1-bantotal/solução/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 1 - Maratona Behind the Code 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from datetime import timedelta\n",
    "from pathlib import Path\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import requests\n",
    "import seaborn as sns\n",
    "from dotenv import find_dotenv, load_dotenv\n",
    "from ibm_watson_machine_learning import APIClient\n",
    "from IPython.display import HTML, display\n",
    "from sklearn import set_config\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import (AdaBoostClassifier, BaggingClassifier,\n",
    "                              ExtraTreesClassifier, RandomForestClassifier,\n",
    "                              StackingClassifier)\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import f1_score, plot_confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV  # , RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Configurações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed: int = 0\n",
    "data_path: Path = Path(\"../desafio/assets/data/\")\n",
    "set_config(display=\"diagram\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Junção dos datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CHECKING_BALANCE', 'CREDIT_HISTORY', 'EXISTING_SAVINGS', 'EXISTING_CREDITS_COUNT']\n"
     ]
    }
   ],
   "source": [
    "accounts = pd.read_csv(data_path / \"ACCOUNTS.csv\", index_col=\"ID\")\n",
    "account_cols = list(accounts.columns)\n",
    "print(account_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SEX', 'AGE', 'JOB_TYPE', 'DEPENDENTS', 'TELEPHONE', 'FOREIGN_WORKER', 'EMPLOYMENT_DURATION', 'PROPERTY', 'HOUSING', 'CURRENT_RESIDENCE_DURATION']\n"
     ]
    }
   ],
   "source": [
    "demographics = pd.read_csv(data_path / \"DEMOGRAPHICS.csv\", index_col=\"ID\")\n",
    "demographic_cols = list(demographics.columns)\n",
    "print(demographic_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PAYMENT_TERM', 'INSTALLMENT_PLANS', 'INSTALLMENT_PERCENT', 'LOAN_PURPOSE', 'LOAN_AMOUNT', 'OTHERS_ON_LOAN', 'ALLOW']\n"
     ]
    }
   ],
   "source": [
    "loans = pd.read_csv(data_path / \"LOANS.csv\", index_col=\"ID\")\n",
    "loan_cols = list(loans.columns)\n",
    "print(loan_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "answers = pd.read_csv(data_path / \"ANSWERS.csv\")\n",
    "df_all = pd.concat([accounts, demographics, loans], axis=1).reset_index()[answers.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Informações gerais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3999 entries, 0 to 3998\n",
      "Data columns (total 22 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   ID                          3999 non-null   int64  \n",
      " 1   CHECKING_BALANCE            2467 non-null   object \n",
      " 2   PAYMENT_TERM                3975 non-null   float64\n",
      " 3   CREDIT_HISTORY              2462 non-null   object \n",
      " 4   LOAN_PURPOSE                3979 non-null   object \n",
      " 5   LOAN_AMOUNT                 3981 non-null   float64\n",
      " 6   EXISTING_SAVINGS            2467 non-null   object \n",
      " 7   EMPLOYMENT_DURATION         3923 non-null   float64\n",
      " 8   INSTALLMENT_PERCENT         3974 non-null   float64\n",
      " 9   SEX                         3920 non-null   object \n",
      " 10  OTHERS_ON_LOAN              3977 non-null   object \n",
      " 11  CURRENT_RESIDENCE_DURATION  3917 non-null   float64\n",
      " 12  PROPERTY                    3918 non-null   object \n",
      " 13  AGE                         3918 non-null   float64\n",
      " 14  INSTALLMENT_PLANS           3978 non-null   object \n",
      " 15  HOUSING                     3912 non-null   object \n",
      " 16  EXISTING_CREDITS_COUNT      2463 non-null   float64\n",
      " 17  JOB_TYPE                    3910 non-null   float64\n",
      " 18  DEPENDENTS                  3915 non-null   float64\n",
      " 19  TELEPHONE                   3915 non-null   float64\n",
      " 20  FOREIGN_WORKER              3909 non-null   float64\n",
      " 21  ALLOW                       3999 non-null   int64  \n",
      "dtypes: float64(11), int64(2), object(9)\n",
      "memory usage: 687.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_all.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A maioria das variáveis (colunas) são numéricas, mas há algumas categóricas.\n",
    "- Com exceção da variável que identifica cada cliente (`ID`) e da variável de destino (`ALLOW`), todas têm dados faltantes (nulos)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Divisão dos dados entre treino e teste"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de inspecionar os dados, serão reservados alguns exemplos para teste, que não serão vistos durante a análise ou modelagem dos dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensões dos dados de treino: (3499, 22)\n",
      "Dimensões dos dados de teste: (500, 22)\n"
     ]
    }
   ],
   "source": [
    "test_size = 500\n",
    "target = \"ALLOW\"\n",
    "df_train, df_test = train_test_split(df_all, test_size=test_size, random_state=seed, stratify=df_all[[target]])\n",
    "print(f'Dimensões dos dados de treino: {df_train.shape}')\n",
    "print(f'Dimensões dos dados de teste: {df_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Variável destino"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A variável destino para este desafio é a `ALLOW`, significando se um empréstimo deverá ser permitido ou não, baseado nas informações dadas. Vamos dar uma olhada em como está a distribuição dessa variável"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v0NR4ZSGNbhfwnlNqNwNvp83MO1T/w7b8myR/1dYfrKqXJ9kEfKldnTwGvKGqHgKoqkryFeAXq+p/2nFfBvbglYWmyFlnJUld3oaSJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEld/wtj+FovO4tdsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "risk_plot = sns.countplot(\n",
    "    data=df_train, x=target, order=df_train[target].value_counts().index\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dados de exemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CHECKING_BALANCE</th>\n",
       "      <th>PAYMENT_TERM</th>\n",
       "      <th>CREDIT_HISTORY</th>\n",
       "      <th>LOAN_PURPOSE</th>\n",
       "      <th>LOAN_AMOUNT</th>\n",
       "      <th>EXISTING_SAVINGS</th>\n",
       "      <th>EMPLOYMENT_DURATION</th>\n",
       "      <th>INSTALLMENT_PERCENT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>OTHERS_ON_LOAN</th>\n",
       "      <th>CURRENT_RESIDENCE_DURATION</th>\n",
       "      <th>PROPERTY</th>\n",
       "      <th>AGE</th>\n",
       "      <th>INSTALLMENT_PLANS</th>\n",
       "      <th>HOUSING</th>\n",
       "      <th>EXISTING_CREDITS_COUNT</th>\n",
       "      <th>JOB_TYPE</th>\n",
       "      <th>DEPENDENTS</th>\n",
       "      <th>TELEPHONE</th>\n",
       "      <th>FOREIGN_WORKER</th>\n",
       "      <th>ALLOW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>443445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CAR_NEW</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NONE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>REAL_ESTATE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3697</th>\n",
       "      <td>1208610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>APPLIANCES</td>\n",
       "      <td>4545.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NONE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>887509</td>\n",
       "      <td>NO_CHECKING</td>\n",
       "      <td>630.0</td>\n",
       "      <td>CREDITS_PAID_TO_DATE</td>\n",
       "      <td>FURNITURE</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>980.41</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NONE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>SAVINGS_INSURANCE</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OWN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>1291443</td>\n",
       "      <td>144.51</td>\n",
       "      <td>465.0</td>\n",
       "      <td>PRIOR_PAYMENTS_DELAYED</td>\n",
       "      <td>FURNITURE</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>53.87</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NONE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CAR_OTHER</td>\n",
       "      <td>35.0</td>\n",
       "      <td>STORES</td>\n",
       "      <td>OWN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>76618</td>\n",
       "      <td>NO_CHECKING</td>\n",
       "      <td>510.0</td>\n",
       "      <td>CREDITS_PAID_TO_DATE</td>\n",
       "      <td>CAR_NEW</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>201.44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NONE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>SAVINGS_INSURANCE</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3696</th>\n",
       "      <td>1206978</td>\n",
       "      <td>NO_CHECKING</td>\n",
       "      <td>960.0</td>\n",
       "      <td>OUTSTANDING_CREDIT</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>9727.0</td>\n",
       "      <td>829.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>M</td>\n",
       "      <td>GUARANTOR</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CAR_OTHER</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>FREE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3825</th>\n",
       "      <td>1261640</td>\n",
       "      <td>NO_CHECKING</td>\n",
       "      <td>806.0</td>\n",
       "      <td>OUTSTANDING_CREDIT</td>\n",
       "      <td>RADIO_TV</td>\n",
       "      <td>3782.0</td>\n",
       "      <td>997.69</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NONE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OWN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>229882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>744.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CAR_NEW</td>\n",
       "      <td>501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>SAVINGS_INSURANCE</td>\n",
       "      <td>26.0</td>\n",
       "      <td>STORES</td>\n",
       "      <td>OWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>658037</td>\n",
       "      <td>NO_CHECKING</td>\n",
       "      <td>720.0</td>\n",
       "      <td>PRIOR_PAYMENTS_DELAYED</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>1828.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NONE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>SAVINGS_INSURANCE</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OWN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3674</th>\n",
       "      <td>1198137</td>\n",
       "      <td>NO_CHECKING</td>\n",
       "      <td>713.0</td>\n",
       "      <td>PRIOR_PAYMENTS_DELAYED</td>\n",
       "      <td>RADIO_TV</td>\n",
       "      <td>3252.0</td>\n",
       "      <td>133.37</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NONE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CAR_OTHER</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OWN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID CHECKING_BALANCE  PAYMENT_TERM          CREDIT_HISTORY  \\\n",
       "1300   443445              NaN         270.0                     NaN   \n",
       "3697  1208610              NaN        1170.0                     NaN   \n",
       "2710   887509      NO_CHECKING         630.0    CREDITS_PAID_TO_DATE   \n",
       "3914  1291443           144.51         465.0  PRIOR_PAYMENTS_DELAYED   \n",
       "200     76618      NO_CHECKING         510.0    CREDITS_PAID_TO_DATE   \n",
       "3696  1206978      NO_CHECKING         960.0      OUTSTANDING_CREDIT   \n",
       "3825  1261640      NO_CHECKING         806.0      OUTSTANDING_CREDIT   \n",
       "675    229882              NaN         744.0                     NaN   \n",
       "1976   658037      NO_CHECKING         720.0  PRIOR_PAYMENTS_DELAYED   \n",
       "3674  1198137      NO_CHECKING         713.0  PRIOR_PAYMENTS_DELAYED   \n",
       "\n",
       "     LOAN_PURPOSE  LOAN_AMOUNT EXISTING_SAVINGS  EMPLOYMENT_DURATION  \\\n",
       "1300      CAR_NEW        250.0              NaN                  0.0   \n",
       "3697   APPLIANCES       4545.0              NaN                  6.0   \n",
       "2710    FURNITURE       1903.0           980.41                  7.0   \n",
       "3914    FURNITURE       1931.0            53.87                  4.0   \n",
       "200       CAR_NEW       2720.0           201.44                  4.0   \n",
       "3696        OTHER       9727.0            829.3                  7.0   \n",
