xGModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xG\n",
    "\n",
    "xG has gained traction in the past few years in the football analytics community. It tells us about the probability of a shot being scored. It's value ranges from 0 to 1. 1 signifying a definite goal, and 0 signifying a definite miss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 1 = Logistic Regression <br>\n",
    "Model 2 = Random Forests <br>\n",
    "Model 3 = AdaBOOST <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.calibration import calibration_curve\n",
    "\n",
    "sns.set_context('poster')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ROC_curve(fpr, tpr):\n",
    "    plt.plot(fpr, tpr, color='orange', label='ROC')\n",
    "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "def plot_PR_curve(recall, precision):\n",
    "    plt.plot(recall, precision, color='blue', label='PR')\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.title('Precision-Recall Curve')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "def plot_pr_curve(y,ytest_prob):\n",
    "    precision, recall, thresholds = metrics.precision_recall_curve(y, ytest_prob)\n",
    "    plot_PR_curve(recall,precision)\n",
    "    f1score = 2*precision*recall/(precision+recall)\n",
    "    index = np.argmax(f1score)\n",
    "    optimal_threshold= thresholds[index]\n",
    "    return optimal_threshold,f1score[index],recall[index],precision[index]\n",
    "def plot_roc_curve(y,ytest_prob):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y, ytest_prob)\n",
    "    plot_ROC_curve(fpr,tpr)\n",
    "    auc = roc_auc_score(y,ytest_prob)\n",
    "    gmean = np.sqrt(tpr * (1 - fpr))\n",
    "    index = np.argmax(gmean)\n",
    "    optimal_threshold= thresholds[index]\n",
    "    \n",
    "    \n",
    "def optimal_confusion_matrix(y,ytest_prob,optimal_threshold):\n",
    "    ytemp=(ytest_prob > optimal_threshold).astype(int)\n",
    "    mat= confusion_matrix(y,ytemp) \n",
    "    sns.heatmap(mat, square=True, annot=True, fmt='d', cbar=False)\n",
    "    accuracy=accuracy_score(y,ytemp)\n",
    "    print('The optimal accuracy is ' ,accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerId</th>\n",
       "      <th>id</th>\n",
       "      <th>x_coordinates</th>\n",
       "      <th>y_coordinates</th>\n",
       "      <th>blocked</th>\n",
       "      <th>goal</th>\n",
       "      <th>time</th>\n",
       "      <th>matchPeriod</th>\n",
       "      <th>counter</th>\n",
       "      <th>competition</th>\n",
       "      <th>foot</th>\n",
       "      <th>bodypart</th>\n",
       "      <th>preferrence</th>\n",
       "      <th>angles</th>\n",
       "      <th>distance</th>\n",
       "      <th>headers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25413</td>\n",
       "      <td>177959212</td>\n",
       "      <td>92.40</td>\n",
       "      <td>27.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.77</td>\n",
       "      <td>14.01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26150</td>\n",
       "      <td>177959247</td>\n",
       "      <td>89.25</td>\n",
       "      <td>35.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.99</td>\n",
       "      <td>15.81</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14763</td>\n",
       "      <td>177959280</td>\n",
       "      <td>100.80</td>\n",
       "      <td>35.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.79</td>\n",
       "      <td>4.41</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7868</td>\n",
       "      <td>177959289</td>\n",
       "      <td>85.05</td>\n",
       "      <td>22.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.74</td>\n",
       "      <td>23.06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7868</td>\n",
       "      <td>177959429</td>\n",
       "      <td>78.75</td>\n",
       "      <td>20.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.59</td>\n",
       "      <td>29.56</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43073</th>\n",
       "      <td>25575</td>\n",
       "      <td>90588469</td>\n",
       "      <td>81.90</td>\n",
       "      <td>41.48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.36</td>\n",
       "      <td>24.28</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43074</th>\n",
       "      <td>25575</td>\n",
       "      <td>90588583</td>\n",
       "      <td>97.65</td>\n",
       "      <td>28.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.46</td>\n",
       "      <td>9.14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43075</th>\n",
       "      <td>70410</td>\n",
       "      <td>90589205</td>\n",
       "      <td>94.50</td>\n",
       "      <td>25.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.19</td>\n",
       "      <td>13.30</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43076</th>\n",
       "      <td>70410</td>\n",
       "      <td>90589254</td>\n",
       "      <td>80.85</td>\n",
       "      <td>31.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.03</td>\n",
       "      <td>24.30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43077</th>\n",
       "      <td>134513</td>\n",
       "      <td>90589034</td>\n",
       "      <td>91.35</td>\n",
       "      <td>27.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.51</td>\n",
       "      <td>15.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43078 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerId         id  x_coordinates  y_coordinates  blocked  goal  time  \\\n",
       "0         25413  177959212          92.40          27.88      0.0   1.0   2.0   \n",
       "1         26150  177959247          89.25          35.36      0.0   0.0   3.0   \n",
       "2         14763  177959280         100.80          35.36      0.0   1.0   4.0   \n",
       "3          7868  177959289          85.05          22.44      0.0   0.0   7.0   \n",
       "4          7868  177959429          78.75          20.40      0.0   0.0  14.0   \n",
       "...         ...        ...            ...            ...      ...   ...   ...   \n",
       "43073     25575   90588469          81.90          41.48      1.0   0.0  85.0   \n",
       "43074     25575   90588583          97.65          28.56      0.0   0.0  91.0   \n",
       "43075     70410   90589205          94.50          25.84      0.0   0.0  13.0   \n",
       "43076     70410   90589254          80.85          31.28      0.0   1.0   3.0   \n",
       "43077    134513   90589034          91.35          27.20      1.0   0.0  16.0   \n",
       "\n",
       "       matchPeriod  counter  competition  foot  bodypart  preferrence  angles  \\\n",
       "0                0      0.0            0   1.0       1.0          1.0   26.77   \n",
       "1                0      0.0            0   0.0       2.0          1.0   25.99   \n",
