Road Traffic France.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "color = sns.color_palette()\n",
    "sns.set(style=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jessica\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.linear_model import Lasso, LinearRegression, Ridge\n",
    "from sklearn.metrics.regression import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, ParameterGrid\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jessica\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:466: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('limpo7.csv', sep=';',index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_dummies = pd.DataFrame()\n",
    "for coluna in x_col:\n",
    "    temp = pd.get_dummies(df[coluna], prefix=coluna)\n",
    "    df_dummies = pd.concat([df_dummies, temp], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.concat([df,df_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1.0</th>\n",
       "      <th>col_2.0</th>\n",
       "      <th>col_3.0</th>\n",
       "      <th>col_4.0</th>\n",
       "      <th>col_5.0</th>\n",
       "      <th>col_6.0</th>\n",
       "      <th>col_7.0</th>\n",
       "      <th>catr_0.0</th>\n",
       "      <th>catr_1.0</th>\n",
       "      <th>catr_2.0</th>\n",
       "      <th>...</th>\n",
       "      <th>dep_976</th>\n",
       "      <th>atm_1.0</th>\n",
       "      <th>atm_2.0</th>\n",
       "      <th>atm_3.0</th>\n",
       "      <th>atm_4.0</th>\n",
       "      <th>atm_5.0</th>\n",
       "      <th>atm_6.0</th>\n",
       "      <th>atm_7.0</th>\n",
       "      <th>atm_8.0</th>\n",
       "      <th>atm_9.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</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>0</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>0</td>\n",
       "      <td>0</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>0</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_1.0  col_2.0  col_3.0  col_4.0  col_5.0  col_6.0  col_7.0  catr_0.0  \\\n",
       "0        0        0        1        0        0        0        0         0   \n",
       "1        0        0        1        0        0        0        0         0   \n",
       "2        0        0        0        0        0        1        0         0   \n",
       "3        0        0        0        0        0        1        0         0   \n",
       "4        0        0        0        0        0        1        0         0   \n",
       "\n",
       "   catr_1.0  catr_2.0   ...     dep_976  atm_1.0  atm_2.0  atm_3.0  atm_4.0  \\\n",
       "0         0         0   ...           0        0        0        0        0   \n",
       "1         0         0   ...           0        0        0        0        0   \n",
       "2         0         0   ...           0        1        0        0        0   \n",
       "3         0         0   ...           0        1        0        0        0   \n",
       "4         0         0   ...           0        1        0        0        0   \n",
       "\n",
