Predicting Mortality ICU .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicción de mortalidad en pacientes de ICU\n",
    "\n",
    "El presente notebook refiere al desarrollo del proyecto mencionado en:\n",
    "\n",
    "Se realizarán dos stacks de modelos (cada stack es igual, cambia el tratamiento de datos). El primero consta con lecturas de las primeras 48 horas de los pacientes, sin tratamiento adicional. El segundo recibe el mismo pre-procesamiento, sin embargo, para balancear las clases se utilizará una técnica de Oversampling.\n",
    "\n",
    "\n",
    "## 1.- Lectura y preprocesamiento de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166 Index(['Age', 'ICUType', 'Gender', 'SOFA', 'SAPS', 'Weight_min', 'Weight_max',\n",
      "       'Weight_mean', 'Weight_var', 'Weight_first',\n",
      "       ...\n",
      "       'MechVent_first', 'MechVent_last', 'MechVent_diff', 'Lactate_min',\n",
      "       'Lactate_max', 'Lactate_mean', 'Lactate_var', 'Lactate_first',\n",
      "       'Lactate_last', 'Lactate_diff'],\n",
      "      dtype='object', length=166)\n",
      "166\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "import os\n",
    "import seaborn as sns\n",
    "from sklearn.utils import class_weight\n",
    "from sklearn.utils.class_weight import compute_sample_weight\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.impute import SimpleImputer \n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from scipy import interp\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "\n",
    "\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold, train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import preprocessing\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import warnings\n",
    "\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "#Leemos el primer conjunto de datos para prueba y entrenamiento,\n",
    "#en este archivo \"outcomes\" se presentan las variables de \"alta\" de paciente.\n",
    "outcomes=pd.read_csv('DATA/data/Outcomes-a_b.csv')\n",
    "\n",
    "\n",
    "def dict_outcomes(ruta):\n",
    "    \"\"\"\n",
    "    Se crea un archivo para leer cada una de las entradas de outcomes\n",
    "    \n",
    "    Regresa outcomes, un diccionario con clave id paciente, con items lista con id-saps-sofa-surv-stay-death\n",
    "    \"\"\"\n",
    "    with open(ruta) as file:\n",
    "        inpu=csv.reader(file)\n",
    "        next(inpu)\n",
    "        outcomes=defaultdict(list)\n",
    "        for i in inpu:\n",
    "            outcomes[i[0]]=i\n",
    "        return outcomes\n",
    "outcomes={}\n",
    "outcomes=dict_outcomes('./DATA/data/Outcomes-a_b.csv')\n",
    "outcomes_test=dict_outcomes('./DATA/data/Outcomes-c.csv')\n",
    "\n",
    "filepath_patients='DATA/data/set-a_b'\n",
    "def read_pat(filepath_patients,icustay):\n",
    "    \"\"\"\n",
    "    Crea una clave para cada nueva variable, por paciente\n",
    "    \n",
    "    Regresa un diccionario donde las claves son los id de paciente, y el item son diccionarios, donde la clave\n",
    "    es la variable leída (i,e: Weight, heart rate, etc.) con tuplas de (\"Hora leída\", \"valor leído\")\n",
    "    \"\"\"\n",
    "    data_icu = {}\n",
    "    for icustay_id in icustay.keys():\n",
    "        file = '{}.txt'.format(icustay_id)\n",
    "        f = open(os.path.join(filepath_patients,file))\n",
    "        read_data = csv.reader(f)\n",
    "        next(read_data) \n",
    "        next(read_data) \n",
    "        data = defaultdict(list)\n",
    "        for row in read_data:\n",
    "            data[row[1]].append((row[0],row[2]))\n",
    "            data_icu[icustay_id] = data\n",
    "    return data_icu\n",
    "\n",
    "data_icu= read_pat('DATA/data/set-a_b/', outcomes)\n",
    "data_icu_test=read_pat('DATA/data/set-c/', outcomes_test)\n",
    "\n",
    "outcomes_df=pd.read_csv('./DATA/data/Outcomes-a_b.csv', index_col=None)\n",
    "outcomes_df_test=pd.read_csv('./DATA/data/Outcomes-c.csv', index_col=None)\n",
    "\n",
    "outcomes_df.index=outcomes_df['RecordID']\n",
    "outcomes_df_test.index=outcomes_df_test['RecordID']\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Estas son las variables que utilizaremos para iterar nuestro modelo.\n",
    "\"\"\"\n",
    "vars_estaticas = ['Age', 'Gender', 'Height', 'ICUType']\n",
    "vars_dinamicas = ['Albumin','ALP','ALT','AST','Bilirubin','BUN','Cholesterol',\n",
    "                'Creatinine','DiasABP','FiO2','GCS','Glucose','HCO3',\n",
    "                'HCT','HR','K','Lactate','Mg','MAP','MechVent',\n",
    "                'Na','NIDiasABP','NIMAP','NISysABP','PaCO2',\n",
    "                'PaO2','pH','Platelets','RespRate','SaO2',\n",
    "                'SysABP','Temp','TroponinI','TroponinT','WBC','Weight'] \n",
    "vars_outcomes=['RecordID', 'SAPS-I', 'SOFA', 'Length_of_stay', 'Survival',\n",
    "       'In-hospital_death']\n",
    "    \n",
    "def analysis_data(data):\n",
    "    \"\"\" Extraemos cada una de las variables listadas arriba\n",
    "        Regresa una lista de features que vamos a usar\n",
    "    \"\"\"\n",
    "    features = {} \n",
    "    values_features = {}\n",
    "    for name in vars_estaticas:\n",
    "        valor = float(data[name][0][1])\n",
    "        if valor < 0: #Los nulos estaban llenos con -1 (si fueron registrados), los reemplazamos\n",
    "            features[name] = np.nan\n",
    "        else:\n",
    "            features[name] = float(data[name][0][1])\n",
    "    for name in vars_dinamicas:\n",
    "                \n",
    "        if name in data:\n",
    "            values=[]\n",
    "            for d in data[name]:\n",
    "                if int(d[0].split(':')[0])>48:\n",
    "                    break\n",
    "                values.append(float(d[1]))\n",
    "            if valor < 0: \n",
    "                features[name] = np.nan\n",
    "            else:\n",
    "                values_features[name] = values\n",
    "        features.update(values_features)\n",
    "        \n",
    "   \n",
    "    return features\n",
    "\n",
    "obtain_features = [] \n",
    "labels = []\n",
    "ids = []\n",
    "count_missing_weight = 0\n",
    "for id, data in data_icu.items(): \n",
    "    features = analysis_data(data) #metemos en features un diccionario para cada paciente que posee todas sus variables\n",
    "    features.update({'SAPS':float(outcomes[id][1]),\n",
    "                           'SOFA': float(outcomes[id][2]),\n",
    "                           'STAY': float(outcomes[id][3]),\n",
    "                           'SURV': float(outcomes[id][4])})\n",
    "    obtain_features.append(features)\n",
    "    labels.append(outcomes[id][5]) # recuperamos la etiqueta \"In-hospital_death\"\n",
    "    ids.append(id)\n",
    "      \n",
    "\n",
    "obtain_features = pd.DataFrame(obtain_features)\n",
    "labels = pd.DataFrame(labels, columns = ['hospital_death'])\n",
    "pd_data = pd.concat([obtain_features, labels], axis = 1, sort = False) #Unimos todos los features, incluido el target\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Ahora realizaremos lo mismo para test\n",
    "\"\"\"\n",
    "\n",
    "obtain_features_t = [] \n",
    "labels_t = []\n",
    "ids_t = []\n",
    "count_missing_weight_t = 0\n",
    "for id, data in data_icu_test.items(): \n",
    "    features_t = analysis_data(data) \n",
    "    features_t.update({'SAPS':float(outcomes_test[id][1]),\n",
    "                           'SOFA': float(outcomes_test[id][2]),\n",
    "                           'STAY': float(outcomes_test[id][3]),\n",
    "                           'SURV': float(outcomes_test[id][4])})\n",
    "    obtain_features_t.append(features)\n",
    "    labels_t.append(outcomes_test[id][5])\n",
    "    ids_t.append(id)\n",
    "      \n",
    "obtain_features_t = pd.DataFrame(obtain_features_t)\n",
    "labels_t = pd.DataFrame(labels_t, columns = ['hospital_death'])\n",
    "pd_data_test = pd.concat([obtain_features_t, labels_t], axis = 1, sort = False) \n",
    "\n",
    "\"\"\"\n",
    "Ahora, si la variable tiene más del 25% de nulos, la eliminamos, y tenemos una lista de las variables que usarmos\n",
    "\"\"\"\n",
    "pd_data.isnull().sum()\n",
    "pd_data2=pd_data.drop(pd_data.columns[pd_data.isnull().mean()>0.25], axis=1)\n",
    "\n",
    "vars_estaticas_less = ['Age', 'ICUType', 'Gender','SOFA','STAY','SURV','SAPS']\n",
    "vars_dinamicas_less = ['Weight', 'BUN', 'Creatinine', 'HCT','HR', 'GCS', 'Temp', 'Platelets', 'HCO', \n",
    "                       'Na', 'WBC', 'K', 'Mg', 'Glucose', 'NISysABP', 'NIDiasABP', 'NIMAP', 'pH', \n",
    "                       'PaCO', 'PaO', 'DiasABP', 'SysABP', 'MAP', 'FiO', 'MechVent', 'Lactate', 'Height']\n",
    "\n",
    "\n",
    "\n",
    "def set_features_to_nan(name):\n",
    "  \"\"\"\n",
    "  Si el valor de una variable no fue registrado, le asignaremos NAN\n",
    "  \"\"\"\n",
    "  values_features = {}\n",
    "  values_features['{}_min'.format(name)] = np.nan\n",
    "  values_features['{}_max'.format(name)] = np.nan\n",
    "  values_features['{}_mean'.format(name)] = np.nan\n",
    "  values_features['{}_first'.format(name)] = np.nan\n",
    "  values_features['{}_last'.format(name)] = np.nan\n",
    "  values_features['{}_diff'.format(name)] = np.nan\n",
    "  values_features['{}_var'.format(name)] = np.nan\n",
    "  return values_features\n",
    "def features_series(data):\n",
    "    \"\"\"\n",
    "    Esta funcion construye los valores que queremos tener, esto es la media, varianza, etc. Y la agregamos al\n",
    "    diccionario, es la última parte del tratamiento.\n",
    "    \"\"\"\n",
    "    pre_features = {}\n",
    "    pre_values_features = {}\n",
    "    age_value = []\n",
    "    missing_weight = False\n",
    "    for name in vars_estaticas_less:\n",
    "        valor = float(data[name][0][1])\n",
    "        if valor < 0:\n",
    "            pre_features[name] = np.nan\n",
    "        elif name == \"Age\" and valor > 90:\n",
    "            pre_features[name] = 91.4 #Si la persona es muy longeva debemos reemplazar la edad, es una regla\n",
    "        \n",
    "        else:\n",
    "            pre_features[name] = float(data[name][0][1])\n",
    "    for parameter in vars_dinamicas_less:\n",
    "        if parameter in data:\n",
    "            values = [float(d[1]) for d in data[parameter]]\n",
    "            if not values: \n",
    "                missing_weight = True\n",
    "                pre_values_features = set_features_to_nan(parameter)\n",
    "            else: \n",
    "                pre_values_features['{}_min'.format(parameter)] = min(values)\n",
    "                pre_values_features['{}_max'.format(parameter)] = max(values)\n",
    "                pre_values_features['{}_mean'.format(parameter)] = np.mean(values)\n",
    "                pre_values_features['{}_var'.format(parameter)] = np.var(values)\n",
    "                pre_values_features['{}_first'.format(parameter)] = values[0]\n",
    "                pre_values_features['{}_last'.format(parameter)] = values [-1]\n",
    "                pre_values_features['{}_diff'.format(parameter)] = values[-1] - values[0]\n",
    "        pre_features.update(pre_values_features)\n",
    "    return pre_features\n",
    "\n",
    "\n",
    "def preprocessing(data_icu):\n",
    "    \"\"\"\n",
    "    Hacemos la union de los datos anteriores\n",
    "    \"\"\"\n",
    "    pre_features = []\n",
    "    count_missing_weight = 0\n",
    "    for id, data in data_icu.items():\n",
    "        feats = features_series(data)\n",
    "        pre_features.append(feats)\n",
    "    return pre_features\n",
    "\n",
    "\"\"\"\n",
    "Agregamos las claves para las medidas de outcomes y las inicializamos como estáticas\n",
    "\"\"\"\n",
    "for i in data_icu.keys():\n",
    "    data_icu[i].update({'SAPS':[('00:00',float(outcomes[i][1]))],\n",
    "                           'SOFA':[('00:00', float(outcomes[i][2]))],\n",
    "                           'STAY':[('00:00', float(outcomes[i][3]))],\n",
    "                           'SURV':[('00:00', float(outcomes[i][4]))]})\n",
    "    \n",
    "for i in data_icu_test.keys():\n",
    "    data_icu_test[i].update({'SAPS':[('00:00',float(outcomes_test[i][1]))],\n",
    "                           'SOFA':[('00:00', float(outcomes_test[i][2]))],\n",
    "                           'STAY':[('00:00', float(outcomes_test[i][3]))],\n",
    "                           'SURV':[('00:00', float(outcomes_test[i][4]))]})\n",
    "    \n",
    "pre_features_test = preprocessing(data_icu_test)\n",
    "pre_features_test = pd.DataFrame(pre_features_test)\n",
    "pre_features = preprocessing(data_icu)\n",
    "pre_features = pd.DataFrame(pre_features)\n",
    "\n",
    "\n",
    "\n",
    "###########################################################\n",
    "\"\"\"\n",
    "Eliminamos ciertas varaibles altamente descriptivas del target, poseen una correlacion de aprox .49,\n",
    "sin embargo, las variables representan algo que contradice la técnica, ya que mis valores son de las primeras 48 horas,\n",
    "estas variables sólo pueden ser conocidas al final de la estancia del paciente (por esto la alta correlacion)\n",
    "\"\"\"\n",
    "\n",
    "all_data = pd.concat([pre_features, labels], axis = 1, sort = False)\n",
    "x_all = all_data.drop(['hospital_death', 'SURV', 'STAY'], axis = 1) #features\n",
    "y_all = all_data.hospital_death #label\n",
    "feature_list = x_all.columns\n",
    "print(len(feature_list), feature_list) \n",
    "\n",
    "all_data_test = pd.concat([pre_features_test, labels_t], axis = 1, sort = False)\n",
    "x_all_t = all_data_test.drop(['hospital_death', 'SURV', 'STAY'], axis = 1) #features\n",
    "y_all_t = all_data_test.hospital_death #label\n",
    "feature_list_t = x_all_t.columns\n",
    "print(len(feature_list_t)) \n",
    "\n",
    "####\n",
    "all_data['hospital_death']=list(map(int, list(all_data['hospital_death'])))\n",
    "\n",
    "\n",
    "all_data.drop(['SURV','STAY'], axis=1, inplace=True)\n",
    "\n",
    "all_data_test.drop(['SURV','STAY'], axis=1, inplace=True)\n",
    "\n",
    "def imputer(set):\n",
    "    \"\"\"\n",
    "    Dado que existe una gran cantidad de nan, utilizaremos la técnica de llenarlos con la media\n",
    "    \n",
    "    \"\"\"\n",
    "    imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "    set = imputer.fit_transform(set) ##imputando con la media\n",
    "    return set\n",
    "\n",
    "def scaler(set):\n",
    "    \"\"\"\n",
    "    Escalamos los datos, esto sirve para que todos tengan a misma escala.\n",
    "    \"\"\"\n",
    "    scaler = StandardScaler() #normalizar datos\n",
    "    set = scaler.fit_transform(set)\n",
    "    return set\n",
    "\n",
    "x_all = imputer(x_all)\n",
    "x_all = scaler(x_all)\n",
    "\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder() #Codificamos las etiquetas con valor entre 0 y 1\n",
    "y_all = le.fit_transform(y_all)\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Ahora partimos nuestros datos, y creamos nuestro objeto para realizar cross-validation\n",
    "\"\"\"\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_all, y_all, test_size=0.2, random_state=422) \n",
    "sample_weights = class_weight.compute_sample_weight('balanced', y_train)\n",
    "ratio = float(np.sum(y_train == 0)) / np.sum(y_train==1)\n",
    "cv = StratifiedKFold(n_splits=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.- Comenzamos con el tratamiento para los modelos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.- Grid Search y Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid(x_tr=x_train,y_tr=y_train):\n",
    "    \"\"\"\n",
    "    Esta funcion inicializa los modelos y varia los parámetros para el CV\n",
    "    \"\"\"\n",
    "    estimators = {\n",
    "        'RandomForestClassifier': RandomForestClassifier(random_state = 442),\n",
    "        'XGBClassifier': XGBClassifier(seed = 442,eval_metric = 'auc', scale_pos_weight = ratio),\n",
    "        #'XGBClassifier': XGBClassifier(seed = 442,eval_metric = 'auc', scale_pos_weight = ratio),\n",
    "        'LogisticRegression': LogisticRegression(random_state = 442),\n",
    "        'Light':lgb.LGBMClassifier(seed = 442),\n",
    "        'AdaBoostClassifier': AdaBoostClassifier(random_state=442)\n",
    "    }\n",
    "\n",
    "    params = {\n",
    "          'XGBClassifier': {\n",
    "        \"n_estimators\":[100], \"learning_rate\": [0.15,0.25],\n",
    "        \"colsample_bytree\" : [0.8],\n",
    "        \"subsample\" : [0.8, 0.4], \"reg_alpha\" : [0.5], \"reg_lambda\": [2],\n",
    "        \"objective\": ['binary:logistic'], \"max_depth\":[4,2], \n",
