Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0136c2db-0f67-4489-82be-c295736b6715",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import joblib\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, KFold\n",
    "from catboost import CatBoostClassifier, Pool, sum_models\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "import utils\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8fa8b365-23ce-423a-b0f6-3e044a40bb9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7f98825b36d0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_FU = model = CatBoostClassifier()\n",
    "model_FU.load_model('./pretrain_models/FirstUse/db-v4-FirstUse-1_2_0.75_31_0.8_7(allin).cbm', format='cbm')\n",
    "\n",
    "model_AF = model = CatBoostClassifier()\n",
    "model_AF.load_model('./pretrain_models/AfterFraud/db-v4-AfterFraud-1_3_0.8_31_0.8_8(allin).cbm', format='cbm')\n",
    "\n",
    "model_UBF = model = CatBoostClassifier()\n",
    "model_UBF.load_model('./pretrain_models/UsedBeforeFraud/db-v4-UsedBeforeFraud-1_3.5_0.66_31_0.8_8(allin).cbm', format='cbm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12bd627d-2bca-4cf9-bcf7-f6896845211b",
   "metadata": {},
   "outputs": [],
   "source": [
    "D_FU = joblib.load('./dataset_3rd/db-v5-FirstUse.joblib')\n",
    "utils.catboost_fillna(D_FU['train'], D_FU['pred'])\n",
    "\n",
    "D_FU['train'][\"label\"] = D_FU['train'][\"label\"].astype(np.uint8)\n",
    "D_FU['pred'][\"label\"] = D_FU['pred'][\"label\"].astype(np.uint8)\n",
    "\n",
    "train_X_FU = D_FU['train'].drop([\"label\"], axis=1)\n",
    "train_Y_FU = D_FU['train'][\"label\"].values\n",
    "\n",
    "val_X_FU = D_FU['pred'].drop([\"label\"], axis=1)\n",
    "val_Y_FU = D_FU['pred'][\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce06641a-7680-417f-99f6-653252bab0ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features_list = np.nonzero((train_X_FU.dtypes==bool).to_numpy() | (train_X_FU.dtypes=='category').to_numpy())[0]\n",
    "\n",
    "train_data_FU = Pool(train_X_FU,\n",
    "                  train_Y_FU,\n",
    "                  cat_features = list(train_X_FU.columns[cat_features_list]))\n",
    "\n",
    "val_data_FU = Pool(val_X_FU,\n",
    "                  val_Y_FU,\n",
    "                  cat_features = list(val_X_FU.columns[cat_features_list]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9af3fd3-b63b-4864-bff3-23324bcd9ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_FU =  {\n",
    " 'eval_metric': 'F1', #\n",
    " 'iterations': 1000, #\n",
    " 'boosting_type': 'Ordered', # Plain* or Ordered(better but slow)\n",
    " 'one_hot_max_size': 10, # 2*\n",
    " 'l2_leaf_reg': 25, # 2*\n",
    " 'random_strength': 10, #\n",
    " 'od_type': 'Iter',\n",
    " # 'rsm': 0.5, # 1*, 0~1   # CPU only\n",
    " 'max_ctr_complexity': 4, # 允許的特徵交叉的最大特徵數量\n",
    " 'use_best_model': True,\n",
    " 'od_wait': 999,\n",
    " 'random_seed': 8,\n",
    " 'depth': 2,\n",
    " 'loss_function': 'Logloss',\n",
    " 'learning_rate': 0.8,\n",
    " 'task_type': 'CPU',\n",
    " 'max_leaves': 4, # Can be used only with the Lossguide growing policy. should < 64\n",
    " 'verbose': 50,\n",
    "    \n",
    " 'thread_count': 16}\n",
    "\n",
    "# params_FU =  {\n",
    "#             'max_depth': 7,\n",
    "#             'learning_rate': 0.8,\n",