       "3825     RADIO_TV       3782.0           997.69                  6.0   \n",
       "675       CAR_NEW        501.0              NaN                  7.0   \n",
       "1976    EDUCATION       1828.0          UNKNOWN                  3.0   \n",
       "3674     RADIO_TV       3252.0           133.37                  8.0   \n",
       "\n",
       "      INSTALLMENT_PERCENT SEX OTHERS_ON_LOAN  CURRENT_RESIDENCE_DURATION  \\\n",
       "1300                  1.0   M           NONE                         2.0   \n",
       "3697                  3.0   M           NONE                         3.0   \n",
       "2710                  4.0   M           NONE                         4.0   \n",
       "3914                  3.0   M           NONE                         4.0   \n",
       "200                   3.0   M           NONE                         4.0   \n",
       "3696                  4.0   M      GUARANTOR                         5.0   \n",
       "3825                  4.0   F           NONE                         3.0   \n",
       "675                   3.0   F           NONE                         1.0   \n",
       "1976                  3.0   M           NONE                         3.0   \n",
       "3674                  4.0   F           NONE                         4.0   \n",
       "\n",
       "               PROPERTY   AGE INSTALLMENT_PLANS HOUSING  \\\n",
       "1300        REAL_ESTATE  24.0              NONE    RENT   \n",
       "3697            UNKNOWN  35.0              NONE     OWN   \n",
       "2710  SAVINGS_INSURANCE  53.0              NONE     OWN   \n",
       "3914          CAR_OTHER  35.0            STORES     OWN   \n",
       "200   SAVINGS_INSURANCE  34.0              NONE    RENT   \n",
       "3696          CAR_OTHER  57.0              NONE    FREE   \n",
       "3825            UNKNOWN  45.0              NONE     OWN   \n",
       "675   SAVINGS_INSURANCE  26.0            STORES     OWN   \n",
       "1976  SAVINGS_INSURANCE  44.0              NONE     OWN   \n",
       "3674          CAR_OTHER  51.0              NONE     OWN   \n",
       "\n",
       "      EXISTING_CREDITS_COUNT  JOB_TYPE  DEPENDENTS  TELEPHONE  FOREIGN_WORKER  \\\n",
       "1300                     NaN       3.0         1.0        0.0             1.0   \n",
       "3697                     NaN       3.0         2.0        1.0             1.0   \n",
       "2710                     1.0       3.0         1.0        1.0             1.0   \n",
       "3914                     2.0       1.0         1.0        0.0             1.0   \n",
       "200                      1.0       2.0         1.0        0.0             1.0   \n",
       "3696                     2.0       3.0         2.0        1.0             1.0   \n",
       "3825                     1.0       3.0         2.0        1.0             1.0   \n",
       "675                      NaN       1.0         2.0        1.0             1.0   \n",
       "1976                     1.0       3.0         1.0        0.0             1.0   \n",
       "3674                     2.0       3.0         1.0        0.0             1.0   \n",
       "\n",
       "      ALLOW  \n",
       "1300      1  \n",
       "3697      0  \n",
       "2710      1  \n",
       "3914      1  \n",
       "200       1  \n",
       "3696      0  \n",
       "3825      0  \n",
       "675       1  \n",
       "1976      0  \n",
       "3674      0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context(\"display.max_columns\", None):\n",
    "    display(df_train.sample(10, random_state=seed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Variáveis categóricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_numeric_cols = list(df_train.select_dtypes(exclude='number').columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHECKING_BALANCE</th>\n",
       "      <th>CREDIT_HISTORY</th>\n",
       "      <th>LOAN_PURPOSE</th>\n",
       "      <th>EXISTING_SAVINGS</th>\n",
       "      <th>SEX</th>\n",
       "      <th>OTHERS_ON_LOAN</th>\n",
       "      <th>PROPERTY</th>\n",
       "      <th>INSTALLMENT_PLANS</th>\n",
       "      <th>HOUSING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2155</td>\n",
       "      <td>2151</td>\n",
       "      <td>3481</td>\n",
       "      <td>2155</td>\n",
       "      <td>3433</td>\n",
       "      <td>3480</td>\n",
       "      <td>3432</td>\n",
       "      <td>3480</td>\n",
       "      <td>3428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1233</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1965</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NO_CHECKING</td>\n",
       "      <td>PRIOR_PAYMENTS_DELAYED</td>\n",
       "      <td>CAR_NEW</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>M</td>\n",
       "      <td>NONE</td>\n",
       "      <td>SAVINGS_INSURANCE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OWN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>859</td>\n",
       "      <td>735</td>\n",
       "      <td>649</td>\n",
       "      <td>149</td>\n",
       "      <td>2122</td>\n",
       "      <td>2893</td>\n",
       "      <td>1135</td>\n",
       "      <td>2428</td>\n",
       "      <td>2209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CHECKING_BALANCE          CREDIT_HISTORY LOAN_PURPOSE EXISTING_SAVINGS  \\\n",
       "count              2155                    2151         3481             2155   \n",
       "unique             1233                       5           11             1965   \n",
       "top         NO_CHECKING  PRIOR_PAYMENTS_DELAYED      CAR_NEW          UNKNOWN   \n",
       "freq                859                     735          649              149   \n",
       "\n",
       "         SEX OTHERS_ON_LOAN           PROPERTY INSTALLMENT_PLANS HOUSING  \n",
       "count   3433           3480               3432              3480    3428  \n",
       "unique     2              3                  4                 3       3  \n",
       "top        M           NONE  SAVINGS_INSURANCE              NONE     OWN  \n",
       "freq    2122           2893               1135              2428    2209  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context(\"display.max_columns\", None):\n",
    "    display(df_train.describe(include=\"O\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quantidade de valores únicos para as variáveis `CHECKING_BALANCE` e `EXISTING_SAVINGS` é relativamente grande (>30%), e observando-se os dados de exemplo, há indícios de que boa parte dos valores possam ser numéricos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CHECKING_BALANCE', 'EXISTING_SAVINGS']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_unique_treshold = 0.3\n",
    "cols_with_many_unique_values = df_train[non_numeric_cols].nunique() > max_unique_treshold * len(df_train)\n",
    "possibly_numeric_cols = list(df_train[non_numeric_cols].columns[cols_with_many_unique_values])\n",
    "possibly_numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NO_CHECKING</th>\n",
       "      <td>859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97.41</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Quantidade\n",
       "Valor                  \n",
       "NO_CHECKING         859\n",
       "0                    57\n",
       "97.41                 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.92</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.89</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Quantidade\n",
       "Valor              \n",
       "UNKNOWN         149\n",
       "28.92             3\n",
       "100.89            3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in possibly_numeric_cols:\n",
    "    display(\n",
    "        df_train[col]\n",
    "        .value_counts()\n",
    "        .reset_index()\n",
    "        .sort_values([col, \"index\"], ascending=False)\n",
    "        .rename(columns={col: \"Quantidade\", \"index\": \"Valor\"})\n",
    "        .set_index(\"Valor\")\n",
    "        .head(3)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na hora da modelagem, teremos algumas opções do que fazer com estas colunas:\n",
    "- Tratar essas colunas como variáveis numéricas, deixando que valores textuais como `NO_CHECKING` e `UNKNOWN` sejam substituídos por `NaN` (e posteriormente imputar um valor, por exemplo, zero).\n",
    "- Se for relevante distinguir o caso anterior dos zeros que já estavam nestas colunas, poderia ser criada uma nova variável binária cujo valor fosse `True` quando a variável original fosse não numérica, e `False` nos demais casos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remoção de texto em variáveis numéricas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por hora, apenas para visualizar estatísticas sobre os valores numéricos, valores textuais nessas colunas serão trocados por `NaN` usando um *transformer* personalizado:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Instalação de um pacote extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'custom_sklearn_transformers'...\n",
      "remote: Enumerating objects: 12, done.\u001b[K\n",
      "remote: Counting objects: 100% (12/12), done.\u001b[K\n",
      "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
      "Receiving objects: 100% (12/12), done.\n",
      "remote: Total 12 (delta 0), reused 12 (delta 0), pack-reused 0\u001b[K\n"
     ]
    }
   ],
   "source": [
    "!rm -rf custom_sklearn_transformers\n",
    "!git clone git@github.com:he7d3r/custom_sklearn_transformers.git\n",
    "!git --git-dir custom_sklearn_transformers/.git archive -o custom_sklearn_transformers.zip HEAD\n",
    "!pip install --quiet --upgrade pip custom_sklearn_transformers.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Uso do pacote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from custom_sklearn_transformers.transformers import ToNumeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_numeric = ToNumeric(possibly_numeric_cols, errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "Para criar um modelo capaz de fazer transformações nos dados de entrada, vamos criar uma `Pipeline` do `scikit-learn` e aplicar nossas transformações de pré-processamento dentro dos estágios dela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = Pipeline([\n",
    "    ('to_numeric', to_numeric),\n",
    "])\n",
    "clean_df = preprocessor.fit_transform(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estes são os valores que as outras variáveis não numéricas assumem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRIOR_PAYMENTS_DELAYED</th>\n",
       "      <th>CREDITS_PAID_TO_DATE</th>\n",
       "      <th>OUTSTANDING_CREDIT</th>\n",
       "      <th>ALL_CREDITS_PAID_BACK</th>\n",
       "      <th>NO_CREDITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CREDIT_HISTORY</th>\n",
       "      <td>735</td>\n",
       "      <td>633</td>\n",
       "      <td>394</td>\n",
       "      <td>336</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PRIOR_PAYMENTS_DELAYED  CREDITS_PAID_TO_DATE  \\\n",
       "CREDIT_HISTORY                     735                   633   \n",
       "\n",
       "                OUTSTANDING_CREDIT  ALL_CREDITS_PAID_BACK  NO_CREDITS  \n",
       "CREDIT_HISTORY                 394                    336          53  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OWN</th>\n",
       "      <th>RENT</th>\n",
       "      <th>FREE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HOUSING</th>\n",
       "      <td>2209</td>\n",
       "      <td>720</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          OWN  RENT  FREE\n",
       "HOUSING  2209   720   499"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NONE</th>\n",
       "      <th>STORES</th>\n",
       "      <th>BANK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>INSTALLMENT_PLANS</th>\n",
       "      <td>2428</td>\n",
       "      <td>710</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   NONE  STORES  BANK\n",
       "INSTALLMENT_PLANS  2428     710   342"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CAR_NEW</th>\n",
       "      <th>FURNITURE</th>\n",
       "      <th>CAR_USED</th>\n",
       "      <th>RADIO_TV</th>\n",
       "      <th>APPLIANCES</th>\n",
       "      <th>REPAIRS</th>\n",
       "      <th>VACATION</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>RETRAINING</th>\n",
       "      <th>BUSINESS</th>\n",
       "      <th>OTHER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LOAN_PURPOSE</th>\n",