    "        \"gamma\":[10]\n",
    "        },\n",
    "        'RandomForestClassifier':{ \n",
    "                \"n_estimators\"      : [20,80,110, 130],\n",
    "                \"max_depth\":[6,7,8]\n",
    "                },\n",
    "      \n",
    "\n",
    "        'LogisticRegression': {\n",
    "            \"C\":np.logspace(-3,3,7), \n",
    "            \"penalty\":[\"elasticnet\"],\n",
    "            \"solver\":['saga'], \n",
    "            \"l1_ratio\":[0.5]\n",
    "            },\n",
    "        'Light':{\"objective\": ['binary'],\"learning_rate\":[0.1,0.9],\n",
    "                \"metric\":['auc'], \"max_bin\":[130], \n",
    "                \"feature_fraction\":[0.8, 0.6],\"min_data_in_bin\":[1],\n",
    "                \"max_depth\":[10,2],\"min_data_in_leaf\": [10],\n",
    "                \"min_sum_hessian_in_leaf\":[1e-10],\"drop_rate\":[0.5],\n",
    "                \"bagging_fraction\":[0.5,1.0],\"num_leaves\":[31],\n",
    "                \"boost_from_average\":['true'],\"lambda_l2\":[0.09, 0.9],\n",
    "                \"min_gain_to_split\":[10], \"is_unbalance\" : ['true'],\n",
    "                \"num_iterations\":[200]\n",
    "        },\n",
    "        'AdaBoostClassifier':{\"n_estimators\": [5,20,50,100], \"learning_rate\":[.1,.9],\n",
    "              \"algorithm\":[\"SAMME\", \"SAMME.R\"]}\n",
    "    }\n",
    "    for name in estimators.keys():\n",
    "        est = estimators[name]\n",
    "        est_params = params[name]\n",
    "        clf = GridSearchCV(estimator=est, param_grid=est_params, cv=cv,\n",
    "                          scoring='roc_auc', iid=\"warn\", refit = True)\n",
    "        clf.fit(x_tr, y_tr,sample_weight = sample_weights)\n",
    "        print(\"best parameters are: {}\".format(clf.best_estimator_))\n",
    "        print(\"{} best AUC: {}\".format(name,clf.best_score_))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.8395316855694165\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.8385781565456619\n",
      "best parameters are: LogisticRegression(C=0.01, l1_ratio=0.5, penalty='elasticnet', random_state=442,\n",
      "                   solver='saga')\n",
      "LogisticRegression best AUC: 0.8341283144013525\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.6, is_unbalance='true', lambda_l2=0.9,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.8390127829985383\n",
      "best parameters are: AdaBoostClassifier(learning_rate=0.1, n_estimators=100, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.8343853952207624\n"
     ]
    }
   ],
   "source": [
    "grid()\n",
    "#Utilizamos estos parámetros (los mejores segun CV) para entrenar los modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def compute_auc(model, x,y):\n",
    "    \"\"\"Función que calcula el área bajo la curva ROC\"\"\"\n",
    "    y_predicted = model.predict(x)\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y, y_predicted)\n",
    "    auc_score = auc(false_positive_rate, true_positive_rate)\n",
    "    return auc_score,false_positive_rate,true_positive_rate\n",
    "\n",
    "def read_data(raw_clinical_note):\n",
    "    #Leemos los datos y los partimos en x,y\n",
    "    data=raw_clinical_note\n",
    "    col = data.columns\n",
    "    x = data.drop('hospital_death', axis = 1) \n",
    "    y = data.hospital_death \n",
    "    return x, y\n",
    "\n",
    "x_test, y_test = read_data(all_data_test)\n",
    "y_test = list(map(int, y_test))\n",
    "\n",
    "#Imputamos y escalamos el test\n",
    "x_test = imputer(x_test)\n",
    "x_test=scaler(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>ICUType</th>\n",
       "      <th>Gender</th>\n",
       "      <th>SOFA</th>\n",
       "      <th>SAPS</th>\n",
       "      <th>Weight_min</th>\n",
       "      <th>Weight_max</th>\n",
       "      <th>Weight_mean</th>\n",
       "      <th>Weight_var</th>\n",
       "      <th>Weight_first</th>\n",
       "      <th>...</th>\n",
       "      <th>NIDiasABP_first</th>\n",
       "      <th>NIDiasABP_last</th>\n",
       "      <th>NIDiasABP_diff</th>\n",
       "      <th>NIMAP_min</th>\n",
       "      <th>NIMAP_max</th>\n",
       "      <th>NIMAP_mean</th>\n",
       "      <th>NIMAP_var</th>\n",
       "      <th>NIMAP_first</th>\n",
       "      <th>NIMAP_last</th>\n",
       "      <th>NIMAP_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>3996.000000</td>\n",
       "      <td>3878.000000</td>\n",
       "      <td>3834.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3485.000000</td>\n",
       "      <td>3485.000000</td>\n",
       "      <td>3485.000000</td>\n",
       "      <td>3467.000000</td>\n",
       "      <td>3467.000000</td>\n",
       "      <td>3467.000000</td>\n",
       "      <td>3467.000000</td>\n",
       "      <td>3467.000000</td>\n",
       "      <td>3467.000000</td>\n",
       "      <td>3467.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.728500</td>\n",
       "      <td>2.767500</td>\n",
       "      <td>0.557558</td>\n",
       "      <td>6.724085</td>\n",
       "      <td>15.107720</td>\n",
       "      <td>73.741587</td>\n",
       "      <td>77.137107</td>\n",
       "      <td>76.321165</td>\n",
       "      <td>8.518125</td>\n",
       "      <td>74.062030</td>\n",
       "      <td>...</td>\n",
       "      <td>59.596844</td>\n",
       "      <td>56.665710</td>\n",
       "      <td>-2.931133</td>\n",
       "      <td>59.353389</td>\n",
       "      <td>96.677346</td>\n",
       "      <td>76.210280</td>\n",
       "      <td>110.815525</td>\n",
       "      <td>79.285734</td>\n",
       "      <td>76.814073</td>\n",
       "      <td>-2.471661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.816569</td>\n",
       "      <td>1.026502</td>\n",
       "      <td>0.496738</td>\n",
       "      <td>4.030858</td>\n",
       "      <td>5.222933</td>\n",
       "      <td>31.763487</td>\n",
       "      <td>32.470657</td>\n",
       "      <td>31.961677</td>\n",
       "      <td>115.111600</td>\n",
       "      <td>31.818614</td>\n",
       "      <td>...</td>\n",
       "      <td>18.172873</td>\n",
       "      <td>16.586356</td>\n",
       "      <td>18.997231</td>\n",
       "      <td>14.523564</td>\n",
       "      <td>19.411720</td>\n",
       "      <td>12.392760</td>\n",
       "      <td>163.757209</td>\n",
       "      <td>17.999951</td>\n",
       "      <td>15.643532</td>\n",
       "      <td>18.409682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-97.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-87.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>61.200000</td>\n",
       "      <td>63.800000</td>\n",
       "      <td>63.194828</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.675000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>-13.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>67.564048</td>\n",
       "      <td>41.526748</td>\n",
       "      <td>66.670000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>-13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.145938</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>58.670000</td>\n",
       "      <td>95.330000</td>\n",
       "      <td>74.605263</td>\n",
       "      <td>78.069069</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>75.330000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>93.525000</td>\n",
       "      <td>92.500000</td>\n",
       "      <td>0.885764</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>67.330000</td>\n",
       "      <td>107.300000</td>\n",
       "      <td>83.596222</td>\n",
       "      <td>137.479484</td>\n",
       "      <td>90.330000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>278.500000</td>\n",
       "      <td>278.500000</td>\n",
       "      <td>278.500000</td>\n",
       "      <td>4279.164354</td>\n",
       "      <td>278.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>121.700000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>128.750000</td>\n",
       "      <td>5456.222222</td>\n",
       "      <td>154.300000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>152.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age      ICUType       Gender         SOFA         SAPS  \\\n",
       "count  4000.000000  4000.000000  3996.000000  3878.000000  3834.000000   \n",
       "mean     64.728500     2.767500     0.557558     6.724085    15.107720   \n",
       "std      16.816569     1.026502     0.496738     4.030858     5.222933   \n",
       "min      16.000000     1.000000     0.000000     0.000000     1.000000   \n",
       "25%      54.000000     2.000000     0.000000     3.000000    11.000000   \n",
       "50%      67.000000     3.000000     1.000000     7.000000    15.000000   \n",
       "75%      78.000000     4.000000     1.000000    10.000000    19.000000   \n",
       "max      90.000000     4.000000     1.000000    22.000000    38.000000   \n",
       "\n",
       "        Weight_min   Weight_max  Weight_mean   Weight_var  Weight_first  ...  \\\n",
       "count  4000.000000  4000.000000  4000.000000  4000.000000   4000.000000  ...   \n",
       "mean     73.741587    77.137107    76.321165     8.518125     74.062030  ...   \n",
       "std      31.763487    32.470657    31.961677   115.111600     31.818614  ...   \n",
       "min      -1.000000    -1.000000    -1.000000     0.000000     -1.000000  ...   \n",
       "25%      61.200000    63.800000    63.194828     0.000000     61.675000  ...   \n",
       "50%      75.000000    78.000000    77.145938     0.000000     75.000000  ...   \n",
       "75%      90.000000    93.525000    92.500000     0.885764     90.000000  ...   \n",
       "max     278.500000   278.500000   278.500000  4279.164354    278.500000  ...   \n",
       "\n",
       "       NIDiasABP_first  NIDiasABP_last  NIDiasABP_diff    NIMAP_min  \\\n",
       "count      3485.000000     3485.000000     3485.000000  3467.000000   \n",
       "mean         59.596844       56.665710       -2.931133    59.353389   \n",
       "std          18.172873       16.586356       18.997231    14.523564   \n",
       "min          -1.000000       -1.000000      -97.000000     0.000000   \n",
       "25%          48.000000       47.000000      -13.000000    51.000000   \n",
       "50%          59.000000       56.000000       -1.000000    58.670000   \n",
       "75%          71.000000       67.000000        7.000000    67.330000   \n",
       "max         141.000000      117.000000       86.000000   121.700000   \n",
       "\n",
       "         NIMAP_max   NIMAP_mean    NIMAP_var  NIMAP_first   NIMAP_last  \\\n",
       "count  3467.000000  3467.000000  3467.000000  3467.000000  3467.000000   \n",
       "mean     96.677346    76.210280   110.815525    79.285734    76.814073   \n",
       "std      19.411720    12.392760   163.757209    17.999951    15.643532   \n",
       "min      22.000000    22.000000     0.000000     0.000000     0.000000   \n",
       "25%      84.000000    67.564048    41.526748    66.670000    66.000000   \n",
       "50%      95.330000    74.605263    78.069069    78.000000    75.330000   \n",
       "75%     107.300000    83.596222   137.479484    90.330000    86.000000   \n",
       "max     228.000000   128.750000  5456.222222   154.300000   228.000000   \n",
       "\n",
       "        NIMAP_diff  \n",
       "count  3467.000000  \n",
       "mean     -2.471661  \n",
       "std      18.409682  \n",
       "min     -87.300000  \n",
       "25%     -13.000000  \n",
       "50%      -1.000000  \n",
       "75%       8.000000  \n",
       "max     152.000000  \n",
       "\n",
       "[8 rows x 166 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>ICUType</th>\n",
       "      <th>Gender</th>\n",
       "      <th>SOFA</th>\n",
       "      <th>SAPS</th>\n",
       "      <th>Weight_min</th>\n",
       "      <th>Weight_max</th>\n",
       "      <th>Weight_mean</th>\n",
       "      <th>Weight_var</th>\n",
       "      <th>BUN_min</th>\n",
       "      <th>...</th>\n",
       "      <th>MAP_var</th>\n",
       "      <th>MechVent_min</th>\n",
       "      <th>MechVent_max</th>\n",
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       "      <th>Lactate_min</th>\n",
       "      <th>Lactate_max</th>\n",
       "      <th>Lactate_mean</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>7992.000000</td>\n",
       "      <td>7719.000000</td>\n",
       "      <td>7648.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>7880.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5598.000000</td>\n",
       "      <td>5040.0</td>\n",
       "      <td>5040.0</td>\n",
       "      <td>5040.0</td>\n",
       "      <td>5040.0</td>\n",
       "      <td>4398.000000</td>\n",
       "      <td>4398.000000</td>\n",
       "      <td>4398.000000</td>\n",
       "      <td>4398.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.429750</td>\n",
       "      <td>2.783750</td>\n",
       "      <td>0.562938</td>\n",
       "      <td>6.632724</td>\n",
       "      <td>14.945345</td>\n",
       "      <td>74.019159</td>\n",
       "      <td>77.481383</td>\n",
       "      <td>76.614612</td>\n",
       "      <td>7.600651</td>\n",
       "      <td>21.881599</td>\n",
       "      <td>...</td>\n",
       "      <td>184.463168</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.714950</td>\n",
       "      <td>3.129045</td>\n",
       "      <td>2.352909</td>\n",
       "      <td>0.916188</td>\n",
       "      <td>0.140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.352628</td>\n",
       "      <td>1.013096</td>\n",
       "      <td>0.496054</td>\n",
       "      <td>4.069552</td>\n",
       "      <td>5.199350</td>\n",
       "      <td>32.219494</td>\n",
       "      <td>32.869914</td>\n",
       "      <td>32.504830</td>\n",
       "      <td>77.564366</td>\n",
       "      <td>18.484622</td>\n",
       "      <td>...</td>\n",
       "      <td>354.769646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.196205</td>\n",
       "      <td>2.682227</td>\n",
       "      <td>1.681688</td>\n",
       "      <td>3.184083</td>\n",
       "      <td>0.347268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>70.266183</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.335000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>75.600000</td>\n",
       "      <td>78.750000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>115.873060</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>92.829021</td>\n",
       "      <td>0.776805</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>194.877236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>0.497225</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>3500.169796</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>10649.135802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>90.494722</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age      ICUType       Gender         SOFA         SAPS  \\\n",
       "count  8000.000000  8000.000000  7992.000000  7719.000000  7648.000000   \n",
       "mean     64.429750     2.783750     0.562938     6.632724    14.945345   \n",
       "std      17.352628     1.013096     0.496054     4.069552     5.199350   \n",
       "min      15.000000     1.000000     0.000000     0.000000     0.000000   \n",
       "25%      53.000000     2.000000     0.000000     3.000000    11.000000   \n",
       "50%      67.000000     3.000000     1.000000     7.000000    15.000000   \n",
       "75%      78.000000     4.000000     1.000000     9.000000    19.000000   \n",
       "max      90.000000     4.000000     1.000000    22.000000    34.000000   \n",
       "\n",
       "        Weight_min   Weight_max  Weight_mean   Weight_var      BUN_min  ...  \\\n",
       "count  8000.000000  8000.000000  8000.000000  8000.000000  7880.000000  ...   \n",
       "mean     74.019159    77.481383    76.614612     7.600651    21.881599  ...   \n",
       "std      32.219494    32.869914    32.504830    77.564366    18.484622  ...   \n",
       "min      -1.000000    -1.000000    -1.000000     0.000000     0.000000  ...   \n",
       "25%      61.500000    64.000000    63.400000     0.000000    11.000000  ...   \n",
       "50%      75.600000    78.750000    78.000000     0.000000    16.000000  ...   \n",
       "75%      90.000000    94.000000    92.829021     0.776805    26.000000  ...   \n",
       "max     472.000000   472.000000   472.000000  3500.169796   168.000000  ...   \n",
       "\n",
       "            MAP_var  MechVent_min  MechVent_max  MechVent_mean  MechVent_var  \\\n",
       "count   5598.000000        5040.0        5040.0         5040.0        5040.0   \n",