       "   atm_5.0  atm_6.0  atm_7.0  atm_8.0  atm_9.0  \n",
       "0        0        0        0        1        0  \n",
       "1        0        0        0        1        0  \n",
       "2        0        0        0        0        0  \n",
       "3        0        0        0        0        0  \n",
       "4        0        0        0        0        0  \n",
       "\n",
       "[5 rows x 142 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['grav1'] = ((df.grav == 2) | (df.grav == 3)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['secu2'] = df['secu'].apply(lambda x: int(x/10) if x>=10 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    1231559\n",
       "2.0     399508\n",
       "9.0     111747\n",
       "0.0      67503\n",
       "5.0      43356\n",
       "3.0      14322\n",
       "4.0       5472\n",
       "Name: secu2, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['secu2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CREANDO O MODELO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import export_graphviz\n",
    "import graphviz\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x_col= df2.drop(['Num_Acc','grav1','secu2','an','mois','jour','hrmn','lum','agg','int','atm','col','com','dep','date','catu','grav','sexe','trajet','secu','locp','actp','etatp','an_nais','num_veh','secu1','catr','circ','vosp','prof','plan','lartpc','larrout','surf','infra','situ','env1','catv'\n",
    "],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df2['grav1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1.0</th>\n",
       "      <th>col_2.0</th>\n",
       "      <th>col_3.0</th>\n",
       "      <th>col_4.0</th>\n",
       "      <th>col_5.0</th>\n",
       "      <th>col_6.0</th>\n",
       "      <th>col_7.0</th>\n",
       "      <th>catr_0.0</th>\n",
       "      <th>catr_1.0</th>\n",
       "      <th>catr_2.0</th>\n",
       "      <th>...</th>\n",
       "      <th>dep_976</th>\n",
       "      <th>atm_1.0</th>\n",
       "      <th>atm_2.0</th>\n",
       "      <th>atm_3.0</th>\n",
       "      <th>atm_4.0</th>\n",
       "      <th>atm_5.0</th>\n",
       "      <th>atm_6.0</th>\n",
       "      <th>atm_7.0</th>\n",
       "      <th>atm_8.0</th>\n",
       "      <th>atm_9.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</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>0</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>0</td>\n",
       "      <td>0</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>0</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_1.0  col_2.0  col_3.0  col_4.0  col_5.0  col_6.0  col_7.0  catr_0.0  \\\n",
       "0        0        0        1        0        0        0        0         0   \n",
       "1        0        0        1        0        0        0        0         0   \n",
       "2        0        0        0        0        0        1        0         0   \n",
       "3        0        0        0        0        0        1        0         0   \n",
       "4        0        0        0        0        0        1        0         0   \n",
