Projeto_Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Projeto Final \n",
    "**Grupo:**\n",
    "- Adriano Felipe Cabral Filho (afcf)\n",
    "- Felipe Rodrigues de Souza (frs3)\n",
    "- Lucas Vinícius José da Silva (lvjs)\n",
    "- Pedro José Carneiro de Souza (pjcs) \n",
    "- Vitor Sousa Silva (vss2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import optuna\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy import std\n",
    "from numpy import mean\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from random import randint\n",
    "from tensorflow import keras \n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score, average_precision_score, plot_confusion_matrix\n",
    "\n",
    "ds = pd.read_csv('TRNcod.xls', delimiter = \"\\t\")\n",
    "\n",
    "# Shuffle no dataset\n",
    "ds = ds.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "def compute_performance_metrics(y, y_pred_class, y_pred_scores=None):\n",
    "    accuracy = accuracy_score(y, y_pred_class)\n",
    "    recall = recall_score(y, y_pred_class)\n",
    "    precision = precision_score(y, y_pred_class)\n",
    "    f1 = f1_score(y, y_pred_class)\n",
    "    performance_metrics = (accuracy, recall, precision, f1)\n",
    "    if y_pred_scores is not None:\n",
    "        skplt.metrics.plot_ks_statistic(y, y_pred_scores)\n",
    "        plt.show()\n",
    "        y_pred_scores = y_pred_scores[:, 1]\n",
    "        auroc = roc_auc_score(y, y_pred_scores)\n",
    "        aupr = average_precision_score(y, y_pred_scores)\n",
    "        performance_metrics = performance_metrics + (auroc, aupr)\n",
    "        plt.suptitle('Acurácia: {:3.3f}\\nRecall: {:3.3f}\\nPrecision: {:3.3f}\\nF1: {:3.3f}\\nAUROC: {:3.3f}\\nAURP: {:3.3f}'.format(accuracy, recall, precision, f1, auroc, aupr), x=0.25, y=0.8)\n",
    "    return performance_metrics\n",
    "\n",
    "def compute_performance_metrics_sem_plot(y, y_pred_class, y_pred_scores=None):\n",
    "    accuracy = accuracy_score(y, y_pred_class)\n",
    "    recall = recall_score(y, y_pred_class)\n",
    "    precision = precision_score(y, y_pred_class)\n",
    "    f1 = f1_score(y, y_pred_class)\n",
    "    performance_metrics = (accuracy, recall, precision, f1)\n",
    "    if y_pred_scores is not None:\n",
    "        # skplt.metrics.plot_ks_statistic(y, y_pred_scores)\n",
    "        # plt.show()\n",
    "        y_pred_scores = y_pred_scores[:, 1]\n",
    "        auroc = roc_auc_score(y, y_pred_scores)\n",
    "        aupr = average_precision_score(y, y_pred_scores)\n",
    "        performance_metrics = performance_metrics + (auroc, aupr)\n",
    "    return performance_metrics\n",
    "\n",
    "def compute_performance_metrics_sem_plot2(y, y_pred_class, y_pred_scores, rede_trial):\n",
    "    accuracy = accuracy_score(y, y_pred_class)\n",
    "    recall = recall_score(y, y_pred_class)\n",
    "    precision = precision_score(y, y_pred_class)\n",
    "    f1 = f1_score(y, y_pred_class)\n",
    "    performance_metrics = (accuracy, recall, precision, f1)\n",
    "    if y_pred_scores is not None:\n",
    "        skplt.metrics.plot_ks_statistic(y, y_pred_scores)\n",
    "        # plt.show()\n",
    "        y_pred_scores = y_pred_scores[:, 1]\n",
    "        auroc = roc_auc_score(y, y_pred_scores)\n",
    "        aupr = average_precision_score(y, y_pred_scores)\n",
    "        performance_metrics = performance_metrics + (auroc, aupr)\n",
    "        plt.title(label=rede_trial, y=0.9)\n",
    "        plt.suptitle('Acurácia: {:3.3f}\\nRecall: {:3.3f}\\nPrecision: {:3.3f}\\nF1: {:3.3f}\\nAUROC: {:3.3f}\\nAURP: {:3.3f}'.format(accuracy, recall, precision, f1, auroc, aupr), x=0.25, y=0.8)\n",
    "        plt.savefig(rede_trial, dpi=100)\n",
    "        plt.close()\n",
    "    return performance_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print([d for d in ds.columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tratamento do Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tamanhos de inadimplentes: \n",
      "Treino: 127549\n",
      "Teste: 33524\n",
      "Validação: 63775\n",
      "\n",
      "Tamanhos de adimplentes: \n",
      "Treino: 127549\n",
      "Teste: 63774\n",
      "Validação: 63775\n",
      "\n",
      "Colunas: 246\n"
     ]
    }
   ],
   "source": [
    "# inadimplentes = pd.DataFrame(list(filter(lambda x: x == 1, ds['IND_BOM_1_2'])))\n",
    "\n",
    "# Selecionando quem é inadimplente\n",
    "inadimplentes = ds[ds['IND_BOM_1_2'] == 1]\n",
    "\n",
    "# Selecionando quem é adimplente\n",
    "adimplente = ds[ds['IND_BOM_1_2'] == 0]\n",
    "\n",
    "global treino_ina\n",
    "global teste_ina\n",
    "global valid_ina\n",
    "global treino_adi\n",
    "global teste_adi\n",
    "global valid_adi\n",
    "\n",
    "\n",
    "# Dividindo datasets\n",
    "treino_ina = inadimplentes[:int(len(inadimplentes)/2)]\n",
    "teste_ina  = inadimplentes[int(len(inadimplentes)/2):int((len(inadimplentes)*3)/4)]\n",
    "valid_ina  = inadimplentes[int((len(inadimplentes)*3)/4):]\n",
    "\n",
    "treino_adi = adimplente[:int(len(adimplente)/2)]\n",
    "teste_adi  = adimplente[int(len(adimplente)/2):int((len(adimplente)*3)/4)]\n",
    "valid_adi  = adimplente[int((len(adimplente)*3)/4):]\n",
    "\n",
    "# Equalizando tamanho de datasets treino e validação dos inadimplentes ao de adimplentes\n",
    "treino_ina = treino_ina.loc[treino_ina.index.repeat(2)].drop('INDEX', axis=1)\n",
    "treino_ina[\"COPIA\"] = treino_ina.duplicated()\n",
    "treino_ina.sort_values(by=\"COPIA\", inplace=True, ignore_index=True)\n",
    "treino_ina = treino_ina.iloc[ : ( len(treino_adi) - len(treino_ina) ), :  ]\n",
    "treino_ina.drop(columns=[\"COPIA\"], axis=1, inplace=True)\n",
    "\n",
    "treino_adi.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "\n",
    "valid_ina  = valid_ina.loc[valid_ina.index.repeat(2)].drop('INDEX', axis=1)\n",
    "valid_ina[\"COPIA\"] = valid_ina.duplicated()\n",
    "valid_ina.sort_values(by=\"COPIA\", inplace=True, ignore_index=True)\n",
    "valid_ina = valid_ina.iloc[ : ( len(valid_adi) - len(valid_ina) ), :  ]\n",
    "valid_ina.drop(columns=[\"COPIA\"], axis=1, inplace=True)\n",
    "# Fim da equalização\n",
    "\n",
    "print('Tamanhos de inadimplentes: \\nTreino: {}\\nTeste: {}\\nValidação: {}\\n'.format(len(treino_ina.values), len(teste_ina.values), len(valid_ina.values)))\n",
    "print('Tamanhos de adimplentes: \\nTreino: {}\\nTeste: {}\\nValidação: {}\\n'.format(len(treino_adi.values), len(teste_adi.values), len(valid_adi.values)))\n",
    "print('Colunas: {}'.format(len([d for d in ds.columns])))\n",
    "ds.drop(labels=\"INDEX\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/29294983/how-to-calculate-correlation-between-all-columns-and-remove-highly-correlated-on\n",
    "# https://psicometriaonline.com.br/como-testar-a-normalidade-da-amostra-com-kolgomorov-smirnov-e-shapiro-wilk/\n",
    "\n",
    "# # Create correlation matrix\n",
    "# corr_matrix = ds.corr().abs()\n",
    "\n",
    "# # Select upper triangle of correlation matrix\n",
    "# upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "\n",
    "# # Find features with correlation greater than 0.89\n",
    "# to_drop = [column for column in upper.columns if any(upper[column] > 0.89)]\n",
    "\n",
    "# # Drop features \n",
    "# # ds.drop(to_drop, axis=1, inplace=True)\n",
    "# ds.columns\n",
    "\n",
    "\n",
    "# Colunas mais correlacionadas\n",
    "# l = []\n",
    "# for c in ds.columns[:-1]:\n",
    "#     l.append({c: ds[c].corr(ds['IND_BOM_1_1'])})\n",
    "#     # if (ds[c].corr(ds['IND_BOM_1_1']) > 0.0):\n",
    "#         # print( c, ds[c].corr(ds['IND_BOM_1_1']) )\n",
    "# l.sort(key=lambda x: list(x.values())[0])\n",
    "# print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-12-12 20:40:00,301]\u001b[0m A new study created in memory with name: no-name-5eef8c69-9c6f-47d8-a5c4-ecc8d67965ca\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 20:40:18,250]\u001b[0m Trial 0 finished with value: 0.6264288514308114 and parameters: {'layers': 1, 'solver': 'adam', 'neurons': 18, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.09109204532809982}. Best is trial 0 with value: 0.6264288514308114.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 20:40:40,852]\u001b[0m Trial 1 finished with value: 0.6360721285770287 and parameters: {'layers': 2, 'solver': 'adam', 'neurons': 15, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.02671932906926546}. Best is trial 1 with value: 0.6360721285770287.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 20:41:46,359]\u001b[0m Trial 2 finished with value: 0.6304978439827519 and parameters: {'layers': 2, 'solver': 'lbfgs', 'neurons': 12, 'activation': 'identity', 'learning rate': 'adaptive', 'initial learning rate': 0.03621666853560101}. Best is trial 1 with value: 0.6360721285770287.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 20:42:10,535]\u001b[0m Trial 3 finished with value: 0.6264602116816934 and parameters: {'layers': 1, 'solver': 'adam', 'neurons': 21, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.08231492707611938}. Best is trial 1 with value: 0.6360721285770287.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 20:51:22,641]\u001b[0m Trial 4 finished with value: 0.6238102704821639 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 13, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.05759457395850182}. Best is trial 1 with value: 0.6360721285770287.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 20:52:55,717]\u001b[0m Trial 5 finished with value: 0.6397255978047824 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 14, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.05073428158010421}. Best is trial 5 with value: 0.6397255978047824.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 20:53:13,893]\u001b[0m Trial 6 finished with value: 0.6279733437867503 and parameters: {'layers': 2, 'solver': 'adam', 'neurons': 25, 'activation': 'identity', 'learning rate': 'constant', 'initial learning rate': 0.09725322173179218}. Best is trial 5 with value: 0.6397255978047824.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:21:45,168]\u001b[0m Trial 7 finished with value: 0.6137201097608781 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 24, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.07493011350038593}. Best is trial 5 with value: 0.6397255978047824.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:22:10,530]\u001b[0m Trial 8 finished with value: 0.6260446883575068 and parameters: {'layers': 2, 'solver': 'adam', 'neurons': 26, 'activation': 'identity', 'learning rate': 'adaptive', 'initial learning rate': 0.08143607994406217}. Best is trial 5 with value: 0.6397255978047824.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:22:31,764]\u001b[0m Trial 9 finished with value: 0.6298784790278322 and parameters: {'layers': 1, 'solver': 'adam', 'neurons': 31, 'activation': 'identity', 'learning rate': 'adaptive', 'initial learning rate': 0.0029219374686136946}. Best is trial 5 with value: 0.6397255978047824.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:23:17,957]\u001b[0m Trial 10 finished with value: 0.6386044688357507 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 2, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.05601229348856481}. Best is trial 5 with value: 0.6397255978047824.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:24:17,334]\u001b[0m Trial 11 finished with value: 0.6403684829478636 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 3, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.058684739035340376}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:25:00,901]\u001b[0m Trial 12 finished with value: 0.6304272834182674 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 2, 'activation': 'relu', 'learning rate': 'adaptive', 'initial learning rate': 0.040036252787325935}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:26:14,714]\u001b[0m Trial 13 finished with value: 0.6381732653861231 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 7, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.06396536383373262}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:27:27,430]\u001b[0m Trial 14 finished with value: 0.6386907095256762 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 8, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.023461433443856032}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:28:23,501]\u001b[0m Trial 15 finished with value: 0.6306781654253234 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 6, 'activation': 'relu', 'learning rate': 'adaptive', 'initial learning rate': 0.046411131254829784}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:29:37,028]\u001b[0m Trial 16 finished with value: 0.6389651117208938 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 10, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.06814065309124875}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:30:30,872]\u001b[0m Trial 17 finished with value: 0.639952959623677 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 4, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.01783685450605295}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:31:32,469]\u001b[0m Trial 18 finished with value: 0.6382203057624461 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 4, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.0019502841572633378}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:32:23,578]\u001b[0m Trial 19 finished with value: 0.6390591924735398 and parameters: {'layers': 2, 'solver': 'sgd', 'neurons': 2, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.014819792680873717}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:33:10,804]\u001b[0m Trial 20 finished with value: 0.6373814190513524 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 5, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.010432296668493837}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:34:45,892]\u001b[0m Trial 21 finished with value: 0.6339161113288906 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 18, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.044787335905702504}. Best is trial 11 with value: 0.6403684829478636.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:35:52,029]\u001b[0m Trial 22 finished with value: 0.6405331242649941 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 10, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.03244903765068676}. Best is trial 22 with value: 0.6405331242649941.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:37:02,740]\u001b[0m Trial 23 finished with value: 0.6391611132889063 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 10, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.028660478797339396}. Best is trial 22 with value: 0.6405331242649941.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:38:10,534]\u001b[0m Trial 24 finished with value: 0.6386358290866327 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 9, 'activation': 'relu', 'learning rate': 'adaptive', 'initial learning rate': 0.01907698752783516}. Best is trial 22 with value: 0.6405331242649941.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:38:59,282]\u001b[0m Trial 25 finished with value: 0.639890239121913 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 4, 'activation': 'tanh', 'learning rate': 'adaptive', 'initial learning rate': 0.03232268086860157}. Best is trial 22 with value: 0.6405331242649941.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:40:04,229]\u001b[0m Trial 26 finished with value: 0.6405644845158761 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 11, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.038643117434869326}. Best is trial 26 with value: 0.6405644845158761.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:43:35,466]\u001b[0m Trial 27 finished with value: 0.6347785182281458 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 12, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.039904849524831816}. Best is trial 26 with value: 0.6405644845158761.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:44:52,344]\u001b[0m Trial 28 finished with value: 0.6391062328498628 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 16, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.05366628490571898}. Best is trial 26 with value: 0.6405644845158761.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:46:45,291]\u001b[0m Trial 29 finished with value: 0.6375695805566445 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 20, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.06339905039066138}. Best is trial 26 with value: 0.6405644845158761.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:47:55,821]\u001b[0m Trial 30 finished with value: 0.6395923167385339 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 10, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.04409202754598887}. Best is trial 26 with value: 0.6405644845158761.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:49:18,923]\u001b[0m Trial 31 finished with value: 0.6398667189337515 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 7, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.021911482787623882}. Best is trial 26 with value: 0.6405644845158761.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:50:24,758]\u001b[0m Trial 32 finished with value: 0.6409721677773422 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 4, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.009951325676263768}. Best is trial 32 with value: 0.6409721677773422.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:52:01,987]\u001b[0m Trial 33 finished with value: 0.6404782438259506 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 11, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.008408671743724755}. Best is trial 32 with value: 0.6409721677773422.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:54:02,284]\u001b[0m Trial 34 finished with value: 0.6400078400627205 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 15, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.007362889987935016}. Best is trial 32 with value: 0.6409721677773422.