, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.884 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=4, n_estimators=30;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.879 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=5;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.889 total time= 0.0s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.874 total time= 0.0s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.894 total time= 0.0s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.864 total time= 0.0s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=10;, score=0.854 total time= 0.0s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=20;, score=0.854 total time= 0.1s\n","[CV 1/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.889 total time= 0.1s\n","[CV 2/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.879 total time= 0.1s\n","[CV 3/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.894 total time= 0.1s\n","[CV 4/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.864 total time= 0.1s\n","[CV 5/5] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=6, min_samples_split=6, n_estimators=30;, score=0.854 total time= 0.1s\n"]}],"source":["rfs_grid=GridSearchCV(rfscale_model,param_grid=rf_grid_search,cv=5,refit=True,verbose=3,scoring='accuracy').fit(rfscale_X_train,rfscale_y_train)"]},{"cell_type":"code","source":[],"metadata":{"id":"w7qX74mZv3QE"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":100,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WAMUBM6I2x_D","outputId":"c53f85fe-f01f-47a8-918d-22096ac01548","executionInfo":{"status":"ok","timestamp":1716217496947,"user_tz":300,"elapsed":124,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Best hyperarameters: {'bootstrap': True, 'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 6, 'n_estimators': 5}\n","Mean Accuracy: 0.897\n"]}],"source":["rfs_best_params=rfs_grid.best_params_\n","rfs_best_estim=rfs_grid.best_estimator_\n","rfs_feat_importance=rfs_best_estim.feature_importances_\n","rfs_average_score=\"{:.3}\".format(rfs_grid.best_score_)\n","print(f\"Best hyperarameters: {rfs_best_params}\")\n","print(f\"Mean Accuracy: {rfs_average_score}\")"]},{"cell_type":"code","execution_count":101,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":677},"executionInfo":{"elapsed":145,"status":"ok","timestamp":1716217499361,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"ppAaygLx3Ast","outputId":"a1cbe908-af73-4e23-dccd-2c504a2b48e2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Importance\n","blood_pressure 0.392671\n","future_career_concerns 0.158427\n","headache 0.151657\n","depression 0.077150\n","mental_health_history 0.028659\n","self_esteem 0.023657\n","study_load 0.021868\n","peer_pressure 0.019715\n","extracurricular_activities 0.016941\n","anxiety_level 0.016355\n","academic_performance 0.015910\n","social_support 0.015030\n","teacher_student_relationship 0.012752\n","living_conditions 0.012636\n","sleep_quality 0.012199\n","noise_level 0.008402\n","safety 0.005576\n","bullying 0.004845\n","breathing_problem 0.002863\n","basic_needs 0.002689"],"text/html":["\n","
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Importance
blood_pressure0.392671
future_career_concerns0.158427
headache0.151657
depression0.077150
mental_health_history0.028659
self_esteem0.023657
study_load0.021868
peer_pressure0.019715
extracurricular_activities0.016941
anxiety_level0.016355
academic_performance0.015910
social_support0.015030
teacher_student_relationship0.012752
living_conditions0.012636
sleep_quality0.012199
noise_level0.008402
safety0.005576
bullying0.004845
breathing_problem0.002863
basic_needs0.002689
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"feature_imp_df","summary":"{\n \"name\": \"feature_imp_df\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"Importance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0923763262390698,\n \"min\": 0.002688586453395249,\n \"max\": 0.392670884121611,\n \"num_unique_values\": 20,\n \"samples\": [\n 0.392670884121611,\n 0.004845373651420897,\n 0.008401890622603202\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":101}],"source":["feat_imp_col_names=rfscale_X.columns\n","feature_imp_df=pd.DataFrame(rfs_feat_importance,index=feat_imp_col_names,columns=['Importance'])\n","feature_imp_df=feature_imp_df.sort_values(by='Importance',ascending=False)\n","feature_imp_df"]},{"cell_type":"code","source":["rfs_model=rfs_best_estim.fit(rfscale_X_train,rfscale_y_train)\n","\n","rfscale_y_train_pred=rfscale_model.predict(rfscale_X_train)\n","rfs_y_pred=rfc_model.predict(rfscale_X_test)\n","rfs_train_accuracy=accuracy_score(rfscale_y_train,rfscale_y_train_pred).round(4)\n","rfs_accuracy=accuracy_score(rfscale_y_test,rfscale_y_pred).round(4)\n","print('Standard Scaling, Tuned Model\\n')\n","print(rfs_train_accuracy)\n","print(rfs_accuracy)\n","print()\n","print(classification_report(rfscale_y_test,rfs_y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FGn_9LJJwHcE","executionInfo":{"status":"ok","timestamp":1716217505648,"user_tz":300,"elapsed":226,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"e3bf8a74-b97b-4408-b5a9-ae5f26ac46bc"},"execution_count":102,"outputs":[{"output_type":"stream","name":"stdout","text":["Standard Scaling, Tuned Model\n","\n","1.0\n","0.8909\n","\n"," precision recall f1-score support\n","\n"," 0 0.96 0.82 0.89 33\n"," 1 0.80 0.91 0.85 35\n"," 2 0.93 0.93 0.93 42\n","\n"," accuracy 0.89 110\n"," macro avg 0.90 0.89 0.89 110\n","weighted avg 0.90 0.89 0.89 110\n","\n"]}]},{"cell_type":"markdown","metadata":{"id":"ZQCFJmwI-2kM"},"source":["# K Neighbor"]},{"cell_type":"markdown","metadata":{"id":"53-Av8bH3Vhr"},"source":["### Iteration 5)\n"," * Scaled Data Only for KNN"]},{"cell_type":"code","execution_count":103,"metadata":{"id":"ZIamysX3EcCE","executionInfo":{"status":"ok","timestamp":1716217510339,"user_tz":300,"elapsed":168,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["from sklearn.neighbors import KNeighborsClassifier\n"]},{"cell_type":"code","execution_count":104,"metadata":{"id":"oXrlTYxq3Rim","executionInfo":{"status":"ok","timestamp":1716217512054,"user_tz":300,"elapsed":538,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"outputs":[],"source":["knn_df=df\n","knn_X=df.drop('stress_level',axis=1)\n","knn_y=df['stress_level']\n","\n","knn_X_train, knn_X_test,knn_y_train, knn_y_test = train_test_split(knn_X,knn_y, test_size=.3,random_state=43)\n","knn_scaler=StandardScaler()\n","knn_strain=knn_scaler.fit_transform(knn_X_train)\n","knn_stest=knn_scaler.transform(knn_X_test)\n","\n","knn=KNeighborsClassifier(n_neighbors=3)\n","knn.fit(knn_X_train,knn_y_train)\n","\n","knn_y_pred=knn.predict(knn_X_test)\n","knn_y_pred_train=knn.predict(knn_X_train)\n"]},{"cell_type":"code","execution_count":105,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":124,"status":"ok","timestamp":1716217517133,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"RIl5EfzZZC0D","outputId":"501e6d32-cf2b-431b-e54f-7e3116179c56"},"outputs":[{"output_type":"stream","name":"stdout","text":["KNN Accuracy Score: Test: 0.8818181818181818\n","KNN Accuracy Score: Tran 0.9090909090909091\n"]}],"source":["accuracy=accuracy_score(knn_y_test,knn_y_pred)\n","\n","print(\"KNN Accuracy Score: Test:\",accuracy)\n","print(\"KNN Accuracy Score: Tran\",accuracy_score(knn_y_train,knn_y_pred_train))"]},{"cell_type":"markdown","source":[],"metadata":{"id":"5an525VPyk8j"}},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.neighbors import RadiusNeighborsClassifier\n","\n","from sklearn.model_selection import GridSearchCV\n","\n","from sklearn import metrics\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import roc_auc_score\n","\n","from sklearn.preprocessing import StandardScaler\n","\n"],"metadata":{"id":"aRC_3UfZ5B0A","executionInfo":{"status":"ok","timestamp":1716217563991,"user_tz":300,"elapsed":342,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}}},"execution_count":106,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import