ET.Indiv2_GridSearch-.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hyperparameter tuning of the 5 classifiers for emotional state detection\n",
    "* 5 fold cross validation with grid-search\n",
    "* Multiclass classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.bagging module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.forest module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "Using TensorFlow backend.\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from pprint import pprint\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import metrics   \n",
    "from sklearn.feature_selection import SelectFromModel,RFECV\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "from sklearn.model_selection import KFold, StratifiedKFold, cross_val_score, PredefinedSplit\n",
    "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "from sklearn.model_selection import KFold, StratifiedKFold, cross_val_score\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "\n",
    "from sklearn import metrics   \n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.over_sampling import SMOTENC\n",
    "from imblearn.over_sampling import ADASYN\n",
    "from imblearn.over_sampling import SVMSMOTE\n",
    "from imblearn.combine import SMOTEENN\n",
    "from imblearn.combine import SMOTETomek\n",
    "\n",
    "pd.options.mode.chained_assignment = None\n",
    "import re\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "#warnings.filterwarnings('always')\n",
    "import pickle\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from imblearn.pipeline import Pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from imblearn.metrics import specificity_score\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import make_scorer, f1_score, roc_auc_score, precision_score, recall_score, confusion_matrix\n",
    "from sklearn import metrics   \n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from catboost import CatBoostClassifier, Pool, cv\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "\n",
    "#from pandas_ml import ConfusionMatrix\n",
    "\n",
    "#import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_input(p):\n",
    "    #Read input file of each person\n",
    "    filename='data/NonOverlap_w5_emoChange_SelFeat_data_p'+str(p)+'.csv'\n",
    "    raw_df= pd.read_csv(filename)\n",
    "    \n",
    "    #drop all variables that contain all NANs\n",
    "    raw_df.dropna(axis=1,how='all', inplace=True)\n",
    "    #reset the index\n",
    "    raw_df.reset_index(drop=True, inplace=True)\n",
    "    #drop columns with all zeros in pandas dataframe\n",
    "    raw_df=raw_df.T[(raw_df!=0).any()].T\n",
    "    \n",
    "    #print(\"The shape of the dataframe is \",raw_df.shape)\n",
    "    #print(raw_df['emotion'].value_counts())\n",
    "    return raw_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace NANs with -999\n",
    "def prep_data(data):\n",
    "    return data.fillna(-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop columns\n",
    "def drop_cols(data, col_list):\n",
    "    return data.drop(col_list, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize data with minmax\n",
    "def scale_data(trn_x, tst_x):\n",
    "    \n",
    "    sc= StandardScaler()\n",
    "    scaled_trn_x = sc.fit_transform(trn_x)\n",
    "    scaled_tst_x = sc.fit_transform(tst_x)\n",
    "    \n",
    "    return scaled_trn_x, scaled_tst_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# oversampling with SMOTE with 'minority' and 'not majority'\n",
    "def over_sample_SMOTE(X_train, y_train):\n",
    "    sm=SMOTE(sampling_strategy='not majority', random_state=10) # 'minority'\n",
    "    X_train_ovr, y_train_ovr=sm.fit_sample(X_train, y_train)\n",
    "\n",
    "    #print(X_train_ovr.shape, y_train_ovr.shape)\n",
    "    return X_train_ovr, y_train_ovr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# oversampling with SVMSMOTE \n",
    "def over_sample_SVMSMOTE(X_train, y_train):\n",
    "    sm=SVMSMOTE(random_state=10)\n",
    "    \n",
    "    X_train_ovr, y_train_ovr=sm.fit_sample(X_train, y_train)\n",
    "\n",
    "    #print(X_train_ovr.shape, y_train_ovr.shape)\n",
    "    return X_train_ovr, y_train_ovr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_k_features(X_train_scaled,X_test_scaled,y_train,k):\n",
    "    selection = SelectKBest(mutual_info_classif, k)\n",
    "    X_train = selection.fit_transform(X_train_scaled,y_train)\n",
    "    X_test = selection.transform(X_test_scaled)\n",
    "    \n",
    "    return X_train, X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define random state to re-generate the same result\n",
    "random_state = 43\n",
    "\n",
    "# total persons\n",
    "p_list=[8, 10,12,13,15,20,21,25, 27, 33,35,40,46,48,49,52,54,55]\n",
    "#p_list=[8]\n",
    "\n",
    "# #of folds\n",
    "n_fold=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(accu, bl_accu, prec, rec_, spec_, roc_, f1_):   \n",
    "    print('.....................')\n",
    "    print(\"Average Accuracy: %.2f%% (%.2f)\" % (np.mean(accu), np.std(accu)))\n",
    "    print(\"Average Balanced_accuracy: %.2f%% (%.2f)\" % (np.mean(bl_accu),np.std(bl_accu)))\n",
    "    print(\"Average Precision: %.2f%% (%.2f)\" % (np.mean(prec),np.std(prec)))\n",
    "    print(\"Average Recall: %.2f%% (%.2f)\" % (np.mean(rec_),np.std(rec_)))\n",
    "    print(\"Average Specificity: %.2f%% (%.2f)\" % (np.mean(spec_),np.std(spec_)))\n",
    "    print(\"Average ROC AUC: %.2f%% (%.2f)\" % (np.mean(roc_),np.std(roc_)))\n",
    "    print(\"Average F1 score: %.2f%% (%.2f)\" % (np.mean(f1_),np.std(f1_)))\n",
    "    print('..................................................')\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([('scaler', StandardScaler()), # MinMaxScaler()\n",
    "                 ('selector', SelectKBest(mutual_info_classif, k=90)), #\n",
    "                 ('classifier', LogisticRegression())])\n",
    "\n",
    "search_space = [{'selector__k': [ 50, 70, 90]},\n",
    "                \n",
    "                {'classifier': [LogisticRegression(solver='lbfgs')],\n",
    "                 'classifier__C': [0.01, 0.1, 1.0],\n",
    "                 'classifier__penalty': ['l1', 'l2', None],\n",
    "                 'classifier__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
    "                 'classifier__max_iter':[100, 150, 200], \n",
    "                 'classifier__class_weight':[None, 'balanced']},\n",
    "                 \n",
    "                {'classifier': [RandomForestClassifier()],\n",
    "                 'classifier__max_depth': [5, 10, 30, None],\n",
    "                 'classifier__criterion':['gini','entropy'], \n",
    "                 'classifier__bootstrap': [True],\n",
    "                 'classifier__max_features':['log2', None],\n",
    "                 'classifier__n_estimators': [50, 100, 200, 300, 400]},\n",
    "                \n",
    "                {'classifier': [MLPClassifier(random_state=1, early_stopping=True)],\n",
    "                 'classifier__hidden_layer_sizes' : [(50, 50, 50), (50, 100, 50), (20, 20, 20), (30, ), (50,),(100,)], \n",
    "                 'classifier__activation' : ['tanh', 'relu', 'logistic'],\n",
    "                 'classifier__max_iter':[50, 100, 150, 200, 300],\n",
    "                 'classifier__solver': ['sgd', 'adam', 'lbfgs'],\n",
    "                 'classifier__alpha': [0.0001, 0.001, 0.05]},\n",
    "                \n",
    "                {'classifier': [CatBoostClassifier(random_seed=1)],\n",
    "                 'classifier__learning_rate': [0.05, 0.1, 0.15, 0.2]},\n",
    "                \n",
    "                {'classifier': [XGBClassifier(objective='binary:logistic', random_state=1)],\n",
    "                 'classifier__learning_rate': [0.05, 0.1, 0.15, 0.2],\n",
    "                 'classifier__colsample_bytree':[.5, .75, 1],\n",
    "                 'classifier__max_depth': np.arange(3, 6, 10),\n",
    "                 'classifier__n_estimators': [50, 100, 200, 300, 400]}]\n",
    "                  \n",
    "\n",
    "scorer = make_scorer(f1_score, average = 'binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_pipe = Pipeline([('scaler', StandardScaler()), # MinMaxScaler()\n",
    "                 ('selector', SelectKBest(mutual_info_classif, k=90)), #\n",
    "                 ('classifier', LogisticRegression())])\n",
    "\n",
    "LR_search_space = [{'selector__k': [ 50, 70, 90, 110]},\n",
    "                \n",
    "                {'classifier': [LogisticRegression(solver='lbfgs')],\n",
    "                 'classifier__C': [0.01, 0.1, 1.0],\n",
    "                 'classifier__penalty': ['l1', 'l2', None],\n",
    "                 'classifier__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