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 21:57:20,599]\u001b[0m Trial 35 finished with value: 0.642077616620933 and parameters: {'layers': 2, 'solver': 'lbfgs', 'neurons': 11, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.033070586613238195}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 22:01:12,800]\u001b[0m Trial 36 finished with value: 0.6339239513916112 and parameters: {'layers': 2, 'solver': 'lbfgs', 'neurons': 13, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.0319077297544169}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 22:39:57,125]\u001b[0m Trial 37 finished with value: 0.6151626813014505 and parameters: {'layers': 2, 'solver': 'lbfgs', 'neurons': 17, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.03450138547533955}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 22:43:42,885]\u001b[0m Trial 38 finished with value: 0.6339239513916112 and parameters: {'layers': 2, 'solver': 'lbfgs', 'neurons': 13, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.02704411432849982}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-12 23:54:18,071]\u001b[0m Trial 39 finished with value: 0.6213563308506468 and parameters: {'layers': 2, 'solver': 'lbfgs', 'neurons': 15, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.0399957916779562}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:09:31,791]\u001b[0m Trial 40 finished with value: 0.6316268130145041 and parameters: {'layers': 2, 'solver': 'lbfgs', 'neurons': 8, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.049273331692431914}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:09:51,746]\u001b[0m Trial 41 finished with value: 0.6339474715797726 and parameters: {'layers': 1, 'solver': 'adam', 'neurons': 11, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.010854735220074064}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:13:11,356]\u001b[0m Trial 42 finished with value: 0.6347785182281458 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 12, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.00519559261136187}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:14:14,175]\u001b[0m Trial 43 finished with value: 0.6411995295962367 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 11, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.027330145261761467}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:14:40,638]\u001b[0m Trial 44 finished with value: 0.6302391219129753 and parameters: {'layers': 2, 'solver': 'adam', 'neurons': 14, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.03687419172854359}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:15:47,128]\u001b[0m Trial 45 finished with value: 0.6289298314386516 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 20, 'activation': 'identity', 'learning rate': 'adaptive', 'initial learning rate': 0.024662516355434302}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:17:40,359]\u001b[0m Trial 46 finished with value: 0.6418502548020384 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 6, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.028779074513895504}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:18:34,729]\u001b[0m Trial 47 finished with value: 0.6389572716581733 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 5, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.014650655662845234}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:20:42,078]\u001b[0m Trial 48 finished with value: 0.6377577420619365 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 8, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.0275306395503197}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:22:33,489]\u001b[0m Trial 49 finished with value: 0.6384476675813406 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 7, 'activation': 'logistic', 'learning rate': 'constant', 'initial learning rate': 0.03687077552966993}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:23:10,877]\u001b[0m Trial 50 finished with value: 0.6304586436691494 and parameters: {'layers': 2, 'solver': 'lbfgs', 'neurons': 6, 'activation': 'identity', 'learning rate': 'adaptive', 'initial learning rate': 0.020495614484324305}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:24:20,289]\u001b[0m Trial 51 finished with value: 0.6401019208153665 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 9, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.03190980364358179}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:25:30,590]\u001b[0m Trial 52 finished with value: 0.6389337514700117 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 11, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.0400359190111914}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:26:42,131]\u001b[0m Trial 53 finished with value: 0.640219521756174 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 9, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.028037408497633733}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:26:50,444]\u001b[0m Trial 54 finished with value: 0.5 and parameters: {'layers': 1, 'solver': 'adam', 'neurons': 6, 'activation': 'relu', 'learning rate': 'adaptive', 'initial learning rate': 0.015832565019236026}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:28:10,010]\u001b[0m Trial 55 finished with value: 0.6410035280282242 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 32, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.048175673918137527}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:29:45,287]\u001b[0m Trial 56 finished with value: 0.6388318306546452 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 31, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.04841491164720835}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:31:08,128]\u001b[0m Trial 57 finished with value: 0.6387612700901607 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 29, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.05194928908258793}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:42:43,797]\u001b[0m Trial 58 finished with value: 0.6227440219521756 and parameters: {'layers': 1, 'solver': 'lbfgs', 'neurons': 23, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.04466075817322117}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:43:39,819]\u001b[0m Trial 59 finished with value: 0.6408702469619757 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 3, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.024194789209265138}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:44:32,639]\u001b[0m Trial 60 finished with value: 0.6301293610348883 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 3, 'activation': 'relu', 'learning rate': 'adaptive', 'initial learning rate': 0.023153056684111124}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:45:21,609]\u001b[0m Trial 61 finished with value: 0.6391924735397884 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 2, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.04250165681913789}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:46:13,405]\u001b[0m Trial 62 finished with value: 0.6406036848294786 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 3, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.03565707925542141}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 00:47:08,798]\u001b[0m Trial 63 finished with value: 0.6411995295962367 and parameters: {'layers': 1, 'solver': 'sgd', 'neurons': 3, 'activation': 'logistic', 'learning rate': 'adaptive', 'initial learning rate': 0.03015863343420548}. Best is trial 35 with value: 0.642077616620933.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7b8deded30>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_treino = treino_ina.drop(axis=1, labels=['IND_BOM_1_1', 'IND_BOM_1_2']).append(treino_adi.drop(axis=1, labels=['IND_BOM_1_1', 'IND_BOM_1_2']))\n",
    "df_teste = teste_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(teste_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_teste.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "df_validacao = valid_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(valid_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_validacao.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "\n",
    "best_mlp = []\n",
    "\n",
    "def mlp(trial):\n",
    "    layers              = trial.suggest_int('layers', 1, 2)\n",
    "    solver              = trial.suggest_categorical('solver', ['lbfgs', 'sgd', 'adam'])\n",
    "    neurons             = trial.suggest_int('neurons', 2, 32)\n",
    "    activation          = trial.suggest_categorical('activation', ['identity', 'logistic', 'tanh', 'relu'])\n",
    "    learning_rate       = trial.suggest_categorical('learning rate', ['constant', 'adaptive'])\n",
    "    learning_rate_init  = trial.suggest_float('initial learning rate', 0.001, 0.1)\n",
    "    mlp = MLPClassifier(random_state=1, \n",
    "                    max_iter=10000,\n",
    "                    solver=solver,\n",
    "                    activation=activation,\n",
    "                    learning_rate=learning_rate,\n",
    "                    hidden_layer_sizes=(neurons,) if layers==1 else (neurons, neurons),\n",
    "                    learning_rate_init=learning_rate_init, \n",
    "                    early_stopping=True).fit(df_treino, [0]*len(treino_ina)+[1]*len(treino_adi))\n",
    "\n",
    "    best_mlp.append(mlp)\n",
    "\n",
    "    mlp_pred_class = mlp.predict(df_validacao)\n",
    "    mlp_pred_scores = mlp.predict_proba(df_validacao)\n",
    "\n",
    "    accuracy, recall, precision, f1, auroc, aupr = compute_performance_metrics_sem_plot2([0]*len(valid_ina)+[1]*len(valid_adi), mlp_pred_class, mlp_pred_scores, \"MLP/MLP Trial {}\".format(trial.number))\n",
    "\n",
    "    return accuracy\n",
    "\n",
    "study_0 = optuna.create_study(direction=\"maximize\")\n",
    "study_0.optimize(mlp, n_trials=64)\n",
    "\n",
    "compute_performance_metrics([0]*len(teste_ina)+[1]*len(teste_adi), best_mlp[study_0.best_trial.number].predict(df_teste), best_mlp[study_0.best_trial.number].predict_proba(df_teste))\n",
    "plot_confusion_matrix(best_mlp[study_0.best_trial.number], df_teste, [0]*len(teste_ina)+[1]*len(teste_adi), normalize='true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot da matriz de confusão do melhor classificador dentre os testados acima"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resultados do MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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     "metadata": {},
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   ],
   "source": [
    "optuna.visualization.plot_param_importances(study_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comentários acerca dos resultados do MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cuidado: usar esses parâmetros muito elevados, ou o default (100 estimadores e profundidade ilimitada) vai travar seu computador\n",
    "\n",
    "# Parâmetros default:\n",
    "# n_estimators=100, *,\n",
    "# criterion=\"gini\",\n",
    "# max_depth=None,\n",
    "# min_samples_split=2,\n",
    "# min_samples_leaf=1,\n",
    "# min_weight_fraction_leaf=0.,\n",
    "# max_features=\"auto\",\n",
    "# max_leaf_nodes=None,\n",
    "# min_impurity_decrease=0.,\n",
    "# min_impurity_split=None,\n",
    "# bootstrap=True,\n",
    "# oob_score=False,\n",
    "# n_jobs=None,\n",
    "# random_state=None,\n",
    "# verbose=0,\n",
    "# warm_start=False,\n",
    "# class_weight=None,\n",
    "# ccp_alpha=0.0,\n",
    "# max_samples=None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Teste de Kolmogorov-Smirnov (KS) e matriz de confusão da Random Forest\n",
    "Acima, podemos ver os resultados de ambos. O teste de Kolmogorov-Smirnov assemelha-se bastante a uma distribuição normal. A matriz foi bem sucedida para identificar resultados falsos,  para casos verdadeiro seu resultado foi pouco acima de 50%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-12-13 09:53:46,701]\u001b[0m A new study created in memory with name: no-name-645ee9d7-04f2-49f6-b887-c3745391ff0c\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:54:27,698]\u001b[0m Trial 0 finished with value: 0.6122540180321443 and parameters: {'max_depth': 62, 'criterion': 'gini', 'n_estimators': 20, 'min_samples_leaf': 4, 'min_samples_split': 8}. Best is trial 0 with value: 0.6122540180321443.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:54:47,460]\u001b[0m Trial 1 finished with value: 0.6049392395139161 and parameters: {'max_depth': 37, 'criterion': 'gini', 'n_estimators': 10, 'min_samples_leaf': 5, 'min_samples_split': 6}. Best is trial 0 with value: 0.6122540180321443.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:55:08,387]\u001b[0m Trial 2 finished with value: 0.5943237945903567 and parameters: {'max_depth': 55, 'criterion': 'entropy', 'n_estimators': 9, 'min_samples_leaf': 3, 'min_samples_split': 2}. Best is trial 0 with value: 0.6122540180321443.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:55:55,276]\u001b[0m Trial 3 finished with value: 0.6175460603684829 and parameters: {'max_depth': 57, 'criterion': 'gini', 'n_estimators': 24, 'min_samples_leaf': 5, 'min_samples_split': 2}. Best is trial 3 with value: 0.6175460603684829.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:56:17,979]\u001b[0m Trial 4 finished with value: 0.6098706389651117 and parameters: {'max_depth': 57, 'criterion': 'gini', 'n_estimators': 11, 'min_samples_leaf': 7, 'min_samples_split': 7}. Best is trial 3 with value: 0.6175460603684829.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:56:42,197]\u001b[0m Trial 5 finished with value: 0.6196550372402979 and parameters: {'max_depth': 25, 'criterion': 'gini', 'n_estimators': 13, 'min_samples_leaf': 8, 'min_samples_split': 3}. Best is trial 5 with value: 0.6196550372402979.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:57:13,298]\u001b[0m Trial 6 finished with value: 0.621858094864759 and parameters: {'max_depth': 22, 'criterion': 'entropy', 'n_estimators': 15, 'min_samples_leaf': 3, 'min_samples_split': 4}. Best is trial 6 with value: 0.621858094864759.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:57:32,841]\u001b[0m Trial 7 finished with value: 0.6154292434339474 and parameters: {'max_depth': 24, 'criterion': 'gini', 'n_estimators': 11, 'min_samples_leaf': 6, 'min_samples_split': 7}. Best is trial 6 with value: 0.621858094864759.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:58:26,212]\u001b[0m Trial 8 finished with value: 0.6281615052920423 and parameters: {'max_depth': 25, 'criterion': 'entropy', 'n_estimators': 27, 'min_samples_leaf': 8, 'min_samples_split': 3}. Best is trial 8 with value: 0.6281615052920423.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 09:58:55,945]\u001b[0m Trial 9 finished with value: 0.6015209721677773 and parameters: {'max_depth': 61, 'criterion': 'entropy', 'n_estimators': 13, 'min_samples_leaf': 3, 'min_samples_split': 7}. Best is trial 8 with value: 0.6281615052920423.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:00:00,991]\u001b[0m Trial 10 finished with value: 0.6258408467267739 and parameters: {'max_depth': 34, 'criterion': 'entropy', 'n_estimators': 31, 'min_samples_leaf': 8, 'min_samples_split': 5}. Best is trial 8 with value: 0.6281615052920423.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:01:05,384]\u001b[0m Trial 11 finished with value: 0.6252685221481772 and parameters: {'max_depth': 35, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 8, 'min_samples_split': 5}. Best is trial 8 with value: 0.6281615052920423.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:02:12,021]\u001b[0m Trial 12 finished with value: 0.62203057624461 and parameters: {'max_depth': 46, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 7, 'min_samples_split': 4}. Best is trial 8 with value: 0.6281615052920423.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:02:54,762]\u001b[0m Trial 13 finished with value: 0.6353978831830654 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 27, 'min_samples_leaf': 8, 'min_samples_split': 5}. Best is trial 13 with value: 0.6353978831830654.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:03:40,725]\u001b[0m Trial 14 finished with value: 0.6359702077616621 and parameters: {'max_depth': 17, 'criterion': 'entropy', 'n_estimators': 27, 'min_samples_leaf': 7, 'min_samples_split': 3}. Best is trial 14 with value: 0.6359702077616621.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:04:20,609]\u001b[0m Trial 15 finished with value: 0.6345041160329282 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 26, 'min_samples_leaf': 7, 'min_samples_split': 4}. Best is trial 14 with value: 0.6359702077616621.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:04:55,441]\u001b[0m Trial 16 finished with value: 0.6341826734613877 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 22, 'min_samples_leaf': 6, 'min_samples_split': 3}. Best is trial 14 with value: 0.6359702077616621.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:05:42,165]\u001b[0m Trial 17 finished with value: 0.6375068600548804 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 29, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:06:39,220]\u001b[0m Trial 18 finished with value: 0.6207840062720502 and parameters: {'max_depth': 31, 'criterion': 'entropy', 'n_estimators': 29, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:07:14,116]\u001b[0m Trial 19 finished with value: 0.6116346530772246 and parameters: {'max_depth': 43, 'criterion': 'entropy', 'n_estimators': 18, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:08:05,138]\u001b[0m Trial 20 finished with value: 0.6302548020384163 and parameters: {'max_depth': 20, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 4, 'min_samples_split': 8}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:08:48,371]\u001b[0m Trial 21 finished with value: 0.635280282242258 and parameters: {'max_depth': 17, 'criterion': 'entropy', 'n_estimators': 27, 'min_samples_leaf': 7, 'min_samples_split': 5}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:09:33,867]\u001b[0m Trial 22 finished with value: 0.622046256370051 and parameters: {'max_depth': 29, 'criterion': 'entropy', 'n_estimators': 24, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:10:30,295]\u001b[0m Trial 23 finished with value: 0.6310544884359075 and parameters: {'max_depth': 20, 'criterion': 'entropy', 'n_estimators': 29, 'min_samples_leaf': 6, 'min_samples_split': 4}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:11:23,776]\u001b[0m Trial 24 finished with value: 0.6251195609564877 and parameters: {'max_depth': 29, 'criterion': 'entropy', 'n_estimators': 25, 'min_samples_leaf': 8, 'min_samples_split': 5}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:12:12,854]\u001b[0m Trial 25 finished with value: 0.6365817326538612 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:12:55,559]\u001b[0m Trial 26 finished with value: 0.6323010584084673 and parameters: {'max_depth': 19, 'criterion': 'entropy', 'n_estimators': 22, 'min_samples_leaf': 5, 'min_samples_split': 7}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:13:54,519]\u001b[0m Trial 27 finished with value: 0.6251430811446491 and parameters: {'max_depth': 27, 'criterion': 'entropy', 'n_estimators': 29, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:14:36,783]\u001b[0m Trial 28 finished with value: 0.6289925519404155 and parameters: {'max_depth': 21, 'criterion': 'entropy', 'n_estimators': 23, 'min_samples_leaf': 6, 'min_samples_split': 7}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:15:06,389]\u001b[0m Trial 29 finished with value: 0.6340415523324187 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 19, 'min_samples_leaf': 4, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:15:48,125]\u001b[0m Trial 30 finished with value: 0.6263347706781655 and parameters: {'max_depth': 23, 'criterion': 'entropy', 'n_estimators': 21, 'min_samples_leaf': 7, 'min_samples_split': 8}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:16:31,880]\u001b[0m Trial 31 finished with value: 0.6349274794198354 and parameters: {'max_depth': 17, 'criterion': 'entropy', 'n_estimators': 27, 'min_samples_leaf': 8, 'min_samples_split': 5}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:17:20,841]\u001b[0m Trial 32 finished with value: 0.6356017248137985 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:18:19,378]\u001b[0m Trial 33 finished with value: 0.6347706781654253 and parameters: {'max_depth': 18, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 5, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:19:23,979]\u001b[0m Trial 34 finished with value: 0.6195923167385339 and parameters: {'max_depth': 48, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 6, 'min_samples_split': 7}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:20:09,688]\u001b[0m Trial 35 finished with value: 0.6296432771462172 and parameters: {'max_depth': 20, 'criterion': 