       "mean     184.463168           1.0           1.0            1.0           0.0   \n",
       "std      354.769646           0.0           0.0            0.0           0.0   \n",
       "min        0.000000           1.0           1.0            1.0           0.0   \n",
       "25%       70.266183           1.0           1.0            1.0           0.0   \n",
       "50%      115.873060           1.0           1.0            1.0           0.0   \n",
       "75%      194.877236           1.0           1.0            1.0           0.0   \n",
       "max    10649.135802           1.0           1.0            1.0           0.0   \n",
       "\n",
       "       Lactate_min  Lactate_max  Lactate_mean  Lactate_var  hospital_death  \n",
       "count  4398.000000  4398.000000   4398.000000  4398.000000     8000.000000  \n",
       "mean      1.714950     3.129045      2.352909     0.916188        0.140250  \n",
       "std       1.196205     2.682227      1.681688     3.184083        0.347268  \n",
       "min       0.300000     0.400000      0.400000     0.000000        0.000000  \n",
       "25%       1.100000     1.500000      1.335000     0.000000        0.000000  \n",
       "50%       1.400000     2.300000      1.900000     0.060000        0.000000  \n",
       "75%       1.900000     3.700000      2.760000     0.497225        0.000000  \n",
       "max      21.200000    31.000000     24.800000    90.494722        1.000000  \n",
       "\n",
       "[8 rows x 98 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.- Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#RandomForestClassifier best AUC: 0.8395316855694165\n",
    "\n",
    "#XGBClassifier best AUC: 0.8385781565456619\n",
    "\n",
    "#LogisticRegression best AUC: 0.8341283144013525\n",
    "\n",
    "#Light best AUC: 0.8390127829985383\n",
    "\n",
    "#AdaBoostClassifier best AUC: 0.8343853952207624\n",
    "\n",
    "\"\"\"\n",
    "Utilizamos los parámetros obtenidos y entrenamos para cada uno\n",
    "\"\"\"\n",
    "\n",
    "moXGB= XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
    "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
    "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
    "moXGB=moXGB.fit(x_train, y_train)\n",
    "\n",
    "moRand=RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
    "moRand=moRand.fit(x_train, y_train)\n",
    "\n",
    "moLog=LogisticRegression(C=0.01, l1_ratio=0.5, penalty='elasticnet', random_state=442,\n",
    "                   solver='saga')\n",
    "moLog=moLog.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "molgb=lgb.LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
    "               feature_fraction=0.6, is_unbalance='true', lambda_l2=0.9,\n",
    "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
    "               min_data_in_leaf=10, min_gain_to_split=10,\n",
    "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
    "               objective='binary', seed=442)\n",
    "molgb=molgb.fit(x_train, y_train)\n",
    "\n",
    "moAda=AdaBoostClassifier(learning_rate=0.1, n_estimators=100, random_state=442)\n",
    "moAda=moAda.fit(x_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4.- AUC y ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "XGBClassifier\n",
      "AUC de validación:  0.7387276496501685\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "RandomForest\n",
      "AUC de validación:  0.5520520983723886\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "LogisticRegression\n",
      "AUC de validación:  0.5847277236885944\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "LGBMC\n",
      "AUC de validación:  0.7657948642011871\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "AdaBoost\n",
      "AUC de validación:  0.5483810264193784\n"
     ]
    },
    {
     "data": {
      "image/png": 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uSo1hcPdafy0PEakhFOgBMDOeWbCJp+Z/41937TkdeWR0oodViYiUpkAPwJy1P/jD/I6fdGVc7w6c1aqxx1WJiJSmQA/Au2t2AfDvn6cytEdbj6sRESmfpi1W4p3Vu3h/3Q8ADOoa5XE1IiIVU6CfQFZ2Pre/thKAJy9PpkFkhMcViYhUTIF+AjsyswG4ok80V6TGVNJaRMRbCvQADEto53UJIiKVUqCLiIQJzXKpwJsr05m2dIfXZYiIBEyBXoHfTF8NQO+OLYhr19TjakREKqdAL8euAzmAL8zf+PW5HlcjIhIYjaGX41+LvwPQzBYRCSkK9HJs3+ebrjgmRec5F5HQoUAvR6P6dWnbrD6N6mlESkRChwL9OHkFhbyzepe+FSoiIUeBfpwd+30HRKNbNvS4EhGRk6NAP859b6wF4PI+0R5XIiJychTox1m6dT8NIutwcZIOiIpIaFGgl6NzVBPq1dVLIyKhRalVQsahPAAujNN1QkUk9CjQi+UVFHLpc58B0KZZfY+rERE5eQEFunNuuHNuo3Nuk3Pu3hO0u8w5Z8651OCVWD2eW7SZncVf+b+qr74hKiKhp9JAd85FAM8BI4AewNXOuR7ltGsK3AF8Gewiq8Ph3AIAltx7IfXrag66iISeQPbQ+wGbzOw7MzsKTAPGlNPuUeAJIDeI9VWrpvXr0r6F5p+LSGgKJNA7ACVPDJ5evM7POdcbiDGz9060Iefczc655c655RkZGSddbFX5aOMe/vPZFo4WFnldiojIKTvtg6LOuTrAX4G7KmtrZs+bWaqZpbZu3fp0n/q07TqQwxff7WPCi8sA+O3wOI8rEhE5dYGcfWonUPIoYXTxumOaAonAR845gHbAbOfcaDNbHqxCq8LV//qCbcVnVoxr15SJgzp5XJGIyKkLJNCXAV2dc53wBfl44Jpjd5pZFhB1bNk59xFwd00P8w/W/8C2fdkMjW/DxEGdSY5u7nVJIiKnpdIhFzMrAG4DPgA2ADPMbL1zbrJzbnRVF1hV/rbwWwCG9WjHgLNb0bi+TpUrIqEtoBQzsznAnOPWPVhB28GnX1bVenbht6zbeZCh8W25UnPORSRM1Mpviq7acQCAG8/TmLmIhI9aGegACe2bcU7nVl6XISISNLU20EVEwo0CXUQkTNS6QF+14wDzN+yhsMi8LkVEJKhqXaBv3nMYgPGa3SIiYabWBfrKHZkAXBjX1uNKRESCq1YF+uG8Av77xXYAmjeK9LgaEZHgqlWB/tqXvjAfk9Ke5g0V6CISXmpNoBcVGX+YswGAhy9J8LgaEZHgqzWB/td53wAQ3bIhLRvX87gaEZHgqzWBvu9IHgBv3Xqux5WIiFSNWhPoAG2a1ieqSX2vyxARqRK1KtBFRMJZrQj0x+ZsYNqyHdTxXVFJRCQs1YpA3/DDIVo3qc/Do3t4XYqISJWpFYEO0L5FQ4Ynnul1GSIiVabWBLqISLgL+0A/klfAJ99koHMriki4C/tAn/TmWgBiWjb0uBIRkaoV1oG+Ze8R3lq1C4DHxiV5XI2ISNUK20AvKjKG/PkjAG4+vzNNG+hkXCIS3sI20Ddn+C5k0axBXf53RJzH1YiIVL2wDfQNPxwC4PHLknH6QpGI1AJhG+jvrPaNnZ/duonHlYiIVI+wDfRmxWPm3ds19bgSEZHqEbaBDtChhaYqikjtEdaBLiJSm4RtoBeZvhsqIrVLWAZ6ztFC3ly5k6OFRV6XIiJSbQIKdOfccOfcRufcJufcveXcf6dzLs05t8Y5t8A5d1bwSw3c/W+tA2BA51ZeliEiUq0qDXTnXATwHDAC6AFc7Zw7/sTiK4FUM0sGZgJ/CnahgSosMmZ9lQ7Any5P9qoMEZFqF8geej9gk5l9Z2ZHgWnAmJINzGyRmWUXL34BRAe3zMB9s9v3haJxvTvQIDLCqzJERKpdIIHeAdhRYjm9eF1FJgLvl3eHc+5m59xy59zyjIyMwKs8CX94bwMAP01oVyXbFxGpqYJ6UNQ5dy2QCjxZ3v1m9ryZpZpZauvWrYP51ADsPpjLt3t8e+h9Y88I+vZFRGqyugG02QnElFiOLl5XinNuKDAJuMDM8oJT3skZ8fRi9h85ytX9OnJG43pelCAi4plA9tCXAV2dc52cc/WA8cDskg2cc72AfwKjzWxP8MusXH5hEfuPHOXipDP53fDuXpQgIuKpSgPdzAqA24APgA3ADDNb75yb7JwbXdzsSaAJ8LpzbpVzbnYFm6syc9f9AECHlg1p0Uh75yJS+wQy5IKZzQHmHLfuwRK3hwa5rpO277BvlGdMSnuPKxER8UbYfFN0xnLf3PPolo08rkRExBthE+hp3x8EoHlDXWpORGqnsAj0zCNHAYjTuc9FpBYLi0DPL/KdhOtn53h6ChkREU+FRaAfoyuHikhtFhaBPn2p78wEkRGKdBGpvcIi0Od/7fsu08ikMz2uRETEOyEf6HkFhazecQCApg00w0VEaq+QD/Svv/edjOum8zp5XImIiLdCPtCnLfONnw/sEuVxJSIi3gr5QD+Umw9A6lktPa5ERMRbIR/oAJ1bN9b4uYjUeiEf6Eu37MfM6ypERLwX0NkWa6rF32aw51AezQuKvC5FRMRzIb2H/sBb6wB46qqeHlciIuK9kA70lo3r0SCyDhfGtfW6FBERz4V0oDt0MWgRkWNCOtC/2n7A6xJERGqMkA30N77yXaHoYG6Bx5WIiNQMIRvo8zfsBmDy6ASPKxERqRlCNtB3ZubQpU0Tesa08LoUEZEaIWTnoa9Oz+KMxvW8LiMs5efnk56eTm5urteliNRaDRo0IDo6msjIwL8FH5KBvvugL2h0DdGqkZ6eTtOmTYmNjcU5XTREpLqZGfv27SM9PZ1OnQI/k2xIDrl8l3EEgDEp7T2uJDzl5ubSqlUrhbmIR5xztGrV6qQ/JYdkoO89nAdAm6YNPK4kfCnMRbx1Kn+DIRnoh4qnKsac0dDjSkREao6QDPSV2zMBaFJfp8wNZ2+99RbOOb7++usK20yYMIGZM2eecDsTJkygU6dOpKSkEBcXxyOPPBL0OtPS0oK6zdM1d+5cunfvTpcuXXj88cfLbbN9+3aGDBlCr169SE5OZs6cOQDMmzePPn36kJSURJ8+fVi4cKH/Ma+99hpJSUkkJyczfPhw9u7dC8ADDzxAcnIyKSkpDBs2jF27dgHw5JNPkpKSQkpKComJiURERLB//34AnnrqKRISEkhMTOTqq6/2Dy8sXLiQ3r17k5iYyPXXX09BgW8H7pVXXiE5OZmkpCQGDhzI6tWr/XVVtC0zY9KkSXTr1o34+HieeeYZ/2M++ugjUlJSSEhI4IILLgB8w439+vWjZ8+eJCQk8NBDD/nbV1TXifp44MABLr/8cuLi4oiPj+fzzz8H4KqrrvI/JjY2lpSUlJN/k8tjZp789OnTx07Vw7PX2Vm/e/eUHy8nlpaW5nUJZmZ25ZVX2qBBg+zBBx+ssM31119vr7/++gm3U7JNTk6OderUyb777rug1RlIDdWpoKDAOnfubJs3b7a8vDxLTk629evXl2l300032d///nczM1u/fr2dddZZZmb21Vdf2c6dO83MbO3atda+fXszM8vPz7fWrVtbRkaGmZndc8899tBDD5mZWVZWln+7Tz/9tP3yl78s83yzZ8+2IUOGmJlZenq6xcbGWnZ2tpmZXXHFFfbiiy9aYWGhRUdH28aNG83M7IEHHrB///vfZmb22Wef2f79+83MbM6cOdavX78TbsvM7D//+Y9dd911VlhYaGZmu3fvNjOzzMxMi4+Pt23btpVaX1RUZIcOHTIzs6NHj1q/fv3s888/P2FdFfXRzOznP/+5/etf/zIzs7y8PMvMzCzzmDvvvNMeeeSRMuvNyv9bBJZbBbkakrNcPt6YQcPICK/LqBUeeWc9absOBnWbPdo346FLTvyFsMOHD/Ppp5+yaNEiLrnkEv9etZlx++23M2/ePGJiYqhX78epq5MnT+add94hJyeHgQMH8s9//rPMOOSxPbfGjRsDsGDBAu6++24KCgro27cvU6ZMoX79+hWuv/fee5k9ezZ169Zl2LBhjBs3jtmzZ/Pxxx/z+9//nlmzZnH22WeX26elS5dyxx13kJubS8OGDXnxxRfp3r07L730EsuXL+fZZ58FYNSoUdx9990MHjyYuXPnct9991FYWEhUVBQLFiyo9PVdunQpXbp0oXPnzgCMHz+et99+mx49epRq55zj4EHfe5uVlUX79r5JBr169fK3SUhIICcnh7y8POrUqYOZceTIEVq1asXBgwfp0qULAM2aNfM/5siRI+WO/7722mtcffXV/uWCggJycnKIjIwkOzub9u3bs2/fPurVq0e3bt0AuOiii3jssceYOHEiAwcO9D/2nHPOIT09/YTbApgyZQqvvvoqder4BiPatGkDwKuvvsq4cePo2LFjqfXOOZo0aQL4pu/m5+fjnDthXRX1MSsri08++YSXXnoJgHr16pX6fQXf7/OMGTNKfQo6HSE55JJXUMTRQp0DPZy9/fbbDB8+nG7dutGqVStWrFgBwJtvvsnGjRtJS0tj6tSpLFmyxP+Y2267jWXLlrFu3TpycnJ49913/ffdc889pKSkEB0dzfjx42nTpg25ublMmDCB6dOns3btWgoKCpgyZUqF6/ft28ebb77J+vXrWbNmDffffz8DBw5k9OjRPPnkk6xatarCMAeIi4tj8eLFrFy5ksmTJ3Pfffed8DXIyMjgpptuYtasWaxevZrXX38dgEWLFvk/rpf8ORZ4O3fuJCYmxr+d6Ohodu7cWWb7Dz/8MP/973+Jjo5m5MiR/O1vfyvTZtasWfTu3Zv69esTGRnJlClTSEpKon379qSlpZUKtEmTJhETE8Mrr7zC5MmTS20nOzubuXPnctlllwHQoUMH7r77bjp27MiZZ55J8+bNGTZsGFFRURQUFLB8+XIAZs6cyY4dO8rU9cILLzBixIgTbgtg8+bNTJ8+ndTUVEaMGMG3334LwDfffENmZiaDBw+mT58+TJ061b/twsJCUlJSaNOmDRdddBH9+/cPqK7j+7hlyxZat27NDTfcQK9evbjxxhs5cuRIqccsXryYtm3b0rVr1zJ9PCUV7bpX9c+pDrl888NBO+t379oV/1hySo+XytWEIZeLL77YPvzwQzPzfYS/6667zMzsjjvusBdeeMHfbuzYsf7hjpkzZ1q/fv0sMTHR2rdvb4899piZlR4SOXTokPXr188+++wzW7VqlZ133nn+bc2fP9/Gjh1b4fr8/HxLTk62G264wWbNmmV5eXlltn8i27dvt0svvdQSEhIsMTHRunfvbmZmL774ot16662l+r5o0SKbPXu2XXPNNSf92r3++us2ceJE//LUqVNLbf+Yv/zlL/bnP//ZzMyWLFli8fHx/qEJM7N169ZZ586dbdOmTWbmG4K48MILbdOmTVZUVGS33nqrPfroo2W2+8c//rHMMNm0adNs1KhR/uX9+/fbkCFDbM+ePXb06FEbM2aMvfzyy/5aBg0aZH379rVJkyZZz549S21r4cKFFhcXZ3v37q10W40bN/b3cdasWTZo0CAzM7v11lutf//+dvjwYcvIyLAuXbr4h1OOyczMtMGDB9vatWsDquv4Pi5btswiIiLsiy++MDOz//mf/7H777+/1GNuueUWf33lOdkhl4D20J1zw51zG51zm5xz95Zzf33n3PTi+790zsUG599NWQdyfBeFvrx3dFU9hXhs//79LFy4kBtvvJHY2FiefPJJZsyYgZ3gWoO5ubn8+te/ZubMmaxdu5abbrqp3Dm8TZo0YfDgwXz66acnXVfdunVZunQpl19+Oe+++y7Dhw8/qcc/8MADDBkyhHXr1vHOO+/466tbty5FRT9+4qxs7nFle+gdOnQotfeYnp5Ohw4dymznhRde4MorrwRgwIAB5Obm+g9ypqenM3bsWKZOner/1LFq1SoAzj77bJxzXHnllaU+IR3zs5/9jFmzZpVaN23atFLDLfPnz6dTp060bt2ayMhIxo0b59/WgAEDWLx4MUuXLuX888/3D3MArFmzhhtvvJG3336bVq1aVbqt6OhoxiqD7QEAAAjnSURBVI0bB8DYsWNZs2aNf/1Pf/pTGjduTFRUFOeff36pg6wALVq0YMiQIcydO7fSusrrY3R0NNHR0fTv3x+Ayy+/nK+++sp/f0FBAW+88QZXXXVVmdfwVFUa6M65COA5YATQA7jaOdfjuGYTgUwz6wI8BTwRtAor0L6FpiyGq5kzZ3Ldddexbds2tm7dyo4dO+jUqROLFy/m/PPPZ/r06RQWFvL999+zaNEi4McQjIqK4vDhwxXOfCkoKODLL7/k7LPPpnv37mzdupVNmzYB8PLLL3PBBRdUuP7w4cNkZWUxcuRInnrqKX8ANG3alEOHDvmf49lnn/WPh5eUlZXlD9Zj46oAsbGxrFq1iqKiInbs2MHSpUsB3zjxJ598wpYtWwD8MyeGDBnCqlWryvwcC7G+ffvy7bffsmXLFo4ePcq0adMYPXp0mXo6duzoH5PfsGEDubm5tG7dmgMHDnDxxRfz+OOPc+655/rbd+jQgbS0NDIyMgDfbJj4+HgA/1AG+IbL4uLiSvX7448/ZsyYMaWe+4svviA7OxszY8GCBf5t7dmzB4C8vDyeeOIJbrnlFsA3K2fcuHG8/PLLpcL0RNu69NJL/b8jH3/8sf9xY8aM4dNPP6WgoIDs7Gy+/PJL4uPjycjI4MAB32m5c3JymDdvnr8vFdVVUR/btWtHTEwMGzduBHzHa0oex5g/fz5xcXFERwdx57SiXfdjP8AA4IMSy/8L/O9xbT4ABhTfrgvsBdyJtnuqQy4Pve2b4bL4m4xTerxUzushl8GDB9v7779fat3TTz9tt9xyi/+jfrdu3Wzo0KE2YsQI/3DHpEmTrHPnzjZw4ECbMGGCfwbG9ddfb7GxsdazZ0+Lj4+32267zYqKiszMN5ySkpJiiYmJdsMNN1hubm6F63ft2mV9+/a1pKQkS0xMtJdeesnMzD799FOLj4+3lJQU27Rpk91666326quvlunXkiVLrGvXrpaSkmKTJk3yzyopKiqya665xrp3726XXnqpXXDBBbZo0SIz883mSElJseTkZBs6dGjAr+F7771nXbt2tc6dO9vvf/97//oHHnjA3n77bTPzzWwZOHCgJScnW8+ePe2DDz4wM7NHH33UGjVqZD179vT/HJsFMmXKFIuLi7OkpCQbNWqUf9hj3LhxlpCQ4F+fnp7uf84XX3zRrrrqqjI1Pvjgg9a9e3dLSEiwa6+91v/a33333RYXF2fdunWzp556yt9+4sSJ1qJFC39NJTOkom1lZmbayJEjLTEx0c455xxbtWqV/zF/+tOfLD4+3hISEvzPs3r1aktJSbGkpCRLSEgoNfukorpO1MeVK1danz59LCkpycaMGeOfpWPm+72cMmVKOe/ej052yMXZCT7GAjjnLgeGm9mNxcvXAf3N7LYSbdYVt0kvXt5c3Gbvcdu6GbgZoGPHjn22bdt20v+APlz/Ax+m7eaBUT1o3lDz0KvChg0b/Hs4cvJGjRrFG2+8UWZGg8jJKu9v0Tm3wsxSy2tfrdMWzex54HmA1NTUE/8nqcCwhHYMS2gX1LpEgqnk7BqR6hTIQdGdQEyJ5ejideW2cc7VBZoD+4JRoIiIBCaQQF8GdHXOdXLO1QPGA7OPazMbuL749uXAQqtsLEdqNL19It46lb/BSgPdzAqA2/Ad+NwAzDCz9c65yc65Y4fOXwBaOec2AXcCZaY2Suho0KAB+/btU6iLeMSKz4feoMHJnVG20oOiVSU1NdWOfetKahZdsUjEexVdsajGHBSV0BAZGXlSV0kRkZohJM/lIiIiZSnQRUTChAJdRCRMeHZQ1DmXAZz8V0V9ovCdXqA2UZ9rB/W5djidPp9lZq3Lu8OzQD8dzrnlFR3lDVfqc+2gPtcOVdVnDbmIiIQJBbqISJgI1UB/3usCPKA+1w7qc+1QJX0OyTF0EREpK1T30EVE5DgKdBGRMFGjA70mXZy6ugTQ5zudc2nOuTXOuQXOubO8qDOYKutziXaXOefMORfyU9wC6bNz7sri93q9c+7V6q4x2AL43e7onFvknFtZ/Ps90os6g8U59x/n3J7iK7qVd79zzj1T/Hqscc71Pu0nrejadF7/ABHAZqAzUA9YDfQ4rs2vgX8U3x4PTPe67mro8xCgUfHtX9WGPhe3awp8AnwBpHpddzW8z12BlUDL4uU2XtddDX1+HvhV8e0ewFav6z7NPp8P9AbWVXD/SOB9wAHnAF+e7nPW5D30fsAmM/vOzI4C04Axx7UZA/xf8e2ZwE+cc64aawy2SvtsZovMLLt48Qt8V5AKZYG8zwCPAk8A4XBO30D6fBPwnJllApjZnmquMdgC6bMBzYpvNwd2VWN9QWdmnwD7T9BkDDDVfL4AWjjnzjyd56zJgd4B2FFiOb14XbltzHchjiygVbVUVzUC6XNJE/H9hw9llfa5+KNojJm9V52FVaFA3uduQDfn3GfOuS+cc8OrrbqqEUifHwaudc6lA3OA26unNM+c7N97pXQ+9BDlnLsWSAUu8LqWquScqwP8FZjgcSnVrS6+YZfB+D6FfeKcSzKzA55WVbWuBl4ys7845wYALzvnEs2syOvCQkVN3kOvjRenDqTPOOeGApOA0WaWV021VZXK+twUSAQ+cs5txTfWODvED4wG8j6nA7PNLN/MtgDf4Av4UBVInycCMwDM7HOgAb6TWIWrgP7eT0ZNDvTaeHHqSvvsnOsF/BNfmIf6uCpU0mczyzKzKDOLNbNYfMcNRptZKF+/MJDf7bfw7Z3jnIvCNwTzXXUWGWSB9Hk78BMA51w8vkDPqNYqq9ds4OfFs13OAbLM7PvT2qLXR4IrOUo8Et+eyWZgUvG6yfj+oMH3hr8ObAKWAp29rrka+jwf2A2sKv6Z7XXNVd3n49p+RIjPcgnwfXb4hprSgLXAeK9rroY+9wA+wzcDZhUwzOuaT7O/rwHfA/n4PnFNBG4BbinxHj9X/HqsDcbvtb76LyISJmrykIuIiJwEBbqISJhQoIuIhAkFuohImFCgi4iECQW6iEiYUKCLiISJ/w8TCKsWHvlhpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "\"\"\"\n",
    "Para cada variable graficamos su curva roc y el área bajo su curva para validación\n",
    "\"\"\"\n",
    "\n",
    "auc_val,_,_ = compute_auc(moXGB, x_val,y_val)\n",
    "#print(i/100)\n",
    "print(\"\\n\\nXGBClassifier\\nAUC de validación: \",auc_val)\n",
    "\n",
    "\n",
    "y_pred_proba = moXGB.predict_proba(x_val)[::,1]\n",
    "fpr1, tpr1, _ = metrics.roc_curve(y_val,  y_pred_proba)\n",
    "auc1 = metrics.roc_auc_score(y_val, y_pred_proba)\n",
    "plt.plot(fpr1,tpr1,label=\"XGB, auc=\"+str(auc1))\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "auc_val,_,_ = compute_auc(moRand, x_val,y_val)\n",
    "#print(i/100)\n",
    "print(\"\\n\\nRandomForest\\nAUC de validación: \",auc_val)\n",
    "\n",
    "y_pred_proba = moRand.predict_proba(x_val)[::,1]\n",
    "fpr1, tpr1, _ = metrics.roc_curve(y_val,  y_pred_proba)\n",
    "auc1 = metrics.roc_auc_score(y_val, y_pred_proba)\n",
    "plt.plot(fpr1,tpr1,label=\"RandFor, auc=\"+str(auc1))\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "auc_val,_,_ = compute_auc(moLog, x_val,y_val)\n",
    "#print(i/100)\n",
    "print(\"\\n\\nLogisticRegression\\nAUC de validación: \",auc_val)\n",
    "\n",
    "y_pred_proba = moXGB.predict_proba(x_val)[::,1]\n",
    "fpr1, tpr1, _ = metrics.roc_curve(y_val,  y_pred_proba)\n",
    "auc1 = metrics.roc_auc_score(y_val, y_pred_proba)\n",
    "plt.plot(fpr1,tpr1,label=\"LogReg, auc=\"+str(auc1))\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "auc_val,_,_ = compute_auc(molgb, x_val,y_val)\n",
    "#print(i/100)\n",
    "print(\"\\n\\nLGBMC\\nAUC de validación: \",auc_val)\n",
    "\n",
    "y_pred_proba = molgb.predict_proba(x_val)[::,1]\n",
    "fpr1, tpr1, _ = metrics.roc_curve(y_val,  y_pred_proba)\n",
    "auc1 = metrics.roc_auc_score(y_val, y_pred_proba)\n",
    "plt.plot(fpr1,tpr1,label=\"LGBM, auc=\"+str(auc1))\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n",
    "\n",
    "auc_val,_,_ = compute_auc(moAda, x_val,y_val)\n",
    "#print(i/100)\n",
    "print(\"\\n\\nAdaBoost\\nAUC de validación: \",auc_val)\n",
    "\n",
    "y_pred_proba = moAda.predict_proba(x_val)[::,1]\n",
    "fpr1, tpr1, _ = metrics.roc_curve(y_val,  y_pred_proba)\n",
    "auc1 = metrics.roc_auc_score(y_val, y_pred_proba)\n",
    "plt.plot(fpr1,tpr1,label=\"AdaBoost, auc=\"+str(auc1))\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n",
    "\n",
    "import shap\n",
    "\n",
    "\"\"\"\n",
    "Para los modelos basados en árboles podemos verificar la importancia de cada variable para cada clase\n",
    "\"\"\"\n",
    "\n",
    "explainer = shap.TreeExplainer(moRand)\n",
    "shap_values = explainer.shap_values(x_test)\n",
    "shap.summary_plot(shap_values, x_test, feature_names=feature_list_t, class_names=['survive','death'])\n",
    "\n",
    "\n",
    "explainer = shap.TreeExplainer(molgb)\n",
    "shap_values = explainer.shap_values(x_test)\n",
    "shap.summary_plot(shap_values, x_test, feature_names=feature_list_t, class_names=['survive','death'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.- Evaluando en Test\n",
    "Utilizaremos los datos con los que se evaluaban los jugadores y con el Score que usaron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado Test de  XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8) \n",
      "\n",
      "Accuracy is: 83.525\n",
      "TruePositive:  284 \n",
      "TrueNEgative:  3057 \n",
      "F1 Score:  0.23145884270578648 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.48547008547008547 \n",
      "\tP=  0.4423676012461059 \n",
      "\n",
      "Score1:  0.4423676012461059 \n",
      "\n",
      "\n",
      "\n",
      "Resultado Test de  RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442) \n",
      "\n",
      "Accuracy is: 85.7\n",
