reg_log_smote.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import (classification_report, confusion_matrix,\n",
    "                             matthews_corrcoef, roc_auc_score, roc_curve)\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# Definindo funções\n",
    "################################################################################\n",
    "def gerar_transacao(config: dict) -> list:\n",
    "    \"\"\" \n",
    "    Gera uma linha de dados aleatórios de acordo com os parâmetros para\n",
    "    cada variável, definida no dicionário de configuração. \n",
    "    :param config: Dicionário contendo as porcentagens de cada variável\n",
    "    em caso de fraude.\n",
    "    :return: Lista de valores aleatórios gerados para cada coluna do \n",
    "    dataframe a ser criado.\n",
    "    \"\"\"\n",
    "    categoria = np.random.choice(list(config['categoria'].keys()))\n",
    "    localizacao = np.random.choice(list(config['localizacao'].keys()))\n",
    "    faixa_etaria = np.random.choice(list(config['faixa_etaria'].keys()))\n",
    "    dia_semana = np.random.choice(list(config['dia_semana'].keys()))\n",
    "    hora = np.random.choice(list(config['hora'].keys()))\n",
    "    fraude_prob = np.mean([\n",
    "        config['categoria'][categoria],\n",
    "        config['localizacao'][localizacao],\n",
    "        config['faixa_etaria'][faixa_etaria],\n",
    "        config['dia_semana'][dia_semana],\n",
    "        config['hora'][hora]\n",
    "    ])\n",
    "    fraude = np.random.rand() < fraude_prob\n",
    "    if fraude:\n",
    "        valor_transacao = np.random.uniform(*config['valor']['fraude'])\n",
    "    else:\n",
    "        valor_transacao = np.random.uniform(*config['valor']['legitimo'])\n",
    "    return categoria, localizacao, faixa_etaria, dia_semana, hora, valor_transacao, fraude\n",
    "\n",
    "def tratamento_hora(df: pd.DataFrame) -> pd.DataFrame:  \n",
    "    \"\"\"  \n",
    "    Transforma o formato de hora com tipo e numeração ideal para análise.\n",
    "    :param df: Dataframe com coluna de hora.\n",
    "    :return: Dataframe com coluna de hora tratada.\n",
    "    \"\"\"\n",
    "    df['hora'] = df['hora'].astype(str)\n",
    "    df['hora_transacao'] = df['hora'].str.replace('h', '', regex=False)\n",
    "    df['hora_transacao'] = pd.to_datetime(df['hora_transacao'], format='%H', errors='coerce')\n",
    "    df['hora_transacao'] = df['hora_transacao'].dt.hour\n",
    "    df = df.drop(columns=['hora'])\n",
    "    return df\n",
    "\n",
    "def tratamento_categoricas(df: pd.DataFrame, coluna: str) -> pd.DataFrame:\n",
    "    \"\"\"  \n",
    "    Trata coluna de valor categórico para gerar colunas binárias de \n",
    "    classificação.\n",
    "    :param df: Dataframe com coluna de dados categóricos.\n",
    "    :param coluna: Nome da coluna a ser tratada.\n",
    "    :return: Dataframe com coluna categórica trasnformada em colunas binárias.\n",
    "    \"\"\"\n",
    "    encoded_array = encoder.fit_transform(df[[coluna]])\n",
    "    encoded_df = pd.DataFrame(encoded_array, columns=encoder.get_feature_names_out([coluna]))\n",
    "    df_final = pd.concat([df.drop(coluna, axis=1), encoded_df], axis=1)\n",
    "    return df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# Definindo objetos\n",
    "################################################################################\n",
    "\n",
    "# Valores para o dataframe\n",
    "n_transacoes = 10000\n",
    "dados = {\n",
    "    'categoria': [],\n",
    "    'localizacao': [],\n",
    "    'faixa_etaria': [],\n",
    "    'dia_semana': [],\n",
    "    'hora': [],\n",
    "    'valor_transacao': [],\n",
    "    'fraude': []\n",
    "}\n",
    "dados_config = {'localizacao': {\n",
    "    'Norte': 0.034,\n",
    "    'Nordeste': 0.028,\n",
    "    'Centro-Oeste': 0.025,\n",
    "    'Sudeste': 0.021,\n",
    "    'Sul': 0.011},\n",
    "    'categoria' : {\n",
    "        'Celular': 0.082,\n",
    "        'Eletrônicos': 0.080,\n",
    "        'Informática': 0.043,\n",
    "        'Automotivo': 0.034,\n",
    "        'Eletrodomésticos': 0.033,\n",
    "        'Acessórios': 0.030,\n",
    "        'Móveis': 0.023,\n",
    "        'Beleza': 0.022,\n",
    "        'Esportes': 0.020,\n",
    "        'Eletroportáteis': 0.020},\n",
    "    'faixa_etaria': {\n",
    "        'até 25 anos': 0.040,\n",
    "        'entre 26 e 35 anos': 0.019,\n",
    "        'entre 36 e 50 anos': 0.015,\n",
    "        'acima de 51 anos': 0.021},\n",
    "    'dia_semana': {\n",
    "        'Domingo': 0.017,\n",
    "        'Segunda': 0.021,\n",
    "        'Terça': 0.022,\n",
    "        'Quarta': 0.022,\n",
    "        'Quinta': 0.022,\n",
    "        'Sexta': 0.021,\n",
    "        'Sábado': 0.020},\n",
    "    'hora': {\n",
    "        '00h': 0.036,\n",
    "        '01h': 0.042,\n",
    "        '02h': 0.069,\n",
    "        '03h': 0.077,\n",
    "        '04h': 0.067,\n",
    "        '05h': 0.037,\n",
    "        '06h': 0.014,\n",
