HandsON_Regressions.ipynb

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   "source": [
    "![alt text](https://github.com/EnxaisRichard/regressionstarterkit/blob/master/images/blogtitle.PNG?raw=true \"Enaxis ML Blog Title\")"
   ]
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   "source": [
    "Are you ready to get some machine learning experience under your belt?\n",
    "\n",
    "We are in 2017 and every Fortune 500 company are finding ways to leverage machine learning algorithms to future proof and automate their business.  Leading scientists are predicting Moore's Law on the growth for technology will become stale and the shift will move to making machines more smarter to the human race. We see the [effects of machine learning]\n",
    "(https://www.forbes.com/sites/bernardmarr/2016/09/30/what-are-the-top-10-use-cases-for-machine-learning-and-ai/#63c6398e94c9) escalating to make everyday life more personalized to everyone.  Online shopping recommendation, auto-enhancing selfies, self-driving cars, or even unlocking our phones are driven by machine learning technology.\n",
    "\n",
    "The concept of learning math and programming at the same time might be an intimidating thought to most, but rest assured, we will only go into high-level concepts here for this \"regression\" algorithms starter-kit. You might be thinking, what's are regression algorithms used for? Please refer to the diagram below for your quick answer.\n",
    "\n",
    "![alt text](https://github.com/EnxaisRichard/regressionstarterkit/blob/master/images/ds.png?raw=true \"ML Categories\")\n",
    "\n",
    "\n",
    "As Benjamin Franklin always said during his time, “Tell me I forget. Teach me I remember. Involve me I learn.”  So lets focus on predicting numbers in this exercise. We are predicting the \"Initial Spread Index\" on a [Forest Fire dataset](http://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/) provided to us by UC Irvine public datasets. There are a bunch of different regression algorithms out there, but we will just work with three models to get you started on finding the optimal prediction. Again, to keep this kit less intimidating to follow, I wll lightly touch on your typical Data Science procedures, but leave a recommended link for those who seek a more in-depth guide. Afterall, we live in the age where “Googling” is the norm for learning a new subject as soon as possible. "
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   "source": [
    "<a id='toc'></a>\n",
    "## Table of Content\n",
    "These are hyperlinks so you can jump to different parts of this tutorial.\n",
    "\n",
    "## [I. Data Science Environment Setup](#setup)\n",
    "## [II. Data Ingestion](#ingestion)\n",
    "## [III. Data Pre-Processing](#preprocessing)\n",
    "## [IV. Regression Modeling](#regression)\n",
    "\n",
    "For those interested in copying+pasting the whole code at once, you can find the github repo [here](https://github.com/EnxaisRichard/regressionstarterkit/blob/master/HandsON_Regressions.py)"
   ]
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   "source": [
    "<a id='setup'></a>\n",
    "### I. Data Science Environment Setup\n",
    "\n",
    "Step 1. [Download Anaconda based on your system specification](https://www.anaconda.com/download/).\n",
    "\n",
    "Step 2. Open Command Prompt from your start menu.\n",
    "\n",
    "Step 3. Type \"jupyter notebook\".\n",
    "\n",
    "Step 4. Create a folder for all your Data Science Experiments.\n",
    "\n",
    "Step 5. Create a new python 3 file and follow the tutorial below.\n",
    "\n",
    "> For a more in depth guide, please click [here](https://medium.com/k-folds/setting-up-a-data-science-environment-5e6fd1cbd572)."
   ]
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   "source": [
    "![alt text](https://github.com/EnxaisRichard/regressionstarterkit/blob/master/images/jupyternotebook_preview.PNG?raw=true \"Jupyter Preview\")"
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   "source": [
    "<a id='ingestion'></a>\n",
    "### Data Ingestion\n",
    "[Back to Table of Content](#toc)\n",
    "\n",
    "#### Importing your python Data Science Libraries.\n",
    "For any libraries that do not work, go ahead and open command prompt/terminal again and type in \"pip install #library name\" to download/install python libraries automatically for quick usage."
   ]
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  {
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     "output_type": "stream",
     "text": [
      "Initial Python libraries are now ready for usage.\n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# <-- By the way, hastags are used to write comments in Python.\n",
    "#I will be commenting most of the code to give context to what is happening.\n",
    "\n",
    "#dataframe library, we call it with variable \"pd\"\n",
    "import pandas as pd\n",
    "\n",
    "#matrix and math library, we call it with variable \"np\"\n",
    "import numpy as np\n",
    "\n",
    "#python system libraries to hide warning output for cleaner notebook\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#execute and view python code in any IPython IDE\n",
    "%matplotlib inline\n",
    "\n",
    "#print produces a written output after executing your code\n",
    "print(\"Initial Python libraries are now ready for usage.\")\n",
    "print(\"-------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Uploading or referencing a dataset\n",
    "For other different methods of uploading a dataset examples, please click [here](https://chrisalbon.com/python/pandas_dataframe_importing_csv.html)."
