tornadoes_part5_modelling_upsampling_nlp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/GAlogo.png\" style=\"float: left; margin: 15px; height: 100px\">\n",
    "\n",
    "# CAPSTONE PROJECT\n",
    "## US TORNADOES: PREDICTING THEIR MAGNITUDE WITH MACHINE LEARNING\n",
    "### Part 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goals\n",
    "* Pre-work:\n",
    "    - Doing a class gathering all the pre-processing work done in parts [1](tornadoes_part1_project_introduction.ipynb) and [3](tornadoes_part3_eda_modelling_nlp_tsa.ipynb) (see [pre-processing tornado class](tornadoes_part4_pre_processing_class.ipynb)).\n",
    "    - Importing again the raw data from our PostgreSQL database created from Web Scraping in separate notebook [Web Scraping](tornadoes_part2_webscraping_posgres.ipynb).\n",
    "    - Applying the previously mentioned class to get a pre-processed dataframe.\n",
    "<br>\n",
    "<br>\n",
    "* Predicting tornado magnitude from numerical features:\n",
    "    - As a reference: redo best model from [part 3](tornadoes_part3_eda_modelling_nlp_tsa.ipynb).\n",
    "    - Test upsampling for improvement of the prediction of stronger tornadoes.\n",
    "<br>\n",
    "<br>\n",
    "* Predicting tornado magnitude from narratives: NLP\n",
    "    - As a reference: redo best model from [part 3](tornadoes_part3_eda_modelling_nlp_tsa.ipynb).\n",
    "    - Improve modelling.\n",
    "<br>\n",
    "<br>\n",
    "* Gathering both numerical and narrative features to get our best model.\n",
    "<br>\n",
    "<br>  \n",
    "* Predict total damage cost based on tornado magnitude and other numerical features with linear regression.\n",
    "<br>\n",
    "<br>\n",
    "* Main conclusions and further possible work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Packages import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "import calendar\n",
    "from datetime import datetime, date, timedelta\n",
    "\n",
    "from sklearn.utils import resample\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from textblob import TextBlob, Word\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, recall_score, confusion_matrix, classification_report,\n",
    "auc, roc_curve\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier, LinearRegression, Lasso\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing class for data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- When wanting to apply a process in production, it is important to make everyhting repeatable and with an easy implementation. We do not want to copy and past every single line of code and call functions every single time new data needs to be predicted.\n",
    "- This is where a class is important. We can gather in it all the functions and pre-processing code we worked on in part 2 and 3 of the capstone. And then run all that in one go through fit and then transform. This is a more efficient way of working.\n",
    "- Moreover, it is then easily accessible in a pipeline in conjunction with standardization and modelling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class was built and tested in the separate notebook [Pre-Processing](tornadoes_part4_pre_processing_class.ipynb)\n",
    "<br>It was also saved locally as a .py file so we can import it easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tornado_preproc_class import TornadoPreprocessor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to plot confusion matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to plot the confusion matrices in a nicer and more interpretable way:\n",
    "def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues, \n",
    "                          xlab='Predicted magnitude', ylab='True magnitude'):\n",
    "    sns.set(font_scale=1.3)\n",
    "    plt.figure(figsize=(7,5))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.ylabel(ylab)\n",
    "    plt.xlabel(xlab)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the data from local PostgreSQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EF0    7661\n",
       "EF1    4822\n",
       "EF2    1441\n",
       "EF3     435\n",
       "EF4     103\n",
       "EFU      62\n",
       "EF5      14\n",
       "Name: TOR_F_SCALE, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating engine connection to my local \"storms\" database, using sqlalchemy:\n",
    "engine_local = create_engine('postgresql://localhost:5432/storms')\n",
    "\n",
    "# Saving the data from Feb 2007 until now (since the EF scale has been in place):\n",
    "sql_query = \"\"\"\n",
    "SELECT * \n",
    "FROM tornadoes_1950_mid2017 \n",
    "WHERE \"BEGIN_YEARMONTH\" >= 200702;\n",
    "\"\"\"\n",
    "\n",
    "raw_2007_2017 = pd.read_sql(sql_query, engine_local)\n",
    "\n",
    "# Counting the number of tornadoes in each magnitude category:\n",
    "raw_2007_2017.TOR_F_SCALE.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TornadoPreprocessor()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preproc = TornadoPreprocessor()\n",
    "preproc.fit(raw_2007_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = preproc.transform(raw_2007_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14538, 62) (14538, 14)\n"
     ]
    }
   ],
   "source": [
    "print raw_2007_2017.shape, df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOR_F_SCALE</th>\n",
       "      <th>TOR_LENGTH</th>\n",
       "      <th>TOR_WIDTH</th>\n",
       "      <th>Duration</th>\n",
       "      <th>AverageDate</th>\n",
       "      <th>AverageTime</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Injuries</th>\n",
       "      <th>Mean_Lat</th>\n",
       "      <th>Mean_Lon</th>\n",
       "      <th>Azimuth</th>\n",
       "      <th>State</th>\n",
       "      <th>TotalCost</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EF0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>208</td>\n",
       "      <td>15.855</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.7345</td>\n",
       "      <td>-93.3220</td>\n",
       "      <td>120.34</td>\n",
       "      <td>IA</td>\n",
       "      <td>0</td>\n",
       "      <td>Public</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EF0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>208</td>\n",
       "      <td>16.255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.6694</td>\n",
       "      <td>-93.0534</td>\n",
       "      <td>117.98</td>\n",
       "      <td>IA</td>\n",
       "      <td>0</td>\n",
       "      <td>Law_Enforcement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EF1</td>\n",
       "      <td>0.29</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>208</td>\n",
       "      <td>15.575</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.7892</td>\n",
       "      <td>-93.5405</td>\n",
       "      <td>144.90</td>\n",
       "      <td>IA</td>\n",
       "      <td>60000</td>\n",
       "      <td>Trained_Spotter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EF0</td>\n",
       "      <td>1.43</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3</td>\n",
       "      <td>126</td>\n",
       "      <td>15.685</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.2945</td>\n",
       "      <td>-98.6804</td>\n",
       "      <td>1.11</td>\n",
       "      <td>KS</td>\n",
       "      <td>0</td>\n",
       "      <td>Law_Enforcement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EF0</td>\n",
       "      <td>1.38</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>183</td>\n",
       "      <td>17.535</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.7200</td>\n",
       "      <td>-80.2500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>FL</td>\n",
       "      <td>0</td>\n",
       "      <td>Broadcast_Media</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EF0</td>\n",
       "      <td>2.77</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9</td>\n",
       "      <td>111</td>\n",
       "      <td>18.875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43.6830</td>\n",
       "      <td>-98.3963</td>\n",
       "      <td>81.93</td>\n",
       "      <td>SD</td>\n",
       "      <td>0</td>\n",
       "      <td>Storm_Chaser</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EF0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>152</td>\n",
       "      <td>11.925</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.6658</td>\n",
       "      <td>-81.5240</td>\n",
       "      <td>-36.57</td>\n",
       "      <td>FL</td>\n",
       "      <td>20000</td>\n",
       "      <td>NWS_Storm_Survey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EF2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>6.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5300</td>\n",
       "      <td>-82.2500</td>\n",
       "      <td>64.12</td>\n",
       "      <td>FL</td>\n",
       "      <td>0</td>\n",
       "      <td>NWS_Storm_Survey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EF1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>9.170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.7200</td>\n",
       "      <td>-81.2400</td>\n",
       "      <td>64.12</td>\n",
       "      <td>FL</td>\n",
       "      <td>0</td>\n",
       "      <td>Public</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EF1</td>\n",
       "      <td>3.47</td>\n",
       "      <td>150.0</td>\n",
       "      <td>5</td>\n",
       "      <td>295</td>\n",
       "      <td>16.935</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.4369</td>\n",
       "      <td>-90.5177</td>\n",
       "      <td>52.84</td>\n",
       "      <td>MS</td>\n",
       "      <td>180000</td>\n",
       "      <td>NWS_Storm_Survey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TOR_F_SCALE  TOR_LENGTH  TOR_WIDTH  Duration  AverageDate  AverageTime  \\\n",
       "0         EF0        0.27       20.0         1          208       15.855   \n",
       "1         EF0        0.20       20.0         1          208       16.255   \n",
       "2         EF1        0.29      100.0         1          208       15.575   \n",
       "3         EF0        1.43       50.0         3          126       15.685   \n",
       "4         EF0        1.38       20.0         5          183       17.535   \n",
       "5         EF0        2.77      100.0         9          111       18.875   \n",
       "6         EF0        0.55       30.0         1          152       11.925   \n",
       "7         EF2        1.50      300.0         0          105        6.250   \n",
       "8         EF1        0.20       50.0         0          105        9.170   \n",
       "9         EF1        3.47      150.0         5          295       16.935   \n",
       "\n",
       "   Deaths  Injuries  Mean_Lat  Mean_Lon  Azimuth State  TotalCost  \\\n",
       "0       0         0   41.7345  -93.3220   120.34    IA          0   \n",
       "1       0         0   41.6694  -93.0534   117.98    IA          0   \n",
       "2       0         0   41.7892  -93.5405   144.90    IA      60000   \n",
       "3       0         0   37.2945  -98.6804     1.11    KS          0   \n",
       "4       0         0   26.7200  -80.2500     0.00    FL          0   \n",
       "5       0         0   43.6830  -98.3963    81.93    SD          0   \n",
       "6       0         0   24.6658  -81.5240   -36.57    FL      20000   \n",
       "7       0         0   30.5300  -82.2500    64.12    FL          0   \n",
       "8       0         0   29.7200  -81.2400    64.12    FL          0   \n",
       "9       0         0   33.4369  -90.5177    52.84    MS     180000   \n",
       "\n",
       "             Source  \n",
       "0            Public  \n",
       "1   Law_Enforcement  \n",
       "2   Trained_Spotter  \n",
       "3   Law_Enforcement  \n",
       "4   Broadcast_Media  \n",
       "5      Storm_Chaser  \n",
       "6  NWS_Storm_Survey  \n",
       "7  NWS_Storm_Survey  \n",
       "8            Public  \n",
       "9  NWS_Storm_Survey  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers and standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convertEFtonb(magnitude):\n",
    "    '''Converts an EF scale string to integer of the corresponding number.\n",
    "    EFU are mostly weak tornadoes therefore their scale is set to 0'''\n",
    "    \n",
    "    scale = np.nan\n",
    "    scales = {'EF0': 0, 'EF1': 1, 'EF2': 2, 'EF3': 3, 'EF4': 4, 'EF5': 5, 'EFU': 0}\n",
    "    \n",
    "    if magnitude in scales.keys():\n",
    "        scale = scales[magnitude]\n",
    "    \n",
    "    return scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DCtucZhnZGKWteklfhYX5VFXtTvUwJEX0/AcuPfuBTc9/YMvU519YmN/m8q20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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1206e5a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "dfnum = df.drop(['TOR_F_SCALE','State','Source'],axis=1)\n",
    "dfnum['Magnitude'] = df['TOR_F_SCALE'].map(convertEFtonb)\n",
    "dfstd = pd.DataFrame(scaler.fit_transform(dfnum), columns=dfnum.columns)\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "sns.set(font_scale=1.2)\n",
    "sns.boxplot(data=dfstd, orient='h')\n",
    "plt.title(\"Box plots of numerical features\")\n",
    "plt.xlabel(\"Number of times the standard deviation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As seen in part 3, the outlier for TOR_LENGTH = 100 miles has to be removed, it was most likely entered wrongly in the original database\n",
    "- The negative outliers for Mean_Lon correspond to Hawaii\n",
    "- The other outliers actually correspond to stronger tornadoes and should not be removed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing outlier and keeping only standardized numerical features:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saw in [part 3](tornadoes_part3_eda_modelling_nlp_tsa.ipynb) that the dummy features created from \"State\" and \"Source\" did not improve our model accuracy. We are then just going to use the numerical features. As a consequence, our modelling will run faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Removing tornado with outlier TOR_LENGTH > 20*std:\n",
    "tornado_2007_2017 = dfstd[dfstd.TOR_LENGTH<20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14538, 62) (14538, 14) (14537, 12)\n"
     ]
    }
   ],
   "source": [
    "print raw_2007_2017.shape, df.shape, tornado_2007_2017.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOR_LENGTH</th>\n",
       "      <th>TOR_WIDTH</th>\n",
       "      <th>Duration</th>\n",
       "      <th>AverageDate</th>\n",
       "      <th>AverageTime</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Injuries</th>\n",
       "      <th>Mean_Lat</th>\n",
       "      <th>Mean_Lon</th>\n",
       "      <th>Azimuth</th>\n",
       "      <th>TotalCost</th>\n",
       "      <th>Magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.649575</td>\n",
       "      <td>-0.553936</td>\n",
       "      <td>-0.663909</td>\n",
       "      <td>0.71888</td>\n",
       "      <td>0.064657</td>\n",
       "      <td>-0.048934</td>\n",
       "      <td>-0.06017</td>\n",
       "      <td>0.992225</td>\n",
       "      <td>-0.148975</td>\n",
       "      <td>1.408556</td>\n",
       "      <td>-0.0423</td>\n",
       "      <td>-0.775497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.665453</td>\n",
       "      <td>-0.553936</td>\n",
       "      <td>-0.663909</td>\n",
       "      <td>0.71888</td>\n",
       "      <td>0.142239</td>\n",
       "      <td>-0.048934</td>\n",
       "      <td>-0.06017</td>\n",
       "      <td>0.978331</td>\n",
       "      <td>-0.115564</td>\n",
       "      <td>1.354156</td>\n",
       "      <td>-0.0423</td>\n",
       "      <td>-0.775497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TOR_LENGTH  TOR_WIDTH  Duration  AverageDate  AverageTime    Deaths  \\\n",
       "0   -0.649575  -0.553936 -0.663909      0.71888     0.064657 -0.048934   \n",
       "1   -0.665453  -0.553936 -0.663909      0.71888     0.142239 -0.048934   \n",
       "\n",
       "   Injuries  Mean_Lat  Mean_Lon   Azimuth  TotalCost  Magnitude  \n",
       "0  -0.06017  0.992225 -0.148975  1.408556    -0.0423  -0.775497  \n",
       "1  -0.06017  0.978331 -0.115564  1.354156    -0.0423  -0.775497  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_2007_2017.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling using numerical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target / Feature matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X1 = tornado_2007_2017.drop(['Magnitude'], axis=1)\n",
    "y1 = raw_2007_2017[raw_2007_2017.TOR_LENGTH<50]['TOR_F_SCALE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xtrain1, Xtest1, ytrain1, ytest1 = train_test_split(X1, y1, test_size=0.3, stratify=y1,\n",
    "                                                    random_state=31)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy | Test\t\t | Baseline\t\t | EF0 tornados:\t0.527\n"
     ]
    }
   ],
   "source": [
    "accuracy_baseline = ytest1.value_counts().get_values()[0]/float(ytest1.count())\n",
    "print \"Accuracy | Test\\t\\t | Baseline\\t\\t | EF0 tornados:\\t\", round(accuracy_baseline, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extensive modelling tests were performed in [part 3](Capstone_Yves_Tornadoes_Part3.ipynb) of the capstone. The next model is our best one obtained in part 3. This is our new reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy | Test\t\t | Random Forest\t | Optimized params:\t0.732\n"
     ]
    }
   ],
   "source": [
    "# Taking the best RF hyperparameters from part 3:\n",
    "rf6 = RandomForestClassifier(criterion='entropy', max_depth=18, max_features=0.4, \n",
    "                             min_samples_leaf=1, min_samples_split=9,\n",
    "                             n_estimators=72, random_state=31)\n",
    "rf6.fit(Xtrain1, ytrain1)\n",
    "\n",
    "yhat1_rf6 = rf6.predict(Xtest1)\n",
    "accuracy_rf6 = accuracy_score(ytest1, yhat1_rf6)\n",
    "print \"Accuracy | Test\\t\\t | Random Forest\\t | Optimized params:\\t\", round(accuracy_rf6, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aa6kNWVlZmDtnJjq0kYe0tDRGjRiGxDdv/sq6OWGbvx9UupZ8j9RioCni4+Ia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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac24150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Computing the confusion matrix\n",
    "confmatrix_rf6 = confusion_matrix(ytest1, yhat1_rf6)\n",
    "\n",
    "# Plotting it using the function:\n",
    "plot_confusion_matrix(confmatrix_rf6, rf6.classes_,\n",
    "                      'Confusion matrix - Random Forest - Optimized hyperparameters',\n",
    "                      xlab='Predicted EF scale magnitude', ylab='True EF scale magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report from our optimized random forest:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        EF0       0.82      0.87      0.84      2298\n",
      "        EF1       0.64      0.67      0.65      1447\n",
      "        EF2       0.53      0.40      0.46       432\n",
      "        EF3       0.65      0.35      0.46       131\n",
      "        EF4       0.67      0.26      0.37        31\n",
      "        EF5       0.00      0.00      0.00         4\n",
      "        EFU       0.00      0.00      0.00        19\n",
      "\n",
      "avg / total       0.72      0.73      0.72      4362\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Yves/anaconda2/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "clasreport_rf6 = classification_report(ytest1, yhat1_rf6)\n",
    "print \"Classification report from our optimized random forest:\\n\", clasreport_rf6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we had noticed in part 3 with our analysis of the confusion matrix, the weaker tornadoes are well predicted, but the stronger ones less. This is clearly visible in the classification report. \n",