    "#             'max_leaves': 31,\n",
    "#             'subsample': 0.75,\n",
    "#             'reg_lambda': 2,\n",
    "#             'min_data_in_leaf':1\n",
    "#         } "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32cc5779-8aec-446e-bb31-79249f06c397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.Pool at 0x7f98265838e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_FU = model_FU.predict(train_data_FU, prediction_type='RawFormulaVal')\n",
    "train_data_FU.set_baseline(base_FU)\n",
    "val_data_FU.set_baseline(model_FU.predict(val_data_FU, prediction_type='RawFormulaVal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af8cd06c-9e05-4e41-88ec-3a6aa503e538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b603d3ee559416f9692397a589c2744",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.8422998\ttest: 0.8547215\tbest: 0.8547215 (0)\ttotal: 148ms\tremaining: 2m 27s\n",
      "50:\tlearn: 0.8441217\ttest: 0.8491811\tbest: 0.8547215 (0)\ttotal: 14.7s\tremaining: 4m 33s\n",
      "100:\tlearn: 0.8455521\ttest: 0.8510846\tbest: 0.8547215 (0)\ttotal: 27.7s\tremaining: 4m 6s\n",
      "150:\tlearn: 0.8455857\ttest: 0.8482624\tbest: 0.8547215 (0)\ttotal: 40.3s\tremaining: 3m 46s\n",
      "200:\tlearn: 0.8460051\ttest: 0.8478314\tbest: 0.8547215 (0)\ttotal: 53.5s\tremaining: 3m 32s\n",
      "250:\tlearn: 0.8466652\ttest: 0.8474743\tbest: 0.8547215 (0)\ttotal: 1m 5s\tremaining: 3m 16s\n",
      "300:\tlearn: 0.8467714\ttest: 0.8460594\tbest: 0.8547215 (0)\ttotal: 1m 17s\tremaining: 3m 1s\n",
      "350:\tlearn: 0.8473302\ttest: 0.8459081\tbest: 0.8547215 (0)\ttotal: 1m 30s\tremaining: 2m 47s\n",
      "400:\tlearn: 0.8477293\ttest: 0.8476821\tbest: 0.8547215 (0)\ttotal: 1m 43s\tremaining: 2m 35s\n",
      "450:\tlearn: 0.8479903\ttest: 0.8476724\tbest: 0.8547215 (0)\ttotal: 1m 56s\tremaining: 2m 21s\n",
      "500:\tlearn: 0.8479271\ttest: 0.8503517\tbest: 0.8547215 (0)\ttotal: 2m 8s\tremaining: 2m 8s\n",
      "550:\tlearn: 0.8484717\ttest: 0.8475728\tbest: 0.8547215 (0)\ttotal: 2m 21s\tremaining: 1m 55s\n",
      "600:\tlearn: 0.8485888\ttest: 0.8449744\tbest: 0.8547215 (0)\ttotal: 2m 34s\tremaining: 1m 42s\n",
      "650:\tlearn: 0.8493228\ttest: 0.8463035\tbest: 0.8547215 (0)\ttotal: 2m 46s\tremaining: 1m 29s\n",
      "700:\tlearn: 0.8488668\ttest: 0.8481166\tbest: 0.8547215 (0)\ttotal: 2m 58s\tremaining: 1m 15s\n",
      "750:\tlearn: 0.8491557\ttest: 0.8488824\tbest: 0.8547215 (0)\ttotal: 3m 9s\tremaining: 1m 2s\n",
      "800:\tlearn: 0.8497101\ttest: 0.8495016\tbest: 0.8547215 (0)\ttotal: 3m 22s\tremaining: 50.2s\n",
      "850:\tlearn: 0.8495702\ttest: 0.8484554\tbest: 0.8547215 (0)\ttotal: 3m 32s\tremaining: 37.2s\n",
      "900:\tlearn: 0.8498709\ttest: 0.8484554\tbest: 0.8547215 (0)\ttotal: 3m 44s\tremaining: 24.6s\n",
      "950:\tlearn: 0.8501736\ttest: 0.8488089\tbest: 0.8547215 (0)\ttotal: 3m 54s\tremaining: 12.1s\n",
      "999:\tlearn: 0.8501095\ttest: 0.8482490\tbest: 0.8547215 (0)\ttotal: 4m 4s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.8547215496\n",
      "bestIteration = 0\n",
      "\n",
      "Shrink model to first 1 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7f9851b84820>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_FU_final=CatBoostClassifier(**params_FU)\n",