       "      <td>649</td>\n",
       "      <td>609</td>\n",
       "      <td>584</td>\n",
       "      <td>528</td>\n",
       "      <td>370</td>\n",
       "      <td>194</td>\n",
       "      <td>143</td>\n",
       "      <td>114</td>\n",
       "      <td>113</td>\n",
       "      <td>102</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              CAR_NEW  FURNITURE  CAR_USED  RADIO_TV  APPLIANCES  REPAIRS  \\\n",
       "LOAN_PURPOSE      649        609       584       528         370      194   \n",
       "\n",
       "              VACATION  EDUCATION  RETRAINING  BUSINESS  OTHER  \n",
       "LOAN_PURPOSE       143        114         113       102     75  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NONE</th>\n",
       "      <th>CO-APPLICANT</th>\n",
       "      <th>GUARANTOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OTHERS_ON_LOAN</th>\n",
       "      <td>2893</td>\n",
       "      <td>502</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                NONE  CO-APPLICANT  GUARANTOR\n",
       "OTHERS_ON_LOAN  2893           502         85"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAVINGS_INSURANCE</th>\n",
       "      <th>CAR_OTHER</th>\n",
       "      <th>REAL_ESTATE</th>\n",
       "      <th>UNKNOWN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PROPERTY</th>\n",
       "      <td>1135</td>\n",
       "      <td>1056</td>\n",
       "      <td>748</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SAVINGS_INSURANCE  CAR_OTHER  REAL_ESTATE  UNKNOWN\n",
       "PROPERTY               1135       1056          748      493"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <td>2122</td>\n",
       "      <td>1311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        M     F\n",
       "SEX  2122  1311"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_cat_cols = list(sorted(set(non_numeric_cols) - set(possibly_numeric_cols)))\n",
    "for col in sorted_cat_cols:\n",
    "    display(clean_df[col].value_counts().to_frame().T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Todas as variáveis poderiam ser convertidas em valores numéricos usando \"One-Hot-Encoding\".\n",
    "- Alternativamente, algumas variáveis poderiam poderia ser convertidas para valores crescentes em uma ordem que aparenta ir da pior para a melhor situação (mas esta é uma avaliação subjetiva):\n",
    "  - **`CREDIT_HISTORY`**: `PRIOR_PAYMENTS_DELAYED`, `NO_CREDITS`, `CREDITS_PAID_TO_DATE`, `ALL_CREDITS_PAID_BACK`, `OUTSTANDING_CREDIT`\n",
    "  - **`OTHERS_ON_LOAN`**: `NONE`, `CO-APPLICANT`, `GUARANTOR`\n",
    "  - **`HOUSING`**: `FREE`, `RENT`, `OWN`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Variáveis numéricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CHECKING_BALANCE</th>\n",
       "      <th>PAYMENT_TERM</th>\n",
       "      <th>LOAN_AMOUNT</th>\n",
       "      <th>EXISTING_SAVINGS</th>\n",
       "      <th>EMPLOYMENT_DURATION</th>\n",
       "      <th>INSTALLMENT_PERCENT</th>\n",
       "      <th>CURRENT_RESIDENCE_DURATION</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EXISTING_CREDITS_COUNT</th>\n",
       "      <th>JOB_TYPE</th>\n",
       "      <th>DEPENDENTS</th>\n",
       "      <th>TELEPHONE</th>\n",
       "      <th>FOREIGN_WORKER</th>\n",
       "      <th>ALLOW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.499000e+03</td>\n",
       "      <td>1296.000000</td>\n",
       "      <td>3478.000000</td>\n",
       "      <td>3486.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>3437.000000</td>\n",
       "      <td>3479.000000</td>\n",
       "      <td>3430.000000</td>\n",
       "      <td>3431.000000</td>\n",
       "      <td>2155.000000</td>\n",
       "      <td>3427.000000</td>\n",
       "      <td>3429.000000</td>\n",
       "      <td>3427.000000</td>\n",
       "      <td>3423.000000</td>\n",
       "      <td>3499.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.564330e+05</td>\n",
       "      <td>500.561914</td>\n",
       "      <td>652.430995</td>\n",
       "      <td>3471.581469</td>\n",
       "      <td>1366.428509</td>\n",
       "      <td>4.250509</td>\n",
       "      <td>2.979304</td>\n",
       "      <td>2.856851</td>\n",
       "      <td>36.105800</td>\n",
       "      <td>1.465429</td>\n",
       "      <td>2.425445</td>\n",
       "      <td>1.161855</td>\n",
       "      <td>0.414357</td>\n",
       "      <td>0.976337</td>\n",
       "      <td>0.66419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.729361e+05</td>\n",
       "      <td>2742.738362</td>\n",
       "      <td>338.976026</td>\n",
       "      <td>2482.036344</td>\n",
       "      <td>4827.286202</td>\n",
       "      <td>3.586829</td>\n",
       "      <td>1.116362</td>\n",
       "      <td>1.110505</td>\n",
       "      <td>10.572843</td>\n",
       "      <td>0.569324</td>\n",
       "      <td>0.934158</td>\n",
       "      <td>0.368371</td>\n",
       "      <td>0.492682</td>\n",
       "      <td>0.152021</td>\n",
       "      <td>0.47234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>-2617.350000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.362930e+05</td>\n",
       "      <td>-186.057500</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>1328.250000</td>\n",
       "      <td>64.845000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.586480e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>3237.000000</td>\n",
       "      <td>253.825000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.710270e+05</td>\n",
       "      <td>109.872500</td>\n",
       "      <td>899.000000</td>\n",
       "      <td>5308.000000</td>\n",
       "      <td>726.065000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.320082e+06</td>\n",
       "      <td>20104.120000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>11676.000000</td>\n",
       "      <td>50177.310000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  CHECKING_BALANCE  PAYMENT_TERM   LOAN_AMOUNT  \\\n",
       "count  3.499000e+03       1296.000000   3478.000000   3486.000000   \n",
       "mean   6.564330e+05        500.561914    652.430995   3471.581469   \n",
       "std    3.729361e+05       2742.738362    338.976026   2482.036344   \n",
       "min    9.800000e+01      -2617.350000    120.000000    250.000000   \n",
       "25%    3.362930e+05       -186.057500    390.000000   1328.250000   \n",
       "50%    6.586480e+05          0.000000    651.000000   3237.000000   \n",
       "75%    9.710270e+05        109.872500    899.000000   5308.000000   \n",
       "max    1.320082e+06      20104.120000   1984.000000  11676.000000   \n",
       "\n",
       "       EXISTING_SAVINGS  EMPLOYMENT_DURATION  INSTALLMENT_PERCENT  \\\n",
       "count       2006.000000          3437.000000          3479.000000   \n",
       "mean        1366.428509             4.250509             2.979304   \n",
       "std         4827.286202             3.586829             1.116362   \n",
       "min            0.020000             0.000000             1.000000   \n",
       "25%           64.845000             1.000000             2.000000   \n",
       "50%          253.825000             4.000000             3.000000   \n",
       "75%          726.065000             7.000000             4.000000   \n",
       "max        50177.310000            18.000000             6.000000   \n",
       "\n",
       "       CURRENT_RESIDENCE_DURATION          AGE  EXISTING_CREDITS_COUNT  \\\n",
       "count                 3430.000000  3431.000000             2155.000000   \n",
       "mean                     2.856851    36.105800                1.465429   \n",
       "std                      1.110505    10.572843                0.569324   \n",
       "min                      1.000000    19.000000                1.000000   \n",
       "25%                      2.000000    28.000000                1.000000   \n",
       "50%                      3.000000    36.000000                1.000000   \n",
       "75%                      4.000000    44.000000                2.000000   \n",
       "max                      5.000000    74.000000                3.000000   \n",
       "\n",
       "          JOB_TYPE   DEPENDENTS    TELEPHONE  FOREIGN_WORKER       ALLOW  \n",
       "count  3427.000000  3429.000000  3427.000000     3423.000000  3499.00000  \n",
       "mean      2.425445     1.161855     0.414357        0.976337     0.66419  \n",
       "std       0.934158     0.368371     0.492682        0.152021     0.47234  \n",
       "min       0.000000     1.000000     0.000000        0.000000     0.00000  \n",
       "25%       2.000000     1.000000     0.000000        1.000000     0.00000  \n",
       "50%       3.000000     1.000000     0.000000        1.000000     1.00000  \n",
       "75%       3.000000     1.000000     1.000000        1.000000     1.00000  \n",
       "max       3.000000     2.000000     1.000000        1.000000     1.00000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context(\"display.max_columns\", None):\n",
    "    display(clean_df.describe(exclude=\"O\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sinta-se livre para ver a distribuição de outras colunas do conjunto de dados, utilizar os outros conjuntos de dados, explorar as correlações entre variáveis e outros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = list(clean_df.select_dtypes(include='number').columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "                      3.0  2.0  4.0  1.0  5.0  6.0\n",
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       "<p>1 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     19.0  40.0  36.0  ...  64.0  73.0  67.0\n",
       "AGE   278   129   127  ...     1     1     1\n",
       "\n",
       "[1 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "      <th>3.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EXISTING_CREDITS_COUNT</th>\n",
       "      <td>1233</td>\n",
       "      <td>841</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         1.0  2.0  3.0\n",
       "EXISTING_CREDITS_COUNT  1233  841   81"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>0.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JOB_TYPE</th>\n",
       "      <td>2331</td>\n",
       "      <td>467</td>\n",
       "      <td>426</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           3.0  1.0  2.0  0.0\n",
       "JOB_TYPE  2331  467  426  203"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "<div>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DEPENDENTS</th>\n",
       "      <td>2874</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             1.0  2.0\n",
       "DEPENDENTS  2874  555"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TELEPHONE</th>\n",
       "      <td>2007</td>\n",
       "      <td>1420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0.0   1.0\n",
       "TELEPHONE  2007  1420"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1.0</th>\n",
       "      <th>0.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FOREIGN_WORKER</th>\n",
       "      <td>3342</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 1.0  0.0\n",
       "FOREIGN_WORKER  3342   81"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALLOW</th>\n",
       "      <td>2324</td>\n",
       "      <td>1175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1     0\n",
       "ALLOW  2324  1175"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_columns', 6):\n",
    "    for col in numeric_cols:\n",
    "        display(clean_df[col].value_counts().to_frame().T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Correlações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variáveis sobre as contas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583.125x540 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_account_cols = list(sorted(set(account_cols + [target])))\n",
    "sns.pairplot(clean_df[sorted_account_cols], hue=target, palette='Set1', corner=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variáveis sobre dados demográficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1303.12x1260 with 35 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_demographic_cols = list(sorted(set(demographic_cols + [target])))\n",