GridSearchCV\n","k_range = list(range(1, 31))\n","param_grid = dict(n_neighbors=k_range)\n","\n","# defining parameter range\n","grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', return_train_score=False,verbose=1)\n","\n","# fitting the model for grid search\n","grid_search=grid.fit(knn_X_train, knn_y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_Br5qUzr5Bwo","executionInfo":{"status":"ok","timestamp":1716217571558,"user_tz":300,"elapsed":6102,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"2e732ad7-4e7f-4c37-ff89-34a3596b43da"},"execution_count":107,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 10 folds for each of 30 candidates, totalling 300 fits\n"]}]},{"cell_type":"code","source":["print(grid_search.best_params_)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wtLCtBTJ5BuA","executionInfo":{"status":"ok","timestamp":1716217575401,"user_tz":300,"elapsed":134,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"35f258e9-105d-45cd-9423-f6f6b2024ba3"},"execution_count":108,"outputs":[{"output_type":"stream","name":"stdout","text":["{'n_neighbors': 12}\n"]}]},{"cell_type":"code","source":["accuracy = grid_search.best_score_ *100\n","print(\"Accuracy for our training dataset with tuning is : {:.2f}%\".format(accuracy) )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8MtldC745Bq7","executionInfo":{"status":"ok","timestamp":1716217577366,"user_tz":300,"elapsed":157,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"57eeaf0c-7221-4249-87d6-eaa108f6b532"},"execution_count":109,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy for our training dataset with tuning is : 88.44%\n"]}]},{"cell_type":"code","execution_count":110,"metadata":{"id":"bY4P4dNu8LE9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1716217579607,"user_tz":300,"elapsed":3,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"}},"outputId":"234b8930-9412-46b9-c958-fc60c9cbdbbf"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy for our testing dataset with tuning is : 87.58%\n"]}],"source":["knn = KNeighborsClassifier(n_neighbors=26)\n","\n","knn.fit(knn_X,knn_y)\n","\n","y_test_hat=knn.predict(knn_X_test)\n","\n","test_accuracy=accuracy_score(knn_y_test,y_test_hat)*100\n","\n","print(\"Accuracy for our testing dataset with tuning is : {:.2f}%\".format(test_accuracy) )"]},{"cell_type":"markdown","source":["## TEST SPLIT RATIO\n"," * Each of these was tested and trained at a 70/30, 80/20, 90/10 ratio. Upon review of the results, it was determined that the 90/10 ratio on the Unscaled Grid Search Random Forest Classifier model gave us the most accurate projection."],"metadata":{"id":"jzswBcg6HTlX"}},{"cell_type":"code","source":[],"metadata":{"id":"rhskZ34-G92v"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"P4f00jxJ8MEK"},"source":["# Feature Importance Visual"]},{"cell_type":"code","execution_count":113,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"executionInfo":{"elapsed":1485,"status":"ok","timestamp":1716217741446,"user":{"displayName":"Daniel Meier","userId":"06365571306976914087"},"user_tz":300},"id":"-tU2E0gR8gON","outputId":"ea510729-078b-46d1-fc6a-480cf54c38bb"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Placeholder for final visual (will adjust code once modeling is done to pull from the right variables)\n","\n","\n","indices = np.argsort(rf_feat_importance)[::-1]\n","\n","plt.figure(figsize=(10, 6))\n","sns.barplot(x=rf_feat_importance[indices], y=rfc_X.columns[indices], palette=\"crest\",)\n","plt.title('Feature Importance related to Stress Level')\n","plt.ylabel(\"Features\")\n","plt.xlabel(\"Importance Level\")\n","plt.show()\n"]},{"cell_type":"code","source":[],"metadata":{"id":"fShzDxexmY4y"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Energy b/Energy deleted file mode 100644 index 0ddf45b..0000000 --- a/Energy +++ /dev/null @@ -1 +0,0 @@ -https://github.com/0verby/DSP.git/energy diff --git a/Energy.html b/Energy.html new file mode 100644 index 0000000..216a5bc --- /dev/null +++ b/Energy.html @@ -0,0 +1,14 @@ +https://github.com/0verby/DSP.git/energy + + + + + Learning + + + + all about html + + + + diff --git a/EnergyConsumptionPresentation.pptx b/EnergyConsumptionPresentation.pp.html similarity index 100% rename from EnergyConsumptionPresentation.pptx rename to EnergyConsumptionPresentation.pp.html diff --git a/EnergyProject b/EnergyProject deleted file mode 100644 index 0249b39..0000000 --- a/EnergyProject +++ /dev/null @@ -1,52 +0,0 @@ -# DSP -Data Science Projects - Bellevue University - -## **APPLIED DATA SCIENCE** - -This page is devoted to my final applied data science project class at Bellevue University. Featured on this page will include copies of my projects and resume. - -## **ABOUT ME** -I decided halfway through my career life to change the course and explore data more. The decision was based on a love for cleaning up databases and exploring and creating reports for others to understand the information more efficiently. This lead to attending Bellevue University for a bachelor's in Data Science. While proficient in word and excel from working for almost 20 years as an administrative assistant, I had very very little experience in any kind of computer language or coding experience. I am now in my last term at Bellevue and will earn my bachelor's degree shortly. - -## **PROJECTS DURING COURSE** -
    -
  • ENERGY CONSUMPTION PREDICTIVE MODEL
    • -
      -
      Source
      -
        - Available from Kaggle.com
      -
        - Energy-Consumption-Prediction
      - -
      Purpose
      -
        - What are the main factors influencing energy consumption?
      -
        - Can we predict future enegy consumption in buildins using historical data?
      - -
      Outcome
      -
        - I was tasked with the data wrangling in this project.
      -
        - Utilize our model as a predictive engine
      - -
      Reflection
      -
        - More analysis is needed to determine additional relevent features.
      -
        - Experiment more with different model types and hyperparameters.
      - -
    -

    -
  • DATA SALARY PREDICTIVE MODEL
    • -
      -
      Source
      -
      Purpose
      -
      Outcome
      -
      Reflection
      -
    -

    -
  • STRESS LEVEL PREDICTIVE MODEL
    • -
      -
      Source
      -
      Purpose
      -
      Outcome
      -
      Reflection
      -
    -

    -
- -## **SEND A MESSAGE** -smurf_62@hotmail.com diff --git a/README.md b/README.md index 0249b39..9698171 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ +gh repo clone pages-themes/slate # DSP Data Science Projects - Bellevue University - + ## **APPLIED DATA SCIENCE** This page is devoted to my final applied data science project class at Bellevue University. Featured on this page will include copies of my projects and resume. @@ -10,7 +11,7 @@ I decided halfway through my career life to change the course and explore data m ## **PROJECTS DURING COURSE**
    -
  • ENERGY CONSUMPTION PREDICTIVE MODEL
    • +
  • ENERGY CONSUMPTION PREDICTIVE MODEL
    • Energy

      Source
        - Available from Kaggle.com
      @@ -30,15 +31,27 @@ I decided halfway through my career life to change the course and explore data m


    -
  • DATA SALARY PREDICTIVE MODEL
    • +
  • DATA SALARY PREDICTIVE MODEL
    • Salary
      -
      Source
      +
      Source Salary +
      +
      Purpose
      +
        - What are the main factors influencing energy consumption?
      +
        - Can we predict future enegy consumption in buildins using historical data?
      + +
      Outcome
      +
        - I was tasked with the data wrangling in this project.
      +
        - Utilize our model as a predictive engine
      + +
      Reflection
      +
        - More analysis is needed to determine additional relevent features.
      +
        - Experiment more with different model types and hyperparameters.