    "                 'classifier__max_iter':[100, 150, 200], \n",
    "                 'classifier__class_weight':[None, 'balanced']}]\n",
    "                 \n",
    "################################################################################          \n",
    "\n",
    "RF_pipe = Pipeline([('scaler', StandardScaler()), # MinMaxScaler()\n",
    "                 ('selector', SelectKBest(mutual_info_classif, k=90)), #\n",
    "                 ('classifier', RandomForestClassifier())])\n",
    "\n",
    "RF_search_space = [{'selector__k': [ 50, 70, 90, 110]},\n",
    "                \n",
    "                {'classifier': [RandomForestClassifier()],\n",
    "                 'classifier__max_depth': [5, 10, 30, None],\n",
    "                 'classifier__criterion':['gini','entropy'], \n",
    "                 'classifier__bootstrap': [True],\n",
    "                 'classifier__max_features':['log2', None],\n",
    "                 'classifier__n_estimators': [50, 100, 200, 300, 400]}]\n",
    "                  \n",
    "################################################################################\n",
    "\n",
    "MLP_pipe = Pipeline([('scaler', StandardScaler()), # MinMaxScaler()\n",
    "                 ('selector', SelectKBest(mutual_info_classif, k=90)), #\n",
    "                 ('classifier', MLPClassifier(random_state=1, early_stopping=True))])\n",
    "\n",
    "MLP_search_space = [{'selector__k': [ 50, 70, 90, 110]},\n",
    "                \n",
    "                {'classifier': [MLPClassifier(random_state=1, early_stopping=True)],\n",
    "                 'classifier__hidden_layer_sizes' : [(50, 50, 50), (50, 100, 50), (20, 20, 20), (30, ), (50,),(100,)], \n",
    "                 'classifier__activation' : ['tanh', 'relu', 'logistic'],\n",
    "                 'classifier__max_iter':[50, 100, 150, 200, 300],\n",
    "                 'classifier__solver': ['sgd', 'adam', 'lbfgs'],\n",
    "                 'classifier__alpha': [0.0001, 0.001, 0.05]}]\n",
    "\n",
    "################################################################################\n",
    "\n",
    "CB_pipe = Pipeline([('scaler', StandardScaler()), # MinMaxScaler()\n",
    "                 ('selector', SelectKBest(mutual_info_classif, k=90)), #\n",
    "                 ('classifier', CatBoostClassifier(random_seed=1))])\n",
    "\n",
    "CB_search_space = [{'selector__k': [ 50, 70, 90, 110]},\n",
    "                \n",
    "                {'classifier': [CatBoostClassifier(random_seed=1, verbose=False)],\n",
    "                 'classifier__learning_rate': [0.05, 0.1, 0.15, 0.2]}]\n",
    "\n",
    "################################################################################\n",
    "\n",
    "XGB_pipe = Pipeline([('scaler', StandardScaler()), # MinMaxScaler()\n",
    "                 ('selector', SelectKBest(mutual_info_classif, k=90)), #\n",
    "                 ('classifier', XGBClassifier(objective='binary:logistic', random_state=1))])\n",
    "\n",
    "XGB_search_space = [{'selector__k': [ 50, 70, 90, 110]},\n",
    "                \n",
    "                {'classifier': [XGBClassifier(objective='binary:logistic', random_state=1)],\n",
    "                 'classifier__learning_rate': [0.05, 0.1, 0.15, 0.2],\n",
    "                 'classifier__colsample_bytree':[.5, .75, 1],\n",
    "                 'classifier__max_depth': np.arange(3, 6, 10),\n",
    "                 'classifier__n_estimators': [50, 100, 200, 300, 400]}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_search_wrapper(pipe = pipe, search_space = search_space, verbose= False,refit_score=scorer):\n",
    "    \"\"\"\n",
    "    fits a GridSearchCV classifiers using refit_score for optimization\n",
    "    prints classifier performance metrics\n",
    "    \"\"\"\n",
    "    cross_validation = StratifiedKFold(n_splits=5, shuffle=True, random_state=random_state)\n",
    "\n",
    "    grid_search = GridSearchCV(pipe, search_space, cv=cross_validation, verbose=verbose,  n_jobs = -1) #scoring=scorer, refit=scorer\n",
    "    \n",
    "    grid_search.fit(X, y)\n",
    "    \n",
    "    return grid_search\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_list=[8, 10,12,13,15,20,21,25, 27, 33,35,40,46,48,49,52,54,55]\n",
    "\n",
    "best_LR = []\n",
    "best_RF = []\n",
    "best_MLP = []\n",
    "best_XGB = []\n",
    "best_CB = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 8\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   22.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   49.1s\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  3.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.01, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.940251572327044\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    4.7s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   32.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=5, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=100, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9512928022361985\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    4.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   20.5s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   47.3s\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  3.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  4.5min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed:  5.9min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed:  7.4min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed:  9.2min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed:  9.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='tanh', alpha=0.0001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(50,),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=50, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.940461215932914\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    4.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   24.0s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:   53.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=0.5, gamma=0,\n",
      "                               learning_rate=0.15, max_delta_step=0,\n",
      "                               max_depth=3, min_child_weight=1, missing=None,\n",
      "                               n_estimators=200, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9625436757512229\n",
      "-------------------------------------------------------\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a43e35710>)],\n",
      "         verbose=False)\n",
      "0.940251572327044\n",
      "-------------------------------------------------------\n",
      "Person 10\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   11.8s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   56.0s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  4.0min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  6.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  8.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.01, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=200,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9168627450980391\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   13.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  6.5min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  8.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='entropy',\n",
      "                                        max_depth=30, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=400, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9184313725490195\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   12.0s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  4.3min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  6.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed: 10.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed: 13.5min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 17.7min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 22.2min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 27.3min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 27.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=110,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.0001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(100,),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=200, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='adam',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9160784313725492\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   14.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  3.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=0.75, gamma=0,\n",
      "                               learning_rate=0.2, max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=300, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9168627450980391\n",