    "sns.pairplot(clean_df[sorted_demographic_cols], hue=target, palette='Set1', corner=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Talvez seja melhor considerar que o tipo de trabalho (`JOB_TYPE`) é uma variável categórica e fazer One-Hot-Encoding, assumindo que não exista necessariamente uma ordenação natural dos tipos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Variáveis sobre os empéstimos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583.125x540 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_loan_cols = list(sorted(set(loan_cols + [target])))\n",
    "sns.pairplot(clean_df[sorted_loan_cols], hue=target, palette='Set1', corner=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Variáveis categóricas (gráficos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in sorted_cat_cols:\n",
    "    risk_plot = sns.countplot(\n",
    "        data=df_train, y=col, order=df_train[col].value_counts().index, orient='h', hue='ALLOW', palette='Set1'\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=len(sorted_cat_cols), sharex=True, figsize=(10,20))\n",
    "for i, col in enumerate(sorted_cat_cols):\n",
    "    group_means = df_train.groupby([col])['ALLOW'].mean().rename('ALLOW_RATE').sort_values(ascending=False).to_frame()\n",
    "    sns.barplot(x='ALLOW_RATE', y=group_means.index, data=group_means, ax=axes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamento dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma vez que exploramos os dados, entendemos a importância de cada coluna e podemos fazer alterações nelas para para obter um melhor resultado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Dados de entrada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "O desafio espera um modelo que aceite todas as variáveis dos conjuntos de dados disponíveis (exceto a variável destino, `ALLOW`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "challenge_columns = [\n",
    "    \"ID\",\n",
    "    \"CHECKING_BALANCE\",\n",
    "    \"PAYMENT_TERM\",\n",
    "    \"CREDIT_HISTORY\",\n",
    "    \"LOAN_PURPOSE\",\n",
    "    \"LOAN_AMOUNT\",\n",
    "    \"EXISTING_SAVINGS\",\n",
    "    \"EMPLOYMENT_DURATION\",\n",
    "    \"INSTALLMENT_PERCENT\",\n",
    "    \"SEX\",\n",
    "    \"OTHERS_ON_LOAN\",\n",
    "    \"CURRENT_RESIDENCE_DURATION\",\n",
    "    \"PROPERTY\",\n",
    "    \"AGE\",\n",
    "    \"INSTALLMENT_PLANS\",\n",
    "    \"HOUSING\",\n",
    "    \"EXISTING_CREDITS_COUNT\",\n",
    "    \"JOB_TYPE\",\n",
    "    \"DEPENDENTS\",\n",
    "    \"TELEPHONE\",\n",
    "    \"FOREIGN_WORKER\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variáveis categóricas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observando a execução do método `.info()` acima, podemos ver que existem colunas do tipo `object`. O modelo do `scikit-learn` que vamos usar não é capaz de processar uma variável desse tipo. Portanto, para dar seguimento ao experimento, será utilizada a técnica de _one-hot encoding_ para tratamento de variáveis categóricas. Além disso, a coluna `ID` será desconsiderada, pois sabemos que ela não é uma informação útil para a predição (é apenas um número identificando um cliente).\n",
    "\n",
    "Primeiramente, especificaremos quais variáveis serão tratadas como categóricas e quais serão tratadas como numéricas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = [\n",
    "    \"CREDIT_HISTORY\",\n",
    "    \"LOAN_PURPOSE\",\n",
    "    \"SEX\",\n",
    "    \"OTHERS_ON_LOAN\",\n",
    "    \"PROPERTY\",\n",
    "    \"INSTALLMENT_PLANS\",\n",
    "    \"HOUSING\",\n",
    "    \"TELEPHONE\",  # Boolean  # TODO: Impute some value instead OneHotEncoding the missing values\n",
    "    \"FOREIGN_WORKER\",  # Boolean  # TODO: Impute some value instead OneHotEncoding the missing values\n",
    "]\n",
    "numeric_features = [\n",
    "    \"CHECKING_BALANCE\",\n",
    "    \"PAYMENT_TERM\",\n",
    "    \"LOAN_AMOUNT\",\n",
    "    \"EXISTING_SAVINGS\",\n",
    "    \"EMPLOYMENT_DURATION\",\n",
    "    \"INSTALLMENT_PERCENT\",\n",
    "    \"CURRENT_RESIDENCE_DURATION\",\n",
    "    \"AGE\",\n",
    "    \"EXISTING_CREDITS_COUNT\",\n",
    "    \"JOB_TYPE\", # NOTE: This could also be considered categorical\n",
    "    \"DEPENDENTS\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qualquer outra variável será desconsiderada:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ID']\n"
     ]
    }
   ],
   "source": [
    "features = numeric_features + categorical_features\n",
    "unwanted_columns = list(\n",
    "    sorted((set(challenge_columns) - set([target])) - set(features))\n",
    ")\n",
    "print(unwanted_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A pipeline de pré-processamento será atualizada para que realize transformações específicas para cada tipo de variável:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('to_numeric', to_numeric),\n",
    "])\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features),\n",
    "    ]\n",
    ")\n",
    "# display(preprocessor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesta nova versão:\n",
    "- As variáveis `CHECKING_BALANCE` e `EXISTING_SAVINGS` continuarão sendo convertidas para tipos numéricos\n",
    "- One-Hot-Encoding será aplicado às variáveis categóricas, resultando em uma coluna para cada valor\n",
    "\n",
    "A título de exemplo, a antiga coluna `SEX` foi transformada em novas colunas, uma para cada valor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x2_F</th>\n",
       "      <th>x2_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2416</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x2_F  x2_M\n",
       "1866   0.0   1.0\n",
       "2416   1.0   0.0\n",
       "639    1.0   0.0\n",
       "281    0.0   1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor.fit(df_train)\n",
    "# onehot_features = list(preprocessor.named_transformers_[\"cat\"][\"onehot\"].get_feature_names_out())\n",
    "onehot_features = list(preprocessor.named_transformers_[\"cat\"][\"onehot\"].get_feature_names())  # scikit-learn=0.23\n",
    "clean_df = pd.DataFrame(\n",
    "    preprocessor.transform(df_train),\n",
    "    columns=numeric_features + onehot_features,\n",
    "    index=df_train.index\n",
    ")\n",
    "# clean_df.head(4)\n",
    "clean_df[clean_df.columns[clean_df.columns.str.endswith('_F') | clean_df.columns.str.endswith('_M')]].head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta é a lista completa das colunas criadas pelo One-Hot-Encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x0_ALL_CREDITS_PAID_BACK', 'x0_CREDITS_PAID_TO_DATE', 'x0_NO_CREDITS', 'x0_OUTSTANDING_CREDIT', 'x0_PRIOR_PAYMENTS_DELAYED', 'x1_APPLIANCES', 'x1_BUSINESS', 'x1_CAR_NEW', 'x1_CAR_USED', 'x1_EDUCATION', 'x1_FURNITURE', 'x1_OTHER', 'x1_RADIO_TV', 'x1_REPAIRS', 'x1_RETRAINING', 'x1_VACATION', 'x2_F', 'x2_M', 'x3_CO-APPLICANT', 'x3_GUARANTOR', 'x3_NONE', 'x4_CAR_OTHER', 'x4_REAL_ESTATE', 'x4_SAVINGS_INSURANCE', 'x4_UNKNOWN', 'x5_BANK', 'x5_NONE', 'x5_STORES', 'x6_FREE', 'x6_OWN', 'x6_RENT', 'x7_0.0', 'x7_1.0', 'x8_0.0', 'x8_1.0']\n"
     ]
    }
   ],
   "source": [
    "print(onehot_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variáveis faltantes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com as etapas de pré-processamento definidas até aqui, algumas colunas ainda têm valores faltantes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3499 entries, 1866 to 415\n",
      "Data columns (total 11 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   CHECKING_BALANCE            1296 non-null   float64\n",
      " 1   PAYMENT_TERM                3478 non-null   float64\n",
      " 2   LOAN_AMOUNT                 3486 non-null   float64\n",
      " 3   EXISTING_SAVINGS            2006 non-null   float64\n",
      " 4   EMPLOYMENT_DURATION         3437 non-null   float64\n",
      " 5   INSTALLMENT_PERCENT         3479 non-null   float64\n",
      " 6   CURRENT_RESIDENCE_DURATION  3430 non-null   float64\n",
      " 7   AGE                         3431 non-null   float64\n",
      " 8   EXISTING_CREDITS_COUNT      2155 non-null   float64\n",
      " 9   JOB_TYPE                    3427 non-null   float64\n",
      " 10  DEPENDENTS                  3429 non-null   float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 457.1 KB\n"
     ]
    }
   ],
   "source": [
    "clean_df[clean_df.columns[clean_df.isnull().sum() > 0]].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nestes casos, faremos apenas um tratamento simples, de imputar o valor zero nas linhas que tiverem faltando algum valor. Não necessariamente essa técnica é a melhor para se utilizar no desafio, é apenas um exemplo de como tratar o dataset.\n",
    "\n",
    "Tratamentos mais avançados, como modificação de colunas ou criação de novas colunas, serão incluídos na `Pipeline` posteriormente, se necessário."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='constant', fill_value=0)\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('to_numeric', to_numeric),\n",
    "    ('imputer', imputer),\n",
    "])\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features),\n",
    "    ]\n",
    ")\n",
    "# display(preprocessor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor.fit(df_train)\n",
    "clean_df = pd.DataFrame(\n",
    "    preprocessor.transform(df_train),\n",
    "    columns=numeric_features + onehot_features,\n",
    "    index=df_train.index\n",
    ")\n",
    "# display(clean_df.head(2))\n",
    "# clean_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A nova pipeline de pré-processamento zera o número de colunas com dados faltantes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Escalamento de variáveis contínuas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para evitar problemas com modelos sensíveis a diferentes escalas numéricas, vamos normalizar as variáveis numéricas para que tenham média zero e desvio padrão unitário. A título de exemplo, antes de normalizar os dados, estas são as variáveis com maior e menor desvio padrão, respectivamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Desvio padrão</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EXISTING_SAVINGS</th>\n",
       "      <td>3716.668486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x0_NO_CREDITS</th>\n",
       "      <td>0.122156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Desvio padrão\n",
       "EXISTING_SAVINGS    3716.668486\n",
       "x0_NO_CREDITS          0.122156"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_std = clean_df.std().sort_values(ascending=False).rename('Desvio padrão').to_frame()\n",
    "display(pd.concat([top_std.head(1), top_std.tail(1)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='constant', fill_value=0)\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('to_numeric', to_numeric),\n",
    "    ('imputer', imputer),\n",
    "    ('scaler', StandardScaler()),\n",
    "])\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features),\n",
    "    ]\n",
    ")\n",
    "# display(preprocessor)\n",
    "\n",
    "preprocessor.fit(df_train)\n",
    "clean_df = pd.DataFrame(\n",
    "    preprocessor.transform(df_train),\n",
    "    columns=numeric_features + onehot_features,\n",
    "    index=df_train.index\n",
    ")\n",
    "# display(clean_df.head(2))\n",
    "# clean_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, o desvio padrão está próximo de um:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Desvio padrão</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EXISTING_SAVINGS</th>\n",
       "      <td>1.000143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x0_NO_CREDITS</th>\n",
       "      <td>0.122156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Desvio padrão\n",
       "EXISTING_SAVINGS       1.000143\n",
       "x0_NO_CREDITS          0.122156"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_std_normalized = clean_df.std()[top_std.index].rename('Desvio padrão').to_frame()\n",
    "display(pd.concat([top_std_normalized.head(1), top_std_normalized.tail(1)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criação do modelo\n",
    "\n",
    "Com os dados prontos, podemos selecionar um modelo de Machine Learning para treinar com nossos dados. Nesse exemplo, vamos utilizar um modelo de classificação básico, o de Árvore de Decisão."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = DecisionTreeClassifier(random_state=seed)\n",
    "pipe = Pipeline([\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('clf', classifier)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pronto! Essa pipeline agora está pronta para receber todas as variáveis do desafio, transformá-las e passar para o modelo aquelas que forem relevantes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display(pipe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abaixo, separamos os dados que queremos predizer dos dados que utilizamos como informações para a predição."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3499, 21)\n",
