{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "rili7kp4jsn",
   "metadata": {},
   "source": [
    "# <a id='toc1_'></a>[Imports](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba526a4",
   "metadata": {},
   "source": [
    "**Table of contents**<a id='toc0_'></a>    \n",
    "- [Imports](#toc1_)    \n",
    "- [Chargement des données](#toc2_)    \n",
    "  - [Lecture des fichiers CSV](#toc2_1_)    \n",
    "  - [Sélection du dataset](#toc2_2_)    \n",
    "  - [Aperçu](#toc2_3_)    \n",
    "- [Utilitaires](#toc3_)    \n",
    "  - [Fonctions de logging MLflow](#toc3_1_)    \n",
    "  - [Gestion des runs](#toc3_2_)    \n",
    "  - [Pipeline sklearn](#toc3_3_)    \n",
    "- [Préparation des données](#toc4_)    \n",
    "  - [Features & target](#toc4_1_)    \n",
    "  - [Statistiques du split](#toc4_2_)    \n",
    "- [Configuration MLflow](#toc5_)    \n",
    "- [Modélisation](#toc6_)    \n",
    "  - [Fonction run_cv](#toc6_1_)    \n",
    "  - [Entraînement des baselines](#toc6_2_)    \n",
    "  - [Entraînement avec sample weights (balanced)](#toc6_3_)    \n",
    "  - [Entraînement avec seuil optimisé (Fbeta β=1.5, CV-aware)](#toc6_4_)    \n",
    "\n",
    "<!-- vscode-jupyter-toc-config\n",
    "\tnumbering=false\n",
    "\tanchor=true\n",
    "\tflat=false\n",
    "\tminLevel=1\n",
    "\tmaxLevel=6\n",
    "\t/vscode-jupyter-toc-config -->\n",
    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0861f057",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import lightgbm as lgb\n",
    "from lightgbm import LGBMClassifier\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import (\n",
    "    roc_auc_score,\n",
    "    average_precision_score,\n",
    "    f1_score,\n",
    "    fbeta_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    confusion_matrix,\n",
    "    make_scorer,\n",
    ")\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, train_test_split\n",
    "from sklearn.utils.class_weight import compute_sample_weight\n",
    "import mlflow\n",
    "from mlflow import MlflowClient"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9wwx5h3559",
   "metadata": {},
   "source": [
    "# <a id='toc2_'></a>[Chargement des données](#toc0_)\n",
    "\n",
    "## <a id='toc2_1_'></a>[Lecture des fichiers CSV](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d8874081",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full = pd.read_csv(\"df.csv\")\n",
    "df_red = pd.read_csv(\"df_red.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zb1j2c50cd",
   "metadata": {},
   "source": [
    "## <a id='toc2_2_'></a>[Sélection du dataset](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3ed1c72b",
   "metadata": {},
   "outputs": [],
   "source": [
    "use_reduced = True  # flip to False for full run\n",
    "df = df_red.copy() if use_reduced else df_full.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "n557jot7frr",
   "metadata": {},
   "source": [
    "## <a id='toc2_3_'></a>[Aperçu](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "66c701c4",
   "metadata": {},
   "outputs": [
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        },
        {
         "name": "OCCUPATION_TYPE_Drivers",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_HR staff",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_High skill tech staff",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_IT staff",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Laborers",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Low-skill Laborers",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Managers",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Medicine staff",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Private service staff",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Realty agents",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Sales staff",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Secretaries",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Security staff",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "OCCUPATION_TYPE_Waiters/barmen staff",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WEEKDAY_APPR_PROCESS_START_FRIDAY",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WEEKDAY_APPR_PROCESS_START_MONDAY",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WEEKDAY_APPR_PROCESS_START_SATURDAY",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WEEKDAY_APPR_PROCESS_START_SUNDAY",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WEEKDAY_APPR_PROCESS_START_THURSDAY",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WEEKDAY_APPR_PROCESS_START_TUESDAY",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WEEKDAY_APPR_PROCESS_START_WEDNESDAY",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Advertising",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Agriculture",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Bank",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Business Entity Type 1",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Business Entity Type 2",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Business Entity Type 3",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Cleaning",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Construction",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Culture",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Electricity",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Emergency",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Government",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Hotel",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Housing",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 1",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 10",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 11",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 12",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 13",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 2",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 3",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 4",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 5",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 6",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 7",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 8",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Industry: type 9",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Insurance",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Kindergarten",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Legal Services",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Medicine",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Military",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Mobile",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Other",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Police",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Postal",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Realtor",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Religion",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Restaurant",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_School",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Security",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Security Ministries",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Self-employed",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Services",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Telecom",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Trade: type 1",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Trade: type 2",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Trade: type 3",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Trade: type 4",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Trade: type 5",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Trade: type 6",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Trade: type 7",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Transport: type 1",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Transport: type 2",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Transport: type 3",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_Transport: type 4",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_University",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "ORGANIZATION_TYPE_XNA",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "FONDKAPREMONT_MODE_not specified",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "FONDKAPREMONT_MODE_org spec account",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "FONDKAPREMONT_MODE_reg oper account",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "FONDKAPREMONT_MODE_reg oper spec account",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "HOUSETYPE_MODE_block of flats",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "HOUSETYPE_MODE_specific housing",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "HOUSETYPE_MODE_terraced