Quora_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=green><h3>Machine Learning Models</h3></font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=blue>Reading Data from File and Storing into SQL Table</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import sqlite3\n",
    "from sqlalchemy import create_engine #database_connection_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180000 rows\n"
     ]
    }
   ],
   "source": [
    "#creating_database_file_from_csv\n",
    "disk_engine = create_engine('sqlite:///train.db')\n",
    "start = dt.datetime.now() #\n",
    "chunksize = 180000\n",
    "j = 0\n",
    "index_start = 1\n",
    "for data in pd.read_csv('final_quora.csv', names=['Unnamed: 0', 'id', 'is_duplicate', 'cwc_min', 'cwc_max',\n",
    "       'csc_min', 'csc_max', 'ctc_min', 'ctc_max', 'last_word_eq',\n",
    "       'first_word_eq', 'abs_len_diff', 'mean_len', 'token_set_ratio',\n",
    "       'token_sort_ratio', 'fuzz_ratio', 'fuzz_partial_ratio',\n",
    "       'longest_substr_ratio', 'freq_qid1', 'freq_qid2', 'q1len', 'q2len',\n",
    "       'q1_n_words', 'q2_n_words', 'word_Common', 'word_Total',\n",
    "       'word_share', 'freq_q1+q2', 'freq_q1-q2', '0_x', '1_x', '2_x',\n",
    "       '3_x', '4_x', '5_x', '6_x', '7_x', '8_x', '9_x', '10_x', '11_x',\n",
    "       '12_x', '13_x', '14_x', '15_x', '16_x', '17_x', '18_x', '19_x',\n",
    "       '20_x', '21_x', '22_x', '23_x', '24_x', '25_x', '26_x', '27_x',\n",
    "       '28_x', '29_x', '30_x', '31_x', '32_x', '33_x', '34_x', '35_x',\n",
    "       '36_x', '37_x', '38_x', '39_x', '40_x', '41_x', '42_x', '43_x',\n",
    "       '44_x', '45_x', '46_x', '47_x', '48_x', '49_x', '50_x', '51_x',\n",
    "       '52_x', '53_x', '54_x', '55_x', '56_x', '57_x', '58_x', '59_x',\n",
    "       '60_x', '61_x', '62_x', '63_x', '64_x', '65_x', '66_x', '67_x',\n",
    "       '68_x', '69_x', '70_x', '71_x', '72_x', '73_x', '74_x', '75_x',\n",
    "       '76_x', '77_x', '78_x', '79_x', '80_x', '81_x', '82_x', '83_x',\n",
    "       '84_x', '85_x', '86_x', '87_x', '88_x', '89_x', '90_x', '91_x',\n",
    "       '92_x', '93_x', '94_x', '95_x', '0_y', '1_y', '2_y', '3_y', '4_y',\n",
    "       '5_y', '6_y', '7_y', '8_y', '9_y', '10_y', '11_y', '12_y', '13_y',\n",
    "       '14_y', '15_y', '16_y', '17_y', '18_y', '19_y', '20_y', '21_y',\n",
    "       '22_y', '23_y', '24_y', '25_y', '26_y', '27_y', '28_y', '29_y',\n",
    "       '30_y', '31_y', '32_y', '33_y', '34_y', '35_y', '36_y', '37_y',\n",
    "       '38_y', '39_y', '40_y', '41_y', '42_y', '43_y', '44_y', '45_y',\n",
    "       '46_y', '47_y', '48_y', '49_y', '50_y', '51_y', '52_y', '53_y',\n",
    "       '54_y', '55_y', '56_y', '57_y', '58_y', '59_y', '60_y', '61_y',\n",
    "       '62_y', '63_y', '64_y', '65_y', '66_y', '67_y', '68_y', '69_y',\n",
    "       '70_y', '71_y', '72_y', '73_y', '74_y', '75_y', '76_y', '77_y',\n",
    "       '78_y', '79_y', '80_y', '81_y', '82_y', '83_y', '84_y', '85_y',\n",
    "       '86_y', '87_y', '88_y', '89_y', '90_y', '91_y', '92_y', '93_y',\n",
    "       '94_y', '95_y'], chunksize=chunksize, iterator=True, encoding='utf-8', ):\n",
    "    \n",
    "    data.index += index_start\n",
    "    j+=1\n",
    "    print('{} rows'.format(j*chunksize))\n",
    "    data.to_sql('data', disk_engine, if_exists='append')\n",
    "    index_start = data.index[-1] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ref:_http://www.sqlitetutorial.net/sqlite-python/create-tables/\n",
    "\n",
    "def create_connection(db_file):\n",
    "    \"\"\" create a database connection to the SQLite database\n",
    "        specified by db_file\n",
    "    :param db_file: database file\n",
    "    :return: Connection object or None\n",
    "    \"\"\"\n",
    "    try:\n",
    "        conn = sqlite3.connect(db_file)\n",
    "        return conn\n",
    "    except Error as e:\n",
    "        print(e)\n",
    " \n",
    "    return None\n",
    "\n",
    "\n",
    "def checkTableExists(dbcon):\n",
    "    cursr = dbcon.cursor()\n",
    "    str = \"select name from sqlite_master where type='table'\"\n",
    "    table_names = cursr.execute(str)\n",
    "    print(\"Tables in the databse:\")\n",
    "    tables =table_names.fetchall() \n",
    "    print(tables[0][0])\n",
    "    return(len(tables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tables in the databse:\n",
      "data\n"
     ]
    }
   ],
   "source": [
    "read_db = 'train.db'\n",
    "conn_r = create_connection(read_db)\n",
    "checkTableExists(conn_r)\n",
    "conn_r.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#try_to_sample_data_according_to_the_computing_power\n",
    "import os\n",
    "\n",
    "if os.path.isfile(read_db):\n",
    "    conn_r = create_connection(read_db)\n",
    "    if conn_r is not None:\n",
    "        #_for_selecting_random_points\n",
    "        data = pd.read_sql_query(\"SELECT * From data;\", conn_r) #\"SELECT * From data ORDER BY RANDOM() LIMIT 100001;\n",
    "        conn_r.commit()\n",
    "        conn_r.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#_remove_the_first_row_\n",
    "\n",
    "data.drop(data.index[0], inplace=True)\n",
    "y_true = data['is_duplicate']\n",
    "data.drop(['Unnamed: 0', 'id','index','is_duplicate'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cwc_min</th>\n",
       "      <th>cwc_max</th>\n",
       "      <th>csc_min</th>\n",
       "      <th>csc_max</th>\n",
       "      <th>ctc_min</th>\n",
       "      <th>ctc_max</th>\n",
       "      <th>last_word_eq</th>\n",
       "      <th>first_word_eq</th>\n",
       "      <th>abs_len_diff</th>\n",
       "      <th>mean_len</th>\n",
       "      <th>...</th>\n",
       "      <th>86_y</th>\n",
       "      <th>87_y</th>\n",
       "      <th>88_y</th>\n",
       "      <th>89_y</th>\n",
       "      <th>90_y</th>\n",
       "      <th>91_y</th>\n",
       "      <th>92_y</th>\n",
       "      <th>93_y</th>\n",
       "      <th>94_y</th>\n",
       "      <th>95_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.999980000399992</td>\n",
       "      <td>0.8333194446759221</td>\n",
       "      <td>0.9999833336111064</td>\n",
       "      <td>0.9999833336111064</td>\n",
       "      <td>0.9166590278414348</td>\n",
       "      <td>0.7857086735094749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.76825027167797</td>\n",
       "      <td>-20.440071880817413</td>\n",
       "      <td>-25.349166497588158</td>\n",
       "      <td>12.486474052071571</td>\n",
       "      <td>-19.637780040502548</td>\n",
       "      <td>16.586845815181732</td>\n",
       "      <td>30.42620912194252</td>\n",
       "      <td>43.3457567691803</td>\n",
       "      <td>5.500876009464264</td>\n",
       "      <td>2.2623480558395386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7999840003199936</td>\n",
       "      <td>0.3999960000399996</td>\n",
       "      <td>0.7499812504687383</td>\n",
       "      <td>0.5999880002399952</td>\n",
       "      <td>0.6999930000699993</td>\n",
       "      <td>0.4666635555762962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>...</td>\n",
       "      <td>63.48698341846466</td>\n",
       "      <td>0.21716612577438354</td>\n",
       "      <td>-24.077536888420582</td>\n",
       "      <td>-5.16056339815259</td>\n",
       "      <td>-23.868522435426712</td>\n",
       "      <td>4.3169752061367035</td>\n",
       "      <td>2.1708393692970276</td>\n",
       "      <td>41.508874736726284</td>\n",