       "2                0      0.0            0   1.0       3.0          1.0   78.79   \n",
       "3                0      0.0            0   1.0       2.0          0.0   15.74   \n",
       "4                0      0.0            0   1.0       1.0          1.0   12.59   \n",
       "...            ...      ...          ...   ...       ...          ...     ...   \n",
       "43073            1      0.0            6   1.0       2.0          0.0   16.36   \n",
       "43074            1      0.0            6   1.0       1.0          1.0   37.46   \n",
       "43075            2      0.0            6   1.0       3.0          1.0   25.19   \n",
       "43076            3      0.0            6   1.0       1.0          1.0   17.03   \n",
       "43077            3      0.0            6   1.0       1.0          1.0   24.51   \n",
       "\n",
       "       distance  headers  \n",
       "0         14.01      0.0  \n",
       "1         15.81      0.0  \n",
       "2          4.41      1.0  \n",
       "3         23.06      0.0  \n",
       "4         29.56      0.0  \n",
       "...         ...      ...  \n",
       "43073     24.28      0.0  \n",
       "43074      9.14      0.0  \n",
       "43075     13.30      1.0  \n",
       "43076     24.30      0.0  \n",
       "43077     15.25      0.0  \n",
       "\n",
       "[43078 rows x 16 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allshots=pd.read_csv('shots.csv')\n",
    "allshots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features \n",
    "**x_coordinates** - the closer the player is to the goal, higher is the probability of the goal being scored.  <br><br>\n",
    "**y_coordinates** - the closer the player is to the centre of the goal, higher is the probaboility of the goal being scored.<br>  <br>\n",
    "**angles** - greater angles imply that the player is close to the goal with a better view of of it and hence a larger area to aim at. <br><br>\n",
    "**counter** - goals are easier to score in a counterattack. <br><br>\n",
    "**preference** - the quality of shot is directly dependent on the preferred foot being used or not.<br><br>\n",
    "**distance** - less is better. <br><br>\n",
    "**headers** - Headers might positively or negatively affect the goal scoring chance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_input = allshots[['x_coordinates','y_coordinates','angles','counter','preferrence','distance','headers']]\n",
    "model_output = np.ravel(allshots[['goal']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X1, X2, y1, y2 = train_test_split(model_input, model_output, random_state=0,\n",
    "train_size=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "pipe_1=make_pipeline(LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_values = [{'logisticregression__C': [0.001,0.01,0.1,1,3,5,7,10,100]}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_1 = GridSearchCV(pipe_1, param_grid = grid_values,scoring = 'f1',verbose=1, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    7.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('logisticregression',\n",
       "                                        LogisticRegression())]),\n",
       "             param_grid=[{'logisticregression__C': [0.001, 0.01, 0.1, 1, 3, 5,\n",
       "                                                    7, 10, 100]}],\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_1.fit(X1,y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.181408159787053"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'logisticregression__C': 1}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_1.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytest_labels1=grid_1.predict(X2)\n",
    "ytest_prob1=grid_1.predict_proba(X2)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold1,f1score1,recall1,precision1=plot_pr_curve(y2,ytest_prob1)\n",
    "threshold1=np.around(threshold1,4)\n",
    "f1score1=np.around(f1score1,4)\n",
    "recall1=np.around(recall1,4)\n",
    "precision1=np.around(precision1,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(y2,ytest_prob1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss1=log_loss(y2, ytest_prob1, eps=1e-15)\n",
    "log_loss1=np.around(log_loss1,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Reliability Curve')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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01iSau0VkNfWTXaYBb2JNeqmUUh1Cj9SEgOciaywjI4O4uDhPgJkwYQKzZs0iMTExaPUMlkCuYNKAyU2ktzpkGmMeEZGNwKNYjfDu6fqfJ7jT9SulVNgtfGASC363EQCnQ1j/nRn06xrY7azk5GTmzZvHmjVryMvLY+BAX32lwiuQyS7XUT9+xd9t7qeFwZDGmL8BfwukXKWUikbPbDjm+ff8sZktBpezZ89y9OhRpk6d6pU+atQohg8fTlxcZD8zMrJrp5RSHcSpyxW8vfuM5/VDtw5uNm9tbS3vvvsuGzduxOVykZmZyaBB9bNmiUjEBxcI31xkSikVU17YlE+dy5q8ZOqQ7lzXt+mx36dOneKZZ55hw4YNuFxWK8HSpUsxpi0Tn4RH5IdApZSKcsVXa3j5/QLP6y83cfVSXV3N2rVr2bp1q1d6v379yMvLC/uYltbQAKOUUiH29/cLKK+2nv4+rFcqM4b39Fp//PhxFi9ezOXLlz1p8fHx5OTkMHHiRByO6LzZpAFGKaVCqLrWxQubjntef/nWwZ6rkcrKSlauXMmHH3pPWDJ48GByc3Pp0qVLu9Y12DTAKKVUCC36qJCzJdaYlV6dE7kzu49n3dtvv82ePXs8r5OSkpgzZw433HBDVN4Sa0wDjFJKhYgxhmcbdE2+/+YsEuOcntczZ87kwIED1NbWMnLkSObNm0fnzk0+AisqaYBRSqkQ2XD4AgfPWtPnpyQ4+PSE/l7ru3Xrxty5c0lKSmL06NFNFRHVorPlSCmlooD76qWTVPOprgXs3vHeNXluvPHGDhlcQAOMUkqFxN7CYjYeOc8I5zk+lrgHSorYsGED58+fD3fV2o3eIlNKqRB4fs1u5iYcpLezzJNWV1fHsWPH6Nmzp48tOw4NMEopFUQul4sV72wg5egG0pz1o++7d+9OXl5eRDxpsr1ogFFKqSApKipi0aJFnDlzhji7l7FBmHbLzUyfPj0q5g8Lptj6tEopFQJ1dXWsX7+eTZs2eeYPA7joSmHWHfPImTImjLULHw0wSinVRsYYDhw44AkutUbYWduHyq5D+M3kjtlDzB/ai0wppdooLi6OvLw8AC5JGm9VjWF3bSYP3joEhyP6R+S3lgYYpZQK0OnTp6+ZPr9fv34MuXk+b1UMo8Qk0SM1kTuz+4aphpFBA4xSSvnp6tWrvPXWWzz33HN89NFHXuuMMfx1TwXuB//eP3UgSfHOJkqJHdoGo5RSfti/fz9Lly6lrMwa17JixQqGDh1KamoqABuPXOBAkTUtTHK8k8/dNDBsdY0UGmCUUsqHsrIyli1bxr59+7zSBw8e7DXj8TMNJrX81MT+dElJaLc6RioNMEop1QRjDLt27WL58uVUVlZ60lNTU5k3bx6jRo3ypO0/U8K7hy8A4BD44s2D2r2+kUgDjFKqRROeWMWFsmq/8/dITWD792aHsEahdeXKFZYsWcLRo0e90rOzs7n99ttJTk72Sn/23fqrl7nXZTKge0q71DPSaYBRSrUokODSmvyR5NixY7zyyitUV9d/hi5dupCbm8vgwYOvyX+m+CqLdhZ6Xj84Ta9e3DTAKKVUA5mZmSQkJHgCzOTJk7nttttISGi6TWXhpnxqXVaX5UlZ3Rg3oGu71TXSaYBRSqkGkpOTmTdvHu+88w55eXn079+/2byllTX87b0Cz+uHbr32CieWaYBRSsWsoqIijhw5wi233OKVPmrUKIYPH47T6XscyyvbTlJaVQvA4J6duG1kr5DVNRppgFFKxZza2lrP5JTGGPr06XNN+0pLwaWmzsXzG497Xn952uCYnhamKTqSXykVUwoKCvjjH//Ixo0bPdO9LF++/JqpX1qydPcZCout7ss9UhO4a1xsTwvTFL2CUUrFhKqqKtasWcO2bdu80gcOHEhubq7XoMmWGGO8BlbeNyUr5qeFaYoGGKVUSLy6/SSfuLFfRNw2OnLkCEuWLKG4uNiTlpCQwOzZsxk/fnxAwQVg89GL7C0sASAp3sHndVqYJmmAUUqFxL+9vou/bM7nBwtGM3lw97DU4erVq6xYseKaiSmHDh3KggULSE9Pb1W5Da9ePjmhP1076bQwTdEAo5QKmb2FJXzqma3Mva43j88d1e4j3JcuXcqePXs8r5OTk7njjjsYO3Zsi1ct/s5e8OKWE7y45UTUz14QChpglFIhkRTvoLLGesLjsj1FrNl/ji/eMohHZw6hc1J8u9Thtttu4+DBg9TU1HDddddxxx130KlTJ7+2jaXZC0JFe5EppVrUPcBbQD1SE3jnWzP4WHYfT1p1nYs/rj/KzP9ex8vvF1DnCqzXVkuMMZ5HFrt17dqVuXPncu+993L33Xf7HVxUcOgVjFKqRXeN68tz9piPwT06seKbtxLvbPn36W/uHcd9U7P4yeJ97Dx5BbB+6X/3jd38ZcsJfrBgNFOGtL195sqVKyxevJgBAwYwffp0r3Xjxo1rc/mqdfQKRinl08lLFby45YTn9b/dMcKv4OJ244CuvPHwVH57bzaZ6Ume9P1nSvj0s1v5l5e2c+Jieavq5nK5eO+993jqqac4duwYGzZs4Ny5c60qSwWfXsEopXz61cqDVNdZt55uHNCFOWN6B1yGwyHcmd2X20f35pkNx/jj+qNcrakDYMXes6w9cJ4Hbs7i0duGkuZn+8z58+dZvHgxJ0+e9KQZYzhx4gS9erVtypbSypo2ba8sGmCUUs3ac7qYNxtMRf/4vFEBjxlpKDnByWOzhvHJif34r+UHeePD04DVPvOnDcd4/YNTfOv2EXxqYn8m/2x1kw3ngouxcUVkx53BKfXtOCUk880vfoZ+/foFVKeLZVXsLSxhT2ExewtL2FdYwvELrbuiUt40wCilmvWL5Qc8/549OoOJWd2CUm5mejK//lS23T6zlx0FVvvMxfJq/uOfu3lxS36TwaW7lHNzQj7dHVc9aXVG2FXbm121mfzKR3AxxlBYXMne08XsKSxhnx1QzhRXNruNahsNMEqpJm04dN7rMcD/fseIoL9Hdv8u/OPhqSzedYafL93vmdvrQFGpVz4nLrLjCrkuroiGEwOcd6WwqTqLy8Z7fI3LZTh+sZy9hSXsLSxm72lrebnCv1tfcQ7xPONFtZ4GGKXUNVwuw8+X1V+9fGpif4b26hyS9xIR8m7ow+2jM3h2wzGeWlffPuNmgP7OYk9wqTUOdtT2YV9tBob6iPOjRXvZc7qY/WdKKK/2LqM5iXEORmWmMaZPGtf1TWdMnzSGZ3Rm5PeXB+sjxiwNMEqpa7z10Wn2nbHm2kqOd/KNWcND/p5J8U6+ljOMT07szy+XH+QfO0551rlwsLF6IPMTD3DW1ZlNNQMpNUnXlLFwc77P9+icFMeYPmmM6ZPOdX2t5eAenYgLoFec8p8GGKWUl8qaOv57xSHP6wenDSIj7dqTeahkpCXxjSnd+MeOk9Dg6uSCSWVp1UjOm05e6c3pkZpoBxE7oPRJp3+35DZ1UlCB0QCjlPLy0pYTnL5iNaJ365TQro8BrqioYMWKFezatYthziwO1/XwWn/epPrc/luzhzOmbxrX9UmnVxuDYo/UhICmf+mRqhNeNqYBRinlUVxRw+/XHvG8fixnWLvMG2aMYd++fSxdupSKigoAJsaf5FRdGlfx/8T9tZxhQauTTlzZdhpglFIeT607QvFVq6fVwO4pfHrSgJC/Z2lpKW+//TYHDx70Sj9Vl47Lj1thKnJpgFFKAXD6ylVeaNBI/p05I0iIC13jtzGGDz/8kJUrV1JVVeVJ79y5MwsWLOD25w+H7L1V+9AAo5QC7Clhaq0pYW7ol878sZkhe6/Lly+zePFijh8/7pU+fvx4Zs2aRVJSEqABJtppgFFKsa+whH/a07ZA26eE8eXo0aO8/PLL1NbWetK6detGbm4uWVlZIXlPFR4aYJRS/GL5AYw9cD1nZC9uCuEjjvv27UtSUhJlZWWICFOmTGHGjBnEx3t3JtBeXNFPA4xSMW7TkQusP3QesKeEmTsypO+XlJTE/PnzWbt2LXfeeSd9+vRpMp/24op+GmCUimEul+HJZfs9rz8xvh/DM4I3Jczp06c5evQot956q1f6yJEjGT58OA6HjqDvyDTAKBXDFu8qZM9pa0qYpHgH35wdnClhampqWLt2LVu3bsUYQ9++fRkyZIhXHg0uHZ9+w0rFqKraOv5rRf3Yky/ePIjM9OQ2l5ufn8/TTz/Nli1bMHbDzooVKzz/VrFDr2CUilF/3VrAqcvWlDBdU+L5yowhLWzhW2VlJatWrWLHjh1e6YMGDSI3N1fnAItBGmCUikHFV2v43Tv140y+dtswvx9V3JRDhw6xZMkSSkvrn+OSmJjInDlzyM7O1uASozTAKBWD/rj+KFfsh2/175bMZ29q3ZQw5eXlLF++nD179niljxgxgvnz59O5c2ieIaOigwYYpWLMmeKrPL+xfgT9t28fQWKcs1VlLVu2jL1793ped+rUiblz5zJ69Gi9alEaYJSKNb9eeYgqe0qYsX3Tyb2+6XEo/pg1axaHDh2ipqaG66+/njlz5pCSktLyhiomaIBRKoYcLCr1elLk43NH4nD4d6VhjMEY49W9uEuXLsyfP5+UlBSGDQveVPmqY9AAo1QM+cXyA7js3sLTh/dk6tAevjewXbp0icWLFzNgwABmzpzpte6GG24IdjVVB6EBRqkYseXoRd45cA4AEfiuH1PCuFwutm7dytq1a6mtraWgoIDRo0eTkZER6uqqDkADjFIxoPGUMB8f149RmWk+tzl79iyLFi2isLDQk2aM4eTJkxpglF80wCgVA97efYZdp4oBSIhz8K3bm58Spra2lnfffZeNGzficrk86b179yYvL4/MzNA9J0Z1LBpglOrgqmtdXlPCPHBzFn26ND0lzKlTp1i0aBHnz5/3pDmdTqZPn87UqVNxOlvXnVnFJg0wSnVwf3vvBAWXKgBIT47nkelDr8lTW1vLmjVr2Lp1q1d6//79ycvLo0cP/zoDKNWQBhilOrDSyhr+950jntdfu20o6SnXTgkjIl6PL46PjycnJ4dJkybpgEnVajqbslId2J/WH+NSufVUyL5dkvn8lIFN5nM6neTl5SEiDB48mEceeYTJkydrcFFtolcwSnVQZ0sqeW7jMc/rb88Z7pkS5sSJEwwYMMArgPTp04cHH3yQzMxMDSwqKPQKRqkO6n9WHaKyxuoFNjozjTtv6EtZWRmvv/46Cxcu5MMPP7xmmz59+mhwUUGjVzBKdUCHz5by6vaTntffnTuCPXt2s3z5cq5etZ4Bs3LlSoYOHUpamu/xMEq1lgYYpTqgXyw/6JkSZuagTpzctoq1R4545Rk1ahTx8a1/BoxSLdEAo1QH8/7xS6zefxYwjIw7z9CLZzhSVONZn56eTm5uLkOGtO0Jlkq1RAOMUh2IMYafLd1PmlRyc3w+vZ1l1NbHFiZNmkROTg4JCQnhq6SKGRpglOpAlu0p4tzpE9yZeIQ4MZ707t27k5eXx4ABrXtypVKtoQFGqQ6ips6aEua8qxNVxBFHDSLCzTffzPTp04mL0//uqn3pEadUB/Hy+wUcv1AOxPERg7mz1xU+ftfH6N27d7irpmKUBhilotjJkyc5cuQIE6dO4zerD3vSPz5zIl+eNsjr6ZNKtTcNMEpFoerqatasWcP7778PwLbzDi7aU8L0SU/iC1OzNLiosNMAo1SUmPDEKi6UVdPHUczU+BN0dlR71hXu3QqMAYTC4kpGfn85PVIT2P692WGrr1IaYJSKEiVlFdwcf5LhcRe90k/VpbG5ZiDgPcXLhbJqlAonDTBKRYH9+/dzV9JeUqR+UEulcfJ+TX+O1nWncXBRKhIEHGBE5DPAw8D1gBM4ALwAPG2McfnatlE5C4Ev+Mhy0BgzMtD6KdWRlJWVsWzZMvbt20dKgxhyvLYrW2sGUIlO9aIiV0ABRkT+ADwCVAJrgBogB/g9kCMi9xhj6gKswybgSBPpZwIsR6kOZ8WKFezbt8/zusLEs6V6AAWurmGslVL+8TvAiMjdWMGlCLjVGHPYTs8A1gJ3AV8FfhtgHZ4zxiwMcBulYsKsWbM4dOgQ1dXVHKrtwbaaflTrnYPqgn4AABsuSURBVG0VJQLpx/i4vfx3d3ABMMacxbplBvBdEdG+kUq1gjGGujrvGwDp6eksWLCAz3/+82yqydLgoqKKX8FARPoB44Fq4LXG640x64HTQG/gpmBWUKlYcOHCBV544QXWr19/zbqxY8cyePDgMNRKqbbx9+fQOHu51xhztZk824C+dt7NAdRhpohcD6QCZ4GNwKpAOgwoFa3q6urYvHkz69evp66ujtOnTzN69Gid3kV1CP4GmEH28oSPPAWN8vrrvibS9onIvcaY3QGWpVTUOHPmDIsWLaKoqMgrvbCw0CvAuFyG/155sL2rp1Sb+RtgUu1luY88Zfays59l7gQ+wOqNdgJIA24E/h9wA7BaRG40xpxuamMRuR+438/3yvYzn1IhV1tby7p169i8eTPG1E+pn5mZSV5enldwqaqt4zuv7WLRR4XhqKpSbeJvgHH3wDc+cwXAGPObRknlwNsisgpYj9WW8zhWz7SmZAHTg1UfpdpDQUEBixYt4uLF+tH4cXFxzJgxgylTpnjNH1ZcUcNDL23nveOXwlFVpdrM3wBTai9TfeRxryv1kadFxphqEXkSeAuY5yNrPlYg8kc2kN6WeinVFjU1NaxatYpt27Z5pQ8cOJDc3Fy6d+/ulX7yUgUPLNzGkXNlnrSkeAeVNf43TfZI1adWqvDyN8Dk28uBPvL0b5S3LQ7Yy77NZbDHziz0pzARWYde7agwcjgcFBQUeF4nJCQwe/Zsxo8fj4j3NC+7Tl3hiwu3c6GsypP23bkj+ZdbB1+TV6lI5u+YlQ/t5RgRSW4mz8RGedvC/XOuzGcupaKE0+nkzjvvREQYNmwYjzzyCBMmTLgmYKzZf5ZP/WmrJ7gkOB387tPj+Mr0IRpcVNTx6wrGGHNSRHZgNcLfA7zYcL2ITAf6YY3y3xKEen3SXm7zmUupCHX8+HGysrK8gkJmZiYPPfQQGRkZTQaLl7ae4Idv7cFlt3SmJ8fz7H0TmDSoW3tVW6mgCmTU/ZP28hciMtSdKCK9gKfslz9vOH5FRJ4UkQN2mwoN0rNFZIGIOBulx4nIvwJft5P+J4D6KRV2paWlvPrqq7z44ovs2LHjmvW9e/e+Jri4XIYnl+3n+2/WB5f+3ZL5x8NTNbioqOb3vBPGmNdF5GmsaWF2i8hq6ie7TAPexJr0sqFMYIS9bCgL+CdwSUQOAaewujePBfoALqwpaVYE+oGUCgdjDDt37mTlypVUVlYCsGrVKoYNG0ZaWlqz21XW1PHt1z5iya76uV1v6JfOc1+YSM/OiSGvt1KhFNDERsaYR0RkI/AoVqO5e7r+5wlsuv6PsCbFnITVcWAcVhfoU1hT///BGPNBIHVTKlwuX77MkiVLOHbsmFf6mDFjSEhovifXlYpqvvzidrblX/akzRrVi//99DhSEnTOMRX9Aj6KjTF/A/7mZ977aWIwpDHmOPCNQN9bqUjicrnYtm0ba9asoaam/kFgXbt2JTc3l0GDmp/UouBiBfcvfJ9j5+vHLt83ZSA/zB2D06GN+apj0J9JSrXC+fPnWbRoEadOnfKkiQiTJ09m5syZPq9cPjp5hS/9ZZvXI43/c94oHpw2SHuKqQ5FA4xSATp8+DCvvPKK19T6PXv2JC8vj379+vncdtW+s3zt7zs8AyYT4hz8zyezmX9942ZKpaKfBhilAjRgwAA6depESUkJDoeDadOmMW3aNJxOp8/tXtySz48W7fX0FOuSEs9z901gQpb2FFMdkwYYpQKUmJjI/Pnz2bBhA7m5uWRkZPjM7+6G/Oy7xz1pA7qlsPCBiQzu6Wv2JaWimwYYpXw4ceIER48e5bbbbvNKHz58OMOGDWuxzaSypo5/fXUnS3fXT8mf3b8Lz31hAj1StRuy6tg0wCjVhKqqKlavXs327dsB6N+/P8OGDfPK01JwuVRudUP+4ER9N+TbR2fw23vHkZzg+3aaUh2BBhilGjl8+DBLliyhpKTEk7Z69WqGDh3qdy+vExfLuf+FbRy/UN8N+f6pWXx/wWjthqxihgYYpWwVFRWsWLGCXbt2eaUPHz6c+fPn+x1cdhRc5sG/bOdSudUNWQS+N380X7ol0Ie9KhXdNMComGeMYd++fSxdupSKigpPekpKCnPnzmXMmDF+B5fle4p47OUPqaq1uiEnxjn4zaeymTtWuyGr2KMBRsW00tJS3n77bQ4e9H7m/dixY7njjjtISUlhwhOrvAZF+qtrSjzPfWEi4wd2DVZ1lYoqGmBUTFu5cqVXcElLS2P+/PkMHz7ck9aa4JLVPYUXHpjEoB6dglJPpaKRBhgV02bNmsWhQ4eorq5m/PjxzJ49m8TEtncf/sfDU+mu3ZBVjNMAo2KGy+XCGOM14j49PZ3c3FxSU1PJysoK2ntpcFFKA4yKEefOnWPRokUMGjSInJwcr3XXXXddmGqlVMemAUZFjUAb23ukJvDe47exceNGNmzYgMvlorCwkDFjxtC7d2+/yqip8/cRR0qpxjTAqKgRcGN7+WWeeeYZzp0750kSEc6cOXNNgCmtrOHY+XKOnCvjyPkyjtrLgosVjUtVSvlJA4zqcJzUcWNcIaPjztIgttC3b19uybmDS3VJvLQlv0EwKaeopDJs9VWqo9IAozqU3o4Sbo4/QZqjypNmxMnplCG8fqorT/zpozDWTqnYogFGdQhx1DEp/iQj4i54pZ+u68zmmizKKhKBuqY3BuKdQlb3TgzpmcrQXqkM6dWJoT07M7hnJ8b8cEWIa69Ux6QBRnUIdQg9HPXtJVXGybaa/hyu6w7UT/OSmhjHkF6pDO3pDiKpDOmVyoBuKcQ7HWGouVIdlwYY1SEYHGysziI3cR8nXV3YUj2AqyRw35SBnquSob1S6dU5UZ97r1Q70QCjopChr6OE0640Gl6dXDIpvFU1hismyZP+kzvbPsalR2pCwN2jlVIaYFQUKLxylR8u2gtACtVMSTjBAGcxm6sHcLCul1feKyY56O+//Xuzg16mUrFAA4yKWHUuw1825/OrlQcpr65lhPMCE+JPkiDW4McJ8ac45epCudErBqUikQYYFZH2nC7m8Td2s/t0MZ2lkjsSTpDpLPXKc7SuO9VGHz2sVKTSAKMiSnlVLb9edYgXNh3HGMN1cWcZF3eaODGePMWuRDbVZHHW1TmMNVVKtUQDjIoYq/ad5Ydv7aGwuJKuUsHNifn0bND12AXsqenNzto+1NFyl2JtbFcqvDTAqLArKq7