   ]
  },
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     "text": [
      "Pull dataset from http://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv\n",
      "-------------------------------------\n",
      "   X  Y month  day  FFMC   DMC     DC  ISI  temp  RH  wind  rain  area\n",
      "0  7  5   mar  fri  86.2  26.2   94.3  5.1   8.2  51   6.7   0.0   0.0\n",
      "1  7  4   oct  tue  90.6  35.4  669.1  6.7  18.0  33   0.9   0.0   0.0\n",
      "2  7  4   oct  sat  90.6  43.7  686.9  6.7  14.6  33   1.3   0.0   0.0\n",
      "3  8  6   mar  fri  91.7  33.3   77.5  9.0   8.3  97   4.0   0.2   0.0\n",
      "4  8  6   mar  sun  89.3  51.3  102.2  9.6  11.4  99   1.8   0.0   0.0\n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Link from the UC Irvine public dataset on Forest Fires\n",
    "url=\"http://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv\"\n",
    "print(\"Pull dataset from {}\".format(url))\n",
    "print(\"-------------------------------------\")\n",
    "\n",
    "#assign a variable to the read URL dataset\n",
    "dataframe = pd.read_csv(url)\n",
    "\n",
    "#preview the first 5 rows/index of the dataset\n",
    "print(dataframe.head(5))\n",
    "print(\"-------------------------------------\")\n"
   ]
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   "source": [
    "#### Forest Fire Dataset\n",
    "We will be experimenting with the Forest Fire Data. The target value we will be predicting will be \"ISI - Initial Spread Index.\"  \n",
    "UC Irvine has kindly provided the full variable descriptions below incase you wanted more details.\n",
    "\n",
    "   1. X - x-axis spatial coordinate within the Montesinho park map: 1 to 9\n",
    "   2. Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9\n",
    "   3. month - month of the year: \"jan\" to \"dec\" \n",
    "   4. day - day of the week: \"mon\" to \"sun\"\n",
    "   5. FFMC - Fine Fuel Moisture Code Index from the FWI system: 18.7 to 96.20\n",
    "   6. DMC - Duff Moisture Code Index from the FWI system: 1.1 to 291.3 \n",
    "   7. DC - Drought Code Index from the FWI system: 7.9 to 860.6 \n",
    "   8. ISI - Initial Spread Index from the FWI system: 0.0 to 56.10\n",
    "   9. temp - temperature in Celsius degrees: 2.2 to 33.30\n",
    "   10. RH - relative humidity in %: 15.0 to 100\n",
    "   11. wind - wind speed in km/h: 0.40 to 9.40 \n",
    "   12. rain - outside rain in mm/m2 : 0.0 to 6.4 \n",
    "   13. area - the burned area of the forest (in ha): 0.00 to 1090.84 \n",
    "   (this output variable is very skewed towards 0.0, thus it may make\n",
    "    sense to model with the logarithm transform)\n",
    "    \n",
    "For more details about the dataset, please refer to the [link](http://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.names).\n",
    "\n",
    "\n",
    "For more details about the Fire index, please refer to this [link](http://www.fbfrg.org/cffdrs/fire-weather-index-fwi-system)."