house",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WALLSMATERIAL_MODE_Block",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WALLSMATERIAL_MODE_Mixed",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WALLSMATERIAL_MODE_Monolithic",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WALLSMATERIAL_MODE_Others",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WALLSMATERIAL_MODE_Panel",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WALLSMATERIAL_MODE_Stone, brick",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "WALLSMATERIAL_MODE_Wooden",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "EMERGENCYSTATE_MODE_No",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "EMERGENCYSTATE_MODE_Yes",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "DAYS_EMPLOYED_PERC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INCOME_CREDIT_PERC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INCOME_PER_PERSON",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ANNUITY_INCOME_PERC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PAYMENT_RATE",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_DAYS_CREDIT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_DAYS_CREDIT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_DAYS_CREDIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_DAYS_CREDIT_VAR",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_DAYS_CREDIT_ENDDATE_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_DAYS_CREDIT_ENDDATE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_DAYS_CREDIT_ENDDATE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_DAYS_CREDIT_UPDATE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_DAY_OVERDUE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_DAY_OVERDUE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_MAX_OVERDUE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_SUM_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_SUM_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_SUM_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_SUM_DEBT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_SUM_DEBT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_SUM_DEBT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_SUM_OVERDUE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_SUM_LIMIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_CREDIT_SUM_LIMIT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_ANNUITY_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_AMT_ANNUITY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CNT_CREDIT_PROLONG_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_MONTHS_BALANCE_MIN_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_MONTHS_BALANCE_MAX_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_MONTHS_BALANCE_SIZE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_MONTHS_BALANCE_SIZE_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_ACTIVE_Active_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_ACTIVE_Bad debt_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_ACTIVE_Closed_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_ACTIVE_Sold_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_ACTIVE_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_CURRENCY_currency 1_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_CURRENCY_currency 2_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_CURRENCY_currency 3_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_CURRENCY_currency 4_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_CURRENCY_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Another type of loan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Car loan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Consumer credit_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Credit card_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Loan for business development_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Loan for working capital replenishment_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Microloan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Mortgage_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Real estate loan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_Unknown type of loan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_CREDIT_TYPE_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_STATUS_0_MEAN_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_STATUS_1_MEAN_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_STATUS_2_MEAN_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_STATUS_3_MEAN_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_STATUS_4_MEAN_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_STATUS_5_MEAN_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_STATUS_C_MEAN_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_STATUS_X_MEAN_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BURO_STATUS_nan_MEAN_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_DAYS_CREDIT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_DAYS_CREDIT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_DAYS_CREDIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_DAYS_CREDIT_VAR",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_DAYS_CREDIT_ENDDATE_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_DAYS_CREDIT_ENDDATE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_DAYS_CREDIT_ENDDATE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_DAYS_CREDIT_UPDATE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_CREDIT_DAY_OVERDUE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_CREDIT_DAY_OVERDUE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_SUM_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_SUM_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_SUM_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_SUM_DEBT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_SUM_DEBT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_ANNUITY_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_AMT_ANNUITY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_CNT_CREDIT_PROLONG_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_MONTHS_BALANCE_MIN_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_MONTHS_BALANCE_MAX_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_MONTHS_BALANCE_SIZE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ACTIVE_MONTHS_BALANCE_SIZE_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_DAYS_CREDIT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_DAYS_CREDIT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_DAYS_CREDIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_DAYS_CREDIT_VAR",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_DAYS_CREDIT_ENDDATE_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_DAYS_CREDIT_ENDDATE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_DAYS_CREDIT_ENDDATE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_DAYS_CREDIT_UPDATE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_CREDIT_DAY_OVERDUE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_CREDIT_DAY_OVERDUE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_SUM_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_SUM_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_SUM_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_SUM_DEBT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_SUM_DEBT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_SUM_DEBT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_SUM_OVERDUE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_SUM_LIMIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_CREDIT_SUM_LIMIT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_ANNUITY_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_AMT_ANNUITY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_CNT_CREDIT_PROLONG_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_MONTHS_BALANCE_MIN_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_MONTHS_BALANCE_MAX_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_MONTHS_BALANCE_SIZE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CLOSED_MONTHS_BALANCE_SIZE_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_ANNUITY_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_ANNUITY_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_ANNUITY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_APPLICATION_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_APPLICATION_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_APPLICATION_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_CREDIT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_CREDIT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_CREDIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_APP_CREDIT_PERC_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_APP_CREDIT_PERC_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_APP_CREDIT_PERC_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_APP_CREDIT_PERC_VAR",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_DOWN_PAYMENT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_DOWN_PAYMENT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_DOWN_PAYMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_GOODS_PRICE_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_GOODS_PRICE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_AMT_GOODS_PRICE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_HOUR_APPR_PROCESS_START_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_HOUR_APPR_PROCESS_START_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_HOUR_APPR_PROCESS_START_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_RATE_DOWN_PAYMENT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_RATE_DOWN_PAYMENT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_RATE_DOWN_PAYMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_DAYS_DECISION_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_DAYS_DECISION_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_DAYS_DECISION_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CNT_PAYMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CNT_PAYMENT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_TYPE_Cash