      "TruePositive:  23 \n",
      "TrueNEgative:  3405 \n",
      "F1 Score:  0.0372168284789644 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.039316239316239315 \n",
      "\tP=  0.696969696969697 \n",
      "\n",
      "Score1:  0.039316239316239315 \n",
      "\n",
      "\n",
      "\n",
      "Resultado Test de  AdaBoostClassifier(learning_rate=0.1, n_estimators=100, random_state=442) \n",
      "\n",
      "Accuracy is: 85.95\n",
      "TruePositive:  41 \n",
      "TrueNEgative:  3397 \n",
      "F1 Score:  0.06366459627329192 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.07008547008547009 \n",
      "\tP=  0.6949152542372882 \n",
      "\n",
      "Score1:  0.07008547008547009 \n",
      "\n",
      "\n",
      "\n",
      "Resultado Test de  LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.6, is_unbalance='true', lambda_l2=0.9,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442) \n",
      "\n",
      "Accuracy is: 82.1\n",
      "TruePositive:  362 \n",
      "TrueNEgative:  2922 \n",
      "F1 Score:  0.2513888888888889 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.6188034188034188 \n",
      "\tP=  0.4233918128654971 \n",
      "\n",
      "Score1:  0.4233918128654971 \n",
      "\n",
      "\n",
      "\n",
      "Resultado Test de  LogisticRegression(C=0.01, l1_ratio=0.5, penalty='elasticnet', random_state=442,\n",
      "                   solver='saga') \n",
      "\n",
      "Accuracy is: 86.425\n",
      "TruePositive:  82 \n",
      "TrueNEgative:  3375 \n",
      "F1 Score:  0.11598302687411598 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.14017094017094017 \n",
      "\tP=  0.6721311475409836 \n",
      "\n",
      "Score1:  0.14017094017094017 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def accTest(m, regresa=False, val=.5, x=x_test, disc=True):\n",
    "    \"\"\"\n",
    "    Función que calcula las métricas utilizadas para la competencia, los jugadores fueron rankeados por\n",
    "    el score \"Score1\", es el que debe importar.\n",
    "    \"\"\"\n",
    "    if(disc):\n",
    "        y_pred = m.predict(x)\n",
    "        pred = list()\n",
    "        for i in range(len(y_pred)):\n",
    "            if(y_pred[i]>=val):\n",
    "                pred.append(1)\n",
    "            else:\n",
    "                pred.append(0)\n",
    "\n",
    "        test=list(y_test)\n",
    "\n",
    "        a = accuracy_score(pred,test)\n",
    "        print('Accuracy is:', a*100)\n",
    "\n",
    "        CM = confusion_matrix(test, pred)\n",
    "        TN = CM[0][0]\n",
    "        FN = CM[1][0]\n",
    "        TP = CM[1][1]\n",
    "        FP = CM[0][1]\n",
    "        precision=TP/(TP+FP)\n",
    "        exha= TP/(TP+FN)\n",
    "        print(\"TruePositive: \", TP,\n",
    "             \"\\nTrueNEgative: \",TN,\n",
    "             \"\\nF1 Score: \", (precision*exha)/(precision+exha),\n",
    "             \"\\n\\t Scoring for the challenge: \\n\\n\\tSe= \", exha,\n",
    "             \"\\n\\tP= \", precision,\n",
    "             \"\\n\\nScore1: \", min(exha, precision),\"\\n\\n\\n\")\n",
    "        if regresa:\n",
    "            return y_pred, a, CM\n",
    "    elif (disc==False):\n",
    "        y_pred = m.predict(x)\n",
    "        test=list(y_test)\n",
    "\n",
    "        a = accuracy_score(pred,test)\n",
    "        print('Accuracy is:', a*100)\n",
    "        \n",
    "        CM = confusion_matrix(test, pred)\n",
    "        TN = CM[0][0]\n",
    "        FN = CM[1][0]\n",
    "        TP = CM[1][1]\n",
    "        FP = CM[0][1]\n",
    "        precision=TP/(TP+FP)\n",
    "        exha= TP/(TP+FN)\n",
    "        print(\"TruePositive: \", TP,\n",
    "             \"\\nTrueNEgative: \",TN,\n",
    "             \"\\nF1 Score: \", (precision*exha)/(precision+exha),\n",
    "             \"\\n\\t Scoring for the challenge: \\n\\n\\tSe= \", exha,\n",
    "             \"\\n\\tP= \", precision,\n",
    "             \"\\n\\nScore1: \", min(exha, precision),\"\\n\\n\\n\")\n",
    "        if regresa:\n",
    "            return y_pred, a, CM\n",
    "        \n",
    "\n",
    "\n",
    "modelos=[moXGB, moRand, moAda, molgb, moLog]\n",
    "\n",
    "for i in modelos:\n",
    "    print(\"Resultado Test de \", i,\"\\n\")\n",
    "    accTest(i, disc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Como podemos ver, los dos mejores modelos son LGBM y XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Veamos sus curvas\n",
    "\n",
    "\n",
    "y_pred_proba = moXGB.predict_proba(x_test)[::,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"XGB, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n",
    "\n",
    "y_pred_proba = molgb.predict_proba(x_test)[::,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"LGBM, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4.- Probamos con modelos naive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 40.675\n",
      "TruePositive:  539 \n",
      "TrueNEgative:  1088 \n",
      "F1 Score:  0.1561866125760649 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.9213675213675213 \n",
      "\tP=  0.18806699232379623 \n",
      "\n",
      "Score1:  0.18806699232379623 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Esto lo dejé al final debido a que la complejidad y el análisis que hice de los datos me hace saber que\n",
    "#un modelo naive no iba a tener el mejor acercamiento\n",
    "\n",
    "from sklearn.naive_bayes import BernoulliNB, GaussianNB\n",
    "\n",
    "clf=BernoulliNB(alpha=0.005)\n",
    "clf.fit(x_train, y_train)\n",
    "accTest(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 84.95\n",
      "TruePositive:  8 \n",
      "TrueNEgative:  3390 \n",
      "F1 Score:  0.012944983818770227 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.013675213675213675 \n",
      "\tP=  0.24242424242424243 \n",
      "\n",
      "Score1:  0.013675213675213675 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf=GaussianNB(priors=[.5, .5])\n",
    "clf.fit(x_train, y_train)\n",
    "accTest(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.- Explorando con PCA\n",
    "\n",
    "Ahora, vamos a ver si utilizando menos dimensiones puede aumentar el Score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Dependencies\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=16)\n",
    "x_train_pca=pca.fit_transform(x_train)\n",
    "x_val_pca=pca.transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_pca=pca.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 65.375\n",
      "TruePositive:  390 \n",
      "TrueNEgative:  2225 \n",
      "F1 Score:  0.18013856812933024 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.6666666666666666 \n",
      "\tP=  0.2468354430379747 \n",
      "\n",
      "Score1:  0.2468354430379747 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf=GaussianNB(priors=[.5, .5])\n",
    "clf.fit(x_train_pca, y_train)\n",
    "accTest(clf, x=x_test_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Como podemos ver, el Score1 aumentó para un modelo tan simple, ahora veamos que pasa con mis modelos más complejos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 166)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  2\n",
      "best parameters are: RandomForestClassifier(max_depth=6, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.6932035724467974\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=2, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.4)\n",
      "XGBClassifier best AUC: 0.6747913287083901\n",
      "best parameters are: LogisticRegression(C=100.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.675185520004423\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.9,\n",
      "               max_bin=130, max_depth=2, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.6999329597724685\n",
      "best parameters are: AdaBoostClassifier(learning_rate=0.1, n_estimators=100, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.6986747138977946\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  18\n",
      "best parameters are: RandomForestClassifier(max_depth=7, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.7380113965436372\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=1000.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.7040200248687094\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n",
      "best parameters are: AdaBoostClassifier(algorithm='SAMME', learning_rate=0.9, n_estimators=100,\n",
      "                   random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.7106269907023003\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  34\n",
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=80, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.7482865616699966\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=1000.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.750618993099025\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n",
      "best parameters are: AdaBoostClassifier(algorithm='SAMME', learning_rate=0.9, n_estimators=100,\n",
      "                   random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.7192596085822609\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  50\n",
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.6712274973027267\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=1000.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.6187998744563129\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n",
      "best parameters are: AdaBoostClassifier(learning_rate=0.9, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.6190489277254854\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  66\n",
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.6233399595131608\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=1000.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.5196650459146811\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n",
      "best parameters are: AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.5768563824243899\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  82\n",
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.5821414735973819\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=100.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.5522354942551364\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n",
      "best parameters are: AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.5170168467259869\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  98\n",
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.5701716555387628\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=100.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.5482671171860907\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best parameters are: AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.4971592398341164\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  114\n",
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.5546144540295862\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=100.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.5820234684601275\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n",
      "best parameters are: AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.5235531314923909\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  130\n",
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.5698143560816028\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=100.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.5528443714997513\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n",
      "best parameters are: AdaBoostClassifier(algorithm='SAMME', learning_rate=0.1, n_estimators=5,\n",
      "                   random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.5242420076449553\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  146\n",
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.5565109744950876\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=100.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.5598337930255121\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n",
      "best parameters are: AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.5125670466299538\n",
      "\n",
      "\n",
      "\n",
      "##########################################\n",
      "PCA=  162\n",
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=80, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.5350119869033959\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.5\n",
      "best parameters are: LogisticRegression(C=100.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga')\n",
      "LogisticRegression best AUC: 0.5130104932834656\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.5\n",
      "best parameters are: AdaBoostClassifier(learning_rate=0.9, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.4732375320129954\n"
     ]
    }
   ],
   "source": [
    "for i in range(2,166, 16):\n",
    "    print(\"\\n\\n\\n##########################################\\nPCA= \", i)\n",
    "    pca=PCA(n_components=i)\n",
    "    x_train_pca=pca.fit_transform(x_train)\n",
    "    x_val_pca=pca.transform(x_train)\n",
    "    \n",
    "    grid(x_tr=x_train_pca, y_tr=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### El mejor modelo fue:\n",
    "PCA=  34\n",
    "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=80, random_state=442)\n",
    "RandomForestClassifier best AUC: 0.7482865616699966\n",
    "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
    "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
    "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
    "XGBClassifier best AUC: 0.5\n",
    "best parameters are: LogisticRegression(C=1000.0, l1_ratio=0.5, penalty='elasticnet',\n",
    "                   random_state=442, solver='saga')\n",
    "LogisticRegression best AUC: 0.750618993099025\n",
    "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
    "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
    "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
    "               min_data_in_leaf=10, min_gain_to_split=10,\n",
    "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
    "               objective='binary', seed=442)\n",
    "Light best AUC: 0.5\n",
    "best parameters are: AdaBoostClassifier(algorithm='SAMME', learning_rate=0.9, n_estimators=100,\n",
    "                   random_state=442)\n",
    "AdaBoostClassifier best AUC: 0.7192596085822609\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado Test de  XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8) \n",
      "\n",
      "Accuracy is: 79.07499999999999\n",
      "TruePositive:  343 \n",
      "TrueNEgative:  2820 \n",
      "F1 Score:  0.2252133946158897 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.5863247863247864 \n",
      "\tP=  0.3656716417910448 \n",
      "\n",
      "Score1:  0.3656716417910448 \n",
      "\n",
      "\n",
      "\n",
      "Resultado Test de  RandomForestClassifier(max_depth=8, n_estimators=80, random_state=442) \n",
      "\n",
      "Accuracy is: 85.425\n",
      "TruePositive:  9 \n",
      "TrueNEgative:  3408 \n",
      "F1 Score:  0.014975041597337773 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.015384615384615385 \n",
      "\tP=  0.5625 \n",
      "\n",
      "Score1:  0.015384615384615385 \n",
      "\n",
      "\n",
      "\n",
      "Resultado Test de  AdaBoostClassifier(algorithm='SAMME', learning_rate=0.9, n_estimators=100,\n",
      "                   random_state=442) \n",
      "\n",
      "Accuracy is: 85.6\n",
      "TruePositive:  108 \n",
      "TrueNEgative:  3316 \n",
      "F1 Score:  0.13636363636363635 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.18461538461538463 \n",
      "\tP=  0.5217391304347826 \n",
      "\n",
      "Score1:  0.18461538461538463 \n",
      "\n",
      "\n",
      "\n",
      "Resultado Test de  LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442) \n",
      "\n",
      "Accuracy is: 76.375\n",
      "TruePositive:  373 \n",
      "TrueNEgative:  2682 \n",
      "F1 Score:  0.2205795387344766 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.6376068376068376 \n",
      "\tP=  0.337251356238698 \n",
      "\n",
      "Score1:  0.337251356238698 \n",
      "\n",
      "\n",
      "\n",
      "Resultado Test de  LogisticRegression(C=1000.0, l1_ratio=0.5, penalty='elasticnet',\n",
      "                   random_state=442, solver='saga') \n",
      "\n",
      "Accuracy is: 86.25\n",
      "TruePositive:  112 \n",
      "TrueNEgative:  3338 \n",
      "F1 Score:  0.144702842377261 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.19145299145299147 \n",
      "\tP=  0.5925925925925926 \n",
      "\n",