   ]
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   "source": [
    "<a id='preprocessing'></a>\n",
    "### Data Pre-Processing\n",
    "[Back to Table of Content](#toc)\n",
    "\n",
    "Most dataset you work with requires a lot of data cleaning before modeling.  In this exercise, we asssume most of the data have already been cleansed from missing, wrong, duplicates, or out of range data.\n",
    "\n",
    "For a further in-depth guide to data pre-processing, please refer [here](https://www.analyticsvidhya.com/blog/2016/07/practical-guide-data-preprocessing-python-scikit-learn/)"
   ]
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     "text": [
      "Data Types\n",
      "X          int64\n",
      "Y          int64\n",
      "month     object\n",
      "day       object\n",
      "FFMC     float64\n",
      "DMC      float64\n",
      "DC       float64\n",
      "ISI      float64\n",
      "temp     float64\n",
      "RH         int64\n",
      "wind     float64\n",
      "rain     float64\n",
      "area     float64\n",
      "dtype: object\n",
      "-------------------------------------\n",
      "Data Statistical Summary\n",
      "                X           Y        FFMC         DMC          DC         ISI  \\\n",
      "count  517.000000  517.000000  517.000000  517.000000  517.000000  517.000000   \n",
      "mean     4.669246    4.299807   90.644681  110.872340  547.940039    9.021663   \n",
      "std      2.313778    1.229900    5.520111   64.046482  248.066192    4.559477   \n",
      "min      1.000000    2.000000   18.700000    1.100000    7.900000    0.000000   \n",
      "25%      3.000000    4.000000   90.200000   68.600000  437.700000    6.500000   \n",
      "50%      4.000000    4.000000   91.600000  108.300000  664.200000    8.400000   \n",
      "75%      7.000000    5.000000   92.900000  142.400000  713.900000   10.800000   \n",
      "max      9.000000    9.000000   96.200000  291.300000  860.600000   56.100000   \n",
      "\n",
      "             temp          RH        wind        rain         area  \n",
      "count  517.000000  517.000000  517.000000  517.000000   517.000000  \n",
      "mean    18.889168   44.288201    4.017602    0.021663    12.847292  \n",
      "std      5.806625   16.317469    1.791653    0.295959    63.655818  \n",
      "min      2.200000   15.000000    0.400000    0.000000     0.000000  \n",
      "25%     15.500000   33.000000    2.700000    0.000000     0.000000  \n",
      "50%     19.300000   42.000000    4.000000    0.000000     0.520000  \n",
      "75%     22.800000   53.000000    4.900000    0.000000     6.570000  \n",
      "max     33.300000  100.000000    9.400000    6.400000  1090.840000  \n",
      "-------------------------------------\n",
      "Category columns have been encoded for machine learning\n",
      "   X  Y month  day  FFMC   DMC     DC  ISI  temp  RH  wind  rain  area  \\\n",
      "0  7  5   mar  fri  86.2  26.2   94.3  5.1   8.2  51   6.7   0.0   0.0   \n",
      "1  7  4   oct  tue  90.6  35.4  669.1  6.7  18.0  33   0.9   0.0   0.0   \n",
      "2  7  4   oct  sat  90.6  43.7  686.9  6.7  14.6  33   1.3   0.0   0.0   \n",
      "3  8  6   mar  fri  91.7  33.3   77.5  9.0   8.3  97   4.0   0.2   0.0   \n",
      "4  8  6   mar  sun  89.3  51.3  102.2  9.6  11.4  99   1.8   0.0   0.0   \n",
      "\n",
      "   month_code  day_code  \n",
      "0           7         0  \n",
      "1          10         5  \n",
      "2          10         2  \n",
      "3           7         0  \n",
      "4           7         3  \n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#preview the data type for each columns\n",
    "print(\"Data Types\")\n",
    "print(dataframe.dtypes)\n",
    "print(\"-------------------------------------\")\n",
    "\n",
    "#code to summarize statistics of your current dataset BEFORE Label Encoding and Data Scaling\n",
    "print(\"Data Statistical Summary\")\n",
    "print(dataframe.describe())\n",
    "print(\"-------------------------------------\")\n",
    "\n",
    "\n",
    "#sklearn libraries are the Data Science Gold Standard library for Python\n",
    "#pre-processing library is used for data preparation\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "#create encoding object to convert categorical value to numeric\n",
    "encode = LabelEncoder()\n",
    "#create additional 2 columns to convert category columns (Month & Days) into numeric values\n",
    "dataframe['month_code'] = encode.fit_transform(dataframe['month'])\n",
    "dataframe['day_code'] = encode.fit_transform(dataframe['day'])\n",
    "\n",
    "print(\"Category columns have been encoded for machine learning\")\n",
    "print(dataframe.head(5))\n",
    "print(\"-------------------------------------\")\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our purpose for statisical summary is to examine our dataset for any unusual outliers or missing data that could throw off our model."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Visualization - Correlation Plot\n",
    "There are many creative method to visually analyze your dataset in order to make hypothesis on your prediction. The most commonly used visual for any regression algorithms are correlation plots. \n",
    "\n",
