RIIAA_XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V5zIY2gdgqKB"
   },
   "source": [
    "**Cargar librerias**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": 3,
   "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": 3,
     "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": 4,
   "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": 5,
   "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": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 546
    },
    "colab_type": "code",
    "id": "FHAjQafJnEHr",
    "outputId": "f2955195-604a-435a-fa77-1c8384bdd63d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('escalar', StandardScaler(copy=True, with_mean=True, with_std=True)), ('classifier', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=200,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1))])"
      ]
     },
     "execution_count": 11,
     "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",
    "from xgboost import XGBClassifier\n",
    "\n",
    "f = [('escalar', StandardScaler()),('classifier', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
    "       max_depth=3, min_child_weight=1, missing=None, n_estimators=200,\n",
    "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
    "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
    "       silent=True, subsample=1))]\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": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aARn8xbmnELW"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tkalnik\\AppData\\Local\\Continuum\\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\\tkalnik\\AppData\\Local\\Continuum\\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": [
    "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": 13,
   "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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vH9rUeNbHZpXlaufxXknS13+/XzcuGl/VvnNN7aRfGwAAYLqxgo0JmnuGYrfrfFPTfx1vcVVB7Pbelr4pfz0AAICpRsDGBK29/tjtGYVZU/56Cyry5RldJW/p9ceOZwcAAEhWBGzEWJal1r7xgF1ZOHX912MyM9yaE7eRcn8rq9gAACC5EbAR0zMclD8YkSRlZ7hVkDU9LfrzK/Njtw+0DbzBZwIAACQ+AjZi4ttDKguzZIz9B8ycyYKK8YB9pHNAwXBkWl4XAABgKhCwEdNySsCeLiW5XpXmeSVJwbClo3GTTAAAAJINARsx8f3XVQXTF7AlaX7cKvbBNuZhAwCA5EXARsypLSLTKT5g76cPGwAAJDECNiRJgVBEXQMjkiQjqTx/egP2rNJcZbijPd+dAyOM6wMAAEmLgA1JUnu/X9bobV9eprye6f3WyHC7NKt0/GCbA7SJAACAJEXAhqSJ7SFV09weMia+TYSADQAAkhUBG5KcmyASL35c3+GOAfmDYUfqAAAAmAwCNiRNnCBSOc0TRMb48jLlyx0f1/daQ7cjdQAAAEwGARvRI9IToEVEmtgm8tz+DsfqAAAAuFgEbKh3OKjh0XaMrAyXCrMzHKslPmBvOEDABgAAyYeAjdPaQ6briPQzmVWaK48r+vqH2gd0/OSQY7UAAABcDAI2TjlgJtvBSiSvZ+K4PtpEAABAsiFgw9Ej0s+EPmwAAJDMCNhIiBF98eLH9W083KmREOP6AABA8iBgp7lgOKLO/vEj0isSYAXbl+dVyei4vqFAWJsbTjpcEQAAwPkjYKe59v6R2BHpJbneaT8i/UyMMZpfkRe7fm5/u4PVAAAAXBjn0xQc1do7HLudCO0hY+jDBgAAyYqAneYS5YCZU80uzZPXHf32PNg+oOae4XN8BQAAQGIgYKe5lgkzsJ0d0RfP63FpzeyS2PUGVrEBAECSIGCnsVOPSE+kFhFJunZ+Wew2fdgAACBZELDTWL8/pKFAdARepsel4