    "<br>EF0 precision and recall scores are great. However EF2 to EF5 tornadoes tend to be predicted as weaker. \n",
    "<br>The is due to the strong imbalance between classes (strong tornadoes are rare)\n",
    "<br>The precision and recall scores can benefit from upsampling the rarer events. This is the next step we are going to perform. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upsampling and redoing the modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The upsampling will be performed on the trained dataset to try and improve the precision and recall of the different categories, except the EF0 ones.\n",
    "<br>The model scoring and validation will still be done on the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10175, 11)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the original dimensions of the train features:\n",
    "Xtrain1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example \"stolen\" from Raja (thank you!):**<br>\n",
    "table_train_r=table_train[table_train['Relegated']==1]<br>\n",
    "table_train_s=table_train[table_train['Relegated']==0]\n",
    "\n",
    "table_train_r_upsample = resample(table_train_r, replace=True, n_samples=table_train_s.shape[0])<br>\n",
    "table_train=pd.concat([table_train_r_upsample,table_train_s],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10175, 12)\n"
     ]
    },
    {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOR_LENGTH</th>\n",
       "      <th>TOR_WIDTH</th>\n",
       "      <th>Duration</th>\n",
       "      <th>AverageDate</th>\n",
       "      <th>AverageTime</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Injuries</th>\n",
       "      <th>Mean_Lat</th>\n",
       "      <th>Mean_Lon</th>\n",
       "      <th>Azimuth</th>\n",
       "      <th>TotalCost</th>\n",
       "      <th>TOR_F_SCALE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13291</th>\n",
       "      <td>-0.658648</td>\n",
       "      <td>-0.452255</td>\n",
       "      <td>-0.521007</td>\n",
       "      <td>0.421973</td>\n",
       "      <td>0.038473</td>\n",
       "      <td>-0.048934</td>\n",
       "      <td>-0.06017</td>\n",
       "      <td>0.243112</td>\n",
       "      <td>1.263833</td>\n",
       "      <td>0.354663</td>\n",
       "      <td>-0.041447</td>\n",
       "      <td>EF0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>2.473960</td>\n",
       "      <td>0.869589</td>\n",
       "      <td>1.908334</td>\n",
       "      <td>1.080332</td>\n",
       "      <td>0.219821</td>\n",
       "      <td>-0.048934</td>\n",
       "      <td>-0.06017</td>\n",
       "      <td>1.206906</td>\n",
       "      <td>1.025230</td>\n",
       "      <td>0.418744</td>\n",
       "      <td>0.156800</td>\n",
       "      <td>EF2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TOR_LENGTH  TOR_WIDTH  Duration  AverageDate  AverageTime    Deaths  \\\n",
       "13291   -0.658648  -0.452255 -0.521007     0.421973     0.038473 -0.048934   \n",
       "1056     2.473960   0.869589  1.908334     1.080332     0.219821 -0.048934   \n",
       "\n",
       "       Injuries  Mean_Lat  Mean_Lon   Azimuth  TotalCost TOR_F_SCALE  \n",
       "13291  -0.06017  0.243112  1.263833  0.354663  -0.041447         EF0  \n",
       "1056   -0.06017  1.206906  1.025230  0.418744   0.156800         EF2  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-incorporating the magnitude:\n",
    "Xtrain2 = pd.concat([Xtrain1, pd.DataFrame(ytrain1)], axis=1)\n",
    "print Xtrain2.shape\n",
    "Xtrain2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating a dictionary of dataframes, one for each magnitude:\n",
    "Xtrain2_perMag = {}\n",
    "for mag in ['EFU', 'EF0', 'EF1', 'EF2', 'EF3', 'EF4', 'EF5']:\n",
    "    Xtrain2_perMag[mag] = Xtrain2[Xtrain2['TOR_F_SCALE']==mag]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EF0 Original shape :\t(5362, 12) \tUpsample shape EF0: (5362, 12)\n",
      "EFU Original shape :\t(43, 12) \tUpsample shape EFU: (5362, 12)\n",
      "EF1 Original shape :\t(3375, 12) \tUpsample shape EF1: (5362, 12)\n",
      "EF2 Original shape :\t(1009, 12) \tUpsample shape EF2: (5362, 12)\n",
      "EF3 Original shape :\t(304, 12) \tUpsample shape EF3: (5362, 12)\n",
      "EF4 Original shape :\t(72, 12) \tUpsample shape EF4: (5362, 12)\n",
      "EF5 Original shape :\t(10, 12) \tUpsample shape EF5: (5362, 12)\n"
     ]
    }
   ],
   "source": [
    "# Upsampling each dataframe based on the number of rows of the majority class EF0:\n",
    "Xtrain2_upsampled = {}\n",
    "\n",
    "for mag in ['EF0', 'EFU', 'EF1', 'EF2', 'EF3', 'EF4', 'EF5']:\n",
    "    \n",
    "    if mag == 'EF0':\n",
    "        Xtrain2_upsampled['EF0'] = Xtrain2_perMag['EF0']\n",
    "    else:\n",
    "        # Bootstrapping method is used:\n",
    "        Xtrain2_upsampled[mag] = resample(Xtrain2_perMag[mag], replace=True,\n",
    "                                          n_samples=Xtrain2_perMag['EF0'].shape[0],\n",
    "                                          random_state=31)\n",
    "    # QCs:\n",
    "    print \"{} Original shape :\\t\".format(mag), Xtrain2_perMag[mag].shape,\\\n",
    "          \"\\tUpsample shape {}:\".format(mag), Xtrain2_upsampled[mag].shape\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37534, 12)\n"
     ]
    }
   ],
   "source": [
    "# Concatenating all the upsampled dataframes\n",
    "Xtrain2_df = pd.concat([Xtrain2_upsampled['EF0'], Xtrain2_upsampled['EF1'], Xtrain2_upsampled['EF2'], \n",
    "                        Xtrain2_upsampled['EF3'], Xtrain2_upsampled['EF4'], Xtrain2_upsampled['EF5'],\n",
    "                        Xtrain2_upsampled['EFU']], ignore_index=True, axis=0)\n",
    "print Xtrain2_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37534, 11) (37534,)\n"
     ]
    }
   ],
   "source": [
    "# Recreating the feature matrix and target for modelling:\n",
    "ytrain2 = Xtrain2_df.TOR_F_SCALE\n",
    "Xtrain2_df.drop(['TOR_F_SCALE'], axis=1, inplace=True)\n",
    "print Xtrain2_df.shape, ytrain2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy | Test\t\t | Random Forest\t | Original sampling\t | Part 3 params:\t0.723\n"
     ]
    }
   ],
   "source": [
    "# Taking the best RF hyperparameters from part 3:\n",
    "rf7 = RandomForestClassifier(criterion='entropy', max_depth=18, max_features=0.4, min_samples_leaf=1,\n",
    "                             min_samples_split=9, n_estimators=72, random_state=31)\n",
    "rf7.fit(Xtrain2_df, ytrain2)\n",
    "\n",
    "yhat1_rf7 = rf7.predict(Xtest1)\n",
    "accuracy_rf7 = accuracy_score(ytest1, yhat1_rf7)\n",
    "print \"Accuracy | Test\\t\\t | Random Forest\\t | Original sampling\\t | Part 3 params:\\t\", round(accuracy_rf7, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Ip7srI0eNxs3DSybs/fv3OPTrzYnjR1m/YTMdO3eRe/7fMrq6GSXR+Ph4NDU/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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c970150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Computing the confusion matrix\n",
    "confmatrix_rf7 = confusion_matrix(ytest1, rf7.predict(Xtest1))\n",
    "\n",
    "# Plotting it using the function:\n",
    "plot_confusion_matrix(confmatrix_rf7, rf7.classes_,\n",
    "                      'Confusion matrix - Random Forest - Upsampling',\n",
    "                      xlab='Predicted EF scale magnitude', ylab='True EF scale magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report after upsampling:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        EF0       0.83      0.85      0.84      2298\n",
      "        EF1       0.64      0.64      0.64      1447\n",
      "        EF2       0.47      0.50      0.49       432\n",
      "        EF3       0.53      0.40      0.45       131\n",
      "        EF4       0.79      0.35      0.49        31\n",
      "        EF5       0.40      0.50      0.44         4\n",
      "        EFU       0.33      0.05      0.09        19\n",
      "\n",
      "avg / total       0.72      0.72      0.72      4362\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clasreport_rf7 = classification_report(ytest1, yhat1_rf7)\n",
    "print \"Classification report after upsampling:\\n\", clasreport_rf7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments on the confusion matrix and classification report:\n",
    "- This is a successful test. The true positives of EF1 to EF5 tornadoes has increased. The upsampling helps predicting, for each magnitude over EF0, the majority of tornadoes in their true magnitude. It was not the case before.\n",
    "- Precision and recall of EF5 tornadoes has considerably improved (none were preductied correctly before upsampling).\n",
    "- Nevertheless, EF0 tornadoes are not predicted as well. This is why overall the accuracy has decreased from 0.732 to 0.723.\n",
    "- Using still the upsampled data, we are now going to fine tune the Random Forest hyper parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Flag to specify if GridSearch has to be redone or not:\n",
    "# Just not to rerun accidently the following whole gridsearch (which takes ages) accidently\n",
    "redo_RFgridsearch1 = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 96 candidates, totalling 480 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed: 16.7min\n",
      "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed: 41.2min\n",
      "[Parallel(n_jobs=2)]: Done 480 out of 480 | elapsed: 45.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimized parameters are:\n",
      "{'min_samples_leaf': 1, 'n_estimators': 80, 'min_samples_split': 8, 'criterion': 'entropy', 'max_features': 0.4, 'max_depth': 24}\n",
      "\n",
      "Accuracy | Train CV5\t | Random Forest\t | Upsampling\t | Optimal params:\t0.958\n"
     ]
    }
   ],
   "source": [
    "if redo_RFgridsearch1:\n",
    "    rf1 = RandomForestClassifier(random_state=31)\n",
    "    params_randfor_v1 = {'criterion': ['entropy'],\n",
    "                         'max_depth': [16,18,20,22,24,26],\n",
    "                         'max_features': [0.3,0.4,0.5,0.6],\n",
    "                         'n_estimators': [80],\n",
    "                         'min_samples_split': [8,9,10,11],\n",
    "                         'min_samples_leaf': [1]\n",
    "                        }\n",
    "\n",
    "    rf8 = GridSearchCV(rf1, param_grid=params_randfor_v1, cv=5, verbose=1, n_jobs=2)\n",
    "\n",
    "    rf8.fit(Xtrain2_df, ytrain2)\n",
    "    print \"The optimized parameters are:\\n\", rf8.best_params_\n",
    "    print \"\\nAccuracy | Train CV5\\t | Random Forest\\t | Upsampling\\t | Optimal params:\\t\", round(rf8.best_score_, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy | Test\t\t | Random Forest\t | Upsampling\t | Optimized params:\t0.722\n"
     ]
    }
   ],
   "source": [
    "if redo_RFgridsearch1:\n",
    "    yhat2_rf8 = rf8.best_estimator_.predict(Xtest1)\n",
    "else:\n",
    "    rf8 = RandomForestClassifier(random_state=31, min_samples_leaf=1, n_estimators=80, \n",
    "                                 min_samples_split=8,\n",
    "                                 criterion='entropy', max_features=0.4, max_depth=24)\n",
    "    rf8.fit(Xtrain2_df, ytrain2)\n",
    "    yhat2_rf8 = rf8.predict(Xtest1)\n",
    "accuracy_rf8 = accuracy_score(ytest1, yhat2_rf8)\n",
    "print \"Accuracy | Test\\t\\t | Random Forest\\t | Upsampling\\t | Optimized params:\\t\", round(accuracy_rf8, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments about GridSearch on upsampled data:\n",
    "- This is a clear example of overfitting: the train set gives an excellent score of 96% whereas the test gives 72%.\n",
    "- It was kind of expected with an upsampled train dataset. It is not recommended to gridsearch upsampled data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions and summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy, precision and recall:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **Accuracy**: overall correct prediction of a tornado, whatever its scale. <br>As there is a big imabalance, this statistic mostly shows how well the EF0 and EF1 tornadoes are correctly predicted.\n",
    "<br><br>\n",
    "+ True Postive of an EFx tornado (with x varying from 0 to 5): number of correctly predicted EFx tornadoes.\n",
    "+ False Negatives of an EFx tornado: total number of EFx tornadoes wrongly predicted as another type.\n",
    "+ False Postives of an EFx tornado: total number of tornadoes wrongly predicted as an EFx tornado.\n",
    "<br><br>\n",
    "+ **Recall of an EFx tornado**: $$ Recall = \\frac{TP}{TP + FN}\\ $$ It is a useful statistic to measure how well a certain type of tornado is predicted.\n",
    "    - The lower the recall of EFx, the higher the number of EFx tornadoes are wrongly predicted.\n",
    "    - The higher the recall of EFx, the better the EFx tornado is predicted.\n",
    "<br><br>\n",
    "+ **Precision of an EFx tornado**: $$ Precision = \\frac{TP}{TP + FP}\\ $$ It measures how well other types of tornadoes are not predicted as EFx.\n",
    "    - The lower the precision of EFx, the higher the number of other types of tornadoes are predicted as EFx.\n",
    "    - The higher the precision of EFx, the lower other types of tornadoes are wrongly predicted as EFx.\n",
    "<br><br>\n",
    "+ **To summarize:**\n",
    "    - **The accuracy gives us a measure of how well the model performs globally.**\n",
    "    - **The recall gives us a detailed measure of how well each type of tornado is correctly predicted. Because of the big imbalance between weaker and stronger tornadoes, this is the most important measure in this project.**\n",
    "\n",
    "Let's compare the statistics with / without upsampling. \n",
    "<br>*Note: the different statistics were computed on the test set*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Accuracy | Model description | Score |\n",
    "| :---: | :--- | :--- |\n",
    "|  | Baseline | 0.527 |\n",
    "|  | Random Forest - no upsampling | 0.732 |\n",
    "|  | Random Forest - with upsampling | 0.723 |\n",
    "\n",
    "| Random Forest Recall | No upsampling | With upsampling |\n",
    "| :---: | :---: | :---: |\n",
    "| EF0 | 0.87 | 0.85 |\n",
    "| EF1 | 0.67 | 0.64 |\n",
    "| EF2 | 0.40 | 0.50 |\n",
    "| EF3 | 0.35 | 0.40 |\n",
    "| EF4 | 0.26 | 0.35 |\n",
    "| EF5 | 0.00 | 0.50 |\n",
    "\n",
    "*For comparisons with other types of models including logistic regression, please check [part 3](Capstone_Yves_Tornadoes_Part3.ipynb)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Random Forest model generated by upsampling the training set is the best option to an efficient prediction of all types of tornadoes.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The Receiver Operating Characteristic curve is another graph used in statistics to compare models. It is defined for binomial targets. \n",
    "- If used in a multinomial case, it compares each class with the total of remaining classes as one separate class.\n",
    "- It represents the True Postive Rate (or recall) as a function of the False Positive Rate (FPR). Logically, a good model will therefore produce a point close to (0;1) as a recall of 1 and a FPR of 0 correspond to perfection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first have to derive the associated probabilities for each tornado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOR_F_SCALE</th>\n",
       "      <th>pred_rf6</th>\n",
       "      <th>pred_rf7</th>\n",
       "      <th>rf6_EF0</th>\n",
       "      <th>rf6_EF1</th>\n",
       "      <th>rf6_EF2</th>\n",
       "      <th>rf6_EF3</th>\n",
       "      <th>rf6_EF4</th>\n",
       "      <th>rf6_EF5</th>\n",
       "      <th>rf6_EFU</th>\n",
       "      <th>rf7_EF0</th>\n",
       "      <th>rf7_EF1</th>\n",
       "      <th>rf7_EF2</th>\n",
       "      <th>rf7_EF3</th>\n",
       "      <th>rf7_EF4</th>\n",
       "      <th>rf7_EF5</th>\n",
       "      <th>rf7_EFU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EF1</td>\n",
       "      <td>EF0</td>\n",
       "      <td>EF0</td>\n",
       "      <td>0.534267</td>\n",
       "      <td>0.337432</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>0.001389</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009645</td>\n",
       "      <td>0.382310</td>\n",
       "      <td>0.346674</td>\n",
       "      <td>0.271016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EF1</td>\n",
       "      <td>EF1</td>\n",
       "      <td>EF1</td>\n",
       "      <td>0.300914</td>\n",
       "      <td>0.620458</td>\n",
       "      <td>0.076578</td>\n",
       "      <td>0.002050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.282633</td>\n",
       "      <td>0.597741</td>\n",
       "      <td>0.119626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EF0</td>\n",
       "      <td>EF0</td>\n",
       "      <td>EF0</td>\n",
       "      <td>0.739870</td>\n",
       "      <td>0.260130</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.825108</td>\n",
       "      <td>0.174892</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EF0</td>\n",
       "      <td>EF1</td>\n",
       "      <td>EF1</td>\n",
       "      <td>0.205096</td>\n",
       "      <td>0.582587</td>\n",
       "      <td>0.169694</td>\n",
       "      <td>0.035431</td>\n",
       "      <td>0.007192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.212897</td>\n",
       "      <td>0.483391</td>\n",
       "      <td>0.275934</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EF0</td>\n",
       "      <td>EF2</td>\n",
       "      <td>EF2</td>\n",
       "      <td>0.106334</td>\n",
       "      <td>0.440584</td>\n",
       "      <td>0.449378</td>\n",
       "      <td>0.003704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065985</td>\n",
       "      <td>0.379462</td>\n",
       "      <td>0.540664</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EF0</td>\n",
       "      <td>EF0</td>\n",
       "      <td>EF0</td>\n",
       "      <td>0.807659</td>\n",
       "      <td>0.138022</td>\n",
       "      <td>0.018519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.035801</td>\n",
       "      <td>0.803041</td>\n",
       "      <td>0.171959</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EF1</td>\n",
       "      <td>EF1</td>\n",
       "      <td>EF1</td>\n",
       "      <td>0.368901</td>\n",
       "      <td>0.600438</td>\n",
       "      <td>0.030661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.383022</td>\n",
       "      <td>0.538993</td>\n",
       "      <td>0.077985</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EF0</td>\n",
       "      <td>EF0</td>\n",
       "      <td>EF0</td>\n",
       "      <td>0.582347</td>\n",
       "      <td>0.402375</td>\n",
       "      <td>0.015278</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.520041</td>\n",
       "      <td>0.460327</td>\n",
       "      <td>0.005743</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EF1</td>\n",
       "      <td>EF0</td>\n",
       "      <td>EF0</td>\n",
       "      <td>0.800220</td>\n",
       "      <td>0.198539</td>\n",
       "      <td>0.001157</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.654793</td>\n",
       "      <td>0.310458</td>\n",
       "      <td>0.034749</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EF0</td>\n",
       "      <td>EF1</td>\n",
       "      <td>EF1</td>\n",
       "      <td>0.310378</td>\n",
       "      <td>0.448605</td>\n",
       "      <td>0.141745</td>\n",
       "      <td>0.079635</td>\n",
       "      <td>0.013580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006057</td>\n",
       "      <td>0.276614</td>\n",
       "      <td>0.380976</td>\n",
       "      <td>0.281646</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TOR_F_SCALE pred_rf6 pred_rf7   rf6_EF0   rf6_EF1   rf6_EF2   rf6_EF3  \\\n",
       "0         EF1      EF0      EF0  0.534267  0.337432  0.117267  0.001389   \n",
       "1         EF1      EF1      EF1  0.300914  0.620458  0.076578  0.002050   \n",
