Churn Prediction Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer Churn Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Preparation based on EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datapreparation(filepath):\n",
    "    \n",
    "    df = pd.read_csv(filepath)\n",
    "    df.drop([\"customerID\"], inplace = True, axis = 1)\n",
    "    \n",
    "    df.TotalCharges = df.TotalCharges.replace(\" \",np.nan)\n",
    "    df.TotalCharges.fillna(0, inplace = True)\n",
    "    df.TotalCharges = df.TotalCharges.astype(float)\n",
    "    \n",
    "    cols1 = ['Partner', 'Dependents', 'PaperlessBilling', 'Churn', 'PhoneService']\n",
    "    for col in cols1:\n",
    "        df[col] = df[col].apply(lambda x: 0 if x == \"No\" else 1)\n",
    "   \n",
    "    df.gender = df.gender.apply(lambda x: 0 if x == \"Male\" else 1)\n",
    "    df.MultipleLines = df.MultipleLines.map({'No phone service': 0, 'No': 0, 'Yes': 1})\n",
    "    \n",
    "    cols2 = ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\n",
    "    for col in cols2:\n",
    "        df[col] = df[col].map({'No internet service': 0, 'No': 0, 'Yes': 1})\n",
    "    \n",
    "    df = pd.get_dummies(df, columns=['InternetService', 'Contract', 'PaymentMethod'], drop_first=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>...</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       1              0        1           0       1             0   \n",
       "1       0              0        0           0      34             1   \n",
       "2       0              0        0           0       2             1   \n",
       "3       0              0        0           0      45             0   \n",
       "4       1              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "0              0               0             1                 0  ...   \n",
       "1              0               1             0                 1  ...   \n",
       "2              0               1             1                 0  ...   \n",
       "3              0               1             0                 1  ...   \n",
       "4              0               0             0                 0  ...   \n",
       "\n",
       "   MonthlyCharges  TotalCharges  Churn  InternetService_Fiber optic  \\\n",
       "0           29.85         29.85      0                            0   \n",
       "1           56.95       1889.50      0                            0   \n",
       "2           53.85        108.15      1                            0   \n",
       "3           42.30       1840.75      0                            0   \n",
       "4           70.70        151.65      1                            1   \n",
       "\n",
       "   InternetService_No  Contract_One year  Contract_Two year  \\\n",
       "0                   0                  0                  0   \n",
       "1                   0                  1                  0   \n",
       "2                   0                  0                  0   \n",
       "3                   0                  1                  0   \n",
       "4                   0                  0                  0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = datapreparation(filepath = \"C:/Data/Telco-Customer-Churn.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am going to build and tune random forest model because in this case tree based method would perform better. I am also interested in individual customer's churning probability and in understanding how the model calculates it using Shap values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, precision_score, recall_score, f1_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(df, test_size=0.2, random_state=111, stratify = df.Churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.columns[df.columns!=\"Churn\"]\n",
    "y = \"Churn\"\n",
    "train_x = train[x]\n",
    "train_y = train[y]\n",
    "test_x = test[x]\n",
    "test_y = test[y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function for model fitting\n",
    "def churn_prediction(algo, training_x, training_y, testing_x, testing_y, cols, cf = 'coefficients'):\n",
    "    algo.fit(training_x,training_y)\n",
    "    predictions = algo.predict(testing_x)\n",
    "    probabilities = algo.predict_proba(testing_x)[:,1]\n",
    "    \n",
    "    #coeffs\n",
    "    if cf == \"coefficients\":\n",
    "        coefficients = pd.DataFrame(algo.coef_.ravel())\n",
    "    elif cf == \"features\":\n",
    "        coefficients = pd.DataFrame(algo.feature_importances_)\n",
    "        \n",
    "    column_df = pd.DataFrame(cols)\n",
    "    coef_sumry = (pd.merge(coefficients,column_df,left_index= True,\n",
    "                              right_index= True, how = \"left\"))\n",
    "    coef_sumry.columns = [\"coefficients\",\"features\"]\n",
    "    coef_sumry = coef_sumry.sort_values(by = \"coefficients\",ascending = False)\n",
    "    \n",
    "    print (algo)\n",
    "    print (\"\\n Classification report : \\n\",classification_report(testing_y,predictions))\n",
    "    print (\"Accuracy   Score : \",accuracy_score(testing_y,predictions))\n",
    "    \n",
    "    #confusion matrix\n",
    "    conf_matrix = confusion_matrix(testing_y,predictions)\n",
    "    plt.figure(figsize=(12,12))\n",
    "    plt.subplot(221)\n",
    "    sns.heatmap(conf_matrix, fmt = \"d\",annot=True, cmap='Blues')\n",
    "    plt.title('Confuion Matrix')\n",
    "    plt.ylabel('True Values')\n",
    "    plt.xlabel('Predicted Values')\n",
    "    \n",
    "    #roc_auc_score\n",
    "    model_roc_auc = roc_auc_score(testing_y,probabilities) \n",
    "    print (\"Area under curve : \",model_roc_auc,\"\\n\")\n",
    "    fpr,tpr,thresholds = roc_curve(testing_y,probabilities)\n",
    "    \n",
    "    plt.subplot(222)\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=1, label = \"Auc : %.3f\" %model_roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    \n",
    "    plt.subplot(212)\n",
    "    sns.barplot(x = coef_sumry[\"features\"] ,y = coef_sumry[\"coefficients\"])\n",
    "    plt.title('Feature Importances')\n",
    "    plt.xticks(rotation=\"vertical\")\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid 1: Selecting class weight and estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 24 candidates, totalling 72 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   28.9s\n",
      "[Parallel(n_jobs=-1)]: Done  72 out of  72 | elapsed:  1.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'max_features': ['auto', 'sqrt', 'log2', None],\n",
       "                         'n_estimators': [300, 500, 700, 900, 1100, 1300]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid1 = {'max_features':['auto', 'sqrt', 'log2', None],\n",
    "          'n_estimators':[300, 500, 700, 900, 1100, 1300]\n",
    "         }\n",
    "\n",
    "rf_model = RandomForestClassifier()\n",
    "grid1 = GridSearchCV(estimator=rf_model, param_grid=param_grid1, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\n",
    "grid1.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid1.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e31749898>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = pd.DataFrame(grid1.cv_results_)\n",
    "dt.param_max_features = dt.param_max_features.astype(str)\n",
    "dt.param_n_estimators = dt.param_n_estimators.astype(str)\n",
    "\n",
    "table = pd.pivot_table(dt, values='mean_test_score', index='param_n_estimators', \n",
    "                       columns='param_max_features')\n",
    "     \n",
    "sns.heatmap(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5664538956289195"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid 2: Selecting max depth and split criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   38.8s\n",
      "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed:  2.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'criterion': ['entropy', 'gini'],\n",
       "                         'max_depth': [7, 9, 11, 13, 15, None],\n",
       "                         'max_features': ['auto'],\n",
       "                         'n_estimators': [1000, 1100, 1200]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid2 = {'max_features':['auto'],\n",
    "          'n_estimators':[1000, 1100, 1200],\n",
    "           'criterion': ['entropy', 'gini'],    \n",
    "          'max_depth': [7, 9, 11, 13, 15, None],\n",
    "         }\n",
    "\n",
    "rf_model = RandomForestClassifier()\n",
    "grid2 = GridSearchCV(estimator=rf_model, param_grid=param_grid2, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\n",
    "grid2.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=11, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid2.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e26d720f0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = pd.DataFrame(grid2.cv_results_)\n",
    "\n",
    "table = pd.pivot_table(dt, values='mean_test_score', index='param_max_depth', \n",
    "                       columns='param_criterion')\n",
    "     \n",
    "sns.heatmap(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e3e7c6550>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.pivot_table(dt, values='mean_test_score', index='param_max_depth', \n",
    "                       columns='param_n_estimators')\n",
    "     \n",
    "sns.heatmap(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5754368499916416"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid2.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking if other depth and estimator value results better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:   23.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'criterion': ['entropy'], 'max_depth': [10, 11, 12],\n",
       "                         'max_features': ['auto'],\n",
       "                         'n_estimators': [950, 1000, 1050]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid2_2 = {'max_features':['auto'],\n",
    "          'n_estimators':[950, 1000, 1050],\n",
    "           'criterion': ['entropy'],    \n",
    "          'max_depth': [10, 11, 12],\n",
    "         }\n",
    "\n",
    "rf_model = RandomForestClassifier()\n",
    "grid2_2 = GridSearchCV(estimator=rf_model, param_grid=param_grid2_2, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\n",
    "grid2_2.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=10, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid2_2.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57924338341986"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid2_2.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid 3: Selecting minimum samples leaf and split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  48 out of  48 | elapsed:   42.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'criterion': ['entropy'], 'max_depth': [10],\n",
       "                         'max_features': ['auto'],\n",
       "                         'min_samples_leaf': [1, 3, 5, 7],\n",
       "                         'min_samples_split': [2, 4, 6, 8],\n",
       "                         'n_estimators': [1000]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid3 = {'max_features':['auto'],\n",
    "          'n_estimators':[1000],\n",
    "           'criterion': ['entropy'],    \n",
    "          'max_depth': [10],\n",
    "          'min_samples_leaf': [1, 3, 5, 7],\n",
    "          'min_samples_split': [2, 4, 6, 8]\n",
    "         }\n",
    "\n",
    "rf_model = RandomForestClassifier()\n",
    "grid3 = GridSearchCV(estimator=rf_model, param_grid=param_grid3, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\n",
    "grid3.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=10, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=8,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid3.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e3e9f2470>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = pd.DataFrame(grid3.cv_results_)\n",
    "\n",
    "table = pd.pivot_table(dt, values='mean_test_score', index='param_min_samples_leaf', \n",
    "                       columns='param_min_samples_split')\n",
    "     \n",
    "sns.heatmap(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5780629261081371"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid3.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid 4: Selecting class weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   9 | elapsed:    5.2s remaining:    6.6s\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:    8.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,...\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'class_weight': [{0: 1, 1: 1}, {0: 1, 1: 2},\n",
       "                                          {0: 1, 1: 3}],\n",
       "                         'criterion': ['entropy'], 'max_depth': [10],\n",
       "                         'max_features': ['auto'], 'min_samples_leaf': [1],\n",