      "Score1:  0.19145299145299147 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Entrenamos todos los modelos con el mejor auc\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pca=PCA(n_components=29)\n",
    "x_train_pca=pca.fit_transform(x_train)\n",
    "x_val_pca=pca.transform(x_val)\n",
    "x_test_pca=pca.transform(x_test)\n",
    "    \n",
    "\n",
    "moRand_pca=RandomForestClassifier(max_depth=8, n_estimators=80, random_state=442)\n",
    "moRand_pca=moRand_pca.fit(x_train_pca,y_train)\n",
    "\n",
    "\n",
    "moXGB_pca=XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
    "              learning_rate=0.15, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
    "              scale_pos_weight=6.239819004524887, seed=442, subsample=0.8)\n",
    "moXGB_pca=moXGB_pca.fit(x_train_pca, y_train)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "moLog_pca=LogisticRegression(C=1000.0, l1_ratio=0.5, penalty='elasticnet',\n",
    "                   random_state=442, solver='saga')\n",
    "\n",
    "moLog_pca=moLog_pca.fit(x_train_pca, y_train)\n",
    "\n",
    "\n",
    "molgb_pca=lgb.LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
    "               feature_fraction=0.8, is_unbalance='true', lambda_l2=0.09,\n",
    "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
    "               min_data_in_leaf=10, min_gain_to_split=10,\n",
    "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
    "               objective='binary', seed=442)\n",
    "\n",
    "molgb_pca=molgb_pca.fit(x_train_pca, y_train)\n",
    "\n",
    "\n",
    "moAda_pca=AdaBoostClassifier(algorithm='SAMME', learning_rate=0.9, n_estimators=100,\n",
    "                   random_state=442)\n",
    "\n",
    "moAda_pca=moAda_pca.fit(x_train_pca, y_train)\n",
    "\n",
    "\n",
    "modelos_pca=[moXGB_pca, moRand_pca, moAda_pca, molgb_pca, moLog_pca]\n",
    "\n",
    "for i in modelos_pca:\n",
    "    print(\"Resultado Test de \", i,\"\\n\")\n",
    "    accTest(i, disc=True, x=x_test_pca)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "como podemos ver, el mejor es:\n",
    "![image.png](attachment:image.png)\n",
    "sin embargo, está .1 debajo del mejor anterior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.- Explorando con redes neuronales\n",
    "\n",
    "Ahora trataré de lograr un resultado similar al obtenido, utilizando redes neuronales y mi casi nulo conocimiento xd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def accTest(m, regresa=False, val=.5, x=x_test, disc=False):\n",
    "    \"\"\"\n",
    "    Función que calcula las métricas utilizadas para la competencia, los jugadores fueron rankeados por\n",
    "    el score \"Score1\", es el que debe importar.\n",
    "    \"\"\"\n",
    "    if(disc==False):\n",
    "        y_pred = m.predict(x)\n",
    "        predi = list()\n",
    "        for i in range(len(y_pred)):\n",
    "            if(y_pred[i]>=val):\n",
    "                predi.append(1)\n",
    "            else:\n",
    "                predi.append(0)\n",
    "\n",
    "        test=list(y_test)\n",
    "\n",
    "        a = accuracy_score(predi,test)\n",
    "        print('Accuracy is:', a*100)\n",
    "\n",
    "        CM = confusion_matrix(test, predi)\n",
    "        TN = CM[0][0]\n",
    "        FN = CM[1][0]\n",
    "        TP = CM[1][1]\n",
    "        FP = CM[0][1]\n",
    "        precision=TP/(TP+FP)\n",
    "        exha= TP/(TP+FN)\n",
    "        print(\"TruePositive: \", TP,\n",
    "             \"\\nTrueNEgative: \",TN,\n",
    "             \"\\nF1 Score: \", (precision*exha)/(precision+exha),\n",
    "             \"\\n\\t Scoring for the challenge: \\n\\n\\tSe= \", exha,\n",
    "             \"\\n\\tP= \", precision,\n",
    "             \"\\n\\nScore1: \", min(exha, precision),\"\\n\\n\\n\")\n",
    "        if regresa:\n",
    "            return y_pred, a, CM\n",
    "    elif (disc!=False):\n",
    "        y_pred = m.predict(x)\n",
    "        test=list(y_test)\n",
    "        predi = list()\n",
    "\n",
    "        a = accuracy_score(predi,test)\n",
    "        print('Accuracy is:', a*100)\n",
    "        \n",
    "        CM = confusion_matrix(test, predi)\n",
    "        TN = CM[0][0]\n",
    "        FN = CM[1][0]\n",
    "        TP = CM[1][1]\n",
    "        FP = CM[0][1]\n",
    "        precision=TP/(TP+FP)\n",
    "        exha= TP/(TP+FN)\n",
    "        print(\"TruePositive: \", TP,\n",
    "             \"\\nTrueNEgative: \",TN,\n",
    "             \"\\nF1 Score: \", (precision*exha)/(precision+exha),\n",
    "             \"\\n\\t Scoring for the challenge: \\n\\n\\tSe= \", exha,\n",
    "             \"\\n\\tP= \", precision,\n",
    "             \"\\n\\nScore1: \", min(exha, precision),\"\\n\\n\\n\")\n",
    "        if regresa:\n",
    "            return y_pred, a, CM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6400 samples, validate on 1600 samples\n",
      "Epoch 1/10\n",
      "6400/6400 [==============================] - 1s 218us/step - loss: 0.1782 - accuracy: 0.8561 - val_loss: 0.3510 - val_accuracy: 0.8487\n",
      "Epoch 2/10\n",
      "6400/6400 [==============================] - 1s 139us/step - loss: 0.1582 - accuracy: 0.8673 - val_loss: 0.3466 - val_accuracy: 0.8456\n",
      "Epoch 3/10\n",
      "6400/6400 [==============================] - 1s 131us/step - loss: 0.1529 - accuracy: 0.8662 - val_loss: 0.3423 - val_accuracy: 0.8444\n",
      "Epoch 4/10\n",
      "6400/6400 [==============================] - 1s 130us/step - loss: 0.1530 - accuracy: 0.8628 - val_loss: 0.3302 - val_accuracy: 0.8581\n",
      "Epoch 5/10\n",
      "6400/6400 [==============================] - 1s 135us/step - loss: 0.1517 - accuracy: 0.8648 - val_loss: 0.3306 - val_accuracy: 0.8500\n",
      "Epoch 6/10\n",
      "6400/6400 [==============================] - 1s 135us/step - loss: 0.1502 - accuracy: 0.8677 - val_loss: 0.3315 - val_accuracy: 0.8562\n",
      "Epoch 7/10\n",
      "6400/6400 [==============================] - 1s 134us/step - loss: 0.1474 - accuracy: 0.8695 - val_loss: 0.3307 - val_accuracy: 0.8562\n",
      "Epoch 8/10\n",
      "6400/6400 [==============================] - 1s 141us/step - loss: 0.1471 - accuracy: 0.8695 - val_loss: 0.3437 - val_accuracy: 0.8475\n",
      "Epoch 9/10\n",
      "6400/6400 [==============================] - 1s 133us/step - loss: 0.1467 - accuracy: 0.8708 - val_loss: 0.3363 - val_accuracy: 0.8531\n",
      "Epoch 10/10\n",
      "6400/6400 [==============================] - 1s 133us/step - loss: 0.1461 - accuracy: 0.8702 - val_loss: 0.3428 - val_accuracy: 0.8487\n",
      "Accuracy is: 84.575\n",
      "TruePositive:  264 \n",
      "TrueNEgative:  3119 \n",
      "F1 Score:  0.23056768558951965 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.4512820512820513 \n",
      "\tP=  0.4714285714285714 \n",
      "\n",
      "Score1:  0.4512820512820513 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#solo mostraré el mejor modelo logrado\n",
    "model3= Sequential()\n",
    "model3.add(Dense(64, input_dim=166, activation='sigmoid'))\n",
    "model3.add(Dropout(.2))\n",
    "model3.add(Dense(1, activation='sigmoid'))\n",
    "opt=keras.optimizers.Adam()\n",
    "model3.compile(loss='binary_crossentropy', optimizer=opt,\n",
    "              metrics=['accuracy'])\n",
    "history3 = model3.fit(x_train, y_train, epochs=10,validation_data = (x_val,y_val),\n",
    "                      class_weight={0:.35, 1:.65}, batch_size=32)\n",
    "\n",
    "accTest(model3, disc=False)\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Como podemos ver, obtuvimos un accuracy mejor que con los modelos tradicionales.\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.- Utilizando over sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "#ROS=RandomOverSampler(random_state=0)\n",
    "X_resampled, y_resampled= SMOTE().fit_resample(x_all, y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 6878), (1, 6878)]\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print(sorted(Counter(y_resampled).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13756, 166)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 166)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_ovs, x_val_ovs, y_train_ovs, y_val_ovs = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=422) #división de los datos\n",
    "sample_weights = class_weight.compute_sample_weight('balanced', y_train_ovs)\n",
    "ratio = float(np.sum(y_train_ovs == 0)) / np.sum(y_train_ovs==1)\n",
    "cv = StratifiedKFold(n_splits=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1.- Grid search con SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best parameters are: RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "RandomForestClassifier best AUC: 0.9492413421939855\n",
      "best parameters are: XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.25, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=0.981274756931941, seed=442, subsample=0.8)\n",
      "XGBClassifier best AUC: 0.9741773023110074\n",
      "best parameters are: LogisticRegression(l1_ratio=0.5, penalty='elasticnet', random_state=442,\n",
      "                   solver='saga')\n",
      "LogisticRegression best AUC: 0.8630227904405563\n",
      "best parameters are: LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.6, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Light best AUC: 0.9736709437864441\n",
      "best parameters are: AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
      "AdaBoostClassifier best AUC: 0.9565552808351339\n"
     ]
    }
   ],
   "source": [
    "grid(x_tr=x_train_ovs, y_tr=y_train_ovs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
      "Accuracy is: 69.22500000000001\n",
      "TruePositive:  443 \n",
      "TrueNEgative:  2326 \n",
      "F1 Score:  0.2092583845063769 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.7572649572649572 \n",
      "\tP=  0.2891644908616188 \n",
      "\n",
      "Score1:  0.2891644908616188 \n",
      "\n",
      "\n",
      "\n",
      "XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
      "              learning_rate=0.25, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
      "              scale_pos_weight=0.981274756931941, seed=442, subsample=0.5)\n",
      "Accuracy is: 26.125\n",
      "TruePositive:  559 \n",
      "TrueNEgative:  486 \n",
      "F1 Score:  0.1372452737539897 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.9555555555555556 \n",
      "\tP=  0.1602637614678899 \n",
      "\n",
      "Score1:  0.1602637614678899 \n",
      "\n",
      "\n",
      "\n",
      "LogisticRegression(l1_ratio=0.5, penalty='elasticnet', random_state=442,\n",
      "                   solver='saga')\n",
      "Accuracy is: 76.14999999999999\n",
      "TruePositive:  441 \n",
      "TrueNEgative:  2605 \n",
      "F1 Score:  0.24019607843137253 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.7538461538461538 \n",
      "\tP=  0.35251798561151076 \n",
      "\n",
      "Score1:  0.35251798561151076 \n",
      "\n",
      "\n",
      "\n",
      "LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
      "               feature_fraction=0.6, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Accuracy is: 17.325\n",
      "TruePositive:  575 \n",
      "TrueNEgative:  118 \n",
      "F1 Score:  0.1290105452097824 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.9829059829059829 \n",
      "\tP=  0.1485020661157025 \n",
      "\n",
      "Score1:  0.1485020661157025 \n",
      "\n",
      "\n",
      "\n",
      "AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
      "Accuracy is: 51.824999999999996\n",
      "TruePositive:  445 \n",
      "TrueNEgative:  1628 \n",
      "F1 Score:  0.15796947106851258 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.7606837606837606 \n",
      "\tP=  0.19937275985663083 \n",
      "\n",
      "Score1:  0.19937275985663083 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "mo1=RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442)\n",
    "mo1.fit(x_train_ovs, y_train_ovs)\n",
    "print(mo1)\n",
    "accTest(mo1)\n",
    "\n",
    "\n",
    "mo2=XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
    "              learning_rate=0.25, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
    "              scale_pos_weight=0.981274756931941, seed=442, subsample=0.5)\n",
    "mo2.fit(x_train_ovs, y_train_ovs)\n",
    "print(mo2)\n",
    "accTest(mo2)\n",
    "\n",
    "\n",
    "\n",
    "mo3=LogisticRegression(l1_ratio=0.5, penalty='elasticnet', random_state=442,\n",
    "                   solver='saga')\n",
    "print(mo3)\n",
    "mo3.fit(x_train_ovs, y_train_ovs)\n",
    "accTest(mo3)\n",
    "\n",
    "mo4=lgb.LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
    "               feature_fraction=0.6, is_unbalance='true', lambda_l2=0.09,\n",
    "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
    "               min_data_in_leaf=10, min_gain_to_split=10,\n",
    "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
    "               objective='binary', seed=442)\n",
    "mo4.fit(x_train_ovs, y_train_ovs)\n",
    "print(mo4)\n",
    "accTest(mo4)\n",
    "\n",
    "mo5= AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
    "mo5.fit(x_train_ovs, y_train_ovs)\n",
    "print(mo5)\n",
    "accTest(mo5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Como podemos observar, se ha sobreajustado cada modelo, realizando una busqueda manual de pesos encontré el mejor para los modelos y se presentan a continuación con el mejor SCORE obtenido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(class_weight={0: 0.76, 1: 0.24}, max_depth=8,\n",
      "                       n_estimators=130, random_state=442)\n",
      "Accuracy is: 80.75\n",
      "TruePositive:  286 \n",
      "TrueNEgative:  2944 \n",
      "F1 Score:  0.21311475409836067 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.4888888888888889 \n",
      "\tP=  0.37780713342140027 \n",
      "\n",
      "Score1:  0.37780713342140027 \n",
      "\n",
      "\n",
      "\n",
      "XGBClassifier(class_weight={0: 0.76, 1: 0.24}, colsample_bytree=0.8,\n",
      "              eval_metric='auc', gamma=10, learning_rate=0.25, max_depth=4,\n",
      "              reg_alpha=0.5, reg_lambda=2, scale_pos_weight=0.981274756931941,\n",
      "              seed=442, subsample=0.5)\n",
      "Accuracy is: 26.125\n",
      "TruePositive:  559 \n",
      "TrueNEgative:  486 \n",
      "F1 Score:  0.1372452737539897 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.9555555555555556 \n",
      "\tP=  0.1602637614678899 \n",
      "\n",
      "Score1:  0.1602637614678899 \n",
      "\n",
      "\n",
      "\n",
      "LogisticRegression(class_weight={0: 0.76, 1: 0.24}, l1_ratio=0.5,\n",
      "                   penalty='elasticnet', random_state=442, solver='saga')\n",
      "Accuracy is: 85.275\n",
      "TruePositive:  270 \n",
      "TrueNEgative:  3141 \n",
      "F1 Score:  0.2391496899911426 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.46153846153846156 \n",
      "\tP=  0.4963235294117647 \n",
      "\n",
      "Score1:  0.46153846153846156 \n",
      "\n",
      "\n",
      "\n",
      "LGBMClassifier(bagging_fraction=0.5, boost_from_average='true',\n",
      "               class_weight={0: 0.76, 1: 0.24}, drop_rate=0.5,\n",
      "               feature_fraction=0.6, is_unbalance='true', lambda_l2=0.09,\n",
      "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
      "               min_data_in_leaf=10, min_gain_to_split=10,\n",
      "               min_sum_hessian_in_leaf=1e-10, num_iterations=200,\n",
      "               objective='binary', seed=442)\n",
      "Accuracy is: 19.45\n",
      "TruePositive:  555 \n",
      "TrueNEgative:  223 \n",
      "F1 Score:  0.12811634349030468 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.9487179487179487 \n",
      "\tP=  0.14811849479583666 \n",
      "\n",
      "Score1:  0.14811849479583666 \n",
      "\n",
      "\n",
      "\n",
      "AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
      "Accuracy is: 51.824999999999996\n",
      "TruePositive:  445 \n",
      "TrueNEgative:  1628 \n",
      "F1 Score:  0.15796947106851258 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.7606837606837606 \n",
      "\tP=  0.19937275985663083 \n",
      "\n",
      "Score1:  0.19937275985663083 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mo1=RandomForestClassifier(max_depth=8, n_estimators=130, random_state=442, class_weight={0:.76, 1:.24})\n",
    "mo1.fit(x_train_ovs, y_train_ovs)\n",
    "print(mo1)\n",
    "accTest(mo1)\n",
    "\n",
    "\n",
    "mo2=XGBClassifier(colsample_bytree=0.8, eval_metric='auc', gamma=10,\n",