    "For more details on other visual methods, please refer [here](http://pbpython.com/simple-graphing-pandas.html)"
   ]
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------\n"
     ]
    },
    {
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DB6vq7keVbc2uynT7iuwKRqfTMXz4cN577z26dOnCJ598Qv369fnggw+Kfdzbqkal2k/B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5/I92XMmrRHdXyP4vGA/Hdgj7y32sS7xK/7CW28flym6vhejKxtMapQEYO8/o9ZeX8unlaWrvD3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\nizynubxaW/GMd6JjHttP319Pq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KAYsQUFO5aACIhGYkejWLAlv4g9poBdUDAKCrss+/sD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5T6vOdzt27Bhjxoxh/Pjx3L59m7Vr16JUKlttfun58+c5duwYu3fvBuDmzZtAzZSarl27AmBk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234d21d9198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#python math plotting/chart library\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#code to plot correlation strength amongst all varaibles/columns\n",
    "fig, ax = plt.subplots(figsize=(12,10))\n",
    "sns.heatmap(dataframe.corr(), annot=True, fmt=\".2f\")\n",
    "\n",
    "print(\"-------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that ISI is mostly correlated with the variables: FFMC(.53), temp(.39), DMC(.31), AND DC(.23). It will be interesting to find how [feature importance](https://en.wikipedia.org/wiki/Feature_selection) result are effected from the correlation plot.  In other words, does having highly correlated variable make the determining factor for your machine learning algorithms?  We shall wait and see the results below. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Scaling/Normalization\n",
    "It is generally best practice to scale your training dataset from values 0-1. Doing so won't harm your correlation, but allows your models to run faster as well as be more effective with usage of \"non-linear\" regression models.\n",
    "\n",
    "Fore more details about data scaling or normalization, please [refer here](https://en.wikipedia.org/wiki/Feature_scaling)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicting variables used are: ['X', 'Y', 'month', 'day', 'FFMC', 'DMC', 'DC', 'temp', 'RH', 'wind', 'rain', 'area', 'month_code', 'day_code']\n",
      "-------------------------------------\n",
      "The target variable we are predicting is: ISI\n",
      "-------------------------------------\n",
      "Training dataset have been scaled from 0 to 1.\n",
      "                X           Y        FFMC         DMC          DC        temp  \\\n",
      "count  517.000000  517.000000  517.000000  517.000000  517.000000  517.000000   \n",
      "mean     0.458656    0.328544    0.928318    0.378264    0.633329    0.536629   \n",
      "std      0.289222    0.175700    0.071227    0.220698    0.290918    0.186708   \n",
      "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "25%      0.250000    0.285714    0.922581    0.232598    0.504046    0.427653   \n",
      "50%      0.375000    0.285714    0.940645    0.369400    0.769673    0.549839   \n",
      "75%      0.750000    0.428571    0.957419    0.486906    0.827958    0.662379   \n",
      "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
      "\n",
      "               RH        wind        rain        area  month_code    day_code  \n",
      "count  517.000000  517.000000  517.000000  517.000000  517.000000  517.000000  \n",
      "mean     0.344567    0.401956    0.003385    0.011777    0.523475    0.456157  \n",
      "std      0.191970    0.199073    0.046244    0.058355    0.397570    0.320844  \n",
      "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
      "25%      0.211765    0.255556    0.000000    0.000000    0.090909    0.166667  \n",
      "50%      0.317647    0.400000    0.000000    0.000477    0.545455    0.500000  \n",
      "75%      0.447059    0.500000    0.000000    0.006023    1.000000    0.666667  \n",
      "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000  \n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#call scaler function(converting range of value in column from 0-1) from sklearn\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "#filter the dataframe with a variable: train:predictors & target:target\n",
    "\n",
    "target_column = 'ISI'\n",
    "#every column except target column\n",
    "train = dataframe.loc[:, dataframe.columns != target_column]\n",
    "target = dataframe[target_column]\n",
    "\n",
    "print(\"The predicting variables used are: {}\".format(list(train)))\n",
    "print(\"-------------------------------------\")\n",
    "print(\"The target variable we are predicting is: {}\".format(target_column))\n",
    "print(\"-------------------------------------\")\n",
    "\n",
    "\n",
    "#select only numeric columns for data scaling\n",
    "train = train.select_dtypes(exclude=['object'])\n",
    "#apply scaler function to the train or predictors dataset\n",
    "scaled_train = scaler.fit_transform(train)\n",
    "print(\"Training dataset have been scaled from 0 to 1.\")\n",
    "\n",
    "#code to summarize statistics of your current dataset AFTER Label Encoding\n",
    "scaled_features_df = pd.DataFrame(scaled_train, index=train.index, columns=train.columns)\n",
    "print(scaled_features_df.describe())\n",