      "(3499,)\n",
      "(500, 21)\n",
      "(500,)\n"
     ]
    }
   ],
   "source": [
    "X_train = df_train[challenge_columns]\n",
    "y_train = df_train[target]\n",
    "X_test = df_test[challenge_columns]\n",
    "y_test = df_test[target]\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {\n",
    "    'clf':[LinearSVC(dual=False, max_iter=100000, random_state=seed)],\n",
    "    'clf__C': [0.001],\n",
    "    'clf__penalty': ['l2'],\n",
    "    },\n",
    "    {\n",
    "    'clf': [GaussianNB()],\n",
    "    },\n",
    "    {\n",
    "    'clf': [DecisionTreeClassifier(random_state=seed)],\n",
    "    'clf__max_depth': [5],\n",
    "    'clf__max_features': [0.9],\n",
    "    'clf__min_samples_split': [10],\n",
    "    'clf__min_samples_leaf': [2],\n",
    "    'clf__criterion': ['entropy'],\n",
    "    },\n",
    "    {\n",
    "    'clf': [KNeighborsClassifier()],\n",
    "    'clf__n_neighbors': [80],\n",
    "    'clf__weights': ['uniform'],\n",
    "    },\n",
    "    {\n",
    "    'clf': [LogisticRegression(solver='liblinear', random_state=seed)],\n",
    "    'clf__C': [0.01],\n",
    "    'clf__penalty': ['l2'],\n",
    "    },\n",
    "    {\n",
    "    'clf': [ExtraTreesClassifier(random_state=seed)],\n",
    "    'clf__n_estimators': [100],\n",
    "    },\n",
    "    {\n",
    "    'clf': [AdaBoostClassifier(random_state=seed)],\n",
    "    'clf__n_estimators': [100],\n",
    "    'clf__learning_rate': [1.0],\n",
    "    },\n",
    "    {\n",
    "    'clf':[SVC(random_state=seed)],\n",
    "    'clf__C': [50],\n",
    "    'clf__gamma': [0.005],\n",
    "    },\n",
    "    {\n",
    "    'clf': [RandomForestClassifier(random_state=seed)],\n",
    "    'clf__max_depth': [8],\n",
    "    'clf__n_estimators': [100],\n",
    "    'clf__max_features': [0.7],\n",
    "    'clf__min_samples_split': [6],\n",
    "    'clf__min_samples_leaf': [3],\n",
    "    'clf__criterion': ['entropy'],\n",
    "    },\n",
    "    {\n",
    "    'clf': [BaggingClassifier(random_state=seed)],\n",
    "    'clf__base_estimator': [SVC(C=50, gamma=0.005, random_state=seed)],\n",
    "    'clf__n_estimators': [8],\n",
    "    'clf__max_samples': [0.9],\n",
    "    'clf__max_features': [0.6],\n",
    "    },\n",
    "    {\n",
    "    'clf': [MLPClassifier(max_iter=10000, random_state=seed)],\n",
    "    'clf__alpha': [0.001],\n",
    "    'clf__hidden_layer_sizes': [25],\n",
    "    'clf__learning_rate_init': [0.0001],\n",
    "    },\n",
    "    {\n",
    "    'clf': [StackingClassifier(estimators=[\n",
    "        ('lsvc', LinearSVC(C=0.001, penalty='l2', dual=False, max_iter=100000, random_state=seed)),\n",
    "        ('gnb', GaussianNB()),\n",
    "        ('dtc', DecisionTreeClassifier(max_depth=5, max_features=0.9, min_samples_split=10, min_samples_leaf=2, criterion='entropy', random_state=seed)),\n",
    "        ('knn', KNeighborsClassifier(n_neighbors=80, weights='uniform')),\n",
    "        ('lr', LogisticRegression(C=0.01, penalty='l2', solver='liblinear', random_state=seed)),\n",
    "        ('etc', ExtraTreesClassifier(n_estimators=100, random_state=seed)),\n",
    "        ('abc', AdaBoostClassifier(n_estimators=100, learning_rate=1.0, random_state=seed)),\n",
    "        ('svc', SVC(C=50, gamma=0.005, random_state=seed)),\n",
    "        ('rfc', RandomForestClassifier(max_depth=8, n_estimators=100, max_features=0.7, min_samples_split=6, min_samples_leaf=3, criterion='entropy', random_state=seed)),\n",
    "        ('mlpc', MLPClassifier(alpha=0.001, hidden_layer_sizes=25, learning_rate_init=0.0001, max_iter=10000, random_state=seed)),\n",
    "    ])],\n",
    "    'clf__final_estimator': [SVC()]\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 12 candidates, totalling 120 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:  2.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo gasto para treinar o modelo 10 vezes com cada combinação de parâmetros para encontrar a melhor: 0:02:55.558340\n"
     ]
    }
   ],
   "source": [
    "cv_num = 10\n",
    "verbose = 1\n",
    "scoring = ['f1', 'accuracy', 'precision', 'recall']\n",
    "\n",
    "cv_train_size = int(((cv_num - 1) / cv_num) * len(X_train))\n",
    "cv_test_size = len(X_train) - cv_train_size\n",
    "\n",
    "\n",
    "# cv = RandomizedSearchCV(pipe, param_distributions=param_grid, n_jobs=-1, verbose=verbose, random_state=seed,\n",
    "#                           cv=cv_num, n_iter=15, scoring=scoring)\n",
    "cv = GridSearchCV(pipe, param_grid, cv=cv_num, n_jobs=-1, verbose=verbose, scoring=scoring, refit=scoring[0])\n",
    "\n",
    "start = timer()\n",
    "cv.fit(X_train, y_train)\n",
    "end = timer()\n",
    "print(f'Tempo gasto para treinar o modelo {cv_num} vezes com cada combinação de parâmetros para encontrar a melhor: {timedelta(seconds=end-start)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>Dados brutos da validação cruzada</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cada combinação de parâmetros foi utilizada em 10 rodadas de treino com 3149 exemplos e teste com 350.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_test_f1</th>\n",
       "      <th>mean_test_accuracy</th>\n",
       "      <th>mean_test_precision</th>\n",
       "      <th>mean_test_recall</th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_f1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.864094</td>\n",
       "      <td>0.803085</td>\n",
       "      <td>0.798363</td>\n",
       "      <td>0.941905</td>\n",
       "      <td>74.787731</td>\n",
       "      <td>{'clf': StackingClassifier(estimators=[('lsvc',\n",
       "                                LinearSVC(C=0.001, dual=False, max_iter=100000,\n",
       "                                          random_state=0)),\n",
       "                               ('gnb', GaussianNB()),\n",
       "                               ('dtc',\n",
       "                                DecisionTreeClassifier(criterion='entropy',\n",
       "                                                       max_depth=5,\n",
       "                                                       max_features=0.9,\n",
       "                                                       min_samples_leaf=2,\n",
       "                                                       min_samples_split=10,\n",
       "                                                       random_state=0)),\n",
       "                               ('knn', KNeighborsClassifier(n_neighbors=80)),\n",
       "                               ('lr',\n",
       "                                LogisticRegression(C=0.01, random_state=0,...\n",
       "                                AdaBoostClassifier(n_estimators=100,\n",
       "                                                   random_state=0)),\n",
       "                               ('svc', SVC(C=50, gamma=0.005, random_state=0)),\n",
       "                               ('rfc',\n",
       "                                RandomForestClassifier(criterion='entropy',\n",
       "                                                       max_depth=8,\n",
       "                                                       max_features=0.7,\n",
       "                                                       min_samples_leaf=3,\n",
       "                                                       min_samples_split=6,\n",
       "                                                       random_state=0)),\n",
       "                               ('mlpc',\n",
       "                                MLPClassifier(alpha=0.001,\n",
       "                                              hidden_layer_sizes=25,\n",
       "                                              learning_rate_init=0.0001,\n",
       "                                              max_iter=10000,\n",
       "                                              random_state=0))],\n",
       "                   final_estimator=SVC()), 'clf__final_estimator': SVC()}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.862710</td>\n",
       "      <td>0.803086</td>\n",
       "      <td>0.804298</td>\n",
       "      <td>0.930711</td>\n",
       "      <td>2.162953</td>\n",
       "      <td>{'clf': RandomForestClassifier(random_state=0), 'clf__criterion': 'entropy', 'clf__max_depth': 8, 'clf__max_features': 0.7, 'clf__min_samples_leaf': 3, 'clf__min_samples_split': 6, 'clf__n_estimators': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.862170</td>\n",
       "      <td>0.801655</td>\n",
       "      <td>0.801306</td>\n",
       "      <td>0.933310</td>\n",
       "      <td>1.402650</td>\n",
       "      <td>{'clf': SVC(random_state=0), 'clf__C': 50, 'clf__gamma': 0.005}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.860462</td>\n",
       "      <td>0.798797</td>\n",
       "      <td>0.797959</td>\n",
       "      <td>0.933734</td>\n",
       "      <td>2.862758</td>\n",
       "      <td>{'clf': BaggingClassifier(random_state=0), 'clf__base_estimator': SVC(C=50, gamma=0.005, random_state=0), 'clf__max_features': 0.6, 'clf__max_samples': 0.9, 'clf__n_estimators': 8}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.854029</td>\n",
       "      <td>0.794796</td>\n",
       "      <td>0.809748</td>\n",
       "      <td>0.903606</td>\n",
       "      <td>8.990917</td>\n",
       "      <td>{'clf': MLPClassifier(max_iter=10000, random_state=0), 'clf__alpha': 0.001, 'clf__hidden_layer_sizes': 25, 'clf__learning_rate_init': 0.0001}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.851725</td>\n",
       "      <td>0.787078</td>\n",
       "      <td>0.792672</td>\n",
       "      <td>0.920405</td>\n",
       "      <td>0.135280</td>\n",
       "      <td>{'clf': KNeighborsClassifier(), 'clf__n_neighbors': 80, 'clf__weights': 'uniform'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.848544</td>\n",
       "      <td>0.787364</td>\n",
       "      <td>0.805421</td>\n",
       "      <td>0.896722</td>\n",
       "      <td>0.126943</td>\n",
       "      <td>{'clf': LinearSVC(dual=False, max_iter=100000, random_state=0), 'clf__C': 0.001, 'clf__penalty': 'l2'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.846967</td>\n",
       "      <td>0.786223</td>\n",
       "      <td>0.807416</td>\n",
       "      <td>0.890706</td>\n",
       "      <td>0.127395</td>\n",
       "      <td>{'clf': LogisticRegression(random_state=0, solver='liblinear'), 'clf__C': 0.01, 'clf__penalty': 'l2'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.846193</td>\n",
       "      <td>0.784502</td>\n",
       "      <td>0.804423</td>\n",
       "      <td>0.892848</td>\n",
       "      <td>0.937918</td>\n",
       "      <td>{'clf': ExtraTreesClassifier(random_state=0), 'clf__n_estimators': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.844676</td>\n",
       "      <td>0.786506</td>\n",
       "      <td>0.817140</td>\n",
       "      <td>0.874362</td>\n",
       "      <td>1.006280</td>\n",
       "      <td>{'clf': AdaBoostClassifier(random_state=0), 'clf__learning_rate': 1.0, 'clf__n_estimators': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.843512</td>\n",
       "      <td>0.774222</td>\n",
       "      <td>0.782412</td>\n",
       "      <td>0.915669</td>\n",
       "      <td>0.113297</td>\n",
       "      <td>{'clf': DecisionTreeClassifier(random_state=0), 'clf__criterion': 'entropy', 'clf__max_depth': 5, 'clf__max_features': 0.9, 'clf__min_samples_leaf': 2, 'clf__min_samples_split': 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.720650</td>\n",
       "      <td>0.683915</td>\n",
       "      <td>0.872478</td>\n",
       "      <td>0.614030</td>\n",
       "      <td>0.106632</td>\n",
       "      <td>{'clf': GaussianNB()}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean_test_f1  mean_test_accuracy  mean_test_precision  \\\n",
       "rank_test_f1                                                          \n",
       "1                 0.864094            0.803085             0.798363   \n",
       "2                 0.862710            0.803086             0.804298   \n",
       "3                 0.862170            0.801655             0.801306   \n",
       "4                 0.860462            0.798797             0.797959   \n",
       "5                 0.854029            0.794796             0.809748   \n",