       "2         EF0      EF0      EF0  0.739870  0.260130  0.000000  0.000000   \n",
       "3         EF0      EF1      EF1  0.205096  0.582587  0.169694  0.035431   \n",
       "4         EF0      EF2      EF2  0.106334  0.440584  0.449378  0.003704   \n",
       "5         EF0      EF0      EF0  0.807659  0.138022  0.018519  0.000000   \n",
       "6         EF1      EF1      EF1  0.368901  0.600438  0.030661  0.000000   \n",
       "7         EF0      EF0      EF0  0.582347  0.402375  0.015278  0.000000   \n",
       "8         EF1      EF0      EF0  0.800220  0.198539  0.001157  0.000000   \n",
       "9         EF0      EF1      EF1  0.310378  0.448605  0.141745  0.079635   \n",
       "\n",
       "    rf6_EF4  rf6_EF5   rf6_EFU   rf7_EF0   rf7_EF1   rf7_EF2   rf7_EF3  \\\n",
       "0  0.000000      0.0  0.009645  0.382310  0.346674  0.271016  0.000000   \n",
       "1  0.000000      0.0  0.000000  0.282633  0.597741  0.119626  0.000000   \n",
       "2  0.000000      0.0  0.000000  0.825108  0.174892  0.000000  0.000000   \n",
       "3  0.007192      0.0  0.000000  0.212897  0.483391  0.275934  0.027778   \n",
       "4  0.000000      0.0  0.000000  0.065985  0.379462  0.540664  0.013889   \n",
       "5  0.000000      0.0  0.035801  0.803041  0.171959  0.025000  0.000000   \n",
       "6  0.000000      0.0  0.000000  0.383022  0.538993  0.077985  0.000000   \n",
       "7  0.000000      0.0  0.000000  0.520041  0.460327  0.005743  0.013889   \n",
       "8  0.000000      0.0  0.000084  0.654793  0.310458  0.034749  0.000000   \n",
       "9  0.013580      0.0  0.006057  0.276614  0.380976  0.281646  0.046875   \n",
       "\n",
       "   rf7_EF4  rf7_EF5   rf7_EFU  \n",
       "0      0.0      0.0  0.000000  \n",
       "1      0.0      0.0  0.000000  \n",
       "2      0.0      0.0  0.000000  \n",
       "3      0.0      0.0  0.000000  \n",
       "4      0.0      0.0  0.000000  \n",
       "5      0.0      0.0  0.000000  \n",
       "6      0.0      0.0  0.000000  \n",
       "7      0.0      0.0  0.000000  \n",
       "8      0.0      0.0  0.000000  \n",
       "9      0.0      0.0  0.013889  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_probs_rf6_7 = pd.concat([pd.DataFrame(ytest1).reset_index(drop=True),\n",
    "                                   pd.DataFrame(yhat1_rf6, columns=['pred_rf6']),\n",
    "                                   pd.DataFrame(yhat1_rf7, columns=['pred_rf7']),\n",
    "                                   pd.DataFrame(rf6.predict_proba(Xtest1), \n",
    "                                                columns=[\"rf6_{}\".format(str(i)) \n",
    "                                                         for i in list(rf6.classes_)]),\n",
    "                                   pd.DataFrame(rf7.predict_proba(Xtest1), \n",
    "                                                columns=[\"rf7_{}\".format(str(i)) \n",
    "                                                         for i in list(rf7.classes_)])],\n",
    "                                axis=1)\n",
    "predicted_probs_rf6_7.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am going to do the plotting of the ROC curves for each tornado scale, and comparing the model without and with upsampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WnQqBZW+4dywsxD/P7/nyZmPGvMKZM6eoV68+q1d/SkhI4VtvJIS4pSOXjjF7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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1244b5ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initializing the roc/auc attributes:\n",
    "fpr_rf6 = {}\n",
    "tpr_rf6 = {}\n",
    "fpr_rf7 = {}\n",
    "tpr_rf7 = {}\n",
    "roc_auc_rf6 = {}\n",
    "roc_auc_rf7 = {}\n",
    "\n",
    "# Computing the FPR, TPR and area under the ROC curve for each tornado and each model:\n",
    "for i in range(6):\n",
    "    fpr_rf6[i], tpr_rf6[i], _ = roc_curve(predicted_probs_rf6_7.TOR_F_SCALE.map(convertEFtonb),\n",
    "                                          proba_rf6[i],\n",
    "                                          pos_label=i)\n",
    "    roc_auc_rf6[i] = auc(fpr_rf6[i], tpr_rf6[i])\n",
    "\n",
    "    fpr_rf7[i], tpr_rf7[i], _ = roc_curve(predicted_probs_rf6_7.TOR_F_SCALE.map(convertEFtonb),\n",
    "                                          proba_rf7[i],\n",
    "                                          pos_label=i)\n",
    "    roc_auc_rf7[i] = auc(fpr_rf7[i], tpr_rf7[i])\n",
    "\n",
    "# Dictionnaries of the probabilities for each tornado:\n",
    "proba_rf6 = {0: predicted_probs_rf6_7.rf6_EF0, 1: predicted_probs_rf6_7.rf6_EF1,\n",
    "             2: predicted_probs_rf6_7.rf6_EF2, 3: predicted_probs_rf6_7.rf6_EF3, \n",
    "             4: predicted_probs_rf6_7.rf6_EF4, 5: predicted_probs_rf6_7.rf6_EF5}\n",
    "proba_rf7 = {0: predicted_probs_rf6_7.rf7_EF0, 1: predicted_probs_rf6_7.rf7_EF1,\n",
    "             2: predicted_probs_rf6_7.rf7_EF2, 3: predicted_probs_rf6_7.rf7_EF3,\n",
    "             4: predicted_probs_rf6_7.rf7_EF4, 5: predicted_probs_rf6_7.rf7_EF5}\n",
    "\n",
    "# Setting the plots:\n",
    "fig, ax = plt.subplots(2,3, figsize=(16,10), sharex=True, sharey=True)\n",
    "\n",
    "# Plotting first the EF0 to EF2 tornadoes:\n",
    "for i in range(3):\n",
    "    ax[0][i].plot(fpr_rf6[i], tpr_rf6[i],\n",
    "                  label='RF no upsampling (area = %0.3f)' %roc_auc_rf6[i], linewidth=2)\n",
    "    ax[0][i].plot(fpr_rf7[i], tpr_rf7[i],\n",
    "                  label='RF with upsampling (area = %0.3f)' %roc_auc_rf7[i], linewidth=2) \n",
    "    ax[0][i].plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "    ax[0][i].set_xlim([-0.05, 1.05])\n",
    "    ax[0][i].set_ylim([-0.05, 1.05])\n",
    "    ax[0][i].set_xlabel('False Positive Rate', fontsize=12)\n",
    "    ax[0][i].set_ylabel('True Positive Rate', fontsize=12)\n",
    "    ax[0][i].set_title('ROC for EF{} tornado prediction'.format(i), fontsize=14)\n",
    "    ax[0][i].legend(loc=\"lower right\")\n",
    "\n",
    "# Plotting the EF3 to EF5 tornadoes:\n",
    "for i in range(3,6):\n",
    "    ax[1][i-3].plot(fpr_rf6[i], tpr_rf6[i],\n",
    "                  label='RF no upsampling (area = %0.3f)' %roc_auc_rf6[i], linewidth=3)\n",
    "    ax[1][i-3].plot(fpr_rf7[i], tpr_rf7[i],\n",
    "                  label='RF with upsampling (area = %0.3f)' %roc_auc_rf7[i], linewidth=3)\n",
    "    ax[1][i-3].plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "    ax[1][i-3].set_xlim([-0.05, 1.05])\n",
    "    ax[1][i-3].set_ylim([-0.05, 1.05])\n",
    "    ax[1][i-3].set_xlabel('False Positive Rate', fontsize=12)\n",
    "    ax[1][i-3].set_ylabel('True Positive Rate', fontsize=12)\n",
    "    ax[1][i-3].set_title('ROC for EF{} tornado prediction'.format(i), fontsize=14)\n",
    "    ax[1][i-3].legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments:\n",
    "- The results are completely unexpected: EF5 tornadoes perform the best, and for EF4 tornadoes, without upsampling is better. The results do not correspond to our previous analyses of the confusion matrix and classification report.\n",
    "- Either displaying the ROC curves for each tornado against the rest of tornadoes is not a reliable way of interpreting the validity of the models, or I need to think more to understand these results, or I made some mistake in my previous computations of the probabilities!\n",
    "- Further investigation will be necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest most important features:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display the features with the most influence to the prediction of the magnitude.\n",
    "<br>We will do it for the two models without and with upsampling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_RF_important_features(XtrainDF=Xtrain1, fitted_rf=rf6, weight_threshold_for_display=0,\n",
    "                               figsize=(6,4),\n",
    "                               title_test=\"Most important features taken from our best RF model so far\\n\"):\n",
    "    \n",
    "    \"\"\"Plots for the train dataset 'Xtrain1' the most important features of a fitted random forest\n",
    "    model 'fitted_rf' over the weight 'weight_threshold_for_display'\"\"\"\n",
    "    \n",
    "    importanceDF = pd.DataFrame({'Importances': fitted_rf.feature_importances_},\n",
    "                                index = XtrainDF.columns)\n",
    "    most_imp = importanceDF.sort_values(by=\"Importances\", ascending=False)\n",
    "    # Horizontal bar plot\n",
    "    plt.figure(figsize=figsize)\n",
    "    sns.barplot(list(most_imp[most_imp['Importances']>weight_threshold_for_display].values), \n",
    "                [str(i) for i in \n",
    "                 list(most_imp[most_imp['Importances']>weight_threshold_for_display].index)], \n",
    "                orient='h')\n",
    "    plt.xlabel(\"Weight\")\n",
    "    plt.ylabel(\"Predictors\")\n",
    "    plt.xlim([0, 0.25])\n",
    "    plt.title(title_test)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i5uZG7dq1mTx5MsePH6dNmza0bNky1/nS/DRq1IhNmzaRnp7O2bNnuXTpEqdPnwbQ2Ka0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12413ac90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_RF_important_features()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/PjVq1MDX15f+/fujq6v7xDr56KOP8PT0JDExkQoVKijl3rlzB3t7e/bs2UOt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\nA+iKp6pM2pKvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12382add0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_RF_important_features(fitted_rf=rf7, title_test=\"Most important features for RF with upsampling\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments on the most important features:\n",
    "- It is equally very interesting to discover which features are the most influential on the prediction of the tornado's magnitude, and to compare the lists between with and without upsampling.\n",
    "- The tornado's path maximum width is by far the parameter which predicts the best the tornado strength\n",
    "- Without upsampling, the emphasis of the model is put on the weaker tornadoes. Deaths and injuries are not a big factor to predict them, as even for larger tornadoes, the casualties are usually and fortunately not very high.<br>However numerous casualties for a particular tornado indicates that it is a strong one, which is why with upsampling, the number of deaths goes up in the list.\n",
    "- As we had seen before in part 2 and 3, the location of the tornado (latitude and longitude) is a big influencer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Modelling using narratives - NLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Each tornado includes a narrative, which we can use as a predictor for the magnitude.\n",
    "- In [part 3](tornadoes_part3_eda_modelling_nlp_tsa.ipynb), I managed to get a good accuracy score of 0.787 with a simple CountVectorizer and Multinomial Naive Bayes model. For a reminder the baseline is 0.527 and the best accuracy obtained using RF with numerical features is 0.732.\n",
    "- Lemmatization and Tf-IDF did not help improving the accuracy. \n",
    "- Pending things were: \n",
    "    - enhancing the stop word list, especially with any reference to the magnitude in the text\n",
    "    - testing of removing any numbers\n",
    "    - creating new word nodamage when there is actually no damage\n",
    "    - Testing more ngrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best CountVectorizer and Multinomial Naive Bayes model obtained in part 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10175,) (4362,) (10175,) (4362,)\n"
     ]
    }
   ],
   "source": [
    "# Feature matrix (removing the bad row already removed before for tornado_2007_2017)\n",
    "X3 = raw_2007_2017[raw_2007_2017.TOR_LENGTH<50]['EVENT_NARRATIVE']\n",
    "\n",
    "# Train/test split\n",
    "Xtrain3, Xtest3, ytrain3, ytest3 = train_test_split(X3, y1, stratify=y1, test_size=0.3, random_state=31)\n",
    "print Xtrain3.shape, Xtest3.shape, ytrain3.shape, ytest3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function that accepts a vectorizer and calculates the accuracy \n",
    "def tokenize_naiveb_scores(vect, Xtrain=Xtrain3, Xtest=Xtest3,\n",
    "                  ytrain=ytrain3, ytest=ytest3, \n",
    "                  print_scores=False, print_extra_info='Part3 count vectorizer'):\n",
    "    \n",
    "    '''From a CountVectorizer model input and a train and test feature matrix and target\n",
    "    Fits a Naive Bayes multinomial model on train data\n",
    "    Outputs and optionally prints the test accuracy score and recall score for each magnitude'''\n",
    "    \n",
    "    Xtrain_vect = vect.fit_transform(Xtrain)\n",
    "    Xtest_vect = vect.transform(Xtest)\n",
    "    \n",
    "    nb = MultinomialNB()\n",
    "    nb.fit(Xtrain_vect, ytrain)\n",
    "    yhat = nb.predict(Xtest_vect)\n",
    "    \n",
    "    accuracy = round(accuracy_score(ytest, yhat), 3)\n",
    " \n",
    "    recall=[]\n",
    "    for i in range(6):\n",
    "        recall.append(round(recall_score(ytest, yhat, labels=['EF{}'.format(i)], average='macro'),\n",
    "                            3))\n",
    "        \n",
    "    if print_scores:\n",
    "        print \"Multinomial NB\\t | {} features | {}\\t\\t\\t | Accuracy:\\t\".format(Xtrain_vect.shape[1],\n",
    "                                                                               print_extra_info),\\\n",
    "              accuracy\n",
    "        for i in range(6):\n",
    "            print \"\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t | Recall EF{}:\\t\".format(i), recall[i]\n",
    "\n",
    "    return [accuracy]+recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial NB\t | 20000 features | Part3 count vectorizer\t\t\t | Accuracy:\t0.795\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF0:\t0.85\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF1:\t0.811\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF2:\t0.65\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF3:\t0.427\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF4:\t0.032\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF5:\t0.25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.795, 0.85, 0.811, 0.65, 0.427, 0.032, 0.25]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best countvec and modelling from part 3:\n",
    "countvec2 = CountVectorizer(stop_words='english', ngram_range=(1,2), max_features=20000)\n",
    "tokenize_naiveb_scores(countvec2, print_scores=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tornado    23072\n",
       "damage     14396\n",
       "county      8999\n",
       "dtype: int64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the most frequent words:\n",
    "Xtrain3_vec2 = countvec2.fit_transform(Xtrain3)\n",
    "pd.DataFrame(Xtrain3_vec2.todense(),\n",
    "             columns=countvec2.get_feature_names()).sum(axis=0).sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating scores dictionnaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For later overall graphic analysis, I am going to create dictionnaries containing the accuracy and recall scores for each test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accuracy_nlp = {}\n",
    "recallEF0_nlp = {}\n",
    "recallEF1_nlp = {}\n",
    "recallEF2_nlp = {}\n",
    "recallEF3_nlp = {}\n",
    "recallEF4_nlp = {}\n",
    "recallEF5_nlp = {}\n",
    "scores_nlp = [accuracy_nlp, recallEF0_nlp, recallEF1_nlp, recallEF2_nlp, recallEF3_nlp,\n",
    "              recallEF4_nlp, recallEF5_nlp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cntv2 - part3 reference': 0.65}"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_score_list = tokenize_naiveb_scores(countvec2)\n",
    "for i, score in enumerate(scores_nlp):\n",
    "    score['cntv2 - part3 reference'] = tmp_score_list[i]\n",
    "\n",
    "recallEF2_nlp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing nltk stopwords:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "153"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopnltk = [str(t) for t in stopwords.words('english')]\n",
    "len(stopnltk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial NB\t | 20000 features | nltk stopwords\t\t\t | Accuracy:\t0.792\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF0:\t0.848\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF1:\t0.802\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF2:\t0.657\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF3:\t0.412\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF4:\t0.032\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF5:\t0.25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.792, 0.848, 0.802, 0.657, 0.412, 0.032, 0.25]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Countvec using nltk stopwords and modelling from part 3:\n",
    "countvec3 = CountVectorizer(stop_words=stopnltk, ngram_range=(1,2), max_features=20000)\n",
    "tokenize_naiveb_scores(countvec3, print_scores=True, print_extra_info='nltk stopwords')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The accuracy score is not as good when using nltk stopwords\n",
    "- Let's go back to the count vectorizer ones and work from there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cntv2 - part3 reference': 0.65, 'cntv3 - nltk stopwords': 0.657}"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the scores:\n",
    "tmp_score_list = tokenize_naiveb_scores(countvec3)\n",
    "for i, score in enumerate(scores_nlp):\n",
    "    score['cntv3 - nltk stopwords'] = tmp_score_list[i]\n",
    "recallEF2_nlp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling \"no damage\" references"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "318"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countvec_stopwords1 = list(countvec2.get_stop_words())\n",
    "len(countvec_stopwords1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of stopwords in countvectorizer is more important than nltk. That may be the reason why it gives better result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing 'no' from the list as it can usefully be associated with 'damage' for instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "317"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countvec_stopwords2 = countvec_stopwords1\n",
    "for word in ['no']:\n",
    "    countvec_stopwords2.remove(word)\n",
    "len(countvec_stopwords2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Redoing the modelling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial NB\t | 20000 features | countvec stopwords including \"no\"\t\t\t | Accuracy:\t0.796\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF0:\t\t0.844\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF1:\t\t0.82\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF2:\t\t0.667\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF3:\t\t0.397\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF4:\t\t0.0\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF5:\t\t0.25\n"
     ]
    }
   ],
   "source": [
    "countvec4 = CountVectorizer(stop_words=countvec_stopwords2, ngram_range=(1,2),\n",
    "                            max_features=20000)\n",
    "tokenize_naiveb_scores(countvec4, print_scores=True,\n",
    "                       print_extra_info='countvec stopwords including \"no\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bit better. We can now check the most important words and/or combination of words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tornado      23072\n",
       "damage       14396\n",
       "county        8999\n",
       "trees         8257\n",
       "road          6959\n",
       "near          4731\n",
       "path          4437\n",
       "miles         4230\n",
       "touched       4033\n",
       "northeast     3951\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain3_vec4 = countvec4.fit_transform(Xtrain3)\n",
    "pd.DataFrame(Xtrain3_vec4.todense(),\n",