    "model_FU_final.fit(train_data_FU, eval_set=val_data_FU, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "389355cb-6ce8-4110-90ab-864558f90ea7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a270bd4a-845e-47a8-852f-4b30171bd9c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b1eb79fa-e827-41e4-a158-f3191dd92760",
   "metadata": {},
   "outputs": [],
   "source": [
    "D_AF = joblib.load('./dataset_3rd/db-v5-AfterFraud.joblib')\n",
    "utils.catboost_fillna(D_AF['train'], D_AF['pred'])\n",
    "\n",
    "D_AF['train'][\"label\"] = D_AF['train'][\"label\"].astype(np.uint8)\n",
    "D_AF['pred'][\"label\"] = D_AF['pred'][\"label\"].astype(np.uint8)\n",
    "\n",
    "train_X_AF = D_AF['train'].drop([\"label\"], axis=1)\n",
    "train_Y_AF = D_AF['train'][\"label\"].values\n",
    "\n",
    "val_X_AF = D_AF['pred'].drop([\"label\"], axis=1)\n",
    "val_Y_AF = D_AF['pred'][\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8815af51-35d5-48ee-a1e9-374036fba391",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features_list = np.nonzero((train_X_AF.dtypes==bool).to_numpy() | (train_X_AF.dtypes=='category').to_numpy())[0]\n",
    "\n",
    "train_data_AF = Pool(train_X_AF,\n",
    "                  train_Y_AF,\n",
    "                  cat_features = list(train_X_AF.columns[cat_features_list]))\n",
    "\n",
    "val_data_AF = Pool(val_X_AF,\n",
    "                  val_Y_AF,\n",
    "                  cat_features = list(val_X_AF.columns[cat_features_list]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "12b698b0-60be-45ca-be71-6df849f983d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_AF =  {\n",
    " 'eval_metric': 'F1', #\n",
    " 'iterations': 1000, #\n",
    " 'boosting_type': 'Ordered', # Plain* or Ordered(better but slow)\n",
    " 'one_hot_max_size': 10, # 2*\n",
    " 'l2_leaf_reg': 25, # 2*\n",
    " 'random_strength': 10, #\n",
    " 'od_type': 'Iter',\n",
    " # 'rsm': 0.5, # 1*, 0~1   # CPU only\n",
    " 'max_ctr_complexity': 4, # 允許的特徵交叉的最大特徵數量\n",
    " 'use_best_model': True,\n",
    " 'od_wait': 999,\n",
    " 'random_seed': 8,\n",
    " 'depth': 2,\n",
    " 'loss_function': 'Logloss',\n",
    " 'learning_rate': 0.8,\n",
    " 'task_type': 'CPU',\n",
    " 'max_leaves': 4, # Can be used only with the Lossguide growing policy. should < 64\n",
    " 'verbose': 50,\n",
    "    \n",
    " 'thread_count': 16}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bcf6a5f3-4bd3-4944-85a3-aa0edd3f9974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.Pool at 0x7f97b60d5160>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_AF = model_AF.predict(train_data_AF, prediction_type='RawFormulaVal')\n",
    "train_data_AF.set_baseline(base_AF)\n",
    "val_data_AF.set_baseline(model_AF.predict(val_data_AF, prediction_type='RawFormulaVal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7cb5688f-441e-4d2e-be88-f8a421496e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9473684210526316"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(val_Y_AF, model_AF.predict(val_X_AF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fa3980de-d01b-4203-91b0-f5f5a53317c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87229b7a43fd4fd59111de140b65e8b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.9988485\ttest: 0.9465318\tbest: 0.9465318 (0)\ttotal: 34.5ms\tremaining: 34.5s\n",
