classification_conTime copy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classification es el notebook oficial de clasificación\n",
    "\n",
    "### - Limpiamos los datos según conclusiones del notebook main\n",
    "### - Probaremos distintos algorítmos de clasificación y compararemos los resultados\n",
    "### - Utilizamos el dataset de train ya recortado por la cátedra\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACA TODOS LOS IMPORTS DEL NOTEBOOK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, cross_val_predict, validation_curve\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression, Perceptron\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, VotingClassifier\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, precision_recall_curve, auc, roc_curve\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LECTURA Y LIMPIEZA DEL DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(239, 13)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig = pd.read_csv(r\"DS_G3_HeartFailure_FULL_train.csv\",index_col=0)  #datos de entrenamiento (80% del dataset original)\n",
    "df_orig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(239, 12)\n",
      "(239,)\n"
     ]
    }
   ],
   "source": [
    "df_cleaned = df_orig.copy()\n",
    "feat_train, targ_train = df_orig.drop([\"DEATH_EVENT\"],axis=1),df_orig[\"DEATH_EVENT\"]\n",
    "print(feat_train.shape)\n",
    "print(targ_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformaciones necesarias al df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>age</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>95.0</td>\n",
       "      <td>371</td>\n",
       "      <td>30</td>\n",
       "      <td>461000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>132</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>40.0</td>\n",
       "      <td>478</td>\n",
       "      <td>30</td>\n",
       "      <td>303000.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>136</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>70.0</td>\n",
       "      <td>232</td>\n",
       "      <td>30</td>\n",
       "      <td>173000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>132</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>70.0</td>\n",
       "      <td>2695</td>\n",
       "      <td>40</td>\n",
       "      <td>241000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>137</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>59.0</td>\n",
       "      <td>176</td>\n",
       "      <td>25</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  creatinine_phosphokinase  ejection_fraction  platelets  \\\n",
       "55   95.0                       371                 30   461000.0   \n",
       "180  40.0                       478                 30   303000.0   \n",
       "237  70.0                       232                 30   173000.0   \n",
       "280  70.0                      2695                 40   241000.0   \n",
       "181  59.0                       176                 25   221000.0   \n",
       "\n",
       "     serum_creatinine  serum_sodium  time  DEATH_EVENT  \n",
       "55                2.0           132    50            1  \n",
       "180               0.9           136   148            0  \n",
       "237               1.2           132   210            0  \n",
       "280               1.0           137   247            0  \n",
       "181               1.0           136   150            1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_cleaned = df_orig.drop([\"time\",\"anaemia\",\"diabetes\",\"high_blood_pressure\",\"sex\",\"smoking\"],axis=1)\n",
    "\n",
    "df_cleaned = df_orig.drop([\"anaemia\",\"diabetes\",\"high_blood_pressure\",\"sex\",\"smoking\"],axis=1)\n",
    "# NOS QUEDAMOS CON AGE, CREATININE_PHOSPHOKINASE, EJECTION_FRACTION, PLATELETS, SERUM_CREATININE, SERUM_SODIUM Y DEATH_EVENT\n",
    "df_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(239, 8)\n"
     ]
    }
   ],
   "source": [
    "print(df_cleaned.shape)  #6 features + target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feat_train, feat_test, targ_train, targ_test = train_test_split(df_cleaned.drop([\"DEATH_EVENT\"],axis=1),df_cleaned[\"DEATH_EVENT\"],test_size=0.1,random_state=420)\n",
    "#HAY QUE SEPARAR EL TEST ANTES DE ESTANDARIZAR PARA QUE NO INFLUYAN LOS FEATURES DE TEST EN LA MEDIA Y VARIANZA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(239, 7)\n",
      "(239,)\n"
     ]
    }
   ],
   "source": [
    "feat_train, targ_train = df_cleaned.drop([\"DEATH_EVENT\"],axis=1),df_cleaned[\"DEATH_EVENT\"]\n",
    "print(feat_train.shape)\n",
    "print(targ_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "163\n",
      "76\n",
      "163\n",
      "163\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "feat_train = scaler.fit_transform(feat_train)\n",
    "\n",
    "print(np.sum(targ_train==0))\n",
    "print(np.sum(targ_train==1))\n",
    "\n",
    "#APLICAMOS SMOTE PARA AUMENTAR LA CANTIDAD DE SAMPLES Y BALANCEAR EL TARGET\n",
    "feat_train, targ_train = SMOTE(random_state=420).fit_resample(feat_train, targ_train)\n",
    "feat_train.shape\n",
    "\n",
    "print(np.sum(targ_train==0))\n",
    "print(np.sum(targ_train==1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACA TENEMOS EL DATASET RECORTADO Y STANDARIZADO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_metric = \"recall\" # Queremos pocos FN, es decir pacientes q decimos q NO tienen riesgo pero SI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ALGUNAS FUNCIONES UTILES PARA GRAFICAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aoc_curves (model,feat,targ):\n",
    "    predicted_p = cross_val_predict(model,feat,targ,cv=10,method=\"predict_proba\")\n",
    "    fpr, tpr, thres = roc_curve(targ, predicted_p[:,1])\n",
    "    precisions, recalls, thres = precision_recall_curve(targ, predicted_p[:,1])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    pr_auc = auc(recalls, precisions)\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 8))\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.plot(fpr,tpr,label = \"ROC AOC:{:.3f}\".format(roc_auc))\n",
    "    plt.plot([0,1],[0,1],'--k', alpha=0.5)\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Curva ROC')\n",
    "    plt.legend()\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.plot(recalls,precisions,label = \"PR AOC:{:.3f}\".format(pr_auc))\n",
    "    plt.plot([1,0],[0,1],'--k', alpha=0.5)\n",
    "    plt.ylabel('Precisión')\n",
    "    plt.xlabel('Recall')\n",
    "    plt.title('Curva PR')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_curve (model,param,param_range): \n",
    "    train__scores, test__scores = validation_curve(model,feat_train,targ_train,param_name=param,param_range=param_range,scoring=score_metric,cv=20)\n",
    "\n",
    "    train__mean = np.mean(train__scores,axis=1)\n",
    "    #train__std = np.std(train__scores,axis=1)\n",
    "    test__mean = np.mean(test__scores,axis=1)\n",
    "    #test__std = np.std(test__scores,axis=1)\n",
    "    plt.figure(figsize=(20,10))\n",
    "    plt.subplot(2,2,2)\n",
    "    plt.plot(param_range,train__mean,color='blue',marker='o',markersize=5,label='training recall')\n",
    "    plt.xscale(\"log\")\n",
    "    plt.plot(param_range,test__mean,color='green',marker='x',markersize=5,label='test recall') \n",
    "    plt.xlabel(param)\n",
    "    plt.ylabel(score_metric)\n",
    "    plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plt_eb (model,rmax):\n",
    "    means,errors = [],[]\n",
    "    \n",
    "    for j in range(2,rmax):\n",
    "        scorings = cross_val_score(model, feat_train, targ_train,cv=j, scoring=score_metric)\n",
    "        means.append(scorings.mean())\n",
    "        errors.append(scorings.std())\n",
    "\n",
    "    plt.figure(figsize=(15,8))\n",
    "    plt.xlabel(\"Cantidad de Folds en CV\")\n",
    "    plt.ylabel(\"Recall Score\")\n",
    "    plt.title(\"Mean: {:.3f}+/-{:.3f}\".format(np.mean(means),np.mean(errors)))\n",
    "    plt.ylim((0,1.1))\n",
    "    plt.errorbar(x=range(2,rmax),y=means,yerr=errors,fmt=\"-o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_setup (model):\n",
    "    model.fit(feat_train,targ_train)\n",
    "\n",
    "    scoring = cross_val_score(model, feat_train, targ_train,cv=10, scoring=score_metric)\n",
    "    print(\"Cross Validation Score: {:.3f} +/- {:.3f}\".format(scoring.mean(),scoring.std()))\n",
    "\n",
    "    predicted = cross_val_predict(model,feat_train,targ_train,cv=10)\n",
    "    print(classification_report(targ_train, predicted))\n",
    "    \n",
    "    cm = confusion_matrix(targ_train, predicted)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "    disp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BASELINE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score: 0.721 +/- 0.192\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.78      0.76       163\n",
      "           1       0.76      0.72      0.74       163\n",
      "\n",
      "    accuracy                           0.75       326\n",
      "   macro avg       0.75      0.75      0.75       326\n",
      "weighted avg       0.75      0.75      0.75       326\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "percep1 = Perceptron(fit_intercept=True, max_iter=40000)\n",
    "model_setup(percep1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kT2Ol/QJVdVeSL7ES4ADXVdXxXs/PBp6f5I+7+w8CFln5H8PrkpwKvK+q7p76AKU1GOCaG12vmxuAG5LcDrxijJf51qrbAX5rjS8MOZTkZuBc4MNJXl5VHx+nZmkSHgPXXEhyxsCx6DOBQ8ApSZ7UrXNSkl3AfwC/2y17DCuz6rW+1emjwKvSdZ5Kclb3++eAL1TVP7DSXe8JUxmUNIQzcM2LhwJv7o5VH2PlK/SWgXd0yx8MfAc4B3gr8LZuln4MuKg7c2XwNf8ceBNwW5IHAF9k5UyXFwMvTfJ9Vr5l5g3THZq0Nj/ElKRGeQhFkhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RG/R9kTvM/WjQpNgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_scores = cross_val_predict(percep1, feat_train, targ_train, cv=10, method=\"decision_function\")\n",
    "\n",
    "A = plt.hist(y_scores,100)\n",
    "plt.xlabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f630804fe50>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ldrtJTk7WvzMREekyFR9ERHqJaFtiAbBq1Sq2bdvGueeeS0pKChdeeKHTkUSi2oABAxgwYAAAzc3NvPnmm5xyyikaRikiIp3SsgsRkV4impZYtNq7dy87d+6ksbHR6SgivU5ZWRmfffYZpaWlnR8sIiK9njofREQi0NG6GDrT2uUQ6UssSktLcblc5OfnM23aNKZOnarZDiIO6Nu3L3feeSdJSUkAlJSUkJubi9frdTiZiIiEI3U+iIhEoKN1MXQm0rscIDBl/9lnn+XNN98EwOVyqfAg4qDWwkN9fT2PP/44r776qsOJREQkXKnzQUQkQrTvdoiWLoZgVVdXk5SUhMvl4vLLLyc1NdXpSCLSTlxcHJdccgmZmZkANDU14Xa7McY4nExERMKFOh9ERCJE+26HaOhiCFZ5eTl/+ctfWLRoEQC5ubnExcU5nEpEDjdo0CDS0tIAeOWVV3jiiSfw+/3OhhIRkbChzgcREYd0dW5Db+t2aJWens6ECRMYOnSo01FEJAjWWgoKCqirq8Pl0udcIiISoFcEERGHdHVuQ2/qdigrK+Phhx+mtrYWYwwzZswgIyPD6VgiEgRjDBMmTGDatGkA7Nq1i9deew2fz+dwMhERcZI6H0REHNQbOxmC4XK5qK2tpbq6msTERKfjiMhXsGXLFtasWcNpp52mnTBERHoxFR9ERLpRV5ZStC6jkIDKyko2b97MuHHjyM3N5dZbb1XLtkgUmDJlCuPGjSMuLg5rLWvXrmX48OEaRiki0svorE5EpBt1ZSlFb1pGEYyPP/6YBQsWUFdXB6DCg0iQ/uPFldyzcONx3ffRj7cy54FPj+u+r68s48zfv8f2/XWdHts6JHb9+vU8/fTTrF279rieU0REIpc6H0REukFrx0NvHQp5vBobG2loaCA5OZkZM2YwefJkEhISnI4lEjH2VNXzj0XbmDY0u8v3/WjjPu56aVWX72et5Z6FG/mfN9YDsL28jsLM4P7dDh06lMsvv7xtgGx9fb12rxER6SWOWXwwxny/oztaa3/f/XFERCJT+8KDuhmCY63l0UcfxePxcO211xITE6OhkiJdNP+Lnfht1++3s+Ig33nic/wWXF1Y/dDY5OdHz33JC5+XMqoghZWlwQ/NhcAwymHDhgFw8OBB7r33XiZOnMjUqVO79DgiIhJ5Oup8SO6xFCIiEeRocx3U8RA8ay3GGIwxTJ06lfj4eK39FjlOL3we/Ha9rXzNfm75xzIam/ycNzqf11aWBXW/usYmbnl8Ge+t38sPzhzK5IGZXPq3T7r8/K28Xi+jRo1i8ODBx/0YIiISOY5ZfLDW3tWTQUREIkX7LodW6ngITl1dHU8//TSTJk2iuLiY4cOHOx1JJGKt313Nqp1d6zwAuGfhRr7YUcFf5oxnTZAzairrfHzrkcUs31HBr78+mismFbJka3mXn7s9j8fDWWed1Xb5/fffJyYmhsmTJ6sgKSIShTpadvHHju5orb2j++OIiISn9t0O6nI4frGxsbhcLpqbm52OIhLxXvi8FLfLkJcS/MyEVTsr+fM7G7lobB/OHZ0fVPGhqt7HNx9axJqyau65cjznjM7/KrGPylpLWVkZsbGxKjyIiESpjpZdfNZjKUREwlz7bgd1OXRNY2MjH374IVOnTsXr9XL11VfrzYXIV+T3W+Z9Xsq0IVmU1zYGdZ/GJj8/ePoL0hNj+PmFI4O6T01DE9c+tJhVO6v461UnMrM496vEPiZjDJdeemlbYbKyspLdu3e3DaYUEZHI19Gyi0d7MoiISLhTt8Px2blzJx9++CEFBQUMGzZMhQeRbrBkazk7K+v50TnDeejDLUHd5953N7F2VzX3f3MCaQkxnR5f72vmukeW8EVJJfdcOS5khYdWxhg8nsCp6QcffMCKFSu48847tQOOiEiU6HSrTWNMNvAjoBho6+uz1p4RwlwiIo7paKCkBMfv97Nr1y769OlDUVERd9xxB2lpaU7HEoka763fi8dlmDEiN6jiw86Kg9z73kbOOyGfM4MoIvj9lh888wWLt5Rz9+VjmTWq+5dadGTWrFmMGzeurfBQXV1NcrJmoYuIRDJXEMf8A1gDDADuArYCS0KYSUTEUa1LLNrTUouuWbhwIQ8//DCVlZUAKjyIdLOPN+1nTL80kmI7/RwJgN8tWIffwr+dE9yQ19+8vpZXvizj384Z3unvPovl0Y+38ujHW4N67GB4PB4KCgLPu27dOu6++262bdvWbY8vIiI9L5hXrExr7YPGmDutte8B7xljVHwQkajT2vGggZLHr7m5GbfbzeTJk8nKyiIlRd0iIt2tut7HitJKbp0+KKjjl++o4IXPS7l1+iD6pne+hOGJxdv52/ub+eaU/tw0bWCnx//x7Q0s2XqAEfkpXHNyUVCZuqJv375MmjSJvn37dvtji4hIzwmm88HX8meZMeY8Y8w4ICOEmUREHNG+8KAuh657+eWXee6557DWkpSUxJgxYzTfQSQElmwtp9lvmTIos9NjrbX86uXVZCXFcuvpgzs9/osdFfxs3iqmDc3mZxeMDOrf8JKtB4j1BHNKeXwSExM566yzcLvdNDU18fe//52NGzeG7PlERCQ0gul8+JUxJhX4AfAnIAX4XkhTiYg4RB0Pxy8zM5OEhASstSo6iITQxxv3E+NxMb4wvdNjF67bw9JtB/j110d3ukSjvLaRWx7/jOzkWO6+bCxuV8f/jtMTYnAZuHPGUFaUVlJacbBLP8fxqK2t5eDBg1hrQ/5cIiLSvTotPlhrX275thI4PbRxREQkUvj9fj766CP69+9PYWEhU6aoaCPSEz7etJ8J/dOJ87o7PfYvCzdRkBbPN07seMmC32+588nP2VfbyHPfPpn0xM53wxick8Squ2YRH+Pmxr8vDTr/sRxsbObPCzcwsSiD6cNyjnpMamoqN954I43NlrrGJrZuXE9mZia5uaHdiUNERL66TnvkjDGPGmPS2l1ON8Y8FMyDG2NmGWPWGWM2GmN+fJTbC40xC40xnxtjvjTGnNul9CIi4hifz8fnn3/OmjVrnI4i0qFoOh85UNvI6rIqTg5iycWSreUs3XaAG6cOwOvu+JTvkY+38sGGffzsgmJG900NOk98TOcFkGCs21XN7Hs+5J6Fm5i/fOcxj7PW8sqKXZz6m4Xc9OgS3nrrLd5+++1uyRBtahuaeHP1bnaU1zkdRUQECG7ZxQnW2orWC9baAy1zHzpkjHED9wBnAiXAEmPMfGvt6naH/RR42lp7rzGmGHgVKOpCfhER6WGbN2+mqKiI2NhYrr/++rat8ETCUbSdj3y6eT8AUwZldXrsXxZuJDMxhssmFnZ4nN8Gdrc4Y3gOV07q+NjuZq3l8UXb+dXLq0mO83a4NGRHeR3/MW8l767bC8C+pBiuv/H6tiUY9fX1NDU1kZSU1CPZw1FFXSNvrdnD6yt38cGGvTQ0+fn6uAJ+f9lYp6OJiARVfHAZY9KttQcAjDEZQd5vErDRWru55X5PArOB9i/2lsAMCYBU4NilbhGRbtK6q8XhWodNyrFt376dxx57jAsuuIDx48eTmJjodCSRzkTV+cjHm/aTGOPmhE66E1bvrGLhur38y1lDg+pOSIhx898Xj+7ReS0VdY386LkvWbBqN9OGZvO/l4zh6/d+dMRxTc1+Hv5oK79/cz3GwH+cX8wnm/ZRcuDgIb+DXn/9dTZv3sztt99OTEzny0aixe6qet5YtYvXV+3i082BYaR9UuO4YlIhr6woo6HZDwR2SXlv/V7eWbOHSQMyuLyHC00iIsEUEf4X+MQY80zL5UuA/wrifgXAjnaXS4DJhx3zc+ANY8x3gERgZhCPKyLylbTf1aI97XJxbI2NjcTExNCvXz8uvvhiRowY4XQkkWBF1fnIJ5v3M3FARqfLKO57fxNJsR6unlLU4XGulmLDf31tNDnJcV8536eb9/ODp7/gT1eO63Ag5oqSSr79+Gfsqa7n388dwfWnDsB1lAGXm/bW8L2nlvNlSSUzR+Rw1+xRFKTFs3jL/iOOPeWUU+jfv39b4SGah9/ur2ng1RVlzP9iJ0u2HgBgYHYiN08byKxReYwuSMUYwwcb9rJ+VzXXPLSYTzbtp7GlEFFScVDFBxHpccEMnPy7MWYpcEbLVV8/rFXxq7gCeMRa+7/GmCnAY8aYUdZaf/uDjDE3ATcBFBbqF6WIfHXa1SJ4X3zxBW+99RY33XQTycnJjBo1yulIIt0tIs5H6n3NbNpbw3mj8zs8rqKukVdX7uKKif1Ijfd2eOwlE/oyMDuRczt5zGCUHqjj2ocXU+/zs31/3TGLD88vK+Hfnl9BZmIMz377ZMb0SzviGGst/1i0nV+9spp4r5t7rhzPuaPzOiwmZGdnk52dDUBJSQkvv/wyl156KRkZ0bFDfHW9jwWrdjP/i518tHEfzX7LkJwkvn/mUM4dncfgnOQj7pMQ42FFaSWNzX6uObk/Zxbn8T8L1jmQXkQkuM4HgAyg1lr7sDEm2xgzwFq7pZP7lAL92l3u23Jde9cDswCstZ8YY+KALGBP+4OstfcB9wFMmDBBeyuJyHFpXW6h5RVdU1BQwKBBg/B4gn3JEAkrUXM+snFPDdbCsLwj32S29+LnpTQ2+bl0Yr8OjwPom55A3/TumdtSVd9EXkocu3z1R73d77f85vW1/O39zUwekME9c8aTlRR7xHH7axu54dGlvL12D1OHZPE/l4whN6VrXRlNTU14vd6In0nT0NTMO2v2MG/5Tt5Zt4fGJj990+O5adpALhzTh+F5yR0WZP4yZzwHfc0MyUlqO87lCsz5EBHpaZ2eSRpjfgZMAIYBDwNe4HHglE7uugQYYowZQOBF/nLgysOO2Q7MAB4xxowA4oC9XfkBRESC1b7woOUVHVu9ejW7d+/m9NNPJysri4suusjpSCLHK2rOR9btqgZgaO6xiw/WWp5aWsKoghRG9gl+14qvamhuEhV1jfzk3BF87S8fH3H7wcZmvv/0cl5buYs5kwv5+YUjj7l05L31e4nxuPjZBcVcM6XoqMsxOlNUVMR1112HMQZrLQsXLmTSpEkRM4xyZWklz35WwovLS6mo85GVFMuVkwq5YEwfxhemBb2cpF9GcMWXHeV1xHhcXS7yiIh0RTAfY30NGAcsA7DW7jTGdFxyDxzXZIy5HVgAuIGHrLWrjDG/AJZaa+cDPwDuN8Z8j8Cwp2tt68hiEZEgHWuA5OFaCw9abtG5bdu2UVpaytSpU9XxIBEtms5H1u+pJsbtoijz2G8oV5ZWsaasil/OHtmDyeCHZw8HYMu+2iNuK69t5LpHlvBFSQU/PS8w3+FYb56zkmJJjPFw9+XjOu3w6Ezrc+zevZtPPvmE9PR0xo3rdMM2x5TXNvLi56U881kJa8qqiPG4OKs4l0sm9OPUwVm4j6MIc0wW1u6qYsHK3by+ahdryqo4oW8q828/tfueQ0TkMMGcUTZaa60xxgIYY4IebW6tfZXAdlXtr/vPdt+vpvMOChGRDgW7lEIdDx0rLS0lLi6OzMxMzjzzTIwxuN2dT8kXCXfRcj6yflc1g3KS8HQwbPKppduJ9bi4MEx+1+2sOMjVDy6i5MBB7p1zIrNG5XV4/JM3nYTX5TqubodjycvL47bbbiM1NdAJsnv3bjIyMvB6O56H0RP8fsv7G/by1JIdvLVmN75my+iCVH4xeyQXjulDWkJodu1YvLWcWX/4AGNgfGE6A7MSqalvCslziYi0Cqb48LQx5m9AmjHmRgLrIh8IbSwR6a2C7WJoTx0NX11TUxNPPvkkBQUFXH755ep2EAlD63fXMLHo2DtI1Puambd8J+eMyut00GRP2Ly3hqseWER1fRN/v24SkwdmdnqfWE9oCp5paWkA+Hw+HnvsMYqKivjGN74RkucKRkVdI88sLeHxRdvYtr+OjMQYvjmliEsm9GV4XmhnEs0ckUu8183M4lzOLM4lJzmO7zzxOatKK0P6vCIiwex28T/GmDOBKgJzH/7DWvtmyJOJSK90PAMh1dFw/Orq6oiPj8fj8XDZZZeRmdn5mwMR6XnV9T5KKw4yJ+/Yu2y8s3YP1fVNXDKh80GTobZpbw3/9eoa/H7LEzedxKiCnps/0RGv18vXvvY1kpMDSzqam5txuVydzlBobPJjsV+5OLKytJK/f7KVect30tDkZ2JROt8/cyjnjMonxtPx9qnd5YapA7lh6sBOj6tpaGLh2j0sWLWLTzbt55cXjeqWXVFEpPfqsPhgjHED6S3FhjeNMTHAtcaYNdZabfIuIiGhLoaesXfvXh566CFmzZrFmDFj6Nu3r9ORROQY1u+uAWBYB8Mm31y9m7QEL5MHOL+15J/e2UhWUixP3XzSUbeAdNKgQYPavn/77bfZu3cvl19++VGXmdX7mnli8XbuWbiJ4XnJPH7D5C4/X0NTM698WcbfP9nG8h0VxHvdfH18X64+qT/FfcJr56Xy2kbeWh2YA/Hhxn00NvnJSIyhvLaRTXtq8PstPr8/ZB0qIhLdjll8MMZcDvwNqDXGbAD+C3iIwNToOT0TT0REQiUzM5PRo0fTr5/zn5KKSMfW7+54p4umZss7a/cwY0ROhzMhekpWUixP3jQ57AoPh8vIyMDv9x9RePA1+3lmaQl/emcDZZX1xLhd7Kk++haix1JV72Puou08/NEWdlc1MDArkf88v5iLT+wbFstiDretvI4Jv3oTv4WCtHiumtyfWaPyGNMvlWE/fZ15X+zk0U+24beWxT+ZERZ/z0QksnTU+fBT4ERr7UZjzHjgE+Ab1tqXeiaaiIh0t507d7Jw4UK+8Y1vEBsby7nnnut0JBEJwrpd1STGuClIiz/q7V/sqKC6oYmzijse6Bhq/dLjuWX6IL5xYl8GZYf/tpYTJkxo+768vJzFixdTmzWcu9/ZwvbyOsYVpvG/l4zhsU+3sWlvTVCPubuqnoc+3MI/Fm2npqGJUwZn8ttvjGHq4KxuHaTZnSb0T2fz3hpmDM/hrJF5jOyT0rYUxe+3pCV42VVZT2ZSDNv219Hkt6j5QUS6qqPiQ6O1diOAtXaZMWaDCg8i8lV1NlCyq/MepGt8Ph/79u2jsrKSnJwcp+OISJDW765mSG7yMd+8Vjc0EetxMW1oVg8nO5TH7eJHs4Y7muForLUs217BwKxE0hOPvoPEW4u+YMWSz3j+4EH652Xx0LUTOH1YDsYYHvt0W6fPsWF3Nfe9v5kXl5fS7LecOzqfm6cNYnTf8Jh30ZFrTi7impOLjnqby2V474enE+d18eCHW/jt6+t6NpyIRI2Oig85xpjvt7uc1v6ytfb3oYslItGqs4GSGh7Z/SorKykpKWHkyJH079+f22+/XVtoikSY9burmTE8t8NjTh2cRUKMdqo5XGnFQf7zxZW8vXYP3zljMD84a9ght2/ZV8t/v7aGBavqKEw5kV9eMoqLxhawefMmoPMi7YqSSu5+ewNvrdlNnNfFFZMKueHUgRRmJoToJ+p5x1omUlHXyDtr91BWWc+t0wd1OrhTRHq3jl6h7geSO7gsInJcNFCyZy1cuJD169czePBgYmNjVXgQiTD7ahrYV9PI0LyOT8POLO64OBGN5i0v5XcL1nH35WM5sf+hgzab/ZZHP97K/7yxDmvBZeBgY3Pb7dX1Pv7w1gYe/XgrsR4X/3LWUK4/dSDxMW62bNnCP/7xDy666CLGjBlz1OdeWVrJH95az1tr9pAa7+WOGUO4Zkp/MpNiQ/ozh4PHP93GwnV7+HRzOc1+C8DssX3omx49BRcR6X7HLD5Ya+/qySAiItJ9GhsbaWpqIiEhgbPOOotp06YRGxv9J8Qi0ah12GRHO10YAzNG9K7iw/rd1dz55HIAtu6rO6T4sHpnFf/2/Jd8UVLJ9GHZ/HL2KGb94X0gsARj/hc7+dUra9hX08DlE/vxvTOHkpMc13b/oqIiLr74YoqLiwEw/qa22wJFh0CnQ0qchx+cOZRrTykiOS78hkh2N3dLZ8OvXlnDwOxEbpo2kKZmP/d/sIX/e3MDy3cc4Bsn9uOW6YM6eSQR6Y3UmyciPaJ11oNmOoSe3+/noYceIjU1lSuuuIKEhAQSEvRplEik2rgnMOhwSO7RBzjGed1M6J9OdnLvKTDGeNwYY7h8Ql+eXLKj7fqmZj/3vruJu9/eQFqClz9eMY4LTshvWw6wrbyOK+9fxCeb93NC31QevGYCJ/RNO+LxjTGMGjUKCBRzM0s+oKo5k9v+kcwrK8pIifPw/ZaiQ0ovKDq0+tq4AuJj3Jw8KLNtJ5OXv9wJwEtf7MRi+bKkgtKKg