      "-------------------------------------------------------\n",
      "Learning rate set to 0.011428\n",
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      "999:\tlearn: 0.0532375\ttotal: 4.27s\tremaining: 0us\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=70,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a44ed38d0>)],\n",
      "         verbose=False)\n",
      "0.9176470588235294\n",
      "-------------------------------------------------------\n",
      "Person 12\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    5.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   28.1s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  3.4min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  4.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=70,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9665974385600553\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    6.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   55.2s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  2.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=50,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=None, max_features='auto',\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=100, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9665628245067497\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    6.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   31.7s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  3.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  5.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  6.9min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed:  9.0min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 11.3min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 13.9min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 14.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.0001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(20, 20, 20),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=50, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9647282796815506\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   37.2s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  1.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=0.5, gamma=0, learning_rate=0.2,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=200, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9684319833852545\n",
      "-------------------------------------------------------\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a449eb750>)],\n",
      "         verbose=False)\n",
      "0.962876427829699\n",
      "-------------------------------------------------------\n",
      "Person 13\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   11.7s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   55.7s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.9min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  6.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  8.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.01, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l1', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9461847389558233\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   15.7s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  5.6min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  6.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=10, max_features='log2',\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=50, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9477911646586346\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   11.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  4.3min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  6.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  9.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed: 13.1min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 17.2min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 22.0min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 27.1min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 27.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(50, 100, 50),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=50, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='adam',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9477911646586346\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   14.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  3.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=110,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=100, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9445783132530121\n",
      "-------------------------------------------------------\n",
      "Learning rate set to 0.011313\n",
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      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=110,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a44bb23d0>)],\n",
      "         verbose=False)\n",
      "0.9453815261044177\n",
      "-------------------------------------------------------\n",
      "Person 15\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    6.3s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   31.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  3.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  4.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.1, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l1', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9637018837018838\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    9.0s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   57.9s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  2.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='entropy',\n",
      "                                        max_depth=10, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=200, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9745945945945946\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   33.9s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  3.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  4.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  6.5min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed:  8.4min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 10.7min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 13.2min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 13.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='tanh', alpha=0.0001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(20, 20, 20),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=50, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.961916461916462\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.0s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   38.0s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  1.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.2,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=50, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9709909909909911\n",
      "-------------------------------------------------------\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a44de32d0>)],\n",
      "         verbose=False)\n",
      "0.970941850941851\n",
      "-------------------------------------------------------\n",
      "Person 20\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   33.9s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  3.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  5.2min finished\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.1, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l1', random_state=None,\n",
      "                                    solver='saga', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9261935483870968\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.0s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.7min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  3.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='entropy',\n",
      "                                        max_depth=10, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=100, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9245548387096776\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   37.1s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  5.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  8.0min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 10.5min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 13.4min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 16.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 16.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(50,),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=300, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='adam',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9309935483870969\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   47.2s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=0.5, gamma=0, learning_rate=0.1,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=200, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9293677419354838\n",