'gini', 'n_estimators': 25, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:21:15,639]\u001b[0m Trial 36 finished with value: 0.6259427675421403 and parameters: {'max_depth': 26, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 7, 'min_samples_split': 2}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:22:01,328]\u001b[0m Trial 37 finished with value: 0.6262877303018424 and parameters: {'max_depth': 22, 'criterion': 'gini', 'n_estimators': 30, 'min_samples_leaf': 5, 'min_samples_split': 7}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:22:37,810]\u001b[0m Trial 38 finished with value: 0.6375068600548804 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 25, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:23:18,640]\u001b[0m Trial 39 finished with value: 0.6210348882791062 and parameters: {'max_depth': 38, 'criterion': 'gini', 'n_estimators': 25, 'min_samples_leaf': 6, 'min_samples_split': 3}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:24:04,972]\u001b[0m Trial 40 finished with value: 0.6144021952175618 and parameters: {'max_depth': 52, 'criterion': 'entropy', 'n_estimators': 24, 'min_samples_leaf': 5, 'min_samples_split': 8}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:24:48,979]\u001b[0m Trial 41 finished with value: 0.6371854174833399 and parameters: {'max_depth': 18, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:25:35,580]\u001b[0m Trial 42 finished with value: 0.6276127009016073 and parameters: {'max_depth': 23, 'criterion': 'entropy', 'n_estimators': 26, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:26:21,413]\u001b[0m Trial 43 finished with value: 0.6293923951391611 and parameters: {'max_depth': 19, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 2, 'min_samples_split': 7}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:27:07,457]\u001b[0m Trial 44 finished with value: 0.6343551548412387 and parameters: {'max_depth': 18, 'criterion': 'entropy', 'n_estimators': 26, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:28:13,325]\u001b[0m Trial 45 finished with value: 0.6287416699333594 and parameters: {'max_depth': 24, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 5, 'min_samples_split': 5}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:29:13,042]\u001b[0m Trial 46 finished with value: 0.619921599372795 and parameters: {'max_depth': 63, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 6, 'min_samples_split': 7}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:30:18,779]\u001b[0m Trial 47 finished with value: 0.6325519404155233 and parameters: {'max_depth': 22, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 7, 'min_samples_split': 2}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:31:26,828]\u001b[0m Trial 48 finished with value: 0.6248451587612701 and parameters: {'max_depth': 28, 'criterion': 'entropy', 'n_estimators': 31, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:32:00,787]\u001b[0m Trial 49 finished with value: 0.6139004312034496 and parameters: {'max_depth': 31, 'criterion': 'gini', 'n_estimators': 17, 'min_samples_leaf': 5, 'min_samples_split': 4}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:32:45,580]\u001b[0m Trial 50 finished with value: 0.6224852998823991 and parameters: {'max_depth': 25, 'criterion': 'entropy', 'n_estimators': 23, 'min_samples_leaf': 6, 'min_samples_split': 5}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:33:26,897]\u001b[0m Trial 51 finished with value: 0.6372951783614269 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 17 with value: 0.6375068600548804.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:34:11,745]\u001b[0m Trial 52 finished with value: 0.6378518228145825 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 31, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 52 with value: 0.6378518228145825.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:34:57,130]\u001b[0m Trial 53 finished with value: 0.6372559780478244 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 31, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 52 with value: 0.6378518228145825.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:35:47,459]\u001b[0m Trial 54 finished with value: 0.6356958055664446 and parameters: {'max_depth': 19, 'criterion': 'entropy', 'n_estimators': 31, 'min_samples_leaf': 8, 'min_samples_split': 6}. Best is trial 52 with value: 0.6378518228145825.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:36:43,169]\u001b[0m Trial 55 finished with value: 0.6318149745197962 and parameters: {'max_depth': 21, 'criterion': 'entropy', 'n_estimators': 31, 'min_samples_leaf': 7, 'min_samples_split': 7}. Best is trial 52 with value: 0.6378518228145825.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:37:35,757]\u001b[0m Trial 56 finished with value: 0.6356566052528421 and parameters: {'max_depth': 18, 'criterion': 'entropy', 'n_estimators': 29, 'min_samples_leaf': 8, 'min_samples_split': 5}. Best is trial 52 with value: 0.6378518228145825.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:38:28,012]\u001b[0m Trial 57 finished with value: 0.6381967855742846 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 57 with value: 0.6381967855742846.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:39:22,348]\u001b[0m Trial 58 finished with value: 0.6395531164249314 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 58 with value: 0.6395531164249314.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:40:27,236]\u001b[0m Trial 59 finished with value: 0.6305292042336339 and parameters: {'max_depth': 21, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 6, 'min_samples_split': 5}. Best is trial 58 with value: 0.6395531164249314.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:40:41,075]\u001b[0m Trial 60 finished with value: 0.6258173265386123 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 8, 'min_samples_leaf': 7, 'min_samples_split': 7}. Best is trial 58 with value: 0.6395531164249314.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:41:29,399]\u001b[0m Trial 61 finished with value: 0.6369188553508428 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 58 with value: 0.6395531164249314.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:42:20,026]\u001b[0m Trial 62 finished with value: 0.6380086240689925 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 31, 'min_samples_leaf': 8, 'min_samples_split': 6}. Best is trial 58 with value: 0.6395531164249314.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:43:17,386]\u001b[0m Trial 63 finished with value: 0.6343551548412387 and parameters: {'max_depth': 19, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 8, 'min_samples_split': 6}. Best is trial 58 with value: 0.6395531164249314.\u001b[0m\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'df_teste' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-23bc67534477>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0mstudy_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_study\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"maximize\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0mstudy_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mcompute_performance_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mteste_ina\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mteste_adi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest_rf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstudy_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_teste\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest_rf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstudy_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_teste\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0mplot_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_rf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstudy_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_teste\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mteste_ina\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mteste_adi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'true'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df_teste' is not defined"
     ]
    }
   ],
   "source": [
    "ds_dropado = ds.drop(labels=['IND_BOM_1_2', 'IND_BOM_1_1'], axis=1)\n",
    "df_treino = treino_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(treino_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_validacao = valid_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(valid_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_validacao.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "\n",
    "best_rf = []\n",
    "\n",
    "def rf(trial):\n",
    "    max_depth           = trial.suggest_int(\"max_depth\", 16, 64)\n",
    "    criterion           = trial.suggest_categorical(\"criterion\", [\"gini\", \"entropy\"])\n",
    "    n_estimators        = trial.suggest_int(\"n_estimators\", 8, 32)\n",
    "    min_samples_leaf    = trial.suggest_int(\"min_samples_leaf\", 2, 8)\n",
    "    min_samples_split   = trial.suggest_int(\"min_samples_split\", 2, 8)\n",
    "    random_forest = RandomForestClassifier(n_estimators=n_estimators, \n",
    "                                        max_depth=max_depth, \n",
    "                                        criterion=criterion, \n",
    "                                        bootstrap=True, \n",
    "                                        min_samples_leaf=min_samples_leaf, \n",
    "                                        min_samples_split=min_samples_split).fit(df_treino, [0]*len(treino_ina)+[1]*len(treino_adi))\n",
    "\n",
    "    # Código opcional para logar e cronometrar tempo\n",
    "    # print('Random Forest com {} estimadores e profundidade máxima {}, critério {}, min amostras de folhas {} e min divisão de amostras {}\\n'.format(\n",
    "    #     n_estimators, max_depth, criterion, min_samples_leaf, min_samples_split\n",
    "    # ))\n",
    "    # start = time.time()\n",
    "    # n_scores = cross_val_score(random_forest, ds_dropado, ds['IND_BOM_1_2'], scoring='accuracy', n_jobs=1, error_score='raise')\n",
    "    # print('Acurácia e desvio padrão: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))\n",
    "    # print(\"Tempo: \", format(time.time() - start, '.3f'), 's\\n', sep='')\n",
    "\n",
    "    best_rf.append(random_forest)\n",
    "\n",
    "    rf_pred_class  = random_forest.predict(df_validacao)\n",
    "    rf_pred_scores = random_forest.predict_proba(df_validacao)\n",
    "\n",
    "    accuracy, recall, precision, f1, auroc, aupr = compute_performance_metrics_sem_plot2([0]*len(valid_ina)+[1]*len(valid_adi), rf_pred_class, rf_pred_scores, \"RF/RF Trial {}\".format(trial.number))\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "study_1 = optuna.create_study(direction=\"maximize\")\n",
    "study_1.optimize(rf, n_trials=64)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot da matriz de confusão do melhor classificador dentre os testados acima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f0a35b8b1c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_teste = teste_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(teste_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_teste.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "compute_performance_metrics([0]*len(teste_ina)+[1]*len(teste_adi), best_rf[study_1.best_trial.number].predict(df_teste), best_rf[study_1.best_trial.number].predict_proba(df_teste))\n",
    "plot_confusion_matrix(best_rf[study_1.best_trial.number], df_teste, [0]*len(teste_ina)+[1]*len(teste_adi), normalize='true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resultados da Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "mode": "markers",
         "name": "Objective Value",
         "type": "scatter",
         "x": [
          0,
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8,
          9,
          10,
          11,
          12,
          13,
          14,
          15,
          16,
          17,
          18,
          19,
          20,
          21,
          22,
          23,
          24,
          25,
          26,
          27,
          28,
          29,
          30,
          31,
          32,
          33,
          34,
          35,
          36,
          37,
          38,
          39,
          40,
          41,
          42,
          43,
          44,
          45,
          46,
          47,
          48,
          49,
          50,
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   "metadata": {},
   "source": [
    "#### Comentários acerca dos resultados da Random Forest\n",
    "\n",
    "A profundidade máxima é, sem dúvidas, o maior diferencial entre todos os hiperparâmetros, e menores profundidades são melhores; é possível perceber através do gráfico que quanto maior a profundidade melhor vai ser o desempenho, até atingir-se um limite. A quantidade de estimadores tem um impacto bastante menor, no gráfico, é possível ver que, excluindo-se os outliers, o número de estimadores realmente não interfere bastante no resultado final. A quantidade mínima de samples nas folhas tem impacto ainda menor, pode-se dizer que é inútil alterar este parâmetro.\n",
    "\n",
    "\n",
    "### Teste com profundidade menor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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      "\u001b[32m[I 2021-12-13 10:51:57,381]\u001b[0m A new study created in memory with name: no-name-f01eceaf-879c-435b-b6ec-274904bcbc97\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:52:39,920]\u001b[0m Trial 0 finished with value: 0.6385966287730301 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 26, 'min_samples_leaf': 7, 'min_samples_split': 3}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:52:55,472]\u001b[0m Trial 1 finished with value: 0.624076832614661 and parameters: {'max_depth': 6, 'criterion': 'entropy', 'n_estimators': 20, 'min_samples_leaf': 8, 'min_samples_split': 6}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:53:07,963]\u001b[0m Trial 2 finished with value: 0.626765974127793 and parameters: {'max_depth': 7, 'criterion': 'entropy', 'n_estimators': 14, 'min_samples_leaf': 4, 'min_samples_split': 7}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:53:30,821]\u001b[0m Trial 3 finished with value: 0.6291807134457076 and parameters: {'max_depth': 7, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 6, 'min_samples_split': 8}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:53:33,727]\u001b[0m Trial 4 finished with value: 0.5870717365738926 and parameters: {'max_depth': 2, 'criterion': 'entropy', 'n_estimators': 9, 'min_samples_leaf': 5, 'min_samples_split': 4}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:53:43,658]\u001b[0m Trial 5 finished with value: 0.619890239121913 and parameters: {'max_depth': 5, 'criterion': 'gini', 'n_estimators': 19, 'min_samples_leaf': 3, 'min_samples_split': 3}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:54:03,966]\u001b[0m Trial 6 finished with value: 0.6362524500196002 and parameters: {'max_depth': 11, 'criterion': 'gini', 'n_estimators': 19, 'min_samples_leaf': 4, 'min_samples_split': 6}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:54:15,594]\u001b[0m Trial 7 finished with value: 0.6327165817326539 and parameters: {'max_depth': 11, 'criterion': 'entropy', 'n_estimators': 10, 'min_samples_leaf': 5, 'min_samples_split': 7}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:54:20,507]\u001b[0m Trial 8 finished with value: 0.5952881223049784 and parameters: {'max_depth': 2, 'criterion': 'gini', 'n_estimators': 21, 'min_samples_leaf': 8, 'min_samples_split': 6}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:54:44,117]\u001b[0m Trial 9 finished with value: 0.6287259898079185 and parameters: {'max_depth': 16, 'criterion': 'gini', 'n_estimators': 16, 'min_samples_leaf': 2, 'min_samples_split': 4}. Best is trial 0 with value: 0.6385966287730301.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:55:32,696]\u001b[0m Trial 10 finished with value: 0.6388318306546452 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 7, 'min_samples_split': 2}. Best is trial 10 with value: 0.6388318306546452.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:56:24,752]\u001b[0m Trial 11 finished with value: 0.6388631909055272 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 7, 'min_samples_split': 2}. Best is trial 11 with value: 0.6388631909055272.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:57:14,551]\u001b[0m Trial 12 finished with value: 0.6370364562916503 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 7, 'min_samples_split': 2}. Best is trial 11 with value: 0.6388631909055272.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:57:59,903]\u001b[0m Trial 13 finished with value: 0.6392551940415523 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 7, 'min_samples_split': 2}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:58:33,113]\u001b[0m Trial 14 finished with value: 0.6382987063896511 and parameters: {'max_depth': 13, 'criterion': 'entropy', 'n_estimators': 25, 'min_samples_leaf': 6, 'min_samples_split': 2}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:59:19,846]\u001b[0m Trial 15 finished with value: 0.6379772638181106 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 8, 'min_samples_split': 3}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 10:59:49,959]\u001b[0m Trial 16 finished with value: 0.6384790278322227 and parameters: {'max_depth': 12, 'criterion': 'entropy', 'n_estimators': 24, 'min_samples_leaf': 6, 'min_samples_split': 4}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:00:17,885]\u001b[0m Trial 17 finished with value: 0.6363857310858487 and parameters: {'max_depth': 9, 'criterion': 'entropy', 'n_estimators': 29, 'min_samples_leaf': 7, 'min_samples_split': 2}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:01:07,539]\u001b[0m Trial 18 finished with value: 0.6390827126617012 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 6, 'min_samples_split': 3}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:01:47,663]\u001b[0m Trial 19 finished with value: 0.6385182281458252 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 27, 'min_samples_leaf': 6, 'min_samples_split': 5}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:02:17,313]\u001b[0m Trial 20 finished with value: 0.6374206193649549 and parameters: {'max_depth': 10, 'criterion': 'gini', 'n_estimators': 30, 'min_samples_leaf': 4, 'min_samples_split': 3}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:03:09,793]\u001b[0m Trial 21 finished with value: 0.6387142297138377 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 7, 'min_samples_split': 2}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:04:02,376]\u001b[0m Trial 22 finished with value: 0.6373814190513524 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 6, 'min_samples_split': 3}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:04:34,656]\u001b[0m Trial 23 finished with value: 0.6388867110936888 and parameters: {'max_depth': 13, 'criterion': 'entropy', 'n_estimators': 23, 'min_samples_leaf': 8, 'min_samples_split': 2}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:05:06,942]\u001b[0m Trial 24 finished with value: 0.6366601332810663 and parameters: {'max_depth': 13, 'criterion': 'entropy', 'n_estimators': 24, 'min_samples_leaf': 8, 'min_samples_split': 4}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:05:34,747]\u001b[0m Trial 25 finished with value: 0.637765582124657 and parameters: {'max_depth': 12, 'criterion': 'entropy', 'n_estimators': 22, 'min_samples_leaf': 8, 'min_samples_split': 3}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:05:57,087]\u001b[0m Trial 26 finished with value: 0.6376636613092904 and parameters: {'max_depth': 13, 'criterion': 'entropy', 'n_estimators': 16, 'min_samples_leaf': 5, 'min_samples_split': 2}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:06:17,250]\u001b[0m Trial 27 finished with value: 0.6322304978439828 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 12, 'min_samples_leaf': 6, 'min_samples_split': 5}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:07:02,586]\u001b[0m Trial 28 finished with value: 0.6391297530380243 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 7, 'min_samples_split': 3}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:07:43,808]\u001b[0m Trial 29 finished with value: 0.6383535868286946 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 27, 'min_samples_leaf': 7, 'min_samples_split': 3}. Best is trial 13 with value: 0.6392551940415523.