loans_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_TYPE_Consumer loans_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_TYPE_Revolving loans_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_TYPE_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_TYPE_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_WEEKDAY_APPR_PROCESS_START_TUESDAY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_WEEKDAY_APPR_PROCESS_START_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_FLAG_LAST_APPL_PER_CONTRACT_N_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_FLAG_LAST_APPL_PER_CONTRACT_Y_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_FLAG_LAST_APPL_PER_CONTRACT_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Building a house or an annex_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Business development_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Buying a garage_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Buying a home_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Buying a new car_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Buying a used car_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Car repairs_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Education_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Everyday expenses_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Furniture_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Gasification / water supply_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Hobby_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Journey_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Other_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Payments on other loans_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Refusal to name the goal_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Repairs_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Urgent needs_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_XAP_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CASH_LOAN_PURPOSE_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_STATUS_Approved_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_STATUS_Canceled_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_STATUS_Refused_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_STATUS_Unused offer_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CONTRACT_STATUS_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PAYMENT_TYPE_Cash through the bank_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PAYMENT_TYPE_Cashless from the account of the employer_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PAYMENT_TYPE_Non-cash from your account_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PAYMENT_TYPE_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PAYMENT_TYPE_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_CLIENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_HC_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_LIMIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_SCO_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_SCOFR_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_SYSTEM_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_VERIF_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_XAP_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CODE_REJECT_REASON_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_TYPE_SUITE_Children_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_TYPE_SUITE_Family_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_TYPE_SUITE_Group of people_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_TYPE_SUITE_Other_A_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_TYPE_SUITE_Other_B_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_TYPE_SUITE_Spouse, partner_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_TYPE_SUITE_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CLIENT_TYPE_New_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CLIENT_TYPE_Refreshed_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CLIENT_TYPE_Repeater_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CLIENT_TYPE_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_CLIENT_TYPE_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Additional Service_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Audio/Video_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Auto Accessories_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Clothing and Accessories_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Computers_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Construction Materials_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Consumer Electronics_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Direct Sales_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Education_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Fitness_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Furniture_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Gardening_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Homewares_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Insurance_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Jewelry_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Medical Supplies_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Medicine_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Mobile_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Office Appliances_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Other_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Photo / Cinema Equipment_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Sport and Leisure_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Tourism_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Vehicles_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_Weapon_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_GOODS_CATEGORY_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PORTFOLIO_Cards_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PORTFOLIO_Cars_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PORTFOLIO_Cash_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PORTFOLIO_POS_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PORTFOLIO_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PORTFOLIO_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PRODUCT_TYPE_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PRODUCT_TYPE_walk-in_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PRODUCT_TYPE_x-sell_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_PRODUCT_TYPE_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CHANNEL_TYPE_AP+ (Cash loan)_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CHANNEL_TYPE_Car dealer_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CHANNEL_TYPE_Channel of corporate sales_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CHANNEL_TYPE_Contact center_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CHANNEL_TYPE_Country-wide_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CHANNEL_TYPE_Credit and cash offices_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CHANNEL_TYPE_Regional / Local_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CHANNEL_TYPE_Stone_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_CHANNEL_TYPE_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_Auto technology_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_Construction_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_Consumer electronics_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_Furniture_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_Industry_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_Jewelry_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_MLM partners_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_Tourism_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_SELLER_INDUSTRY_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_YIELD_GROUP_XNA_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_YIELD_GROUP_high_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_YIELD_GROUP_low_action_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_YIELD_GROUP_low_normal_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_YIELD_GROUP_middle_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_NAME_YIELD_GROUP_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_Card Street_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_Card X-Sell_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_Cash_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_Cash Street: high_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_Cash Street: low_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_Cash Street: middle_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_Cash X-Sell: high_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_Cash X-Sell: low_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_Cash X-Sell: middle_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_POS household with interest_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_POS household without interest_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_POS industry with interest_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_POS industry without interest_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_POS mobile with interest_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_POS mobile without interest_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_POS other with interest_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_POS others without interest_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PREV_PRODUCT_COMBINATION_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_ANNUITY_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_ANNUITY_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_ANNUITY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_APPLICATION_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_APPLICATION_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_APPLICATION_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_CREDIT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_CREDIT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_CREDIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_APP_CREDIT_PERC_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_APP_CREDIT_PERC_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_APP_CREDIT_PERC_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_APP_CREDIT_PERC_VAR",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_DOWN_PAYMENT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_DOWN_PAYMENT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_DOWN_PAYMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_GOODS_PRICE_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_GOODS_PRICE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_AMT_GOODS_PRICE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_HOUR_APPR_PROCESS_START_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_HOUR_APPR_PROCESS_START_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_HOUR_APPR_PROCESS_START_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_RATE_DOWN_PAYMENT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_RATE_DOWN_PAYMENT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_RATE_DOWN_PAYMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_DAYS_DECISION_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_DAYS_DECISION_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_DAYS_DECISION_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_CNT_PAYMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "APPROVED_CNT_PAYMENT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_ANNUITY_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_ANNUITY_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_ANNUITY_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_APPLICATION_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_APPLICATION_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_APPLICATION_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_CREDIT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_CREDIT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_CREDIT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_APP_CREDIT_PERC_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_APP_CREDIT_PERC_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_APP_CREDIT_PERC_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_APP_CREDIT_PERC_VAR",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_DOWN_PAYMENT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_DOWN_PAYMENT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_DOWN_PAYMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_GOODS_PRICE_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_GOODS_PRICE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_AMT_GOODS_PRICE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_HOUR_APPR_PROCESS_START_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_HOUR_APPR_PROCESS_START_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_HOUR_APPR_PROCESS_START_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_RATE_DOWN_PAYMENT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_RATE_DOWN_PAYMENT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_RATE_DOWN_PAYMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_DAYS_DECISION_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_DAYS_DECISION_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_DAYS_DECISION_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_CNT_PAYMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "REFUSED_CNT_PAYMENT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_MONTHS_BALANCE_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_MONTHS_BALANCE_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_MONTHS_BALANCE_SIZE",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_SK_DPD_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_SK_DPD_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_SK_DPD_DEF_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_SK_DPD_DEF_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_NAME_CONTRACT_STATUS_Active_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_NAME_CONTRACT_STATUS_Approved_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_NAME_CONTRACT_STATUS_Completed_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_NAME_CONTRACT_STATUS_Demand_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_NAME_CONTRACT_STATUS_Returned to the store_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_NAME_CONTRACT_STATUS_Signed_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_NAME_CONTRACT_STATUS_nan_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "POS_COUNT",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_DPD_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_DPD_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_DPD_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_DBD_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_DBD_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_DBD_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_PAYMENT_PERC_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_PAYMENT_PERC_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_PAYMENT_PERC_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_PAYMENT_PERC_VAR",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_PAYMENT_DIFF_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_PAYMENT_DIFF_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_PAYMENT_DIFF_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_PAYMENT_DIFF_VAR",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_AMT_INSTALMENT_MAX",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_AMT_INSTALMENT_MEAN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_AMT_INSTALMENT_SUM",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_AMT_PAYMENT_MIN",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "INSTAL_AMT_PAYMENT_MAX",
         "rawType": "float64",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>134822</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>1288350.0</td>\n",
       "      <td>37800.0</td>\n",
       "      <td>1125000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>134825</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>269982.0</td>\n",
       "      <td>27792.0</td>\n",
       "      <td>238500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>134826</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>247275.0</td>\n",
       "      <td>17338.5</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>134827</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>21775.5</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 755 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SK_ID_CURR  TARGET  CODE_GENDER  FLAG_OWN_CAR  FLAG_OWN_REALTY  \\\n",
       "0          100002       1            0             0                0   \n",
       "1          100003       0            1             0                1   \n",
       "2          100004       0            0             1                0   \n",
       "3          100006       0            1             0                0   \n",
       "4          100007       0            0             0                0   \n",
       "...           ...     ...          ...           ...              ...   \n",
       "29995      134821       0            0             1                1   \n",
       "29996      134822       0            1             0                0   \n",
       "29997      134825       0            1             1                0   \n",
       "29998      134826       0            1             0                0   \n",
       "29999      134827       0            1             0                1   \n",
       "\n",
       "       CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0                 0          202500.0    406597.5      24700.5   \n",
       "1                 0          270000.0   1293502.5      35698.5   \n",
       "2                 0           67500.0    135000.0       6750.0   \n",
       "3                 0          135000.0    312682.5      29686.5   \n",
       "4                 0          121500.0    513000.0      21865.5   \n",
       "...             ...               ...         ...          ...   \n",
       "29995             2          202500.0    738000.0      26275.5   \n",
       "29996             1          225000.0   1288350.0      37800.0   \n",
       "29997             1          135000.0    269982.0      27792.0   \n",
       "29998             0           99000.0    247275.0      17338.5   \n",
       "29999             0           99000.0    675000.0      21775.5   \n",
       "\n",
       "       AMT_GOODS_PRICE  ...  CC_SK_DPD_DEF_MEAN  CC_SK_DPD_DEF_SUM  \\\n",
       "0             351000.0  ...                 NaN                NaN   \n",