    "print(\"-------------------------------------\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Splitting\n",
    "\n",
    "It is recommended in Data Science procedure to split your dataset by a large percent chunk for training and use the model created on training set to evaluate it's competency in our remaining test set. In other words, the \"math\" is built with the train set, and we test our \"math\" in the test set.  In our example, we will split the training set by 80% and leave the remaining 20% as testing.\n",
    "\n",
    "![alt text](https://github.com/EnxaisRichard/regressionstarterkit/blob/master/images/splitting_data.PNG?raw=true \"Train and Test Datasets\")\n",
    "\n",
    "For more explanation, please refer to this link [here](https://info.salford-systems.com/blog/bid/337783/Why-Data-Scientists-Split-Data-into-Train-and-Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset have been split and assign to new variables.\n",
      "@_train set are the 80% of the data.\n",
      "(413, 12)\n",
      "@_test set are the remaining 20% of the data.\n",
      "(104, 12)\n",
      "-------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Richard\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "#split the dataset into a training set(80% of the data) and test set(20% of the data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_features_df, target, test_size = 0.2)\n",
    "print(\"Training dataset have been split and assign to new variables.\")\n",
    "print(\"@_train set are the 80% of the data.\")\n",
    "print(X_train.shape)\n",
    "print(\"@_test set are the remaining 20% of the data.\")\n",
    "print(X_test.shape)\n",
    "print(\"-------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='regression'></a>\n",
    "### Regressions Modeling\n",
    "[Back to Table of Content](#toc)\n",
    "\n",
    "Lets get to the juicy part and actually apply some machine learning models to our ready splitted dataset.  Most of the algorithms have been packaged by [sklearn](http://scikit-learn.org/stable/) which makes the code application or usage of machine learning easier than you think.\n",
    "\n",
    "For more details about regression analysis, please refer to the link [here](https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyper-Parameter Tuning & Cross Validation\n",
    "\n",
    "##### Hyper-Parameter Tuning\n",
    "Most machine learning models have a fixed numbers of parameters that can be tuned for a given solution. Finding the optimal combination manually is challenge. Therefore, we automated the parameter search process by running all possible combinations for the best score in a given list(GridSearchCV). \n",
    "\n",
    "For a more in-depth guide for hyper-parameter tuning, please refer to the link [here](https://machinelearningmastery.com/how-to-tune-algorithm-parameters-with-scikit-learn/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regressions' parameters defined.\n",
      "-------------------------------------\n",
      "Regression objects assigned to variables.\n",
      "-------------------------------------\n",
      "variable cycles aligned and set.\n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#sklearn libraries for actually preforming machine learning\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "#python library tool to switch variables for independent loops\n",
    "import itertools\n",
    "\n",
    "#Gradient Boost Machine Parameters\n",
    "gbm_params = {'n_estimators': [50, 100], \n",
    "              'max_depth': [1,5,7], \n",
    "              'min_samples_split': [2,3,4],\n",
    "              'learning_rate':[0.01,0.1,0.5,1]}\n",
    "\n",
    "#Adaboost Parameters\n",
    "ada_params = {'n_estimators': [50, 100],             \n",
    "              'base_estimator': [DecisionTreeRegressor(max_depth=1),\n",
    "                                 DecisionTreeRegressor(max_depth=5), \n",
    "                                 DecisionTreeRegressor(max_depth=5)], \n",
    "              'learning_rate':[0.01,0.1,0.5,1], \n",
    "              'random_state':[88]}\n",
    "\n",
    "#Random Forest Parameters\n",
    "rf_params = {'n_estimators': [50, 100], \n",
    "              'max_depth': [1,5,7], \n",
    "              'min_samples_split': [2,3,4], \n",
    "             'random_state':[88]}\n",
    "\n",
    "print(\"Regressions' parameters defined.\")\n",
    "print(\"-------------------------------------\")\n",
    "\n",
    "#Assign Regression object from Sklearn to a variable\n",
    "gbm_estimator = GradientBoostingRegressor(**gbm_params)\n",
    "ada_estimator = AdaBoostRegressor(**ada_params)\n",
    "rf_estimator = RandomForestRegressor(**rf_params)\n",
    "print(\"Regression objects assigned to variables.\")\n",
    "print(\"-------------------------------------\")\n",
    "\n",
    "#Create a cycle for different regression variables to be used in a set loop we will create\n",
    "reg_names_cycle = [\"Gradient Boost Machine Regression\", \"AdaBoost Regession\", \"Random Forest Regression\"]\n",
    "reg_params_cycle = [gbm_params, ada_params, rf_params]\n",
    "reg_estimator_cycle = [gbm_estimator, ada_estimator, rf_estimator]\n",
    "print(\"variable cycles aligned and set.\")\n",