      "-------------------------------------------------------\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a4852fc10>)],\n",
      "         verbose=False)\n",
      "0.9245548387096776\n",
      "-------------------------------------------------------\n",
      "Person 21\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   10.7s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   51.5s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.6min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  5.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  7.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.1, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=200,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9692539914724099\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   11.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  4.2min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  5.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='entropy',\n",
      "                                        max_depth=5, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=100, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9668793377112446\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   55.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.9min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  6.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  8.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed: 12.0min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 15.6min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 19.9min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 24.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 24.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(50,),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=200, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9739784009212288\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   13.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  2.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=0.75, gamma=0,\n",
      "                               learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=400, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9668855622296224\n",
      "-------------------------------------------------------\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a44006f10>)],\n",
      "         verbose=False)\n",
      "0.969241542435654\n",
      "-------------------------------------------------------\n",
      "Person 25\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.3s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   15.7s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   36.6s\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  2.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.01, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='newton-cg', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9650075414781296\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    4.0s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   28.1s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  1.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=70,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=None, max_features='auto',\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=100, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9650075414781296\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.3s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   17.0s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   38.9s\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  2.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  3.7min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed:  4.8min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed:  6.1min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed:  7.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed:  7.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(50, 100, 50),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=50, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9571644042232277\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.8s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   21.3s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:   45.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.2,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=100, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9611613876319758\n",
      "-------------------------------------------------------\n",
      "Learning rate set to 0.005767\n",
      "0:\tlearn: 0.6829863\ttotal: 5.05ms\tremaining: 5.05s\n",
      "1:\tlearn: 0.6744226\ttotal: 7.41ms\tremaining: 3.7s\n",
      "2:\tlearn: 0.6652686\ttotal: 9.76ms\tremaining: 3.24s\n",
      "3:\tlearn: 0.6559027\ttotal: 12.1ms\tremaining: 3.02s\n",
      "4:\tlearn: 0.6462190\ttotal: 14.4ms\tremaining: 2.87s\n",
      "5:\tlearn: 0.6365017\ttotal: 16.8ms\tremaining: 2.79s\n",
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      "993:\tlearn: 0.0068641\ttotal: 2.71s\tremaining: 16.4ms\n",
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      "996:\tlearn: 0.0068202\ttotal: 2.72s\tremaining: 8.19ms\n",
      "997:\tlearn: 0.0068112\ttotal: 2.72s\tremaining: 5.46ms\n",
      "998:\tlearn: 0.0067915\ttotal: 2.73s\tremaining: 2.73ms\n",
      "999:\tlearn: 0.0067799\ttotal: 2.73s\tremaining: 0us\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=50,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a43c47090>)],\n",
      "         verbose=False)\n",
      "0.9611613876319758\n",
      "-------------------------------------------------------\n",
      "Person 27\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    4.3s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   20.1s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   46.6s\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  3.0min finished\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.1, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=150,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='saga', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9811011904761905\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    4.8s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   30.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  1.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='entropy',\n",
      "                                        max_depth=5, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=100, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9811507936507937\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    4.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   21.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   49.6s\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  3.4min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  4.6min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed:  6.1min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed:  7.7min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed:  9.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed:  9.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='tanh', alpha=0.05, batch_size='auto',\n",
      "                               beta_1=0.9, beta_2=0.999, early_stopping=True,\n",
      "                               epsilon=1e-08, hidden_layer_sizes=(50, 50, 50),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=50, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9874503968253968\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    4.7s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   24.6s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:   54.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=100, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9874503968253968\n",
      "-------------------------------------------------------\n",
      "Learning rate set to 0.006308\n",
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      "999:\tlearn: 0.0024937\ttotal: 5.21s\tremaining: 0us\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=110,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a4414d990>)],\n",
      "         verbose=False)\n",
      "0.9811011904761905\n",
      "-------------------------------------------------------\n",