       "1            1129500.0  ...                 NaN                NaN   \n",
       "2             135000.0  ...                 NaN                NaN   \n",
       "3             297000.0  ...                 NaN                NaN   \n",
       "4             513000.0  ...                 NaN                NaN   \n",
       "...                ...  ...                 ...                ...   \n",
       "29995         738000.0  ...                 NaN                NaN   \n",
       "29996        1125000.0  ...                 NaN                NaN   \n",
       "29997         238500.0  ...                 NaN                NaN   \n",
       "29998         225000.0  ...                 NaN                NaN   \n",
       "29999         675000.0  ...                 NaN                NaN   \n",
       "\n",
       "       CC_SK_DPD_DEF_VAR  CC_NAME_CONTRACT_STATUS_Active_MEAN  \\\n",
       "0                    NaN                                  NaN   \n",
       "1                    NaN                                  NaN   \n",
       "2                    NaN                                  NaN   \n",
       "3                    NaN                                  NaN   \n",
       "4                    NaN                                  NaN   \n",
       "...                  ...                                  ...   \n",
       "29995                NaN                                  NaN   \n",
       "29996                NaN                                  NaN   \n",
       "29997                NaN                                  NaN   \n",
       "29998                NaN                                  NaN   \n",
       "29999                NaN                                  NaN   \n",
       "\n",
       "       CC_NAME_CONTRACT_STATUS_Completed_MEAN  \\\n",
       "0                                         NaN   \n",
       "1                                         NaN   \n",
       "2                                         NaN   \n",
       "3                                         NaN   \n",
       "4                                         NaN   \n",
       "...                                       ...   \n",
       "29995                                     NaN   \n",
       "29996                                     NaN   \n",
       "29997                                     NaN   \n",
       "29998                                     NaN   \n",
       "29999                                     NaN   \n",
       "\n",
       "       CC_NAME_CONTRACT_STATUS_Demand_MEAN  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                      NaN   \n",
       "4                                      NaN   \n",
       "...                                    ...   \n",
       "29995                                  NaN   \n",
       "29996                                  NaN   \n",
       "29997                                  NaN   \n",
       "29998                                  NaN   \n",
       "29999                                  NaN   \n",
       "\n",
       "       CC_NAME_CONTRACT_STATUS_Sent proposal_MEAN  \\\n",
       "0                                             NaN   \n",
       "1                                             NaN   \n",
       "2                                             NaN   \n",
       "3                                             NaN   \n",
       "4                                             NaN   \n",
       "...                                           ...   \n",
       "29995                                         NaN   \n",
       "29996                                         NaN   \n",
       "29997                                         NaN   \n",
       "29998                                         NaN   \n",
       "29999                                         NaN   \n",
       "\n",
       "       CC_NAME_CONTRACT_STATUS_Signed_MEAN  CC_NAME_CONTRACT_STATUS_nan_MEAN  \\\n",
       "0                                      NaN                               NaN   \n",
       "1                                      NaN                               NaN   \n",
       "2                                      NaN                               NaN   \n",
       "3                                      NaN                               NaN   \n",
       "4                                      NaN                               NaN   \n",
       "...                                    ...                               ...   \n",
       "29995                                  NaN                               NaN   \n",
       "29996                                  NaN                               NaN   \n",
       "29997                                  NaN                               NaN   \n",
       "29998                                  NaN                               NaN   \n",
       "29999                                  NaN                               NaN   \n",
       "\n",
       "       CC_COUNT  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  \n",
       "...         ...  \n",
       "29995       NaN  \n",
       "29996       NaN  \n",
       "29997       NaN  \n",
       "29998       NaN  \n",
       "29999       NaN  \n",
       "\n",
       "[30000 rows x 755 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fsbp5eeilys",
   "metadata": {},
   "source": [
    "# <a id='toc3_'></a>[Utilitaires](#toc0_)\n",
    "\n",
    "## <a id='toc3_1_'></a>[Fonctions de logging MLflow](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1d8830f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_model_fn = {\n",
    "    LGBMClassifier: mlflow.lightgbm.log_model,\n",
    "    XGBClassifier: mlflow.xgboost.log_model,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "log323w8xvo",
   "metadata": {},
   "source": [
    "## <a id='toc3_2_'></a>[Gestion des runs](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5p13d1dx40i",
   "metadata": {},
   "outputs": [],
   "source": [
    "def delete_run_if_exists(experiment_name, run_name):\n",
    "    client = MlflowClient()\n",
    "    exp = client.get_experiment_by_name(experiment_name)\n",
    "    if exp is None:\n",
    "        return\n",
    "    runs = client.search_runs(\n",
    "        experiment_ids=[exp.experiment_id],\n",
    "        filter_string=f\"tags.mlflow.runName = '{run_name}'\",\n",
    "    )\n",
    "    for run in runs:\n",
    "        client.delete_run(run.info.run_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rdqlr7eglu",
   "metadata": {},
   "source": [
    "## <a id='toc3_3_'></a>[Pipeline sklearn](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "yqhlc7nbljh",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_sklearn_pipeline(model):\n",
    "    \"\"\"Wrapping imputation + scaling pour les modèles sklearn qui ne gèrent pas les NaN.\"\"\"\n",
    "    return Pipeline(\n",
    "        [\n",
    "            (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"model\", model),\n",
    "        ]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "pc3u4ngxy",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost_score(y_true, y_pred):\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
    "    return 10 * fn + fp  # positif = à minimiser\n",
    "\n",
    "\n",
    "scorer = make_scorer(cost_score, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "nn9v7jz2vkk",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_threshold(y_true, y_proba, n_steps=100):\n",
    "    \"\"\"Retourne le seuil qui minimise le coût métier (10*FN + FP).\"\"\"\n",
    "    thresholds = np.linspace(0.01, 0.99, n_steps)\n",
    "    costs = [cost_score(y_true, (y_proba >= t).astype(int)) for t in thresholds]\n",
    "    best_idx = np.argmin(costs)\n",
    "    return thresholds[best_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "twkds8iz6k",
   "metadata": {},
   "source": [
    "# <a id='toc4_'></a>[Préparation des données](#toc0_)\n",
    "\n",
    "## <a id='toc4_1_'></a>[Features & target](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c41f5bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=[\"TARGET\", \"SK_ID_CURR\"])\n",
    "y = df[\"TARGET\"]\n",
    "\n",
    "\n",
    "# Fix noms de colonnes pour LightGBM\n",
    "import re\n",
    "\n",
    "X.columns = [re.sub(r\"[^A-Za-z0-9_]+\", \"_\", col) for col in X.columns]\n",
    "\n",
    "# on enleve les inf pour eviter de faire bugger certains modeles\n",
    "X = X.replace([np.inf, -np.inf], np.nan)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.20, random_state=78\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cn45l9kye4j",
   "metadata": {},
   "source": [
    "## <a id='toc4_2_'></a>[Statistiques du split](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "df81edcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "TARGET",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "count",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "606eda85-81e8-44dd-95d7-4e7c926cae53",
       "rows": [
        [
         "0",
         "22110"
        ],
        [
         "1",
         "1890"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 2
       }
      },
      "text/plain": [
       "TARGET\n",
       "0    22110\n",
       "1     1890\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1356b14b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "TARGET",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "count",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "88c197c8-5ea5-4a81-9265-7d489dd356c6",
       "rows": [
        [
         "0",
         "5487"
        ],