2zYXc1pQ7O1FENE2qj4ICI9on3hQTMdQsNaizEGl8vFlClTSE0N/yFnItK5ssp6vG5DzjGKC/9zyRi8vWzbw387Zzh3nDGYWI+7rfiwZV8t3396OZ9vr+DCMX34xeyRpCUcOlzyzdWBboVfXTSKKyYV4g5i9wmXy0VDQg4b93ipXreHO2YM4YapA3pV0aFVTkoc35xSdMh1ZxbnMvfGyYwuSOXiez/m7bV7eG3lLgBeu3MqI/SBg4i0OGbxwRgzBfjUWmt7MI+IRJH2O1u0Fh406yE0amtrefbZZ5k6dSoDBw485hplEYk8uyvryUmOO+YnyP0yel9nU5+WLUe3768D4JUVZfz0xZV43YY/XjGOC8f0OeI+pw/PId7r5kfnDCerC3MZPB4P554zi5ytB7j2lCK2rPmSNV8sY9KkSfpUH4j1uDl5UGCXlSkDM0mO81KQFs/8L3Yyd9F2Nu+r4eRBWdx2+uC2+1Qe9OEy9IqlKiLyTx11PnwTuMcYsx54HXjdWrurZ2KJSDRo3+2gjofQiomJobGxkYMHDzodRUS62e7qenJTes+SiuPxzto9TB2Sxe++MYa81LijHvPnK8cf9+NPH5bD9GE5WGtZuHUrTU1NTJo06bgfL1rdNTuwVOWjjfuY/8VOHvt0Gy4DjU1+Zo/tw5urd/Pm6t0s3lLO6L6pvHDrKQ4nFpGe1NHAyVsAjDHDgXOAR4wxqcBCAsWIj6y1zce6v4j0XofPd1C3Q2g0NjayaNEiTj75ZLxeLzfccIM+hROJQrurGhh6jHkPvV1WcgwT+qdzwZg+fHNK/5D/DjTG8I1vfAOfz4cxhrq6OkpLSxkyZEhInzfSTB6QwR+vGMfoglT+/YUVLNpSzqm/WQjAoOxE8lLjOFDb6HBKEelpnc58sNauBdYC/2eMiQdOBy4Bfg9MCG08EYlEmu/QMzZv3szChQspKChg4MCBKjyIRKndlfWcOjjL6RhhKSHGw7O3nNyjz2mMISYmMEvigw8+YMmSJdx5550kJ2tH+lYet6tt6cupQ7Jo8ltmDM/hzOJcBmYnceeTn/PFjgpnQ4pIj+vSwElr7UHg1ZYvEYlS7Wc1HA91PISO3+9n//79ZGdnM3z4cG699VaysvSmRCRa1TY0Ud3QRG7K0ZcSiLNmzJjBsGHD2goPdXV12l3oMLdOH8yt0wd3fqCIRL3eNRpZRILS2rlwvNTxEDpvvPEGDz74ILW1tQAqPIhEud1V9QDkpWrmQzjyeDwUFRUBsG3bNv7whz+wefNmZ0OJiIQpbbUpIkelzoXw4vf7cblcTJ48mby8PH2yJtJL7K5qACA3WZ0P4S4rK4sxY8bQt29fp6OIiISloIsPxphEoF5DJkWiQ0dLK1qXTYjzrLW8/PLLAFxwwQWkp6eTnp7ucCoR6Sl7qgOdDzladhH2EhMTOe+884BAwfiZZ55h/PjxGkYpItLimMsujDEuY8yVxphXjDF7CAydLDPGrDbG/M4Yo8VbIhGso6UVWjYRPowxJCQkkJCQgLXW6Tgi0sN2VbYuu1DxIZLU1tZy4MABbX/cgWZrWbylnHsWbmRfTYPTcUSkB3TU+bAQeAv4N2CltdYPYIzJILDjxW+MMS9Yax8PfUyR3uOrDnsMloZChi+/38/HH3/MkCFDyM3NZcaMGU5HEhGH7K5qIDHGTVKsVspGkuTkZG688UbcbjcAmzZtIikpidzcXIeThY8d5Qe59G+fAJCeEMMJfVOxFkb3TXU4mYiESkevZDOttb7Dr7TWlgPPAc8ZY7whSybSS7XfpjKU1N0Qvurr6/n0009paGjQiapIL7e7ql47XUSo1sKDtZYFCxYQHx/Ptddeq22RgfNP6IPbGMYWpvGf81Zx10uraGjyk5kYw9KfzmTb/jr6pMUT49FsfJFocsziw9EKDwDGmDTgNmvtfx3rGBHputaOB3Uk9F7bt2+nX79+JCQkcNNNN2nPeBFR8SEKGGO45ppr8Pl8GGPw+Xw0NDSQlJTkdDTHnFmcy5nFuTQ0NfPsZyXkJMdSedDHZ9sOcOpvFlJacZCfX1DM1VOK8DX7ifO6nY4sIt2go5kP/Ywx9xljXjbG3GCMSTTG/C+wHsjpuYgivUP7woM6EnqfTZs28fDDD7N69WoAUlJS9OmYiLC7up7cFG2zGekSExNJS0sD4O233+avf/2r5kEAsR43828/lQeumch5o/Pxul0MzwsU3v/+6TYm/ddbTP5/b+Nr9jucVES6Q0fLLv4OvEdgicUsYCmwHDjBWrsr9NFEeh91PPQ+TU1NeDweBg4cyAUXXMDw4cOdjiQiYcJay+6qBnU+RJnx48eTlpZGfHw8EPj/rGIzXHvKAL45pQhjYPwv32RPVQPZybFs2VdLU7OlffOD/puJRKaOig8Z1tqft3y/wBhzCTCndfCkiHx17YdLanvL3mfZsmV8+OGH3HTTTcTFxTF+/HinI4lIGKmo89HY5FfxIcrk5OSQkxNoIt67dy/z5s3joosuIisry+FkznO5AgWF9/71dGI9Lh7+aCv//dpaLJa1u6pYsHI3b6zexc6Kg7z3r6eTEqfxcyKRpMPRycaYdKC1rLgfSDUtZcaWwZMi8hW0X2qh5Ra9T25uLgUF+n8uIke3uzqwzaaKD9Grrq4On89HbKyW1rR3eFHhrP97n5IDBzEGcpJjOVDno6LWp+KDSITpqPiQCiw77LrWyxYYGJJEIhHmq2yNqeGSvc/q1auprKxkypQpFBQUcPHFFzsdSUTC1K7KQPEhL1VvTKNV//79+fa3v40xBmstH3/8MWPGjOnVwyjbK8xIIM7rYkhOEredPpgZI3L4YP0+fvDMF/itZWVpJfmpcWQmde+/kW37a/lk036mDMqkf2Zitz62SG/W0W4XRT2YQyRifZWtMdXt0PusXbuWAwcOMHnyZFwubSEmIse2p6oBgJxkdT5Es9bZBfv37+fdd9/F5XIxZYo+lAA4d3Q+54zKO+p8h9n3fETlQR9fH1fA7y8be8TtDU3NbNpTy/C85LblHADb99fxztrdvLNuL8mxHu6ZM556XzOfbt7Pu+v28t76vWzZVwvArJF5TBqQwe7qev717OG4XYbahiYWbdnP5r21XHNyEV73sV/LaxuaWLy1nI827OPDjftIiffy9DE+cKqq97F4czmfbN7P3uoGfnfJCcR6tMuHRJdjFh+MMTnAT4DBwJfAf1trq3oqmEg4O9qsBnUvyLHs3LmTpKQkUlJSOP/883G73So8iEindlUFOh9ytNtFr5CVlcUtt9zStivG/v37SU1NxePpcJV01Du88DAgO5HclFhO7J/O4i3l1DY2td1WUdfIO2v38Obq3by/fi+1jc38/bpJxHpcvLN2D2+v3cPGPTUAxHhcNPst1z68mE8376fe5yfW42LKoEyumdKfP7y9gddX7eL1VYE5+41NftaUVfHZtgP4mi0A4wrTObF/etvzNzX7+aKkko82BooNn28PHBvjcZES52kragDUNDSxZGs5n27azyeb97OytBK//efPeefMIQzKDnTA7KqsZ3t5HROL0jsctGmtZU91A9lJsYcUXETCRWe7XXwG/Ak4H/gjcG0PZBIJe5rVIMFqaGjgscceY8iQIXz9618nJibG6UgiEiF2V9WTkRijTz97kYyMDACam5v5xz/+QXZ2NldccYXDqcLL+MJ0Fv1kJgCz/vA+B2p9PPDBZt5as5slWw/Q7LfkJMcyZVAWb63ZzXWPLKHJb/G6DZMHZHLFpELOGJ7DayvL+O3r69i6r5bLJxYyfVg2Jw3MJK5lW43clDj21zbS0OTnly+v5uGPtlKcn8J1pw4gMcbD799cT7PfsnFPNR9u2MeHG/ezaPN+qhuaMAZG9Unl+lMHcurgLCYUpfN/b63nwQ+28JvX1/LJpv2sKK2k2W+JcbsYW5jGd84YwpRBmWwvr+Nfn/2S11fuYvv+OhZt2c/W/XUAvHT7qYzum9r236LZb1lTVsWiLeUs2VLOkq3l7K9t5O7Lx+rcVMJSR8WHfGvtv7d8v8AYc/j8B5FeTd0O0pGGhgZiY2OJjY3lkksuIT8/3+lIIhJhdlc1kJOsrofeyO12c+6557YNorQ28JG4tpc80uKt5SzeWs6w3GRuOW0QZxbnMrogldrGJq57ZAlFmYnMGJHDKYOzSG43oPKmqQO5eHzfYw50PWd04HW7oamZQdmJFPdJaVsC9eGGfQBc/8gSqhsCnRf9MxO4YGwfTh2cxZSBmaQnHvphQ4LXQ5Pfcv/7mxnTL41bThvElEGZjC9MJz7mnwXGA7WNAPxuwTpS471MGpDB5AGZPLV0BxUHG1m6tZxFW8pZvKWcZdsOtD1/v4x4ThqYySsryihveQyRcNOV3S7c7S9rtwsRkaMrKyvjscce46KLLmLo0KEMHKj5vCLSdbur6rXTRS82ePDgtu8/+ugjtm3bxqWXXorXqx0eWv3w7GFs21/HzBG5FGYmHHJbcpyXZ7598jHv63G7gvr3FetxM31YziHXDcxOZHheMoNzkjh1cBanDM6iX0bCMR4h4LpTi5gyKJNRBSkkxBz7Ldj0YTn84bKxDM1NbptX8fHGfTy1dAfffGgxLXUohuYmceHYPkwakMGkARnkp8ZTUdfIKyvKOv2ZRJzS2W4Xn/HP4gNotwsRkU5lZ2czdOhQMjMznY4iIhFsd1X9cQ0zlugTFxdHQkJCr5//cLgZI3Ided4+afG8/t1pXbpPclygi6Ez8TFuLhp36JKJ4fkpzByRQ1FmIhMHZDCxKIOMRC3jlMjT0W+w06y123osiYhIBNu5cycfffQRX/va1/B4PFx00UVORxKRCGatZX9tI1nJeoMhMGHCBCZMmABAdXU1ixYt4rTTTlMXRC+RkRjDA9dMPK77+pr9rNpZxZIt5Szasp8mv+WvV53Ixj019M9MOGQpikiodVR8eAEY31NBRJzUfveKYBzv1poSvaqrqykpKaGiooKsrCyn44hIhDvoa6bZb0mK1RsDOdSGDRtYvHgxY8eO1euNHNW6XdX84a31LNlazrJtFRz0NR9y+/D/eB2Ab51SxM8uGOlEROmlOio+aKKN9Brtd68Ihna4EICqqir27NnD4MGDGTZsGAMHDtSnUCLSLWpahsglxWqnCznU+PHjGTJkCMnJyQBs27aNwsJCDaOUtu01n1yyA2NgeF4Kl07o27ZUY3dVPb9+dS3D85N59rMSahuaOnlEke7VUfGhwBjzx2PdaK29IwR5RByj3Sukq1577TVKSkq488478Xg8KjyISLepbQh8UpkUpzX+cqTWwkNpaSmPPPII5557LhMnHl9bvkSPlDgv984ZT1yMm/GF6aTGH3pekpsSxxM3nQTA6yt3ORFRermOXtEOEhg4KSIiLRobG7HWEhsbyznnnIPP59MAMBHpdq2fSCZ2MBVfpE+fPlx00UUUFxcD0NTUpNekXq51i1CRcNTRb6f91tpHeyyJiEiYa2pq4oEHHmg72UtJ0dwPEQmNfy670BtJOTZjDGPGjAGgubmZhx9+mEGDBnHGGWc4nExE5EgdvaI19lgKEZEI4PF4OPHEE8nNdWZrLxHpPWrqWzofVHyQIFlr6devH3l5eU5HkQjS7LfUNDQdskSjss5HSUUdxfkpmiUi3aqjV7TLO7qjCfxNLLDWlnRvJBGR8FFTU8OLL77IGWecQZ8+fZg8ebLTkUSkF6htVPFBusbj8TBr1qy2yytXrqS2tpZJkybpDaQc1Zurd/P00lcB+Ol5I9i6v5alWw+wdlc1AOeNzqehqZkJRRl8+7RBTkaVKNHRK9rvjDEuYB6B2Q97gThgMHA6MAP4GaDig0SUo22rqa0z5VjcbjeVlZVUVFTQp08fp+OISC/RuuwiWQMn5TitX7+eiooKJk6cqOKDHGF8YTob9lTjdrnYV9PAr15ZQ1Ksh/H90ynMSOCN1bt5ZUUZHpdhd1WDig/SLY75imatvcQYUwzMAa4D8oE6YA3wKvBf1tr6Hkkp0o2Otq2mts6U9hobG1m2bBmTJ08mPj6eW265BZfL5XQsEelF2gZOqvNBjtPXvvY1GhoacLlcNDY2smPHDgYN0htICbhnzngA6n3NLFi1i8E5SQzPS8HtMlhr+bKkkr7p8fzw2S/ZW93gcFqJFh2+ollrVwP/3kNZRHqMttWUjqxZs4YFCxZQUFBAv379VHgQkR5X07LVZoLX7XASiVTGGOLi4gD46KOP+OCDD7j99tvJyMhwOJmEkziv+4gP4IwxjOmX5kwgiWoqp0tU0xILCZbf76eyspL09HROOOEEcnNzNbRLRBxTU99EYowbl0vt8vLVTZ06lYKCgrbCQ319fVthQkSkp+jjPIlqrUss2tMSCzmaV199lYceeoj6+nqMMSo8iIijahuatORCuo3H42Ho0KEA7Nq1iz/84Q+sX7/e4VQi0tvoVU2inpZYSEestRhjmDhxIgUFBcTGxjodSUSEmsYmkjRsUkIgOTmZESNG0LdvX6ejiEgv02nngzHmFGNMYsv3Vxljfm+M6R/6aCIioeP3+5k/fz7vvPMOALm5uYwbN04TwUUkLNQ2NJGkzgcJgcTERGbPnk1CQgLWWubNm8eGDRucjiUivUAwyy7uBeqMMWOAHwCbgL+HNJWISIi5XC6MMSo2iEhYqm1oIjFGxQcJrYMHD7Jz507Ky8udjiIivUAwr2pN1lprjJkN/Nla+6Ax5vpQBxMR6W7WWj755BNGjBhBeno6559/vooPIhKWquub6Jue4HQMiXIJCQnceOONuN2BXVV27NhBbGwsOTk5DieTcOJr9vPZtnI27qnhgjF9SFBhVI5TMJ0P1caYfwOuBl4xxrgAbzAPboyZZYxZZ4zZaIz58TGOudQYs9oYs8oYMzf46CIiXVNdXc3777/PF198AaDCg0gvEYnnI7WNTSTFaptNCT2Px4MxBmstr732Gs8//zzWWqdjSRhZu6uai+/9hB89t4L31u11Oo5EsGDKVpcBVwLXWWt3GWMKgd91didjjBu4BzgTKAGWGGPmW2tXtztmCPBvwCnW2gPGGJVZRaTblZWVkZ+fT0pKCjfffDNpaWlORxKRHhKp5yO1Dc0aOCk9yhjDnDlzqKurwxiD3+/n4MGDJCYmOh1NHPTNKf0ZlJ1Ibkocv3plDY3NfjbuqWHp1nKWbD3AmrIqfn7hSCYNyHA6qkSATl/VWgoO/wAmGmPOBxZba4OZ+TAJ2Git3QxgjHkSmA2sbnfMjcA91toDLc+1p6s/gAjA3EXbmbe89IjrV5dVUZyf4kAiCRdr1qzh6aefZs6cOQwePJj09HSnI4lIz4rI85EabbUpDkhMTGwrNrz//vssXbqUb3/72yQlJTmcTJwyfVgO04flsHFPDb96ZQ0/fOZLGpv9AKQleKmo87GitFLFBwlKp69qxphLCXQ6vAsY4E/GmB9aa5/t5K4FwI52l0uAyYcdM7TlOT4C3MDPrbWvHyXDTcBNAIWFhZ1Fll5o3vLSoxYaivNTmD22wKFU4iS/34/L5WLo0KGcc845DBgwwOlIIuKMiDsfaWzy09jkJ0nrqsVBxcXFACo8CAAFafGcNjSbrKRYJhalM3FABlmJsYz5xRtOR5MIEsyr2r8DE1s/BTDGZANvAZ0VH4J9/iHAdKAv8L4xZrS1tqL9Qdba+4D7ACZMmKBFaHJUxfkpPHXzFKdjSBhYunQpS5cu5frrr8fr9TJp0iSnI4lIeAur85HahiYAdT6Io3JyctoGT1ZWVvLCCy9w3nnnkZ2d7XAycUJ8jJtHrzv0fKryoM+hNBKpghk46Tqs/XB/kPcrBfq1u9y35br2SoD51lqftXYLsJ7Ai7+IyHFLT08nMzOT5uZmp6OIiPMi7nykpqX4kKTig4SJqqoqqqur23bFEDlcdb2P8trGoI7VQNPeK5hXtdeNMQuAJ1ouXwa8GsT9lgBDjDEDCLzIX05gcGV7LwJXAA8bY7IItD1uDuKxRUQOsWbNGhoaGhg7diyDBg1i0KBBTkcSkfAQcecjtY0txQcNnJQw0a9fP2677TZcrsDnj0uXLmXEiBEaRikA/PmdDfzy5cAYnfNOyOejjfuYMTyXvNRY6n1+rphUyLJtB/hs2wGWbitnf20j7/xgOhmJMYc8jq/Zz9qyajKTYuiTFu/EjyIhFszAyR8aY74OnNpy1X3W2heCuF+TMeZ2YAGB9ZMPWWtXGWN+ASy11s5vue0sY8xqoBn4obV2//H+MCLSO1lrWbZsGY2NjYwZM0ZbaIpIm0g8H9GyCwlHrYWHyspKFixYQHV1NaeffrrDqcRJiTFuJhal43W7WFlaSVV9E8u3V1BR5+O5ZSVtxz344RYAUuO95KbEUlHnY19NA0CgKLE9UJj4sqSCep+fiUXpPPPtkx35mSS0ghk4+X3gKWvt8119cGvtqxzWJWGt/c9231vg+y1fIiJdUlZWRlpaGvHx8Xz9618nJiZGhQcROUKknY/UNASWjCXFqsVdwk9qaio33XRT2+5RlZWVJCYm4vGoWNbbeNyutiKB32+pOOgjIzGGJVvL8TX7yUqKZe6i7QzLS2ZC/3QGZSfx2spd3DZ3GVc/uIjdVYEChMdlGFmQypWT+vPxpn3UNmjZbLQK5rdEMvCGMaYceAp4xlq7O7SxREQ6VldXx8MPP8wJJ5zA+eefT3y82vNEJDrU1KvzQcJb69BJv9/PE088QWJiIldffbXDqcRJLpdpW0Yxseif227+/MKRhxw3JDeJQdmJDMxOYnxhOif2T+eEvqnEeQPF1hseXcrOioM9F1x6VDDLLu4C7jLGnEBg3sN7xpgSa+3MkKcTETmMz+fD6/WSkJDAxRdfrO13RSTqtC270FabEuZcLhczZsxoW5LROkhQXYhyLENzk3n7B9M7PGZn5UGuuO9TNu+r4b6rJzCmX1qPZJPQ68qr2h5gF4HdLnJCE0ekc3MXbWfe8kMHla8uq6I4P8WhRNJTSktLmTt3LpdddhmFhYUMGzbM6UgiIt2udbeLZA2clAgwZMg/N4ZZtmwZ69at4+KLLyY2NtbBVBKp8lPjqK5vYk91PburGtiyr/a4ig9V9T6SYjy4XCqEhZNgZj7cClwKZAPPADdaa1eHOpjIscxbXnpEsaE4P4XZYwscTCU9ISsri6KiIk3XFpGopoGTEsmMMcTExHR+oMhR/GL2SH56/gh2VtRz+v+8y+Z9tTz2yVZyU+Io7pPC59srWLb9AJ9vryAp1sPjN0ymscnPmrIqlu+o4PPtB1i+o4Kt++sAGNMvjfLaBubfdirxMW5W7axk+Y5KdpTXMXtsHzbtrWVAViIn9k93+CfvHYJ5VesHfNdauzzEWUSCVpyfwlM3T3E6hvSAnTt3smTJEi644AJiY2O55JJLnI4kIhJSNY1NxHhceN0up6OIdMmJJ57I+PHjMcZQX1/PJ598wtSpUzWMUoJmjCHW46a1YeGPb2844pg4r4ukWA/7ahr5+l8+YuXOKhqb/ADkJMcytl9aW/Ghsq6RHeUH+dpfPmLHgYM0+23b4zzy8VYAijITuPbkIrbsq+XskXnsrKwnOzmW04Zmh/aH7YWO+ZvAGJNira0CftdyOaP97dba8hBnEzlE63ILLbHoXfbs2cOmTZuorKxsm6wtIr2TMeYU4OdAfwLnMIbAZhUDnczV3Wrqm0hS14NEqNZ5D+vXr+fDDz9k2LBh9OnTx+FUEmkKMxK468KRpCV4aWzys3hLOaP7pjK+MJ1hecm8/OVO/vPFVbhdhmum9Gdsv3TGFaaRnxqHMQa/32KBjXtquPHvS+mXkcD5J/ThhL6pFPdJYd7yneSlxPHi8lI+2LCPn78UaOx/9JNtAOSmxLLoJxpx2N06emWbC5wPfAZYAi/wrSwQVS/0Ev7aFx60xCK6VVVVceDAAfr378+YMWMYMWKE1o6KCMCDwPcInJtE7V5stQ1NJGqbTYlwJ5xwAv369Wv74GDnzp3k5+drGKUExRjDNScXtV2+ZEK/Q27/2ri+fG1c32Pev3XWw7C8ZN7/19OPuP220wcDMGlABmvKqhjdN5UFK3eRHOdlwapdfLbtAFv31fJFSQUrSir5srSSxBg3D39rUjf8dL3XMYsP1trzW/4c0HNxRA7Vfrhka+FByy2i37x58ygvL+f222/H7Xar8CAirSqtta85HSLUahqaSYr1Oh1D5CtrLTzs3buXBx54gJkzZ3LyySc7nErkn/plJNAvIwGAa08JvO1dtv0A+2sbmf4/7wIQ6wks89hf2+hUzKgRzMDJt621Mzq7TiQU2nc7qOMhujU2NuJyufB4PJx77rkAuN365E9EDrHQGPM74HmgofVKa+0y5yJ1v9qGJpLU+SBRJCsri/POO4+RI0cC0NzcrNd4CVsXn9gXj8tQ3CeF0QVpDMlN4s/vbOTutzfga/azbX8tfdMT8LgMjc1+ErQtctA6mvkQByQAWcaYdP657CIF0DtA6THqdoh+jY2N3H///QwaNIhZs2aRmZnpdCQRCU+TW/6c0O46C5zhQJaQqW1sIiNRuwVI9DDGcOKJJwJgrWXu3Lnk5+czc6bW1Ev4GV+YzvjCo88ZG/mzBW3DLeO8LgyGpT+dqd2JgtTRf6Wbge8CfYD2nyhUAX8OYSYR6WViYmIYNWoUhYWFTkcRkTBmrT1y4W4UqqlvamsDFok2fr+frKwsDZGWiHLqkCyW76hgWF4yj3+6jWF5ybiNYem2A9Q2Nqn4EKSOZj7cDdxtjPmOtfZPPZhJRHqB6upqXn75Zc466ywyMzM57bTTnI4kImHOGJMK/AyY1nLVe8AvrLWVzqXqfjUNTSSpjVeilNvt5pxzzmm7vHHjRsrLy5k4caKGUUrYmliUwaPXBYZN/uTcEQA8/uk2lm47wIKVu9hZWc/MEbmc2D+dZr9ly75aVu2spDg/hSG5yQA0NfvZtLeW9AQvOSlxjv0sTupo2cUZ1tp3gFJjzNcPv91a+3xIk4lIVLPWsnv3bvbu3atlFiISrIeAlcClLZevBh4GjjhPiWS1DU0kxan4IL3DqlWr2LlzJ+PHj8fj0d97iRwxHhcA/zFvFQD3vruJiUXprN5ZRW3jPzdkuuqkQlaWVrGmrIqGJj8n9k/nuVt65+DVjv6Fnwa8A1xwlNssgWFPIiHRustF67BJiQ6NjY2sXLmS8ePHk5KSwu23364TDRHpikHW2ovbXb7LGLPcqTCh4Pdbahub1cIrvcaFF17IwYMH8Xg8NDc3s2PHDoqKipyOJdKp80bnkxDjZkBWInfNX83ireX4LXzjxL6MKkjlD29toLTiIPM+38nIghSuPqk/H27cx/6aBt5Zu5vt++v4+ol9SYnrPbsbdbTs4mctf36r5+KIBLQvPGiHi+ixfPlyXnvtNfr06UNeXp4KDyLSVQeNMadaaz8EMMacAhx0OFO3qvMFPi3TbhfSWxhjSEgIzDhZtGgRb775Jrfccgs5OTkOJxPpWGKsh/NP6APAkzedhN9aPG5X2+1nj8qjvKaRwowEXK7AkqLb/rGMV1aUcd0jSwGI87oZnp9CUqyH3JRYtu6rY2heErGe6HwNCGarzTsJtDRWA/cD44EfW2vfCHE26eW0y0V0sNZSXV1NSkoKEyZMoKCggLy8PKdjiUhkugV4tGX2gwHKgWsdTdTNauqbANT5IL3SxIkTSUlJaSs8+Hw+vN7e86mwRC6Xy+Di0JklKXHeI7oabj9jMBOL0klPjOHOJ5fz4+dXHPFYE/qnM64wjcRYD9+dOZR6XzNx3ugoRgTzynadtfZuY8zZQCaB9ZWPASo+iEin5s2bx/bt27nlllvwer0UFKiTRUSOj7V2OTDGGJPScrnK2UTdr6YhUHxIUvFBeiGv18uoUaMAKC8v58EHH+T8889nxIgRDicT6R4j8lMYkZ9Cs9/yZUklCTFuqg76KK04yMg+qdz99gaWbjvA0m0HAHj2sxJKDhzk6+MKmDIok4o6H+MK01i7q5qTBmYyOCfJ4Z+oa4J5ZWst4ZwL/N1au8poFK2IdMJaizGGcePGUVhYqCUWInLcjDFXWWsfN8Z8/7DrAbDW/t6RYCFQq+KDCBDYhnvgwIH06dPH6Sgi3c7tMvzH+cVHXH/2yDxcLig9cJBfv7aWITlJlBw4yPOfl/L856VHHD+2XxrLd1QwY3gOu6rqOWdUHrefMaQnfoTjEswr22fGmDeAAcC/GWOSAX9oY0lv1DpkEtCgyQjW3NzMq6++SmZmJieffDL9+/enf//+TscSkciW2PJnsqMpekBr8UHLLqS3S0pK4uKL/zlf9o033mDAgAEMGRK+b6xEvqriPoH3P8PzUpgxIheADzbspaLOR5+0eD7ffoABWYn89vV1rNtdzZqyQAPgx5v2Ywx8tHF/xBcfrgfGAputtXXGmExAQyil27UfMqlBk5HL5XJx8OBBGhoanI4iIlHCWvu3lj/vcjpLqFWr80HkCA0NDWzatAmv16vig/Q6U4dkt31/Yv90AM4YnoO1gVkTDU3NxHrcXPrXT/A1+1lZWsnuqnpOG5p9yADMcNDpK5u11m+M6Qtc2dLe+J619qWQJ5NeSUMmI5O1lkWLFjF69GgSExO55JJL0OosEeluxpjfAr8isMPF68AJwPestY87GqwbqfNB5EixsbHccMMNuFyBN1K7d+/GGKMdMaTXMsbQeqrdtjOGgaVbD3D+nz4EYO4Nkzl5cJZDCY+u01KIMea/gTuB1S1fdxhj/l+og4lI5Dhw4ABvvfUWy5cvB1DhQURC5ayWIZPnA1uBwcAPHU3Uzf5ZfIiOyeYi3cXr9eJ2B/5dvPbaazz11FP4/VoJLtLqlumDuO30QXx3ZqA76A9vbWDWH97nP+etdDjZPwVTVj8XGGut9QMYYx4FPgd+EspgIhL+9u7dS3Z2NhkZGdx8881kZYVXdVVEok7rect5wDPW2spoK3bWNDQDkByr7QVFjuUb3/gGVVVVuFwurLXU19cTHx/vdCwRR50+LIfTh+Wws+Ig9767iW3ltTQ0+Xln7R6mDdlNyYE6vja+LylxgZdSYwJLNpr9loSYnum2C/ZZ0gjspQ2QGpooIhJJVqxYwfPPP891111Hv379yM7O7vxOIiJfzcvGmLUEll3cYozJBuodztStahuacBmI84bXOl2RcJKUlERSUmCLwcWLF/PBBx9www03kJaW5mwwkTDQJy2e1b+YhdtluOOJz5n/xU5u+PtSAH7+0mpi3C4am/0MzU1i895afnzOcG6YOrBHsgVTfPg18LkxZiGBbTenAT8OaSqJeu13tmilHS4iQ+sWmsOHD2fmzJnaAktEeoy19sctcx8qrbXNxphaYLbTubqTr9lPjMel5WsiQSoqKqKiooLUVH0+KtLK7Qq8htwxYzBTBmVSmJHAj5//kpQ4L9v215EQ66ZfegJnFue2DbHsCcEMnHzCGPMuMBGwwI+stbtCHUyix9EKDYu2BBppJg/IaLtOO1yEvyVLlrBq1SquvvpqvF4vp5xyitORRKQXMMacYa19xxjz9XbXtT/k+Z5PFRq+ZovHpa4HkWDl5uZy9tlnA1BXV8cLL7zAWWedpY5MEWBwTjKDcwK7VH/wr2c4nCb4ZRdTgFMJFB88wAshSyRRp/0Wmq0mD8hg9tgCrpxc6GAy6ar4+HgSEhJoampqG/okItIDTgPeAS44ym2WKCo+NPv9bZ9YiUjXHDhwgD179tDc3Ox0FBE5ik6LD8aYvxCYJv1Ey1U3G2NmWmtvC2kyiSraQjNyrV27FmstI0aMYNSoUYwcOVLtwCLSo6y1P2v581tOZwm1Jr/F69bvWJHjUVBQwB133NH2AcmKFSsYOHAgiYmJDicTEQhiq03gDOBsa+3D1tqHCex+4XzPhoiEnLWWjz76iKVLl2KtBbSNpog4xxjz/4wxae0upxtjfuVgpG7X1GzV+SDyFbQWHmpqanjppZf44IMPHE4kIq2CWXaxESgEtrVc7tdyncgRNEgyOuzatYuMjAxiYmK47LLLiI+PV9FBRMLBOdbatq2+rbUHjDHnAj91MFO3avJr5oNId0hKSjpkB4za2lpiY2PxeHpmS0EROVIwr27JwBpjzLstO16sBlKMMfONMfNDG08iTet8h/Y0SDKyVFdX88ADD/Dee+8BgRdvzXcQkTDhNsbEtl4wxsQDsR0cH3Ga/H48WnYh0i1ycnKIiYnBWsvTTz/NY4891tbJKSI9L5jS33+GPIVEtPbdDq1dDprvEHmamprweDwkJycze/ZsBg0a5HQkEZHD/QN42xjzcMvlbwGPOpin2zX5texCpLsZYzj11FPx+XxtnZytW4eLSM8JZqvN93oiiESu9rtZqMshMm3fvp1nn32WOXPmkJuby+jRo52OJCJyBGvtb4wxXwAzW676pbV2gZOZultzs8WrZRci3W7IkCFt369Zs4bly5fzta99jbi4OAdTifQuWvQkx62140HdDpEvMzOTvLw8rYMUkUiwBmiy1r5ljEkwxiRba6udDtVdmrTVpkjIHTx4kPr6erxer9NRRHoVldbluLUvPKjbIfKUlZWxYMECrLUkJiZy5ZVXkpmZ6XQsEZFjMsbcCDwL/K3lqgLgRccChYC22hQJvfHjx3Pttdfidrvx+Xx88MEHNDU1OR1LJOp16WNOY0w60M9a+2WI8kiY03yH6LF161ZWrVrFySefTHJystNxRESCcRswCVgEYK3dYIzJcTZS99JWmyI9o3Xew4YNG3jnnXfo168fRUVFzoYSiXKdFh+MMe8CF7Yc+xmwxxjzkbX2+yHOJj3oaFtkHs2iLeUATB6QoY6HCFRVVUVtbS35+fmcdNJJjBs3TmsdRSSSNFhrG1vfNBhjPEBUja5v8vu11aZIDyouLubWW28lOzsbgD179pCdna1hlCIhEEznQ6q1tsoYcwPwd2vtz4wx6nyIMu2XUHRk8oAMZo8t4MrJhT2UTLqLtZZnnnmG+vp6br31VowxKjyISKR5zxjzEyDeGHMmcCvwksOZulWz3+J1q/gg0pNaCw8VFRXcf//9nHrqqZx22mkOpxKJPsEUHzzGmHzgUuDfQ5xHHKQlFNHJ5/PhdrtxuVyce+65eL1eVfNFJFL9CLgBWAHcDLwKPOBoom7ma7bEefU7WsQJqampnHXWWQwfPhzQdpwi3S2Y4sMvgAXAR9baJcaYgcCG0MYSke5w8OBBHnzwQU444QSmTZtGfn6+05FERI6LMcYNrLLWDgfudzpPqKjzQcQ5xhgmTpwIBAoPzz33HKmpqZx55pkOJxOJDp0WH6y1zwDPtLu8Gbg4lKGkZxxteKREl7i4OIYMGUK/fv2cjiIi8pVYa5uNMeuMMYXW2u1O5wkVX7O22hQJB627gcXHxzsdRSRqdFpaN8b0Nca8YIzZ0/L1nDGmb0+Ek9BqnfMAaHhkFKmuruaZZ56hqqoKYwxnn302AwYMcDqWiEh3SAdWGWPeNsbMb/1yOlR3avZbPCo+iDjO5XJxzjnncMoppwCwY8cOFi9ejLVRNeNWpEcFs+ziYWAucEnL5atarlP/UYRq7XjQVpnRqbGxka1bt7Jr1y5SUtTNIiJR5T+cDhBqzX6LR8suRMJG68yHL774gk2bNjF27FhiYmIcTiUSmYIpPmRbax9ud/kRY8x3Q5RHekD7woO6HaKDz+dj/fr1jBw5kszMTL773e/i9XqdjiUi0i2MMXHAt4HBBIZNPmitbXI2VWj4/H51PoiEofPOO4/q6mpiYmLw+/2UlpZqWatIFwVTfNhvjLkKeKLl8hXA/tBFklA42nwHdTxEj08//ZSFCxeSl5dHZmamCg8iEm0eBXzAB8A5QDFwp6OJQqS5WcsuRMKRMaato3TZsmW88sorXH/99fTtq9XoIsEKpvhwHfAn4P8AC3wMfCuUoaT7tBYdFm0pB2DygAx1PEQJay11dXUkJiYyZcoU+vfvT2ZmptOxRERCodhaOxrAGPMgsNjhPCHj81s8bhUfRMLZmDFjcLlcFBQEzqebmprweIJ5WyXSu3X4r6RlW6v/Z629sIfySDdrXWIxeUAGs8cWcOXkQqcjSTd5/vnn2bdvHzfccAMej4fCQv2/FZGo5Wv9xlrb1LoGOxo1+612uxAJc16vl/HjxwNQU1PDAw88wJlnnsnIkSMdTiYS3josPrRsa9XfGBNjrW3sqVDSvbTEIjqNHj2a6upqXC4NJhORqDfGGFPV8r0B4lsuG8Baa6Nmum5Tsx+Pfq+LRAxjDPn5+eTk5DgdRSTsBdMftBn4qGUrq9rWK621vw9ZKhE5QlNTE6+//jp9+/Zl7NixDB061OlIIiI9wlrrdjpDT2nSVpsiESUxMZHLLrus7fL7779Pnz59GDx4sIOpRMJTMMWHTS1fLiA5tHHkq2g/VLJV63BJiXwul4v9+/eTlJTkdBQREQmRJm21KRKxfD4fq1atorq6WsUHkaPotPhgrb0LwBiTYK2t68qDG2NmAXcDbuABa+1/H+O4i4FngYnW2qVdeQ75p/ZbaLbScMnIZq1l6dKlnHDCCcTGxnL11VdrmYWISBdF0vlIYNmFOh9EIpHX6+WGG25ou1xeXk5zczPZ2dkOphIJH50WH4wxU4AHgSSg0BgzBrjZWntrJ/dzA/cAZwIlwBJjzHxr7erDjksmsF3WouP7EXo3baEZ3Xbv3s1rr72G3+9n8uTJKjyIiHRRJJ2P+G3gSwMnRSJX++3OX3/9dXbt2sUdd9yh3TBECCyl6MwfgLOB/QDW2i+AaUHcbxKw0Vq7uWVY5ZPA7KMc90vgN0B9MIHlUK3dDqAuh2hy4MABAPLy8rjxxhuZNGmSw4lERCJWxJyP+P0WAK+22hSJChdeeCEXX3xxW+GhoaHB4UQizgrqY1Rr7Y7DrmoO4m4FQPv7lbRc18YYMx7oZ619paMHMsbcZIxZaoxZunfv3mAi9yqt3Q5P3TxFW2lGgWXLlvHnP/+ZXbt2AZCfn080bysnIhJiEXM+4vP7AXCry00kKiQlJdG/f38AVqxYwZ/+9Cf279/vcCoR5wTT/7PDGHMyYI0xXgItiWu+6hMbY1zA74FrOzvWWnsfcB/AhAkT7Fd9bpFwZK3FGMOIESOorq7W+kARkR4QTucjzc3qfBCJVjk5OQwdOpT09HSno4g4JpjS+reB2wh8SlAKjG253JlSoF+7y31brmuVDIwC3jXGbAVOAuYbYyYE8dgiUWXRokU89dRTWGuJj4/ntNNOw+3uNTvLiYiEUsScj/hall1o5oNI9MnNzeXCCy/E5XLR2NjI008/jTq6pbcJZreLfcCc43jsJcAQY8wAAi/ylwNXtnvcSiCr9bIx5l3gX7TbxbFpK83o5XK5MMbg8/mIiYlxOo6ISDSJmPOR5pZlF9rtQiS67d+/nx07dlBbW6tOV+lVjll8MMb8CThmS6G19o6OHtha22SMuR1YQGBrq4estauMMb8Allpr5x9n5l5LW2lGl7Vr1xITE8PAgQOZMGECEyZM0GwHEZFuFknnI00tnQ8et2Y+iESz/Px87rjjjradMdatW0ffvn1JTEx0OJlIaHXU+dBa8T8FKAaearl8CbD6qPc4jLX2VeDVw677z2McOz2Yx+zttJVmdGhubuadd94hPT2dgQMHquggIhJCkXI+0tSsZRcivUVr4aG+vp4XXniBESNGMHv20TbiEYkexyw+WGsfBTDG3AKcaq1tarn8V+CDnoknEl327NlDZmYmbrebOXPmkJSU5HQkEREJE83aalOk14mLi+Nb3/oWycnJQKAY4fF42rbnFIkmwfytTgdSgPKWy0kt10kIab5D9CkvL+e+++7jtNNOY+rUqaSmpjodSUREwoivWVttivRGubm5QGDnsxdeeIHa2lquu+46XPpdIFEmmOLDfwOfG2MWAgaYBvw8lKFE8x2iid/vx+VykZGRwaxZsyguLnY6koiIhKHWzgcNnBTpnYwxnHjiidTU1KjwIFEpmN0uHjbGvAZMbrnqR9baXaGNJaD5DtFg69atzJ8/n29+85ukpaUxYYJ2khURkaNrUvFBpNcbOnRo2/dbt27l008/Zfbs2cTHxzuYSqR7BFtSawDKgAPAUGPMtNBFEokeqamppKam4m/ZPk1ERORYmlqWXXg080FEgAMHDlBRUYHb7XY6iki36LTzwRhzA3An0BdYDpwEfAKcEdJkvVD7OQ+a7xC5ysrKWL9+Paeddhrp6elcc801TkcSEZEI8M/OB7VbiwiMGzeOMWPG4HK58Pv9LF68mAkTJmgYpUSsYF7d7gQmAtustacD44CKUIbqrVrnPIDmO0Sy1atX89lnn1FXV+d0FBERiSCtW21q2YWItGqd/bBp0yYWLFjApk2bHE4kcvyCKZvVW2vrjTEYY2KttWuNMcNCnqyX0pyHyFRdXU1DQwNZWVlMnz6dKVOmkJCQ4HQsERGJIK2dD24VH0TkMEOGDOGmm24iPz8fCOyilp6ejjH6fSGRI5jOhxJjTBrwIvCmMWYesC2UoUQiibWWxx9/nBdeeAFrLW63W4UHERHpsmZ/68wHLbsQkSO1Fh5qamq4//77efvttx1OJNI1wex28bWWb3/est1mKvB6SFOJRACfz4fH48EYw3nnnUdCQoKqzyIicty024WIBCMxMZHp06czZMgQIPBBmM5BJRIcs/hgjMk4ytUrWv5MAspDkkgkAtTU1PDoo48yadIkJk6cSGFhodORREQkwrXNfNBuFyLSAWMMkydPbrv86quv4vV6OfPMM1WEkLDWUefDZ4AFDFBIYJtNA6QB24EBoQ4nEq4SExPp27cvmZmZTkcREZEo0dS67EK7XYhIkNp3PajwIOHumK9u1toB1tqBwFvABdbaLGttJnA+8EZPBRQJF9XV1bzwwgscPHgQYwyzZ89m4MCBTscSEZEooYGTItJVxhjOPfdczjzzTAD27NnDkiVLsNY6nEzkSMGU1k+y1r7aesFa+xpwcugiiYSn6upq1q1bx86dO52OIiIiUai5pfjg1bILEemi1q6HZcuW8e6771JfX+9wIpEjBbPV5k5jzE+Bx1suzwH07kt6BZ/Px9atWxkyZAh9+vThe9/7HrGxsU7HEhGRKORrVueDiHw1Z599NpMnTyY+Ph5rLbt27WrbJUPEacF0PlwBZAMvAM+3fH9FKEOJhIv33nuPJ598ksrKSgAVHkREJGRat9r0aqtNETlOxhjS09MBWLVqFffddx9btmxxOJVIQIedD8YYN/Ana+2cHsoj4jhrLQ0NDcTFxTF16lQGDx5Mamqq07FERCTKNanzQUS60bBhw5g1axZFRUUA+P1+XBpoKw7q8G+ftbYZ6G+MiemhPCKOe/rpp3nqqafw+/3Exsa2/cIWEREJpdaBkx4VH0SkG3i9XiZPnowxhvr6eu69915WrFjhdCzpxYKZ+bAZ+MgYMx+obb3SWvv7kKUScdCIESPw+XzarkhERHpU68BJj5ZdiEg3a25uJiMjo21JhogTgik+bGr5cgHJoY0j0vN8Ph8LFixgyJAhDBs2jBNOOMHpSCIi0gv5mgMzH9T5ICLdLTExkSuu+OfYvsWLF5OZmcmgQYMcTCW9TafFB2vtXQDGmARrbV3oI4n0LGMMO3fuJCUlhWHDhjkdR0REeqlmLbsQkR7Q3NzMsmXLyM7OVvFBelSnxQdjzBTgQSAJKDTGjAFuttbeGupwIqFirWX58uWMHj0aj8fDddddh8cTTCOQiIhIaLR2PmjgpIiEktvt5vrrr8ffssNOdXU19fX1ZGdnO5xMol0wiwr/AJwN7Aew1n4BTAthJpGQKykpYf78+W1Dd1R4EBERpzX7LW6X0cwhEQk5r9fbtoX8G2+8wcMPP0xjY6PDqSTaBfWOy1q747AXwubQxBEJraqqKlJSUujXrx/f+ta36Nevn9ORREREgMBuF1pyISI97eyzz6asrIyYmMAGhz6fD6/X63AqiUbBdD7sMMacDFhjjNcY8y/AmhDnEul2ixcv5s9//jPl5eUAFBYW6tMlEREJGyo+iIgTkpKSGDJkCAAbN27kj3/8I3v27HE4lUSjYDofvg3cDRQApcAbwG2hDCXSnay1GGMYPnw4NTU1pKSkOB1JRETkCM1+q202RcRRycnJFBYWkpGR4XQUiULBvMIZa+0ca22utTbHWnuVtXZ/yJOJdINPP/2U+fPnA5CSksIZZ5yh+Q4iIhK21PkgIk7Kzc3lkksuwePx0NzczIsvvsjevXudjiVRIpjiw0fGmDeMMdcbY9JCHUikOzU0NHDw4EGampqcjiIiItIp7XQhIuFi//79bNiwgf379bmzdI9OPwK21g41xkwCLgf+3RizGnjSWvt4yNOJHId169aRlJREQUEB06YFNmbRbAcREYkEXi27EJEwkZOTwx133NG2K8aWLVvIyckhMTHR4WQSqYJ6hbPWLrbWfh+YBJQDj4Y0lchxampq4rXXXuPjjz8GAkUHFR5ERCRSqPNBRMJJa+HB5/Px7LPP8uqrrzqcSCJZp50PxpgU4GsEOh8GAS8QKEKIhI19+/aRmZmJx+Ph6quvJjU11elIIiIiXeZxq/ggIuHH6/Vy9dVXEx8fD0BjYyMul0uz1KRLgul8+AIYC/zCWjvUWvsja+1noY0lErzdu3fz17/+lSVLlgC0FSFEREQijQZOiki4ysvLa/uA77XXXuOhhx6iubnZ4VQSSYJ5hzbQWmtDnkSki1q30MzJyeH0009n1KhRTkcSERH5StwuzXwQkfA3YsQIsrOzcbvdTkeRCNLpK5wKDxKONm/ezF//+ldqa2sxxnDKKaeQkJDgdCwREZGvxKtlFyISAYYOHcrJJ58MQFlZGU8//TR1dXUOp5Jwp/K6RKTExERiYmJobGx0OoqIiEi30cBJEYk0e/fuZdeuXU7HkAighfESMXbt2sW2bduYPHkyubm5XHfdddrJQkREoopmPohIpDnhhBMoLi7G4/FgrWXZsmWMGTNGM9jkCJ12Phhjhhpj3jbGrGy5fIIx5qehjyZyqM8++4yPPvqIhoYGABUeREQk6ng080FEIlBroWHbtm28/PLLrF692uFEEo6CKUfdD/wQ+BuAtfZLY8xc4FehDBbt5i7azrzlpYdct7qsiuL8FIcShafq6mqam5tJS0vjzDPP5PTTT2/bb1hERCTaaKtNEYlkRUVFXHfddfTt2xeAqqoqkpOT9aGhAMHNfEiw1i4+7LqmUITpTeYtL2V1WdUh1xXnpzB7bIFDicKP3+/nkUceYf78+QDExMRoqKSIiEQ1LbsQkUjXr18/jDHU19fzwAMP8MYbbzgdScJEMJ0P+4wxgwALYIz5BlAW0lS9RHF+Ck/dPMXpGGGnubkZt9uNy+XinHPOIS0tzelIIiIiPUJbbYpItIiNjWXKlCkUFRUBYK1VB0QvF0zx4TbgPmC4MaYU2AJcFdJU0mtVVVXx2GOPMX36dEaOHMngwYOdjiQiItJj1PkgItHCGMOUKf/8oPXdd9+lsbGRs846S0WIXqrT4oO1djMw0xiTCListdWhjyW9VWJiIllZWcTHxzsdRUREpMdp5oOIRCNrLfX19TQ2Nqrw0Isds/hgjPn+Ma4HwFr7+xBlkl6murqa9957j7PPPhuv18tll13mdCQRERFHqPNBRKKRMYZzzjkHay0AFRUVbNq0ifHjx6sY0Yt0tLAwueVrAnALUNDy9W1gfOijSW+xb98+vvzyS3bu3Ol0FBEREUd53Jr5ICLRq7XQsHTpUt544w1qamocTiQ96ZidD9bauwCMMe8D41uXWxhjfg680iPpJGr5fD5KS0spKipiwIABfPe739VOFiIi0uup80FEeoMZM2YwZswYkpOTAdi7dy/Z2dkOp5JQC6a8ngs0trvc2HKdyHF78803+cc//kFtbS2ACg8iIiKAW8UHEekFjDFtxYYNGzbwl7/8hfXr1zucSkItmN0u/g4sNsa80HL5IuCRUAWS6GWtxefzERMTw2mnncbw4cNJTEx0OpaIiEjY8GrZhYj0MkVFRcyYMYNBgwYB2pIzmnX6Cmet/S/gW8CBlq9vWWt/HepgEl2stTz55JO88MILWGtJTExk4MCBTscSEREJK+p8EJHexuv1cuqpp+J2u/H5fDz44IN88cUXTseSEAim8wFr7TJgWYiz9ApzF21n3vJSVpdVUZyf4nScHmOMYeDAgbjdbqejiIiIhC1ttSkivZnP5yMuLk7d0VEqqOKDdJ/2hYfZYwucjhNSPp+PN998k5EjR9K/f38mT57sdCQREZGwpoGTItKbJSQkMGfOnLZlF19++SWJiYltSzIksoV0YaExZpYxZp0xZqMx5sdHuf37xpjVxpgvjTFvG2P6hzJPuCjOT+Gpm6dw5eRCp6OElN/vZ9OmTZSUlDgdRUREerFIOh9xuzTzQUR6t9bCg7WWRYsW8cknn2CtdTiVdIeQdT4YY9zAPcCZQAmwxBgz31q7ut1hnwMTrLV1xphbgN8Cl4Uqk4SetZaVK1cycuRIYmNjufnmm4mJiXE6loiI9FKRdj7iVeeDiAgQKEJce+21+Hw+jDHU19dTU1NDVlaW09HkOHVaXjfGnGSMWWKMqTHGNBpjmo0xVUE89iRgo7V2s7W2EXgSmN3+AGvtQmttXcvFT4G+Xf0BJLxs2rSJ559/ntWrA+d0KjyIiIjDIup8xK2ZDyIibbxeLwkJCQC8+eabPPDAAxw8eNDhVHK8gul8+DNwOfAMMAH4JjA0iPsVADvaXS4BOlr0fz3wWhCPK2Gotra2bT3WVVddpZ0sREQkXETU+YhXyy5ERI5q+vTpDBw4kPj4eACam5s1zD7CBPUKZ63dCLittc3W2oeBWd0ZwhhzFYHCxu+OcftNxpilxpile/fu7c6nlm7w8ccfc88991BdXY0xhkGDBmlvXhERiTjhcD6irTZFRI4uOTmZkSNHAlBSUsIf//hHdu3a5XAq6YpgOh/qjDExwHJjzG+BMoIrWpQC/dpd7tty3SGMMTOBfwdOs9Y2HO2BrLX3AfcBTJgwQdNGwszQoUOpq6trq0KKiIiEkYg6H9FWmyIinfN6veTk5JCWluZ0FOmCYIoIV7ccdztQS+AF/OIg7rcEGGKMGdBSvLgcmN/+AGPMOOBvwIXW2j1dCR5J5i7azmV/+4TL/vYJq8uCGZcR/j799FPeeOMNALKyspg5cyYej3ZuFRGRsBNR5yMeLbsQEelUbm4uc+bMIS4uDmstr776Kvv27XM6lnSi03eL1tptLd/WG2P+CPRrWYbR2f2ajDG3AwsAN/CQtXaVMeYXwFJr7XwCbY1JwDMtbfrbrbUXHufP4qi5i7Yzb/kRH6QAsGhLOQCTB2RQnJ/C7LEFPRktJCoqKqioqMDv9+PSiZKIiISpSDsf8WjZhYhIl5SXl7Nq1Sry8vK0E0aY67T4YIx5F7iw5djPgD3GmI+std/v7L7W2leBVw+77j/bfT+zq4HD1bzlpawuq6I4P+WI2yYPyGD22AKunFzoQLLus27dOjIzM8nKyuLMM8/E5XJptoOIiIS9SDof0bILEZGuyczM5PbbbycuLg4IzIPIyMho2yVDwkcwffKp1toqY8wNwN+ttT8zxnwZ6mCRqDg/hadunuJ0jJBoaGhg/vz5DBkyhIsuukiTZUVEREJAAydFRLqu/Q4YzzzzDDk5OcyZM8fhVHK4YIoPHmNMPnApgUFM0oscOHCAtLQ0YmNj+eY3v0lmZqbTkURERKKWZj6IiBw/t9vNFVdc0fZBaXNzM9ZazaYLE8G8wv2CwDrJjdbaJcaYgcCG0MaScFBaWso999zDihUrgMBgF/3DFRERCR0tuxAR+Wry8vLIzs4G4O233+bBBx/E5/M5nEoguIGTzwDPtLu8meB2u5AIZa3FGEN+fj6nnHIKgwcPdjqSiIhIr6CBkyIi3ad///54PB68Xq/TUYTgBk7GAdcDI4G41uuttdeFMJc4ZNOmTbz77rtcddVVxMbGcvrppzsdSUREpNfwuLXsQkSkuwwbNoxhw4YBgV0xFi5cyDnnnKNhlA4J5hXuMSAPOBt4D+gLVIcylDjH6/XS3NzMwYMHnY4iIiLS66jzQUQkNHbu3MnWrVtpampyOkqvdczOB2OMx1rbBAy21l5ijJltrX3UGDMX+KDnIoavuYu2M295KcAxt9mMBLt27WLXrl2MHTuWwsJCbrzxRm2hKSIi4gDtdiEiEhqjRo1i2LBhbUswVqxYwYgRIzTTrgd11PmwuOXP1ukcFcaYUUAqkBPSVBFi3vJSVpdVAYFtNmePLXA40fH56KOPePfdd9uqgCo8iIiIOMOrgZMiIiHTWnjYuXMnzz//PJ9//rnDiXqXYMo89xlj0oGfAvOBJOA/QpoqghTnp/DUzVOcjtFlNTU1GGNITEzk3HPP1RY0IiIiYcCtrTZFREKuT58+fPOb36R///4A1NbWkpCQoA9hQ6yjd5s5xpjvt3z/rZY/72n5MzF0kSTUmpqaeOCBB8jPz+eyyy4jPj7e6UgiIiKCZj6IiPSUAQMGAODz+XjooYcYOHAg5513nsOpoltHxQc3gS6Ho70K2tDEkVDy+/24XC48Hg9nnnkmOTlaPSMiIhJOPFp2ISLSozweDxMmTCA/P9/pKFGvo+JDmbX2Fz2WRELqwIEDPPnkk5x99tkMHDiQkSNHOh1JREREDqPOBxGRnmWMYcqUfy6jX7RoEZWVlcycOROXlsJ1q46KD3r1iyJJSUkkJiZqHZOIiEgY8+hEV0TEUQcOHKCiokLvm0Kgo+LDjB5LISFRXV3Nxx9/zMyZM/F6vXzzm990OpKIiIh0QFttiog4a9asWfj9fowx1NbWsn79esaOHatiRDc4ZnndWlvek0Gk++3YsYOlS5dSVlbmdBQREREJgtetzgcREae1LrdYunQpr7zyChUVFc4GihLaWzHK+Hw+9uzZQ0FBAcXFxfTr14/k5GSnY4mIiEgQ1PkgIhI+pk2bxtChQ0lPTwcCSzJav5euU3k9yrz66qs8/vjj1NfXA6jwICIiEkE0cFJEJHwYY9p2wdi+fTt/+tOfWL16tcOpIpc6H47D3EXbmbe8lNVlVRTnpzgdB2stzc3NeDwepk2bxqhRo4iLi3M6loiIiHSRttoUEQlP+fn5TJ06lcGDBwOB92CaA9E1Kj4EqbXgALBoS2AcxuQBGcweW+BkLPx+P0888QTJyclceOGFpKenqxVIREQkQmm3CxGR8OT1ejn99NOBwHuwf/zjH4wePZqxY8c6GyyCqPgQpPadDq1FhysnFzodC5fLRUFBAYmJiU5HERERka9InQ8iIuGvsbERALfb7XCSyKLiQwfadzu0Fh6eunmKw6kCQyXfeustxo8fT25uLtOnT3c6koiIiHQDt1p4RUTCXlxcHFdddVXbsov169fj8XgYOHCgw8nCm3r7OtDa7QBQnJ/i+BKLVo2NjaxevZrNmzc7HUVERES6icuASwMnRUQiQmvhwVrLhx9+yMKFC7HWOpwqvKnzoRPh0u1grWXdunUMGzaMxMREbrvtNg2VFBERiSIetz4TEhGJNMYYrr76ag4ePIgxBp/PR2VlJVlZWU5HCzt6lYsQa9as4amnnmLDhg0AKjyIiIhEGW2zKSISmbxeLykpgV0QFy5cyH333UdNTY3DqcKPOh/CXH19PXFxcYwYMYLLLruMIUOGOB1JREREQkDFBxGRyDdlyhSysrJISkoCAjtjuLSTEaDOh7D2wQcfcO+997a18AwfPlx7yYqIiEQpLbsQEYl8ycnJjB8/HoA9e/bw5z//mdLSUodThQd1PoSxQYMGcfDgQbxer9NRREREJMTc6nwQEYkq1lpSUlJITU11OkpYUPEhjFhrWbRoEU1NTZx66qn06dOHPn36OB1LREREeoBXxQcRkaiSm5vLtddeCwTe6y1cuJATTjih1w6jVPEhjBhj2LlzJz6fD2utlliIiIj0Im63XvdFRKJVVVUVS5cuJTY2VsUHcc769evJzc0lNTWVCy+8ELfbrcKDiIhIL+PVQDIRkaiVmprKrbfeSmJiIgC7d+8mOTmZhIQEh5P1HL3KOayuro7nnnuODz74AACPx6PCg4iISC+kmQ8iItEtKSkJYwx+v59nnnmGp556yulIPUqdD0cxd9F25i0vZXVZFcX5KSF5jurq6rZK19VXX01eXl5InkdEREQig4oPIiK9g8vl4uKLL8ZaCwS247TW4na7HU4WWup8OIr2hYfZYwu6/fG3bdvG3Xffzfr16wHo27cvHo/qQCIiIr2ZV1ttioj0Gvn5+W2bC3z00Uc8+OCD1NfXO5wqtPSO9xiK81N46uYpIXnsgoICJk6cSEFB9xc2REREJDKp80FEpHfKycmhqqqK2NhYp6OElErsPWTjxo089thjNDU14fF4OPvss9uGjYiIiIh4tduFiEivNGzYMM477zyMMVRXVzNv3jzq6uqcjtXtVHzoIX6/n7q6uqj8SyQiIiJfnTofRESkpKSEtWvXRuX7Ri27CKHdu3dTXl7OiBEjGDp0KIMHD8albbRERETkKDw6RxAR6fVGjBjBgAEDiIuLA2DdunUMHjw4KoZRqvgQQm+99Rbl5eUMHToUt9utwoNIhPL5fJSUlET9ECAJb263m7S0NLKysvR6EqU8WnYhIt3I7/ezb98+KioqaG5udjqOHIfm5maqq6v58ssv24oRTouLi6Nv3754vd4u31fFh25WU1ODx+MhLi6OCy+8EJfLFRVVKpHerKSkhOTkZIqKijBGbw6k51lr8fl87N69m5KSEgoLC52OJCHg0bILEelGJSUlGGMoKirC6/XqHCZC1dfXExMTg8vlwu/3Y4xx7P+ltZb9+/dTUlLCgAEDunx/fXTSjRobG/nb3/7GggULAEhOTtZQSZEoUF9fT2Zmpl60xTHGGGJiYigoKKC2ttbpOBIiWnYhIt2ptraWgoICYmJidA4TweLi4nC5XG1v/CsrKx3LYowhMzPzuLuB1fnQDay1bSeGp59+Ov369XM6koh0M71oSzjQcovo5tayCxHpZnrdiC7x8fGOd9V/lXNi/W38isrLy7nvvvsoLS0FYPz48WRnZzucSkRERCKNll2IiMixGGNISkoiPj4egLq6OqqqqrDWOpwseOp8aDF30XbmLQ8UEFaXVVGcnxLU/VqrTz6fL5TxREREJMpp2YWIiATL5/NF3HtQvcq1mLe8lNVlVQAU56cwe2zBMY+tqalh4cKFWGuJj4/n+uuvp6ioqIeSioiISDRS54OIiAQrNTW1bSaZ3++nrq4u7LsgVHxopzg/hadunsJTN0/hysnHniS+fv16Pv74Y3bv3g1oLbiIhK9ly5bhdrs55ZRTjrht69atGGNYunTpEbdNnz6d22+//ZDrli9fzmWXXUZeXh6xsbEMHjyYa6+9lhUrVhzz+a+99tq2qcwej4fCwkJuueUWDhw4cMSxixYt4sILLyQjI4PY2FiGDx/OXXfdddShRseT5WgaGhr4zne+Q1ZWFomJiVx44YWUlJR0er+ysjKuueYasrOziYuLo7i4mPfee6/t9t27d3PttdfSp08fEhISmDVrFhs2bDjqY1lrOeecczDG8Oyzz3Ypv0QXbbUpIvJP4XQO4/V6GThwIP/yL//SNvi5NUPrV2pqKieddBIvvfTScf28zz33HMXFxcTGxlJcXMwLL7wQ1P3+8Ic/MHz4cNLT0+nTpw8//vGPD7l97ty5jB07loSEBPLy8rjqqqvYtWtX2+2PPPLIIT9H61cotphX8SFIrVucAYwbN47bbruNvLw8h1OJiHTsgQce4NZbb2XlypWsWbPmuB/n5ZdfZvLkydTU1PDYY4+xdu1annzySfLz8494kTvczJkzKSsrY+vWrTzwwAO89NJL3HrrrYccM3/+fKZOnUpmZiZvvfUW69ev52c/+xn33XcfZ511Fo2Njd2S5XDf/e53ee6553jiiSf44IMPqKqq4vzzz+9wP/SKigpOOeUUrLW88sorrFmzhj/96U/k5OQAgWLCRRddxIYNG3jxxRf5/PPP6d+/PzNnzjzqThX/+7//q4FgAqjzQUSkvXA6h9m8eTO/+tWv+Mtf/sK//Mu/HHLM66+/TllZGYsWLWLSpElcfPHFrFy5sksZP/nkEy677DLmzJnD8uXLmTNnDpdccgmLFi3q8H4/+MEP+Mtf/sJvf/tbli9fzquvvsq0adPazmM++ugjrr76aq655hpWrVrFiy++yOrVq5kzZ84hj5OQkEBZWdkhX3FxcV36GYJirY2orxNPPNGGwqV//dhe+tePj3n7008/bf/nf/7HNjY2huT5RSR8rV692ukIx6Wurs6mpqbaL7/80l533XX2Bz/4wSG3b9myxQJ2yZIlR9z3tNNOs7fddpu11tra2lqblZVlL7jggqM+z4EDB46Z4ZprrrHnnXfeIdd9//vftxkZGW2XWx9/9uzZR9z/s88+s8YY+9vf/vYrZzlcRUWF9Xq99vHHH2+7bvv27dYYY19//fVj3u/f/u3f7Mknn3zM29etW2cBu3z58rbrmpubbXZ2tr3//vsPOXbx4sW2b9++dvfu3RawzzzzTKe5O/r7CCy1YfBa3Ru+uvN85EBtg+3/o5ftXfNXddtjiohE6vmLteF7DnPDDTfYvLy8Y2aoqqqygP3jH/8Y1M/Z6tJLL7UzZ8485LoZM2bYyy+//Jj3Wbt2rfV4PEf8f25sbLSlpaW2trbW/u53v7OFhYWH3P7QQw/ZxMTEtssPP/zwIZeDcbznIho42YHW/0gul4tp06ZRXV2N1+t1OpaIOOyul1axemdVjz5ncZ8UfnbByC7d59lnn6V///6MHj2aq6++mksvvZRf//rXXf49tmDBAvbt23fMTwfS0tKCfqzNmzfz+uuvH5Kh9fH/9V//9Yjjx48fz4wZM5g7dy4//OEPu5SlqKiI6dOn88gjjxz12M8++wyfz8dZZ53Vdl2/fv0YMWIEH3/8MWefffZR7/fiiy8ya9YsLrvsMhYuXEifPn244YYbuO222zDG0NDQAHDIJwYul4vY2Fg+/PBDbrjhBgCqq6u58sorue+++9q6JqR382rZhYiEmM5hDtWVcxgIbDZwrCGPPp+P+++/H+CQnD//+c+56667CLwvP7pPPvmE73znO4dcd/bZZ/PnP//5mPeZN28eAwcO5PXXX+e8887D7/dz2mmn8Zvf/IakpCRiY2M55ZRT+MlPfsJLL73E+eefz/79+3nyySc599xzD3msgwcP0r9/f5qbmxk7diy//OUvGTduXKf/PbpKfZ7H0NzczNy5c3nnnXcAyM3NZfDgwQ6nEhEJ3oMPPsjVV18NwGmnnUZCQgLz5s3r8uO0zioYMWLEceV4/fXX27aGGjRoEKtXr+ZHP/pR2+3r16/v8PGLi4tZt25dl7MMGjSI/Pz8Y96+a9cu3G43WVlZh1yfm5t7yFrIw23evJm//OUvDBw4kAULFnDnnXfy4x//mHvuuQeA4cOHU1hYyE9+8hPKy8tpbGzkN7/5DSUlJZSVlbU9zre//W1mzZrFOeec0+nPIr2DW8suRESA8DmHaW/x4sXMnTuXGTNmHHL9tGnTSEpKIi4ujh/84AcMGDCASy+9tO32rKwshg0b1uFj79q1i9zc3EOuC+Z8ZNu2bTz55JM88sgjbUtKZs+eTVJSEm63m5NOOon777+fOXPmEBMTQ3Z2NtZaHn300bbHGTZsGA899BDz5s3jiSeeIC4ujlNOOeWYs6q+CnU+HIPb7SYjI4PU1FSno4hImOlq9d4JGzdu5MMPP2Tu3LlAYDDunDlzePDBB/nGN77RpcfqqFIfjGnTpnHfffdx8OBB7r//fjZt2sQdd9xxXI/VlSxvv/32cT1HZ/x+PxMmTODXv/41EJgDtGHDBu655x5uv/12vF4vzz//PNdffz2ZmZm43W5mzpzJOeec05b/scce44svvjjqoCzpvTxufSYkIqGlc5iuaf0ApampCZ/Px+zZs/nTn/50yDFz585l5MiRrF+/nu9973vcd999ZGRktN1+++23HzEAszv4/X4aGhp47LHHGDp0KBA4vxg2bBhLlixh8uTJrFq1ih//+Mf867/+KxdeeCFlZWX88Ic/5Ob/396dh0lVnXkc/752dSOKgNogBuxGFFEiiCwqqIgkOgZREoeIYhLCGBNNXLI485DREaJGYXRmIjg+Ro0bcRwxEkJcQOKGkYBCs4gQtBtcWEQxgjrdJHT3O3/c052iqG4KvFXVRf8+z1OPdc89deq9pwvvW+eee+p73+Phhx8GYPDgwQwePLix3SFDhtCvXz+mTZvG1KlTY41ZZ7kk5nXMnTu3cRX2r3zlKwwaNCjPUYmI7Ln77ruPuro6ysrKSCQSJBIJJk+ezLPPPst7770HQPv27QHYtm3bLq/funVr4+Brwwltbxd7OuCAAzj66KPp06cPU6dOpbq6mptuuqlxf0P7q1atSvv6VatWNdb5vLEk69KlC3V1dWzZsmWn8s2bNze7oPDhhx9O7969dyo77rjjePfddxu3BwwYwLJly9i6dSubNm1izpw5fPTRR/To0QOIBkZWrVpFu3btGv8+AGPGjOG000773McmhUkLToqItKwcZujQoSxbtow1a9awfft2Zs6cucutkt26daNnz56ce+653HvvvVx44YW75Ba706VLl8YfN2iQST6SSCQajxGgZ8+eFBUVNeYkU6ZM4ZRTTuG6666jb9++DBs2jF/84hdMnz69yV/3KioqYuDAgVmZ+ZDVwQczO8fM1phZpZntcqONmbUxs8fC/kVm1j2b8exOov5vLFu2jMrKynyGISLyudTW1vLQQw9x6623smzZssbH8uXL6du3Lw888AAAhxxyCKWlpSxZsmSn13/yySdUVlY2ThE8++yzKS0tZfLkyWnfb+vWrXsU38SJE5kyZQobN25sbP/QQw/ltttu26VuRUUFzz33XOOqzHHGMmDAAIqLi5k3b15j2fr161m9ejVDhgxp8nWnnnpq420gDd58803Ky8t3qduhQwc6derEW2+9xeLFixk1ahQAP//5z1mxYsVOfx+A22+/vfFKhMSnUPIR3XYhIq1dS8thGi6glJeXZ7TexBlnnEHv3r258cYbMzjavxs8ePBO+QjAvHnzdpuP1NbWUlVV1Vi2du1a6urqGnOS6upqioqKMIvOL59++ik1NTVANHMiHXdnxYoVzd66uteaWony8z6AIqAK6AGUAMuB3il1vg/cHZ5fBDy2u3bj/rWL+vp6r6ysbPy1i88++yzW9kWk8BXaatGzZs3yRCLhW7Zs2WXf5MmTvXv37l5fX+/u7rfccosffPDBPn36dK+srPRFixb5yJEjvXv37l5dXb1Tm8XFxT5ixAh/9tlnfd26db5kyRK//vrrfcSIEU3Gkm6laHf3/v37+xVXXNG4PXPmTE8kEj5+/HivqKjwd955xx999FHv2rWrn3766f7Xv/51j2MZPny4T5gwodm+uvzyy71r164+b948r6io8GHDhvkJJ5zgtbW1jXV69erl06ZNa9x+9dVXPZFI+M033+xvvfWWz5gxw9u3b+933nlnY50ZM2b4888/71VVVT5r1iwvLy/3Cy64oNlY0K9dtNp8pOHXLn75UmVsbYqIFFr+4l4YOUyDpn5xY/bs2d6mTRt/99133d192rRp3qtXr2aP+5VXXvGioiK/9dZbffXq1X7LLbd4IpHwhQsXNtaZMGGCDx8+vHG7rq7O+/fv70OHDvWKigqvqKjwoUOH+sknn+x1dXXuHv2SRSKR8Lvuusurqqp8/vz5PmDAAO/fv7/X19f7jh07fNKkST5nzhyvqqrypUuX+vjx4z2RSPiiRYuajHdvc5FsnuwHA3OTtn8K/DSlzlxgcHieALYA1ly7cQ8+THrgSZ80aZIPmzij2Z/aFJHWq9BO3uedd56fddZZafdVVVU54HPnznV399raWp86dar36dPHDzzwQO/atauPGTPG161bt8trFy9e7KNHj/bOnTt7SUmJ9+jRw8eNG+crV65sMpamTtyPPPKIl5SU+Ntvv91YtmDBAj/33HO9Y8eOXlJS4sccc4xPnDjRa2pq9iqW8vJyHzduXJOxubtv377dr7zySj/kkEO8bdu2PnLkyMZkoQHgEydO3KnsySef9L59+3qbNm28Z8+efscddzQmQ+7ud9xxh3fr1s2Li4u9rKzMr7/++p0GUNLR4EPrzUcaBh/ue3ltbG2KiBRa/uJeGDlMg6YGH+rr671Xr15+2WWXubv7xIkTPbrm37zHH3/ce/Xq5cXFxX7sscf6E088sUs85eXlO5Vt3LjRR48e7e3atfNOnTr52LFj/f3339+pztSpU713797etm1b79Kli48dO9bfe+89/+STT3zTpk1+zTXXeFlZmZeUlHinTp387LPP9gULmv9evLe5iEX742dmo4Fz3P07YfubwMnufmVSnZWhzvqwXRXqbElp67vAdwHKysoGvPPOO7HF+bPZr7N+7Vt8sn8XRp3YjbEnl8XWtojsG1avXh3LKskicWju82hmS9x9YI5DatEKIR9xd2595s+MGXQER3VqF0ubIiLKX6Q5dXV11NbW0qZNmz1+7d7mIgXxaxfufg9wD8DAgQNjHS2ZeH4foE+cTYqIiMg+KFv5iJnxryP0BUFERHKnqKiIoqKinL5nNhec3AAckbTdLZSlrWNmCaAD8FEWYxIREZHWRfmIiIhIC5DNwYfXgJ5mdqSZlRAt4DQ7pc5sYFx4Php43rN1H4iIiIi0RspHREREWoCs3Xbh7rVmdiXRIk5FwP3u/oaZ3Ui0CMVs4FfAdDOrBP5ClBCIiIiIxEL5iIiISMuQ1TUf3P1p4OmUshuSnm8Hvp7NGERE4uDujb+RLJIvuhi/d5SPiEhrpfxF4vZ5cpFs3nYhIrJPKCoqYseOHfkOQ4SamhqKi4vzHYaIiBSA4uJiampq8h2G7GN27NhBIrF3cxg0+CAishsdO3Zk8+bN1NfX5zsUaaXcnerqajZs2EDnzp3zHY6IiBSAzp07s2HDBqqrqzVzTmJRX1/P5s2b6dChw169viB+alNEJJ9KS0tZv349a9asyXco0ooVFxdz2GGH0b59+3yHIiIiBaDhfLFx40bN4JTYHHjggZSWlu7VazX4ICKyG/vttx9lZWX5DkNERERkj7Rv316D1tJi6LYLEREREREREckqDT6IiIiIiIiISFZp8EFEREREREREskqDDyIiIiIiIiKSVRp8EBEREREREZGsskL7zVcz+xB4J+ZmS4EtMbfZWqkv46O+jI/6Mj7qy/hkoy/L3b1TzG1KGspHWjz1ZXzUl/FRX8ZHfRmfuPuyyVyk4AYfssHMFrv7wHzHsS9QX8ZHfRkf9WV81JfxUV9KKn0m4qO+jI/6Mj7qy/ioL+OTy77UbRciIiIiIiIiklUafBARERERERGRrNLgQ+SefAewD1Ffxkd9GR/1ZXzUl/FRX0oqfSbio76Mj/oyPurL+Kgv45OzvtSaDyIiIiIiIiKSVZr5ICIiIiIiIiJZ1aoGH8zsHDNbY2aVZjYhzf42ZvZY2L/IzLrnIcyCkEFf/tjMVpnZCjN7zszK8xFnIdhdXybV+0czczPTyr5NyKQvzezC8Nl8w8z+J9cxFooM/o2XmdkLZrY0/DsfkY84Wzozu9/MPjCzlU3sNzObGvp5hZn1z3WMklvKReKjXCQ+ykXio1wkPspF4tNi8hF3bxUPoAioAnoAJcByoHdKne8Dd4fnFwGP5TvulvjIsC/PBA4Iz69QX+59X4Z6BwHzgYXAwHzH3RIfGX4uewJLgYPDdud8x90SHxn25T3AFeF5b+DtfMfdEh/AUKA/sLKJ/SOAZwADTgEW5TtmPbL6eVAuktu+VC4SU1+GespFYuhL5SKx9qVykcz7s0XkI61p5sNJQKW7r3X3vwH/C4xKqTMKeCg8/w3wJTOzHMZYKHbbl+7+grtXh82FQLccx1goMvlcAtwETAG25zK4ApNJX14G/Le7fwzg7h/kOMZCkUlfOtA+PO8AbMxhfAXD3ecDf2mmyijgYY8sBDqa2eG5iU7yQLlIfJSLxEe5SHyUi8RHuUiMWko+0poGH7oC7yVtrw9laeu4ey2wDTg0J9EVlkz6MtmlRCNpsqvd9mWY9nSEuz+Vy8AKUCafy2OAY8zsFTNbaGbn5Cy6wpJJX04CvmFm64GngatyE9o+Z0//fyqFTblIfJSLxEe5SHyUi8RHuUhu5SQfScTdoEgyM/sGMBA4I9+xFCIz2w/4T+DbeQ5lX5Egmu44jOgK2Hwz6+PuW/MZVIG6GHjQ3f/DzAYD083seHevz3dgIiLJlIt8PspFYqdcJD7KRQpMa5r5sAE4Imm7WyhLW8fMEkTTdz7KSXSFJZO+xMy+DFwHnO/uf81RbIVmd315EHA88KKZvU10D9ZsLfSUViafy/XAbHff4e7rgDeJEgDZWSZ9eSkwA8Dd/wTsD5TmJLp9S0b/P5V9hnKR+CgXiY9ykfgoF4mPcpHcykk+0poGH14DeprZkWZWQrSI0+yUOrOBceH5aOB5DytwyE5225dmdiLwS6KTve5la1qzfenu29y91N27u3t3ontWz3f3xfkJt0XL5N/4LKIrDZhZKdHUx7U5jLFQZNKX7wJfAjCz44hO+B/mNMp9w2zgW2GV6VOAbe6+Kd9BSdYoF4mPcpH4KBeJj3KR+CgXya2c5COt5rYLd681syuBuUSrp97v7m+Y2Y3AYnefDfyKaLpOJdGCHBflL+KWK8O+vA1oBzwe1sl6193Pz1vQLVSGfSkZyLAv5wJnm9kqoA74Z3fXFcUUGfblT4B7zexHRAs+fVtfkHZlZo8SJZml4Z7UiUAxgLvfTXSP6gigEqgGxucnUskF5SLxUS4SH+Ui8VEuEh/lIvFqKfmI6e8jIiIiIiIiItnUmm67EBEREREREZE80OCDiIiIiIiIiGSVBh9EREREREREJKs0+CAiIiIiIiIiWaXBBxERERERERHJKg0+iDTBzA41s2Xh8b6ZbUjaLsnSe04ys2v3oH4bM/tDiGlMM/UeNLPR8UQZPzO70cy+HJ7/0MwOSNr3tJl1zOJ7/8bMeoTnb5vZ60l/5yFm1t3MasL2KjO728z2S1P+sJkVh3b6mNmD2YpZRERkX2RmdeG8utLMfh/3+T+c50vD88/ibFtEdk+DDyJNcPeP3L2fu/cD7gb+q2Hb3f+W5/AanAgQYnos38HsLXe/wd3/EDZ/CByQtG+Eu2/Nxvua2ReBIndfm1R8ZtLfeUEoqwqfg75Ab+CrKeV9gG7AhSHm14FuZlaWjbhFRET2UTXh/Hs88BfgB/kOSETio8EHkT1gZpeZ2WtmttzMnmi4Qm9mXw+j9MvNbH4o625mL5tZRXgMaaLN68zsTTP7I9ArqfwoM5tjZktCO8emvK4z8GtgULhKcJSZ3RDiW2lm95iZpXm/yeFK/Qozuz0p1udD2XMNX5rTHVdKW8PMbL6ZPWVmaxpmBYR9F4dZBCvNbEooKwqzMFaGfT8K5Q+a2Wgzuxr4AvCCmb0Q9r1tZqUh7h8kvfckM7vWIrcltTkm7D88xNZwBeX0NN1/CfC7tH/sNNy9FlgAHJ1SXge8CnRNKv49cFGmbYuIiMhO/kQ4rzaVE5nZYWb225CnLG/ItcxsVqj7hpl9N4/HICJJNPggsmdmuvsgdz8BWA1cGspvAP4hlJ8fyj4AznL3/sAYYGpqY2Y2gOgLaj9gBDAoafc9wFXuPgC4Frgr+bXu/gHwHeDlcJWgCrgzxHc80BYYmfJ+hwJfA77o7n2Bm8OuacBDoeyRpFjTHVeqk4CriGYEHAVcYGZfAKYAw8OxDTKzr4bnXd39eHfvAzyQckxTgY1Esw/OTHmfxwgzC4ILQ9kFod0TgC8Dt5nZ4cBYYG6YmXACsCxN7KcCS1LKXggDFotSK4fBpi8Br6eU7w+cDMxJKl4MpBvwEBERkWaYWRHR+XZ2KGoqJ5oKvBTylP7AG6H8n0LdgcDVIf8RkTxL5DsAkQJzvJndDHQE2gFzQ/krwINmNgOYGcqKgTvNrB9QBxyTpr3Tgd+6ezWAmc0O/20HDAEeT5q80CaD+M40s38hum3hEKKT8O+T9m8DtgO/MrMngSdD+WCiL/EA04F/b+a4Ur3acNuCmT0KnAbsAF509w9D+SPAUOAmoIeZTQOeAp7N4JgAcPelZtY5DGx0Aj529/fM7MfAo2H2wWYze4loEOc14H6L1mGY5e7L0jR7OPBhStmZ7r4lpewoM1sGOPA7d3/GzLonlR8JPOXuK5Je8wHRLA4RERHJTNtwXu1KdJFn3m5youHAt6BxFuK2UH61mX0tPD8C6Al8lPXoRaRZmvkgsmceBK4MV+1/BuwP4O6XA9cTneCWhBH2HwGbia66DwT2ZJHK/YCtSWsP9HP345p7Qbj6fhcwOsR3b0N8DcJtAycBvyGaFTEntZ2U+umOa5dqu9lObu9jov54EbgcuK+590/jcWA00UySZte4cPf5RAMeG4gGUL6VploNKX3UhKrwNzjR3SellhPN+BhgZsmzQ/YP7YuIiEhmasJ5tRwwojUf9ignMrNhRDMhB4cZEUvJ7FwvIlmmwQeRPXMQsClcTb+kodDMjnL3Re5+A9GV9COADsAmd68HvgkUpWlvPvBVM2trZgcB5wG4+yfAOjP7emjfzOyE3cTWcGLdEq4S7PLrFqG8g7s/TTQ40tDmAv6+PsElwMvNHFeqk8zsyLDWwxjgj0TrH5wR1mooAi4GXrJohen93P0JokGN/mna+5Son9N5LMQ5mmggghDrmLCeRCeiAYdXzawc2Ozu9xINcqR7r9WkrN+wN8JMiQnAT5OKjwFWft62RUREWpswI/Rq4CdANU3nRM8BV4TyIjPrQJR/fezu1WFtiFNyfgAikpYGH0T2zL8Bi4huR/hzUvltDYsrEn2RX040C2GcmS0HjgX+L7Uxd68g+kK9HHiG6FaBBpcAl4bXvwGMai6w8IsQ9xJ94Z2b0laDg4AnzWwF0SDBj0P5VcD4UP5N4JpmjivVa8CdRF/k1xHdRrKJ6Mv4C+E1S9z9d0TTKF8MUyp/zc5f1hvcA8xpWHAy5RjfCMewIbwHwG+BFeF9ngf+xd3fB4YBy81sKdGgyB1p3uupUC8Os4ADkha2PDO0LyIiInvI3ZcSnd8vpumc6BqiW05fJ1rDqTfRrM6Ema0GJgMLcx27iKRn7k3OkBYRaVaY2nitu4/cTdUWyczaEg2QnBruFY2r3TbAS8Bp4VYXEREREZFWTTMfRKTVcvcaYCI7/0RmHMqACRp4EBERERGJaOaDiIiIiIiIiGSVZj6IiIiIiIiISFZp8EFEREREREREskqDDyIiIiIiIiKSVRp8EBEREREREZGs0uCDiIiIiIiIiGSVBh9EREREREREJKv+H/E4dGr0IItLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precisions, recalls, thresholds = precision_recall_curve(targ_train, y_scores)\n",