       "6                 0.851725            0.787078             0.792672   \n",
       "7                 0.848544            0.787364             0.805421   \n",
       "8                 0.846967            0.786223             0.807416   \n",
       "9                 0.846193            0.784502             0.804423   \n",
       "10                0.844676            0.786506             0.817140   \n",
       "11                0.843512            0.774222             0.782412   \n",
       "12                0.720650            0.683915             0.872478   \n",
       "\n",
       "              mean_test_recall  mean_fit_time  \\\n",
       "rank_test_f1                                    \n",
       "1                     0.941905      74.787731   \n",
       "2                     0.930711       2.162953   \n",
       "3                     0.933310       1.402650   \n",
       "4                     0.933734       2.862758   \n",
       "5                     0.903606       8.990917   \n",
       "6                     0.920405       0.135280   \n",
       "7                     0.896722       0.126943   \n",
       "8                     0.890706       0.127395   \n",
       "9                     0.892848       0.937918   \n",
       "10                    0.874362       1.006280   \n",
       "11                    0.915669       0.113297   \n",
       "12                    0.614030       0.106632   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                params  \n",
       "rank_test_f1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            \n",
       "1             {'clf': StackingClassifier(estimators=[('lsvc',\n",
       "                                LinearSVC(C=0.001, dual=False, max_iter=100000,\n",
       "                                          random_state=0)),\n",
       "                               ('gnb', GaussianNB()),\n",
       "                               ('dtc',\n",
       "                                DecisionTreeClassifier(criterion='entropy',\n",
       "                                                       max_depth=5,\n",
       "                                                       max_features=0.9,\n",
       "                                                       min_samples_leaf=2,\n",
       "                                                       min_samples_split=10,\n",
       "                                                       random_state=0)),\n",
       "                               ('knn', KNeighborsClassifier(n_neighbors=80)),\n",
       "                               ('lr',\n",
       "                                LogisticRegression(C=0.01, random_state=0,...\n",
       "                                AdaBoostClassifier(n_estimators=100,\n",
       "                                                   random_state=0)),\n",
       "                               ('svc', SVC(C=50, gamma=0.005, random_state=0)),\n",
       "                               ('rfc',\n",
       "                                RandomForestClassifier(criterion='entropy',\n",
       "                                                       max_depth=8,\n",
       "                                                       max_features=0.7,\n",
       "                                                       min_samples_leaf=3,\n",
       "                                                       min_samples_split=6,\n",
       "                                                       random_state=0)),\n",
       "                               ('mlpc',\n",
       "                                MLPClassifier(alpha=0.001,\n",
       "                                              hidden_layer_sizes=25,\n",
       "                                              learning_rate_init=0.0001,\n",
       "                                              max_iter=10000,\n",
       "                                              random_state=0))],\n",
       "                   final_estimator=SVC()), 'clf__final_estimator': SVC()}  \n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       {'clf': RandomForestClassifier(random_state=0), 'clf__criterion': 'entropy', 'clf__max_depth': 8, 'clf__max_features': 0.7, 'clf__min_samples_leaf': 3, 'clf__min_samples_split': 6, 'clf__n_estimators': 100}  \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      {'clf': SVC(random_state=0), 'clf__C': 50, 'clf__gamma': 0.005}  \n",
       "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 {'clf': BaggingClassifier(random_state=0), 'clf__base_estimator': SVC(C=50, gamma=0.005, random_state=0), 'clf__max_features': 0.6, 'clf__max_samples': 0.9, 'clf__n_estimators': 8}  \n",
       "5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        {'clf': MLPClassifier(max_iter=10000, random_state=0), 'clf__alpha': 0.001, 'clf__hidden_layer_sizes': 25, 'clf__learning_rate_init': 0.0001}  \n",
       "6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   {'clf': KNeighborsClassifier(), 'clf__n_neighbors': 80, 'clf__weights': 'uniform'}  \n",
       "7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               {'clf': LinearSVC(dual=False, max_iter=100000, random_state=0), 'clf__C': 0.001, 'clf__penalty': 'l2'}  \n",
       "8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                {'clf': LogisticRegression(random_state=0, solver='liblinear'), 'clf__C': 0.01, 'clf__penalty': 'l2'}  \n",
       "9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              {'clf': ExtraTreesClassifier(random_state=0), 'clf__n_estimators': 100}  \n",
       "10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    {'clf': AdaBoostClassifier(random_state=0), 'clf__learning_rate': 1.0, 'clf__n_estimators': 100}  \n",
       "11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               {'clf': DecisionTreeClassifier(random_state=0), 'clf__criterion': 'entropy', 'clf__max_depth': 5, 'clf__max_features': 0.9, 'clf__min_samples_leaf': 2, 'clf__min_samples_split': 10}  \n",
       "12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               {'clf': GaussianNB()}  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML('<h4>Dados brutos da validação cruzada</h4>'))\n",
    "print(f'Cada combinação de parâmetros foi utilizada em {cv_num} rodadas de treino com {cv_train_size} exemplos e teste com {cv_test_size}.')\n",
    "\n",
    "rank_col = 'rank_test_' + scoring[0]\n",
    "cv_df = pd.DataFrame(cv.cv_results_).sort_values(rank_col).set_index(rank_col)\n",
    "\n",
    "cols = ['mean_test_f1', 'mean_test_accuracy', 'mean_test_precision', 'mean_test_recall', 'mean_fit_time', 'params']\n",
    "with pd.option_context('display.max_colwidth', None):\n",
    "    display(cv_df[cols].head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seconds = timedelta(seconds=end-start).seconds\n",
    "# pickle_file = f'cv_df_{cv_num}x{len(cv_df)}_{seconds}sec.zip'\n",
    "# cv_df.to_pickle(pickle_file)\n",
    "# temp = pd.read_pickle(pickle_file)\n",
    "# print(temp.sort_values('mean_fit_time')[['mean_fit_time', 'param_clf']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #### Melhor combinação de parâmetros para cada modelo\n",
    "# model_names = cv_df['param_clf'].astype(str).replace(r'\\([\\w\\W]+', r'', regex=True)\n",
    "# largest_by_model = cv_df[cv_df.groupby(model_names)['mean_test_f1'].rank(ascending=False) <= 2]\n",
    "# with pd.option_context('display.max_colwidth', None):\n",
    "#     display(largest_by_model[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>Maior média de f1</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ul>\n",
       "<li>Maior média de f1: 0.86409 (desvio padrão: 0.00992)</li>\n",
       "<li>Parâmetros que maximizaram a média de f1:<br/><code>{'clf': StackingClassifier(estimators=[('lsvc',\n",
       "                                LinearSVC(C=0.001, dual=False, max_iter=100000,\n",
       "                                          random_state=0)),\n",
       "                               ('gnb', GaussianNB()),\n",
       "                               ('dtc',\n",
       "                                DecisionTreeClassifier(criterion='entropy',\n",
       "                                                       max_depth=5,\n",
       "                                                       max_features=0.9,\n",
       "                                                       min_samples_leaf=2,\n",
       "                                                       min_samples_split=10,\n",
       "                                                       random_state=0)),\n",
       "                               ('knn', KNeighborsClassifier(n_neighbors=80)),\n",
       "                               ('lr',\n",
       "                                LogisticRegression(C=0.01, random_state=0,...\n",
       "                                AdaBoostClassifier(n_estimators=100,\n",
       "                                                   random_state=0)),\n",
       "                               ('svc', SVC(C=50, gamma=0.005, random_state=0)),\n",
       "                               ('rfc',\n",
       "                                RandomForestClassifier(criterion='entropy',\n",
       "                                                       max_depth=8,\n",
       "                                                       max_features=0.7,\n",
       "                                                       min_samples_leaf=3,\n",
       "                                                       min_samples_split=6,\n",
       "                                                       random_state=0)),\n",
       "                               ('mlpc',\n",
       "                                MLPClassifier(alpha=0.001,\n",
       "                                              hidden_layer_sizes=25,\n",
       "                                              learning_rate_init=0.0001,\n",
       "                                              max_iter=10000,\n",
       "                                              random_state=0))],\n",
       "                   final_estimator=SVC()), 'clf__final_estimator': SVC()}</code></li>\n",
       "<li>Modelo com a maior média de f1:</li>\n",
       "</ul>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5708a25d-fa1f-4311-9cc3-006c0095cdbb\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"5708a25d-fa1f-4311-9cc3-006c0095cdbb\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('to_numeric',\n",
       "                                                                   ToNumeric(columns=['CHECKING_BALANCE',\n",
       "                                                                                      'EXISTING_SAVINGS'])),\n",
       "                                                                  ('imputer',\n",
       "                                                                   SimpleImputer(fill_value=0,\n",
       "                                                                                 strategy='constant')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['CHECKING_BALANCE',\n",
       "                                                   'PAYMENT_TERM',\n",
       "                                                   'LOAN_AMOUNT',\n",
       "                                                   'EXISTING_SAVINGS',\n",
       "                                                   'EMPLOYMENT_DURATION',\n",
       "                                                   'INSTALLM...\n",
       "                                                 AdaBoostClassifier(n_estimators=100,\n",
       "                                                                    random_state=0)),\n",
       "                                                ('svc',\n",
       "                                                 SVC(C=50, gamma=0.005,\n",
       "                                                     random_state=0)),\n",
       "                                                ('rfc',\n",
       "                                                 RandomForestClassifier(criterion='entropy',\n",
       "                                                                        max_depth=8,\n",
       "                                                                        max_features=0.7,\n",
       "                                                                        min_samples_leaf=3,\n",
       "                                                                        min_samples_split=6,\n",
       "                                                                        random_state=0)),\n",