        [
         "1",
         "513"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 2
       }
      },
      "text/plain": [
       "TARGET\n",
       "0    5487\n",
       "1     513\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sttzzbfevtq",
   "metadata": {},
   "source": [
    "# <a id='toc5_'></a>[Configuration MLflow](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3dbxv71rla6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Experiment: artifact_location='mlflow-artifacts:/2', creation_time=1774003960636, experiment_id='2', last_update_time=1774003960636, lifecycle_stage='active', name='First trial', tags={}, workspace='default'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if mlflow.active_run():\n",
    "    mlflow.end_run()\n",
    "\n",
    "mlflow.set_tracking_uri(\"http://127.0.0.1:5000/\")\n",
    "EXPERIMENT_NAME = \"First trial\"\n",
    "mlflow.set_experiment(EXPERIMENT_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ut02x0xok5p",
   "metadata": {},
   "source": [
    "# <a id='toc6_'></a>[Modélisation](#toc0_)\n",
    "\n",
    "## <a id='toc6_1_'></a>[Fonction run_cv](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e89d4a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "FBETA = 1.5\n",
    "\n",
    "\n",
    "def run_cv(\n",
    "    model,\n",
    "    model_name,\n",
    "    X,\n",
    "    y,\n",
    "    n_splits=5,\n",
    "    tags=None,\n",
    "    use_sample_weights=False,\n",
    "    optimize_threshold=False,\n",
    "):\n",
    "    delete_run_if_exists(EXPERIMENT_NAME, model_name)\n",
    "    skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
    "    aucs, pr_aucs, recalls, precisions, f1s, fbetas, costs, thresholds = (\n",
    "        [],\n",
    "        [],\n",
    "        [],\n",
    "        [],\n",
    "        [],\n",
    "        [],\n",
    "        [],\n",
    "        [],\n",
    "    )\n",
    "    sw_key = \"model__sample_weight\" if isinstance(model, Pipeline) else \"sample_weight\"\n",
    "\n",
    "    with mlflow.start_run(run_name=model_name, tags=tags or {}):\n",
    "        mlflow.log_params({k: str(v) for k, v in model.get_params().items()})\n",
    "        mlflow.log_param(\"model_name\", model_name)\n",
    "        mlflow.log_param(\"sample_weight\", \"balanced\" if use_sample_weights else \"none\")\n",
    "        mlflow.log_param(\"optimize_threshold\", str(optimize_threshold))\n",
    "        base = model.steps[-1][1] if isinstance(model, Pipeline) else model\n",
    "        mlflow.set_tag(\"model_family\", type(base).__name__)\n",
    "        for fold, (tr, val) in enumerate(skf.split(X, y)):\n",
    "            sw = (\n",
    "                {sw_key: compute_sample_weight(\"balanced\", y.iloc[tr])}\n",
    "                if use_sample_weights\n",
    "                else {}\n",
    "            )\n",
    "            model.fit(X.iloc[tr], y.iloc[tr], **sw)\n",
    "            preds_proba = model.predict_proba(X.iloc[val])[:, 1]\n",
    "            if optimize_threshold:\n",
    "                thresh = find_best_threshold(y.iloc[val], preds_proba)\n",
    "                thresholds.append(thresh)\n",
    "            else:\n",
    "                thresh = 0.5\n",
    "            preds = (preds_proba >= thresh).astype(int)\n",
    "            aucs.append(roc_auc_score(y.iloc[val], preds_proba))\n",
    "            pr_aucs.append(average_precision_score(y.iloc[val], preds_proba))\n",
    "            recalls.append(recall_score(y.iloc[val], preds, zero_division=0))\n",
    "            precisions.append(precision_score(y.iloc[val], preds, zero_division=0))\n",
    "            f1s.append(f1_score(y.iloc[val], preds, zero_division=0))\n",
    "            fbetas.append(fbeta_score(y.iloc[val], preds, beta=FBETA, zero_division=0))\n",
    "            costs.append(cost_score(y.iloc[val], preds))\n",
    "        mlflow.log_metric(\"roc_auc_mean\", round(float(np.mean(aucs)), 2))\n",
    "        mlflow.log_metric(\"roc_auc_std\", round(float(np.std(aucs)), 2))\n",
    "        mlflow.log_metric(\"pr_auc_mean\", round(float(np.mean(pr_aucs)), 2))\n",
    "        mlflow.log_metric(\"pr_auc_std\", round(float(np.std(pr_aucs)), 2))\n",
    "        mlflow.log_metric(\"recall_mean\", round(float(np.mean(recalls)), 2))\n",
    "        mlflow.log_metric(\"recall_std\", round(float(np.std(recalls)), 2))\n",
    "        mlflow.log_metric(\"precision_mean\", round(float(np.mean(precisions)), 2))\n",
    "        mlflow.log_metric(\"precision_std\", round(float(np.std(precisions)), 2))\n",
    "        mlflow.log_metric(\"f1_mean\", round(float(np.mean(f1s)), 2))\n",
    "        mlflow.log_metric(\"f1_std\", round(float(np.std(f1s)), 2))\n",
    "        mlflow.log_metric(\"fbeta_mean\", round(float(np.mean(fbetas)), 2))\n",
    "        mlflow.log_metric(\"fbeta_std\", round(float(np.std(fbetas)), 2))\n",
    "        mlflow.log_metric(\"cost_mean\", round(float(np.mean(costs)), 2))\n",
    "        mlflow.log_metric(\"cost_std\", round(float(np.std(costs)), 2))\n",
    "        if optimize_threshold:\n",
    "            mlflow.log_metric(\n",
    "                \"best_threshold_mean\", round(float(np.mean(thresholds)), 2)\n",
    "            )\n",
    "            mlflow.log_metric(\"best_threshold_std\", round(float(np.std(thresholds)), 2))\n",
    "\n",
    "        sw = (\n",
    "            {sw_key: compute_sample_weight(\"balanced\", y)} if use_sample_weights else {}\n",
    "        )\n",
    "        model.fit(X, y, **sw)\n",
    "        log_fn = log_model_fn.get(type(model), mlflow.sklearn.log_model)\n",
    "        log_fn(model, name=model_name)\n",
    "\n",
    "        thr_info = (\n",
    "            f\" | Threshold={np.mean(thresholds):.3f} ± {np.std(thresholds):.3f}\"\n",
    "            if optimize_threshold\n",
    "            else \"\"\n",
    "        )\n",
    "        print(\n",
    "            f\"{model_name} | ROC-AUC={np.mean(aucs):.4f} | PR-AUC={np.mean(pr_aucs):.4f} | \"\n",
    "            f\"Recall={np.mean(recalls):.4f} | Precision={np.mean(precisions):.4f} | \"\n",
    "            f\"Fbeta(β={FBETA})={np.mean(fbetas):.4f} | Cost={np.mean(costs):.1f}{thr_info}\"\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "z3f5do04s9i",
   "metadata": {},
   "source": [
    "## <a id='toc6_2_'></a>[Entraînement des baselines](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fd3ff110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,010225 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40433\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 707\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,078750 -> initscore=-2,459453\n",
      "[LightGBM] [Info] Start training from score -2,459453\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,025692 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40599\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 708\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,078750 -> initscore=-2,459453\n",
      "[LightGBM] [Info] Start training from score -2,459453\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,010915 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40736\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 708\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,078750 -> initscore=-2,459453\n",
      "[LightGBM] [Info] Start training from score -2,459453\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,016241 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40695\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 708\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,078750 -> initscore=-2,459453\n",
      "[LightGBM] [Info] Start training from score -2,459453\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,022516 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40909\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 707\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,078750 -> initscore=-2,459453\n",
      "[LightGBM] [Info] Start training from score -2,459453\n",
      "[LightGBM] [Info] Number of positive: 1890, number of negative: 22110\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,014313 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 44432\n",
      "[LightGBM] [Info] Number of data points in the train set: 24000, number of used features: 711\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,078750 -> initscore=-2,459453\n",
      "[LightGBM] [Info] Start training from score -2,459453\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026/03/30 17:24:59 WARNING mlflow.lightgbm: Saving the models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:25:02 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lgbm_baseline | ROC-AUC=0.7257 | PR-AUC=0.2075 | Recall=0.0312 | Precision=0.4195 | Fbeta(β=1.5)=0.0436 | Cost=3678.4\n",
      "🏃 View run lgbm_baseline at: http://127.0.0.1:5000/#/experiments/2/runs/3aadb34a7cd645afafd3f4dced2e1b54\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026/03/30 17:25:12 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb_baseline | ROC-AUC=0.6969 | PR-AUC=0.1808 | Recall=0.0487 | Precision=0.3234 | Fbeta(β=1.5)=0.0658 | Cost=3635.2\n",
      "🏃 View run xgb_baseline at: http://127.0.0.1:5000/#/experiments/2/runs/7ea363ed94de4e0487e207e65b3f51db\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "2026/03/30 17:25:34 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:25:35 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP_baseline | ROC-AUC=0.6887 | PR-AUC=0.1705 | Recall=0.0079 | Precision=0.1457 | Fbeta(β=1.5)=0.0112 | Cost=3759.8\n",