    "fpr, tpr, thresholds = roc_curve(targ_train, y_scores)\n",
    "\n",
    "auc_score_PR = auc(recalls, precisions)\n",
    "auc_score_ROC = auc(fpr,tpr)\n",
    "\n",
    "fig = plt.figure(figsize=(18, 6))\n",
    "ax = fig.add_subplot(122)\n",
    "ax.plot(recalls,precisions, label = \"AUC PR: {:.3f}\".format(auc_score_PR))\n",
    "ax.plot([0, 1], [1, 0], color='0.5', ls=':')\n",
    "ax.set_xlabel('Recall')\n",
    "ax.set_ylabel('Precision')\n",
    "ax.set_title('Curva PR')\n",
    "ax.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax2 = fig.add_subplot(121)\n",
    "ax2.plot(fpr, tpr,label = \"AUC ROC: {:.3f}\".format(auc_score_ROC))\n",
    "ax2.plot([0, 1], [0, 1], color='0.5', ls=':')\n",
    "ax2.set_xlabel('Tasa de falsos positivos (FPR)')\n",
    "ax2.set_ylabel('Tasa de verdaderos positivos (TPR) / Recall')\n",
    "ax2.set_title('Curva ROC')\n",
    "ax2.legend(loc='lower right', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score: 0.731 +/- 0.104\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.75      0.75       163\n",
      "           1       0.75      0.73      0.74       163\n",
      "\n",
      "    accuracy                           0.74       326\n",
      "   macro avg       0.74      0.74      0.74       326\n",
      "weighted avg       0.74      0.74      0.74       326\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "percep2 = Perceptron(max_iter=40000,penalty=\"l2\",random_state=420,alpha=0.0012689610031679235,eta0=0.041753189365604)\n",
    "model_setup(percep2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 2.8072162039411756e-06, 'eta0': 3.039195382313198}\n"
     ]
    }
   ],
   "source": [
    "hyperparam = {\"eta0\":np.logspace(-2,1,30),\"alpha\":np.logspace(-7,0,30)}\n",
    "per_model = Perceptron(max_iter=40000,penalty=\"l2\",random_state=420,alpha=5e-5,eta0=0.05)\n",
    "per_grilla = GridSearchCV(per_model,hyperparam,cv=10,scoring=score_metric,n_jobs=-1)\n",
    "per_grilla.fit(feat_train,targ_train)\n",
    "print(per_grilla.best_params_)\n",
    "svm_model = per_grilla.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_test_curve(percep2,\"alpha\",np.logspace(-6,2,40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_eb(percep2,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_scores = cross_val_predict(percep2, feat_train, targ_train, cv=10, method=\"decision_function\")\n",
    "\n",
    "A = plt.hist(y_scores,100)\n",
    "plt.xlabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f63005ac340>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precisions, recalls, thresholds = precision_recall_curve(targ_train, y_scores)\n",
    "fpr, tpr, thresholds = roc_curve(targ_train, y_scores)\n",
    "\n",
    "auc_score_PR = auc(recalls, precisions)\n",
    "auc_score_ROC = auc(fpr,tpr)\n",
    "\n",
    "fig = plt.figure(figsize=(18, 6))\n",
    "ax = fig.add_subplot(122)\n",
    "ax.plot(recalls,precisions, label = \"AUC PR: {:.3f}\".format(auc_score_PR))\n",
    "ax.plot([0, 1], [1, 0], color='0.5', ls=':')\n",
    "ax.set_xlabel('Recall')\n",
    "ax.set_ylabel('Precision')\n",
    "ax.set_title('Curva PR')\n",
    "ax.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax2 = fig.add_subplot(121)\n",
    "ax2.plot(fpr, tpr,label = \"AUC ROC: {:.3f}\".format(auc_score_ROC))\n",
    "ax2.plot([0, 1], [0, 1], color='0.5', ls=':')\n",
    "ax2.set_xlabel('Tasa de falsos positivos (FPR)')\n",
    "ax2.set_ylabel('Tasa de verdaderos positivos (TPR) / Recall')\n",
    "ax2.set_title('Curva ROC')\n",
    "ax2.legend(loc='lower right', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score: 0.811 +/- 0.094\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.80      0.80       163\n",
      "           1       0.80      0.81      0.80       163\n",
      "\n",
      "    accuracy                           0.80       326\n",
      "   macro avg       0.80      0.80      0.80       326\n",
      "weighted avg       0.80      0.80      0.80       326\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_model = LogisticRegression(dual=False, max_iter=40000,C=0.7906043210907697)\n",
    "model_setup(lr_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 0.7906043210907697}\n"
     ]
    }
   ],
   "source": [
    "hyperparam = {\"C\":np.logspace(-1,3,50)}\n",
    "lr_model = LogisticRegression(dual=False, max_iter=40000,C=2)\n",
    "lr_grilla = GridSearchCV(lr_model,hyperparam,cv=10,scoring=score_metric,n_jobs=-1)\n",
    "lr_grilla.fit(feat_train,targ_train)\n",
    "print(lr_grilla.best_params_)\n",
    "lr_model = lr_grilla.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_eb(lr_model,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_test_curve(lr_model,\"C\",np.logspace(-3,3,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aoc_curves(lr_model,feat_train,targ_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BEST PARAMS => C = 0.85, Gamma = 0.61 -> Recall = 0.872+/-0.075"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score: 0.916 +/- 0.070\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.80      0.85       163\n",
      "           1       0.82      0.91      0.87       163\n",
      "\n",
      "    accuracy                           0.86       326\n",
      "   macro avg       0.86      0.86      0.86       326\n",
      "weighted avg       0.86      0.86      0.86       326\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_model = SVC(probability=True,gamma=0.6210169418915616,C=0.6210169418915616)\n",
    "model_setup(svm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_eb(svm_model,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 0.6210169418915616, 'gamma': 0.6210169418915616}\n"
     ]
    }
   ],
   "source": [
    "hyperparam = {\"C\":np.logspace(-2,0,30),\"gamma\":np.logspace(-2,2,30)}\n",
    "svm_model = SVC(break_ties=True,probability=True)\n",
    "svm_grilla = GridSearchCV(svm_model,hyperparam,cv=10,scoring=score_metric,n_jobs=-1)\n",
    "svm_grilla.fit(feat_train,targ_train)\n",
    "print(svm_grilla.best_params_)\n",
    "svm_model = svm_grilla.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_test_curve(svm_model,\"gamma\",np.logspace(-2,0.5,40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aoc_curves(svm_model,feat_train,targ_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score: 0.917 +/- 0.092\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.83      0.87       163\n",
      "           1       0.84      0.93      0.88       163\n",
      "\n",
      "    accuracy                           0.88       326\n",
      "   macro avg       0.88      0.88      0.88       326\n",
      "weighted avg       0.88      0.88      0.88       326\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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1aZD8bWbVUIQWXFegD+sntjYN8uuZWVU0SAboKMEtiYhzN1skZtYYKrurVlV1lOA2feNDMyukInRRJ262KMyssTR6gouIps0ZiJk1jiK9qmVm9qGCjMGZmX2EaJwBeic4M8vPLTgzK6pGmUXNsvGzmdn6IuPRCUkDJN0k6TlJ8yTtJWmQpHskvZD+ObDcMJ3gzCyfdMHLLEcGFwJ3RcQuwFhgHnAWcF9EjALuS8/L4gRnZvlVoAUnqT+wD3AFQESsjYhVwGHAtPSyacDh5YbpBGdmuSmyHcBgSbNKjikl1YwE3gB+K+kJSZdL6g0MiYgl6TVLgSHlxulJBjPLL/skw/KIGNfOd91I1pw8JSIekXQhG3RHIyKk8qc03IIzs9xytOA6sghYFBGPpOc3kSS81yUNBUj/XFZunE5wZpZPkCx4meXoqJqIpcCrkkanRROBucDtwOS0bDJwW7mhuotqZrlUeNOZU4BrJG0FLAC+SdLwulHS8cArwBHlVu4EZ2b5VSjBRcSTwMbG6CqympETnJnlpmiMVxmc4MwsH68mYmZF1ijvojrBmVluXvDSzIrLLTgzK6SC7WxvZrY+JzgzK6IKP+hbVU5wZpabWhsjwznBmVk+fg5uy3XGLxayx/5vs2p5N07YL3mH+Fv/9zX2POAt1q0VS17ZivNP/zir3+pa40i3XOefPoJH7u3HgMHNTP3z8wD87ucf485rB9F/UAsA3zz7NcZPfJu3mrrykyk78rcne3HAEU2c/K+Laxl63WiUx0SqtpqIpCslLZM0p1r3qEd33zCIHx4zcr2y2TP6MmXf0Xx7/9EsXrA1R57yeo2iM4ADv9rET69Z8JHyL/3jG1x67/Nceu/zjJ/4NgBb9Qgmf38p//ij1zZ3mPWtQnsyVFs1l0u6CphUxfrr0pxH+vD2yvUbxrP/0pfWlmQnyXmP92bw0HW1CM1Sn95zNX0HtmS6tkevVnbbYzVbbV0H/1rrSIXWg6u6qiW4iJgBNFWr/kZ10FFNPHZ/v1qHYRvxx99uy4kTR3P+6SN4e5WHENoVQES2o8ZqvuClpClt67Wv4/1ah1NVR536Oi3NcP8tA2odim3g0MnL+e3MuVxyz/MMGrKOqf+yfa1DqmsV3FWrqmqe4CJiakSMi4hx3dm61uFUzQFHNDF+/7f495N3IHmSyOrJwG2b6doVunSBg49p4vkne9U6pLrV9hzcFt1FtQ+Nm/AW/3DSMn78jZG8v8Z/5fVoxesfjpv+9c7+7Dj6vRpGU+eydk/roIvqx0Qq7KxLXuEze71D/0HN/H7WXH53/hCOPHkZ3bcO/u2GFwF47vHeXHTW8BpHuuX6t2/vwNMz+/BmUzeO+R9j+PqZS3l6Zh9efLYnEgwZvpZTf/bqB9cfO34Mq9/pQvNaMXN6f/71uhfZ4ZPFHk7pTD20zrKoWoKTdB0wgWRfxEXAORFxRbXuVy/OO2mHj5RNv26bGkRi7Tn70lc+Ujbp6Pbnw65+dG41w2lMW3qCi4ijqlW3mdXWFt+CM7OCCqClMTKcE5yZ5dYoLThP6ZlZfhWcRZXUVdITku5Iz0dKekTSfEk3pHumlsUJzsxyq/BzcKcB80rO/x24ICJ2BlYCx5cbpxOcmeWT9UX7DAlO0nDgC8Dl6bmA/YCb0kumAYeXG6rH4MwsFwHKPskwWNKskvOpETG15PyXwA+Avun5NsCqiGhOzxcBw8qN1QnOzHLLsbP98ogYt9E6pEOBZRHxuKQJFQptPU5wZpZP5dZ62xv4oqRDgB5AP+BCYICkbmkrbjhQ9iqjHoMzs5wq8y5qRJwdEcMjYkfgSOD+iDgG+DPwlfSyycBt5UbqBGdmuVV5NZF/As6QNJ9kTK7sVzzdRTWz/Cq8UkhEPAA8kH5eAIyvRL1OcGaWT+SaRa0pJzgzy68x8psTnJnll+MxkZpygjOz/JzgzKyQAqiDDWWycIIzs1xEuItqZgXW2hhNOCc4M8vHXVQzKzJ3Uc2suJzgzKyY6mNT5yyc4MwsH++qZWZF5jE4MysuJzgzK6QAWp3gzKyQPMlgZkXmBGdmhRRAS2O8yuAEZ2Y5BYQTnJkVlbuoZlZInkU1s0JrkBac90U1s/wqsPGzpBGS/ixprqRnJZ2Wlg+SdI+kF9I/B5YbphOcmeUTAS0t2Y6ONQNnRsQYYE/gO5LGAGcB90XEKOC+9LwsTnBmll8FWnARsSQiZqef3wbmAcOAw4Bp6WXTgMPLDdNjcGaWX/YxuMGSZpWcT42IqRteJGlH4HPAI8CQiFiSfrUUGFJumE5wZpZT5JlFXR4R4zq6QFIf4GbguxHxlqQP7xQRksqe0XCCM7N8AqJCD/pK6k6S3K6JiFvS4tclDY2IJZKGAsvKrd9jcGaWX0trtqMDSppqVwDzIuIXJV/dDkxOP08Gbis3TLfgzCyfiEptG7g38HXgGUlPpmX/DJwH3CjpeOAV4Ihyb+AEZ2b5VeBB34h4CFA7X0/c5BvgBGdmZQhv/GxmxeQFL82sqPyyvZkVVQDR+WtYdcEJzszyCS94aWYFFu6imllhNUgLTlFHsyGS3iB5sK9oBgPLax2E5VLU/2Y7RMS2m1KBpLtI/n6yWB4RkzblfpuirhJcUUma1dkLx1Zf/N+sGPwuqpkVlhOcmRWWE9zm8ZEF/qzu+b9ZAXgMzswKyy04MyssJzgzKywnuCqSNEnS85LmSyp76zPbfCRdKWmZpDm1jsU2nRNclUjqClwMHAyMAY5K93y0+nYVULMHU62ynOCqZzwwPyIWRMRa4HqS/R6tjkXEDKCp1nFYZTjBVc8w4NWS80VpmZltJk5wZlZYTnDVsxgYUXI+PC0zs83ECa56HgNGSRopaSvgSJL9Hs1sM3GCq5KIaAZOBqYD84AbI+LZ2kZlnZF0HTATGC1pUbo3pzUov6plZoXlFpyZFZYTnJkVlhOcmRWWE5yZFZYTnJkVlhNcA5HUIulJSXMk/UFSr02o6ypJX0k/X97RQgCSJkj6fBn3eFnSR3Zfaq98g2veyXmvH0v6Xt4Yrdic4BrLmoj4bETsBqwFTiz9UlJZ+9xGxLciYm4Hl0wAcic4s1pzgmtcDwI7p62rByXdDsyV1FXSf0h6TNLTkk4AUOLX6fp09wLbtVUk6QFJ49LPkyTNlvSUpPsk7UiSSE9PW4//S9K2km5O7/GYpL3Tn91G0t2SnpV0OaDOfglJ/0/S4+nPTNnguwvS8vskbZuW7STprvRnHpS0S0X+Nq2QvLN9A0pbagcDd6VFuwO7RcRLaZJ4MyL+TtLWwH9Luhv4HDCaZG26IcBc4MoN6t0WuAzYJ61rUEQ0SfoN8E5E/Dy97lrggoh4SNLHSd7W+BRwDvBQRJwr6QtAlrcAjkvv0RN4TNLNEbEC6A3MiojTJf0orftkks1gToyIFyTtAVwC7FfGX6NtAZzgGktPSU+mnx8EriDpOj4aES+l5QcCn2kbXwP6A6OAfYDrIqIFeE3S/Rupf09gRltdEdHeumj7A2OkDxpo/ST1Se/x5fRn/0vSygy/06mSvpR+HpHGugJoBW5Iy38P3JLe4/PAH0ruvXWGe9gWygmusayJiM+WFqT/0FeXFgGnRMT0Da47pIJxdAH2jIj3NhJLZpImkCTLvSLiXUkPAD3auTzS+67a8O/ArD0egyue6cC3JXUHkPRJSb2BGcBX0zG6ocC+G/nZh4F9JI1Mf3ZQWv420LfkuruBU9pOJH02/TgDODotOxgY2Ems/YGVaXLbhaQF2aYL0NYKPZqk6/sW8JKkf0jvIUljO7mHbcGc4IrncpLxtdnpxin/SdJSvxV4If3uapIVM9YTEW8AU0i6g0/xYRfxj8CX2iYZgFOBcekkxlw+nM39F5IE+SxJV3VhJ7HeBXSTNA84jyTBtlkNjE9/h/2Ac9PyY4Dj0/iexcvAWwe8moiZFZZbcGZWWE5wZlZYTnBmVlhOcGZWWE5wZlZYTnBmVlhOcGZWWP8frzotXEc+dwIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators=100)\n",
    "model_setup(rf_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_eb(rf_model,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': None, 'min_samples_split': 2, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "hyperparam = {\"n_estimators\":[20,50,100,200,400],\"max_depth\":[5,6,7,8,9,None],\"min_samples_split\":[2,20,30,40]}\n",
    "rf_model = RandomForestClassifier(min_impurity_decrease=2e-4)\n",
    "rf_grilla = GridSearchCV(rf_model,hyperparam,cv=10,scoring=score_metric,n_jobs=-1)\n",
    "rf_grilla.fit(feat_train,targ_train)\n",
    "rf_model = rf_grilla.best_estimator_\n",
    "print(rf_grilla.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_test_curve(rf_model,\"n_estimators\",[20,50,100,200,400])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aoc_curves(rf_model,feat_train,targ_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ADA BOOSTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score: 0.837 +/- 0.101\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.79      0.81       163\n",
      "           1       0.80      0.85      0.82       163\n",
      "\n",
      "    accuracy                           0.82       326\n",
      "   macro avg       0.82      0.82      0.82       326\n",
      "weighted avg       0.82      0.82      0.82       326\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada_model = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(),learning_rate=2e-2)\n",
    "model_setup(ada_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([163.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 163.]),\n",
       " array([2.22044605e-16, 1.00000000e-01, 2.00000000e-01, 3.00000000e-01,\n",
       "        4.00000000e-01, 5.00000000e-01, 6.00000000e-01, 7.00000000e-01,\n",
       "        8.00000000e-01, 9.00000000e-01, 1.00000000e+00]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ada_model.predict_proba(feat_train)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_test_curve(ada_model,\"learning_rate\",np.logspace(-5,-0,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_eb(ada_model,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aoc_curves(ada_model,feat_train,targ_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRADIENT BOOSTING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BEST PARAMS => n_estimators = 500, learning_rate = 0.05,objective=\"binary:hinge\" -> Recall = 0.843+/-0.158"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cambia levemente la metodologia, usa early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_train_2, feat_val, targ_train_2, targ_val = train_test_split(feat_train,targ_train,random_state=420)\n",
    "# NECESARIO CREAR DATOS DE VALIDACION PARA USAR XGBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[1]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[2]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[3]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[4]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[5]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[6]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[7]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[8]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[9]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[10]\tvalidation_0-error:0.20902\tvalidation_1-error:0.19512\n",
      "[11]\tvalidation_0-error:0.14344\tvalidation_1-error:0.18293\n",
      "[12]\tvalidation_0-error:0.11066\tvalidation_1-error:0.17073\n",
      "[13]\tvalidation_0-error:0.11066\tvalidation_1-error:0.18293\n",
      "[14]\tvalidation_0-error:0.07787\tvalidation_1-error:0.19512\n",
      "[15]\tvalidation_0-error:0.06557\tvalidation_1-error:0.20732\n",
      "[16]\tvalidation_0-error:0.06148\tvalidation_1-error:0.19512\n",
      "[17]\tvalidation_0-error:0.05738\tvalidation_1-error:0.19512\n",
      "[18]\tvalidation_0-error:0.04918\tvalidation_1-error:0.20732\n",
      "[19]\tvalidation_0-error:0.04918\tvalidation_1-error:0.20732\n",
      "[20]\tvalidation_0-error:0.04918\tvalidation_1-error:0.20732\n",
      "[21]\tvalidation_0-error:0.04508\tvalidation_1-error:0.20732\n",
      "[22]\tvalidation_0-error:0.04508\tvalidation_1-error:0.20732\n",
      "[23]\tvalidation_0-error:0.04508\tvalidation_1-error:0.20732\n",
      "[24]\tvalidation_0-error:0.04508\tvalidation_1-error:0.20732\n",
      "[25]\tvalidation_0-error:0.03689\tvalidation_1-error:0.19512\n",
      "[26]\tvalidation_0-error:0.03279\tvalidation_1-error:0.20732\n",
      "[27]\tvalidation_0-error:0.03279\tvalidation_1-error:0.20732\n",
      "[28]\tvalidation_0-error:0.02459\tvalidation_1-error:0.20732\n",
      "[29]\tvalidation_0-error:0.01639\tvalidation_1-error:0.20732\n",
      "[30]\tvalidation_0-error:0.01639\tvalidation_1-error:0.20732\n",
      "[31]\tvalidation_0-error:0.01639\tvalidation_1-error:0.21951\n",
      "[32]\tvalidation_0-error:0.01639\tvalidation_1-error:0.21951\n",
      "Cross Validation Score: 0.905 +/- 0.112\n"
     ]
    }
   ],
   "source": [
    "xgbc_model = XGBClassifier(use_label_encoder=False,objective='binary:hinge',learning_rate=0.05,n_estimators=500)\n",
    "xgbc_model.fit(feat_train_2,targ_train_2,eval_set=[(feat_train_2, targ_train_2), (feat_val, targ_val)],eval_metric=\"error\",early_stopping_rounds=20,verbose=True)\n",
    "xgbc_scoring = cross_val_score(xgbc_model, feat_train, targ_train,cv=10, scoring=score_metric)\n",
    "print(\"Cross Validation Score: {:.3f} +/- {:.3f}\".format(xgbc_scoring.mean(),xgbc_scoring.std())) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.82      0.85       163\n",
      "           1       0.83      0.90      0.86       163\n",
      "\n",
      "    accuracy                           0.86       326\n",
      "   macro avg       0.86      0.86      0.86       326\n",
      "weighted avg       0.86      0.86      0.86       326\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgbc_predicted = cross_val_predict(xgbc_model,feat_train,targ_train,cv=10)\n",
    "print(classification_report(targ_train, xgbc_predicted))\n",
    "cm = confusion_matrix(targ_train, xgbc_predicted)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "aoc_curves(xgbc_model,feat_train,targ_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_eb(xgbc_model,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VOTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "vot_hard_model = VotingClassifier(\n",
    "    estimators=[('lr', lr_model), ('tree', rf_model), ('svc', svm_model)],\n",
    "    voting='hard')\n",
    "vot_soft_model = VotingClassifier(\n",
    "    estimators=[('lr', lr_model), ('tree', rf_model), ('svc', svm_model)],\n",
    "    voting='soft')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HARD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score: 0.916 +/- 0.070\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.83      0.87       163\n",
      "           1       0.85      0.92      0.88       163\n",
      "\n",