    "print(\"-------------------------------------\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cross-Validation\n",
    "[Back to Table of Content](#toc)\n",
    "To test your model consistent, typically you have to divide your data out K number of times as well as run the model K number of times with different subsets of training and validation datasets each round.\n",
    "\n",
    "![alt text](https://github.com/EnxaisRichard/regressionstarterkit/blob/master/images/cross-validation.PNG?raw=true \"4KFolds Cross-Validation\")\n",
    "\n",
    "\n",
    "For a more in-depth guide for cross-validation, please refer to the link [here](https://www.analyticsvidhya.com/blog/2015/11/improve-model-performance-cross-validation-in-python-r/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is 0.\n",
      "Current_reg_name is Gradient Boost Machine Regression.\n",
      "Current_regression is GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
      "             learning_rate=[0.01, 0.1, 0.5, 1], loss='ls',\n",
      "             max_depth=[1, 5, 7], max_features=None, max_leaf_nodes=None,\n",
      "             min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "             min_samples_split=[2, 3, 4], min_weight_fraction_leaf=0.0,\n",
      "             n_estimators=[50, 100], presort='auto', random_state=None,\n",
      "             subsample=1.0, verbose=0, warm_start=False).\n",
      "Current_parameters is {'n_estimators': [50, 100], 'max_depth': [1, 5, 7], 'min_samples_split': [2, 3, 4], 'learning_rate': [0.01, 0.1, 0.5, 1]}.\n",
      "Fitting 4 folds for each of 72 candidates, totalling 288 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 288 out of 288 | elapsed:   12.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            importance\n",
      "feature               \n",
      "temp             0.171\n",
      "DC               0.138\n",
      "DMC              0.130\n",
      "FFMC             0.125\n",
      "RH               0.116\n",
      "area             0.092\n",
      "wind             0.068\n",
      "X                0.056\n",
      "Y                0.044\n",
      "day_code         0.036\n",
      "month_code       0.023\n",
      "rain             0.002\n",
      "\n",
      "The optimal 4 KFolds Cross Validation Mean score on our training data with a Gradient Boost Machine Regression is 0.7295895455525695\n",
      "Using the recommended parameters:\n",
      "{'learning_rate': 0.5, 'max_depth': 7, 'min_samples_split': 3, 'n_estimators': 50}\n",
      "\n",
      "The Gradient Boost Machine Regression provides a Train Data score of: 0.9999999787938295\n",
      "The Gradient Boost Machine Regression provides a Test Data score of: 0.7159907873383698\n",
      "-------------------------------------\n",
      "Saved current_regression optimal parameters back to reg_names_cycle variable\n",
      "-------------------------------------\n",
      "x is 1.\n",
      "Current_reg_name is AdaBoost Regession.\n",
      "Current_regression is AdaBoostRegressor(base_estimator=[DecisionTreeRegressor(criterion='mse', max_depth=1, max_features=None,\n",
      "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "           splitter='best'),...        min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "           splitter='best')],\n",
      "         learning_rate=[0.01, 0.1, 0.5, 1], loss='linear',\n",
      "         n_estimators=[50, 100], random_state=[88]).\n",
      "Current_parameters is {'n_estimators': [50, 100], 'base_estimator': [DecisionTreeRegressor(criterion='mse', max_depth=1, max_features=None,\n",
      "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "           splitter='best'), DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
      "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "           splitter='best'), DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
      "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "           splitter='best')], 'learning_rate': [0.01, 0.1, 0.5, 1], 'random_state': [88]}.\n",
      "Fitting 4 folds for each of 24 candidates, totalling 96 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  96 out of  96 | elapsed:   11.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            importance\n",
      "feature               \n",
      "FFMC             0.445\n",
      "DC               0.291\n",
      "RH               0.061\n",
      "DMC              0.044\n",
      "Y                0.038\n",
      "temp             0.024\n",
      "X                0.023\n",
      "wind             0.023\n",
      "month_code       0.023\n",
      "day_code         0.022\n",
      "area             0.006\n",
      "rain             0.000\n",
      "\n",
      "The optimal 4 KFolds Cross Validation Mean score on our training data with a AdaBoost Regession is 0.7109722093434867\n",
      "Using the recommended parameters:\n",
      "{'base_estimator': DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
      "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "           splitter='best'), 'learning_rate': 0.5, 'n_estimators': 100, 'random_state': 88}\n",
      "\n",
      "The AdaBoost Regession provides a Train Data score of: 0.9369752238496655\n",