      "50:\tlearn: 0.9990196\ttest: 0.9457701\tbest: 0.9485880 (1)\ttotal: 1.16s\tremaining: 21.6s\n",
      "100:\tlearn: 0.9991424\ttest: 0.9456915\tbest: 0.9485880 (1)\ttotal: 2.2s\tremaining: 19.6s\n",
      "150:\tlearn: 0.9991424\ttest: 0.9456915\tbest: 0.9485880 (1)\ttotal: 3.18s\tremaining: 17.9s\n",
      "200:\tlearn: 0.9991669\ttest: 0.9456915\tbest: 0.9485880 (1)\ttotal: 4.19s\tremaining: 16.6s\n",
      "250:\tlearn: 0.9991669\ttest: 0.9456915\tbest: 0.9485880 (1)\ttotal: 5.25s\tremaining: 15.7s\n",
      "300:\tlearn: 0.9991669\ttest: 0.9456915\tbest: 0.9485880 (1)\ttotal: 6.32s\tremaining: 14.7s\n",
      "350:\tlearn: 0.9991669\ttest: 0.9456915\tbest: 0.9485880 (1)\ttotal: 7.36s\tremaining: 13.6s\n",
      "400:\tlearn: 0.9991669\ttest: 0.9456915\tbest: 0.9485880 (1)\ttotal: 8.41s\tremaining: 12.6s\n",
      "450:\tlearn: 0.9992404\ttest: 0.9456915\tbest: 0.9485880 (1)\ttotal: 9.49s\tremaining: 11.6s\n",
      "500:\tlearn: 0.9992404\ttest: 0.9456915\tbest: 0.9485880 (1)\ttotal: 10.6s\tremaining: 10.5s\n",
      "550:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 11.7s\tremaining: 9.51s\n",
      "600:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 12.8s\tremaining: 8.48s\n",
      "650:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 13.9s\tremaining: 7.43s\n",
      "700:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 15s\tremaining: 6.38s\n",
      "750:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 16.1s\tremaining: 5.33s\n",
      "800:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 17.2s\tremaining: 4.27s\n",
      "850:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 18.3s\tremaining: 3.21s\n",
      "900:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 19.4s\tremaining: 2.13s\n",
      "950:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 20.4s\tremaining: 1.05s\n",
      "999:\tlearn: 0.9992404\ttest: 0.9464544\tbest: 0.9485880 (1)\ttotal: 21.4s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.9485879797\n",
      "bestIteration = 1\n",
      "\n",
      "Shrink model to first 2 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7f986813b670>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_AF_final=CatBoostClassifier(**params_AF)\n",
    "model_AF_final.fit(train_data_AF, eval_set=val_data_AF, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "484fb922-2080-47a9-8821-48d796312dce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.948587979724837"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(val_Y_AF, model_AF_final_2.predict(val_data_AF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "95d218ac-24bf-4924-9ddf-437728a89962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9523809523809523"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(val_Y_AF, model_AF_final_2.predict(val_data_AF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "20d1bdf0-d320-4fd5-ad60-37cd855b512f",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_AF_2 =  {\n",
    " 'eval_metric': 'F1', #\n",
    " 'iterations': 5000, #\n",
    " 'boosting_type': 'Ordered', # Plain* or Ordered(better but slow)\n",
    " 'one_hot_max_size': 20, # 2*\n",
    " 'l2_leaf_reg': 25, # 2*\n",
    " 'random_strength': 10, #\n",
    " 'od_type': 'Iter',\n",
    " # 'rsm': 0.5, # 1*, 0~1   # CPU only\n",
    " 'max_ctr_complexity': 4, # 允許的特徵交叉的最大特徵數量\n",
    " 'use_best_model': True,\n",