    "             columns=countvec4.get_feature_names()).sum(axis=0).sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No combination of words can be seen in the top 10 list. Let's check now that \"no damage\" is indeed included in the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"no damage\" in countvec4.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"no\" in countvec4.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slight improve may be explained by the fact that \"no damage\" is now a feature. However \"no\" is also becoming a feature on its own. One way to fix this is to replace in the original text \"no damage\" by a new word \"no_damage\" and leave \"no\" as a stopword:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def replace_nodamage(txt):\n",
    "    '''Replaces 'no damage' by 'nodamage' in a text'''\n",
    "    txt = txt.replace('no damage','nodamage')\n",
    "    txt = txt.replace('no reported damage','noreporteddamage')\n",
    "    txt = txt.replace('any damage','nodamage')\n",
    "    txt = txt.replace('not damage','nodamage')\n",
    "    return txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This was a rope shaped tornado that was over range land and did not damage anything.\n",
      "This was a rope shaped tornado that was over range land and did nodamage anything.\n"
     ]
    }
   ],
   "source": [
    "# Applying and checking the function:\n",
    "Xtrain3_nodamage = Xtrain3.map(replace_nodamage)\n",
    "Xtest3_nodamage = Xtest3.map(replace_nodamage)\n",
    "print Xtrain3[3]\n",
    "print Xtrain3_nodamage[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial NB\t | 20000 features | nodamage new word\t\t\t | Accuracy:\t0.796\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF0:\t\t0.848\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF1:\t\t0.817\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF2:\t\t0.657\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF3:\t\t0.405\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF4:\t\t0.0\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF5:\t\t0.25\n"
     ]
    }
   ],
   "source": [
    "countvec5 = CountVectorizer(stop_words='english', ngram_range=(1,2), max_features=20000)\n",
    "tokenize_naiveb_scores(countvec5, Xtrain3_nodamage, Xtest3_nodamage, print_scores=True,\n",
    "                       print_extra_info=\"nodamage new word\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get the same accuracy, but the words are better prepared. So we will take this result as our new reference.\n",
    "<br>Note: recall for EF4 tornadoes is still bad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cntv2 - part3 reference': 0.65,\n",
       " 'cntv3 - nltk stopwords': 0.657,\n",
       " 'cntv5 - nodamage new word': 0.657}"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the scores:\n",
    "tmp_score_list = tokenize_naiveb_scores(countvec5, Xtrain3_nodamage, Xtest3_nodamage)\n",
    "for i, score in enumerate(scores_nlp):\n",
    "    score['cntv5 - nodamage new word'] = tmp_score_list[i]\n",
    "recallEF2_nlp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing references to target:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any reference to the supposed EF scale (our target) in the narrative would not provide a fair judgement of a model's quality. We should add ef0, ef1, ..., ef5 to the stopwords for a fairer modelling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ef4', 'ef5', 'f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'efu', 'fu']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countvec_stopwords3 = countvec_stopwords1 + [\"ef{}\".format(i) for i in range(6)] \\\n",
    "                                          + [\"f{}\".format(i) for i in range(6)] \\\n",
    "                                          + ['efu', 'fu']\n",
    "countvec_stopwords3[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial NB\t | 20000 features | target references removed\t\t\t | Accuracy:\t0.771\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF0:\t0.829\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF1:\t0.79\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF2:\t0.618\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF3:\t0.321\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF4:\t0.032\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF5:\t0.25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.771, 0.829, 0.79, 0.618, 0.321, 0.032, 0.25]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countvec6 = CountVectorizer(stop_words=countvec_stopwords3, ngram_range=(1,2), max_features=20000)\n",
    "tokenize_naiveb_scores(countvec6, Xtrain3_nodamage, Xtest3_nodamage, \n",
    "                       print_scores=True, print_extra_info='target references removed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, with no reference to the target, the accuracy score is worse. But much fairer. And better than our previous models using the numerical features (0.732)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cntv2 - part3 reference': 0.65,\n",
       " 'cntv3 - nltk stopwords': 0.657,\n",
       " 'cntv5 - nodamage new word': 0.657,\n",
       " 'cntv6 - no reference to target': 0.618}"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the scores:\n",
    "tmp_score_list = tokenize_naiveb_scores(countvec6, Xtrain3_nodamage, Xtest3_nodamage)\n",
    "for i, score in enumerate(scores_nlp):\n",
    "    score['cntv6 - no reference to target'] = tmp_score_list[i]\n",
    "recallEF2_nlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ojg6NGjVCVVW1wPPk1zvIpkuXLixatIgqVapQt27dPNOdOXOGoUOHSoUVZiwq+w1yzpw5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\nEonkgUIGJolEIpE8UPwLiJzzWFszztUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120deda90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb = MultinomialNB()\n",
    "nb.fit(Xtrain3_vec6, ytrain3)\n",
    "yhat3_vec6 = nb.predict(Xtest3_vec6)\n",
    "\n",
    "# Computing the confusion matrix\n",
    "confmatrix_nb = confusion_matrix(ytest3, yhat3_vec6)\n",
    "\n",
    "# Plotting it using the function:\n",
    "plot_confusion_matrix(confmatrix_nb, nb.classes_,\n",
    "                      'Confusion matrix - Naive Bayes - Default hyperparameters',\n",
    "                      xlab='Predicted EF scale magnitude', ylab='True EF scale magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- EF0 to EF2 tornadoes: their predictions are in majority correct.\n",
    "- EF3 to EF5 tornadoes: they are predicted in majority as weaker.\n",
    "- In all cases it is better than RF from numerical features\n",
    "- To improve on this, we could try upsampling on just the EF3 to EF5 tornadoes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But first let's test removing any reference to numbers and digits, which could give info on wind speed, and I would preferably like to base our modelling only on narrative text."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing references to numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def remove_nbs_from_text(txt):\n",
    "    '''Removes any digit from a text'''\n",
    "    for digitchar in [str(digit) for digit in range(10)]:\n",
    "        txt = txt.replace(digitchar,'')\n",
    "    txt = txt.replace('-',' ').replace('  ',' ').replace(' .','.').replace(' ,',',')\n",
    "    return txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xtrain3_nonb = Xtrain3_nodamage.map(remove_nbs_from_text)\n",
    "Xtest3_nonb = Xtest3_nodamage.map(remove_nbs_from_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A tornado touched down along Moorefield Road, just to the east of State Route 4.  Trees were either uprooted or snapped off towards their tops with additional large limbs down from other trees.  Some minor structural damage occured as well along the 250 yard damage path.  The tornado was rated EF-0 with top wind speeds of 70 mph. \n",
      "\n",
      "A tornado touched down along Moorefield Road, just to the east of State Route. Trees were either uprooted or snapped off towards their tops with additional large limbs down from other trees. Some minor structural damage occured as well along the yard damage path. The tornado was rated EF with top wind speeds of mph.\n"
     ]
    }
   ],
   "source": [
    "# QCing the function:\n",
    "print Xtrain3[10],\"\\n\"\n",
    "print Xtrain3_nonb[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['so', 'five', 'the', 'first', 'whereas', 'once', 'ef', 'f', 'efu', 'fu']"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As number have already been removed, we just need to add ef or f to the stopword list:\n",
    "countvec_stopwords4 = countvec_stopwords1 + [\"ef\", \"f\", 'efu', 'fu']\n",
    "countvec_stopwords4[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial NB\t | 20000 features | no number\t\t\t | Accuracy:\t0.705\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF0:\t\t0.768\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF1:\t\t0.749\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF2:\t\t0.444\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF3:\t\t0.237\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF4:\t\t0.0\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF5:\t\t0.25\n"
     ]
    }
   ],
   "source": [
    "countvec7 = CountVectorizer(stop_words=countvec_stopwords4, ngram_range=(1,2),\n",
    "                            max_features=20000)\n",
    "tokenize_naiveb_scores(countvec7, Xtrain3_nonb, Xtest3_nonb, print_scores=True,\n",
    "                       print_extra_info='no number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, we have lost even more accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, it is probably better to keep references to numbers and wind speed in particular, as they are an indication about the strength of a tornado, and not a direct reference to the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cntv2 - part3 reference': 0.65,\n",
       " 'cntv3 - nltk stopwords': 0.657,\n",
       " 'cntv5 - nodamage new word': 0.657,\n",
       " 'cntv6 - no reference to target': 0.618,\n",
       " 'cntv7 - no reference to numbers': 0.444}"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the scores:\n",
    "tmp_score_list = tokenize_naiveb_scores(countvec7, Xtrain3_nonb, Xtest3_nonb)\n",
    "for i, score in enumerate(scores_nlp):\n",
    "    score['cntv7 - no reference to numbers'] = tmp_score_list[i]\n",
    "recallEF2_nlp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying this other model widely used in NLP when having big datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function that accepts a vectorizer and calculates the accuracy via Stochastic Gradient Descent\n",
    "# modelling\n",
    "def tokenize_sgd_scores(vect, Xtrain=Xtrain3, Xtest=Xtest3, ytrain=ytrain3, ytest=ytest3,\n",
    "                        print_scores=False, print_extra_info='Default stopwords'):\n",
    "    \n",
    "    '''From a CountVectorizer model input and a train and test feature matrix and target\n",
    "    Fits a Stochastic Gradient Descent model on train data\n",
    "    Outputs and optionally prints the test accuracy score and recall score for each magnitude'''\n",
    "    \n",
    "    Xtrain_vect = vect.fit_transform(Xtrain)\n",
    "    Xtest_vect = vect.transform(Xtest)\n",
    "    \n",
    "    sgd = SGDClassifier(random_state=31)\n",
    "    sgd.fit(Xtrain_vect, ytrain)\n",
    "    yhat = sgd.predict(Xtest_vect)\n",
    "    \n",
    "    accuracy = round(accuracy_score(ytest, yhat), 3)\n",
    " \n",
    "    recall=[]\n",
    "    for i in range(6):\n",
    "        recall.append(round(recall_score(ytest, yhat, labels=['EF{}'.format(i)],\n",
    "                                         average='macro'), 3))\n",
    "        \n",
    "    if print_scores:\n",
    "        print \"Stochastic GD\\t | {} features | {}\\t | Accuracy:\\t\".format(Xtrain_vect.shape[1],\n",
    "                                                                          print_extra_info),\\\n",
    "              accuracy\n",
    "        for i in range(6):\n",
    "            print \"\\t\\t\\t\\t\\t\\t\\t\\t\\t | Recall EF{}:\\t\".format(i), recall[i]\n",
    "\n",
    "    return [accuracy]+recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stochastic GD\t | 20000 features | cntv6 - no reference to target\t | Accuracy:\t0.78\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF0:\t0.888\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF1:\t0.724\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF2:\t0.528\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF3:\t0.565\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF4:\t0.226\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF5:\t0.25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.78, 0.888, 0.724, 0.528, 0.565, 0.226, 0.25]"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenize_sgd_scores(countvec6, Xtrain3_nodamage, Xtest3_nodamage, \n",
    "                    print_scores=True, print_extra_info='cntv6 - no reference to target')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The accuracy is better than Naive Bayes (0.771). \n",
    "- Recall for EF0, EF3 and EF4 tornadoes has improved. \n",
    "- Recall for EF1 and EF2 has worsened.\n",
    "- Overall I would say it is a better model, as it improved the accuracy and our goal to get better recall for stronger tornadoes\n",
    "- Thanks to Hannah for advising me to test Stochasting Gradient Descent!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cntv2 - part3 reference': 0.65,\n",
       " 'cntv3 - nltk stopwords': 0.657,\n",
       " 'cntv5 - nodamage new word': 0.657,\n",
       " 'cntv6 - no reference to target': 0.618,\n",
       " 'cntv6b - SGD model': 0.528,\n",
       " 'cntv7 - no reference to numbers': 0.444}"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the scores:\n",
    "tmp_score_list = tokenize_sgd_scores(countvec6, Xtrain3_nodamage, Xtest3_nodamage)\n",
    "for i, score in enumerate(scores_nlp):\n",
    "    score['cntv6b - SGD model'] = tmp_score_list[i]\n",
    "recallEF2_nlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4xtKlS4mXlxcpLS0lxsbGHPkJIWTZsmVk+PDhhJCf16DiPZebm0v69OlD9u7dy5GmYjtp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120ded690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sgd = SGDClassifier(random_state=31)\n",
    "\n",
    "sgd.fit(Xtrain3_vec6, ytrain3)\n",
    "yhat3_vec6_sgd = sgd.predict(Xtest3_vec6)\n",
    "\n",
    "# Computing the confusion matrix\n",
    "confmatrix_sgd = confusion_matrix(ytest3, yhat3_vec6_sgd)\n",
    "\n",
    "# Plotting it using the function:\n",
    "plot_confusion_matrix(confmatrix_sgd, sgd.classes_,\n",
    "                      'Confusion matrix - Stochastic Gradient Descent - Default hyperparameters',\n",
    "                      xlab='Predicted EF scale magnitude', ylab='True EF scale magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It is better, now the majority of EF3 predictions are correct.\n",
    "- And true positives of EF4 tornadoes have gone from 1 to 7, this is a huge improvement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More ngrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countvec8 = CountVectorizer(stop_words=countvec_stopwords3, ngram_range=(1,3), max_features=20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial NB\t | 20000 features | ngrams 3\t\t\t | Accuracy:\t0.777\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF0:\t0.84\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF1:\t0.771\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF2:\t0.65\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF3:\t0.45\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF4:\t0.032\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF5:\t0.25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.777, 0.84, 0.771, 0.65, 0.45, 0.032, 0.25]"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenize_naiveb_scores(countvec8, Xtrain3_nodamage, Xtest3_nodamage, print_scores=True,\n",
    "                       print_extra_info='ngrams 3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stochastic GD\t | 20000 features | ngrams 3\t | Accuracy:\t0.784\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF0:\t0.897\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF1:\t0.755\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF2:\t0.44\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF3:\t0.458\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF4:\t0.29\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF5:\t0.25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.784, 0.897, 0.755, 0.44, 0.458, 0.29, 0.25]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenize_sgd_scores(countvec8, Xtrain3_nodamage, Xtest3_nodamage, print_scores=True,\n",
    "                    print_extra_info='ngrams 3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We improved the accuracy compared to ngrams=(1,2), in both cases Naive Bayes and Stochastic Gradient Descent\n",
    "- Recall of EF0, EF1 and EF4 got better too. EF2 and EF3 got worse.\n",
    "- Overall, SGD is better than NB, even though NB models better middle size tornadoes (EF1 and EF2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cntv2 - part3 reference': 0.65,\n",
       " 'cntv3 - nltk stopwords': 0.657,\n",
       " 'cntv5 - nodamage new word': 0.657,\n",
       " 'cntv6 - no reference to target': 0.618,\n",
       " 'cntv6b - SGD model': 0.528,\n",
       " 'cntv7 - no reference to numbers': 0.444,\n",
       " 'cntv8 - ngrams 1 to 3': 0.44}"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the scores:\n",
    "tmp_score_list = tokenize_sgd_scores(countvec8, Xtrain3_nodamage, Xtest3_nodamage)\n",
    "for i, score in enumerate(scores_nlp):\n",
    "    score['cntv8 - ngrams 1 to 3'] = tmp_score_list[i]\n",
    "recallEF2_nlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7adEyeavbkiYmNG9iz907d6hkZZWj2r4kNDSUjRvWsXzF7/TtPwBtdSgvq4SF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e196cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xtrain3_vec8 = countvec8.fit_transform(Xtrain3_nodamage)\n",
    "Xtest3_vec8 = countvec8.transform(Xtest3_nodamage)\n",
    "\n",
    "sgd = SGDClassifier(random_state=31)\n",
    "sgd.fit(Xtrain3_vec8, ytrain3)\n",
    "yhat3_vec8_sgd = sgd.predict(Xtest3_vec8)\n",
    "\n",
    "# Computing the confusion matrix\n",
    "confmatrix_sgd = confusion_matrix(ytest3, yhat3_vec8_sgd)\n",
    "\n",
    "# Plotting it using the function:\n",
    "plot_confusion_matrix(confmatrix_sgd, sgd.classes_,\n",
    "                      'Confusion matrix - SGD - Default hyperparameters',\n",
    "                      xlab='Predicted EF scale magnitude', ylab='True EF scale magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The true positives of EF4 tornadoes went again up: from 7 to 9.\n",
    "- Even though the recall of EF5 tornadoes is the same, ie the true positive is still equal to 1, and the sum of false negatives = 3, the distribution of the false negatives is worse: one EF5 tornado is even predicted as EF0! Analysing the recall is not enough, thorough analysis of the confusion matrix is needed.\n",
    "- Nevertherless, in terms of scoring, this result using SGD with ngrams 1 to 3 is the best so far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I also tested higher ngrams, but it was giving worse results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upsampling EF3 to EF5 tornadoes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upsampling the train dataset can help improving the recall on stronger tornadoes on the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10175,) (10175, 2)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVENT_NARRATIVE</th>\n",
       "      <th>TOR_F_SCALE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13291</th>\n",
       "      <td>A survey team found damage consisten tiwht an ...</td>\n",