      Purpose

      Outcome

      Reflection


    -
  • STRESS LEVEL PREDICTIVE MODEL
    • +
  • STRESS LEVEL PREDICTIVE MODEL
    • Stress

      Source

      Purpose
      diff --git a/Salary b/Salary new file mode 100644 index 0000000..4654cd6 --- /dev/null +++ b/Salary @@ -0,0 +1 @@ +Salary holder diff --git a/Student_Stress_Levels.csv b/Student_Stress_Levels.csv new file mode 100644 index 0000000..9987584 --- /dev/null +++ b/Student_Stress_Levels.csv @@ -0,0 +1,1101 @@ +anxiety_level,self_esteem,mental_health_history,depression,headache,blood_pressure,sleep_quality,breathing_problem,noise_level,living_conditions,safety,basic_needs,academic_performance,study_load,teacher_student_relationship,future_career_concerns,social_support,peer_pressure,extracurricular_activities,bullying,stress_level +"$1,4)",($20),0,11,2,1,2,4,2,3,3,2,3,2,3,3,2,3,3,2,1 +15,8,1,15,5,3,1,4,3,1,2,2,1,4,1,5,1,4,5,5,2 +12,18,1,14,2,1,2,2,2,2,3,2,2,3,3,2,2,3,2,2,1 +16,12,1,15,4,3,1,3,4,2,2,2,2,4,1,4,1,4,4,5,2 +16,28,0,7,2,3,5,1,3,2,4,3,4,3,1,2,1,5,0,5,1 +20,13,1,21,3,3,1,4,3,2,2,1,2,5,2,5,1,4,4,5,2 +4,26,0,6,1,2,4,1,1,4,4,4,5,1,4,1,3,2,2,1,0 +17,3,1,22,4,3,1,5,3,1,1,1,1,3,2,4,1,4,4,5,2 +13,22,1,12,3,1,2,4,3,3,3,3,3,3,2,3,3,3,2,2,1 +6,8,0,27,4,3,1,2,0,5,2,2,2,2,1,5,1,5,3,4,1 +17,$12 ,1,25,4,3,1,3,4,2,1,1,1,3,1,4,1,4,4,5,2 +17,15,1,"($2,2)",3,3,1,5,5,2,1,1,1,3,1,4,1,5,5,4,2 +5,28,0,8,1,2,4,2,2,3,5,5,5,2,4,1,3,1,1,1,0 +9,23,1,24,4,3,1,0,1,2,4,3,1,2,3,3,0,1,0,1,2 +2,28,0,3,1,2,4,2,1,3,4,4,4,2,5,1,3,1,2,1,0 +11,21,0,14,3,1,2,4,2,2,2,2,3,3,3,3,2,3,2,2,1 +6,28,0,1,1,2,4,2,1,4,5,4,5,1,5,1,3,2,2,1,0 +7,25,0,3,1,2,4,2,2,4,5,4,4,2,5,1,3,1,1,1,0 +11,($23),0,12,3,1,2,2,3,2,3,3,2,3,2,2,3,3,2,3,1 +21,1,1,25,4,3,1,4,4,1,2,1,1,5,2,5,1,4,4,5,2 +3,27,0,0,1,2,4,1,1,3,5,4,5,2,5,1,3,1,2,1,0 +18,1,1,21,4,3,1,3,5,1,1,2,2,5,1,4,1,4,4,5,2 +7,27,0,5,1,2,4,1,1,3,5,5,4,2,5,1,3,1,2,1,0 +20,5,1,"($2,6",3,3,1,4,4,2,1,2,1,3,1,4,1,5,4,4,2 +13,$21 ,1,14,3,1,2,2,3,2,2,3,3,2,3,2,3,3,3,2,1 +6,26,0,8,1,2,5,2,2,4,5,4,4,1,4,1,3,2,1,1,0 +18,6,1,27,5,3,1,5,3,2,2,1,1,3,1,4,1,5,5,4,2 +7,28,0,20,2,3,3,1,5,1,2,5,4,0,2,2,1,1,2,2,0 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b/salary_model.csv @@ -0,0 +1,14200 @@ + work_year ,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,work_setting,company_location,company_size,job_category +2024,Entry-level,Freelance,Applied Data Scientist,30000,USD,30000,United Kingdom,Remote,United Kingdom,M,Data Science and Research +2024,Executive,Full-time,Business Intelligence,230000,USD,230000,United States,In-person,United States,M,BI and Visualization +2024,Executive,Full-time,Business Intelligence,176900,USD,176900,United States,In-person,United States,M,BI and Visualization +2024,Senior,Full-time,Data Architect,171210,USD,171210,Canada,In-person,Canada,M,Data Architecture and Modeling +2024,Senior,Full-time,Data Architect,92190,USD,92190,Canada,In-person,Canada,M,Data Architecture and Modeling +2024,Mid-level,Full-time,Data Science,46203,GBP,57753,United Kingdom,In-person,United Kingdom,M,Data Science and Research +2024,Mid-level,Full-time,Data Science,38280,GBP,47850,United Kingdom,In-person,United Kingdom,M,Data Science and Research +2024,Entry-level,Full-time,Insight Analyst,50000,USD,50000,United States,Remote,United States,M,Data Analysis +2024,Entry-level,Full-time,Insight Analyst,40000,USD,40000,United States,Remote,United States,M,Data Analysis +2024,Senior,Full-time,Data Engineer,276000,USD,276000,United States,In-person,United States,M,Data Engineering +2024,Senior,Full-time,Data Engineer,148000,USD,148000,United States,In-person,United States,M,Data Engineering +2024,Senior,Full-time,Research Scientist,234000,USD,234000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Research Scientist,146000,USD,146000,United States,In-person,United States,M,Data Science and Research +2024,Entry-level,Full-time,Business Intelligence Analyst,192300,USD,192300,United States,In-person,United States,M,BI and Visualization +2024,Entry-level,Full-time,Business Intelligence Analyst,120200,USD,120200,United States,In-person,United States,M,BI and Visualization +2024,Entry-level,Full-time,Analytics Engineer,132500,USD,132500,United States,In-person,United States,M,Leadership and Management +2024,Entry-level,Full-time,Analytics Engineer,111500,USD,111500,United States,In-person,United States,M,Leadership and Management +2024,Entry-level,Fulltime,Data Engineer,234000,USD,234000,United States,In-person,United States,M,Data Engineering +2024,Entry-level,Full-time,Data Engineer,146000,USD,146000,United States,In-person,United States,M,Data Engineering +2024,Senior,Full-time,Data Scientist,,USD,322000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,168000,USD,168000,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,152000,USD,152000,Canada,In-person,Canada,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,98000,USD,98000,Canada,In-person,Canada,M,Data Science and Research +2024,Senior,Full-time,Research Engineer,138000,USD,138000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Research Engineer,86000,USD,86000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Science,268700,USD,268700,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Science,130300,USD,130300,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,BI Developer,60000,GBP,75000,United Kingdom,In-person,United Kingdom,M,BI and Visualization +2024,Senior,Full-time,BI Developer,40000,GBP,50000,United Kingdom,In-person,United Kingdom,M,BI and Visualization +2024,Senior,Full-time,Data Analyst,115000,USD,115000,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Data Analyst,105000,USD,105000,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Data Scientist,80000,GBP,100000,United Kingdom,In-person,United Kingdom,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,70000,GBP,87500,United Kingdom,In-person,United Kingdom,M,Data Science and Research +2024,Senior,Full-time,Business Intelligence Engineer,156476,USD,156476,United States,Remote,United States,M,BI and Visualization +2024,Senior,Full-time,Business Intelligence Engineer,104981,USD,104981,United States,Remote,United States,M,BI and Visualization +2024,Mid-level,Full-time,Data Engineer,146000,USD,146000,United States,Remote,United States,M,Data Engineering +2024,Mid-level,Full-time,Data Engineer,110000,USD,110000,United States,Remote,United States,M,Data Engineering +2024,Senior,Full-time,Data Scientist,297000,USD,297000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,138000,USD,138000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Quality Engineer,190000,USD,190000,United States,In-person,United States,M,Data Quality and Operations +2024,Senior,Full-time,Data Quality Engineer,25000,USD,25000,United States,In-person,United States,M,Data Quality and Operations +2024,Mid-level,Full-time,Machine Learning Engineer,160480,USD,160480,United States,In-person,United States,M,Machine Learning and AI +2024,Mid-level,Full-time,Machine Learning Engineer,109920,USD,109920,United States,In-person,United States,M,Machine Learning and AI +2024,Senior,Full-time,Data Engineer,224000,USD,224000,United States,In-person,United States,M,Data Engineering +2024,Senior,Full-time,Data Engineer,150000,USD,150000,United States,In-person,United States,M,Data Engineering +2024,Entry-level,Full-time,Data Analyst,84000,USD,84000,United States,In-person,United States,M,Data Analysis +2024,Entry-level,Full-time,Data Analyst,75800,USD,75800,United States,In-person,United States,M,Data Analysis +2024,Mid-level,Full-time,Cloud Database Engineer,146125,USD,146125,United States,Remote,United States,M,Cloud and Database +2024,Mid-level,Full-time,Cloud Database Engineer,82000,USD,82000,United States,Remote,United