IAmigos_RIIAA_Reto_Machine_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V5zIY2gdgqKB"
   },
   "source": [
    "**Cargar librerias**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "75kuTvbCgk2t"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sc\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "from IPython.display import display\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-34S39vohJF_"
   },
   "source": [
    "**Metricas**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UesFGEoHhLDM"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "import sklearn as sk\n",
    "\n",
    "    \n",
    "def AMSScore(s,b): \n",
    "    return np.sqrt (2.*( (s + b + 10.)*np.log(1.+s/(b+10.))-s))\n",
    "\n",
    "def eval_model(Y_true_train,Y_pred_train,w_train,Y_true_test,Y_pred_test,w_test):\n",
    "    ratio =  float(len(X_train)) /float(len(X_test))\n",
    "    TruePositive_train = w_train*(Y_true_train==1.0)*(1.0/ratio)\n",
    "    TrueNegative_train = w_train*(Y_true_train==0.0)*(1.0/ratio)\n",
    "    TruePositive_valid = w_test*(Y_true_test==1.0)*(1.0/(1-ratio))\n",
    "    TrueNegative_valid = w_test*(Y_true_test==0.0)*(1.0/(1-ratio))\n",
    "    s_train = sum ( TruePositive_train*(Y_pred_train==1.0) )\n",
    "    b_train = sum ( TrueNegative_train*(Y_pred_train==1.0) )\n",
    "    s_test = sum ( TruePositive_valid*(Y_pred_test==1.0) )\n",
    "    b_test = sum ( TrueNegative_valid*(Y_pred_test==1.0) )\n",
    "    score_train = AMSScore(s_train,b_train)\n",
    "    score_test = AMSScore(s_test,b_test)\n",
    "    print('--- Resultados --')\n",
    "    print('- AUC train: {:.3f} '.format(sk.metrics.roc_auc_score(Y_train,Y_train_pred)))\n",
    "    print('- AUC test : {:.3f} '.format(sk.metrics.roc_auc_score(Y_test,Y_test_pred)))\n",
    "    print('- AMS train: {:.3f} sigma'.format(score_train))\n",
    "    print('- AMS test : {:.3f} sigma'.format(score_test))\n",
    "    return score_train, score_test\n",
    "\n",
    "def plot_roc(clf,Y_test,Y_test_prob):\n",
    "    fpr, tpr, thresholds = roc_curve(Y_test, Y_test_prob)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, lw=1, alpha=0.3, label=str(clf.__class__.__name__))\n",
    "    plt.plot(np.linspace(0,1,100),np.linspace(0,1,100), lw=2, alpha=0.3, label='Suerte')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.xlim([0,1])\n",
    "    plt.ylim([0,1])\n",
    "    plt.tight_layout()\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5E2PnPllhQlL"
   },
   "source": [
    "**Datos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "colab_type": "code",
    "id": "17eCeoWohTKx",
    "outputId": "1a7b6d5b-dc71-47ac-98aa-8b076caa1b64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(250000, 33)\n"
     ]
    },
    {
     "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>EventId</th>\n",
       "      <th>DER_mass_MMC</th>\n",
       "      <th>DER_mass_transverse_met_lep</th>\n",
       "      <th>DER_mass_vis</th>\n",
       "      <th>DER_pt_h</th>\n",
       "      <th>DER_deltaeta_jet_jet</th>\n",
       "      <th>DER_mass_jet_jet</th>\n",
       "      <th>DER_prodeta_jet_jet</th>\n",
       "      <th>DER_deltar_tau_lep</th>\n",
       "      <th>DER_pt_tot</th>\n",
       "      <th>...</th>\n",
       "      <th>PRI_jet_num</th>\n",
       "      <th>PRI_jet_leading_pt</th>\n",
       "      <th>PRI_jet_leading_eta</th>\n",
       "      <th>PRI_jet_leading_phi</th>\n",
       "      <th>PRI_jet_subleading_pt</th>\n",
       "      <th>PRI_jet_subleading_eta</th>\n",
       "      <th>PRI_jet_subleading_phi</th>\n",
       "      <th>PRI_jet_all_pt</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000</td>\n",
       "      <td>138.47</td>\n",
       "      <td>51.655</td>\n",
       "      <td>97.827</td>\n",
       "      <td>27.98</td>\n",
       "      <td>0.91</td>\n",
       "      <td>124.711</td>\n",
       "      <td>2.666</td>\n",
       "      <td>3.064</td>\n",
       "      <td>41.928</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>67.435</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0.444</td>\n",