      "    accuracy                           0.88       326\n",
      "   macro avg       0.88      0.88      0.88       326\n",
      "weighted avg       0.88      0.88      0.88       326\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_setup(vot_hard_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_eb(vot_hard_model,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SOFT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score: 0.910 +/- 0.071\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.85      0.87       163\n",
      "           1       0.86      0.91      0.88       163\n",
      "\n",
      "    accuracy                           0.88       326\n",
      "   macro avg       0.88      0.88      0.88       326\n",
      "weighted avg       0.88      0.88      0.88       326\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_setup(vot_soft_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_eb(vot_soft_model,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RED NEURONAL SIMPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "input = keras.layers.Input(shape=[7,])\n",
    "flatten = keras.layers.Flatten()(input)\n",
    "hidden1 = keras.layers.Dense(100, activation=\"relu\",kernel_initializer='he_uniform')(flatten)\n",
    "dropout = keras.layers.Dropout(rate=0.2)(hidden1)\n",
    "hidden2 = keras.layers.Dense(100, activation=\"relu\",kernel_initializer='he_uniform')(hidden1)\n",
    "output = keras.layers.Dense(1, activation=\"sigmoid\")(hidden2)\n",
    "\n",
    "model = keras.models.Model(inputs=[input], outputs=[output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 7)]               0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 7)                 0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 100)               800       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 11,001\n",
      "Trainable params: 11,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "              optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
    "              metrics=[tf.keras.metrics.Recall()])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "9/9 [==============================] - 1s 20ms/step - loss: 0.7385 - recall: 0.5464 - val_loss: 0.6738 - val_recall: 0.5606\n",
      "Epoch 2/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4968 - recall: 0.6392 - val_loss: 0.4775 - val_recall: 0.7727\n",
      "Epoch 3/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4360 - recall: 0.7010 - val_loss: 0.4737 - val_recall: 0.7879\n",
      "Epoch 4/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4154 - recall: 0.7010 - val_loss: 0.4882 - val_recall: 0.7727\n",
      "Epoch 5/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4006 - recall: 0.7423 - val_loss: 0.4496 - val_recall: 0.7879\n",
      "Epoch 6/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3893 - recall: 0.7526 - val_loss: 0.4391 - val_recall: 0.8030\n",
      "Epoch 7/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3799 - recall: 0.7320 - val_loss: 0.4860 - val_recall: 0.7879\n",
      "Epoch 8/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3626 - recall: 0.7320 - val_loss: 0.4175 - val_recall: 0.8030\n",
      "Epoch 9/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3531 - recall: 0.7629 - val_loss: 0.3733 - val_recall: 0.8182\n",
      "Epoch 10/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3464 - recall: 0.7938 - val_loss: 0.3790 - val_recall: 0.8182\n",
      "Epoch 11/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3354 - recall: 0.7938 - val_loss: 0.4088 - val_recall: 0.7879\n",
      "Epoch 12/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3244 - recall: 0.7835 - val_loss: 0.4268 - val_recall: 0.7879\n",
      "Epoch 13/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3210 - recall: 0.7526 - val_loss: 0.4527 - val_recall: 0.7727\n",
      "Epoch 14/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3124 - recall: 0.8041 - val_loss: 0.3125 - val_recall: 0.8788\n",
      "Epoch 15/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3126 - recall: 0.8041 - val_loss: 0.3787 - val_recall: 0.8485\n",
      "Epoch 16/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2985 - recall: 0.7938 - val_loss: 0.3267 - val_recall: 0.8788\n",
      "Epoch 17/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2910 - recall: 0.7938 - val_loss: 0.3475 - val_recall: 0.8636\n",
      "Epoch 18/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2846 - recall: 0.7938 - val_loss: 0.3567 - val_recall: 0.8485\n",
      "Epoch 19/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2786 - recall: 0.7938 - val_loss: 0.3640 - val_recall: 0.8485\n",
      "Epoch 20/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2714 - recall: 0.8041 - val_loss: 0.3099 - val_recall: 0.8788\n",
      "Epoch 21/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2696 - recall: 0.8557 - val_loss: 0.3019 - val_recall: 0.9091\n",
      "Epoch 22/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2639 - recall: 0.8763 - val_loss: 0.3372 - val_recall: 0.8939\n",
      "Epoch 23/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2558 - recall: 0.8557 - val_loss: 0.3534 - val_recall: 0.8788\n",
      "Epoch 24/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2558 - recall: 0.8351 - val_loss: 0.2923 - val_recall: 0.9091\n",
      "Epoch 25/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2492 - recall: 0.8763 - val_loss: 0.2821 - val_recall: 0.9545\n",
      "Epoch 26/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2399 - recall: 0.9072 - val_loss: 0.2994 - val_recall: 0.9545\n",
      "Epoch 27/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2352 - recall: 0.8969 - val_loss: 0.2904 - val_recall: 0.9394\n",
      "Epoch 28/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2333 - recall: 0.8866 - val_loss: 0.2851 - val_recall: 0.9091\n",
      "Epoch 29/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2266 - recall: 0.9072 - val_loss: 0.2918 - val_recall: 0.9091\n",
      "Epoch 30/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2216 - recall: 0.8969 - val_loss: 0.2962 - val_recall: 0.9394\n",
      "Epoch 31/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2198 - recall: 0.9381 - val_loss: 0.2327 - val_recall: 0.9545\n",
      "Epoch 32/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2150 - recall: 0.9485 - val_loss: 0.3213 - val_recall: 0.9091\n",
      "Epoch 33/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2273 - recall: 0.8351 - val_loss: 0.4187 - val_recall: 0.8030\n",
      "Epoch 34/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2154 - recall: 0.9072 - val_loss: 0.2540 - val_recall: 0.9545\n",
      "Epoch 35/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2059 - recall: 0.9175 - val_loss: 0.3163 - val_recall: 0.9242\n",
      "Epoch 36/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2036 - recall: 0.9278 - val_loss: 0.2579 - val_recall: 0.9545\n",
      "Epoch 37/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1989 - recall: 0.9381 - val_loss: 0.2272 - val_recall: 0.9545\n",
      "Epoch 38/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1915 - recall: 0.9588 - val_loss: 0.2629 - val_recall: 0.9545\n",
      "Epoch 39/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1870 - recall: 0.9381 - val_loss: 0.3014 - val_recall: 0.9394\n",
      "Epoch 40/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1829 - recall: 0.9278 - val_loss: 0.2731 - val_recall: 0.9242\n",
      "Epoch 41/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1802 - recall: 0.9381 - val_loss: 0.2618 - val_recall: 0.9242\n",
      "Epoch 42/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1813 - recall: 0.9485 - val_loss: 0.2118 - val_recall: 0.9545\n",
      "Epoch 43/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1748 - recall: 0.9588 - val_loss: 0.2322 - val_recall: 0.9545\n",
      "Epoch 44/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1694 - recall: 0.9485 - val_loss: 0.2450 - val_recall: 0.9545\n",
      "Epoch 45/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1657 - recall: 0.9485 - val_loss: 0.2368 - val_recall: 0.9545\n",
      "Epoch 46/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1636 - recall: 0.9691 - val_loss: 0.2322 - val_recall: 0.9545\n",
      "Epoch 47/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1666 - recall: 0.9278 - val_loss: 0.3204 - val_recall: 0.9242\n",
      "Epoch 48/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1611 - recall: 0.9278 - val_loss: 0.2036 - val_recall: 0.9697\n",
      "Epoch 49/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1579 - recall: 0.9691 - val_loss: 0.1988 - val_recall: 0.9545\n",
      "Epoch 50/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1518 - recall: 0.9485 - val_loss: 0.2818 - val_recall: 0.9394\n",
      "Epoch 51/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1525 - recall: 0.9381 - val_loss: 0.1926 - val_recall: 0.9697\n",
      "Epoch 52/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1458 - recall: 0.9485 - val_loss: 0.2141 - val_recall: 0.9697\n",
      "Epoch 53/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1428 - recall: 0.9588 - val_loss: 0.2287 - val_recall: 0.9394\n",
      "Epoch 54/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1447 - recall: 0.9588 - val_loss: 0.2147 - val_recall: 0.9545\n",
      "Epoch 55/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1377 - recall: 0.9588 - val_loss: 0.2236 - val_recall: 0.9545\n",
      "Epoch 56/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1352 - recall: 0.9691 - val_loss: 0.1795 - val_recall: 0.9697\n",
      "Epoch 57/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1371 - recall: 0.9588 - val_loss: 0.2418 - val_recall: 0.9545\n",
      "Epoch 58/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1382 - recall: 0.9588 - val_loss: 0.2268 - val_recall: 0.9697\n",
      "Epoch 59/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1302 - recall: 0.9691 - val_loss: 0.1840 - val_recall: 0.9697\n",
      "Epoch 60/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1282 - recall: 0.9691 - val_loss: 0.1857 - val_recall: 0.9697\n",
      "Epoch 61/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1218 - recall: 0.9588 - val_loss: 0.2294 - val_recall: 0.9545\n",
      "Epoch 62/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1214 - recall: 0.9691 - val_loss: 0.1956 - val_recall: 0.9697\n",
      "Epoch 63/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1168 - recall: 0.9691 - val_loss: 0.1870 - val_recall: 0.9697\n",
      "Epoch 64/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1148 - recall: 0.9691 - val_loss: 0.1905 - val_recall: 0.9697\n",
      "Epoch 65/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1124 - recall: 0.9691 - val_loss: 0.1459 - val_recall: 0.9697\n",
      "Epoch 66/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1122 - recall: 0.9691 - val_loss: 0.1837 - val_recall: 0.9697\n",
      "Epoch 67/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1162 - recall: 0.9588 - val_loss: 0.2231 - val_recall: 0.9394\n",
      "Epoch 68/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1104 - recall: 0.9691 - val_loss: 0.1631 - val_recall: 0.9697\n",
      "Epoch 69/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1084 - recall: 0.9691 - val_loss: 0.1532 - val_recall: 0.9697\n",
      "Epoch 70/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1047 - recall: 0.9794 - val_loss: 0.1775 - val_recall: 0.9697\n",
      "Epoch 71/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1028 - recall: 0.9691 - val_loss: 0.1754 - val_recall: 0.9394\n",
      "Epoch 72/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1020 - recall: 0.9691 - val_loss: 0.1367 - val_recall: 0.9697\n",
      "Epoch 73/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1005 - recall: 0.9691 - val_loss: 0.1624 - val_recall: 0.9697\n",
      "Epoch 74/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0974 - recall: 0.9794 - val_loss: 0.1709 - val_recall: 0.9697\n",
      "Epoch 75/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0953 - recall: 0.9794 - val_loss: 0.1828 - val_recall: 0.9697\n",
      "Epoch 76/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0924 - recall: 0.9794 - val_loss: 0.1434 - val_recall: 0.9697\n",
      "Epoch 77/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0913 - recall: 0.9794 - val_loss: 0.1854 - val_recall: 0.9697\n",
      "Epoch 78/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0908 - recall: 0.9794 - val_loss: 0.1595 - val_recall: 0.9697\n",
      "Epoch 79/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0875 - recall: 0.9794 - val_loss: 0.1631 - val_recall: 0.9697\n",
      "Epoch 80/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0857 - recall: 0.9691 - val_loss: 0.1602 - val_recall: 0.9697\n",
      "Epoch 81/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0848 - recall: 0.9794 - val_loss: 0.1305 - val_recall: 0.9697\n",
      "Epoch 82/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0838 - recall: 0.9794 - val_loss: 0.1524 - val_recall: 0.9697\n",
      "Epoch 83/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0871 - recall: 0.9794 - val_loss: 0.1818 - val_recall: 0.9697\n",
      "Epoch 84/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0892 - recall: 0.9691 - val_loss: 0.1321 - val_recall: 0.9697\n",
      "Epoch 85/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - recall: 0.9897 - val_loss: 0.1458 - val_recall: 0.9545\n",
      "Epoch 86/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0784 - recall: 0.9794 - val_loss: 0.1574 - val_recall: 0.9545\n",
      "Epoch 87/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0785 - recall: 0.9897 - val_loss: 0.1183 - val_recall: 0.9697\n",
      "Epoch 88/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0756 - recall: 0.9897 - val_loss: 0.1393 - val_recall: 0.9697\n",
      "Epoch 89/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0751 - recall: 0.9794 - val_loss: 0.1424 - val_recall: 0.9697\n",
      "Epoch 90/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0705 - recall: 0.9897 - val_loss: 0.0986 - val_recall: 0.9697\n",
      "Epoch 91/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0754 - recall: 1.0000 - val_loss: 0.1201 - val_recall: 0.9697\n",
      "Epoch 92/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0727 - recall: 0.9897 - val_loss: 0.1682 - val_recall: 0.9545\n",
      "Epoch 93/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0668 - recall: 0.9897 - val_loss: 0.1070 - val_recall: 0.9697\n",
      "Epoch 94/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0710 - recall: 1.0000 - val_loss: 0.1069 - val_recall: 0.9697\n",
      "Epoch 95/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0659 - recall: 1.0000 - val_loss: 0.1495 - val_recall: 0.9697\n",
      "Epoch 96/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0647 - recall: 1.0000 - val_loss: 0.1369 - val_recall: 0.9697\n",
      "Epoch 97/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0612 - recall: 1.0000 - val_loss: 0.1130 - val_recall: 0.9697\n",
      "Epoch 98/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0604 - recall: 1.0000 - val_loss: 0.1192 - val_recall: 0.9697\n",
      "Epoch 99/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0582 - recall: 1.0000 - val_loss: 0.1357 - val_recall: 0.9697\n",
      "Epoch 100/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0583 - recall: 1.0000 - val_loss: 0.1181 - val_recall: 0.9697\n",
      "Epoch 101/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0676 - recall: 1.0000 - val_loss: 0.0815 - val_recall: 0.9697\n",
      "Epoch 102/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0621 - recall: 1.0000 - val_loss: 0.1367 - val_recall: 0.9697\n",
      "Epoch 103/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0559 - recall: 1.0000 - val_loss: 0.0948 - val_recall: 0.9697\n",
      "Epoch 104/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0576 - recall: 1.0000 - val_loss: 0.1108 - val_recall: 0.9697\n",
      "Epoch 105/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0564 - recall: 1.0000 - val_loss: 0.1281 - val_recall: 0.9697\n",
      "Epoch 106/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0556 - recall: 1.0000 - val_loss: 0.0960 - val_recall: 0.9848\n",
      "Epoch 107/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0524 - recall: 1.0000 - val_loss: 0.1129 - val_recall: 0.9848\n",
      "Epoch 108/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0521 - recall: 1.0000 - val_loss: 0.1244 - val_recall: 0.9848\n",
      "Epoch 109/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0493 - recall: 1.0000 - val_loss: 0.0923 - val_recall: 0.9848\n",
      "Epoch 110/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0489 - recall: 1.0000 - val_loss: 0.1055 - val_recall: 0.9697\n",
      "Epoch 111/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0469 - recall: 1.0000 - val_loss: 0.1145 - val_recall: 0.9697\n",
      "Epoch 112/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0470 - recall: 1.0000 - val_loss: 0.1021 - val_recall: 0.9697\n",
      "Epoch 113/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0465 - recall: 1.0000 - val_loss: 0.1011 - val_recall: 0.9697\n",
      "Epoch 114/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0450 - recall: 1.0000 - val_loss: 0.1314 - val_recall: 0.9697\n",
      "Epoch 115/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0517 - recall: 1.0000 - val_loss: 0.1545 - val_recall: 0.9697\n",
      "Epoch 116/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0469 - recall: 0.9897 - val_loss: 0.0926 - val_recall: 0.9697\n",
      "Epoch 117/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0453 - recall: 1.0000 - val_loss: 0.0913 - val_recall: 0.9697\n",
      "Epoch 118/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0434 - recall: 1.0000 - val_loss: 0.1129 - val_recall: 0.9697\n",
      "Epoch 119/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0412 - recall: 1.0000 - val_loss: 0.1008 - val_recall: 0.9697\n",
      "Epoch 120/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0417 - recall: 1.0000 - val_loss: 0.1009 - val_recall: 0.9697\n",
      "Epoch 121/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0403 - recall: 1.0000 - val_loss: 0.1011 - val_recall: 0.9697\n",