States,M,Cloud and Database +2024,Mid-level,Full-time,Business Intelligence,207600,USD,207600,United States,In-person,United States,M,BI and Visualization +2024,Mid-level,Full-time,Business Intelligence,138400,USD,138400,United States,In-person,United States,M,BI and Visualization +2024,Entry-level,Full-time,Data Analyst,94600,USD,94600,United States,In-person,United States,M,Data Analysis +2024,Entry-level,Full-time,Data Analyst,75700,USD,75700,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Data Science,331292,USD,331292,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Science,244868,USD,244868,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,190000,USD,190000,United States,Remote,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,155000,USD,155000,United States,Remote,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Engineer,168000,USD,168000,United States,In-person,United States,M,Data Engineering +2024,Mid-level,Full-time,Data Engineer,158000,USD,158000,United States,In-person,United States,M,Data Engineering +2024,Executive,Full-time,Head of Data,243000,USD,243000,United States,In-person,United States,M,Leadership and Management +2024,Executive,Full-time,Head of Data,163000,USD,163000,United States,In-person,United States,M,Leadership and Management +2024,Senior,Full-time,Data Scientist,190000,USD,190000,United States,Remote,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,155000,USD,155000,United States,Remote,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,170000,USD,170000,United States,Remote,United States,M,Data Science and Research +2024,Senior,Full-time,Machine Learning Engineer,112800,EUR,125333,Lithuania,In-person,Lithuania,M,Machine Learning and AI +2024,Senior,Full-time,Machine Learning Engineer,73200,EUR,81333,Lithuania,In-person,Lithuania,M,Machine Learning and AI +2024,Senior,Full-time,Business Intelligence,192500,USD,192500,United States,In-person,United States,M,BI and Visualization +2024,Senior,Full-time,Business Intelligence,134900,USD,134900,United States,In-person,United States,M,BI and Visualization +2024,Senior,Full-time,Data Scientist,227000,USD,227000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,192780,USD,192780,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,BI Analyst,85000,USD,85000,United States,Remote,United States,M,BI and Visualization +2024,Mid-level,Full-time,BI Analyst,70000,USD,70000,United States,Remote,United States,M,BI and Visualization +2024,Senior,Full-time,Analytics Engineer,180000,USD,180000,United States,In-person,United States,M,Leadership and Management +2024,Senior,Full-time,Analytics Engineer,112000,USD,112000,United States,In-person,United States,M,Leadership and Management +2024,Senior,Full-time,Data Engineer,130000,USD,130000,United States,In-person,United States,M,Data Engineering +2024,Senior,Full-time,Data Engineer,100000,USD,100000,United States,In-person,United States,M,Data Engineering +2024,Senior,Full-time,Data Engineer,140000,USD,140000,United States,Remote,United States,M,Data Engineering +2024,Senior,Full-time,Data Engineer,100000,USD,100000,United States,Remote,United States,M,Data Engineering +2024,Mid-level,Full-time,Data Scientist,175000,USD,175000,United States,Remote,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,135000,USD,135000,United States,Remote,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Manager,110000,USD,110000,United States,In-person,United States,M,Leadership and Management +2024,Mid-level,Full-time,Data Manager,70000,USD,70000,United States,In-person,United States,M,Leadership and Management +2024,Senior,Full-time,Computational Biologist,236000,USD,236000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Computational Biologist,174000,USD,174000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Computational Biologist,242000,CAD,186153,Canada,In-person,Canada,M,Data Science and Research +2024,Senior,Full-time,Computational Biologist,215000,CAD,165384,Canada,In-person,Canada,M,Data Science and Research +2024,Senior,Full-time,Data Architect,363000,USD,363000,United States,In-person,United States,M,Data Architecture and Modeling +2024,Senior,Full-time,Data Architect,139800,USD,139800,United States,In-person,United States,M,Data Architecture and Modeling +2024,Senior,Full-time,Research Scientist,345000,USD,345000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Research Scientist,180000,USD,180000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Analytics Engineer,170000,USD,170000,United States,Remote,United States,M,Leadership and Management +2024,Senior,Full-time,Analytics Engineer,130000,USD,130000,United States,Remote,United States,M,Leadership and Management +2024,Mid-level,Full-time,Data Integration Specialist,75000,GBP,93750,United Kingdom,Remote,United Kingdom,M,Data Management and Strategy +2024,Mid-level,Full-time,Data Integration Specialist,55000,GBP,68750,United Kingdom,Remote,United Kingdom,M,Data Management and Strategy +2024,Senior,Full-time,Machine Learning Engineer,184800,USD,184800,Canada,Remote,Canada,M,Machine Learning and AI +2024,Senior,Full-time,Machine Learning Engineer,123200,USD,123200,Canada,Remote,Canada,M,Machine Learning and AI +2024,Senior,Full-time,Data Engineer,115000,USD,115000,United States,Remote,United States,M,Data Engineering +2024,Senior,Full-time,Data Engineer,105000,USD,105000,United States,Remote,United States,M,Data Engineering +2024,Mid-level,Full-time,Data Science Manager,110000,USD,110000,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Science Manager,70000,USD,70000,United States,In-person,United States,M,Data Science and Research +2024,Entry-level,Full-time,Machine Learning Engineer,157900,USD,157900,Canada,Remote,Canada,M,Machine Learning and AI +2024,Entry-level,Full-time,Machine Learning Engineer,105300,USD,105300,Canada,Remote,Canada,M,Machine Learning and AI +2024,Mid-level,Full-time,Data Scientist,252000,USD,252000,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,168000,USD,168000,United States,In-person,United States,M,Data Science and Research +2024,Executive,Full-time,AI Engineer,225000,USD,225000,United States,In-person,United States,M,Machine Learning and AI +2024,Executive,Full-time,AI Engineer,150000,USD,150000,United States,In-person,United States,M,Machine Learning and AI +2024,Mid-level,Full-time,Data Engineer,150000,USD,150000,United States,Remote,United States,M,Data Engineering +2024,Mid-level,Full-time,Data Engineer,90000,USD,90000,United States,Remote,United States,M,Data Engineering +2024,Mid-level,Full-time,Data Analyst,150000,USD,150000,United States,In-person,United States,M,Data Analysis +2024,Mid-level,Full-time,Data Analyst,117000,USD,117000,United States,In-person,United States,M,Data Analysis +2024,Mid-level,Full-time,Research Engineer,440000,USD,440000,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Research Engineer,295000,USD,295000,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,70000,GBP,87500,United Kingdom,In-person,United Kingdom,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,50000,GBP,62500,United Kingdom,In-person,United Kingdom,M,Data Science and Research +2024,Mid-level,Full-time,Data Analyst,150000,USD,150000,United States,Remote,United States,M,Data Analysis +2024,Mid-level,Full-time,Data Analyst,120000,USD,120000,United States,Remote,United States,M,Data Analysis +2024,Mid-level,Full-time,Data Engineer,148000,USD,148000,United States,In-person,United States,M,Data Engineering +2024,Mid-level,Full-time,Data Engineer,106500,USD,106500,United States,In-person,United States,M,Data Engineering +2024,Senior,Full-time,Applied Scientist,209000,USD,209000,Poland,Remote,Poland,M,Data Science and Research +2024,Senior,Full-time,Applied Scientist,155000,USD,155000,Poland,Remote,Poland,M,Data Science and Research +2024,Senior,Full-time,AI Engineer,210000,USD,210000,United States,In-person,United States,M,Machine Learning and AI +2024,Senior,Full-time,AI Engineer,180000,USD,180000,United States,In-person,United States,M,Machine Learning and AI +2024,Senior,Full-time,Data Scientist,170000,USD,170000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,130000,USD,130000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,70000,GBP,87500,United Kingdom,In-person,United Kingdom,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,50000,GBP,62500,United Kingdom,In-person,United Kingdom,M,Data Science and Research +2024,Senior,Full-time,Data Engineer,220000,USD,220000,Canada,In-person,Canada,M,Data Engineering +2024,Senior,Full-time,Data Engineer,180000,USD,180000,Canada,In-person,Canada,M,Data Engineering +2024,Senior,Full-time,Machine Learning Engineer,278700,USD,278700,United States,In-person,United States,M,Machine Learning and AI +2024,Senior,Full-time,Machine Learning Engineer,159300,USD,159300,United States,In-person,United States,M,Machine Learning and AI +2024,Senior,Full-time,Data Analyst,145000,USD,145000,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Data Analyst,110000,USD,110000,United States,In-person,United States,M,Data Analysis +2024,Entry-level,Full-time,Data Science,51550,EUR,57277,France,Hybrid,France,M,Data Science and Research +2024,Senior,Full-time,BI Data Analyst,60000,EUR,66666,Germany,Hybrid,Germany,S,Data Analysis +2024,Mid-level,Full-time,Analytics Engineer,126000,USD,126000,United States,In-person,United States,M,Leadership and Management +2024,Mid-level,Full-time,Analytics Engineer,78000,USD,78000,United States,In-person,United States,M,Leadership and Management +2024,Senior,Full-time,Research Scientist,290000,USD,290000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Research Scientist,240000,USD,240000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Machine Learning Engineer,210000,USD,210000,United States,In-person,United States,M,Machine Learning and AI +2024,Senior,Full-time,Machine Learning Engineer,178500,USD,178500,United States,In-person,United States,M,Machine Learning and AI +2024,Senior,Full-time,AI Research Scientist,150000,USD,150000,Saudi Arabia,Hybrid,Saudi Arabia,M,Data Science and Research +2024,Executive,Full-time,Machine Learning Engineer,290000,USD,290000,United States,In-person,United States,M,Machine Learning and AI +2024,Executive,Full-time,Machine Learning Engineer,232000,USD,232000,United States,In-person,United States,M,Machine Learning and AI +2024,Senior,Full-time,Research Scientist,234000,USD,234000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Research Scientist,146000,USD,146000,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Engineer,172400,USD,172400,United States,In-person,United States,M,Data Engineering +2024,Mid-level,Full-time,Data Engineer,109000,USD,109000,United States,In-person,United States,M,Data Engineering +2024,Entry-level,Full-time,Data Analyst,88800,USD,88800,United States,In-person,United States,M,Data Analysis +2024,Entry-level,Full-time,Data Analyst,64600,USD,64600,United States,In-person,United States,M,Data Analysis +2024,Mid-level,Full-time,MLOps Engineer,156200,USD,156200,Canada,In-person,Canada,M,Machine Learning and AI +2024,Mid-level,Full-time,MLOps Engineer,106500,USD,106500,Canada,In-person,Canada,M,Machine Learning and AI +2024,Senior,Full-time,Data Scientist,185900,USD,185900,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Data Scientist,109400,USD,109400,United States,In-person,United States,M,Data Science and Research +2024,Entry-level,Full-time,Research Analyst,106000,USD,106000,United States,In-person,United States,M,Data Science and Research +2024,Entry-level,Full-time,Research Analyst,66000,USD,66000,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Machine Learning Engineer,234300,USD,234300,United States,In-person,United States,M,Machine Learning and AI +2024,Senior,Full-time,Machine Learning Engineer,96900,USD,96900,United States,In-person,United States,M,Machine Learning and AI +2024,Entry-level,Full-time,Data Analyst,157600,USD,157600,United States,In-person,United States,M,Data Analysis +2024,Entry-level,Full-time,Data Analyst,96800,USD,96800,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Research Engineer,339250,USD,339250,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Research Engineer,180000,USD,180000,United States,In-person,United States,M,Data Science and Research +2024,Entry-level,Full-time,Admin & Data Analyst,40000,USD,40000,India,Remote,United States,M,Data Analysis +2024,Entry-level,Full-time,Data Analyst,70000,USD,70000,United States,In-person,United States,M,Data Analysis +2024,Entry-level,Full-time,Data Analyst,50000,USD,50000,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Research Analyst,94600,USD,94600,United States,In-person,United States,M,Data Science and Research +2024,Senior,Full-time,Research Analyst,77400,USD,77400,United States,In-person,United States,M,Data Science and Research +2024,Entry-level,Full-time,Data Analyst,192500,USD,192500,United States,In-person,United States,M,Data Analysis +2024,Entry-level,Full-time,Data Analyst,140000,USD,140000,United States,In-person,United States,M,Data Analysis +2024,Mid-level,Full-time,Data Scientist,154500,USD,154500,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,75705,USD,75705,United States,In-person,United States,M,Data Science and Research +2024,Entry-level,Full-time,Business Intelligence Analyst,95000,USD,95000,United States,In-person,United States,M,BI and Visualization +2024,Entry-level,Full-time,Business Intelligence Analyst,85000,USD,85000,United States,In-person,United States,M,BI and Visualization +2024,Mid-level,Full-time,Business Intelligence Manager,164700,USD,164700,United States,In-person,United States,M,BI and Visualization +2024,Mid-level,Full-time,Business Intelligence Manager,117500,USD,117500,United States,In-person,United States,M,BI and Visualization +2024,Entry-level,Full-time,Data Analyst,80000,USD,80000,United States,In-person,United States,M,Data Analysis +2024,Entry-level,Full-time,Data Analyst,45700,USD,45700,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Data Analyst,117000,USD,117000,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Data Analyst,115765,USD,115765,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Data Analyst,140000,USD,140000,Canada,In-person,Canada,M,Data Analysis +2024,Senior,Full-time,Data Analyst,107000,USD,107000,Canada,In-person,Canada,M,Data Analysis +2024,Senior,Full-time,Machine Learning Engineer,178250,USD,178250,United States,Remote,United States,M,Machine Learning and AI +2024,Senior,Full-time,Machine Learning Engineer,105500,USD,105500,United States,Remote,United States,M,Machine Learning and AI +2024,Senior,Full-time,Data Developer,50000,GBP,62500,United Kingdom,In-person,United Kingdom,M,Leadership and Management +2024,Senior,Full-time,Data Developer,50000,GBP,62500,United Kingdom,In-person,United Kingdom,M,Leadership and Management +2024,Senior,Full-time,Prompt Engineer,197011,USD,197011,United States,Remote,United States,M,Machine Learning and AI +2024,Senior,Full-time,Prompt Engineer,115889,USD,115889,United States,Remote,United States,M,Machine Learning and AI +2024,Mid-level,Full-time,Research Scientist,138000,USD,138000,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Research Scientist,86000,USD,86000,United States,In-person,United States,M,Data Science and Research 