      "🏃 View run MLP_baseline at: http://127.0.0.1:5000/#/experiments/2/runs/10df0e5c363344219eb4f07f1a334b05\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1184: FutureWarning: 'n_jobs' has no effect since 1.8 and will be removed in 1.10. You provided 'n_jobs=-1', please leave it unspecified.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1184: FutureWarning: 'n_jobs' has no effect since 1.8 and will be removed in 1.10. You provided 'n_jobs=-1', please leave it unspecified.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1184: FutureWarning: 'n_jobs' has no effect since 1.8 and will be removed in 1.10. You provided 'n_jobs=-1', please leave it unspecified.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1184: FutureWarning: 'n_jobs' has no effect since 1.8 and will be removed in 1.10. You provided 'n_jobs=-1', please leave it unspecified.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1184: FutureWarning: 'n_jobs' has no effect since 1.8 and will be removed in 1.10. You provided 'n_jobs=-1', please leave it unspecified.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1184: FutureWarning: 'n_jobs' has no effect since 1.8 and will be removed in 1.10. You provided 'n_jobs=-1', please leave it unspecified.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "2026/03/30 17:25:51 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:25:53 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log_reg_baseline | ROC-AUC=0.7129 | PR-AUC=0.1843 | Recall=0.0286 | Precision=0.2673 | Fbeta(β=1.5)=0.0394 | Cost=3701.8\n",
      "🏃 View run log_reg_baseline at: http://127.0.0.1:5000/#/experiments/2/runs/e82489d3c1bd45229e51de575d8d3a1f\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "2026/03/30 17:26:07 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:26:09 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf_baseline | ROC-AUC=0.6846 | PR-AUC=0.1670 | Recall=0.0005 | Precision=0.2000 | Fbeta(β=1.5)=0.0008 | Cost=3778.0\n",
      "🏃 View run rf_baseline at: http://127.0.0.1:5000/#/experiments/2/runs/c7a2ede490af4f309bc6e2ed283fedbb\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    }
   ],
   "source": [
    "baseline_tags = {\"dataset\": \"reduced\", \"phase\": \"baseline\", \"fbeta\": str(FBETA)}\n",
    "\n",
    "run_cv(\n",
    "    LGBMClassifier(random_state=42, n_jobs=-1),\n",
    "    \"lgbm_baseline\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=baseline_tags,\n",
    ")\n",
    "run_cv(\n",
    "    XGBClassifier(random_state=42, n_jobs=-1, eval_metric=\"auc\"),\n",
    "    \"xgb_baseline\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=baseline_tags,\n",
    ")\n",
    "run_cv(\n",
    "    make_sklearn_pipeline(\n",
    "        MLPClassifier(\n",
    "            random_state=42,\n",
    "            hidden_layer_sizes=(100, 50),\n",
    "            early_stopping=True,\n",
    "            max_iter=300,\n",
    "        )\n",
    "    ),\n",
    "    \"MLP_baseline\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=baseline_tags,\n",
    ")\n",
    "run_cv(\n",
    "    make_sklearn_pipeline(\n",
    "        LogisticRegression(max_iter=1000, random_state=42, n_jobs=-1)\n",
    "    ),\n",
    "    \"log_reg_baseline\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=baseline_tags,\n",
    ")\n",
    "run_cv(\n",
    "    make_sklearn_pipeline(RandomForestClassifier(random_state=42, n_jobs=-1)),\n",
    "    \"rf_baseline\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=baseline_tags,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1a99b3e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### Entrainement sur le full\n",
    "\n",
    "# baseline_tags = {\"dataset\": \"full\", \"phase\": \"baseline\", \"fbeta\": str(FBETA)}\n",
    "\n",
    "# run_cv(\n",
    "#     LGBMClassifier(random_state=42, n_jobs=-1),\n",
    "#     \"lgbm_baseline_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=baseline_tags,\n",
    "# )\n",
    "# run_cv(\n",
    "#     XGBClassifier(random_state=42, n_jobs=-1, eval_metric=\"auc\"),\n",
    "#     \"xgb_baseline_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=baseline_tags,\n",
    "# )\n",
    "# run_cv(\n",
    "#     make_sklearn_pipeline(\n",
    "#         MLPClassifier(\n",
    "#             random_state=42,\n",
    "#             hidden_layer_sizes=(100, 50),\n",
    "#             early_stopping=True,\n",
    "#             max_iter=300,\n",
    "#         )\n",
    "#     ),\n",
    "#     \"MLP_baseline_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=baseline_tags,\n",
    "# )\n",
    "# run_cv(\n",
    "#     make_sklearn_pipeline(\n",
    "#         LogisticRegression(max_iter=1000, random_state=42, n_jobs=-1)\n",
    "#     ),\n",
    "#     \"log_reg_baseline_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=baseline_tags,\n",
    "# )\n",
    "# run_cv(\n",
    "#     make_sklearn_pipeline(RandomForestClassifier(random_state=42, n_jobs=-1)),\n",
    "#     \"rf_baseline_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=baseline_tags,\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fut1smyf6te",
   "metadata": {},
   "source": [
    "## <a id='toc6_3_'></a>[Entraînement avec sample weights (balanced)](#toc0_)\n",
    "\n",
    "MLP exclu : `MLPClassifier` ne supporte pas `sample_weight` dans `fit()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ca659263",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,010398 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40433\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 707\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,014013 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40599\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 708\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,015612 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40736\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 708\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,012151 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40695\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 708\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,010453 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40909\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 707\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1890, number of negative: 22110\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,016161 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 44432\n",
      "[LightGBM] [Info] Number of data points in the train set: 24000, number of used features: 711\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026/03/30 17:26:16 WARNING mlflow.lightgbm: Saving the models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:26:18 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lgbm_sw | ROC-AUC=0.7185 | PR-AUC=0.2052 | Recall=0.4593 | Precision=0.1930 | Fbeta(β=1.5)=0.3223 | Cost=2768.8\n",
      "🏃 View run lgbm_sw at: http://127.0.0.1:5000/#/experiments/2/runs/4ee00fb101af45c8b46467af84eeb84a\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026/03/30 17:26:59 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb_sw | ROC-AUC=0.6898 | PR-AUC=0.1815 | Recall=0.3026 | Precision=0.2029 | Fbeta(β=1.5)=0.2628 | Cost=3087.2\n",
      "🏃 View run xgb_sw at: http://127.0.0.1:5000/#/experiments/2/runs/9f935ba5d4b547ff9dba5d25bb0b1608\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "2026/03/30 17:27:20 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:27:22 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log_reg_sw | ROC-AUC=0.7074 | PR-AUC=0.1779 | Recall=0.6090 | Precision=0.1485 | Fbeta(β=1.5)=0.3116 | Cost=2798.4\n",
      "🏃 View run log_reg_sw at: http://127.0.0.1:5000/#/experiments/2/runs/81f62a5e0526442e9c139ac6d1ec5fc6\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "2026/03/30 17:27:35 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:27:37 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf_sw | ROC-AUC=0.6973 | PR-AUC=0.1750 | Recall=0.0011 | Precision=0.2667 | Fbeta(β=1.5)=0.0015 | Cost=3776.4\n",
      "🏃 View run rf_sw at: http://127.0.0.1:5000/#/experiments/2/runs/b9cd0ebeca494a3ca534d854e1311c72\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    }
   ],
   "source": [
    "sw_tags = {\"dataset\": \"reduced\", \"phase\": \"sample_weights\", \"fbeta\": str(FBETA)}\n",
    "\n",
    "run_cv(\n",
    "    LGBMClassifier(random_state=42, n_jobs=-1),\n",
    "    \"lgbm_sw\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=sw_tags,\n",
    "    use_sample_weights=True,\n",
    ")\n",
    "run_cv(\n",
    "    XGBClassifier(random_state=42, n_jobs=-1, eval_metric=\"auc\"),\n",
    "    \"xgb_sw\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=sw_tags,\n",
    "    use_sample_weights=True,\n",
    ")\n",
    "run_cv(\n",
    "    make_sklearn_pipeline(LogisticRegression(max_iter=1000, random_state=42)),\n",
    "    \"log_reg_sw\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=sw_tags,\n",
    "    use_sample_weights=True,\n",
    ")\n",
    "run_cv(\n",
    "    make_sklearn_pipeline(RandomForestClassifier(random_state=42, n_jobs=-1)),\n",
    "    \"rf_sw\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=sw_tags,\n",
    "    use_sample_weights=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e12303c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### Entrainement sur le full\n",
    "\n",
    "# sw_tags = {\"dataset\": \"full\", \"phase\": \"sample_weights\", \"fbeta\": str(FBETA)}\n",
    "\n",
    "# run_cv(\n",