       "      <td>EF0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>This tornado first touched down approximately ...</td>\n",
       "      <td>EF2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         EVENT_NARRATIVE TOR_F_SCALE\n",
       "13291  A survey team found damage consisten tiwht an ...         EF0\n",
       "1056   This tornado first touched down approximately ...         EF2"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-incorporating the magnitude:\n",
    "Xtrain4 = pd.concat([Xtrain3_nodamage, pd.DataFrame(ytrain3)], axis=1)\n",
    "print Xtrain3_nodamage.shape, Xtrain4.shape\n",
    "Xtrain4.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating a dictionary of dataframes, ones for each magnitude:\n",
    "Xtrain4_perMag = {}\n",
    "for mag in ['EF0', 'EFU', 'EF1', 'EF2', 'EF3', 'EF4', 'EF5']:\n",
    "    Xtrain4_perMag[mag] = Xtrain4[Xtrain4['TOR_F_SCALE']==mag]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EF0 Original shape :\t(5362, 2) \tUpsample shape EF0: (5362, 2)\n",
      "EFU Original shape :\t(43, 2) \tUpsample shape EFU: (43, 2)\n",
      "EF1 Original shape :\t(3375, 2) \tUpsample shape EF1: (3375, 2)\n",
      "EF2 Original shape :\t(1009, 2) \tUpsample shape EF2: (1009, 2)\n",
      "EF3 Original shape :\t(304, 2) \tUpsample shape EF3: (1009, 2)\n",
      "EF4 Original shape :\t(72, 2) \tUpsample shape EF4: (1009, 2)\n",
      "EF5 Original shape :\t(10, 2) \tUpsample shape EF5: (1009, 2)\n"
     ]
    }
   ],
   "source": [
    "# Upsampling each dataframe based on the number of rows of the majority class EF0:\n",
    "Xtrain4_upsampled = {}\n",
    "\n",
    "for mag in ['EF0', 'EFU', 'EF1', 'EF2', 'EF3', 'EF4', 'EF5']:\n",
    "    \n",
    "    if mag in ['EF0', 'EFU', 'EF1', 'EF2']:\n",
    "        Xtrain4_upsampled[mag] = Xtrain4_perMag[mag]\n",
    "    else:\n",
    "        # Bootstrapping method is used:\n",
    "        Xtrain4_upsampled[mag] = resample(Xtrain4_perMag[mag], replace=True,\n",
    "                                          n_samples=Xtrain4_perMag['EF2'].shape[0],\n",
    "                                          random_state=31)\n",
    "    # QCs:\n",
    "    print \"{} Original shape :\\t\".format(mag), Xtrain4_perMag[mag].shape,\\\n",
    "          \"\\tUpsample shape {}:\".format(mag), Xtrain4_upsampled[mag].shape\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12816, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVENT_NARRATIVE</th>\n",
       "      <th>TOR_F_SCALE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A survey team found damage consisten tiwht an ...</td>\n",
       "      <td>EF0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This tornado occurred in open country and impa...</td>\n",
       "      <td>EF0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     EVENT_NARRATIVE TOR_F_SCALE\n",
       "0  A survey team found damage consisten tiwht an ...         EF0\n",
       "1  This tornado occurred in open country and impa...         EF0"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating all the upsampled dataframes\n",
    "Xtrain4_df = pd.concat([Xtrain4_upsampled['EF0'], Xtrain4_upsampled['EF1'],\n",
    "                        Xtrain4_upsampled['EF2'], Xtrain4_upsampled['EF3'], \n",
    "                        Xtrain4_upsampled['EF4'], Xtrain4_upsampled['EF5'],\n",
    "                        Xtrain4_upsampled['EFU']], ignore_index=True, axis=0)\n",
    "print Xtrain4_df.shape\n",
    "Xtrain4_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12816,) (12816,)\n"
     ]
    }
   ],
   "source": [
    "# Recreating the feature matrix and target for modelling:\n",
    "ytrain4 = Xtrain4_df.TOR_F_SCALE\n",
    "Xtrain4_df.drop(['TOR_F_SCALE'], axis=1, inplace=True)\n",
    "Xtrain4_df = Xtrain4_df.EVENT_NARRATIVE\n",
    "print Xtrain4_df.shape, ytrain4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stochastic GD\t | 20000 features | upsampling EF3 to EF5\t | Accuracy:\t0.771\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF0:\t0.903\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF1:\t0.668\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF2:\t0.549\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF3:\t0.565\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF4:\t0.226\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF5:\t0.25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.771, 0.903, 0.668, 0.549, 0.565, 0.226, 0.25]"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Redoing the modeling, with best obtained so far \n",
    "# (maxfeatures 20000, ngrams 3, new word no_damage, stopwords including references to scale, sgd)\n",
    "countvec9 = CountVectorizer(stop_words=countvec_stopwords3, ngram_range=(1,3),\n",
    "                            max_features=20000)\n",
    "tokenize_sgd_scores(countvec9, Xtrain4_df, Xtest3_nodamage, ytrain4, ytest3,\n",
    "                    print_scores=True, print_extra_info='upsampling EF3 to EF5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Accuracy is lower\n",
    "- Recall of EF0, EF2 and EF3 tornadoes has increased. Recall EF1 and EF4 has decreased. \n",
    "- It is not a very conclusive test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cntv2 - part3 reference': 0.65,\n",
       " 'cntv3 - nltk stopwords': 0.657,\n",
       " 'cntv5 - nodamage new word': 0.657,\n",
       " 'cntv6 - no reference to target': 0.618,\n",
       " 'cntv6b - SGD model': 0.528,\n",
       " 'cntv7 - no reference to numbers': 0.444,\n",
       " 'cntv8 - ngrams 1 to 3': 0.44,\n",
       " 'cntv9 - sgd - upsampling': 0.549}"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the scores:\n",
    "tmp_score_list = tokenize_sgd_scores(countvec9, Xtrain4_df, Xtest3_nodamage, ytrain4, ytest3)\n",
    "for i, score in enumerate(scores_nlp):\n",
    "    score['cntv9 - sgd - upsampling'] = tmp_score_list[i]\n",
    "recallEF2_nlp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introducing the length of the narrative as a feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By inspecting more the data, I could realize the that stronger tornadoes have a bigger narrative. Let's include this as a new variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVENT_NARRATIVE</th>\n",
       "      <th>txt_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A survey team found damage consisten tiwht an ...</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This tornado first touched down approximately ...</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     EVENT_NARRATIVE  txt_length\n",
       "0  A survey team found damage consisten tiwht an ...         390\n",
       "1  This tornado first touched down approximately ...        1562"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain3_nodamage_df = pd.DataFrame(Xtrain3_nodamage.reset_index(drop=True))\n",
    "Xtrain3_nodamage_df['txt_length'] = Xtrain3_nodamage_df.EVENT_NARRATIVE.map(lambda txt: len(txt))\n",
    "Xtrain3_nodamage_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    EF0\n",
       "1    EF2\n",
       "Name: TOR_F_SCALE, dtype: object"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain3.reset_index(drop=True, inplace=True)\n",
    "ytrain3.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two lists above illustrate my point: the EF2 tornado has more narrative text than the EF0 one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Same kind of DF for test set:\n",
    "Xtest3_nodamage_df = pd.DataFrame(Xtest3_nodamage.reset_index(drop=True))\n",
    "Xtest3_nodamage_df['txt_length'] = Xtest3_nodamage_df.EVENT_NARRATIVE.map(lambda txt: len(txt))\n",
    "\n",
    "ytest3.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a new feature matrix with the length of the narrative and the best count vectorizer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>txt_length</th>\n",
       "      <th>00</th>\n",
       "      <th>00 cst</th>\n",
       "      <th>00 pm</th>\n",
       "      <th>000</th>\n",
       "      <th>000 chickens</th>\n",
       "      <th>000 dollars</th>\n",
       "      <th>000 feet</th>\n",
       "      <th>000 pounds</th>\n",
       "      <th>001</th>\n",
       "      <th>...</th>\n",
       "      <th>years old</th>\n",
       "      <th>yellow</th>\n",
       "      <th>york</th>\n",
       "      <th>york county</th>\n",
       "      <th>young</th>\n",
       "      <th>zandt</th>\n",
       "      <th>zandt county</th>\n",
       "      <th>zero</th>\n",
       "      <th>zion</th>\n",
       "      <th>zion road</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>390</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1562</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 20001 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   txt_length  00  00 cst  00 pm  000  000 chickens  000 dollars  000 feet  \\\n",
       "0         390   0       0      0    0             0            0         0   \n",
       "1        1562   0       0      0    0             0            0         0   \n",
       "\n",
       "   000 pounds  001    ...      years old  yellow  york  york county  young  \\\n",
       "0           0    0    ...              0       0     0            0      0   \n",
       "1           0    0    ...              0       0     0            0      0   \n",
       "\n",
       "   zandt  zandt county  zero  zion  zion road  \n",
       "0      0             0     0     0          0  \n",
       "1      0             0     0     0          0  \n",
       "\n",
       "[2 rows x 20001 columns]"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain5 = pd.concat([Xtrain3_nodamage_df[['txt_length']],\n",
    "                     pd.DataFrame(Xtrain3_vec8.todense(),\n",
    "                                  columns=countvec8.get_feature_names())], axis=1)\n",
    "Xtrain5.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Same for test set:\n",
    "Xtest5 = pd.concat([Xtest3_nodamage_df[['txt_length']],\n",
    "                    pd.DataFrame(Xtest3_vec8.todense(),\n",
    "                                 columns=countvec8.get_feature_names())], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we now have another feature and it is numerical, I am going to use RF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:  2.8min\n",
      "[Parallel(n_jobs=2)]: Done  80 out of  80 | elapsed:  4.9min finished\n",
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=2)]: Done  80 out of  80 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy | Test\t\t | Random Forest\t | length + cvect\t | Prev test param:\t0.785\n"
     ]
    }
   ],
   "source": [
    "# Best RF so far:\n",
    "rf9 = RandomForestClassifier(random_state=31, min_samples_leaf=1, n_estimators=80,\n",
    "                             min_samples_split=8, criterion='entropy', \n",
    "                             max_features=0.4, max_depth=24, \n",
    "                             verbose=1, n_jobs=2)\n",
    "rf9.fit(Xtrain5, ytrain3)\n",
    "yhat5_rf9 = rf9.predict(Xtest5)\n",
    "accuracy_rf9 = accuracy_score(ytest3, yhat5_rf9)\n",
    "print \"Accuracy | Test\\t\\t | Random Forest\\t | length + cvect\\t | Prev test param:\\t\", round(accuracy_rf9, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is very similar to the best accuracy score previously obtained with SGD without the text length as a feature (0.784). Let's analyse the recall:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t\t\t\t\t\t\t\t | Recall EF0:\t\t0.905\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF1:\t\t0.759\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF2:\t\t0.479\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF3:\t\t0.252\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF4:\t\t0.129\n",
      "\t\t\t\t\t\t\t\t\t\t | Recall EF5:\t\t0.25\n"
     ]
    }
   ],
   "source": [
    "for i in range(6):\n",
    "    print \"\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t | Recall EF{}:\\t\\t\".format(i),\\\n",
    "          round(recall_score(ytest3, yhat5_rf9, labels=['EF{}'.format(i)], average='macro'), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The recall of the weaker tornadoes has increased, but the one of the stronger ones has decreased.\n",
    "- This test is not going to be chosen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Saving the scores:\n",
    "accuracy_nlp['cntv8b - rf - text length feat'] = round(accuracy_rf9, 3)\n",
    "for i, score in enumerate(scores_nlp[1:]):\n",
    "    score['cntv8b - rf - text length feat'] = \\\n",
    "    round(recall_score(ytest3, yhat5_rf9, labels=['EF{}'.format(i)], average='macro'), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cntv2 - part3 reference': 0.795,\n",
       " 'cntv3 - nltk stopwords': 0.792,\n",
       " 'cntv5 - nodamage new word': 0.796,\n",
       " 'cntv6 - no reference to target': 0.771,\n",
       " 'cntv6b - SGD model': 0.78,\n",
       " 'cntv7 - no reference to numbers': 0.705,\n",
       " 'cntv8 - ngrams 1 to 3': 0.784,\n",
       " 'cntv8b - rf - text length feat': 0.785,\n",
       " 'cntv9 - sgd - upsampling': 0.771}"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_nlp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions on our Natural Language Processing analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We gathered all the important test scores in the list of dictionaries scores_nlp. \n",
    "<br>We can now use it to plot the scores as a function of the test, and have a better overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1mNd3Bnr7KO1tmlNgyrHGCSjHGtHpIMFGEIRDodJW4nj2KSRnpSKv0rD4erBr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\nVQgLC5PcqYscDQ8Po6OjA2VlZZg9ezYmT54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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128f60650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xTicks = sorted(scores_nlp[0].keys())\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title(\"Scores of the different tests performed for NLP\")\n",
    "plt.xticks(range(9), xTicks, rotation=70)\n",
    "plt.ylabel(\"Score\")\n",
    "labels = ['Accuracy','EF0 recall','EF1 recall','EF2 recall','EF3 recall','EF4 recall',\n",
    "          'EF5 recall']\n",
    "for i, score in enumerate(scores_nlp):\n",
    "    y = [value for (key, value) in sorted(score.items())]\n",
    "    plt.plot(y, label=labels[i])\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It is clearly visible on this graph that the introduction of the Stochastic Gradient Descent at test 6b, brought a significant improvement on all scores except EF1 and EF2 recalls.\n",
    "- Test 7 shows us that numbers are important in the modelling of narrative features. They correspond to references to wind speed for example.\n",
    "- Test 8: apart from EF2 and EF3 tornadoes, all other predictions are better than test 6b.\n",
    "- Test 8b shows that the text length is not very representative of EF3 and EF4 tornadoes (their recall decreases)\n",
    "- Test 9 representing the upsampling is interesting: EF2 and EF3 are getting better, but EF1 tornadoes are getting their lowest recall.\n",
    "<br><br>\n",
    "- Test 8 seems like a good compromise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary of the chosen parameters based on the various statistics:**\n",
    "- Creation of 'nodamage' new word.\n",
    "- Count Vectorizer default stop words.\n",
    "- Removal of references to target.\n",
    "- Keeping the numbers references.\n",
    "- Stochastic Gradient Descent model.\n",
    "- No upsampling of stronger tornadoes\n",
    "- ngrams 1 to 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gathering the numerical features + count vectorized narrative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14537, 11) (14537,)\n"
     ]
    }
   ],
   "source": [
    "# We have to join X1 and X3\n",
    "X1.head(2)\n",
    "print X1.shape, X3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X1.reset_index(drop=True, inplace=True)\n",
    "\n",
    "X3_vec8 = countvec8.fit_transform(X3.map(replace_nodamage))\n",
    "\n",
    "X3_vec8_df = pd.DataFrame(X3_vec8.todense(), columns=countvec8.get_feature_names())\n",
    "\n",
    "X4 = pd.concat([X1, X3_vec8_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14537, 20011)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOR_LENGTH</th>\n",
       "      <th>TOR_WIDTH</th>\n",
       "      <th>Duration</th>\n",
       "      <th>AverageDate</th>\n",
       "      <th>AverageTime</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Injuries</th>\n",
       "      <th>Mean_Lat</th>\n",
       "      <th>Mean_Lon</th>\n",
       "      <th>Azimuth</th>\n",
       "      <th>...</th>\n",
       "      <th>years old</th>\n",
       "      <th>yellow</th>\n",
       "      <th>york</th>\n",
       "      <th>york county</th>\n",
       "      <th>young</th>\n",
       "      <th>zandt</th>\n",
       "      <th>zandt county</th>\n",
       "      <th>zero</th>\n",
       "      <th>zion</th>\n",
       "      <th>zion road</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.649575</td>\n",
       "      <td>-0.553936</td>\n",
       "      <td>-0.663909</td>\n",
       "      <td>0.71888</td>\n",
       "      <td>0.064657</td>\n",
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       "      <td>0.992225</td>\n",
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       "      <td>1.408556</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.665453</td>\n",
       "      <td>-0.553936</td>\n",
       "      <td>-0.663909</td>\n",
       "      <td>0.71888</td>\n",
       "      <td>0.142239</td>\n",
       "      <td>-0.048934</td>\n",
       "      <td>-0.06017</td>\n",
       "      <td>0.978331</td>\n",
       "      <td>-0.115564</td>\n",
       "      <td>1.354156</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 20011 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TOR_LENGTH  TOR_WIDTH  Duration  AverageDate  AverageTime    Deaths  \\\n",
       "0   -0.649575  -0.553936 -0.663909      0.71888     0.064657 -0.048934   \n",
       "1   -0.665453  -0.553936 -0.663909      0.71888     0.142239 -0.048934   \n",
       "\n",
       "   Injuries  Mean_Lat  Mean_Lon   Azimuth    ...      years old  yellow  york  \\\n",
       "0  -0.06017  0.992225 -0.148975  1.408556    ...              0       0     0   \n",
       "1  -0.06017  0.978331 -0.115564  1.354156    ...              0       0     0   \n",
       "\n",
       "   york county  young  zandt  zandt county  zero  zion  zion road  \n",
       "0            0      0      0             0     0     0          0  \n",
       "1            0      0      0             0     0     0          0  \n",
       "\n",
       "[2 rows x 20011 columns]"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print X4.shape\n",
    "X4.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xtrain4, Xtest4, ytrain4, ytest4 = train_test_split(X4, y1, test_size=0.3, stratify=y1,\n",
    "                                                    random_state=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy | Test\t\t | Random Forest\t | num + cvect\t | Prev test param:\t0.812\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF0:\t\t0.911\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF1:\t\t0.8\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF2:\t\t0.542\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF3:\t\t0.397\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF4:\t\t0.161\n",
      "\t\t\t\t\t\t\t\t\t | Recall EF5:\t\t0.0\n"
     ]
    }
   ],
   "source": [
    "rf9 = RandomForestClassifier(random_state=31, min_samples_leaf=1, n_estimators=80,\n",
    "                             min_samples_split=8,\n",
    "                             criterion='entropy', max_features=0.4, max_depth=24)\n",
    "rf9.fit(Xtrain4, ytrain4)\n",
    "yhat4_rf9 = rf9.predict(Xtest4)\n",
    "accuracy_rf9 = accuracy_score(ytest4, yhat4_rf9)\n",