       "      <td>46.062</td>\n",
       "      <td>1.24</td>\n",
       "      <td>-2.475</td>\n",
       "      <td>113.497</td>\n",
       "      <td>0.002653</td>\n",
       "      <td>s</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EventId  DER_mass_MMC  DER_mass_transverse_met_lep  DER_mass_vis  DER_pt_h  \\\n",
       "0   100000        138.47                       51.655        97.827     27.98   \n",
       "\n",
       "   DER_deltaeta_jet_jet  DER_mass_jet_jet  DER_prodeta_jet_jet  \\\n",
       "0                  0.91           124.711                2.666   \n",
       "\n",
       "   DER_deltar_tau_lep  DER_pt_tot  ...    PRI_jet_num  PRI_jet_leading_pt  \\\n",
       "0               3.064      41.928  ...              2              67.435   \n",
       "\n",
       "   PRI_jet_leading_eta  PRI_jet_leading_phi  PRI_jet_subleading_pt  \\\n",
       "0                 2.15                0.444                 46.062   \n",
       "\n",
       "   PRI_jet_subleading_eta  PRI_jet_subleading_phi  PRI_jet_all_pt    Weight  \\\n",
       "0                    1.24                  -2.475         113.497  0.002653   \n",
       "\n",
       "   Label  \n",
       "0      s  \n",
       "\n",
       "[1 rows x 33 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !wget https://raw.githubusercontent.com/beangoben/RIIAA_Escuela18/master/datos/training.csv\n",
    "\n",
    "df=pd.read_csv('C:\\\\Users\\\\tkalnik\\\\PycharmProjects\\\\RIIAA_Escuela18-master\\\\datos\\\\training.csv')\n",
    "print(df.shape)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J33BxKh6hqPI"
   },
   "source": [
    "**Preprocesamiento para X y Y**\n",
    "\n",
    "Utilicé random_state para que en la division de los set se seleccionen los elementos de los grupos de forma aleatoria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tprhFrXQht2e"
   },
   "outputs": [],
   "source": [
    "Y = df['Label'].replace(to_replace=['s','b'],value=[1,0]).values\n",
    "weights = df['Weight'].values\n",
    "X = df.drop(['EventId','Label','Weight'],axis=1).values\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 110
    },
    "colab_type": "code",
    "id": "7y8t4sP-r0Uy",
    "outputId": "8f416d17-e5dc-4be4-87bb-1d826ad02e29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(75000, 30) (75000,) (75000,)\n",
      "(175000, 30) (175000,) (175000,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tkalnik\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test,w_train,w_test = train_test_split(X,Y,weights,random_state=42,train_size=0.3)\n",
    "print(X_train.shape,Y_train.shape,w_train.shape)\n",
    "print(X_test.shape,Y_test.shape,w_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7xE6slybiPSF"
   },
   "source": [
    "**ML con SKLearn**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 546
    },
    "colab_type": "code",
    "id": "FHAjQafJnEHr",
    "outputId": "f2955195-604a-435a-fa77-1c8384bdd63d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Iter       Train Loss      OOB Improve   Remaining Time \n",
      "         1           1.2263           0.0616           12.15m\n",
      "         2           1.1719           0.0521           12.10m\n",
      "         3           1.1311           0.0417           12.00m\n",
      "         4           1.0930           0.0374           11.95m\n",
      "         5           1.0633           0.0307           11.96m\n",
      "         6           1.0355           0.0266           11.89m\n",
      "         7           1.0117           0.0256           11.79m\n",
      "         8           0.9886           0.0203           11.76m\n",
      "         9           0.9691           0.0176           11.66m\n",
      "        10           0.9499           0.0180           11.58m\n",
      "        20           0.8568           0.0062           10.89m\n",
      "        30           0.8125           0.0031           10.23m\n",