    "              learning_rate=0.25, max_depth=4, reg_alpha=0.5, reg_lambda=2,\n",
    "              scale_pos_weight=0.981274756931941, seed=442,class_weight={0:.76, 1:.24}, subsample=0.5)\n",
    "mo2.fit(x_train_ovs, y_train_ovs)\n",
    "print(mo2)\n",
    "accTest(mo2)\n",
    "\n",
    "\n",
    "\n",
    "mo3=LogisticRegression(l1_ratio=0.5, penalty='elasticnet',class_weight={0:.76, 1:.24}, random_state=442,\n",
    "                   solver='saga')\n",
    "print(mo3)\n",
    "mo3.fit(x_train_ovs, y_train_ovs)\n",
    "accTest(mo3)\n",
    "\n",
    "mo4=lgb.LGBMClassifier(bagging_fraction=0.5, boost_from_average='true', drop_rate=0.5,\n",
    "               feature_fraction=0.6, is_unbalance='true', lambda_l2=0.09,\n",
    "               max_bin=130, max_depth=10, metric='auc', min_data_in_bin=1,\n",
    "               min_data_in_leaf=10, min_gain_to_split=10,\n",
    "               min_sum_hessian_in_leaf=1e-10, class_weight={0:.76, 1:.24},num_iterations=200,\n",
    "               objective='binary', seed=442)\n",
    "mo4.fit(x_train_ovs, y_train_ovs)\n",
    "print(mo4)\n",
    "accTest(mo4)\n",
    "\n",
    "mo5= AdaBoostClassifier(learning_rate=0.9, n_estimators=100, random_state=442)\n",
    "mo5.fit(x_train_ovs, y_train_ovs)\n",
    "print(mo5)\n",
    "accTest(mo5)\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El modelo ganador es:\n",
    "![image.png](attachment:image.png)\n",
    "Que nos ubicaría en el 8° lugar del challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Probando SMOTE con NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11004 samples, validate on 2752 samples\n",
      "Epoch 1/200\n",
      "11004/11004 [==============================] - 2s 213us/step - loss: 0.2263 - accuracy: 0.6457 - val_loss: 0.5581 - val_accuracy: 0.6995\n",
      "Epoch 2/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.1937 - accuracy: 0.7064 - val_loss: 0.5896 - val_accuracy: 0.6857\n",
      "Epoch 3/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.1893 - accuracy: 0.7145 - val_loss: 0.5506 - val_accuracy: 0.7071\n",
      "Epoch 4/200\n",
      "11004/11004 [==============================] - 2s 154us/step - loss: 0.1874 - accuracy: 0.7196 - val_loss: 0.5379 - val_accuracy: 0.7118\n",
      "Epoch 5/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.1845 - accuracy: 0.7241 - val_loss: 0.5538 - val_accuracy: 0.7042\n",
      "Epoch 6/200\n",
      "11004/11004 [==============================] - 2s 156us/step - loss: 0.1808 - accuracy: 0.7340 - val_loss: 0.5551 - val_accuracy: 0.7137\n",
      "Epoch 7/200\n",
      "11004/11004 [==============================] - 2s 156us/step - loss: 0.1782 - accuracy: 0.7370 - val_loss: 0.5078 - val_accuracy: 0.7398\n",
      "Epoch 8/200\n",
      "11004/11004 [==============================] - 2s 156us/step - loss: 0.1734 - accuracy: 0.7513 - val_loss: 0.5247 - val_accuracy: 0.7286\n",
      "Epoch 9/200\n",
      "11004/11004 [==============================] - 2s 157us/step - loss: 0.1708 - accuracy: 0.7584 - val_loss: 0.4907 - val_accuracy: 0.7536\n",
      "Epoch 10/200\n",
      "11004/11004 [==============================] - 2s 154us/step - loss: 0.1677 - accuracy: 0.7628 - val_loss: 0.5027 - val_accuracy: 0.7431\n",
      "Epoch 11/200\n",
      "11004/11004 [==============================] - 2s 156us/step - loss: 0.1638 - accuracy: 0.7667 - val_loss: 0.5030 - val_accuracy: 0.7445\n",
      "Epoch 12/200\n",
      "11004/11004 [==============================] - 2s 157us/step - loss: 0.1597 - accuracy: 0.7808 - val_loss: 0.4794 - val_accuracy: 0.7536\n",
      "Epoch 13/200\n",
      "11004/11004 [==============================] - 2s 182us/step - loss: 0.1558 - accuracy: 0.7858 - val_loss: 0.4324 - val_accuracy: 0.7943\n",
      "Epoch 14/200\n",
      "11004/11004 [==============================] - 2s 155us/step - loss: 0.1514 - accuracy: 0.7953 - val_loss: 0.4638 - val_accuracy: 0.7696\n",
      "Epoch 15/200\n",
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      "Epoch 16/200\n",
      "11004/11004 [==============================] - 2s 154us/step - loss: 0.1431 - accuracy: 0.8072 - val_loss: 0.4177 - val_accuracy: 0.8049\n",
      "Epoch 17/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.1383 - accuracy: 0.8192 - val_loss: 0.4324 - val_accuracy: 0.7922\n",
      "Epoch 18/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.1340 - accuracy: 0.8299 - val_loss: 0.4027 - val_accuracy: 0.8110\n",
      "Epoch 19/200\n",
      "11004/11004 [==============================] - 2s 154us/step - loss: 0.1306 - accuracy: 0.8349 - val_loss: 0.3810 - val_accuracy: 0.8314\n",
      "Epoch 20/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.1261 - accuracy: 0.8427 - val_loss: 0.4007 - val_accuracy: 0.8180\n",
      "Epoch 21/200\n",
      "11004/11004 [==============================] - 2s 155us/step - loss: 0.1226 - accuracy: 0.8489 - val_loss: 0.3560 - val_accuracy: 0.8467\n",
      "Epoch 22/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.1179 - accuracy: 0.8573 - val_loss: 0.3579 - val_accuracy: 0.8441\n",
      "Epoch 23/200\n",
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      "Epoch 24/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.1104 - accuracy: 0.8662 - val_loss: 0.3351 - val_accuracy: 0.8572\n",
      "Epoch 25/200\n",
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      "Epoch 26/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.1049 - accuracy: 0.8715 - val_loss: 0.3311 - val_accuracy: 0.8586\n",
      "Epoch 27/200\n",
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      "Epoch 28/200\n",
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      "Epoch 29/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0964 - accuracy: 0.8882 - val_loss: 0.3146 - val_accuracy: 0.8703\n",
      "Epoch 30/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.0930 - accuracy: 0.8909 - val_loss: 0.2998 - val_accuracy: 0.8775\n",
      "Epoch 31/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0928 - accuracy: 0.8911 - val_loss: 0.2886 - val_accuracy: 0.8848\n",
      "Epoch 32/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0911 - accuracy: 0.8969 - val_loss: 0.2935 - val_accuracy: 0.8819\n",
      "Epoch 33/200\n",
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      "Epoch 34/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0900 - accuracy: 0.8949 - val_loss: 0.2774 - val_accuracy: 0.8870\n",
      "Epoch 35/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0824 - accuracy: 0.9078 - val_loss: 0.2768 - val_accuracy: 0.8906\n",
      "Epoch 36/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0819 - accuracy: 0.9049 - val_loss: 0.2767 - val_accuracy: 0.8921\n",
      "Epoch 37/200\n",
      "11004/11004 [==============================] - 2s 156us/step - loss: 0.0793 - accuracy: 0.9099 - val_loss: 0.2572 - val_accuracy: 0.8972\n",
      "Epoch 38/200\n",
      "11004/11004 [==============================] - 2s 156us/step - loss: 0.0788 - accuracy: 0.9093 - val_loss: 0.2571 - val_accuracy: 0.8993\n",
      "Epoch 39/200\n",
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      "Epoch 40/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.0755 - accuracy: 0.9146 - val_loss: 0.2517 - val_accuracy: 0.8964\n",
      "Epoch 41/200\n",
      "11004/11004 [==============================] - 2s 156us/step - loss: 0.0758 - accuracy: 0.9148 - val_loss: 0.2527 - val_accuracy: 0.9033\n",
      "Epoch 42/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.0737 - accuracy: 0.9141 - val_loss: 0.2543 - val_accuracy: 0.8986\n",
      "Epoch 43/200\n",
      "11004/11004 [==============================] - 2s 155us/step - loss: 0.0712 - accuracy: 0.9201 - val_loss: 0.2350 - val_accuracy: 0.9077\n",
      "Epoch 44/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.0704 - accuracy: 0.9222 - val_loss: 0.2329 - val_accuracy: 0.9135\n",
      "Epoch 45/200\n",
      "11004/11004 [==============================] - 2s 156us/step - loss: 0.0713 - accuracy: 0.9206 - val_loss: 0.2379 - val_accuracy: 0.9044\n",
      "Epoch 46/200\n",
      "11004/11004 [==============================] - 2s 154us/step - loss: 0.0690 - accuracy: 0.9221 - val_loss: 0.2420 - val_accuracy: 0.9015\n",
      "Epoch 47/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.0677 - accuracy: 0.9239 - val_loss: 0.2224 - val_accuracy: 0.9146\n",
      "Epoch 48/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0636 - accuracy: 0.9296 - val_loss: 0.2274 - val_accuracy: 0.9077\n",
      "Epoch 49/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0654 - accuracy: 0.9258 - val_loss: 0.2240 - val_accuracy: 0.9146\n",
      "Epoch 50/200\n",
      "11004/11004 [==============================] - 2s 155us/step - loss: 0.0672 - accuracy: 0.9253 - val_loss: 0.2247 - val_accuracy: 0.9146\n",
      "Epoch 51/200\n",
      "11004/11004 [==============================] - 2s 156us/step - loss: 0.0644 - accuracy: 0.9307 - val_loss: 0.2196 - val_accuracy: 0.9164\n",
      "Epoch 52/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0597 - accuracy: 0.9326 - val_loss: 0.2167 - val_accuracy: 0.9124\n",
      "Epoch 53/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.0597 - accuracy: 0.9340 - val_loss: 0.2255 - val_accuracy: 0.9132\n",
      "Epoch 54/200\n",
      "11004/11004 [==============================] - 2s 153us/step - loss: 0.0587 - accuracy: 0.9350 - val_loss: 0.2086 - val_accuracy: 0.9219\n",
      "Epoch 55/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0586 - accuracy: 0.9338 - val_loss: 0.2059 - val_accuracy: 0.9237\n",
      "Epoch 56/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0568 - accuracy: 0.9357 - val_loss: 0.2096 - val_accuracy: 0.9193\n",
      "Epoch 57/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0580 - accuracy: 0.9375 - val_loss: 0.2162 - val_accuracy: 0.9117\n",
      "Epoch 58/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0587 - accuracy: 0.9329 - val_loss: 0.2006 - val_accuracy: 0.9241\n",
      "Epoch 59/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0557 - accuracy: 0.9386 - val_loss: 0.1960 - val_accuracy: 0.9270\n",
      "Epoch 60/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0538 - accuracy: 0.9419 - val_loss: 0.1983 - val_accuracy: 0.9255\n",
      "Epoch 61/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0544 - accuracy: 0.9400 - val_loss: 0.2115 - val_accuracy: 0.9168\n",
      "Epoch 62/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0548 - accuracy: 0.9397 - val_loss: 0.2019 - val_accuracy: 0.9241\n",
      "Epoch 63/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0507 - accuracy: 0.9437 - val_loss: 0.2058 - val_accuracy: 0.9237\n",
      "Epoch 64/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0516 - accuracy: 0.9433 - val_loss: 0.1929 - val_accuracy: 0.9277\n",
      "Epoch 65/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0520 - accuracy: 0.9444 - val_loss: 0.1947 - val_accuracy: 0.9262\n",
      "Epoch 66/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0513 - accuracy: 0.9438 - val_loss: 0.1918 - val_accuracy: 0.9270\n",
      "Epoch 67/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0509 - accuracy: 0.9445 - val_loss: 0.1904 - val_accuracy: 0.9262\n",
      "Epoch 68/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0511 - accuracy: 0.9471 - val_loss: 0.1896 - val_accuracy: 0.9248\n",
      "Epoch 69/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0477 - accuracy: 0.9468 - val_loss: 0.1856 - val_accuracy: 0.9270\n",
      "Epoch 70/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0483 - accuracy: 0.9483 - val_loss: 0.1883 - val_accuracy: 0.9295\n",
      "Epoch 71/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0478 - accuracy: 0.9452 - val_loss: 0.1850 - val_accuracy: 0.9317\n",
      "Epoch 72/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0489 - accuracy: 0.9470 - val_loss: 0.1909 - val_accuracy: 0.9281\n",
      "Epoch 73/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0477 - accuracy: 0.9487 - val_loss: 0.1805 - val_accuracy: 0.9331\n",
      "Epoch 74/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0463 - accuracy: 0.9503 - val_loss: 0.1863 - val_accuracy: 0.9306\n",
      "Epoch 75/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0455 - accuracy: 0.9525 - val_loss: 0.1868 - val_accuracy: 0.9295\n",
      "Epoch 76/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0440 - accuracy: 0.9518 - val_loss: 0.1761 - val_accuracy: 0.9339\n",
      "Epoch 77/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0453 - accuracy: 0.9507 - val_loss: 0.1719 - val_accuracy: 0.9335\n",
      "Epoch 78/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0435 - accuracy: 0.9519 - val_loss: 0.1791 - val_accuracy: 0.9328\n",
      "Epoch 79/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0439 - accuracy: 0.9527 - val_loss: 0.1833 - val_accuracy: 0.9335\n",
      "Epoch 80/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0419 - accuracy: 0.9541 - val_loss: 0.1741 - val_accuracy: 0.9364\n",
      "Epoch 81/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0414 - accuracy: 0.9554 - val_loss: 0.1743 - val_accuracy: 0.9368\n",
      "Epoch 82/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0427 - accuracy: 0.9514 - val_loss: 0.1797 - val_accuracy: 0.9339\n",
      "Epoch 83/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0420 - accuracy: 0.9559 - val_loss: 0.1801 - val_accuracy: 0.9342\n",
      "Epoch 84/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0402 - accuracy: 0.9566 - val_loss: 0.1695 - val_accuracy: 0.9382\n",
      "Epoch 85/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0419 - accuracy: 0.9543 - val_loss: 0.1734 - val_accuracy: 0.9353\n",
      "Epoch 86/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0418 - accuracy: 0.9553 - val_loss: 0.1759 - val_accuracy: 0.9364\n",
      "Epoch 87/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0432 - accuracy: 0.9536 - val_loss: 0.1821 - val_accuracy: 0.9328\n",
      "Epoch 88/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0422 - accuracy: 0.9541 - val_loss: 0.1720 - val_accuracy: 0.9382\n",
      "Epoch 89/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0423 - accuracy: 0.9536 - val_loss: 0.1791 - val_accuracy: 0.9379\n",
      "Epoch 90/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0404 - accuracy: 0.9551 - val_loss: 0.1677 - val_accuracy: 0.9404\n",
      "Epoch 91/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0409 - accuracy: 0.9561 - val_loss: 0.1689 - val_accuracy: 0.9393\n",
      "Epoch 92/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0382 - accuracy: 0.9575 - val_loss: 0.1679 - val_accuracy: 0.9419\n",
      "Epoch 93/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0393 - accuracy: 0.9571 - val_loss: 0.1696 - val_accuracy: 0.9375\n",
      "Epoch 94/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0383 - accuracy: 0.9582 - val_loss: 0.1670 - val_accuracy: 0.9382\n",
      "Epoch 95/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0367 - accuracy: 0.9612 - val_loss: 0.1769 - val_accuracy: 0.9353\n",
      "Epoch 96/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0385 - accuracy: 0.9597 - val_loss: 0.1656 - val_accuracy: 0.9390\n",
      "Epoch 97/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0361 - accuracy: 0.9605 - val_loss: 0.1684 - val_accuracy: 0.9400\n",
      "Epoch 98/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0362 - accuracy: 0.9597 - val_loss: 0.1689 - val_accuracy: 0.9379\n",
      "Epoch 99/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0378 - accuracy: 0.9605 - val_loss: 0.1636 - val_accuracy: 0.9400\n",
      "Epoch 100/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0348 - accuracy: 0.9629 - val_loss: 0.1638 - val_accuracy: 0.9426\n",
      "Epoch 101/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0354 - accuracy: 0.9607 - val_loss: 0.1607 - val_accuracy: 0.9411\n",
      "Epoch 102/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0362 - accuracy: 0.9623 - val_loss: 0.1620 - val_accuracy: 0.9408\n",
      "Epoch 103/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0376 - accuracy: 0.9576 - val_loss: 0.1687 - val_accuracy: 0.9375\n",
      "Epoch 104/200\n",
      "11004/11004 [==============================] - 2s 163us/step - loss: 0.0376 - accuracy: 0.9595 - val_loss: 0.1693 - val_accuracy: 0.9382\n",
      "Epoch 105/200\n",
      "11004/11004 [==============================] - 2s 160us/step - loss: 0.0335 - accuracy: 0.9645 - val_loss: 0.1696 - val_accuracy: 0.9368\n",
      "Epoch 106/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0347 - accuracy: 0.9634 - val_loss: 0.1720 - val_accuracy: 0.9368\n",
      "Epoch 107/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0364 - accuracy: 0.9618 - val_loss: 0.1621 - val_accuracy: 0.9382\n",