    "print \"Accuracy | Test\\t\\t | Random Forest\\t | num + cvect\\t | Prev test param:\\t\", round(accuracy_rf9, 3)\n",
    "for i in range(6):\n",
    "    print \"\\t\\t\\t\\t\\t\\t\\t\\t\\t | Recall EF{}:\\t\\t\".format(i),\\\n",
    "          round(recall_score(ytest4, yhat4_rf9, labels=['EF{}'.format(i)], average='macro'), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Accuracy has increased a lot\n",
    "- Recall of all tornadoes has improved, except EF3, EF4 and EF5.\n",
    "- Even though the accuracy is much better due to great predictions of the lower magnitudes, I would not select this test as the stronger tornadoes do not get predicted as well. This can be noted in the confusion matric too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LoqKilC1Nps3NzY2tO1RHW0EoM1XUJoeaWu42wTchmtn/YxTS1MC1lz0z/Q/g\nv/0MAGeC71KpXAlG9W3BobN/YWygy5zRnRjYtTERkTHp5lOtUklWTP8fVSuVTDe+t1M9wp6HM3z2\nDtk5GtpU4ocujTh39V629cbHx7Nx7Srmz5mBjk5hubjSZcpSukxZ2X50dDQ/uv5A15596NGnHwAW\nVavJHfPP7b85cmAvc32WYW4pH6doPn78yKaNgSz3X8WAgYPQ0YRKFtWxrFyBE8eP0blL1zw9f2ZI\nksT8eV6MGjOWiZPc0dGEBo3tsahUnm1bNuM2YqRKaHN3d+dLPDS1U762glJmqqYtDcLLVgqi1P9j\nGOoXZtO+Sxw9f1su/P7jN1QoXRSAEf9rjoOtJZ1HreLW3Wfp5rNi+v/4GhtPM5cl6cZra2kSFf1V\nLuz9p88YG+rmSO+VS7+zeIEXU2d6MXDYiEzTrlvlx5s3b5jptSDDNLM8JlGthhV9XQblSEdu0NPT\n49yFy3Tv2UsWpqWVtDzs169fMzosX3j44AFPnzyhfcdOsjBDQ0Ps7Jtx6uRxJSpTXW2qqgtUW1sa\n8sgzv3btGj179qRu3bq0bNmSn3/+GYCIiAhGjhxJ3bp1ad68Obt27ZIdI0kSS5YsoWHDhtSvXx9v\nb28SEv5dB/fQoUM4OjpSq1YtXF1dCQ8PV3x55BPCmP/HCP8QxbgFO3nw5I0sTE1NjVaNq3H30WsA\nfj56FZtuXpy6nHF/20jvn2jn5k/Y8/Qr99YDl6lWqSSDujXBQE+Hbi1r42hbld3HrudIr0XValy6\nGcLQEWNQy+SB/hQRwSq/JYwfPx7TkqXSTXPx/DnOnz3N9NnzMs1LUWhqalK7Th0MDAxISEggJCQE\nt6GDKFW6NG3aOuX5+TPj/v2k1pHKlavIhVeoaMbDhw+UIUmGqmpTVV2g2trSkAd95hEREYwYMQIX\nFxeuXr3K8uXLWbp0KRcvXmTGjBno6upy8eJF/Pz8WLx4MTdv3gRg+/btnD17lgMHDnDkyBGuX7/O\nhg0bAAgJCcHT05OlS5dy+fJlihcvztSpU/O8ePIKYcy/A6YMbUO1SqXw25rU7P7wyVvi4xMzPSYk\n9FWm8ZdvheG7+RQrZ/yP1+cXs91nMIF7fmfbweAcaStewoSiRYtlmW7nT1uJi41l1KiMf8QhcM1K\nrGvVoYl98xxpUARTJk+kWrVqnDh+jLnzFmJoaJjvGlLy6dMnAPT19eXC9fX1iYyMVIYkGaqqTVV1\ngWprS0OMFr6BAAAgAElEQVQeeOYvXrygWbNmdOzYEXV1dWrUqIGtrS3Xr1/n5MmTjBkzBm1tbayt\nrenQoQP79u0DYP/+/QwYMAATExNKlCiBq6sre/fuBeDgwYM4OjpiY2ODjo4OEydO5Pz58wXWOxfG\n/D/O8D7NmDm8A0s2nuB0sOJGvs4a2ZHxP7Rk9qpDtB6ynOnL9+HcsSGTBrVW2DlSsmPLBjp160WJ\nEiXSjX/75jXHjx5iUBZN9XlFv/4unD59msFDhjF4oAu/7N6V9UF5SGJi0sdaei0U+dFqkRmqqk1V\ndYFqa0tDHnjm1apVkw1+hSRP/dq1a0BSC1m5cv8O0jUzMyP0/2eUhIaGUqVKFbm4sLAwJElKE2ds\nbIyhoSFhYWEKKYb8RgyAUzCWlpbo6Oigri5fOVu2bImPjw/BwcG4uLigqyvft6ylpUVwcJJXu2nT\nJgIDA/n8+TMODg7MmTMnTfrs4OHajulu7QgI+o3pfvtzf1Gp0CqkyY/ODvhuPsWCdb8CcP6P+8TF\nJzJndEfW777Ah0+KG839+FEoIXduM2PO/AzTnPj1CBoaGjh16Kyw8+aEWrVro6MJjexa8PDhA/yW\nLaV7j55K0QLIWgaioqIoUqSILDwyMhJDA+W2GqTUVsxQdbQVlDJTNW1pyOMBcJGRkbi5ucm88y1b\ntsjF6+jo8OXLFwBiYmLQ0dGRxRUuXJjExERiY2PTxCXHx8SkPyhY1RGeeR6wa9cubty4Ibel/Ko0\nMjJKE59syM+cOUNgYCBbtmzh3LlzREREsGjRohxr8HXvyXS3dizZeIJxC3Yq7NoATIrqoaNdiCt/\nyX/BXr4VirZWIczKFlfo+c6cPI6+gQFNmzlkmObc6RM0bdYCfQMDhZ47M169esWWTRvTPPxW1ja8\nevUy33SkR5Uq5gBp5rw/fhRGFYvcTR9UFKqqTVV1gWpry0+ePn1Knz59MDQ0ZMWKFejq6qYZbPrl\nyxeZ86OjoyMXHxMTg6amJtra2nJGP2V8bhwnVUAYcxVj//799OjRAzMzM/T19fnxxx/Zv3+/3AjM\nrJg0qDVufZoxa+VBhXrkybx694lPUTE0tKkkF16vZgUSExN5+vK9Qs9368Z1rG3qUKhQoUzS/EGd\nerYKPW9WfPzwAdehgzh44N8yTkxM5Py5s1SvXiNftaTG3MKC0mXKcPjQAVlYREQE5387R7NmGa8t\nkB+oqjZV1QWqrS0N6mq527Lg9u3b9OrVi6ZNm7Jq1Sp0dHSoUKECcXFxvHjxQpYuLCxM1nxeuXJl\nuWbzsLAwKlWqlG7c+/fviYiIoHLlyooqiXxFNLOrGKGhobRq1Uq2b2ZmRnR0NK9fv6Z06dJZHl+u\npDHT3dpx6eZDzgTfpYFVRVlcbFw8N0PSn4qWE+LjE1my6SQerk7EfI3lwh8PqVu9PB5u7diw5yJv\nPyh2EYt7IXewssl4dbno6GiePH5EZfP89VCqVqtGx85dmDhuDF9iYihfthSr16zl77//4qT/qnzV\nkho1NTXGT5iM+6Tx6OnpUaeWNXPnzUffwIB+zi4qo83IQI+qNazxWah8bQWlzFRNWxryoJk9PDyc\nIUOGMHDgQIYNGyYL19PTw9HRkSVLluDt7c39+/c5dOgQa9euBaBTp04EBgbSsGFDNDU1WbNmDZ07\nJ3XFdejQgf79+9O9e3esrKxYunQp9vb2GBsbK1x/fiCMeR7Qp0+fNH3mCxcuxNHREUj6oq5Xr55c\nvK+vL3Z2dun28QDZ7sdp07QGWoU0aVSrMue2TJSLexX+CbNW03J8PemxKPAYn6O/MrxPMyYPasOj\n5++Yt+YIy7edVkj+KXkX/hYDQ6MM49+/Sxp9qowR5Bs3b2O25wzmzJ7Ju/Bw6tStx9Hjp6nfoEG+\na0nNyNFjiI7+zOpVK/gUEUED24YcPnoizYhoZWpbtWoFESqkrSCUmSpqkyMPBuTt3r2b9+/fs3r1\nalavXi0Ld3FxwcvLC09PT5o1a4auri6TJk3CxsYGgL59+xIeHk6PHj2Ii4ujY8eODByYtHx1tWrV\n8PLywsPDg7dv31KvXj3mz894XI6qoyZJkqRsEf8lLC0tOXjwIBYZ9GMFBwczZswYWR95ajp27Mjw\n4cNp164dAJ8/f6ZOnTqcO3eOkiXTX40N4PaDF9SokrXnLhAIBCn5Eg86CnTrCrfMeFGnzIg5OUVx\nIr5DhGeuYlSuXFk2rQKS+ngMDAwwMTHJ9Lh6PecpTEPMjRUUrp3xfO6cEnp2qcLyKmWoxcuIWIXl\nZ1xES2F56WgmvRhVEVXVpqq6QGjLNao2Ve47QQyAUzE6depEUFAQ9+/fJyoqCj8/Pzp06JCm2V4g\nEAhUEvGraUpBeOZ5QM+ePdMYXxMTE44dO5blsQ4ODjx79gxXV1c+ffpEs2bNmDx5cl5JFQgEAsUi\nPHOlIIy5grl7926m8ba2thn2lyfj4uKCi4uKjVAVCASC7CC8bKUgjLlAIBAIFIfwzJWCMOYCgUAg\nUBzCM1cKwpgLBAKBQHEIz1wpCGMuEAgEAsUhPHOlIIy5QCAQCBSHMOZKQRhzgUAgECgO0cyuFIQx\nFwgEAoHiEJ65UhDGXCAQCASKQ3jmSkF8QgkEAoFAUMARnrlAIBAIFIdoZlcKwpgLBAKBQHGIZnal\nIIy5QCAQCBSGmjDmSkEYc4FAIBAoDGHMlYMw5gKBQCBQHMKWKwVhzP8jPDizVGXzG/3LXwrLa/eg\nugrNb1O/2grLC0114hMSFZedhhhIJCh4CM9cOQhjLhAIBAKFIYy5chDGXCAQCAQKQxhz5SCMuUAg\nEAgUhjDmykEYc4FAIBAojjy25X/++ScjRozgwoULvHjxgvbt28vFx8bGUrZsWY4dO4YkSdStWxdJ\nkmTxdevWZf369QAcOnQIX19f3r17h62tLXPnzqV48eJ5ewF5hDDmAoFAIFAYeeWZS5LEL7/8woIF\nC9DQ0ACgdOnS3LhxQ5bm7du3dO3aFQ8PDwAeP34MwPXr19PoCgkJwdPTkw0bNmBpaYmXlxdTp05l\n3bp1eaI/rxHDZQUCgUCgMNTU1HK1ZUVAQABbtmzBzc0twzSenp44OTlhb28PwJ07d7C0tEw3/4MH\nD+Lo6IiNjQ06OjpMnDiR8+fPEx4envuLVyLCmH+H/Hp4P7Uty8uFhT64h0uvzpiXMcbU1JQZ7uOI\n/vxZFt+jQyvKGmunuy1d6J0rHfXLG7Kuj7VcmKGOJiOaVmBNbys297PBs605FYsWlktjoq/FtFZV\n2OpcizW9rehqXTJN3h1qmLCyZ022u9RmXgdLrErr50pjRnz48IHKFUozz2t2uvHuE8fxg/P/FHrO\n3LDS34+q5mbo6uri1NqRuyEhypYkY6W/H2ZmZhQ1UC1tql5mqqotmbwy5t27d2f//v1YWVmlG3/p\n0iWuX7/O2LFjZWH//PMPUVFRdO7cmUaNGjFmzBhev34NQGhoKFWqVJGlNTY2xtDQkLCwsG8sAeUg\njPl3xvVrVxg3fIhcWFRkJH26tCP04X18lgewbt06bvxxlYF9u8nSzFvsx/7jv8ltnbv3RrdIETp2\n6Z5jHeYldBllbyYXpq4GU1pVoVpJPbZcfcayc2EkSjCnnSXFihSSpZvRxpwiWhosOxvG0X/e0LtO\nadpXN5HFt69ugkuDslx78pGFJx9w/WkE7o6VqVlKcQbdY8ok3vz/SyE169euZtUKP4WdK7dsDFzP\nlMkTGDLMjaCgIGJiYmjXtiVRUVHKlibT5ubmxtYdqqOtIJSZKmpLSV4ZcxMTk0zTrV27lkGDBlGk\nSBFZmJaWFrVq1SIwMJDjx4+jq6vL6NGjAYiJiUFHR0cuj8KFCxMTE5PLK1cuos/8OyE+Pp5N61Yx\nf84MdHTkPd1dP23l7ZtXnAm+RUWzypQx0sKsRj0a1bLkxNFDtHLqgEXVanLH/HP7b44e3IvXomWY\nW8rHZYa6GjhVM6FvvTLEplpgpaqpHuYlijBp3x3C3ic9ULdfRrK6lxUO5sXZdfMlAEV1tZh2MITI\nrwn88TQCLQ11uliX5MidN0hAN5uSnLn/jo3BzwD480UkWprqONcvg/uBb/dkfjt7hgP79qCnpycX\n/v79eyZOnsLmjYEYGBh883m+BUmSmD/Pi1FjxjJxkjs6mtCgsT0Wlcqzbctm3EaMVAlt7u7ufImH\npnbK11ZQykzVtKVBCYPZX758ydWrV1myZIlceLLhTsbd3Z2GDRvy5s0bdHR0+PLli1x8TEwMurq6\nea43LxCe+XfClcu/s2S+F1NnejFw2Ai5uAf372JWuQoVzSrLwooWK04Vc0vOnTmZbn5zpk+iWg0r\n+roMypGOaqZ69KpTmh3XnnP0zhu5uLgEieMhb2WGHCA2QeLd5zhK6GnJwu69iSLya4Js/+qTjxjr\nFqJi0cLoa2tgWLgQN59FyOUd8jqKysWLoKetkSO9qfny5QtjRroxy2suuik8AAA/Pz/Onj7Fnv2H\nsbZR4MpyueDhgwc8ffKE9h07ycIMDQ2xs2/GqZPHlahMdbWpqi5QbW2pySvPPDPOnDlDgwYNKFq0\nqFz42rVruX37tmw/NjYWAG1tbSpXrizXpP7+/XsiIiKoXLkyBRFhzL8TLCyrcfFmCEOGj0nz4JiY\nluTtmzeyig5JnvyL5894/vRJmrwuXjjH+bOnmTZ7Xo4fwqcfvzBy118cTmXIAe6//czai/LnK15E\ni3LGOryI+PcL+uWnr3Jp3kQm6S5poM3n2ARi4xMpnsL4A5joawPIfRTkhvnecyhWogSDh6YdhNOv\nXz/++PMOjq1af9M5FMH9+/cAqFy5ilx4hYpmPHz4QBmSZKiqNlXVBaqtTRW4desWtWrVShMeGhrK\nggUL+PDhA5GRkcydOxdHR0cMDQ3p0KEDx48f59q1a3z9+pWlS5dib2+PsbGxEq7g2xHG/DuheAkT\njIsWSzeuQ+duRH+OYtyIITx/+oSXL18ybeJoPkV8JDr6c5r0G9asxLpWHZrYNc+xjk9f4olK4VVn\nhroauDUpz9f4RE7ffycL/xInf3zM/+8XLqRBogS/h72nm00p6pYzRLeQOlal9OlsZQqAjmbuPfO/\n//qT1Sv98F8ZkO5HjLm5OYUKFUrnyPzn06dPAOjry48T0NfXJzIyUhmSZKiqNlXVBaqtLTXK8Myf\nP39OiRIl0oRPnz6dsmXL4uTkRPPmzSlUqBDz588HoFq1anh5eeHh4UGjRo148+aNLK4gIvrMBVQ2\nt2TF+q24jx2B7S9BqKur07OvC62cOvAl1WCQt29ec+LoIRb7r8lTTepqMKaZGValDVh06iGfvsTL\n4qQMjkkO3xj8DB1NDaa2SvJi3kZ9ZdeNl4ywq8jX+Nz9EEpCQgKjhg9jxKgfqV6jZq7yyE8SE5Ou\nM72XpLJX6FJVbaqqC1RbW2ryWo+trS3BwcFyYdu2bUs3rZ6eXqYGul27drRr106h+pSFMOYCAJw6\ndKa1Uwcehz2kagVTYgsZ0r2dI2XKyU9hO3nsCBoaGrRt3znPtGhpqDHRoTLWZQzw/y2MP57K93+n\n9q4LF0raj45NkP1dciYUvd+T+s9ffvpC9ZJJHs3n2Hhyw+qV/rwLD2fC5CnEx/+bR2JiIvHx8Whq\nqtajZGhoCEBUVJTc6N7IyEgMDQyVJQuQ11bMUHW0FZQyUzVtaVCtb4vvBtV6A/0HsLS0REdHB3V1\n+R6Mli1b4uPjQ3BwMC4uLmlGTGppaaX52vT29qZQoUK4u7vnqeZnTx5z6fff6Pk/ZypVsaCEkRZP\n3sUQ8s9tWjnJL5V47vQJmti3QD+PRmvraKozo605ZkV1WXomlCuPP6ZJY6qfuj88aT+5X71eOUPC\nP8fy6H0MUf9v4CsWLUx0bIKsfz2nHD54gEePwihdwkgufOF8bxbO9ybyS/a6DvKLKlXMAQgLDcXU\n1FQW/vhRGFUsLJQlC5DXVqGM6mgrKGWmatpSo2otBd8Los88D9i1axc3btyQ23x8fGTxRkZGaeJT\nGvIPHz4wZcoUtm7dmi96X754zrgRQ7gX8o8sbN/uID5FfMShlZNc2j9v/EGd+rZ5pmWiQyUqFtVl\nwckH6RpyAEtTPblR6fXLG/EhOo7n/2/Mnaqb0M3m34VkCmmo4WBRnBvPIjJsos+K5StXc+73YLmt\naLFi/DBoCOd+D846g3zG3MKC0mXKcPjQAVlYREQE5387R7NmLZSoTHW1qaouUG1tqVFGn7lAeOYq\nSd++falTpw5t2rTJl/PVqW9LDSsbxo0YzMRpnsR8eMWU8WPp4zxQbn55THQ0Tx4/onKVvPEEmlQy\nplZZQ47ceUNMXCLmJVI0J36J51Vk0ij2qK8JeLQ255ebLylrpEM3m1JsvfKMxP+31MdD3jK+RSW6\nWEXz8F00HWqYUryIFktOh+Zam4WFZZowTU1NSpUqTZ269XKdb16hpqbG+AmTcZ80Hj09PerUsmbu\nvPnoGxjQz9lFZbQZGehRtYY1PguVr62glJmqaUuNMMzKQRhzFWTTpk2YmpoyZcqUfDmfhoYG67ft\nxGPijwwf1A9jIyOGjRzLuMkecunev0tas9jAMG/66BqUT2rCblfdhHYpVnQD+D30Pb5nk+aEeh27\nx9BG5RnXohKfvsTz8/XnclPdgh9/ZGPwUzrWNKW7jiYP3n5m9q/3ZJ7798LI0WOIjv7M6lUr+BQR\nQQPbhhw+eiLNiGhlalu1agURKqStIJSZKmpLiTDmykFNSvnbcN8x7969Y9euXYSFhTF58mSCg4Op\nUqUKFjnsj7K0tKRIkSJp+swXLlyIo6MjwcHBDBgwIM3qYb6+vtjZ2cmFTZkyBWNj42z1mcclJFJI\nQ/SaCASCnPElHnQU6NaVdtuTq+NeBHTLOpEgQ4RnTtIv67i4uFClShX+/vtvRo0axcWLF5k6dSoB\nAQE0atQoR/n9/PPPmX4EGBoaphns9q28iczdKO30KGOkxfOPuRsolh4/7vlLYXntHlSXHhv+UFh+\nm/opbqU2PW11or7mbupbemgq8ONMRzPppa1qqKouENpyi/DMlYNw5YD58+czYMAAfv75Z9miH97e\n3jg7O7N48WIlqxMIBIKCgxgApxyEMQdu375Np06d0oT37t2bhw8fKkGRQCAQFEyEMVcOopmdpGbv\nFy9eUKFCBbnw27dvp1m4Pzv07NkzTZ+5iYkJx44d+yadAoFAoPIIu6wUhDEH/ve//zFz5kwmTpwI\nwN27d/ntt9/w9/dn4MCBOcrr7t27mcantxRhRixYsCBH5xYIBAJlI7xs5SCMOTBs2DCKFCnCggUL\niImJYdSoURQvXhw3NzcGDBigbHkCgUBQYBDGXDkIY/7/9OvXj379+hEdHU1CQoLKzd0UCASCgoAw\n5srhuzXmFy5cyHbapk2b5qESgUAg+O8gjLly+G6N+ZAhQ+T21dTUkCSJwoULo6mpSWRkJBoaGhgY\nGHDp0iUlqRQIBAKBIGu+W2MeEhIi+3/Pnj3s3LmTuXPnUrlyZQCePn3KtGnTaNFCtX7EQCAQCFQa\n4ZgrBTHPHFiyZAmzZs2SGXKAcuXK4eHhwZo1a5SoTCAQCAoWYp65cvhuPfOUxMfHExERkSb89evX\naGhopHOEQCAQCNJDGGblIIw50K1bN9zd3Rk1ahRVq1ZFkiT+/PNPVq5cSd++fZUtTyAQCAoMwpYr\nB2HMgYkTJ6KlpcXSpUt5//49ACVKlGDgwIEMHTpUyeoEAoGg4CA8c+UgjDlJv+c9btw4xo0bx/v3\n71FTU8PY2FjZsgQCgaDAIWy5chDGHNi3b1+m8V26dMknJQKBQFCwEZ65chDGHNL8zGl8fDyfPn1C\nS0uLqlWrCmMuEAgE2SSvbfmff/7JiBEjZAt//fXXX/Tq1QsdHR1ZGldXV9zc3JAkiaVLl7Jr1y4S\nEhLo3LkzU6dOlQ1sPnToEL6+vrx79w5bW1vmzp1L8eLF8/YC8ghhzEl/NbiIiAhmzJhBnTp1lKBI\nIBAICibq6nljzSVJ4pdffmHBggVys4z++ecf7O3t051GvH37ds6ePcuBAwdQU1PD1dWVDRs2MHTo\nUEJCQvD09GTDhg1YWlri5eXF1KlTWbduXZ7oz2vEPPMMMDQ0ZOzYsaxfv17ZUgQCgaDAoKaWuy0r\nAgIC2LJlC25ubnLhd+7coWrVqukes3//fgYMGICJiQklSpTA1dWVvXv3AnDw4EEcHR2xsbFBR0eH\niRMncv78ecLDw7+5DJSB8Mwz4dmzZ8TExChbRrYw1i2ksvmt6mmtsLwUnd/byFiF5aWnraPQ/EoZ\n6WSdSCBQMfKqz7x79+64ublx5coVufB//vkHLS0tHBwcSExMxMnJiXHjxqGlpUVoaChVqlSRpTUz\nMyMsLAxJkggNDaV27dqyOGNjYwwNDQkLCyuQTe3CmAMTJkxIExYVFcWVK1fo0KGDEhQJBAJBwSSv\n+sxNTEzSDTc2NsbW1pbevXvz7t07fvzxR/z8/Jg4cSIxMTFyfemFCxcmMTGR2NjYNHHJ8QXFgUuN\nMOaAlpZWmjBTU1OmTZtG586dlaBIIBAICib5PZo9ICBA9r+uri6urq4sXbqUiRMnoqOjw9evX2Xx\nMTExaGpqoq2tjY6ODl++fJHLKyYmBl1d3XzTrkiEMSdpBbhatWpRqJB803JsbCznzp2jVatWSlIm\nEAgEBYv8NOYREREEBAQwcuRI9PT0APj69Sva2toAVK5cmbCwMGxsbAAICwujUqVKcnHJvH//noiI\nCLnf6ChIfLcD4BISEoiNjSU2NhYXFxfevXsn20/ebt++nW4TvEAgEAjSJ68GwKWHvr4+J06cYMWK\nFcTFxfH48WMCAgLo1q0bAJ06dSIwMJBXr14RHh7OmjVrZK2tHTp04Pjx41y7do2vX7+ydOlS7O3t\nC+yCYd+tZ7579248PT1lv2Oe0U+dNmnSJJ+VCQQCQcElPz1zdXV1AgIC8Pb2pmHDhujo6NC7d28G\nDBgAQN++fQkPD6dHjx7ExcXRsWNHBg4cCEC1atXw8vLCw8ODt2/fUq9ePebPn59v2hWNmiRJkrJF\nKIurV6+SmJjIgAED8Pf3x9DQUBanpqaGrq4uFhYWaZrfVZHoWMXdRl0tNYXmF/U1XmF5megX4k1k\nnMLy+/w1QWF5mRXXISz8S9YJs4kiR7PraMIXxd0GhaGquuD70qajQLeu9uzTuTruhqeD4kR8h3y3\nzewA9evXx9bWllOnTtGyZUsaNGgg2+rXr0+NGjUKhCH/Vg7u34dZ+VKy/VmzZlFEWz3dzal13j1w\nRw7tp2aVchnGX7l8EXV1dX4/f04u/GrwZTq0akbFkkY0sK7KutUrFKInNjYW/yXzadGgJjUqFKN9\nc1sO79sti5ckidXLfbCrY0n18kVp2LAhVy//nmF+K30XUalEYYVoyw6JiYks9llIdcvKGBgY0KZl\nC27dvJlv58+Klf5+mJmZUdRAF6fWjtwNCVG2JCBJV1VzM3R1VUsXqLa2ZPKzmV3wL99tM/uECROY\nPXs2enp6LF26NNO0S5YsySdV+c/VK8EMG/IDOoX/NTJDhgyhuWMbuXQXf7+Ax9TJOA8YmCc6/rh6\nhTFug9HRSd/YxcbGMmF00vKMKQl7+IA+3drj0KoNk6bO4M7tv5jlMRlNTU0GDnVLN6/s4uM9g5+3\nbGDslBlYVqvJ6WOHGT3UGS1tbVo5dWTZIm/W+C/BfYY3VSyr8cvWtQz+X1eOnr9GmbLl0+hcsTR/\nm/D8ly9j1gwPZnnNpUHd2ixZ6ku7No5c//MOpqam+aolNRsD1zNl8gS8vb2pYlkdn4Xzade2Jbf+\nDpENZFKmLs853tjUrM7ceaqhS9W1pUSsza4cvltjnnI6WnpT0/7rxMfHE7BqBZ4zplG4sLwBLVu2\nLEVNysj2o6OjGTSgP7369KVvP2eF6whcs4p5s6dnaMgBli9eQGRkZJrwrZvWY1y0GKvXb0FTU5Nm\nDi0J+ecOWzau+yZjHhsby9YNa5g0fQ6D3cYA0LSZA4/CQtm4ZgW2je1Y47+EGd4+9Psh6Wdye3Vw\npGr1mly5eIGuvfrK5ecxYSTGxkV59fJFrjXllJ+2b6XP//oxcZI7OppQp0FjypYszv69exjmNjzf\ndKRGkiTmz/Ni1JixuLu78yUemtrZY1GpPNu2bMZtxEil60ouswaNla9L1bWlRthy5fDdGvOUAx0K\n8qCH3HLx9wvM9ZrFbK95fPz4gcD1azNMu8LPl7dv3zBvgY/CdQRf+h2f+XOY5ulNxMcPbNmYdvnc\nuyF3WOXvy7KVaxn2Qz+5OLfR4+j5v/5oav5blbW0tIhNMbc0N0R+iqBX3x9o0cpJLrxSFXOOHznA\nb6dPANC9d39ZnI6ODmeu/J0mr6CtG3n29DGDho9h3swp36QrJ3z5+gV9AwPZvq6uLtra2nz48D7f\nNKTHwwcPePrkCe07dpKFGRoaYmffjFMnjyvNMKmqLlXXlhrhmSuH77rPPCX3799nz5497Ny5k6Cg\nINm2c+dOZUvLE6pWq87fIQ8ZNWZspg9fREQEvkt8GP3jOEqVKpVhutxiUbUaV27dxXXkmHR1SJLE\nhNHDGTRsONVrWqWJNzExpVr1mgB8iohgd9AOdu7YSr8Bg75JV7HiJZizaBmVKpvLwhITE/nt9Akq\nV7Hk/t1/KFuuApd//4229vWwKKVP7dq1uX71slw+b1+/YsEcD7wW+VG4cP4uRjFkqBs/79jGb+fO\n8uHDBzxnePD161c6dFTuQkj3798DoHLlKnLhFSqa8fDhA2VIAlRXF6i2ttSIPnPl8N165ilZu3Yt\nS5cuxdDQkCJFisjFqamp0atXLyUpyzsyWhoxNdu3biY2NhbX4aPyREeJEpnr2LR+DW9ev2bilBk8\ne/o4w3SfIiIwL1cCgFp16tHP5duMeXqsWLqAB/dCmDF3MccO7+f9u3CmjHVj/NRZlCxVmi1rlvFD\n73+kdMUAACAASURBVM6cuvwnJUyS+qRnT5uAXYuWNHNszfZN+ftrTIOGDOXkiWO0aZk07VJNTY3A\njVuoUbNmvupIzadPn4CkOcIp0dfXT7crJb9QVV2g2tpSIzxz5SCMObBp0yYmTJjA0KFDlS1F5di4\nYT09evWhRIkS+X7uly+eM3f2dNZt2pGmXz81Ghoa7D54jDevXzFv9gy6dWjFsbOX5Jrfv4XN61ax\nbKEXw0aPx665I4f37ebjh/cEbA6idbukps9uTs2pUNGMLetXM2HaLE4dO8yFc6c5cTH/R5BLkkSX\nju24G/IPq9esx6KKGUE7d+M2bDCmJUvi4Ngy3zUlk5iYCKT/0lemIVBVXaDa2lKjYnK+G4QxJ2k9\n3rZt2ypbhsoRFhrKndt/M3f+IqWc3338aBxatsGuuQPx8fEkJCTNCU9ISCAhIUHuN42L6Olh1yzJ\nAy1XvgIdWzfn/NnTtGjZ+pt1LFvkjZ/PXPoPdMV9hjcAuv/fgmPX/F+jqK+vj3WtutwLuU1UVCQz\nJ//IRI/ZGBctRnx8vOyFHB8fj7q6OurqedfLdfH33zn/2zn2HTxCm7ZO6GhCY3sHXr1+xTT3SVy+\ndiPPzp0Vyes5REVFUczw35awyMhIDA0MMzosz0mpK2ULnbJ1gWprS42qfVx8L4g+c6B169YcOHBA\nIXlZWlpiY2ND7dq15bZJkyYBEBwcjKWlZZp4W1tbWR6rVq2iefPm1KtXD2dnZ+7du6cQbTnlxPFf\nMTAwoIWDo1LOf+zIIfbv2UWZorqUKapLM9uknyvs2aktPTomTZ07d/okFy/8JndcjZpJP5H6+vWr\nb9bg6T4WP5+5DBs9njmLlsleVBUqJq3vHBcn/5OncfFxqKmp8fetG7x88ZyZk3/EopQ+FqX08XQf\nC4BFKX38Fs/7Zm2Z8ezZUwAa2DaUC2/YqDEhIf/k6bmzokqVpHEIYaGhcuGPH4VRxcJCGZIA1dUF\nqq0tNaLPXDkIz5wkj2r16tX8+uuvmJmZpVkoJqfzzHft2oVFJg+YkZERwcHB6cbt2bOH/fv3s3Xr\nVkqVKsXatWtxdXXl1KlTeerJpcf169eoVaeu0hbOOXb2ktz+s6ePGezcB59lK2lsZw/Ati0buHvn\nNmcuXZd56ud/OwOAZdXq33T+lb6L2LphDeOneDJqgvwo9CbNkhbPOXJgD32ck/rn379/z5/XrzFy\nvDs1bWqz78QF+es5tJ/Vy33Yd+ICpiUVP5gwJckv/8uXLuLUrr0s/NrVK1SoWDFPz50V5hYWlC5T\nhsOHDtDcrhGQNNDy/G/nmO2Vtx852dXVsJHq6FJ1bakRnrlyEMYc+Pz5Mx07dlS2DAA+fPiAm5sb\n5colrYTm4uLC8uXLefXqFaVLl85XLf/cuUOtWrXz9ZwpqVWnrtx+Eb2k5sXK5hZUMbcEYOSYCXRo\nZc+oYQPp038AYQ8fsMDLE6cOnahdt16uz/382RP8fLypW78hTZq14Ma1fz++ChXSoqZNbTr36MPc\nGe7ExcVRtlwFNqxcjE5hXfo4D0JPL6nJPSV/3bwOkCY8L6hbrx6t27TFbeggZnvNw7xyRX7Zu589\nu3cRuHFLnp8/M9TU1Bg/YTLuk8ZjZKBH1RrW+Cycj76BAf2cXVRCl56eHnVqWTN3nvJ1qbq21Ahb\nrhyEMUe15pkPHjxYbv/06dMYGRlRsmTJfNcS/vYthkZG+X7enFCrTl127j/K3FnTGfC/7hgYGNLX\n5Qfcp8/+pnzPnjxGXFwcf1y9THen5nJxxUuYcuXOIxYuX8PSUrNZseT/2Lvv8Brv/4/jz5OdyDAT\ntEokROxYsVfUqlG1KakWiVnUrCJWUWpHzdhE7VWltDGqTetLixhVyc9qqQQRspP790eaU0fMOOfc\nd+T96HWuq7nvM17nHOe8z2fcn3s6Dx7cp26dOoTuOoBLXm2cdWnDpi2MGzuGoPFjiYuLo4x3Wb7e\nuoPWbdQ9NA1gwKDBxMc/ZNGihcTGxlLDtyZ7932XZba2Wrm+WrSQ+xrKpfVsj5KWuTpy9YlWMu3Y\nseOp+2xsbHB1daVSpUov1N3s5eVFnjx5snSJz5gxAz8/P8LDw/H398+y/OKcOXOoV6+ewbZffvmF\ngIAAJk2a9Nyeg/R0BQsL+RAJIV5OYqpxT7RSf/bTz0/wLEeGyRkqX4W0zMkYpz5x4gS2traU+Hc8\n8f/+7/9ITEykaNGi3Lt3j7x58xISEkLx4sWfe3+hoaHPHDN3cXF56ph5ph07djBx4kTGjRv3QkMA\nGWdQMs7vMjlrWvbIWdNenlZzgWTLLmmYq0OKORnntbWzs+OLL74g77/dynFxcYwZMwZ3d3c+/vhj\npkyZwpQpU1i2zPQLfwQHB7NmzRoWLVpErX8nuwghRE4g3ezqkEPTyGiZjxw5Ul/IIWOG+5AhQwgN\nDcXKyooPPviAkydPmjzL1q1bWb16NRs2bJBCLoTIceTQNHVIy5yMcfEbN27g6Wm47vGNGzf0Y98p\nKSkvvJpYx44ds4yZu7q6sn///ufedunSpTx8+JAOHToYbN+yZQseHh4v9PhCCKEWaZmrQ4o50KlT\nJ8aMGcPAgQOpWLEi6enpREREEBwcTMeOHblz5w7Tpk0zWNjlaS5evPjM/b6+vs8cL3+Rgi+EEEI8\nSoo58PHHH2Nvb8/ixYv5559/AChcuDC9e/fG39+fo0ePkidPHsaPH69yUiGE0DZpmKtDDk17zN27\nd7G2ts5y6JjWGXP2ucxmzx6Zzf7ytJoLclc2Yx6a9vbCn59/pSf4bmDN519JPJW0zP/122+/cenS\nJf3JMBRFITk5mYiICGbMmKFyOiGEyBmkZa4OKebA3LlzWbJkCa6urvzzzz+4ubkRHR1NWloaTZu+\n+lm3hBAit5AJcOqQQ9PIOBwsKCiIw4cPU7hwYdauXcvx48epWbPmCy0SI4QQIoOFLnuXF3X69Gnq\n1q2r//vmzZv0798fX19f6tSpw+TJk0lOzjiboaIoVKlSxeAMlb1799bfds+ePfj5+VG5cmUCAgKI\njo422utgblLMyRgnz1xKtUyZMvz+++84OzszdOhQ9u7dq3I6IYTIOXQ6XbYuz6MoClu2bOHDDz8k\nJeW/eTMjRoygcOHCHDlyhB07dnDmzBmCg4MBuHLlCgAnT57k1KlTnDp1iuXLlwNw4cIFJkyYwOzZ\ns/n5558pWLAgY8aMMcErYh5SzIFChQpx69YtAEqWLMn58xnne86XLx8xMTFqRhNCiBzFVIvGLF68\nmDVr1hAYGKjflpycjL29Pf369cPW1pZChQrRunVrTp06BcC5c+fw8vJ64o+F3bt34+fnR6VKlbCz\ns2P48OEcPXo0x7bOpZgDLVu2ZMSIEZw4cYL69euzdetWdu3axdy5cylZsqTa8YQQIsfQZfO/52nf\nvj07d+6kQoUK+m02NjYsXbqUQoUK6bf98MMPlClTBoDz58/z4MED2rZtS61atRg8eLC+4RYZGWmw\nUFi+fPlwcXEhKirKWC+FWUkxB4YNG0b79u2JjY3F19eXbt26ERQUxLlz55gwYYLa8YQQIscw1Zi5\nq6vrM7vjFUVhypQpREZGEhAQAGQU+8qVK7NixQoOHDiAg4MDgwYNAiAhIQE7O8PDP+3t7UlISMj+\nk1eRzGYHLC0t6devn/7vwYMHM3jwYBUTCSFEzqTGbPbExERGjhzJxYsXWbt2LQUKFADQF+5Mo0aN\nombNmvzzzz/Y2dmRmGi4LkRCQgIODg5my21MUsyB9PR09u/fz+XLl/WzIB81bNgwFVIJIUTOY+5a\nfu/ePXr37o2DgwObNm0yOGHW0qVLqVOnDuXKlQPQf7/b2tri4eFh0KV+584dYmNjc+w5MKSYA6NH\nj2bfvn14e3tja2trsE+OmRRCiBdnYcbvTEVRGDRoEAULFmTBggVYW1sb7I+MjOTo0aPMnz8fKysr\npk6dip+fHy4uLrRq1Yr333+f9u3bU6FCBWbPnk39+vXJly+f2fIbkxRz4ODBg8yfP59GjRqpHUUI\nIXI0c7Z/Tp06xS+//IKtrS01atTQby9btizr16/ns88+Y+rUqbRo0YKUlBQaNmzI5MmTAfD29mby\n5MmMHTuW27dvU61aNaZNm2a+8EYma7MDTZs2Zd68eXh7e6sdJduMvU6zMe8vNS3daPflaGvBgyTj\n3Z8xe17y2Oh4aMQ17dPSjXdfznYW3E80zutmY2W8ebO5af1zY9Ly2uwdVp7M1u229KpivBC5kLTM\ngbFjxzJx4kT69evHm2++meVc5O7u7iolE0KInEVGJtUhxZyMFeAuXLigP5wBMlpsiqKg0+n0i8gI\nIYR4NnOOmYv/SDEHZs2aRYcOHejatWuW4w6FEEIIrZNiTsaxhf7+/hQrVkztKEIIkaNJu1wdsgIc\n0KVLFzZs2IDMBRRCiFdjqhOtiGeTljnw119/cfDgQbZv384bb7yR5VjF0NBQlZIJIUTO8jKnMxXG\nI8Uc8PDwyLGr/gghhJZIK1sd0s0ODBw48JmX3CApKYmxY0bhUeJNnJycaP9ua6IiI1XLMm7saLw8\n3qJIQRc6vtdGnyUoKAgnO8snXlo29TNPtk9HUbpkMQoXcKZjuzZPfZ1GfjIE//e7mjxTpn17d1PX\ntwpu+fJQpUIZVoUs0++Lj49n6OD+lCzmxluF8zMg4CNiY2PNlu1JghfMx93dnfzODrRo6sfFCxdU\nzZMpeMF8ypRyx8FBW7lA29kymeoUqOLZpJgLAEYMG8KqkOWMnzCJzZs3c+fOHZo3bUx8fLzZs4wa\nPpTVK1cwdlwQazZs4u6du7Rq3oT4+Hh69+7NocM/GlymTPsCgB7+H5g828hPhrB65Qo+Gz+RtRu+\n5u7dO7zT3C/L67RsyVcsWjjf5Hky/fD9Qbp1eg/fWrXZtG0Xbd59j48HBLJ753YA+vbty55dO5gx\ncw6z5izgwP599Ov9gdnyPW7liuWMHvkJgYGBrN2wiYSEBFo2b8KDBw9Uy/Rort59A9m0STu5tJ7t\nUTJmrg5ZAe418SqrQd27d4+3iroyb8Eien3UGzsr+CPyKl4exQndvI2277Z7pWwvswLcvXv3KFms\nMLPnLeSDD3sDcO3qVcqWdmf9pi1069TeYAW4+Ph4alatRPUavqxYve6ls73Ml8i9e/dwf9ONOfOD\nDbJ5lyrBhq+30rXje1y7GcOEz8aweuUKHB0debtZC1av2/jSueDlVoBrXK8WxUuUYOXa/x6rZ7dO\nFCxUiAGDh1K1Qhk2bd1JsxbvAHD0SBitmvnx8/9O41223EvletUV4BRFwcuzBO07dGLOlzNJTIXY\n2FhKl3yLiZM/J7D/gFe6f2PkmjZjJnZWcCtG/VzmyGbMFeA+2Hg6W7db1bWi8ULkQtIyFzg6OnL4\n2M+079hJv83GxgbI6FY2d5bvjxznvQ5ZsyQ/IUvw/Lncvv0PU6fPNEu2H47+9MRsma/TV8HzCfv+\nENt3fUPFyj4mzwTwz61b/O/EL3zwUR+D7Ws2fM3secEcPfwDNjY2NG7SVL+vbr0GuLi48P3B78yS\n8VGX//yTa1ev8k7rNvptLi4u1KvfgEMHD5g9j9ZzgbazPU5a5uqQYi6wsrLCp0oVnJ2dSUtL48KF\nCwT2+ZAiRYvSrHkLs2ep7PNflosXL9A/4COKFC3K280Ms8TGxjJ39kwGDh5K4SJFzJ/twgX69c3I\n1vTfbJ27dOfkmfP4vd30OfdmPOfPRwBgbWVN23eaUsjFnrKexVm9cjkAf166RPHixQ2O0tDpdBR7\nqziRkX+aLWemS5f+AMDDw9Nge/ES7ly+bP48mbSaC7Sd7XG6bF7Eq5HZ7P86cuQIK1eu5MqVK6xd\nu5YtW7ZQtGhROnbsqHY0sxo9cjgL58/FwsKC5SGrcXFxUS3L2NEjCF4wDwsLC5YsX5kly4Z1a0hO\nTqZvP/N3f346arg+29IVq/TZPEuVMnuW6Nu3Aej9wfv0+OBDho0YzZ6dOxjcP4CiRd8gLu4+Tk5O\nWW7n6OjEg7g4c8fl/v37AFkyOTk5EadCnkxazQXazvY4Wc5VHVLMgb179zJhwgS6d+/OyZMnSU9P\nJ2/evEyePJmEhAR69uypdkSz6f5+T957tw0bQ7/mo149sbG1pX0HdX7QdO3eg5bvtGbb1s30/egD\nbG1t6dGts37/qpDltO/YmUKFCpk9W7f3e9KyVRu2bfmaPh/6Y2NrS4+unZ5/QxNITU0BoM277zHm\nswkANGjYmEuXLjJrxjRKlynz1G5MNbo309PTn/rYana3ajUXaDvb4zQWJ9eQbnZgyZIljB8/nqFD\nh+rPmObv78+UKVNYs2aNyunMq7KPD40aNWJ+8Fc0auzH/LmzVctSqbIP9Rs2Yu6CRTRs1JgF8+fq\n90VFRnIu4qzBOL+5szVo2Ih5C7+iYSM/Fs6bo0oOgDx5HAGydO03aOjH+XNncXZ2eeKM5wcP4nB2\nNn/PS2YvxuOZ4uLicFEhTyat5gJtZ3ucjJmrQ4o5cOXKFXx8sk5Wqly5Mv/8889L3ZeXlxeVKlXC\nx8fH4DJixAgAwsPD8fLyyrLf19cXgOTkZIKCgqhZsyZVq1alX79+3Lp169Wf5DPcvHmTNatWkpCQ\nYLC9QsVK3Lz5t0kf+3G3bt5k7eqsWcpXrMStv//LcvC7/Tg7O9OwkemPLX9etgoVK5r9dXqUe8mM\nBY8en6yYmpoCOh0enp5cvXqVtLQ0/T5FUbh29QqepUqbNSuAp2fGUMTjx+df+b8oPEubP08mreYC\nbWd7nBxnrg4p5kDx4sU5ceJElu379++nRIkSL31/mzdv5tSpUwaXmTP/m22dN2/eLPvDw8MBCA4O\n5vLly3z77bf89NNP+u5+U7p39y4BfT5k966d+m3p6ekcPRxG2Zc8bOlV3b13l/4Bvdm72zDLsSOH\nKVO2rH7byf+doLJP1SxL75o029279Ov7EXsey3b0yGG8vc37Oj3Ku2w5Chcuwo5tWwy2Hzywnxq+\nNWnQMGO9gB++P6jfd+zoYWJjY6lbv4G541KqdGmKvvEGe/fs0m+LjY3l6JHDNGjQyOx5tJ4LtJ3t\ncRY6XbYu4tXImDkwdOhQhg0bxtmzZ0lLS+Prr7/m6tWrHDp0iLlz5z7/Doxo8ODBpKSkYGdnx927\nd3n48CH58uUz6WOW8famddt3GT50MIkJCbz1ZhG+WrKUs2fPcHDBIpM+dpYsZbxp1aYtIz4ZQkJC\nAoWLFGHlimVEnD3D/kOH9de7cP4clcx06Jc+m7c3rdq8y4hhH5OYkEDhwkUI+TfbnO+DzZrlURYW\nFowZN4GPBwTiVrgITZo2Y+vmTfwS/hPfHPgBD89StGvXjr4f9mTq9JlYWVoxdswI3mnVhrLlyps9\nr06nY9gnIxk1Yhh5nR0pU64iM2dMw8nZme491Juf8mguR0dHqlSuyNTP1c+l9WyPk7qsDinmQKNG\njQgNDSUkJIRSpUpx9OhRPDw82LRpE+XKmbfFZWlpiaWlJQsXLmThwoW4urqyfv16kz/uytXrmDhh\nHJMmjicmOpoqVaux78D3VK9Rw+SP/bjlK9cyeeJ4pk4KIiYmGp8q1djz7UGqVf8vS/Tt27i45DV7\nthWr1jI5aBxTJk0gJjoan6rV2Lv/kEE2NXzwYR+srKyY++VMli0OplRpL0K37KBWnboArF69mn4D\nBjFy2MdYWlnxTqs2TJup3jj/gEGDiY9/yKJFC4mNjaWGb0327vvuibPu1cj11aKF3NdQLq1ne5SM\nf6tDVoAzMi8vL/LkyaOfSJdpxowZ+Pn5ER4ejr+/P46Ojgb758yZQ7169fR/JyUloSgKs2bN4siR\nI+zdu/eZXcrpipytSAjx8hJTjbsC3IDt57N1u+B23sYLkQvl2pb5J5988sLX/fLLL1/qvkNDQyn9\njEkpLi4u+jHyp7G1tQVg5MiRhIaG8scffzyzlyA57am7Xpqd1astD/u4l1nO9XkcbS0MlnN9VcZs\nReSx0fEw2Xi/jV9mOdfncbaz4H6icV63V13O9VHG/rdmTJIte2QiljpybTHPXIZTa8aMGUOFChXo\n1q0bAGlpaaSnp+Ps7KxyMiGEeD7pZldHri3m06ZNUzvCE1WsWJEVK1ZQv359ChQowNSpU6latSrF\nihVTO5oQQjyXqYf7Tp8+Tf/+/Tl27BiQMav/008/5eeff8bJyYkBAwboV+5UFIXZs2ezefNm0tLS\naNu2LWPGjMHS0hKAPXv2MGfOHGJiYvD19WXq1KkULFjQtE/ARHJtMX9UWloa3377LZcuXdKvtKQo\nCsnJyURERLBu3cudjatjx45ZxsxdXV3Zv3//c2/bpUsXYmJi6Nq1KykpKdSpU4d58+a91OMLIYRa\nTFXMFUVh69atTJ8+XV+MAcaNG4eDgwPHjx/n4sWL9OnTh1KlSlG5cmXWr19PWFgYu3btQqfTERAQ\nQEhICH369OHChQtMmDCBkJAQvLy8mDx5MmPGjGHZsmWmeQImJsUcmDRpEjt27KBcuXL89ttv+Pj4\ncPXqVaKjo+nevftL3dfFixefud/X1/eZ4+U6nY6BAwcycODAl3pcIYTQAlN1sy9evJh9+/YRGBio\nL7gPHz7k4MGD7N+/H1tbWypWrEirVq3YsWMHlStXZufOnfj7++Pq6gpAQEAA8+bNo0+fPuzevRs/\nPz8qVaoEwPDhw6lVqxbR0dE5snUucxXIWBzmiy++YMOGDRQrVoyJEyfyww8/0Lx5c5KTk9WOJ4QQ\nOYaFLnuX52nfvj07d+6kQoUK+m1XrlzBysrKYBjS3d2dyH9XyouMjMTT09NgX1RUFIqiZNmXL18+\nXFxciIqKMsKrYH5SzMlY77hixYpAxqFlZ86cwcrKioCAAA4fPvycWwshhMhkquVcXV1ds7T64+Pj\nsbOzM9hmZ2dHYmIiAAkJCQb77e3tSU9PJzk5Ocu+zP2PL9ecU0gxB4oWLar/JVeyZEkiIjLOD21j\nY6M/9aAQQojnM+dyrvb29lnOSZCYmIiDgwOQUdgf3Z+QkICVlRW2trYGRf/R/Zm3zWlkzBzo1KkT\nw4YNY/r06TRp0oT333+fAgUK8PPPP1P2kfXAhRBCPJs5W4jFixcnJSWFv/76i6JFiwIQFRWl7z73\n8PAgKipKPy4eFRVFyZIlDfZlunPnDrGxsXh4eJjxGRiPtMyB3r17M3bsWPLkyUP58uUZN24c33zz\nDdbW1kyZMkXteEIIkWOY86xpjo6O+Pn58eWXX5KQkMDp06fZs2cPrVu3BqBNmzasWLGCmzdvEh0d\nzZIlS2jbti0ArVq14sCBA5w4cYKkpCRmz55N/fr1TX4uDFORlvm/mjVrxr1794CMiRZFixalUqVK\nObbLRQgh1GDuM6BNnjyZCRMm0KBBAxwcHBgxYoS+Jd6tWzeio6Pp0KEDKSkptG7dml69egHg7e3N\n5MmTGTt2LLdv36ZatWqaXX/kRcja7MCZM2cICAigbdu2jBo1CoC3336blJQUli1bRqlSpVRO+HzG\nXNpRlnPNHlnO9eVpeVnS3JTNmGuzj99/KVu3m9RM+9+zWibd7MDUqVNp2bIlw4YN0287cOAAfn5+\nTJo0ScVkQgiRs5jq0DTxbFLMgQsXLuDv729wVjKdToe/vz9nz55VMZkQQuQs5pzNLv4jxRxwc3Pj\n1KlTWbZHRESQN6/5z5kthBA5lTknwIn/yAQ4wN/fnwkTJnDp0iXKly8PwLlz59iwYQMDBgxQOZ0Q\nQuQc0mWuDinmZMx4tLW1ZePGjaxbtw5ra2tKlCjBxIkTadmypdrxhBBCiGeSYv6v9u3b0759e7Vj\nCCFEjqZDmuZqkGIOpKSkEBoaSqNGjXjzzTeZPXs233zzDeXLlycoKEjGzV+RlaVxp2YY+/6MydKI\nfYzG7q60tpQvWWF60s2uDu1+K5rRjBkzWLJkCXFxcRw6dIiQkBA6depEdHQ0kydPVjueEELkGHJo\nmjqkZQ7s27ePBQsW4O3tzYoVK6hduzZ9+/alYcOGdOvWTe14QgiRY5jqfObi2aRlTsZp9Nzc3EhP\nT+fo0aM0aNAAAEtLSyws5CUSQogXJS1zdUjLHChXrhxfffUVefPmJS4uDj8/P27cuMHMmTPx8fFR\nO54QQuQY0jBXhzQ7gQkTJnD27FlCQ0MZPnw4hQsXZvXq1dy+fZtx48apHU8IIXIMWQFOHXKiladI\nTk7GxsZG7RgvTMsnWjGm3JTNmB9Ne2sdCSnGuT9jjonmpvfTmLR8opX5x6Kef6UnGFzX3XghciHp\nZn+KnFTIhRBCK6SRrQ7pZhd6wQvmU6aUOw4ODrRo6sfFCxfUjgTArVu3sLfWYW+tQ6fT6f//s09H\nq5rryOEwfZbHs9lbq/uNtmvnDkoUK/LU/T8d/5E8tpYcORxmvlCPyHxPH3/N1H5PQbufA9B2tkwW\n6LJ1Ea9GWuYCgJUrljN65CdMmDSFSuXLMvXzabRs3oTfz17A0dFR1WwRZ8+g0+k4cCgMR3sbkv7t\nXiz6xhuq5qrsU4Wwoz/p/7a1gpu379C1U3s6d1HvkMZffgmn70cfYGdv/8T9ycnJDAjsa9Ru/JeV\n+Z6GhYWhWPzXC6b2e6rlz4GWsz1KWubqkGIuUBSFaZ9PZuDgIQwfMQo7K6hRuz6lS77FujWrCeyv\n7slmzpw+TQl3d+rWq6+pcUxnZ2d8a9bU/21nBZ26dOWNN99k9rwFZs+TmprK4kULGf/Zp9g/pZAD\nfDH9c+7H3Tdjsqwy39P69etr5v3U8udAy9keJ4eZqSPXdrMPHDiQBw8eqB1DEy7/+SfXrl7lndZt\n9NtcXFyoV78Bhw4eUDFZhoizZyhXvoLaMZ7rxx9/ZPOmUGbMnI2Dg4PZH//4j8eYMimISVM+J7D/\nwCde5/y5c8yb8yUzvvjSzOkMafE91fLnQMvZHiez2dWRa4v5oUOHSEpKMthWt25drl+/rlIiG+s+\n/wAAIABJREFU9Vy69AcAHh6eBtuLl3Dn8uU/1YhkIOLsGWLv3aNurRrY2triXbok69asVjtWFmPH\njqVuvfq806q1Ko9fxrssERcvM3DwkCfOOFcUhQH9+hLYbwDlK1RUIeF/Mt/TGjVq4JJHG++plj8H\nWs72ODmfuTpybTf7k8YLHz58qOo4olru38/ocnVycjLY7uTkRFxcnBqR9NLS0jh//hwuLi5M++JL\nihV1Y8PGTfT56APyFyhAy3daqZov05nTpzl8+DBbd+xWLYOrq+sz9y9d8hW3bt3k03ETuHrliplS\nZfXoe/rll1+Sr6AbW75W/z3V8udAy9keJ61sdeTaYi7+k56eDjz5+GEtrLO8beceihcvgXvJkthZ\nQZ0Gfvz11w2mTgrSTDEPWbGMkiVL0qLlO2pHeaIbN24w4bNPWbth0zPH080l8z31Ll2SxFRo1Fj9\n91TLnwMtZ3ucxuLkGlLMjczLyws7O7ssa7o3adKEmTNnEh4eTs+ePbOMqdrY2BAeHm6wbcuWLfrb\nmJKLiwsADx48IE+ePPrtcXFxuDi7mPSxn8fS0pKGjRpn2e7XpCmffToKRVE08WW2Z/dOunfrpoks\nT9K/f3+aNmtBo8Z+pKamkpaWBmS0ktPS0rC0tDRbFq2+p1r+HGg5m9CGXF3Mr169qu++ynT9+nVS\nUw2n17q7v9zKRJs3b6Z06dJP3Z83b97nFuhr164xffp0s3zJenqWAiAqMhI3Nzf99iv/F4XnM56H\nOfz99998s2c3777XngIFCui3JyYlkidPHk0Uz/PnznH92jXee+89taM81a5duwDYsnmTwfZ3mr9N\nvfoN2H/wB7NlefQ9fcNNO++plj8HWs72OFNMxNq1axcTJkww2JaQkEDHjh3p1KkTnTp1ws7OTr8v\nICCAwMBAFEVh9uzZbN68mbS0NNq2bcuYMWPM+uPVXHJ1Me/WrVuWMfJevXoBGV1XmS2E8+fPmzVX\nWloaI0eOpHPnzmzZssXkj1eqdGmKvvEGe/fsomatWgDExsZy9MhhJk7+3OSP/yzJSUkM7B9AWloa\nfQP76bfv3rmD2nXrqZjsPyf/dwIbGxt8fHxIUzvMU/z6668kpv73b/3alSt069KR+cFfUb9+Q7Nm\nefQ9HTxQO++plj8HWs72OFP8GGvTpg1t2vw3k//48eOMHDmSAQMGcOTIEerXr8+SJUuy3G79+vWE\nhYWxa9cudDodAQEBhISE0KdPH6NnVFuuLeaHDh1SO8JTLV26lFKlSlG/fn2zFHOdTsewT0YyasQw\nHB0dqVK5IlM/n4aTszPde/Q0+eM/S/ESJejQqTPjxo5GURS8SpVk6fIV/P7bKQ4f+1nVbJnOnYvA\nvWRJrK2tSdPIMdOPq1atmsHa7I55MhYZKV3ai9JeXmbN8uh7amWh8Gbxkqxaqf57quXPgZazPc7U\n/SoPHz5k9OjRBAUFUbhwYc6dO0eZMmWeeN2dO3fi7++vnxwaEBDAvHnzpJi/Tt4w4UpTXbp0yTJm\nPmPGDPz8/ICMX9TVqlUz2D9nzhzq1avH2bNn2bVrF1u2bOHs2bMmy/i4AYMGEx//kK8WLeR+bCw1\nfGuyd993WWbPqmHJshCmTAriy1kz+OfWLSpV9mHPvgNUqlxZ7WgARN++jYtLXrVj5CiZ7+mMGTO4\npaH3VMufAy1ne5SpZ7MvX76c0qVL06RJEwDOnz+PjY0NjRs3Jj09nRYtWjB06FBsbGyIjIzE0/O/\nw/nc3d2JiorSzFwbY8q1Z00bOHAg06dPN1gG8fDhw/j6+urHXu7cucM777zDTz/99LS7ycLLy4vd\nu3c/dcw8PDycwYMHP3HMPDExkQ4dOjBx4kSqVq36zOs+Ll2RlZeEEC8vMdW4Z01b/7/srdXRveqb\nz73Ow4cPadiwIcuWLaPyvz/8AgMD8fX1pXPnzsTExPDxxx9Tu3Zthg8fTtmyZdm2bZu+5X7t2jWa\nNGnC6dOnsbW1zVZOrcq1LfPMRWMeLeZDhw5l586dFCtWDMg4HOTu3btmy3T27FmuXbtGQEAAkDF2\nnpCQQLVq1di1axdFixZ96m2TjThYq6UlUx+Xm7LJKVDVJdmyx5QN3oMHD1K0aFF9IQdYvHix/v8d\nHBwICAhg9uzZDB8+HDs7O4PFwRISErCysnrtCjnk4hXgnvRF+aRt5uyKqVatGr///jsnTpzgxIkT\nLF68GBcXF06cOPHMQi6EEFqh0+mydXkRP/zwAy1atND/HRsby4wZMwyW5k5KStIXaw8PD6Ki/ju/\nelRUFCVLljTSM9WWXNsyN6WOHTtmGTN3dXVl//79KiUSQgjzMGUL8ffff6dLly76v52cnPjuu+9Q\nFIVPPvmEv/76i8WLF9OpUycgYxb8ihUrqFmzJlZWVixZsoS2bduaMKF6pJgb2cWLF5+539fX94UX\ngXmZ6wohhBaYqjczLS2Nv//+m0KFCum3WVhYsHjxYqZMmULNmjWxs7Ojc+fO+Pv7AxmHH0dHR9Oh\nQwdSUlJo3bq1/vDj102uLeYv07UjhBDixZjqW9XS0pILFy5k2e7p6cmqVaueepuhQ4cydOhQE6XS\njlxbzBVFYfz48QYTIVJSUpg6dap+ucTHz6omhBDi2aSRpI5cW8zbtWuXZVvr1oanrrSxseHdd981\nVyQhhMjxcu2sapXl2mI+bdo0tSMIIcRrR1rm6si1xVwIIYTxSSlXhxRzIYQQRiMNc3VIMRdCCGE0\nFtI2V4UUcyGEEEYjLXN1yMTDf8XExLB48WJGjRpFTEwM33zzDX/88YfasYQQQojnkmIOnDt3jmbN\nmhEWFsbevXuJj4/n+PHjdOzY8aXOmCaEELmdLpv/iVcjxZyMw9T8/f0JDQ3F2toagClTptCjRw9m\nzZqlcjohhMg5dLrsXcSrkWIORERE0KZNmyzbO3fuzOXLl1VIJIQQOZMFumxdxKuRYg64uLjw119/\nZdkeERFB/vz5VUgkhBA5k7TM1SGz2YGuXbsyfvx4hg8fDmSc+ezIkSMsWLDgtT3DjhBCmIIUZnVI\nMQf69u1Lnjx5mD59OgkJCQwcOJCCBQsSGBioP5WeEOZm7GUxZZlNYQ4ymU0dOkVRFLVDaEl8fDxp\naWk4OTmpHeWlJKYa777srIx7f8Yk2bJHq9m0mgtyVzY7IzbrDl2Iztbt/MoUNF6IXEha5sCOHTue\nuV/OnCaEEC9GWubqkGIOWQ4/S01N5f79+9jY2FCmTBkp5kII8YJkNEcdUsyBY8eOZdkWGxvLuHHj\nqFKligqJhBAiZ5KWuTrk0LSncHFxYciQISxfvlztKEIIkWNY6LJ3Ea9GWubPcP36dRISEtSOIYQQ\nOYa0zNUhxRz45JNPsmx78OABv/zyC61atVIhkRBC5EwyZq4O6WYHbGxsslzc3Nz49NNPGTdunNrx\nzCZ4wXzKlHLHwcGBFk39uHjhgtqRstixYwcl3iysdownkmzZs2untrJp+XOg5WyZdNm8iFcjx5kD\nq1atokWLFri5uakdJdte9ZjTlSuWM3hgPyZMmkKl8mWZ+vk0rl27yu9nL+Do6GickK/ol/BwWrds\nir29Pf93/abacQxItuz5/X/hNG2qnWxa/hyYMpsxjzP/6c972bpdLc+8z9y/YsUK5syZoz8ZFsCy\nZcsoVaoUn376KT///DNOTk4MGDCAjh07AqAoCrNnz2bz5s2kpaXRtm1bxowZg6WlZbYyapm0zIHg\n4GCSk5PVjqEaRVGY9vlkBg4ewvARo2jdujU79+zjQVwc69asVjseqampLJg3l2ZNGmruQyjZsicz\nW8OG2smm5c+BlrM9zlQt83PnzjF06FBOnTqlv1SrVo1x48bh4ODA8ePHmT9/PrNmzeK3334DYP36\n9YSFhbFr1y6++eYbTp48SUhIiNGfsxZIMQfq16/PunXruH//vtpRVHH5zz+5dvUq77T+78xxLi4u\n1KvfgEMHD6iYLMPxH48xZdIEJk2ZxqBBg9SOY0CyZU9mtmnTptFvgDayaflzoOVs5nL+/Hm8vb0N\ntj18+JCDBw8yePBgbG1tqVixIq1atdIvBLZz5078/f1xdXWlUKFCBAQEsH37djXim5wUc+DKlSus\nXr0aX19fqlevTt26dQ0ur7tLl/4AwMPD02B78RLuXL78pxqRDJTxLsu5PyIZ9PEQza0vLtmyJzPb\nkCHayablz4GWs2VhgqZ5QkICUVFRrFmzhjp16tCiRQu2bNnClStXsLKyolixYvrruru7ExkZCUBk\nZCSenp4G+6KiongdR5dlNjvQvXt3tSOoKrNH4vH16J2cnIiLi1MjkgFXV1e1IzyVZMseLWbT8udA\ny9keZ4pD06Kjo6latSpdu3Zl/vz5nD59msDAQHr16oWdnZ3Bde3s7EhMTAQyfgQ8ut/e3p709HSS\nk5OxtbU1ek415dpivnDhQj766CPs7e1p166d2nFUlZ6eDjz5rFpaaTUJYWpa/hxoOdvjTBGnWLFi\nrFu3Tv93tWrVaNu2LSdOnCApKcnguomJiTg4OAAZhf3R/QkJCVhZWb12hRxycTEPDg6ma9eu2Nvb\nG/V+vby8sLOzw8LCcASjSZMmzJw5k/DwcHr27Kn/x5bJxsaG8PBwAFq1asW1a9f091G0aFH27t1r\n1JyPcnFxATKOrc+TJ49+e1xcHC7OLiZ7XCG0RMufAy1ne5wpflpERETw448/0rdvX/22pKQkihQp\nQkpKCn/99RdFixYFICoqSt+17uHhQVRUFJUqVdLvK1mypAkSqi/XFnNTjpls3ryZ0qVLP3V/3rx5\n9YX7cYmJiURGRnLs2DHy589vqogGPD1LARAVGWlweN6V/4vC8xnPQ4jXiZY/B1rOloUJqrmDgwML\nFy7krbfeomnTpoSHh7N3717WrVtHXFwcX375JVOmTOHSpUvs2bOHpUuXAtCmTRtWrFhBzZo1sbKy\nYsmSJbRt29b4ATUgV0+AS0lJITk5+bkXc/rjjz8oWLCg2Qo5QKnSpSn6xhvs3bNLvy02NpajRw7T\noEEjs+UQQk1a/hxoOdvjdNn871nc3d2ZO3cuwcHBVKlShaCgIKZNm0a5cuWYPHkyqampNGjQgMGD\nBzNixAh9S7xbt240btyYDh068M4771ClShV69epljpfB7HJtyxygUaMX+xCcP3/exEn+c+7cOays\nrOjcuTNXrlyhbNmyjB07Fg8PD5M9pk6nY9gnIxk1YhiOjo5UqVyRqZ9Pw8nZme49eprscYXQEi1/\nDrSc7XGmGsJv3LgxjRs3zrI9b968zJs374m3sbS0ZOjQoQwdOtQ0oTQkVxfz+fPn68eijKlLly5Z\nxsxnzJiBn58fkPGLulq1agb758yZQ7169QCoUKECI0aMoGDBgixatIg+ffrwzTffZJm1aUwDBg0m\nPv4hXy1ayP3YWGr41mTvvu+yzJ4V4nWm5c+BlrM9SlvT8XKPXLucq7e3N8eOHaNAgQJGvV8vLy92\n79791DHz8PBwBg8e/NQx88cpikK1atVYvnw5Pj4+T71euiKnERRCvLzEVOMu53rySvYW36pS3Nl4\nIXKhXNsy1+pvmE2bNlGsWDFq164NQFpaGqmpqc89lCI5zXgZ7Kxefa13U5Fs2aPVbFrNBZItu+QU\nqOrItRPg2rVrp8ljDf/55x+mTp3K33//TWJiItOnT6dkyZKUKVNG7WhCCPFcOl32LuLV5NqW+bRp\n00x23x07dswyZu7q6sr+/fufe9vAwEAePHhAx44defjwIdWrVyc4ODjL/QkhhBZJXVZHrh0zf90Y\ns8tNy114ki17tJpNq7kgd2Uz5pj579eyt7xspWLamsiX0+TalrkQQgjjkzFzdUgxF0IIYTQy/q0O\nKeZCCCGMRmq5OmRWlRBCCJHDSctcCCGE8UjTXBVSzIUQQhiNTIBThxRzIYQQRiMT4NQhxVwIIYTR\nSC1XhxRzIYQQxiPVXBVSzIUQQhiNjJmrQ4q5EEIIo5Exc3VIMRdCCGE0UsvVIcVcCCGE8Ug1V4UU\ncyGEEEYjY+bqkGL+mjDumWx1Rr0/nQyiCZFryMddHVLMhRBCGI3UcnXIiVaEEEIYjy6bl+c4ceIE\nHTt2pGrVqjRp0oTQ0FAAzpw5g7e3Nz4+PvrL4sWLgYweyy+//JKaNWtSvXp1pkyZQlpampGfsDZI\ny1wIIYTRmGLMPDY2lv79+zNu3Djeeecdzp8/T69evXjrrbe4fv069evXZ8mSJVlut379esLCwti1\naxc6nY6AgABCQkLo06eP0TOqTVrmQgghjEany97lWf766y8aNGhA69atsbCwoFy5cvj6+nLy5EnO\nnTtHmTJlnni7nTt34u/vj6urK4UKFSIgIIDt27eb4FmrT4q5EEIIozFFL7u3tzczZ87U/x0bG8uJ\nEycoU6YM58+f5+TJkzRu3JiGDRsyY8YMkpOTAYiMjMTT01N/O3d3d6Kioow8YVgbpJgLIYTIMeLi\n4ggMDKRcuXI0btyYfPny0bhxY/bs2cPatWsJDw9n/vz5ACQkJGBnZ6e/rb29Penp6fpi/zqRYi6E\nEMJ4TDQBDuDatWt06dIFFxcXFi5ciIWFBYsXL6ZXr144ODhQrFgxAgIC+O677wCws7MjKSlJf/uE\nhASsrKywtbU10pPVDinmgl07d1CiWBGDbYqiMOuL6ZQp5U4Blzw0qFuL4z8eUykhBC+YT5lS7jg4\nONCiqR8XL1xQLcvjJFv2BC+Yj7u7O/mdtZVNq69ZUlISY8eMwqPEmzg5OdH+3dZERUaqHSsLXTb/\ne56IiAg6depE3bp1WbRoEXZ2dsTGxjJjxgwePHigv15SUpK+WHt4eBAVFaXfFxUVRcmSJY3/pDVA\ninku98sv4fT96IMs26dMCmLq5IkMGjyEr7fuwNXVjXZt3uHa1atmz7hyxXJGj/yE3n0D2bRpEwkJ\nCbRs3sTgA6wWyfZq2QIDA1m7QTvZtPyajRg2hFUhyxk/YRKbN2/mzp07NG/amPj4eLWjGTDFBLjo\n6Gh69+5Nr169GDNmDBYWGaXLycmJ7777joULF5KSksKVK1dYvHgx7733HgBt2rRhxYoV3Lx5k+jo\naJYsWULbtm1N/RKoQxGvhfjk9Je63I9PVr6YNVuxs7NT8uXLp7i6uen33b17V7G1tVXmLVyk33bn\nfrzi4empLA9Z/dKPlZCiZPsSn5yuFHvrLWXIsOFKQkrGc70ZfU9xdnZW5sxb+Er3/aoXyfbq2RQl\nY5sWsmn5Nfv79l3F2tpaWbR4mT7bxctXFEAJ3bztle/fmK7GJGbr8ixfffWVUrp0aaVy5coGl9mz\nZyuXLl1S/P39lSpVqii1a9dW5s2bp6SnpyuKoiipqanK7NmzlTp16ig1atRQJk+erKSmphr3CWuE\nTlFew2l9uVBCysu9jUcOh9Gp/bt8Nj6Iu3fvsmL5Uv7v2t8A7Nr2Nf7+/vz1zx3s7e1fOdurLOf6\n56VLVChbmu9+OELduvWws4LEVOjQrg06nY7N23a+cj7Jpl62Jg3rkZiasV3tbFp+zVJTUzlz+jQe\nnp44OztjZwX/d/0m7sWKsHrdRjp17vJK929nxBVHrt9Nev6VnuDNfK/fOLY5STd7LlXGuywRFy8z\ncPCQLMU2IiKC4sVLcORwGNV9KuLsYEPN6lUI//kns+e8dOkPADw8PA22Fy/hzuXLf5o9z6MkW/Zo\nNZtWcwFYWVnhU6UKzs7OpKWlceHCBQL7fEiRokVp1ryFqtmyMuEMOPFUUsxzKVdXVwoUKPDEfbdv\n3yY6+jb9Anoz8OMhbN2xm/z589O2VQtu3bpl1pz3798HMsbGHuXk5ERcXJxZszxOsmWPVrNpNdfj\nRo8cjre3N98d2M/Uz2fg4uKidiQDphgzF88nxVxkkZKSwp07d5g7Pxj/Dz7k7abN2LRlO9bW1nwV\nvMCsWdLT04End9WrfTY2yZY9Ws2m1VyP6/5+T77//ns+6t2Xj3r1ZOuWzWpHMiDtcnXI2uxG4uXl\nhZ2dnX6WZaYmTZowc+ZMwsPD6dmzJw4ODgb7bWxsCA8PZ9u2baxbt45t27YZ7A8PD2fw4MGEh4eb\n/DlkcnR0zMj+dlP9NicnJ6pUq865iAiz5QD0rY4HDx6QJ08e/fa4uDhcnNVtkUi27Hk0WwEX7WTT\n8mv2qMo+PthZQa16jbh8+U/mz51N+w4d1Y6lp6HfPbmKFHMj2rx5M6VLl37q/rx585q1KGeXh4cH\nAMnJyQY/PlJTUrCztXvazUzC07MUAFGRkbi5uem3X/m/KDyf8Vqbg2TLnkezFX9DO9m0/JrdvHmT\nA9/uo2PnLgaTUitUrMT2bVtUTJaVKU60Ip5PutlFFk2aNAFg29b/uu/u3LnDiV9/wbdmTbNmKVW6\nNEXfeIO9e3bpt8XGxnL0yGEaNGhk1iyPk2zZo9VsWs0FcO/uXQL6fMjuXf/NqE9PT+fo4TDKli2n\nYrInkH52VUjLXGTh7e1Nl67dGT3iE1JSUihevAQzZ0zD3sGBXh+Z99SBOp2OYZ+MZNSIYTg6OlKl\nckWmfj4NJ2dnuvfoadYsks342fI6O1KmXEVmzlA/m5ZfszLe3rRu+y7Dhw4mMSGBt94swldLlnL2\n7BkOLlikarbHSV1WhxRzI+rSpUuWMfMZM2bg5+cHZPzKr1atmsH+OXPmUK9ePbNlfFGLl61g0oSi\nTP98CnH371Ordh0OHAwjX758Zs8yYNBg4uMf8tWihdyPjaWGb0327vsuy6xjNUi2V8u2aNFCYjWU\nTcuv2crV65g4YRyTJo4nJjqaKlWrse/A91SvUUPtaAZkzFwdsmiMkXh5ebF79+6njpk/byLbnj17\nWLZsGTt3Gi5Mcfz4cUaNGsXRo0ef+fjpioKFfIqEEC8pMdW4i8bcjkvN1u0KOUnb8lXIq6cRbm5u\n3Lx5E0VRDA6DuXbtGoULF37u7ZNSAYzzu8zeWvfSK8o9izEP68lclUuLJNvL02oukGzZJm0KVcgE\nOI2oVKkSefLkYdasWTx8+JC0tDTOnDlDSEgIrVu3VjueEEK8EJn/pg5pmRtRx44ds4yZu7q6sn//\n/ufe1sbGhpCQEL744gsaN25MUlISbm5udO3alR49epgqshBCiNeAjJm/JozZLS7d7Nkj2V6eVnNB\n7spmzDHzmIfZC1Ygj7QtX4W8ekIIIYxGFo1RhxRzIYQQRiMH1ahDJsAJIYQQOZy0zIUQQhiNtMzV\nIcVcCCGE0ciYuTqkmAshhDAaaZmrQ4q5EEIIo5Farg4p5kIIIYxHqrkqpJgLIYQwGhkzV4ccmiaE\nEMJodLrsXZ7n3LlzdOjQgcqVK9O2bVt+++030z+ZHESKuRBCCKMxxYlWkpKSCAwM5L333uPXX3+l\nR48e9OvXj4cPH5rqaeQ4UsyFEEIYjwmq+c8//4yFhQXdunXD2tqaDh06ULBgQQ4fPmyyp5HTyJi5\nEEIIozHFmHlUVBQeHh4G29zd3YmMjDT6Y+VUUsyFEEIYjSmOM4+Pj8fe3t5gm52dHYmJicZ/sBxK\nivlrwt7auJ8gY9+fMRnzdI3GJtlenlZzgWTLDlPksre3z1K4ExMTcXBwMP6D5VAyZi6EEELTSpYs\nSVRUlMG2qKgoPD09VUqkPVLMhRBCaFqtWrVITk5m7dq1pKSksGXLFqKjo6lbt67a0TRDpyiKonYI\nIYQQ4lkuXLhAUFAQFy9epHjx4gQFBVG5cmW1Y2mGFHMhhBAih5NudiGEECKHk2IuhBBC5HBSzIUQ\n4gXJqKTQKinm4oXJF9nrRYvvZ3p6utoRnuibb74BQGeKFVGEMAKNLjsgtGD79u3Y2dlhaWlJ06ZN\nNfVFtm/fPsqXL0+xYsXUjmJg8eLFJCQkkJiYyIgRI7CyskJRFE28dmvWrMHS0hJra2s6deqkiUyZ\npkyZQuPGjalduzbp6elYWGinndGvXz/u3r1Ly5Yt1Y7yRFOnTiVPnjwMGTJE7ShCRdr5xAhN6dOn\nD6tWrWLHjh3MmjWLwMBA4uPj1Y6FoihcvXqVxYsXs2nTJv7++2+1I+n169ePgwcPYm9vz3fffUdA\nQACgjdZc37592blzJxEREYSGhnLt2jW1I+nFx8fz448/MmLECE6cOIGFhYVmWuiBgYFER0cTGhqq\ndpQnSktLo3Tp0mzevJnly5erHUeoSFrmIovDhw9z//59du7cSVpaGvfu3aNnz5707t2bpUuX4ujo\nqFprU6fTYWNjw99//83vv/9Oeno63bt354033jB7lkcdOHCAe/fusWXLFgA6depEhw4dOH/+PN7e\n3qpmCw0N5e7du2zdupV79+7Rp08fTp48yaFDh+jUqZPqS2I6ODhQuXJl/vjjDwYNGsQXX3xBvXr1\nVM0E8PnnnxMZGcmBAwcA+Pbbb4mLiyMmJoZu3brh7OysckKwtLSkbdu22NvbM23aNBRFoU+fPmrH\nEiqQlrnIIj4+nocPH5KYmIilpSUFChRg165d3L9/n48//hhQt7UZHx9PxYoVadCgAZcvX2b9+vXc\nuHFDtTwADx8+JD09neTkZBRFQVEUHj58SGxsrKq5AO7evasfjtixYwdnzpzhyJEjLF++HH9/fx48\neKBatuTkZAAcHR1p2LAhffr0YeTIkVy6dImYmBhVz1ddoEAB/fsXGhrK559/zu+//87mzZvp06cP\nN2/eVC0bQGpqKgA2NjY0bdqU0aNHs2rVKlasWKFqLqEOKeZCLyQkBABPT0/c3NwIDw8nLS0NyGgB\nbNy4kevXr7N69WqzZ1u5cqX+/y9cuICbmxu9e/emRYsWREZGqlbQM78433zzTTw8PIiOjkan01Gg\nQAHc3NywtbXVX9fcZ3jKzObm5sb7778PgK2tLYcPH+bLL7/k2LFjJCYmMnfuXLPmgv/eTxsbGwDq\n1q1LXFwc3bt354MPPqBbt27UqVOHf/75x+zZMl+3gIAA/Pz8aNmyJTt37mTz5s1MmTIP9CQVAAAU\nW0lEQVSFQ4cOkZ6ezvz5882eDWDVqlXcuXMHKysr/efTxsaG5s2bM3r0aNavX8/WrVtVySbUI8Vc\nANC/f382b94MQIkSJXB1dWX9+vWcP39eP37p5OREgwYNuHv3rtmzff311/q/W7ZsyYQJEwB49913\nadasGZGRkWYfC+7fv7++W93Hx4cuXbpQtGhRIKM1nJCQQMGCBYGMAhEUFKRvTZkjW+b7+d5771Gl\nShUAOnbsiJubGwkJCfq/M//fXB5/PwGsra355ZdfsLW1xcPDg9TUVFxcXFTJlvmeAnzwwQe4u7uT\nP39+ChUqpJ830qlTJ1V6Df7++2/279/PmDFjuHfvHpaWlvqCbm1tTdOmTenSpQvHjh3j4cOHmjxi\nQZiGFHNB//79uXPnDvv27QMyvhSCgoJIS0tj3rx5nDhxgqSkJCBjAtq9e/f0Xcnmzpb5mDY2Nvov\nsXbt2tG8eXNOnTrFjh07zFIwH89lZWVFxYoV9ftjY2O5d+8eb7zxBuvXr2fJkiV069YNKyvTT1PJ\nzPbtt99m2WdlZUVCQoI+R0xMjH7GvRrvZyZfX18qVqxIWFgYY8eOZdy4cfTo0YMhQ4aYrSg9KZun\npyfvvfceH330ERYWFvqelqtXr+oLqbkK5po1ayhSpAjDhg3D1taWUaNGGRR0RVGwtbXF1dWVmJgY\nbGxsNDH5UpiHrM2ey/Xv35/Y2FjWr1+v33bnzh0cHBzQ6XSMGDGC1NRU4uPjKVOmDJs3b2bjxo2U\nLl1alWyxsbEkJibi5uYGYHAY0549e6hatSpFihRRPVdkZCQjR46kcePGrFy5klWrVlGuXDmT5nqR\nbDdu3GDJkiXcuXMHd3d3Nm3axPr16ylVqpSq2QoVKkSLFi24cuUKc+fOpXnz5ty/f5+UlBQKFCig\nSra7d++iKAr58+cnMTGRb775hpMnT5I/f37WrVvHhg0bKFOmjMmzZea7fPky+/fvR1EU/ve//7Fq\n1SpSUlKYNm0a+fPnJzk5GRsbG5YuXcq5c+eYPn06dnZ2ZsknNEARuda8efMULy8vg23Xrl1Tateu\nrYSFhSmKoihJSUlKWFiYsmDBAmXJkiXKn3/+qXq2Y8eOGWxPS0szS6aXyXX79m2lfPnySu3atZWI\niAjNZEtOTlY2btyoTJ8+XZk1a5byxx9/qJ7t8OHDiqIoyrFjx5Rff/3VLHleNNvRo0cVRVGU1NRU\nZf369cqoUaOUCRMmmO11UxRF6devn9K5c2eDbenp6cqvv/6qDBw4UOnZs6cSExOjJCUlKRs3blSq\nVaumnD9/3mz5hDZIMc/Fjh8/rjRv3lwZN26coiiKEh0drTRq1EiZN2+eoigZXxhazab1XCkpKcrw\n4cPN+qX/su+nOd/fF3ndMn+Upaenay7bo8z547Ffv35Kt27dDLbduXNHiYmJURRFUc6cOaMMGjRI\nqVSpkvLhhx8qLVq0UM6ePWu2fEI7pJjnQrNnz1YuXLigpKWlKb/++qvSrFkzpX///kqjRo2UJUuW\n6K9nzi8trWd7mVxazvYocxRMrb6f2cmW+XqZ64fGs3oMjhw5YrD9xx9/VC5evKjcunXLLNmE9kgx\nz2X69u2rdO3aVf93Zndd27ZtlWbNmum3JyUlSTaN55Jsr2e2TC/SY6Bm75nQFsugoKAgtcfthXn0\n79+fe/fusXHjRv02nU5H0aJF8fT05NChQ0RERNCkSRP9DFlzrZGt1WxazSXZXs9sAHPmzCF//vxU\nrFgRb29v1q5dy08//cSiRYvo0qUL/fv3B9DcGvZCZWr/mhDmMWDAgCyTaP766y9l+/btSnJysr5l\n0qpVK2XgwIGSTcO5JNvrmU1RckaPgdAm+VmXCwwcOJDz588bnCzixo0btG7dmpiYGKytrdHpdFSu\nXJmxY8dy48YNbt26lauzaTWXZHs9s8F/h8dt2LBBv02n01GtWjU+++wzFEVh9OjRgOE6C0IA0jLP\nDcLCwpSqVasqZ86cURRFUa5fv67Ur19fCQ4ONrje1atXFUVRlISEhFyfTau5JNvrmU3rPQZC+6SY\n5xJhYWFK9erVlb179yqNGzdWFi5caLB/zZo1St26dZXY2FizT6rRajat5pJsr1e2AQMGKI0bNzbY\ndv36daVq1arK8uXL9dtSUlKUn376SWnXrp1y8+ZNs2QTOYcU81wkLCxM8fLyUkaPHm2wfd26dUqN\nGjX0LRY1aDWbVnMpimTLLq1l03KPgcg5pJjnMseOHVNq1KihX2lr3bp1SvXq1TWx0IRWs2k1l6JI\ntuzSWjYt9hiInEWKeS4UFham1K5dWwkKClJq1qypiS/XTFrNptVciiLZsktr2bTWYyByFinmudT3\n33+veHl5mW3d8Jeh1WxazaUoki27tJZNaz0GIueQs6blYgkJCdjb26sd44m0mk2ruUCyZZfWsh0+\nfJhPP/2Upk2b8u2337J8+XKznHFP5GxSzIUQQmN++OEH+vXrx7Zt2yhbtqzacUQOIMVcCCE0SGs9\nBkLbpJgLIYQQOZws5yqEEELkcFLMhRBCiBxOirkQQgiRw0kxF0IIIXI4KebCbBo3boyXl5f+Uq5c\nORo2bMjMmTNJSUkx2uOEh4fj5eVFUlKS/nE3btz43NslJycbnH7yZV2/fh0vLy8uX778xP2jR482\neP6PXvr3729wH8+6jrmMHj2aoUOHmvUxTeXR56IoCqGhoa/0b87Ly4sjR44YK54Qr8xK7QAidxk+\nfDjvvvsuAGlpaZw9e5YRI0bg4ODAgAEDTPKYW7ZswcHB4bnX27t3L8HBwXTr1s0kOQD8/PyYOHFi\nlu22trYGf69fv57ixYs/8zrixY0dO1b//7/++isTJkygXbt2KiYSwrikmAuzcnR0pFChQvq/Cxcu\nTOvWrTlw4IDJinn+/Plf6HrmOErT1tbW4Pk/Tb58+V7oeuLFODk56f9fjsYVryPpZheqs7Kywtra\nGsjoDh05ciTt27fH19eX//3vf6SkpDBjxgxq165NtWrVCAgI4Nq1a/rbx8TE0L9/f3x8fGjevDln\nz541uP9Hu9nT09MJDg6mQYMG+Pj48OGHH3LlyhXCw8MZM2YM0dHReHl5cf36dRRFYenSpTRs2BAf\nHx/ef/99IiIi9PcbHx/PmDFjqFq1Kg0bNlSt2/WPP/6ge/fuVK5cmdq1azNlyhSSk5P1+zds2EDT\npk2pVKkSnTt35vTp0/p9y5cv5+2336Z8+fL4+voyfvz4p3Y///DDD7Ru3ZqKFSvSunVr9uzZ89RM\nCxYsYMiQIXzxxRdUqVKFunXrsn37dsLCwnj77bfx8fFhxIgRpKamApCamsrMmTNp2LAh5cqVo27d\nusyZM0d/f4qiMG/ePGrXrk3VqlX5/PPP6dGjB9u2bQMy/t1MnjyZkSNH4uPjQ61atViyZIn+9pnd\n7NevX6dnz54AVKxYkfDw8CcOJ9SpU0d/36mpqcyYMQNfX19q1arF1q1bDa77vH8nQpiDFHOhmrS0\nNH766Sd27tyJn5+ffvuuXbvo1asXISEhVKhQgTlz5hAeHs6CBQvYtGkThQoVomfPniQmJgIwaNAg\n7t+/z8aNGxkzZgwhISFPfcyFCxeybt06xo0bx/bt23F0dNT/EPj000/Jnz8/x44do0iRImzYsIFN\nmzYxZcoUtm3bRvXq1enRowe3b98GYPz48Zw5c4aQkBBmzZrF6tWrTfuCPcWIESMoXrw4u3fvZv78\n+ezbt0//42Xr1q188cUX9O/fn927d1O+fHn69u1LQkICO3fuZOnSpYwbN479+/cTFBTEjv9v7/5j\nqqz+AI6/ISCGQhiEGtxIYzfpXsJoEXMwbXOzkFVDZ7TiR0TO5mq0wiug7g5jgAHySxyYq5nm/MGG\nrbQf4rxbrautFqtW3EAS/rBgYgIhV+69n+8fjiceAe83v98v4r7ntbFxzznPcz7nuYd7nuecw25r\nK59++umkOjo6OnjjjTfIzs7m448/5uWXX2bbtm3YbLZp4zp58iQul4vW1lZWrVqF1WqlsbGRnTt3\nUllZyYkTJ/j8888BaG5u5sSJE1RWVvLZZ5+xceNGmpqa+O677wDYs2cPBw8e5O233+bgwYN0d3fz\nzTff6Oo7dOgQBoOBY8eO8dJLL1FdXU1HR4euzMKFC6mvrwfg1KlTPPLII16vb319PZ988gk1NTXs\n3buXw4cP6/K99RNFmRG34ttdlP9PTzzxhJjNZlm6dKksXbpUYmNjxWw2S2FhoTidThERsVgskpaW\nph1z5coVMZvN0t7erqW53W5JSUmR1tZWcTgcYjQapbu7W8vfv3+/GI1GGR0d1er98MMPxePxSFJS\nkuzfv18r29/fL+Xl5TI0NCQtLS2ybNkyLW/58uVy/PhxXRuee+452bVrlwwODkpsbKx8+eWXWt7p\n06fFaDRKZ2fnlO23WCzy0EMPae2f+HPq1CkREent7RWj0Sjx8fG6/IlxXS8hIUEqKirE5XKJiMiP\nP/4oPT09IiKSnp4uFRUVWlmn0yllZWVy4cIF+frrr+XkyZO6c2VkZEh1dbUWb35+voiIFBQUiNVq\n1ZWtqqqS7OzsKWOqq6uTxMRELaZff/1VjEaj1k4RkbVr10ptba2IiHzxxRdy5swZ3TlSUlLk0KFD\nIiKSnJwsH3zwgZY3MDAg8fHx0tLSosW6evVq3fGJiYly5MiRSW2x2+26/jExb9yyZcukpaVF6zPj\ncUxsi81mE5Eb9xNFmSlqzVyZURs2bCAtLQ2AgIAAwsPDtSn2cVFRUdrvPT09XL16laysLHx8fLT0\n0dFRuru7CQgIICgoiPvvv1/Li4uLm7LuS5cuMTAwoMsPDw/HYrFMKvvXX39x4cIFNm/eTFFRkZZ+\n9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1242ab2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Computing the confusion matrix\n",