    " 'od_wait': 999,\n",
    " 'random_seed': 8,\n",
    " 'depth': 2,\n",
    " 'loss_function': 'Logloss',\n",
    " 'learning_rate': 0.66,\n",
    " 'task_type': 'CPU',\n",
    " 'max_leaves': 4, # Can be used only with the Lossguide growing policy. should < 64\n",
    " 'verbose': 100,\n",
    "    \n",
    " 'thread_count': 16}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "426b1568-3085-4a11-a1ad-c073739e6984",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.9989461\ttest: 0.9523121\tbest: 0.9523121 (0)\ttotal: 30.6ms\tremaining: 2m 33s\n",
      "100:\tlearn: 0.9990689\ttest: 0.9523810\tbest: 0.9523810 (23)\ttotal: 2.03s\tremaining: 1m 38s\n",
      "200:\tlearn: 0.9990689\ttest: 0.9523810\tbest: 0.9523810 (23)\ttotal: 3.88s\tremaining: 1m 32s\n",
      "300:\tlearn: 0.9990689\ttest: 0.9523810\tbest: 0.9523810 (23)\ttotal: 5.73s\tremaining: 1m 29s\n",
      "400:\tlearn: 0.9990689\ttest: 0.9516245\tbest: 0.9523810 (23)\ttotal: 7.56s\tremaining: 1m 26s\n",
      "500:\tlearn: 0.9990689\ttest: 0.9516245\tbest: 0.9523810 (23)\ttotal: 9.45s\tremaining: 1m 24s\n",
      "600:\tlearn: 0.9990933\ttest: 0.9516245\tbest: 0.9523810 (23)\ttotal: 11.3s\tremaining: 1m 22s\n",
      "700:\tlearn: 0.9991179\ttest: 0.9508671\tbest: 0.9523810 (23)\ttotal: 13.1s\tremaining: 1m 20s\n",
      "800:\tlearn: 0.9991179\ttest: 0.9508671\tbest: 0.9523810 (23)\ttotal: 15s\tremaining: 1m 18s\n",
      "900:\tlearn: 0.9991914\ttest: 0.9508671\tbest: 0.9523810 (23)\ttotal: 16.8s\tremaining: 1m 16s\n",
      "1000:\tlearn: 0.9991914\ttest: 0.9508671\tbest: 0.9523810 (23)\ttotal: 18.7s\tremaining: 1m 14s\n",
      "Stopped by overfitting detector  (999 iterations wait)\n",
      "\n",
      "bestTest = 0.9523809524\n",
      "bestIteration = 23\n",
      "\n",
      "Shrink model to first 24 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7f983ae1f310>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_AF_final_2=CatBoostClassifier(**params_AF_2)\n",
    "model_AF_final_2.fit(train_data_AF, eval_set=val_data_AF, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7ad80a2f-c89a-4bc0-8b87-b4b5912580dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0.66 =  0.9486623283\n",
    "# 'one_hot_max_size': 20 = 0.9523809524\n",
    "model_AF_final_2.save_model('AF_model_final', format='cbm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c42cbc03-3216-428e-993b-34d5d570d001",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cbff836-0cd5-4077-b43c-a8b4cc467219",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc5dda5c-9705-4dc3-ba74-d3c7238fac46",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62d75645-d26d-49f1-9e71-48cb51b2a071",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b21dfeab-d878-4001-991f-a12e4681aece",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3b54f63-3f48-4604-b718-d1e1c6f84c3b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e85c721-2c1b-42e9-8e07-23dc97f1282e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ed0b1fd-9b56-4937-a428-d7c659cbc699",
   "metadata": {},
   "outputs": [],
   "source": [
    "D_AF = joblib.load('./dataset_3rd/db-v5-AfterFraud.joblib')\n",
    "utils.catboost_fillna(D_AF['train'], D_AF['pred'])\n",
    "\n",
    "D_AF['train'][\"label\"] = D_AF['train'][\"label\"].astype(np.uint8)\n",
    "D_AF['pred'][\"label\"] = D_AF['pred'][\"label\"].astype(np.uint8)\n",