      "Epoch 122/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0408 - recall: 1.0000 - val_loss: 0.0941 - val_recall: 0.9697\n",
      "Epoch 123/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0393 - recall: 1.0000 - val_loss: 0.1118 - val_recall: 0.9697\n",
      "Epoch 124/2000\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0378 - recall: 1.0000 - val_loss: 0.0890 - val_recall: 0.9697\n",
      "Epoch 125/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0379 - recall: 1.0000 - val_loss: 0.0943 - val_recall: 0.9697\n",
      "Epoch 126/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0368 - recall: 1.0000 - val_loss: 0.0998 - val_recall: 0.9697\n",
      "Epoch 127/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0369 - recall: 1.0000 - val_loss: 0.1050 - val_recall: 0.9697\n",
      "Epoch 128/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0379 - recall: 1.0000 - val_loss: 0.1376 - val_recall: 0.9697\n",
      "Epoch 129/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0374 - recall: 1.0000 - val_loss: 0.0844 - val_recall: 0.9848\n",
      "Epoch 130/2000\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0360 - recall: 1.0000 - val_loss: 0.0933 - val_recall: 0.9697\n",
      "Epoch 131/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0344 - recall: 1.0000 - val_loss: 0.1033 - val_recall: 0.9697\n",
      "Epoch 132/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0338 - recall: 1.0000 - val_loss: 0.0807 - val_recall: 0.9848\n",
      "Epoch 133/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0338 - recall: 1.0000 - val_loss: 0.0909 - val_recall: 0.9848\n",
      "Epoch 134/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0328 - recall: 1.0000 - val_loss: 0.0873 - val_recall: 0.9848\n",
      "Epoch 135/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0329 - recall: 1.0000 - val_loss: 0.0699 - val_recall: 0.9848\n",
      "Epoch 136/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0343 - recall: 1.0000 - val_loss: 0.0739 - val_recall: 0.9848\n",
      "Epoch 137/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0312 - recall: 1.0000 - val_loss: 0.0827 - val_recall: 0.9848\n",
      "Epoch 138/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0311 - recall: 1.0000 - val_loss: 0.0963 - val_recall: 0.9697\n",
      "Epoch 139/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0305 - recall: 1.0000 - val_loss: 0.0910 - val_recall: 0.9848\n",
      "Epoch 140/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0291 - recall: 1.0000 - val_loss: 0.0841 - val_recall: 0.9848\n",
      "Epoch 141/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0286 - recall: 1.0000 - val_loss: 0.0837 - val_recall: 0.9848\n",
      "Epoch 142/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0288 - recall: 1.0000 - val_loss: 0.0819 - val_recall: 0.9697\n",
      "Epoch 143/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0273 - recall: 1.0000 - val_loss: 0.1002 - val_recall: 0.9697\n",
      "Epoch 144/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0297 - recall: 1.0000 - val_loss: 0.1078 - val_recall: 0.9697\n",
      "Epoch 145/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0280 - recall: 1.0000 - val_loss: 0.0754 - val_recall: 0.9848\n",
      "Epoch 146/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0280 - recall: 1.0000 - val_loss: 0.0813 - val_recall: 0.9697\n",
      "Epoch 147/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0268 - recall: 1.0000 - val_loss: 0.0995 - val_recall: 0.9697\n",
      "Epoch 148/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0266 - recall: 1.0000 - val_loss: 0.0778 - val_recall: 0.9697\n",
      "Epoch 149/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0274 - recall: 1.0000 - val_loss: 0.0783 - val_recall: 0.9697\n",
      "Epoch 150/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0260 - recall: 1.0000 - val_loss: 0.0903 - val_recall: 0.9697\n",
      "Epoch 151/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0260 - recall: 1.0000 - val_loss: 0.0702 - val_recall: 0.9848\n",
      "Epoch 152/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0330 - recall: 0.9897 - val_loss: 0.1109 - val_recall: 0.9697\n",
      "Epoch 153/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0266 - recall: 0.9897 - val_loss: 0.0662 - val_recall: 0.9848\n",
      "Epoch 154/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0258 - recall: 1.0000 - val_loss: 0.0664 - val_recall: 0.9697\n",
      "Epoch 155/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0242 - recall: 1.0000 - val_loss: 0.0845 - val_recall: 0.9697\n",
      "Epoch 156/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0247 - recall: 1.0000 - val_loss: 0.0687 - val_recall: 0.9697\n",
      "Epoch 157/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0240 - recall: 1.0000 - val_loss: 0.1065 - val_recall: 0.9697\n",
      "Epoch 158/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0250 - recall: 1.0000 - val_loss: 0.0901 - val_recall: 0.9697\n",
      "Epoch 159/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0228 - recall: 1.0000 - val_loss: 0.0729 - val_recall: 0.9848\n",
      "Epoch 160/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0219 - recall: 1.0000 - val_loss: 0.0762 - val_recall: 0.9848\n",
      "Epoch 161/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0219 - recall: 1.0000 - val_loss: 0.0715 - val_recall: 0.9697\n",
      "Epoch 162/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0212 - recall: 1.0000 - val_loss: 0.0741 - val_recall: 0.9848\n",
      "Epoch 163/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0206 - recall: 1.0000 - val_loss: 0.0761 - val_recall: 0.9697\n",
      "Epoch 164/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0214 - recall: 1.0000 - val_loss: 0.0715 - val_recall: 0.9697\n",
      "Epoch 165/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0199 - recall: 1.0000 - val_loss: 0.0838 - val_recall: 0.9697\n",
      "Epoch 166/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0198 - recall: 1.0000 - val_loss: 0.0793 - val_recall: 0.9697\n",
      "Epoch 167/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0193 - recall: 1.0000 - val_loss: 0.0814 - val_recall: 0.9697\n",
      "Epoch 168/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0224 - recall: 1.0000 - val_loss: 0.1108 - val_recall: 0.9697\n",
      "Epoch 169/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0206 - recall: 1.0000 - val_loss: 0.0756 - val_recall: 0.9848\n",
      "Epoch 170/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0201 - recall: 1.0000 - val_loss: 0.0681 - val_recall: 0.9848\n",
      "Epoch 171/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0189 - recall: 1.0000 - val_loss: 0.0903 - val_recall: 0.9697\n",
      "Epoch 172/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0189 - recall: 1.0000 - val_loss: 0.0796 - val_recall: 0.9697\n",
      "Epoch 173/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0179 - recall: 1.0000 - val_loss: 0.0735 - val_recall: 0.9848\n",
      "Epoch 174/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0175 - recall: 1.0000 - val_loss: 0.0761 - val_recall: 0.9848\n",
      "Epoch 175/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0171 - recall: 1.0000 - val_loss: 0.0755 - val_recall: 0.9848\n",
      "Epoch 176/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0170 - recall: 1.0000 - val_loss: 0.0822 - val_recall: 0.9697\n",
      "Epoch 177/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0169 - recall: 1.0000 - val_loss: 0.0785 - val_recall: 0.9848\n",
      "Epoch 178/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0167 - recall: 1.0000 - val_loss: 0.0760 - val_recall: 0.9848\n",
      "Epoch 179/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0163 - recall: 1.0000 - val_loss: 0.0746 - val_recall: 0.9697\n",
      "Epoch 180/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0161 - recall: 1.0000 - val_loss: 0.0754 - val_recall: 0.9848\n",
      "Epoch 181/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0159 - recall: 1.0000 - val_loss: 0.0771 - val_recall: 0.9697\n",
      "Epoch 182/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0157 - recall: 1.0000 - val_loss: 0.0751 - val_recall: 0.9697\n",
      "Epoch 183/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0155 - recall: 1.0000 - val_loss: 0.0688 - val_recall: 0.9697\n",
      "Epoch 184/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0154 - recall: 1.0000 - val_loss: 0.0723 - val_recall: 0.9697\n",
      "Epoch 185/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0156 - recall: 1.0000 - val_loss: 0.0807 - val_recall: 0.9697\n",
      "Epoch 186/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0150 - recall: 1.0000 - val_loss: 0.0681 - val_recall: 0.9848\n",
      "Epoch 187/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0155 - recall: 1.0000 - val_loss: 0.0712 - val_recall: 0.9848\n",
      "Epoch 188/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0146 - recall: 1.0000 - val_loss: 0.0819 - val_recall: 0.9697\n",
      "Epoch 189/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0144 - recall: 1.0000 - val_loss: 0.0719 - val_recall: 0.9848\n",
      "Epoch 190/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0143 - recall: 1.0000 - val_loss: 0.0676 - val_recall: 0.9848\n",
      "Epoch 191/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0138 - recall: 1.0000 - val_loss: 0.0740 - val_recall: 0.9848\n",
      "Epoch 192/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0140 - recall: 1.0000 - val_loss: 0.0771 - val_recall: 0.9848\n",
      "Epoch 193/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0140 - recall: 1.0000 - val_loss: 0.0656 - val_recall: 0.9848\n",
      "Epoch 194/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0137 - recall: 1.0000 - val_loss: 0.0719 - val_recall: 0.9697\n",
      "Epoch 195/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0134 - recall: 1.0000 - val_loss: 0.0706 - val_recall: 0.9848\n",
      "Epoch 196/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0131 - recall: 1.0000 - val_loss: 0.0731 - val_recall: 0.9848\n",
      "Epoch 197/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0131 - recall: 1.0000 - val_loss: 0.0704 - val_recall: 0.9848\n",
      "Epoch 198/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0128 - recall: 1.0000 - val_loss: 0.0711 - val_recall: 0.9848\n",
      "Epoch 199/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0126 - recall: 1.0000 - val_loss: 0.0734 - val_recall: 0.9848\n",
      "Epoch 200/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0126 - recall: 1.0000 - val_loss: 0.0675 - val_recall: 0.9848\n",
      "Epoch 201/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0124 - recall: 1.0000 - val_loss: 0.0723 - val_recall: 0.9848\n",
      "Epoch 202/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0122 - recall: 1.0000 - val_loss: 0.0665 - val_recall: 0.9848\n",
      "Epoch 203/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0123 - recall: 1.0000 - val_loss: 0.0605 - val_recall: 0.9848\n",
      "Epoch 204/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0121 - recall: 1.0000 - val_loss: 0.0676 - val_recall: 0.9848\n",
      "Epoch 205/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0122 - recall: 1.0000 - val_loss: 0.0671 - val_recall: 0.9848\n",
      "Epoch 206/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0116 - recall: 1.0000 - val_loss: 0.0684 - val_recall: 0.9848\n",
      "Epoch 207/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0114 - recall: 1.0000 - val_loss: 0.0684 - val_recall: 0.9848\n",
      "Epoch 208/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0112 - recall: 1.0000 - val_loss: 0.0715 - val_recall: 0.9697\n",
      "Epoch 209/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0110 - recall: 1.0000 - val_loss: 0.0728 - val_recall: 0.9848\n",
      "Epoch 210/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0110 - recall: 1.0000 - val_loss: 0.0688 - val_recall: 0.9848\n",
      "Epoch 211/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0109 - recall: 1.0000 - val_loss: 0.0681 - val_recall: 0.9848\n",
      "Epoch 212/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0107 - recall: 1.0000 - val_loss: 0.0734 - val_recall: 0.9848\n",
      "Epoch 213/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0109 - recall: 1.0000 - val_loss: 0.0711 - val_recall: 0.9697\n",
      "Epoch 214/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0125 - recall: 1.0000 - val_loss: 0.0607 - val_recall: 0.9697\n",
      "Epoch 215/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0115 - recall: 1.0000 - val_loss: 0.0730 - val_recall: 0.9697\n",
      "Epoch 216/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0105 - recall: 1.0000 - val_loss: 0.0706 - val_recall: 0.9848\n",
      "Epoch 217/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0103 - recall: 1.0000 - val_loss: 0.0597 - val_recall: 0.9848\n",
      "Epoch 218/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0108 - recall: 1.0000 - val_loss: 0.0679 - val_recall: 0.9848\n",
      "Epoch 219/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0102 - recall: 1.0000 - val_loss: 0.0670 - val_recall: 0.9848\n",
      "Epoch 220/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0098 - recall: 1.0000 - val_loss: 0.0651 - val_recall: 0.9848\n",
      "Epoch 221/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0100 - recall: 1.0000 - val_loss: 0.0620 - val_recall: 0.9848\n",
      "Epoch 222/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0096 - recall: 1.0000 - val_loss: 0.0654 - val_recall: 0.9848\n",
      "Epoch 223/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0094 - recall: 1.0000 - val_loss: 0.0665 - val_recall: 0.9848\n",
      "Epoch 224/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0093 - recall: 1.0000 - val_loss: 0.0659 - val_recall: 0.9848\n",
      "Epoch 225/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0093 - recall: 1.0000 - val_loss: 0.0647 - val_recall: 0.9848\n",
      "Epoch 226/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0094 - recall: 1.0000 - val_loss: 0.0595 - val_recall: 0.9848\n",
      "Epoch 227/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0095 - recall: 1.0000 - val_loss: 0.0645 - val_recall: 0.9848\n",
      "Epoch 228/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0091 - recall: 1.0000 - val_loss: 0.0672 - val_recall: 0.9848\n",
      "Epoch 229/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0099 - recall: 1.0000 - val_loss: 0.0902 - val_recall: 0.9697\n",
      "Epoch 230/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0105 - recall: 1.0000 - val_loss: 0.0644 - val_recall: 0.9848\n",
      "Epoch 231/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0090 - recall: 1.0000 - val_loss: 0.0581 - val_recall: 0.9848\n",
      "Epoch 232/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0089 - recall: 1.0000 - val_loss: 0.0608 - val_recall: 0.9848\n",
      "Epoch 233/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0087 - recall: 1.0000 - val_loss: 0.0669 - val_recall: 0.9848\n",
      "Epoch 234/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0089 - recall: 1.0000 - val_loss: 0.0693 - val_recall: 0.9848\n",
      "Epoch 235/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0085 - recall: 1.0000 - val_loss: 0.0530 - val_recall: 0.9848\n",
      "Epoch 236/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0086 - recall: 1.0000 - val_loss: 0.0554 - val_recall: 0.9848\n",
      "Epoch 237/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0085 - recall: 1.0000 - val_loss: 0.0650 - val_recall: 0.9848\n",
      "Epoch 238/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0079 - recall: 1.0000 - val_loss: 0.0681 - val_recall: 0.9848\n",
      "Epoch 239/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0079 - recall: 1.0000 - val_loss: 0.0630 - val_recall: 0.9848\n",
      "Epoch 240/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0078 - recall: 1.0000 - val_loss: 0.0519 - val_recall: 0.9848\n",
      "Epoch 241/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0080 - recall: 1.0000 - val_loss: 0.0582 - val_recall: 0.9848\n",
      "Epoch 242/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0076 - recall: 1.0000 - val_loss: 0.0547 - val_recall: 0.9848\n",
      "Epoch 243/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0075 - recall: 1.0000 - val_loss: 0.0579 - val_recall: 0.9848\n",
      "Epoch 244/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0073 - recall: 1.0000 - val_loss: 0.0604 - val_recall: 0.9848\n",
      "Epoch 245/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0072 - recall: 1.0000 - val_loss: 0.0579 - val_recall: 0.9848\n",
      "Epoch 246/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0071 - recall: 1.0000 - val_loss: 0.0544 - val_recall: 0.9848\n",
      "Epoch 247/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0073 - recall: 1.0000 - val_loss: 0.0560 - val_recall: 0.9848\n",
      "Epoch 248/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0070 - recall: 1.0000 - val_loss: 0.0619 - val_recall: 0.9848\n",
      "Epoch 249/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0069 - recall: 1.0000 - val_loss: 0.0621 - val_recall: 0.9848\n",
      "Epoch 250/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0068 - recall: 1.0000 - val_loss: 0.0545 - val_recall: 0.9848\n",
      "Epoch 251/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0068 - recall: 1.0000 - val_loss: 0.0548 - val_recall: 0.9848\n",
      "Epoch 252/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0069 - recall: 1.0000 - val_loss: 0.0593 - val_recall: 0.9848\n",
      "Epoch 253/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0067 - recall: 1.0000 - val_loss: 0.0566 - val_recall: 0.9848\n",
      "Epoch 254/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0066 - recall: 1.0000 - val_loss: 0.0571 - val_recall: 0.9848\n",
      "Epoch 255/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0066 - recall: 1.0000 - val_loss: 0.0560 - val_recall: 0.9848\n",
      "Epoch 256/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0065 - recall: 1.0000 - val_loss: 0.0560 - val_recall: 0.9848\n",
      "Epoch 257/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0067 - recall: 1.0000 - val_loss: 0.0528 - val_recall: 0.9848\n",