      "Epoch 108/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0335 - accuracy: 0.9652 - val_loss: 0.1588 - val_accuracy: 0.9400\n",
      "Epoch 109/200\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0356 - accuracy: 0.9620 - val_loss: 0.1606 - val_accuracy: 0.9422\n",
      "Epoch 110/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0346 - accuracy: 0.9606 - val_loss: 0.1559 - val_accuracy: 0.9448\n",
      "Epoch 111/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0322 - accuracy: 0.9656 - val_loss: 0.1600 - val_accuracy: 0.9437\n",
      "Epoch 112/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0344 - accuracy: 0.9635 - val_loss: 0.1610 - val_accuracy: 0.9408\n",
      "Epoch 113/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0320 - accuracy: 0.9654 - val_loss: 0.1603 - val_accuracy: 0.9426\n",
      "Epoch 114/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0331 - accuracy: 0.9640 - val_loss: 0.1566 - val_accuracy: 0.9430\n",
      "Epoch 115/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0350 - accuracy: 0.9639 - val_loss: 0.1598 - val_accuracy: 0.9411\n",
      "Epoch 116/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0330 - accuracy: 0.9671 - val_loss: 0.1567 - val_accuracy: 0.9422\n",
      "Epoch 117/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0328 - accuracy: 0.9632 - val_loss: 0.1618 - val_accuracy: 0.9386\n",
      "Epoch 118/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0322 - accuracy: 0.9653 - val_loss: 0.1615 - val_accuracy: 0.9433\n",
      "Epoch 119/200\n",
      "11004/11004 [==============================] - 2s 146us/step - loss: 0.0332 - accuracy: 0.9662 - val_loss: 0.1619 - val_accuracy: 0.9415\n",
      "Epoch 120/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0325 - accuracy: 0.9652 - val_loss: 0.1572 - val_accuracy: 0.9440\n",
      "Epoch 121/200\n",
      "11004/11004 [==============================] - 2s 146us/step - loss: 0.0319 - accuracy: 0.9656 - val_loss: 0.1532 - val_accuracy: 0.9451\n",
      "Epoch 122/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0300 - accuracy: 0.9701 - val_loss: 0.1601 - val_accuracy: 0.9415\n",
      "Epoch 123/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0296 - accuracy: 0.9691 - val_loss: 0.1583 - val_accuracy: 0.9426\n",
      "Epoch 124/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0331 - accuracy: 0.9648 - val_loss: 0.1574 - val_accuracy: 0.9444\n",
      "Epoch 125/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0290 - accuracy: 0.9673 - val_loss: 0.1580 - val_accuracy: 0.9448\n",
      "Epoch 126/200\n",
      "11004/11004 [==============================] - 2s 145us/step - loss: 0.0287 - accuracy: 0.9684 - val_loss: 0.1563 - val_accuracy: 0.9422\n",
      "Epoch 127/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0338 - accuracy: 0.9652 - val_loss: 0.1577 - val_accuracy: 0.9415\n",
      "Epoch 128/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0309 - accuracy: 0.9664 - val_loss: 0.1634 - val_accuracy: 0.9430\n",
      "Epoch 129/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0311 - accuracy: 0.9658 - val_loss: 0.1609 - val_accuracy: 0.9419\n",
      "Epoch 130/200\n",
      "11004/11004 [==============================] - 2s 145us/step - loss: 0.0322 - accuracy: 0.9646 - val_loss: 0.1618 - val_accuracy: 0.9430\n",
      "Epoch 131/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0286 - accuracy: 0.9687 - val_loss: 0.1629 - val_accuracy: 0.9408\n",
      "Epoch 132/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0293 - accuracy: 0.9692 - val_loss: 0.1642 - val_accuracy: 0.9408\n",
      "Epoch 133/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0299 - accuracy: 0.9669 - val_loss: 0.1586 - val_accuracy: 0.9459\n",
      "Epoch 134/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0285 - accuracy: 0.9705 - val_loss: 0.1537 - val_accuracy: 0.9455\n",
      "Epoch 135/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0285 - accuracy: 0.9685 - val_loss: 0.1605 - val_accuracy: 0.9444\n",
      "Epoch 136/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0282 - accuracy: 0.9702 - val_loss: 0.1494 - val_accuracy: 0.9462\n",
      "Epoch 137/200\n",
      "11004/11004 [==============================] - 2s 145us/step - loss: 0.0283 - accuracy: 0.9692 - val_loss: 0.1500 - val_accuracy: 0.9477\n",
      "Epoch 138/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0290 - accuracy: 0.9683 - val_loss: 0.1514 - val_accuracy: 0.9466\n",
      "Epoch 139/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0290 - accuracy: 0.9686 - val_loss: 0.1511 - val_accuracy: 0.9451\n",
      "Epoch 140/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0279 - accuracy: 0.9709 - val_loss: 0.1523 - val_accuracy: 0.9499\n",
      "Epoch 141/200\n",
      "11004/11004 [==============================] - 2s 145us/step - loss: 0.0290 - accuracy: 0.9706 - val_loss: 0.1521 - val_accuracy: 0.9473\n",
      "Epoch 142/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0289 - accuracy: 0.9690 - val_loss: 0.1614 - val_accuracy: 0.9455\n",
      "Epoch 143/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0261 - accuracy: 0.9727 - val_loss: 0.1498 - val_accuracy: 0.9455\n",
      "Epoch 144/200\n",
      "11004/11004 [==============================] - 2s 146us/step - loss: 0.0270 - accuracy: 0.9716 - val_loss: 0.1630 - val_accuracy: 0.9455\n",
      "Epoch 145/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0274 - accuracy: 0.9697 - val_loss: 0.1527 - val_accuracy: 0.9466\n",
      "Epoch 146/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0273 - accuracy: 0.9720 - val_loss: 0.1540 - val_accuracy: 0.9448\n",
      "Epoch 147/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0276 - accuracy: 0.9699 - val_loss: 0.1662 - val_accuracy: 0.9433\n",
      "Epoch 148/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0292 - accuracy: 0.9692 - val_loss: 0.1663 - val_accuracy: 0.9444\n",
      "Epoch 149/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0257 - accuracy: 0.9740 - val_loss: 0.1604 - val_accuracy: 0.9455\n",
      "Epoch 150/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0285 - accuracy: 0.9696 - val_loss: 0.1562 - val_accuracy: 0.9473\n",
      "Epoch 151/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0272 - accuracy: 0.9726 - val_loss: 0.1537 - val_accuracy: 0.9462\n",
      "Epoch 152/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0277 - accuracy: 0.9697 - val_loss: 0.1554 - val_accuracy: 0.9440\n",
      "Epoch 153/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0267 - accuracy: 0.9707 - val_loss: 0.1538 - val_accuracy: 0.9495\n",
      "Epoch 154/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0267 - accuracy: 0.9714 - val_loss: 0.1530 - val_accuracy: 0.9480\n",
      "Epoch 155/200\n",
      "11004/11004 [==============================] - 2s 168us/step - loss: 0.0247 - accuracy: 0.9732 - val_loss: 0.1551 - val_accuracy: 0.9466\n",
      "Epoch 156/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0255 - accuracy: 0.9729 - val_loss: 0.1590 - val_accuracy: 0.9459\n",
      "Epoch 157/200\n",
      "11004/11004 [==============================] - 2s 159us/step - loss: 0.0251 - accuracy: 0.9746 - val_loss: 0.1497 - val_accuracy: 0.9477\n",
      "Epoch 158/200\n",
      "11004/11004 [==============================] - 2s 154us/step - loss: 0.0264 - accuracy: 0.9723 - val_loss: 0.1493 - val_accuracy: 0.9469\n",
      "Epoch 159/200\n",
      "11004/11004 [==============================] - 2s 165us/step - loss: 0.0263 - accuracy: 0.9718 - val_loss: 0.1532 - val_accuracy: 0.9466\n",
      "Epoch 160/200\n",
      "11004/11004 [==============================] - 2s 162us/step - loss: 0.0255 - accuracy: 0.9739 - val_loss: 0.1546 - val_accuracy: 0.9473\n",
      "Epoch 161/200\n",
      "11004/11004 [==============================] - 2s 155us/step - loss: 0.0280 - accuracy: 0.9713 - val_loss: 0.1443 - val_accuracy: 0.9495\n",
      "Epoch 162/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0246 - accuracy: 0.9746 - val_loss: 0.1556 - val_accuracy: 0.9448\n",
      "Epoch 163/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0259 - accuracy: 0.9733 - val_loss: 0.1458 - val_accuracy: 0.9520\n",
      "Epoch 164/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0257 - accuracy: 0.9731 - val_loss: 0.1466 - val_accuracy: 0.9480\n",
      "Epoch 165/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0245 - accuracy: 0.9746 - val_loss: 0.1508 - val_accuracy: 0.9459\n",
      "Epoch 166/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0258 - accuracy: 0.9736 - val_loss: 0.1491 - val_accuracy: 0.9469\n",
      "Epoch 167/200\n",
      "11004/11004 [==============================] - 2s 152us/step - loss: 0.0246 - accuracy: 0.9742 - val_loss: 0.1488 - val_accuracy: 0.9480\n",
      "Epoch 168/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0248 - accuracy: 0.9731 - val_loss: 0.1539 - val_accuracy: 0.9444\n",
      "Epoch 169/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0236 - accuracy: 0.9756 - val_loss: 0.1570 - val_accuracy: 0.9477\n",
      "Epoch 170/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0255 - accuracy: 0.9726 - val_loss: 0.1574 - val_accuracy: 0.9433\n",
      "Epoch 171/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0268 - accuracy: 0.9743 - val_loss: 0.1539 - val_accuracy: 0.9484\n",
      "Epoch 172/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0259 - accuracy: 0.9710 - val_loss: 0.1586 - val_accuracy: 0.9480\n",
      "Epoch 173/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0246 - accuracy: 0.9776 - val_loss: 0.1516 - val_accuracy: 0.9469\n",
      "Epoch 174/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0219 - accuracy: 0.9753 - val_loss: 0.1597 - val_accuracy: 0.9466\n",
      "Epoch 175/200\n",
      "11004/11004 [==============================] - 2s 146us/step - loss: 0.0232 - accuracy: 0.9762 - val_loss: 0.1536 - val_accuracy: 0.9455\n",
      "Epoch 176/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0250 - accuracy: 0.9741 - val_loss: 0.1464 - val_accuracy: 0.9499\n",
      "Epoch 177/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0232 - accuracy: 0.9748 - val_loss: 0.1461 - val_accuracy: 0.9491\n",
      "Epoch 178/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0256 - accuracy: 0.9739 - val_loss: 0.1534 - val_accuracy: 0.9462\n",
      "Epoch 179/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0256 - accuracy: 0.9734 - val_loss: 0.1524 - val_accuracy: 0.9448\n",
      "Epoch 180/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0259 - accuracy: 0.9736 - val_loss: 0.1583 - val_accuracy: 0.9426\n",
      "Epoch 181/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0259 - accuracy: 0.9723 - val_loss: 0.1548 - val_accuracy: 0.9440\n",
      "Epoch 182/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0223 - accuracy: 0.9763 - val_loss: 0.1563 - val_accuracy: 0.9444\n",
      "Epoch 183/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0234 - accuracy: 0.9756 - val_loss: 0.1599 - val_accuracy: 0.9440\n",
      "Epoch 184/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0223 - accuracy: 0.9760 - val_loss: 0.1517 - val_accuracy: 0.9440\n",
      "Epoch 185/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0240 - accuracy: 0.9746 - val_loss: 0.1540 - val_accuracy: 0.9451\n",
      "Epoch 186/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0249 - accuracy: 0.9746 - val_loss: 0.1460 - val_accuracy: 0.9484\n",
      "Epoch 187/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0225 - accuracy: 0.9746 - val_loss: 0.1567 - val_accuracy: 0.9480\n",
      "Epoch 188/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0252 - accuracy: 0.9733 - val_loss: 0.1551 - val_accuracy: 0.9473\n",
      "Epoch 189/200\n",
      "11004/11004 [==============================] - 2s 146us/step - loss: 0.0216 - accuracy: 0.9787 - val_loss: 0.1525 - val_accuracy: 0.9455\n",
      "Epoch 190/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0206 - accuracy: 0.9778 - val_loss: 0.1558 - val_accuracy: 0.9462\n",
      "Epoch 191/200\n",
      "11004/11004 [==============================] - 2s 146us/step - loss: 0.0218 - accuracy: 0.9777 - val_loss: 0.1502 - val_accuracy: 0.9491\n",
      "Epoch 192/200\n",
      "11004/11004 [==============================] - 2s 150us/step - loss: 0.0225 - accuracy: 0.9755 - val_loss: 0.1650 - val_accuracy: 0.9466\n",
      "Epoch 193/200\n",
      "11004/11004 [==============================] - 2s 154us/step - loss: 0.0218 - accuracy: 0.9757 - val_loss: 0.1554 - val_accuracy: 0.9484\n",
      "Epoch 194/200\n",
      "11004/11004 [==============================] - 2s 154us/step - loss: 0.0230 - accuracy: 0.9763 - val_loss: 0.1510 - val_accuracy: 0.9466\n",
      "Epoch 195/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0225 - accuracy: 0.9759 - val_loss: 0.1482 - val_accuracy: 0.9491\n",
      "Epoch 196/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0220 - accuracy: 0.9772 - val_loss: 0.1427 - val_accuracy: 0.9488\n",
      "Epoch 197/200\n",
      "11004/11004 [==============================] - 2s 147us/step - loss: 0.0253 - accuracy: 0.9726 - val_loss: 0.1464 - val_accuracy: 0.9488\n",
      "Epoch 198/200\n",
      "11004/11004 [==============================] - 2s 148us/step - loss: 0.0242 - accuracy: 0.9761 - val_loss: 0.1493 - val_accuracy: 0.9484\n",
      "Epoch 199/200\n",
      "11004/11004 [==============================] - 2s 151us/step - loss: 0.0225 - accuracy: 0.9765 - val_loss: 0.1488 - val_accuracy: 0.9469\n",
      "Epoch 200/200\n",
      "11004/11004 [==============================] - 2s 149us/step - loss: 0.0220 - accuracy: 0.9766 - val_loss: 0.1511 - val_accuracy: 0.9495\n",
      "Accuracy is: 75.8\n",
      "TruePositive:  223 \n",
      "TrueNEgative:  2809 \n",
      "F1 Score:  0.15770862800565771 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.3811965811965812 \n",
      "\tP=  0.2689987937273824 \n",
      "\n",
      "Score1:  0.2689987937273824 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model3= Sequential()\n",
    "model3.add(Dense(128, input_dim=166, activation='sigmoid'))\n",
    "model3.add(Dropout(.2))\n",
    "model3.add(Dense(1, activation='sigmoid'))\n",
    "opt=keras.optimizers.Adam()\n",
    "model3.compile(loss='binary_crossentropy', optimizer=opt,\n",
    "              metrics=['accuracy'])\n",
    "history3 = model3.fit(x_train_ovs, y_train_ovs, epochs=200,validation_data = (x_val_ovs,y_val_ovs),\n",
    "                      class_weight={0:.76, 1:.24}, batch_size=32)\n",
    "\n",
    "accTest(model3, disc=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(history3.history['accuracy'])\n",
    "plt.plot(history3.history['val_accuracy'])\n",
    "plt.plot(history3.history['loss'])\n",
    "plt.plot(history3.history['val_loss'])\n",
    "plt.title('Model accuracy and loss')\n",
    "plt.ylabel('Accuracy-loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test', 'loss_train', 'loss_test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11004 samples, validate on 2752 samples\n",
      "Epoch 1/25\n",
      "11004/11004 [==============================] - 5s 452us/step - loss: 0.2709 - accuracy: 0.4953 - specificity_at_sensitivity_23: 0.5433 - val_loss: 0.7934 - val_accuracy: 0.5189 - val_specificity_at_sensitivity_23: 0.8242\n",
      "Epoch 2/25\n",
      "11004/11004 [==============================] - 3s 242us/step - loss: 0.2542 - accuracy: 0.4953 - specificity_at_sensitivity_23: 0.8859 - val_loss: 0.7647 - val_accuracy: 0.5189 - val_specificity_at_sensitivity_23: 0.8873\n",
      "Epoch 3/25\n",
      "11004/11004 [==============================] - 3s 263us/step - loss: 0.2330 - accuracy: 0.4953 - specificity_at_sensitivity_23: 0.8822 - val_loss: 0.6927 - val_accuracy: 0.5189 - val_specificity_at_sensitivity_23: 0.9013\n",
      "Epoch 4/25\n",
      "11004/11004 [==============================] - 3s 270us/step - loss: 0.2141 - accuracy: 0.4953 - specificity_at_sensitivity_23: 0.8974 - val_loss: 0.6445 - val_accuracy: 0.5189 - val_specificity_at_sensitivity_23: 0.9020\n",
      "Epoch 5/25\n",
      "11004/11004 [==============================] - 3s 266us/step - loss: 0.2019 - accuracy: 0.4953 - specificity_at_sensitivity_23: 0.9194 - val_loss: 0.6194 - val_accuracy: 0.5189 - val_specificity_at_sensitivity_23: 0.9104\n",
      "Epoch 6/25\n",
      "11004/11004 [==============================] - 3s 266us/step - loss: 0.1936 - accuracy: 0.5713 - specificity_at_sensitivity_23: 0.9226 - val_loss: 0.5936 - val_accuracy: 0.7017 - val_specificity_at_sensitivity_23: 0.9118\n",
      "Epoch 7/25\n",
      "11004/11004 [==============================] - 3s 275us/step - loss: 0.1880 - accuracy: 0.7256 - specificity_at_sensitivity_23: 0.9242 - val_loss: 0.5908 - val_accuracy: 0.7242 - val_specificity_at_sensitivity_23: 0.9111\n",