       "\n",
       "   catr_1.0  catr_2.0   ...     dep_976  atm_1.0  atm_2.0  atm_3.0  atm_4.0  \\\n",
       "0         0         0   ...           0        0        0        0        0   \n",
       "1         0         0   ...           0        0        0        0        0   \n",
       "2         0         0   ...           0        1        0        0        0   \n",
       "3         0         0   ...           0        1        0        0        0   \n",
       "4         0         0   ...           0        1        0        0        0   \n",
       "\n",
       "   atm_5.0  atm_6.0  atm_7.0  atm_8.0  atm_9.0  \n",
       "0        0        0        0        1        0  \n",
       "1        0        0        0        1        0  \n",
       "2        0        0        0        0        0  \n",
       "3        0        0        0        0        0  \n",
       "4        0        0        0        0        0  \n",
       "\n",
       "[5 rows x 142 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_col.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2[\"idade\"] = df.an - df.an_nais\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, \n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zscore = StandardScaler().fit(X_train)\n",
    "# x_train = zscore.transform(X_train)\n",
    "# x_test = zscore.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=12,\n",
       "            max_features=5, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=12, max_features=5, random_state=42)\n",
    "model.fit(X=x_train, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAD3CAYAAADsd3iFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xu4neOd//F3pDmZxDFNQ2fqNNOvQxXN0DiEaEhRx5q5iijREoJWw/xa+UUYGtVfHaqKICbIREeYMqlWlMlIBHEo1Ssh+ZAZKVFC1KlDzvv3x31vWba19zrstdfaO/m8ritX93oO93M/Xe3+7vt57u/37tbU1ISZmVlbNmp0B8zMrPNzsDAzs5IcLMzMrCQHCzMzK+lTje5ArUVEL2BP4DVgTYO7Y2bWVXQHtgKekrSi5c71LliQAsWcRnfCzKyLGgI80nLj+hgsXgO4/fbbGThwYKP7YmbWJbz++uuMGDEC8u/QltbHYLEGYODAgfz1X/91o/tiZtbVFH18vz4GCwDGznqO3lu88dHnSYd9qYG9MTPr2jo8WETELOAMSQvbOKY7MA24WdL9Lfb1AaYCA4D3gZMlvdlxPTYzs5YaPnU2InYAZpNeTBczGpgnaQgwBbigXn0zM7Ok6pFF/ov/FmAboAcwBhgF7ECagnWVpGllNNUXOA34QSv79wN+kn+eAYyvts9mZlad9owszgAWS9obGAkcACyTtA9wEDAhIvqXakTSHyQtaOOQTYB388/vA5u2o89mZlaF9gSLAOYCSJpPSuZ4OH9+H3ieNMpor/eAfvnnfsA7NWjTzMwq0J4X3AtI7xmmR8T2wPHACuCeiOgH7Aq81P4u8ihwGPAkcChlJtxdNnQXT501M6uR9gSLG4HJETGb9I7iEOCsiHgE6ANcLOmNiKiq8Yh4ADgcmAjclttdCZzQjj6bmVkVuq1vix9FxLbASzNnzvTIwsysTEuWLGHYsGEA20la3HJ/XZLyImIv1s1oKjRN0sR69MHMzKpXl2Ah6UlgaD2uZWZmtVdRsIiI3sBCSdu256IRsTtwdcGmwcDRwLPA7UBPUjGrkZI+iIgjgAuB1cBkSZNKXWP+nMtYuuXGH30eNPzy9nTZzGyD1pAMbknPShoqaShwHXB3LvNxPnBbztZ+Hjg9InoAPwWGk3I5RkWEy8mamdVRyZFFRPQl/bW/ObAob9sVuAboBrwFfAvYAxgHrAUGAjdJuq5E238FXAzsnzeNAbpFxEbA3wAvADsBiyS9nc95hFRv/a5KbtTMzKpXzshiJDBf0v6k6bIAk4Cz8sjgPuD7eftngSNJj5XGRMSAEm1/G7hL0jIASU2kabjzgQNJORaFGdzgLG4zs7orJ1jsQkqIQ9ITwCrSX/vX54qy3wK2zsc+JmmFpA9Jv/BLZXCPAG4u3CBplaSdSXWmpvDxDG5wFreZWd2V84J7IbA3KVN7D1LRQAEnSXo5IvYllfoA2D2XG+9FCjIvttZoRGwK9JL0SsG260kjjYdII4i1pEzxv4uILYC/kB5ZXVGq018YMtZ5FmZmNVJOsLgOuCW/K1hIKukxGpiSAwOkx0lbkwLJDGBLYELz46VWfB5Y3GLbNcANEXEhKVCcKWlVRJwL/JY0Epos6dVybs7MzGqjZLCQtBr4ZpFdQws/RMTWwAJJx5VzYUlPkabLFm5b2LLdvP1e4N5y2jUzs9rr0KS8iBhF8VpOYyXN7chrm5lZ7dQsWEiaBcxqse0m4KZaXcPMzBqj4cuqmplZ51eX2lDtERHDgAmkKbtvkGZhfVDqvGsunUnfjbf46POFVx7RYX00M1vfdYWRxfXA0Tkp8EXg1Ab3x8xsg1OzkUVEbEJKsNsM6E/K8n6aNPX2fdKoYLmkkRExHjgGeBPYGBif33kUM1TS0oL+Lq9Vn83MrDy1HFn8LXCHpOGkFe7OBW4gVY79CvDfABGxG2l51D1JU2e3Kt5cIum1fN4xpBIgU2rYZzMzK0Mt31m8DnwvIr5OKtHRA9ha0nN5/xzgOFKpkCclrQE+jIjflWo4IsYA/wAcIskjCzOzOqtlsPgnYK6kiRFxIPA14JWI2FnS86TiggDPAd/JlWV7kKrVtioixgGDgINyzamyfHfcMJf7MDOrkVoGi3uBiRExglS2fDVwNjA5Iv4CrARelTQvIu4DHgeWkWY5rSrWYER8BrgIeAaYERHgpVjNzOqulkl5DwE7Fm6LiLOAIyS9GRETgJW5bPnbkvaKiF6kkcYrn2wR8ovtnrXqo5mZVaej8yyWAg/kkcW7wMnA28CeEfEU0ESaQTUwIoq9uPYowsysE+jQYCHp34F/L7LrlCLbhnZkX8zMrHpdISnPzMwarKKRRUT0BhZK2rY9F42I3YGrCzYNJuVcPA/8K2lt7z8DJ0j6ICKOAC4kvTSfLGlSqWs8fdpo/thz3euOfaf/sj1dNjPboDVkZCHpWUlD8xre1wF3S7ofGEN6T7E/6cX3tyOiB/BTYDhwADAqIgY2ot9mZhuqkiOLiOgL3A5sDizK23YlrWrXjTRN9lukfIlxpBXuBgI3SbquRNt/BVxMWioV4FmgOTliE9IsqZ2ARZLezuc8AgwB7ir3Js3MrH3KGVmMBObnv/ZvzNsmAWflkcF9wPfz9s8CR5IeK43J02Tb8m3SmtvNy68uAc6OiOdIJUHuIgWNdwvOeR/YtIx+m5lZjZQTLHYBngSQ9AQpgW4n4PqImEUaVWydj31M0oqcaT0f2KFE2yNIU2ebXU6qJbULcA6pDtR7QL+CY/oB75TRbzMzq5FyXnAvBPYGpkfEHqQSHSKtK/FyROzLumKAu0dEd6AXKci82FqjEbEp0EtSYULe26wbRfyJ9OhrAfB3EbEF8BfSI6srSnV60KSJLvdhZlYj5QSL64Bb8ruChcAKYDQwJQcGSI+TtiYFkhnAlsCEgsdLxXweWNxi23eAa3O73UiPulZFxLnAb0kjocmSXi3n5szMrDZKBgtJq4FvFtk1tPBDRGwNLJB0XDkXlvQUabps4bbnga8UOfZeUu0pMzNrgA7N4I6IUcAJRXaNlTS3I69tZma1U8tCgrOAWS223QTcVKtrmJlZY7jch5mZlVTzx1A5YW9zSQ+XefwQ0uymJuA+SZe02N8HmAoMIOVYnCzpzVLtnv3rC+i5eZ9PbL/zGy5ia2ZWqY4YWRwL7FzB8VcDx0kaDByYp+cWGg3MkzSElHdxQW26aWZm5Sp7ZJH/wr8F2IY0RfY84CxgM6A/Kav7V6SM75UR8QwwGXgBWCHp+Faa/rKk1bmsyKak8iGF9gN+kn+eAYwvt89mZlYblYwszgAWS9qbFBAGAXdIGg4cDpyb8x9uBa6S9CTQF/hhG4GCHCgGkzK+XwdaPmIqLPfhUh9mZg1QSbAIYC6ApPnAHcDRETGV9GioRyvnqVTDkh7PZc+fAc5vsbuw3IdLfZiZNUAlL7gXAHuSyn5sT3op/aCkiRFxIPC1fNxaPh6E1rbWYER0Ax4GjsxVZd8Herc47FHgMFJ9qkOBOeV09trDJ7jch5lZjVQSLG4EJkfEbKA7MB04JyJGkN4zrI6IXsDTwOURsaBUg5KaIuIKYEZErABeA04FiIgHSI+3JgK35XIjKyme5GdmZh2oW1NTU6P7UFMRsS3w0syZMz2yMDMr05IlSxg2bBjAdpIWt9zfoeU+mkXEXqyb0VRomiQnPpiZdXJ1CRZ5ZtTQelzLzMxqr6JgERG9gYV55lK7RMQPgONJs51+IunXEfE5Um7Gp0glykdJUkQcAVwIrCaVKJ9Uqv1TL32QHhtv8Ynt9155VHu7bma2wWlIbahcEuQE0vKrw4FLImJj4IfAtXm51h8Bl0VED+Cn+bgDgFERMbAR/TYz21CVHFnkzOrbSavWLcrbdgWuIf31/xZpadU9gHGkqbIDgZskXddKszsBsyQtz+29CHyRlBXenID3KWB5PnZRnlpLnhU1hLQ+t5mZ1UE5I4uRwHxJ+5Omz0Iq7XFWHgHcB3w/b/8scCRpxDAmIga00uY8YP+I6BcRWwL7AH8laVleGS9IeRwX8/EMbnAWt5lZ3ZUTLHYhJcQh6QlgFemv/esjYhZpVLF1PvYxSSskfUgq37FDsQYlLQCuJdV6uhJ4AlgGkBP8/gP4piTx8QxucBa3mVndlfOCeyGwNylzew9SWQ8BJ0l6OSL2BbbKx+6e18/uRQoyLxZrMCI+DfSXtF9EbAo8AMzPgeJnwCGS/pgPXwD8XURsAfwF2J806jAzszopJ1hcB9yS3xUsBFaQyoZPyYEB4Nuk0UUP0mhhS2CCpGWttLkM2D4iniJlZf8fSWsi4mqgJyljG0CSTo+Ic4HfkkZCk3PBwjbdPO5gJ+WZmdVIyWAhaTXwzSK7hhZ+iIitgQWSjiujzSbg9CLbd2vl+HuBe0u1a2ZmHaNDk/IiYhTFazmNlTS3I69tZma1U7NgIWkWMKvFtpuAm2p1DTMza4yGJOWZmVnX0tnKfQwkJQD2JJUrHynpA5f7MDNrrM5W7uN84DZJQ4DngdNd7sPMrPE6W7mPMUC3iNgI+BvgBVzuw8ys4TpbuY8