    "        '07h': 0.017,\n",
    "        '08h': 0.014,\n",
    "        '09h': 0.015,\n",
    "        '10h': 0.016,\n",
    "        '11h': 0.018,\n",
    "        '12h': 0.020,\n",
    "        '13h': 0.021,\n",
    "        '14h': 0.021,\n",
    "        '15h': 0.022,\n",
    "        '16h': 0.023,\n",
    "        '17h': 0.022,\n",
    "        '18h': 0.022,\n",
    "        '19h': 0.021,\n",
    "        '20h': 0.021,\n",
    "        '21h': 0.020,\n",
    "        '22h': 0.021,\n",
    "        '23h': 0.026},\n",
    "    'valor': {'fraude': (500.00, 2000.00),\n",
    "        'legitimo': (50.00, 700.00)}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# Encoder e seed\n",
    "################################################################################\n",
    "\n",
    "np.random.seed(42)\n",
    "encoder = OneHotEncoder(sparse_output=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# Iteração para geração de dataframe\n",
    "################################################################################\n",
    "\n",
    "for i in range(n_transacoes):\n",
    "    categoria, localizacao, faixa_etaria, dia_semana, hora, valor_transacao, fraude = gerar_transacao(dados_config)\n",
    "    transacao = {\n",
    "        'categoria': categoria,\n",
    "        'localizacao': localizacao,\n",
    "        'faixa_etaria': faixa_etaria,\n",
    "        'dia_semana': dia_semana,\n",
    "        'hora': hora,\n",
    "        'valor_transacao': valor_transacao,\n",
    "        'fraude': fraude\n",
    "    }\n",
    "    for key, value in transacao.items():\n",
    "        dados[key].append(value)\n",
    "df = pd.DataFrame(dados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         categoria   localizacao        faixa_etaria dia_semana hora  \\\n",
      "0           Móveis       Sudeste         até 25 anos     Sábado  10h   \n",
      "1  Eletroportáteis  Centro-Oeste  entre 36 e 50 anos      Terça  10h   \n",
      "2           Beleza  Centro-Oeste  entre 26 e 35 anos     Quinta  01h   \n",
      "3      Eletrônicos       Sudeste         até 25 anos    Domingo  11h   \n",
      "4         Esportes         Norte  entre 36 e 50 anos      Terça  09h   \n",
      "\n",
      "   valor_transacao  fraude  \n",
      "0       437.952603   False  \n",
      "1       440.724758   False  \n",
      "2       660.059261   False  \n",
      "3        54.593098   False  \n",
      "4       682.941087   False  \n"
     ]
    }
   ],
   "source": [
    "################################################################################\n",
    "# Verificando as colunas do dataframe\n",
    "################################################################################\n",
    "\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# Tratamento de colunas\n",
    "################################################################################\n",
    "\n",
    "df_data = tratamento_hora(df)\n",
    "df_cat = tratamento_categoricas(df_data, 'categoria')\n",
    "df_loc = tratamento_categoricas(df_cat, 'localizacao')\n",
    "df_faixa = tratamento_categoricas(df_loc, 'faixa_etaria')\n",
    "df_transacoes = tratamento_categoricas(df_faixa, 'dia_semana')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>416.380697</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      valor_transacao  fraude  hora_transacao  categoria_Acessórios  \\\n",
       "0          437.952603   False              10                   0.0   \n",
       "1          440.724758   False              10                   0.0   \n",
       "2          660.059261   False               1                   0.0   \n",
       "3           54.593098   False              11                   0.0   \n",
       "4          682.941087   False               9                   0.0   \n",
       "...               ...     ...             ...                   ...   \n",
       "9995       217.340580   False               4                   0.0   \n",
       "9996       273.640842   False              21                   1.0   \n",
       "9997        68.896961   False               0                   0.0   \n",
       "9998      1400.769500    True               0                   0.0   \n",
       "9999       416.380697   False               7                   0.0   \n",
       "\n",
       "      categoria_Automotivo  categoria_Beleza  categoria_Celular  \\\n",
       "0                      0.0               0.0                0.0   \n",
       "1                      0.0               0.0                0.0   \n",
       "2                      0.0               1.0                0.0   \n",
       "3                      0.0               0.0                0.0   \n",
       "4                      0.0               0.0                0.0   \n",
       "...                    ...               ...                ...   \n",
       "9995                   0.0               0.0                0.0   \n",
       "9996                   0.0               0.0                0.0   \n",