      "Person 33\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.6s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   41.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  6.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.01, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l1', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9582403548864095\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    9.7s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  3.6min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  4.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=None, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=300, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9640341443742437\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   45.5s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  5.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  7.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  9.7min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 12.7min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 16.1min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 19.9min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 20.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='logistic', alpha=0.001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(30,),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=200, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='sgd',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9605726576152709\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   10.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   58.4s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  2.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.05,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=50, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9628713536765694\n",
      "-------------------------------------------------------\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a43d74710>)],\n",
      "         verbose=False)\n",
      "0.9628579110095442\n",
      "-------------------------------------------------------\n",
      "Person 35\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    9.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   44.8s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  6.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.01, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=200,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='sag', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9725635020716987\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   10.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  3.6min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  4.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=50,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=None, max_features='auto',\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=100, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9692667987749954\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    9.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   47.2s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.3min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  5.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  7.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed: 10.2min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 13.2min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 16.8min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 20.7min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 20.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.05, batch_size='auto',\n",
      "                               beta_1=0.9, beta_2=0.999, early_stopping=True,\n",
      "                               epsilon=1e-08, hidden_layer_sizes=(100,),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=150, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9725695069957364\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  2.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=50,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=100, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9725635020716987\n",
      "-------------------------------------------------------\n",
      "Learning rate set to 0.0099\n",
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      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=50,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a44130650>)],\n",
      "         verbose=False)\n",
      "0.9692667987749954\n",
      "-------------------------------------------------------\n",
      "Person 40\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.7s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   36.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  5.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.01, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9747373068432671\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  3.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='entropy',\n",
      "                                        max_depth=30, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=50, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9813863134657836\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   39.9s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.8min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.4min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  6.4min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  8.6min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 11.2min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 14.3min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 17.6min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 17.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='logistic', alpha=0.0001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(100,),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=200, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='adam',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9774039735099338\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   50.1s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  1.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=110,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=100, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.982719646799117\n",
      "-------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.009122\n",
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      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=50,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a43e51fd0>)],\n",
      "         verbose=False)\n",
      "0.9800529801324502\n",
      "-------------------------------------------------------\n",
      "Person 46\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.7s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   36.5s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.5min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  5.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.01, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=150,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9629139072847682\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.6s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  3.0min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  3.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=10, max_features='log2',\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=100, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9629139072847682\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   39.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.8min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  6.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  8.5min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 11.1min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 14.1min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 17.4min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 17.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='logistic', alpha=0.05,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(100,),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=200, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='sgd',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.96158940397351\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   50.5s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  1.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=0.5, gamma=0, learning_rate=0.2,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=400, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.96158940397351\n",
      "-------------------------------------------------------\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a44efe890>)],\n",