       "      <td>10.780357360839844</td>\n",
       "      <td>21.472071304917336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3999920001599968</td>\n",
       "      <td>0.3333277778703688</td>\n",
       "      <td>0.3999920001599968</td>\n",
       "      <td>0.24999687503906198</td>\n",
       "      <td>0.3999960000399996</td>\n",
       "      <td>0.28571224491253633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.4096200466156</td>\n",
       "      <td>-35.57620549201965</td>\n",
       "      <td>-20.263143956661224</td>\n",
       "      <td>39.94573229551315</td>\n",
       "      <td>-22.443190425634384</td>\n",
       "      <td>12.674661040306091</td>\n",
       "      <td>0.09520073235034943</td>\n",
       "      <td>21.34302520751953</td>\n",
       "      <td>24.57832145690918</td>\n",
       "      <td>-7.446970224380493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.468349575996399</td>\n",
       "      <td>-21.470735728740692</td>\n",
       "      <td>0.5468772947788239</td>\n",
       "      <td>40.3733349442482</td>\n",
       "      <td>-9.026010990142822</td>\n",
       "      <td>18.770623445510864</td>\n",
       "      <td>-1.5563985109329224</td>\n",
       "      <td>1.3486743569374084</td>\n",
       "      <td>18.06006595492363</td>\n",
       "      <td>17.89223536849022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.3999920001599968</td>\n",
       "      <td>0.19999800001999984</td>\n",
       "      <td>0.9999500024998748</td>\n",
       "      <td>0.6666444451851604</td>\n",
       "      <td>0.5714204082798817</td>\n",
       "      <td>0.3076899408466089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.49354112148285</td>\n",
       "      <td>-11.318888233043253</td>\n",
       "      <td>-11.371670164167881</td>\n",
       "      <td>14.175069153308868</td>\n",
       "      <td>-32.5538332760334</td>\n",
       "      <td>6.133713513612747</td>\n",
       "      <td>20.308695316314697</td>\n",
       "      <td>17.38372352719307</td>\n",
       "      <td>12.348763644695282</td>\n",
       "      <td>-12.703654170036316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 218 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              cwc_min              cwc_max             csc_min  \\\n",
       "1   0.999980000399992   0.8333194446759221  0.9999833336111064   \n",
       "2  0.7999840003199936   0.3999960000399996  0.7499812504687383   \n",
       "3  0.3999920001599968   0.3333277778703688  0.3999920001599968   \n",
       "4                 0.0                  0.0                 0.0   \n",
       "5  0.3999920001599968  0.19999800001999984  0.9999500024998748   \n",
       "\n",
       "               csc_max             ctc_min              ctc_max last_word_eq  \\\n",
       "1   0.9999833336111064  0.9166590278414348   0.7857086735094749          0.0   \n",
       "2   0.5999880002399952  0.6999930000699993   0.4666635555762962          0.0   \n",
       "3  0.24999687503906198  0.3999960000399996  0.28571224491253633          0.0   \n",
       "4                  0.0                 0.0                  0.0          0.0   \n",
       "5   0.6666444451851604  0.5714204082798817   0.3076899408466089          0.0   \n",
       "\n",
       "  first_word_eq abs_len_diff mean_len  ...               86_y  \\\n",
       "1           1.0          2.0     13.0  ...  23.76825027167797   \n",
       "2           1.0          5.0     12.5  ...  63.48698341846466   \n",
       "3           1.0          4.0     12.0  ...  -10.4096200466156   \n",
       "4           0.0          2.0     12.0  ...  6.468349575996399   \n",
       "5           1.0          6.0     10.0  ...  17.49354112148285   \n",
       "\n",
       "                  87_y                 88_y                89_y  \\\n",
       "1  -20.440071880817413  -25.349166497588158  12.486474052071571   \n",
       "2  0.21716612577438354  -24.077536888420582   -5.16056339815259   \n",
       "3   -35.57620549201965  -20.263143956661224   39.94573229551315   \n",
       "4  -21.470735728740692   0.5468772947788239    40.3733349442482   \n",
       "5  -11.318888233043253  -11.371670164167881  14.175069153308868   \n",
       "\n",
       "                  90_y                91_y                 92_y  \\\n",
       "1  -19.637780040502548  16.586845815181732    30.42620912194252   \n",
       "2  -23.868522435426712  4.3169752061367035   2.1708393692970276   \n",
       "3  -22.443190425634384  12.674661040306091  0.09520073235034943   \n",
       "4   -9.026010990142822  18.770623445510864  -1.5563985109329224   \n",
       "5    -32.5538332760334   6.133713513612747   20.308695316314697   \n",
       "\n",
       "                 93_y                94_y                 95_y  \n",
       "1    43.3457567691803   5.500876009464264   2.2623480558395386  \n",
       "2  41.508874736726284  10.780357360839844   21.472071304917336  \n",
       "3   21.34302520751953   24.57832145690918   -7.446970224380493  \n",
       "4  1.3486743569374084   18.06006595492363    17.89223536849022  \n",
       "5   17.38372352719307  12.348763644695282  -12.703654170036316  \n",
       "\n",
       "[5 rows x 218 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=blue><h3>Converting Strings to Numerics</h3></font>**\n",
    "- After reading data from sql table each entry was read it as a string; we convert all the features into numaric before we apply any model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cwc_min\n",
      "cwc_max\n",
      "csc_min\n",
      "csc_max\n",
      "ctc_min\n",
      "ctc_max\n",
      "last_word_eq\n",
      "first_word_eq\n",
      "abs_len_diff\n",
      "mean_len\n",
      "token_set_ratio\n",
      "token_sort_ratio\n",
      "fuzz_ratio\n",
      "fuzz_partial_ratio\n",
      "longest_substr_ratio\n",
      "freq_qid1\n",
      "freq_qid2\n",
      "q1len\n",
      "q2len\n",
      "q1_n_words\n",
      "q2_n_words\n",
      "word_Common\n",
      "word_Total\n",
      "word_share\n",
      "freq_q1+q2\n",
      "freq_q1-q2\n",
      "0_x\n",
      "1_x\n",
      "2_x\n",
      "3_x\n",
      "4_x\n",
      "5_x\n",
      "6_x\n",
      "7_x\n",
      "8_x\n",
      "9_x\n",
      "10_x\n",
      "11_x\n",
      "12_x\n",
      "13_x\n",
      "14_x\n",
      "15_x\n",
      "16_x\n",
      "17_x\n",
      "18_x\n",
      "19_x\n",
      "20_x\n",
      "21_x\n",
      "22_x\n",
      "23_x\n",
      "24_x\n",
      "25_x\n",
      "26_x\n",
      "27_x\n",
      "28_x\n",
      "29_x\n",
      "30_x\n",
      "31_x\n",
      "32_x\n",
      "33_x\n",
      "34_x\n",
      "35_x\n",
      "36_x\n",
      "37_x\n",
      "38_x\n",
      "39_x\n",
      "40_x\n",
      "41_x\n",
      "42_x\n",
      "43_x\n",
      "44_x\n",
      "45_x\n",
      "46_x\n",
      "47_x\n",
      "48_x\n",
      "49_x\n",
      "50_x\n",
      "51_x\n",
      "52_x\n",
      "53_x\n",
      "54_x\n",
      "55_x\n",
      "56_x\n",
      "57_x\n",
      "58_x\n",
      "59_x\n",
      "60_x\n",
      "61_x\n",
      "62_x\n",
      "63_x\n",
      "64_x\n",
      "65_x\n",
      "66_x\n",
      "67_x\n",
      "68_x\n",
      "69_x\n",
      "70_x\n",
      "71_x\n",
      "72_x\n",
      "73_x\n",
      "74_x\n",
      "75_x\n",
      "76_x\n",
      "77_x\n",
      "78_x\n",
      "79_x\n",
      "80_x\n",
      "81_x\n",
      "82_x\n",
      "83_x\n",
      "84_x\n",
      "85_x\n",
      "86_x\n",
      "87_x\n",
      "88_x\n",
      "89_x\n",
      "90_x\n",