       "                         'min_samples_split': [8], 'n_estimators': [1000]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid4 = {'class_weight':[{0:1, 1:1}, {0:1, 1:2}, {0:1, 1:3}],\n",
    "            'max_features':['auto'],\n",
    "          'n_estimators':[1000],\n",
    "           'criterion': ['entropy'],    \n",
    "          'max_depth': [10],\n",
    "          'min_samples_leaf': [1],\n",
    "          'min_samples_split': [8]\n",
    "         }\n",
    "\n",
    "rf_model = RandomForestClassifier()\n",
    "grid4 = GridSearchCV(estimator=rf_model, param_grid=param_grid4, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\n",
    "grid4.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight={0: 1, 1: 3},\n",
       "                       criterion='entropy', max_depth=10, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=8,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid4.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e527cf748>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD7CAYAAAChScXIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAG7BJREFUeJzt3Xu0XWV57/Hvzo0AJmIJ6AGFHo7yA0RjEWnCTYIGRUoTrRdkCBqIgQ5aijkM5NZ6wRuDE7kIlIab3ATSDqJRuVlCCCSkYtA2cMIDOZRWsESSAwEFQ7L36h/vXGSx2XuvubPm2nvOvX4fxhzZc8255vvG4Xj2m2e+7/t01Wo1zMysGkYNdwfMzCw/B20zswpx0DYzqxAHbTOzCnHQNjOrEAdtM7MKcdA2M6sQB20zswpx0DYzq5Axw92B3saM29VLNM0sl82vPtPV6jM2rXsyd8wZO2mPlttrlUfaZmYVUrqRtpnZkOrpHu4eDIqDtpl1tu7Nw92DQXHQNrOOVqv1DHcXBsVB28w6W4+DtplZdXikbWZWIX4RaWZWIR5pm5lVR82zR8zMKsQvIs3MKsTpETOzCinoRaSkUcDlwGRgIzA7ItY0XD8S+Ep2+jBwCrAd8APgj4DfA8dFxHMDteO9R8yss9V68h8DmwmMj4ipwJnAvPoFSROAC4A/i4gpwFPAJOCLwMqIOAS4BTi3WSMeaZtZZyvuReTBwJ0AEbFC0v4N1w4EVgHzJO0BXJWNqC+SNDq7ZzdgbbNGPNI2s87W05P/GNhEYEPDebek+sB4EjAN+DJwJHCapD0BIqJb0mLgr4HbmzXikbaZdbRaLX9OW9IcYE7DR/MjYn7284vAhIZroyKiPoxfDzwUEc9mz1kKvA94HCAiDpe0F/BT4H8N1AcHbTPrbIOYPZIF6Pn9XF4GHA0skDSFlA6pWwnsK2kS8AIwBbhS0lnA0xFxA+lFZNPfIA7aZtbZipunvRCYLmk50AXMkjQXWBMRi7IAfVd274KIeETSc8B1kk4ERgOzmjXSVauVq7qXy42ZWV5FlBv7w8of5o45498/c9jLjXmkbWadrXvTcPdgUBy0zayzeRm7mVmFeBm7mVmFeKRtZlYhDtpmZtVR84tIM7MKcU7bzKxCnB4xM6sQj7TNzCrEI20zswrxSNvMrEI2uxq7mVl1eKRtZlYhFctpt7XcmKRPtvP5ZmYtK66w75AodKQtaSdSHbRtgXcDh0g6AHiUVFJ+Sb3cjplZKXT4SPsuUt2zd5DK6xwErMjO3wvcUXB7Zmat6eSRNnBhVuus0W3ZgaTVBbdnZtaaTp490kfAHtR1M7MhV7KSi80UndOe09+1hjLzZmblUbGcdtHpkb1IJeRvIFUjrqvWrzIz6xydHLQjYq6kvYA7IuKhIp9tZtYWJXnBmFc7FtccD7ypDc81Myted/dw92BQCg/aEbEOWFf0c83M2qKT0yNmZpXjoG1mViEVy2m3Ze8RSWMGOjczK4taTy33UQbt2jDqJ03OzczKoacn/1ECbRkBR8RHBzo3MyuNTp89YmZWKQWNoCWNAi4HJpN2NZ0dEWsarh8JfCU7fRg4BZgI3Jj9OQ6YGxEPDtRO0cvYj+jvWkTcXWRbZmaFKC7tMRMYHxFTJU0B5gEzACRNAC4ADouIdZLOACYBfwXcExEXSRJwM7DfQI0UPdL+IrA/cC9vXMbuoG1m5VPchlEHA3cCRMQKSfs3XDsQWAXMk7QHcFVEPCfpQtKoHFI8/kOzRooO2scAS4DzIyIKfraZWfGKG2lPBDY0nHdLGhMRm0mj6mmkegO/A+6X9GBEPA4g6W2kNMlpzRopeu+RbkmfB7Yv8rlmZm0ziKl82U6mjbuZzm/YwfRFYELDtVFZwAZYDzxUr9wlaSkpgD8u6T3ALcDpEXFfsz60Yxn7k0U/08ysbQYxeyQL0P1tM72MtMvpgiynvarh2kpgX0mTgBeAKcCVkvYB/hH4TET8a54+ePaImXW0WnHpkYXAdEnLSe/0ZkmaC6yJiEWSziKVZARYEBGPSPoRMB64OL2HZENEzBioka5ayao2jBm3a7k6ZGaltfnVZ7qa3zWw33/z+NwxZ/tzrm+5vVZ5pG1mna1ie48MSdCWdDPwNHBBRPx2KNo0M8ulJHuK5DVUI+3TSHtsD/s/LczMXmezl7G/QUSsHYp2zMwGrZPTI5L27O9afRK5mVmpdHh65BpgD+Ax3riM/fCC2zIza1mBU/6GRNFB+wjgPuC4iHim4GebmRWvYiPtQosgRMTLwMnAbkU+18ysbXpq+Y8SyBW0JZ3b6/zb/d0bESub7QdrZlYa3d35jxIYMD0i6URgNrC3pI9lH48GxgJntblvZmZtV5baj3k1y2nfCNwDnA18M/usB/ACGTMbGUZS0I6IjcBTWZWFDwPbNVy+vp0dMzMbEhWbPZL3ReRtwFHA3tmx10A3S7p0oHMzs9Ko2IvIvFP+uiJi1iCee1mTczOzcihJMM6r2YvIcdmPT0qaSqogXAOIiFf7+c7OwD7Z/S8AD0bE6uK6bGZWnFp3tdIjzUbaQQrSXbx+RWONtPLxdSTNJpXieQB4CdgXOFvSVRFxRZ4OvXTHV5rfZGZWlJE00o6I/znI580CDoqITfUPstH6MiBX0DYzG0ojbcofAJKeIM3PrtsE/Bo4IyIebvh8LLBtdr1uO7KUiplZ6YzEoA0sJhWfvB+YSlpwcy1wCXBww33nASuzIL+BVFL+ncDcojpsZlaoaqW0cwftPSPin7Ofl0j624i4R1LvBPRytkwLnEgqKb+6XkZe0o4Rsb6IjpuZFaG2uVpRO2/QflXSyaSgfCCwUdL7+/j+YuATpCo1/zciuiWNkbQLMAG4FXhfMV03MytAtWJ27qB9LHAOMANYBRwHHACc0Ou+i4BLSXntiZK+D5wEPAe8Any39S6bmRVnRL2IlPT2iHga2JE0+6OL9FJxx4i4o/f9EXEtKdddnzWyENgvWw5vZlY+I2ykPTc7/oEtM0DqgXvASjTZ4pujWu2gmVk7jaiRdkTMzf6cJunNwO7AkxHxu6HonJlZ21VspJ23CMJfAEuAm4Av9S6KYGZWVbXN+Y8yyLvL31xgCmlWyDeAj7etR2ZmQ6jWk/8og7xBuyd7mViLiBrw+zb2ycxs6PQM4iiBvFP+7pd0M/B2SVcAD7WxT2ZmQ6YsI+i8cgXtiDhb0kdJW7M+FhE/bm+3zMyGRlFBW9Io4HJgMrARmB0RaxquHwnUV5E/DJySZS6Q9HHgUxFxbLN28r6IXAkcCix2wDazkaTW3ZX7aGImMD4ipgJnAvPqFyRNAC4A/iwipgBPAZOyaxcD3yZnPM6b055K2izqREnLJV2Y83tmZqVW4IvIg4E7ASJiBbB/w7UDSavJ50m6H1gbEc9l15YDf5m3v3mD9vbZMQbYBtg5bwNmZmVW6+nKfTQxkbS7aV23pHoKehIwDfgycCRwmqQ9ASLiVgaxfXXeF5HPkX5LnBMRc/I+3Mys7AaT05Y0h1Sdq25+RMzPfn6RtDFe3aj6DqfAeuChiHg2e85S0uZ5jw+2v3mD9juAjwCfk3QasDIizhpsY2ZmZVOrNR1BvyYL0PP7ubwMOBpYIGkKaaBbtxLYV9IkUu3cKcCVW9PfvEF7LfAEsCdpKfsfb01jZmZlU+CUv4XAdEnLSXs0zZI0F1gTEYsknQXcld27ICIe2ZpGumq15qkUSY8DS4HbgH/urxJ7EV65Z361dm8xs2Gz7Yfm5B8m9+M/9/9Q7piz2y/uabm9VuUdae8VEW/4fSRpYUR4SbuZVVaOF4ylkndxTX//gNihwL6YmQ25ERm0B+BUhplVWo4Mcam0GrTNzCqt00baZmaVNpgpf2XQatB+vpBemJkNk+7me4qUSq6gLendpCWaPcC3gG9FxD0R8Rft7JyZWbtVbaSdd++RK0hbDZ4LnMOW7QXNzCqtwL1HhkTeoL0JeBQYl+1e5Vy4mY0ItVr+owzyBt8a8APgdkmfxuXGzGyEKMsIOq+8QfszwAHAHcAHs3Mzs8rr7smbcCiHvL0dS6q08C7gOGC3dnXIzGwoVS09kjdoXw+8lTRz5GeAK9eY2YjQU+vKfZRB3qA9hrTL3w4RcQswOs+XJH1yaztmZjYUarWu3EcZ5M1pjwO+CyyVNK2/70naiVRSZ1vg3cAhkg4gzTzZCCypV24wMyuDsqQ98so70v4CEMD5wE7A5/q57y7gT0iVblYCBwErsvP3kl5kmpmVRtXSI3lH2v8O/BL4U1IVmz8Fnuzjvgsj4oZen92WHUhavZX9NDNri6rNHskbtG8jpUh2JeWzfwPc3Md9d0n6P8AfSAF8PYCkr0TE1/oI6GZmw6pi2ZHc6ZE3R8RHgX8B3g+M7+e+60jVhX9Dyn/vnn3+wZZ6aWbWJiM1PbIp+3P7iHhF0rh+7htfLycv6VfAjyQdRipyaWZWOmWZFZJX3pH2Qkl/B/yrpBXAS/3cN1rSewAiYjnwbWAR8OaWe2pm1gY9gzjKIG+NyMvqP0v6KfBEP7eeCnxP0mciYm1E3CppLHBx6101MyterWKJgAGDtqSb6T9Pf2zvDyLiV8BhvT67UdIPtraDZmbttLli6ZFmI+0rimhkgGruZmbDqmoj7QFz2hFxX0TcR6pa86Hs57Pof/aImVmlVC2nnfdF5NeAS7OfP0OTyjWSxgx0bmZWFjW6ch9lkLtyTUT8FiAiNgDdTe7/SZNzM7NSqNpIO+8I+OfZy8QHgQ+QlrT3K1uI0++5mVlZdJdkBJ1X3pH2qcACYDvgnyLiVICGFY9mZpXU05X/KIO887RrwA/7uHQtcHj9RNIRAzzj7kH3zsyszXoqNtJu9QVh77/tF4H9gXt7XasBDtpmVjpFbRglaRRwOTCZVD9gdkSsabh+JFsmcTwMnEKaiXcjsDNppfnnI+K5gdppNWj3/vseAywBzo+IaPHZZmZtV+ALxpmk/ZemSpoCzANmAEiaAFwAHBYR6ySdAUwi1dxdFRFflXQMcC7wNwM1UuhGshHRDXyetI2rmVnp9XR15T6aOBi4EyAiVpCyDnUHAquAeZLuB9ZmI+rXvkMqEvPhZo0UnR4hIvoqjmBmVkrN5i83kjQHmNPw0fz6zqakRYgbGh8taUxEbCaNqqcB7wN+B9wv6cFe33mJHJvrtRq0F7f4fTOzYTWYWSFZgJ7fz+UXgQkN56OygA2wHnioXiNX0lJSAG/8zgTghWZ9yBW0JZ0EnAxsQxpd1yJin4g4L8/3zczKqsDZI8uAo4EFWU57VcO1lcC+kiaRAvMU4MrsOx8Dfg4cCdzfrJG8I+2/yR78fN7em5lVQYHlxhYC0yUtJw1uZ0maC6yJiEWSziIVPwdYEBGPSHoSuE7SA8Cr9LF7am95g/a/Ab/OXjQOWrbF69PABfXl8GZmZVDUoplsN9OTe338WMP1W4Bben3nZeBTg2knb9BeDDwp6f+xJT1yeJPvNDoNWIfLjplZyZRlT5G88gbtk4BPkyNJ3peIWLs13zMza7fuig0l8wbtp0lvPgf8pSRpz/6uRcTjg+mYmdlQGKkj7W1IRX0fIcvbR0RfCfNrgD1IeZzey9gHk04xMxsSIzVofzvnfUcA9wHHRcQzW9clM7OhU7ESkbmXsa8CdgF2B/6YtCTzDbI3oScDuxXROTOzdhupRRD+CXgceA/wB+Dl/m6MiJUF9MvMbEhs1TzmYZR7w6iIOBkIYDrwlrb1yMxsCI3IIggAksYD25NeKr6pbT0yMxtCZUl75JV3pH0ZaYHM3cCvaVjlY2ZWZVXLaecN2uMj4jsRcTWwT0QcM9DNki4d6NzMrCxqgzjKIG/Qfm3/2Ih4Mcf9lzU5NzMrhZGa095G0i9JLyJ7oN/FNUjaGdhH0lTSsvcHI2J1EZ01Myta1WaP5A3aX85zk6TZpFH5A6QqDPsCZ0u6KiKuyNWhyU2r7ZiZFaanNImPfPIG7VXAR4CxpOXpu5BWPvY2CzgoIjbVP5A0jrTRd66gbWY2lMrygjGvohfXjAW2BTY1fLYd5cnhm5m9TtWCU+552hFxsqRrgNnA0n5uOw9YKekJUrHKicA7gbmtdtTMrB1G6kg77+Ka5cDe2TGRVLRydb24paQdI2J9Sz02MyvQ5q5qjbUHs7jmdFJu+2n6X1yzmLSp1DpgRUSsApC0iyQB97TWXTOzYlVtnnbekXaNlBZ5HtgIXNvPfRcBl5Ly2hMlfZ9U9eY54BXgu6101sysaCM1PfK3wAER8VtJbwV+zJaqwq+JiGvJAno2a2QhsF9EbCyov2ZmhRqpU/7W16uoR8RaSU1XRUbEq8BRrXTOzKzdqhWy8wftFyXdRZqb/X5gO0nfAoiIs9vVOTOzdhup6ZEfNfzsMmJmNmJ0V2ysnStoR8R17e6ImdlwGKkjbTOzEak2EkfaZmYjlUfaZmYVMlKn/JmZjUjVCtkO2mbW4TYXFLYljQIuByaTVo7Pjog1DdcvAQ4i1RoAmAH8EXAdacvr/wDmRER/u6gC+fceMTMbkWqD+K+JmaR6ulOBM4F5va7vB3wkIg7Ljg3ABcAVEXEIsIQcO6I6aJtZRyuwGvvBwJ0AEbEC2L9+IRuFvwuYL2mZpBOyS/sAd2Q/L8ueMSCnR8ysow1myp+kOTQUOgfmR8T87OeJpDoCdd2SxmRbU28PfI+0ad5o4F5JvwB+Bfw5KUXy59l9A3LQNrOONpgpf1mAnt/P5ReBCQ3no+q1BEjVvi6u56slLSblvv83cKmkz5K2rl7XrA9Oj5hZR+uu1XIfTSwDPgYgaQqp/kDdnsADkkZLGktKgzwMTAe+FhEfJf3++FmzRjzSNrOOVuA87YXAdEnLSbNBZkmaC6yJiEWSbgJWkGroXh8Rj0p6E3CNpI3Ao8ApzRrpqjX/7TGkNq17slwdMrPSGjtpj65Wn/HZ3Wfmjjk3/8cPW26vVR5pm1lH8zJ2M7MK8TJ2M7MK8S5/ZmYVkmNWSKk4aJtZR3N6xMysQvwi0sysQpzTNjOrEKdHzMwqpGwLDJtx0DazjtZdsZF2WzeMkvTJdj7fzKxVPdRyH2VQ6Ehb0k7ANGBb4N3AIZIOIG2EshFYEhHPFtmmmVkrqpYeKXqkfRfwJ8A7gJWkemgrsvP3sqVCg5lZKXT0SBu4MCJu6PXZbdmBpNUFt2dm1pJOn/J3k6QZwFoggAuBbuDsiFjbR0A3MxtWnb6M/RJSjbO3ATsC/0AqF38VcHTBbZmZtawsaY+8is5pT46IWcAMYIeIuDoiFgDbFdyOmVkhqpbTLnzKn6SDIuJV4MPZ+TuBbYpux8ysCLVaLfdRBkUH7ZOA0yV1RcR/Zp/NA04vuB0zs0JUbaTtGpFmVllF1Ij8wC6H5o45D/1mqWtEmpkNp+5atTZnddA2s45WtmxDM23Ze0TSmIHOzczKomo57XZtGPWTJudmZqVQG8R/ZeAXkWZWWUW8iNz3rVNyx5xH1q7wi0gzs+FUlhF0XkVvzXpEf9ci4u4i2zIzK0Knzx75IrA/cC/Q+M+IGuCgbWal01OyFHEzRQftY4AlwPkREQU/28yscB2dHomIbkmfJ+30Z2ZWekWNtCWNAi4HJpMqdc2OiDUN1y8hFYZ5KftoBvBm4AZSZuL/A8dGxMsDtVP4lL+IeDIiVhX9XDOzdihwyt9MYHxETAXOJO271Gg/4CMRcVh2bAC+BNwaEYeSyjKe2KyRthb2NTMru+5ad+6jiYOBOwEiYgXp/R7w2ij8XcB8ScsknZBd+hXwluznicCmZo14yp+ZdbTBrFWRNAeY0/DR/IiYn/08EdjQcK1b0piI2ExKGX8P+C4wGrhX0i+Ap4HvSDqWtIX1V5v1wUHbzDraYJanZwF6fj+XXwQmNJyPygI2wMvAxfV8taTFpNz3l4AvRMRdko4CrgeOGqgPQxK0Jd1M+o1yQUT8dijaNDPLo8BV4ctIZRUXSJoCNL7b2xO4RdJ+pLT0wcB1wPNsGZ3/hi2pkn4N1Uj7NGAdr5+7bWY27Aqcp70QmC5pOSnWzZI0F1gTEYsk3QSsIOWtr4+IRyX9NXCppNHZd05p1oj3HjGzyipi75G37bB37pjz7Aurh33gWfQy9j37uxYRjxfZlplZETp9Gfs1wB7AY7xxGfvhBbdlZtaysmUbmik6aB8B3AccFxHPFPxsM7PCVW3vkUIX12TTWU4GdivyuWZm7VKr1XIfZVD47JGIWFn0M83M2qUsZcTy8uIaM+toZRlB5+WgbWYdrdNnj5iZVUpHv4isk3TpQOdmZmXR8S8iM5c1OTczK4WOrlwDIGlnYB9JU4EXgAcjYnXR7ZiZFaEsI+i8Ck2PSJoN/IRUUmd30k5WP5Z0cpHtmJkVpadWy32UQdEj7VnAQRHxWvUFSeNIWxZekecBRWwAY2aW1+ZXn6lUzCn6ReRYYNten20HFUsamZmVVNEj7fOAlZKeIG3sPRF4JzC34HbMzDpS0UF7ObB3dkwkld9ZXS+5I2nHiFhfcJtmZh2j6PTIYtILyHXAiohYBSBpF0kC7im4PTOzjlL0SPsi4FJSXnuipO8DJwHPAa+QKhGbmdlWalu5sWzWyELgExGxsS2NmJl1mNLViLTOJGk3YHJE/HiQ35sDXNs4zdRsJGvL3iNmW+Fw0qKswTobGF1wX8xKy7v82etI+gJwNOm9xP8ALgZmAPsCpwPjSFM4u4EHIuJMSW8H/h4YD+wIfD0ifijp30jl595Lmqs/IyI29NHmaOBMYDtJy4F/By4h1RldD5yQtXsraaAxllQh6QDgbcAtwMx+/j6fAL4MbAKeAo4HJgHfB3bI2jie9N7lRtKspzHAuRGxWNIjwOPAxqzNq7O/I8Cp9ZftZkPFI23ry4SI+BhwPvCXwCeAOcCJwNeAD0XEwcCukqYDewHzImI68FfAKdlzJgI3R8QHgWeAI/tqLCK6ge8AP4iIRcCVwCkRcRhwO3AGKUBvyJ5xKjAxIq4GngWOGeDv8lngwqy/d2d9OgdYFBEHZj8fAJwL/CwiDgU+BVwtaRTwJuC8iPgsaVR/T0RMy/73+Ps8/2OaFclB2/ryy+zPF0jz7GvA86QAthNwu6QlwD7AHsB/ASdJuoE0Gh3bx7N+TRqJ57E3cHnWxgnALsAdpFH7j4CvA3l3rp8LHCrpPuDA7HsCHgSIiMURcVPW5tLss2dIawx2yp4R2Z/vAU7I+nUl8JacfTArjIO29aW/t9M1UvCdno2Cvwf8C2kl7PURcRxwLynl0OxZvfWw5f+PARyftXEG8FPgMOC/IuII4BvAt/r4Xl/mAF/NRvtdwMeB1cAHACQdKun87LNDss92JQXk+kKw+i+Ix0ij9sOATwM35fy7mRXGOW0bjE2kufb3ZXnop4AFwD8Cl0h6lhTUJ23Fs1cB50h6mJSSuT5rA1JaZj1wq6TTSPn0r2fX7ieN/Kdl/yLo7efAzyStB14i7UJ5O3CNpM+RfqmcSPpXxTWSPknK58+JiM1pTdhrvklKm8whpVm+uhV/T7OWeMqfmVmFeKRtQyZbcHV3H5ciIk4q23PNysgjbTOzCvGLSDOzCnHQNjOrEAdtM7MKcdA2M6sQB20zswpx0DYzq5D/BvmUe+zJmToLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = pd.DataFrame(grid4.cv_results_)\n",
    "dt.param_class_weight = dt.param_class_weight.astype(str)\n",
    "table = pd.pivot_table(dt, values='mean_test_score', index='param_class_weight')\n",
    "     \n",
    "sns.heatmap(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.577445110448134"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid4.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight={0: 1, 1: 2},\n",
    "                       criterion='entropy', max_depth=10, max_features='auto',\n",
    "                       max_leaf_nodes=None, max_samples=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=1, min_samples_split=8,\n",
    "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
    "                       n_jobs=None, oob_score=False, random_state=None,\n",
    "                       verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight={0: 1, 1: 2},\n",
      "                       criterion='entropy', max_depth=10, max_features='auto',\n",
      "                       max_leaf_nodes=None, max_samples=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=8,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=0, warm_start=False)\n",
      "\n",
      " Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.83      0.85      1035\n",
      "           1       0.59      0.66      0.62       374\n",
      "\n",
      "    accuracy                           0.79      1409\n",
      "   macro avg       0.73      0.75      0.74      1409\n",
      "weighted avg       0.80      0.79      0.79      1409\n",
      "\n",
      "Accuracy   Score :  0.7877927608232789\n",
