churn_supervised_model_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import functions.visualization_functions as VisualizationFunctions\n",
    "import functions.model_functions as ModelFunctions\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_churn_processed = pd.read_csv('datasets/churn_pre_processed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>married</th>\n",
       "      <th>n_of_dependents</th>\n",
       "      <th>n_of_referrals</th>\n",
       "      <th>tenure_in_months</th>\n",
       "      <th>offer</th>\n",
       "      <th>phone_service</th>\n",
       "      <th>multiple_lines</th>\n",
       "      <th>internet_service</th>\n",
       "      <th>...</th>\n",
       "      <th>streaming_music</th>\n",
       "      <th>unlimited_data</th>\n",
       "      <th>contract</th>\n",
       "      <th>paperless_billing</th>\n",
       "      <th>payment_method</th>\n",
       "      <th>ttl_refunds</th>\n",
       "      <th>ttl_extra_data_chg</th>\n",
       "      <th>ttl_l_distance_chg</th>\n",
       "      <th>ttl_revenue</th>\n",
       "      <th>customer_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>No Offer</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One Year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>381.51</td>\n",
       "      <td>974.81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>46</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>No Offer</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>38.33</td>\n",
       "      <td>10</td>\n",
       "      <td>96.21</td>\n",
       "      <td>610.28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Offer E</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>134.60</td>\n",
       "      <td>415.45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>78</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>Offer D</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>361.66</td>\n",
       "      <td>1599.51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>75</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>No Offer</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>22.14</td>\n",
       "      <td>289.54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  age married  n_of_dependents  n_of_referrals  tenure_in_months  \\\n",
       "0  Female   37     Yes                0             2.0                 9   \n",
       "1    Male   46      No                0             0.0                 9   \n",
       "2    Male   50      No                0             0.0                 4   \n",
       "3    Male   78     Yes                0             1.0                13   \n",
       "4  Female   75     Yes                0             3.0                 3   \n",
       "\n",
       "      offer phone_service multiple_lines internet_service  ...  \\\n",
       "0  No Offer           Yes             No              Yes  ...   \n",
       "1  No Offer           Yes            Yes              Yes  ...   \n",
       "2   Offer E           Yes             No              Yes  ...   \n",
       "3   Offer D           Yes             No              Yes  ...   \n",
       "4  No Offer           Yes             No              Yes  ...   \n",
       "\n",
       "  streaming_music  unlimited_data        contract paperless_billing  \\\n",
       "0              No             Yes        One Year               Yes   \n",
       "1             Yes              No  Month-to-Month                No   \n",
       "2              No             Yes  Month-to-Month               Yes   \n",
       "3              No             Yes  Month-to-Month               Yes   \n",
       "4              No             Yes  Month-to-Month               Yes   \n",
       "\n",
       "    payment_method ttl_refunds ttl_extra_data_chg ttl_l_distance_chg  \\\n",
       "0      Credit Card        0.00                  0             381.51   \n",
       "1      Credit Card       38.33                 10              96.21   \n",
       "2  Bank Withdrawal        0.00                  0             134.60   \n",
       "3  Bank Withdrawal        0.00                  0             361.66   \n",
       "4      Credit Card        0.00                  0              22.14   \n",
       "\n",
       "  ttl_revenue customer_status  \n",
       "0      974.81               0  \n",
       "1      610.28               0  \n",
       "2      415.45               1  \n",
       "3     1599.51               1  \n",
       "4      289.54               1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_churn_processed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gender', 'age', 'married', 'n_of_dependents', 'n_of_referrals', 'tenure_in_months', 'offer', 'phone_service', 'multiple_lines', 'internet_service', 'internet_type', 'avg_monthly_gb_download', 'online_security', 'online_backup', 'device_protection_plan', 'premium_tech_support', 'streaming_tv', 'streaming_movies', 'streaming_music', 'unlimited_data', 'contract', 'paperless_billing', 'payment_method', 'ttl_refunds', 'ttl_extra_data_chg', 'ttl_l_distance_chg', 'ttl_revenue', 'customer_status']\n"
     ]
    }