       "9997                   0.0               0.0                1.0   \n",
       "9998                   1.0               0.0                0.0   \n",
       "9999                   0.0               1.0                0.0   \n",
       "\n",
       "      categoria_Eletrodomésticos  categoria_Eletroportáteis  \\\n",
       "0                            0.0                        0.0   \n",
       "1                            0.0                        1.0   \n",
       "2                            0.0                        0.0   \n",
       "3                            0.0                        0.0   \n",
       "4                            0.0                        0.0   \n",
       "...                          ...                        ...   \n",
       "9995                         0.0                        0.0   \n",
       "9996                         0.0                        0.0   \n",
       "9997                         0.0                        0.0   \n",
       "9998                         0.0                        0.0   \n",
       "9999                         0.0                        0.0   \n",
       "\n",
       "      categoria_Eletrônicos  ...  faixa_etaria_até 25 anos  \\\n",
       "0                       0.0  ...                       1.0   \n",
       "1                       0.0  ...                       0.0   \n",
       "2                       0.0  ...                       0.0   \n",
       "3                       1.0  ...                       1.0   \n",
       "4                       0.0  ...                       0.0   \n",
       "...                     ...  ...                       ...   \n",
       "9995                    0.0  ...                       0.0   \n",
       "9996                    0.0  ...                       0.0   \n",
       "9997                    0.0  ...                       0.0   \n",
       "9998                    0.0  ...                       0.0   \n",
       "9999                    0.0  ...                       0.0   \n",
       "\n",
       "      faixa_etaria_entre 26 e 35 anos  faixa_etaria_entre 36 e 50 anos  \\\n",
       "0                                 0.0                              0.0   \n",
       "1                                 0.0                              1.0   \n",
       "2                                 1.0                              0.0   \n",
       "3                                 0.0                              0.0   \n",
       "4                                 0.0                              1.0   \n",
       "...                               ...                              ...   \n",
       "9995                              1.0                              0.0   \n",
       "9996                              0.0                              1.0   \n",
       "9997                              0.0                              1.0   \n",
       "9998                              0.0                              1.0   \n",
       "9999                              0.0                              1.0   \n",
       "\n",
       "      dia_semana_Domingo  dia_semana_Quarta  dia_semana_Quinta  \\\n",
       "0                    0.0                0.0                0.0   \n",
       "1                    0.0                0.0                0.0   \n",
       "2                    0.0                0.0                1.0   \n",
       "3                    1.0                0.0                0.0   \n",
       "4                    0.0                0.0                0.0   \n",
       "...                  ...                ...                ...   \n",
       "9995                 0.0                0.0                0.0   \n",
       "9996                 0.0                0.0                1.0   \n",
       "9997                 0.0                0.0                0.0   \n",
       "9998                 0.0                0.0                1.0   \n",
       "9999                 0.0                0.0                0.0   \n",
       "\n",
       "      dia_semana_Segunda  dia_semana_Sexta  dia_semana_Sábado  \\\n",
       "0                    0.0               0.0                1.0   \n",
       "1                    0.0               0.0                0.0   \n",
       "2                    0.0               0.0                0.0   \n",
       "3                    0.0               0.0                0.0   \n",
       "4                    0.0               0.0                0.0   \n",
       "...                  ...               ...                ...   \n",
       "9995                 0.0               0.0                0.0   \n",
       "9996                 0.0               0.0                0.0   \n",
       "9997                 0.0               1.0                0.0   \n",
       "9998                 0.0               0.0                0.0   \n",
       "9999                 0.0               0.0                1.0   \n",
       "\n",
       "      dia_semana_Terça  \n",
       "0                  0.0  \n",
       "1                  1.0  \n",