      "91_x\n",
      "92_x\n",
      "93_x\n",
      "94_x\n",
      "95_x\n",
      "0_y\n",
      "1_y\n",
      "2_y\n",
      "3_y\n",
      "4_y\n",
      "5_y\n",
      "6_y\n",
      "7_y\n",
      "8_y\n",
      "9_y\n",
      "10_y\n",
      "11_y\n",
      "12_y\n",
      "13_y\n",
      "14_y\n",
      "15_y\n",
      "16_y\n",
      "17_y\n",
      "18_y\n",
      "19_y\n",
      "20_y\n",
      "21_y\n",
      "22_y\n",
      "23_y\n",
      "24_y\n",
      "25_y\n",
      "26_y\n",
      "27_y\n",
      "28_y\n",
      "29_y\n",
      "30_y\n",
      "31_y\n",
      "32_y\n",
      "33_y\n",
      "34_y\n",
      "35_y\n",
      "36_y\n",
      "37_y\n",
      "38_y\n",
      "39_y\n",
      "40_y\n",
      "41_y\n",
      "42_y\n",
      "43_y\n",
      "44_y\n",
      "45_y\n",
      "46_y\n",
      "47_y\n",
      "48_y\n",
      "49_y\n",
      "50_y\n",
      "51_y\n",
      "52_y\n",
      "53_y\n",
      "54_y\n",
      "55_y\n",
      "56_y\n",
      "57_y\n",
      "58_y\n",
      "59_y\n",
      "60_y\n",
      "61_y\n",
      "62_y\n",
      "63_y\n",
      "64_y\n",
      "65_y\n",
      "66_y\n",
      "67_y\n",
      "68_y\n",
      "69_y\n",
      "70_y\n",
      "71_y\n",
      "72_y\n",
      "73_y\n",
      "74_y\n",
      "75_y\n",
      "76_y\n",
      "77_y\n",
      "78_y\n",
      "79_y\n",
      "80_y\n",
      "81_y\n",
      "82_y\n",
      "83_y\n",
      "84_y\n",
      "85_y\n",
      "86_y\n",
      "87_y\n",
      "88_y\n",
      "89_y\n",
      "90_y\n",
      "91_y\n",
      "92_y\n",
      "93_y\n",
      "94_y\n",
      "95_y\n"
     ]
    }
   ],
   "source": [
    "cols = list(data.columns)\n",
    "for i in cols:\n",
    "    data[i] = data[i].apply(pd.to_numeric)\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert_all_strings_in_a_list_to_int_\n",
    "#ref:_https://stackoverflow.com/questions/7368789/convert-all-strings-in-a-list-to-int\n",
    "y_true = list(map(int, y_true.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=blue><h3>Random Train Test Split (70:30)</h3></font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, y_true, stratify=y_true, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF DATA POINTS IN TRAIN DATA:  (35000, 218)\n",
      "NUMBER OF DATA POINTS IN TEST DATA:  (15000, 218)\n"
     ]
    }
   ],
   "source": [
    "print(\"NUMBER OF DATA POINTS IN TRAIN DATA: \",X_train.shape)\n",
    "print(\"NUMBER OF DATA POINTS IN TEST DATA: \",X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- DISTRIBUTION OF OUTPUT VARIABLE IN TRAIN DATA ----------\n",
      "Class 0:  0.6270285714285714 Class 1:  0.37297142857142856 \n",
      "\n",
      "---------- DISTRIBUTION OF OUTPUT VARIABLE IN TEST DATA ----------\n",
      "Class 0:  0.373 Class 1:  0.373\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "print(\"-\"*10, \"DISTRIBUTION OF OUTPUT VARIABLE IN TRAIN DATA\", \"-\"*10)\n",
    "train_distr = Counter(y_train)\n",
    "train_len = len(y_train)\n",
    "print(\"Class 0: \",int(train_distr[0])/train_len,\"Class 1: \", int(train_distr[1])/train_len,\"\\n\")\n",
    "\n",
    "print(\"-\"*10, \"DISTRIBUTION OF OUTPUT VARIABLE IN TEST DATA\", \"-\"*10)\n",
    "test_distr = Counter(y_test)\n",
    "test_len = len(y_test)\n",
    "print(\"Class 0: \",int(test_distr[1])/test_len, \"Class 1: \",int(test_distr[1])/test_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this_function_plots_the_confusion_matrices_given_y_i_(true_labels), y_i_hat_(predicted_labels).\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_confusion_matrix(test_y, predict_y):\n",
    "    C = confusion_matrix(test_y, predict_y)\n",
    "    \n",
    "    A =(((C.T)/(C.sum(axis=1))).T)\n",
    "    \n",
    "    B =(C/C.sum(axis=0))\n",
    "    \n",
    "    plt.figure(figsize=(20,4))\n",
    "    labels = [0,1]\n",
    "    \n",
    "    cmap=sns.light_palette(\"#2ecc71\")\n",
    "    \n",
    "    plt.subplot(1,3,1)\n",
    "    #representing_C_in_heatmap_format\n",
    "    sns.heatmap(C, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Confusion matrix\")\n",
    "    \n",
    "    plt.subplot(1,3,2)\n",
    "    #representing_B_in_heatmap_format\n",
    "    sns.heatmap(B, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Precision matrix\")\n",
    "    \n",
    "    plt.subplot(1, 3, 3)\n",
    "    #representing_A_in_heatmap_format\n",
    "    sns.heatmap(A, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Recall matrix\")\n",
    "    \n",
    "    plt.savefig('cm.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=blue><h3>Building a Random Model (Finding Worst-Case Log-Loss)</h3></font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-Loss on Test Data using Random Model - 0.8826696876931132\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "#we_need_to_generate_9_numbers_and_the_sum_of_numbers_should_be_1_\n",
    "#one_solution_is_to_genarate_9_numbers_and_divide_each_of_the_numbers_by_their_sum_\n",
    "\n",
    "#ref: https://stackoverflow.com/a/18662466/4084039\n",
    "#we_create_a_output_array_that_has_exactly_same_size_as_the_CV_data_\n",
    "predicted_y = np.zeros((test_len,2))\n",
    "for i in range(test_len):\n",
    "    rand_probs = np.random.rand(1,2)\n",
    "    predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])\n",
    "print(\"Log-Loss on Test Data using Random Model -\",log_loss(y_test, predicted_y, eps=1e-15))\n",
    "\n",
    "predicted_y =np.argmax(predicted_y, axis=1)\n",
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=blue><h3>Logistic Regression with Hyperparameter Tuning</h3></font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOR VALUES OF ALPHA =  1e-05 LOG LOSS IS:  0.49654685488456696\n",
      "FOR VALUES OF ALPHA =  0.0001 LOG LOSS IS:  0.49380219976152245\n",
      "FOR VALUES OF ALPHA =  0.001 LOG LOSS IS:  0.4949909248786381\n",
      "FOR VALUES OF ALPHA =  0.01 LOG LOSS IS:  0.482925357093655\n",
      "FOR VALUES OF ALPHA =  0.1 LOG LOSS IS:  0.4978345558275487\n",
      "FOR VALUES OF ALPHA =  1 LOG LOSS IS:  0.5232445444029195\n",
      "FOR VALUES OF ALPHA =  10 LOG LOSS IS:  0.5455059917104451\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOR VALUES OF BEST ALPHA =  0.01 TRAIN LOG LOSS IS:  0.4800758003928714\n",
      "FOR VALUES OF BEST ALPHA =  0.01 TEST LOG LOSS IS: 0.482925357093655\n",
      "TOTAL NUMBER OF DATA POINTS:  15000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "alpha = [10 ** x for x in range(-5, 2)] #hyperparameter_for_SGDclassifier_\n",
    "\n",
    "#ref_:_http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "\n",
    "log_error_array=[]\n",
    "\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    #SGDClassifier_with_'l2'_penalty_and_'log'_loss_>>_logistic_regression_\n",
    "    clf.fit(X_train, y_train)\n",
    "    \n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(X_train, y_train)\n",
    "    \n",
    "    predict_y = sig_clf.predict_proba(X_test)\n",
    "    log_error_array.append(log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    \n",
    "    print('FOR VALUES OF ALPHA =', i, \"LOG LOSS IS: \",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, log_error_array,c='g')\n",
    "\n",
    "for i, txt in enumerate(np.round(log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],log_error_array[i]))\n",
    "    \n",
    "plt.grid()\n",
    "plt.title(\"CROSS VALIDATION ERROR FOR EACH ALPHA\")\n",
    "plt.xlabel(\"ALPHA i's\")\n",