hznjkg/k+sWlMduv3SoU4FQxMFqAAAAzg8BO43FHzBT4fAR6WcypyxXM4ujbSuDgbA2H+t2uCIAAIBzI2Cnsc6Bkdjt8vxMBys5M2OMrlsw3iZCHzYAAEgGBOw01tE/HrDLEjBgS9K188fbRBjXBwAAkgEBO43Fr2CX5iVmwF43xxcb17e/rV8tvYzrAwAAiY2AncY6BwKx22UJGrBzMz1aNas4dk2bCAAASHQE7DQ1EgqrdzgoSXIZqTjX63BFZ3cdbSIAACCJELDTVPzqdUluptyuxJogEi9+o+NLhzoVDDOuDwAAJC4CdprqjN/gmJe4q9eSNLc8T9VF0XF9/SMhbTl20uGKAAAAzo6AnaY64jc4JugEkTHGGF0z4VRH2kQAAEDiImCnqfgJIom6wTFefJsIx6YDAIBERsBOU/EtIok6oi/elXNLY+P69rX2q7mHcX0AACAxEbDTkGVZEzY5JnqLiCTlZXq0ZnZJ7PoPe9scrAYAAODsCNhpqLXPr8DoJI7sDLdyvW6HKzo/Ny6qiN1+ei9tIgAAIDERsNPQkY7B2O3SPK+MSdwRffHetHB8HvbLh7s0OBJysBoAAIAzI2CnoSMdA7HbZflZDlZyYWpKcrSgIl+SFAhH9OKhTocrAgAAOB0BOw0djlvBTvQZ2Ke6YdH4KvYz9GEDAIAERMBOQ0c641pEkmCDY7wb4vqw/7CvQ5GI5WA1AAAApyNgp6H4FpFkGNEX7/KaIvlyo6vunQMj2tnc63BFAAAAExGw04w/GI7NkDZSLKwmC7fL6LoFtIkAAIDERcBOMw1dg7JGuyqKc73yuJPvW+DGuD5sxvUBAIBEk3zpCpNyZMIGx+RqDxlz9fyy2KmOe1v6dIJTHQEAQAIhYKeZif3XydUeMubUUx2f2ccqNgAASBwE7DQz4ZCZJJsgEi/+VEf6sAEAQCIhYKeZw53J3yIiTTzVcePhLg0FONURAAAkBgJ2GrEsa2KLSBKvYE841TEU0YsHOdURAAAkBgJ2GukcCKjfH13pzfS4lJ/pcbiiyblhwjQR2kQAAEBiIGCnkVMPmDHGOFjN5N24eLwP+/d72hQMRxysBgAAIIqAnUbij0gvS+L2kDGXzyxSVWGWJKlnKKhNR7odrggAAMCGgG2M8RljPmaM+aUx5pAxZtgY02uMedEY81FjDCE+QaTCiL54LpfRW5dUxa5/s6vFwWoAAACi7Ai/t0v6oaQ1kjZJ+pakxyQtkfQjSetNsvcipIgJI/qSeIJIvLddWhm7/fvXWxWiTQQAADjMjl1uByTdIuk3lmXF0o0x5m8kvSrpNkm3Khq64aBkaxF5aFPjOT8nYlnKz/Ko3x9S12BArzZ0a92c0mmoDgAA4MwmvYJtWdYfLMv6dXy4Hr2/VdL3Ry+vm+zrYHKC4Ygau4di177cxA/Y58NljC6ZURi7foI2EQAA4LCp7o8Ojn7kFBCHNXYPKRyxJEkzCrPk9aROa/yS6oLY7d/ubov97wQAAHDClA1CNsZ4JH1w9PK35/H5W87y0ELbikpjx7rG20PqS3MdrMR+9b5c5WV6NDASUufAiDY3dGvNbJ/TZQEAgDQ1lcuYX1N0o+MTlmX9bgpfB+fhaOd4e0iqBexom8j4KvaTu1sdrAYAAKS7KQnYxpjPSrpH0j5JHzifr7Esa8WZ/ow+ByapIW6D4yxfagVsSVpSPd6H/eTuFkVoEwEAAA6xPWAbY/5M0rcl7ZF0vWVZnP6RABpSuEVEiraJ5HjdkqS2vhFtbTzpcEUAACBd2RqwjTGfk/RdSbsVDde8V58gjsavYJfmOFjJ1HC7JraJPLGLbz0AAOAM2wK2MeavJH1T0nZFw3W7Xc+NyRkJhXWiZ1iS5DJSTUnqBWxJWjKDNhEAAOA8WwK2MebvFd3UuEXSDZZlddrxvLBHU/eQxrLmjKJsZXrczhY0RWaX5akoJ0OS1NLr17amHocrAgAA6WjSY/qMMR+S9GVJYUkvSPrsGU5Gb7As677JvhYuTvwEkVkp2H89xu0yumlxhdZvPi5J+vWOE1pRV+xwVQAAIN3YMQd71uhHt6TPneVzNki6z4bXwkWInyBSn4ITROLdsrQ6FrD/Z3uz/uZti1LqUB0AAJD47Dgq/UuWZZlz/LnOhlpxkY7GTRCp86Vm//WYtXN8qirMkiSdHArquf1sBQAAANOLpb00EH+KYyq3iEjRNpF3LauOXT+29biD1QAAgHREwE4DDSl8iuOZ3Lp8Zuz2H/a16+RgwMFqAABAuiFgpzh/MKwTvXEj+opTu0VEkuaW52lpTZEkKRi29OudJxyuCAAApBMCdopr7B6SNTqib2ZxTtps+Hv38rg2kS20iQAAgOmTHmkrjcWf4JgO7SFj3rF0hrzu6Lf3juO9OtTe73BFAAAgXRCwU1z8iL5ZKT5BJF5Rjlc3LCqPXT+2tdnBagAAQDohYKe4hq70XMGWJm52/OXWZoU5Oh0AAEwDAnaKS9cWEUm6bkGZfLleSVJrn18bD3c6XBEAAEgHBOwUFz+ib1aKn+J4qgy3S7dcPiN2/QvaRAAAwDQgYKew4UBYrX1+SdEDWKqLsx2uaPrdFtcm8uTuFvX7gw5WAwAA0gEBO4Ud6x5vD6kpzlaGO/3+c18yo0ALKvIlSf5gRL/azkxsAAAwtdIvcaWRhjTuvx5jjNGda2pj1/dvbJBlsdkRAABMHQJ2Cjsaf0R6mvVfx7t1ebVyvW5J0sH2Ab18uMvhigAAQCojYKewCTOw03QFW5LyszJ024rxXuyfvtzgWC0AACD1EbBT2NE0noF9qg+urYvdfmpPm5p7hh2sBgAApDICdgqbeIpjegfsueX5unKuT5IUsaQHXznmcEUAACBVEbBT1OBISO39I5KkDLfRjKIshyty3gfX1sduP/xak/zBsHPFAACAlEXATlHxR6TXlOTIk4Yj+k51w8JyVRdFZ4F3Dwb0+M4WhysCAACpiNSVotL5BMez8bhdev8V473YP2VkHwAAmAIE7BQVv4JdR8COee+qGnk90W/7Xc292t7U43BFAAAg1RCwU9TEEX05DlaSWEpyvbpl6YzY9f0vs9kRAADYi4CdohoY0XdWH4rb7Pj4zhM6wcg+AABgI4/TBWBqHI0/Jj2NWkQe2tR4Xp9XV5KjY91DCoYt3bN+h37+8SumuDIAAJAuWMFOQb3DQXUOBCRJXo8rNjkD465bUB67/VpDt9r7/Q5WAwAAUgkBOwWdesCMy2UcrCYxza/Ii/3iEYpY+tELRx2uCAAApAoCdgo60jkQuz27LH3aQy6EMUbXLyiLXT/wyjF1DwYcrAgAAKQKAnYKOtoRP0GEgH02C6sKVFkQPeFyKBDWT15kFRsAAEweATsFHe4kYJ8PlzG6Lm4V+6cbG9Q7HHSwIgAAkAoI2CkofgV7dlmeg5UkviXVhSrNy5Qk9Y+EdP/GBmcLAgAASY+AnWIiEWvCiL7ZrGC/oVNXsX/80lENjIQcrAgAACQ7AnaKaev3azgYliQV52SoONfrcEWJb+nMItWURCeK9AwF9cArnO4IAAAuHgE7xbDB8cK5XUZ/et3c2PX3NxxW7xC92AAA4OIQsFPMxA2O9F+fr9uWz5ywiv3dZw86XBEAAEhWBOwUM3GDIyvY58vrcemLNy+KXd+3sUHHugbf4CsAAADOjICdYiYcMkOLyAV526WVWlFXLEkKhi197cl9DlcEAACSEQE7xcRPEJnFCvYFMcbo794+vor95O5WvXq028GKAABAMiJgp5CRUFhN3UOSJGOkeh8B+0Itqy3WLUtnxK6/+ps9ikQsBysCAADJhoCdQpq6hzSWBWcUZisrw+1sQUnqL29eIK8n+ldjx/Fe/e+OEw5XBAAAkgkBO4UcZoOjLWYW5+hjV82KXf/bb/dpOBB2sCIAAJBMCNgphBMc7fOp6+aoNC96SE9Lr1/f33DY4YoAAECyIGCnEA6ZsU9+Vob+4s3zY9f/+dwhHWjrd7AiAACQLAjYKWTCiL4yDpmZrDtW1erymiJJ0bF9f/noToXZ8AgAAM6BgJ1CJozoYwV70twuo39/92XKcBtJ0vamHt23scHZogAAQMIjYKeI3uGgOgcCkqKnElYXZTtcUWqYX5Gvz7xpXuz667/br8auIQcrAgAAiY6AnSImrF77cuVyGQerSS2fvHaOFlbmS5KGg2F98Rc7ZVm0igAAgDMjYKeIIx3j/de0h9jL63Hp3267TGO/s2w83KX1m5ucLQoAACQsAnaKmDCijxnYtltaU6S7r54du/7Kb/aqpXfYwYoAAECi8jhdAOxxhA2Ok/LQpsZzfk5VYbZ8uV51DQbU7w/pz3++XQ/dvUYeN7+nAgCAcSSDFHGEUxynnNfj0q3LZ2qsu/3Vhm598+kDjtYEAAASDwE7BUQilhomnOLIDOypMqs0Vzcurohd/99nD+u5/e0OVgQAABINATsFtPb5NRwMS5KKcjJUnOt1uKLUdu38Ms0rH/8l5v9bv4N+bAAAEEPATgETNjjSfz3lXMbo9pU1qijIlCR1Dwb02Z9vUygccbgyAACQCAjYKWDiiD7aQ6ZDXqZH/3HHstjovtcaTuobT9GPDQAACNgp4Qgj+hyxZrZP99y0IHb9vecO6393nHCwIgAAkAgI2CngUPv4CjYtItPrU9fO0TXzy2LXn39khzY3dDtYEQAAcBoBOwUcaOuP3Z4/eqQ3pofLZfSdO5Zpzug7B4FQRHffv3nCVBcAAJBeCNhJrncoqLa+EUmS1+1SXUmOwxWln8KcDN33kdUqzYtObzk5FNSH731V3YMBhysDAABOIGAnuQPt46vXc8rzOFXQITUlOfrhB1cq0xP9/7+ha0gfv3+z/KPjEwEAQPogjSW5Ce0hFUwQcdKy2mJ9+47LZUYni2w+dlL3rN/B+D4AANIMATvJHWiND9j0Xzvt5iVV+tu3LYpd/2ZXi+55ZIfCEcvBqgAAwHQiYCe5/XEr2AsI2Anho1fN0keurI9d/8/2E/oCIRsAgLRBwE5yB9vGR/Sxgp0YjDH6hz9arPdfURu77xfbmvVXj+1UhJANAEDKI2Ansc6BEXWNTqrIznBrZnG2wxVhjDFGX76as+RtAAAgAElEQVRlid63ejxkP7rluL74C0I2AACpjoCdxOI3OM6ryJNr7NxuJASXy+ir71yiO1bVxO5bv/m4Pv/oDgXZ+AgAQMryOF0ALh4bHJ310KbG8/q8JdWFWtE2oC2NJyVJv9jarK6BgP7zruXKzeSvIAAAqYYV7CR2oD2+/5oRfYnKZYzetbxaK+uKY/dtONCh9/3wFXUOjDhYGQAAmAq2BGxjzLuNMd8xxrxgjOkzxljGmAfseG6cHSvYycNljN61rFpvWlgeu2/n8V7d9r2NHKsOAECKsWsF++8kfVrS5ZKabXpOvAHLsiaO6KskYCc6Y4xuXFShf3nXpRprlz/WNaTbvrdRW451O1scAACwjV0B+y8kzZdUIOlTNj0n3kBb34j6/SFJUn6mR5UFWQ5XhPN155pa/dcHxo9V7xoM6I4fvKIHNx1zuDIAAGAHWwK2ZVnPWpZ10LIs5o9Nk/jV6/mV+TKGCSLJ5M2LK/TQ3VeoOCdDkhQMW/rbX+7WX/9ip0ZCYYerAwAAk8EmxyR1MD5gs8ExKa2oK9b/fvoqLa4qiN3381ebdMcPXlFbn9/BygAAwGQkzIwwY8yWszy0cFoLSRL72eCYtE4d7/eelTX61fZmbW/qkSRta+zRDd/YoNtXztS88uh/2zvX1J72PAAAIDGxgp2k4g+ZWUDATmpej0u3r5