m0ip884EDSUuqutyHmVmDdbpyH5JWSdoZGAVMweU+zMwarpxg0VzugyLlPoaSRhW/ycfuHhHd8/uHssp9AOeQHjnNj4jrc8kPSCOItRSU+4iInqRyH87RMDOro85W7uMa4IaIuJAUKM7MVWhd7sPMrIE6W7mPhS3bzdtd7sPMrIFc7sPMzEpyuQ8zMyvJ5T7MzKykdo8salwCZAzQ/M7jPkkXR0QfYCowgDRD6mRJb5Zqa+ys5+i9xRtF90067Evt7aqZ2Qal04wsImJ7YAQpQW9vYHhEfJE082peLgEyBbigcb00M9swVTWy6KASIK+QllNdk9vrASwH9gN+ko+ZAYyvps9mZla9akcWI6lxCZCcub0sIrpFxBXA7yW9wMfLfbjUh5lZA1QbLGpeAgQ+ev9xO6mkx5l5c2G5D5f6MDNrgGpfcDeXAJlepATIyxGxL7BVPnb3nOndi7ZLgHQDpgP/Jen/Fex6FDiMFJwOBeZU2WczM6tStcGiI0qAHE1ar6JXRByat40FJgK35WutpHiS3ydcNnQXl/swM6uRqoJFB5UAuQfo3cruf6y0j2ZmVjsdWu6jGJcAMTPrejo0WLgEiJnZ+qHTJOWZmVnnVffHUPUyf85lLN1y41b3Dxp+eR17Y2bWtdUtWOQM780lPVzheZ8DJpP62g0YJUkd0EUzM2tFPR9DHQvsXMV5PwSuzZnhPwIuq2WnzMystFpUne0D3AJsQ8qpOA84C9gM6E8qA/IrUomQlRHxDHAnsKOk5RHxY1LV2ltbucR5rCv38SlSvSgzM6ujWowszgAWS9qbFBAGAXdIGg4cDpyb18y+FbhK0pOVNC5pWV6HO4ArgItr0GczM6tALd5ZBClDG0nzI+LPwI8j4uukuk49SpzfreQFIg4Erge+We77ii8MGesMbjOzGqnFyGIBsCd8tCbFtcBcSScCd7EuGKwtuN5yYKtcD2r3thrPgeJnpPLlv6tBf83MrEK1GFncCEyOiNlAd1IxwHMiYgRpXYvVEdELeBq4PCIWkNanuA9YDLxdov2rgZ6k+lAAknR6DfptZmZlanewkLScT5bvKJbE8Jv8D+Ah0nTYctrfrfremZlZLXSKpLyI6Ak8UGSXRxFmZp1ApwgWklbSomKtmZl1Hp0iWHSEay6dSd+Nt2h1/4VXHlHH3piZdW3tChYRsTtwpKRLIuIY4AlJf6qine7ANOBmSfe32NcHmAoMIK3BfbKkN9vTbzMzq0y7ps5KelbSJfnjOcAmlbYRETsAs8nTb4sYDcyTNASYAlxQTV/NzKx6FY0sIuLzpEzsVcBq0oymI4B/JeVLTImIE4Epkgbncx4HjpO0uJVm+wKnAT9oZf9+pKm2kJL/xlfSZzMza79KRxYHk/IlDgIuBTYHkPQb4FngJNI62WWT9AdJC9o4ZBPW1YZ6H9i0wj6bmVk7VfrO4l9II4D7Sb/Ai013balkOY8S3gP65Z/7Ae+Uc9J3xw1zuQ8zsxqpdGRxFDBH0jBSKY/CR0fN5TyWAwMiontEbAZs184+Pgocln8+FJjTzvbMzKxClY4sfgdMjYjVpODwc2CvvO8x0gvo4cCDwFPAovyvYhHxAKlq7URSqY9HSI+4WmaLm5lZB+vW1NTU6D7UVERsC7w0c+ZMP4YyMyvTkiVLGDZsGMB2xSYk1SUpLyL2Yt2MpkLTJE2sRx/MzKx6dQkWecGjofW4lpmZ1V6leRa9SUugbtveC0fEocBF+eMzpKVYNyFla29CKkt+rqS5ETGYtKbFauABSSVXy3v6tNH8sWfPNo/Zd/ovq78BM7MNSC0WP6pYRPQjlTE/PCfvLSat130uMFPSAaQlWq/Lp9xAerG9H/DliPhSvftsZrYhKzmyiIi+wO2kBLxFeduuwDWkHIq3gG8BewDjSLOkBgI3SbquWJvAPsA84Mq8ut7Nkt6MiJ8CKwr6tjwiNgF6SfrvfO3fAsNIoxEzM6uDckYWI4H5kvYnrYoHMAk4S9JQ0op338/bPwscCQwGxkTEgFba7A8cSMrTOBT4XkR8XtI7kj6MiIGkx1FjSY+k3is411ncZmZ1Vk6w2AV4EkDSE6S6UDsB10fELNKoYut87GOSVkj6EJgP7NBKm28BT0l6XdJfgIfJa3HnUctM4P9Kms3HM7ihgixuMzOrjXJecC8E9gamR8QeQA9AwEmSXo6IfYGt8rG753LjvUhB5sVW2nwa+EJE9Cf94h8MTIqInUmZ4d+Q9AcASe9FxMpcnfZ/gK8CJV9wD5o00XkWZmY1Uk6wuA64JWdQLyS9UxhNqjDbPR/zbdLoogepMuyWwARJy4o1mN9PjAV+mzfdKWl+REwHegM/iwiAdyUdBZxBem/SnTQb6onKb9XMzKpVMlhIWg18s8iuoYUfImJrYIGk48q5sKQ7gDtabDuqlWMfJ40+zMysATo0KS8iRlG8ltNYSXM78tpmZlY7NQsWkmYBs1psuwm4qVbXMDOzxmhIUp6ZmXUtdakN1Qhn//oCem7ep+Rxd37DdQzNzEqpebDIeRKbS3q4zOOHARNI+RtvkKbkflCwvw8pQW8AKSHvZElv1rrfZmbWuo54DHUssHMFx18PHJ0zxF8ETm2xfzQwT9IQ0uJKF9Skl2ZmVrayRxb5L/xbgG1I+RTnkSrFbkYq3zEJ+BWpPMjKiHgGmAy8AKyQdHwrTQ+VtLSgP8tb7N+PdWthzADGl9tnMzOrjUpGFmcAiyXtTQoIg4A7JA0nLX96rqRXgVuBq/IaFn2BH7YRKJD0GkBEHEOqFzWlxSGbAO/mn10XysysASp5ZxGkv+zJ2dZ/Bn4cEV8n1W/q0cp5KtlwxBjgH4BDJLUcWRTWhiq7LtS1h09wuQ8zsxqpZGSxANgTIJcVvxaYK+lEUj2nbvm4tS3aXdtWoxExDhgCHNRKeZBHgcPyz4cCcyros5mZ1UAlI4sbgckRMZtUo2k6cE5EjCBVkV0dEb1IRQIvj4gFpRqMiM+QVst7BpiR60FNkzQxIh4gPd6aCNyWa1OtpHhGuJmZdaBuTU1Nje5DTUXEtsBLM2fO