    "plt.ylabel(\"ERROR MEASURE\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "best_alpha = np.argmin(log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(X_train, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(X_train)\n",
    "print('FOR VALUES OF BEST ALPHA =', alpha[best_alpha], \"TRAIN LOG LOSS IS: \",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "predict_y = sig_clf.predict_proba(X_test)\n",
    "print('FOR VALUES OF BEST ALPHA =', alpha[best_alpha], \"TEST LOG LOSS IS:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "predicted_y = np.argmax(predict_y,axis=1)\n",
    "print(\"TOTAL NUMBER OF DATA POINTS: \", len(predicted_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=blue><h3>Linear SVM with Hyperparameter Tuning</h3></font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOR VALUES OF ALPHA =  1e-05 LOG LOSS IS:  0.5071704235676304\n",
      "FOR VALUES OF ALPHA =  0.0001 LOG LOSS IS:  0.5085132652154267\n",
      "FOR VALUES OF ALPHA =  0.001 LOG LOSS IS:  0.5246881379786456\n",
      "FOR VALUES OF ALPHA =  0.01 LOG LOSS IS:  0.5432954728978588\n",
      "FOR VALUES OF ALPHA =  0.1 LOG LOSS IS:  0.5301794992713046\n",
      "FOR VALUES OF ALPHA =  1 LOG LOSS IS:  0.6360956942361801\n",
      "FOR VALUES OF ALPHA =  10 LOG LOSS IS:  0.6510421907973826\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOR VALUES OF BEST ALPHA = 1e-05 TRAIN LOG LOSS IS:  0.5026956580808021\n",
      "FOR VALUES OF BEST ALPHA = 1e-05 TEST LOG LOSS IS:  0.5071704235676304\n",
      "TOTAL NUMBER OF DATA POINTS:  15000\n"
     ]
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-5, 2)] #hyperparameter_for_SGDclassifier_\n",
    "\n",
    "log_error_array=[]\n",
    "\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l1', loss='hinge', random_state=42)\n",
    "    #SGDClassifier_with_'l1'_penalty_and_'hinge'_loss_>>_linear_svm_\n",
    "    clf.fit(X_train, y_train)\n",
    "    \n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(X_train, y_train)\n",
    "    \n",
    "    predict_y = sig_clf.predict_proba(X_test)\n",
    "    log_error_array.append(log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    \n",
    "    print('FOR VALUES OF ALPHA =', i, \"LOG LOSS IS: \",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, log_error_array,c='g')\n",
    "\n",
    "for i, txt in enumerate(np.round(log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],log_error_array[i]))\n",
    "    \n",
    "plt.grid()\n",
    "plt.title(\"CROSS VALIDATION ERROR FOR EACH ALPHA\")\n",
    "plt.xlabel(\"ALPHA i's\")\n",
    "plt.ylabel(\"ERROR MEASURE\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "best_alpha = np.argmin(log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l1', loss='hinge', random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(X_train, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(X_train)\n",
    "print('FOR VALUES OF BEST ALPHA =', alpha[best_alpha], \"TRAIN LOG LOSS IS: \",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "predict_y = sig_clf.predict_proba(X_test)\n",
    "print('FOR VALUES OF BEST ALPHA =', alpha[best_alpha], \"TEST LOG LOSS IS: \",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "predicted_y = np.argmax(predict_y,axis=1)\n",
    "print(\"TOTAL NUMBER OF DATA POINTS: \", len(predicted_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIAAAAEWCAYAAAAer+yjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xd8FVX6x/HPk0AIVap0AQWkWUAF7IoNRUV3dUWxgIV1V+x9fy4orN1V2ZUVUcEuuiiKwoqKoisLUiwoHQEllEjvIe35/TGTeAnJzQVyU7/v1+u+cufMmZkzIcwz97nnnDF3R0REREREREREyq+Ekm6AiIiIiIiIiIjElxJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAsk/MrKqZfWBmm83s3/uxn75m9nFRtq2kmNmJZrawpNshIlKamdlcMzulkDoHmdk2M0sspmbFVXguB5d0O0REyhszO8XMUiKWl5vZ6SXZpkhmNsLM/lrS7RDJoQRQOWdml5nZrPDmc7WZ/cfMTiiCXV8ENATqufvF+7oTd3/d3c8sgvbElZm5mbWOVsfd/+vuhxZXm0REilJ407wzjBepZjbazGoU9XHcvaO7Tymkzi/uXsPds4r6+EXJzKaY2bWF1QvPZWlxtElEpKTkiSNrzOyleMSR0sDM+pnZV4XVc/fr3X1ocbRJJBZKAJVjZnYb8DTwEEGy5iDgX0DvIth9C2CRu2cWwb7KPDOrVNJtEBEpAue5ew2gC3AMcF/eChbQ/UMMFBtEpALKiSNHAp2Be0u4PSWmvPRilfJFN3DllJkdAAwBbnD3d919u7tnuPsH7n5nWKeKmT1tZqvC19NmViVcd4qZpZjZ7Wb2a9h7qH+47gFgEHBJmOG/xszuN7PXIo7fMuw1Uylc7mdmS81sq5ktM7O+EeVfRWx3nJnNDIeWzTSz4yLWTTGzoWY2NdzPx2ZWv4Dzz2n/XRHtv8DMzjGzRWa2wcz+ElG/q5lNM7NNYd1nzCwpXPdlWO378Hwvidj/3Wa2Bhgd2QXVzA4Jj9ElXG5iZusKG/YgIlIauPtK4D9AJ8i9/j5oZlOBHcDBZnaAmb0YXjNXmtnfIm92zew6M5sfXq/nRVwPc7vnh9feWWa2Jex19GRYnjeGNDGz8eF1dYmZXRdxnPvN7G0zeyU81lwzO7qgcwv3+2czWxzWHxpes6eF7Xg74vpfx8w+NLO1ZrYxfN8sXPcgcCLwTBgbnonY/w1mthhYHFHW2sySzOw7M7sxLE8MY9qgIvmHExEpJdx9DTCJIBEE5H72eMLMfgmv+SPMrGrE+t7hNXKLmf1kZj3D8v4R8WSpmf1xX9pkQY+kf1kwImJbeP1tZMFnoI1mtsDMOkfUvydsR04cuzAsbw+MAI4N97MpYv/PmtlEM9sOnBqW/S1cf7eZTY+IbX8KY1byvpyPyL5QAqj8OhZIBsZFqfN/QHeCC/MRQFd2/7a3EXAA0BS4BhhuZnXcfTBBr6K3wm7tL0ZriJlVB/4BnO3uNYHjgO/yqVcXmBDWrQc8CUwws3oR1S4D+gMHAknAHVEO3Yjgd9CUIGH1PHA5cBTBTfsg+21OhizgVqA+we/uNODPAO5+UljniPB834rYf12C3lADIg/s7j8BdwOvm1k1YDTwUmHDHkRESgMzaw6cA3wbUXwFwbWuJvAz8DKQCbQm+Jb3TODacPuLgfuBK4FawPnA+nwONQwY5u61gEOAtwto0ptACtCEYAjyQ2Z2WsT684ExQG1gPPBMIafYkyAWdAfuAkYCfYHmBEmvS8N6CQTX7xYEvWh35uzb3f8P+C8wMIwNAyP2fwHQDegQeVB3TyeIQ0PCDxD3AInAg4W0V0SkTAmT5WcDSyKKHwXaEnz2aM1v9+iYWVfgFeBOgmv5ScDycLtfgXMJ4kl/4KmcLxX2wR8IPu/UB3YB04BvwuWxBJ8/cvxE8JnhAOAB4DUza+zu84HrgWnh9b92xDaXEVzTawJ5h4g9DqQD95lZG4LPU5e7e9o+novIXlMCqPyqB6wrZIhWX2CIu//q7msJLmxXRKzPCNdnuPtEYBuwr3PcZAOdzKyqu69297n51OkFLHb3V909093fBBYA50XUGe3ui9x9J8EHhSPz2U9k+x909wyCDwb1CT5obA2PPxc4HMDdZ7v79PC4y4HngJNjOKfB7r4rbM9u3P15gm9/vwYaEyTcRERKs/fCbzK/Ar4guDnN8ZK7zw3jSl2CG/tbwh6mvwJPAX3CutcCj7n7TA8scfef8zleBtDazOq7+zZ3n563QpiMOgG4293T3P074AV2j1dfufvEcM6gVwm+1IjmUXffEsaCH4GP3X2pu28m6PnUGcDd17v7O+6+w923EtzUFxYbAB529w0FxIYfgb8RfEFzB3BFaZ/rSERkL7xnZluBFQSJm8EQDB8GrgNuDa+PWwliTE7cuAYY5e6fuHu2u6909wUA7j7B3X8K48kXwMcEiZl9MS68708juA6nufsr4XX4LcLrf3jcf7v7qrA9bxHc13ctZP/vu/vUcJvdEjvunk3wxchNBF9WPObu3+a3E5F4UQKo/FoP1Lfo8w80IfgWN8fPYVnuPvIkkHYAez2Rm7tvBy4hyJSvNrMJZtYuhvbktKlpxPKavWjP+oib6pyb8NSI9TtztjeztmHX/jVmtoUgIOU7vCzC2hgy9s8TfJv8T3ffVUhdEZGSdoG713b3Fu7+5zwJjBUR71sAlQmu6ZvCpNFzBL0zIehJ81MMx7uG4NvgBRYM+z03nzpNgJwPCzkKiw3JhcS/vLGgoNhQzcyeM7Ofw9jwJVDbCp/XYUUh618GWgIT3X1xIXVFRMqSC8Ie/6cA7fjtfroBUA2YHRE3PgrLIUrcMLOzw6FTG8LtzqHw+/SCxHT9D497ZTgkLae9nWI4btTrf/hF8+cEMWB47M0WKRpKAJVf04A0gm7oBVlFcBOf46CwbF9sJ7io52gUudLdJ7n7GQQ9YRYQJEYKa09Om1buY5v2xrME7WoTDkX4C2CFbOPRVlrw1IOngReB+8MhbiIiZVXkNW8FQdf5+mHCqLa713L3jhHrDyl0h+6L3f1SgsTRo8DYcNhwpFVAXTOrGVFWXLHhdoKer93C2JAzJDgnPhQUB6LGB4IHMnwInGVF82ROEZFSJeyp8xLwRFi0jiDB0jEibhzgwYTRUEDcsGB+0nfC/TQMh1tNpPD79P1iZi0IPq8MJHjqcW2CHqP7df03s3MIppuYTDAkTKRYKQFUToXd2AcRzNtzQfgtZuUwg/5YWO1NgjGoDSyYTHkQ8FpB+yzEd8BJZnaQBRNQ5874b2YNzez88KZ+F8FQsvy6u08E2lrw6PpKZnYJwfwJH+5jm/ZGTWALsC3snfSnPOtTgYP32Cq6YcBsd7+WYG6jEfvdShGRUsDdVxN0wf+7mdUyswQLJlLOGR71AnCHmR1lgdbhzfRuzOxyM2sQdovfFBbvFh/cfQXwP+BhM0s2s8MJeg69Hq/zi1CT4APLpjCJPzjP+r2ODWZ2BcH8Q/0IhgG8bOX0MckiUuE9DZxhZkeG1/nnCebvORDAzJqa2Vlh3ReB/mZ2WhhTmob35ElAFWAtkGlmZxPMORdv1QmSOWvDtvYnfDBCKBVoZuFDA2IRft56kWCY9FXAeWFCSKTYKAFUjrn7k8BtBBOdrSXIrA8E3gur/A2YBcwBfiCYAO1v+3isTwjGzc4BZrN70iaB4FvUVcAGgvkT/pzPPtYTTPB2O8EQtruAc9193b60aS/dQTBp21aC4PRWnvX3E9ykbzKzPxS2MzPrTTDJ6PVh0W1AFwuffiYiUg5cSXBjPg/YSDB5ZmMI5k0gmC/nDYLr6nsE8wbl1ROYa2bbCJLmfQoYWnspQXf5VQRzNgwO4068PQ1UJfjmejrBcIVIw4CLLHh6zD8K25mZHRTu88pwzqM3COLwU0XbbBGRkhfOMfoK8New6G6CSaGnh8NqPyWcX9TdZxBO8AxsJpiHrkU4/Pcmgrk/NxLcr48vhrbPA/5OMKoiFTgMmBpR5TOC+UTXmFmsn1VGEswRNDH83HMN8EKeB96IxJW5F9ZLWUREREREREREyjL1ABIRERERERERKeeUABIRERERERERKeeUABIRERERERERKeeUABIRERERERERKecqlXQDCvKAPaDZqQWAwSvzPnVXKrQm2P5svjfXlsE+eL+OJfGlOCE5FCdkN4oTElKckByKE7KbChwn1ANIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRERERERERKScUwJIRGQfmdmhZvZdxGuLmd1iZveb2cqI8nMitrnXzJaY2UIzOyuivGdYtsTM7imZMxIRkaJW2PXdzJ6KiBeLzGxTSbRTRETKv0ol3QARkbLK3RcCRwKYWSKwEhgH9AeecvcnIuubWQegD9ARaAJ8amZtw9XDgTOAFGCmmY1393nFciIiIhIXYWyIen1391sj6t8IdC72hoqISIWgHkAiIkXjNOAnd/85Sp3ewBh33+Xuy4AlQNfwtcTdl7p7OjAmrCsiImXb3l7fLwXeLJaWiYhIhaMEkIhIAcxsgJnNingNiFK9D7vftA80szlmNsrM6oRlTYEVEXVSwrKCykVEpBSLIU7EfH03sxZAK+Cz+LRWREQqOg0BExEpgLuPBEYWVs/MkoDzgXvDomeBoYCHP/8OXA1Yfoch/2S870OTRUSkGMUQJwq67uenDzDW3bP2u2EiIiL5UA8gEZH9dzbwjbunArh7qrtnuXs28DzBEAAIvvltHrFdM2BVlHIRESnb9ub6nrcnqYiISJFSAkhEZP/tNmeDmTWOWHch8GP4fjzQx8yqmFkroA0wA5gJtDGzVmFvoj5hXRERKdtiur6b2aFAHWBaMbdPREQqEA0BExHZD2ZWjeDpLn+MKH7MzI4k6Oa/PGedu881s7eBeUAmcENOV38zGwhMAhKBUe4+t9hOQkRE4sLdM/O7vpvZEGCWu+ckgy4leEiAhv+KiEjcKAEkIrIf3H0HUC9P2RVR6j8IPJhP+URgYpE3UERESlR+13d3H5Rn+f7ibJOIiFRMGgImIiIiIiIiIlLOKQEkIiIiIiIiIlLOKQEkIiIiIiIiIlLOKQEkIiIiIiIiIlLOKQEkIiIiIiIiIlLOKQEkIiIiIiIiIlLMzKynmS00syVmdk8+658ys+/C1yIz2xSxLiti3fhYjqfHwIuIiIiIiIiIFCMzSwSGA2cAKcBMMxvv7vNy6rj7rRH1bwQ6R+xip7sfuTfHVA8gEREREREREZHi1RVY4u5L3T0dGAP0jlL/UuDN/TmgEkAiIiIiIiIiIkXMzAaY2ayI14CI1U2BFRHLKWFZfvtpAbQCPosoTg73Od3MLoilPRoCJiIiIiIiIiJSxNx9JDCygNWW3yYF1O0DjHX3rIiyg9x9lZkdDHxmZj+4+0/R2qMeQCIiIiIiIiIixSsFaB6x3AxYVUDdPuQZ/uXuq8KfS4Ep7D4/UL7UA0hEKpTGhzcu6SaIiEgppjghIiLRFGGcmAm0MbNWwEqCJM9leSuZ2aFAHWBaRFkdYIe77zKz+sDxwGOFHVAJIBERERERERGRYuTumWY2EJgEJAKj3H2umQ0BZrl7zqPdLwXGuHvk8LD2wHNmlk0wsuuRyKeHFUQJIBERERERERGRYubuE4GJecoG5Vm+P5/t/gcctrfH0xxAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlnBJAIiIiIiIiIiLlXKWSbkB50f2W7nS+tjM4pP6Qyvv936fLtV3ofkt36rauy2P1H2Pn+p0AHHr+oZw69FQ828nOzOajWz5ixdQVABxx5RGceN+JAPz3b//l+1e+3+NYyXWSueiti6jdsjablm9i7B/GkrYpDYCew3rS5pw2ZOzI4L1+77Hm2zUx71eKxr2P3suU6VOoV7seH47+EIBHRzzK5//7nMqVK3NQk4N4+O6HqVWjFuM/Gc+Lb72Yu+3CpQsZN3Ic7Vu354pbruDXDb+SnJQMwKjHR1GvTr09jvfc688xduJYEhITuG/gfZzYNfh3/nLGlzz4zINkZ2Vzca+LGXDZAABWrF7BbUNuY/PWzXRo04HH/vIYSZWT4v1rEZEYHHLWIfQc1pOExAS+eeEbpj46dbf1Zz15Fi1PbQlA5WqVqX5gdR6t82ju+qSaSdww/wYWjFvAf278DwBXfX4VNRrXIHNnJgCvnvkqO9buKJbzkX1X0DU8x5vj3+SN994gISGBalWrMfT2obRu2ZqNmzdy0/038eOCH7mw54UMunkQADvTdnLz/Tfzy6pfSExI5NTjTuWOAXeUxKmJyH4oLE7Ual6LC16+gOTaySQkJvDpPZ+y5D9LADjwsAM597lzqVKrCp7tPH/M82Ttysrdts/7fahzcB2ePezZYj0n2TeFxYmZ38/koeEPsfCnhTw56El6ntwTgJVrVnLjoBvJys4iMzOTy393OZeefykA6RnpDB02lBnfz8DMuPWaWznr5LOK/dwkfpQAKgI1m9Sk601d+VeHf5GZlslFb11Epz6dWDF1BYs+XES/Kf12q7908lIWjl8IBBfii9++mOHth5NcJ5mTB5/MyKNHgsOA2QNYOH5hbnInxwn3nMCyycuY+uhUjr/7eE645wQ+vedTWp/dmrpt6vLPNv+kabem9Hq2Fy92fzHm/UrR+F3P33H5hZdz98N355Ydf9Tx3H7d7VRKrMTjzz3Oc68/x51/vJPzzzif8884HwiSP3++78+0b90+d7sn/u8JDjv0sAKPtWT5EiZ8NoEJoyeQuj6V/nf0Z9IrkwAYMmwIox8fTcMGDbno+ovocVwPWrdszRPPPUG/i/vRq0cvBj05iLETx3JZ78vi9NsQkVhZgnHO8HN49YxX2ZKyhetmXsfC8QtZN39dbp1Jt03Kfd91YFcadW602z56DO3Bz1/8vMe+3+37Lqtnr45f46VIZWVlFXgNz3Heaefl3rBPnjqZh//1MC8+9iJVkqpw89U3s3jZYhYvW7zbfq++5Gq6d+5OekY6/W7vxxdff8HJ3U4u1nMTkX0XS5w46b6TmPf2PGaNmEX99vXpO7Evw1oNwxKN3732O8ZdMY7UOalUrVuV7Izs3O3aXdiO9G3pJXFasg9iiRONGzbm4bsfZtRbo3bbtkG9Box5ZgxJSUls37md8/qfR4/jetCwfkNGvDaCunXqMunVSWRnZ7Np66biPjWJs7gNATOzdmZ2t5n9w8yGhe/bF75l2ZRQKYFKVSthiUblapXZumora75bw+afN+9RN2N7Ru77pOpJuDsArc9qzdJPlpK2MY20TWks/WQprXu23mP7Q3sfyvcvBz14vn/5ew694FAA2vVux5xX5gCw8uuVJNdOpkajGjHvV4rGMUccwwG1Dtit7IRjTqBSYpBvPbLDkaxZu2aP7SZMnsC5Pc7dq2NNnjqZXj16kZSURPPGzWnRpAVzFsxhzoI5tGjSguZNmpNUOYlePXoxeepk3J3p307PzeRfeNaFTP5q8j6eqcj+qWhxojBNuzZlw5INbFq2ieyMbOaOmUu73u0KrN/p0k78+OaPucuNuzSmesPq/PTxT8XRXImjgq7hkWpUr5H7fmfaTswMgGpVq3H0YUdTJanKbvWrJlele+fuACRVTqJDmw6krk2N85mI7B/Fid3FFCccqtQK/v8nH5DM1lVbATjkzENInZNK6pzg//3ODTvx7OAzSOXqlTn2tmP58m9fFt/JyH6JJU40a9SMdoe0IyFh94/8SZWTSEoKev+np6eT7b8lAt/5zzv88bI/ApCQkEDdA+rG+UykuMUlAWRmdwNjAANmADPD92+a2T3xOGZJ2rpqK9OemMatv9zK7atvJ21zkGSJpt0F7bhh/g1cNuEyxl89HoCaTWuyecVvCaMtKVuo2bTmHtvWaFiDbWu2AbBtzTaqH1g96vax7leKxzv/eYeTup20R/nEKRPpdVqv3cr+8uhf6H1tb4a/Mjw3URgpdV0qjQ78rQdAwwYNSV2XWmD5xi0bqVWjVm4yqlGDRqSu0wcAKX4VLU7EombTmmxZsSV3Odq1+oCDDqB2q9os+2xZUGBw5t/P5JM7P8m3fu/Rvfnjt3/kpPv2vPZI6VPQNTyv18e9zul9T+fx5x7nvhvvi3n/W7Zt4fNpn3Nsl2OLpL0i8aA4sadY4sSU+6dw2OWHceuKW7ls4mW5w4Hrta2Hu9P3o74MmD2A4+48LnebHkN7MO3v08jYkYGUDbHGiYKs/nU1511zHqdccgrX9bmOhvUbsmVb8Lc1bNQwLhxwITfdfxPrNqwrZE9S1sSrB9A1wDHu/oi7vxa+HgG6huvyZWYDzGyWmc2axaw4Na3oJddO5tDehzKs1TCebPIkSdWTOKxvwcN2ABa8t4Dh7Ycz5oIxnDr0VIDcb+92s+dn/oLlszleBPuVIvPsa8+SmJjI+aefv1v59/O+p2qVqrRt1Ta37In/e4IPRn3A6/94ndk/zOb9j9/fY3/5JYUMy7/cLN9/93z/PkTir0LFiVjszbW6U59OzB87P/fb22P+fAyLJy5mS8qWPeq+2/ddRhw+gtEnjuagEw/i8CsOL8pmSxwUeA3Po++Fffn09U+5Y8AdPPtqbHN2ZGZlctvQ27jid1fQvEnz/W6rSBwpTuQRS5zodGknvn/pe55q/hRvnPMGF756IVgwWuGgEw7i3b7vMuqEUbS7sB2terSi4RENqdO6DgveW1A8JyFFItY4UZDGBzbmgxc/4OPXPmbcx+NYt2EdmVmZrFm7hi6dujBu5Dg6d+jMoyMeLXxnUqbEKwGUDTTJp7xxuC5f7j7S3Y9296OP5ug4Na3oHXz6wWxatokd63aQnZnN/Hfn0/y42G6qfvnvL9Q5pA5V61VlS8oWDmj+29ChWs1q5XbbjLQtdRs1GgVdv2s0qsH2X7cDsDVla77bx7pfia9xH41jyrQpPPF/T+xxgZ7w+QR69di990/DBg0BqFGtBueedi5zFszZY5+NGjRiza+/DSdLXZvKgfUPzL+83oHUOaAOW7ZtITMrmAx2zdo1HFjvwCI7R5G9UKHiRCy2pGyhVvNaucvRrtUd+3Tkhzd/yF1udmwzug7sys3LbubMJ87kiCuP4LSHTwPI3Uf6tnR+eOMHmnZtGsezkKJQ0DW8IL169OLTqZ/GtO+/PvFXWjZtSb+L+u1vM0XiTXEij1jiROdrOjP37bkApExPoVJyJarVr8aWlC38/MXP7Fy/k8ydmSyZuITGXRrT/NjmNDmqCTcvu5mrv7qaem3rcdXnVxXrecne29s4UZCG9RvSpmUbZv0wizq16lA1uSpnnHgGAD1P6cm8RfOKrM1SOsQrAXQLMNnM/mNmI8PXR8Bk4OY4HbPEbP5lM027N6VS1WBYTavTWu02GVtedQ6pk/u+UedGJCYlsnP9TpZMWsLBZx5Mcu1kkmsnc/CZB7Nk0pI9tl80fhFHXHUEAEdcdQQL3w8mlF44fiGHXxl8s9u0W1N2bd7FtjXbYt6vxM+XM77k+THP8+yDz1I1uepu67Kzs/loyke7JYAyszLZsHkDABmZGUyZNoU2rdrssd8ex/VgwmcTSE9PZ8XqFSxfuZzD2x3OYe0OY/nK5axYvYL0jHQmfDaBHsf1wMzo1rkbk74IJpIdN2kcPY7vEcczFylQhYoTsVg5cyX12tSjdsvaJFROoGOfjrkPDIhUr209qtapSsq0lNyycZeP4+kWTzOs1TA+vuNjvn/leybfOxlLNKrWC645CZUSaHtuW3798ddiOyfZNwVdwyMtT1me+37K9Cm0aNqi0P0+9eJTbNu+jb8M/EtRN1kkHhQn8oglTmz+ZTOtTmsFQP129amUXIkda3fw06SfaHh4w9w5S1uc3IK189Yya8Qsnmz6JMNaDWPUCaNYv2g9L5/6ckmcnuyFWOJEQdasXUParuBhQJu3buabH7+hVfNWmBmnHnsqX3/3NQDTvpnGIS0Pids5SMmIy1PA3P0jM2tL0EWzKcHgpBRgprtnRd24DFo5YyXzx87nj9/8kezMbFZ/u5rZI2fT9cauHH/X8dRoVIM/zfkTiycu5oPrPqDD7ztw+JWHk52RTcbODMZeMhaAtI1pfDn0S66beR0AXw75krSNwX/O854/j1kjZrF69mq+euQrLnr7Ijpf05nNv2zm3xf/G4DFExfT5pw23LjkRjJ2ZPB+//cL3a8UvduG3saM72awcfNGTrr4JG7sdyMj3xhJekY6/e/oD8ARHY5gyG1DAJg5ZyaNGjTarSt+eno61955LRlZGWRnZXPsUcfyh15/AIKJn39c+CM3X30zbVq14exTz+ac/ueQmJjIoJsHkZiYCMCgmwZx7V3XkpWdxe/P/n1uAunOAXdy69BbefrFp2nfpj0Xn3Nxcf56RICKFydi4VnOxIETuXzS5Vii8d2o71g7by2nPHAKq2atYtEHi4Bw8ucxPxayt0ClKpW4fNLlJFZOxBKNZZ8u45vnv4nnaUgRqJRYKd9r+LBRw+h0aCdOO/40Xhv3GtNmT6NSpUrUqlmLR+/5rZt+jz492LZjGxkZGXz61aeMenwUNarXYMRrIzj4oIO5cMCFAFx+4eVc3EsxQEonxYk9xRInPr79Y857/jy639odHN7r9x4AaZvSmPbktODzgAefGxZPXFzIEaW0iiVOzFkwh4F/HZg779s/R/+9n6vYAAAgAElEQVSTCS9N4Keff+KRZx8Jpo3AufoPV3PowcFDhe4YcAd3PXwXDw1/iLoH1OXhux8u4TOVomb5jR8sDR6wB0pnw6TYDV45uKSbIKVJk3xnu4rZyCNGxnxtGfD9AE2QVIopTkgOxQnZjeKEhBQnJIfihOymAseJuD0GXkRERERERERESgclgEREREREREREyjklgERERERE4sTMeprZQjNbYmb3FFDnD2Y2z8zmmtkbxd1GERGpGOIyCbSIiIiISEVnZonAcOAMwgmMzWy8u8+LqNMGuBc43t03mtneP8tZREQkBuoBJCIiIiISH12BJe6+1N3TgTFA7zx1rgOGu/tGAHf/tZjbKCIiFYQSQCIi+8HMapvZWDNbYGbzzexYM6trZp+Y2eLwZ52wrpnZP8JhAHPMrEvEfq4K6y82s6tK7oxERCRWZjbAzGZFvAbkqdIUWBGxnBKWRWoLtDWzqWY23cx6xrPNIiJScWkImIjI/hkGfOTuF5lZElAN+Asw2d0fCed7uAe4GzgbaBO+ugHPAt3MrC4wGDgacGB2OERgY/GfjoiIxMrdRwIjo1TJ7/G/eR8fXIkgLpwCNAP+a2ad3H1TkTRSREQkpB5AIiL7yMxqAScBLwK4e3p4w94beDms9jJwQfi+N/CKB6YDtc2sMXAW8Im7bwiTPp8A+gZYRKTsSwGaRyw3A1blU+d9d89w92XAQoKEkIiISJFSAkhEpAAxdO0/GFgLjDazb83sBTOrDjR099UA4c+cCT0LGgoQyxABEREpe2YCbcysVdhLtA8wPk+d94BTAcysPsGQsKXF2koREakQNARMRKQAMXTtrwR0AW5096/NbBjBcK+CFDQUIJYhAiIiUsa4e6aZDQQmAYnAKHefa2ZDgFnuPj5cd6aZzQOygDvdfX3JtVpERMorJYBERPZdCpDi7l+Hy2MJEkCpZtbY3VeHQ7x+jaif31CAFIK5HyLLp8Sx3SIiUkzcfSIwMU/ZoIj3DtwWvkREROJGQ8BERPaRu68BVpjZoWHRacA8gu79OU/yugp4P3w/HrgyfBpYd2BzOEQs59vfOuETw84My0RERERERIqEegCJiOyfG4HXw7kdlgL9CZLrb5vZNcAvwMVh3YnAOcASYEdYF3ffYGZDCeaKABji7huK7xRERERERKS8UwJIRCqUJoc3KdL9uft3BI9vz+u0fOo6cEMB+xkFjCrSxomIyF4r6jghIiLlS1mOExoCJiIiIiIiIiJSzikBJCIiIiIiIiJSzhWaADKz6maWEL5va2bnm1nl+DdNRETKAsUJERGJRnFCRCR/ZtbTzBaa2RIzu6eAOn8ws3lmNtfM3ogov8rMFoevq/LbNq9YegB9CSSbWVNgMsGkpS/FsnMREakQFCdERCQaxQkRkTzMLBEYDpwNdAAuNbMOeeq0Ae4Fjnf3jsAtYXldYDDQDegKDA6fJhxVLAkgc/cdwO+Af7r7hWHjREREQHFCRESiU5wQEdlTV2CJuy9193RgDNA7T53rgOHuvhHA3X8Ny88CPnH3DeG6T4CehR0wpgSQmR0L9AUmhGV6epiIiORQnBARkWgUJ0SkQjKzAWY2K+I1IGJ1U2BFxHJKWBapLdDWzKaa2XQz67kX2+4hlgvvLQRdjsa5+1wzOxj4PIbtRESkYlCcEBGRaBQnRKRCcveRwMgCVlt+m+RZrgS0AU4BmgH/NbNOMW67h0ITQO7+BfAFQDh52zp3v6mw7UREpGJQnBARkWgUJ0RE8pUCNI9YbgasyqfOdHfPAJaZ2UKChFAKQVIoctsphR0wlqeAvWFmtcysOjAPWGhmdxa2nYiIVAyKEyIiEo3ihIhIvmYCbcyslZklAX2A8XnqvAecCmBm9QmGhC0FJgFnmlmdcPLnM8OyqGKZA6iDu28BLgAmAgcBV8R2PiIiUgEoToiISDSKEyIiebh7JjCQIHEzH3g7HCY7xMzOD6tNAtab2TyCobN3uvt6d98ADCVIIs0EhoRlUcUyB1BlM6tMcMF+xt0zzKzQsWUiIlJhKE6IiEg0ihMiIvlw94kEifHIskER7x24LXzl3XYUMGpvjhdLD6DngOVAdeBLM2sBbNmbg4iISLmmOCEiItEoToiIlAKxTAL9D+AfEUU/m9mp8WuSiIiUJYoTIiISjeKEiEjpEMsQMMysF9ARSI4oHhKXFomISJmjOCEiItEoToiIlLxYngI2ArgEuJHgWfMXAy3i3C4RESkjFCdERCQaxQkRkdIhljmAjnP3K4GN7v4AcCy7P6teREQqNsUJERGJRnFCRKQUiCUBtDP8ucPMmgAZQKv4NUlERMoYxQkREYlGcUJEpBSIZQ6gD82sNvA48A3gwAtxbZWIiJQlihMiIhKN4oSISCkQy1PAhoZv3zGzD4Fkd98c32aJiEhZoTghIiLRKE6IiJQOBSaAzOx3Udbh7u/Gp0kiIlIWKE6IiEg0ihMiIqVLtB5A50VZ54Au2CIiFZvihIiIRKM4ISJSihSYAHL3/sXZEBERKVsUJ0REJBrFCRGR0qXAp4CZ2W1mdk0+5Tea2S3xbZaIiJR2ihMiIhKN4oSISOkS7THwVwOv5lM+MlwnIiIVm+KEiIhEozghIlKKREsAubun51O4C7D4NUlERMoIxQkREYlGcUJEpBSJlgDCzBrGUiYiIhWT4oSIiESjOCEiUnpESwA9Dkwws5PNrGb4OgX4AHiiWFonIiKlmeKEiIhEozghIlKKRHsK2CtmthYYAnQieFTjXGCwu/+nmNonIiKllOKEiIhEozghIlK6FJgAAggvzLo4i4hIvhQnREQkGsUJEZHSI+ocQCIiIiIiIiIiUvYpASQiIiIiIiIiUs4pASQiIiIiIiIiUs4VOAeQmd0WbUN3f7LomyMiImWF4oSISOHMrCcwDEgEXnD3R/Ks70fwtKyVYdEz7v5CsTYyThQnRERKl2iTQNcstlaIiEhZpDghIhKFmSUCw4EzgBRgppmNd/d5eaq+5e4Di72B8ac4ISJSikR7DPwDxdkQEREpWxQnREQK1RVY4u5LAcxsDNAbyJsAKpcUJ0RESpeoj4EHMLNk4BqgI5CcU+7uV8exXQz4aEA8dy9lyOofVpd0E6QUadykcUk3QfIoqTgxeOXgeO5eypAHmuozpvxmsBfftcHMBgCRN60j3X1kxHJTYEXEcgrQLZ9d/d7MTgIWAbe6+4p86pRZJRUnzn3s3HjuXsqQD+/+sKSbIKXIua9W3GtDoQkg4FVgAXAWMAToC8yPZ6NEROKl8eFKIMWB4oSIlBt7EyfCZM/IKFUsv83yLH8AvOnuu8zseuBloEfMjSgbFCdEpNwoy58nYnkKWGt3/yuw3d1fBnoBh8W3WSIiUoYoToiI5C8FaB6x3AxYFVnB3de7+65w8XngqGJqW3FSnBARKQViSQBlhD83mVkn4ACgZdxaJCJSxphZopl9a2YfhssvmdkyM/sufB0ZlpuZ/cPMlpjZHDPrErGPq8xscfi6qqTOZR8pToiI5G8m0MbMWplZEtAHGB9Zwcwiv0o+n/LZM0ZxQkSkFIhlCNhIM6sD/JUgYNUABsW1VSIiZcvNBDfstSLK7nT3sXnqnQ20CV/dgGeBbmZWFxgMHE0wNGB2+JSYjXFvedFQnBARyYe7Z5rZQGASwWPgR7n7XDMbAsxy9/HATWZ2PpAJbAD6lViD40dxQkSkFCg0AeTuL4RvvwAOjm9zRETKFjNrRtCV/UHgtkKq9wZecXcHpptZ7fCb31OAT9x9Q7jPT4CewJtxa3gRUpwQESmYu08EJuYpGxTx/l7g3uJuV3FSnBARKR1ieQpYFeD3BN00c+u7+5D4NUtEpMx4GrgLqJmn/EEzGwRMBu4J53fI72kwTaOUlwmKEyIiEo3ihIhI6RDLHEDvE3xrnQlsj3iJiJRrZjbAzGZFvAbkWX8u8Ku7z86z6b1AO+AYoC5wd84m+RzGo5SXFYoTIiISjeKEiEgpEMscQM3cvWfcWyIiUsrE8Hjf44HzzewcIBmoZWavufvl4fpdZjYauCNcLuhpMCkEw8Aiy6fs9wkUH8UJERGJRnFCRKQUiKUH0P/MTI9pFBHJw93vdfdm7t6S4Mkun7n75TlPdDEzAy4Afgw3GQ9cGT4NrDuw2d1XE0wOeqaZ1QknyTwzLCsrFCdERCQaxQkRkVIglh5AJwD9zGwZsItgqIK7++FxbZmISNn1upk1ILhefgdcH5ZPBM4BlgA7gP4A7r7BzIYSPC4YYEjOhNBlhOKEiIhEozghIlIKxJIAOjvurRARKePcfQrhsC1371FAHQduKGDdKGBUnJoXb4oTIiISjeKEiEgpUOAQMDOrFb7dWsBLREQqMMUJERGJRnFCRCQ6M+tpZgvNbImZ3ROl3kVm5mZ2dLjc0sx2mtl34WtELMeL1gPoDeBcYDZ7PqXGgYNjOYCIiJRbihMiIhKN4oSISAHMLBEYDpxB8FCYmWY23t3n5alXE7gJ+DrPLn5y9yP35pgFJoDc/dzwZ6u92aGIiFQMihMiIhKN4oSISFRdgSXuvhTAzMYAvYF5eeoNBR7jtycL77NC5wAysy75FG8Gfnb3zP1tgIiIlG2KEyIiEo3ihIhUVGY2ABgQUTTS3UeG75sCKyLWpQDd8mzfGWju7h+aWd4EUCsz+xbYAtzn7v8trD2xTAL9L6ALMIeg2+ZhwPdAPTO73t0/jmEfIiJSfilOiIhINIoTIlIhhcmekQWstnzKPHelWQLwFNAvn3qrgYPcfb2ZHQW8Z2Yd3X1LtPYUOAl0hOVAZ3c/2t2PAo4EfgROJ+iGJCIiFdtyFCdERKRgy1GcEBHJKwVoHrHcDFgVsVwT6ARMMbPlQHdgvJkd7e673H09gLvPBn4C2hZ2wFgSQO3cfW7OQjghUeeccWoiIlLhKU6IiEg0ihMiInuaCbQxs1ZmlgT0AcbnrHT3ze5e391buntLYDpwvrvPMrMG4STSmNnBQBug0GtqLEPAFprZs8CYcPkSYJGZVQEy9uLkRESkfFKcEBGRaBQnRETycPdMMxsITAISgVHuPtfMhgCz3H18lM1PAoaYWSaQBVzv7hsKO2YsCaB+wJ+BWwjGqH1FMPt0BnBqDNuLiEj51g/FCRERKVg/FCdERPbg7hOBiXnKBhVQ95SI9+8A7+zt8QpNALn7TuDv4SuvbXt7QBERKV8UJ0REJBrFCRGR0qHABJCZve3ufzCzH4iYiTqHux8e15aJiEippjghIiLRKE6IiJQu0XoA3Rz+PLc4GiIiImWO4oSIiESjOCEiUooUmABy99XhrNIvuvvpxdgmEREpAxQnREQkGsUJEZHSJepj4N09C9hhZgcUU3tERKQMUZwQEZFoFCdEREqPWJ4Clgb8YGafANtzCt39pri1SkREyhLFCRERiUZxQkSkFIglATQhfImIiORHcUJERKJRnBARKQViSQC9BbQmmLn/J3dPi2+TRESkjFGcEBGRaBQnRERKgQLnADKzSmb2GJACvAy8Bqwws8fMrHJxNVBEREonxQkREYlGcUJEpHSJNgn040BdoJW7H+XunYFDgNrAE8XROBERKdUUJ0REJBrFCRGRUiRaAuhc4Dp335pT4O5bgD8B58S7YSIiUuopToiISDSKEyIipUi0BJC7u+dTmEUwfldERCo2xQkREYlGcUJEpBSJlgCaZ2ZX5i00s8uBBfFrkoiIlBGKEyIiEo3ihIhIKRLtKWA3AO+a2dXAbIIs/TFAVeDCYmibiIiUbooTIiISjeKEiEgpUmACyN1XAt3MrAfQETDgP+4+ubgaJyIipZfihIiIRKM4ISJSukTrAQSAu38GfFYMbRERkTJIcUJERKJRnBARKR0KTQCJiJQnTQ5vUtJNEBGRUkxxQkREoinLcSLaJNAiIiIiIiIiIlIOKAEkIiIiIiIiIlLOKQEkIiIiIiIiIlLOKQEkIiIiIhInZtbTzBaa2RIzuydKvYvMzM3s6OJsn4iIVBxKAImIiIiIxIGZJQLDgbOBDsClZtYhn3o1gZuAr4u3hSIiUpEoASQiIiIiEh9dgSXuvtTd04ExQO986g0FHgPSirNxIiJSsSgBJCIiIiKyD8xsgJnNingNyFOlKbAiYjklLIvcR2egubt/GOfmiohIBVeppBsgIiIiIlIWuftIYGSUKpbfZrkrzRKAp4B+RdsyERGRPakHkIiIiIhIfKQAzSOWmwGrIpZrAp2AKWa2HOgOjNdE0CIiEg9KAImIiIiIxMdMoI2ZtTKzJKAPMD5npbtvdvf67t7S3VsC04Hz3X1WyTRXRETKMyWARERERETiwN0zgYHAJGA+8La7zzWzIWZ2fsm2TkREKhrNASQiIiIiEifuPhGYmKdsUAF1TymONomISMWkBFAR+HXjrzz06kNs2LqBBEvg3OPO5aJTLuKB0Q/wy6+/ALBt5zZqVK3Bi3e/SGZWJo+/+TiLViwiKzuLs445i75n9gVg646tPP7m4yxbvQwz4+7L7qZjq467Hc/d+ec7/2T6vOkkJyVzT997aNu8LQAfff0Rr378KgBXnHkFPbv1BGDhLwt55PVH2JWxi+4dunPj72/ELL95CWV/FPS3sDhlMU++9STpmekkJiRy6x9upX2L9lH/LZ97/zmmzZsGwJVnXUmPLj32OF56RjoPv/YwC1cs5IDqBzCo3yAa12sMwOsfv86E6RNITEjkxt/fSNf2XQH4et7XPPPuM2RlZ9Hr2F70PaNvMf12RKQwX874kgefeZDsrGwu7nUxAy7b/YFCo98ezb8n/pvExETqHlCXh+56iKaNggcKtT+tPW1bBdePxg0bM+LBEQDc88g9zPh+BjWr1wTgkXseoX3r9sV4VrIvDjnrEHoO60lCYgLfvPANUx+dukedDhd34JT7T8HdSf0+lXf7vgvA6Y+eTptebbAEY+knS/no5o8A6NSnEyf85QRw2LpqK+9e/i471+8s1vMSkf1Tq2Utmp/WHAzWzVlH6ozUfOvVblubQ3ofwvxX5rMjdQeWYBx05kFUb1Qdd2fFZyvYtmIbAHXa1aFx98Y4Tsa2DJZNWEbWzqziPC3ZBw0Oa0DHKzpiCcYvU37hpw9/2m19sxOb0b5Pe9I2pgGw/JPlrPgieChhsxOa0aZ3GwAWv7+YlK9SSEhK4Kgbj6L6gdXxbCf121QWvL2geE9K4k4JoCKQmJDIny/8M22bt2VH2g4GPD6Aow89msH9B+fW+de4f1E9uToAU76dQnpmOqPvHU1aehpXPXQVPY7qQeN6jXnm3Wfo2r4rQ64ZQkZmBmnpaXsc7+t5X5OyNoXX//o685bP46m3n+LZ259ly/YtvPzRyzx3x3OYGQMeH8Dxhx1PzWo1eertp7ijzx10aNmBu0fczYz5M+jWoVux/Y4qioL+Fp57/zn6nd2Pbh26MX3udEa8P4JhNw0r8N9y2txpLEpZxAt3vUBGZgY3/+NmurXvRvWq1Xc73sTpE6lRrQZvDHqDybMnM3L8SAb3H8zy1cv57JvPeOnel1i/ZT23P3M7r/41SAwO+/cwnrjhCRrUbsD1T1zP8Z2Op2XjlsX/yyoHzCwZ+BKoQnA9Hevug82sFTAGqAt8A1zh7ulmVgV4BTgKWA9c4u7Lw33dC1wDZAE3ufuk4j4fKVlZWVkMGTaE0Y+PpmGDhlx0/UX0OK4HrVu2zq3Tvk173hnxDlWTq/LG+2/w+HOP8/TgpwFITkrm/Rfez3ffd11/Fz1P7lks5yH7zxKMc4afw6tnvMqWlC1cN/M6Fo5fyLr563Lr1G1dlxPuPYFRx48ibVMa1RpUA6DZsc1ofnxzRhweJAD7f9WfFie34JevfqHnsJ4M7zCcnet3cvqjp9N1YFe+eOCLEjlHEdkHBgedcRCL3l5ExtYM2l3Rjs0/bSZt/e6fFxIqJ3BglwPZtmpbbln9I+oDMO+leVSqVonWv2/NglcXgEHzHs2ZO3ouWTuzaHpyUw7sfCCr/7e6WE9N9pJBp6s68fWjX7Nzw05OHHIiqd+k7vZvDrD669X8+MqPu5VVrl6ZNhe24atBX4HDCUNPIPWbVLIys1g6cSnr56/HEo3u93anweENWDtnbXGemcSZ5gAqAvUOqJfba6NacjVaNGzBus2/3aS5O59/+zmnHXUaAGZG2q40MrMy2ZWxi8qJlameXJ3tO7fz/ZLv6XVsLwAqV6pMzWo19zje1B+mclbXszAzOrbqyLad21i/eT0zF8zk6EOPplb1WtSsVpOjDz2aGfNnsH7zeranbadjq46YGWd1PYuv5nxVDL+ZiqegvwUzY3vadgC2p22n/gFBEC7o3/LnNT9zROsjqJRYiapVqtK6aWtmzJ+xx/Gm/jCVnl2DD3UnH3kysxfNxt2Z+sNUenTpQVLlJBrXa0zTBk1Z8PMCFvy8gKYNmtKkfhMqV6pMjy49mPrDnt8qS8x2AT3c/QjgSKCnmXUHHgWecvc2wEaCxA7hz43u3prgsb+PAphZB4KJQTsCPYF/mVlisZ6JlLg5C+bQokkLmjdpTlLlJHr16MXkqZN3q9O9c3eqJlcF4MgOR7Jm7ZqSaKrEWdOuTdmwZAOblm0iOyObuWPm0q53u93qdLmuCzOHzyRtU/DBb8faHcEKh0rJlUhMSiSxSiKJlRPZnro96PVrkFQ9CYAqtaqwddXWYj0vEdk/1RtXJ21jGumb0/FsZ+OCjdRuXXuPek1OaELqjFQ803PLkusls/WX4P985o5MsnZlUa1RNTDAILFycNuRmJRIxraMYjkf2Xe1D6nN9tTt7Fi7A89yVk5fScOjGsa0bYPDGrDux3VkbM8gY0cG635cR4PDG5Cdns36+esB8Cxny/ItVK1bNZ6nISVACaAitnr9ahavXEz7Fr91r5/z0xzq1KxDswObAcEH9eQqyfz+vt9zyeBLuKTHJdSqXotV61dRu0ZtHnn9Ea599Foee+Mxdu7as2v22s1raVC7Qe5yg9oNWLt5LWs3raVBnTzlm9YWWF/iK/JvYeDvBjLi/RFcPOhinn3vWa477zqg4H/LQ5ocwox5M0hLT2PTtk18u/hbft306x7HiNy+UmIlaiTXYPP2zUF5fn8Lm/S3UJQ8kPNVS+Xw5UAPYGxY/jJwQfi+d7hMuP40C8Zi9gbGuPsud18GLAG6FsMpSCmSui6VRgc2yl1u2KAhqevy79oPMHbiWE7qdlLu8q7/b+/ew60q60WPf38sWFxUEJcgyE2ihYjSBi/IwTJ1u1O6aLmtg2ZlWZTi0fKys6eOpqWZe0en82QWpaeLqduOlZwtRRruzNJCkbgpCIiwgI2Q3ER0Aet3/phzLRasC9d1cfL9PM98mPMd7xjjHXMNxm+t3xjv+1a/yQWfvYCPXPERHnvysZ3qfvvub/OByz7AbXfeRnV19YFvvA6ow/odxsblG+s+b6zayGH9dr4hVDG0goqhFXzyyU9y2VOXMeScIQBUPV3F0seXcu2qa7l21bUsnraYtS+spWZbDY9c/giXz7mca1ZeQ6/hvXju7uda9bgk7Z9Oh3Zi66YdyZnqTdV0OrTTTnW69u5KefdyNizZsFP5lle2FJJFAeU9yul2VDfKu5dDDSx7dBnDLx3OOy5/B10qurB2zlrUvnXt2ZU3Xt3x5Ncbr75B154NkzV9TunD6beezkn/4yS6HNEFgC5HdGHLq1t2Wrd2Wa2O3TrSe1Rv1s7zXCg1rZ4AiohPNrNsQkQ8ExHP3Dv13tZs1gHx+puvc9PdN3HlBVfu1FXn98/+vu7pH4DnX36esijjoa8/xP033c+Djz/IyrUr2V6znYVVCzn/nefzoy/+iK6du3LfY/c13FE2LAoaH88nIvaqvg6MXc+Fh598mIkfmsgvbvkFEz80kTvuu6NQsYmfzSnHncKpw09l4rcn8rWffI3jjzmesg6NPBDS2PpN/cybGPPJsaCaVv+aVHxNaKROWUTMAl4BHgUWA+uLM78AVAH9iu/7AcuhbmaYDUBF/fJG1jno7GmcmHzv5NZsVovLbPgft6n/nw8/+jBzF8zl0//903Vlj//74/zyB7/kW1/5Frd99zaWrSiMQXfNZ67htz/5LQ/d9RAbNm5g8v2l9b2VokZ/7rucHh06duCIyiP4yRk/4aGLHuK8H51H5x6d6TmkJ0cedyST+k9iUr9JHHPWMQx810A6dOzAyZefzA9G/YBJR09i9ezVvPNL72ydA5IOsD2NE798+pet2ax2YcCZA6h6vKpB+do5a6neVM1xHz+OAWcOYPPKzWRNQgfoNbIX8386n9l3zWbLmi30ObVPI1tWu9JYmNjl94jVz61m+hem88SXn2DNvDWM/OzIprdXb9XoEJx4xYks/d3SHU+XqmS0xRNANze1IDMnZ+bJmXnyJe+9pDXbtN+2bd/GTXffxNknn83p/3D6TuV/nP1Hzhx1Zl3Z75/5PaOPG03Hso70PKwnJww+gQXLFtDr8F70OrwXw48ZDhSeFHpx+YsN9lX7NEetNevXcGSPIwvl65oob6S+WkZj58K0v06re3/GqDN44eXCgGrN/Ww+ds7HuPuLd/Otid8iSfr36t9gX/XX37Z9G6+98Rrdu3Xfu3Ohu+dCU+pfk4qvBn85Z+b2zBwJ9Kfw1E5jo+vWhtXG/prPZsoPVnsUJyZc0iAf95bWp1cf/uuVHV26Vq9ZTe+K3g3q/fnZP/P9e7/PXbfeRXl5eV35UUcWHv0ecPQARo8czfxF8wHoXdGbiKC8vJwLxl3AnBfmtPCRaH9trNpI9wHd6z5379+9QXetjVUbWfDwAmq21bB+6XrWLlhLRWUFx33oOFY8vaLwaP/mrSz6zSL6j+lPn5GFP+jWLVkHwLwH5zFg7IDWOyjpwNqjOHHBmAtas00tbutrW+l02I4nfsoPK9+pu1aH8g50PbIrQ8cP5YQJJ3DI0Ycw5MldhKkAABKXSURBVIIhdDuqGyRUPV7F8z95nsW/XkxZ5zLeXPcm3XoXxg+rXl94OnTdgnUc2u/Q1j0w7bUtr27Z6amdLkd0qesSXGvra1up2VYDwLLHl9HjmB5A8Wmhel27uhzRpW6gaIARnxrB5tWbeWnaSy15CGojLZIAiojZTbzmAHvWOfEtJDO54747GHjUQD5y1kd2WvbsgmcZ2HsgvXvu+CW+d8/ezHxxJpnJlje3MH/pfAYeNZCK7hX0Prw3y1Yvq1t3UJ9BDfY3dsRYpv11GpnJvJfmcUiXQ6joUcEpw05hxgsz2PT6Jja9vokZL8zglGGnUNGjgm5dujHvpXlkJtP+Oo3TRpzWsl/KQaqpc6GiRwWzFs0CYObCmXXJnKZ+lttrtrNhc+HR3cUrFrN45WJOHnZyg/2NPWEsv/1rYXaXP8z6AydWnkhEMHbEWKbPnE711mpW/X0VVWuqGDZoGMcOPJaqNVWs+vsqtm7byvSZ0xk7YmxLfy0HhcxcD/wnMAY4PCJqB9nvD6wsvq8CBgAUl/cAXq1f3sg6JelgixN7YsSwESxdsZTlq5ZTvbWaR6Y/wlljd579b/6L87lx0o3cdetdVPSsqCvfsGlDXdeuVze8ysy5M3n7oMLg0a/8vdB9NDN57MnHqBxc2UpHpH21YsYKKiorOPyYw+nQqQPHjz+eBVMW7FTnhV+/wDFnHgNA14quVAytYN2SdWxYtoFB7x5ElAUdOnZg0LsHsfb5tWxcsZFew3vR7cjCH3tD/mnIToNKS+2NcaKhzas206VnF8p7lBMdgp7DerJ+0fq65TXVNfztzr8xd/Jc5k6ey+aVm1n8y8WFWcA6Bh06Ff70O2zQYWRN8sbf32Drpq10rehKx66FX1u6D+reYFBptT8blmzgkD6H0LVXV6Is6DemH6tn7txtvHOPznXv+5zYp26A6DVz1tBrRC86detEp26d6DWiF2vmFG4QH3vhsXTq1ol5985rvYNRq2qpWcCOAs6hMPhpfQH8uYX22WbmLJnD72b8jrcd/TYu+2ZhrNfPvP8zjDl+DNNnTuesk3b+Bf6Dp3+Qb/78m3zyG58kMxk3ZhxD+hX67l914VV8/adfZ9v2bfSt6MsNH70BgIefLMzscv47z2fM8DH8Zd5f+OgtH6VzeWe++NEvAtD9kO58/JyP89l/+ywAnzj3E3Q/pHAH8Qsf+QK3//x2qqurGT18tDOAtZCmzoXrxl/Hdx8qTL1e3qmca8dfC9Dkz3Lb9m1c9b+uAgqDSX/5Y1+mY1nhv+s9j9zDsQOP5bQRp/He//ZebvvZbVx8y8V079adGy+9EYDBfQdzxqgzuPS2SykrK+PzH/58XReyqy+8muu/dz01NTWMGzOOwX0Ht+p3VEoiohewNTPXR0RX4GwKAzs/DlxIYSawTwC1UzNNKX5+qrh8emZmREwB7ouIScDRQCXQcNTv0nJQxYk90bGsIzdedSOf/pdPs71mO/887p+pHFzJd+75DiccewL/eNo/csf37+D1La9z9VevBnZM97745cXcNOkmIoLM5DMXfaZu9rDrbr2OdevXkZkMe/swbr6myRvnaidyezL1yqlcMu0SoiyYdc8s1sxfwxk3n8HKZ1ay8P8tZPG0xQx5zxCumHcFNdtrePT6R9ny6hbm/9/5DD5rMJfPuRwSFv12EQv/YyEAf7j5D1z6xKXUbK1h/cvrefjSxmeNk9oJ48SuEpY9tozKCyuJDsHaOWt54+9v0Pe0vrz+X6+zYfGGJlft1K0TlR+uJLMw1fvSqUsB2Lp5Kyv/vJKhFw0la5LqDdUs/c3S1jke7bOsSeb9dB6nXn8q0SFY/sRyXlvxGkMvGMqGlzaw+rnVDD5nMEeNOqrwc32tmlmTCzejt27eyou/fpF33lLoBrzwVwvZunkrXXp2ofL8Sjat2MS7vvYuYOep49UyIuJc4DtAGfCjzLx9l+WfAyZSmCn4NWBCZs4vLtvrWYSjsTEH9ldE3A38n8xsMNVURNyXmRfvbhurpq06mLs/SGpC33P67tegRXtzbdndviLiHRQGdS6j8ETlg5l5S0S8jR3TwD8HXJKZbxanjf8ZMIrCkz/jM3NJcVtfBj4FbAM+n5m/2euDews5EHGClQd1NznVc3M/k1ra4aa8qd3ECe27AxEnnv3XZ40TAmDVbKe11w7v/9n720WcKM76uxD4Jwo9AmYAF9UmeIp1umfmxuL784ArMvPc4izC91MYguJo4DFgaGZub649LfIEUGZe1syy3f9SL0lvAZk5m0IyZ9fyJTQyi1dmvgF8uIlt3QrceqDb2F4ZJyRJzTFOSDoIjAYW1bsh/ACF2YHrEkC1yZ+iQ9gxTmjdLMLASxFRO4vwU83t0GngJUmSJEmSDrDdzCq8RzMBR8TEiFgM3AFctTfr7qqlxgCSJEmSJEk6aBVnEW4wk3DRHs0EnJl3AndGxMXAVyiMKbpPswj7BJAkSZIkSVLr2tuZgB8APriP6wImgCRJkiRJklrbDKAyIgZHRDkwnsKswXUiorLex/cBLxbfTwHGR0TniBjMHs4ibBcwSZIkSZKkVpSZ2yLiSmAahVmF78nMeRFxC/BMZk4BroyIs4GtwDoK3b8o1nuQwoDR24CJu5sBDEwASZIkSZIktbrMnApM3aXsxnrvr25m3b2eRdguYJIkSZIkSSXOBJAkSZIkSVKJMwEkSZIkSZJU4kwASZIkSZIklTgTQJIkSZIkSSXOBJAkSZIkSVKJMwEkSZIkSZJU4jq2dQMkqTX1HdG3rZsgSWrHjBOSpOa8leOETwBJkiRJkiSVOBNAkiRJkiRJJc4EkCRJkiRJUokzASRJkiRJklTiTABJkiRJkiSVOBNAkiRJkiRJJc4EkCRJkiRJUokzASRJkiRJklTiTABJkiRJkiSVOBNAkiRJkiRJJc4EkCRJkiRJUokzASRJkiRJklTiTABJkiRJLSQizo2IBRGxKCJuaGT55yJiTkTMiognI2J4W7RTklT6TABJkiRJLSAiyoA7gXHAcOCiRhI892XmiMwcCdwBTGrlZkqSDhImgCRJkqSWMRpYlJlLMrMaeAA4v36FzNxY7+MhQLZi+yRJB5GObd0ASZIk6a0oIiYAE+oVTc7MyfU+9wOW1/tcBZzayHYmAtcA5cBZLdBUSZJMAEmSJEn7opjsmdxMlWhstUa2cydwZ0RcDHwF+MSBaaEkSTvYBUySJElqGVXAgHqf+wMrm6n/APDBFm2RJOmgZQJIkiRJahkzgMqIGBwR5cB4YEr9ChFRWe/j+4AXW7F9kqSDiF3AJEmSpBaQmdsi4kpgGlAG3JOZ8yLiFuCZzJwCXBkRZwNbgXXY/UuS1EJMAEmSJEktJDOnAlN3Kbux3vurW71RkqSDkl3AJEmSJEmSSpwJIEmSJEmSpBJnAkiSJEmSJKnEmQCSJEmSJEkqcSaAJGkfRcQ9EfFKRMytV/bViFgREbOKr/fWW/aliFgUEQsi4px65ecWyxZFxA2tfRySJEmSSp8JIEnadz8Gzm2k/NuZObL4mgoQEcOB8cDxxXW+FxFlEVEG3AmMA4YDFxXrSpIkSdIBYwJIkvZRZj4BvLqH1c8HHsjMNzPzJWARMLr4WpSZSzKzGnigWFeSJElSCdtdT4CIOD0iZkbEtoi4cJdl2+v1OpiyJ/szASRJTYiICRHxTL3XhD1c9cqImF3sItazWNYPWF6vTlWxrKlySZIkSSVqD3sCLAMuBe5rZBNb6vU6OG9P9mkCSJKakJmTM/Pkeq/Je7DaXcAQYCSwCvhWsTwa20Uz5ZIkSZJK1257AmTm0sycDdQciB2aAJKkAygzV2fm9sysAX5I4cIOhSd7BtSr2h9Y2Uy5JEmSpLew3fQo2N+eAF2K23w6Ij64Jyt03IuNS5J2IyL6Zuaq4scPAbUzhE0B7ouIScDRQCXwVwpPAFVGxGBgBYWBoi9u3VZLkiRJOtCKPQia6kWwvz0BBmbmyoh4GzA9IuZk5uLmVjABJEn7KCLuB84AjoyIKuAm4IyIGEnh4r0U+CxAZs6LiAeB+cA2YGJmbi9u50pgGlAG3JOZ81r5UCRJkiS1rv3qCZCZK4v/LomI/wRGASaAJKklZOZFjRTf3Uz9W4FbGymfCkw9gE2TJEmS1L7NYB97AhQnmnk9M9+MiCOB04A7dreeYwBJkiRJkiS1oszcBtT2BHgeeLDYa+CWiDgPICJOKfY0+DDwg4io7SlwHPBMRPwNeBy4PTPn726fkelkM+1ZREzYw5mHVOI8FyQ1xmuDankuSGqM1wbV8lyQTwC1fxN2X0UHCc8FSY3x2qBanguSGuO1QbU8Fw5yJoAkSZIkSZJKnAkgSZIkSZKkEmcCqP2zj6ZqeS5IaozXBtXyXJDUGK8NquW5cJBzEGhJkiRJkqQS5xNAkiRJkiRJJc4EkCRJkiRJUokzAdRORcS5EbEgIhZFxA1t3R61nYi4JyJeiYi5bd0WSe2HcUK1jBOSGmOcUC3jhGqZAGqHIqIMuBMYBwwHLoqI4W3bKrWhHwPntnUjJLUfxgnt4scYJyTVY5zQLn6McUKYAGqvRgOLMnNJZlYDDwDnt3Gb1EYy8wng1bZuh6R2xTihOsYJSY0wTqiOcUK1TAC1T/2A5fU+VxXLJEkC44QkqXnGCUkNmABqn6KRsmz1VkiS2ivjhCSpOcYJSQ2YAGqfqoAB9T73B1a2UVskSe2PcUKS1BzjhKQGTAC1TzOAyogYHBHlwHhgShu3SZLUfhgnJEnNMU5IasAEUDuUmduAK4FpwPPAg5k5r21bpbYSEfcDTwHHRkRVRFzW1m2S1LaME6rPOCFpV8YJ1WecUK3ItCuoJEmSJElSKfMJIEmSJEmSpBJnAkiSJEmSJKnEmQCSJEmSJEkqcSaAJEmSJEmSSpwJIEmSJEmSpBJnAkg7iYjtETErIuZGxC8iott+bOuMiPiP4vvzIuKGZuoeHhFX7MM+vhoR1zWx7OPF45gXEfNr60XEjyPiwr3dlyTJOCFJap5xQmq/TABpV1syc2RmngBUA5+rvzAK9vq8ycwpmXl7M1UOB/b6gt2UiBgHfB54T2YeD5wIbDhQ25ekg5hxQpLUHOOE1E6ZAFJz/gi8PSKOiYjnI+J7wExgQES8JyKeioiZxcz+oQARcW5EvBARTwIX1G4oIi6NiO8W3x8VEb+KiL8VX2OB24EhxbsF/1qsd31EzIiI2RFxc71tfTkiFkTEY8CxTbT9S8B1mbkSIDPfyMwf7lopIm4s7mNuREyOiCiWX1XM8s