ipt19aFdv8ODAS0r0vNejJ3S0KRTiUBgCAZJIwK9iWZa040/2jK9vLp7mchBaJWDoYNwN7HgE76RljdOXcUlUWZunh15o0OBLdwPrCwU4d6RjUNfPKVF+a63CVAADgfLCCnYSae4Y1FIhuhCvJ9ao0z+twRbDLnLI8ffZNcyf01Tf3DOvt//GCHtncJPYRAwCQ+BJmBRvnL749ZF55HhNEUkx+VoY+uLZeGw936Xe7WxW2LA0GwvrCozv1xK4W/eutl6mykLGMANLTqftYphL7X3CxWMFOQhwwk/pcxuiquaX61HVzVJqXGbv/2f0devM3N2j9a6xmAwCQqAjYSehg23j/NRNEUtuMomx9+vq5+vC6+th9/f6Q/vKxnfrQva+puWfYueIAAMAZ2RKwjTHvNMbcZ4y5T9IXR+9eO3afMebrdrwOohjRl168Hpe+dMslWv+Jtar35cTuf/5Ah278xgZ9f8NhBUJMGgEAIFHY1YN9uaQPnXLf7NE/knRM0udteq20Fo5YOtQRv4LNITPpYvWsEj3559foG7/frx+/dFSWJQ0Hw/rak/v02Jbj+ud3LtEVs31OlwkAU8ayLHUNjOh4z7Bae/3q94c0OBLSwEj043AwLK/bpcwMlzI9bnk9LuV63aoqylZ1UbZmFGUrL5PtZ5h6tnyXWZb1JUlfsuO58MaOdQ3GVivL8zNVlMMEkXSS7XXr7/5osd56aZX+9pe7tG/03YyD7QO64wev6NZl1fri2xaqPJ9NkACSXzhiaWvjST23v13bm3q063iv+vyhN/yakVBE/SMT79t9oi92uzA7Q7UlObq0ulALKvOV4aZbFvbj17gkc4ANjlD0mPXHP3OV7tvYoG8+dUCDo2Mbf7GtWb99vVV/et0cfezq2crKcDtcKQCc3ZkmgviDYR1o69e+1n7tb+3XcDBs62v2Dge1q7lXu5p7lelxaXFVgS6bWai55flyu5jKBXsQsJPMnhb6r9PR2cZS5Xg9+vSb5umJXS3a1dwrSRoKhPX13x/QD184qi//8SV6x2Uz5OKHBoAEFo5YOtDWr22NJ7W3tV/hyNmnJOV43aoebfkozvUqL9Oj3EyP8jI9yspwKRi2NBIMKxCOaCQU0cnBgJp7hnWiZ1gtvX6F4p57JBTRtqYebWvqUUGWR1fNK9Pq+hJ5PaxqY3II2ElmR1NP7Pal1YUOVoJEUZidofetrtXK9n49satFbX3R90Z7h4P684e36ycvNeivbl6gdXNKHa4UACY60TOsLcdOasfxntgBaqfKz/JoYWW+5pTlaWZxjopzMs59/kN2xvjtMmnl6M1wxFJbn197Wvq0o6lHXYOB2Kf1+UN6YleLntvfrivnlmote1owCQTsJGJZlnYeHw/YS2uKHKwGiWZeeb4+fX2eNh/r1tN72mJtIzuaenTnDzfpyrk+ff6mBVpWW+xwpQDSmT8Y1uM7W/SzV45NWDSKN6MwS4uqCrSwskAzirJsO1DN7TKaMbrZ8YaF5TrR69fO4z3a3tij/pFob/dQIKyn9rTphYMdGgqEaLfDRSFgJ5Gm7mGdHApKkgqyPBNGtgFS9IfHmlk+LZ1ZpOf2d+iVI10KhKObYl861KWXDm3UjYsqdM9N87WoqsDhagGkk2Ndg3rglWN6ZMtx9Yz+LItXmJ2hy2uKdHlNkSoKpn6jtjEm1mpy46IKbW08qQ0HOmK1+YMRff33B/ToluP6pz9eomvnl015TUgdBOwksv2U1WuOSMfZZGW4dfOSSv3rbZfqP54+qEe3Ho/1ND69t01P723TmxdX6NPXz+WdEABTxrIsbTrarR+/eFRP723TqQfQul1Gl1YXakVdsWaV5srl0M+1DLdLa2b5tLKuRDuaevTcgXZ1DkTbRxq6hvShn7yqmy+p1N+/Y7Gqi7IdqRHJhYCdRHbGvZW2dCahCOdWXZStf3v3ZfrEtbP1zacP6tc7TsQee2pPm57a06ar55Xq09fP1Rr6DQHYZCQU1uM7WvSTl47q9bgReWNqSrJ115o6uYxJqLnUbpfR8rpiXV5bpFePduu5/e2xsYC/fb1VGw506AtvWaAPr6tn8zjeUOJ8V+OcdsStYF82kw2OOLf46SNrZ/tU78vRM3vbtadl/AfeCwc79cLBTtWW5OjKuaX65z++RB7mwgK4AGP/1gyMhPTq0S5tOtId62mON78iT2tn+zSvIt+x1erz4TJGV8z26R/esVj/+sQ+Pbb1uKTo4V5ffnyPnjvQoa+/+zKVT0MrC5ITATtJhMIR7W4eD0WX87Y+LkJVYbbef0WdWvv82rC/XTuP92rsHdvG7iE1vtqo5w906MPr6vXe1TUqyMp4w+cDAElq7fNr46FObW/qmTAGT5Iy3EbLaoq1bo4v6QJpaV6mvvGepbpjdY3+/le7Y4d7PX+gQzd/+wX9+22X6cbFFQ5XiUREwE4SB9sHYsP2Kwuyku4fKSSWyoIsvXdVrW5cNKINBzq0rbFH4dHmyOaeYX31ib361tMH9K7l1bprTR0bIgGcJhyx9Oy+dt278aheOtR12uMFWR5dMdun1fUlykmgNpCLsaq+RP/76av0jaf26wfPH5FlSd2DAX3s/s16/xW1+ru3L2bSCCZI7u/4NDJxPB/tIbCHLy9Tty6fqTcvrtArR7q16WhXbBbtYCCsB15p1AOvNGp5bZHuXFOnP7qsih8iQJrr8wf1yObj+unGBjV2D532eHVRtq6cW6ol1QXyuFKn3czrcemv37pI184r01+s3x47c+CBVxq1o6lX//WBFZrBBkiMImAnie1NvbHbTH2A3fKzMvTmxRW6bkGZMj0u/fjFozrYPhB7fGtjj7Y29ujLv35d71g6Q7etmKllTLIB0srhjgHdv7FBj245HpuzP8ZIWjyjQFfNLVVtSU7K/NtwtlN0775qtn65vTm2gXNXc6/e/M3nddfqWtWX5urONbXTWSYSEAE7SUxYwWaCCKZIhtulO1bX6r2ravTKkW499Gqjfru7RcFwtH2kzx/Sg5sa9eCmRs0uy9Vty2fqncuqGVsFpKhIxNLzBzt070sN2nCg47THC7MzdMfofo3iHK8DFTojJ9OjO1fXatPRbj2+84QiljQ4EtKPXzyqW5bOIGCDgJ0M/MFwbGOFJF3KBBFMMWOM1s7xae0cnzoHFuvRLcf181cbdaxr/O3gIx2D+j+/26//87v9WlZbpLdfWqW3XlpF2AZSQHu/X49tadZ/v9aohq7T20DmV+Tpw+tm6V3LqpXtdZ91pTeVmdFJI+UFmXpoU6OGAmGFLUu/3N6s3Cy3/vEdlyiDiUxpi4CdBF4/0Rs7JGR2WS6THTCtSvMy9clr5+jjV8/Waw3demzrcT2xq1UDcSO4tjX2aFtjj77ym726vGYsbFdqZjGnjQLJIhSO6PmDHXr41SY9s6899nNnjDHSDQsr9JEr67Vuji9l2kAma3Zpnv7s+rl64JVjaun1S4r2Zbf0+PXdO5cr28u+lXREwE4CO+L6ry+nPQRT7FwrUZfXFGtxVaH2tPRqW2OPDncMKP7n8PamHm1v6tFXn9irpTVFevullXrrkirVlBC2gUTU1D2k9Zub9Mjm42rt85/2eFaGSytqi3XFbJ98eZk61jU04d0sSMU5Xn3imjl6bOtx7WqO/sx+Zl+7PvDjTfrxh1apMIeFsXRDwE4CHDCDROP1uHR5TbEurynW4EhIe1r61Dkwoo2Huyaseu1o6tGOph79yxP7dMmMAr1pYbnetLBcS2cWcQoa4CB/MKyn97bpv19r0ouHOk87wlyS6n25WlVfrEtmFMrrodXhXLwel+5YVaOSXG+sX33zsZO6/b826v4/WaPKQsbrphMCdhLYeZwJIkhcuZkeraovkSRdO69Me1r6tPtErw61T1zZfv1En14/0afv/OGQcr1uLajM10evmq2r55fS9gRMg3DE0suHu/Q/25v1292tZzxp0Zfr1eIZBVpZV6Ky/EwHqkxuxhi95ZJKXTO/TP/8+B5J0oG2Ad32vY26/6OrNacsz+EKMV0I2Amudyioo52DkqKnYXHgBxJZTqZHK+tLtLK+REOBkPa29GlXc68Otw/GDrKRojO2tzb2aOtDW+VxGa2qL9ENi8p1/cJyzS7NpbcTsMFDmxoVsSwd6xrS7hO92nW8d8LeiTFG0ryKPK2sK9HCqvyUml3tlI9eNUu+XK8+/8gOhSKWmnuGdfv3X9ZDd6/Rwkp+jqcDAnaC29k83h6ysLKAQz6QNHK8Hq2oK9GKuhL5g2Edah/Q/tZ+7Wvr12DcD/lQxNLLR7r08pEufeU3e1VVmKW1c3xaN6dU6+b4OLgBuEChcESvHOnWr7Y3a8+JvjOGakkqyfVqWU2RVtQVqyiNRuxNh7G9LO+/ok4PbjqmYNhS92BAt/3nRn3s6tmqsPE0ZkYCJiYCdoLb0cQJjkh+WRluLaku1JLqQkUsSyd6hrWvtV8d/SOxDUFjWnr9+sXWZv1ia7Mkqd6Xo7WjYXvtHJ9K83jbGjhVz1BAGw506A/72vXc/g71DgfP+Hn5mR5dOrNQS2cWaWZxNu8WTbH5Ffn66JWzdO/GBo2EIhoMhPXjF4/qY1fPUnk+PdmpjICd4OJPcLyMCSJIAS5jNLM4RzOLc3Tnmlq19/n17P52PbO3XS8f7jqtL7Sha0gNXY36+avRFaGFlflaPatEy2uLtby2WDUlhASkn1A4ol3Nvdp4uEsbDnRoy7GTp43VG5Ob6dElVQVaUl2o2WW5cvH3ZVrV+nL14XX1undjgwKhiAZGD6S5+6rZKqXPPWURsBOYZVkTJohczgZHpJj4kYDXLSjX1fPK1NI7rMMdgzrcMaBjXYOxUyTH7Gvt177Wft3/8jFJUmmeV5fXFGvpzEItmVmoJTMK2ZyFlGNZlg62D+ilQ5166VCXNh05/ZfReFWFWaovzdWSGYWq8+UQqh1W58vVh9fW676NDQqEI+r3h/SjF4/o7qtny8e7cimJgJ3AWvv86ugfkSTleN3sPkbKc7vGV7evnV+mUDiippPDOtwxoCMdA2rqHp6wWVKSOgcCenpvm57e2xa7r6IgU5fMKNS8ijzNKcvT3PLoH6aVIFlYlqXjJ4e18XA0UG883KXOgZGzfr4x0tKZRbphYbluWFShRVX5+vmrTdNYMc6lvjRXH1pXr/s2HlUwbKnPH13J/sS1c1SYzb9NqYaAncA2HemO3b60ulBu5gYjzXjcLs0qzdWs0lxpUYUCoYiOdQ+qMDtDWxt7tK3xpPr9p6/itfWNqK2vXX/Y1z7h/vL8zAmBe255nmaX5aoiP4u53HCUPxjWruZebT12UlsbT2pbY4/a+88eqCWpsiBL6+b6dOWcUl0zv4x3bpLArNJcfWhtvX76coOCYUs9w0H9dGOD7r56Nic+phgCdgIbG1QvSVfNLXWwEiAxeD0uzSvPj+2aj0QsHe4Y0LamHr3e3KvdJ/q050SfhoPhM359e/+I2vtH9PKRrgn3Z3pcqi3JUZ0vR3W+3PGPJTmqLs5WhpuxZbDHQ5saZVmWTg4F1dg9pMbuITV1D6mld1hnaaGOKcrJ0NrZPq2b49O6uaWMtExSs8vydNeaOt3/coMiVvTd6gc2HdOH19Xzb00KIWAnqEjE0gsHxwP2tQvKHKwGSEwul9G8inzNq8iXVtZIih6mcbRzQHta+nWofUCH2wd0uGNAB9sHzroJbCQU0cH26Oecyu0yqi7KVp0vR/Xx4duXo9qSHEZn4g0FQhEdbO/X3pZ+7W3p07P729Xa69dQ4My/BMbzelyqK8nRnLI8zSnPU1VhVqyXetOR7gnvciK5zK/I123LZ+qRLcclSUc7B/XI5ibdsbqWfvkUQcBOUNGjpwOSorNKl8xgRB8wJn5z5BupLMhSZUGWrpxbqohl6eRgQO39I+oYXcnu6PerazDwhmEnHLFiK40vHOw87fGCLI98eZkqyfXKl+tVSa5X77+iTrW+HHq+00gkYqmt36/D7YPa29KnvS192tPSp8MdA6dt1D2bsvxM1RZHf3GrKclReUEmYSuFLastVr8/pN++3ipJ2n2iT4/vbNE7LqvinYkUQMBOUM8fnNgeQn8oMDkuY+TLy5QvL1OLqiY+NhwIq3swoK7BkdGPAXUNBNQ9OKK+M/R4x+vzh9TnD8VOXJWkh1+Lbi4ryfWqtiRH9b4c1fpyVe8bb0Px5Xr5IZpkLMtSR/+IjnYOqqFrUEc7h9Qweruha1D+YOS8nysrw6Wa4miQri3JUU1xDj24aejqeaXq9Qf18uFo29orR7pUlJ2ha+bzrnWyI2AnqOfj+q/5iwZMrWyvW9XebFUXn35qZCAU0cmh8cDdNRiIhfCeocAb9s12j37u9rgDo8bkZ3pUX5qr+tJczRoN3fWjGzqLczII39MsErHUPRRQa69f7f1+tfaOqLXPr7Zef/Rjn19N3UMaPI/WjlPVlGRrcVWBFlUVqLN/RJWF2fw3hiTJGKO3X1qlAX8odujW715vVWmeV4t55zqpEbAT0OBISFuOnYxdXzOPDY6AU7welyoKss54tHE4YqlnaHTFezCg7oHxAN49GFDoDdJ3/0j0B+qpJ1lK0baTOl+uZhRlqaowO+5j9HZ5fhZThc7AsiyNhCIaCoQ1FAhpOBBWnz+onqHRP8NB9QwFJtzuHQ6qayCg9n7/ebdynE1RTobqfblaWJmvRVUFWjyjQAsr85Uf1yp0vu1NSB8uY3T7ipnq94fU0DUoS9L6zcf1iWu9qio8/Zd+JAcCdgJ6+XBX7B/6hZX5Kj/DD3YAznO7xttOThWxLPX7Q+oaiGs7GQ3hnYMBBUJnbyfo8589fI+9bkV+pqqKslWen6miHK9KcjNUnONVcY5XeVke5WV6lON1KzfTo+wMtzxuI7cxMsbI7Rq97ZLco9cuY2TJUjhiKRiOfgyFIwpFxu6LTHgsGIlMuD8UtkY/NzL+OWNfE7EUHn2u0GnPO/FrQpFI7LlO/9zx1xoJR+QPhDUUjAbpoUBYw8GwrMll5HPKz/JoVmmu6n25GhgJyZfrVWlepnx5XuV4J/5IPdg2oINtp2+cBU7lcbt015pafW/DYXUPBhQIR3T/y8f0p9fNmfALGpIHATsBxfdfX0t7CJCUXMaoMDtDhdkZmn3KX2PLsjQwElLXQLTvu3MgGr4jEUsNXYPnnDARjlg60evXiV7/FP4vSD9ZGS4VZGWoIDtj9KMn+nH0dlGOV7leN60dmBK5mR594Io6fX/DYY2EIuodDuqBV47pY1fPZnxfEiJgJyD6r4HUZoxRflaG8rMyVF+aO+GxsfDdPRhtX+gZCqp3ePxPz3BQg29wRHa687iMMtwueT0ued0uZWW4lOP1KNvrVrbXrZyM0Y9et7Izoqv8OV638rMy5PUQYuCsioIsvW91rX66sUGWpKaTw/rltmbdvmImv9glGQJ2gmnsGlJD15AkKTvDrZX1xQ5XBGA6xYfvswmGI+obDduX1xSpZyigk0NBdY9uvBwYifYgD46ENBgIa2gkpLBlKWJFN/OFrWjLhWVFV8PDliVrtLfC43LJ4zLRlpK42x6X0WAgLLcxcrmiK/TR20Yuo1ibiWv0PrcZ/Zyx+0fbUVyxrzFyu079nNHnmvA5p9/nckkeY5QxGqK9HlcsVDPWDslufkW+3nZplX6zq0WStL2pRxX5mbp2QbnDleFCELATzIa49pC1c3zK9DC2CcBEGW5XrPe73x+S2+VSaV6mSs/QCw4g+ayb41N7/4hea4geJvT7PW2qLMzWgsp8hyvD+eL9sAQzoT2E6SEAAKQdY4xuWTpD9b5oC5kl6b83N6pzYMTZwnDeCNgJJBCKxIbNS/RfAwCQrtwuozvX1KowO9ou5g9G9LNXjmkkeOGz2DH9CNgJZGvjSQ2Mbl6aWZytWadsfgIAAOkjL9Oj96+pk2d07n1H/4ge2XJckameR4lJI2AnkFOnh7BjGACA9FZdnK13LauOXe9p6dOz+9sdrAjng4CdQOLnX18zj/YQAAAgLast1pVzfLHrZ/a2a29Ln4MV4VwI2AmisWtIu5ujf1ncLqN1c33n+AoAAJAubl5Spdll462j6zc3qb2Pw6YSFQE7QTz8WmPs9rXzy1TA0agAAGCU22X0vlW1Ks6J5oORUHTTY+9w0OHKcCYE7AQQDEe0fvPx2PX7Vtc6WA0AAEhEuZkevf+KOmW4o3u0ugYD+tzD2xSOsOkx0RCwE8Aze9tisy0rCjJ1/QL6rwEAwOmqCrN12/KZsetn93fom08dcLAinAkBOwH8/NWm2O33rKyRx81/FgAAcGaXzSyaMAzhu88e0hOjR6sjMZDkHNbUPRSbHmJMNGADAAC8kZsuqdC88rzY9T3rd2h3c6+DFSEeAdth6zc3aWxe/NXzylRTkuNsQQAAIOG5jNEdq2pV54vmhuFgWB/96Wtq6R12uDJIBGxHhcIRrd883h5y52pWrwEAwPnJ9rr14w+tVH6WR5LU1jeij963OXYqNJxDwHbQs/s71NYX3dxYmpepGxZVOFwRAABIJnPL8/X996+IHae+p6VPn/35NoXCEYcrS28EbAc9/Or47OvbV85UBpsbAQDABbpybqm++q4lses/7GvXV36z18GKQKJzyImeYT27vz12fccq2kMAAMDFee+qWn3qujmx6/s2NugnLx51sKL0RsB2yPrNTRqbC3/lXJ/qfLlv