      "Area under curve :  0.8479681727763569 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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df/2+HHDA5uy7b5sqf28l3yISTap8i0g5NBsZJFlKpDNTQUEhd9/9Kf367cTGGzcgFoslJfGG9L8QkYhIxcRiFb+JSMbJystlfq9fww5DQrBqVT4DBrzKvfd+Tt++LxOPx5O6PVW+RSSaVPkWkQ0o2a8tmWfp0jz69ZvAhx/OolGj2txwQ5cq7e8ujZJvEYkmVbBFhPX3catfO7MtXLiC3r1fZMqUv2jevB6jRh1Phw4bJX27Sr5FREQkUtY1A4lIkb/+WkrPnuOYPn0+rVs3YsyY7rRr1yQl21byLSLRpLYTkYykgZNSFuPHO9Onz2ebbZoyenR3WrVqmLJtK/kWkWhS24lIRsrKy1XiLRt01lkdAejRYzuaNaub0m0r+RaRaFLlWyTjNBvZRgMnZZ0mT55Ny5YN2XTThsRiMQYO7BRKHEq+RSSalHyLZISS/d2aLlBK8957P9Ov3wQ226wRr77ai5ycOqHFouRbRKJJbScikVaUdGtApWzI+PHOuee+zurVhey6a0saNKgVajxKvkUkmlT5FoksDaqUsho+/Fsuv/wd4nE4++xO3Hjjfkmfx3tDlHyLSDSp8i0SSUWJt9pLZH3i8TgPPPAlt976MQDXXNOZCy7YLfTEG5R8i4iISJoq7QI56uuWsvj449+49daPicXgzjsPpG/fncIOaQ0l3yISTWo7EanW1FoildG5c2suvHB3tt++Bccea2GHsxYl3yISTWlwalFEykYVbqkKK1fms2DBClq1CqYSvOaazmGHVCol3yISSenQ1yciG6YKt1SFpUvzOO20l/jtt8VMmNCLjTeuH3ZI66TzsiISSbFYrMI3EUmdrLxcVbilUhYsWEH37mOYOPE3li1bzYIFK8IOab1U+RaRaFIOLZLWis/TLVJRf/65hJ49X8B9Pm3aNGL06O60a9ck7LDWS8m3iESSKtgi6UutJlIVZs5cSI8e4/jtt8Vsu20zRo/uziabNAg7rA1S8i0iUg5m1hfom3hYB9gZ6A3cDfyWWH4DMBF4BNgJWAWc4e4/pTJWkXRR47EWtFi1cM1jDaaUypo/fwVHHjmKefOW06lTS5577liaNKkbdlhlouRbRCIpWZVvd38KeArAzB4GngA6Ape7+7ii15nZ8UAdd9/LzPYE7gGOSUpQImkutmqhqtxSpZo1q0ufPh2YMuUvnnzyqNAvGV8eSr5FJJKS3XZiZrsC27v7uWb2OrCLmV0EfAFcAXQG3gBw988SrxfJGMWnD4zXTu8eXKk+Vq3Kp3btIH294oq9yc8vpGbN7JCjKh8l3yISSZVJvs1sADCg2KIh7j6kxMuuBv6VuP82MB74GXgUGAg0AhYVe32BmdVw9/wKByZSjWTl5a6pdufk1IPc5SFHJNXdCy9M5447JvHiiz3ZdNNgLu/qlniDkm8RiapKFL4TiXbJZHsNM8sBtnX39xOLnnD33MRzLwHdCRLvhsVWy1LiLVG1rovkiFSVJ574D1dd9R7xOEyY8CMDB3YKO6QKU/ItIpGU5LaT/YB3AMwsBnxrZnu7++/AgcAUYA5wFDA60fM9NZkBiaRCaUk2BIm2erolGeLxOPfd9zl33PEJANde27laJ96g5FtEIirJybcBMwHcPW5mZwAvmNkK4AdgKFAAHGxmnxDU4fslMyCRZNP0gJJqhYVxbrjhQx577CtiMRg06CBOPXXHsMOqNCXfIiLl5O53l3j8FvBWKS8dmJqIRJKveA+3SLLF43EuuugtRo78npo1sxg8+AiOPnqbsMOqEkq+RSSSdJEdkarTbGQb9XBLSsViMbbcsgn16tXgySePZv/9Nw87pCqj5FtEIknJt0jlFO/v1kVxJAwXXLAbxx+/La1bNwo7lCqVFXYAIiJJEavETUTWtJnM7bNYibekxPz5K+jT5yV+/TWYpTUWi0Uu8QZVvkUkolT5Fqk4tZlIqv3xxxJ69hzHf/+7gFWr8hk1qnvYISWNkm8RiSQl3yLlV9RqojYTSaUZMxbSo8dYfv99Ce3bN+eBBw4NO6SkUvItIpGk5Fuk/DSjiaTat9/OoVevF5g3bwW77tqS5547jpycOmGHlVRKvkUkmpR7i2xQyYvmqNVEUunTT3/nlFPGs2RJHvvv35Ynnjia+vVrhh1W0in5FhERyVCqdEuYvv9+LkuW5HHMMdvw8MOHU6tWdtghpYSSbxGJJLWdiKyfBlVK2M44Yxdat27EQQdtQXZ25kzAlzl7KiIZJRaLVfgmkgmy8nI1qFJSbsSIb5kxY+Gax4ceumVGJd6gyreIRJSSaJF/KnnhHJFUicfjDBr0GXff/SmtWzfio49Oy4j+7tIo+RaRSFLyLfJP6vGWMBQWxrnuug8YOvRrsrJiXHLJHhmbeIOSbxGJKuXeIiKhW726gAsvfIuxY6dRq1Y2jz56BEceuXXYYYVKybeIRJIq3yIi4VqxYjUDBrzKm2/OpF69mgwffjRdurQNO6zQKfkWERERkSr3/vuzePPNmTRpUofnnjuOTp1ahh1SWlDyLSKRpMq3yN+KXzZeJFWOOGIrbr99f/bZpzXbbts87HDShpJvEYkkJd/JYWZZwCPATsAq4Ax3/6nY85cBJwGFwG3u/mIogcpaNNBSUuX33xezbNlqzJoB0L//LiFHlH4ya2JFEckcsUrcZH2OBeq4+17AlcA9RU+YWQ5wAbAXcAhwfygRylp0MR1JlenT53HkkSPp0WMsv/66KOxw0paSbxGJJF1kJ2k6A28AuPtnwK7FnlsGzALqJ26FKY9O1tJsZBsAXUxHku6bb+ZwwAEjmD17KW3aNKZx49phh5S21HZSDdSokcXjN/WhbaumFBQUcs7Nz7Nw0TIevr43TRrVIzsrRv/rnubn3+cBQdLx4oMDeeWDqTw+9uOQo5fy+n7qtzzywL08PPQprrvyMhbMDz7XP2f/wfYdduLmOwYB8Puvs7ji0gt4dsxLYYabtpREJ00joHhJq8DMarh7fuLxb8APQDZwe6qDk78p8ZZUmTTpN0499SWWLs3jwAM3Z9iwo6hXL3Pn8d6QtE6+zSzL3TO+cnJY5+2pkZ3F/n3v5YA9tuVf5x3FkmUrGfXal4x7+2v223VrbPON1yTfN557JE0b1Q85aqmIZ54axhuvTaBunboAaxLtxYsXcf6Aflx46RUAvP7Ky4x5/hkW5S5c53tlOiXfSbMYaFjscVaxxPtwoCWwReLxm2Y2yd2/WNebxWKQk1MvOZGmqezsrJTsc1ZeLqsvyCcdGk5Stc/pJFP2ecKEH+nd+wVWrSrgxBO3Z9iwo6lVKzvssNJa2iXfZtYOuJfgVGZ+YnDPVOBid/8x1OBC8t9Z/6NGdhaxWIxGDeqwOr+AvXZux3f/nc2rj57HrNkLuOyusQAcd9DOFBbGeXPSDyFHLRWxaevW3D7o39x07ZVrLX/80Yc5odfJNG/RAoBGjRrx8ONP0ePow8IIUzLbJOAoYLSZ7UlwfC6yEFgBrHL3uJnlwvpzv3gccnOXJy3YdJSTUy/p+1zU550uP9tU7HO6yYR9njVrESeeOJb8/EL69t2JwYO7sWTJSpZHe7fX0qJFww2/qIR07Pl+HLjd3Tdz983dvQ1wM/BkyHGFZtnyVbRp1YxvXryOh687iUee/4C2LZuxcPFyug18iN/+WsCl/Q5muy1bcuJhu3LT4FfDDlkqaP8DD6FGjbV/J16wYD5TvviMI446ds2yffbrSt260a+oVIZ6vpPmRWClmX0C3AdcbGaXmNnR7j4R+BL4zMw+BX4E3g4x1oykdhNJlbZtG3PDDftx8cV7cOedB5CdnY5pZfpJu8o3wSj6z4svcPfPzCyseEJ3/ikH8M6n07j+wZfZbOMcXh9yAfMXLePVD4OC02sffseN5x1FnVo1aLVRDm8MuYC2rZqSt7qAWbPn8/Yn00LeA6mM9995i4MP60Z2tk7jlYty6KRItAIOLLF4erHnbwBuSGlQshZNKyjJFI/HmTNnGZts0gCAs87qGHJE1U86Jt/fmNkTBKPpFxH0Fh4BfBtqVCFauHg5+fkFACxYtJyaNbL54tufObTzdjz/6pd07rgV02b8yTX//nvg3TVnHcGc+YuVeEfA5M8/o+8ZZ4UdRrWjCrZkIk0rKMlUWBjn6qvf4+WX/8srr5xIu3ZNwg6pWkrH5PscgnlkOxOMql8MvEJwqjMjPfjMezx24ym8M+wiatWswQ0PTuDTb2bwyPUnM6DHvixauoK+Vz0VdpiSJL/O+plWm20WdhjVjpJvySTFr2CpdhNJhtWrCzj//Dd54YXp1KqVzYwZC5V8V1AsHo+HHUOVqrvLedHaIfmH3z/WdTsyRbP6NSqcQW912esVPhb8NOhwZe4pUlgYj8+fvzTsMFKqqgfiVYce70wYfFhSlPZ5xYrVnHHGK7z99s/Ur1+TESOOYd992/zjdVHa57Jq0aLhFNa+3sEGpWPlW0Sk0lT5lqhTtVtSYfHiVZxyyng+++wPmjatw/PPH88uu2wSdljVmpJvEYkk5d4SdRpYKcmWn19I9+5j+eabObRs2YAxY7qzzTbNwg6r2lPyLSIiIiL/UKNGFiefvANLl+YxenR3WrduFHZIkaDkW0QiSW0nEmWa1USSqaCgcM2c3X377sSJJ25H3bq6XHxV0WzoIhJJsVjFbyLpLisvV33ekhRff/0XnTsPx33+mmVKvKuWKt8iEklZWcqiJVqKBlgCqnpLUkyc+Ct9+rzEsmWrefTRKdx33yFhhxRJSr5FJJJUwZao0QBLSabXXvuJAQNeJS+vgOOP35a77jow7JAiS8m3iESSer4lStTjLcn0/PPfcfHFb1NYGKd//5259db9dfYwiZR8i0gkKfeWqKgOF9GR6uuxx77iuus+AODSS/fk8sv3UvEiyZR8i4iIpDG1m0gyNW5cm1gMbr65KwMGdAw7nIyg5FtEIkmVG6nONLhSUqVXr+3p1KklW2/dNOxQMoaSbxGJJCXfUl0VtZmo2i3JkJdXwFVXvUffvjvRocNGAEq8U0zzfItIJGmeb6mO1N8tybR8+WpOO+0lnn56Kmec8Qr5+YVhh5SRVPkWkUhS5VuqGyXekky5uSs5+eTxfPnlbJo1q8uQId2oUUM12DAo+RaRSFLuLdWJEm9JpjlzlnHiieP44Yd5bLppQ0aP7q5WkxAp+RaRSFLlW6oTzWgiyTJr1iJ69BjLL78sYqutmjB6dHc226xR2GFlNCXfIiIiIdIFdCSZvv12DrNmLWLHHTdi5Mjjad68XtghZTwl3yISSSp8S3WgdhNJtqOO2oYnnjiK/fZrQ8OGtcMOR1DyLSIRpbYTSXdKvCVZPvxwFg0b1qJjx5YAdOu2dcgRSXFKvkUkkpR7SzoqunhOC4KL5yjxlqo2YcKPnH3269SvX5N33z1F/d1pSMm3iESSKt+SjrLycll9QT65ucvDDkUi6Nlnp3Lppe9QWBjntNN2pFWrhmGHJKVQ8i0ikaTcW9KNBlZKMj300JfcdNNEAC6/fC8uvXRPFSHSlJJvEYkkfelIOihqM4G/20yUfktVisfj3HrrxzzwwJcA3H77/vTvv0vIUcn6KPkWkUhS7i3pQPN3S7J9991cHnpoMtnZMR588DBOOKF92CHJBij5FhEREammOnTYiPvvP4SmTetyyCHtwg5HykDJt4hEUjLbTszsKuBooBbwCPAh8BQQB74DznX3QjO7AegG5AMXufsXSQtK0o56vCVZli1bzcyZC+nQYSMAevXaPuSIpDyywg5ARCQZYrGK39bHzLoCewP7AF2A1sC9wLXuvi8QA44xs46J5/cAegEPJ21nJS1l5eVqKkGpcgsXrqBHj7Ecd9wYpk79X9jhSAUo+RaRSIrFYhW+bcChwFTgRWAC8ArQiaD6DfA6cBDQGXjL3ePu/itQw8xaJGVnRSQjzJmzlGOPHc3kyX/SqFEt6tWrGXZIUgFqOxGRSEpi20lzoC1wJLAF8DKQ5e7xxPNLgMZAI2B+sfWKls9NVmCSPtRyIlXtl19y6dFjHLNmLWLrrZsyZkx3zeNdTSn5FpFIqkzubWYDgAHFFg1x9yGJ+/OB6e6eB7iZrSRoPSnSEMgFFiful1wuGUCznEhV+uHfiSpOAAAgAElEQVSHuZx44gvMmbOMnXfemOefP55mzeqGHZZUkJJvEYmkylS+E4n2kHU8/TFwoZndC7QE6gPvmllXd/8AOBx4H/gJuMvMBgGbEVTH51U4qCQzsybuvjDsOKJAVW+pSkuX5nHCCeOYN285nTu3ZsSIY2jQoFbYYUklKPkWESkHd3/FzPYDviAYN3Mu8DMw1MxqAdOAse5eYGYTgU+LvS7tmFkXgsGg2WY2Bpjl7sNCDqvaajayDYAGWkqVadCgFrfc0pUJE35k8OAjqFNHqVt1p09QRCIpmRfZcffLS1ncpZTX3QjcmLxIqsTNwH7AOOA2YBKg5LuC1G4iVWXBghU0bRq0lhx//LYcd5zpyr0RodlORCSSkjjbSdQUuvsCIO7uKwkGhko5NRvZhhYjGqndRKrE009/y+67P8HXX/+1ZlkGHpsiS5VvEYkkfU+V2U9mdjvQzMyuBGaFHVB1pIq3VJUHHviCW275GIDPPvuDXXbZJOSIpKop+RaRSMpS9l1WA4EzCAaSLkvcl3LQAEupCvF4nJtumsjDD08mFoM77jiQfv12CjssSQIl3yISScq9y+x+dz+v6IGZjQD6hBhPtaOqt1RWQUEhl132Ds8++x01amTx0EOHcfzx24YdliSJkm8RiST1R66fmZ0LXAs0NbPjE4tjwA/hRSWSmc477w3GjZtOnTrZPPHEURx0ULuwQ5IkUvItIpKB3P1h4GEzu9rdbws7nuqo2cg2ZOXlquVEKu3II7fmvfd+YfjwY9hzz03DDkeSTMm3iERSlgrfZfWomZ0E1CSofLdy99tDjimtFU+61W4iFRWPx9ecoevWbWv23bcNjRrVDjkqSQUl3yISSWo7KbOxwI/AjsAKYHm44aQ/9XhLZf355xL69n2Zf/2r65pKtxLvzKF5vkUkkmKxit8yjbsPBKYDBwNNQg4nrWlmE6msmTMXctRRo/j66zncfPNE4vF42CFJiqnyLSKRFCMDs+gKMrM6QH0gDjQIOZy0VLzVRJeOl4r6/vu59Ow5jrlzl9Ox4yY8/fQxOkuXgZR8i0gkqee7zB4GLgLeAn4jmO9bSlCriVTW55//wcknj2fx4lXsu28bhg8/mgYNaoUdloRAybeIRJKqSWXj7uOK7pvZGIIKuBSjVhOprPfe+5l+/SawYkU+3bptxaOPHkHt2krBMlXSP3kza0nQQ5gPXAE86O7/SfZ2RURk3cysI/AvYAFwmbvPBU4BrgE011kxqnpLZRUWxlm9upCTT96Bu+8+iBo1NOQuk6Xi164RwG3AuQSj6u8D9k/BdkUkg6nwvUFDgauAtsCtZlafIOneb30rmVkW8AiwE7AKOMPdfyr2/OHADYmHXwHnurtGlElGO+igdrz5Zm922KGFzspJSmY7qQF8BOS4+0ggOwXbFJEMlxWLVfiWIZa5+1vuPhQ4HPgZ2N/dZ2xgvWOBOu6+F3AlcE/RE2bWELgbONLd9wR+AZonI3iRdBaPx7n77k/46KO/B+d26LCREm8BUlP5rgXcC3xkZvunaJsikuH0HbdB+cXuz3b3a8u4XmfgDQB3/8zMdi323N7AVOAeM2sHPJ5oZ6m21O8t5RWPx7nxxo8YPHgKDRvW4ssv+9O0ad2ww5I0kopEuC/B3LGPE1RMTknBNkUkw6nCtEFZZlaT4AzoisT9GIC7561nvUbAomKPC8yshrvnE1S59wd2BpYCE83sU3f/cV1vFotBTk69Su5KctR4rAXEYuSfNZeqTL+zs7PSdp+TJVP2OT+/kHPOeZWnnvqGmjWzGDy4G+3aNQs7rJTJlM+5slKRfM8E8ggG8bwPaNSKiCSdcu8Nagt44n4scT9GMNd3u/WstxhoWOxxViLxBpgPfOnufwGY2UcEifg6k+94HHJz0+uimv+Y07uK48vJqZd2+5xsmbDPK1fmM3Dga7z22k/Uq1eD0aN7sPvuLSO/38VlwudcUosWDTf8ohJSkXw/BswmqH5PJhiAeUQKtisiGSyDercrxN23qOCqk4CjgNFmtidBm0mRKcAOZtYcyAX2JBjYWa1odhMpr6VL8zjttJeYOPE3GjeuzbPPHschh2yZcYmolE0qBlxu6e7XAyvcfQLQOAXbFJEMF6vETdbrRWClmX1CMHvVxWZ2iZkdnejvvgp4E/gceMHdvwsxVpGU+OGHeXz++Ww22qg+48f3ZPfdW4UdkqSxVFS+aySqIEUj4QtTsE0REUkCdy8EBpZYPL3Y8yOBkSkNqooUbzcRKY/dd2/Fk08exVZbNWWLLfTvR9YvFcn3tQSnKVsCnxFcxlhEJKk04LLszKwRQQ/4THdfFnY8YVG7iZTHzJkL+e23xXTp0haAgw9e31AJkb8lPfl29w8BM7MWwDxdbEFEUiFLuXeZmNkJBAPiaxD0ccfd/ZaQw0o5TSko5TF16v848cQXWLYsj5de6snOO28SdkhSjaTi8vLvE4yeL3qMux+Q7O2KSGZT5bvMLiYYGPkGcAvBwPiMS75V9Zay+uyz3zn55PEsWZJHly5t2WqrpmGHJNVMKtpOinoDY0AngksSi4gklXLvMit091WJinfczDKu7URVbymrt9+eSf/+E1i5soCjjtqaRx45nNq1de1AKZ9UtJ14sYfTzez0ZG9TRESV7zKbaGbPA5uZ2aPAl2EHlGqqektZjBs3jfPPf5P8/EJOOWUH7r77ILKzUzFpnERNKtpOBhR72JK1L84gIpIU6vkuG3e/2swOA74CpiemhBWRYubOXc6ll75Dfn4h5523K9ddt69+wZcKS8W5kpbF7q8EeqZgmyIiUgZmNhl4AnjM3TOu/KuWEymLFi3q8fjj3Zg2bT7nn79b2OFINZe05NvMtkncfb7EU7WStU0RkSKqSpVZN+BU4F0z+x4Y6u6TQo4pZdRyIutSWBjnhx/mscMOLQA46KB2HHSQphOUyktm5fuxdSyPA5rtRESSSql32bj7HGCQmY0G7gImAJq+QTJafn4hF130FuPHOyNHHkfnzm3CDkkiJGnJt7vvX9pyM1PlW0SSLkuV7zIxsz7AaUA2QftJv3AjEgnXypX5DBjwKm+8MYN69WqQn6/Lk0jVSsWAy7OAS4CaBMWo1cA2611JRKSSlHuX2U7AOSVmphLJSEuWrKJPn5eYNOl3cnJq89xzx7Hrrq3CDksiJhUDLs8EuhJcZn4Mury8iKSAer7Xz8yOdPdXgB+BLmbWpeg5dx8SXmQi4Zg3bzknnfQi33wzh403rs/o0d1p37552GFJBKVigsp57v4n0NDdP0C9hCKSArFYxW8Zolni700IZqUquuk62ZJx4vE4vXsHiffmmzfmlVd6KfGWpElF5XuRmR0LxBMtKC1SsE0REVkPdx+euFvg7msuJ29mt4cUkkhoYrEY11zTmdtu+5gRI45l443rhx2SRFgqku8zgK2AK4HLgLNTsE0RyXAacLl+Ztaf4Pjc3syOSCzOIpgO9qrQAkshzfEtS5fm0aBBMA9Ely5t2XffNmTpCl2SZMmc53siwXSDY9z968TiS5O1PRGR4pR7b9AzwLvA1cCtiWWFwP9CiyjFNMd3Zvvkk984/fQJPPLI4RxwwBYASrwlJZJZ+T4P6A9cb2avAkPcfVoStwfAwi8fSvYmJGS/zF0edgiSIs3qV/wQpQGXG9TB3Seb2TjAii1vD7wVUkwpo6p3ZnvzzRmceeYrrFxZwPjxP65JvkVSIZnzfH8DXJCY1/sY4B4zqwsMc/dnkrVdERFIzWjyau5AYDLQq8TyOBFPvpuNDC6YMr/XryFHImEYM+YHLrjgTQoK4vTpsyN33qnr/klqJb3n293zgDFmNgk4H3iA4HSniEjSqPK9fu5+Z+LvfmaWTXAdhr2Az0MNLAXUbpK5Hn/8a66++n0ALrpod666ah8dKyTlkpp8Jyrd3YE+QGNgGNA2mdsUEQFQ62bZmNmdwEyCY3NH4C+gb5gxJUuzkW3IystVu0mGevDBL7n55okA3Hjjfpxzzq4hRySZKpkDLocDBwAvA1cUG3QpIpJ0Sr7LrLO7X2Fm77v7/mb2btgBJYsq3pltt91aUr9+TW69dX96994h7HAkgyWz8v0BMNDdVyRxGyIiUjnZZrY78EtijI6uxSCREY/H17SV7LnnZnz5ZX+aN68XclSS6ZI2Jsndn1TiLSJhicViFb5lmBHAg8Ag4C7g3+GGI1I1VqxYTd++L/P66z+tWabEW9JBKi6yIyKScmo7KRt3f8TMRgHtgFvcfV7YMYlU1uLFqzj11PF8+ukffP31X3Tt2pa6dWuGHZYIkKLZuMyskZl1MDNdr1VEUiIWq/gtk5hZT+AT4BrgMzM7JeSQkkLzemeOuXOXc9xxY/j00z/YZJP6jB7dXYm3pJWkJ99mdgLwIfAccImZXZvsbYqIZMViFb5lmIuBTu5+LLALcGHI8SRFVl6u5vXOAL//vpijjx7F1Kn/Y4stcnjllV5su23zsMMSWUsqKt8XA3sC84BbgONSsE0RyXBZlbhlmEJ3Xwrg7kuAlSHHI1Ih//3vAo48ciQzZixkhx1aMGHCibRp0zjssET+IRU934XuvsrM4u4eN7NlKdimiGS4zCtgV9gMM7sH+AjYD5gRcjwiFbJkySpyc1exxx6b8swzx9C4cZ2wQxIpVSqS74lm9jywmZk9CnyZgm2KiEjZnA6cBRwMTAOuDDcckYrp2LEl48f3YJttmlGvnnq8JX2l4vLyV5vZYcBXwDR3fyXZ2xQRycDe7XJJDIDvBywFBrt7YcghiZTb66//RF5eAcccYwDsvPMmIUcksmGpGHDZB9gImAM0TTwWEUkqzXayQcOBzQjG5NwSciwi5TZy5PecfvoEzjnndaZP1wyZUn2kou2kfeLvGLAzsIDgog4iIkmjeb43qLm7n2BmWcBbYQeTTJpmMHoee+wrrrvuAwAuvHB3zJqFG5BIOaSi7eSqovtmFgPUdiIiSZfsthMz2wiYQtArXQ+YAPw38fRgdx9lZjcA3YB84CJ3/yKpQZVPIYC7FyYS8MjKystlbp/FYYchVSAej3PnnZ9w772fA3DzzV0566yOIUclUj5JT77NrFaxhy2BLZK9TRGRZObeZlYTeAxYkVjUEbjX3e8p9pqOQBdgD6A1MA7YLXlRlVtWYj+yit2PAbh7XqiRVZFmI9uQlZerqndEFBbGueqq93jyyW/Izo5x332H0KvX9mGHJVJuqWg7cSBOcFBfAdydgm2KSIZLctvJIOBRoOjMXifAzOwYgur3RUBn4C13jwO/mlkNM2vh7nOTGlnZtSU4PkNwfPbE33GCS81Xa81GtgFQxTtCZs1axNix06hdO5shQ7px+OFbhR2SSIWkIvm+zt2fScF2RESqhJkNAAYUWzTE3YcknusLzHX3N82sKPn+Anjc3aeY2TXADUAuML/YeywBGgNpkXy7e6TPQqrVJHq22CKHZ589lvz8Qjp3bhN2OCIVlork+0xAybeIpFSMipe+E4n2kHU8fToQN7ODCAaRjwCOdve/Es+/CDwIvAQ0LLZeQ4KEXETKaNGilXzxxWwOPjg4GbPnnpuFHJFI5aUi+a5tZl8TnNIsGuDTOwXbFZEMlqy2E3ffr+i+mX0ADAReMrPzEwMqDyQYiDkJuMvMBhFM6Zfl7poPTaSM/ve/ZfTq9QLTps1j+PBjOOSQat8NJQKkJvm+IgXbEBFZS4qnGjwbeMjM8oC/gAHuvtjMJgKfEgxqPDelEZWDmTUi6AGf6e7Lwo5H5NdfF9Gjxzh+/jmXLbdswnbbNQ87JJEqk7Tk28xGufuJ7v5hsrYhIrIusRRcLcfduxZ7uHcpz98I3Jj0QCrBzE4AriH4PhhtZnF3r9YX3dG83tWb+3x69hzHn38upUOHjRg58nhatKgXdlgiVSaZc7u2SOJ7i4isV1as4rcMczHBVS7nEVzp8rhww6m8rLxc5vf6NewwpAK++upPjj56FH/+uZS99tqUF1/socRbIieZbSdbmtltpT3h7lcncbsiIpl0mfjKKnT3VYmKd9zM1HYiocjLK+CMM15h4cKVHHJIO4YO7UbdujXDDkukyiUz+V7O33PIioikVLKvcBkhE83seWAzM3sU+DLsgCpDLSfVV61a2QwdeiTPPfcdd9xxADVrZocdkkhSJDP5/svdhyfx/UVEpJLc/WozOwz4Cpjm7q+EHVNlaH7v6mfmzIW0a9cEgE6dWtKpU8uQIxJJrmT2fE9J4nuLiKyXer7Lxsz6ABsBc4CmicciKTF48BT23vspxo/XiXLJHEmrfLv7Zcl6bxGRDVHXSZm1T/wdI7ho0AKCCwdVO2o5qT7i8Th33PEJ9933OQDz5y8POSKR1EnFPN8iIimXVYkrXGYSd7+q6L6ZxYBq23ailpPqoaCgkCuvfI/hw78lOzvGv/99KD17bhd2WCIpo+RbRCJJle+yMbNaxR62BLYIKxaJvry8As477w3Gj3dq187m8ceP5NBDtww7LJGUUvItIpGUab3bleBAnKDtZAVw9/pebGZZwCPATsAq4Ax3/6mU17wKvOTujyYjaKmeLr74LcaPdxo0qMUzzxzD3nu3DjskkZRT8i0ikaSpBsvsOnd/phyvPxao4+57mdmewD3AMSVecwvQtKoClOgYOLATkyf/ydCh3dhxx43DDkckFMmc7URERNLfmeV8fWfgDQB3/wzYtfiTicvVFwKvV0l0Uu2tXJm/5n6HDhsxaVJfJd6S0ZR8i0gkxWIVv2WY2mb2tZmNNLPnzOy5Dby+EbCo2OMCM6sBYGY7AL2B65MU6zppppP09MsvuXTpMoKnn/52zbIaNZR6SGZT24mIRJLaTsrsinK+fjHQsNjjLHcvKm32ATYF3gM2B/LM7Bd3f2NdbxaLQU5OvXKG8E9ZebmsviCf6pB+Z2dnVck+p7vvvvsfxxwzmj//XMpjj02md+8dyM7OnMQ7Uz7n4jJxnytCybeIRJJy7/Uzs1HufqK7f1jOVScBRwGjEz3fU4uecPfLi73/jQRXOl5n4g0Qj0NubuXneG5B1bxPKuTk1Ks2sVbU5Mmz6d37RXJzV7H33pvx8ssnsWTJyrDDSqlM+JxLysR9btGi4YZfVELm/AoqIhklqxK3DNGiguu9CKw0s0+A+4CLzewSMzu66kKT6uyDD2ZxwgnjyM1dxWGHbcnzzx9Ho0a1ww5LJG2o8i0ikRRT6XtDtjSz20p7wt2vXtdK7l4IDCyxeHopr7uxUtFJtfTGGzPo338Cq1cX0rPndtx//yHq8RYpQcm3iESSUu8NWk4wx7dIldl888Y0aFCLHj3ac9NNXcnShPsi/6DkW0QkM/3l7sPDDkKiZdttm/P++6fSsmUDnX0SWQedCxKRSMqKxSp8yxBTwg6gqmiawfDE43FuuWUiw4f/PZVgq1YNlXiLrIcq3yISSfrqXz93vyzsGKpKVl4uc/ssDjuMjFNQUMjll7/L009PpVatbA4+eAtatSr/zA8imUbJt4hEkgpvmUFV73CsWpXPuee+wcsv/0idOtkMG3aUEm+RMlLyLSKRpNPemUFV79RbujSPfv0m8OGHs2jYsBbPPnsse+65WdhhiVQbSr5FJJI0oEWk6i1cuILevcczZcqfNG9el1GjutOhw0ZhhyVSrSj5FpFIUuU7+tRyknoLF65k1qxcNtusIWPGnMCWWzYJOySRakfJt4hEklLvaGs2sg0A83v9GnIkmaVduyaMHn0CTZvWUY+3SAXpzKyIiFQ7WXm5SrxT5Icf5vL0039PJbjDDi2UeItUgirfIhJJajsRqbwvvpjNySe/yKJFq2jVqiEHHrhF2CGJVHtKvkUkknRaT6Ry3nvvF04//WWWL8/n8MO3ZJ99WocdkkgkKPkWkUhS5Vuk4l56yTnnnNdZvbqQXr225957D6ZGDf1KK1IV9D9JRCIpVombSCYbMeJbBgx4ldWrCxk4sBP333+IEm+RKqTKt4hEkgrf0dRsZBuy8nI1xWCSLFmyikGDPiUeh6uv3ocLL9xdZ5FEqpiSbxGJpCzVsCNJV7RMroYNazNqVHemTPmTU07pEHY4IpGk80giIiIZrKCgkHff/XnN4/btmyvxFkkiJd8iEkmxWMVvIpli1ap8zjzzVU466cW15vIWkeRR24mIRFJMbSci67V0aR59+77MRx/9SqNGtdl662ZhhySSEZR8i0gkqYItsm4LFqzg5JNfZMqUv2jRoh6jRnVnhx1ahB2WSEZQ8i0ikaQBlyKl+/PPJfTs+QLu82nTphGjR3enXbsmYYclkjGUfItIJKnyLVK6s89+Hff5mDVj9OjjadmyYdghiWQUDbgUkUjSgMvoaTayjeb3rgKDBh3EIYe046WXeirxFgmBkm8REakWsvJymd/r17DDqJb++GPJmvtbbdWUZ545lqZN64YYkUjmUvItIpEUq8QfkSh5992f2XvvJxky5KuwQxER1PMtIhGVpRxahBdemM55571Bfn4h06fPIx6P63LxIiFT8i0ikaQKdjQ0G9mGrLxcAPV7l9OTT37DlVe+SzwO5567K9dfv68Sb5E0oORbRCJJOUY0ZOXlMrfP4rDDqFbi8Tj33/8Ft98+CYBrr+3MBRfsHnJUIlJEybeIRJIq35KpHnzwS26/fRKxGNx110GcdtqOYYckIsVowKWIRFJWrOI3SQ+aWrBijjnGaN26EY891k2Jt0gaUuW7Gvn222/4972DGPbU00yfNo07bruZ7Oxsatasxa2330mz5s0Z/uQwXn/tVbJiMfoPGMiBBx0cdthSRvn5q3nwzn/xv79ms3p1Hj1OPYM99ukKwIfvvM6rLzzPXY+MAGDK5x8z8qkhAGy5zbacddFV6uUsQZXv6k8tJ2WXn19IjRpBPa1t28ZMmtSXOnX0FS+SjvQ/s5p4cthQXpnwMnXrBvOy3nXHrVx59XVs2749Y0aP5IlhQznr7HN57pmneeX1t1ixYgU9ux+r5Lsa+eDt12jYqDEXX3MLixflcvGZJ7HHPl2Z+V/nndfGE48Hr1u+fBlPDb6fW+8fSqOcJrzw/FMsXrSQxjlNw90BEQnF0qV5nHbaS3Tp0nZNb7cSb5H0pbaTaqJ16zbc++8H1zy+c9C9bNu+PQAF+QXUrl2bunXr0rJVK1asWMGKFSuI6fx5tbJPl4Pp3f+cNY+zs7NZvCiXEUMeoP95l61ZPv27b2jbbiueGHwvV51/OjlNmirxLoWucCmZYP78FXTvPoaJE39j6NCvWbRoZdghicgG6FfjauKgQw7ljz9+X/O4RYuNAPjP118x8vlneGL4swBssklLjju6GwWFBfQ/46xQYpWKqVuvHhBUtu+84f/offo5PHTXv+h/3qXUqlVnzesWL8pl6teTuf/xkdSpW4+rLjgd234nNm3dNqzQ05JyaIm62bOX0LPnOH78cQFt2jRm7NjuNG5cZ8MrikiolHxXY2+8/hqPDxnMQ48MoWnTpnzw3rvMnfs/XnvrXQDOHtCfnXfpSIcdNeCmupj7v7+4/dpLOOLYnrTarA2z//iVR++9jby8PH6bNZPHH7ybjrvvzdbbbk+TZs0B2H6njvz8kyv5LiFLJexqTYMt12/GjIX06DGW339fQvv2zRg1qjubbNIg7LBEpAzSLvk2s/eB2iUWx4C4u+8dQkhp6ZUJLzF29CiGPfk0jXOCL6hGjRtTp04datWqRSwWo2HDhixZosFK1UXugvnceNk5DLjwCnbqtAcADz01DoA5f85m0E1Xcsb5/8ei3AXM+vknFucupH6DhvgPUzmk2/Fhhp6WlHpXbxpsuW7ffTeXnj3HMm/eCjp1aslzzx1LkyZ1ww5LRMoo7ZJv4EpgKHAckB9yLGmpoKCAO2+7lZYtW3LJRecD0GnX3TjnvAv47NNPOOWknmRlZbFLx47stfc+IUcrZTXm2WEsXbKY0SOGMnrEUACuv+shatde+zRy45ymnHrm+dx4+bkA7NP1YNq22yrl8aa9JGXfZpZNcIwyoADol9jaU0Ac+A44190LzewGoBvBsewid/8iOVFJJmnSpA61a9ega9e2PPnk0dSvXzPskESkHGLxoikU0oiZ/R/wk7u/WN51V+aTfjskVeqXucvDDkFSZNuW9SqcQn8+Y1GFjwV7bNl4nds1s2OBo939dDPrClxMkHzf6+4fmNmjwJvALGAQcCDQGhjn7rtVNKaoKiyMx+fPX/qP5S1GNIps5Tsnpx65uZU7jv3222I22qgetWunYw3tn6pin6sb7XNmaNGi4RRg1/Ksk5aznbj73RVJvEVEks3dxwMDEg/bAnOATsCHiWWvAwcBnYG33D3u7r8CNcysRarjrY7U7/1PY8dO4557PlvzuHXrRtUm8RaRtel/rohEUmXGW5rZAP5OsAGGuPuQogfunm9mwwna404AjnT3okr7EqAx0AiYX+w9ipbPrXhkmUH93msbNuxrrrrqfQA6d27NHntsGnJEIlIZSr5FJJIq0/KdSLSHbOA1p5nZFcDnQPHRbg2BXGBx4n7J5SJlEo/Huffez7nzzk8AuO66fZV4i0RAWradiIhUWqwSt/Uws1PN7KrEw+VAITA50f8NcDgwEZgEHGpmWWbWBshy93lVtHcScYWFca677gPuvPMTsrJi3HvvwZx/voYMiESBKt8iEkmx5E02+ALwpJl9BNQELgKmAUPNrFbi/lh3LzCzicCnBIWOc5MVkERLfn4hF130FqNH/0CtWtkMHnw4Rx21TdhhiUgVUfItIpGUrGvsuPsyoGcpT3Up5bU3AjcmJxKJqtzclXz++R/Uq1eT4cOPpksXXUBLJEqUfItIJOkiO1JdNW9ejzFjujN/fnARHRGJFvV8i4hI2sjUaQbnzVvOsGH/WfN4881zlHiLRJQq3yISTSp9V0uZOM3gH38soUePsfz000Kys2P07btT2OkmQugAACAASURBVCGJSBIp+RaRSErigEuRKvPTTwvo0WMcf/yxhO22a87hh28VdkgikmRKvkUkkpI14FKkqnz77RxOPPEF5s9fwW67teLZZ48lJ6dO2GGJSJIp+RaRSFLunRxmlgU8AuwErALOcPefij1/MdAr8fA1d/9X6qNMf5988v/s3Xl4XWW59/FvxoaWDkAriCg44C1OOCBQDzggCCLYCqKIOICVSUXhoIKioAdQEEQRC4rwgnIUi8zIICoqM4JHBYFbq4IeOJUiTVta2qZJ3j/2Cm5KkqZt1kqz+/1w5epe0/PcKy17//LkWWv9g/33v4InnljGTjttwbnn7sm4cW0jXZakCnjBpaTGVNJDdsR0oCMzpwJHA6f1bYiIFwDvA14PTAXeGhGvHJEq12I9Pb0ce+wveeKJZUyfHnzve9MM3tI6xPAtqSE1rcF/GtQOwHUAmXk7sE3dtn8Au2Vmd2b2UHsI0ZLqS1y7NTc38f3vT+eII7bjrLPeRnt7y0iXJKlCTjuR1JCc812aCcD8uuXuiGjNzOWZ2QU8FhFNwFeB/8nMPw3WWFMTTJo09mnrVlxuFDfd9BA77PA8WlqaednLNubkk3cZ6ZIq09LS3LB/rwPxnDUQw7ckaVUsAMbXLTdn5vK+hYjoAM4DFgKHrayx3l7o7Fz81PIUnr7cCHp7e/nqV2/j1FNv51OfmsqJJ76l4c5xZSZNGus5rwPWxXOeMmX8yndageFbUkNy4Ls0twB7ArMiYnvgnr4NxYj3FcAvMvPkEapvrdLT08vnPncj5577O5qbm9hss1X/oJbUWAzfkhqT6bsslwG7RMSt1L7LB0TEkcBsoAV4IzAmIt5W7H9MZt42MqWOrK6ubg4//HouueQB2ttb+Pa3d+ftb99ypMuSNMIM35IakhdOlqO4kPKQFVY/UPd6tW9U3UiPln/yyS5mzLiaG274G+PGtXHBBdN4wxueN9JlSVoLGL4lNSQvuBx9GunR8sce+0tuuOFvbLhhBz/84V68+tWbjHRJktYShm9JDcnsrZH06U9PZfbsxznllJ2J2Giky5G0FjF8S2pMpm9V7LHHFrPRRuvR1NTExhuvz+WXv5smfwUjaQU+ZEeSpDX0pz/9i513vpATTrj5qXUGb0n9ceRbUkPygktV5Xe/m8O++17K448v4Te/eYSlS5czZowfr5L657uDpIbkoKOqcPPNf+f977+CRYu62GWX53POOXsYvCUNymknkhpS0xp8SUNxzTWzee97L2PRoi722uslnH/+Oxg7tm2ky5K0lvPHc0mNyRStEl177WwOPPAqenp6OfDArTnppJ1obvYfnaSVM3xLakjO+VaZttlmU57//ElMm/ZiPvOZ13txpaQhM3xLakhmIQ233t5eenuhubmJKVPGcsMN72P99dtHuixJo4xzviVJWomenl6OPvoXHHvsjfT29gIYvCWtFke+JTUkB741XLq6uvn4x6/j0kuTMWNaOOCAV7HllhuOdFmSRinDt6TGZPrWMFi8uIsZM67mZz/7G+PGtfH9708zeEtaI4ZvSQ3JCy61pubPX8L++1/BHXc8zIYbdnDRRXvxqldtMtJlSRrlDN+SGpIXXGpNPProIt7znkv54x/nsumm6zNr1t68+MUbjXRZkhqA4VtSQzJ7jy4bXfQ8etonjXQZT2lra6anp4cXvnADLr54bzbbbMJIlySpQRi+JTUm0/eo0rysk7kfWDDSZTxlgw3WY9asvWlubmbKlLEjXY6kBuKtBiU1pKY1+E/rprvv/j+OO+5XT91KcOON1zd4Sxp2jnxLktZ5v/rVQ3zwg1eyeHEXW201mX33fdlIlySpQRm+JTUkL7jUUF199Z855JBrWLasm3e9ayv23vslI12SpAbmtBNJDalpDb607vjBD+5lxoyrWbasmxkzXsWZZ+5GW1vLSJclqYE58i2pITnyrZWZOfMujj/+1wB86lNTOeqo7WnyH46kkhm+JTUoQ5QGtnTpci699AEATjrpzcyY8eoRrkjSusLwLakhOYCpwYwZ08pFF+3F7bc/zB57bDnS5UhahzjnW1JDcs63VrRsWTfnn/97enpqtxKcPHmswVtS5Rz5liQ1vEWLujjwwCu58caH+N//XcCxx+440iVJWkcZviU1JKedqE9n5xLe977L+c1vHmHy5PV4xztePNIlSVqHGb4lNSSfVCmAf/7zCd797ku5//7H2Gyz8Vx88bt44Qs3GOmyJK3DDN+SGpPZe5334IOd7LPPJTz00Hy23HJDZs3am+c8Z/xIlyVpHWf4ltSQzN46/vhf89BD89l664354Q/fyeTJY0e6JEkyfEtqTM751umn78LkyWM57rgdGT9+zEiXI1Wiu3s58+bNZfnyZZX3/c9/NtHb21t5v1VobW1ngw2m0NKy5tHZ8C2pITnne910zz2P8tKXTqalpZkNNliPU0/deaRLkio1b95cOjrGMm7cJpU/sbWlpZnu7p5K+6xCb28vixYtYN68uUye/Ow1bs/7fEuSGsJVV/2J3Xb7AZ/+9M8bdvRNWpnly5cxbtyEyoN3I2tqamLcuAnD9tsEw7ekxuRTdtYp3//+H/jIR35CV1cPY8e2YfbWuszgPfyG83vqtBNJDcmPnnXHGWfcyQkn3AzA0Ue/niOO2M7wIa0FLrzwfC6++IfMmnUlY8YM73UX9957D9/4xqm0trbwutdtz4EHHvS07XPmzOGEE75Ab28vEyZM4LjjTqSjo+Op7SeffCITJkzg0EM/zvLlyznhhOOYM+f/aG5u5jOfOZbNN99iWOut58i3pIbU1LT6Xxodent7+dKXfs0JJ9xMUxN85Ss7ceSR2xu8pbXEDTdcx1ve8lZ+/vOfDnvbp576ZY4//kRmzjyX++67l8wHnrZ91qz/ZqedduFb3zqHLbZ4AVdffflT2y6//BL++tfZTy3fdtvNdHd3c/bZ53HAATP4zne+Nez11jN8S2pITWvwn6q10UXPo6d90iof9+1v/5Yzz7yL1tZmzjprdw488FUlVCdpdfz2t3ex6aabMX363lx66cUAfOxjB/HQQw8CcPnlP+bcc78NwPnnf5cPf/j9fOhD+3H55Zc8rZ0FC+bz2c9+6mnrFi16gq6uZTznOZvR1NTEtttO5e6773zaPltuGSxcuACAxYsX0dpam+xx771/4L777mXatL2e2ve5z92c7u5uenp6WLTo3/uWxWknkhqSg5+jR/OyTuZ+YMEqH7fffi/n6qv/zCc+sS277PKCEiqTRr8NrtyO1s77h6295ZO2Yt477ljpfldffQV77jmd5z1vC9ra2vjjH+/td78//ekB7rjjVr7znfPp6uri7LPPpLe396nfYE2YMJGTTvrq045ZtGgRY8eOe2p57NixPPLIw0/bZ8qUZ3H22d/khhuup6trGQceeBCPPfYY5533HU466VR+8Ysbntp3vfXWY86cR9hvv3cxf34np5xy+pC/H6vD8C1JGjUWLeqivb2ZtrYWJkwYw1VXvcdpJtIghhKUh9uCBQu47bZbmDfvcX784x+xaNETXHrpj562T99F0X//+0NstdXLaGlpoaWlhU9+8qiVtj9u3DiefHLxU8uLFy9m/fWf/vTamTO/wWc/ezzbbTeVW2+9mRNOOI5tt51KZ2cnRx11OI8//i+WLFnC5ptvwV/+8me23XYqhxzyMf75zzl84hOHcsEFFw37PPU+hm9JDansPBYR2wEnZ+abIuI1wFXAn4vNZ2XmjyLiOODtwHLgk5l55wDNaQjmzXuS/fa7nBe8YBLf/OZuNDc3GbyltdBPf3oNe+wxjY9+9BMALFmyhH32eQcveMEL+de/HmPzzbfgT396gMmTp7D55ltw+eWX0NPTQ09PD0cddTinnPJ12tvbB2x/3Lj1aW1t4+GH/5dNN30Od955Gwcc8PQLLsePn8C4cesDMHnyZBYuXMg+++zLPvvsC8A111zFQw89yO6778n553/3qYfnTJgwkeXLl9PTU979yg3fkrSKIuLTwPuBRcWq1wBfy8zT6vZ5DfBGYDvgucAlwOsqLrVhzJnzBO95zyXcf/+/ePTRRcydu5iNNx638gMlVe6qq67g85//0lPLHR0dvPGNO/GsZz2Lr33tZJ71rI2ZPHkKUJubvd12Uzn00A/T09PDO9/5rqcF7wUL5vOVr5zwjKknRx11DF/84rH09PTwutdtx8te9vKn7fvJT36K008/hZ6eHnp7eznyyE8PWO+7370fX/7ylzjssBl0dXVx0EEfZb311hvm78q/NTXagwiWLKexTkjP8ODcxSvfSQ3hJc8eu9rDmvOf7Fnt94KJ6zUP2m9E7A38Afh+Zm4fEWcBQW1A48/AJ4EDgLGZ+ZXimP8B3pqZc1e3rkbU09Pb23x686Bzvv/2t0722ecS/v73+bz4xRsya9bebLrp+AH3X9tNmjSWzs51633Mc67OnDkPsckmm1feLzTuEy779Pe9nTJl/N3ANqvSjnc7kdSQyrzVYGZeAnTVrboT+FRmvgH4K3AcMAGYX7fPQmDisJ3gOuKPf5zLnnv+iL//fT6vfvXGXHHFe0Z18JYkp51IakhrMhM4Ig4C6icQficzvzPIIZdlZmffa+CbwBVAfUocD3SueKAGdu+9c3nnO2cxf/5SdtzxuVxwwTTWX3/geaCSNBoYviU1pjVI30XQHixsr+j6iPh4cUHlW4C7gVuAUyLiVGAzoDkzH1v9qtY9W2wxkRe9aEM23ngcZ5+9Ox0dfmRJGv18J5PUkCp+WM6hwJkRsQyYAxyUmQsi4ibgNmpT/D5aZUGjWd89ftdfv50f/Wgvxo5to7XVWZLSUNXfJ1vDYzivkTR8S2pIZX/uZOaDwPbF698Cr+9nn+OB48utpLFccMEfuPXWfzBz5ttoaWlmwoRy7rMrNarW1nYWLVrAuHETDODDpLe3l0WLFtDaOjzT3gzfkqQR19vbyxln/IYTT7wZgHe/+6W85S3PH+GqpNFngw2mMG/eXJ54ovpLTJqamoZ1hHht0trazgYbTBmetoalFUlayzjeU46IaAZmAlsDS4EZmTm7bvtHgIOpPVjohMy8erD2mh77Hd1tk/jiF3/NzJl309QEJ5/8FoO3tJpaWlqZPPnZI9L3unhLydXhJDpJjalpDb40mOlAR2ZOBY4G6h8stAlwOPAfwK7AlyNi8HkjPd184I5zmDnzblpbm/n2t9/Ohz60dXnVS9IIM3xLakhNa/CfBrUDcB1AZt7O0x8usS1wS2Yuzcz5wGzglYM19pd/bcAPfvBH1luvlQsvnMb06VFW3ZK0VjB8S2pIZT5kZx234sODuiOidYBtK32w0PKe2kWVs2btzU47OdVEUuNruDnfHa0OWzW6lzx77EiXoFHA94LSLODpDw9qzszlA2xb6YOFXvKK5z82f/7RDw1viWu/KVPWvad0es7rhnXwnDdf+S5P13DhW5JUqluAPYFZEbE9cE/dtjuBEyOiAxgDbAXcu5L2huf2AZI0SjQ16i1hJEnDr+5uJ6+kdnnqAcDuwOzMvLK428lB1KY1npSZl4xYsZK0FjJ8S5IkSRXxgktJkiSpIoZvSZIkqSKGb0mSJKki3u1kFFvZY57VOCJiO+DkzHzTSNcirY7hfiz9aDCEcz4C2LdYvCYzv1h9lcNrKJ9LxT4/Aa7IzLOrr3L4DOHv+G3AccXib4GPZuaovthuCOd8FPBeoIfaRdeXjUihJRjoszgi9gS+QO3967zMPGewdhz5Ht0GfMyzGkdEfBr4LtAx0rVIa2B4H0s/Ogx2zi8A3ge8HpgKvDUiBn0a6CgxlM+lE4ANK62qPIP9HY8HvgrskZnbAw8Ck0eiyGE22DlPovb/8lTgrcDXR6TCEgz0WRwRbcDp1M73jcBBxXvagAzfo9tgj3lW4/gLsNdIFyGtoWF9LP0oMdg5/wPYLTO7M7MHaAOWVF/isBv0cyki3kVtRPTa6ksrxWDn+3pq98E/LSJuAv6ZmXOrL3HYDXbOi4CHgHHFV0/l1ZVnoM/irajdanVeZi4DbgZ2HKwhw/foNthjntUgivskd410HdIaGtbH0o8SA55zZnZl5mMR0RQRpwL/k5l/GpEqh9eA5xwRLwf2o/br+UYx2L/rycCbgc8AbwM+GREvrri+Mqwse/wDuI/aNJszqiysTIN8Fq/y+5fhe3Qb7DHPkrQ2GdbH0o8Sg75HF08C/e9in8Mqrq0sg53zB4DnAL8APgQcGRG7VVvesBvsfP8F/CYz52TmE8CvgVdVXWAJBjvntwHPBp4PPA+YHhHbVlxf1Vb5/cvwPbrdQu3JcvTzmGdJWpsM9n51J7BjRHRExESG9lj60WDAc46IJuAK4PeZeXBmdo9MicNuwHPOzE9n5nbFxWrnA1/LzOtGoshhNNi/67uBl0fE5GJkeHtqI8Kj3WDnPA94EliamUuohdBJlVdYrfuBLSNiw4hoB94A3DbYAU5RGN0uA3aJiFv592OeJWlt9Iz3q4g4kn8/lv4M4CZqg0KfKz64R7sBzxlooXZx1pjijhgAx2TmoB/ao8Cgf88jW1opVvbv+hjg+mLfWZnZCD9UruycdwZuj4geavOfbxjBWksTEfsB62fmd4rzv57a+9d5mfnwYMf6eHlJkiSpIk47kSRJkipi+JYkSZIqYviWJEmSKmL4liRJkipi+JYkSZIq4q0GNaiIeBMwi9q9SXuB9YD/zsxvrkZbXwEeAH4HvCMzvzTAfu8E7sjMR4bQ5m7Avpn5obp13wN+mZnn1a07AtgoM48doJ05mbnJqpyPJK2LImIL4A/UnmDY5xeDvKefD1y0uvf0jogHgb8D3dQGDf8FfDAzF65CG0dTe7jPH4D9M/O7EfEh4PHVvQViXV091G4duT7wkcy8a5BjPpaZZ65Of2ochm8NxS8yc1+AiBgDZER8PzNX6wl0mfk7agF8IJ8ADgFWGr4H8B3gBOC8unUfBKavZnuSpKe7r3hYTlXe2nfv94g4mdpzLYb86PLM/Epx7BbADOC7mXn+MNe1K3A8sMcg+x8LGL7XcYZvrarx1EYflkfEL4G5wAbA24GZwJbURiaOzcxfRsTe1N5s5gLtwAPFaPohmblvRHwYOJTaqMEVwG+oPX73exGxA3AwsB+1UfeLMvOMiNiKWrBeVHzNqy8wM2+OiCkRsXlmPhQRrwPmZOaDEfFy4GtFjZOAwzPz1r5ji3M6JDMfiIhDgE0y8/iI+Hg/dewFfAboAh4EPpCZPcPxTZak0SYiWoBvA88FNgKuzczP121/MbUnW3YBy6m9Zz4cEV+m9lTAZmpPvbx4kD763rszItqofRa8kNpnyNcy80cRcRi1AZce4ObM/FTf6DuwN/DSiPhC0d8c4MXUnjR6QURsAvwkM1+7KnUVNqf4PIqIdwEfpfYQGoB3Ufs82zAiZlIbZDqbFT4zV9K+GoRzvjUUO0XELyPiF8B/Ax/PzCeKbT/IzJ2BA4HHMvMNwDTgW8X2U4CdgV2BxfWNRsSzgKOBHYHXAhOBX1EbFf8A8CLgPcAOxdf0iAjgv4AvFP3eSv/OBfYvXh9A7QMB4GXAfxbHfo0hPBU0Il46QB3vBU7PzB2AnwITVtaWJDWIlxafC31fz6EWum/PzF2pvVceusIxu1B75PrOwInABsXTPZ+fmf8BvBn4XET09zjyn0bEjcDPqAXc71ELs49l5uuLNk+IiMnU3tc/kZlTgb8Wj3bvcyK1Ufv6KTLnUAvrAO8H/t8q1nVnRPwvsC1wVLH+xcDbi98OJLBrZp5IbZrLYdRG3/v7zNQ6wJFvDcVT0076kcWfrwB2jIjtiuXWiNgYWJCZ/wIoHkVb7wXAvZn5ZLF8RLFf3/aXUxtJ+HmxvAG1QP4y4M5i3S3AVv3U9T3g5xFxGvAm4PBi/cPA5yPiSWqj+AsGOC/494jFQHUcCRwTEYcC9wOXD9KWJDWSZ0w7iYgJwOsi4s3U3lvHrHDMudR+W3gdMB/4LLXPjtcWv3UEaKP2frvitManpnfU9bcVtTBOZi6MiPuojYIfABxVTE+5jX+/l/crM++PiNaI2JzaQMvOwEGrUldEnAQ8H3i0WP8ocEFEPAG8pKijXn+fmRv1fV6qsTnyrTXVN83iAeCHxZvx24CLqY1OTIyIKcU+r1vh2L8ALynmkRMRPy5GT3qo/dtM4I/Am4t2zwfuKfqaOkCbAGTmY9QC8eeByzJzebHpDOC4zPxg0daKb8pLgGcXr1/T19wAdRwEHJ+ZbyzaeWd/tUjSOuJDQGdmvg84DRgbEfXvsdOAmzLzLdQ+Iz5D7f38xuK9dSdqF/j/dYj93U/tN6dExHhqgfZvwEeoTR98I/Bq4PV1x/R9vqzoXGq/qb2vuJ5pVes6FtgUOCwiJgJfBPalNsL9JP/+rOn7c6DPTK0DDN8aLt+mFqR/RW0qyEOZuYzaCMT1EfEzanO+n5KZc4GTgV9FxG3AbzPz4eL47wH/oDbafHNE3EVtbtzDwGHAZyPi58B2DOwcar8CPKdu3YXAFRFxE7VfC266wjFnAN+KiOupzSEkM38/QB13AjcU03E2Aa4e0ndKkhrTz4Hdi99yngX8mae/x94FnFi8/x4CfBO4CniiWHc30LsKdzH5DrBRRNwM/BL4YmY+Sm1w5DfFe/OjwB11xzwKtBej4vUupjY98rvF8irVVVzv82FqIXx9ar+V/S1wE7Xw3fd9uC8iLqT/z0yvGVpHNPX29o50DZIkSdI6wZFvSZIkqSKGb0mSJKkihm9JkiSpIoZvSZIkqSKGb0mSJKkihm9JkiSpIoZvSZIkqSKGb0mSJKkihm9JkiSpIoZvSZIkqSKGb0mSJKkihm9JkiSpIoZvSZIkqSKGb0mSJKkihm9JkiSpIoZvSZIkqSKGb0mSJKkirSNdgCSNJhHRC9wLdNetviszZ6xme68DPpyZhwxHfQP00QtMyczHyupjgH5nAO2ZObPKfiVpbWb4lqRV9+ZhDLIvAzYbprbWNjtQ+0FFklQwfEvSMImIrYBvABsBLcAZmXleRDQDpwPbA+OBJmAG8HfgS8DEiPh/wAXAmZn58qK9N/UtR8TxwFRgU+D3mbl/RHwO2JvaFMIHgcMy85FB6tsC+AVwA/Baap8BXwAOBl4C3AW8F3ge8CvgOmC7ot6PZeZNEdEGfA14C7XR/zuAIzJzYUQ8WCy/Evgs8A5gl4h4Evgx8G1gY2AT4CHg3Zn5aHHc+UWbzwO+l5mfL2o+EPjPoq/HgA9m5j8iYk/gWKAdWAwclZm3RcRLgHOBjqLu7zryLmlt4pxvSVp1N0bE7+q+nhURrdQC5tGZ+VrgjcBREbE9tQC7KTA1M19KLWQfnZn/oBZ+b8rMA4bQ7+bAq4vg/QHgFcC2mfkq4Brgu0No4/nATzJzG+A2aj8svJfaCPyO1H5AgCKAF20fDfyoCN7HFueydfHVDHy1rv17M3OrzLwMuBI4PTO/BewL3JaZU4EXUAvM7687bv3M3BF4ffF9e35EbA2cDOyWma8s2vtcRGwJnATsnpmvBg4CLo2IccCngKuKv4PdgTcUP/xI0lrBkW9JWnXPmHYSES8FXgicFxF9q9ejFpbPiohjgYMj4oXAm4CFq9Hv7Zm5vHi9B7AtcFfRXwswdghtdAFXFa//AtyamQuKc3gE2BB4BJiXmT8AyMxrI6Kb2oj224DPZWZXccw3gcvr2r+pv04z8xsRsWNEHAlsCbyc2ih5nyuK/R6OiEeLOt4IXF/8kEJmfr3o8zDg2cDP677XPcCLgMuA70XEtsDPgMMzs2cI3xdJqoThW5KGRwswvxgpBiAiNgbmR8TbqY0wn0YtZD4A7N9PG73Upkr0aV9h+xMr9HdyZp5V9DUG2GAIdS7LzN665a4B9lu+wnIztakfLUWd9evbBqjxKRFxMrUfFs4DbiyOqT/XJ+te930fltf3FRHrURv9bwF+npnvqdv2XOCRzPx9MTK+C7VpLMdFxGsz838HOE9JqpS/ipOk4ZHAkxGxPzwVBu+lNrd6F2pTIc6iNq96OrUACbWA2Rde5wLPK6axNFGbqjGQ64EZETGhWP4S8P1hPJ8pEbFbcS57Ugvp91CbB35oRLQV0zk+Sm0OeX/qz21X4OuZ+X3gUWrfk5YBjutzI7BzRDy7WD4YOAX4OfDWYn43EbE78AdgvYj4AfCezLwIOAxYQO03EpK0VjB8S9IwyMxlwDRqgfgPwE+Bz2fmLcDZwJsi4h7gt9Smezy/CK+3Ay+IiEsz8z5qFyXeVaz/2yBdfhe4Grg9Iv5IbUrIh4bxlJYA74+I3wOfA6ZnZjdwAjAH+B1wP7Vw/YkB2rgWOCQijqH2w8GpxffmSuBmatNEBpSZ91Cbw31dUcduwCHF9+kg4KJi/X8B78jMJ4rX7yvW30FtGsqvV/N7IEnDrqm3t3fle0mS1hnFXVHuzcz1R7oWSWo0jnxLkiRJFXHkW5IkSaqII9+SJElSRQzfkiRJUkUM35IkSVJFGu4hOz09Pb3d3c5jlyRJUrna2loeA6asyjENF767u3vp7Fw80mVIkiSpwU2ZMv6hVT3GaSeSJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFWke6gDJtOLGDlva2UtruXtbF4/OXlNK2JEmSGlNDh++W9jbmnnVhKW1POXR/wPAtSZKkoXPaiSRJklQRw7ckSZJUkVKmnUREMzAT2BpYCszIzNkr7DMFuBV4RWYuiYijgd2KzZOATTJzk4g4EvgwMLfYdnBmZhl1S5IkSWUqa873dKAjM6dGxPbAacC0vo0RsSvwFWDjvnWZ+ZViHRFxNfCZYtNrgA9k5t0l1SpJkiRVoqxpy4JCWQAAIABJREFUJzsA1wFk5u3ANits7wF2Bh5f8cCI2AuYl5nXF6teCxwTETdHxDEl1StJkiSVrqzwPQGYX7fcHRFPjbJn5g2Z+a8Bjj0G+GLd8kXAIcBOwA4RscdwFytJkiRVoaxpJwuA8XXLzZm5fGUHRcRLgc6++eER0QR8PTPnF8s/AV4NXD1QGy0tTUyaNHZNah+yqvqRJElSYygrfN8C7AnMKuZ83zPE43YGrq1bngDcGxFbAYuojX6fN1gD3d29dHYuBmDKlPGD7brG+vqRJEnSumd1smZZ004uA5ZExK3A6cAREXFkRLxjJccF8Ne+hWLE+7PAjcBNwB8z85qSapYkSZJK1dTb2zvSNQyrrq7u3vqR7zKfcDl37sJS2pYkSdLab8qU8XfzzBuLDMqH7EiSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFWkto9GIaAZmAlsDS4EZmTl7hX2mALcCr8jMJRHRBPwv8Odil9sy85iI2BP4ArAcOC8zzymj5uGy4cQOWtrbSmu/e1kXj89fUlr7kiRJKk8p4RuYDnRk5tSI2B44DZjWtzEidgW+Amxcd8wLgd9m5p51+7UBpwOvAxYBt0TEVZk5p6S611hLexv/POuU0trf+NBPA4ZvSZKk0ais8L0DcB1AZt4eEdussL0H2Bm4u27da4HnRMSNwJPAEcAYYHZmzgOIiJuBHYGLS6pbkiRJKk1Z4XsCML9uuTsiWjNzOUBm3gAQEfXH/B/w5cy8OCJ2AC6kFsDr21kITBys45aWJiZNGrvmZzAEVfWztvQrSZKkNVNW+F4AjK9bbu4L3oO4i9q8bjLz5oh4DrWwXd/OeKBzsEa6u3vp7FwMwJQp4wfbdY319VOv7D4H6leSJEnVWp3cV9bdTm4Bdgco5nzfM4RjjgM+WRyzNfB34D5gy4jYMCLagTcAt5VSsSRJklSyska+LwN2iYhbgSbggIg4ktr87SsHOOYrwIUR8XZqI+Afysyu4rjrqf2gcF5mPlxSzZIkSVKpSgnfmdkDHLLC6gf62W+LutfzgLf3s89VwFXDXKIkSZJUOR+yI0mSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFWkdaQL0JrbcGI7Le1jSmu/e9lSHp+/rLT2JUmS1hWG7wbQ0j6GB8+YXlr7Wxx+OWD4liRJWlNOO5EkSZIqUsrId0Q0AzOBrYGlwIzMnL3CPlOAW4FXZOaSiJgIXAhMANqBIzPztojYC/gq8I/i0OMy81dl1C1JkiSVqaxpJ9OBjsycGhHbA6cB0/o2RsSuwFeAjeuOORL4eWZ+PSIC+CHwmuLr05l5SUm1SpIkSZUoK3zvAFwHkJm3R8Q2K2zvAXYG7q5bdzq1UfK+upYUr18LvDoiPgncCXwmM5eXVLckSZJUmrLC9wRgft1yd0S09oXmzLwBoDbAXZOZncW6TahNP/lksekG4HLgb8DZwCHAmQN13NLSxKRJY4ftRAZTVT9rQ78jda6SJEmNpKzwvQAYX7fcPJTR6oh4BXARcFTdvO7z6oL5FcDeg7XR3d1LZ+diAKZMGT/Yrmusr596ZffZX78j0ackSdK6bnUyWFl3O7kF2B2gmPN9z8oOiIiXAhcD+2XmtcW6JuAPEbFZsdtbePpUFUmSJGnUKGvk+zJgl4i4FWgCDoiII4HZmXnlAMd8GegAvlFMR5mfmdMiYgZwaUQ8CdwHnFNSzZIkSVKpSgnfmdlDbW52vQf62W+LutfTVtxerP8p8NPhrE+SJEkaCT5kR5IkSaqI4VuSJEmqiOFbkiRJqojhW5IkSaqI4VuSJEmqiOFbkiRJqojhW5IkSaqI4VuSJEmqiOFbkiRJqojhW5IkSaqI4VuSJEmqiOFbkiRJqojhW5IkSaqI4VuSJEmqiOFbkiRJqojhW5IkSaqI4VuSJEmqiOFbkiRJqojhW5IkSaqI4VuSJEmqyCqH74gwsEuSJEmroXUoO0XEPkALMAb4akSckpmnllqZJEmS1GCGOop9FHADsD/wXGDP0iqSJEmSGtRQw/fS4s+FmbkUGF9SPZIkSVLDGmr4/itwF3BeRBwH3FFeSZIkSVJjGtKcb+BYoDMzn4iIu4CJJdYkSZIkNaRBw3dEvBx4DnAy8OmIgNqFl18GXlV6dZIkSVIDWdnI9wbAvsDGwHuLdT3AzDKLkiRJkhrRoOE7M28CboqI12TmbyuqSaPABhPbaW0fU1r7y5ctZd78ZaW1L0mSNBKGOud7o4i4BujoW5GZO5VTkkaD1vYx3PHtPUprf7uDrwYM35IkqbEMNXyfDnwS+EeJtUiSJEkNbajh+++Z+bOhNlo8gn4msDW1e4TPyMzZK+wzBbgVeEVmLomI9YALgWcBC4EPZubciNgT+AKwHDgvM88Zah2SJEnS2mSo9/l+NCLOjoiDI+KgiDhoJftPBzoycypwNHBa/caI2BX4KbULOfscCtyTmTsC3wOOjYg2aqPubwXeCBwUEZsMsWZJkiRprTLU8P034P+ATYBnF1+D2QG4DiAzbwe2WWF7D7Az8Hh/xwDXFtu3AmZn5rzMXAbcDOw4xJolSZKktcqQwndmfhG4BXgEuIzafb8HMwGYX7fcHRFPTXHJzBsy81+DHLOQ2oN8Vmynb70kSZI06gxpzndEnARsRm0kehlwDP++73d/FgDj65abM3P5SrqpP2Y80NlPO33rB9TS0sSkSWNX0tXwqKqftaHfdaVPSZKkMg31gssdMvMNEXFjZl4QEYeuZP9bgD2BWRGxPXDPEPq4BdgduBN4G3ATcD+wZURsCDwBvAE4dbBGurt76excDMCUKeMH23WN9fVTr+w+++t3XelTkiRpbbI6eWio4bs1IjqA3ohoAbpXsv9lwC4RcSvQBBwQEUdSm7995QDHnAVcEBE3Uxtd3y8zu4rjrqc2Rea8zHx4iDVLkiRJa5VVuc/33cAU4I5ieUCZ2QMcssLqB/rZb4u614uBffrZ5yrgqiHWKUmSJK21hhS+M/PiiPgZ8CLgb5n5WLllSZIkSY1n0LudRMSxxZ8/BL4FHAGcERE/qKA2SZIkqaGsbOS7b7rH2WUXIkmSJDW6QUe+M/P3xcsJwFsy81fUbjPYUXZhkiRJUqMZ6gWXXwR2K16/h9oTKK8vpSJJkiSpQQ318fJdmfkoQGbOZ+W3GpQkSZK0gqGOfN9ZXGR5G7At8D/llSRJkiQ1pqGG78OBaUAAs4p7b0uSJElaBSu71eAexcuPAM8C5gHPjoiDyi5MkiRJajQrG/l+YfHns8suRJIkSWp0KwvfewPfADbJzEMrqEeSJElqWCsL34sj4jfAlhGxdf2GzHx9eWVJkiRJjWdl4fsIYCHwbeCw8suRJEmSGtfKwve5mfn6iFiWmQ9VUpEkSZLUoFYWvmdHxBxgUkQ8AjQV63szc9NyS5MkSZIay6DhOzM/ABAR38rMj1ZTkiRJktSYhvqQnWMi4r+ATYGfAH/IzNnllSVJkiQ1nkEfslPnXOBvwIuBOcWyJEmSpFUw1PC9UWaeB3Rl5q38e+63JEmSpCEaavgmIl5S/LkZ0F1aRZIkSVKDGuqc78OB/wdsBfwY7/ktSZIkrbIhjXxn5r3AHsBbgT0y87elViVJkiQ1oCGF74h4N3Ar8Fng9ojYv9SqJEmSpAY01DnfRwCvzczpwKuBT5RXkiRJktSYhhq+ezLzCYDMXAgsKa8kSZIkqTEN9YLLv0TEacCvgR2Bv5RXkiRJktSYhjry/R3gcWAX4ADgzNIqkiRJkhrUUMP314DLMvNjwOuKZUmSJEmrYKjhe3lm3geQmX8FesorSZIkSWpMQ53z/VBEnATcBmwLPFxeSdLAJk1sp619TCltdy1bSuf8ZaW0LUmSBEMP3wcAhwC7A/cDJ5RWkTSItvYxXHvu7qW0/bYPXwMYviVJUnmGFL4zcwnw9aE2GhHNwExga2ApMCMzZ9dt/whwMLAcOCEzr46IrwOvKnbZBOjMzO0j4gzgP4CFxbZpmTl/qLVIkiRJa4uhjnyvqulAR2ZOjYjtgdOAaQARsQlwOLAN0AHcHBE3ZOYni+1twM3AR4q2XgPsmpmPlVSrJEmSVImhXnC5qnYArgPIzNupBe0+2wK3ZObSYgR7NvDKuu0fB36amfcUI+hbAt+JiFsi4sCS6pUkSZJKV1b4ngDUTw3pjojWAbYtBCYCREQ7tekopxbbxgHfBPYHdgMOi4j6oC5JkiSNGmVNO1kAjK9bbs7M5QNsGw90Fq93Bn5dN6d7MfCNzFwMEBG/oDaP/A8DddzS0sSkSWPX/AyGoKp+1oZ+7VOSJGnNlRW+bwH2BGYVc77vqdt2J3BiRHQAY4CtgHuLbTsD19bt+2Lgooh4DbVR+h2ACwbruLu7l87OxQBMmTJ+sF3XWF8/9crus79+15U+q+i3vz4lSZL6szq5pKxpJ5cBSyLiVuB04IiIODIi3pGZc4AzgJuAXwCfK+6mAhDAX/saycz7gf8Gbgd+BXwvM/9YUs2SJElSqUoZ+c7MHmr3Ba/3QN32c4Bz+jnu7f2sOwU4ZbhrlCRJkqpW1si3JEmSpBUYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKtJbRaEQ0AzOBrYGlwIzMnF23/SPAwcBy4ITMvDoiNgT+BNxb7HZZZn6jv33LqFmSJEkqWynhG5gOdGTm1IjYHjgNmAYQEZsAhwPbAB3AzRFxA/Aa4IeZ+fG+RgbaNzOXllS3JEmSVJqywvcOwHUAmXl7RGxTt21b4JYiQC+NiNnAK4HXAq+JiF8Bj1IL3a8bYN/flFS3JEmSVJqywvcEYH7dcndEtGbm8n62LQQmAg8Ad2fmzyLifcA3gcsH2HdALS1NTJo0dhhOYeWq6mdt6Hdd7rOpaTmtrWNK6W/58qX09pb1v6EkSVrblPWpvwAYX7fcXATv/raNBzqBO4DFxbrLgC8B3xtg3wF1d/fS2VlrZsqU8YPtusb6+qlXdp/99buu9FlFvwP1+d/n71pKf+/70PXMnbuwlLYlSVK5VieXlHW3k1uA3QGKOd/31G27E9gxIjoiYiKwFbWLLL8L7F3s8xbg7kH2lSRJkkadska+LwN2iYhbgSbggIg4EpidmVdGxBnATdTC/+cyc0lEHA2cFxGHAYuo3SFlTn/7llSzJEmSVKpSwndm9gCHrLD6gbrt5wDnrHDM34A399PWM/aVJEmSRiMfsiNJkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVaR1pAuQ9EwTJ7XR3tZRStvLupYwv7OrlLYlSdLgDN/SWqi9rYMzL9y1lLY/tv/1gOFbkqSR4LQTSZIkqSKOfEsCYMKkdsa0jSml7aVdS1nQuayUtiVJGk0M35IAGNM2hs/P2q2Utv/r3dcBhm9Jkpx2IkmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVaS1jEYjohmYCWwNLAVmZObsuu0fAQ4GlgMnZObVEfE84LyipibgoMzMiDgS+DAwtzj84MzMMuqWJEmSylRK+AamAx2ZOTUitgdOA6YBRMQmwOHANkAHcHNE3AD8F3BmZl4eEbsCXwb2Al4DfCAz7y6pVkmSJKkSZU072QG4DiAzb6cWtPtsC9ySmUszcz4wG3gl8J/AT4p9WoElxevXAsdExM0RcUxJ9UqSJEmlK2vkewIwv265OyJaM3N5P9sWAhMz8zGAiAjgVGqj5wAXAd8CFgCXRcQemXn1QB23tDQxadLY4TuTQVTVz9rQr33a52jsU5KktU1Z4XsBML5uubkI3v1tGw90AkTEm6nNFX9/Md+7Cfh6MUJORPwEeDUwYPju7u6ls3MxAFOmjB9ot2HR10+9svvsr991pc8q+rXPavscP6mdjrYxpfS3pGspCzuXldK2JEmwep+dZYXvW4A9gVnFnO976rbdCZwYER3AGGAr4N4ieH8D2C0zHyr2nVBs2wpYBOxE7aJMSQ2go20Mb7vyHaW0fe07rmQhzwzf4yeNoaOtvZQ+l3QtY2Hn0n767KCjra2UPmv9drGwc8nKd5QkjbiywvdlwC4RcSu1O5ccUNy1ZHZmXhkRZwA3UZtz/rnMXBIRXwfagQtqM0/IzDw4Ij4L3Ejtrik/z8xrSqpZ0jqgo62d3S87rpS2r3nnF1nIM8N3R1sbb7/0jFL6BPjJXoezEMO3JI0GpYTvzOwBDllh9QN1288BzlnhmK0HaOv7wPeHu0ZJkiSpaj5kR5IkSaqI4VuSJEmqiOFbkiRJqojhW5IkSaqI4VuSJEmqiOFbkiRJqojhW5IkSaqI4VuSJEmqiOFbkiRJqojhW5IkSapIKY+XlySNrPGTOuhoayut/SVdXSzsXFJa+5LUqAzfktSAOtra2OOS80tr/+q9P8RCnh6+x09aj4628j5WlnQtZ2Hnk89YX2a/A/UpSavL8C1JGhYdba3s8eOLS2v/6nftw8IB+p324+tK6fOKd+3Wb58jEfjHTxpLR1tLSX12s7BzcSltS3o6w7ckSauoo62VvS65rZS2L9176gA/ZLTwnkv/WkqfP9rrBf32OXHSONrbyrk8bFlXD/M7Fz1j/aRJ42grqc+urh46++lTqpLhW5Ik9au9rZlvXDanlLY/8c5N+l3f1tbMFRc/Vkqf0/aZXEq70qrwbieSJElSRRz5liRJ67QNJo2jtaSpLsu7epjXz1SXDSaOo7W9vDHQ5ct6mDf/6f1uOHEsLe3lXDcA0L2sm8fne+3Ayhi+JUnSOq21rZnbLphbSttTPzil/z7bm5n9zX+W0ifAiz6+8TPWtbS3MOe0P5fW5yb/ueUz1hn4n8nwLUmSpFK0tLfwz2/cXlr7G39i+9LaLotzviVJkqSKGL4lSZKkihi+JUmSpIo451uSJEkNZcOJ69HSXk7M7V62nMfnP/MptENl+JYkSVJDaWlv5dEzry2l7Wd97G1rdLzTTiRJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKlHKf74hoBmYCWwNLgRmZObtu+0eAg4HlwAmZeXVETAZ+AKwHPAIckJmL+9u3jJolSZKkspU18j0d6MjMqcDRwGl9GyJiE+Bw4D+AXYEvR8QY4AvADzJzR+B/gIMH2VeSJEkadcoK3zsA1wFk5u3ANnXbtgX+P3vnHWZZVaXvt5soUWhyMqB8osg4iIgMQcWEKIoBAZFRBFQEFESJRkCEUTELkpEcJBkQFZEoDkGCyIdkHQQEBEEyXb8/1j5dt8ruZn7TtfaB2/t9nnpuqKq7dt2655y1V/jWRbYfs/0AcCOw6uDvAD8D3jCTn200Go1Go9FoNJ51ZDnfCwEPDDx+StKcM/jeg8DC456f3nODzzcajUaj0Wg0Gs86Jo2MjEz4i0r6OvBb2yeVx3+xvVy5vxHwFtvblcenAfsCPyjP3y3p38Y9N+ZnbV82E/N/A26b8D+q0Wg0Go1Go9EYy/OAxf9/fiGl4RK4CHg7cJKkNYFrBr73O2BfSfMC8wArA9eW33krcCSwAXDBTH52Zvx/vQGNRqPRaDQajUYtsiLfndrJqsAk4EOEY32j7TOLgsm2RNnLl22fKmlJ4ChgQeAeYHPb/5zez074ghuNRqPRaDQajQqkON+NRqPRaDQajUbjX2lDdhqNRqPRaDQajUo057vRaDQajUaj0ahEc74bjUaj0Wg0Go1KNOe7kYakST3YnEfSK8r9d0iaq4LNhcY9/o8KNo/NttFoNBqNRmPiac43IGmRvtcwpPy8B5vHAmuW+yIUdLI5TdK8kuaUtB/w7Qo255W0arE7t6S5K9ichqSXSXpvt9FJtpW+gXomMH6zKmnBvtYybPQRCCh2J0uaQ9I6NY5RScq28UxE0vIVbGw2cH8pST9LtreVpOdk2nimIOkFkr4m6XJJt0i6VNKXJT2v77VlkaXz/axA0nrAd4E5JJ0M3Gb7sAp21wbmIzY/3wQ+Z/vEZJtLA4sATwK7At+2/ftMm8D9kt4BGJgKYPuGZJvL2j6o2DpA0q+T7QEcCJxOvL8/B15dweZKwBkDj0eAF1awi6Qdgc2B3wKflnSS7a8mmrxc0rnAobafTud/QpD0J2COgaeeAP4MfMb2FRNsawlgAeBYSZsT8qyTiY1k6mdJ0pbA7sQchUnAiO3Uz5Gk+Ylj5QlCRvZo29mD0X4OvCnZxhgk7Q/cTAzgWA24C/jPZLOHAWsn2/gXJK1FyAsvCfwPsI3tK5Nt7gg8AjwX+JCks23vnGhyS0kPAvMSQwA/n2gLQqp5D0nnAD+ocL2ehqQ9gM8ADzN6XlgmydbngBWBkwl/6K/E+eHVxJyXG21/Icn2goQ/tDTwE+Bq2zdm2BrPbO18A3sD6wKnAl8mBv2kO9/AfwEfICKk6wPHA6nON3A08Td+HDiFcBhfl2xzceCTA49HgNcn20TSSrZvkLQiYx2oCbdT7t4A/Ib4244BXlCeS8P2yzNf/2nYDFjb9pMlKn0xkOl8vwJ4C/B5SYsT7/EJth9KtHkucTG4AHgNsDVwBPAtJt65WYc4TlYmMjWTiM3qrybYzvTYlRiI9ucKtjqOJd7LdwPXEZOM35xss49AwNq2d5X0a9uvk1Tj//lPSQcy9u/8QQW73yZmc1wnaRXif7pWss3NgPWAs4GXkX+8vAs4i3C+17b9t0xjtj8p6dPAOwgn9LnA4cDxth/OtA1sAixTwQ7Aj6YTVLmbeK/PkpR5rTsc+BnxOTqsfK2XaG8as7vzPdX2fZJGbD9adrU1eITY3T1l+y+VSgbmBM4H9rR9gqTtsg3aHuPcV/o7P0lMVu0iMB9NtHXwdJ47qNymbjIkbURspOYinLUptlfNtDnAJNtPAth+QtITmcZsTy0p3hHCCd6BiHQdlehYrGT7l+X+eZI+a/tXkiY82lUGh50q6e22z5ro138abq4V6RlgEeBMYEfbW0p6SwWbfQQC5pC0BnBrOffVmL58cbldsoKtQe63fR2A7Wsl1XDaRoiI5V22RyQtmmFE0vHFFsS1ew3gm5KwvXmGzQ7bTxDBslNK9voTwO3AYpl2gVuJvzWdzvEuwbI1bB9fyjcPtn2r7Wtm/gqzxBTbh0vawvbFNcvTZnfn+8byT54iaTcgO/XZ8U/gHOAQSR8lnMRs5ga+Dpwv6XVU+N9L+giwM6MO4hNEuUQati8lIqXpdJsLSfMCK9u+UtI7ifRVNp8jnNCPAr8G3ljBZseFkk4hosJrExmjNCQdQER/fgPsb/t3ZYru5USELYPHy7F5MRHBe0zSK8k9bj5NRHtq8nDZ2Pye4mDY3iPZ5tzAp4ArJL2UKLlJpUSeFyZKQG5Ozpp0HE1EhLcCDiBS6qnY/qKkNxDZt0tJzsANcLekQ4mM0SuByZK2LWvKOkZ/TQSUNivR/qzp1weNe5yZ5fsXyvVlY2BLYgL4ZyqYnRu4RlLn+I5kbzSI42XPcv9nRBR6/WSbSHpJuV0OeCrbXsfs7nxvR5wYLyQc4m0q2X0vEVm7WtK/AUdWsPlBwkE7FHgnsEUFm9sArwX2IlL4n5zpT88Ckk6x/R5Jf2U0SpFaqzbAMcAvgSuJzcUmRE10JvfavkTSR20fKelDyfamYXsXSRsSZRJH2s7ebNwAvLJzmCQ91/b9kjZOtLk5cSF4B3ANUSa2BnG+yGJq6T0ZLBn4XKI9gJ8mv/70+BRxDtoXeD/wsWyDkt5NnIfmJDJjI7b3ybRp+3sKVaIViIzjPzPtAUj6MrAccWw+TtTzbzbTX5oYri+3Lwb+QWyUl2b0XDzh2N4T2FMhmLCr7ceT7PwGQNLbgVfZ/pyks4nSzTQkvZboEXgt0d/z6Vo9L8D+leyMwfZ55fb8EmDJZkeiBG5lIsOQXhHQMbs73z+2XbUJp7AIsHNptDqViAxflmzzZuJkvCcRMfhHsj2Ae2z/VdKCts+T9KUsQ7bfU+6uYXta/Wq3q02mjybPxyStC8wl6c3EhS6VYq/jQeB33fO2z0+wtxSwEFFq8puSEpxMREjWsH3rRNvssH2R/78jAAAgAElEQVSvpK8Q9Z0A89tOVTcAfpj8+tPjWGJjvjxxXqhxcX+97S56992SffzvZJs7EypIZwP7EOfbVOe7D4efqEVet9SZHyUpfWNTOGL8E7ZvzzRYzkffI/p6TpaULZjwRaL3BOB9RHQ2U9Hri0Rp40dtP5ZoZ3pcCXwWeCkR/Ni7gs37S7bkEiLQUaMM+E/AdgNZ68wSlzHM7s73/aV29gbqNeEAHEI0bu1BfNAOI5q6MjkYuIOIfl9GODBvTbb5QPlAj5QSlLSax9Lksyywf2lS6Ry1r1ChDKVWk+cAHwNeQjgQexNlKDVsDtLVzc5DbCgnmjWJGkcxWl4ylQoSlpK+B2xA9GZMIv7W7AayI4mNRnfBq9EodxCVzguSPkz8fStL6mxMJlLcu2fYHGCq7ceKAzwiKT0KTQ8OPzBnKVMYkTQH9dLoJxLHyGSi5OVP5Kuu7ENdwYQnbN8NYPsBSdnv7VW2j0u2MSMOJ7IXxxINiEcCGyXb/CARHNyYaMTOzDJ2HEv9rDXQnO/FgZ0GHldR4wDms32OpN1Kd3hKumwcK9reWtLats8qNe7ZbE1ICO0G7EJuenkRYFOi0ag7eKYSkZFsuibPJQhHJrPJEwDb/1Oi+v9BREjSN422B3VuFyXe22tIOknaPh04XdJbbdcuj1iDOGamVrR5ECHtdQFxwfsBkF1O1J0X1qlwXjiGUKTYgyg5gThG70602XFBaZxbTtJB5EfaoR+H/0CiF2JxouY7tTSiw/a04FFR5ZheM/pEU1sw4XeSjmM0MpsqpQiskvz6M2OK7W5exe8lvWemPz0LSFrO9l+Ia/jg9XpR4J4su4U+stbAbO58j1fjqMhjktYnmlJWBx6tYHNOSYvBNG3LGk7Fw8DqREr7LBJT2rYvIC6wqxHNY4sDd9tOqzkcsH2pQjO+WjNXj7WdlKjlN4Bv2v5uop29Spr+A5LG9ChUaP65kSg5qaHa0CHbXWnPKZIunulPTwzdeWEk+7xQUue3lkbW1Rkt6XkB0TiXhu09iqrKFcAfbf84016husNv+2RJvwReBNxiO9t5mR4PEEGXbGoLJuxI9ICsBJxUQZlo2a5pdTwV5COfI2kp23cqlMMys7k7l6+DiQBopzgyVNLE45mtnW9JtzC2IeQB2/9ewfRHCOWRpYmawBpF/nsRabmlieEoac2PA/RR6vJ84EfAfcBCkj5m+xeZBmeX2k5JCxCRtJWBDWzflGyyu7idCPw92dZ4VgBuk9TJ8I3Yzi47mVfSvCWKNy91JhCPPy98ooLNU4AlGNUWHyHZ+S4bi4WIQTeLStrS9tGZNms6/JKOYDrNjQo5vPT0vaRLBuwvQaTys/kokV29EHiIfMGEBYhN49KE4/+iZJnOuYGlGHVGO9IDSkS998WS/kEorEx3EzAReHQw0tcHNzSSNsmyOUBNaeIxzNbON1EzC/HhfiWhQlKDJ4lddMcTkuawnVZDVjq2JWlxJw8HGKCPUpfPEs14d5cD6iwg1flm9qntvAZ4DrGJ+rAGJllnyNPZvqrc3cV27al9VbII4/gOcJWkq4mUc/ZnCOB221IML7qHOgMmlqqwkRnPGUQgYNDhT0HS22z/eCBq+QCwjKRtEyOWJ5TbjxHymBcBryLKI2qw6cD9R4ngRzZPERub68rjNcndxNUeyHKr7TSRgplRAlYvlLRYdvZE0tuI8snNJHXlS5OJLMNJmbZrShOPZ7Z2vsd1EF9UUlg1OIcoGbiBSA8+AkyS9Cnbx2cY1MC47OI0pY3LHqCPUpd7B5pi7io792yeCbWdX69g8wsVbEyP+yR9grESfOdkGJK0te1DiQjIeActVf/a9tEKze0XAjdVKhlwyQ4dBtNGPZ+XbPN6ScvYviPZziCTbdeQVwWYUm7TFYg6bP8coFxDDihPXyQpO/DQsYnt/yprWIVwUldLtvkjYtjMnxltis50vmsPZKkx/2MMkr5je/vBTEYXZEncMF9FHDOPEOd4iPP8CTP8jVlE05cmBsD50sTAbO58F2e7e+OXoY5zCFFP+voSnZ1CkRMihrOkON/UHZfdsSf1U9r/kPRzolN7