   ],
   "source": [
    "print(df_churn_processed.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4925, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_churn_processed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pré Processamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "TARGET = 'customer_status'\n",
    "R_STATE = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = ModelFunctions.model_train_test(df_churn_processed, TARGET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train - Proporção da classe 0: 0.6771105308964317\n",
      "y_train - Proporção da classe 1: 0.3228894691035683\n",
      "X_test - Proporção da classe 0: 0.6771105308964317\n",
      "y_test - Proporção da classe 1: 0.3228894691035683\n"
     ]
    }
   ],
   "source": [
    "print(f'X_train - Proporção da classe 0: {y_train.value_counts(normalize=True)[0]}')\n",
    "print(f'y_train - Proporção da classe 1: {y_train.value_counts(normalize=True)[1]}')\n",
    "print(f'X_test - Proporção da classe 0: {y_train.value_counts(normalize=True)[0]}')\n",
    "print(f'y_test - Proporção da classe 1: {y_train.value_counts(normalize=True)[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_processed, X_test_processed = ModelFunctions.model_pre_process(df_churn_processed, TARGET, X_train, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelagem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/12/01 16:49:20 INFO mlflow.tracking.fluent: Experiment with name 'Predição de Churn - modelagem supervisionada' does not exist. Creating a new experiment.\n"
     ]
    }
   ],
   "source": [
    "ModelFunctions.mlflow_up('Predição de Churn - modelagem supervisionada')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model = SVC(random_state=R_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/12/01 16:57:08 INFO mlflow.tracking._tracking_service.client: 🏃 View run Supervised_RandomSearchCV_SVC at: http://127.0.0.1:5000/#/experiments/641201155062328783/runs/cbb0642a4ea5497eb461207440afd749.\n",
      "2024/12/01 16:57:08 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/641201155062328783.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLflow Run ID: cbb0642a4ea5497eb461207440afd749\n",
      "Melhores parâmetros: {'kernel': 'linear', 'gamma': 'scale', 'degree': 2, 'C': 1}\n",
      "Precisão (acurácia): 0.8531799729364006\n"
     ]
    }
   ],
   "source": [
    "svm_rs_run_id, svm_rs_model = ModelFunctions.supervised_rand_search_cv(\n",
    "    svm_model, \n",
    "    ModelFunctions.PARAM_GRID_SVM, \n",
    "    X_train_processed, \n",
    "    X_test_processed, \n",
    "    y_train, \n",
    "    y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/12/01 18:59:29 INFO mlflow.tracking._tracking_service.client: 🏃 View run Supervised_RandomSearchCV_XGBClassifier at: http://127.0.0.1:5000/#/experiments/641201155062328783/runs/bdb77f6041574a5cac9e557b713931f5.\n",
      "2024/12/01 18:59:29 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/641201155062328783.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLflow Run ID: bdb77f6041574a5cac9e557b713931f5\n",
      "Melhores parâmetros: {'subsample': 1.0, 'n_estimators': 100, 'max_depth': 3, 'learning_rate': 0.1}\n",
      "Precisão (acurácia): 0.8653585926928281\n"
     ]
    }
   ],
   "source": [
    "xgb_rs_run_id, xgb_rs_model = ModelFunctions.supervised_rand_search_cv(\n",
    "    xgb_model, \n",
    "    ModelFunctions.PARAM_GRID_XGB, \n",
    "    X_train_processed, \n",
    "    X_test_processed, \n",
    "    y_train, \n",
    "    y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad_model = GradientBoostingClassifier(random_state=R_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/12/01 19:02:07 INFO mlflow.tracking._tracking_service.client: 🏃 View run Supervised_RandomSearchCV_GradientBoostingClassifier at: http://127.0.0.1:5000/#/experiments/641201155062328783/runs/36a7f63dcdd94369933a46cff0c0a03b.\n",
      "2024/12/01 19:02:07 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/641201155062328783.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLflow Run ID: 36a7f63dcdd94369933a46cff0c0a03b\n",
      "Melhores parâmetros: {'subsample': 0.8, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_depth': 3, 'learning_rate': 0.1}\n",
      "Precisão (acurácia): 0.8694181326116374\n"
     ]
    }
   ],
   "source": [
    "grad_rs_rund_id, grad_rs_model = ModelFunctions.supervised_rand_search_cv(\n",
    "    grad_model, \n",
    "    ModelFunctions.PARAM_GRID_GRADIENTBOOST, \n",
    "    X_train_processed, \n",
    "    X_test_processed, \n",
    "    y_train, \n",
    "    y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada_model = AdaBoostClassifier(random_state=R_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "/home/marqsleal/Dev/Python/ml2-projeto-final/venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "2024/12/01 19:03:22 INFO mlflow.tracking._tracking_service.client: 🏃 View run Supervised_RandomSearchCV_AdaBoostClassifier at: http://127.0.0.1:5000/#/experiments/641201155062328783/runs/f700abece190449da83f170f242079d1.\n",