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:08:27,730]\u001b[0m Trial 30 finished with value: 0.6394825558604469 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 7, 'min_samples_split': 4}. Best is trial 30 with value: 0.6394825558604469.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:09:10,202]\u001b[0m Trial 31 finished with value: 0.6385966287730301 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 7, 'min_samples_split': 4}. Best is trial 30 with value: 0.6394825558604469.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:09:52,904]\u001b[0m Trial 32 finished with value: 0.6395060760486084 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 29, 'min_samples_leaf': 7, 'min_samples_split': 3}. Best is trial 32 with value: 0.6395060760486084.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:10:24,566]\u001b[0m Trial 33 finished with value: 0.6377577420619365 and parameters: {'max_depth': 12, 'criterion': 'entropy', 'n_estimators': 26, 'min_samples_leaf': 7, 'min_samples_split': 4}. Best is trial 32 with value: 0.6395060760486084.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:11:06,749]\u001b[0m Trial 34 finished with value: 0.6369266954135633 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 29, 'min_samples_leaf': 8, 'min_samples_split': 5}. Best is trial 32 with value: 0.6395060760486084.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:11:47,956]\u001b[0m Trial 35 finished with value: 0.6369972559780478 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 7, 'min_samples_split': 4}. Best is trial 32 with value: 0.6395060760486084.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:12:22,140]\u001b[0m Trial 36 finished with value: 0.6362054096432771 and parameters: {'max_depth': 11, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 8, 'min_samples_split': 3}. Best is trial 32 with value: 0.6395060760486084.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:12:37,271]\u001b[0m Trial 37 finished with value: 0.6194590356722853 and parameters: {'max_depth': 5, 'criterion': 'entropy', 'n_estimators': 28, 'min_samples_leaf': 5, 'min_samples_split': 3}. Best is trial 32 with value: 0.6395060760486084.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:12:59,036]\u001b[0m Trial 38 finished with value: 0.6326617012936103 and parameters: {'max_depth': 8, 'criterion': 'entropy', 'n_estimators': 26, 'min_samples_leaf': 7, 'min_samples_split': 4}. Best is trial 32 with value: 0.6395060760486084.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:13:39,947]\u001b[0m Trial 39 finished with value: 0.639937279498236 and parameters: {'max_depth': 14, 'criterion': 'gini', 'n_estimators': 31, 'min_samples_leaf': 6, 'min_samples_split': 5}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:14:11,577]\u001b[0m Trial 40 finished with value: 0.6372246177969424 and parameters: {'max_depth': 10, 'criterion': 'gini', 'n_estimators': 31, 'min_samples_leaf': 6, 'min_samples_split': 5}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:14:50,899]\u001b[0m Trial 41 finished with value: 0.6387377499019992 and parameters: {'max_depth': 14, 'criterion': 'gini', 'n_estimators': 29, 'min_samples_leaf': 7, 'min_samples_split': 5}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:15:32,659]\u001b[0m Trial 42 finished with value: 0.6363622108976872 and parameters: {'max_depth': 15, 'criterion': 'gini', 'n_estimators': 31, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:16:10,575]\u001b[0m Trial 43 finished with value: 0.638000784006272 and parameters: {'max_depth': 13, 'criterion': 'gini', 'n_estimators': 31, 'min_samples_leaf': 7, 'min_samples_split': 6}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:16:52,128]\u001b[0m Trial 44 finished with value: 0.6355546844374755 and parameters: {'max_depth': 16, 'criterion': 'gini', 'n_estimators': 27, 'min_samples_leaf': 6, 'min_samples_split': 4}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:17:27,710]\u001b[0m Trial 45 finished with value: 0.6395374362994904 and parameters: {'max_depth': 12, 'criterion': 'gini', 'n_estimators': 29, 'min_samples_leaf': 5, 'min_samples_split': 4}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:18:01,366]\u001b[0m Trial 46 finished with value: 0.6373030184241474 and parameters: {'max_depth': 12, 'criterion': 'gini', 'n_estimators': 28, 'min_samples_leaf': 4, 'min_samples_split': 7}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:18:28,643]\u001b[0m Trial 47 finished with value: 0.6382987063896511 and parameters: {'max_depth': 11, 'criterion': 'gini', 'n_estimators': 25, 'min_samples_leaf': 5, 'min_samples_split': 5}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:19:09,123]\u001b[0m Trial 48 finished with value: 0.6384555076440611 and parameters: {'max_depth': 14, 'criterion': 'gini', 'n_estimators': 31, 'min_samples_leaf': 5, 'min_samples_split': 4}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:19:43,378]\u001b[0m Trial 49 finished with value: 0.637577420619365 and parameters: {'max_depth': 13, 'criterion': 'gini', 'n_estimators': 29, 'min_samples_leaf': 4, 'min_samples_split': 4}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:20:05,046]\u001b[0m Trial 50 finished with value: 0.6363151705213642 and parameters: {'max_depth': 12, 'criterion': 'gini', 'n_estimators': 19, 'min_samples_leaf': 6, 'min_samples_split': 6}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:20:49,425]\u001b[0m Trial 51 finished with value: 0.6386907095256762 and parameters: {'max_depth': 15, 'criterion': 'gini', 'n_estimators': 31, 'min_samples_leaf': 7, 'min_samples_split': 3}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:21:37,440]\u001b[0m Trial 52 finished with value: 0.6382595060760486 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 6, 'min_samples_split': 3}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:22:19,715]\u001b[0m Trial 53 finished with value: 0.63546844374755 and parameters: {'max_depth': 16, 'criterion': 'gini', 'n_estimators': 28, 'min_samples_leaf': 3, 'min_samples_split': 4}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:23:04,333]\u001b[0m Trial 54 finished with value: 0.6387063896511173 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 29, 'min_samples_leaf': 7, 'min_samples_split': 5}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:23:55,136]\u001b[0m Trial 55 finished with value: 0.6391532732261858 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 5, 'min_samples_split': 2}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:24:47,569]\u001b[0m Trial 56 finished with value: 0.6376793414347315 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 5, 'min_samples_split': 2}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:25:27,509]\u001b[0m Trial 57 finished with value: 0.6390670325362603 and parameters: {'max_depth': 13, 'criterion': 'gini', 'n_estimators': 32, 'min_samples_leaf': 5, 'min_samples_split': 2}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:25:37,163]\u001b[0m Trial 58 finished with value: 0.6043041944335554 and parameters: {'max_depth': 3, 'criterion': 'entropy', 'n_estimators': 27, 'min_samples_leaf': 4, 'min_samples_split': 2}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:25:46,486]\u001b[0m Trial 59 finished with value: 0.6308192865542924 and parameters: {'max_depth': 10, 'criterion': 'entropy', 'n_estimators': 8, 'min_samples_leaf': 5, 'min_samples_split': 2}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:26:35,694]\u001b[0m Trial 60 finished with value: 0.6386201489611917 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 31, 'min_samples_leaf': 6, 'min_samples_split': 4}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:27:24,511]\u001b[0m Trial 61 finished with value: 0.6386907095256762 and parameters: {'max_depth': 15, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 7, 'min_samples_split': 3}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:28:14,807]\u001b[0m Trial 62 finished with value: 0.6389651117208938 and parameters: {'max_depth': 16, 'criterion': 'entropy', 'n_estimators': 30, 'min_samples_leaf': 8, 'min_samples_split': 3}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 11:29:04,177]\u001b[0m Trial 63 finished with value: 0.6376479811838495 and parameters: {'max_depth': 14, 'criterion': 'entropy', 'n_estimators': 32, 'min_samples_leaf': 6, 'min_samples_split': 2}. Best is trial 39 with value: 0.639937279498236.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "def rf(trial):\n",
    "    max_depth           = trial.suggest_int(\"max_depth\", 2, 16)\n",
    "    criterion           = trial.suggest_categorical(\"criterion\", [\"gini\", \"entropy\"])\n",
    "    n_estimators        = trial.suggest_int(\"n_estimators\", 8, 32)\n",
    "    min_samples_leaf    = trial.suggest_int(\"min_samples_leaf\", 2, 8)\n",
    "    min_samples_split   = trial.suggest_int(\"min_samples_split\", 2, 8)\n",
    "    random_forest = RandomForestClassifier(n_estimators=n_estimators, \n",
    "                                        max_depth=max_depth, \n",
    "                                        criterion=criterion, \n",
    "                                        bootstrap=True, \n",
    "                                        min_samples_leaf=min_samples_leaf, \n",
    "                                        min_samples_split=min_samples_split).fit(df_treino, [0]*len(treino_ina)+[1]*len(treino_adi))\n",
    "\n",
    "    rf_pred_class  = random_forest.predict(df_validacao)\n",
    "    rf_pred_scores = random_forest.predict_proba(df_validacao)\n",
    "\n",
    "    accuracy, recall, precision, f1, auroc, aupr = compute_performance_metrics_sem_plot([0]*len(valid_ina)+[1]*len(valid_adi), rf_pred_class, rf_pred_scores)\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "study_1 = optuna.create_study(direction=\"maximize\")\n",
    "study_1.optimize(rf, n_trials=64)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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           "bgcolor": "#E5ECF6",
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          },
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           "x": 0.05
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         "text": "Hyperparameter Importances"
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        "xaxis": {
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          "text": "Importance for Objective Value"
         }
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        "yaxis": {
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          "text": "Hyperparameter"
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     },
     "metadata": {},
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   ],
   "source": [
    "optuna.visualization.plot_param_importances(study_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comentários acerca dos resultados da segunda execução\n",
    "\n",
    "Os resultados foram melhores, é possível perceber uma tendência de crescimento em torno do 16, entretanto, a melhora foi de menos de 1% (a melhora foi de 0.04%). Tendo em vista que esta execução foi mais rápida (11 minutos a menos) que a primeira (que demorou um total de 50 minutos), então, o teste foi válido."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parâmetros default:\n",
    "# *,loss='deviance', \n",
    "# learning_rate=0.1, \n",
    "# n_estimators=100,\n",
    "# subsample=1.0, \n",
    "# criterion='friedman_mse', \n",
    "# min_samples_split=2,\n",
    "# min_samples_leaf=1, \n",
    "# min_weight_fraction_leaf=0.,\n",
    "# max_depth=3, \n",
    "# min_impurity_decrease=0.,\n",
    "# min_impurity_split=None, \n",
    "# random_state=None, \n",
    "# max_features=None, verbose=0,\n",
    "# max_leaf_nodes=None, \n",
    "# warm_start=False,\n",
    "# validation_fraction=0.1, \n",
    "# n_iter_no_change=None, \n",
    "# tol=1e-4,\n",
    "# ccp_alpha=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-12-14 12:35:04,754]\u001b[0m A new study created in memory with name: no-name-b8adeb50-a23d-463b-94fc-944a708e9a49\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 12:38:01,910]\u001b[0m Trial 0 finished with value: 0.5818424147393179 and parameters: {'loss': 'deviance', 'max_depth': 51, 'subsample': 0.41731688066640243, 'n_estimators': 9, 'learning_rate': 0.42063373317223757, 'min_samples_leaf': 7}. Best is trial 0 with value: 0.5818424147393179.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 12:39:17,529]\u001b[0m Trial 1 finished with value: 0.6161662093296746 and parameters: {'loss': 'exponential', 'max_depth': 31, 'subsample': 0.13141235973746762, 'n_estimators': 14, 'learning_rate': 0.06671473937284195, 'min_samples_leaf': 5}. Best is trial 1 with value: 0.6161662093296746.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 12:43:08,316]\u001b[0m Trial 2 finished with value: 0.5892512740101921 and parameters: {'loss': 'deviance', 'max_depth': 22, 'subsample': 0.5739603892662606, 'n_estimators': 10, 'learning_rate': 0.4509134670537174, 'min_samples_leaf': 5}. Best is trial 1 with value: 0.6161662093296746.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 12:51:59,330]\u001b[0m Trial 3 finished with value: 0.5873069384555076 and parameters: {'loss': 'exponential', 'max_depth': 49, 'subsample': 0.48936838369940494, 'n_estimators': 18, 'learning_rate': 0.4457168114947387, 'min_samples_leaf': 6}. Best is trial 1 with value: 0.6161662093296746.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:06:53,904]\u001b[0m Trial 4 finished with value: 0.5679184633477068 and parameters: {'loss': 'deviance', 'max_depth': 59, 'subsample': 0.4111795716780603, 'n_estimators': 26, 'learning_rate': 0.668913103794306, 'min_samples_leaf': 6}. Best is trial 1 with value: 0.6161662093296746.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:08:28,060]\u001b[0m Trial 5 finished with value: 0.5932575460603685 and parameters: {'loss': 'exponential', 'max_depth': 29, 'subsample': 0.14915101810919126, 'n_estimators': 15, 'learning_rate': 0.2699796443430259, 'min_samples_leaf': 7}. Best is trial 1 with value: 0.6161662093296746.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:13:08,496]\u001b[0m Trial 6 finished with value: 0.6314386515092121 and parameters: {'loss': 'exponential', 'max_depth': 50, 'subsample': 0.25797250165436475, 'n_estimators': 28, 'learning_rate': 0.04752114921679849, 'min_samples_leaf': 8}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:14:41,642]\u001b[0m Trial 7 finished with value: 0.5917287338298707 and parameters: {'loss': 'exponential', 'max_depth': 33, 'subsample': 0.14246311405493958, 'n_estimators': 15, 'learning_rate': 0.27233566868925524, 'min_samples_leaf': 7}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:27:52,328]\u001b[0m Trial 8 finished with value: 0.5986828694629557 and parameters: {'loss': 'deviance', 'max_depth': 36, 'subsample': 0.4913296374188595, 'n_estimators': 32, 'learning_rate': 0.24636709272396276, 'min_samples_leaf': 6}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:30:16,812]\u001b[0m Trial 9 finished with value: 0.5820070560564484 and parameters: {'loss': 'exponential', 'max_depth': 40, 'subsample': 0.2140168898881506, 'n_estimators': 12, 'learning_rate': 0.41168077342549714, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:36:31,477]\u001b[0m Trial 10 finished with value: 0.6226421011368091 and parameters: {'loss': 'exponential', 'max_depth': 60, 'subsample': 0.2657729684854506, 'n_estimators': 26, 'learning_rate': 0.041826506711636735, 'min_samples_leaf': 2}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:43:25,624]\u001b[0m Trial 11 finished with value: 0.6241787534300275 and parameters: {'loss': 'exponential', 'max_depth': 64, 'subsample': 0.2956698853797405, 'n_estimators': 26, 'learning_rate': 0.011678327716671377, 'min_samples_leaf': 2}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:50:35,465]\u001b[0m Trial 12 finished with value: 0.6238494707957664 and parameters: {'loss': 'exponential', 'max_depth': 50, 'subsample': 0.3072154643925413, 'n_estimators': 26, 'learning_rate': 0.01420892885396989, 'min_samples_leaf': 2}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 13:51:53,525]\u001b[0m Trial 13 finished with value: 0.6002195217561741 and parameters: {'loss': 'exponential', 'max_depth': 64, 'subsample': 0.05099780566798767, 'n_estimators': 32, 'learning_rate': 0.14529728390732555, 'min_samples_leaf': 3}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 14:03:13,494]\u001b[0m Trial 14 finished with value: 0.6132497059976479 and parameters: {'loss': 'exponential', 'max_depth': 44, 'subsample': 0.6937230361822643, 'n_estimators': 23, 'learning_rate': 0.13897398485854534, 'min_samples_leaf': 8}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 14:18:05,511]\u001b[0m Trial 15 finished with value: 0.5748490787926304 and parameters: {'loss': 'exponential', 'max_depth': 56, 'subsample': 0.34523407420553, 'n_estimators': 29, 'learning_rate': 0.6067669229563805, 'min_samples_leaf': 3}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 14:18:20,596]\u001b[0m Trial 16 finished with value: 0.5962289298314386 and parameters: {'loss': 'exponential', 'max_depth': 64, 'subsample': 0.021208741476975462, 'n_estimators': 21, 'learning_rate': 0.14153073915135736, 'min_samples_leaf': 8}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 14:23:37,398]\u001b[0m Trial 17 finished with value: 0.624264994119953 and parameters: {'loss': 'exponential', 'max_depth': 55, 'subsample': 0.23219876695659292, 'n_estimators': 29, 'learning_rate': 0.07499051604044929, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 14:29:33,620]\u001b[0m Trial 18 finished with value: 0.6029949039592317 and parameters: {'loss': 'exponential', 'max_depth': 54, 'subsample': 0.22372240701152032, 'n_estimators': 30, 'learning_rate': 0.19934408569947926, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 14:31:26,238]\u001b[0m Trial 19 finished with value: 0.6150921207369658 and parameters: {'loss': 'exponential', 'max_depth': 45, 'subsample': 0.08796253613940774, 'n_estimators': 29, 'learning_rate': 0.08207508291383014, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 14:36:10,382]\u001b[0m Trial 20 finished with value: 0.5887416699333594 and parameters: {'loss': 'exponential', 'max_depth': 45, 'subsample': 0.20327581955022128, 'n_estimators': 22, 'learning_rate': 0.3377122823801209, 'min_samples_leaf': 3}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 14:42:31,861]\u001b[0m Trial 21 finished with value: 0.621889455115641 and parameters: {'loss': 'exponential', 