dWC+Uh+dOTykj2au84n/W7XaTttHzez7kk6znaG9fS8RnegiFCPEBjaDLjJ6/Ux/KgFJryYajpYlaqO3tX11stlLgdeW+s59+dcUdwZrA7dLuof4X9bQ4r+6vL+Dw4SydKG742QR4Acukx8rsYCk1xPnoLWI0oUavLzU8i9ADIOpkb5fsnYGRRUHsnSbRY2VeIUSOCtNihNNJym4JdFL1JEyPRTAIQ18lKQfDja4KyZ6ZtnsGkhXrVgJMIbZ2vlm7AX2KqJsoAZLDkRn75W0pO17JGU6/9XGZQ/wz8GUdo3mRyK9DHGBrRI56KmZ61Tgb0Tq8/IK9v43PDfjRW1XGyBElPG8iZAYrM33gA/avkYxfOv7RDo2kydsf0DStyV9m7iwp2I7dcrtDFgPePvA4xEiw5DJhcABJet3BHCi7eyR3VsRDtR3gD8SetQ1+CAh27Y4MYimhi517QxKXwNZ9iFqvy8H/p1wiueVdEiXbZhAJklaiSgt/ACjkr0Hk1/C9HlJ2xEbxvmIyoCXJdv8eenrOSS7N2w8s7vzfTRRF9d13f8byY0/hasl/ZCQLHoNcK2k9xLOVBa9jMuW9HxCYuwY4P5EWx3HEg2tnVby97MiXB3lb1yFqIdeTdJq2bV6tteWtDJxsd1L0q+Aw2zfnGn3aUjZXA2kBicREZibba+cYYsZ13pnRts77rd9DUS9u6RsRw1KpNv2DpL2JqbppSLpZYSs4nOJ4/XaChvWrW1Py0gppgemYvsUQrVmaaJM7BskbVAHbF5Pvd6l8Y2WcxHX0F+XRs/sqPQ6RAalu26mZlAc0yWz53FMj4eJCO2jkuYhAi/vInyViXa+B2crHEycH6rMViDmKixHHCtfp4JMsO3VFIpzHyqVEKeVDGA6s7vzfSqxU+/SN+ld94VdCAWQlYFTbJ9RHKnMJq/q47Jtb6oY/bs5MYHsbmKHeV6WTeKEcT/RZLkecCiRQsvkeCJrcmeynfHcQUwufSXh/H9T0pXOH0leFdvT0o+Snkdu7flHEl/76birlC2dS/xPJ0naCsD24Uk2u4l92P6spDNm9sMTxLcI/fJDiMzNz4AU51vSOsRGfCdJXV/EZGB7knWUJa1AjAd/DxG13CDTXrG5B/AZwmGbRH5Jz2CjZVd3PQ+QHvm2/eJsG4OULPHHCcGEbg016oMXt/1osfdYaYJ8XAnj1z0wWwE4z/bDFbML95a/b0HbN0qar4JNgD8QgdAXERu6KszuzncfXfcAP7U95p9s+4/JNr9l+/3jnsselw0x9GYFojHmOuC9ReYry+l/sUe1kk9XHa3kh21/sYKdaUg6iXAejgG26E6Oki6jzrTLXrB9W1d3mWWCf43gd05FdplCJ924CpFavpjQwJ7wjELXWEWUoI1//fRzYrm4jtj+m3KHo/ydSNnPw2gD5FTCQc3mVGLzv7btGqOyISb0LWO7ij697dsAJG0DvNT2TpLOAX5YviYcFf3/0mczvlkuU///bcDzKpQOjed0SRcCvyMy9WcqpGXT5mYUO+sDnyKCOpfZ3j/RHsBfSrDhnyUKvVCyPSQdTkT7TwE+YvvWbJsds7vz3UfXPYR6w8cZq95wbrLNeSWtSpRidDazyzEuJSIwhwCf6+oAS0NkFvNKmq/s2J9Domh+qY2DiFhuTkS3umau7ImTM6pRqy3JN0iKFve4i+wyhFZzCrZfkPXa/wvbn5X0dmKIxx9sZ/agVG+sGuA+SR8B5pe0KYnlaKVU4Nri6FeVbbP9KkkbAh+VdIPtGlmFWwnViNp8jNFN24ZEBjnF+WZU//+gpNefEXdToSdiPLb3LhmplYHDbV9b+qgy//6NbL+y2H+vpIuAbOf7I8RAvpOJHoIa/QqnEyVpNScZA8357qPrHuAfxG5rzfJ4hEg1Z7ISo82Inc3sSN4Wtv80/knbb060+U1CK/laIt2c2VA6OEJ5G0aHPKRP5ppRc0iXnsxE0rLEiXhxImJwte1Lbb87yeTgReYREhtMpye11ZGdJZN0MCG5dQmwjaT1bX86yVyfjVUfJmQb7yEUiT6cbA9C0WVfJ85SGE+J3r2YaLz8T0nr2v5Ustm5gWskXcNoICB7GizAUwOlEU9MJ5syYbjo/ztmV0yj9FH9Zrq/NAsMbP6XBK4s1xYIfyH9vZW0PFEeNm881LsqbCSnSpq7lLfMRZ2BX88jmqK7Hry3E03Dmexi+8xkG9Nltna+e+q6x/YHBh+XXWy2zZdn25iOzX9xvCvYPFajWsm32L430dbrABTDbla2faWkdxKSkcPMD4CvEQONzgeOYnQjmcFChILC5ySdTTTjZDU/dhHhTWf6Uzn8u+3O8f1ayRxlMdhY1Q1+SW2sKjXQHYPNVAuQP5RlceAOjU41rjGxdF3b/wEg6ZuE3Go22dHJGXGGpAuI0ojVgD4cGj39j/yfqB1hH8/JxMTQPz/dD04gBxEZo2uIYYQ1PldnENrtNacZj0g6jbFVCKnzHDpma+e7p677bpDF9kR3+PxErWeWekNncyOiWWQuIso1xfaqmTb7wvZ91Jmw1nEMcXK8ksgwbEI0maYiaSEiWnCz7RqDfTrmtX1uqb20pOxo+xcZbQx8H9GrkOV83yPpXcDtxAn568Qx84Uke4PcJGkF27crhlOlXWwHG6ts/zTLzjhOLLdTiJHV1xBSYneRP5DlbcmvPz3mkjS5pLS7voFsrgB2JerbfwJk68QDUGqwf0w4wEd7dDrts54uwl5KwgaDAAdWWsKDtveqZAsA24dJOpO6A7/+bPsLFewMktXI/rTM1s43Fbvux7Eh0YT4tbKGb1Ww+TlgB2L4wa8JtZV0ygbnpcCfbP++hs0eWNb2QQC2D5D062yDkt5DqNfMCZxUalprjCOHkKl8MzCHpDWJcdKZPOFRXfwHJGWWDnyPiMQuCCxBRIL/TJykU0uJiBIMS7qVqH18TNKfiSjtCjP7xf9fuqY14AOSxkx+zEql235NsX0asKXtByXNT95gsUHmIiT4uuDDMuQr25xITJn8LfBqRjcfmdQegQ6ApBcRai5zAS+RtJ3tlPdXocM/nknkN+hNLwhQQ4Lv2tIbcSWVeorGByYl1QhMniXpK4QwAxBTf5NtHkvUly9P+EWZTaxjqFHH84zG9o3Exe1vQK2O9L+X+riFbJsQlM/mXtuXANg+ktDTTEXSjsTJ/z+AH0japYLNdBszsLtSuV2RxCbPAXYiSgfuIYYwZEyWnBHbEpvWxQjZzI8l2/udpOMk7VDqOq9MtPXyogq0MbCI7c87xs3XaMh5CeH4rwIsTDj/zycUTyaawaa1g8d9ZbNcp/5RMjY1+my6i/jaxPs5ZSY/OyHY/hrRB3IRsK3tGpHSKQ5ZyidsX0ydiaVQ9/3dbDpfmxK9EpmMCQKQPOFygFcQG8XuWK1RBtMFJu8hruFfqGBzU+K8t3L5ylS16jiICIS+iQi4ZDv705jdI9/ju+5r1Rr9j6QPAo8oBlukDl4oPKYYUztXiVqmjW4dYDNCZuvJ0rRxMfDVZJtvlXRgzcYqonb2JElLENrbNbSipzo0UUdsj0iqWXbyCDHQ5xeStif/uNmR0KdfCTjJ9llP8/OzQtc09pSkwQmpNQIV3x//hO1tMww5hvhsS6gnPKnQw35Zl8FJ5hxJvwEuI5o7s1QxBnnY9n6SXmx7q1KfnEIpKxzPSys1ylUdgT5AtffXAxNvK5fe/U7ScYST/ypygwDT6HqLauN6cqAdj9nODuSMZ0XbW0tax/ZZknarZXh2d76vISJLfyNSvpkTJgfZlkhz/Ijo9M8crtPxMWInuQ/RVPbZCjYn2X4SpnXA15BpWozKjVW2f0dEJ2pyQenCX04xmOW/n+4XJpATGI2Q3kfUvGfW1C5AHJ9LAzdKelHJWGUwpaS1JwGLDt5PsjdIp0Y0iaiBXjLLkKQvMKoT/yRRWrOTpMVt7z2z351VbO9Z0tovo1598CRJSwELlFKXzP/nDoR84vHEALda0WeIjerhROTwZOqNQO/e3wUrvL8ASHo3sBf1Su+6IICI4XipTaWSTrH9Ho1O+IU6g5OgohzoALdJ2p3oW+jKa7KnCs9Z+mtGJC1InQxnGK5l6JmEpA8DWxMnqE7KZh2iXq0GywLvJMpNJgEbkV9rdCfhvCxGpJRqNP9cJOkU4AIiHXlRBZtvr2Cjd2zvIektxInqjzUahQeY3zE6G9vHSdo62V7NOtYrGN0MXznufiq2B1VyfqxcPfwNgDVtdxe5WyW9j8hOpTrfRTptQ0JS7CWS3pEZES7R0S8S59xjgFvITS8vTdQGbwb8OzFs5xTbDyXa7Pg5sWnrAkq/kXQXsJ1nIE86QXyRKNX6Ifnvb8fOROnd2URQ6bJym0I5Vk7Pev3p2HtPua2RpR7PeDnQtEnYA8xFZDc7FboR8hrrO/Yi/JKlCTWiTybbm8Zs6XwTJ+BfER+ufctzUwkR/RqcUOzXHEd+ClHe0tkcIWTi0rD9KcWQiZWBI8c5F1k8yTgNauC2CnarUnbpCxFKEYsqpobWqld7XNIbiZPVGuRHC6bYPlzSFrYvlpQWSRxMaXdIWt52usyXpMGGzqXJLQ17qHO8O0p2qkZ6uZp0WimL+hRxXtjBMbhoiUybJdv3Y2IDtQDwLuA4SQ/bzpawPB/4QlEhWpGYc/Al4pqX5nzbPh84X9I8wAquMG+AfkvvqlFUVj7EqP41tt+abHZH29NKMBSa9btnGrT9IUmrEAINN9QQaChKNpK0RFfPX4vZ0vl2TFq8lSj/6INHbNco+xhkMY8baZ+NpMuJSMypttMGo4yjtgZ112S5hu3jy0nqYOePqT2DqC/vHJgamYyOrYna/W8RnenpNe6161hLs/AjxIb1Q5LOtr1zstlBx/9RcofPPCLphbZv7p6Q9ELqfI5qSqdtTpQJLEREZTOnhk6PVxIN588j0fkdYLnSxI/tTrryRklPZhgrjeZfI6LdpwCnESn8nWxn1/L3WXpXk68S59j0nrTBqgBJnYM/BxGVTnW+Je1AHK+XArtIOsl2So+YpBcQMrLvBV4DnCzpIWIwYA09/tnT+e6LcnEDuFPSexlb23TzDH9xYritVgRvgNcA6wMflvRt4FLbOyXbrK1BDZFi3bPc/xlRFrF+ss3Jtrd4+h+bOCTNWaJ6txNa5rW0i3cEjiAyKKdQp451M6K05WyiNvlX2QZdhm9JEqGskHlO2JXQ+f4VcDPR8f9m4D8TbXbUlE571PbjhH773Ek2xiDpVcTn541Ec95xwEfHZxqS+KtCru1iYtz7nSVL9XiSvcOJkpNFCV3x1YiSl7NJbqStVXon6QhmcJ6zXaMc4w+2z6tgB/qtCtgcWKeSQMM3CNGAJyV9nZjyex0hPfjaJJtjaM53XY4auL/jwP0RYN0MgwPNGvMCm0jqJj7WaNqYv3zNCcxDcrq3UFuDGoDu5Gj7fEk1lDGulvRq4PeMOjBZF9iOo4kTpBnXAEQMY0jB9rXAayQtAjzZydQlM0KUfdxVUtppDWSl3OQI4EWE5uwehLP4PdtHZNi0/YeicPIOQurvCuBLld7bVzC2QXmEfA11qNf4eCnRS3Q24fS+CXiTpBrT87YkMrobEH1EXyDqzrOa+p/sasklfcJlqnGJIqagUOkZ5AFgGUnb2v7B9H5nFjmh3H6McAgvItRO1pjhb0wsZ0i6hIFR61lOf1cVIGk74nP0MuAGpqPElEBNgYZ5bJ8paQqw/MBnuJr8dnO+K2J7HY1OPKPUAz7iRFm8npo1Ov5GKMrsmSWZNh22JXbLtTSoAe4vF4RLiBNyDQdmPcY2l6Y6wDBm+MrGNerxJK1GZBHWINRUDiLe612cKzcIMXDhfGAzSQcSTXNZ7AasVS44uxPO2p/LGlKcb5g2sGgL29MbWpKG7ddJWphRibjMRsSXKeThJg3c79aRNYX2X/oGalFqrccPbcvUvx7s9xgMdGQ6MeOvaSMkbqxs/xxA0qdsH1CevkhSjTIiiEDdAdRRHOk4mNjU/IK41hxKbOwyqSnQ0H1e1gfOhWmO98KJNscuYGSkZqno7I2klxLd0q+2/XdJGxMH1dttX59kc0HiQPqIY6LcZkS0a+vs7ntJSxOp7DcRzvDltlPrxordVYmO6Wuz3tdx9hYnyk5EpK72c4VxvJImVUplj7d7FjFE4wjg+KzPkaSfAbvavlrSdURq8E/Az2z/R4bNGaxjLttpURhJ59h+U6lDPMf2i8vzF9peO8tusXESkWq+geJIJZaAdDbHSMQRWbgUlQpJM1TFKc1WaSjmKgzyBDFC+y+ZdmtSlFR+RTgzrx+4/zrbSyXbnkQo2KxElGakKj5Jupj43P43UdKzh+0a00N/YnvDbDvjbJ5ve92Bxxc7WbK32OkEGv6YKdAg6WvEJm51YhDWDcB+xADE7NJYoEW+a/Mt4AO2/w5g+zRJ9wDfJm/c+8HA74DOQTqZkDo8CMiuGb6LcJZWIqJcz0+2h6S9CJmv/yZ0i0+2/Y0kW8uVC+kixFjyjkUJiaYUFFM8twXmk/Q48L2sxpTpYfvtCk3fDxADU66znSE3OLk43ssQ8oaXA0hK12ItPRlzEOVSB0j6r8T3eLKkOYG3UsZVl8jw/En2BlmcmJbaUaMEpJpEXLaD/TTsAywFXE6UfjwOzCvpENv/1eO6JpJNBu4fNIP7WRxCzAC4BNhS0uuTm6K3ImQ4vw1cT4yYr8Ejks5mbI9EdvnSvJLms/2wpOeQOLVZ0hzl9U8g3tNfEGWj59rOOhftQvgJX7Z9raSXA1fxr1mjNJrzXZc5bV86+ITtCxTyTFksP5heLTVVXy01ZNlcT6Tuf0TIX2XXJEM4MGvbnlocmguJ5ooMdi5f40dypzkwkj5JbGZeWTIZCwEHSvp05Qv6XIRjOpmQccugS12/hZCmoxwrCybZG2QX4rN0AtGMeA55zT/HAn8A5gbeqBhAcwxjN3QplBKQKcCKRAlIesaG2UQiDngYWNX2o+VzeyohO3g+MBTOd7e5KQ7UB4lj5dfkz60AeLntV5f735SUqlJh+3pJexK9GVcTwaUaZJfYTY9vAFdJupaQ/vt8oq2tiD6XpYh+okmEotWFWQZLxvhnAJLWJ0o2f0Vc16pMhW3Od11mVAeX+X+YUZQwzRGWtLvt/YCXdPXtFbmbGF70EOHMpE0t7aIsrjv+9z3Aut37avsfkj5KxQt6UciYl6Lq4ryxzr+UdBExDXYjhaTj94ETk+wN0tWvPlgcxTSH3/YRkk4H/uEYa78MsK3tdOm0EuHfh2jmWkXSF2wfk2x2dpGIW7zUYFM+Q4vZfrxmU1dFDiakT99IZDKOJjavmdwo6QW2b5G0BKHClIZCM35jIrN5JPBiYPtMm4VjiQbPuQjHNFsoAeCvwKsJp/QW2/c+zc//n7F9CHCIpK1sH55lZ3pI+jKwHFHq8jghp1hj4nhzvivzc0n7A3vbfkjSfMDngMzU6J8UE+S60dVI2og4uLJ4I1H3XM3xLpH8EUJR5U+SriJ27GknjQHbWxJNc4NDELKaHx8f/75W6Awfz2+cOJGww/b+ks4E7rZ9b+d82z4t2zahW3wZsIOkzxMKFmmUHpAVJL2L2Dwi6Y22v5xpl8jcvLKcjxYkmo9SnW/3O521JqdLupAo+3sVcKakj1EnKlybFW1vLWlt22dJ2u3pf2WWeQ1wvaTbiVLKx1TUvZyj5LUpMQn7XNvflFRr0/gjIpC0LFGecQdwfLLNL5aa7/uS7QyyvKTPDT5R4Tqztu11Jf3a9lHl+KxCc77rsh+RXvmDQnP2AUIL9SuJNncBji8OxC1EFPFvRL1uFlMkTVdBwXbWuNjsyXEzY1dgIypM7CMGWIyZxiVpSfKnTA6yPjE1Lx3bf5Q0h2L4wwrAr0sEMbU8wvYHJS1QnNLLbNeYRtvH5NupXcNsKWNKl+YsUcoNiAblJSVd1PXBDBO295Z0BhFVO7zUli5OnXro2swpaTGY1uSffj5KDHDMiC5j0TW5P1bJ7sK215N0KLADdYY1jUg6jSgD6bKs2XXm3XlvEqEXXyNDNKekeYm/dw4qlZxAc76rUiKW+5DUXDQDm/cDG0hagUhX3W77jmSzSxDO8Hj5pxGidjaDN9o+VDFhcrwCSPZJ42bbNybb6NgH+GlJl90EvIBQWsn+GweZR9KVjD0xZ8m2QQ8p7VJ3fZCk5wLHSrq2QoS2j8m3N5XO//OJWQM3VbB5Yvk6nJj++ENCSnKokLQ80a8wbzzUu2pkjHpiL0Iabmngt8AnswwpBqjtU0qXxpzrk89DxxHHyfMk/ZRQLqtBl9Wc3/YjqjMsqmr5B4DtMb1TCrWrbA4kGqIXJ7KbB1awCTTnuyqSLmDGk7JShuwM8GMipXwo4chkcr3rTP4apIs6p0sLToeHy4licOBNijNs+9elzOVjxPjxvxD1wVdm2JsBu1a0Bf2ktL9F6DUfQtS2/4w4hiYc9Tv5ditidPUbCZnMGu8ttrvo71WSNpnpDz97OZloFK45VbgXSuOlSmT/HudKoHYNiFUzCLa/U/pdVomHvrqS6dNKOcZVpan0H1mGJM1PnPceAo6uXDq60sDDpYlMZyq2T5b0S6KJ9pZKDedAc75r88Eebb+CiMJ8vpwgjwFOcI5Gc7XUzQDLA5S6rdr61z+taAvb1xHpx39B0vdtZ9etXUE44EsTI6WzL0LVU9oAtm8sihx/k5Q5OOkoRgeF1Jp8u7rtywhVnj+VL4DXkZed6rhe0vsJVYxXAvd2F14na4xX5kHbe/W9iBooRtjvROl7UUzzzJKJu7ZEfz9BSNNNImqhf0KCytQMsqn/LmnTCqUY2P7uwFp+AmRmWSt7qhkAACAASURBVI8qr/9cQlWrZkZ1MPL9KFEym4qkVxCyvYOf2yqBw+Z8V8T2TQCKYRrvYWz38seTbU8t0dkRYGvCefuQpKM8wSN5bb9hZt9PchA3J6L6EHWzNcZVdxxLRA9fSr1RvDNCFWwcTkSC1yOiwoeV+1lUS2kPcJ+kjwDzS9qUxOlytteBaTKKcmibv50ihZXE+kQJz/jO/szSsI6XlK9BbfiDqTdmvhbXls/OoD7zMG0uBjmQOC5rRPlrS9P1kU2dRlcCRzjExxINu1klcIvZfk9R5Mk+D4xhvGqYpLkqmD0S+A49ZKea890PxxADJtYltEKfk21Q0gHEZMvfAPvb/l05wC4HJtT5/t8sJ+E1J83gfg0OJpyzmqN4+2SK7cMVo8kvVkyaS6NySrvjw8QF/h5iCtqHK9j8IXF8Xk2ktt9H0iAs2/uX2+qj0F13vHyfvKJ8dQzb5mKQ223/soahHqTp/mD7shmJCFSgWgkcoz08U2tLYpZgx86MBiWfIKLvmdxp+9Cn/7GJpznf/fBI6YQ/3PZWpRY8mz9RJMW6J8oBtnEF2zUYmcH9Grx4oGb/dMUY4qFG0kvK7XIklxnVTGmPqzscvLAvRr7s1vJditn2fpJ+nWwPSbsTJUQPExe8LJm2QZtjxsuX0p5qTei1GB/JG3LuVmi2D0b5U4I6pdn8X+4XmxllEq+nvywRULUEbnKJOE8euD+prCF7SN42wGuJc8PJ1Mlw3lp6iAY/t1X+p8357odJJYq3gGJ066JZhsbVq+0hjQadbe9h+9Ys25V5maTjiBNFdx9I74CHiqN4nyHsCBxBSKidAmyXbK9mSrurO+xqsAdv06OWkl5ULrTPp87n6H3AMrYfrmCro9p4+T6QdEpJ3f+V0XNvlY1Nj9xSbpcqt5kBECe+9r8asw8ot2OyRJKWrrSEaiVwRDaqK+WBKKOE+H9mSzveY/uvkha0fZ6kGspA8xCZ+M4xqrahas53P+xDXPSOJ6ZyHZtoa0b1arWjw9kMKibU1tGtOYr36UgvubF9LfAaSYsAT9rOjMRA3ZT26wAkfdp27RHgOwM/Uox7vxv4aAWbtwKPVLAzyIiHeLy87feU21rOWW9IWs72X8gf+jIN20cV29kKYWOQ9EUi0DA3MQjrBuBlFUxXK4Gz/YKs1/5f8ICkdxKa2x8h5P9Ssf0hSatQ+rVs/z7bZkdzvvthXtvfKfdPK2nYFAZOVAsTcmLzZdn6/2DCHcRSFzxDJJ1mO6vEptooXsUggDmIgSxdp/9k4KelFCOtLlHSakTN4RqELvNBwP2SdrF91kx/edaoltIeYANJX7ddTbnH9iWS1iKiT7dUikbPDVwj6RpG39vsTNH5JTM11OPlS9Pshxg7+TZ75HptPkWUhB087vkamaKuaX8S4QTfSuhwZ7EBMYr8QODrwPcSbQ3yfdvvr2QLmH6TZ4U5B1sTkn+7EUon6dMmJe1AiDVcCuwi6STbX822C835roqkDYl06xaSfliengy8Gzg12fxpxMnprvI4LfLdp4M4E56b+No1R/FOr9N/KnABxKj5RNv7Av/pGGe/LzHo5k9EA1Cm810zpd2xOHCHpFuKvRHba2UaLFGfLxLHzsmSHre9X6ZNYP/k1x+DpFWJHoHViAbT+21/u+YaKvJVQgVp6KZ3dtjeqdxWr2+3Pa0Gu0gPnpRs8t6SsVmwlIbVCmTNW46bGxhtiMyuv6455+Adts8oGdRuXsWnBr6/se3TMmwTjvc6tp8s9e0XE8dtOs35rsu1hAPxGHBbeW4qSYoG45hUS7+Sfh3EGZHpsFUbxTu9Tn9Jc1c4GQNMLjJ4yxDT1i4v9lN0t/tIaQ/Qx8TFTxMZlJ8BexMR4Wznu5pmu2KA0K5ERO0zRIR/G0m32z4jy26P/MH2eX0vIpNxde0dfdS3zwmsmGzjL5K2Av5ZeqkWSrbXsRIweHzUqL+u2eS5QJFBPoc4/9xFBMvWBN5MTDTOYpLtJyH8EknVfJPmfFfE9m3AYZKOJk4UKwN/KjW0KWh0FO3Nkl7D2Ol5KQ5bzw5iH4yXu6oRmZ1T0oEl8vRjST+0/cOn/a1Zo5Oeegsxua/Tpl4wyV6fKe0niajw4kRT6dWMbpizeMr2o+WCN7VSLXRNzfZPAOvZnvZ3SToSOJOxzsWwcIakS4A/dk9UDIBUoc+69nGO/1xE700mHyGGuZ1MDMzbNNlex1a2p5VmSXptBZs15xwcW4JX7ycCd4sRPS/nAe8cPF8kcKGkU4jA4DrEPIkqNOe7H7YhUjqXAntJOsb2gUm2zKhiw6DDUmP33IeD2Aevsr1996BsrjJ36xDNeF0ZxIZErWP2e/tLSRcRF6CNJK1IDBRKSff2mdImtO+/BnyWeG+PIiIxmVxSytGWlfQdYqOcTU3N9ifHX0htPyipj4m4NdgROIBcdYpnBBo3KRDyNhqSumDH2eO+lRL5ljS9mQ0PEM2P12XYLHbXIRoBd5L09fL0ZGB7Yg5AJlXnHJT+lkPKVzVs71LKgVcGDrddbVp1c7774QPAWiXNMTex20pxvrvuZUmv6mH33IeDOCMmvO5S0scJTdJFJb2rPD0Z+MNE25oOT9l+FKaly9Kj7bb3l3QmcLftezvnO6ser+eU9ry2z5W0l21LejTLkKQTbb/P9q6S3kZc0K9PrHMcb7+WZvuMypOqDvOoyJ22T+x7EZU4knqTAlcnhAOOIWp0IVfl6VAi63UWMfa81hC3vxOlm/OU20WBe4mSrWwWB66yvZukrxAlNjV6mqpSJF1XJj5Pq0ta3XYNicPmfPfEpK7u2fbjktLKMSStTeyed+5h91zdQZS0LOPKBWxfanvCFWUcA1G+K2kP219+2l+YWM5QDGf6HdG8dmYNo7b/WKKy29u+CbhJ0tG2J3yiZ89SbY9JejMwh6Q1iYtuFtMktYqiQLaqwCCdZvtLgdOJrFwWY/T3C5OK7WHkEUlnM1alJ6UX5BlAtUmBtlct8nBbEMoY5wPH2L4xyeSyRInJ2yjSwJVq+ecGNibKwFYnsoz3A1dVsH00sGe5/1OiHG39CnZrczyRQbmztuHmfPfDJZJOIOqM1ibKT7K4n2immqfcQkSgauye+3AQ+ygXuFbSl2x/rlxsv+7kKVm295H0Y2I4wNG200/I04n0dxGgtNRrsVstpT3AtkTX+2Lky16tqHGT+jqynLVxspH/RVzYFyJKiq6cya/OCpvM4Pnauvy1yFQAeqZRdVJg6ZPaDabpfe8naXnbE36ut/034NvAt0u27/2S9gAut737RNsbYF9gS9u3l+vKBsCNRI9G+rW022DYPl+Jo+YlrTCTNdyeZbfwsO0vJtuYLs35rshAenknSe8g0h0nZHb6l5PUtZIOsX1Hlp0Z2K7uIFKxXGCALxBNiBDSil3ndhqSXkScjOcCXiJpO9sfybTZY6T/SOqltAGw/RdJ+xOf3T/YvuXpfmcWeJjKU/sYKxu5DxUu7H4aLf4h5FjgVcQxOgkY1umWAM8hNqznM1pelH0OXIiIDG8GzE+UoWTzFPAEsVF9UbKt8epSV0CeutQ47pe0LXAJsUHPVDvpSrOmEM373bC6u4ig3YQjaaVy9y5JmwOXM7ppvGGGvziBNOe7LoPp5TOo2+H/Bkm7ExHwrm42teGyDweRuuUCHU/YvhvA9gOVGsiOJiJrawN3AAtUsNnxHUl7E87ET4jSnqyUL1RMaXcUh/R1RNZmR8WQpqyJl3e6DMOqSJ8X9tmFHxGlA8sS2u130I9sZhqSFiD+psWA3xLDbu4mHOIsm+8tr78C8R5/1PatifaWIrI27yU2yscDb7L9jyybhdrqUoN8kCg72ZjIbKZlGm2/BmIQHhHpf1DS/OQeKwcDCxOqVtswWm5XQ0kLaM53baqnlwfYFXg7FaOH9OMg1iwX6PhdqWXtogRZaftBHra9n6QX296qlPfUohu68Fry5emgckq78BZgjSL5Nwfxv81yvi9Pet2Z0eeFfXZhYdvrSToU2AH4Rd8LSuArwMm2p6k7SfowcaxkBVpOBK4nap9fDnxZEpA2nfUvRGbqRCIaOzewqaTsSbszUpdKa+LV6GyFRRg7wXNRQvkkk+Ucg3aw/c8SGMjiVELK9ingc7bHK+ek05zvuvSRXu64OTk6OT36cBAfAQ6z/QtJ21NnutyOwDuIYQgnOXfUesekEpFZoEQJFq1gs6OmPB30kNImLrgLEpJiczE6GXbCsb1L1mvPhOoX9tmQbmDH/LYf0ejMhWHi3wZlVgFsHyZp60SbtaVH92FUrnepp/nZCaO2ulRh5/J1MKN/M9SJCJ8j6TfAZUQQK1MZbXPier1wsdOc7yGnj/Ryx8OKKVK/p17nfR8O4gmMDmW5j6gDzJ5WuADRjb40cKOkF1XY6HwReCfx991Cvq74GGrI0/WR0h5gGeAGSVcR9YePS7oYwMlj5mvQ04V9duM0SZ8DrpL0WyC7TKEPZjQR8Mksg7V7B2x/YWbfl/R92ykZVtuDA5puAm7KsDNgY+dy95vAmbZrlqEdDRxHnOeze8QeLYpz9/S1KW7Od136SC93VBOPH6APB3F+26cA2D4uOQLTUXNKYMcatr9a7i+RbGs8nTzdyoSc43ZJdvpIaXe8N/n1e6f2hX12ozQoAyDpJ0RD67BxX9FGvqx7QtLqDKEm9ExQ3wtI4A3A3mWDfpjtmyvYPMz22tSZkzFILd32MTTnuyI9pZc7jiUclpcCNxAp5mz6cBAfl/RGIlK6BjMe7DGR1C7DAHirYnpo9emARUHnNRVM9ZHS7piLcMCnKVVUaBZuDBGSXkbIKD6XOP9eS10N9xrsApwp6Txi8/YCwnF7e5+LaswatrcvEeF3EA32c9t+Q7LZf0o6kCjNnVrWkVVT380cmMS4+QNJfQP/QnO+Zx8OJjS/f0FEZQ8FJnwwyjj6cBC3Jhouv0V0aVdxmGqUYYxjceAOSbcQZUQjtcohFOOWd2Os7naGck71lPYAfarJNIaDbwEfIkZmd03KQ+V8275V0hrEBOMXEupAe9r+Z78ra0wAawBvBpYkMpzZdNNKlyy3mUP5BmcO9DJnoDnfsw8vtr1uuX96V7+aTDUHUdKctp8kJpBtQpFTzLA1HT5BnTKMQd4FDE5GrdlwuSuwEfnKOX2mtPtUk2kMCbZvlDRi+2+SMrWSe8MxxfjUvtfRmDgkXUeoyRxqu0amEdtflLQ0FXTxnwkzB5rzPfswr6T5bD8saT5Cdzabmg7i0UQHsxl1ujsHPFXP3PY11CnD6DRnFyL+3g8Qf+NkIrOxRo01UE85p8+Udp9qMo3h4D5JHwHml7QpkXlsDB+91Awnsw6ReXyepPlrZDIkHUZcR+cnFK5uJn86dW8053v24RvA7yX9gaj7/nyWoT4cxIE6rY1t/z7Dxngk/ZUZRNdtZ+3a1yQi7QK6eripwM+T7E2PKso5faW0y+S8XtVkGkPBh4E9CH3k1cvjxrOMUmY3XUoz+JsqLqcWrwX2InzEk0r2Zp9kmysTSicHE8dNjVKX3mjO95Aj6fCBh9cTKZ0biBPGCUlm+3QQ95Y0hSgDOd72Q1mGbC+d9dozsXk6UTb0Vtt9KNhA/B+r1PHXTmkXbfhPEXXlO5ThC7XVZBrDwfdtv7/vRTRmmZXL7ZrErI6LgVcR19Kji2TdsLEz8feeTeicX1ZuM3nQ9kiJtPcmAViL5nwPP6sD8xERvBOokCLr00G0/fYSef8AIdp/XVbNmqTdbe9X7q9v+1flfpru6wB3SPoeY5se00YAj2NT28MY7YEoXRKRuell+EJjaJhX0qpEsKNTb3h85r/SeKZhe3cASWfb3rB7XlL2oK8+GbH9WIl4j0iq0UB7uaRdiGvbCQy5fzrUf1wDbK8qaRVgC0Kh4nzgmEo1u305iHMB8xClLpnKGG8E9iv39wR+Ve6/JNFmx5HAd8hvepwe90vaiLFOxQ09rCODR4uDNPSRl0Y6KwFnDDxO7z9ppLKEpOfavr9kV6f0vaBEzi/ye8tJOgj472yDtveQtCAxpfqtwKXZNvukOd+zAUWXeTcASesC+0la3nZ2M8ORVHYQJf2KcPYPA9ZPrg+eNIP7NVRW7rR9aAU702NxYKeBxzVGD/fBMDZSNeqxle1pTouk1/a4lsassy9wmaR/EJmxWpnGqpRszVPAakT2737b365gdxvgpbZ3KuV/3ej3oaQ537MJpYlsY2I09/xEGUo2fTiIv7H9pUq2RmZwvwa3StoNuJLRpscqaVDbr5O0MPA8Qvkkra6+B3ofvtB4diNpHaKpfSdJXy9PTwa2B1bpbWGNWcL2qZJ+RAQf7qk8er0Kkt5LSMkeBHyGOMdvI+l222fM9JdnnY8BnRTxhkSWvjnfjWcn5WDaDFgB+BHwUdu3VjLfh4O4PlDL+V5W0raEozZ4P02fdIB5iNrkbrTxCFDF+Zb0bup3wtei9+ELjWc9fweWIo7RpQiZynsJZ6bxLEXS64iM6gPAIpK2sf2Lnpc10XwCWG8wYyzpSOBMxpZQZfBUabDH9hOSage0qtKc7+HnRELl5Crg5cCXpfDXKkTy+nAQ55F0JWNH1Gb9nccBS0/n/vFJ9qZh+0Ollv+lwA215BULfXTCV+GZMHyh8axnbiLLuB7R8P59QuP7qj4X1Zhl9gbWtn2HpGWJYNawOd9Pji/VtP2gpBrqVmeUYWa/I0pesp39XmnO9/Dzur4M9+Qg7lrBBhATuWb2/UzVE0k7EMoclwK7SDrJ9lczbE2HqT10wjcazxb2Bba0fbuks4ENgBuJ8fJn9rqyxqzwlO07AGz/j6RH+15QAjMqpZmcbdj2PpJ+TATrjrY91JvV5nwPOX1G8npyEK8gHPClgZ8AVyfbmxl6+h/5P7M5sI7tJyXNRWjP1nK+L6jdCd9oPIuYbPtqScsA89u+AkDS0NUIz2b8o1zTzgfWBe7reT0ZjOlzKUwiAmjplABdzSxubzTnu5FJHw7i4USEaT2iPu+wcn/YmGT7SZhWH1dt0EORhHoLUct/ve2zatluNJ4FdFHCtwC/BJA0D7BgbytqTARbEL0u+wLXMZxqJ5vM4PnW/zLBNOe7kUkfDuIU24dL2sL2xZKGVS7uQkmnABcA6wAXZRuUNAcwBzGs6X3AucAcks61PYxSg43G/4VfSroIWB7YSNKKRN33if0uqzGLPET8D+cn+pdeTkTBh4aeM+Wr275s4PF6w9yD05zvRibVHUQASS8pt8tRaQx6bWzvImlDYvTxEbZ/UsHsVsAehIKDiXTkU8CFFWw3Gs8KbO8v6Uzgbtv3ds637dP6XltjljiF0J6+kzj3jTBkzncfzECacw7g4wyxNGdzvhtp9OQg7ggcUWyeAmxXweaMSIm6F0nDw23/pAx8eFmGnfHYPgQ4RNJWtg+vYbPReDZi+48D928CbupxOY2JYTHb6/S9iCFkUJqzUwybypBLczbnu5FCjw7itcBrJC1CyCY9mGVL0pYzWcfRwJsSbH6BiAYcAzxJTA/dSdISFYcL/ULSZ4hJogBUtN1oNBp9cFuZDF1tYvPsQLlmXyvpB7b/2vd6atGc78aE04eDKGk1orlyDeBtRIPI/ZJ2SWwIXLncrgk8TDSUvgqYi5BKyqhx3wBY03Y3tOhWSe8rtms5wCcTjWTtItRoNIYaSX8lSkzmBTaRdG/51ojtGgPVhhpJp9h+D3DFwGCdSQz5+9uc70YGfTiI+wL/WRo79wXeCvyJUD5Jcb5t7w4g6WzbG3bPS8ocJPRQ974OrOMJSWkR/unwoO29KtprNBqNXrC9NMD4qHfXW9SYNYrjPe19nl1ozncjgz4cxPHaupdDNW3dJSQ91/b9kqYAUxJtPSLphbZv7p6Q9EIiMlOLayVtSkgNdhusGyrabzQajSqUQXHLAAdI+jQRlZ0MfAV4RZ9rGwYkHcEMrl+2h1HOEWjOdyOHPhzEPrV19wUuK7XtC5Gr/7orcLqkXwE3AysAbwb+M9HmeF4B/Nu455rUYKPRGEYWATYDliRmV0A0BH6vtxUNFyeU248R2fGLiPLNNXpbUQUmjYzUDJg1ZgckvQw4HvgXB9H2lUk2dwU2omjrAg8S2rrn2f5yhs1x9icBiwP32E6NtktaGHgHEY25DfhxZmPpgN1LiA3UeBWXEdtrZdtvNBqNvpC0mu0rSjP//eOzu41ZQ9I5tt808PgXtt/Y55oyac53I4U+HERJKzNWW3fVGtq6kl5HNHs+QERJtrH9i2y7tZH0vBl9z/ZtNdfSaDQaNZG0HvBdQoP6ZOA224f1u6rhQdLFxATR/wbWAvawPYzTqYHmfDeGDEnfsb39wOOjbc9QEnCCbF4IbGL7DknLAj+y/epMm41Go9Goh6TzgXcCpxKiAhfZfmW/qxoeSgPr3sTAneuBj9u+s99V5TH56X+k0XjmI+njRRJqG0l3SPprebxsBfNP2b4DwPb/AI9WsNloNBqNeky1fR9RZvcoUdrYmCBsXw/sCXwa+ARwV78ryqU1XDaGAtvfBb4raY8aNd7j+IekHYhRw+sC91W232g0Go1cbpS0HzBF0m5EOWVjgpC0PbAxsChwJPBiYPuZ/c6zmRb5bgwb35G0t6TDJL1L0osq2NyCaCrdl2j4HFp5pEaj0ZhN+SjhcF8IPARs0+9yho5NgTcQzazfBIa6dLNFvhvDxmHEYJ3XlvuHAdlNGw8BJwLzE2ogLyei4I1Go9F4FiNp3YGH15UviMnG7Tw/cXTB4K4R8bG+FlKD5nw3ho0ptg+XtIXti4sEYDanAAsDd1LG4tJOyo1GozEMnAfcRKhwwKjUajvPTyzHEe/n8yT9FDi95/Wk0pzvxtDRjf2VtBzwVAWTi9lep4KdRqPRaNRldWK4zmrAucCxtm/pd0lDyS+I2SCrALZ9dc/rSaU5341hY0fgCGBlIiK9XQWbt0la3vafK9hqNBqNRiVsXwFcUbKorwf2krQUcKbtg/td3VBxmO21gT/2vZAaNJ3vRuP/SJEyHAHmBRYA7i3fGrG9TG8LazQajcaEI+k5hNb3lkTG81U9L2lokPRzop7ewFQA2z/odVGJtMh3Y6iQtCWwG+EQA2D7hRm2bC9dbI6JendlL41Go9F4diNpLmKozubASsCZwCds39DrwoaPi8vtkr2uohIt8t0YKiT9gRhrP80Ztp3SNS1pFWAZ4ABiMMAkomP7K7ZfkWGz0Wg0GvWQ9HfgDuB4oulymtNk+5y+1jVMSHpJGbKDpBWB59i+tudlpdIi341h42bbN1aytQiwGbFT37w8NxX4XiX7jUaj0cjlDMLhXrF8dYwAzfmeRSS9G/iypDVsP0BcT4+QtKvtoVU8aZHvxlAh6URgIeD3lAiF7T2Sba5m+wpJixADAtpB1Wg0GkOEpLfZ/vHA401sn9TnmoYBSZcAG9q+b+C5JYiG1jX7W1kuLfLdGDZ+Th15wUEWlHQtMAdwsqTbbB9WeQ2NRqPRmGAkvQ1YC9hc0lrl6clEeWNzvmedRwcdbwDbd0t6tK8F1aA5341hY1Pbb6psc29gXeBU4MvARcRkzUaj0Wg8u7kKmAI8QihxQJQXntDbioaLEUnPsf1I94Sk+YC5elxTOs35bgwb90vaCLiBUbmi7K70qbbvkzRi+1FJDybbazQajUYFipLVUZJ+aHtq3+sZQr4F/FTSN4CbgeUJAYPv9LqqZJrz3Rg2Fgd2Gng8QgxGyORGSfsBUyTtBtyWbK/RaDQaddlV0q7Aw4SyVZvnMAHYPl3SXcA2hHrYrcDutn/b68KSaQ2XjaFD0sLA8wjlk4cq2JsT2Bp4OTGd6we2H8+222g0Go06SPo9sJbth/tey+yEpO/b/ljf65hoWuS7MVQU2aK9iM/2SaUUZJ8kW+sOPLyufAGsCZyfYbPRaDQavXArUffdqIv6XkAGzfluDBs7E87v2cA+wGXlNoPzgJuIwQsQqUiIUpfmfDcajcbwMDdwjaRrGJWx3Xzmv9JoTJ/mfDeGjam2HysR7xFJ/0y0tToxXGc14FzgWNu3JNprNBqNRj/s3/cCGsNDc74bw8YFko4DlpN0EKNR6QnH9hXAFZImEU2de0laihgOcHCW3Uaj0WhU5wpgV2Bp4CfA1f0up/FsZnLfC2g0JpIyzfJo4BDgJ7Y/VcHmCHAx8EvimNo622aj0Wg0qnI4IYW3EnAnbZZDLSY9/Y88+2iR78ZQIGkOYsLkCcD7iDKQOSSdaztFalDSXMAGROnJSsCZwCcq6Io3Go1Goy5TbB8uaQvbF5eMZ2MWkbTljL5n+2ig9tC8KjTnuzEsbAXsASxFTCGbRIyZvzDR5t3AHcDxwBFEE87zJT3f9jmJdhuNRqNRGUkvKbfLEdeXxqyzcrldk9BQvxh4FTHh8mjbT/S1sEya890YCmwfAhwiaSvbh1cyewbhcK9YvjpGgOZ8NxqNxvCwIxFkWRk4Bdiu3+UMB7Z3B5B0tu0Nu+clDfU1tDnfjWHjF5I+A8zbPWH7SxmGbH8QQNLbbP+4e17SJhn2Go1Go9EPtq8FXtP3OoaYJSQ91/b9kqYAU/peUCbN+W4MGycTjY9/zjYk6W3AWsDmktYqT08G3gGclG2/0Wg0GnWQtC9R3jhtLHgbLz+h7AtcJukfwELEez20NOe7MWw8aHuvSrauInbnjxB15gBTiabPRqPRaAwPGwLPt/1Y3wsZRmyfKulHwOL8v/buP9buur7j+PNe14alpQsUZtvRRNDuhXFuSGOykQJClojDRbsQOhsxylAcAWayLGzLkoURxtqQ+Nc2RvixKk5YZMl0ypiBiSjLnEJqG+JbBGy2UVxQYZauVLh3f3zPhTsHVXru93x6vn0+kpN7zrk957xO/zj3dT/38wOeqqq51pn6NDM/P//j/5U0JZJ8FPhX4CFeOoWs191HkswO/YNCko5mC633ZgAACg9JREFUSW4FPlJVz7TOMkRJzqHbvvEZ4Djgg1X1+bap+uPIt4bmtNFlwTzdATh9uirJVXQrtWeAef8cKUmDshvYm+RJXvqcP6VxpiG5BthUVU8k+Tng7wDLtzQNquqcBi+7BVhXVfsbvLYkqX9bgJOBp1sHGagXquoJgKr6zyQHWgfqk+Vbg5DkX1i0EGaxqjrj5e5fQt+mm/ctSRqmPcCzzvnuzX8nuQL4InAW8L3GeXpl+dZQ/GbD114O7Eqyi5fmmW9tmEeStLTWA48meWx0e34CAztHk/cCf0S368nDuNuJdOSrqj0NX35bw9eWJPXvIuBg6xADtg+4A1hBN4j1ZrpR8EGyfEvjexC4ClgLfBb4ets4kqQldidwL3DT6MAdLa1PAT8DvLigFcu3pEO4BbgLOJtuq6SbR9clScNwGnAe8MdJTgRuA26vqn1tYw3GCVV1ZusQkzLbOoA0AKur6hbgh1X1AN1v7ZKkgRid5XAX3WDLd4ErgLuTfKhpsOHYk2R96xCT4si3tASSnDr6ehLwQuM4kqQllGQ78C7gPmBbVX0lySzwNeDGpuGmWJK9dFNMjgEuTPLd0bcGfV6G5Vsa35XArcAb6eatXdY2jiRpiT0CbFw8zaSq5pJsbphp6lXVWoAk66vq3xfuXxjQGiqPl5ckSTqEJBuAC4BldFML11XVpW1TTb8kvwCsA7YDv0f3fzsL/FlVnXaox04zR76lMSW5lm5P0hd/kx3yn8sk6Si0A/gMsAl4AljZNs5gHAe8B3gtsHA+xhzwF80STYDlWxrf+cDrPPlMkgZrf1Vdl2RDVV2c5P7WgYagqu4H7k9yelU9mOQ44OmqGvS0DHc7kcb3EN1iEUnSMM0kWQOsTLICOL51oIE5Nslu4AHg6iS/1TpQnxz5lsa3G9ib5MXDAarqlMaZJElL52pgM93+3o8DH2sbZ3CuAc6iO8zoT4Ev052ZMUiWb2l8W4CTgadbB5EkLa0kq4CvVtXCiYs/2zLPQM1V1feSzFfVgSQ/aB2oT047kca3B3i2qp5buLQOJEkaX5LLgZ3AziRvb51nwL6V5DpgdZLfp/u5OliOfEvjWw88muSx0e35qjqjZSBJ0pLYCgRYBXwcuLttnMH6MHAJ8CVgH/DBtnH6ZfmWxncRcLB1CEnSkjtQVQeBp5Isbx1maJKctejmw6MLwC8DX/z/jxgGy7c0vjuBe4Gbqmp36zCSpF7MtA4wQF8AHgX+bXR74f94ngGXb0+4lMaUZBY4D/gAcCLdavjbFx9DLEmaPkm+A9xDVwrPHV0HoKq2vtLj9JNJcjrd1J7T6QaxPlFVj7dN1T/Lt7QEkszQFfBLgDfQzVnbUVU3Ng0mSTpsSc5+pe9V1X2TzDJko5+h59IV8TXAp6vqr9qm6o/lWxpTku3Au4D76KaefGU0Gv61qnpL23SSJB35kvw08G7gfcAJVfXWxpF645xvaXyPABsXTzOpqrkkmxtmkiTpiJZkGfAOuhHvnwc+DfxOVX2zabCeOfItjSnJBuACYBndvMB1VXVp21SSJB3ZknwfeAL4JN2iyxdLaVX9U6tcfXPkWxrfDuAzwCa6D5GVbeNIkjQV/p6ucL9+dFkwD1i+Jb2i/VV1XZINVXVxkvtbB5Ik6UhXVe8HSPLOqvqHhfuTXNgs1ARYvqXxzSRZA6xMsgI4vnUgSZKOdEneCZwBbE2ycDL0LN0mBn/bLFjPLN/S+K4GNtPt7/048LG2cSRJmgo7gdXA/wA1um8OuL1ZoglwwaU0hiSrgOeran/rLJIkTaMks1U11zrHpFi+pcOU5HLgd4Hngcur6u7GkSRJmjpJ/gC4CthPt2vYfFWta5uqP047kQ7fViDAKuDjgOVbkqRXbwvdNr1HxV+RZ1sHkKbYgao6WFVPActbh5EkaUp9m27e91HBkW9pacy0DiBJ0pRaDuxKsovRQTtVtbVtpP5YvqXD96Ykf0NXvBeuA8P+0JAkaYltax1gkizf0uFbfAjADc1SSJI03R6kW3C5Fvgs8PW2cfpl+ZYOU1Xd1zqDJEkDcAtwF3A2cPPocnbTRD1ywaUkSZJaWl1VtwA/rKoHGPg6Ksu3JEmSmkpy6ujrScALjeP0ymknkiRJaulK4FbgjcCngMvaxumXJ1xKkiRJE+LItyRJkppJci1wMaM9vgE8Xl6SJEnqx/nA66rqudZBJsEFl5IkSWrpIeCY1iEmxZFvSZIktbQb2JvkSbptBuer6pTGmXpj+ZYkSVJLW4CTgadbB5kEy7ckSZJa2gM8e7TM+bZ8S5IkqaX1wKNJHhvdnq+qM1oG6pPlW5IkSS1dBBxsHWJSLN+SJElq6U7gXuCmqtrdOkzfPOFSkiRJzSSZBc4DPgCcCNwG3F5V+5oG64nlW5IkSU0lmaEr4JcAbwD2ATuq6samwXrgITuSJElqJsl24BvAZmBbVf0ScCbw202D9cQ535IkSWrpEWDj4mkmVTWXZHPDTL1x2okkSZKaSbIBuABYRnfC5bqqurRtqv447USSJEkt7Rh93UR30uXqhll6Z/mWJElSS/ur6jrgP6rq/cBrG+fpleVbkiRJLc0kWQOsTLICOL51oD5ZviVJktTS1XQ7ndwGPA7c1TZOv1xwKUmSpCaSrAKer6r9rbNMiiPfkiRJmrgklwM7gZ1J3t46z6RYviVJktTCViDArwAfaZxlYizfkiRJauFAVR2sqqeA5a3DTIrlW5IkSa3NtA4wKS64lCRJ0sQl+Q5wD13xPnd0HYCq2toqV99+qnUASZIkHZUuXHT9hmYpJsyRb0mSJGlCnPMtSZIkTYjlW5IkSZoQ53xL0hRL8hrgc8AK4Ner6vs/wWOOAd5bVTf1nU+S9H9ZviVpuq0FTqiqja/iMWuASwDLtyRNmAsuJWmKJfkcsAm4EzgWWD361pVVtWt0fPNvAMuAZ0bX/xzYAlxPN/3wyaq6IcmpwA1V9bYku4FvAs8BHwZufpnn/mvg9cAxwPVVdUfvb1iSppxzviVpul0GPAz8F3BPVZ0DfAj4yySzdIX5V6vqTLoC/lbgWuDhqvqTQzzvSuCaqnoP8Icv89zHAufQlfl3AK/p5d1J0sA47USShuHNwLlJtoxuH1dVc0kOAp9Msg84ia6Av5IfPWGuDvHcPxiNqt8IrAJuW5J3IUkD58i3JA3DN4CPVtXb6A6u+ESSXwTeXVVbgCvoPvNngDle+vw/QDdvHOD0H3nOuUM891pgY1VtBs4HtidxQEeSfgzLtyQNw7XAhUm+APwjsBv4FvBskq8Cnwf2AuvopqgsT7INuAP4tST/DLzlVTz3k8CaJA+Nnvv6qnq+p/cmSYPhgktJkiRpQhz5liRJkibE8i1JkiRNiOVbkiRJmhDLtyRJkjQhlm9JkiRpQizfkiRJ0oRYviVJkqQJsXxLkiRJE/K/DWuFXu19z7sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "churn_prediction(model, train_x, train_y, test_x, test_y, x,\"features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the model's performance on train data itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.60522273, 0.66346154, 0.62243286, 0.58139535, 0.63836478])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores = cross_val_score(model, train_x, train_y, cv = 5, scoring='f1')\n",
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6221754521655275"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(train_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that the performance of the model on test data is same as training data. So, we can conclude that there is no overfitting and underfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(model, open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explaining the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\archd\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:144: FutureWarning: The sklearn.metrics.scorer module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "C:\\Users\\archd\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:144: FutureWarning: The sklearn.feature_selection.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.feature_selection. Anything that cannot be imported from sklearn.feature_selection is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "\n",
    "from pdpbox import pdp, info_plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0185\n",
       "                \n",
       "                    &plusmn; 0.0058\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                InternetService_Fiber optic\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 90.48%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0064\n",
       "                \n",
       "                    &plusmn; 0.0088\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Contract_Two year\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 92.50%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0045\n",
       "                \n",
       "                    &plusmn; 0.0058\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OnlineSecurity\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0041\n",
       "                \n",
       "                    &plusmn; 0.0134\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Contract_One year\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.34%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0038\n",
       "                \n",
       "                    &plusmn; 0.0086\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PaymentMethod_Electronic check\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.52%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0037\n",
       "                \n",
       "                    &plusmn; 0.0071\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                InternetService_No\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.61%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0028\n",