+OiAeKZe8utm9WRDwXEYft5/crSW91xgnjhCQ1xzhhnFA7YgJIjYqIjsA4YE6x6Fjgp5k5CtgMfAU4OzNPBJ4BromILsAPgQ8A7wL6NLH5/w38ITP/gUImfR5wA7C4eLfg+oh4D1AJjAZGAidFxOkRcRIwHhhFISCc0sQ+TgCe3YND/W5mnlK8Q9EVeH+x/AZgVGa+gx13La4DJmbmyOLxbdmD7UtSSTJOGCckqTnGCeOE2h8TQNpV14iYReEivAy4u1j+cmY+XXw/BhgO/KlY9xPAIGAY8FJmvpiZCdzbxD7OAu4CyMztmdnYo5TvKb6eo3CXYBiFC/i7gF9l5uuZuRGYsl9HC2dGxF8iYk6xXccXy2cDP4+IS4BtxbI/AZMi4irg8Mzc1nBzklTyjBMFxglJapxxosA4oXanY1s3QO3OlmJGuk7xKcbN9YuARzPzol3qjQTyALUjgG9k5g922cfn93Af84CTgOlN7qBwh+F7wMmZuTwivgp0KS5+H3A6cB7wPyPi+My8PSIeAd4LPB0RZ2fmC3t5XJL0VmecKDBOSFLjjBMFxgm1Oz4BpH3xNHBaRLwdICK6RcRQ4AVgcEQMKda7qIn1fw9cXly3LCK6A5uA+n1gpwGfqtcXuF9E9AaeAD4UEV2LfWY/0MQ+vgHcERF9iut3Lmba66u9OK8t7ufCYt0OwIDMfBz4FwoDyh0aEUMyc05mfpPCHY1hzX1JknQQM04YJySpOcYJ44TagE8Aaa9l5pqIuBS4PyI6F4u/kpkLI2IC8EhErAWepNB3dldXA5Mj4jJgO3B5Zj4VEX+KiLnAb4r9do8DnireMXgNuCQzZ0bEvwOzgJcpDCzXWBunRsRRwGNR2EAC9+xSZ31E/JBCv+SlwIziojLg3ojoQeHOwbeLdb8WEWcW2zwf+M3efXOSdHAwThgnJKk5xgnjhNpGFLpWSpIkSZIkqVTZBUySJEmSJKnEmQCSJEmSJEkqcSaAJEmSJEmSSpwJIEmSJEmSpBJnAkiSJEmSJKnEmQCSJEmSJEkqcSaAJEmSJEmSStz/BzAYGGuRVdPvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=blue><h3>LightGBM</h3></font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=LGBMClassifier(), n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3],\n",
       "                         'n_estimators': [5, 10, 50, 75, 100, 200]},\n",
       "             return_train_score=True, scoring='roc_auc')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#build_the_lightgbm_model\n",
    "gbdt_set = lgb.LGBMClassifier()\n",
    "params = {'learning_rate':[0.0001, 0.001, 0.01, 0.1, 0.2, 0.3], 'n_estimators':[5, 10, 50, 75, 100, 200]}\n",
    "\n",
    "clf_set = GridSearchCV(gbdt_set, params, cv=3, scoring='roc_auc', n_jobs=-1, return_train_score=True)\n",
    "clf_set.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyper_Parameter : 0.1\n",
      "Best Score:  0.8900322279325273\n",
      "Best n_estimators:  200\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Hyper_Parameter :\",clf_set.best_params_['learning_rate'])\n",
    "print(\"Best Score: \",clf_set.best_score_)\n",
    "print(\"Best n_estimators: \",clf_set.best_params_['n_estimators'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "y_train_pred = clf_set.predict_proba(X_train)[:,1]  \n",
    "y_test_pred = clf_set.predict_proba(X_test)[:,1]\n",
    "\n",
    "train_fpr, train_tpr, train_thresholds = roc_curve(y_train, y_train_pred)\n",
    "test_fpr, test_tpr, test_thresholds = roc_curve(y_test, y_test_pred)\n",
    "\n",
    "#store_the_auc_value_of_train_and_test_for_further_step_\n",
    "train_auc_set = auc(train_fpr, train_tpr)\n",
    "test_auc_set = auc(test_fpr, test_tpr)\n",
    "\n",
    "plt.plot(train_fpr, train_tpr, label=\"train AUC =\"+str(auc(train_fpr, train_tpr)))\n",
    "plt.plot(test_fpr, test_tpr, label=\"test AUC =\"+str(auc(test_fpr, test_tpr)))\n",
    "plt.legend()\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC AUC Plot\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.9824446982007494\n",
      "Test AUC Score 0.8967067029829835\n"
     ]
    }
   ],
   "source": [
    "print(\"Train AUC Score\",train_auc_set)\n",
    "print(\"Test AUC Score\",test_auc_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Value of test_tpr*(1-test_fpr) 0.650672930328638 for threshold 0.506\n"
     ]
    }
   ],
   "source": [
    "t = test_thresholds[np.argmax(test_fpr*(1-test_tpr))]\n",
    "#this_will_be_maximum_if_fpr_is_very_low_and_tpr_is_very_high\n",
    "    \n",
    "print(\"Maximum Value of test_tpr*(1-test_fpr)\", max(test_tpr*(1-test_fpr)), \"for threshold\", np.round(t,3))\n",
    "\n",
    "pred_y = []\n",
    "for i in y_test_pred:\n",
    "    if i>=t:\n",
    "        pred_y.append(1)\n",
    "    else:\n",
    "        pred_y.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=blue><h3>XGBoost</h3></font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-logloss:0.68605\tvalid-logloss:0.68602\n",
      "[10]\ttrain-logloss:0.62637\tvalid-logloss:0.62643\n",
      "[20]\ttrain-logloss:0.58320\tvalid-logloss:0.58381\n",
      "[30]\ttrain-logloss:0.55090\tvalid-logloss:0.55200\n",
      "[40]\ttrain-logloss:0.52625\tvalid-logloss:0.52782\n",
      "[50]\ttrain-logloss:0.50679\tvalid-logloss:0.50888\n",
      "[60]\ttrain-logloss:0.49133\tvalid-logloss:0.49383\n",
      "[70]\ttrain-logloss:0.47877\tvalid-logloss:0.48174\n",
      "[80]\ttrain-logloss:0.46849\tvalid-logloss:0.47175\n",
      "[90]\ttrain-logloss:0.45998\tvalid-logloss:0.46359\n",
      "[100]\ttrain-logloss:0.45312\tvalid-logloss:0.45716\n",
      "[110]\ttrain-logloss:0.44723\tvalid-logloss:0.45162\n",
      "[120]\ttrain-logloss:0.44210\tvalid-logloss:0.44689\n",
      "[130]\ttrain-logloss:0.43762\tvalid-logloss:0.44268\n",
      "[140]\ttrain-logloss:0.43364\tvalid-logloss:0.43901\n",
      "[150]\ttrain-logloss:0.43036\tvalid-logloss:0.43607\n",
      "[160]\ttrain-logloss:0.42730\tvalid-logloss:0.43338\n",
      "[170]\ttrain-logloss:0.42466\tvalid-logloss:0.43105\n",
      "[180]\ttrain-logloss:0.42222\tvalid-logloss:0.42893\n",
      "[190]\ttrain-logloss:0.42000\tvalid-logloss:0.42708\n",
      "[200]\ttrain-logloss:0.41795\tvalid-logloss:0.42540\n",
      "[210]\ttrain-logloss:0.41602\tvalid-logloss:0.42384\n",
      "[220]\ttrain-logloss:0.41402\tvalid-logloss:0.42218\n",
      "[230]\ttrain-logloss:0.41211\tvalid-logloss:0.42067\n",
      "[240]\ttrain-logloss:0.41055\tvalid-logloss:0.41951\n",
      "[250]\ttrain-logloss:0.40891\tvalid-logloss:0.41818\n",
      "[260]\ttrain-logloss:0.40735\tvalid-logloss:0.41707\n",
      "[270]\ttrain-logloss:0.40549\tvalid-logloss:0.41568\n",
      "[280]\ttrain-logloss:0.40398\tvalid-logloss:0.41470\n",
      "[290]\ttrain-logloss:0.40220\tvalid-logloss:0.41358\n",
      "[300]\ttrain-logloss:0.40059\tvalid-logloss:0.41245\n",
      "[310]\ttrain-logloss:0.39914\tvalid-logloss:0.41149\n",
      "[320]\ttrain-logloss:0.39770\tvalid-logloss:0.41070\n",
      "[330]\ttrain-logloss:0.39620\tvalid-logloss:0.40976\n",
      "[340]\ttrain-logloss:0.39488\tvalid-logloss:0.40901\n",
      "[350]\ttrain-logloss:0.39346\tvalid-logloss:0.40820\n",
      "[360]\ttrain-logloss:0.39212\tvalid-logloss:0.40754\n",
      "[370]\ttrain-logloss:0.39082\tvalid-logloss:0.40681\n",
      "[380]\ttrain-logloss:0.38951\tvalid-logloss:0.40623\n",
      "[390]\ttrain-logloss:0.38825\tvalid-logloss:0.40562\n",
      "[399]\ttrain-logloss:0.38713\tvalid-logloss:0.40510\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "params = {}\n",
    "params['objective'] = 'binary:logistic'\n",
    "params['eval_metric'] = 'logloss'\n",
    "params['eta'] = 0.02\n",
    "params['max_depth'] = 4\n",
    "\n",
    "d_train = xgb.DMatrix(X_train, label=y_train)\n",
    "d_test = xgb.DMatrix(X_test, label=y_test)\n",
    "\n",
    "watchlist = [(d_train, 'train'), (d_test, 'valid')]\n",
    "\n",
    "bst = xgb.train(params, d_train, 400, watchlist, early_stopping_rounds=20, verbose_eval=10)\n",
    "\n",
    "xgdmat = xgb.DMatrix(X_train,y_train)\n",
    "predict_y = bst.predict(d_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE TEST LOG IS:  0.405098605657885\n"
     ]
    }
   ],
   "source": [
    "print(\"THE TEST LOG IS: \",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOTAL NUMBER OF DATA POINTS:  15000\n"
     ]
    }
   ],
   "source": [
    "predicted_y =np.array(predict_y>0.5,dtype=int)\n",
    "print(\"TOTAL NUMBER OF DATA POINTS: \", len(predicted_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}