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States,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,73100,USD,73100,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,212000,USD,212000,Egypt,In-person,Egypt,M,Data Science and Research +2024,Mid-level,Full-time,Data Scientist,93300,USD,93300,Egypt,In-person,Egypt,M,Data Science and Research +2024,Senior,Full-time,Data Analyst,96600,USD,96600,United States,In-person,United States,M,Data Analysis +2024,Senior,Full-time,Data Analyst,55200,USD,55200,United States,In-person,United States,M,Data Analysis +2024,Mid-level,Full-time,Data Science,132600,USD,132600,United States,In-person,United States,M,Data Science and Research +2024,Mid-level,Full-time,Data Science,80845,USD,80845,United States,In-person,United States,M,Data Science and Research +2024,Entry-level,Full-time,Data Scientist,144200,USD,144200,United States,In-person,United States,M,Data Science and Research +2024,Entry-level,Full-time,Data 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Science,85000,USD,85000,Russian Federation,In-person,Russian Federation,M,Data Science and Research +2021,Executive,Full-time,Head of Data,230000,USD,230000,Russian Federation,Hybrid,Russian Federation,L,Leadership and Management +2021,Entry-level,Full-time,Machine Learning Engineer,125000,USD,125000,United States,Remote,United States,S,Machine Learning and AI +2021,Senior,Full-time,Data Analytics Manager,120000,USD,120000,United States,Remote,United States,M,Leadership and Management +2020,Mid-level,Full-time,Research Scientist,450000,USD,450000,United States,In-person,United States,M,Data Science and Research +2020,Mid-level,Full-time,Data Analyst,41000,EUR,46759,France,Hybrid,France,L,Data Analysis +2020,Mid-level,Full-time,Data Engineer,65000,EUR,74130,Austria,Hybrid,Austria,L,Data Engineering +2021,Senior,Full-time,Data Science Engineer,159500,CAD,127221,Canada,Hybrid,Canada,L,Data Science and Research +2021,Senior,Full-time,Data Science Manager,144000,USD,144000,United 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Engineer,270000,USD,270000,United States,Remote,United States,L,Machine Learning and AI +2021,Mid-level,Full-time,Applied Data Scientist,68000,CAD,54238,United Kingdom,Hybrid,Canada,L,Data Science and Research +2021,Mid-level,Full-time,Machine Learning Engineer,40000,EUR,47282,Spain,Remote,Spain,S,Machine Learning and AI +2021,Executive,Full-time,Director of Data Science,130000,EUR,153667,Italy,Remote,Poland,L,Data Science and Research +2021,Mid-level,Full-time,Data Engineer,110000,PLN,28476,Poland,Remote,Poland,L,Data Engineering +2021,Mid-level,Full-time,Data Analytics Engineer,110000,USD,110000,United States,Remote,United States,L,Leadership and Management +2021,Entry-level,Full-time,Research Scientist,60000,GBP,82528,United Kingdom,Hybrid,United Kingdom,L,Data Science and Research +2020,Entry-level,Full-time,Machine Learning Engineer,250000,USD,250000,United States,Hybrid,United States,L,Machine Learning and AI +2021,Entry-level,Full-time,Data Analyst,50000,EUR,59102,France,Hybrid,France,M,Data Analysis +2021,Senior,Full-time,Data Analyst,80000,USD,80000,Bulgaria,Remote,United States,S,Data Analysis +2020,Entry-level,Full-time,Machine Learning Engineer,138000,USD,138000,United States,Remote,United States,S,Machine Learning and AI +2021,Mid-level,Full-time,Data Engineer,140000,USD,140000,United States,Remote,United States,L,Data Engineering +2021,Senior,Full-time,Data Analytics Engineer,67000,EUR,79197,Germany,Remote,Germany,L,Leadership and Management +2021,Senior,Full-time,Lead Data Analyst,170000,USD,170000,United States,Remote,United States,L,Data Analysis +2021,Entry-level,Full-time,Data Analyst,80000,USD,80000,United States,Remote,United States,M,Data Analysis +2020,Mid-level,Full-time,Data Scientist,45760,USD,45760,Philippines,Remote,United States,S,Data Science and Research +2021,Mid-level,Full-time,BI Data Analyst,100000,USD,100000,United States,Remote,United States,M,Data Analysis +2021,Senior,Full-time,Data Scientist,45000,EUR,53192,France,Hybrid,France,L,Data Science and Research +2021,Executive,Full-time,Head of Data,235000,USD,235000,United States,Remote,United States,L,Leadership and Management +2021,Executive,Full-time,BI Data Analyst,150000,USD,150000,India,Remote,United States,L,Data Analysis +2020,Executive,Full-time,Data Engineer,70000,EUR,79833,Spain,Hybrid,Spain,L,Data Engineering +2021,Entry-level,Full-time,Machine Learning Scientist,225000,USD,225000,United States,Remote,United States,L,Machine Learning and AI +2021,Entry-level,Full-time,Data Science Consultant,65000,EUR,76833,Germany,Remote,Germany,S,Data Science and Research +2020,Mid-level,Full-time,Machine Learning Infrastructure Engineer,44000,EUR,50180,Portugal,In-person,Portugal,M,Machine Learning and AI +2021,Senior,Full-time,Marketing Data Analyst,75000,EUR,88654,Greece,Remote,Denmark,L,Data Analysis +2021,Senior,Full-time,Lead Data Engineer,75000,GBP,103160,United Kingdom,Remote,United Kingdom,S,Data Engineering +2021,Senior,Full-time,Data Engineer,82500,GBP,113476,United Kingdom,Remote,United Kingdom,M,Data Engineering +2021,Senior,Full-time,Machine Learning Engineer,80000,EUR,94564,Germany,Hybrid,Germany,L,Machine Learning and AI +2021,Senior,Full-time,Data Engineer,150000,USD,150000,United States,Remote,United States,M,Data Engineering +2021,Senior,Full-time,Data Engineer,115000,USD,115000,United States,Remote,United States,S,Data Engineering +2021,Mid-level,Full-time,Research Scientist,235000,CAD,187442,Canada,Remote,Canada,L,Data Science and Research +2021,Mid-level,Full-time,Data Analyst,37456,GBP,51519,United Kingdom,Hybrid,United Kingdom,L,Data Analysis +2020,Mid-level,Full-time,Data Engineer,106000,USD,106000,United States,Remote,United States,L,Data Engineering +2020,Mid-level,Full-time,Data Engineer,88000,GBP,112872,United Kingdom,Hybrid,United Kingdom,L,Data Engineering +2021,Senior,Full-time,Data Engineer,150000,USD,150000,United States,Remote,United States,L,Data Engineering +2020,Entry-level,Part-time,ML Engineer,14000,EUR,15966,Germany,Remote,Germany,S,Machine Learning and AI +2021,Mid-level,Full-time,Computer Vision Software Engineer,81000,EUR,95746,Germany,Remote,United States,S,Machine Learning and AI +2021,Entry-level,Full-time,Computer Vision Software Engineer,70000,USD,70000,United States,Remote,United States,M,Machine Learning and AI +2020,Mid-level,Full-time,Data Scientist,60000,GBP,76958,United Kingdom,Remote,United Kingdom,S,Data Science and Research +2021,Mid-level,Full-time,Cloud Data Engineer,120000,SGD,89294,Singapore,Hybrid,Singapore,L,Data Engineering +2021,Senior,Full-time,Lead Data Engineer,276000,USD,276000,United States,In-person,United States,L,Data Engineering +2020,Senior,Full-time,Data Engineer,188000,USD,188000,United States,Remote,United States,L,Data Engineering +2021,Senior,Full-time,Cloud Data Engineer,160000,USD,160000,Brazil,Remote,United States,S,Data Engineering +2020,Mid-level,Full-time,Data Scientist,105000,USD,105000,United States,Remote,United States,L,Data Science and Research +2021,Mid-level,Full-time,Data Engineer,200000,USD,200000,United States,Remote,United States,L,Data Engineering +2021,Senior,Full-time,Data Engineer,174000,USD,174000,United States,Remote,United States,L,Data Engineering +2021,Mid-level,Full-time,Data Analyst,93000,USD,93000,United States,Remote,United States,L,Data Analysis +2021,Senior,Full-time,Research Scientist,51400,EUR,60757,Portugal,Hybrid,Portugal,L,Data Science and Research +2021,Entry-level,Full-time,Data Scientist,90000,USD,90000,United States,Remote,United States,S,Data Science and Research +2020,Mid-level,Full-time,Data Engineer,61500,EUR,70139,France,Hybrid,France,L,Data Engineering +2021,Senior,Full-time,Principal Data Analyst,170000,USD,170000,United States,Remote,United States,M,Data Analysis +2021,Senior,Full-time,Data Engineer,70000,GBP,96282,United Kingdom,Hybrid,United Kingdom,L,Data Engineering +2021,Entry-level,Full-time,Data Scientist,31000,EUR,36643,France,Hybrid,France,L,Data Science and Research +2021,Mid-level,Full-time,Data Engineer,52500,GBP,72212,United Kingdom,Hybrid,United Kingdom,L,Data Engineering +2020,Entry-level,Full-time,Data Analyst,91000,USD,91000,United States,Remote,United States,L,Data Analysis +2021,Senior,Full-time,Big Data Architect,125000,CAD,99703,Canada,Hybrid,Canada,M,Data Architecture and Modeling +2021,Senior,Full-time,Data Scientist,165000,USD,165000,United States,Remote,United States,L,Data Science and Research +2021,Mid-level,Full-time,Data Analyst,80000,USD,80000,United States,Remote,United States,L,Data Analysis +2021,Senior,Full-time,Data Scientist,130000,CAD,103691,Canada,Remote,Canada,L,Data Science and Research +2020,Entry-level,Full-time,Research Scientist,42000,USD,42000,Netherlands,Hybrid,Netherlands,L,Data Science and Research +2020,Mid-level,Full-time,Lead Data Scientist,115000,USD,115000,United Arab Emirates,In-person,United Arab Emirates,L,Data Science and Research +2021,Mid-level,Full-time,Research Scientist,80000,CAD,63810,Canada,Remote,Canada,M,Data Science and Research +2020,Senior,Full-time,Machine Learning Scientist,260000,USD,260000,Japan,In-person,Japan,S,Machine Learning and AI +2021,Mid-level,Full-time,Head of Data Science,110000,USD,110000,United States,In-person,United States,S,Data Science and Research +2021,Mid-level,Full-time,Data Architect,180000,USD,180000,United States,Remote,United States,L,Data Architecture and Modeling +2021,Senior,Full-time,Data Analyst,200000,USD,200000,United States,Remote,United States,L,Data Analysis +2020,Senior,Full-time,Big Data Engineer,85000,GBP,109024,United Kingdom,Hybrid,United Kingdom,M,Data Engineering +2021,Senior,Full-time,Data Engineer,200000,USD,200000,United States,Remote,United States,L,Data Engineering +2021,Senior,Full-time,ML Engineer,256000,USD,256000,United States,Remote,United States,S,Machine Learning and AI +2021,Mid-level,Full-time,Data Engineer,110000,USD,110000,United States,Remote,United States,L,Data Engineering +2020,Mid-level,Full-time,Data Scientist,70000,EUR,79833,Germany,In-person,Germany,L,Data Science and Research +2021,Entry-level,Full-time,Data Engineer,72500,USD,72500,United States,Remote,United States,L,Data Engineering +2021,Senior,Full-time,Machine Learning Engineer,185000,USD,185000,United States,Hybrid,United States,L,Machine Learning and AI +2021,Mid-level,Part-time,Data Engineer,59000,EUR,69741,Netherlands,Remote,Netherlands,L,Data Engineering +2021,Entry-level,Full-time,Research Scientist,100000,USD,100000,Jersey,In-person,China,L,Data Science and Research +2021,Mid-level,Full-time,Data Engineer,112000,USD,112000,United States,Remote,United States,L,Data Engineering +2020,Senior,Full-time,Machine Learning Engineer,150000,USD,150000,United States,Hybrid,United States,L,Machine Learning and AI +2021,Senior,Full-time,Data Scientist,180000,TRY,20171,Türkiye,Hybrid,Türkiye,L,Data Science and Research +2021,Senior,Full-time,AI Scientist,55000,USD,55000,Spain,Remote,Spain,L,Machine Learning and AI +2021,Entry-level,Full-time,Data Scientist,58000,USD,58000,United States,Hybrid,United States,L,Data Science and Research +2021,Entry-level,Full-time,Data Scientist,100000,USD,100000,United States,Remote,United States,M,Data Science and Research +2021,Senior,Full-time,Data Scientist,65720,EUR,77684,France,Hybrid,France,M,Data Science and Research +2021,Entry-level,Full-time,Machine Learning Engineer,85000,USD,85000,Netherlands,Remote,Germany,S,Machine Learning and AI +2021,Entry-level,Full-time,Data Science Consultant,65000,EUR,76833,Germany,In-person,Germany,L,Data Science and Research +2021,Senior,Contract,Staff Data Scientist,105000,USD,105000,United States,Remote,United States,M,Data Science and Research +2020,Entry-level,Full-time,Data Analyst,72000,USD,72000,United States,Remote,United States,L,Data Analysis +2021,Entry-level,Full-time,Data Engineer,55000,EUR,65013,Germany,Hybrid,Germany,M,Data Engineering +2021,Mid-level,Full-time,Data Engineer,250000,TRY,28016,Türkiye,Remote,Türkiye,M,Data Engineering +2021,Mid-level,Full-time,Data Engineer,111775,USD,111775,United States,In-person,United States,M,Data Engineering +2021,Mid-level,Full-time,Data Engineer,93150,USD,93150,United States,In-person,United States,M,Data Engineering +2021,Senior,Full-time,Lead Data Engineer,160000,USD,160000,Puerto Rico,Hybrid,United States,S,Data Engineering +2021,Mid-level,Full-time,Data Scientist,21600,EUR,25532,Serbia,Remote,Germany,S,Data Science and Research +2021,Senior,Full-time,Data Analyst,54000,EUR,63831,Germany,Hybrid,Germany,L,Data Analysis +2020,Senior,Full-time,Lead Data Scientist,190000,USD,190000,United States,Remote,United States,S,Data Science and Research +2021,Executive,Full-time,Director of Data Science,120000,EUR,141846,Germany,In-person,Germany,L,Data Science and Research +2021,Senior,Full-time,Data Analyst,90000,CAD,71786,Canada,Remote,Canada,M,Data Analysis +2021,Senior,Full-time,Data Scientist,135000,USD,135000,United States,In-person,United States,L,Data Science and Research +2021,Entry-level,Full-time,Machine Learning Engineer,21000,EUR,24823,Germany,Hybrid,Germany,M,Machine Learning and AI +2021,Mid-level,Full-time,Data Scientist,147000,USD,147000,United States,Hybrid,United States,L,Data Science and Research +2021,Senior,Full-time,Research Scientist,120500,CAD,96113,Canada,Hybrid,Canada,L,Data Science and Research +2021,Senior,Full-time,Data Science Manager,174000,USD,174000,United States,Remote,United States,L,Data Science and Research +2020,Mid-level,Full-time,Business Data Analyst,135000,USD,135000,United States,Remote,United States,L,Data Analysis +2021,Entry-level,Full-time,Machine Learning Engineer,21844,USD,21844,Colombia,Hybrid,Colombia,M,Machine Learning and AI +2020,Senior,Full-time,Lead Data Engineer,125000,USD,125000,New Zealand,Hybrid,New Zealand,S,Data Engineering +2020,Entry-level,Full-time,Data Scientist,45000,EUR,51321,France,In-person,France,S,Data Science and Research +2021,Executive,Full-time,Data Science Consultant,59000,EUR,69741,France,Remote,Spain,S,Data Science and Research +2021,Senior,Full-time,Data Analytics Engineer,50000,USD,50000,Viet Nam,Remote,United Kingdom,M,Leadership and Management +2020,Entry-level,Full-time,Data Scientist,35000,EUR,39916,France,In-person,France,M,Data Science and Research +2020,Mid-level,Full-time,Lead Data Analyst,87000,USD,87000,United States,Remote,United States,L,Data Analysis +2021,Mid-level,Full-time,Data Engineer,22000,EUR,26005,Romania,In-person,United States,L,Data Engineering +2021,Mid-level,Full-time,Data Scientist,76760,EUR,90734,Germany,Hybrid,Germany,L,Data Science and Research +2021,Entry-level,Full-time,Machine Learning Engineer,81000,USD,81000,United States,Hybrid,United States,S,Machine Learning and AI +2021,Entry-level,Full-time,Data Science Consultant,90000,USD,90000,United States,Remote,United States,S,Data Science and Research +2021,Mid-level,Full-time,Data Scientist,52000,EUR,61467,Germany,Hybrid,Austria,M,Data Science and Research +2021,Senior,Full-time,Machine Learning Infrastructure Engineer,195000,USD,195000,United States,Remote,United States,M,Machine Learning and AI +2021,Mid-level,Full-time,Data Scientist,32000,EUR,37825,Spain,Remote,Spain,L,Data Science and Research +2020,Mid-level,Full-time,Data Analyst,85000,USD,85000,United States,Remote,United States,L,Data Analysis +2021,Executive,Contract,Principal Data Scientist,416000,USD,416000,United States,Remote,United States,S,Data Science and Research +2021,Senior,Full-time,Machine Learning Scientist,225000,USD,225000,United States,Remote,Canada,L,Machine Learning and AI +2021,Mid-level,Full-time,Data Scientist,40900,GBP,56256,United Kingdom,Hybrid,United Kingdom,L,Data Science and Research +2021,Mid-level,Full-time,Data Scientist,85000,GBP,116914,United Kingdom,Hybrid,United Kingdom,L,Data Science and Research +2021,Mid-level,Full-time,Machine Learning Engineer,180000,PLN,46597,Poland,Remote,Poland,L,Machine Learning and AI +2020,Senior,Full-time,Big Data Engineer,100000,EUR,114047,Poland,Remote,United Kingdom,S,Data Engineering +2021,Mid-level,Full-time,Machine Learning Engineer,75000,EUR,88654,Belgium,Remote,Belgium,M,Machine Learning and AI +2020,Mid-level,Full-time,Lead Data Engineer,56000,USD,56000,Portugal,Remote,United States,M,Data Engineering +2021,Entry-level,Part-time,Computer Vision Engineer,180000,DKK,28609,Denmark,Hybrid,Denmark,S,Machine Learning and AI +2021,Mid-level,Full-time,Data Scientist,75000,EUR,88654,Germany,Hybrid,Germany,L,Data Science and Research +2020,Senior,Full-time,Data Engineer,42000,EUR,47899,Greece,Hybrid,Greece,L,Data Engineering +2020,Mid-level,Full-time,BI Data Analyst,98000,USD,98000,United States,In-person,United States,M,Data Analysis +2021,Mid-level,Full-time,Data Engineer,48000,GBP,66022,Hong Kong,Hybrid,United Kingdom,S,Data Engineering +2021,Mid-level,Full-time,Research Scientist,48000,EUR,56738,France,Hybrid,France,S,Data Science and Research +2021,Mid-level,Full-time,Machine Learning Engineer,21000,EUR,24823,Slovenia,Hybrid,Slovenia,L,Machine Learning and AI +2021,Senior,Full-time,Data Analytics Manager,120000,USD,120000,United States,In-person,United States,L,Leadership and Management +2021,Mid-level,Freelance,Data Engineer,20000,USD,20000,Italy,In-person,United States,L,Data Engineering +2020,Executive,Full-time,Director of Data Science,325000,USD,325000,United States,Remote,United States,L,Data Science and Research +2021,Senior,Full-time,Machine Learning Engineer,200000,USD,200000,United States,Remote,United States,L,Machine Learning and AI +2020,Entry-level,Full-time,AI Scientist,300000,DKK,45896,Denmark,Hybrid,Denmark,S,Machine Learning and AI +2021,Mid-level,Full-time,Data Scientist,160000,USD,160000,United States,Remote,United States,L,Data Science and Research +2021,Senior,Full-time,Research Scientist,50000,USD,50000,France,Remote,United States,S,Data Science and Research +2021,Mid-level,Full-time,Data Science Engineer,34000,EUR,40189,Greece,Remote,Greece,M,Data Science and Research +2021,Senior,Full-time,Data Engineer,165000,USD,165000,United States,In-person,United States,M,Data Engineering +2020,Mid-level,Full-time,Data Scientist,37000,EUR,42197,France,Hybrid,France,S,Data Science and Research +2021,Senior,Full-time,Principal Data Engineer,185000,USD,185000,United States,Remote,United States,L,Data Engineering +2020,Entry-level,Full-time,Data Scientist,55000,EUR,62726,Germany,Hybrid,Germany,S,Data Science and Research +2021,Mid-level,Full-time,Data Scientist,76760,EUR,90734,Germany,Hybrid,Germany,L,Data Science and Research +2020,Entry-level,Part-time,Data Scientist,19000,EUR,21669,Italy,Hybrid,Italy,S,Data Science and Research +2020,Mid-level,Full-time,Data Engineer,110000,USD,110000,United States,Remote,United States,L,Data Engineering +2021,Senior,Full-time,Data Analytics Manager,140000,USD,140000,United States,Remote,United States,L,Leadership and Management +2020,Senior,Full-time,Data Scientist,120000,USD,120000,United States,Hybrid,United States,L,Data Science and Research +2021,Senior,Full-time,Data Scientist,110000,CAD,87738,Canada,Remote,Canada,S,Data Science and Research +2021,Senior,Full-time,Finance Data Analyst,45000,GBP,61896,United Kingdom,Hybrid,United Kingdom,L,Data Analysis +2021,Senior,Full-time,Data Engineer,65000,EUR,76833,Romania,Hybrid,United Kingdom,S,Data Engineering +2021,Mid-level,Full-time,Machine Learning Engineer,74000,USD,74000,Japan,Hybrid,Japan,S,Machine Learning and AI +2021,Senior,Full-time,Data Science Manager,152000,USD,152000,United States,Remote,France,L,Data Science and Research +2021,Mid-level,Full-time,Big Data Engineer,18000,USD,18000,"Moldova, Republic of",In-person,"Moldova, Republic of",S,Data Engineering +2020,Senior,Freelance,Computer Vision Engineer,60000,USD,60000,Russian Federation,Remote,United States,S,Machine Learning and AI +2021,Mid-level,Full-time,Data Scientist,130000,USD,130000,United States,Hybrid,United States,L,Data Science and Research +2021,Senior,Full-time,Computer Vision Engineer,102000,BRL,18907,Brazil,In-person,Brazil,M,Machine Learning and AI +2021,Entry-level,Full-time,Business Data Analyst,50000,EUR,59102,Luxembourg,Remote,Luxembourg,L,Data Analysis +2021,Senior,Full-time,Principal Data Scientist,147000,EUR,173762,Germany,Remote,Germany,M,Data Science and Research +2020,Senior,Full-time,Principal Data Scientist,130000,EUR,148261,Germany,Remote,Germany,M,Data Science and Research +2020,Mid-level,Full-time,Data Scientist,34000,EUR,38776,Spain,Remote,Spain,M,Data Science and Research +2021,Mid-level,Full-time,Data Scientist,39600,EUR,46809,Spain,Remote,Spain,M,Data Science and Research +2020,Senior,Full-time,Data Scientist,80000,EUR,91237,Austria,In-person,Austria,S,Data Science and Research +2020,Mid-level,Full-time,Data Scientist,55000,EUR,62726,France,Hybrid,Luxembourg,S,Data Science and Research +2021,Mid-level,Full-time,Data Scientist,115000,USD,115000,United States,Hybrid,United States,L,Data Science and Research +2021,Senior,Full-time,Principal Data Scientist,235000,USD,235000,United States,Remote,United States,L,Data Science and Research +2021,Mid-level,Full-time,Data Analyst,75000,USD,75000,United States,In-person,United States,L,Data Analysis +2021,Mid-level,Full-time,Data Analyst,62000,USD,62000,United States,In-person,United States,L,Data Analysis +2021,Mid-level,Full-time,Data Scientist,73000,USD,73000,United States,In-person,United States,L,Data Science and Research +2021,Mid-level,Full-time,Data Engineer,38400,EUR,45391,Netherlands,Remote,Netherlands,L,Data Engineering +2020,Senior,Full-time,Data Science Manager,190200,USD,190200,United States,Remote,United States,M,Data Science and Research +2020,Mid-level,Full-time,Data Scientist,118000,USD,118000,United States,Remote,United States,M,Data Science and Research +2020,Mid-level,Full-time,Data Scientist,,USD,138350,United States,Remote,United States,M,Data Science and Research +2020,Mid-level,Full-time,Data Engineer,130800,USD,130800,Spain,Remote,United States,M,Data Engineering +2020,Senior,Full-time,Machine Learning Engineer,40000,EUR,45618,Croatia,Remote,Croatia,S,Machine Learning and AI +2021,Senior,Full-time,Director of Data Science,168000,USD,168000,Japan,In-person,Japan,s,Data Science and Research +2021,Mid-level,Full-time,Data Scientist,160000,SGD,119059,Singapore,Remote,Israel,M,Data Science and Research +2021,Mid-level,Full-time,Applied Machine Learning Scientist,423000,USD,423000,United States,Hybrid,United States,L,Machine Learning and AI +2021,Mid-level,Full-time,Data Engineer,24000,EUR,28369,Malta,Hybrid,Malta,L,Data Engineering +2021,Senior,Full-time,Data Specialist,165000,USD,165000,United States,Remote,United States,L,Data Management and Strategy +2020,Senior,Full-time,Data Scientist,412000,USD,412000,United States,Remote,United States,L,Data Science and Research +2021,Mid-level,Full-time,Principal Data Scientist,151000,USD,151000,United States,Remote,United States,L,Data Science and Research +2020,Entry-level,Full-time,Data Scientist,105000,USD,105000,United States,Remote,United States,S,Data Science and Research +2020,Entry-level,Contract,Business Data Analyst,100000,USD,100000,United States,Remote,United States,L,Data Analysis