      "         verbose=False)\n",
      "0.957615894039735\n",
      "-------------------------------------------------------\n",
      "Person 48\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   10.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   52.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.7min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  5.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  8.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.1, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l1', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9424448719846085\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   12.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  5.2min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  6.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=None, max_features='log2',\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=50, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9398660648216663\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   11.3s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  4.2min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  6.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  9.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed: 13.1min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 17.0min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 21.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 26.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 26.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(50, 50, 50),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=150, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='adam',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9407207340535741\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   13.6s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  3.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=0.5, gamma=0, learning_rate=0.2,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=300, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9390076957229538\n",
      "-------------------------------------------------------\n",
      "Learning rate set to 0.010992\n",
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      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=50,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a45eca650>)],\n",
      "         verbose=False)\n",
      "0.9415902027527009\n",
      "-------------------------------------------------------\n",
      "Person 49\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   34.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  3.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  5.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.1, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=150,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l2', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.939391095818997\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.8min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  3.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='entropy',\n",
      "                                        max_depth=None, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=50, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9494734647064955\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   37.9s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  6.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  8.1min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 10.6min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 13.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 16.7min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 16.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.05, batch_size='auto',\n",
      "                               beta_1=0.9, beta_2=0.999, early_stopping=True,\n",
      "                               epsilon=1e-08, hidden_layer_sizes=(100,),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=300, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9422062350119905\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.6s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   48.0s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=200, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9436763632572204\n",
      "-------------------------------------------------------\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a44c96bd0>)],\n",
      "         verbose=False)\n",
      "0.9393598164946304\n",
      "-------------------------------------------------------\n",
      "Person 52\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   40.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.8min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  6.1min finished\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l1', random_state=None,\n",
      "                                    solver='saga', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9585409607335063\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    9.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  3.0min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  3.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=5, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=300, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9596903860208623\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.6s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   44.0s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  6.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  9.4min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 12.3min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 15.6min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 19.3min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 19.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.0001,\n",
      "                               batch_size='auto', beta_1=0.9, beta_2=0.999,\n",
      "                               early_stopping=True, epsilon=1e-08,\n",
      "                               hidden_layer_sizes=(20, 20, 20),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=100, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9654640887648661\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   10.3s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   56.5s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  2.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=400, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9608530994618298\n",
      "-------------------------------------------------------\n",
      "Learning rate set to 0.009698\n",
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      "999:\tlearn: 0.0049193\ttotal: 5.29s\tremaining: 0us\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=110,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a43d3a290>)],\n",
      "         verbose=False)\n",
      "0.9619892365955749\n",
      "-------------------------------------------------------\n",
      "Person 54\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    5.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   26.0s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  4.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.1, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=150,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l1', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9811141060197663\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    6.3s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   48.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  2.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='entropy',\n",
      "                                        max_depth=None, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=50, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9830008984725966\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    5.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   28.4s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  4.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  6.1min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed:  8.0min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 10.1min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 12.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 12.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.05, batch_size='auto',\n",
      "                               beta_1=0.9, beta_2=0.999, early_stopping=True,\n",