    "\n",
    "train_X_AF = D_AF['train'].drop([\"label\"], axis=1)\n",
    "train_Y_AF = D_AF['train'][\"label\"].values\n",
    "\n",
    "val_X_AF = D_AF['pred'].drop([\"label\"], axis=1)\n",
    "val_Y_AF = D_AF['pred'][\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bbc615a6-0a4c-4f7b-8a7e-c44b79e0be57",
   "metadata": {},
   "outputs": [],
   "source": [
    "D_UBF = joblib.load('./dataset_3rd/db-v5-UsedBeforeFraud.joblib')\n",
    "utils.catboost_fillna(D_UBF['train'], D_UBF['pred'])\n",
    "\n",
    "D_UBF['train'][\"label\"] = D_UBF['train'][\"label\"].astype(np.uint8)\n",
    "D_UBF['pred'][\"label\"] = D_UBF['pred'][\"label\"].astype(np.uint8)\n",
    "\n",
    "train_X_UBF = D_UBF['train'].drop([\"label\"], axis=1)\n",
    "train_Y_UBF = D_UBF['train'][\"label\"].values\n",
    "\n",
    "val_X_UBF = D_UBF['pred'].drop([\"label\"], axis=1)\n",
    "val_Y_UBF = D_UBF['pred'][\"label\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb050279-d14d-448f-8433-cf605fa8d59b",
   "metadata": {},
   "source": [
    "# First Use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "794737c9-3cee-4e3e-97ae-b363e556dc58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8550689571739656"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(val_Y_FU, model_FU.predict(val_data_FU))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f96762a5-89a6-4bcc-97d2-8740f2e8e029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8443308348858369"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(train_Y_FU, model_FU.predict(train_data_FU))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "57cb8211-49c5-42c0-90b1-84d4f56be555",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features_list = np.nonzero((train_X_FU.dtypes==bool).to_numpy() | (train_X_FU.dtypes=='category').to_numpy())[0]\n",
    "\n",
    "train_data_FU = Pool(train_X_FU,\n",
    "                  train_Y_FU,\n",
    "                  cat_features = list(train_X_FU.columns[cat_features_list]))\n",
    "\n",
    "val_data_FU = Pool(val_X_FU,\n",
    "                  val_Y_FU,\n",
    "                  cat_features = list(val_X_FU.columns[cat_features_list]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4758089a-975e-4372-8ec8-19afaf1e7cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_FU =  {\n",
    " 'eval_metric': 'F1', #\n",
    " 'iterations': 500, #\n",
    " 'boosting_type': 'Ordered', # Plain* or Ordered(better but slow)\n",
    " 'one_hot_max_size': 10, # 2*\n",
    " 'l2_leaf_reg': 25, # 2*\n",
    " 'random_strength': 10, #\n",
    " 'od_type': 'Iter',\n",
    " # 'rsm': 0.5, # 1*, 0~1   # CPU only\n",
    " 'max_ctr_complexity': 4, # 允許的特徵交叉的最大特徵數量\n",
    " 'use_best_model': True,\n",
    " 'od_wait': 168,\n",
    " 'random_seed': 8,\n",
    " 'depth': 2,\n",
    " 'loss_function': 'Logloss',\n",
    " 'learning_rate': 0.8,\n",
    " 'task_type': 'CPU',\n",
    " 'max_leaves': 4, # Can be used only with the Lossguide growing policy. should < 64\n",
    " 'verbose': 50,\n",
    "    \n",
    " 'thread_count': 16}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "534f3a0c-8670-4b40-88b5-28f7bdfc1270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.Pool at 0x7fabd504d100>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_FU = model_FU.predict(train_data_FU, prediction_type='RawFormulaVal')\n",