      "2024/12/01 19:03:22 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/641201155062328783.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLflow Run ID: f700abece190449da83f170f242079d1\n",
      "Melhores parâmetros: {'n_estimators': 200, 'learning_rate': 0.1}\n",
      "Precisão (acurácia): 0.8572395128552097\n"
     ]
    }
   ],
   "source": [
    "ada_rs_rund_id, ada_rs_model = ModelFunctions.supervised_rand_search_cv(\n",
    "    ada_model, \n",
    "    ModelFunctions.PARAM_GRID_ADABOOST, \n",
    "    X_train_processed, \n",
    "    X_test_processed, \n",
    "    y_train, \n",
    "    y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analisando Modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_rs_name = f'SVM RandSearch (ID: {svm_rs_run_id})'\n",
    "xgb_rs_name = f'XGBoost RandSearch (ID: {xgb_rs_run_id})'\n",
    "grad_rs_name = f'GradientBoost RandSearch (ID: {grad_rs_rund_id})'\n",
    "ada_rs_name = f'AdaBoost RandSearch (ID: {ada_rs_rund_id})'\n",
    "\n",
    "models = {\n",
    "    svm_rs_name: svm_rs_model,\n",
    "    xgb_rs_name: xgb_rs_model,\n",
    "    grad_rs_name: grad_rs_model,\n",
    "    ada_rs_name: ada_rs_model \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Modelo</th>\n",
       "      <th>Precisão</th>\n",
       "      <th>Acurácia</th>\n",
       "      <th>Recall</th>\n",
       "      <th>f1-score (weighted)</th>\n",
       "      <th>f1-score (macro avg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM RandSearch (ID: cbb0642a4ea5497eb461207440...</td>\n",
       "      <td>0.760396</td>\n",
       "      <td>0.853180</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.854112</td>\n",
       "      <td>0.834800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost RandSearch (ID: bdb77f6041574a5cac9e55...</td>\n",
       "      <td>0.822989</td>\n",
       "      <td>0.865359</td>\n",
       "      <td>0.745833</td>\n",
       "      <td>0.863532</td>\n",
       "      <td>0.842506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GradientBoost RandSearch (ID: 36a7f63dcdd94369...</td>\n",
       "      <td>0.826879</td>\n",
       "      <td>0.869418</td>\n",
       "      <td>0.756250</td>\n",
       "      <td>0.867819</td>\n",
       "      <td>0.847621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AdaBoost RandSearch (ID: f700abece190449da83f1...</td>\n",
       "      <td>0.812065</td>\n",
       "      <td>0.857240</td>\n",
       "      <td>0.729167</td>\n",
       "      <td>0.855111</td>\n",
       "      <td>0.832604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Modelo  Precisão  Acurácia  \\\n",
       "0  SVM RandSearch (ID: cbb0642a4ea5497eb461207440...  0.760396  0.853180   \n",
       "1  XGBoost RandSearch (ID: bdb77f6041574a5cac9e55...  0.822989  0.865359   \n",
       "2  GradientBoost RandSearch (ID: 36a7f63dcdd94369...  0.826879  0.869418   \n",
       "3  AdaBoost RandSearch (ID: f700abece190449da83f1...  0.812065  0.857240   \n",
       "\n",
       "     Recall  f1-score (weighted)  f1-score (macro avg)  \n",
       "0  0.800000             0.854112              0.834800  \n",
       "1  0.745833             0.863532              0.842506  \n",
       "2  0.756250             0.867819              0.847621  \n",
       "3  0.729167             0.855111              0.832604  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VisualizationFunctions.model_metrics(\n",
    "    models,\n",
    "    X_train_processed, \n",
    "    X_test_processed, \n",
    "    y_train, \n",
    "    y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoost RandSearch (ID: 36a7f63dcdd94369933a46cff0c0a03b) \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.92      0.91       998\n",
      "           1       0.83      0.76      0.79       480\n",
      "\n",
      "    accuracy                           0.87      1478\n",
      "   macro avg       0.86      0.84      0.85      1478\n",
      "weighted avg       0.87      0.87      0.87      1478\n",
      "\n",
      "ROC AUC score: 0.9351\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 768x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VisualizationFunctions.cm_roc_recall_plot(\n",
    "    grad_rs_name,\n",
    "    grad_rs_model,\n",
    "    X_train_processed,\n",
    "    X_test_processed,\n",
    "    y_train,\n",
    "    y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost RandSearch (ID: bdb77f6041574a5cac9e557b713931f5) \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.92      0.90       998\n",
      "           1       0.82      0.75      0.78       480\n",
      "\n",
      "    accuracy                           0.87      1478\n",
      "   macro avg       0.85      0.83      0.84      1478\n",
      "weighted avg       0.86      0.87      0.86      1478\n",
      "\n",
      "ROC AUC score: 0.9354\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 768x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VisualizationFunctions.cm_roc_recall_plot(\n",
    "    xgb_rs_name,\n",
    "    xgb_rs_model,\n",
    "    X_train_processed,\n",
    "    X_test_processed,\n",
    "    y_train,\n",
    "    y_test\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}