kR4v2snxvEQD9HFfISTiKo8FVS+/evcnLyyMzUx89rFS00ACjwqbOZXhpSz7/vfIQZVW1nvTqpG4kJp6hprIcp9PJ9OnTmTp1qtdzXJRSkU8DjAqLvYXF/Mcbu/noVLFX+icn9OPxuaO4UDiUd999l7y8PHr06BGmWiql2kIDjGpXFdW1/M+qQzy/KZ86lyHTUUIfRwmXu47gZ3eN5abB3QHoOmwYQ4cO1VH3SkUxDTCq3bxz4Czff3Mvp69cJYFaJsef8kxO+Yl50xljBxc3DS5KRTcNMCrkzpZU8uPFe1m622rEH+C4zJSEAlKkxpNnw7q1jB45QoOKUh2IBhgVkEAfW5ya6EQQSqtqSaKGyfEFDI677JVn1KhRzJs3T4OLUh2MBhgVkEAfW1xWVQcYBjsvMTm+gCSpfyZLp06dmDdvHqNHjw5yLZVSkUADjAqpTlLF1PgT9HOWeKVnZ2dz++23k5wc/MkplVKRQQOMCqkJcae9gkt6ejq5ubkMGTIkjLVSSrUHDTAqpLbV9GNUp3KqqqqYNGkSOTk5JCToFC5KxQINMCpoBINgcDWYJ6yCBPLy8khNTWXAgAFhrJ1Sqr1pgFFB0VUquCUhn8K6ND6o7ee1ThvxlYpNGmBUmzhxcUPcGcbGFeEQQzepIL+uKxdNp3BXTSkVZhpglF+qa138eeNxr7RejjJujs+ni6PSk+ZC6OK4ysU6DTBKxToNMKpFGw6d50eL9nLsQjkAcdQxPv40o5znaDg2sqgulU01WZSYpDDVVCkVSTTAqGadvFTBE2/vY8Xes560Po5ipsafoLOjfsBljXGwvaYfB+p6AjoaXyll0QCjrlFZU8czG47xh7VHqKp1AdZVy7SkU2TJea+8p+rS2FwzkHKTGI6qKqUimAYY5WX1vrP8ZMk+Ci5VeKXfOa4/Qy6f4bx9MVNpnLxXM4Bjdd3wddWijy1WKnZpgFEA5F8o5ydL9vHOgXNe6aMz0/jJnWOYkNWNoqJMnn32WUaNGsXcuXPp1Ekb8pVSzdMAE+Mqqmt5au1RntlwjOo6l51qGJFcxmdvv4nPTh6I02FdofTu3ZuHH35YnzCplPKLBpgYZYxh2Z4inliyj8Li+m7GqVLFx3ucxVl2jlFxQ3A6sry20+CilPKXBpgYdORcKT9ctJdNRy42SDXM6lFGVtUx6sqsB4GtXr2aYcOG0aVLl/BUVCkV1TTAxJCyqlr+d81hnt94nFqX8aQPSKllQZdCrl4+R12D/OPGjSMlJaX9K6qU6hA0wMQAYwxv7SzkZ0v3c660ypPuFBefH1RF3LkDXL1cH1p69OhBXl4e/fv3D0d1lVIdhAaYKBToY4vjHOJ1xQIwra+TCXKCK2fOe65aHA4Ht9xyC9OmTSMuTg8NpVTb6FkkCgX62OKGwaVn50S+emMKJ7at5oqpT8/MzCQvL4/evXsHrZ5KqdimASZGxDmEB27O4us5w0gQF08ffJ/i4mLi4uKYMWMGU6ZMweFwtFyQUkr5SQNMjFj22DSGZXT2vF6wYAEbN24kNzeX7t27h7FmSqmOSgNMB9fHUUxfR4lXcAEYOnQoQ4YMQUQnp1RKhYYGmA4qkVomxZ9kaJw11uXgwYOMGDHCK48GF6VUKOlN9w7HMNBxibuS9niCC8DatWsxxvjYTimlgkuvYDqQZKqZklDAQOcVr/Rjtd349uc/r1csSql2pQGmQzAMc15kYvxJEqV+wGS5iWdL9UBOurrozMdKqXanASbKrNl/1ut1qlQxNT6fvs5Sr/SDtT3YVtOPGv2KlVJhomefKPJ/753g+2/u8UobH3faK7iUuBLZXDOQM6609q6eUkp50QATBYwx/GrlIX6/9sg1696v6UdfZzHx1LGvNoMdtX2owxmGWiqllDcNMBGuutbFd9/YxRs7TiO4cADicHqmf7lKApuqsyg38VwwqU2WoY8tVkqFgwaYCFZaWcPDf93BxiMX6C7l3JKQjyO9N088+hk6JepXp5SKbHqWilBFxZXc/8L7HC4qZkJcIWPiinAISFk+xRfP0alPn3BXUSmlfNIAE4EOnS3l/uffp670PB9LzCfN0eAZLk4nFy5coI8GGKVUhNMAE2G2HrvIwy9uZWTdCUYmnvdal5WVRW5uLt26dQtT7ZRSyn8aYCLI4o8K+dVr65ntPE6nuBpPemJiIrNnz+bGG2/U0fhKqaihASYCGGN4Zu0hNq1dycz4S17rhg8fzvz580lL03EtSqnoogEmzOpchp8u2cdfNh9nfmKlJz0pOYX58+YyZswYvWpRSkUlDTBhVFlTxzde3snyvUWAsKk6i7yk/YwaNZq8BfNISUkJdxWVUqrVNMCEgTGG7Tv38PMtJXxQUD/z8ZTrBvOVWTPIzOgZxtoppVRwaIBpZ5cvX+a1N97kzKkCKqr7AxkAfOmWQfznvFE4HHo7TCnVMWiAaScul4v33nuPNWveoa6uFoDx8ac5Zbrw2NxsHpw2OMw1VEqp4NIAEwQTnljFhbLqZtd3kavcnJBPL0e5J81l4JArg5/dM5Hccf3bo5pKKdWuNMAEQXPBxYGLsXFF3BB3BqfUP674kiuZnY6h/NcDM5g0SAdNKqU6Jg0wIdJDyrk5IZ9ujquetDojfFSbye7a3qz81xkM7dU5jDVUSqnQ0gATAv0dV7gt4QgN2+vPuTqxsTqLYpMMoMFFKdXhaYAJgTOuzpSbBDpLNTXGwY6avuyv64VBe4gppWKHBpgQqMXJ5pqBXBdXxOaaLMpMYrirpJRS7c4R6AYi8hkReVdEikWkTES2i8ijIhJwWaEor70dPHiQyfEF16QXutJZWT1cg4tSKmYFdAUjIn8AHgEqgTVADZAD/B7IEZF7jDF14SqvPZWXl7N8+XL27NnD6Dg4U9eZAlfXRrn0lphSKnb5HWBE5G6sYFAE3GqMOWynZwBrgbuArwK/DUd57cUYw549e1i2bBlXr9b3EMuOL6SgqgsaVJRSyhLIbajH7eW/u4MBgDHmLPCw/fK7AdzaCnZ5IVdSUsLLL7/MG2+84RVcjtR2Z0XVCDS4KKVUPb+uYESkHzAeqAZea7zeGLNeRE4DfYGbgM3tWV6oGWP44IMPWLVqFdXV9YMq09PTWbBgAbOeOxjG2imlVGTy9xbZOHu51xhztZk827ACwjhaDgjBLi9kLl26xOLFi8nPz/dKnzhxIjk5OSQmJgIaYJRSqjF/A8wge3nCRx53V6pBPvIErTwRuR+434/3Asj2M9811q5d6xVcunfvTm5uLgMHDvSk9UhN8DkXWWM9UhNaWx2llIoa/gaYVHtZ7iNPmb30Z4h6MMrLAqb78V5tcvvtt3P48GGqq6uZOnUqM2bMIC7Oe7dt/97sUFdDKaWijr8Bxt16bXzm8l8wyssH1vuZNxtIb82bdO7cmY997GOkp6eTmZnZmiKUUiom+RtgSu1lqo887nWlPvIErTxjzEJgoR/vhYisow1XOyNHjmztpkopFbP87QKcby8H+sjjfqhJvo88oSpPKaVUhPE3wHxoL8eISHIzeSY2ytue5SmllIowfgUYY8xJYAeQANzTeL2ITAf6YY3K39Le5SmllIo8gYySf9Je/kJEhroTRaQX8JT98ufGGFeDdU+KyAEReZJrBVyeUkqp6OH3XGTGmNdF5GmsaVx2i8hq6ienTAPexJqksqFMYIS9DEZ5SimlokRAsykbYx4RkY3Ao1i9spzAAeB54OlArzaCXZ5SSqnIIcYEa2hL5BKRU0Df9PR0srNbPahfKaViys6dOykuLgY4bYzpF+j2sRJgrtDKgZZKKaUoNsZ0CXSjWHlk8nGsOc3KgCMBbuueBaAY2BnkenVEur8Co/srMLq/AtPW/TUUa9D78da8eUwEGGPMuJZzNa3BLAA7jTEzglWnjkr3V2B0fwVG91dgwr2/IuZhXkoppToWDTBKKaVCQgOMUkqpkNAAo5RSKiQ0wCillAoJDTBKKaVCQgOMUkqpkNAAo5RSKiQ0wCillAqJmBjJ30YLgXXoo5v9tRDdX4FYiO6vQCxE91cgFhLG/RUTk10qpZRqf3qLTCmlVEhogFFKKRUSHTLAiMhnRORdESkWkTIR2S4ij4pIqz5va8sLdj1CJRj1FJF4EckRkV+JyFYROSMi1SJyWkReF5EZPrZdKCLGx9+BoHzQIAnW99rWzx1jx9eMFvZVw78BjbaNiuNLREaIyGMi8lcROSAiLrt+n2hjuWE7f3W4Rn4R+QPwCFAJrAFqgBzg90COiNxjjKkLdXnBrkeoBLGe04FV9r+LgA+AcmA0cDdwt4j81BjzAx9lbKLp5/Wc8eeztIcQfa8Bf+4YPL6KgL/4WD8JGAUcBU42kyfSj6+HgceCWWDYz1/GmA7zh3UiM1gHzLAG6RnAPnvdY6EuL9j1iIb9BdwGvA5Ma2Ldp4Bau7yZTaxfaK+7P9z7pJ2Pr1Z97lg8vvx4r712ef8RxcfXg8AvgU8CQ7B6fxngE+25/4N6Xgj3Tg3yF7Td/vD3NbFueoOd5ghlecGuR7Tsrxbe6zm7vD83sS5aTgDBPr5aG2D0+PIua4pdVi3QN1qPrybq3dYAE/bzV9h3YhC/jH72B68CkpvJc8rOMzVU5QW7HtGyv/x4v0ftslY0sS7iTwCh2F+t+dx6fDVZzrN2OUuCtZ8j4a8tASZSzl8R1RjYRu7HIu81xlxtJs+2RnlDUV6w6xEq7V3PYfbS1/3umSLyaxF5RkR+KiJzIqjBOpT7K5DPrcdXAyKSgnULFuDPLWSP5OMr2CLi/NWRGvkH2csTPvIUNMobivKCXY9Qabd6ikhv4H775T98ZL2vibR9InKvMWZ3W+oQBKHcX4F8bj2+vN0DdAbOAUtayBvJx1ewRcT5qyNF71R7We4jT5m97BzC8oJdj1Bpl3qKSBzwVyAdWGOMWdxEtp3A14Exdr36AAuAj7B6oa0Wkb6trUOQhGJ/teZz6/Hl7Yv28kVjTE0zeaLh+Aq2iDh/daQrGLGXJszlBbseodJe9fwjVvfGk8DnmspgjPlNo6Ry4G0RWQWsB24CHge+GsJ6tiTo+6uVn1uPL/cbiAwFbrVfPt9cvig5voItIs5fHekKptRepvrI415X6iNPW8sLdj1CJeT1FJHfAl/CGsOQY4wpCmR7Y0w18KT9cl5r6hBE7fa9tvC59fiq57562WKM2R/oxhF2fAVbRJy/OlKAybeXA33k6d8obyjKC3Y9QsX93iGpp4j8Cuu2xHms4HI40DJs7lHW4b6FkW8v2+t7be5zt3c9Wsv93qE6vpzUt6m01LjvS6QcX8GWby/Dev7qSAHmQ3s5RkSSm8kzsVHeUJQX7HqESsjqKSK/BP4VuAjMNsbsa10VAehuL8t85gq99v5em/vcMX982eZgBYVy4JVWbO8WKcdXsEXE+avDBBhjzElgB5CA1bPEi4hMx+rjXQRsCVV5wa5HqISqniLyc+A7wGWs4PJRG6v6SXu5zWeuEAvD99rk547146uBL9nLV4wxbQkOEXF8BVvEnL/CPZgoyAOTPkH9KNOhDdJ7UT+VxGONtnkS6zL5yWCU15btOsD++qm9zWVgvJ91yMbq0eNslB6HdRVUZ5c5pyPtr7Z87lg9vhrk6YE1ELDFwX7RdHw1Ufd1tDDQMtLPX2HfiSH4Up6yd8BVYDHwBlBsp/2ziQNtob1uYTDKa+t20bq/gDw73WD9GlzYzN93G233MXubi1i/iF4DlgOn7fQ64N/CvZ9CsL/a9Llj7fhqlOebdp79frx/1BxfwI3A1gZ/JXYdDzVMD2R/tfY4CdbxFfadGqIv6jNYM6eWYN2j/QBrqpJr5s7x84D2u7xgbBeN+wtrIKXx429do+0GAb8BNtv/6Svtg/owVtdTv66EonB/tflzx9Lx1SjPLjvPd/x476g5voAZ/vwfasX+Ctv5Sx+ZrJRSKiQ6TCO/UkqpyKIBRimlVEhogFFKKRUSGmCUUkqFhAYYpZRSIaEBRimlVEhogFFKKRUSGmCUUkqFhAYYpZRSIaEBRimlVEj8f7L/LfxX1K8tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraction_of_positives1, mean_predicted_values1 = calibration_curve(y2, ytest_prob1, n_bins=10)\n",
    "plt.plot(mean_predicted_values1, fraction_of_positives1, 's-')\n",
    "plt.plot([0, 1], [0, 1], '--', color='gray')\n",
    "plt.title('Reliability Curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_2 = make_pipeline(RandomForestClassifier())\n",
    "grid_values_2 = [{'randomforestclassifier__n_estimators': [100,200], 'randomforestclassifier__max_depth':[6,8,10]}]\n",
    "grid_2 = GridSearchCV(pipe_2, param_grid = grid_values_2,scoring = 'f1',verbose=1, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   51.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('randomforestclassifier',\n",
       "                                        RandomForestClassifier())]),\n",