9GMoM7MyLVmyhGHDhgFsJ2lxy/11ScqLiL1YN6Op0DRJzoozM+vk6hIs8syoofW4lpmZ1V5VwSIiFgM7Fpm5VLW8/rYk3dBi+0akxL3dSGtpnCppUan2Tr30QXpsvEXJ6957ZdGq6GZmVqDhhQQj4tMRMYO0dncxRwO9c37H+cCVdeucmZkBZYwsImIkcBQpOa4/cEnBvi8AV5GCzmbAdyU9FhEvkqa8BrAUOFbSmlYu0Rf4Z9K02GL2A+6HtAhSRPx9ybsyM7OaKndk0Rc4GBhOCg7NQWYX4DxJB+Xtp+Tt2wPj82jg0+T8jGIkvVRimdTCDG6ANRGx3lbLNTPrjMr9pTtb0lpgaUS8DeyUt78KjI+ID0nZ1e/l7cskvZJ/foW0rna1CjO4ATbKS72amVmdlBssBsFHSXSbkEqJA1wDjJC0ICIuBrbN22uZvPEocARwZ0QMBuaVc9LN4w52noWZWY2UGywGRsRMUhG/M4HmGUtTgekRsRRYQnqnURMR0VyO/B7g4Ih4jFRS5JQ2TzQzs5ormcGdX3DvKOn8uvSonZzBbWZWuc6Swf05Pll6HNK7kIvq0QczM6teyWAh6db2XkTSyziD28ysy1pvp6CWm8FdKWd8m9mGqMODRUTMAs6QtLCV/cOACcAq0iyrkyR9ULC/D+lF+gDSSnknS3qzo/ttZmbrNLzcB6nu09GS9gdeBE5tsX80ME/SENJ7jwvq3D8zsw1e1SOL/Bf/LcA2pCVVxwCjgB1IiyNdJWlaGU0NlbS0oD8tixPux7ry5jOA8dX22czMqtOekcUZwOJc0mMkcAApc3sf4CBgQkSUzLuQ9BpARBwDHMgnZ00Vlvt4n5TrYWZmddSeYBHAXABJ84GtgIfz5/eB50mjjNINRYwB/gk4pEjZ88JyH/2Ad9rRZzMzq0J7XnAvIBUInB4R2wPHk9abuCci+gG7Ai+VaiQixpHKiRwk6cMihzwKHAY8SapMO6eczrnch5lZ7bRnZHEjsH1EzCY9OjoE2DIiHgFmARdLeqON85trTV0EbA3MiIhZETE673sgInoCE4FdcrujgIvb0WczM6tC1SOL/LjohBabnyxy3NA22lgK9Gxl3/D840rgH6vrpZmZ1UK9yn3sxboZTYWmSZpYjz6YmVn16hIsJD2Jy32YmXVZVQWLiFhMqkTbcuZSNW3tDvwcWEN6QX5SQd4FEbERKXFvt7z/VEmLSrU7dtZz9N6izVcm67VJh32p0V0ws/VIZ8jg/hnwnfxu427gBy32Hw30zvkc5wNX1rd7ZmZWcmSR17M4ipQc1x+4pGDfF0hrb28EbAZ8V9JjEfEiacprAEuBYyWtaeUSxzUn5tF6Bvf9AJIej4i/L+/WzMysVsodWfQFDgaGk4JDc5DZBThP0kF5e/MqdtsD4/No4NOkfIyiCjK49wHOBn7a4pDCDG6ANRGx3lbLNTPrjMr9pTtb0lpgaUS8DeyUt78KjI+ID0nZ1e/l7cskvZJ/fgXo3VbjEfENYBzwtSIVZQszuAE2krS6zH6bmVkNlDuyGAQfJdFtQiolDnANcJGkk4F5pDWyAdpeq7VARJxIGlEMlfQ/RQ5pzuAmIgbn65iZWR2VO7IYGBEzSUX8zgRuyNunksp9LAWWkN5plC0iupMCzsvA3REBeanViGguR34PcHBEPEYKRqe01l6hy4bu4nIfZmY1UsljqPMLPm+b//Oq/O9jJA0s+Pm41hrNL72LLmcn6aSCj2eU2U8zM+sA9crg/hyfLD0OeRRRjz6YmVn1SgYLSbe29yKSXsYZ3GZmXVZnSMozM7NObr3NV5g/5zKWbrlxo7uxXho0/PJGd8HM6qzmwSIidgU2l/RwheeNA3Zt+UI8r/U9FRhAWlb15CK5GGZm1oE64jHUscDOlZwQEYeSVsErZjQwT9IQ0kvyC9rXPTMzq1TZI4v8F/4twDZAD+A84CxSTaj+wCTgV8BIYGVEPANMBl4AVkg6vpV2/xY4Hfhn4NQih+zHurUwZgDjy+2zmZnVRiUjizOAxbne00hSVvcdeUW7w4FzJb0K3Apcldew6Av8sI1A0Re4jhQsWivhUVgb6n1SYqCZmdVRJcEigLkAkuYDdwBHR8RU0qOhHq2cpzbaHA4MBKYBVwNfiYjzWxxTWBuqH/BOBX02M7MaqOQF9wJS9djpEbE9cAXwoKSJEXEg8LV83Fo+HoTWttagpLtJa1gQEUOBMyT9uMVhzbWhniS915hTTme/MGSsy32YmdVIJcHiRmByRMwGugPTgXMiYgTwFrA6InoBTwOXR8SC9nQsIh4gPd6aCNwWEY8AK4ET2tOumZlVrltTU9kFYruEiNgWeGnmzJkeWZiZlWnJkiUMGzYMYDtJi1vur1dtqL1YN6Op0DRJE+vRBzMzq15dgkWeGTW0HtcyM7PaW2/LfVxz6Uz6bly0+rnV0IVXHtHoLphZHXR4sIiIWaRZTgvb2N9sR+DWwrUzIqI/8AugD/An4BRJH3RYh83M7BMaXnVW0lBJQ4FvkVbbm9DikAuBX+RyH78nJfCZmVkdVT2yKFL+YwwwCtiBNLX2KknTKmjyauAHkv7SYvt+wI/yzzPyzz+ttt9mZla59owsWpb/OABYJmkf4CBgQn6EVFJEfBHYRNLMIrtd7sPMrMHa884iSH/pI2l+RIwG/jN/fj8inieNMspxIqkQYTHN5T4+pIJyH98dN8x5FmZmNdKekUVz+Q9y+Y/jgSH5cz9gV+ClMtsaBtzfyr7mch9QQbkPMzOrnfYEixuB7XP5jynAIcCWuSzHLOBiSW+U2dZASW81f4iILSLi7vxxAnBcRDwK7A1c244+m5lZFVzuw8zMXO7DzMzaz+U+zMyspPW23MfTp43mjz17NrobG7R9p/+y0V0wsxppV7CIiN2BIyVdEhHHAE9I+lOFbQwhLaTUBNwn6ZIW+/sAU4EBpDyLkyW92Z5+m5lZZdpV7kPSswW/3M8hJdBV6mrgOEmDgQMjYo8W+0cD83K5jymkJVzNzKyOKhpZRMTngVuBVcBqYDJwBPCvwO7AlIg4EZiSf/kTEY+TgsHiVpr9sqTVEdGXlJ39Vov9+7Hu5fgMYHwlfTYzs/ardGRxMGnZ1IOAS4HNAST9BngWOIm09GnZcqAYDMwHXgdaPmJyuQ8zswar9J3FvwA/IGVbvws8UMY53UodIOlxYNuImACcD1xUsLu53AdUUO5j0KSJzrMwM6uRSkcWRwFzJA0D7iIFjmZrc3vLgQER0T0iNgO2a62xiOgWEXMiYvO86f3cTiGX+zAza7BKRxa/A6ZGxGrSL/WfA3vlfY+RXkAPBx4EngIW5X9FSWqKiCuAGRGxAngNOBUgIh4ADgcmArflMiIrgRMq7LOZmbWTy32YmZnLfZiZWfu53IeZmZW03pb7OPvXF9Bz8z6N7oZZl3PnNzzYt09qVwZ3JSL0KpsLAAAGsElEQVRi14jYvx3nfy8iflzLPpmZWXnqObI4lpR093AlJ+XaUJOALwOuTGdm1gDtDhb5l/ktwDZAD+A84CxgM6A/6Rf9r4CRwMqIeAa4E9hR0vI8Wlgo6dZWLtGbNCX3P4Ed29tfMzOrXC0eQ50BLJa0NykgDALukDSclCdxrqRXSTWlrsovu8sm6W1J5WSKm5lZB6nFY6ggFfhD0vyI+DPw44j4OqlUR48S55csB1KNaw+f4DwLM7MaqcXIYgGwJ0BEbA9cC8yVdCKpJEhzMGguBwKpJMhWEdGNVK3WzMw6sVqMLG4EJkfEbKA7MB04JyJGkMqNr46IXqRqtZdHxAJSgt59wGLg7Rr0wczMOlC7g4Wk5XyyXtPlRQ79Tf4H8BBpLYxKrnNrxZ0zM7Oa6BRJeRHRk+LlziXp9Hr3x8zMPq5TBAtJK3E5EDOzTqtTBItSIqI7MA24WdL95Zxz6qUP0mPjLTq2Y2Zmnci9Vx7VYW3XrdxHtSJiB2A2ecaVmZnVX81GFhGxCXAzH8/cfhq4jrQC3hvAckkjI2I8cAxpve2NgfGSZrXSdF/gND6+Kp+ZmdVRLUcWf0uLzG3gBmCkpK8A/w0QEbuRlkfdEzga2KqtRiX9QdKCGvbTzMwqVMtg8TpwdERMBS4gZW5vLem5vL957eydgCclrZH0IWmpVjMz68Rq+YL7n0iZ2xMj4kDga8ArEbGzpOeBwfm454DvRMRGpICyRw378JGbxx3sch9mZjVSy2BxLzCxMHMbOJuU3f0XYCXwqqR5EXEf8DiwDFiV/5mZWSdVs2Ah6SFalBCPiLOAIyS9GRETSCXKBwBvS9orlwF5DniljPZHltmV7gCvv/56Jd03M9ugFfzO7F5sf0fnWSwFHsgji3eBk0m1oPaMiKeAJtIMqoERMaXI+dMkVbrG41YAI0aMqL7XZmYbrq3IE5IKdWtqampAXzpOHq3sCbwGrGlwd8zMuorupEDxlKQVLXeud8HCzMxqr9NncJuZWeM5WJiZWUkOFmZmVpKDhZmZleRgYWZmJXWJ9SwK5TIh1wO7ASuAUyUtKth/GnA6KYN8gqRfR0R/4BdAH+BPwCmSPqh754uo8n62AF4A5ufD7pH0s/r2/JNK3Us+5tPAY8CukpZHRB9gKjCAVJ34ZElv1rfnxVV5P92AJcCL+ZC5ksbWsdutKuN/a2OA4/LH+yRd3Fm/nyrvpSt/N2cBI0m5aZfk3wN1/W664sjiaKC3pL2B84Erm3dExEDgu8C+wFeBy3LexYXALyQNAX5P+uXbWVRzP18C/k3S0Pyv4YEia/VeACLiq6Tlcz9TsHk0MC9/N1NIRSg7i2ruZwfgmYLvplP8Msra+t/a9sAIYB9gb2B4RHyRzvv9VHMvXfW76Q+cSbqfYaSySt2o83fTFYPFfsD9AJIeB/6+YN9ewKOSVkh6F1gEfLHwHGAGcFD9ultSNfczCPhSRMyOiLsios0y73XU1r0ArCX9d//nYufQtb4bKH4/g4DPRsRDEXFfRERdelqetu7nFeCQXA16LanI53I67/dTzb10ye9G0jJgN0mrgIHAO5KaqPN30xWDxSak0iHN1kTEp1rZ9z6waYvtzds6i2ruZyFwkaQDgP8Afl6PjpahrXtB0oOS3mrjnK703bR2P68Bl0k6EPgR6TFBZ9Hq/UhaJWlZRHSLiCuA30t6gc77/VRzL13yuwGQtDoiziYVYP33Iud0+HfTFYPFe0C/gs8bSVrdyr5+wDsttjdv6yyquZ//Ah7K2+6hg8q8V6GteynnnK703bTmd8B0AEmPkP6S7dZB/atUm/cTEb2B2/MxZxY5pzN9P9XcS5f9bgAkXUsqx7F/Xgairt9NVwwWjwKHAUTEYGBewb4ngSER0TsiNiUttDS/8BzSKn1z6DyquZ+bgWPzMcNIy9d2Bm3dS8lz6FrfTWsuAr6Xz9kNeDk/MugMWr2f/EtzOvAHSadLWtPyHDrX91PNvXTV7yYi4u58X6tIL8DXUufvpsvVhiqYNfBFoBtwCum/sEWSfpVnD40iBcIfSfplRHwGuI0UfZcBJ0j634bcQAtV3s92wOR8/P+SZk681pAbKFDqXgqOWwzsmGcPbUz6brYirXlygqROUV++yvvZnPR4oy9pBttZkhbWuetFtXU/pCJy/0Z6zNFsLPAHOuH3U+W9LKQLfjf598BFpIDQBMyQdEm9/7/T5YKFmZnVX1d8DGVmZnXmYGFmZiU5WJiZWUkOFmZmVpKDhZmZleRgYWZmJTlYmJlZSf8f436S8oMDjUEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x227ad691ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(model.feature_importances_, index=x.columns)\n",
    "feat_importances = feat_importances.nlargest(20)\n",
    "feat_importances.plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mse_tr = mean_squared_error(y_true=y_train, y_pred=model.predict(x_train))\n",