      "Epoch 258/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0065 - recall: 1.0000 - val_loss: 0.0599 - val_recall: 0.9848\n",
      "Epoch 259/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0063 - recall: 1.0000 - val_loss: 0.0631 - val_recall: 0.9848\n",
      "Epoch 260/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0063 - recall: 1.0000 - val_loss: 0.0592 - val_recall: 0.9848\n",
      "Epoch 261/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0061 - recall: 1.0000 - val_loss: 0.0589 - val_recall: 0.9848\n",
      "Epoch 262/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0060 - recall: 1.0000 - val_loss: 0.0632 - val_recall: 0.9848\n",
      "Epoch 263/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0060 - recall: 1.0000 - val_loss: 0.0596 - val_recall: 0.9848\n",
      "Epoch 264/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0059 - recall: 1.0000 - val_loss: 0.0572 - val_recall: 0.9848\n",
      "Epoch 265/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0058 - recall: 1.0000 - val_loss: 0.0580 - val_recall: 0.9848\n",
      "Epoch 266/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0057 - recall: 1.0000 - val_loss: 0.0596 - val_recall: 0.9848\n",
      "Epoch 267/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0056 - recall: 1.0000 - val_loss: 0.0581 - val_recall: 0.9848\n",
      "Epoch 268/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0056 - recall: 1.0000 - val_loss: 0.0562 - val_recall: 0.9848\n",
      "Epoch 269/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0056 - recall: 1.0000 - val_loss: 0.0587 - val_recall: 0.9848\n",
      "Epoch 270/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0056 - recall: 1.0000 - val_loss: 0.0571 - val_recall: 0.9848\n",
      "Epoch 271/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0054 - recall: 1.0000 - val_loss: 0.0572 - val_recall: 0.9848\n",
      "Epoch 272/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0054 - recall: 1.0000 - val_loss: 0.0550 - val_recall: 0.9848\n",
      "Epoch 273/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0059 - recall: 1.0000 - val_loss: 0.0505 - val_recall: 0.9848\n",
      "Epoch 274/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0057 - recall: 1.0000 - val_loss: 0.0615 - val_recall: 0.9848\n",
      "Epoch 275/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0054 - recall: 1.0000 - val_loss: 0.0687 - val_recall: 0.9697\n",
      "Epoch 276/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0061 - recall: 1.0000 - val_loss: 0.0664 - val_recall: 0.9848\n",
      "Epoch 277/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0060 - recall: 1.0000 - val_loss: 0.0537 - val_recall: 0.9848\n",
      "Epoch 278/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0052 - recall: 1.0000 - val_loss: 0.0571 - val_recall: 0.9848\n",
      "Epoch 279/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0052 - recall: 1.0000 - val_loss: 0.0544 - val_recall: 0.9848\n",
      "Epoch 280/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0049 - recall: 1.0000 - val_loss: 0.0598 - val_recall: 0.9848\n",
      "Epoch 281/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0050 - recall: 1.0000 - val_loss: 0.0554 - val_recall: 0.9848\n",
      "Epoch 282/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0049 - recall: 1.0000 - val_loss: 0.0526 - val_recall: 0.9848\n",
      "Epoch 283/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0048 - recall: 1.0000 - val_loss: 0.0566 - val_recall: 0.9848\n",
      "Epoch 284/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0047 - recall: 1.0000 - val_loss: 0.0548 - val_recall: 0.9848\n",
      "Epoch 285/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0047 - recall: 1.0000 - val_loss: 0.0514 - val_recall: 0.9848\n",
      "Epoch 286/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0047 - recall: 1.0000 - val_loss: 0.0540 - val_recall: 0.9848\n",
      "Epoch 287/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0046 - recall: 1.0000 - val_loss: 0.0560 - val_recall: 0.9848\n",
      "Epoch 288/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0045 - recall: 1.0000 - val_loss: 0.0544 - val_recall: 0.9848\n",
      "Epoch 289/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0045 - recall: 1.0000 - val_loss: 0.0548 - val_recall: 0.9848\n",
      "Epoch 290/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0044 - recall: 1.0000 - val_loss: 0.0551 - val_recall: 0.9848\n",
      "Epoch 291/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0044 - recall: 1.0000 - val_loss: 0.0549 - val_recall: 0.9848\n",
      "Epoch 292/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0043 - recall: 1.0000 - val_loss: 0.0554 - val_recall: 0.9848\n",
      "Epoch 293/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0043 - recall: 1.0000 - val_loss: 0.0560 - val_recall: 0.9848\n",
      "Epoch 294/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0043 - recall: 1.0000 - val_loss: 0.0540 - val_recall: 0.9848\n",
      "Epoch 295/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0043 - recall: 1.0000 - val_loss: 0.0517 - val_recall: 0.9848\n",
      "Epoch 296/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0042 - recall: 1.0000 - val_loss: 0.0581 - val_recall: 0.9848\n",
      "Epoch 297/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0043 - recall: 1.0000 - val_loss: 0.0557 - val_recall: 0.9848\n",
      "Epoch 298/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0042 - recall: 1.0000 - val_loss: 0.0535 - val_recall: 0.9848\n",
      "Epoch 299/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0055 - recall: 1.0000 - val_loss: 0.0624 - val_recall: 0.9848\n",
      "Epoch 300/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0050 - recall: 1.0000 - val_loss: 0.0486 - val_recall: 0.9848\n",
      "Epoch 301/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0043 - recall: 1.0000 - val_loss: 0.0479 - val_recall: 0.9848\n",
      "Epoch 302/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0042 - recall: 1.0000 - val_loss: 0.0560 - val_recall: 0.9848\n",
      "Epoch 303/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0041 - recall: 1.0000 - val_loss: 0.0561 - val_recall: 0.9848\n",
      "Epoch 304/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0040 - recall: 1.0000 - val_loss: 0.0481 - val_recall: 0.9848\n",
      "Epoch 305/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0039 - recall: 1.0000 - val_loss: 0.0473 - val_recall: 0.9848\n",
      "Epoch 306/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0039 - recall: 1.0000 - val_loss: 0.0498 - val_recall: 0.9848\n",
      "Epoch 307/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0038 - recall: 1.0000 - val_loss: 0.0551 - val_recall: 0.9848\n",
      "Epoch 308/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0037 - recall: 1.0000 - val_loss: 0.0535 - val_recall: 0.9848\n",
      "Epoch 309/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0037 - recall: 1.0000 - val_loss: 0.0532 - val_recall: 0.9848\n",
      "Epoch 310/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0037 - recall: 1.0000 - val_loss: 0.0566 - val_recall: 0.9848\n",
      "Epoch 311/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0036 - recall: 1.0000 - val_loss: 0.0513 - val_recall: 0.9848\n",
      "Epoch 312/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0036 - recall: 1.0000 - val_loss: 0.0521 - val_recall: 0.9848\n",
      "Epoch 313/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0035 - recall: 1.0000 - val_loss: 0.0528 - val_recall: 0.9848\n",
      "Epoch 314/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0035 - recall: 1.0000 - val_loss: 0.0524 - val_recall: 0.9848\n",
      "Epoch 315/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0035 - recall: 1.0000 - val_loss: 0.0470 - val_recall: 0.9848\n",
      "Epoch 316/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0036 - recall: 1.0000 - val_loss: 0.0494 - val_recall: 0.9848\n",
      "Epoch 317/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0037 - recall: 1.0000 - val_loss: 0.0495 - val_recall: 0.9848\n",
      "Epoch 318/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0036 - recall: 1.0000 - val_loss: 0.0559 - val_recall: 0.9848\n",
      "Epoch 319/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0036 - recall: 1.0000 - val_loss: 0.0585 - val_recall: 0.9848\n",
      "Epoch 320/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0034 - recall: 1.0000 - val_loss: 0.0499 - val_recall: 0.9848\n",
      "Epoch 321/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0034 - recall: 1.0000 - val_loss: 0.0535 - val_recall: 0.9848\n",
      "Epoch 322/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0033 - recall: 1.0000 - val_loss: 0.0546 - val_recall: 0.9848\n",
      "Epoch 323/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0032 - recall: 1.0000 - val_loss: 0.0520 - val_recall: 0.9848\n",
      "Epoch 324/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0032 - recall: 1.0000 - val_loss: 0.0514 - val_recall: 0.9848\n",
      "Epoch 325/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0032 - recall: 1.0000 - val_loss: 0.0545 - val_recall: 0.9848\n",
      "Epoch 326/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0032 - recall: 1.0000 - val_loss: 0.0548 - val_recall: 0.9848\n",
      "Epoch 327/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0031 - recall: 1.0000 - val_loss: 0.0503 - val_recall: 0.9848\n",
      "Epoch 328/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0031 - recall: 1.0000 - val_loss: 0.0520 - val_recall: 0.9848\n",
      "Epoch 329/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0030 - recall: 1.0000 - val_loss: 0.0524 - val_recall: 0.9848\n",
      "Epoch 330/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0030 - recall: 1.0000 - val_loss: 0.0542 - val_recall: 0.9848\n",
      "Epoch 331/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0030 - recall: 1.0000 - val_loss: 0.0535 - val_recall: 0.9848\n",
      "Epoch 332/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0033 - recall: 1.0000 - val_loss: 0.0479 - val_recall: 0.9848\n",
      "Epoch 333/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0031 - recall: 1.0000 - val_loss: 0.0538 - val_recall: 0.9848\n",
      "Epoch 334/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0029 - recall: 1.0000 - val_loss: 0.0524 - val_recall: 0.9848\n",
      "Epoch 335/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0029 - recall: 1.0000 - val_loss: 0.0514 - val_recall: 0.9848\n",
      "Epoch 336/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0029 - recall: 1.0000 - val_loss: 0.0536 - val_recall: 0.9848\n",
      "Epoch 337/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0029 - recall: 1.0000 - val_loss: 0.0542 - val_recall: 0.9848\n",
      "Epoch 338/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0037 - recall: 1.0000 - val_loss: 0.0684 - val_recall: 0.9848\n",
      "Epoch 339/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0037 - recall: 1.0000 - val_loss: 0.0493 - val_recall: 0.9848\n",
      "Epoch 340/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0032 - recall: 1.0000 - val_loss: 0.0435 - val_recall: 0.9848\n",
      "Epoch 341/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0029 - recall: 1.0000 - val_loss: 0.0549 - val_recall: 0.9848\n",
      "Epoch 342/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0028 - recall: 1.0000 - val_loss: 0.0564 - val_recall: 0.9848\n",
      "Epoch 343/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0027 - recall: 1.0000 - val_loss: 0.0516 - val_recall: 0.9848\n",
      "Epoch 344/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0026 - recall: 1.0000 - val_loss: 0.0495 - val_recall: 0.9848\n",
      "Epoch 345/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0026 - recall: 1.0000 - val_loss: 0.0532 - val_recall: 0.9848\n",
      "Epoch 346/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0026 - recall: 1.0000 - val_loss: 0.0505 - val_recall: 0.9848\n",
      "Epoch 347/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0026 - recall: 1.0000 - val_loss: 0.0476 - val_recall: 0.9848\n",
      "Epoch 348/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0027 - recall: 1.0000 - val_loss: 0.0518 - val_recall: 0.9848\n",
      "Epoch 349/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0026 - recall: 1.0000 - val_loss: 0.0542 - val_recall: 0.9848\n",
      "Epoch 350/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0025 - recall: 1.0000 - val_loss: 0.0501 - val_recall: 0.9848\n",
      "Epoch 351/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0025 - recall: 1.0000 - val_loss: 0.0493 - val_recall: 0.9848\n",
      "Epoch 352/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0024 - recall: 1.0000 - val_loss: 0.0508 - val_recall: 0.9848\n",
      "Epoch 353/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0024 - recall: 1.0000 - val_loss: 0.0512 - val_recall: 0.9848\n",
      "Epoch 354/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0024 - recall: 1.0000 - val_loss: 0.0543 - val_recall: 0.9848\n",
      "Epoch 355/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0024 - recall: 1.0000 - val_loss: 0.0516 - val_recall: 0.9848\n",
      "Epoch 356/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0024 - recall: 1.0000 - val_loss: 0.0498 - val_recall: 0.9848\n",
      "Epoch 357/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0023 - recall: 1.0000 - val_loss: 0.0511 - val_recall: 0.9848\n",
      "Epoch 358/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0024 - recall: 1.0000 - val_loss: 0.0555 - val_recall: 0.9848\n",
      "Epoch 359/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0023 - recall: 1.0000 - val_loss: 0.0526 - val_recall: 0.9848\n",
      "Epoch 360/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0023 - recall: 1.0000 - val_loss: 0.0513 - val_recall: 0.9848\n",
      "Epoch 361/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0023 - recall: 1.0000 - val_loss: 0.0520 - val_recall: 0.9848\n",
      "Epoch 362/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0022 - recall: 1.0000 - val_loss: 0.0530 - val_recall: 0.9848\n",
      "Epoch 363/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0022 - recall: 1.0000 - val_loss: 0.0496 - val_recall: 0.9848\n",
      "Epoch 364/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0022 - recall: 1.0000 - val_loss: 0.0472 - val_recall: 0.9848\n",
      "Epoch 365/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0023 - recall: 1.0000 - val_loss: 0.0435 - val_recall: 0.9848\n",
      "Epoch 366/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0022 - recall: 1.0000 - val_loss: 0.0481 - val_recall: 0.9848\n",
      "Epoch 367/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0023 - recall: 1.0000 - val_loss: 0.0538 - val_recall: 0.9848\n",
      "Epoch 368/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0024 - recall: 1.0000 - val_loss: 0.0498 - val_recall: 0.9848\n",
      "Epoch 369/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0022 - recall: 1.0000 - val_loss: 0.0484 - val_recall: 0.9848\n",
      "Epoch 370/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0024 - recall: 1.0000 - val_loss: 0.0564 - val_recall: 0.9848\n",
      "Epoch 371/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0023 - recall: 1.0000 - val_loss: 0.0496 - val_recall: 0.9848\n",
      "Epoch 372/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0021 - recall: 1.0000 - val_loss: 0.0478 - val_recall: 0.9848\n",
      "Epoch 373/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0021 - recall: 1.0000 - val_loss: 0.0526 - val_recall: 0.9848\n",
      "Epoch 374/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0021 - recall: 1.0000 - val_loss: 0.0463 - val_recall: 0.9848\n",
      "Epoch 375/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0022 - recall: 1.0000 - val_loss: 0.0503 - val_recall: 0.9848\n",
      "Epoch 376/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0023 - recall: 1.0000 - val_loss: 0.0630 - val_recall: 0.9848\n",
      "Epoch 377/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0024 - recall: 1.0000 - val_loss: 0.0505 - val_recall: 0.9848\n",
      "Epoch 378/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0021 - recall: 1.0000 - val_loss: 0.0425 - val_recall: 0.9848\n",
      "Epoch 379/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0020 - recall: 1.0000 - val_loss: 0.0427 - val_recall: 0.9848\n",
      "Epoch 380/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0020 - recall: 1.0000 - val_loss: 0.0478 - val_recall: 0.9848\n",
      "Epoch 381/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0019 - recall: 1.0000 - val_loss: 0.0516 - val_recall: 0.9848\n",
      "Epoch 382/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0019 - recall: 1.0000 - val_loss: 0.0509 - val_recall: 0.9848\n",
      "Epoch 383/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0018 - recall: 1.0000 - val_loss: 0.0519 - val_recall: 0.9848\n",
      "Epoch 384/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0018 - recall: 1.0000 - val_loss: 0.0472 - val_recall: 0.9848\n",
      "Epoch 385/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0019 - recall: 1.0000 - val_loss: 0.0478 - val_recall: 0.9848\n",
      "Epoch 386/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0018 - recall: 1.0000 - val_loss: 0.0497 - val_recall: 0.9848\n",
      "Epoch 387/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0018 - recall: 1.0000 - val_loss: 0.0511 - val_recall: 0.9848\n",
      "Epoch 388/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0018 - recall: 1.0000 - val_loss: 0.0471 - val_recall: 0.9848\n",
      "Epoch 389/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0017 - recall: 1.0000 - val_loss: 0.0475 - val_recall: 0.9848\n",
      "Epoch 390/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0018 - recall: 1.0000 - val_loss: 0.0472 - val_recall: 0.9848\n",
      "Epoch 391/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0017 - recall: 1.0000 - val_loss: 0.0492 - val_recall: 0.9848\n",
      "Epoch 392/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0017 - recall: 1.0000 - val_loss: 0.0495 - val_recall: 0.9848\n",
      "Epoch 393/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0017 - recall: 1.0000 - val_loss: 0.0514 - val_recall: 0.9848\n",