      "        40           0.7846           0.0017            9.84m\n",
      "        50           0.7717           0.0011            9.16m\n",
      "        60           0.7529           0.0011            8.43m\n",
      "        70           0.7482           0.0003            7.75m\n",
      "        80           0.7344           0.0008            7.17m\n",
      "        90           0.7301           0.0004            6.64m\n",
      "       100           0.7294           0.0003            5.99m\n",
      "       200           0.6951           0.0000            0.00s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('escalar', StandardScaler(copy=True, with_mean=True, with_std=True)), ('polinomial', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('modelo', GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_dept...         presort='auto', random_state=0, subsample=0.7, verbose=1,\n",
       "              warm_start=False))])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "f = [('escalar', StandardScaler()),('polinomial', PolynomialFeatures(degree=2)),\n",
    "    ('modelo', GradientBoostingClassifier(n_estimators=200, learning_rate=0.1,max_depth=3, random_state=0,verbose=1,subsample=0.7))]\n",
    "\n",
    "\n",
    "\n",
    "pipeline = Pipeline(f)\n",
    "pipeline.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q2FTgBKnTD5F"
   },
   "source": [
    "**Predicciones**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aARn8xbmnELW"
   },
   "outputs": [],
   "source": [
    "Y_train_pred = pipeline.predict(X_train)\n",
    "Y_test_pred = pipeline.predict(X_test)\n",
    "Y_train_prob=pipeline.predict_proba(X_train)[:,1]\n",
    "Y_test_prob =pipeline.predict_proba(X_test)[:,1]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VXoTAsXoV8wF"
   },
   "source": [
    "**Punto de corte**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "colab_type": "code",
    "id": "_fuIPw-5-SXA",
    "outputId": "3d7c4ac4-6b53-4079-8dee-a69946706d9d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tkalnik\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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G68clvdFay1JlChkYDaprKCBJ8nmM6stm3n89IT/bp8sXl8WPn2YVGwAApBlHA7a1tsZaayTVSLpD0mJJO4wxG8/hazdN9yFpv5M14uyOdE22h9SX5SnL6+zPYddfVBV//vSBDkfPDQAA4LZZ6cG21rZba38q6Y2SyiX9z2xcB7NjanuIc/3XE66/qDL+/PnDXQqEwo5fAwAAwC2zepOjtfaYpL2SVhtjKs72+UgOU29wdK7/esLiivx428nweFivNPU6fg0AAAC3zMVW6fNjjyxTpoCe4XH1jgQlSX6vR7Wlzt+AaIzRDQmr2E/tp00EAACkjxkHbGPMCmNMzTSve2IbzVRJesFayzJlCkhcvW4oz5PPMzs/g12/YrIP+yn6sAEAQBpxYkzfmyX9szHmGUmNkrolVUu6TtGbHNsk/Y4D18EcONE3Gn++qML5/usJVywuV7bPo0AoosbOYbX0jGiBg9NKAAAA3OLE8uQTkv5T0ZsZ75D0p5LulNQj6a8lrbbW7nXgOpgDrf2Tm3LOK569+dQ5WV5duaQ8fsw0EQAAkC5mvIJtrd0j6Q8cqAUui1irtoHEgJ0zq9e7/qIqPRWbg/3UgU6994qFs3o9AACAuTAXNzkiRfQOj2s8FJEk5fm9KsxxdKPP17khYR72C41dGgtyHywAAEh9BGzETW0PyZExZlavV1+eF5+zPRaM6KUj3bN6PQAAgLlAwEbc1PaQ2eu/TnTDlF0d2TYdAACkPgI24hJXsGtmuf96wg1smw4AANIMARtxbf2TI/pm+wbHCZcuKlWe3ytJauoemTKHGwAAIBURsCFJGguG4zs4eo1RZWH2nFw32+fVlUsq4sfPHe6ak+sCAADMFgI2JE1tD6kszJ61HRync82yyYD97CECNgAASG0EbEhypz1kwtUJAfulxm6FwpE5vT4AAICTCNiQ9PoRfXNpcUV+/JqDgZB2Hu+f0+sDAAA4iYANSadOEJmbEX0TjDG6emlCHzZtIgAAIIURsKFwxKp9DrdIn05im8hzh5mHDQAAUhcBG+oeCigUsZKkohyf8rNnd4v06VyVsIK9o7lPQ4HQnNcAAADgBAI21Dow9xvMnKqiIFsr5xVJkkIRqy1smw4AAFIUARtq65/7LdKnw7g+AACQDgjYUGvCiD63VrClqW0iz7PhDAAASFEEbExdwS5yL2BftrBMfm/0j+ShjqEpdQEAAKQKAnaGGw6ENDAWvaHQ5zEqL5ibLdKnk+v3alNDafyYbdMBAEAqImBnuMT519VFOfJ6jIvVTB3XR5sIAABIRQTsDOfmFunTuWbKPOwuWWtdrAYAAOD8EbAz3NQdHN0P2KvnF6s4N0uS1DkY0IH2QZcrAgAAOD8E7AzXNpAcI/omeD1GVy0tjx+zbToAAEg1BOwMFopE1DEQiB/XuDhBJFHiuD5udAQAAKmGgJ3BOgcDCsd6nEvyspTr97pcUdQ1Syvjz7cc6VEgFHaxGgAAgPNDwM5gHYPJt3otSfXleaovy5MkjQbD2nas1+WKAAAAzh0BO4N1JgTsqkL35l9Ph23TAQBAqiJgZ7DEgF2ZZAH72uWTbSLPHup0sRIAAIDz43O7ALinayghYM/BDo73bmk+588dC4blMVLESntODKhrKKAKF3eZBAAAOFesYGeoSMROWcGuSLIV7JwsrxaU5sWP2dURAACkCgJ2hjrRN6pQJDpBJD/bpzx/8v0yY1l1Qfz5MwcJ2AAAIDUQsDNUY+dQ/PlctIdciGVVhfHnzx7qZNt0AACQEgjYGaqxczj+PNlucJxQW5qr3KzobO4Otk0HAAApgoCdoaauYPtdrOT0PMZoSdVkm8iztIkAAIAUQMDOUI0dCQE7SVewJWlZQsB+hnF9AAAgBRCwM9SRrsQWkeTZxfFUiQF7y9EejQXZNh0AACQ3AnYG6h8Nxkf0+TxGJXlZLld0eiV5fi2pzJckjYci2nq0x+WKAAAAzoyAnYGOJPRfVxRky2OMi9WcXeKujs8cpE0EAAAkNwJ2BkqcIJJsG8xM59plidumc6MjAABIbgTsDJQKM7ATXb64TH5v9I/qgfZBtQ+MuVwRAADA6RGwM1CqTBCZkOf36ZKFpfFj2kQAAEAyI2BnoCkr2CkQsCXpGtpEAABAiiBgZ5hgOKJj3SPx44ok3WTmVNcur4g/f+ZQp8IRtk0HAADJiYCdYZp7RhSKhdPi3Cxl+7wuV3RuVs0rUnVRdLW9bySo7c29LlcEAAAwPQJ2hpnSf50CNzhOMMboxhXV8eMn9rW7WA0AAMDpEbAzTOIOjqkwoi/RTSuq4s//d1+Hi5UAAACcHgE7w6TaBJFEVy2tULYv+kf2cMeQjnUPn+UrAAAA5h4BO8Ok2gzsRLl+r65eOnmz4xOsYgMAgCREwM4g1topuzim2gq2JN20crIP+3/pwwYAAEmIgJ1BuofH1T8alCTl+70qyvG5XNH5u2nlZB/21qM9GhgLulgNAADA6xGwM0hi//WSqgIZY1ys5sJUF+VobW2xJCkUsdp8gF0dAQBAciFgZ5DE9pAllQUuVjIziavYtIkAAIBkQ8DOIIk3OC6pzHexkpm5KWEe9lMHOhUKR1ysBgAAYCoCdgaZGrBTdwV7Te3kro79o0Ftb+5zuSIAAIBJBOwMkhiwF6dwwD51V0faRAAAQDIhYGeIsWBYx3tHJUkeIzWU57lc0czcnNCHzbbpAAAgmRCwM8Sx7hFZG31eV5qnnCyvuwXN0FVLK5STFf3j29g5rKYudnUEAADJgYCdIY52JbaHpO4NjhNysk7d1ZFVbAAAkBwI2BniSMIK76KK1A/Y0tRdHX/5WpuLlQAAAEyaccA2xpQbYz5sjPmpMeawMWbUGNNvjHnOGPPbxhhCfBI4kjADe3GaBOxbVlXLE9sr5+WmXp3sG3W3IAAAADmzgn2XpG9KulzSFklfkvSApDWSviXpfpOKWwammaNTVrBTd4JIooqCbF25ZLJN5LHdrS5WAwAAEOVEwD4o6a2S6qy177bWfspa+yFJKyS1SLpT0h0OXAczMCVgp0EP9oTb1s2LP//5zpMuVgIAABA144BtrX3SWvtza23klNfbJH09dnj9TK+DC9c3Mq6e4XFJUk6WR/OKclyuyDlvWl2jLG/0FyQ7j/eruXvE5YoAAECmm+3+6GDsMTTL18EZJK5eLyzPl8eTPh07JXl+XbOsMn78yG5WsQEAgLtmLWAbY3yS3hc7fPwcPn/bdB+KtppgBhIDdjqM6DvV1DYR+rABAIC7ZnMF+wuK3uj4mLX2l7N4HZzF0TQc0Zfo5pXV8vuif5T3tQ7ocMfQWb4CAABg9sxKwDbGfFzSJyXtl/Tec/kaa+2m6T5i58AMJI7oS5cJIokKc7J040WTW6c/sos2EQAA4B7HA7Yx5g8k/bukvZJusNb2OH0NnJ903GTmVL95yjQRO7EvPAAAwBxzNGAbYz4h6SuS9igartlez2WRiFVTQsBekoY92JJ044oq5fm9kqTGzmHtbxt0uSIAAJCpHAvYxpg/l/Rvkl5VNFx3OHVuXLj2wTGNBsOSpNK8LJXk+V2uaHbk+X1Ttk6nTQQAALjFkYBtjPkrRW9q3CbpJmttlxPnxcwd7Uz/9pAJt108dZoIbSIAAMANvpmewBjzfkl/Iyks6VlJH59mZ/Qma+13Z3otnL8jabhF+ulcd1GlCrN9GgyE1Nwzol3H+7VuQYnbZQEAgAwz44AtaVHs0SvpE6f5nM2SvuvAtXCeEieIpOMM7ETZPq/euLpGD2w/Lkl6cPtxAjYAAJhzTmyV/jlrrTnLx/UO1IoLcLRrciZ0ureISNKdm2rjzx/ccUKj42EXqwEAAJnIiRVsJLF02GTm3i3N5/y51lqV5/vVPTyuwbGQHt3dqndsqpvF6gAAAKaazZ0c4bLxUEQtvaPx41QN2OfDGKNLF5bFj+/beu7hHAAAwAkE7DTW0juicCQ6SaO2JFc5WV6XK5obGxtK5Y3daLvtWK8OMBMbAADMIQJ2GsukEX2JCrJ9Wjm/KH7MKjYAAJhLBOw0diTDbnBMdFlCm8iD249rLMjNjgAAYG4QsNNYOtzgeKEWV+arvixPkjQwFtJju1tdrggAAGQKAnYaS5yBvSjNZ2CfymOM3nXZgvgxbSIAAGCuELDTWOIK9pI038VxOu/YVCefJ3qz48tNvTrUzs2OAABg9hGw09RQIKSOwYAkKctrVFua63JFc6+qMEe3rKqOH9+3tcXFagAAQKYgYKeppoTV64byfHljK7mZ5l2X1cefP8DNjgAAYA4QsNNUY2fmThBJdM3SCtWWRFfv+0eD+umOEy5XBAAA0h0BO00l9l8vzuCA7fEYvf/KhvjxNzY3xjffAQAAmA0E7DSVySP6TvVbl9WrKMcnSWrqHtHje9pcrggAAKQzAnaamrKCXZl5E0QSFeZk6X1XLIwff23zYVnLKjYAAJgdBOw0ZK2dOgM7w1ewJekDVy1Uti/6x33PiQE9f7jb5YoAAEC6ImCnofaBgIYCIUlSUY5PFQV+lytyX0VBtu6+dHLjma9tPuxiNQAAIJ0RsNNQ4gSRpVUFMiYzR/Sd6neuWRwfV/j84W7tbOlzuSIAAJCOCNhpKDFgL8nw/utEC8ry9JsXz4sff31zo4vVAACAdEXATkOHOxICdhUBO9FHrlsSf/74a21TfhgBAABwAgE7DU1pEWEFe4qV84p0w0WVkiRrpW8+c8TligAAQLohYKehxo7JCSKsYL/eR69fGn/+wPbjOtY9fIbPBgAAOD8E7DQzOBZU28CYJCnLa7SgNNflipLPpQtLddnCMklSMGz1z7884HJFAAAgnRCw00zi/OuF5fnyefkWn8oYoz//jRXx40d2tepVJooAAACHkL7SzKkj+jC9TQ2lunVtTfz484/tY3dHAADgCJ/bBcBZjOibdO+W5jO+v6KmSI/vaVPESluP9ugzP3tNf/v2NXNUHQAASFesYKeZqSP62CL9TCoKsnX5ovL48eN72hQKR1ysCAAApAMCdpppTOjBzvQV7HNxw4oqZfui/xl0DgX0o1daXK4IAACkOgJ2GgmGI1NGzhGwz64g26frllfGj//t14c0HAi5WBEAAEh1BOw00tIzomA4eqPevOIc5WfTYn8urlxSoaKc6P9XXUMBfYPNZwAAwAwQsNPIlP5rVq/Pmd/n0S2rquPHX9/cqCNsoQ4AAC4QATuNTO2/5gbH87GhvlS1JdFNecZDEX3qwd2KRBjbBwAAzh8BO40wA/vCeYzR7Rtq5fUYSdKWoz26nxseAQDABSBgpxFmYM/M/JJcffiaRfHjzz+2Tx2DYy5WBAAAUhEBO01Ya0+ZgU3AvhCfuGm56svyJEkDYyH99cN7Xa4IAACkGgJ2mugcCmhwLDperjDbp6rCbJcrSk25fq8+f/va+PGju1v1xN52FysCAACphoCdJho7Jm9wXFxVIGOMi9WktquXVejOjXXx47/62R4NjgVdrAgAAKQSAnaamNp/zQSRmfr0W1aqPN8vSWrtH9PfP7rP5YoAAECqIGCnCWZgO6s036/P3LYqfvzDl1v0yK6TLlYEAABSBQE7TTCiz3lvXTdfv3nxvPjxpx7crZaeERcrAgAAqYCAnSaOTNlkhoDtBGOMPn/HWi0oi25AMzgW0sd/uEPBcMTlygAAQDIjYKeB4UBIJ/pGJUk+j1FDeZ7LFaWPopws/b93bZAvtgHNjuY+/duvD7pcFQAASGYE7DRwtGty9bq+PE9ZXr6tTtpQX6pPvvGi+PHXNjfq+cNdLlYEAACSGUksDUzpv6Y9ZFb83rWLdc2yCkmStdInfvSqOgcDLlcFAACSEQE7DTSyg+Os83iMvvjOdaooiI7u6xwM6Pe+94oCobDLlQEAgGRDwE4DhxjRNyeqCnP0r+9cr1g7trY39+kvf7pH1lp3CwMAAEnF53YBmLn9