      "Epoch 8/25\n",
      "11004/11004 [==============================] - 3s 250us/step - loss: 0.1839 - accuracy: 0.7457 - specificity_at_sensitivity_23: 0.9242 - val_loss: 0.5694 - val_accuracy: 0.7467 - val_specificity_at_sensitivity_23: 0.9139\n",
      "Epoch 9/25\n",
      "11004/11004 [==============================] - 3s 248us/step - loss: 0.1811 - accuracy: 0.7565 - specificity_at_sensitivity_23: 0.9272 - val_loss: 0.5631 - val_accuracy: 0.7507 - val_specificity_at_sensitivity_23: 0.9139\n",
      "Epoch 10/25\n",
      "11004/11004 [==============================] - 3s 251us/step - loss: 0.1789 - accuracy: 0.7613 - specificity_at_sensitivity_23: 0.9272 - val_loss: 0.5630 - val_accuracy: 0.7558 - val_specificity_at_sensitivity_23: 0.9146\n",
      "Epoch 11/25\n",
      "11004/11004 [==============================] - 3s 249us/step - loss: 0.1775 - accuracy: 0.7668 - specificity_at_sensitivity_23: 0.9297 - val_loss: 0.5544 - val_accuracy: 0.7627 - val_specificity_at_sensitivity_23: 0.9132\n",
      "Epoch 12/25\n",
      "11004/11004 [==============================] - 3s 250us/step - loss: 0.1762 - accuracy: 0.7726 - specificity_at_sensitivity_23: 0.9284 - val_loss: 0.5588 - val_accuracy: 0.7565 - val_specificity_at_sensitivity_23: 0.9139\n",
      "Epoch 13/25\n",
      "11004/11004 [==============================] - 3s 252us/step - loss: 0.1753 - accuracy: 0.7714 - specificity_at_sensitivity_23: 0.9288 - val_loss: 0.5507 - val_accuracy: 0.7653 - val_specificity_at_sensitivity_23: 0.9146\n",
      "Epoch 14/25\n",
      "11004/11004 [==============================] - 3s 270us/step - loss: 0.1744 - accuracy: 0.7760 - specificity_at_sensitivity_23: 0.9268 - val_loss: 0.5475 - val_accuracy: 0.7649 - val_specificity_at_sensitivity_23: 0.9146\n",
      "Epoch 15/25\n",
      "11004/11004 [==============================] - 3s 268us/step - loss: 0.1736 - accuracy: 0.7768 - specificity_at_sensitivity_23: 0.9299 - val_loss: 0.5403 - val_accuracy: 0.7714 - val_specificity_at_sensitivity_23: 0.9174\n",
      "Epoch 16/25\n",
      "11004/11004 [==============================] - 3s 250us/step - loss: 0.1731 - accuracy: 0.7780 - specificity_at_sensitivity_23: 0.9281 - val_loss: 0.5387 - val_accuracy: 0.7740 - val_specificity_at_sensitivity_23: 0.9153\n",
      "Epoch 17/25\n",
      "11004/11004 [==============================] - 3s 257us/step - loss: 0.1726 - accuracy: 0.7845 - specificity_at_sensitivity_23: 0.9275 - val_loss: 0.5487 - val_accuracy: 0.7660 - val_specificity_at_sensitivity_23: 0.9167\n",
      "Epoch 18/25\n",
      "11004/11004 [==============================] - 3s 255us/step - loss: 0.1720 - accuracy: 0.7813 - specificity_at_sensitivity_23: 0.9284 - val_loss: 0.5451 - val_accuracy: 0.7693 - val_specificity_at_sensitivity_23: 0.9160\n",
      "Epoch 19/25\n",
      "11004/11004 [==============================] - 3s 262us/step - loss: 0.1717 - accuracy: 0.7842 - specificity_at_sensitivity_23: 0.9312 - val_loss: 0.5407 - val_accuracy: 0.7711 - val_specificity_at_sensitivity_23: 0.9167\n",
      "Epoch 20/25\n",
      "11004/11004 [==============================] - 3s 267us/step - loss: 0.1713 - accuracy: 0.7849 - specificity_at_sensitivity_23: 0.9305 - val_loss: 0.5395 - val_accuracy: 0.7714 - val_specificity_at_sensitivity_23: 0.9153\n",
      "Epoch 21/25\n",
      "11004/11004 [==============================] - 3s 270us/step - loss: 0.1710 - accuracy: 0.7847 - specificity_at_sensitivity_23: 0.9299 - val_loss: 0.5351 - val_accuracy: 0.7740 - val_specificity_at_sensitivity_23: 0.9174\n",
      "Epoch 22/25\n",
      "11004/11004 [==============================] - 3s 270us/step - loss: 0.1707 - accuracy: 0.7886 - specificity_at_sensitivity_23: 0.9303 - val_loss: 0.5451 - val_accuracy: 0.7671 - val_specificity_at_sensitivity_23: 0.9167\n",
      "Epoch 23/25\n",
      "11004/11004 [==============================] - 3s 254us/step - loss: 0.1704 - accuracy: 0.7864 - specificity_at_sensitivity_23: 0.9273 - val_loss: 0.5447 - val_accuracy: 0.7653 - val_specificity_at_sensitivity_23: 0.9167\n",
      "Epoch 24/25\n",
      "11004/11004 [==============================] - 3s 252us/step - loss: 0.1701 - accuracy: 0.7874 - specificity_at_sensitivity_23: 0.9316 - val_loss: 0.5505 - val_accuracy: 0.7660 - val_specificity_at_sensitivity_23: 0.9167\n",
      "Epoch 25/25\n",
      "11004/11004 [==============================] - 3s 255us/step - loss: 0.1700 - accuracy: 0.7876 - specificity_at_sensitivity_23: 0.9305 - val_loss: 0.5367 - val_accuracy: 0.7725 - val_specificity_at_sensitivity_23: 0.9188\n",
      "Accuracy is: 81.175\n",
      "TruePositive:  336 \n",
      "TrueNEgative:  2911 \n",
      "F1 Score:  0.23578947368421052 \n",
      "\t Scoring for the challenge: \n",
      "\n",
      "\tSe=  0.5743589743589743 \n",
      "\tP=  0.4 \n",
      "\n",
      "Score1:  0.4 \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model3= Sequential()\n",
    "model3.add(Dense(2, input_dim=166, activation='sigmoid'))\n",
    "model3.add(Dense(2, activation='softmax'))\n",
    "#model3.add(Dropout(.2))\n",
    "model3.add(Dense(1, activation='sigmoid'))\n",
    "opt=keras.optimizers.Adam(.001)\n",
    "model3.compile(loss='binary_crossentropy', optimizer=opt,\n",
    "              metrics=['accuracy', keras.metrics.SpecificityAtSensitivity(.5)])\n",
    "history3 = model3.fit(x_train_ovs, y_train_ovs, epochs=25,validation_data = (x_val_ovs,y_val_ovs),\n",
    "                      class_weight={0:.78, 1:.22})\n",
    "\n",
    "accTest(model3, disc=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history3.history['accuracy'])\n",
    "plt.plot(history3.history['val_accuracy'])\n",
    "plt.plot(history3.history['loss'])\n",
    "plt.plot(history3.history['val_loss'])\n",
    "plt.title('Model accuracy and loss')\n",
    "plt.ylabel('Accuracy-loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')#, 'loss_train', 'loss_val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por último\n",
    "Para u ntrabajo futuro, con más poder de cómputo podría intentar con distintas arquitecturas y una búsqueda de parámetros más exhaustiva y considero que llegar a un buen SCORE (lograr subir .1) no suena tan imposible. Para mayores detalles visitar el artículo en:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.title('Cantidad por clase')\n",
    "sns.countplot(x=\"hospital_death\", data=pd_data)\n",
    "plt.xlabel('Hospital death')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.title(\"Distribución de edades\")\n",
    "plt.hist(pd_data['Age'], bins = 30)\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.xlabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAV7UlEQVR4nO3de7RedX3n8feHhItWuUgyVBMgXuiawdYRGwF1ZpUlM4KIwlAveCNqHLTDWLt0qsLYoYMyXiuK9VJa0JC2IqVW42UtRFCrVYGg1gqUIaJA8EIggIIjNPidP57foQ+Hc/J7guc55yTn/VrrrOz9++3929/nJDmfs397P/tJVSFJ0tbsNNcFSJLmP8NCktRlWEiSugwLSVKXYSFJ6jIsJEldhoXmhSQfTvJHMzTWfknuTLKorX8pyStnYuxJx7kzyWMmte2U5FNJVs/gcT6a5K0zNd40x3hekouS7DbO42j7tXiuC9COL8kPgH2ALcC9wFXAucBZVfVLgKp69TaM9cqq+sJ021TVDcDDfrWq+6pqqmO8Fbi4qs4e9/FnSpKDgFcCx1bVL+a6Hs1PhoVmy7Or6gtJ9gB+B3gfcAjw8pk8SJLFVbVlJsfcFlV1ylwd+8Gqqm8BR4xr/Ln+O9HMcBpKs6qq7qiqdcALgFVJfhPuP9WSZEmSzyS5PcnmJF9p0ztrgf2AT7cpoDckWZGkkqxOcgNwyVDb8C9Dj01yWZKftmmiR7RjHZZk43CNSX6Q5D+15UVJTknyvSQ/S3JFkn1bXyV5XFveI8m5STYluT7Jm5Ps1PpeluSrSd6d5LYk30/yzOm+R0kOSvLNdryPA7tN6j86ybfb9+drSZ6wlbGekeSaJHck+WCSLw9PySV5RZKrW10XJtl/qK+SvDrJte1YH0iSbdj3pCTXAte2tqcmubzVcnmSp05Xt+Yfw0JzoqouAzYC/3GK7te3vqUMpq9OGexSLwVuYHCW8rCqeufQPr8D/Dum/w35BOAVwCMZTIedOWKprwNeCBwF7N7G+PkU270f2AN4TKvlBO5/1nQIcA2wBHgncPbwD94JSXYBPgmsBR4B/A3wu0P9BwHnAK8C9gb+DFiXZNcpxloCXACc3La9BnjqUP8xDL63xzH4Xn8F+NikYY4Gngw8AXg+7fs74r7Httd9YAvnzzL4vu8NvAf4bJK9J9et+cmw0Fz6IYMfiJP9C4Mf6vtX1b9U1Veq/xCzP66qu6rq/03Tv7aqvltVdwF/BDx/4gJ4xyuBN1fVNTXwj1V16/AGbZzjgZOr6mdV9QPgT4CXDm12fVX9eVXdC6xpr2+fKY53KLAz8N722i8ALh/qPxH4s6q6tKrurao1wN1tv8mOAq6sqk+0aaAzgR8P9b8aeFtVXd36/w/wxOEzBODtVXV7uw70ReCJ27Dv26pqc/s7eRZwbVWtraotVfUx4J+BZ09Rt+Yhw0JzaRmweYr2dwEbgM8nuS7Jm0YY68Zt6L+ewQ/kJSOMuy/wvc42S9p41086xrKh9ft+SFfVxJnJVBfIHwXcNCkch8fdH3h9mxa6PcntrcZHTTPWfa+7jTk85bY/8L6hcTYDma5uBmdUD9uGfYe/54+a9DomXtcytF0wLDQnkjyZwQ+Kr07ua7+dv76qHgM8B3hdksMnuqcZsnfmse/Q8n4Mzl5uAe4CHjpU1yIG0yoTbgQe2xn7ljbe8G/V+wE3dfabyo+AZZOmqPabVM/pVbXn0NdD22/qU421fGKljbl8qP9G4FWTxnpIVX1thDpH2Xf47+SH3P/7M/G6Hsz3SHPAsNCsSrJ7kqOB84C/rKp/mmKbo5M8rv1wu4PB7ba/bN0/YXBdYFu9JMmBSR4KnAZc0KaE/i+wW5JnJdkZeDMwPP//F8BbkhyQgSdMnmdv45wPnJ7k4W0q5nXAXz6IOr/O4JrK7yfZOclxwMFD/X8OvDrJIa2eX2u1P3yKsT4L/FaSYzO42H8S8OtD/R8GTk7yeLjvIv3zRqxzW/f9HPAbSV6UZHGSFwAHAp8Z8XiaY4aFZsunk/yMwW+k/5PBBc7pbps9APgCcCeDH54frKovtr63AW9u0x//YxuOvxb4KINpld2A34fB3VnAf2MQCjcxONMYnqp5D4Mg+DzwU+Bs4CFTjP+atu91DM6W/prBhehtUlX3MLho/DIGUzsvAD4x1L8e+K/AnwK3MZiue9k0Y90CPI/BBfVbGfxwXs/gGgdV9XfAO4DzkvwU+C4w7V1ak8bepn3bdZ6jGdy8cCvwBuDoVqO2A/HDj6SFod3KuxF48VD4SiPxzELagSU5Isme7dbaUxhchP7GHJel7ZBhIe3YnsLgbq5bGNymeuxWbi+WpuU0lCSpyzMLSVLXDvkgwSVLltSKFSvmugxJ2q5cccUVt1TV0qn6dsiwWLFiBevXr5/rMiRpu5Jk8rvs7+M0lCSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqWuHfAe3tCO74bTfmusSNA/t978e8KGTM8ozC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldYw+LJIuSfCvJZ9r6o5NcmmRDko8n2aW179rWN7T+FUNjnNzar0lyxLhrliTd32ycWbwWuHpo/R3AGVX1OOA2YHVrXw3c1trPaNuR5EDgeODxwJHAB5MsmoW6JUnNWMMiyXLgWcBftPUATwcuaJusAY5ty8e0dVr/4W37Y4Dzquruqvo+sAE4eJx1S5Lub9xnFu8F3gD8sq3vDdxeVVva+kZgWVteBtwI0PrvaNvf1z7FPvdJcmKS9UnWb9q0aaZfhyQtaGMLiyRHAzdX1RXjOsawqjqrqlZW1cqlS5fOxiElacFYPMaxnwY8J8lRwG7A7sD7gD2TLG5nD8uBm9r2NwH7AhuTLAb2AG4dap8wvI8kaRaM7cyiqk6uquVVtYLBBepLqurFwBeB57bNVgGfasvr2jqt/5KqqtZ+fLtb6tHAAcBl46pbkvRA4zyzmM4bgfOSvBX4FnB2az8bWJtkA7CZQcBQVVcmOR+4CtgCnFRV985+2ZK0cM1KWFTVl4AvteXrmOJupqr6BfC8afY/HTh9fBVKkrbGd3BLkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSuubiY1W3C7/9h+fOdQmah6541wlzXYI0JzyzkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktQ1trBIsluSy5L8Y5Irk/zv1v7oJJcm2ZDk40l2ae27tvUNrX/F0Fgnt/ZrkhwxrpolSVMb55nF3cDTq+rfA08EjkxyKPAO4IyqehxwG7C6bb8auK21n9G2I8mBwPHA44EjgQ8mWTTGuiVJk4wtLGrgzra6c/sq4OnABa19DXBsWz6mrdP6D0+S1n5eVd1dVd8HNgAHj6tuSdIDjfWaRZJFSb4N3AxcBHwPuL2qtrRNNgLL2vIy4EaA1n8HsPdw+xT7DB/rxCTrk6zftGnTOF6OJC1YYw2Lqrq3qp4ILGdwNvBvx3iss6pqZVWtXLp06bgOI0kL0qzcDVVVtwNfBJ4C7JlkcetaDtzUlm8C9gVo/XsAtw63T7GPJGkWjPNuqKVJ9mzLDwH+M3A1g9B4bttsFfCptryurdP6L6mqau3Ht7ulHg0cAFw2rrolSQ+0uL/Jg/ZIYE27c2kn4Pyq+kySq4DzkrwV+BZwdtv+bGBtkg3AZgZ3QFFVVyY5H7gK2AKcVFX3jrFuSdIkYwuLqvoOcNAU7dcxxd1MVfUL4HnTjHU6cPpM1yhJGo3v4JYkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSukYKiyQXj9ImSdoxbfWps0l2Ax4KLEmyF5DWtTtTfLSpJGnH1HtE+auAPwAeBVzBv4bFT4E/HWNdkqR5ZKthUVXvA96X5DVV9f5ZqkmSNM+M9OFHVfX+JE8FVgzvU1XnjqkuSdI8MlJYJFkLPBb4NjDxkaYFGBaStACM+rGqK4EDq6rGWYwkaX4a9X0W3wV+fZyFSJLmr1HPLJYAVyW5DLh7orGqnjOWqiRJ88qoYfHH4yxCkjS/jXo31JfHXYgkaf4a9W6onzG4+wlgF2Bn4K6q2n1chUmS5o9RzywePrGcJMAxwKHjKkqSNL9s81Nna+CTwBFjqEeSNA+NOg113NDqTgzed/GLsVQkSZp3Rr0b6tlDy1uAHzCYipIkLQCjXrN4+bgLkSTNX6N++NHyJH+X5Ob29bdJlo+7OEnS/DDqBe6PAOsYfK7Fo4BPtzZJ0gIwalgsraqPVNWW9vVRYOkY65IkzSOjhsWtSV6SZFH7eglw6zgLkyTNH6OGxSuA5wM/Bn4EPBd42ZhqkiTNM6PeOnsasKqqbgNI8gjg3QxCRJK0gxv1zOIJE0EBUFWbgYPGU5Ikab4ZNSx2SrLXxEo7sxj1rESStJ0bNSz+BPh6krckeQvwNeCdW9shyb5JvpjkqiRXJnlta39EkouSXNv+3Ku1J8mZSTYk+U6SJw2Ntaptf22SVQ/upUqSHqyRwqKqzgWOA37Svo6rqrWd3bYAr6+qAxk8ofakJAcCbwIurqoDgIvbOsAzgQPa14nAh+C+s5hTgUOAg4FTh89yJEnjN/JUUlVdBVy1Ddv/iMGdU1TVz5JcDSxj8Eypw9pma4AvAW9s7edWVQHfSLJnkke2bS9q10lIchFwJPCxUWuRJP1qtvkR5Q9GkhUMLohfCuzTggQGt+Lu05aXATcO7baxtU3XPvkYJyZZn2T9pk2bZrR+SVroxh4WSR4G/C3wB1X10+G+dhZRU+64jarqrKpaWVUrly71zeWSNJPGGhZJdmYQFH9VVZ9ozT9p00u0P29u7TcB+w7tvry1TdcuSZolYwuL9vGrZwNXV9V7hrrWARN3NK0CPjXUfkK7K+pQ4I42XXUh8Iwke7UL289obZKkWTLO90o8DXgp8E9Jvt3aTgHeDpyfZDVwPYPHiAB8DjgK2AD8HHg5DN4A2G7Xvbxtd9rExW5J0uwYW1hU1VeBTNN9+BTbF3DSNGOdA5wzc9VJkrbFrNwNJUnavhkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVLX2MIiyTlJbk7y3aG2RyS5KMm17c+9WnuSnJlkQ5LvJHnS0D6r2vbXJlk1rnolSdMb55nFR4EjJ7W9Cbi4qg4ALm7rAM8EDmhfJwIfgkG4AKcChwAHA6dOBIwkafaMLSyq6u+BzZOajwHWtOU1wLFD7efWwDeAPZM8EjgCuKiqNlfVbcBFPDCAJEljNtvXLPapqh+15R8D+7TlZcCNQ9ttbG3TtT9AkhOTrE+yftOmTTNbtSQtcHN2gbuqCqgZHO+sqlpZVSuXLl06U8NKkpj9sPhJm16i/Xlza78J2Hdou+Wtbbp2SdIsmu2wWAdM3NG0CvjUUPsJ7a6oQ4E72nTVhcAzkuzVLmw/o7VJkmbR4nENnORjwGHAkiQbGdzV9Hbg/CSrgeuB57fNPwccBWwAfg68HKCqNid5C3B52+60qpp80VySNGZjC4uqeuE0XYdPsW0BJ00zzjnAOTNYmiRpG/kObklSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHVtN2GR5Mgk1yTZkORNc12PJC0k20VYJFkEfAB4JnAg8MIkB85tVZK0cGwXYQEcDGyoquuq6h7gPOCYOa5JkhaMxXNdwIiWATcOrW8EDhneIMmJwIlt9c4k18xSbQvBEuCWuS5iPsi7V811Cbo//21OODUzMcr+03VsL2HRVVVnAWfNdR07oiTrq2rlXNchTea/zdmzvUxD3QTsO7S+vLVJkmbB9hIWlwMHJHl0kl2A44F1c1yTJC0Y28U0VFVtSfLfgQuBRcA5VXXlHJe1kDi9p/nKf5uzJFU11zVIkua57WUaSpI0hwwLSVKXYaH79B6pkmTXJB9v/ZcmWTH7VWohSnJOkpuTfHea/iQ5s/3b/E6SJ812jTs6w0LAyI9UWQ3cVlWPA84A3jG7VWoB+yhw5Fb6nwkc0L5OBD40CzUtKIaFJozySJVjgDVt+QLg8CQz8rZRaWuq6u+BzVvZ5Bjg3Br4BrBnkkfOTnULg2GhCVM9UmXZdNtU1RbgDmDvWalO2rpR/v3qV2BYSJK6DAtNGOWRKvdtk2QxsAdw66xUJ22djwQaM8NCE0Z5pMo6YOKxq88FLinf1an5YR1wQrsr6lDgjqr60VwXtSPZLh73ofGb7pEqSU4D1lfVOuBsYG2SDQwuNh4/dxVrIUnyMeAwYEmSjcCpwM4AVfVh4HPAUcAG4OfAy+em0h2Xj/uQJHU5DSVJ6jIsJEldhoUkqcuwkCR1GRbSDEvyoiT7zXUd0kwyLKRtkGSfJH+d5LokVyT5epL/MtS/Gvg3VXXDHJYpzTjfZyGNqD008ZPAmqp6UWvbH3jOxDZVdfYMH3Nxew6XNKc8s5BG93TgnvYmMACq6vqqen+SRUneleTy9nkKrwJIcliSLyW5IMk/J/mriSf1JvntJF9uZygXTjwltW3/3iTrgdcmWZHkkjbuxU5xaS54ZiGN7vHAN6fpW83gERNPTrIr8A9JPt/6Dmr7/hD4B+BpSS4F3g8cU1WbkrwAOB14Rdtnl6paCZDk0wzOZtYkeQVwJnDsGF6fNC3DQnqQknwA+A/APcD1wBOSPLd178Hgg3juAS6rqo1tn28DK4Dbgd8ELmonGouA4WcZfXxo+SnAcW15LfDOMbwcaasMC2l0VwK/O7FSVSclWQKsB24AXlNVFw7vkOQw4O6hpnsZ/L8LcGVVPWWaY901g3VLvzKvWUijuwTYLcnvDbU9tP15IfB7SXYGSPIbSX5tK2NdAyxN8pS2/c5JHj/Ntl/jXx/a+GLgKw/2BUgPlmcW0oiqqpIcC5yR5A3AJgZnAG8E/obB9NI32wXsTWzlukJV3dOmrM5MsgeD/4vvZXD2MtlrgI8k+cM2rk9U1azzqbOSpC6noSRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUtf/B7YCJtfFL8yiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "sns.countplot(x=\"Gender\", data=pd_data) #0: femenino, 1: masculino\n",
    "plt.title('Distribución de género')\n",
    "plt.xlabel('Género')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=pd_data,hue='Gender',x='hospital_death')\n",
    "plt.title('Muertes por género')\n",
    "plt.legend(['Femenino', 'Masculino'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>ICUType</th>\n",
       "      <th>SAPS</th>\n",
       "      <th>SOFA</th>\n",
       "      <th>STAY</th>\n",
       "      <th>SURV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8000.000000</td>\n",
       "      <td>7992.000000</td>\n",
       "      <td>4176.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.429750</td>\n",
       "      <td>0.562938</td>\n",
       "      <td>169.724569</td>\n",
       "      <td>2.783750</td>\n",
       "      <td>14.243750</td>\n",
       "      <td>6.364625</td>\n",
       "      <td>13.584875</td>\n",
       "      <td>130.426875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.352628</td>\n",
       "      <td>0.496054</td>\n",
       "      <td>17.378792</td>\n",
       "      <td>1.013096</td>\n",
       "      <td>6.044825</td>\n",
       "      <td>4.237236</td>\n",
       "      <td>12.715975</td>\n",
       "      <td>364.766144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>162.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>170.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>177.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>462.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>2620.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age       Gender       Height      ICUType         SAPS  \\\n",
       "count  8000.000000  7992.000000  4176.000000  8000.000000  8000.000000   \n",
       "mean     64.429750     0.562938   169.724569     2.783750    14.243750   \n",
       "std      17.352628     0.496054    17.378792     1.013096     6.044825   \n",
       "min      15.000000     0.000000     1.800000     1.000000    -1.000000   \n",
       "25%      53.000000     0.000000   162.600000     2.000000    11.000000   \n",
       "50%      67.000000     1.000000   170.200000     3.000000    15.000000   \n",
       "75%      78.000000     1.000000   177.800000     4.000000    18.000000   \n",
       "max      90.000000     1.000000   462.300000     4.000000    34.000000   \n",
       "\n",
       "              SOFA         STAY         SURV  \n",
       "count  8000.000000  8000.000000  8000.000000  \n",
       "mean      6.364625    13.584875   130.426875  \n",
       "std       4.237236    12.715975   364.766144  \n",
       "min      -1.000000    -1.000000   -23.000000  \n",
       "25%       3.000000     6.000000    -1.000000  \n",
       "50%       6.000000    10.000000    -1.000000  \n",
       "75%       9.000000    17.000000    17.000000  \n",
       "max      22.000000   171.000000  2620.000000  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
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}