'max_depth': 59, 'subsample': 0.29680967056260316, 'n_estimators': 24, 'learning_rate': 0.014074752280582241, 'min_samples_leaf': 2}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 14:52:15,721]\u001b[0m Trial 22 finished with value: 0.6214190513524108 and parameters: {'loss': 'exponential', 'max_depth': 55, 'subsample': 0.39809636715883623, 'n_estimators': 28, 'learning_rate': 0.09287860687893003, 'min_samples_leaf': 3}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 15:00:49,117]\u001b[0m Trial 23 finished with value: 0.6276048608388867 and parameters: {'loss': 'exponential', 'max_depth': 64, 'subsample': 0.351878266203777, 'n_estimators': 32, 'learning_rate': 0.016322221599021667, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 15:10:53,484]\u001b[0m Trial 24 finished with value: 0.6064445315562524 and parameters: {'loss': 'exponential', 'max_depth': 61, 'subsample': 0.34849747803331643, 'n_estimators': 32, 'learning_rate': 0.17788653455169182, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 15:16:36,402]\u001b[0m Trial 25 finished with value: 0.6231360250882007 and parameters: {'loss': 'exponential', 'max_depth': 51, 'subsample': 0.2419694161331569, 'n_estimators': 31, 'learning_rate': 0.09041653157548707, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 15:20:54,991]\u001b[0m Trial 26 finished with value: 0.6006507252058016 and parameters: {'loss': 'exponential', 'max_depth': 55, 'subsample': 0.17761608514060412, 'n_estimators': 28, 'learning_rate': 0.2151699690282359, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 15:27:34,582]\u001b[0m Trial 27 finished with value: 0.6180086240689926 and parameters: {'loss': 'deviance', 'max_depth': 41, 'subsample': 0.4834293819226928, 'n_estimators': 19, 'learning_rate': 0.016231473948409902, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 15:35:24,070]\u001b[0m Trial 28 finished with value: 0.6152410819286555 and parameters: {'loss': 'exponential', 'max_depth': 47, 'subsample': 0.3715425212670863, 'n_estimators': 24, 'learning_rate': 0.11625962686694558, 'min_samples_leaf': 3}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 15:45:35,957]\u001b[0m Trial 29 finished with value: 0.6192316738533908 and parameters: {'loss': 'deviance', 'max_depth': 53, 'subsample': 0.45870540357989786, 'n_estimators': 31, 'learning_rate': 0.05080678509819957, 'min_samples_leaf': 6}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 16:07:28,987]\u001b[0m Trial 30 finished with value: 0.5765660525284202 and parameters: {'loss': 'exponential', 'max_depth': 58, 'subsample': 0.5604924316968237, 'n_estimators': 28, 'learning_rate': 0.5392764834510377, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 16:14:15,682]\u001b[0m Trial 31 finished with value: 0.6221952175617405 and parameters: {'loss': 'exponential', 'max_depth': 64, 'subsample': 0.29347391987091254, 'n_estimators': 26, 'learning_rate': 0.014186818779906685, 'min_samples_leaf': 2}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 16:21:01,778]\u001b[0m Trial 32 finished with value: 0.6291023128185026 and parameters: {'loss': 'exponential', 'max_depth': 62, 'subsample': 0.32075896171722496, 'n_estimators': 30, 'learning_rate': 0.05549490976336203, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 16:28:14,515]\u001b[0m Trial 33 finished with value: 0.6275813406507252 and parameters: {'loss': 'exponential', 'max_depth': 61, 'subsample': 0.3382906578549879, 'n_estimators': 30, 'learning_rate': 0.05902615527448696, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 16:35:38,121]\u001b[0m Trial 34 finished with value: 0.6281301450411604 and parameters: {'loss': 'exponential', 'max_depth': 63, 'subsample': 0.3368774421216339, 'n_estimators': 31, 'learning_rate': 0.05253744795324095, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 16:46:38,765]\u001b[0m Trial 35 finished with value: 0.6180243041944335 and parameters: {'loss': 'exponential', 'max_depth': 63, 'subsample': 0.4436555687867192, 'n_estimators': 32, 'learning_rate': 0.11773442007845447, 'min_samples_leaf': 6}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 16:53:45,584]\u001b[0m Trial 36 finished with value: 0.6145276362210897 and parameters: {'loss': 'deviance', 'max_depth': 18, 'subsample': 0.4022687317313694, 'n_estimators': 30, 'learning_rate': 0.16454165830233736, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 16:55:17,303]\u001b[0m Trial 37 finished with value: 0.5953508428067424 and parameters: {'loss': 'exponential', 'max_depth': 58, 'subsample': 0.2634052743050265, 'n_estimators': 8, 'learning_rate': 0.3224330090785183, 'min_samples_leaf': 7}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:07:14,748]\u001b[0m Trial 38 finished with value: 0.6034731477851822 and parameters: {'loss': 'exponential', 'max_depth': 62, 'subsample': 0.5348526841758569, 'n_estimators': 27, 'learning_rate': 0.2263816606934624, 'min_samples_leaf': 6}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:11:15,767]\u001b[0m Trial 39 finished with value: 0.6266797334378675 and parameters: {'loss': 'exponential', 'max_depth': 48, 'subsample': 0.37350566808381086, 'n_estimators': 17, 'learning_rate': 0.04713891892149584, 'min_samples_leaf': 8}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:15:09,123]\u001b[0m Trial 40 finished with value: 0.5769345354762838 and parameters: {'loss': 'deviance', 'max_depth': 26, 'subsample': 0.1855605964526241, 'n_estimators': 31, 'learning_rate': 0.2945668454007403, 'min_samples_leaf': 7}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:22:08,767]\u001b[0m Trial 41 finished with value: 0.6298157585260682 and parameters: {'loss': 'exponential', 'max_depth': 60, 'subsample': 0.3308673540869401, 'n_estimators': 30, 'learning_rate': 0.05007781004029695, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:30:04,222]\u001b[0m Trial 42 finished with value: 0.617828302626421 and parameters: {'loss': 'exponential', 'max_depth': 58, 'subsample': 0.3247065661907484, 'n_estimators': 32, 'learning_rate': 0.10762421496513438, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:35:18,802]\u001b[0m Trial 43 finished with value: 0.6308584868678949 and parameters: {'loss': 'exponential', 'max_depth': 52, 'subsample': 0.26452814885866976, 'n_estimators': 30, 'learning_rate': 0.039914203903854986, 'min_samples_leaf': 6}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:39:56,302]\u001b[0m Trial 44 finished with value: 0.6279733437867503 and parameters: {'loss': 'exponential', 'max_depth': 36, 'subsample': 0.2594239631976873, 'n_estimators': 27, 'learning_rate': 0.04734402416427355, 'min_samples_leaf': 6}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:42:26,112]\u001b[0m Trial 45 finished with value: 0.6015288122304978 and parameters: {'loss': 'exponential', 'max_depth': 53, 'subsample': 0.113134608819972, 'n_estimators': 30, 'learning_rate': 0.17140575203818215, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:47:04,633]\u001b[0m Trial 46 finished with value: 0.62421795374363 and parameters: {'loss': 'exponential', 'max_depth': 51, 'subsample': 0.27944072640278145, 'n_estimators': 25, 'learning_rate': 0.07120912744176179, 'min_samples_leaf': 7}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 17:57:17,538]\u001b[0m Trial 47 finished with value: 0.582391219129753 and parameters: {'loss': 'exponential', 'max_depth': 57, 'subsample': 0.3168330896002284, 'n_estimators': 29, 'learning_rate': 0.39111194079477785, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:05:17,888]\u001b[0m Trial 48 finished with value: 0.617985103880831 and parameters: {'loss': 'exponential', 'max_depth': 60, 'subsample': 0.38499780280745965, 'n_estimators': 27, 'learning_rate': 0.12483920986814033, 'min_samples_leaf': 6}. Best is trial 6 with value: 0.6314386515092121.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:08:42,549]\u001b[0m Trial 49 finished with value: 0.6334300274402195 and parameters: {'loss': 'exponential', 'max_depth': 50, 'subsample': 0.1654729951887813, 'n_estimators': 31, 'learning_rate': 0.021201155244168258, 'min_samples_leaf': 6}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:11:36,807]\u001b[0m Trial 50 finished with value: 0.6314778518228146 and parameters: {'loss': 'exponential', 'max_depth': 47, 'subsample': 0.15802015753140622, 'n_estimators': 28, 'learning_rate': 0.010519353142034338, 'min_samples_leaf': 6}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:14:46,002]\u001b[0m Trial 51 finished with value: 0.6323951391611133 and parameters: {'loss': 'exponential', 'max_depth': 42, 'subsample': 0.16280308336513538, 'n_estimators': 29, 'learning_rate': 0.028596553591318043, 'min_samples_leaf': 6}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:17:28,031]\u001b[0m Trial 52 finished with value: 0.6320188161505292 and parameters: {'loss': 'exponential', 'max_depth': 42, 'subsample': 0.14736515685372004, 'n_estimators': 28, 'learning_rate': 0.016641431323441203, 'min_samples_leaf': 6}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:20:03,361]\u001b[0m Trial 53 finished with value: 0.629266954135633 and parameters: {'loss': 'exponential', 'max_depth': 43, 'subsample': 0.15601425634189764, 'n_estimators': 25, 'learning_rate': 0.02340944562385857, 'min_samples_leaf': 6}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:21:17,434]\u001b[0m Trial 54 finished with value: 0.6297765582124657 and parameters: {'loss': 'exponential', 'max_depth': 36, 'subsample': 0.07438771500325936, 'n_estimators': 28, 'learning_rate': 0.010136829107792527, 'min_samples_leaf': 7}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:23:20,802]\u001b[0m Trial 55 finished with value: 0.6184319874558997 and parameters: {'loss': 'exponential', 'max_depth': 39, 'subsample': 0.11347494879616236, 'n_estimators': 27, 'learning_rate': 0.09003986716630792, 'min_samples_leaf': 7}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:26:29,590]\u001b[0m Trial 56 finished with value: 0.6313288906311251 and parameters: {'loss': 'exponential', 'max_depth': 46, 'subsample': 0.16071463152126725, 'n_estimators': 29, 'learning_rate': 0.031091349667572658, 'min_samples_leaf': 6}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:27:16,783]\u001b[0m Trial 57 finished with value: 0.559827518620149 and parameters: {'loss': 'exponential', 'max_depth': 47, 'subsample': 0.03503689937623003, 'n_estimators': 25, 'learning_rate': 0.49428655220336254, 'min_samples_leaf': 7}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:30:13,941]\u001b[0m Trial 58 finished with value: 0.6327636221089769 and parameters: {'loss': 'exponential', 'max_depth': 42, 'subsample': 0.16181694168567, 'n_estimators': 29, 'learning_rate': 0.030016547923153756, 'min_samples_leaf': 8}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:32:15,654]\u001b[0m Trial 59 finished with value: 0.6208545668365347 and parameters: {'loss': 'exponential', 'max_depth': 42, 'subsample': 0.13684678831463543, 'n_estimators': 23, 'learning_rate': 0.07924144571123334, 'min_samples_leaf': 8}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:36:17,709]\u001b[0m Trial 60 finished with value: 0.6115954527636221 and parameters: {'loss': 'exponential', 'max_depth': 39, 'subsample': 0.20160435671581026, 'n_estimators': 28, 'learning_rate': 0.14811803315461564, 'min_samples_leaf': 8}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:39:04,310]\u001b[0m Trial 61 finished with value: 0.6326303410427283 and parameters: {'loss': 'exponential', 'max_depth': 45, 'subsample': 0.1597842435516696, 'n_estimators': 26, 'learning_rate': 0.031211724432891844, 'min_samples_leaf': 6}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:40:56,445]\u001b[0m Trial 62 finished with value: 0.6306938455507644 and parameters: {'loss': 'exponential', 'max_depth': 49, 'subsample': 0.10785475222934351, 'n_estimators': 27, 'learning_rate': 0.02965155218920697, 'min_samples_leaf': 6}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n",
      "\u001b[32m[I 2021-12-14 18:42:14,617]\u001b[0m Trial 63 finished with value: 0.6146452371618973 and parameters: {'loss': 'exponential', 'max_depth': 43, 'subsample': 0.07044256760588798, 'n_estimators': 29, 'learning_rate': 0.09844070708162139, 'min_samples_leaf': 7}. Best is trial 49 with value: 0.6334300274402195.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f6168865be0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_dropado = ds.drop(labels=['IND_BOM_1_2', 'IND_BOM_1_1'], axis=1)\n",
    "df_treino = treino_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(treino_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_validacao = valid_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(valid_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_validacao.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "df_teste = teste_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(teste_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_teste.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "\n",
    "best_gb = []\n",
    "\n",
    "def gb(trial):\n",
    "    loss                = trial.suggest_categorical(\"loss\", [\"deviance\", \"exponential\"])\n",
    "    max_depth           = trial.suggest_int(\"max_depth\", 16, 64)\n",
    "    subsample           = trial.suggest_float(\"subsample\", 0.01, 0.7)\n",
    "    n_estimators        = trial.suggest_int(\"n_estimators\", 8, 32)\n",
    "    learning_rate       = trial.suggest_float(\"learning_rate\", 0.01, 0.7)\n",
    "    min_samples_leaf    = trial.suggest_int(\"min_samples_leaf\", 2, 8)\n",
    "    \n",
    "    # Código opcional para logar execuções\n",
    "    # print('Gradient Boosting com {} estimadores e profundidade máxima {}, critério {}, min amostras de folhas {}, subsample de {} e taxa de aprendizagem {}\\n'.format(\n",
    "    #     n_estimators, max_depth, loss, min_samples_leaf, subsample, learning_rate\n",
    "    # ))\n",
    "\n",
    "\n",
    "    gradient_boost = GradientBoostingClassifier(n_estimators = n_estimators, \n",
    "                                            max_depth = max_depth, \n",
    "                                            min_samples_leaf = min_samples_leaf, \n",
    "                                            learning_rate = learning_rate, \n",
    "                                            loss = loss, \n",
    "                                            subsample = subsample).fit(df_treino, [0]*len(treino_ina)+[1]*len(treino_adi))\n",
    "    best_gb.append(gradient_boost)\n",
    "    \n",
    "    gb_pred_class = gradient_boost.predict(df_validacao)\n",
    "    gb_pred_scores = gradient_boost.predict_proba(df_validacao)\n",
    "    \n",
    "    accuracy, recall, precision, f1, auroc, aupr = compute_performance_metrics_sem_plot2([0]*len(valid_ina)+[1]*len(valid_adi), gb_pred_class, gb_pred_scores, \"GB/GB Trial {}\".format(trial.number))\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "study_2 = optuna.create_study(direction=\"maximize\")\n",
    "study_2.optimize(gb, n_trials=64)\n",
    "\n",
    "compute_performance_metrics([0]*len(teste_ina)+[1]*len(teste_adi), best_gb[study_2.best_trial.number].predict(df_teste), best_gb[study_2.best_trial.number].predict_proba(df_teste))\n",
    "plot_confusion_matrix(best_gb[study_2.best_trial.number], df_teste, [0]*len(teste_ina)+[1]*len(teste_adi), normalize='true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Teste de Kolmogorov-Smirnov (KS) e matriz de confusão da Gradient Boosting\n",
    "Acima, podemos ver os resultados de ambos. O teste de Kolmogorov-Smirnov assemelha-se, assim como a Random Forest, bastante a uma distribuição normal. Mais uma vez (semelhante a Random Forest), a matriz foi bem sucedida para identificar resultados verdadeiros e falsos. Entretanto, o Gradient Boosting peca pela sua lenta execução, a qual apresenta resultados inferiores aos do Random Forest."
   ]
  },
  {
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   "metadata": {},
   "source": [
    "### Resultados do Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   ],
   "source": [
    "optuna.visualization.plot_param_importances(study_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comentários acerca dos resultados do Gredient Boosting\n",
    "\n",
    "O learning rate é certamente o mais importante dos hiperparâmetros, vemos que quanto mais próximo a 0 melhor o seu desempenho. Um número maior de estimadores foi o que obteve melhores resultados. Para subsamples, o intervalo ao redor de 0.2 obteve os melhores resultados. O min samples leaf obteve resultados consistentes no valor de 6. Por fim, a profundidade máxima se saiu bem com valores entre 40 e 50, o que foi uma surpresa, pois intuitivamente, imaginávamos que maiores profundidades iriam obter melhores resultados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regressão Logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parâmetros default\n",
    "# penalty='l2', \n",
    "# *, \n",
    "# dual=False, \n",
    "# tol=1e-4, \n",
    "# C=1.0,\n",
    "# fit_intercept=True, \n",
    "# intercept_scaling=1, \n",
    "# class_weight=None,\n",
    "# random_state=None, \n",
    "# solver='lbfgs', \n",
    "# max_iter=100,\n",
    "# multi_class='auto', \n",
    "# verbose=0, \n",
    "# warm_start=False, \n",
    "# n_jobs=None,\n",
    "# l1_ratio=None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Teste de Kolmogorov-Smirnov (KS) e matriz de confusão da Regressão Logística\n",