       "                \n",
       "                    &plusmn; 0.0094\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                tenure\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.99%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0026\n",
       "                \n",
       "                    &plusmn; 0.0011\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OnlineBackup\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 95.80%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0020\n",
       "                \n",
       "                    &plusmn; 0.0078\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MonthlyCharges\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.41%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0010\n",
       "                \n",
       "                    &plusmn; 0.0014\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                DeviceProtection\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.68%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0009\n",
       "                \n",
       "                    &plusmn; 0.0083\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PaperlessBilling\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.96%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0007\n",
       "                \n",
       "                    &plusmn; 0.0030\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TechSupport\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.57%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0004\n",
       "                \n",
       "                    &plusmn; 0.0032\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                StreamingMovies\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.92%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0017\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                gender\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.34%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0001\n",
       "                \n",
       "                    &plusmn; 0.0019\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PhoneService\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0000\n",
       "                \n",
       "                    &plusmn; 0.0009\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MultipleLines\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.34%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0001\n",
       "                \n",
       "                    &plusmn; 0.0006\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                StreamingTV\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 98.57%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0004\n",
       "                \n",
       "                    &plusmn; 0.0044\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                SeniorCitizen\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 97.68%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0009\n",
       "                \n",
       "                    &plusmn; 0.0033\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Dependents\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 95.80%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0020\n",
       "                \n",
       "                    &plusmn; 0.0026\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PaymentMethod_Credit card (automatic)\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 93.17%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0040\n",
       "                \n",
       "                    &plusmn; 0.0064\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotalCharges\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 93.17%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0040\n",
       "                \n",
       "                    &plusmn; 0.0039\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Partner\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 89.33%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0075\n",
       "                \n",
       "                    &plusmn; 0.0033\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PaymentMethod_Mailed check\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm = PermutationImportance(model, random_state=1).fit(test_x, test_y)\n",
    "eli5.show_weights(perm, feature_names = test_x.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing how the partial dependance plots look for top features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Internet Service: Fiber Optic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, \n",
    "                        feature='InternetService_Fiber optic')\n",
    "pdp.pdp_plot(pdp_p, 'InternetService_Fiber optic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Payment Method: Mailed Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, \n",
    "                        feature='PaymentMethod_Mailed check')\n",
    "pdp.pdp_plot(pdp_p, 'PaymentMethod_Mailed check')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Monthly Charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, feature='MonthlyCharges')\n",
    "pdp.pdp_plot(pdp_p, 'MonthlyCharges')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total Charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, feature='TotalCharges')\n",
    "pdp.pdp_plot(pdp_p, 'TotalCharges')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Contract - Two years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, \n",
    "                        feature='Contract_Two year')\n",
    "pdp.pdp_plot(pdp_p, 'Contract_Two year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, \n",
    "                        feature='tenure')\n",
    "pdp.pdp_plot(pdp_p, 'tenure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shap Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap \n",
    "shap.initjs()\n",
    "\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iH7Z33BJNOTRHVFX7DSMN'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": 0.419519584349919, \"outValue\": 0.32080927151412986, \"link\": \"identity\", \"featureNames\": [\"gender\", \"SeniorCitizen\", \"Partner\", \"Dependents\", \"tenure\", \"PhoneService\", \"MultipleLines\", \"OnlineSecurity\", \"OnlineBackup\", \"DeviceProtection\", \"TechSupport\", \"StreamingTV\", \"StreamingMovies\", \"PaperlessBilling\", \"MonthlyCharges\", \"TotalCharges\", \"InternetService_Fiber optic\", \"InternetService_No\", \"Contract_One year\", \"Contract_Two year\", \"PaymentMethod_Credit card (automatic)\", \"PaymentMethod_Electronic check\", \"PaymentMethod_Mailed check\"], \"features\": {\"0\": {\"effect\": -0.0009280962004301445, \"value\": 0.0}, \"1\": {\"effect\": 0.027691307039627525, \"value\": 1.0}, \"2\": {\"effect\": -0.002548569210156824, \"value\": 1.0}, \"3\": {\"effect\": 0.00391810413344745, \"value\": 0.0}, \"4\": {\"effect\": -0.06167881936624929, \"value\": 51.0}, \"5\": {\"effect\": -0.0014683690618521308, \"value\": 1.0}, \"6\": {\"effect\": 0.00716010480236766, \"value\": 1.0}, \"7\": {\"effect\": -0.033958620067986126, \"value\": 1.0}, \"8\": {\"effect\": 0.011496568426159546, \"value\": 0.0}, \"9\": {\"effect\": -0.012370221952043473, \"value\": 1.0}, \"10\": {\"effect\": 0.01611983032273219, \"value\": 0.0}, \"11\": {\"effect\": -0.016037826336813683, \"value\": 0.0}, \"12\": {\"effect\": 0.017652934410720512, \"value\": 1.0}, \"13\": {\"effect\": 0.016342375381324707, \"value\": 1.0}, \"14\": {\"effect\": 0.014874028740313188, \"value\": 93.65}, \"15\": {\"effect\": -0.04995914844276259, \"value\": 4839.15}, \"16\": {\"effect\": 0.05288222158871119, \"value\": 1.0}, \"17\": {\"effect\": 0.015580451090576368, \"value\": 0.0}, \"18\": {\"effect\": -0.1100423927686902, \"value\": 1.0}, \"19\": {\"effect\": 0.03001245645551721, \"value\": 0.0}, \"20\": {\"effect\": 0.0007424956195935921, \"value\": 0.0}, \"21\": {\"effect\": -0.02611054063839842, \"value\": 0.0}, \"22\": {\"effect\": 0.0019194131985026338, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iH7Z33BJNOTRHVFX7DSMN')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explainer = shap.TreeExplainer(model)\n",
    "\n",
    "shap_values = explainer.shap_values(np.array(test_x.iloc[0]))\n",
    "shap.force_plot(explainer.expected_value[1], shap_values[1], test_x.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "['explainer.bz2']"
      ],
      "text/plain": [
       "['explainer.bz2']"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving Explainer\n",
    "ex_filename = 'explainer.bz2'\n",
    "joblib.dump(explainer, filename=ex_filename, compress=('bz2', 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer = joblib.load(filename=\"explainer.bz2\")\n",
    "shap_values = explainer.shap_values(np.array(test_x.iloc[0]))\n",
    "shap.force_plot(explainer.expected_value[1], shap_values[1], list(test_x.columns), matplotlib = True, show = False).savefig('static/images/shap.png', bbox_inches=\"tight\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gauge Chart "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.patches import Circle, Wedge, Rectangle\n",
    "\n",
    "def degree_range(n): \n",
    "    start = np.linspace(0,180,n+1, endpoint=True)[0:-1]\n",
    "    end = np.linspace(0,180,n+1, endpoint=True)[1::]\n",
    "    mid_points = start + ((end-start)/2.)\n",
    "    return np.c_[start, end], mid_points\n",
    "\n",
    "def rot_text(ang): \n",
    "    rotation = np.degrees(np.radians(ang) * np.pi / np.pi - np.radians(90))\n",
    "    return rotation\n",
    "\n",
    "def gauge(labels=['LOW','MEDIUM','HIGH','EXTREME'], \\\n",
    "          colors=['#007A00','#0063BF','#FFCC00','#ED1C24'], Probability=1, fname=False): \n",
    "    \n",
    "    N = len(labels)\n",
    "    colors = colors[::-1]\n",
    "\n",
    "    \"\"\"\n",
    "    begins the plotting\n",
    "    \"\"\"\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    ang_range, mid_points = degree_range(4)\n",
    "\n",
    "    labels = labels[::-1]\n",
    "    \n",
    "    \"\"\"\n",
    "    plots the sectors and the arcs\n",
    "    \"\"\"\n",
    "    patches = []\n",
    "    for ang, c in zip(ang_range, colors): \n",
    "        # sectors\n",
    "        patches.append(Wedge((0.,0.), .4, *ang, facecolor='w', lw=2))\n",
    "        # arcs\n",
    "        patches.append(Wedge((0.,0.), .4, *ang, width=0.10, facecolor=c, lw=2, alpha=0.5))\n",
    "    \n",
    "    [ax.add_patch(p) for p in patches]\n",
    "\n",
    "    \n",
    "    \"\"\"\n",
    "    set the labels (e.g. 'LOW','MEDIUM',...)\n",
    "    \"\"\"\n",
    "\n",
    "    for mid, lab in zip(mid_points, labels): \n",
    "\n",
    "        ax.text(0.35 * np.cos(np.radians(mid)), 0.35 * np.sin(np.radians(mid)), lab, \\\n",
    "            horizontalalignment='center', verticalalignment='center', fontsize=14, \\\n",
    "            fontweight='bold', rotation = rot_text(mid))\n",
    "\n",
    "    \"\"\"\n",
    "    set the bottom banner and the title\n",
    "    \"\"\"\n",
    "    r = Rectangle((-0.4,-0.1),0.8,0.1, facecolor='w', lw=2)\n",
    "    ax.add_patch(r)\n",
    "    \n",
    "    ax.text(0, -0.05, 'Churn Probability ' + np.round(Probability,2).astype(str), horizontalalignment='center', \\\n",
    "         verticalalignment='center', fontsize=22, fontweight='bold')\n",
    "\n",
    "    \"\"\"\n",
    "    plots the arrow now\n",
    "    \"\"\"\n",
    "    \n",
    "    pos = (1-Probability)*180\n",
    "    ax.arrow(0, 0, 0.225 * np.cos(np.radians(pos)), 0.225 * np.sin(np.radians(pos)), \\\n",
    "                 width=0.04, head_width=0.09, head_length=0.1, fc='k', ec='k')\n",
    "    \n",
    "    ax.add_patch(Circle((0, 0), radius=0.02, facecolor='k'))\n",
    "    ax.add_patch(Circle((0, 0), radius=0.01, facecolor='w', zorder=11))\n",
    "\n",
    "    \"\"\"\n",
    "    removes frame and ticks, and makes axis equal and tight\n",
    "    \"\"\"\n",
    "    \n",
    "    ax.set_frame_on(False)\n",
    "    ax.axes.set_xticks([])\n",
    "    ax.axes.set_yticks([])\n",
    "    ax.axis('equal')\n",
    "    plt.tight_layout()\n",
    "    if fname:\n",
    "        fig.savefig(fname, dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gauge(Probability=model.predict_proba(test_x.iloc[0:1])[0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
       "       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
       "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
       "       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges',\n",
       "       'InternetService_Fiber optic', 'InternetService_No',\n",
       "       'Contract_One year', 'Contract_Two year',\n",
       "       'PaymentMethod_Credit card (automatic)',\n",
       "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x.columns"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}