       "2                  0.0  \n",
       "3                  0.0  \n",
       "4                  1.0  \n",
       "...                ...  \n",
       "9995               1.0  \n",
       "9996               0.0  \n",
       "9997               0.0  \n",
       "9998               0.0  \n",
       "9999               0.0  \n",
       "\n",
       "[10000 rows x 29 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Verificando o dataframe gerado\n",
    "################################################################################\n",
    "\n",
    "df_transacoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fraude\n",
      "False    9733\n",
      "True      267\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "################################################################################\n",
    "# Verificando a quantidade de fraudes nos dados\n",
    "################################################################################\n",
    "\n",
    "print(df_transacoes['fraude'].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# Gerando divisão dos dados em método SMOTE\n",
    "################################################################################\n",
    "\n",
    "X = df_transacoes.drop(['fraude'], axis=1) \n",
    "y = df_transacoes['fraude']\n",
    "smote = SMOTE(random_state=42)\n",
    "X_plan, y_plan = smote.fit_resample(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fraude\n",
      "False    9733\n",
      "True     9733\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "################################################################################\n",
    "# Verificando a quantidade de fraudes nos dados após SMOTE\n",
    "################################################################################\n",
    "\n",
    "print(y_plan.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.1, max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.1, max_iter=500)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.1, max_iter=500)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Criando o modelo\n",
    "################################################################################\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_plan, y_plan, test_size=0.3, random_state=42)\n",
    "rl_smote = LogisticRegression(max_iter=500, C=0.1)\n",
    "rl_smote.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC: 0.9849709196866807\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.91      0.93      0.92      2943\n",
      "        True       0.92      0.91      0.92      2897\n",
      "\n",
      "    accuracy                           0.92      5840\n",
      "   macro avg       0.92      0.92      0.92      5840\n",
      "weighted avg       0.92      0.92      0.92      5840\n",
      "\n",
      "Matriz de Confusão:\n",
      " [[2726  217]\n",
      " [ 262 2635]]\n",
      "Validação cruzada:  [0.91232575 0.91412844 0.92807339 0.92183486 0.91522936]\n",
      "Acurácia média:  0.9183183614126958\n",
      "Desvio padrão:  0.005840123262354112\n",
      "Coeficiente de correlação: 0.8360281281740621\n"
     ]
    }
   ],
   "source": [
    "\n",
    "################################################################################\n",
    "# Avaliação do modelo\n",
    "################################################################################\n",
    "\n",
    "y_pred_proba = rl_smote.predict_proba(X_test)[:,1]\n",
    "auc_score = roc_auc_score(y_test, y_pred_proba)\n",
    "print(f\"AUC-ROC: {auc_score}\")\n",
    "\n",
    "y_pred = rl_smote.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(\"Matriz de Confusão:\\n\", conf_matrix)\n",
    "\n",
    "cv_scores = cross_val_score(rl_smote, X_train, y_train, cv=5, scoring='accuracy')\n",
    "print(\"Validação cruzada: \", cv_scores)\n",
    "print(\"Acurácia média: \", cv_scores.mean())\n",
    "print(\"Desvio padrão: \", cv_scores.std())\n",
    "mcc = matthews_corrcoef(y_test, y_pred)\n",
    "print(f\"Coeficiente de correlação: {mcc}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.91      0.93      0.92      2943\n",
      "        True       0.92      0.91      0.92      2897\n",
      "\n",
      "    accuracy                           0.92      5840\n",
      "   macro avg       0.92      0.92      0.92      5840\n",
      "weighted avg       0.92      0.92      0.92      5840\n",
      "\n",
      "ROC AUC: 0.9849709196866807\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Curva ROC\n",
    "################################################################################\n",
    "\n",
    "y_pred = rl_smote.predict(X_test)\n",
    "y_prob = rl_smote.predict_proba(X_test)[:, 1]\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"ROC AUC:\", roc_auc_score(y_test, y_prob))\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"Curva ROC\")\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel(\"Falso positivo\")\n",
    "plt.ylabel(\"Verdadeiro Positivo\")\n",
    "plt.title(\"Curva ROC\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}