    "Acima, podemos ver os resultados de ambos. O teste de Kolmogorov-Smirnov assemelha-se, assim como a Random Forest e Gradient Boosting, bastante a uma distribuição normal. Semelhante aos já supracitados, a matriz foi melhor sucedida para identificar resultados falsos, porém, os resultados caíram na identificação de resultados falsos mas foram os melhores até então para identificar resultados verdadeiros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-12-13 17:28:29,982]\u001b[0m A new study created in memory with name: no-name-ea7c6a75-d17e-45ea-8b7f-6053360cb5ec\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:28:45,474]\u001b[0m Trial 0 finished with value: 0.631344570756566 and parameters: {'solver': 'saga', 'C': 0.5849769857357946}. Best is trial 0 with value: 0.631344570756566.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:29:00,180]\u001b[0m Trial 1 finished with value: 0.631485691885535 and parameters: {'solver': 'saga', 'C': 0.2842694673818212}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:29:24,171]\u001b[0m Trial 2 finished with value: 0.6313524108192865 and parameters: {'solver': 'sag', 'C': 0.6668490171508832}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:29:45,635]\u001b[0m Trial 3 finished with value: 0.6313288906311251 and parameters: {'solver': 'liblinear', 'C': 0.6562292021435742}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:30:02,812]\u001b[0m Trial 4 finished with value: 0.631313210505684 and parameters: {'solver': 'saga', 'C': 0.7034896202550367}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:30:19,197]\u001b[0m Trial 5 finished with value: 0.6313524108192865 and parameters: {'solver': 'lbfgs', 'C': 0.7197685296802497}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:31:29,515]\u001b[0m Trial 6 finished with value: 0.6314072912583301 and parameters: {'solver': 'newton-cg', 'C': 0.4162968317742919}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:31:46,629]\u001b[0m Trial 7 finished with value: 0.6313837710701685 and parameters: {'solver': 'saga', 'C': 0.45572740122819116}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:32:04,158]\u001b[0m Trial 8 finished with value: 0.6314151313210505 and parameters: {'solver': 'saga', 'C': 0.4131170914789713}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:32:26,437]\u001b[0m Trial 9 finished with value: 0.6313210505684046 and parameters: {'solver': 'liblinear', 'C': 0.789271822152566}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:33:26,179]\u001b[0m Trial 10 finished with value: 0.6313210505684046 and parameters: {'solver': 'newton-cg', 'C': 0.038839105626143844}. Best is trial 1 with value: 0.631485691885535.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:33:45,271]\u001b[0m Trial 11 finished with value: 0.6315013720109761 and parameters: {'solver': 'saga', 'C': 0.20663194752199038}. Best is trial 11 with value: 0.6315013720109761.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:34:03,176]\u001b[0m Trial 12 finished with value: 0.631485691885535 and parameters: {'solver': 'saga', 'C': 0.168295196141211}. Best is trial 11 with value: 0.6315013720109761.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:34:21,116]\u001b[0m Trial 13 finished with value: 0.6315170521364171 and parameters: {'solver': 'saga', 'C': 0.21342254963865626}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:34:40,620]\u001b[0m Trial 14 finished with value: 0.631172089376715 and parameters: {'solver': 'sag', 'C': 0.016941920740670358}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:34:58,054]\u001b[0m Trial 15 finished with value: 0.6314308114464916 and parameters: {'solver': 'lbfgs', 'C': 0.23564256637746384}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:35:17,987]\u001b[0m Trial 16 finished with value: 0.6313053704429635 and parameters: {'solver': 'saga', 'C': 0.9932414743438174}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:35:38,429]\u001b[0m Trial 17 finished with value: 0.6314229713837711 and parameters: {'solver': 'saga', 'C': 0.1380865293926395}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:35:56,890]\u001b[0m Trial 18 finished with value: 0.6314308114464916 and parameters: {'solver': 'saga', 'C': 0.30825183139007606}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:36:35,659]\u001b[0m Trial 19 finished with value: 0.6314621716973736 and parameters: {'solver': 'sag', 'C': 0.0859798302036967}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:36:57,934]\u001b[0m Trial 20 finished with value: 0.6314151313210505 and parameters: {'solver': 'liblinear', 'C': 0.357885375301218}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:37:16,157]\u001b[0m Trial 21 finished with value: 0.6315092120736966 and parameters: {'solver': 'saga', 'C': 0.2567440157756343}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:37:36,533]\u001b[0m Trial 22 finished with value: 0.6315013720109761 and parameters: {'solver': 'saga', 'C': 0.19101557903025057}. Best is trial 13 with value: 0.6315170521364171.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:37:53,870]\u001b[0m Trial 23 finished with value: 0.6315248921991375 and parameters: {'solver': 'saga', 'C': 0.2530567675509275}. Best is trial 23 with value: 0.6315248921991375.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:38:11,501]\u001b[0m Trial 24 finished with value: 0.6314229713837711 and parameters: {'solver': 'saga', 'C': 0.33459591611069894}. Best is trial 23 with value: 0.6315248921991375.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:38:30,957]\u001b[0m Trial 25 finished with value: 0.631297530380243 and parameters: {'solver': 'lbfgs', 'C': 0.5311214530379987}. Best is trial 23 with value: 0.6315248921991375.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:39:35,089]\u001b[0m Trial 26 finished with value: 0.6314072912583301 and parameters: {'solver': 'newton-cg', 'C': 0.10494444636335767}. Best is trial 23 with value: 0.6315248921991375.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:39:53,818]\u001b[0m Trial 27 finished with value: 0.631485691885535 and parameters: {'solver': 'saga', 'C': 0.2869459413780808}. Best is trial 23 with value: 0.6315248921991375.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:40:11,577]\u001b[0m Trial 28 finished with value: 0.6315640925127401 and parameters: {'solver': 'saga', 'C': 0.23216200775438173}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:40:29,994]\u001b[0m Trial 29 finished with value: 0.631375931007448 and parameters: {'solver': 'saga', 'C': 0.569675094227418}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:41:07,216]\u001b[0m Trial 30 finished with value: 0.6315327322618581 and parameters: {'solver': 'saga', 'C': 0.06388184901496352}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:41:45,511]\u001b[0m Trial 31 finished with value: 0.6314778518228146 and parameters: {'solver': 'saga', 'C': 0.0527578981823673}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:42:00,570]\u001b[0m Trial 32 finished with value: 0.6314229713837711 and parameters: {'solver': 'saga', 'C': 0.13968279418122004}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:42:11,718]\u001b[0m Trial 33 finished with value: 0.6315484123872991 and parameters: {'solver': 'saga', 'C': 0.229259702423482}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:42:22,377]\u001b[0m Trial 34 finished with value: 0.6314151313210505 and parameters: {'solver': 'saga', 'C': 0.3855739753054221}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:42:48,895]\u001b[0m Trial 35 finished with value: 0.6314229713837711 and parameters: {'solver': 'sag', 'C': 0.09985295367135301}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:43:01,973]\u001b[0m Trial 36 finished with value: 0.631109368874951 and parameters: {'solver': 'liblinear', 'C': 0.016512103852271226}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:43:13,006]\u001b[0m Trial 37 finished with value: 0.6315013720109761 and parameters: {'solver': 'saga', 'C': 0.27216893174449597}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:43:24,899]\u001b[0m Trial 38 finished with value: 0.631313210505684 and parameters: {'solver': 'lbfgs', 'C': 0.46842789059036777}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:43:36,948]\u001b[0m Trial 39 finished with value: 0.6314151313210505 and parameters: {'solver': 'saga', 'C': 0.14468623959222282}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:44:12,358]\u001b[0m Trial 40 finished with value: 0.6314308114464916 and parameters: {'solver': 'newton-cg', 'C': 0.3444907385797983}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:44:23,687]\u001b[0m Trial 41 finished with value: 0.6315484123872991 and parameters: {'solver': 'saga', 'C': 0.22114137732095895}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:44:34,980]\u001b[0m Trial 42 finished with value: 0.6315640925127401 and parameters: {'solver': 'saga', 'C': 0.23180877186234763}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:44:46,306]\u001b[0m Trial 43 finished with value: 0.6315013720109761 and parameters: {'solver': 'saga', 'C': 0.1789596585249374}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:45:14,903]\u001b[0m Trial 44 finished with value: 0.6314700117600941 and parameters: {'solver': 'saga', 'C': 0.06970439647332966}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:45:25,814]\u001b[0m Trial 45 finished with value: 0.6314151313210505 and parameters: {'solver': 'saga', 'C': 0.31241240553059696}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:45:37,859]\u001b[0m Trial 46 finished with value: 0.6314151313210505 and parameters: {'solver': 'saga', 'C': 0.41471476891886294}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:45:49,167]\u001b[0m Trial 47 finished with value: 0.6315170521364171 and parameters: {'solver': 'saga', 'C': 0.21215788966190618}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:46:05,066]\u001b[0m Trial 48 finished with value: 0.6314464915719326 and parameters: {'solver': 'liblinear', 'C': 0.12443291331195848}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:46:15,986]\u001b[0m Trial 49 finished with value: 0.6313994511956096 and parameters: {'solver': 'saga', 'C': 0.4623321889705078}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:46:35,975]\u001b[0m Trial 50 finished with value: 0.6313053704429635 and parameters: {'solver': 'sag', 'C': 0.8537853763331291}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:46:48,625]\u001b[0m Trial 51 finished with value: 0.6314935319482556 and parameters: {'solver': 'saga', 'C': 0.2618101050476938}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:46:59,931]\u001b[0m Trial 52 finished with value: 0.6315640925127401 and parameters: {'solver': 'saga', 'C': 0.23150423611955934}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:47:11,233]\u001b[0m Trial 53 finished with value: 0.631485691885535 and parameters: {'solver': 'saga', 'C': 0.17387524321300313}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:47:22,525]\u001b[0m Trial 54 finished with value: 0.6315092120736966 and parameters: {'solver': 'saga', 'C': 0.21581937086086203}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:47:33,328]\u001b[0m Trial 55 finished with value: 0.6314151313210505 and parameters: {'solver': 'saga', 'C': 0.3860063462522674}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:48:21,245]\u001b[0m Trial 56 finished with value: 0.631454331634653 and parameters: {'solver': 'newton-cg', 'C': 0.2972336940778451}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:48:32,530]\u001b[0m Trial 57 finished with value: 0.6315248921991375 and parameters: {'solver': 'saga', 'C': 0.2187454883791752}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:48:44,854]\u001b[0m Trial 58 finished with value: 0.6313916111328891 and parameters: {'solver': 'lbfgs', 'C': 0.17720251004516163}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:49:17,619]\u001b[0m Trial 59 finished with value: 0.6313210505684046 and parameters: {'solver': 'saga', 'C': 0.040364038465462396}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:49:28,954]\u001b[0m Trial 60 finished with value: 0.6315327322618581 and parameters: {'solver': 'saga', 'C': 0.2503863913752739}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:49:39,781]\u001b[0m Trial 61 finished with value: 0.6314151313210505 and parameters: {'solver': 'saga', 'C': 0.3223366806518014}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:49:51,095]\u001b[0m Trial 62 finished with value: 0.6315170521364171 and parameters: {'solver': 'saga', 'C': 0.2419893551273464}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:50:02,026]\u001b[0m Trial 63 finished with value: 0.6313994511956096 and parameters: {'solver': 'saga', 'C': 0.36343712697564384}. Best is trial 28 with value: 0.6315640925127401.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f4a1d2ab160>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_dropado = ds.drop(labels=['IND_BOM_1_2', 'IND_BOM_1_1'], axis=1)\n",
    "df_treino = treino_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(treino_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_validacao = valid_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(valid_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_validacao.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "df_teste = teste_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(teste_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_teste.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "\n",
    "best_rl = []\n",
    "\n",
    "def rl(trial):\n",
    "    # penalty = trial.suggest_categorical(\"penalty\", [\"none\", \"l2\", \"l1\", \"elasticnet\"])\n",
    "    solver = trial.suggest_categorical(\"solver\", [\"newton-cg\", \"lbfgs\", \"liblinear\", \"sag\", \"saga\"])\n",
    "    C = trial.suggest_float(\"C\", 0.01, 1.0)\n",
    "    multi_class = \"ovr\"\n",
    "    \n",
    "    # Código opcional para log\n",
    "    # print('Regressão Logística com solver {} e C {}\\n'.format(\n",
    "    #     solver, C\n",
    "    # ))\n",
    "\n",
    "    regressao_log = LogisticRegression(max_iter=1000,\n",
    "                                    random_state=1, \n",
    "                                    solver=solver, \n",
    "                                    C=C, \n",
    "                                    multi_class=multi_class).fit(df_treino, [0]*len(treino_ina)+[1]*len(treino_adi))\n",
    "\n",
    "    best_rl.append(regressao_log)\n",
    "    \n",
    "    rl_pred_class = regressao_log.predict(df_validacao)\n",
    "    rl_pred_scores = regressao_log.predict_proba(df_validacao)\n",
    "    \n",
    "    accuracy, recall, precision, f1, auroc, aupr = compute_performance_metrics_sem_plot2([0]*len(valid_ina)+[1]*len(valid_adi), rl_pred_class, rl_pred_scores,  \"LR/LR Trial {}\".format(trial.number))\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "study_3 = optuna.create_study(direction=\"maximize\")\n",
    "study_3.optimize(rl, n_trials=64)\n",
    "\n",
    "compute_performance_metrics([0]*len(teste_ina)+[1]*len(teste_adi), best_rl[study_3.best_trial.number].predict(df_teste), best_rl[study_3.best_trial.number].predict_proba(df_teste))\n",
    "plot_confusion_matrix(best_rl[study_3.best_trial.number], df_teste, [0]*len(teste_ina)+[1]*len(teste_adi), normalize='true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resultados da Regressão Logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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         "text": "Hyperparameter Importances"
        },
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   "source": [
    "optuna.visualization.plot_param_importances(study_3)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comentários acerca dos resultados da Regressão Logística\n",
    "Vimos que há uma tendência de resultados melhores na região de 0.2 até 0.26 para a variável C. Vamos afunilar e gerar mais testes nesse intervalo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-12-13 17:53:19,003]\u001b[0m A new study created in memory with name: no-name-ce42977e-cad4-43c8-a88c-c1c38867c807\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:53:30,829]\u001b[0m Trial 0 finished with value: 0.6315327322618581 and parameters: {'solver': 'saga', 'C': 0.24683243924552206}. Best is trial 0 with value: 0.6315327322618581.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:53:49,486]\u001b[0m Trial 1 finished with value: 0.6315327322618581 and parameters: {'solver': 'liblinear', 'C': 0.24644369495349933}. Best is trial 0 with value: 0.6315327322618581.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:54:47,903]\u001b[0m Trial 2 finished with value: 0.6315405723245786 and parameters: {'solver': 'newton-cg', 'C': 0.23096729387561515}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:55:00,556]\u001b[0m Trial 3 finished with value: 0.6315327322618581 and parameters: {'solver': 'saga', 'C': 0.2502997486409192}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:55:48,734]\u001b[0m Trial 4 finished with value: 0.6315092120736966 and parameters: {'solver': 'newton-cg', 'C': 0.20775547948277212}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:56:00,787]\u001b[0m Trial 5 finished with value: 0.6315170521364171 and parameters: {'solver': 'saga', 'C': 0.2124200236063185}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:56:13,063]\u001b[0m Trial 6 finished with value: 0.6315092120736966 and parameters: {'solver': 'saga', 'C': 0.1963930960061364}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:56:28,126]\u001b[0m Trial 7 finished with value: 0.6313367306938455 and parameters: {'solver': 'lbfgs', 'C': 0.21235618670109935}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:56:50,338]\u001b[0m Trial 8 finished with value: 0.6315092120736966 and parameters: {'solver': 'sag', 'C': 0.25813895016555877}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:58:04,165]\u001b[0m Trial 9 finished with value: 0.6315092120736966 and parameters: {'solver': 'newton-cg', 'C': 0.20316898685680146}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:58:54,780]\u001b[0m Trial 10 finished with value: 0.6315405723245786 and parameters: {'solver': 'newton-cg', 'C': 0.22778793145687184}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 17:59:42,598]\u001b[0m Trial 11 finished with value: 0.6315405723245786 and parameters: {'solver': 'newton-cg', 'C': 0.2336088773707623}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:00:40,429]\u001b[0m Trial 12 finished with value: 0.6315405723245786 and parameters: {'solver': 'newton-cg', 'C': 0.2279261916805474}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:01:22,170]\u001b[0m Trial 13 finished with value: 0.6315405723245786 and parameters: {'solver': 'newton-cg', 'C': 0.23512507970331903}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:02:11,794]\u001b[0m Trial 14 finished with value: 0.6315248921991375 and parameters: {'solver': 'newton-cg', 'C': 0.2212772126531317}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:02:29,754]\u001b[0m Trial 15 finished with value: 0.6315327322618581 and parameters: {'solver': 'liblinear', 'C': 0.22220047554576147}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:02:41,781]\u001b[0m Trial 16 finished with value: 0.6313680909447276 and parameters: {'solver': 'lbfgs', 'C': 0.23870672131520754}. Best is trial 2 with value: 0.6315405723245786.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:03:07,009]\u001b[0m Trial 17 finished with value: 0.6315640925127401 and parameters: {'solver': 'sag', 'C': 0.23832960730630523}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:03:28,454]\u001b[0m Trial 18 finished with value: 0.6315092120736966 and parameters: {'solver': 'sag', 'C': 0.25890860331921356}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:03:51,667]\u001b[0m Trial 19 finished with value: 0.6315484123872991 and parameters: {'solver': 'sag', 'C': 0.2401800322848446}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:04:15,591]\u001b[0m Trial 20 finished with value: 0.6315405723245786 and parameters: {'solver': 'sag', 'C': 0.24104428209663428}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:04:37,834]\u001b[0m Trial 21 finished with value: 0.6315405723245786 and parameters: {'solver': 'sag', 'C': 0.2417431880495569}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:05:01,687]\u001b[0m Trial 22 finished with value: 0.6315484123872991 and parameters: {'solver': 'sag', 'C': 0.22379859313737488}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:05:25,730]\u001b[0m Trial 23 finished with value: 0.6315248921991375 and parameters: {'solver': 'sag', 'C': 0.22026105693489417}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:05:47,359]\u001b[0m Trial 24 finished with value: 0.6315092120736966 and parameters: {'solver': 'sag', 'C': 0.2536171285242527}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:06:10,405]\u001b[0m Trial 25 finished with value: 0.6315484123872991 and parameters: {'solver': 'sag', 'C': 0.23775937954220988}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:06:35,660]\u001b[0m Trial 26 finished with value: 0.6315248921991375 and parameters: {'solver': 'sag', 'C': 0.2175890470774522}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:06:59,570]\u001b[0m Trial 27 finished with value: 0.6315092120736966 and parameters: {'solver': 'sag', 'C': 0.24573443801061115}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:07:23,476]\u001b[0m Trial 28 finished with value: 0.6315484123872991 and parameters: {'solver': 'sag', 'C': 0.2340819591755565}. Best is trial 17 with value: 0.6315640925127401.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:07:47,802]\u001b[0m Trial 29 finished with value: 0.6315719325754606 and parameters: {'solver': 'sag', 'C': 0.2360967624268411}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:08:10,189]\u001b[0m Trial 30 finished with value: 0.6315092120736966 and parameters: {'solver': 'sag', 'C': 0.25197453496046585}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:08:35,262]\u001b[0m Trial 31 finished with value: 0.6315484123872991 and parameters: {'solver': 'sag', 'C': 0.23282664125706914}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:08:52,585]\u001b[0m Trial 32 finished with value: 0.6315484123872991 and parameters: {'solver': 'liblinear', 'C': 0.2312379023723466}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:09:09,639]\u001b[0m Trial 33 finished with value: 0.6315327322618581 and parameters: {'solver': 'liblinear', 'C': 0.22844020304631202}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:09:32,935]\u001b[0m Trial 34 finished with value: 0.6315092120736966 and parameters: {'solver': 'sag', 'C': 0.24489411551753681}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:09:49,699]\u001b[0m Trial 35 finished with value: 0.6315484123872991 and parameters: {'solver': 'liblinear', 'C': 0.23157341024317407}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:10:06,543]\u001b[0m Trial 36 finished with value: 0.6315327322618581 and parameters: {'solver': 'liblinear', 'C': 0.23601376815604988}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:10:22,526]\u001b[0m Trial 37 finished with value: 0.6315248921991375 and parameters: {'solver': 'liblinear', 'C': 0.24723329622367238}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:10:47,445]\u001b[0m Trial 38 finished with value: 0.6315248921991375 and parameters: {'solver': 'sag', 'C': 0.24206920159005874}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:10:58,955]\u001b[0m Trial 39 finished with value: 0.6315092120736966 and parameters: {'solver': 'saga', 'C': 0.21477802999147932}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:11:11,393]\u001b[0m Trial 40 finished with value: 0.6315013720109761 and parameters: {'solver': 'lbfgs', 'C': 0.2233535218428334}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:11:36,181]\u001b[0m Trial 41 finished with value: 0.6315719325754606 and parameters: {'solver': 'sag', 'C': 0.23695225760055608}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:11:52,887]\u001b[0m Trial 42 finished with value: 0.6315484123872991 and parameters: {'solver': 'liblinear', 'C': 0.23016013645390984}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:12:09,310]\u001b[0m Trial 43 finished with value: 0.6315327322618581 and parameters: {'solver': 'liblinear', 'C': 0.22985042634483688}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:12:32,569]\u001b[0m Trial 44 finished with value: 0.6315719325754606 and parameters: {'solver': 'sag', 'C': 0.23656835024485606}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:12:44,052]\u001b[0m Trial 45 finished with value: 0.6315327322618581 and parameters: {'solver': 'saga', 'C': 0.2489927140039956}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:13:08,158]\u001b[0m Trial 46 finished with value: 0.6315484123872991 and parameters: {'solver': 'sag', 'C': 0.22624251586718486}. Best is trial 29 with value: 0.6315719325754606.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:13:32,783]\u001b[0m Trial 47 finished with value: 0.6315797726381811 and parameters: {'solver': 'sag', 'C': 0.23708588227231753}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:13:55,442]\u001b[0m Trial 48 finished with value: 0.6315248921991375 and parameters: {'solver': 'sag', 'C': 0.24322517198264107}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:14:18,722]\u001b[0m Trial 49 finished with value: 0.6315719325754606 and parameters: {'solver': 'sag', 'C': 0.2372060807103527}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:14:42,986]\u001b[0m Trial 50 finished with value: 0.6315797726381811 and parameters: {'solver': 'sag', 'C': 0.23664838991030696}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:15:07,265]\u001b[0m Trial 51 finished with value: 0.6315640925127401 and parameters: {'solver': 'sag', 'C': 0.2354383883269074}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:15:30,491]\u001b[0m Trial 52 finished with value: 0.6315719325754606 and parameters: {'solver': 'sag', 'C': 0.23610408821306403}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:15:55,283]\u001b[0m Trial 53 finished with value: 0.6315640925127401 and parameters: {'solver': 'sag', 'C': 0.2389454478740418}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:16:09,191]\u001b[0m Trial 54 finished with value: 0.6313367306938455 and parameters: {'solver': 'lbfgs', 'C': 0.25576384551400083}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:16:33,287]\u001b[0m Trial 55 finished with value: 0.6315484123872991 and parameters: {'solver': 'sag', 'C': 0.22594710511617722}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:16:55,513]\u001b[0m Trial 56 finished with value: 0.6315092120736966 and parameters: {'solver': 'sag', 'C': 0.24407676624418756}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:17:16,827]\u001b[0m Trial 57 finished with value: 0.6315248921991375 and parameters: {'solver': 'sag', 'C': 0.24859897970991518}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:17:41,445]\u001b[0m Trial 58 finished with value: 0.6315797726381811 and parameters: {'solver': 'sag', 'C': 0.23733509116013135}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:18:05,986]\u001b[0m Trial 59 finished with value: 0.6315562524500196 and parameters: {'solver': 'sag', 'C': 0.24027629709865264}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:18:17,875]\u001b[0m Trial 60 finished with value: 0.6315640925127401 and parameters: {'solver': 'saga', 'C': 0.23347016311973762}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:18:45,128]\u001b[0m Trial 61 finished with value: 0.6315405723245786 and parameters: {'solver': 'sag', 'C': 0.23674017787132007}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:19:12,200]\u001b[0m Trial 62 finished with value: 0.6315640925127401 and parameters: {'solver': 'sag', 'C': 0.23920829130306664}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n",
      "\u001b[32m[I 2021-12-13 18:19:38,259]\u001b[0m Trial 63 finished with value: 0.6315640925127401 and parameters: {'solver': 'sag', 'C': 0.23483146826742954}. Best is trial 47 with value: 0.6315797726381811.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "def rl(trial):\n",
    "    # penalty = trial.suggest_categorical(\"penalty\", [\"none\", \"l2\", \"l1\", \"elasticnet\"])\n",
    "    solver = trial.suggest_categorical(\"solver\", [\"newton-cg\", \"lbfgs\", \"liblinear\", \"sag\", \"saga\"])\n",
    "    C = trial.suggest_float(\"C\", 0.19, 0.26)\n",
    "    multi_class = \"ovr\"\n",
    "\n",
    "    regressao_log = LogisticRegression(max_iter=1000,\n",
    "                                    random_state=1, \n",
    "                                    solver=solver, \n",
    "                                    C=C, \n",
    "                                    multi_class=multi_class).fit(df_treino, [0]*len(treino_ina)+[1]*len(treino_adi))\n",
    "\n",
    "    best_rl.append(regressao_log)\n",
    "    \n",
    "    rl_pred_class = regressao_log.predict(df_validacao)\n",
    "    rl_pred_scores = regressao_log.predict_proba(df_validacao)\n",
    "    \n",
    "    accuracy, recall, precision, f1, auroc, aupr = compute_performance_metrics_sem_plot([0]*len(valid_ina)+[1]*len(valid_adi), rl_pred_class, rl_pred_scores)\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "study_3 = optuna.create_study(direction=\"maximize\")\n",
    "study_3.optimize(rl, n_trials=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comentários acerca dos resultados da Regressão Logística 2\n",
    "Os resultados foram pouco distintos dos anteriores, dado que o algoritmo melhorou apenas 0.001%, ou seja, o teste mostrou que atingimos o seu melhor com os parâmetros anteriores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble de MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Voting] ..................... (1 of 3) Processing mlp0, total= 5.5min\n",
      "Iteration 1, loss = 0.65859254\n",
      "Validation score: 0.627871\n",
      "Iteration 2, loss = 0.64850178\n",
      "Validation score: 0.610937\n",
      "Iteration 3, loss = 0.64736500\n",
      "Validation score: 0.630184\n",
      "Iteration 4, loss = 0.64552217\n",
      "Validation score: 0.612544\n",
      "Iteration 5, loss = 0.64443723\n",
      "Validation score: 0.630341\n",
      "Iteration 6, loss = 0.64354886\n",
      "Validation score: 0.615876\n",
      "Iteration 7, loss = 0.64290993\n",
      "Validation score: 0.623167\n",
      "Iteration 8, loss = 0.64191137\n",
      "Validation score: 0.620306\n",
      "Iteration 9, loss = 0.64178602\n",
      "Validation score: 0.630459\n",
      "Iteration 10, loss = 0.64099773\n",
      "Validation score: 0.630694\n",
      "Iteration 11, loss = 0.64020231\n",
      "Validation score: 0.633007\n",
      "Iteration 12, loss = 0.63987681\n",
      "Validation score: 0.632654\n",
      "Iteration 13, loss = 0.63906045\n",
      "Validation score: 0.632419\n",
      "Iteration 14, loss = 0.63838819\n",
      "Validation score: 0.633987\n",
      "Iteration 15, loss = 0.63788467\n",
      "Validation score: 0.635947\n",
      "Iteration 16, loss = 0.63764156\n",
      "Validation score: 0.635751\n",
      "Iteration 17, loss = 0.63718784\n",
      "Validation score: 0.635163\n",
      "Iteration 18, loss = 0.63702467\n",
      "Validation score: 0.636652\n",
      "Iteration 19, loss = 0.63649882\n",
      "Validation score: 0.637162\n",
      "Iteration 20, loss = 0.63606402\n",
      "Validation score: 0.636260\n",
      "Iteration 21, loss = 0.63606826\n",
      "Validation score: 0.638299\n",
      "Iteration 22, loss = 0.63557745\n",
      "Validation score: 0.637436\n",
      "Iteration 23, loss = 0.63544389\n",
      "Validation score: 0.637593\n",
      "Iteration 24, loss = 0.63519591\n",
      "Validation score: 0.637907\n",
      "Iteration 25, loss = 0.63495646\n",
      "Validation score: 0.639945\n",
      "Iteration 26, loss = 0.63475987\n",
      "Validation score: 0.634614\n",
      "Iteration 27, loss = 0.63441440\n",
      "Validation score: 0.637358\n",
      "Iteration 28, loss = 0.63430366\n",
      "Validation score: 0.638965\n",
      "Iteration 29, loss = 0.63432867\n",
      "Validation score: 0.638338\n",
      "Iteration 30, loss = 0.63407835\n",
      "Validation score: 0.640416\n",
      "Iteration 31, loss = 0.63383961\n",
      "Validation score: 0.639396\n",
      "Iteration 32, loss = 0.63387912\n",
      "Validation score: 0.638377\n",
      "Iteration 33, loss = 0.63358630\n",
      "Validation score: 0.636574\n",
      "Iteration 34, loss = 0.63363580\n",
      "Validation score: 0.639044\n",
      "Iteration 35, loss = 0.63374128\n",
      "Validation score: 0.639240\n",
      "Iteration 36, loss = 0.63368041\n",
      "Validation score: 0.639828\n",
      "Iteration 37, loss = 0.63339755\n",
      "Validation score: 0.638495\n",
      "Iteration 38, loss = 0.63364066\n",
      "Validation score: 0.638573\n",
      "Iteration 39, loss = 0.63335142\n",
      "Validation score: 0.639436\n",
      "Iteration 40, loss = 0.63327994\n",
      "Validation score: 0.638965\n",
      "Iteration 41, loss = 0.63319188\n",
      "Validation score: 0.640416\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.011737\n",
      "Iteration 42, loss = 0.63088604\n",
      "Validation score: 0.641004\n",
      "Iteration 43, loss = 0.63070783\n",
      "Validation score: 0.638887\n",
      "Iteration 44, loss = 0.63058488\n",
      "Validation score: 0.637436\n",
      "Iteration 45, loss = 0.63056632\n",
      "Validation score: 0.640886\n",
      "Iteration 46, loss = 0.63058969\n",
      "Validation score: 0.637946\n",
      "Iteration 47, loss = 0.63058279\n",
      "Validation score: 0.638338\n",
      "Iteration 48, loss = 0.63048137\n",
      "Validation score: 0.639867\n",
      "Iteration 49, loss = 0.63048117\n",
      "Validation score: 0.641278\n",
      "Iteration 50, loss = 0.63053991\n",
      "Validation score: 0.638573\n",
      "Iteration 51, loss = 0.63051911\n",
      "Validation score: 0.639828\n",
      "Iteration 52, loss = 0.63044897\n",
      "Validation score: 0.638299\n",
      "Iteration 53, loss = 0.63038055\n",
      "Validation score: 0.640376\n",
      "Iteration 54, loss = 0.63044285\n",
      "Validation score: 0.638887\n",
      "Iteration 55, loss = 0.63032460\n",
      "Validation score: 0.639710\n",
      "Iteration 56, loss = 0.63033801\n",
      "Validation score: 0.637593\n",
      "Iteration 57, loss = 0.63033046\n",
      "Validation score: 0.640024\n",
      "Iteration 58, loss = 0.63037905\n",
      "Validation score: 0.638887\n",
      "Iteration 59, loss = 0.63023855\n",
      "Validation score: 0.639828\n",
      "Iteration 60, loss = 0.63031291\n",
      "Validation score: 0.640494\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.002347\n",
      "Iteration 61, loss = 0.62950877\n",
      "Validation score: 0.639396\n",
      "Iteration 62, loss = 0.62949857\n",
      "Validation score: 0.639161\n",
      "Iteration 63, loss = 0.62950009\n",
      "Validation score: 0.639200\n",
      "Iteration 64, loss = 0.62950112\n",
      "Validation score: 0.639867\n",
      "Iteration 65, loss = 0.62946247\n",
      "Validation score: 0.640102\n",
      "Iteration 66, loss = 0.62944914\n",
      "Validation score: 0.639122\n",
      "Iteration 67, loss = 0.62946002\n",
      "Validation score: 0.639475\n",
      "Iteration 68, loss = 0.62943881\n",
      "Validation score: 0.640612\n",
      "Iteration 69, loss = 0.62945152\n",
      "Validation score: 0.639632\n",
      "Iteration 70, loss = 0.62946220\n",
      "Validation score: 0.640220\n",
      "Iteration 71, loss = 0.62946774\n",
      "Validation score: 0.639200\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000469\n",
      "Iteration 72, loss = 0.62926121\n",
      "Validation score: 0.640141\n",
      "Iteration 73, loss = 0.62923734\n",
      "Validation score: 0.640651\n",
      "Iteration 74, loss = 0.62925218\n",
      "Validation score: 0.639318\n",
      "Iteration 75, loss = 0.62925183\n",
      "Validation score: 0.639632\n",
      "Iteration 76, loss = 0.62924657\n",
      "Validation score: 0.639475\n",
      "Iteration 77, loss = 0.62923922\n",
      "Validation score: 0.639004\n",
      "Iteration 78, loss = 0.62923556\n",
      "Validation score: 0.639357\n",
      "Iteration 79, loss = 0.62924380\n",
      "Validation score: 0.639200\n",
      "Iteration 80, loss = 0.62922735\n",
      "Validation score: 0.639632\n",
      "Iteration 81, loss = 0.62923839\n",
      "Validation score: 0.639553\n",
      "Iteration 82, loss = 0.62924091\n",
      "Validation score: 0.639436\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000094\n",
      "Iteration 83, loss = 0.62919729\n",
      "Validation score: 0.639436\n",
      "Iteration 84, loss = 0.62919330\n",
      "Validation score: 0.639279\n",
      "Iteration 85, loss = 0.62919404\n",
      "Validation score: 0.639318\n",
      "Iteration 86, loss = 0.62919359\n",
      "Validation score: 0.639632\n",
      "Iteration 87, loss = 0.62919304\n",
      "Validation score: 0.639632\n",
      "Iteration 88, loss = 0.62919404\n",
      "Validation score: 0.639592\n",
      "Iteration 89, loss = 0.62919433\n",
      "Validation score: 0.639671\n",
      "Iteration 90, loss = 0.62919442\n",
      "Validation score: 0.639396\n",
      "Iteration 91, loss = 0.62919088\n",
      "Validation score: 0.639592\n",
      "Iteration 92, loss = 0.62919317\n",
      "Validation score: 0.639475\n",
      "Iteration 93, loss = 0.62919261\n",
      "Validation score: 0.639592\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000019\n",
      "Iteration 94, loss = 0.62918210\n",
      "Validation score: 0.639514\n",
      "Iteration 95, loss = 0.62918251\n",
      "Validation score: 0.639514\n",
      "Iteration 96, loss = 0.62918197\n",
      "Validation score: 0.639514\n",
      "Iteration 97, loss = 0.62918214\n",
      "Validation score: 0.639436\n",
      "Iteration 98, loss = 0.62918225\n",
      "Validation score: 0.639357\n",
      "Iteration 99, loss = 0.62918163\n",
      "Validation score: 0.639514\n",
      "Iteration 100, loss = 0.62918187\n",
      "Validation score: 0.639436\n",
      "Iteration 101, loss = 0.62918215\n",
      "Validation score: 0.639514\n",
      "Iteration 102, loss = 0.62918184\n",
      "Validation score: 0.639514\n",
      "Iteration 103, loss = 0.62918171\n",
      "Validation score: 0.639436\n",
      "Iteration 104, loss = 0.62918158\n",
      "Validation score: 0.639357\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000004\n",
      "Iteration 105, loss = 0.62917949\n",
      "Validation score: 0.639436\n",
      "Iteration 106, loss = 0.62917945\n",
      "Validation score: 0.639396\n",
      "Iteration 107, loss = 0.62917942\n",
      "Validation score: 0.639357\n",
      "Iteration 108, loss = 0.62917941\n",
      "Validation score: 0.639357\n",
      "Iteration 109, loss = 0.62917946\n",
      "Validation score: 0.639357\n",
      "Iteration 110, loss = 0.62917939\n",
      "Validation score: 0.639357\n",
      "Iteration 111, loss = 0.62917939\n",
      "Validation score: 0.639279\n",
      "Iteration 112, loss = 0.62917937\n",
      "Validation score: 0.639318\n",
      "Iteration 113, loss = 0.62917940\n",
      "Validation score: 0.639318\n",
      "Iteration 114, loss = 0.62917939\n",
      "Validation score: 0.639318\n",
      "Iteration 115, loss = 0.62917945\n",
      "Validation score: 0.639318\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000001\n",
      "Iteration 116, loss = 0.62917890\n",
      "Validation score: 0.639279\n",
      "Iteration 117, loss = 0.62917890\n",
      "Validation score: 0.639279\n",
      "Iteration 118, loss = 0.62917889\n",
      "Validation score: 0.639279\n",
      "Iteration 119, loss = 0.62917889\n",
      "Validation score: 0.639279\n",
      "Iteration 120, loss = 0.62917889\n",
      "Validation score: 0.639279\n",
      "Iteration 121, loss = 0.62917888\n",
      "Validation score: 0.639279\n",
      "Iteration 122, loss = 0.62917888\n",
      "Validation score: 0.639279\n",
      "Iteration 123, loss = 0.62917888\n",
      "Validation score: 0.639279\n",
      "Iteration 124, loss = 0.62917887\n",
      "Validation score: 0.639279\n",
      "Iteration 125, loss = 0.62917888\n",
      "Validation score: 0.639279\n",
      "Iteration 126, loss = 0.62917887\n",
      "Validation score: 0.639279\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Learning rate too small. Stopping.\n",
      "[Voting] ..................... (2 of 3) Processing mlp1, total= 1.0min\n",
      "Iteration 1, loss = 0.65874036\n",
      "Validation score: 0.628185\n",
      "Iteration 2, loss = 0.64593450\n",
      "Validation score: 0.630419\n",
      "Iteration 3, loss = 0.64272669\n",
      "Validation score: 0.633791\n",
      "Iteration 4, loss = 0.64038335\n",
      "Validation score: 0.633438\n",
      "Iteration 5, loss = 0.63855599\n",
      "Validation score: 0.634379\n",
      "Iteration 6, loss = 0.63713540\n",
      "Validation score: 0.636417\n",
      "Iteration 7, loss = 0.63587003\n",
      "Validation score: 0.638965\n",
      "Iteration 8, loss = 0.63523851\n",
      "Validation score: 0.639279\n",
      "Iteration 9, loss = 0.63459328\n",
      "Validation score: 0.638769\n",