bYPx5ytqCl2sJPXdu6X5rJ/z5tU1emxPmyTpJ9uOazgQ0tfes2m2SwMAACmCFewUNzIeUlN39CZHr8cwA3sOXLW0QpvqS+PHj+9p01P7O1ysCAAAJBMCdoo70DaoiQ6FxRX5ysnyultQBjDG6G3r56uhLDoO0Up3hQPSAAAgAElEQVT6w/t26FD74Jm/EAAAZAQCdoqb0h4yr8jFSjKLz+vRu9/QoJLcLEnSUCCkD/33y+oYHHO5MgAA4DYCdorb1zoQf07/9dwqyPbpvVc0yB+bO97SM6r3/ddW9Y8GXa4MAAC4iYCd4va3Tq5gr2IFe87NK87V3ZcukDc2WmR/26A+9N2XNTIecrkyAADgFgJ2CrPWal9bwgr2PFaw3bByXpH+8c6L48fbjvXqo9/frvFQxMWqAACAWwjYKexE36gGx6IrpcW5WaopynG5osz1jk11+sxvroofbz7YqT++/1WFI8zIBgAg0xCwU1hie8jKeYUyxrhYDT509SJ9/Mal8eNHdrXq0w/tVoSQDQBARiFgp7CpNzjSf50M/uiW5XrfFQ3x4/u2tujPHtjFSjYAABmEgJ3CEkf0raT/OikYY/S521brjg218dd+su24PvGjVxUM05MNAEAmIGCnsMQbHFcyQSRpeDxG/3zXOr3zkrr4az/feVIfu3e7AqGwi5UBAIC54EjANsa8wxjzZWPMs8aYAWOMNcZ834lzY3qj42E1dUW3SPcYaVkVK9jJxOsx+sIdF+u9b5hsF/nla+36yPe2aSxIyAYAIJ05tYL9aUkfk7Re0gmHzokzONg+qIm23oUV+cr1s0V6svF4jP7mbav1O9csir/21IFOve+/tqpvZNzFygAAwGzyOXSeP5J0XNJhSddJesqh8+I09tMeklTu3dJ82vcWlufrhosq9dSBTknS1qYe3fyvz+gDVy7UxxKmjgAAgPTgyAq2tfYpa+0hay2jEubIvsQRfWyRntSMMbplVY1+Y01N/LWuoYC+9vRh7WjudbEyAAAwG7jJMUUxoi/1XLOsUr91Wb18sW3Vh8fDetd/vqTH97S5XBkAAHCSUy0iM2aM2Xaat1bMaSEpwFo7JWCvnE/AThVra4tVlOPT9146ppHxsAKhiD76g2365C3L9fvXL5XHw2ZBAOC2M7X9nck9l9c7XAlSVdIEbJy71v4xDcS2SC/M8Wl+MVukp5KG8nx99Lol+u4LTeoeHpe10r/86qBebenTF9+5XsW5WW6XCABp40LDMjATSdMiYq3dNN2HpP1u15ZsptzgWFPEFukpqLwgWx+5bokuX1QWf+2JfR267cvPae/JgTN8JQAASHZJE7Bx7qbc4MgOjikrP9unH3z4cv3utYvjrzX3jOj2/3heD2w77mJlAABgJmgRSUFTbnBkRF9K83k9+otbV2r9ghL96Y93ajjWl/3JH+/UM4c69TdvXaPiPFpGAOBC9A6P61j3sPpGgxoYDcYfx0MReYyRMdFJTx4jFWT7VFmYrarCHFUWZqsox8dviHHBCNgpaH/b5Ar2Ckb0pYVb187T8upCfeT723S4Y0iS9LNXT2rr0R598a51unJphcsVAkByGwuGtftEv3a29OnVlj7tOt6v5p6RCz5fts+jRRX5WlNbrJU1RWzohvNCwE4xY8GwjnRGA5gx0kUE7LSxtKpAP/uDq/S5h1/Tj2MtIq39Y7rnW1v021cv0p++6SLlZPEXPABMONY9rM0HO/X0gU692Nit0WDYsXMHQhHtbxvU/rZBeY3R0qoCrakt1traYvl9dNjizBwJ2MaYt0t6e+xwYjeNK4wx340977LW/okT18p0h9qHJrdIL89Xnp+fkdJJfrZP/3zXOt20skqfenC3ekeCkqT/eu6oNh/s1OdvX6vLEm6MBIBMYq3VaycH9MiuVv3ytTYd7Ro+4+f7vR5VFPhVkudXcW5W9CMvSzk+r6ysrI2eM2yj7SSdQwF1DkY/EsN62FodaB/UgfZB/eq1Nt2wokqXLCyVz0PQxvScSmfrJb3/lNcWxz4k6ZgkArYDpm4ww+p1unrzmnna2FCqP/vJLj0d22L9cMeQ3vmNF3X3JQv0qVtXqCTP73KVADA39rcN6AuP7dfuE/3qHh4/7eeV5/vVUJ6vutJc1ZXmqqYoRz7v+Ydga616hse15+SA9pzo14m+0fh7g4GQHt55Us8e6tRNK6q1vr5EHnq1cQpHAra19nOSPufEuXBmO1r64s9Xs8FMWqsqzNF3PnCpfrClWf/w2D4Nj0dXU370Soue2Neuv3zLSt2+oZabcACkpe6hgH726kn9eNvxKYtLibK8RosrCrS8plDLqwpUXpDtyLWNMSovyNZ1yyt13fLKaNg+0a8XGrvi+1D0jgT1k+3HtflQp+7YUKuG8nxHro30QH9Bitl+rDf+fGNDqYuVwAnnsgGCxxh97MZl+vnOk9ob+0eme3hcf3z/Tv1wa4v+8i0rtW5ByWyXCgCzLhSO6OkDnbr/lRY9ub9DoYmeyATZPo9WzivS2tpiLa0qUNYFrFCfr7J8v65dXqkrlpTrpSPd2nywUyOxRY/OwYC++ewR3biiWndfukBeduSFCNgpZWAsqIMd0QkiXo/RujpCVaYozs3Se97QoH2tA3p450n1j0Z7s7c29ehtX31eb103X3/6pou0oCzP5UoB4Py1D4zph1tb9MOXm9XaP/a697N9Hi2vLtTFdcVaXl04J6F6Ollej65ZVqlLF5bp+cYuPXeoS4FQRBErPbGvXfd88yV96V3rNa8415X6kDwI2Cnk1eY+2dgP8yvnFSo/m29fplk5r0iLK/N1sm9U33m+Kb668/DOk3r8tTZ98KqF+uh1S+jPBpC0Jn5zF7FWjZ1D2nq0R/taBzTNYrXqy/K0qb5Ua+uKk2qKUk6WVzetqNam+lL96JUWHeuOjgPccrRHN/7LZt25sU6rzqON857L62erVLiEhJZCtiW0h2yqpz0kU2X7vPrLt6zSPZc36J8e369f7GmTJI2HIvrG5iP6wUvN+sCVC/XbVy9SaT5BG0ByGQ6EtO1Yr7Y29ahnmhsW8/1ebWoo06aGUlUWOtNTPVtK8vz68NWL9dSBDj21v0NW0mgwrO9vOabfWFOja5ZVul0iXELATiHbm+m/xqRFFfn62ns26eWmHv3do/u0M3YD7FAgpK88dVjfef6o3n/lQn34msUqI2gDcJG1Vq8c69UPXjqmn+9qVXia5epFFfm6fFGZVs0vSqnxd16P0c0rq7WkskD3v9ISb+H7xZ42jY6Hdcuqam5Gz0AE7BQRjljtaJ6cILKRFWzEXLqwTA/9/pV6dHervvTEofhOkMPjYf3H0436zvNNeucldfrgVYu0sIK73AHMnYGxoB7acUI/eKlZB9oHX/d+TpZHG+tLddnCMlUV5bhQoXMWVeTrD29cqu+/1Kym7uh87qcPdmo0GNZt6+Yzyi/DELBTxMH2QQ0FoqOBqgqzVVfKDRSZ7HTTRz5w5ULtOdGvJ/d3qGMwICn668r/fvGY/ufFY7plVbU+fM1iXbqwlBUVALPCWqtdx/t139Zm/ezVk9PurrigNFeXLSpPu10R8/w+ffCqhbpva7P2t0V/oNhytEejwbDesakupVbmMTME7BQxpf+6gXCE6XmM0cV1JVpTW6zXTg7oqf0dahuI3pFvJf1qb7t+tbdda2qL9O7LG3Tbuvkq4GZZAA4YGAvqZ6+e1H1bmuMjRRPl+b162/palef7Nb8kfReJsrwevfvyBj2w/bhejbXu7Trer0Awone/oZ6QnSH4lzVFJPZfb6L/GmfhMUZra4u1Zn6RGjuH9dzhTh1sH4q/v+fEgD714G793SN79bYNtbrnsnqtqS12sWIAqchaq+3Nffrh1mY9sqt12tXqFTWFevcbGvT29fNVmJN1TvP/U53XY/SOTXXKyfLopSM9kqQD7YP6ybbjeuclC2gXyQAE7BTBBjO4EMYYLa0q0NKqAnUMjKl9MKAHtx9XIBSRFO3TvndLs+7d0qxV84p0x8ZavXXd/JTvhQQwu/pGxvXph/Zo69GeeDtaoixv9If8SxeWqb4sT8YY/XxnqwuVusdjjG67eL5yfF49fbBTUnQluygnS7eunedydZhtBOwU0DUUUFNsxqbf62GLdFyQqqIcfeKW5frzN1+kB7af0H1bm+M3RErS3tYB7X10QJ9/bJ+uWlqhOzbW6pZVNbSQAJAU3WXx+cZu/XT7cT22p03jsR/UE9UU5ejSRWVaX1eiXH/yzK12izFGt6yq1mgwrC1HoyvZzx3uUlGOT1czwi+t8S9nCkhcvV5bV6xsH39p4cJM/Go2N8urD165UE3dI3q5qUd7TvTHN62JWOnZQ1169lCXfJ5duv6iKt26tkY3raxWcW6Wm+UDmGPWWu0+0a+HdpzUwztPqmvo9avVfq9HF9dFV6vrSnO5R+gUxhjdtm6+BsdC8d70x/a0qTAnS+sWsCNzuiJgp4Bt9F9jFhhjtKgiX4sq8vXWdfO150S/drT06WjXcPxzQhGrJ/a164l97cryGl21tEI3r6zWjSuq0vomJSDTtfSM6KEdJ/TTV0/oSOfwtJ9TW5KrSxeW6eIk22UxGXmM0d2XLtC3nzuqYz3R30j/ZNtx5Wf7tLSqwOXqMBsI2ClgSv81868xC3KyvLpkYZkuWVimvpFxvdrSp90n+tXaPxb/nGDY6ukDnXr6QLSXcOW8It20oko3rKjSurpi+bzcGQ+ksu6hgH6xp00P7TihVxL+3UlUWZitt62br7dvqNWu4/1zXGFqy/J69N4rGvSfzxxRx2BAYWt179Zj+v3rl7pdGmYBATvJjYciU/4S29jAr5Mwu0ry/Lr+oipdf1GVuocC8nk9+sWe1tf9Y7qvdUD7Wgf0lacOqyDbp8sWlenKJeW6Ykm5VtYUyePh18RAsjvWPaxf723Xr15r1yvHejTNBovK93v15jXzdPuGWl2xpFze2H/bBOzzl+f36QNXLtTXNzdqYCyksWBE33/pmH776kXK536XtMJ3M8ntbR2IT3yoL8tTVSHTHTB3yguydc/l9fro9UvU0jOiJ/a168n9HXrpSLeC4cl/iYcCIT25v0NP7u+QJJXkZemKxeWxwF2hJZX59GUCScBaqz0nBvSrvW361Wvt0+6uKEkeIy2rKtT6+hKtrCmS3+dRc8+ImmPtDbhwJXl+vfcNC/WNZxoVilh1DAb0Zw/s0ld+awN/T6YRAnaS2zalPYTVa8y9xJm12T6vfmPNPN14UZUOdQzpQNugDncOqX80OOVr+kaC+sWeNv1iT5skqbooW5csLNOGBSXaUF+i1fPp2QTmylgwrJebevTE3nb9em+7Tia0fiUykurL83RxbbHW1pUwQWgW1Zbm6m3ra/XA9uOSpEd3tSoSsbrmPCeL3HN5/WyUBwfwX0+S236MGxyRfLKzvFpTW6w1tcWy1qpneFyNncNq7BzSkc4hDY9P3WyifSCgR3e16tFd0Tm4WV6jVfOKtKG+VOtjoXtiVi6AmYlErF47OaDnDnfp+cNdermpJ/6b0FP5fR4trsjXqnlFWjGviFA9hzY1lOp470h8fN/je9o0vyRXSyq56TEd8F9SErPW6pVjPfFjNphBMjLGqLwgW+UF2bpsUZmstWofDKg0L0svNnbrpSPdGhgLTfmaYNhq5/F+7Uzo4SzL92v9ghKtnl8U+yhm5Bdwjpq7R+KB+vnGLvWNBE/7ucW5WbppRZXeuLpa1yyr1M9ePTmHlSLRWy6ep9b+MTX3jMhKum9rsz52w1KV5PndLg0zRMBOYif7x9Q+EJ05mu/36qLqQpcrAs7OGKOaohzdc3m9PnjVIoUjVvtaB7SjpU87mnv1akvftGO/eobHp/RxS9EgsGpeLHDXRkP34op8JpYgo4UjVgfbB7W9uVfbj/Vpa1O3WnpGz/g1FQXZWlZVoFXzi7SwPF9ej1HPcJBw7TKfx6N7LqvXV586rMFASCPjYd27tVm/e+1i+Tz8PZfKCNhJ7JnY1qqStKG+lFCBlJLYuy1JXmN0SUOZLmko08h4SMd7R9XcM6KWnhG19I5oLPj6X2H3jwb14pFuvXikO/5alteoqjBH1UXZk49FOfr965ew2o201Ds8rh0t0TC9vblXO1v6XteGdaqC2HzlJZUFWlKZz4poEivKzdJvXVavbz13RBErHe8d1a9ea2c79RRHwE5iT+xtjz+/YUWVi5UAzsrz+7S8ulDLY7+ViVirrqGATvSOqrV/TCf7RnWyf3Ta0B0MW53oG9WJvqkrdv/x1GEtrS7U8qoCLa8u1LLqAi2qyFdtSS4/nCJldA0FtL91UPvbBrS3dUCvNvfpSNf0G70kyvN7taA0LxqqqwpUXZjND5wpZGFFvt68ukaPxW4Mf+5wlxZX5GvFvCKXK8OFImAnqdHxsJ473BU/vnklARvpy2Oiq9JVhTnaEHvNWqu+kaBO9o/qZN+YWvtHdbJv9HX93BOGx8Pa2dKnnS19U173eYzqSnPVUJ6vheV50ceK6GNdaa6yfUwzwdwLhMI63DEUD9P72wa1r3Vw2q3Ip1OU49OCsjzVxz5qS3NpKUhxVy2tUGPncHx04o+3HdfHb1qm4twslyvDhSBgJ6nnDnfF7/peVlWghvJ8lysC5pYxRqX5fpXm+7V6fnH89aFASB0DY2ofDEQfBwLqGBzTyGl+ZR6KWDV1j6ipe0SbX3cNqaYoR/OKczSvJFfzi3M0rzh3ynFFQTab5uCCBEJhtfSMqrlnWMe6R2Ifsec9IwpPt6vLNLzGaH5JjurL8uKhujg3ixXqNGOM0Ts21enLTx7SwFhIo8Gwfvhysz589eL45j5IHQTsJJXYHnLzqmoXKwGSS0G2TwWVBVqcMMrKWqtbVlfrcPuQDrYP6mDHkA53DKm5e0RtA9PP/I1+ndTaPxbdEr65b9rPyfIaVRflqLIwW5UF2ao45XHydb/y/PyVmikiEauekXG1D4ypYyCg9tgPe20Do/EwfbJ/VPbcMnRcbpZXy2sKleUxqinOUW1JruaX5CqLNqeMkJ/t092X1utbzx6RlXSse0RP7m/XLatq3C4N54l/DZJQJGL1v/sTAvZKAjZwJiahxeTKpRVT3hsdD6u5Z0RN3cM61j2sptgqYlPXuQWgYNjqeO+ojveeeUqDFJ32U1mYrYpY8K4oyFZ5gV/l+X6V5WerLN+v8gK/yvL9Ks3zsyqVRMZDEfWNjqt/JKi+0aD6RoLqGxlX/2hQvSPj6hsJqmsoEP2NycCYOgYDCp3jCvTpNJTnaUVNoVbUFGnlvOhjfVmePB7zupuEkTkWVeTr5lXV+nVsoe3pA51aVFGgpVXMx04lBOwk9OrxPnUNjUuSKgqis4EBnNm5BJKC7CytmV+sNbGWk1A4ov7R4Gk/+kaCGg2eeVpDouHxsIZj7ShnY4xUkpsVDd2x8F0WD+P+Ka+XF0QDud+X/quY1lqNhyMKhCIaC4YVCEYUCIU1HrIKhiMaD0cUDEUUiD0Gw1bj4bCCIZvwWkTjoejnjo6HNRIMRx/Ho2PQJj+ix8Ox8WhOMyY6anLieznxvZ34ISux/79nOKgXGrv1QmP3Gc6ITHHd8kod7RzW4c4hWUn3v9KiP7xxqQpz6MdOFQTsJJTYHnLjiipWuYBZ4vN64pvknM54KKKBsaAGx0IaCoQ0NBbUYCCkodjx5Oshhc+jH8BaqXckqN6RoBqnmQs+nYJsnwpzfCrKyVJhTux5blbCa9HneX6vsn1eZfs8ys7yTPv8XFoOrKxCYRsPq+OhaPBNPI4+jwbhsWA4FoyjoXgsGNFYKPZeaDIsB055ffLroo/n21bhlpK8LFUX5qiqKFvVRZOjI+vL8lRfnqe60lw9sO2E22UiBXmM0V2X1On/PXlYw4Ho3zH3v9KiD161SB5671MCATsJPbGP9hAgWfh9HlUURNs9zsRaq7FgRIMTATwhhA8HQtHV7fjz0LQjCM9mKHbe1v7T95XjwnhMtP851x/9ASU3yxt99E88+pTv96ooJyv+Q83pfkiZ6OvfMsf/G5BeCnOydPclC/Sd54/KSmrsHNbmg5264SKmiqUCAnaSOdY9rIPtQ5KkbJ9HVy+rOMtXAEgGxhjlxgLZufzzF45YDY+HNBIIa3g8lBC+w697Ho5Y9Y6Ma4YtvynDa4x8XiOf16Msr5HP45HPY+T1mPjj6597pn09y+uR3+eRf+IxtnqfHXstyxd9nu3zMJUDSWdpVYGuu6hSTx+Ibjz3xN52LSzP16IKJoslOwJ2knli3+Q20VcvrWAqAZCmvB4TXQ09x57KiLXxNoyxUFijwcm2jLFgWKPBiAKx94Jhq1A4olAk2uIRjEQUCluFYo/BcCQ6Iu40gTI3a3Jl1usxCQHVK7/Po+yEsDoRXHOyPGruGZHPEwvFXo+yPAkhecpxNDBneT3yeaOPE+/5vIZfgQMJblpRraNd0fGOVtKPXm7WH964TPnZ5INkxncnyfxvQnvITbSHAIjxJKyQJ6v1C0rdLgFIO16P0bsurdeXnzykkfGwBsZC+sm243rfFQ1ul4YzSP9b0lNI/0hQW472xI9vYvdGAAAyXnFult6xsS5+fKB9UM8n7PaM5EPATiJPH+yI7+y1rq5Y1UU5LlcEAACSwYp5Rbo6Yc7/46+16dWW6TfIgvsI2Ekksf+a6SEAACDRG1dXq640V5IUsdIf3rdd/aNBl6vCdAjYSWJ0PKyn9ycEbLZHBwAACXwej951ab1yYjcit/SM6lMP7pJNleHxGYSAnSQe2H5cg4GQpMntcwEAABKV5ft1+4bJfuzHdrfp++ewky3mFgE7CUQiVt9+/mj8+P1XLGQeKwAAmNba2mJdvqgsfvy3j+zVayf7XawIpyJgJ4HNBzt1JLZVckG2T3ddUneWrwAAAJns1rXz4r/tHg9F9LF7d2hgjH7sZEHATgL/9dzk6vXdly5Q4TluPAEAADJTltejr757o/Jis/GPdg3rY/fuUCgccbkySARs1+1vG9BzsVmWHiN94MqF7hYEAABSwpLKAn3hzovjx88c7NTfPbrPxYowgYDtsm8nrF6/aXWNFpTluVgNAABIJW9dN18fv3Fp/Pi7LzTp+y8dc7EiSARsV3UNBfTQqyfjx7999SIXqwEAAKnoEzcv161ra+LHn334Nb3ATo+uImC76PsvHdN4KNorta6uWJsaSl2uCAAApBqPx+iLd63X2tpiSVI4YvXRH2zXkc4hlyvLXARsl4wFw1N+hfOhqxcxmg8AAFyQXL9X33zfJaoqzJYk9Y8G9cHvvqy2/jGXK8tMBGyXPLzzpLqGxiVJNUU5unXtPJcrAgAAqaymOEffev8lyvZF492x7hHd882X1DFIyJ5rBGwXDAVC+vKTh+LH77uyQVlevhUAAGBmLq4r0Vfv2SifJ/pb8SNdw3r3N7eoayjgcmWZhVTngr9/dK9aekYlSUU5Pt1zWb3LFQEAgHRx86pqfeWeDfLGQvahjiG951tb1DM87nJlmYOAPcee3N+u+7a2xI//5m1rVJLnd7EiAACQbt68Zp6+dPd6xTK29rcN6j3f2qK+EUL2XCBgz6He4XH9+QO748e3rq3R29bPd7EiAACQrm5bN19ffOc6TcxQ2Ns6oDu/9oKOdg27W1gGIGDPEWutPv3QHnUORnugKgqy9XdvX8vkEAAAMGtu31Cnf0zY7bGxc1hv/+rzep452bOKgD1HHt55Uo/ubo0f/+Oda1WWT2sIAACYXe+8ZIH+/V3r49NF+keDet+3t+q/X2iStdbl6tITAXsOnOwb1V89tCd+fPclC3TTymoXKwIAAJnkbetrdf/vXaHqouic7HDE6rMPv6a/+OkejQXDLleXfgjYs+xQ+6Du+vqLGhgLSZLqSnP16d9c6XJVAAAg06xbUKKHP3a11i0oib9239Zm3frvz+qFRlpGnETAnkUvNHbpjq+9oBN90ZF8Xo/RF+9ap8KcLJcrAwAAmai6KEc/+t036O0JQxaOdA3rnm9u0Sfv38koP4cQsGfJT3cc1/u/vVWDsZXrPL9X33rfJbp8cbnLlQEAgEyWk+XVv929Xn/39jUqzPbFX39g+3Hd9MWn9cOtzRoPRVysMPURsB02HoroS08c1B/9aKeC4eiNA1WF2br/967QDSuqXK4OAABAMsboPW9o0BOfvE5vuXhe/PXekaD+74O7dc0/Pan/ePowc7MvkGMB2xhTZ4z5tjHmpDEmYIxpMsZ8yRhT6tQ1ktlYMKzvvdikG/7laX3piclt0JdXF+inf3CV1tQWu1ccAADANKqLcvTVezbqOx+4VLUlufHX2wcC+qfHD+iKf3hSn/nZHu1rHWDiyHnwnf1Tzs4Ys0TSC5KqJP1M0n5Jl0n6P5LebIy5ylrb7cS1ks1wIKT7tjbrP585oo7YjOsJVy4p19fes0nFufRcAwCA5HXDiir9+o+v1Xeeb9J3X2iK79sxGgzrf148pv958ZhqS3J108oq3bSyWm9YXKZsn9flqpOXIwFb0n8oGq4/bq398sSLxph/lfRHkv5e0kccuparxoJhbTvWqxcbu/XSkW7tPN4XbwWZUJ7v1+9cu1gfumqR/D66cAAAQPLL8/v0Bzcs1YevWaSf72zVt549ov1tg/H3T/SN/v/27j3GivKM4/j32SuwXbCIKyo0VGSlVqqRVlHqBU2o/cNoBFrrpVJjelFrYvCvaqI2mia9GHqx6S00xUsvNhEbtUpEq8bGUKGU1OyiRRYUENDlJrsrl336x/suHo979nbec86ec36fZPKyM3Peeec8zMxzZt6ZOZpsj2uo5fSTJnDaCeM57cTxfPbE8cxoaVbeE+WdYJvZycB8oAN4IGvyXcA3gevMbIm7l827OVdv6mRLZxdbd3ezdU8XW/d0s21PD2/v7vpYQt2npbmRb104na+dPZVxDal+u4iIiIgUT2NdLQtnT2HBWSfxz43v8cjqLby4YRf7Pzh8dJ6ug0dYvamT1Zs6j46rMWhpHsPkCWM4YUIoj2tupHlMPc2NdTSPqaN5TD1j62upqzXqa2toqK2hrja81brXHffwjO5ed8yMT09qKvr6pzTLtecAAAnpSURBVJAiC7w4livd/SO3nLr7fjN7mZCAzwFWJVheUdz253VHH683mJmTm7nmnE+x6PNTGVOvyyUiIiJS/syMuadMYu4pkzh4uJdXOzp5tm0nq9p3sPm9ro/N3+vwzr4e3tnXw7q38l/+hLH1/Oeu+flXVAIpEuxTY/l6julvEBLsVgZIsM1sTY5JZ7S1tTF79uyRt3AE3nz3AF0Zv9QyNdbX0tRQS1NjHU2NdeyqMZY+CEuL2sL86VmXIiIi5esnTQ0lW/b4I730HOql+9AReuKQ+tF+O2qM2X8bn7TOwbS1tQFMy7eeFAl23+Mx9uaY3jf+mBzTB3Oku7t779q1aztG+PnkDgL7B51r1JoZy/aStkIyKSaji+Ix+igmo4viMUp0fPjPio3J2u1FX+Q0YF++lRSjo7DFcsBnu7h7cU9RV6m+KwX6vkcPxWR0UTxGH8VkdFE8Rh/FZPRJcatn3xnqXA96Hp81n4iIiIhIxUqRYG+IZWuO6TNimauPtoiIiIhIxUiRYD8fy/lm9pH6zKwZmAt0A68kWJaIiIiIyKiWd4Lt7huBlYRO4TdnTb4HaAKWl9MzsEVERERERirVTY43EV6V/jMzuwRoA84B5hG6htyRaDkiIiIiIqOauQ/4cI+hV2Q2Ffg+cClwLLAdWAHc4+6dA31WRERERKRSJEuwRUREREQkzU2OIiIiIiISKcEWEREREUlICbaIiIiISEJKsEVEREREElKCLSIiIiKSkBJsEREREZGElGBXADObYmbLzGybmX1gZh1mttTMPjnMeibGz3XEerbFeqcUqu2VKN94mFmTmV1jZo+YWbuZHTCz/Wb2qpktMbOGQq9DpUm1jWTVeYGZHTEzN7N7U7a30qWMh5nNMrPlZvZWrGunmb1gZl8vRNsrVcLjyBfN7PH4+R4z22JmT5nZpYVqe6Uxs4Vm9nMze8nM9sV9zEMjrCv5vk+GRs/BLnNmNp3wFs0W4HGgHTib8BbNDcBcd39vCPUcG+tpBZ4D/gXMBC4HdgLnuvubhViHSpIiHvFA9HegE3ge+B8