       "             param_grid=[{'randomforestclassifier__max_depth': [6, 8, 10],\n",
       "                          'randomforestclassifier__n_estimators': [100, 200]}],\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_2.fit(X1,y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18120834023466365"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'randomforestclassifier__max_depth': 8,\n",
       " 'randomforestclassifier__n_estimators': 100}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_2.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytest_labels2=grid_2.predict(X2)\n",
    "ytest_prob2=grid_2.predict_proba(X2)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold2,f1score2,recall2,precision2=plot_pr_curve(y2,ytest_prob2)\n",
    "threshold2=np.around(threshold2,4)\n",
    "f1score2=np.around(f1score2,4)\n",
    "recall2=np.around(recall2,4)\n",
    "precision2=np.around(precision2,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(y2,ytest_prob2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss2=log_loss(y2, ytest_prob2, eps=1e-15)\n",
    "log_loss2=np.around(log_loss2,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f46cf4b088>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraction_of_positives2, mean_predicted_values2 = calibration_curve(y2, ytest_prob2, n_bins=10)\n",
    "plt.plot(mean_predicted_values2, fraction_of_positives2, 's-')\n",
    "plt.plot([0, 1], [0, 1], '--', color='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_3=make_pipeline(AdaBoostClassifier())\n",
    "grid_values_3 = [{'adaboostclassifier__n_estimators': [100], 'adaboostclassifier__learning_rate':np.arange(0.1,1.1,0.1)}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_3 = GridSearchCV(pipe_3, param_grid = grid_values_3,scoring = 'f1',verbose=1, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('adaboostclassifier',\n",
       "                                        AdaBoostClassifier())]),\n",
       "             param_grid=[{'adaboostclassifier__learning_rate': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       "                          'adaboostclassifier__n_estimators': [100]}],\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_3.fit(X1,y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adaboostclassifier__learning_rate': 0.6,\n",
       " 'adaboostclassifier__n_estimators': 100}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_3.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19482542809832332"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytest_labels3=grid_3.predict(X2)\n",
    "ytest_prob3=grid_3.predict_proba(X2)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(y2,ytest_prob3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6742704791586309"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(y2, ytest_prob3, eps=1e-15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f46d59eac8>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraction_of_positives3, mean_predicted_values3 = calibration_curve(y2, ytest_prob3, n_bins=10)\n",
    "plt.plot(mean_predicted_values3, fraction_of_positives3, 's-')\n",
    "plt.plot([0, 1], [0, 1], '--', color='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows us that AdaBOOST is not a well calibrated classifier as the probabilities cannot be interpreted as a confidence level. Hence we need to calibrate the predicted probabilities in order to get good 'xG' values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.calibration import CalibratedClassifierCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "calibration = CalibratedClassifierCV(base_estimator = grid_3 , cv='prefit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=GridSearchCV(cv=5,\n",
       "                                                   estimator=Pipeline(steps=[('adaboostclassifier',\n",
       "                                                                              AdaBoostClassifier())]),\n",
       "                                                   param_grid=[{'adaboostclassifier__learning_rate': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       "                                                                'adaboostclassifier__n_estimators': [100]}],\n",
       "                                                   scoring='f1', verbose=1),\n",
       "                       cv='prefit')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calibration.fit(X2,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ytest_prob3C=calibration.predict_proba(X2)[:,1]\n",
    "threshold3,f1score3,recall3,precision3=plot_pr_curve(y2,ytest_prob3C)\n",
    "threshold3=np.around(threshold3,4)\n",
    "f1score3=np.around(f1score3,4)\n",
    "recall3=np.around(recall3,4)\n",
    "precision3=np.around(precision3,4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f46d648288>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OkSNH8uCDD5KZmRnDXko8UcCIyC2VltXwN7/bjcFiMUwb3I2/uLsfDodh9uzZZGZmMmjQoFh3U+KMAkZEmuTxWr77m01MdH9GqTOLM5n9+ekTo3H4r0EeNWpUjHso8UoBIyI35fV6+Zc3F5F/bjfOFEt3RwXPPTiFzu11RZjcmgJGRG7o7NmzvPXO+9RcPIfTf77eYaCTLY9txyRhKGBEJIjb7WbDhg1s3LgRr7dhSP1qZwe+Of8L5PbW4JQSGgWMiAScPHmSoqIizp8/HyjzWMPnjjz+9Zkn6N25fQx7J4lGASMiuN1u1qxZw8cffxxUftbTnk11+fzbV+5VuEjYFDAigjGGY8ca7nGpsw521PVmv+c2vn5Pf6YNuS2GvZNEpdGURYSUlBQKCwsxxnDR0ZEPaoez39OdUXmd+O6DQ2LdPUlQChiRNuj48eNYGzx8fs+ePbmYezdFlQOosOl0SHfyH18YQ5pTHxPSPNpzRNqQiooK3n33XRYsWMDOnTuD5r29rYQPD1ZTP6z+C4+OoE8XDfsizadzMCJtgLWWTz/9lOXLl1NdXQ3AypUrGThwINnZ2XxeWs7/LtobqP/FiX2YM1KXI0vLKGBEklxZWRmLFy/m8OHgIfWHDh1Kamoq1S4P33zrE2rdvnteBnfvwN/PHRaLrkqSUcCIJClrLdu3b2f16tW4XK5AeU5ODnPnzmXAgAEAfO/dPRw6VwFAu1QHLz45hnapKTHpsyQXBYxIErp48SJFRUWcOHEiqHzixIlMnz6dtDTfWGIf7jrFwu0lgfn/UHg7g7rrIWESGQoYkSRz+PBhFi5ciNvtDpR16dKFwsJC+vTpEygrvlDJ//z9Z4HX80b34vHxua3aV0luChiRJJObm0tGRgbl5eUYY7jrrruYMmUKTmfDn3ut28O3fruTCv+jj/t2yeSHD9+up1BKRClgRJJMu3btmD17NmvXrmXevHn06NHjujo/XvY5n57yPfo4NcXw4hfH0qFdamt3VZKcAkYkgZWUlHD48GGmTZsWVD548GAGDRqEw3H9rW6r953ltU0Nw8I8P3MoI3Jzot5XaXsUMCIJyOVysWbNGrZu3QpAXl4eAwcODKpzo3A5faWav3l3d+D1/UO789Rd+VHtq7RdChiRBHPkyBEWL17MlStXAmUrV65kwIABTZ5DcXu8fOftnVypqgOgZ047fvLYSJ13kahRwIgkiOrqalauXMmuXbuCygcMGMCcOXNuGRQ/X3OIbcWXAd+TKX/+xTF00qOPJYoUMCIJYP/+/SxdupSKiopAWUZGBg8++CAjR976KGTz4Qv8x0cNd/L/1f0FTMjvHLX+ikAzAsYY8yTwDDASSAEOAK8Dv7TWepta9pp2FgBfaaLK59ZajRMubVpFRQXLli1j3759QeXDhg1j5syZZGVlATD+h6u4UOG6URM39OstxXxr+qBIdlXkOmEFjDHmF8CzQA2wBqgDpgMvAtONMY9baz1h9mETcPgG5WfCbEck6axYsSIoXLKyspg1axZDhw4NqhdOuDSnvkhzhBwwxphH8YVLKXCvtfaQv7w78BHwCPBN4N/D7MOr1toFYS4j0ibcf//9HDx4EJfLxZgxY5gxYwYZGRmx7pZISMI5gnneP/1efbgAWGvPGmOeAdYC3zfG/Ec4X5WJiI+1Fq/XS0pKw0CTOTk5zJkzh/bt29O/f/8Y9k4kfCE9cMwYkwuMA1zAO9fOt9auA04BPYA7ItlBkbbgwoULvP7666xbt+66eSNGjFC4SEIK9QhmjH+611pbfZM624De/rqbw+jDNGPMSCALOAtsBFbpKEjaAo/Hw+bNm1m3bh0ej4dTp04xbNiwGw7vIpJoQg2Yfv7p8Sbq1I8L3q+JOjfy5RuU7TPGfMFa+2mYbYkkjDNnzlBUVERpaWlQ+enTpxUwkhRCDZgs/7SyiTr1F+iH+jCJXcAOfFejHQeygbHA/wVGAauNMWOttadutLAxZj4wP8R1jQ6xnkjUud1u1q5dy+bNm7HWBsp79uxJYWGhwkWSRqgBU38Xl22yVhistf92TVElsMQYswpYh+9czvP4rky7kXxgSqT6I9IaTpw4QVFRERcvXgyUOZ1Opk6dyuTJk284fphIogo1YMr906wm6tTPK2+izi1Za13GmBeAD4FZTVQtxhdEoRgNaLhYiZm6ujpWrVrFtm3bgsr79u3L3Llz6dKlS7Patdby6oZjt64oEgOhBkyxf9q3iTp519RtiQP+ae+bVfDfO7MglMaMMWvR0Y7EkMPhCHp8cVpaGjNmzGDcuHHNHmzS47X8w6K9/HpLU6dGRWIn1OPxnf7pcGPMze7ymnBN3Zao/+9cRZO1RBJESkoK8+bNwxjDoEGDePbZZxk/fnyzw6XK5eYbb+5QuEhcC+kIxlpbYoz5BN9J+MeBNxrPN8ZMAXLx3eW/JQL9esI/3dZkLZE4dezYMfLz84MCpGfPnjz99NN07969RUPkny+v5Wu/3sbuk2WBsjSnA5c79Cv7u2ZpFGWJvnDu5H8B302WPzbGbLbWHgYwxtwGvOSv86PG96/4z6U8AvzeWvt8o/LR+AJpWeOxy4wxTuDb/h+An4X/lkRip7y8nGXLlrF//37mzJnDuHHjgua39AqxI+crmP/6VkouNdyO9o0p/fneg0NwOPRcF4kvIQeMtfZdY8wv8Y2k/KkxZjUNg11mAx/gG/SysZ7AYP+0sXzg98AlY8xB4CS+y5tHAL0AL74haVaE+4ZEYsFay65du1i5ciU1NTUArFq1ikGDBpGdnR2RdWw9domvv7GdsmrfA8McBv7PvNv58zuaOjUqEjthjaZsrX3WGLMReA7fSfP64fpfI7zh+nfjGxRzIr4LB8bguwT6JL6h/39hrd0RTt9EYuXy5cssXryYo0ePBpUPHz6ctLTIfBVVtPs0f/O73bg8vj+xjNQUXnxyDNOHdo9I+yLREPbzYKy1bwFvhVh3Pje4GdJaewz4b+GuWySeeL1etm3bxpo1a6irqwuUd+rUiblz59KvX7iDWlzPWsvL647y4+UHAmVds9J5bf54RuZ2bHH7ItGkJ1qKNMP58+cpKiri5MmTgTJjDJMmTWLatGkROXJxe7z8fdFefvPHhsubB96WxevzJ5DXObPF7YtEmwJGJEyHDh1i4cKFeDwNz9br1q0bhYWF5ObmRmQdlbVuvvXbnfzhwLlA2aR+nXnlz8eTk5kakXWIRJsCRiRMffr0oX379ly9ehWHw8E999zDPffcE/Qcl5Y4V17DVxds47NTVwNl80b34p8fG0m6MzLrEGkNChiRMKWnpzN79mzWr1/P3Llz6d49cifaD50tZ/7r2zh1peEy5OemDeCvZwzWZciScBQwIk04fvw4R44c4b777gsqLygoYNCgQS26YfJaW45c5Ok3t1Ne4wYgxWH4x3m38+SkPhFbh0hrUsCI3EBtbS2rV69m+/btAOTl5TFo0KCgOpEMlw92nuK77+6mzuMbsLx9Wgov/tlYpg2+LWLrEGltChiRaxw6dIjFixdz9WrDOZDVq1czcODAiIYK+C5DfmntEX6y4vNA2W0d0nlt/gRu760BwCWxKWBE/KqqqlixYgV79uwJKi8oKGD27NkRDxe3x8v/+vAzfru1pGFd3bN4/amJ9O54szFlRRKHAkbaPGst+/btY+nSpVRVVQXKMzMzmTlzJsOHD494uFTUunnuN5+w7uD5QNnk/l14+c/HkZOhy5AlOShgpE0rLy9nyZIlfP7550HlI0aM4KGHHiIzM/I3NJ69WsNTr29j35mGr+D+ZExvfvToSNKceqKlJA8FjLRpK1euDAqX7OxsZs+eTUFBQVTW93lpOU+9vpXTZTWBsm/fN5C/mlEQ8aMkkVhTwEibdv/993Pw4EFcLhfjxo1jxowZpKenR2Vdmw5f4C/f3EF5re8yZKfD8E+PjOCJCXm3WFIkMSlgpM3wer1Ya4PuuM/JyWHu3LlkZWWRn58ftXW/t+Mk33tvD26v7zLkrHQnL/3ZWO4t6Ba1dYrEmgJG2oRz585RVFREv379mD59etC822+/PWrrtdby8zWH+dnqg4GyHtnteG3+BIb1isxzYkTilQJGEsr4H67iQoUr5Prd2jv5l3vbsX79erxeL6dPn2b48OEtfrJkKOo8Xn7w/qe8s6NhxOUhPTrw+lMT6Jmjy5Al+SlgJKGEEy5dTSV3uYtZu7ZhXC9jDGfOnIl6wJTX1PHsbz5hw6ELgbK7B3blpS+NJbudLkOWtkEBI0knBQ9jnacZ5jxL4/Ehc3NzKSwspFu36J73OFNWzVOvb+NAaXmg7LFxubzwJyNITdFlyNJ2KGAkqfRwXOWu1ONkO2oDZampqdx3331MnDgRhyO6H/D7Tl/lqwu2UXq14TLkv7q/gG9Pj/wwMyLxTgEjScGJh4mpJQx2XggqP+XpwI++NZ/OnTu3qP1wz/2A7zLkHz06ksfGReYhZCKJRgEjScGDoaujYZiXWpvCtro8Dnm6sO5f/0he50zyOmWQ1zmTPp0zye3km+Z1zqBDCOdEwg0XgAVPTeTuQV3DXk4kWShgJClYHGx05TM3fR8l3o5scfWhmjQAat1eDp+r4PC5ihsu2zEz1Rc2nTJ9QdQ5gzx/APXqmNHs4VsULtLWKWAkAVl6O65yypsNNJzXuGQz+bB2OFdsu6DyW7lSVceVqjL2nCy7bp7DoEuKRZpJASMJJRMXk9OO0yeljM2uPnzuCX4g1xV7fRjs/vsHKLlU5fu5XEXJpWpO+P998lI1Lo/3puvzWoIeXywioVPASEKw1vL+yg080u4z0owvEMannuSktyOVNq3JZXMyUsnpnXPDB3h5vZZz5bWUXK7ixMWGAKoPo9KrNVgblbckkvQUMBL3Ll26xK/ffo+r50+T1uibryOeLrhsys0XDIHDYeiR044eOe2YkH/9lWa1bg+nLldz30/XtWg9Im2RAkbiltfr5eOPP2b1mj9gvZ5AeZk3nU11+Zz1doh6H9KdKfTvlhX19YgkIwWMxKWzZ89SVFTE6dOnA2VeC3s9PdhZ1wsPoV3Z1TWr6a/PRCR6FDASdw4ePMjChQvxehtOvl/0ZlDa8XZ++bXpdOsQnee1iEhkKWAk7nTvlUudSSOFGjzWsMvdi+y+w3ntyxPIStcuK5Io9NcqcaWsuo6v/2Y3p6ryGOU8zaa6fKaOGsBPHhul59WLJBj9xUpMHTt2jNWrVwNQWlbDEy9vYeuxS5zy5rDUNYTH7xrGz54YHfNwCfdcjs79iOgIRmKkpqaGlStXsnPnTgBSc7rx/OoLnC5rGIX4f84axtfv7R+rLgbZ/rczYt0FkYSjgJFWd+DAAZYsWUJFRcPYYB8uXcnp6qGAwekw/OTxkTwyRqMQiyQyBYy0msrKSpYtW8bevXuDyk94O7G5tg9gyExL4ZdfGseUgug+FExEok8BI1FnreXTTz9l+fLlVFc3jOuVkpbBmspeHHN3AqBL+zRemz+BUXkdY9VVEYkgBYxEVVlZGYsXL+bw4cNB5end+/NacTYu/y6Y1zmDN746iX5d28eimyISBQoYiarVq1cHhUtOTg6Xuozg5b0NJ/OH9cxmwVcncFuHdrHooohEiS5TlqiaMWMG6em+O+/HjZ/AoS6TeaNRuNw5oAsLv3GHwkUkCekIRiJi/A9XcbGiFoPFe83/W/o6elNNKq9vsMCFQPmckT356ROjSHe2bERkEYlPChiJCE/lFeakF3Pak80Od/Dlxce91w+DD/DzL4zB4Qj9yZMiklgUMNIibreb9evXU5i+H4exdDZVFHs6cdHe+mS9wkUkuSlgpNlKSkooKiriwoUL1GeFF0NHRzUXPboaTKStU8BI2FwuF2vWrGHr1q1B5aWeLDbV5XPV6oS9iChgJExHjhxh0aJFlJWVBcrS0tJYV9GDA55ugL72EhEfBYyExOVysWzZMnbt2hVU3q//AEyfsRxYdixGPROReKWAkZA4nU7Onj0beJ2e3g5Xz5H8yxHD1b0KFxG5nm60lJA4HA7mzJmLMQ4qM3vw67LBvLbfy9UaT6y7JiJxSkcwch1rLQcPHqSgoABjDGVVdbyzo4Q3Pz7O5ephXK0KPlj6mgAAAA6qSURBVInfp3MmJy5Vxai3IhKvFDAS5MqVKyxevJgjR44w5q5pbC7ryAe7TlFT5/XX8IWLMTC1oBtfvjOfKYO60f8HS2PXaRGJSwoYAXxHLdu2bWP16tXU1dUBsHXjOhbVDqfGpgfqZbdz8sT4PL50R1/yNfKxiDRBASNcuHCB937/AaWnTwXKrIWDnq7UWt8uMrRnNl+Z3Jd5o3uTkXb92GFds9K4UOEKeZ16Zr1I8lPAtGFut5vfLVnDwd1bMdYbKL/ibcfGunwu04GZo3rylcl9Gde3E8bc/B4XPbNeRK6lgElw43+4Kuwjh/X/Yxpvr93DgT9+RHtvReDWSK817HH34Ey7fL50Tz++ODGP27J1V76INI8CJsGFEy719R/+p3eZzOe0b3RAcsGbyZVuo/javSN4YHh3UlN0BbuItIwCpg06WpPJqPQ0sowLtzXQczjPzJ3OsF4dY901EUkiCpg2yE0KRzIGMyHjHF957BHye3ePdZdEJAkpYJJcL0cZvR1X2ebOC5T9+qsTuWdgV4yhyRP3IiItoYBJUum4mZhawkDnRQBKvR0o8fq+AptS0C2WXRORNkIBk3QsfR2XmZx2ggzjDpSOST1FSW0OGk5fRFqLAiaJZOBictoJ+qZcCSo/6u7Mx3V5KFxEpDUpYBLYyr2l/n9ZBqVcZEJqCemmYXTjSpvKFlffwFdjIiKtSQGTgGrdHn607ACvbyomy9RyZ2oxvVPKg+p87u7Ktrpc6vQrFpEY0adPgjl+sZJvvrWTT0/5Hlk8znkqKFyuetPZXNeXM97sWHVRRARQwCSUxXtO8/33PqWituHk/da6XHqnlJGKh33u7nzi7oWH6wejFBFpbQqYBFBT5+EfFu/jt38s9j+C1EFqiuH5mUN5ae1hNlXlU2lTuWCzbtmWRjEWkdaigIlzh89V8M23PuH82VIK04s55cnmXPZgXnxyDCNzO/LVu/vFuosiIjekgIlj7+04yd9/sIchtoRJ6aU4DHRyVPPk47MoyNWVYSIS3xQwcajK5ebvPtzLhp37eTC1mGxHbWBeqtNJTfkVoE/sOigiEgIFTJw5UHqVb//XVrqUHWRW+vmgefn5+cydO5fOnTvHqHciIqFTwMQJay1vbyvhlUWbmOA4RntnXWBeWno6D8yYwdixYzU4pYgkDAVMHCivqeMH7+2i7MDHTHVeCppXUFDA7Nmzyc7WfS0iklgUMDH22akynnvrE05crGR2ek2gPL1dBnNmz2L48OE6ahGRhKSAiRFrLb/eXMw/LT2Ay+MFDJtc+czL2M/wYcOZPWsmmZmZse6miEizKWBi4Eqli797YwVFxw31Ixy3T0vhf//JndzTZ7pO4otIUlDAtLINe4t5570P6GLLGJqSx35Pd4b3yubFJ8fSr2v7WHdPRCRiFDCtxO328PO3l3H58E66GC8A41JPMWn07Tz/yDjSnRo/TESSiwKmhcb/cBUXKlxN1uloqrkrrZjbHJU4/efrrYU+g0fw1MNjSFW4iEgSUsC0UFPh4sDLCGcpo5xnSDE2UF6dksWfPvoIY4b2b40uiojEhAImSrqaSu5KK6azozpQ5rGG3e6eLHx+Pu3SUmPYOxGR6FPAREGe4wr3pR3G0ej2lXPe9mx05VNmMxQuItImKGCi4Iy3A5U2jQ7GRZ118Eldb/Z7bsOiGyZFpO1QwESBmxQ21/Xldmcpm+vyqbDpse6SiEirc4S7gDHmSWPMBmNMmTGmwhiz3RjznDEm7Lai0V5ry3NcYVLqievKT3tzWOkqULiISJsV1hGMMeYXwLNADbAGqAOmAy8C040xj1trPbFqrzVVVlayfPly7k8/DMAZTwdOeDtdU0tfiYlI2xVywBhjHsUXBqXAvdbaQ/7y7sBHwCPAN4F/j0V7rcVay2effcayZcuorm64Qmx06mlO1HZEoSIi4hPO11DP+6ffqw8DAGvtWeAZ/8vvh/HVVqTbi7qrV6/y9ttv8/777weFy2F3F1bUDkbhIiLSIKQjGGNMLjAOcAHvXDvfWrvOGHMK6A3cAWxuzfaizVrLjh07WLVqFS5Xw42VOTk5vHPuNk55c2LYOxGR+BTq0cEY/3Svtbb6JnW2XVO3NduLmkuXLvHGG2+wZMmSoHCZMGECzzzzjMJFROQmQj0H088/Pd5EnfpLqfo1USdi7Rlj5gPzQ1gXwOgQ613no48+ori4OPC6S5cuzJ07l759+wLQNSvtlmORNdY1K625XRERSSihBkyWf1rZRJ0K/7RDK7WXD0wJYV0t8sADD3Do0CFcLhd33nknU6dOxels2Gzb/3ZGtLsgIpKQQg2Y+rPXtslaoYtEe8XAuhDrjgaa9V1Whw4dePjhh8nJyaFnz57NaUJEpE0KNWDK/dOsJurUzytvok7E2rPWLgAWhLAujDFracHRzpAhQ5q7qIhImxXqSf5i/7RvE3Xyrqnbmu2JiEicCTVgdvqnw40xGTepM+Gauq3ZnoiIxJmQAsZaWwJ8AqQBj1873xgzBcjFd1f+ltZuT0RE4k84d8m/4J/+2BgzsL7QGHMb8JL/5Y+std5G814wxhwwxrzA9cJuT0REEkfIY5FZa981xvwS3zAunxpjVtMwOGU28AG+QSob6wkM9k8j0Z6IiCSIsEZTttY+a4zZCDyH76qsFOAA8Brwy3CPNiLdnoiIxA9jbaRubYlfxpiTQO+cnBxGj272Tf0iIm3Krl27KCsrAzhlrc0Nd/m2EjBXaOaNliIiQpm1tmO4C7WVRyYfwzemWQVwOMxl60cBKAN2RbhfyUjbKzzaXuHR9gpPS7fXQHw3vR9rzsrbRMBYa5s9InOjUQB2WWunRqpPyUrbKzzaXuHR9gpPrLdX3DzMS0REkosCRkREokIBIyIiUaGAERGRqFDAiIhIVChgREQkKhQwIiISFQoYERGJCgWMiIhERZu4k7+FFgBr0aObQ7UAba9wLEDbKxwL0PYKxwJiuL3axGCXIiLS+vQVmYiIRIUCRkREoiIpA8YY86QxZoMxpswYU2GM2W6Mec4Y06z329z2It2PaIlEP40xqcaY6caYnxpjPjbGnDHGuIwxp4wx7xpjpjax7AJjjG3i50BE3miEROr32tL33cb2r6m32FaNf/pcs2xC7F/GmMHGmO8YY/7LGHPAGOP19++xFrYbs8+vpDvJb4z5BfAsUAOsAeqA6cCLwHRjzOPWWk+024t0P6Ilgv2cAqzy/7sU2AFUAsOAR4FHjTH/aK39uyba2MSNn9dzJpT30hqi9HsN+323wf2rFPh1E/MnAkOBI0DJTerE+/71DPCdSDYY888va23S/OD7ILP4dphBjcq7A/v8874T7fYi3Y9E2F7AfcC7wD03mPengNvf3rQbzF/gnzc/1tuklfevZr3vtrh/hbCuvf72fpDA+9fXgH8GngAG4Lv6ywKPteb2j+jnQqw3aoR/Qdv9b/7LN5g3pdFGc0SzvUj3I1G21y3W9aq/vV/dYF6ifABEev9qbsBo/wpua7K/LTfQO1H3rxv0u6UBE/PPr5hvxAj+MnL9b7wWyLhJnZP+OndGq71I9yNRtlcI63vO39aKG8yL+w+AaGyv5rxv7V83bOc//e0sjtR2joeflgRMvHx+xdXJwBaqfyzyXmtt9U3qbLumbjTai3Q/oqW1+znIP23q++5pxph/Nca8Yoz5R2PMg3F0wjqa2yuc9639qxFjTCa+r2ABfnWL6vG8f0VaXHx+JdNJ/n7+6fEm6py4pm402ot0P6Kl1fppjOkBzPe/fK+Jql++Qdk+Y8wXrLWftqQPERDN7RXO+9b+FexxoANwDlh8i7rxvH9FWlx8fiVTemf5p5VN1KnwTztEsb1I9yNaWqWfxhgn8F9ADrDGWrvoBtV2Ad8Ghvv71QuYA+zGdxXaamNM7+b2IUKisb2a8761fwX7qn/6hrW27iZ1EmH/irS4+PxKpiMY45/aGLcX6X5ES2v182V8lzeWAF+6UQVr7b9dU1QJLDHGrALWAXcAzwPfjGI/byXi26uZ71v7V/0KjBkI3Ot/+drN6iXI/hVpcfH5lUxHMOX+aVYTdernlTdRp6XtRbof0RL1fhpj/h34C3z3MEy31paGs7y11gW84H85qzl9iKBW+73e4n1r/2pQf/SyxVq7P9yF42z/irS4+PxKpoAp9k/7NlEn75q60Wgv0v2Ilvp1R6Wfxpif4vta4jy+cDkUbht+9XdZx/orjGL/tLV+rzd7363dj+aqX3e09q8UGs6p3OrkflPiZf+KtGL/NKafX8kUMDv90+HGmIyb1JlwTd1otBfpfkRL1PppjPln4L8DF4EZ1tp9zesiAF3804oma0Vfa/9eb/a+2/z+5fcgvlCoBBY2Y/l68bJ/RVpcfH4lTcBYa0uAT4A0fFeWBDHGTMF3jXcpsCVa7UW6H9ESrX4aY34EfBe4jC9cdrewq0/4p9uarBVlMfi93vB9t/X9q5G/8E8XWmtbEg5xsX9FWtx8fsX6ZqII35j0GA13mQ5sVH4bDUNJfOeaZV7Ad5j8QiTaa8lySbC9/tG/zGVgXIh9GI3vip6Ua8qd+I6CPP42H0ym7dWS991W969GdbriuxHwljf7JdL+dYO+r+UWN1rG++dXzDdiFH4pL/k3QDWwCHgfKPOX/f4GO9oC/7wFkWivpcsl6vYCCv3lFt//Bhfc5Of71yz3sH+Zi/j+R/QOsBw45S/3AP8j1tspCturRe+7re1f19T5K3+d/SGsP2H2L2As8HGjn6v+Ph5sXB7O9mrufhKp/SvmGzVKv6gn8Y2cehXfd7Q78A1Vct3YOSHu0CG3F4nlEnF74buR0obws/aa5foB/wZs9v/R1/h36kP4Lj0N6UgoAbdXi993W9q/rqmzx1/nuyGsO2H2L2BqKH9DzdheMfv80iOTRUQkKpLmJL+IiMQXBYyIiESFAkZERKJCASMiIlGhgBERkahQwIiISFQoYEREJCoUMCIiEhUKGBERiQoFjIiIRMX/B9b0uYsGYiNrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraction_of_positives3C, mean_predicted_values3C = calibration_curve(y2, ytest_prob3C, n_bins=10)\n",
    "plt.plot(mean_predicted_values3C, fraction_of_positives3C, 's-')\n",
    "plt.plot([0, 1], [0, 1], '--', color='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Post calibration the predicted probabilities become reliable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss3=log_loss(y2, ytest_prob3C, eps=1e-15)\n",
    "log_loss3=np.around(log_loss3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f46e6cb748>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precisions1, recalls1, thresholds1 = metrics.precision_recall_curve(y2, ytest_prob1)\n",
    "precisions2, recalls2, thresholds2 = metrics.precision_recall_curve(y2, ytest_prob2)\n",
    "precisions3, recalls3, thresholds3 = metrics.precision_recall_curve(y2, ytest_prob3)\n",
    "plt.plot(recalls1, precisions1, color='blue')\n",
    "plt.plot(recalls2, precisions2, color='red')\n",
    "plt.plot(recalls3, precisions3, color='black')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All three classifiers perform relatively same with logistic regression having a slight edge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f46e72da88>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fprs1, tprs1, thresholds1 = metrics.roc_curve(y2, ytest_prob1)\n",
    "fprs2, tprs2, thresholds2 = metrics.roc_curve(y2, ytest_prob2)\n",
    "fprs3, tprs3, thresholds3 = metrics.roc_curve(y2, ytest_prob3)\n",
    "plt.plot(fprs1, tprs1, color='blue')\n",
    "plt.plot(fprs2, tprs2, color='red')\n",
    "plt.plot(fprs3, tprs3, color='black')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = [['Model','Threshold','F1score','Precision','Recall','Log_loss'],['Logistic Regression',threshold1,f1score1,precision1,recall1,log_loss1],['Random Forest',threshold2,f1score2,precision2,recall2,log_loss2],['AdaBOOST',threshold3,f1score3,precision3,recall3,log_loss3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Model', 'Threshold', 'F1score', 'Precision', 'Recall', 'Log_loss'],\n",
       " ['Logistic Regression', 0.1591, 0.3904, 0.306, 0.5392, 0.2739],\n",
       " ['Random Forest', 0.164, 0.3861, 0.2943, 0.5609, 0.2732],\n",
       " ['AdaBOOST', 0.1624, 0.3847, 0.3049, 0.521, 0.2741]]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒═════════════════════╤═════════════╤═══════════╤═════════════╤══════════╤════════════╕\n",
      "│ Model               │   Threshold │   F1score │   Precision │   Recall │   Log_loss │\n",
      "╞═════════════════════╪═════════════╪═══════════╪═════════════╪══════════╪════════════╡\n",
      "│ Logistic Regression │      0.1591 │    0.3904 │      0.306  │   0.5392 │     0.2739 │\n",
      "├─────────────────────┼─────────────┼───────────┼─────────────┼──────────┼────────────┤\n",
      "│ Random Forest       │      0.164  │    0.3861 │      0.2943 │   0.5609 │     0.2732 │\n",
      "├─────────────────────┼─────────────┼───────────┼─────────────┼──────────┼────────────┤\n",
      "│ AdaBOOST            │      0.1624 │    0.3847 │      0.3049 │   0.521  │     0.2741 │\n",
      "╘═════════════════════╧═════════════╧═══════════╧═════════════╧══════════╧════════════╛\n"
     ]
    }
   ],
   "source": [
    "print(tabulate(table, headers='firstrow', tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion matrix for Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal accuracy is  0.8182451253481894\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimal_confusion_matrix(y2,ytest_prob2,0.164)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion matrix for logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal accuracy is  0.8288068709377902\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimal_confusion_matrix(y2,ytest_prob1,0.1591)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the three classifiers have relatively the same performance. I have chose Random Forest for predicting xG values on the account of it having better recall, log_loss and reliability curve. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "xG=grid_2.predict_proba(model_input)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "xG=np.around(xG,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "allshots['xG']=xG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "allshots.to_csv('allshots.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerId</th>\n",
       "      <th>id</th>\n",
       "      <th>x_coordinates</th>\n",
       "      <th>y_coordinates</th>\n",