      "Iteration 10, loss = 0.63398924\n",
      "Validation score: 0.638769\n",
      "Iteration 11, loss = 0.63355689\n",
      "Validation score: 0.640298\n",
      "Iteration 12, loss = 0.63289817\n",
      "Validation score: 0.639788\n",
      "Iteration 13, loss = 0.63276923\n",
      "Validation score: 0.637123\n",
      "Iteration 14, loss = 0.63234321\n",
      "Validation score: 0.639945\n",
      "Iteration 15, loss = 0.63210204\n",
      "Validation score: 0.640024\n",
      "Iteration 16, loss = 0.63189010\n",
      "Validation score: 0.640455\n",
      "Iteration 17, loss = 0.63164956\n",
      "Validation score: 0.639553\n",
      "Iteration 18, loss = 0.63127024\n",
      "Validation score: 0.637358\n",
      "Iteration 19, loss = 0.63107236\n",
      "Validation score: 0.638534\n",
      "Iteration 20, loss = 0.63097774\n",
      "Validation score: 0.639475\n",
      "Iteration 21, loss = 0.63085636\n",
      "Validation score: 0.637828\n",
      "Iteration 22, loss = 0.63057968\n",
      "Validation score: 0.640572\n",
      "Iteration 23, loss = 0.63053893\n",
      "Validation score: 0.640376\n",
      "Iteration 24, loss = 0.63038481\n",
      "Validation score: 0.640180\n",
      "Iteration 25, loss = 0.63016074\n",
      "Validation score: 0.640455\n",
      "Iteration 26, loss = 0.63006402\n",
      "Validation score: 0.639279\n",
      "Iteration 27, loss = 0.63003233\n",
      "Validation score: 0.639357\n",
      "Iteration 28, loss = 0.62990412\n",
      "Validation score: 0.638652\n",
      "Iteration 29, loss = 0.62987235\n",
      "Validation score: 0.641631\n",
      "Iteration 30, loss = 0.62956052\n",
      "Validation score: 0.639828\n",
      "Iteration 31, loss = 0.62955315\n",
      "Validation score: 0.641239\n",
      "Iteration 32, loss = 0.62936805\n",
      "Validation score: 0.639906\n",
      "Iteration 33, loss = 0.62938160\n",
      "Validation score: 0.638652\n",
      "Iteration 34, loss = 0.62924181\n",
      "Validation score: 0.639553\n",
      "Iteration 35, loss = 0.62918646\n",
      "Validation score: 0.640063\n",
      "Iteration 36, loss = 0.62920010\n",
      "Validation score: 0.642101\n",
      "Iteration 37, loss = 0.62910583\n",
      "Validation score: 0.641356\n",
      "Iteration 38, loss = 0.62888516\n",
      "Validation score: 0.641474\n",
      "Iteration 39, loss = 0.62877433\n",
      "Validation score: 0.639710\n",
      "Iteration 40, loss = 0.62877448\n",
      "Validation score: 0.640141\n",
      "Iteration 41, loss = 0.62883059\n",
      "Validation score: 0.638848\n",
      "Iteration 42, loss = 0.62864917\n",
      "Validation score: 0.641631\n",
      "Iteration 43, loss = 0.62865143\n",
      "Validation score: 0.641827\n",
      "Iteration 44, loss = 0.62857450\n",
      "Validation score: 0.639945\n",
      "Iteration 45, loss = 0.62848227\n",
      "Validation score: 0.640768\n",
      "Iteration 46, loss = 0.62843994\n",
      "Validation score: 0.641121\n",
      "Iteration 47, loss = 0.62854992\n",
      "Validation score: 0.640768\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "[Voting] ..................... (3 of 3) Processing mlp2, total=  24.5s\n",
      "[Voting] ..................... (1 of 3) Processing mlp0, total= 5.2min\n",
      "Iteration 1, loss = 0.65061413\n",
      "Validation score: 0.630655\n",
      "Iteration 2, loss = 0.64105214\n",
      "Validation score: 0.616190\n",
      "Iteration 3, loss = 0.63792377\n",
      "Validation score: 0.632536\n",
      "Iteration 4, loss = 0.63648794\n",
      "Validation score: 0.632536\n",
      "Iteration 5, loss = 0.63531031\n",
      "Validation score: 0.633987\n",
      "Iteration 6, loss = 0.63501860\n",
      "Validation score: 0.631282\n",
      "Iteration 7, loss = 0.63447364\n",
      "Validation score: 0.632967\n",
      "Iteration 8, loss = 0.63420964\n",
      "Validation score: 0.635868\n",
      "Iteration 9, loss = 0.63402057\n",
      "Validation score: 0.634339\n",
      "Iteration 10, loss = 0.63384759\n",
      "Validation score: 0.627911\n",
      "Iteration 11, loss = 0.63364203\n",
      "Validation score: 0.633163\n",
      "Iteration 12, loss = 0.63347399\n",
      "Validation score: 0.634222\n",
      "Iteration 13, loss = 0.63337491\n",
      "Validation score: 0.634927\n",
      "Iteration 14, loss = 0.63334128\n",
      "Validation score: 0.637123\n",
      "Iteration 15, loss = 0.63321087\n",
      "Validation score: 0.636770\n",
      "Iteration 16, loss = 0.63307627\n",
      "Validation score: 0.631870\n",
      "Iteration 17, loss = 0.63317692\n",
      "Validation score: 0.629087\n",
      "Iteration 18, loss = 0.63280983\n",
      "Validation score: 0.634143\n",
      "Iteration 19, loss = 0.63267721\n",
      "Validation score: 0.637475\n",
      "Iteration 20, loss = 0.63276467\n",
      "Validation score: 0.634888\n",
      "Iteration 21, loss = 0.63279217\n",
      "Validation score: 0.634927\n",
      "Iteration 22, loss = 0.63274359\n",
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      "Iteration 30, loss = 0.63225407\n",
      "Validation score: 0.635163\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.011737\n",
      "Iteration 31, loss = 0.63004894\n",
      "Validation score: 0.632967\n",
      "Iteration 32, loss = 0.62979048\n",
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      "Validation score: 0.636417\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.002347\n",
      "Iteration 42, loss = 0.62881666\n",
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      "Validation score: 0.636848\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000469\n",
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      "Validation score: 0.637358\n",
      "Iteration 69, loss = 0.62847639\n",
      "Validation score: 0.637397\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000094\n",
      "Iteration 70, loss = 0.62843565\n",
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      "Iteration 76, loss = 0.62843019\n",
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      "Iteration 80, loss = 0.62843076\n",
      "Validation score: 0.637358\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000019\n",
      "Iteration 81, loss = 0.62842078\n",
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      "Iteration 82, loss = 0.62842026\n",
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      "Iteration 84, loss = 0.62842039\n",
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      "Iteration 86, loss = 0.62842039\n",
      "Validation score: 0.637319\n",
      "Iteration 87, loss = 0.62842017\n",
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      "Iteration 88, loss = 0.62842027\n",
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      "Iteration 89, loss = 0.62842020\n",
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      "Iteration 91, loss = 0.62842017\n",
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      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000004\n",
      "Iteration 92, loss = 0.62841778\n",
      "Validation score: 0.637319\n",
      "Iteration 93, loss = 0.62841780\n",
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      "Iteration 94, loss = 0.62841781\n",
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      "Iteration 95, loss = 0.62841783\n",
      "Validation score: 0.637319\n",
      "Iteration 96, loss = 0.62841774\n",
      "Validation score: 0.637319\n",
      "Iteration 97, loss = 0.62841780\n",
      "Validation score: 0.637319\n",
      "Iteration 98, loss = 0.62841775\n",
      "Validation score: 0.637319\n",
      "Iteration 99, loss = 0.62841771\n",
      "Validation score: 0.637319\n",
      "Iteration 100, loss = 0.62841772\n",
      "Validation score: 0.637319\n",
      "Iteration 101, loss = 0.62841769\n",
      "Validation score: 0.637319\n",
      "Iteration 102, loss = 0.62841770\n",
      "Validation score: 0.637319\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Setting learning rate to 0.000001\n",
      "Iteration 103, loss = 0.62841725\n",
      "Validation score: 0.637319\n",
      "Iteration 104, loss = 0.62841725\n",
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      "Iteration 105, loss = 0.62841724\n",
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      "Iteration 106, loss = 0.62841725\n",
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      "Iteration 107, loss = 0.62841725\n",
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      "Iteration 108, loss = 0.62841723\n",
      "Validation score: 0.637319\n",
      "Iteration 109, loss = 0.62841724\n",
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      "Iteration 110, loss = 0.62841723\n",
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      "Iteration 111, loss = 0.62841723\n",
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      "Iteration 112, loss = 0.62841724\n",
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      "Iteration 113, loss = 0.62841723\n",
      "Validation score: 0.637319\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Learning rate too small. Stopping.\n",
      "[Voting] ..................... (2 of 3) Processing mlp1, total=  55.1s\n",
      "Iteration 1, loss = 0.65815106\n",
      "Validation score: 0.627087\n",
      "Iteration 2, loss = 0.64655583\n",
      "Validation score: 0.630263\n",
      "Iteration 3, loss = 0.64419775\n",
      "Validation score: 0.630968\n",
      "Iteration 4, loss = 0.64220745\n",
      "Validation score: 0.633791\n",
      "Iteration 5, loss = 0.64030094\n",
      "Validation score: 0.633438\n",
      "Iteration 6, loss = 0.63849581\n",
      "Validation score: 0.636025\n",
      "Iteration 7, loss = 0.63688180\n",
      "Validation score: 0.637123\n",
      "Iteration 8, loss = 0.63570633\n",
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      "Iteration 9, loss = 0.63474405\n",
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      "Iteration 10, loss = 0.63402728\n",
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      "Iteration 11, loss = 0.63322733\n",
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      "Iteration 12, loss = 0.63277643\n",
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      "Iteration 13, loss = 0.63264798\n",
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      "Iteration 14, loss = 0.63225570\n",
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      "Iteration 15, loss = 0.63186203\n",
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      "Iteration 16, loss = 0.63167257\n",
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      "Iteration 17, loss = 0.63128129\n",
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      "Iteration 18, loss = 0.63111886\n",
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      "Iteration 19, loss = 0.63110669\n",
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      "Iteration 20, loss = 0.63085328\n",
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      "Iteration 21, loss = 0.63076563\n",
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      "Iteration 22, loss = 0.63055059\n",
      "Validation score: 0.639710\n",
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      "Iteration 24, loss = 0.63018616\n",
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      "Iteration 25, loss = 0.63019343\n",
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      "Iteration 26, loss = 0.63017958\n",
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      "Iteration 27, loss = 0.63003342\n",
      "Validation score: 0.637868\n",
      "Iteration 28, loss = 0.62985011\n",
      "Validation score: 0.640651\n",
      "Iteration 29, loss = 0.62984504\n",
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      "Iteration 30, loss = 0.62967966\n",
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      "Iteration 31, loss = 0.62959930\n",
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      "Iteration 32, loss = 0.62959973\n",
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      "Iteration 34, loss = 0.62949737\n",
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      "Iteration 35, loss = 0.62959642\n",
      "Validation score: 0.637358\n",
      "Iteration 36, loss = 0.62947785\n",
      "Validation score: 0.641239\n",
      "Iteration 37, loss = 0.62926852\n",
      "Validation score: 0.640337\n",
      "Iteration 38, loss = 0.62912497\n",
      "Validation score: 0.640612\n",
      "Iteration 39, loss = 0.62917163\n",
      "Validation score: 0.638181\n",
      "Iteration 40, loss = 0.62913082\n",
      "Validation score: 0.638142\n",
      "Iteration 41, loss = 0.62903024\n",
      "Validation score: 0.638808\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "[Voting] ..................... (3 of 3) Processing mlp2, total=  21.0s\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "cross_val_predict only works for partitions",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-0cc2ceb51439>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_voting\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'IND_BOM_1_1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'IND_BOM_1_2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'IND_BOM_1_1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshowmeans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-0cc2ceb51439>\u001b[0m in \u001b[0;36mevaluate_model\u001b[0;34m(model, X, y)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRepeatedStratifiedKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_repeats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'predict'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m         \u001b[0mscores_proba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'predict_proba'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0mcompute_performance_metrics_sem_plot2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores_proba\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Ensamble/Ensamble'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_val_predict\u001b[0;34m(estimator, X, y, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method)\u001b[0m\n\u001b[1;32m    843\u001b[0m     \u001b[0mtest_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msplits\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    844\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_check_is_permutation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cross_val_predict only works for partitions'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    846\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    847\u001b[0m     \u001b[0;31m# If classification methods produce multiple columns of output,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cross_val_predict only works for partitions"
     ]
    }
   ],
   "source": [
    "from numpy import mean\n",
    "from numpy import std\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from matplotlib import pyplot\n",
    "import scikitplot as skplt\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score, average_precision_score, plot_confusion_matrix\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "ds_dropado = ds.drop(labels=['IND_BOM_1_2', 'IND_BOM_1_1'], axis=1)\n",
    "df_treino = treino_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(treino_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_validacao = valid_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(valid_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_validacao.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    "df_teste = teste_ina.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1).append(teste_adi.drop(['IND_BOM_1_2','IND_BOM_1_1'], axis=1))\n",
    "df_teste.drop(columns=['INDEX'], axis=1, inplace=True)\n",
    " \n",
    "def get_dataset():\n",
    "\tX, y = df_treino, [0]*len(treino_ina)+[1]*len(treino_adi)\n",
    "\treturn X, y\n",
    " \n",
    "def get_voting():\n",
    "\tmodels = list()\n",
    "\tmodels.append(('mlp0', MLPClassifier(verbose=True, max_iter=10000, early_stopping=True, hidden_layer_sizes=(13), solver='lbfgs', learning_rate='constant', activation='logistic', learning_rate_init=0.0319077297544169)))#.fit(get_dataset()[0], get_dataset()[1])))\n",
    "\tmodels.append(('mlp1', MLPClassifier(verbose=True, max_iter=10000, early_stopping=True, hidden_layer_sizes=(3,), solver='sgd', learning_rate='adaptive', activation='tanh', learning_rate_init=0.058684739035340376)\t))#.fit(get_dataset()[0], get_dataset()[1])))\n",
    "\tmodels.append(('mlp2', MLPClassifier(verbose=True, max_iter=10000, early_stopping=True, hidden_layer_sizes=(5,), solver='sgd', learning_rate='constant', activation='tanh', learning_rate_init=0.010432296668493837)\t))#.fit(get_dataset()[0], get_dataset()[1])))\n",
    "\tensemble = VotingClassifier(verbose=True, estimators=models, voting='soft').fit(get_dataset()[0], get_dataset()[1])\n",
    "\treturn ensemble\n",
    " \n",
    "def get_models():\n",
    "\tmodels = dict()\n",
    "\tmodels['mlp0'] = MLPClassifier(max_iter=10000, early_stopping=True)\n",
    "\tmodels['mlp1'] = MLPClassifier(max_iter=10000, early_stopping=True)\n",
    "\tmodels['mlp2'] = MLPClassifier(max_iter=10000, early_stopping=True)\n",
    "\tmodels['soft_voting'] = get_voting()\n",
    "\treturn models\n",
    " \n",
    "\n",
    "def compute_performance_metrics_sem_plot2(y, y_pred_class, y_pred_scores, rede_trial):\n",
    "    accuracy = accuracy_score(y, y_pred_class)\n",
    "    recall = recall_score(y, y_pred_class)\n",
    "    precision = precision_score(y, y_pred_class)\n",
    "    f1 = f1_score(y, y_pred_class)\n",
    "    performance_metrics = (accuracy, recall, precision, f1)\n",
    "    if y_pred_scores is not None:\n",
    "        skplt.metrics.plot_ks_statistic(y, y_pred_scores)\n",
    "        # plt.show()\n",
    "        y_pred_scores = y_pred_scores[:, 1]\n",
    "        auroc = roc_auc_score(y, y_pred_scores)\n",
    "        aupr = average_precision_score(y, y_pred_scores)\n",
    "        performance_metrics = performance_metrics + (auroc, aupr)\n",
    "        plt.title(label=rede_trial, y=0.9)\n",
    "        plt.suptitle('Acurácia: {:3.3f}\\nRecall: {:3.3f}\\nPrecision: {:3.3f}\\nF1: {:3.3f}\\nAUROC: {:3.3f}\\nAURP: {:3.3f}'.format(accuracy, recall, precision, f1, auroc, aupr), x=0.25, y=0.8)\n",
    "        plt.savefig(rede_trial, dpi=100)\n",
    "        plt.close()\n",
    "    return performance_metrics\n",
    "\n",
    "def evaluate_model(model, X, y):\n",
    "\tcv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "\tscores = cross_val_predict(model, X, y, cv=cv, n_jobs=-1, method='predict')\n",
    "\tscores_proba = cross_val_predict(model, X, y, cv=cv, n_jobs=-1, method='predict_proba')\n",
    "\tcompute_performance_metrics_sem_plot2(y, scores, scores_proba, 'Ensamble/Ensamble')\n",
    "\treturn scores\n",
    "\n",
    "# Dataset de treino\n",
    "X, y = get_dataset()\n",
    "\n",
    "# Modelos de ensamble\n",
    "models = get_models()\n",
    "\n",
    "# Avaliar cada modelo e armazenar seus resultados\n",
    "results, names = list(), list()\n",
    "\n",
    "pyplot.boxplot(evaluate_model(get_voting(), ds.drop(axis=1, labels=['IND_BOM_1_1', 'IND_BOM_1_2']), ds['IND_BOM_1_1']), labels=names, showmeans=True)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Utilizando o sistema de Voting Classifier com 3 MLPs, obtivemos o resultado de validação médio: 63,88%."
   ]
  }
 ],
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