      "                               epsilon=1e-08, hidden_layer_sizes=(50, 100, 50),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=200, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='lbfgs',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9697574123989219\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    6.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   35.8s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=1, gamma=0, learning_rate=0.05,\n",
      "                               max_delta_step=0, max_depth=3,\n",
      "                               min_child_weight=1, missing=None,\n",
      "                               n_estimators=100, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9792273135669362\n",
      "-------------------------------------------------------\n",
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a43d74c50>)],\n",
      "         verbose=False)\n",
      "0.9792093441150044\n",
      "-------------------------------------------------------\n",
      "Person 55\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 274 candidates, totalling 1370 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   35.5s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.5min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  3.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1370 out of 1370 | elapsed:  5.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=0.1, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=150,\n",
      "                                    multi_class='auto', n_jobs=None,\n",
      "                                    penalty='l1', random_state=None,\n",
      "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9759664369193887\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    8.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.5min\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  3.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
      "                                        class_weight=None, criterion='gini',\n",
      "                                        max_depth=5, max_features=None,\n",
      "                                        max_leaf_nodes=None, max_samples=None,\n",
      "                                        min_impurity_decrease=0.0,\n",
      "                                        min_impurity_split=None,\n",
      "                                        min_samples_leaf=1, min_samples_split=2,\n",
      "                                        min_weight_fraction_leaf=0.0,\n",
      "                                        n_estimators=50, n_jobs=None,\n",
      "                                        oob_score=False, random_state=None,\n",
      "                                        verbose=0, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9759664369193887\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 814 candidates, totalling 4070 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.6s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   38.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  2.7min\n",
      "[Parallel(n_jobs=-1)]: Done 997 tasks      | elapsed:  4.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1442 tasks      | elapsed:  6.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1969 tasks      | elapsed:  8.2min\n",
      "[Parallel(n_jobs=-1)]: Done 2576 tasks      | elapsed: 10.7min\n",
      "[Parallel(n_jobs=-1)]: Done 3265 tasks      | elapsed: 13.6min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 16.8min\n",
      "[Parallel(n_jobs=-1)]: Done 4070 out of 4070 | elapsed: 16.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 MLPClassifier(activation='relu', alpha=0.05, batch_size='auto',\n",
      "                               beta_1=0.9, beta_2=0.999, early_stopping=True,\n",
      "                               epsilon=1e-08, hidden_layer_sizes=(50, 50, 50),\n",
      "                               learning_rate='constant',\n",
      "                               learning_rate_init=0.001, max_fun=15000,\n",
      "                               max_iter=150, momentum=0.9, n_iter_no_change=10,\n",
      "                               nesterovs_momentum=True, power_t=0.5,\n",
      "                               random_state=1, shuffle=True, solver='sgd',\n",
      "                               tol=0.0001, validation_fraction=0.1,\n",
      "                               verbose=False, warm_start=False))],\n",
      "         verbose=False)\n",
      "0.9759664369193887\n",
      "-------------------------------------------------------\n",
      "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   47.9s\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=90,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                               colsample_bylevel=1, colsample_bynode=1,\n",
      "                               colsample_bytree=0.5, gamma=0,\n",
      "                               learning_rate=0.15, max_delta_step=0,\n",
      "                               max_depth=3, min_child_weight=1, missing=None,\n",
      "                               n_estimators=50, n_jobs=1, nthread=None,\n",
      "                               objective='binary:logistic', random_state=1,\n",
      "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
      "                               seed=None, silent=None, subsample=1,\n",
      "                               verbosity=1))],\n",
      "         verbose=False)\n",
      "0.9759564479073021\n",
      "-------------------------------------------------------\n",
      "Learning rate set to 0.008885\n",
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      "Pipeline(memory=None,\n",
      "         steps=[('scaler',\n",
      "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
      "                ('selector',\n",
      "                 SelectKBest(k=50,\n",
      "                             score_func=<function mutual_info_classif at 0x1a1a1fd8c0>)),\n",
      "                ('classifier',\n",
      "                 <catboost.core.CatBoostClassifier object at 0x1a44802350>)],\n",
      "         verbose=False)\n",
      "0.9745479972030766\n",
      "-------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# for each person in the dataset, find the best hyperparamters for the model in given range \n",
    "for p in p_list:\n",
    "    df=read_input(p)\n",
    "    #df.head()\n",
    "    df=prep_data(df)\n",
    "    # remove day_of_month variable if present in data\n",
    "    if 'day_of_month' in df.columns:\n",
    "        drop_col=['day_of_month']\n",
    "        df=drop_cols(df, drop_col)\n",
    "\n",
    "     #remove classes that have less then 5 samples\n",
    "    min_c=df['emotion_change'].value_counts()\n",
    "    if (min_c <= 5).any():\n",
    "        r_label=min_c[min_c <= 5].index[0]\n",
    "        df = df.drop(df.index[df.emotion == r_label])\n",
    "        \n",
    "    dataset = df   \n",
    "\n",
    "    y = dataset['emotion_change'].copy()\n",
    "    X = dataset.loc[:, dataset.columns != 'emotion_change'].copy()\n",
    "    #X = X.apply(pd.to_numeric)\n",
    "    \n",
    "    #perform grid searc and find the best models \n",
    "    print(\"Person \"+str(p))\n",
    "    print(\"-------------------------------------------------------\")\n",
    "    pipeline_grid_search_LR = grid_search_wrapper(pipe = LR_pipe, search_space = LR_search_space, verbose=2)\n",
    "    print(pipeline_grid_search_LR.best_estimator_)\n",
    "    print(pipeline_grid_search_LR.best_score_)\n",
    "    best_LR.append(pipeline_grid_search_LR.best_estimator_)\n",
    "    \n",
    "    print(\"-------------------------------------------------------\")\n",
    "    pipeline_grid_search_RF = grid_search_wrapper(pipe = RF_pipe, search_space = RF_search_space, verbose=2)\n",
    "    print(pipeline_grid_search_RF.best_estimator_)\n",
    "    print(pipeline_grid_search_RF.best_score_)\n",
    "    best_RF.append(pipeline_grid_search_RF.best_estimator_)\n",
    "    \n",
    "    print(\"-------------------------------------------------------\")\n",
    "    pipeline_grid_search_MLP = grid_search_wrapper(pipe = MLP_pipe, search_space = MLP_search_space, verbose=2)\n",
    "    print(pipeline_grid_search_MLP.best_estimator_)\n",
    "    print(pipeline_grid_search_MLP.best_score_)\n",
    "    best_MLP.append(pipeline_grid_search_MLP.best_estimator_)\n",
    "    \n",
    "    print(\"-------------------------------------------------------\")\n",
    "    pipeline_grid_search_XGB = grid_search_wrapper(pipe = XGB_pipe, search_space = XGB_search_space, verbose=2)\n",
    "    print(pipeline_grid_search_XGB.best_estimator_)\n",
    "    print(pipeline_grid_search_XGB.best_score_)\n",
    "    best_XGB.append(pipeline_grid_search_XGB.best_estimator_)\n",
    "    \n",
    "    print(\"-------------------------------------------------------\")\n",
    "    pipeline_grid_search_CB = grid_search_wrapper(pipe = CB_pipe, search_space = CB_search_space, verbose=False)\n",