    "# r2_tr = r2_score(y_true=y_train, y_pred=model.predict(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# mse_te = mean_squared_error(y_true=y_test, y_pred=model.predict(x_test))\n",
    "# r2_te = r2_score(y_true=y_test, y_pred=model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, confusion_matrix, precision_recall_curve\n",
    "\n",
    "def eval_auc(clf, x, y):\n",
    "    ypred = clf.predict_proba(x)[:,1]\n",
    "    return roc_auc_score(y, ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('LogR', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)), ('DT', Decis...n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False))],\n",
       "         flatten_transform=None, n_jobs=-1, voting='soft', weights=None)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "lr = LogisticRegression()\n",
    "dt = DecisionTreeClassifier(max_depth=12, max_features=5, random_state=42)\n",
    "rf = RandomForestClassifier(max_depth=20, n_estimators=10)\n",
    "\n",
    "clfs = [('LogR', lr),\n",
    "        ('DT', dt),\n",
    "        ('RF', rf)]\n",
    "\n",
    "vot = VotingClassifier(clfs, voting='soft',n_jobs=-1)\n",
    "vot.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogR 0.6887301381540808\n",
      "DT 0.8129308025377266\n",
      "RF 0.8179911788124783\n",
      "\n",
      "all 0.8157845471066235\n"
     ]
    }
   ],
   "source": [
    "for name, clf in clfs:\n",
    "    clf.fit(x_train, y_train)\n",
    "    \n",
    "    auc = eval_auc(clf, x_test, y_test)\n",
    "    print(name, auc)\n",
    "    \n",
    "print()\n",
    "print('all', eval_auc(vot, x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jessica\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\Users\\Jessica\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "p_train = vot.predict(x_train)\n",
    "p_test = vot.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " ---Base de Treinamento---\n",
      "\n",
      "\n",
      " ---Matriz de Confusão---\n",
      "[[979551  21884]\n",
      " [264258  45733]]\n",
      "Accuracy of model on test set: 0.77\n",
      "\n",
      "\n",
      " ---VotingClassifier Model---\n",
      "VotClas AUC = 0.53\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.79      0.98      0.87    428899\n",
      "          1       0.66      0.14      0.24    133142\n",
      "\n",
      "avg / total       0.76      0.78      0.72    562041\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cnf_matrix = confusion_matrix(y_train, p_train)\n",
    "print (\"\\n\\n ---Base de Treinamento---\")\n",
    "print (\"\\n\\n ---Matriz de Confusão---\")\n",
    "print(cnf_matrix)\n",
    "print('Accuracy of model on test set: {:.2f}'.format(model.score(x_train, y_train)))\n",
    "\n",
    "print (\"\\n\\n ---VotingClassifier Model---\")\n",
    "vot_roc_auc2 = roc_auc_score(y_test, model.predict(x_test))\n",
    "print (\"VotClas AUC = %2.2f\" % vot_roc_auc2)\n",
    "print(classification_report(y_test, p_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}