      "Epoch 394/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0017 - recall: 1.0000 - val_loss: 0.0507 - val_recall: 0.9848\n",
      "Epoch 395/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0017 - recall: 1.0000 - val_loss: 0.0472 - val_recall: 0.9848\n",
      "Epoch 396/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0017 - recall: 1.0000 - val_loss: 0.0457 - val_recall: 0.9848\n",
      "Epoch 397/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0017 - recall: 1.0000 - val_loss: 0.0476 - val_recall: 0.9848\n",
      "Epoch 398/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0017 - recall: 1.0000 - val_loss: 0.0473 - val_recall: 0.9848\n",
      "Epoch 399/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0016 - recall: 1.0000 - val_loss: 0.0462 - val_recall: 0.9848\n",
      "Epoch 400/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0016 - recall: 1.0000 - val_loss: 0.0451 - val_recall: 0.9848\n",
      "Epoch 401/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0016 - recall: 1.0000 - val_loss: 0.0492 - val_recall: 0.9848\n",
      "Epoch 402/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0016 - recall: 1.0000 - val_loss: 0.0494 - val_recall: 0.9848\n",
      "Epoch 403/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0016 - recall: 1.0000 - val_loss: 0.0488 - val_recall: 0.9848\n",
      "Epoch 404/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0015 - recall: 1.0000 - val_loss: 0.0478 - val_recall: 0.9848\n",
      "Epoch 405/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0015 - recall: 1.0000 - val_loss: 0.0492 - val_recall: 0.9848\n",
      "Epoch 406/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0015 - recall: 1.0000 - val_loss: 0.0473 - val_recall: 0.9848\n",
      "Epoch 407/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0015 - recall: 1.0000 - val_loss: 0.0447 - val_recall: 0.9848\n",
      "Epoch 408/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0016 - recall: 1.0000 - val_loss: 0.0467 - val_recall: 0.9848\n",
      "Epoch 409/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0015 - recall: 1.0000 - val_loss: 0.0503 - val_recall: 0.9848\n",
      "Epoch 410/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0015 - recall: 1.0000 - val_loss: 0.0513 - val_recall: 0.9848\n",
      "Epoch 411/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0015 - recall: 1.0000 - val_loss: 0.0473 - val_recall: 0.9848\n",
      "Epoch 412/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0014 - recall: 1.0000 - val_loss: 0.0480 - val_recall: 0.9848\n",
      "Epoch 413/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0014 - recall: 1.0000 - val_loss: 0.0491 - val_recall: 0.9848\n",
      "Epoch 414/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0014 - recall: 1.0000 - val_loss: 0.0506 - val_recall: 0.9848\n",
      "Epoch 415/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0014 - recall: 1.0000 - val_loss: 0.0492 - val_recall: 0.9848\n",
      "Epoch 416/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0014 - recall: 1.0000 - val_loss: 0.0506 - val_recall: 0.9848\n",
      "Epoch 417/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0014 - recall: 1.0000 - val_loss: 0.0485 - val_recall: 0.9848\n",
      "Epoch 418/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0014 - recall: 1.0000 - val_loss: 0.0472 - val_recall: 0.9848\n",
      "Epoch 419/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0479 - val_recall: 0.9848\n",
      "Epoch 420/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0491 - val_recall: 0.9848\n",
      "Epoch 421/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0481 - val_recall: 0.9848\n",
      "Epoch 422/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0480 - val_recall: 0.9848\n",
      "Epoch 423/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0480 - val_recall: 0.9848\n",
      "Epoch 424/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0014 - recall: 1.0000 - val_loss: 0.0385 - val_recall: 0.9848\n",
      "Epoch 425/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0015 - recall: 1.0000 - val_loss: 0.0425 - val_recall: 0.9848\n",
      "Epoch 426/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0478 - val_recall: 0.9848\n",
      "Epoch 427/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0498 - val_recall: 0.9848\n",
      "Epoch 428/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0502 - val_recall: 0.9848\n",
      "Epoch 429/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0454 - val_recall: 0.9848\n",
      "Epoch 430/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0446 - val_recall: 0.9848\n",
      "Epoch 431/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0013 - recall: 1.0000 - val_loss: 0.0472 - val_recall: 0.9848\n",
      "Epoch 432/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0488 - val_recall: 0.9848\n",
      "Epoch 433/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0482 - val_recall: 0.9848\n",
      "Epoch 434/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0465 - val_recall: 0.9848\n",
      "Epoch 435/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0484 - val_recall: 0.9848\n",
      "Epoch 436/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0472 - val_recall: 0.9848\n",
      "Epoch 437/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0467 - val_recall: 0.9848\n",
      "Epoch 438/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0467 - val_recall: 0.9848\n",
      "Epoch 439/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0451 - val_recall: 0.9848\n",
      "Epoch 440/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0470 - val_recall: 0.9848\n",
      "Epoch 441/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0468 - val_recall: 0.9848\n",
      "Epoch 442/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0472 - val_recall: 0.9848\n",
      "Epoch 443/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0515 - val_recall: 0.9848\n",
      "Epoch 444/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0487 - val_recall: 0.9848\n",
      "Epoch 445/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0449 - val_recall: 0.9848\n",
      "Epoch 446/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0451 - val_recall: 0.9848\n",
      "Epoch 447/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0442 - val_recall: 0.9848\n",
      "Epoch 448/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0457 - val_recall: 0.9848\n",
      "Epoch 449/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0468 - val_recall: 0.9848\n",
      "Epoch 450/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0463 - val_recall: 0.9848\n",
      "Epoch 451/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0454 - val_recall: 0.9848\n",
      "Epoch 452/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0507 - val_recall: 0.9848\n",
      "Epoch 453/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0012 - recall: 1.0000 - val_loss: 0.0463 - val_recall: 0.9848\n",
      "Epoch 454/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0426 - val_recall: 0.9848\n",
      "Epoch 455/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0011 - recall: 1.0000 - val_loss: 0.0425 - val_recall: 0.9848\n",
      "Epoch 456/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0010 - recall: 1.0000 - val_loss: 0.0453 - val_recall: 0.9848\n",
      "Epoch 457/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0010 - recall: 1.0000 - val_loss: 0.0473 - val_recall: 0.9848\n",
      "Epoch 458/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0010 - recall: 1.0000 - val_loss: 0.0465 - val_recall: 0.9848\n",
      "Epoch 459/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.8132e-04 - recall: 1.0000 - val_loss: 0.0443 - val_recall: 0.9848\n",
      "Epoch 460/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 9.8529e-04 - recall: 1.0000 - val_loss: 0.0459 - val_recall: 0.9848\n",
      "Epoch 461/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.6543e-04 - recall: 1.0000 - val_loss: 0.0457 - val_recall: 0.9848\n",
      "Epoch 462/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.6532e-04 - recall: 1.0000 - val_loss: 0.0458 - val_recall: 0.9848\n",
      "Epoch 463/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 9.5737e-04 - recall: 1.0000 - val_loss: 0.0456 - val_recall: 0.9848\n",
      "Epoch 464/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.4785e-04 - recall: 1.0000 - val_loss: 0.0455 - val_recall: 0.9848\n",
      "Epoch 465/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.4697e-04 - recall: 1.0000 - val_loss: 0.0456 - val_recall: 0.9848\n",
      "Epoch 466/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 9.4330e-04 - recall: 1.0000 - val_loss: 0.0441 - val_recall: 0.9848\n",
      "Epoch 467/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 9.3258e-04 - recall: 1.0000 - val_loss: 0.0444 - val_recall: 0.9848\n",
      "Epoch 468/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.4221e-04 - recall: 1.0000 - val_loss: 0.0419 - val_recall: 0.9848\n",
      "Epoch 469/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 9.4858e-04 - recall: 1.0000 - val_loss: 0.0419 - val_recall: 0.9848\n",
      "Epoch 470/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.2950e-04 - recall: 1.0000 - val_loss: 0.0441 - val_recall: 0.9848\n",
      "Epoch 471/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 9.1076e-04 - recall: 1.0000 - val_loss: 0.0439 - val_recall: 0.9848\n",
      "Epoch 472/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.0817e-04 - recall: 1.0000 - val_loss: 0.0439 - val_recall: 0.9848\n",
      "Epoch 473/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.9933e-04 - recall: 1.0000 - val_loss: 0.0449 - val_recall: 0.9848\n",
      "Epoch 474/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.9065e-04 - recall: 1.0000 - val_loss: 0.0451 - val_recall: 0.9848\n",
      "Epoch 475/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.8356e-04 - recall: 1.0000 - val_loss: 0.0482 - val_recall: 0.9848\n",
      "Epoch 476/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.8775e-04 - recall: 1.0000 - val_loss: 0.0477 - val_recall: 0.9848\n",
      "Epoch 477/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.8185e-04 - recall: 1.0000 - val_loss: 0.0435 - val_recall: 0.9848\n",
      "Epoch 478/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.8413e-04 - recall: 1.0000 - val_loss: 0.0440 - val_recall: 0.9848\n",
      "Epoch 479/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.5573e-04 - recall: 1.0000 - val_loss: 0.0461 - val_recall: 0.9848\n",
      "Epoch 480/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.5093e-04 - recall: 1.0000 - val_loss: 0.0469 - val_recall: 0.9848\n",
      "Epoch 481/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.4239e-04 - recall: 1.0000 - val_loss: 0.0454 - val_recall: 0.9848\n",
      "Epoch 482/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.4339e-04 - recall: 1.0000 - val_loss: 0.0412 - val_recall: 0.9848\n",
      "Epoch 483/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.6621e-04 - recall: 1.0000 - val_loss: 0.0423 - val_recall: 0.9848\n",
      "Epoch 484/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.5162e-04 - recall: 1.0000 - val_loss: 0.0421 - val_recall: 0.9848\n",
      "Epoch 485/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.4159e-04 - recall: 1.0000 - val_loss: 0.0454 - val_recall: 0.9848\n",
      "Epoch 486/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.1401e-04 - recall: 1.0000 - val_loss: 0.0489 - val_recall: 0.9848\n",
      "Epoch 487/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.3218e-04 - recall: 1.0000 - val_loss: 0.0470 - val_recall: 0.9848\n",
      "Epoch 488/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.0323e-04 - recall: 1.0000 - val_loss: 0.0446 - val_recall: 0.9848\n",
      "Epoch 489/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.9819e-04 - recall: 1.0000 - val_loss: 0.0430 - val_recall: 0.9848\n",
      "Epoch 490/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.9603e-04 - recall: 1.0000 - val_loss: 0.0434 - val_recall: 0.9848\n",
      "Epoch 491/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.9819e-04 - recall: 1.0000 - val_loss: 0.0415 - val_recall: 0.9848\n",
      "Epoch 492/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.1177e-04 - recall: 1.0000 - val_loss: 0.0436 - val_recall: 0.9848\n",
      "Epoch 493/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.1432e-04 - recall: 1.0000 - val_loss: 0.0478 - val_recall: 0.9848\n",
      "Epoch 494/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.1724e-04 - recall: 1.0000 - val_loss: 0.0436 - val_recall: 0.9848\n",
      "Epoch 495/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.9538e-04 - recall: 1.0000 - val_loss: 0.0444 - val_recall: 0.9848\n",
      "Epoch 496/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.7283e-04 - recall: 1.0000 - val_loss: 0.0443 - val_recall: 0.9848\n",
      "Epoch 497/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.6095e-04 - recall: 1.0000 - val_loss: 0.0441 - val_recall: 0.9848\n",
      "Epoch 498/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.4429e-04 - recall: 1.0000 - val_loss: 0.0454 - val_recall: 0.9848\n",
      "Epoch 499/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.3996e-04 - recall: 1.0000 - val_loss: 0.0452 - val_recall: 0.9848\n",
      "Epoch 500/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.4548e-04 - recall: 1.0000 - val_loss: 0.0459 - val_recall: 0.9848\n",
      "Epoch 501/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.4271e-04 - recall: 1.0000 - val_loss: 0.0426 - val_recall: 0.9848\n",
      "Epoch 502/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.2238e-04 - recall: 1.0000 - val_loss: 0.0437 - val_recall: 0.9848\n",
      "Epoch 503/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.1938e-04 - recall: 1.0000 - val_loss: 0.0442 - val_recall: 0.9848\n",
      "Epoch 504/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.1456e-04 - recall: 1.0000 - val_loss: 0.0438 - val_recall: 0.9848\n",
      "Epoch 505/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.0666e-04 - recall: 1.0000 - val_loss: 0.0447 - val_recall: 0.9848\n",
      "Epoch 506/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.0244e-04 - recall: 1.0000 - val_loss: 0.0441 - val_recall: 0.9848\n",
      "Epoch 507/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.0178e-04 - recall: 1.0000 - val_loss: 0.0442 - val_recall: 0.9848\n",
      "Epoch 508/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 6.9500e-04 - recall: 1.0000 - val_loss: 0.0449 - val_recall: 0.9848\n",
      "Epoch 509/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.9878e-04 - recall: 1.0000 - val_loss: 0.0440 - val_recall: 0.9848\n",
      "Epoch 510/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 6.8445e-04 - recall: 1.0000 - val_loss: 0.0439 - val_recall: 0.9848\n",
      "Epoch 511/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 6.8763e-04 - recall: 1.0000 - val_loss: 0.0435 - val_recall: 0.9848\n",
      "Epoch 512/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.7785e-04 - recall: 1.0000 - val_loss: 0.0427 - val_recall: 0.9848\n",
      "Epoch 513/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 6.7188e-04 - recall: 1.0000 - val_loss: 0.0411 - val_recall: 0.9848\n",
      "Epoch 514/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.7247e-04 - recall: 1.0000 - val_loss: 0.0415 - val_recall: 0.9848\n",
      "Epoch 515/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 6.6382e-04 - recall: 1.0000 - val_loss: 0.0431 - val_recall: 0.9848\n",
      "Epoch 516/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 6.5852e-04 - recall: 1.0000 - val_loss: 0.0442 - val_recall: 0.9848\n",
      "Epoch 517/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.5206e-04 - recall: 1.0000 - val_loss: 0.0432 - val_recall: 0.9848\n",
      "Epoch 518/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.5314e-04 - recall: 1.0000 - val_loss: 0.0434 - val_recall: 0.9848\n",
      "Epoch 519/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.4381e-04 - recall: 1.0000 - val_loss: 0.0436 - val_recall: 0.9848\n",
      "Epoch 520/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.5564e-04 - recall: 1.0000 - val_loss: 0.0468 - val_recall: 0.9848\n",
      "Epoch 521/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 6.4387e-04 - recall: 1.0000 - val_loss: 0.0452 - val_recall: 0.9848\n",
      "Epoch 522/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.3755e-04 - recall: 1.0000 - val_loss: 0.0443 - val_recall: 0.9848\n",
      "Epoch 523/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.4435e-04 - recall: 1.0000 - val_loss: 0.0428 - val_recall: 0.9848\n",
      "Epoch 524/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.2966e-04 - recall: 1.0000 - val_loss: 0.0442 - val_recall: 0.9848\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(feat_train, targ_train, epochs=2000, batch_size=32,\n",
    "                    validation_split=0.2,\n",
    "                    callbacks=[keras.callbacks.EarlyStopping(patience=100, restore_best_weights=True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 601us/step - loss: 0.0090 - recall: 0.9939\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.00899574439972639, 0.9938650131225586]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(feat_train,targ_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([163.,   1.,   0.,   0.,   0.,   0.,   1.,   0.,   0., 161.]),\n",
       " array([1.1609266e-17, 1.0000000e-01, 2.0000000e-01, 3.0000001e-01,\n",
       "        4.0000001e-01, 5.0000000e-01, 6.0000002e-01, 6.9999999e-01,\n",
       "        8.0000001e-01, 8.9999998e-01, 1.0000000e+00], dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(model.predict(feat_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([163.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 163.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(targ_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2a913028a60>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(targ_train, model.predict(feat_train)>0.5)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d9579fd63862c719ce91d1dd1fb1c44b2ef43bdaa2f86d843d9fd8562746d35b"
  },
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "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": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}