/AUAAABv4As3LdDbL62KXX/58T16dMvxN/gKTBUCtgO6BkZ070sNsWtObgQAAJPlchl94z1Ltay2KHbfXz66Q7/eccLBqtITAdsBX3tyn3qHg5KkmpJs3bS40uGKAABAKsjKcOveD6/SoqoCSVLEkj7339v1u9dbHa4svRCwp9lrDd16JO7tmi/fskReD/8ZAACAPYpyvHrgo6s1d/QgmnDE0mce2qbn4vZ+YWqR7KZRMBzR3/1yd+z6LZdU6PqF5Q5WBAAAUpEvL1MPfWyN6kcPogmEI/rEz7boxYOdDleWHgjY0+inGxu0v61fkpSd4dY/vOMShysCAACpqrwgSw/efYWqi7IlSSOhiD5y36t6jI2PU46APU1aeof1zacOxK4/d+O82Dc8AADAVKguytZDd69RZUGWJCkYtnTPIzv07acPyrIsh6tLXQTsafKVx/dqMBCWJM0rz9OfXDXL4YoAAEA6qPPl6hd/uk4LKvJj933z6QP6wqM7FQhx4uNUIGBPg9+93qrf7GqJXX/lnUs4tREAAEybGUXZeuRTa3XV3NLYfY9uOa6P3PeqTg4GHKwsNZHyptiz+9r1mYe2xa5vXV6tNbN9DlYEAADSUUFWhu79yCrdvmJm7L6XDnXppm89rz/sa3OwstRDwJ5Cz+xt0yd+tkWBcPTtl5nF2frbty1yuCoAAJCuMtwu/fu7L9M9b54fu6+jf0R/ct9m/eWjO9TvDzpYXeogYE+Rp/e06ZMPjIfrmpJsPfzxK+TLy3S4MgAAkM6MMfrMDfP0kw+vVFn+eC5Zv/m4bv7WC3rpEKP8JouAPQWe2tOmTz24RcFwdHdubUmOHv74Ws0sznG4MgAAgKg3LazQ7z93jf7osqrYfc09w7rrR5v0wZ+8qi3HTjpYXXKzLWAbY2YaY35ijDlhjBkxxjQYY75ljCm26zUSXdfAiP7+V7v1yQfGw3WdL0cPf/wKRvIBAICEU5zr1XfvXK7vvG+ZinIyYvc/f6BDt31vI0H7InnseBJjzBxJGyWVS/ofSfskrZb055JuNsZcaVlWlx2vlYj8wbDu29ig//uHQ+ofCcXuHwvXVYWEawAAkLjesXSG1swq0dee3KdfbW9WZHRE9vMHOvT8gQ6tqi/WLUtn6K2XVqmUdtdzsiVgS/pPRcP1Zy3L+s7YncaY/1/SX0j6qqRP2vRaCaO116+n97bp+xsO6/jJ4QmPrZvj0/9r795jrCjPOI5/n3V3YZfLLhRWtGBQZIVEBFmLINUKpFSbGI2KWq/U+Ecr1sTgX2qiJpomvVBq75fQlqptahPFekFSwUtMbHG3SNzuoqCLtgssKNeV6+7bP9731ONhz+6ePe/Zc9nfJ5lMdmbOO++cZ2fmOe+8M7PiullMqBmep9qJiIiI9F/d6OGsuH4WyxaezU/Xb2VNUqK9sW0vG9v28uCzzcw/exxXnHc686eO4/Sa4ZhZfitegCzbt/iY2VnANqANmOKc606aNwrYARhQ55zrHED5jbNnz57d2NiYVViWSAYAAArkSURBVD1jOHK8i3/vOMCG1g7Wt3bQ3H7gpGXOGj+C+y6fzqLpdQX9D/fkPz7MdxVEREQkSzdeeEbOyt62+9BJiXaqU0cP4/xJYzj/jFpmTqrlzHEjGD9yGGVlhZsD9aahoYGmpqYm51xDNuXEaMFeGMbrkpNrAOfcQTN7A1gMzAVejrC+QbGueSfv7+lk+8edtO35lO0fd9K+/0ja5cdUV3DPV+v5xpwz9BIZERERKXpTxo/kR9fP4r6vT+fFd3bwt7fb2dj2+f7Yuw4cZW3zTtY27/z/tGHlZUwaW82kMVWcVltFbVUFtdUV1FZVUlNdQXXlKVSeUsawCj+uLC8DHN0Oup2jq9uRaP8994s1g7jF8cRIsM8J43fTzH8Pn2DXU0QJ9kPPNveaUAOUlxlzzhzLwml1LLlgEjVVFb0uLyIiIlJsxo8axq3zJnPrvMm07zvMc5vbeWXLbt7+aB+dx7pOWv7oiW62dhxia8ehrNZbU1XB2w8uzqqMfImRYCd+WuxPMz8xvba3QswsXR+QmS0tLTQ0ZNVSn7EP9nTSmXTDYkJleRnVleWMHl7OyOHlbDFjC/CLQa1d9j7Ra1FFRESK3g9HVOZt3cMBjnfx6TE/HDnexfGubrrS9SfJ0K4yo+HZ0VHK6q+WlhaAydmWE+smx94kOuEM9NvuOnz48P6mpqa2SPXJyjHgENCR74pkZ1oYt+a1FpKgeBQWxaOwKB6FRfEoMG0lHpOmHYO+ysnAyTfZZShGgp1ooU7XSWZ0ynI9yrYzufRf4mqBvvPCoHgUFsWjsCgehUXxKDyKSWGKcTfeljCuTzN/ahin66MtIiIiIlIyYiTYG8J4sZl9rrzwmL75wGHgzQjrEhEREREpaFkn2M65bcA6fJ+VZSmzHwZGAKsH8gxsEREREZFiE+smxzvxr0p/zMwWAS3AhcACfNeQ+yOtR0RERESkoEV5I0poxb4A+D0+sV4OTAEeA+Y55z6OsR4RERERkUKX9avSRURERETkM3qnt4iIiIhIREqwRUREREQiUoItIiIiIhKREmwRERERkYiUYIuIiIiIRKQEW0REREQkIiXYIiIiIiIRKcEuAWY20cxWmVm7mR01szYzW2lmYzIsZ2z4XFsopz2UOzFXdS9V2cbEzEaY2U1m9qSZtZpZp5kdNLO3zGy5mVXmehtKSax9JKXMS8ysy8ycmT0Ss76lLmY8zGyGma02s49CWR1m9qqZ3ZqLupeiiOeQL5vZmvD5I2b2oZm9YGaX5arupcbMrjWzn5jZ62Z2IBxfHh9gWdGPe9J/etFMkTOzKfjX1NcBa4BWYA7+NfVbgPn9eZOmmX0hlFMPrAc2AtOAK4EO/Bs538/FNpSaGDEJJ6QXgU+ADcBWYCxwBTAhlL/IOXckR5tRMmLtIylljgI2A+OAkcCjzrkHYta7VMWMh5ktBX4LfAo8B7QBtcC5QLtz7obI1S85Ec8h3wZ+DnQCTwP/ASYCVwPVwAPOuUdzsQ2lxMw2ATOBQ/jvcBrwhHPu5gzLiX7ckww55zQU8QC8BDjgOynTV4Tpv+xnOb8Ky69ImX53mL4239taLEOMmACzgJuAypTpo4DGUM7yfG9rMQyx9pGUz67C//i5L5TxSL63s1iGiMesucAJYBMwoYf5Ffne1mIYIh2vKoB9wGHgnJR504Ej+B9Bw/K9vYU+4BPgqYABl4YYPJ6PuGrIblALdhEzs7OAbfhWmynOue6keaOAHfidtM4519lLOSOA3UA3cJpz7mDSvLKwjslhHWrF7kWsmPSxjhuBJ4DnnHNXZF3pEpaLeJjZlcAzwC1AOfA71ILdLzHjYWavARcDM5xz7+Ss0iUs4jnkVGAnsNk5N7OH+ZuBGcA4p1bTfjOzS/FXMDNqwR6M85D0TX2wi9vCMF6XvAMBhCT5Dfylubl9lDMPqALeSE6uQzndwLrw54Ksa1z6YsWkN8fD+EQWZQwVUeNhZnXAb4BnnHMD6hc5xEWJR7gv5GLgLaDZzBaY2b3h/oRFoWFA+hZr/+jAN9LUm9nU5BlmVo9vkd2k5HrQDMZ5SPqgg1BxOyeM300z/70wrh+kcmRwvsvbw3htFmUMFbHj8Wv8cfNb2VRqCIsVjy8lLb8+DN8HfgD8HdhkZmdnUc+hIko8nL8Uvgy/bzSa2R/M7Ltmthrfpa0ZWBKhvtI/OqcXgPJ8V0CyUhPG+9PMT0yvHaRyJMffpZndBVyG73e6aiBlDDHR4mFmt+Nv+r3eObcrQt2GoljxqAvj64A9+BvpXgbGAw/iu+88b2YznHPHBl7dkhdt/3DOPWVm7cCfgOQnuOzCd6NS98LBo3N6AVALdmmzMM62o32sciSL79LMrgZW4vs6XuOcO97HR6Rv/YqHmU3Gf/dPOef+kuM6DWX93T9OSRrf4Zx72jl3wDm3DbgN33WkHrgmN9UcMvp9vDKzm/FXD17H39hYHcYvAz8F/pyjOkrmdE4fBEqwi1viV2hNmvmjU5bLdTmSo+/SzK7Cn6A6gEt1s2m/xYrHKvwTEu6MUakhLFY89obxUeCF5Bmhu8Ka8OecTCs4xESJR+hnvQrfFeQW51yrc+6wc64VfzWhEVgSbtqT3NM5vQAowS5uW8I4XT+qxM0m6fphxS5HcvBdmtkS4Cn8pdavOOe29PER+UyseMzGd0vYHV784MzM4S99A9wfpj2TXXVLXuxj1sHUm7iCRAJelUHdhqJY8ViMf1Tfqz3cVNcNvBb+bBhIJSVjOqcXAPXBLm4bwnixmZX18Cie+fhWtzf7KOfNsNx8MxvVw2P6FqesT9KLFZPEZ24EVgP/BRao5TpjseKxGn/JO9VU4BJ8n/hG4F9Z17i0xYrHZnzf63FmdmoPfeLPDeO27Ktc0mLFY1gYj08zPzFd/eEHR9TzkAyMWrCLWOhvuA7/jOplKbMfBkYAq5Ofc2lm08xsWko5h4A/huUfSinnrlD+S0ru+hYrJmH6bfi4fAhcou8/cxH3kbudc3ekDnzWgv18mPaznG1MCYgYjxP4l2MBfC/5sXxmNgNYin+M5V8jb0JJiXi8ej2MrzWz85JnmNks4Fp8f9/18WovZlYR4jElefpA4irx6UUzRa6H16G2ABfin1n9LnBR8rNHw2VtnHOWUk7qq9L/ib9BJfGq9IvCTit9iBETM1uAv2GoDN+38aMeVrXPObcyR5tRMmLtI2nKXopeNJORiMesavwNdHPxVw5ewbeUXoPvGrLcObcix5tT9CLGYxXwTXwr9dPAdnyCdxVQCax0zt2T480peuF+m6vCnxOAr+GfwJL4EbPHOXdvWHYy8AGw3Tk3OaWcjOIqORDrlZAa8jcAk/An+R34g9t24MfA2B6WdYT7gHqYNzZ8bnsoZwc+uZuY720stiHbmOBb4FwfQ1u+t7NYhlj7SA/LJuKkV6XnIR74bjsPAa34Gx7343+YXp7vbSymIUY88E+mWIr/obMXfwXhE/yPoBvyvY3FMoT/534d9/E/YNKeCzKJq4b4g1qwRUREREQiUh9sEREREZGIlGCLiIiIiESkBFtEREREJCIl2CIiIiIiESnBFhERERGJSAm2iIiIiEhESrBFRERERCJSgi0iIiIiEpESbBERERGRiJRgi4iIiIhEpARbRERERCQiJdgiIiIiIhEpwRYRERERiUgJtoiIiIhIREqwRUREREQiUoItIiIiIhKREmwRERERkYj+B/ZAeQ+cH8V+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 364
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8127681761980057"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "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: 0.694 \n",
      "- AUC test : 0.690 \n",
      "- AMS train: 3.026 sigma\n",
      "- AMS test : 3.732 sigma\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3.02559365301972, 3.7315404851499703)"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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
}