    "#     LGBMClassifier(random_state=42, n_jobs=-1),\n",
    "#     \"lgbm_sw_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=sw_tags,\n",
    "#     use_sample_weights=True,\n",
    "# )\n",
    "# run_cv(\n",
    "#     XGBClassifier(random_state=42, n_jobs=-1, eval_metric=\"auc\"),\n",
    "#     \"xgb_sw_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=sw_tags,\n",
    "#     use_sample_weights=True,\n",
    "# )\n",
    "# run_cv(\n",
    "#     make_sklearn_pipeline(LogisticRegression(max_iter=1000, random_state=42)),\n",
    "#     \"log_reg_sw_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=sw_tags,\n",
    "#     use_sample_weights=True,\n",
    "# )\n",
    "# run_cv(\n",
    "#     make_sklearn_pipeline(RandomForestClassifier(random_state=42, n_jobs=-1)),\n",
    "#     \"rf_sw_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=sw_tags,\n",
    "#     use_sample_weights=True,\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zswd2yxlc9",
   "metadata": {},
   "source": [
    "## <a id='toc6_4_'></a>[Entraînement avec seuil optimisé (Fbeta β=1.5, CV-aware)](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "n5zywru980t",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,009877 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40433\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 707\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,011199 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40599\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 708\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,009209 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40736\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 708\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,011366 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40695\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 708\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1512, number of negative: 17688\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,011484 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 40909\n",
      "[LightGBM] [Info] Number of data points in the train set: 19200, number of used features: 707\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n",
      "[LightGBM] [Info] Number of positive: 1890, number of negative: 22110\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0,011154 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 44432\n",
      "[LightGBM] [Info] Number of data points in the train set: 24000, number of used features: 711\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0,500000 -> initscore=0,000000\n",
      "[LightGBM] [Info] Start training from score 0,000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026/03/30 17:27:43 WARNING mlflow.lightgbm: Saving the models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:27:45 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lgbm_thr | ROC-AUC=0.7185 | PR-AUC=0.2052 | Recall=0.6127 | Precision=0.1622 | Fbeta(β=1.5)=0.3268 | Cost=2701.0 | Threshold=0.382 ± 0.063\n",
      "🏃 View run lgbm_thr at: http://127.0.0.1:5000/#/experiments/2/runs/33b7e9a042aa455ab35432d25c7ce5ba\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026/03/30 17:27:56 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb_thr | ROC-AUC=0.6898 | PR-AUC=0.1815 | Recall=0.5450 | Precision=0.1512 | Fbeta(β=1.5)=0.3020 | Cost=2877.8 | Threshold=0.273 ± 0.023\n",
      "🏃 View run xgb_thr at: http://127.0.0.1:5000/#/experiments/2/runs/661efd6632824fb3b45ca8864c913813\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "2026/03/30 17:28:16 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:28:18 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log_reg_thr | ROC-AUC=0.7074 | PR-AUC=0.1779 | Recall=0.6011 | Precision=0.1587 | Fbeta(β=1.5)=0.3207 | Cost=2726.4 | Threshold=0.523 ± 0.049\n",
      "🏃 View run log_reg_thr at: http://127.0.0.1:5000/#/experiments/2/runs/ecc7dfef53124cec8731cf55e1bdd455\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "2026/03/30 17:28:31 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:28:32 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf_thr | ROC-AUC=0.6973 | PR-AUC=0.1750 | Recall=0.5698 | Precision=0.1492 | Fbeta(β=1.5)=0.3041 | Cost=2864.8 | Threshold=0.099 ± 0.006\n",
      "🏃 View run rf_thr at: http://127.0.0.1:5000/#/experiments/2/runs/17c1783c5dc1419da4badded56629fc3\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kevin\\projects\\OC_P6\\.venv\\Lib\\site-packages\\sklearn\\impute\\_base.py:641: UserWarning: Skipping features without any observed values: ['BURO_MONTHS_BALANCE_MIN_MIN' 'BURO_MONTHS_BALANCE_MAX_MAX'\n",
      " 'BURO_MONTHS_BALANCE_SIZE_MEAN' 'BURO_STATUS_0_MEAN_MEAN'\n",
      " 'BURO_STATUS_1_MEAN_MEAN' 'BURO_STATUS_2_MEAN_MEAN'\n",
      " 'BURO_STATUS_3_MEAN_MEAN' 'BURO_STATUS_4_MEAN_MEAN'\n",
      " 'BURO_STATUS_5_MEAN_MEAN' 'BURO_STATUS_C_MEAN_MEAN'\n",
      " 'BURO_STATUS_X_MEAN_MEAN' 'BURO_STATUS_nan_MEAN_MEAN'\n",
      " 'ACTIVE_MONTHS_BALANCE_MIN_MIN' 'ACTIVE_MONTHS_BALANCE_MAX_MAX'\n",
      " 'ACTIVE_MONTHS_BALANCE_SIZE_MEAN' 'CLOSED_MONTHS_BALANCE_MIN_MIN'\n",
      " 'CLOSED_MONTHS_BALANCE_MAX_MAX' 'CLOSED_MONTHS_BALANCE_SIZE_MEAN']. At least one non-missing value is needed for imputation with strategy='median'.\n",
      "  warnings.warn(\n",
      "2026/03/30 17:28:54 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n",
      "2026/03/30 17:28:55 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mlp_thr | ROC-AUC=0.6887 | PR-AUC=0.1705 | Recall=0.5513 | Precision=0.1562 | Fbeta(β=1.5)=0.3083 | Cost=2837.0 | Threshold=0.097 ± 0.012\n",
      "🏃 View run mlp_thr at: http://127.0.0.1:5000/#/experiments/2/runs/890f0c744f6f48959e0d3677f66689cb\n",
      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/2\n"
     ]
    }
   ],
   "source": [
    "thr_tags = {\"dataset\": \"reduced\", \"phase\": \"threshold_opt\", \"fbeta\": str(FBETA)}\n",
    "\n",
    "run_cv(\n",
    "    LGBMClassifier(random_state=42, n_jobs=-1),\n",
    "    \"lgbm_thr\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=thr_tags,\n",
    "    use_sample_weights=True,\n",
    "    optimize_threshold=True,\n",
    ")\n",
    "run_cv(\n",
    "    XGBClassifier(random_state=42, n_jobs=-1, eval_metric=\"auc\"),\n",
    "    \"xgb_thr\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=thr_tags,\n",
    "    use_sample_weights=True,\n",
    "    optimize_threshold=True,\n",
    ")\n",
    "run_cv(\n",
    "    make_sklearn_pipeline(LogisticRegression(max_iter=1000, random_state=42)),\n",
    "    \"log_reg_thr\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=thr_tags,\n",
    "    use_sample_weights=True,\n",
    "    optimize_threshold=True,\n",
    ")\n",
    "run_cv(\n",
    "    make_sklearn_pipeline(RandomForestClassifier(random_state=42, n_jobs=-1)),\n",
    "    \"rf_thr\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=thr_tags,\n",
    "    use_sample_weights=True,\n",
    "    optimize_threshold=True,\n",
    ")\n",
    "# MLP : threshold optimisé sans sample_weight (non supporté par MLPClassifier)\n",
    "run_cv(\n",
    "    make_sklearn_pipeline(\n",
    "        MLPClassifier(\n",
    "            random_state=42,\n",
    "            hidden_layer_sizes=(100, 50),\n",
    "            early_stopping=True,\n",
    "            max_iter=300,\n",
    "        )\n",
    "    ),\n",
    "    \"mlp_thr\",\n",
    "    X_train,\n",
    "    y_train,\n",
    "    tags=thr_tags,\n",
    "    use_sample_weights=False,\n",
    "    optimize_threshold=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "be27877f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### Entrainement sur le full\n",
    "\n",
    "# thr_tags = {\"dataset\": \"full\", \"phase\": \"threshold_opt\", \"fbeta\": str(FBETA)}\n",
    "\n",
    "# run_cv(\n",
    "#     LGBMClassifier(random_state=42, n_jobs=-1),\n",
    "#     \"lgbm_thr_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=thr_tags,\n",
    "#     use_sample_weights=True,\n",
    "#     optimize_threshold=True,\n",
    "# )\n",
    "# run_cv(\n",
    "#     XGBClassifier(random_state=42, n_jobs=-1, eval_metric=\"auc\"),\n",
    "#     \"xgb_thr_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=thr_tags,\n",
    "#     use_sample_weights=True,\n",
    "#     optimize_threshold=True,\n",
    "# )\n",
    "# run_cv(\n",
    "#     make_sklearn_pipeline(LogisticRegression(max_iter=1000, random_state=42)),\n",
    "#     \"log_reg_thr_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=thr_tags,\n",
    "#     use_sample_weights=True,\n",
    "#     optimize_threshold=True,\n",
    "# )\n",
    "# run_cv(\n",
    "#     make_sklearn_pipeline(RandomForestClassifier(random_state=42, n_jobs=-1)),\n",
    "#     \"rf_thr_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=thr_tags,\n",
    "#     use_sample_weights=True,\n",
    "#     optimize_threshold=True,\n",
    "# )\n",
    "# # MLP : threshold optimisé sans sample_weight (non supporté par MLPClassifier)\n",
    "# run_cv(\n",
    "#     make_sklearn_pipeline(\n",
    "#         MLPClassifier(\n",
    "#             random_state=42,\n",
    "#             hidden_layer_sizes=(100, 50),\n",
    "#             early_stopping=True,\n",
    "#             max_iter=300,\n",
    "#         )\n",
    "#     ),\n",
    "#     \"mlp_thr_full\",\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     tags=thr_tags,\n",
    "#     use_sample_weights=False,\n",
    "#     optimize_threshold=True,\n",
    "# )"
   ]
  }
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