       "                                                ('mlpc',\n",
       "                                                 MLPClassifier(alpha=0.001,\n",
       "                                                               hidden_layer_sizes=25,\n",
       "                                                               learning_rate_init=0.0001,\n",
       "                                                               max_iter=10000,\n",
       "                                                               random_state=0))],\n",
       "                                    final_estimator=SVC()))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a1d0f5f8-4e34-4c3d-8391-9deaf1f38edb\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a1d0f5f8-4e34-4c3d-8391-9deaf1f38edb\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('to_numeric',\n",
       "                                                  ToNumeric(columns=['CHECKING_BALANCE',\n",
       "                                                                     'EXISTING_SAVINGS'])),\n",
       "                                                 ('imputer',\n",
       "                                                  SimpleImputer(fill_value=0,\n",
       "                                                                strategy='constant')),\n",
       "                                                 ('scaler', StandardScaler())]),\n",
       "                                 ['CHECKING_BALANCE', 'PAYMENT_TERM',\n",
       "                                  'LOAN_AMOUNT', 'EXISTING_SAVINGS',\n",
       "                                  'EMPLOYMENT_DURATION', 'INSTALLMENT_PERCENT',\n",
       "                                  'CURRENT_RESIDENCE_DURATION', 'AGE',\n",
       "                                  'EXISTING_CREDITS_COUNT', 'JOB_TYPE',\n",
       "                                  'DEPENDENTS']),\n",
       "                                ('cat',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(strategy='most_frequent')),\n",
       "                                                 ('onehot',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                 ['CREDIT_HISTORY', 'LOAN_PURPOSE', 'SEX',\n",
       "                                  'OTHERS_ON_LOAN', 'PROPERTY',\n",
       "                                  'INSTALLMENT_PLANS', 'HOUSING', 'TELEPHONE',\n",
       "                                  'FOREIGN_WORKER'])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6fe0e5b2-2adf-4623-8f42-6762a75a70a1\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"6fe0e5b2-2adf-4623-8f42-6762a75a70a1\">num</label><div class=\"sk-toggleable__content\"><pre>['CHECKING_BALANCE', 'PAYMENT_TERM', 'LOAN_AMOUNT', 'EXISTING_SAVINGS', 'EMPLOYMENT_DURATION', 'INSTALLMENT_PERCENT', 'CURRENT_RESIDENCE_DURATION', 'AGE', 'EXISTING_CREDITS_COUNT', 'JOB_TYPE', 'DEPENDENTS']</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8c40223e-5e06-4e15-ae90-16de920a3a46\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8c40223e-5e06-4e15-ae90-16de920a3a46\">ToNumeric</label><div class=\"sk-toggleable__content\"><pre>ToNumeric(columns=['CHECKING_BALANCE', 'EXISTING_SAVINGS'])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f45ce8fe-5502-4606-84fd-ddaf7100ebcc\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f45ce8fe-5502-4606-84fd-ddaf7100ebcc\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=0, strategy='constant')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"07a57aa3-4024-4911-a7fa-65c772f202f2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"07a57aa3-4024-4911-a7fa-65c772f202f2\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"91221d4f-2f65-41df-8cf5-bb3e75134a48\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"91221d4f-2f65-41df-8cf5-bb3e75134a48\">cat</label><div class=\"sk-toggleable__content\"><pre>['CREDIT_HISTORY', 'LOAN_PURPOSE', 'SEX', 'OTHERS_ON_LOAN', 'PROPERTY', 'INSTALLMENT_PLANS', 'HOUSING', 'TELEPHONE', 'FOREIGN_WORKER']</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"319d85a0-6979-43b0-bb11-5104779b2ea3\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"319d85a0-6979-43b0-bb11-5104779b2ea3\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy='most_frequent')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"89573c72-8cde-4987-a510-98f0a59a5244\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"89573c72-8cde-4987-a510-98f0a59a5244\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3b7dd3eb-9095-4a7a-92d4-6dc4c670437d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"3b7dd3eb-9095-4a7a-92d4-6dc4c670437d\">clf: StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[('lsvc',\n",
       "                                LinearSVC(C=0.001, dual=False, max_iter=100000,\n",
       "                                          random_state=0)),\n",
       "                               ('gnb', GaussianNB()),\n",
       "                               ('dtc',\n",
       "                                DecisionTreeClassifier(criterion='entropy',\n",
       "                                                       max_depth=5,\n",
       "                                                       max_features=0.9,\n",
       "                                                       min_samples_leaf=2,\n",
       "                                                       min_samples_split=10,\n",
       "                                                       random_state=0)),\n",
       "                               ('knn', KNeighborsClassifier(n_neighbors=80)),\n",
       "                               ('lr',\n",
       "                                LogisticRegression(C=0.01, random_state=0,...\n",
       "                                AdaBoostClassifier(n_estimators=100,\n",
       "                                                   random_state=0)),\n",
       "                               ('svc', SVC(C=50, gamma=0.005, random_state=0)),\n",
       "                               ('rfc',\n",
       "                                RandomForestClassifier(criterion='entropy',\n",
       "                                                       max_depth=8,\n",
       "                                                       max_features=0.7,\n",
       "                                                       min_samples_leaf=3,\n",
       "                                                       min_samples_split=6,\n",
       "                                                       random_state=0)),\n",
       "                               ('mlpc',\n",
       "                                MLPClassifier(alpha=0.001,\n",
       "                                              hidden_layer_sizes=25,\n",
       "                                              learning_rate_init=0.0001,\n",
       "                                              max_iter=10000,\n",
       "                                              random_state=0))],\n",
       "                   final_estimator=SVC())</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lsvc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8dcb3d46-5ef7-4a8b-b795-04dde388c9cf\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8dcb3d46-5ef7-4a8b-b795-04dde388c9cf\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=0.001, dual=False, max_iter=100000, random_state=0)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>gnb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"203180fe-f528-4064-8ebb-c6f416f388f8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"203180fe-f528-4064-8ebb-c6f416f388f8\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>dtc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e1872368-a5a9-4b9c-b299-3668b72e7ccb\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e1872368-a5a9-4b9c-b299-3668b72e7ccb\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion='entropy', max_depth=5, max_features=0.9,\n",
       "                       min_samples_leaf=2, min_samples_split=10,\n",
       "                       random_state=0)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>knn</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f6f34843-3260-4f88-b7db-f901378a7e63\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f6f34843-3260-4f88-b7db-f901378a7e63\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=80)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8daf8f0e-8808-4ed4-8b75-dda6d0eec367\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8daf8f0e-8808-4ed4-8b75-dda6d0eec367\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.01, random_state=0, solver='liblinear')</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>etc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9ba73d39-db07-4000-874d-2a08434ce55b\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"9ba73d39-db07-4000-874d-2a08434ce55b\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier(random_state=0)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>abc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ee7a9f94-b5cd-4c09-ba9c-ad67f5007ade\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"ee7a9f94-b5cd-4c09-ba9c-ad67f5007ade\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(n_estimators=100, random_state=0)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"91f25761-9632-41f9-8f33-640d586cd7da\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"91f25761-9632-41f9-8f33-640d586cd7da\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=50, gamma=0.005, random_state=0)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rfc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9a330097-d33d-4fc9-8610-b18108f7edf0\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"9a330097-d33d-4fc9-8610-b18108f7edf0\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion='entropy', max_depth=8, max_features=0.7,\n",
       "                       min_samples_leaf=3, min_samples_split=6, random_state=0)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>mlpc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e0a72f04-0a6a-4f67-8cc9-da72b73bc3d5\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e0a72f04-0a6a-4f67-8cc9-da72b73bc3d5\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=0.001, hidden_layer_sizes=25, learning_rate_init=0.0001,\n",
       "              max_iter=10000, random_state=0)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8dec5925-0a0a-4ad3-be39-f5d643dde27d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8dec5925-0a0a-4ad3-be39-f5d643dde27d\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('to_numeric',\n",
       "                                                                   ToNumeric(columns=['CHECKING_BALANCE',\n",
       "                                                                                      'EXISTING_SAVINGS'])),\n",
       "                                                                  ('imputer',\n",
       "                                                                   SimpleImputer(fill_value=0,\n",
       "                                                                                 strategy='constant')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['CHECKING_BALANCE',\n",
       "                                                   'PAYMENT_TERM',\n",
       "                                                   'LOAN_AMOUNT',\n",
       "                                                   'EXISTING_SAVINGS',\n",
       "                                                   'EMPLOYMENT_DURATION',\n",
       "                                                   'INSTALLM...\n",
       "                                                 AdaBoostClassifier(n_estimators=100,\n",
       "                                                                    random_state=0)),\n",
       "                                                ('svc',\n",
       "                                                 SVC(C=50, gamma=0.005,\n",
       "                                                     random_state=0)),\n",
       "                                                ('rfc',\n",
       "                                                 RandomForestClassifier(criterion='entropy',\n",
       "                                                                        max_depth=8,\n",
       "                                                                        max_features=0.7,\n",
       "                                                                        min_samples_leaf=3,\n",