       "      <th>blocked</th>\n",
       "      <th>goal</th>\n",
       "      <th>time</th>\n",
       "      <th>matchPeriod</th>\n",
       "      <th>counter</th>\n",
       "      <th>competition</th>\n",
       "      <th>foot</th>\n",
       "      <th>bodypart</th>\n",
       "      <th>preferrence</th>\n",
       "      <th>angles</th>\n",
       "      <th>distance</th>\n",
       "      <th>headers</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25413</td>\n",
       "      <td>177959212</td>\n",
       "      <td>92.40</td>\n",
       "      <td>27.88</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>26.77</td>\n",
       "      <td>14.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26150</td>\n",
       "      <td>177959247</td>\n",
       "      <td>89.25</td>\n",
       "      <td>35.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.99</td>\n",
       "      <td>15.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14763</td>\n",
       "      <td>177959280</td>\n",
       "      <td>100.80</td>\n",
       "      <td>35.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.79</td>\n",
       "      <td>4.41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7868</td>\n",
       "      <td>177959289</td>\n",
       "      <td>85.05</td>\n",
       "      <td>22.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.74</td>\n",
       "      <td>23.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7868</td>\n",
       "      <td>177959429</td>\n",
       "      <td>78.75</td>\n",
       "      <td>20.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.59</td>\n",
       "      <td>29.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0165</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43073</th>\n",
       "      <td>25575</td>\n",
       "      <td>90588469</td>\n",
       "      <td>81.90</td>\n",
       "      <td>41.48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.36</td>\n",
       "      <td>24.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43074</th>\n",
       "      <td>25575</td>\n",
       "      <td>90588583</td>\n",
       "      <td>97.65</td>\n",
       "      <td>28.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.46</td>\n",
       "      <td>9.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43075</th>\n",
       "      <td>70410</td>\n",
       "      <td>90589205</td>\n",
       "      <td>94.50</td>\n",
       "      <td>25.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.19</td>\n",
       "      <td>13.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43076</th>\n",
       "      <td>70410</td>\n",
       "      <td>90589254</td>\n",
       "      <td>80.85</td>\n",
       "      <td>31.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.03</td>\n",
       "      <td>24.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43077</th>\n",
       "      <td>134513</td>\n",
       "      <td>90589034</td>\n",
       "      <td>91.35</td>\n",
       "      <td>27.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.51</td>\n",
       "      <td>15.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43078 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerId         id  x_coordinates  y_coordinates  blocked  goal  time  \\\n",
       "0         25413  177959212          92.40          27.88      0.0   1.0   2.0   \n",
       "1         26150  177959247          89.25          35.36      0.0   0.0   3.0   \n",
       "2         14763  177959280         100.80          35.36      0.0   1.0   4.0   \n",
       "3          7868  177959289          85.05          22.44      0.0   0.0   7.0   \n",
       "4          7868  177959429          78.75          20.40      0.0   0.0  14.0   \n",
       "...         ...        ...            ...            ...      ...   ...   ...   \n",
       "43073     25575   90588469          81.90          41.48      1.0   0.0  85.0   \n",
       "43074     25575   90588583          97.65          28.56      0.0   0.0  91.0   \n",
       "43075     70410   90589205          94.50          25.84      0.0   0.0  13.0   \n",
       "43076     70410   90589254          80.85          31.28      0.0   1.0   3.0   \n",
       "43077    134513   90589034          91.35          27.20      1.0   0.0  16.0   \n",
       "\n",
       "       matchPeriod  counter  competition  foot  bodypart  preferrence  angles  \\\n",
       "0                0      0.0            0   1.0       1.0          1.0   26.77   \n",
       "1                0      0.0            0   0.0       2.0          1.0   25.99   \n",
       "2                0      0.0            0   1.0       3.0          1.0   78.79   \n",
       "3                0      0.0            0   1.0       2.0          0.0   15.74   \n",
       "4                0      0.0            0   1.0       1.0          1.0   12.59   \n",
       "...            ...      ...          ...   ...       ...          ...     ...   \n",
       "43073            1      0.0            6   1.0       2.0          0.0   16.36   \n",
       "43074            1      0.0            6   1.0       1.0          1.0   37.46   \n",
       "43075            2      0.0            6   1.0       3.0          1.0   25.19   \n",
       "43076            3      0.0            6   1.0       1.0          1.0   17.03   \n",
       "43077            3      0.0            6   1.0       1.0          1.0   24.51   \n",
       "\n",
       "       distance  headers      xG  \n",
       "0         14.01      0.0  0.1466  \n",
       "1         15.81      0.0  0.1106  \n",
       "2          4.41      1.0  0.4348  \n",
       "3         23.06      0.0  0.0331  \n",
       "4         29.56      0.0  0.0165  \n",
       "...         ...      ...     ...  \n",
       "43073     24.28      0.0  0.0231  \n",
       "43074      9.14      0.0  0.2714  \n",
       "43075     13.30      1.0  0.0895  \n",
       "43076     24.30      0.0  0.0461  \n",
       "43077     15.25      0.0  0.1332  \n",
       "\n",
       "[43078 rows x 17 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allshots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FEATURE IMPORTANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomforest=RandomForestClassifier(max_depth=8, n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=8)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomforest.fit(X1,y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "coefficients=randomforest.feature_importances_\n",
    "coefficients=np.reshape(coefficients,(7,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=['X-Coordinates','Y-Coordinates','Angles','Counter','Preference','Distance','Headers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(features, coefficients)\n",
    "plt.xticks(rotation=90)\n",
    "plt.savefig('featureimportance.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}