      "The AdaBoost Regession provides a Test Data score of: 0.7416830824108224\n",
      "-------------------------------------\n",
      "Saved current_regression optimal parameters back to reg_names_cycle variable\n",
      "-------------------------------------\n",
      "x is 2.\n",
      "Current_reg_name is Random Forest Regression.\n",
      "Current_regression is RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=[1, 5, 7],\n",
      "           max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "           min_samples_split=[2, 3, 4], min_weight_fraction_leaf=0.0,\n",
      "           n_estimators=[50, 100], n_jobs=1, oob_score=False,\n",
      "           random_state=[88], verbose=0, warm_start=False).\n",
      "Current_parameters is {'n_estimators': [50, 100], 'max_depth': [1, 5, 7], 'min_samples_split': [2, 3, 4], 'random_state': [88]}.\n",
      "Fitting 4 folds for each of 18 candidates, totalling 72 fits\n",
      "            importance\n",
      "feature               \n",
      "FFMC             0.651\n",
      "DC               0.208\n",
      "DMC              0.048\n",
      "wind             0.023\n",
      "RH               0.018\n",
      "day_code         0.014\n",
      "month_code       0.010\n",
      "temp             0.009\n",
      "X                0.007\n",
      "Y                0.005\n",
      "area             0.005\n",
      "rain             0.000\n",
      "\n",
      "The optimal 4 KFolds Cross Validation Mean score on our training data with a Random Forest Regression is 0.7031077308060999\n",
      "Using the recommended parameters:\n",
      "{'max_depth': 7, 'min_samples_split': 4, 'n_estimators': 50, 'random_state': 88}\n",
      "\n",
      "The Random Forest Regression provides a Train Data score of: 0.8595948026485661\n",
      "The Random Forest Regression provides a Test Data score of: 0.7419572238152456\n",
      "-------------------------------------\n",
      "Saved current_regression optimal parameters back to reg_names_cycle variable\n",
      "-------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  72 out of  72 | elapsed:   10.3s finished\n"
     ]
    }
   ],
   "source": [
    "#sklearn libraries for paramter tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#number of regression used\n",
    "number_of_regressions = 3\n",
    "\n",
    "#Set a loop to go through each regression\n",
    "for x in range(number_of_regressions):\n",
    "    #create variables for each cycle iterations\n",
    "    current_regression = reg_estimator_cycle[x]\n",
    "    current_parameters = reg_params_cycle[x]\n",
    "    current_reg_name = reg_names_cycle[x]\n",
    "    print(\"x is {}.\".format(x))\n",
    "    print(\"Current_reg_name is {}.\".format(current_reg_name))\n",
    "    print(\"Current_regression is {}.\".format(current_regression))\n",
    "    print(\"Current_parameters is {}.\".format(current_parameters))\n",
    "    \n",
    "    #run through all different parameters setting to find the best score\n",
    "    current_regression = GridSearchCV(current_regression, current_parameters, verbose=1, cv=4).fit(X_train, y_train)\n",
    "    \n",
    "    #provide feature importance for each regression model\n",
    "    importances = pd.DataFrame({'feature':scaled_features_df.columns,'importance':np.round(current_regression.best_estimator_.feature_importances_,3)})\n",
    "    importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
    "    print(importances)\n",
    "    print()\n",
    "    \n",
    "    #print out best mean cross validation scores\n",
    "    current_best_cv_score =  current_regression.best_score_\n",
    "    print(\"The optimal 4 KFolds Cross Validation Mean score on our training data with a {} is {}\".format(current_reg_name, current_best_cv_score))\n",
    "    print(\"Using the recommended parameters:\")\n",
    "    print(current_regression.best_params_)\n",
    "    print()\n",
    "    \n",
    "    #run best parameters on training and test data, then assign variables\n",
    "    current_r2_train_score =current_regression.score(X_train,y_train)\n",
    "    current_r2_test_score = current_regression.score(X_test,y_test)\n",
    "    print(\"The {} provides a Train Data score of: {}\".format(current_reg_name, current_r2_train_score))\n",
    "    print(\"The {} provides a Test Data score of: {}\".format(current_reg_name, current_r2_test_score))\n",
    "    print(\"-------------------------------------\")\n",
    "    \n",
    "    \n",
    "    #save back to reg_estimator_cycle for scoring later\n",
    "    reg_estimator_cycle[x] = current_regression\n",
    "    print(\"Saved current_regression optimal parameters back to reg_names_cycle variable\")\n",
    "    print(\"-------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation Metric\n",
    "[Back to Table of Content](#toc)\n",
    "\n",
    "In this example we will be using the coefficent of determinination to score our model performance. It rates the model from 0-100 percent, making it very easy to interpret.\n",
    "\n",
    "For more evaluation metric for modeling performance, please check out the link [here](https://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Gradient Boost Machine Regression provides a Train Data score of: 0.9999999787938295\n",