       "                                                                        min_samples_split=6,\n",
       "                                                                        random_state=0)),\n",
       "                                                ('mlpc',\n",
       "                                                 MLPClassifier(alpha=0.001,\n",
       "                                                               hidden_layer_sizes=25,\n",
       "                                                               learning_rate_init=0.0001,\n",
       "                                                               max_iter=10000,\n",
       "                                                               random_state=0))],\n",
       "                                    final_estimator=SVC()))])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = cv.best_estimator_\n",
    "\n",
    "display(HTML(f'<h4>Maior média de {scoring[0]}</h4>'))\n",
    "display(HTML(f'''<ul>\n",
    "<li>Maior média de {scoring[0]}: {cv.best_score_:.5f} (desvio padrão: {cv.cv_results_[\"std_test_\" + scoring[0]][cv.best_index_]:.5f})</li>\n",
    "<li>Parâmetros que maximizaram a média de {scoring[0]}:<br/><code>{cv.best_params_}</code></li>\n",
    "<li>Modelo com a maior média de {scoring[0]}:</li>\n",
    "</ul>'''))\n",
    "display(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score do modelo no conjunto de testes: 0.86621\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "score = f1_score(y_test, y_pred)\n",
    "print(\n",
    "    f\"F1-score do modelo no conjunto de testes: {score:.5f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cmd = ConfusionMatrixDisplay.from_predictions(y_test, y_pred, labels=[0, 1], display_labels=['Negar', 'Aprovar'])\n",
    "cmd = plot_confusion_matrix(model, X_test, y_test, display_labels=['Negar', 'Aprovar'])  # scikit-learn=0.23\n",
    "cmd.ax_.set(xlabel='Previsão', ylabel='Realidade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## Watson Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As etapas da publicação serão colocadas em funções individuais.\n",
    "\n",
    "Primeiro, será usando um cliente da API do Watson Machine Learning para definir o espaço de publicação padrão:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Funções auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_client(API_KEY):\n",
    "    location = 'us-south'\n",
    "    wml_credentials = {\n",
    "        \"apikey\": API_KEY,\n",
    "        \"url\": 'https://' + location + '.ml.cloud.ibm.com'\n",
    "    }\n",
    "    return APIClient(wml_credentials)\n",
    "\n",
    "def set_default_space(client):\n",
    "    # The DEPLOYMENT_SPACE_GUID was copied from the output of\n",
    "    # client.spaces.list(limit=10)\n",
    "    DEPLOYMENT_SPACE_GUID = os.getenv(\"DEPLOYMENT_SPACE_GUID\")\n",
    "    client.set.default_space(DEPLOYMENT_SPACE_GUID)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O transformador customizado será enviado para o WML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_user_package_extension(client):\n",
    "    meta_prop_pkg_extn = {\n",
    "        client.package_extensions.ConfigurationMetaNames.NAME: \"Custom_Sklearn_Transformers\",\n",
    "        client.package_extensions.ConfigurationMetaNames.DESCRIPTION: \"Extensão para transformações personalizadas\",\n",
    "        client.package_extensions.ConfigurationMetaNames.TYPE: \"pip_zip\"\n",
    "    }\n",
    "\n",
    "    # Subir o pacote\n",
    "    pkg_extn_details = client.package_extensions.store(meta_props=meta_prop_pkg_extn,\n",
    "                                                       file_path=\"custom_sklearn_transformers.zip\")\n",
    "\n",
    "    # Salvar as informações sobre o pacote\n",
    "    pkg_extn_uid = client.package_extensions.get_uid(pkg_extn_details)\n",
    "    return pkg_extn_uid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos agora criar uma especificação de software com o nosso pacote customizado, para que o WML possa utilizar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_software_specification(client, pkg_extn_uid):\n",
    "    base_sw_spec_uid = client.software_specifications.get_uid_by_name(\"default_py3.8\")\n",
    "    \n",
    "    # Metadados da nova especificação de software\n",
    "    meta_prop_sw_spec = {\n",
    "        client.software_specifications.ConfigurationMetaNames.NAME: \"sw_spec_custom_sklearn_transformers\",\n",
    "        client.software_specifications.ConfigurationMetaNames.DESCRIPTION: \"Especificação de software com transformações personalizadas\",\n",
    "        client.software_specifications.ConfigurationMetaNames.BASE_SOFTWARE_SPECIFICATION: {\"guid\": base_sw_spec_uid}\n",
    "    }\n",
    "\n",
    "    # Criando a nova especificação de software e obtendo seu ID\n",
    "    sw_spec_details = client.software_specifications.store(meta_props=meta_prop_sw_spec)\n",
    "    sw_spec_uid = client.software_specifications.get_uid(sw_spec_details)\n",
    "\n",
    "    # Adicionando o pacote customizado à nova especificação\n",
    "    client.software_specifications.add_package_extension(sw_spec_uid, pkg_extn_uid)\n",
    "    return sw_spec_uid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, vamos publicar a pipeline utilizando a especificação de software customizada que criamos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_model(client, sw_spec_uid, model):\n",
    "    # Metadados do modelo\n",
    "    model_props = {\n",
    "        client.repository.ModelMetaNames.NAME: \"Pipeline customizada\",\n",
    "        client.repository.ModelMetaNames.TYPE: 'scikit-learn_0.23',\n",
    "        client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: sw_spec_uid\n",
    "    }\n",
    "\n",
    "    # Publicando a Pipeline como um modelo\n",
    "    # This creates a new Model asset in the deployment space on Watson Machine Learning:\n",
    "    # https://dataplatform.cloud.ibm.com/ml-runtime/spaces?context=cpdaas\n",
    "    published_model = client.repository.store_model(model=model, meta_props=model_props)\n",
    "    published_model_uid = client.repository.get_model_uid(published_model)\n",
    "    client.repository.get_details(published_model_uid)\n",
    "    return published_model_uid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora que o modelo está salvo, vamos deixá-lo disponível online, para que possamos testá-lo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_deploy(client, published_model_uid):\n",
    "    # Metadados para publicação do modelo\n",
    "    metadata = {\n",
    "        client.deployments.ConfigurationMetaNames.NAME: \"Publicação do modelo customizado\",\n",
    "        client.deployments.ConfigurationMetaNames.ONLINE: {}\n",
    "    }\n",
    "\n",
    "    # Publicar\n",
    "    created_deployment = client.deployments.create(published_model_uid, meta_props=metadata)\n",
    "\n",
    "    # There should be a new Deployment on the space:\n",
    "    # https://dataplatform.cloud.ibm.com/ml-runtime/spaces?context=cpdaas\n",
    "    return created_deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adaptando um trecho de código fornecido na referência da API, na página de deployment do modelo, podemos testar a API implementada:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_test_request_to_api(client, deployment_id):\n",
    "    meta_props = {\n",
    "        \"input_data\": [\n",
    "            {\n",
    "                \"fields\": [\n",
    "                    \"ID\",\n",
    "                    \"CHECKING_BALANCE\", \"PAYMENT_TERM\", \"CREDIT_HISTORY\", \"LOAN_PURPOSE\",\n",
    "                    \"LOAN_AMOUNT\", \"EXISTING_SAVINGS\", \"EMPLOYMENT_DURATION\", \"INSTALLMENT_PERCENT\",\n",
    "                    \"SEX\", \"OTHERS_ON_LOAN\", \"CURRENT_RESIDENCE_DURATION\", \"PROPERTY\",\n",
    "                    \"AGE\", \"INSTALLMENT_PLANS\", \"HOUSING\", \"EXISTING_CREDITS_COUNT\",\n",
    "                    \"JOB_TYPE\", \"DEPENDENTS\", \"TELEPHONE\", \"FOREIGN_WORKER\",\n",
    "                ],\n",
    "                \"values\": [\n",
    "                    [\n",
    "                        1234,\n",
    "                        None, 987.0, None, \"CAR_NEW\",\n",
    "                        4567.0, None, 5.0, 4.0,\n",
    "                        \"M\", \"NONE\", 3.0, \"SAVINGS_INSURANCE\",\n",
    "                        42.0, \"NONE\", \"OWN\", None,\n",
    "                        3.0, 1.0, 1.0, 1.0,\n",
    "                    ],\n",
    "                    [\n",
    "                        4321,\n",
    "                        \"NO_CHECKING\", 333.0, \"PRIOR_PAYMENTS_DELAYED\", \"BUSINESS\",\n",
    "                        3333.3, 111.1, 13.0, 3.0,\n",
    "                        \"F\", \"BANK\", 2.0, \"REAL_ESTATE\",\n",
    "                        33.0, \"NONE\", \"RENT\", 1.0,\n",
    "                        2.0, 2.0, 0.0, 0.0,\n",
    "                    ],\n",
    "                ],\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "    response_scoring = client.deployments.score(deployment_id, meta_props)\n",
    "\n",
    "    # See documentation at https://cloud.ibm.com/apidocs/machine-learning?code=python#deployments-compute-predictions\n",
    "    print(\"Scoring response\")\n",
    "    display(response_scoring)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Publicação do modelo e teste pela API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quando estiver pronto para publicar o modelo, basta descomentar as linhas da célula abaixo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load_dotenv(find_dotenv())\n",
    "# API_KEY = os.getenv(\"API_KEY\")\n",
    "\n",
    "# client = get_client(API_KEY)\n",
    "# set_default_space(client)\n",
    "# pkg_extn_uid = store_user_package_extension(client)\n",
    "# sw_spec_uid = store_software_specification(client, pkg_extn_uid)\n",
    "# published_model_uid = store_model(client, sw_spec_uid, pipe)\n",
    "# created_deployment = create_deploy(client, published_model_uid)\n",
    "# deployment_uid = client.deployments.get_uid(created_deployment)\n",
    "# make_test_request_to_api(client, deployment_uid)\n",
    "\n",
    "# print(\"O modelo está publicado! \"\n",
    "#       \"Para submeter o desafio, basta acessar https://maratona.dev/challenge/1, \"\n",
    "#       \"e utilizar as credenciais abaixo para realizar a submissão:\")\n",
    "# print(\"Credenciais para envio (não compartilhe esses dados com ninguém!)\\n\\n\"\n",
    "#       f\"API key: {API_KEY}\\nDeployment ID: {deployment_uid}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preenchimento do arquivo de respostas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CHECKING_BALANCE</th>\n",
       "      <th>PAYMENT_TERM</th>\n",
       "      <th>...</th>\n",
       "      <th>TELEPHONE</th>\n",
       "      <th>FOREIGN_WORKER</th>\n",
       "      <th>ALLOW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>42455</td>\n",
       "      <td>-211.72</td>\n",
       "      <td>155</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>243067</td>\n",
       "      <td>NO_CHECKING</td>\n",
       "      <td>660</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID CHECKING_BALANCE  PAYMENT_TERM  ... TELEPHONE FOREIGN_WORKER  \\\n",
       "993   42455          -211.72           155  ...         0              1   \n",
       "859  243067      NO_CHECKING           660  ...         1              1   \n",
       "\n",
       "     ALLOW  \n",
       "993      1  \n",
       "859      0  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "answers['ALLOW'] = model.predict(answers)\n",
    "answers.to_csv(\"ANSWERS.csv\", index=False)\n",
    "\n",
    "with pd.option_context('display.max_columns', 6):\n",
    "    display(answers.sample(2, random_state=seed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALLOW</th>\n",
       "      <td>0.788</td>\n",
       "      <td>0.212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1      0\n",
       "ALLOW  0.788  0.212"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answers['ALLOW'].value_counts(normalize=True).to_frame().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "código.zip\n"
     ]
    }
   ],
   "source": [
    "!rm -rf código.zip\n",
    "!zip --exclude '*/.git/*' -rq código.zip custom_sklearn_transformers notebook.ipynb\n",
    "!ls código.zip"
   ]
  }
 ],
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   "codemirror_mode": {
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