wEbgMmBzrv8Tdewq0GhUl1TaSVWczsB6YBHwCuM/d70zZ7kqVMh5mthj4HdAFPAF0AMcApwPb3P2qxM2vSAmPI98BfgkcAB4D3gamAFcC44A73f2+QqxDJTGzdcAZwPuE73Am8LC7XzvMepLv+2QY3F1DGQ/AM4AD380af38c/6sh1vPrOP/9WeNvjeOfLvW6lsOQIh7AmcA1QEPW+GZgTaxnSanXtVyGVNtI1meXEX4AfS/WcW+p17NchoT7rDnAYWAdMLmf6fWlXtdyGRLtt+qBPUA3cGrWtM8APYQfQo2lXt/RPhAS4BmAARfFGDxUirhqGPmgM9hlzMxOBjYSztpMd/fejGnNhNfVG9Di7gcGqKcJ2AX0Aie4+/6MaTVxGdPiMnQWO4dU8RhkGVcDDwNPuPtleTe6whUiJmZ2ObACuA6oA36PzmAPScp4mNmLwPnALHf/b8EaXeESHkeOB94B1rv7Gf1MXw/MAia5zpoOmZldRLiSOawz2MU4HsnA1Ae7vF0cy5WZGw9ATJJfJlyWmzNIPecCY4GXM5PrWE8vsDL+OS/vFle2VPEYyKFYHs6jjmqSNCZm1gL8Fljh7iPqE1nlksQj3hdyPvAq8JqZzTOz2+M9CpfEEwMyNKm2kZ2EEzWtZjYjc4KZtRLOyK5Tcl00xTgeyQC0Eypvp8by9RzT34hla5HqqXbF+B5viOXTedRRTVLH5DeE/ea382lUFUsVjy9kzP9cHH4E/Bh4FlhnZqfk0c5qkiQmHi6H30zYPtaY2R/M7AdmtpzQte01YFGC9srQ6LheYnWlboDkZUIs9+aY3jf+mCLVU+0K+j2a2S3ApYQ+p8tGUkcVShYTM7uBcNPvV919R4K2VaNU8WiJ5VeAdwk30a0CjgPuInTfedLMZrn7wZE3tyok20bc/VEz2wb8Ech8issOQlcqdTEsHh3XS0xnsCubxTLfjvap6ql2I/4ezexKYCmhj+MCdz80yEdkaIYUEzObRvj+H3X3vxS4TdVsqNtIbUZ5o7s/5u773H0jcD2h60grsKAwzawqQ95vmdm1hCsILxFubBwXy1XAL4A/FaiNMnw6rheYEuzy1vcLdEKO6eOz5it0PdWuIN+jmV1BODDtBC7SjabDkiomywhPR7gpRaOqWKp47I7lB8BTmRNiV4XH459nD7eBVShJTGI/62WEriDXuXu7u3e7ezvhisIaYFG8aU8KT8f1ElOCXd42xDJXH6q+G01y9cFKXU+1S/49mtki4FHCJdYL3X3DIB+Rj0oVk7MI3RJ2xZc+uJk54bI3wB1x3Ir8mlvxUu+z9mffwBX1JeBjh9G2apUqJvMJj+p7oZ+b6nqBF+Ofs0fSSBk2HddLTH2wy9vzsZxvZjX9PIZnLuGs2yuD1PNKnG+umTX385i++VnLk/6likffZ64GlgNbgXk6cz0iqWKynHC5O9sM4AJCv/g1wL/zbnFlSxWP9YS+15PM7Ph++sSfHsuO/Jtc8VLFpDGWx+WY3jdefeKLI+nxSIZPZ7DLWOxvuJLwjOqbsybfAzQByzOfcWlmM81sZlY97wMPxvnvzqrnllj/M0rwBpYqHnH89YSYbAEu0Hc/Mgm3kVvd/cbsgQ/PYD8Zxz1QsJWpAAnjcZjwciyAH2Y+ls/MZgGLCY+y/GviVag4CfdbL8VyoZl9LnOCmZ0JLCT0930uXevFzOpjPKZnjh9JXCUtvWimzPXzKtQ24BzCM6tfB87LfO5ovKyNu1tWPdmvSl9NuDml71Xp58UNVgaQIh5mNo9wo1ANoU/jW/0sao+7Ly3QalSUVNtIjroXoxfNDEvCfdY4ws1zcwhXDv5BOEu6gNA1ZIm731/g1akICWOyDPgG4Sz1Y8BmQoJ3BdAALHX32wq8OmUv3ndzRfxzMvAlwhNY+n7EvOvut8d5pwGbgM3uPi2rnmHFVRJL9UpIDaUbgKmEg/x2wo5tM/BTYGI/8zrxPqB+pk2Mn9sc69lOSPCmlHody2nINx6Es28+yNBR6vUspyHVNtLPvH2x0qvSSxAPQredu4F2wg2Pewk/Tr9c6nUstyFFTAhPplhM+LGzm3AVoZPwQ+iqUq9juQzx//SQ9v+EHzA5jwnDiauGtIPOYIuIiIiIJKQ+2CIiIiIiCSnBFhERERFJSAm2iIiIiEhCSrBFRERERBJSgi0iIiIikpASbBERERGRhJRgi4iIiIgkpARbRERERCQhJdgiIiIiIgkpwRYRERERSUgJtoiIiIhIQkqwRUREREQSUoItIiIiIpKQEmwRERERkYSUYIuIiIiIJKQEW0REREQkISXYIiIiIiIJ/R+ixxwd9MmOEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 364
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8215241033461212"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.distplot(Y_train_prob)\n",
    "plt.show()\n",
    "pcut = np.percentile(Y_train_prob,85)\n",
    "pcut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 126
    },
    "colab_type": "code",
    "id": "Alm90tFz-Z85",
    "outputId": "9d768487-c9ee-4edf-b63d-b962caef1ebb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Resultados --\n",
      "- AUC train: 0.696 \n",
      "- AUC test : 0.690 \n",
      "- AMS train: 3.126 sigma\n",
      "- AMS test : 3.859 sigma\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3.1262187775590173, 3.8586364083871127)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train_pred = Y_train_prob > pcut \n",
    "Y_test_pred = Y_test_prob > pcut\n",
    "\n",
    "\n",
    "eval_model(Y_train,Y_train_pred,w_train,Y_test,Y_test_pred,w_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "MSS6kltTkoN-",
    "outputId": "ca0973ff-2f05-4787-9021-9d1acacd39b0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 424
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(Y_test, Y_test_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, lw=1, alpha=0.3, label=str(''))\n",
    "plt.plot(np.linspace(0,1,100),np.linspace(0,1,100), lw=2, alpha=0.3, label='Suerte')\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlim([0,1])\n",
    "plt.ylim([0,1])\n",
    "plt.xlabel('Falsos Positivos')\n",
    "plt.ylabel('Falsos Negativos')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "IAmigos - RIIAA_Reto_Machine_Learning.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": 1
}