      "The Gradient Boost Machine Regression provides a Test Data score of: 0.7159907873383698\n",
      "The Gradient Boost Machine Regression have been assigned to prediction variable for plotting\n",
      "-------------------------------------\n",
      "The AdaBoost Regession provides a Train Data score of: 0.9369752238496655\n",
      "The AdaBoost Regession provides a Test Data score of: 0.7416830824108224\n",
      "The AdaBoost Regession have been assigned to prediction variable for plotting\n",
      "-------------------------------------\n",
      "The Random Forest Regression provides a Train Data score of: 0.8595948026485661\n",
      "The Random Forest Regression provides a Test Data score of: 0.7419572238152456\n",
      "The Random Forest Regression have been assigned to prediction variable for plotting\n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#create list of empty arrays for predictions\n",
    "length_predictions = len(X_test)\n",
    "gbm_predictions = []\n",
    "ada_predictions = []\n",
    "rf_predictions = []\n",
    "\n",
    "reg_predictions_cycle = [gbm_predictions, ada_predictions, rf_predictions]\n",
    "\n",
    "for x in range(number_of_regressions):   \n",
    "    #create variables for each cycle iterations\n",
    "    current_regression = reg_estimator_cycle[x]\n",
    "    current_reg_name = reg_names_cycle[x]\n",
    "    \n",
    "    #run best parameters on training and test data, then assign variables\n",
    "    current_r2_train_score =current_regression.score(X_train,y_train)\n",
    "    current_r2_test_score = current_regression.score(X_test,y_test)\n",
    "    print(\"The {} provides a Train Data score of: {}\".format(current_reg_name, current_r2_train_score))\n",
    "    print(\"The {} provides a Test Data score of: {}\".format(current_reg_name, current_r2_test_score))\n",
    "    \n",
    "    #assign best parameter predictions\n",
    "    reg_predictions_cycle[x] = current_regression.predict(X_test)\n",
    "    print(\"The {} have been assigned to prediction variable for plotting\".format(current_reg_name))\n",
    "    print(\"-------------------------------------\")\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### Conclusion\n",
    "[Back to Table of Content](#toc)\n",
    "\n",
    "If your accuracy is close to 99% accuracy, you can always assume the model is [overfitted](https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/).  Meaning the model is very bias in nature and will be very inaccurate when introduced new data.  Typically, there is a \"sweet spot\" and it is generally found when your train and test score is close enough (too close indicates [underfitting](https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/) to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "function for graph completed\n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#we create function to plot actual vs predicted target values\n",
    "def actual_vs_predicted(predicted_values, regression_names, test_score, train_score, color):   \n",
    "    plt.figure(figsize=(15,7.5))\n",
    "    plt.scatter(y_test, predicted_values, color=color, label=regression_names, s=10)\n",
    "    test_score = np.round(test_score,3)\n",
    "    train_score = np.round(train_score,3)\n",
    "    plt.title(\"{} - Actual vs Predicted with Test r2  Score: {} & Train r2 Score: {}.\".format(regression_names, test_score, train_score))\n",
    "    plt.xlabel(\"Actual Value\")\n",
    "    plt.ylabel(\"Predicted Value\")\n",
    "    plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "print(\"function for graph completed\")\n",
    "print(\"-------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IqE9paelp/fxea1BQQ3buTCEz8xAnTuTzxBOPAFBcXExWVibduv1xv9+XWSYmbmDJkjdo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spYZS9M1vSEmABXBwAAACCYEOb8wDAMVR4+rfyialUf75ljZ5s6TtkLUzQlflSAqwMAAAAQjAhz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\n0RZarA+LSwEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234d49a06d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#place all variables created through function for plotting actual vs predicted values\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.scatter(y_test, reg_predictions_cycle[0], color=\"r\", label=\"Gradient Boost Machine Regression\", s=10)\n",
    "plt.scatter(y_test, reg_predictions_cycle[1], color=\"b\", label=\"AdaBoost Regession\", s=10)\n",
    "plt.scatter(y_test, reg_predictions_cycle[2], color=\"g\", label=\"Random Forest Regession\", s=10)\n",
    "plt.title(\"Actual vs Predicted\")\n",
    "plt.xlabel(\"Actual Value\")\n",
    "plt.ylabel(\"Predicted Value\")\n",
    "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"-------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We can conclude the fitting for Random Forest seems most promising as GBM and AdaBoost Regression shows greater outliers for their prediction points. Remember, Random Forest train and test score were not the highest, but the delta score between them were the lowest. [Lesson learned here](http://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit) is to always be sure to check your residual plots, because you can't judge a score by it's high value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}