    "confmatrix_nb = confusion_matrix(ytest4, yhat4_rf9)\n",
    "\n",
    "# Plotting it using the function:\n",
    "plot_confusion_matrix(confmatrix_nb, rf9.classes_,\n",
    "                      'Confusion matrix - Random Forest - Numerical and Narrative features',\n",
    "                      xlab='Predicted EF scale magnitude', ylab='True EF scale magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of correctly predicted EF4 tornadoes came down to 4, and there is no EF5 tornado correctly predicted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It could be interesting to redo a gridsearch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OnjxJz5496du3L8HBwWrZPn360KxZMwYPHoyVlRXbt2/H0tKS4OBgduzYocau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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c6dd0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_RF_important_features(XtrainDF=Xtrain4, fitted_rf=rf9, weight_threshold_for_display=0.002,\n",
    "                           figsize=(6,12),\n",
    "                           title_test=\"Most important features taken from our best RF model so far\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- The most important features are interesting and show we could still bring some improvements and gather words like \"damage\" and \"damaged\", or \"90\" and \"90 mph\". Even though we saw in part 3 that they do not bring much to the accuracy, I think more testing on lemmatization and tf-idf could lead to a better result.\n",
    "\n",
    "- Testing other types of models would be good, and also optimizing their hyperparameters.\n",
    "\n",
    "- Another interesting test would be the sentiment analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Predicting the total damage cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have helped determining the tornado magnitude based on narrative text from various sources, we can try and predict the cost of damage, which could help counties plan for future possible protection budgets. Insurance companies could also be a target for this study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14537, 14)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14537 entries, 0 to 14536\n",
      "Data columns (total 14 columns):\n",
      "TOR_F_SCALE    14537 non-null object\n",
      "TOR_LENGTH     14537 non-null float64\n",
      "TOR_WIDTH      14537 non-null float64\n",
      "Duration       14537 non-null int64\n",
      "AverageDate    14537 non-null int64\n",
      "AverageTime    14537 non-null float64\n",
      "Deaths         14537 non-null int64\n",
      "Injuries       14537 non-null int64\n",
      "Mean_Lat       14537 non-null float64\n",
      "Mean_Lon       14537 non-null float64\n",
      "Azimuth        14537 non-null float64\n",
      "State          14537 non-null object\n",
      "TotalCost      14537 non-null int64\n",
      "Source         14537 non-null object\n",
      "dtypes: float64(6), int64(5), object(3)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Removing wrong outlier and displaying info:\n",
    "dfnew = df[df['TOR_LENGTH']<50]\n",
    "dfnew.reset_index(drop=True, inplace=True)\n",
    "print dfnew.shape\n",
    "dfnew.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOR_F_SCALE</th>\n",
       "      <th>TOR_LENGTH</th>\n",
       "      <th>TOR_WIDTH</th>\n",
       "      <th>Duration</th>\n",
       "      <th>AverageDate</th>\n",
       "      <th>AverageTime</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Injuries</th>\n",
       "      <th>Mean_Lat</th>\n",
       "      <th>Mean_Lon</th>\n",
       "      <th>Azimuth</th>\n",
       "      <th>State</th>\n",
       "      <th>TotalCost</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EF0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>208</td>\n",
       "      <td>15.855</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.7345</td>\n",
       "      <td>-93.3220</td>\n",
       "      <td>120.34</td>\n",
       "      <td>IA</td>\n",
       "      <td>0</td>\n",
       "      <td>Public</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EF0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>208</td>\n",
       "      <td>16.255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.6694</td>\n",
       "      <td>-93.0534</td>\n",
       "      <td>117.98</td>\n",
       "      <td>IA</td>\n",
       "      <td>0</td>\n",
       "      <td>Law_Enforcement</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TOR_F_SCALE  TOR_LENGTH  TOR_WIDTH  Duration  AverageDate  AverageTime  \\\n",
       "0         EF0        0.27       20.0         1          208       15.855   \n",
       "1         EF0        0.20       20.0         1          208       16.255   \n",
       "\n",
       "   Deaths  Injuries  Mean_Lat  Mean_Lon  Azimuth State  TotalCost  \\\n",
       "0       0         0   41.7345  -93.3220   120.34    IA          0   \n",
       "1       0         0   41.6694  -93.0534   117.98    IA          0   \n",
       "\n",
       "            Source  \n",
       "0           Public  \n",
       "1  Law_Enforcement  "
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnew.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature matrix and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14537, 14) (14537, 91)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOR_LENGTH</th>\n",
       "      <th>TOR_WIDTH</th>\n",
       "      <th>Duration</th>\n",
       "      <th>AverageDate</th>\n",
       "      <th>AverageTime</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Injuries</th>\n",
       "      <th>Mean_Lat</th>\n",
       "      <th>Mean_Lon</th>\n",
       "      <th>Azimuth</th>\n",
       "      <th>...</th>\n",
       "      <th>Source_Official_NWS_Observations</th>\n",
       "      <th>Source_Other_Federal_Agency</th>\n",
       "      <th>Source_Park/Forest_Service</th>\n",
       "      <th>Source_Public</th>\n",
       "      <th>Source_Social_Media</th>\n",
       "      <th>Source_State_Official</th>\n",
       "      <th>Source_Storm_Chaser</th>\n",
       "      <th>Source_Trained_Spotter</th>\n",
       "      <th>Source_Unknown</th>\n",
       "      <th>Source_Utility_Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>-0.659078</td>\n",
       "      <td>-0.553944</td>\n",
       "      <td>-0.663874</td>\n",
       "      <td>0.718893</td>\n",
       "      <td>0.064674</td>\n",
       "      <td>-0.048936</td>\n",
       "      <td>-0.060172</td>\n",
       "      <td>0.992459</td>\n",
       "      <td>-0.148916</td>\n",
       "      <td>1.408516</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>-0.675226</td>\n",
       "      <td>-0.553944</td>\n",
       "      <td>-0.663874</td>\n",
       "      <td>0.718893</td>\n",
       "      <td>0.142254</td>\n",
       "      <td>-0.048936</td>\n",
       "      <td>-0.060172</td>\n",
       "      <td>0.978564</td>\n",
       "      <td>-0.115505</td>\n",
       "      <td>1.354118</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>2 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TOR_LENGTH  TOR_WIDTH  Duration  AverageDate  AverageTime    Deaths  \\\n",
       "0   -0.659078  -0.553944 -0.663874     0.718893     0.064674 -0.048936   \n",
       "1   -0.675226  -0.553944 -0.663874     0.718893     0.142254 -0.048936   \n",
       "\n",
       "   Injuries  Mean_Lat  Mean_Lon   Azimuth           ...            \\\n",
       "0 -0.060172  0.992459 -0.148916  1.408516           ...             \n",
       "1 -0.060172  0.978564 -0.115505  1.354118           ...             \n",
       "\n",
       "   Source_Official_NWS_Observations  Source_Other_Federal_Agency  \\\n",
       "0                                 0                            0   \n",
       "1                                 0                            0   \n",
       "\n",
       "   Source_Park/Forest_Service  Source_Public  Source_Social_Media  \\\n",
       "0                           0              1                    0   \n",
       "1                           0              0                    0   \n",
       "\n",
       "   Source_State_Official  Source_Storm_Chaser  Source_Trained_Spotter  \\\n",
       "0                      0                    0                       0   \n",
       "1                      0                    0                       0   \n",
       "\n",
       "   Source_Unknown  Source_Utility_Company  \n",
       "0               0                       0  \n",
       "1               0                       0  \n",
       "\n",
       "[2 rows x 91 columns]"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature matrix:\n",
    "Xnew1 = dfnew.drop(['TotalCost'], axis=1)\n",
    "\n",
    "# As we saw in the previous analyses to predict the scale of a tornado, the unidentifies ones \n",
    "# (ie EFU) are in most cases EF0 tornadoes. We are therefore going to replace all the EFU by EF0.\n",
    "Xnew1.TOR_F_SCALE = Xnew1.TOR_F_SCALE.map(lambda scale: 'EF0' if scale=='EFU' else scale)\n",
    "\n",
    "# Standardizing numerical features:\n",
    "numfeat = ['TOR_LENGTH', 'TOR_WIDTH', 'Duration', 'AverageDate', 'AverageTime', 'Deaths',\n",
    "           'Injuries', 'Mean_Lat', 'Mean_Lon', 'Azimuth']\n",
    "scaler = StandardScaler()\n",
    "Xnew1num = pd.DataFrame(scaler.fit_transform(Xnew1[numfeat]), columns=numfeat)\n",
    "\n",
    "# Dummifying categorical features:\n",
    "catfeat = ['TOR_F_SCALE', 'State', 'Source']\n",
    "Xnew1 = pd.concat([Xnew1num, Xnew1[catfeat]], axis=1)\n",
    "Xnew1 = pd.get_dummies(Xnew1, prefix=['Scale','State','Source'], columns=catfeat)\n",
    "Xnew1.drop(['Scale_EF0','State_TX','Source_NWS_Storm_Survey'], axis=1, inplace=True)\n",
    "\n",
    "print dfnew.shape, Xnew1.shape\n",
    "Xnew1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TtW2MGDGC5cuXc/369Sz3aMCze2VWrlzJwoULGT58OHZ2dowbNy7Hu767d+/Ow4cP2bRp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\nyUIIIYRekiyEEELo9f8B4FyAG8PxLBEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d32b450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target:\n",
    "ynew1 = dfnew['TotalCost']\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.title(\"Total damage cost distribution\")\n",
    "plt.ylabel(\"Number of tornadoes (log scale)\")\n",
    "plt.xlabel(\"Cost in billions of dollars\")\n",
    "ynew1.hist(bins=20, log=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data being strongly postively skewed, the median is a better representation than the mean. This will be our baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.453700e+04\n",
       "mean     1.487303e+06\n",
       "std      3.516064e+07\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      5.000000e+03\n",
       "75%      7.500000e+04\n",
       "max      2.800000e+09\n",
       "Name: TotalCost, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ynew1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xnewtrain1, Xnewtest1, ynewtrain1, ynewtest1 = train_test_split(Xnew1, ynew1, test_size=0.3,\n",
    "                                                                random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline RMSE:\t33863232.7319\n",
      "Baseline R2:\t-0.00287911086713\n"
     ]
    }
   ],
   "source": [
    "rmse_baseline = np.sqrt(mean_squared_error(ynewtest1, np.tile(ynewtest1.median(), len(ynewtest1))))\n",
    "R2_baseline = r2_score(ynewtest1, np.tile(ynewtest1.median(), len(ynewtest1)))\n",
    "print \"Baseline RMSE:\\t\", rmse_baseline\n",
    "print \"Baseline R2:\\t\", R2_baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression, no regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "linreg1 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "linreg1.fit(Xnewtrain1, ynewtrain1)\n",
    "yhatnew1 = linreg1.predict(Xnewtest1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Reg 1 - crossvalidated R2:\t-2.80817103928e+21\n",
      "Linear Reg 1 - RMSE:\t\t\t9.40080381137e+16\n",
      "Linear Reg 1 - R2:\t\t\t-7.72897198798e+18\n"
     ]
    }
   ],
   "source": [
    "rmse_linreg1 = np.sqrt(mean_squared_error(ynewtest1, yhatnew1))\n",
    "R2_linreg1 = r2_score(ynewtest1, yhatnew1)\n",
    "print \"Linear Reg 1 - crossvalidated R2:\\t\", np.mean(cross_val_score(linreg1, \n",
    "                                                                     Xnewtrain1,\n",
    "                                                                     ynewtrain1, cv=5))\n",
    "print \"Linear Reg 1 - RMSE:\\t\\t\\t\", rmse_linreg1\n",
    "print \"Linear Reg 1 - R2:\\t\\t\\t\", R2_linreg1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is a very bad modelling.\n",
    "- When splitting the train/set differently, the scores get also completely different. It is very unstable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression, Lasso regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso Reg 1 - crossvalidated R2:\t0.0440209262208\n",
      "Lasso Reg 1 - RMSE:\t\t\t21904184.4034\n",
      "Lasso Reg 1 - R2:\t\t\t0.580389904378\n"
     ]
    }
   ],
   "source": [
    "lassoreg1 = Lasso(random_state=31)\n",
    "lassoreg1.fit(Xnewtrain1, ynewtrain1)\n",
    "yhatnew_lassoreg1 = lassoreg1.predict(Xnewtest1)\n",
    "rmse_lassoreg1 = np.sqrt(mean_squared_error(ynewtest1, yhatnew_lassoreg1))\n",
    "R2_lassoreg1 = r2_score(ynewtest1, yhatnew_lassoreg1)\n",
    "print \"Lasso Reg 1 - crossvalidated R2:\\t\", np.mean(cross_val_score(lassoreg1, \n",
    "                                                                    Xnewtrain1, \n",
    "                                                                    ynewtrain1, cv=5))\n",
    "print \"Lasso Reg 1 - RMSE:\\t\\t\\t\", rmse_lassoreg1\n",
    "print \"Lasso Reg 1 - R2:\\t\\t\\t\", R2_lassoreg1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso regularization gives OK scores. The crossvalidated one is slightly over 0, which means the model is not fitting the data so well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e-02,   1.00000000e-01,   1.00000000e+00,\n",
       "         1.00000000e+01,   1.00000000e+02,   1.00000000e+03])"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(-2, 3, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done  30 out of  30 | elapsed:   32.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=31,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=2,\n",
       "       param_grid={'alpha': array([  1.00000e-02,   1.00000e-01,   1.00000e+00,   1.00000e+01,\n",
       "         1.00000e+02,   1.00000e+03])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_params = {'alpha': np.logspace(-2, 3, 6)}\n",
    "lassoreg2 = GridSearchCV(lassoreg1, param_grid=lasso_params, cv=5, n_jobs=2, verbose=1)\n",
    "lassoreg2.fit(Xnewtrain1, ynewtrain1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.048179138755453425"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lassoreg2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 1000.0}"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lassoreg2.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Getting slightly better, but still a long way to go."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments on damage cost prediction:\n",
    "- It does not seems as straight forward as the magnitude prediction.\n",
    "- It may be due to the highly asymetrical distribution (high postive skew).\n",
    "- Further testing is required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This capstone project consists of 5 notebooks:\n",
    "- [Part 1](tornadoes_part1_project_introduction.ipynb): First analysis of the data and modelling on a subset of 500 tornadoes.\n",
    "- [Web Scraping](tornadoes_part2_webscraping_posgres.ipynb): Importing a bigger dataset from the web and saving as a local PostgreSQL\n",
    "- [Part 3](tornadoes_part3_eda_modelling_nlp_tsa.ipynb): work on the dataset of tornadoes which magnitude was measured with EF scale (2007-2017). Final EDA, more modelling, NLP and time series analysis.\n",
    "- [Pre-Processing class](tornadoes_part4_pre_processing_class.ipynb): Gathering of all the pre-processing steps of part 3 in a single class.\n",
    "- [Part 5](tornadoes_part5_modelling_upsampling_nlp.ipynb): Upsampling, further NLP testing and a first glance at predicting damage costs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Extensive EDA and feature engineering was performed.\n",
    "- Part 1 and part 3 gather most of the insightful discoveries made."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Series Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The count of tornadoes over time was studied.\n",
    "- A clear yearly seasonality of the tornadoes was confirmed. Tornadoes are indeed more frequent in the month of April to July.\n",
    "- Selecting only the count of tornadoes for those months from 1952 until 2017 shows an increase trend. This can be either due to the effects of global warming or (most likely) to better measurments and tornado investigations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the tornadoes' magnitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From numerical charactertics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Logistic Regression, Recurrent Neural Network and Random Forest models were tried.\n",
    "- We succeeded in a good model, improving the baseline accuracy from 0.527 to 0.732.\n",
    "- Because of strong imbalance between weak and strong tornadoes, the recall score of these latest is actually a better proof of model quality. \n",
    "- Better recall of strong tornadoes was achieved through successfull upsampling (accuracy 0.723)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From narrative - NLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Naive Bayes, Random Forest and Stochastic Gradient Descent were tried.\n",
    "- Various tests were perfomed on Count Vectorizer. The best obtained model has an accuracy of 0.784.\n",
    "- There is room for improvement, NLP is a broad topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From both the numerical and text features\n",
    "- Random Forest was tried. Accuracy score of 0.812 was obtained, but at the expense of the recall of strong tornadoes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Possible next steps\n",
    "- Try more models.\n",
    "- Include the tornadoes which magnitude was measures with the F scale (tornadoes between 1952 and 2006) and retest modelling.\n",
    "- Further test neural network.\n",
    "- NLP further tests of TF-IDF and lemmatization.\n",
    "- NLP sentiment analysis.\n",
    "- Time series analysis: analysing the increase in number of tornadoes trend and forecast the next few years with ARIMA model, including yearly seasonality.\n",
    "- Predict the total damage cost via regression. First pass of tests were performed. Further analysis and model testing needed.\n",
    "- Use other datasets already imported in my local postgreSQL database: fatalities and locations datasets.\n",
    "- Incorporate other datasets to improve predictions: weather data or census data (like average income to predict damages).\n",
    "- Further work on probabilites: get the probability per county to have a tornado of a certain magnitude in the next 5 years.\n",
    "- Build an app with Flask and make it publically available: entering the state/county, gives back the probabilities computed previously."
   ]
  }
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