    "    print(pipeline_grid_search_CB.best_estimator_)\n",
    "    print(pipeline_grid_search_CB.best_score_)\n",
    "    best_CB.append(pipeline_grid_search_CB.best_estimator_)\n",
    "    print(\"-------------------------------------------------------\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_models = {} # dictionary of best models with best parameters\n",
    "\n",
    "best_models['Logistic Regression'] = best_LR\n",
    "best_models['RandomForest Classifier'] = best_RF\n",
    "best_models['MLP Classifier'] = best_MLP\n",
    "best_models['XGBoost Classifier'] = best_XGB\n",
    "best_models['CatBoost Classifier'] = best_CB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the best models\n",
    "np.save('Indiv_best_models.npy', best_models) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the best models\n",
    "best_models = np.load('Indiv_best_models.npy',allow_pickle='TRUE').item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restuls for:  Logistic Regression\n",
      ".....................\n",
      "Average Accuracy: 95.36% (1.95)\n",
      "Average Balanced_accuracy: 64.23% (9.42)\n",
      "Average Precision: 50.36% (26.66)\n",
      "Average Recall: 29.49% (19.26)\n",
      "Average Specificity: 98.96% (0.93)\n",
      "Average ROC AUC: 87.31% (13.23)\n",
      "Average F1 score: 35.12% (20.47)\n",
      "..................................................\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restuls for:  RandomForest Classifier\n",
      ".....................\n",
      "Average Accuracy: 93.49% (4.73)\n",
      "Average Balanced_accuracy: 66.88% (10.08)\n",
      "Average Precision: 49.97% (21.23)\n",
      "Average Recall: 37.12% (20.78)\n",
      "Average Specificity: 96.65% (4.91)\n",
      "Average ROC AUC: 88.01% (5.67)\n",
      "Average F1 score: 36.40% (17.87)\n",
      "..................................................\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restuls for:  MLP Classifier\n",
      ".....................\n",
      "Average Accuracy: 94.78% (2.46)\n",
      "Average Balanced_accuracy: 65.65% (12.36)\n",
      "Average Precision: 40.60% (26.71)\n",
      "Average Recall: 33.10% (25.12)\n",
      "Average Specificity: 98.19% (1.35)\n",
      "Average ROC AUC: 80.27% (18.56)\n",
      "Average F1 score: 34.65% (25.38)\n",
      "..................................................\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restuls for:  XGBoost Classifier\n",
      ".....................\n",
      "Average Accuracy: 94.82% (2.55)\n",
      "Average Balanced_accuracy: 65.91% (7.93)\n",
      "Average Precision: 52.34% (17.22)\n",
      "Average Recall: 33.71% (15.69)\n",
      "Average Specificity: 98.12% (1.42)\n",
      "Average ROC AUC: 87.84% (6.81)\n",
      "Average F1 score: 38.00% (15.92)\n",
      "..................................................\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/majed_al-jefri/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restuls for:  CatBoost Classifier\n",
      ".....................\n",
      "Average Accuracy: 94.29% (3.17)\n",
      "Average Balanced_accuracy: 66.89% (8.97)\n",
      "Average Precision: 48.67% (18.09)\n",
      "Average Recall: 36.45% (18.10)\n",
      "Average Specificity: 97.34% (2.33)\n",
      "Average ROC AUC: 87.62% (7.04)\n",
      "Average F1 score: 38.34% (17.07)\n",
      "..................................................\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# this is to get all the detailed performance meterics after selecting the best model parameters\n",
    "nfolds = 5\n",
    "skf=StratifiedKFold(n_splits=nfolds, shuffle=True, random_state=random_state)\n",
    "\n",
    "for model_name, model in best_models.items(): \n",
    "    \n",
    "    accu = []\n",
    "    prec = []\n",
    "    rec_ = []\n",
    "    f1_ = []\n",
    "    bl_accu = []\n",
    "    roc_ = []\n",
    "    spec_ = []\n",
    "\n",
    "    model_index = -1\n",
    "    for p in p_list:\n",
    "        model_index = model_index + 1\n",
    "        model__ = model[model_index]\n",
    "        clf = model__['classifier']\n",
    "        if model_name == 'CatBoost Classifier':\n",
    "            clf = CatBoostClassifier(random_seed=1, verbose=False,learning_rate= 0.1)\n",
    "\n",
    "        df=read_input(p)\n",
    "        #df.head()\n",
    "        df=prep_data(df)\n",
    "        # remove day_of_month variable if present in data\n",
    "        if 'day_of_month' in df.columns:\n",
    "            drop_col=['day_of_month']\n",
    "            df=drop_cols(df, drop_col)\n",
    "\n",
    "         #remove classes that have less then 5 samples\n",
    "        min_c=df['emotion_change'].value_counts()\n",
    "        if (min_c <= 5).any():\n",
    "            r_label=min_c[min_c <= 5].index[0]\n",
    "            df = df.drop(df.index[df.emotion == r_label])\n",
    "\n",
    "        dataset = df   \n",
    "\n",
    "        y = dataset['emotion_change'].copy()\n",
    "        X = dataset.loc[:, dataset.columns != 'emotion_change'].copy()\n",
    "\n",
    "        \n",
    "        avg_ac=0.0\n",
    "        avg_bl_ac=0.0\n",
    "        avg_rc=0.0\n",
    "        avg_pr=0.0\n",
    "        avg_f1=0.0\n",
    "        avg_spec=0.0\n",
    "        avg_roc=0.0\n",
    "        avg_kp=0.0\n",
    "\n",
    "        i = 1\n",
    "        for train_index, test_index in skf.split(X ,y):\n",
    "            #print(\"fold\", i)\n",
    "            i+=1\n",
    "\n",
    "            X_train, X_test = X.iloc[train_index], X.iloc[test_index] \n",
    "            y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "            #scale features\n",
    "            X_train_scaled, X_test_scaled= scale_data(X_train, X_test) \n",
    "            #feature selection\n",
    "            X_train, X_test = select_k_features(X_train_scaled,X_test_scaled,y_train,k=int(str(model__['selector'])[14:16]))\n",
    "\n",
    "            #oversample training data\n",
    "            #X_train_imb,y_train_imb=over_sample_SMOTE(X_train, y_train)\n",
    "            #X_train_imb,y_train_imb=over_sample_SMOTENC(X_train, y_train, index1, index2)\n",
    "            #X_train_imb,y_train_imb=over_sample_SVMSMOTE(X_train, y_train)\n",
    "\n",
    "\n",
    "            # train model on imbalance-handled data\n",
    "            #clf.fit(X_train_imb, y_train_imb)\n",
    "\n",
    "            #train model on imbalance data \n",
    "            clf.fit(X_train, y_train)\n",
    "\n",
    "            # test model, measure class label and probability score\n",
    "            y_pred = clf.predict(X_test)\n",
    "            y_scores = clf.predict_proba(X_test)[:,1]\n",
    "\n",
    "            #calculate metrices\n",
    "        \n",
    "            accuracy = accuracy_score(y_test, y_pred)\n",
    "            bl_accuracy = balanced_accuracy_score(y_test, y_pred)\n",
    "            precision=precision_score(y_test, y_pred, labels=np.unique(y_pred))\n",
    "            recall=recall_score(y_test, y_pred, labels=np.unique(y_pred))\n",
    "            f1=f1_score(y_test, y_pred, labels=np.unique(y_pred))\n",
    "            roc=roc_auc_score(y_test, y_scores, labels=np.unique(y_pred))\n",
    "            spec=specificity_score(y_test, y_pred ,labels=np.unique(y_pred))\n",
    "\n",
    "            ac=accuracy * 100.0\n",
    "            pr=precision*100\n",
    "            rc=recall*100\n",
    "            f1_p=f1*100\n",
    "            bl_ac=bl_accuracy*100\n",
    "            roc=roc*100\n",
    "            spec=spec*100\n",
    "\n",
    "\n",
    "            #update average metrices in each fold\n",
    "            avg_ac+=ac\n",
    "            avg_bl_ac+=bl_ac\n",
    "            avg_rc+=rc\n",
    "            avg_pr+=pr\n",
    "            avg_f1+=f1_p\n",
    "            avg_roc+=roc\n",
    "            avg_spec+=spec\n",
    "    \n",
    "        avg_ac = avg_ac/nfolds\n",
    "        avg_bl_ac = avg_bl_ac/nfolds\n",
    "        avg_rc = avg_rc/nfolds\n",
    "        avg_pr = avg_pr/nfolds\n",
    "        avg_f1 = avg_f1/nfolds\n",
    "        avg_roc = avg_roc/nfolds\n",
    "        avg_spec = avg_spec/nfolds\n",
    "            \n",
    "        accu.append(avg_ac)\n",
    "        prec.append(avg_pr)\n",
    "        rec_.append(avg_rc)\n",
    "        f1_.append(avg_f1)\n",
    "        bl_accu.append(avg_bl_ac)\n",
    "        roc_.append(avg_roc)\n",
    "        spec_.append(avg_spec)\n",
    "    \n",
    "    print('Restuls for: ', model_name)\n",
    "    print_results(accu, bl_accu, prec, rec_, spec_, roc_, f1_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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