    "train_data_FU.set_baseline(base_FU)\n",
    "val_data_FU.set_baseline(model_FU.predict(val_data_FU, prediction_type='RawFormulaVal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8f7e637c-e686-4de4-8508-2e2a814646a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "568169563f034031b9097be1bbdb6539",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.8450879\ttest: 0.7352328\tbest: 0.7352328 (0)\ttotal: 334ms\tremaining: 2m 46s\n",
      "50:\tlearn: 0.8387945\ttest: 0.2589139\tbest: 0.7352328 (0)\ttotal: 13.8s\tremaining: 2m 1s\n",
      "100:\tlearn: 0.8416775\ttest: 0.2192842\tbest: 0.7352328 (0)\ttotal: 27.8s\tremaining: 1m 49s\n",
      "150:\tlearn: 0.8433573\ttest: 0.2211955\tbest: 0.7352328 (0)\ttotal: 40.9s\tremaining: 1m 34s\n",
      "Stopped by overfitting detector  (168 iterations wait)\n",
      "\n",
      "bestTest = 0.7352328006\n",
      "bestIteration = 0\n",
      "\n",
      "Shrink model to first 1 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7fa72f18ab80>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_FU_try=CatBoostClassifier(**params_FU)\n",
    "model_FU_try.fit(train_X_FU, y=train_Y_FU, eval_set=val_data_FU, plot=True,cat_features = list(train_X_FU.columns[cat_features_list])\n",
    "                , baseline = base_FU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "37c862e7-54b0-43a1-99bf-5082ed39eadf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68a9d5a0d8344982bf5838d191e41ae7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.8450879\ttest: 0.7352328\tbest: 0.7352328 (0)\ttotal: 329ms\tremaining: 2m 44s\n",
      "50:\tlearn: 0.8387945\ttest: 0.2589139\tbest: 0.7352328 (0)\ttotal: 14.1s\tremaining: 2m 3s\n",
      "100:\tlearn: 0.8416775\ttest: 0.2192842\tbest: 0.7352328 (0)\ttotal: 28.1s\tremaining: 1m 51s\n",
      "150:\tlearn: 0.8433573\ttest: 0.2211955\tbest: 0.7352328 (0)\ttotal: 41.8s\tremaining: 1m 36s\n",
      "Stopped by overfitting detector  (168 iterations wait)\n",
      "\n",
      "bestTest = 0.7352328006\n",
      "bestIteration = 0\n",
      "\n",
      "Shrink model to first 1 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7fabeebc80d0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_FU_final=CatBoostClassifier(**params_FU)\n",
    "model_FU_final.fit(train_data_FU, eval_set=val_data_FU, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6de87d7f-cddd-4f29-8418-567a58a7790d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed71652e4f984564acb2774bcb2e3bcf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.038316\n",
      "0:\tlearn: 0.0305836\ttest: 0.0877908\tbest: 0.0877908 (0)\ttotal: 389ms\tremaining: 6m 28s\n",
      "1:\tlearn: 0.0301494\ttest: 0.0870545\tbest: 0.0870545 (1)\ttotal: 738ms\tremaining: 6m 8s\n",
      "2:\tlearn: 0.0297268\ttest: 0.0866878\tbest: 0.0866878 (2)\ttotal: 1.08s\tremaining: 5m 58s\n",
      "3:\tlearn: 0.0293491\ttest: 0.0866357\tbest: 0.0866357 (3)\ttotal: 1.41s\tremaining: 5m 51s\n",
      "4:\tlearn: 0.0289808\ttest: 0.0871500\tbest: 0.0866357 (3)\ttotal: 1.75s\tremaining: 5m 48s\n",
      "5:\tlearn: 0.0286583\ttest: 0.0880765\tbest: 0.0866357 (3)\ttotal: 2.1s\tremaining: 5m 47s\n",
      "6:\tlearn: 0.0283144\ttest: 0.0892229\tbest: 0.0866357 (3)\ttotal: 2.38s\tremaining: 5m 37s\n",
      "7:\tlearn: 0.0279983\ttest: 0.0911762\tbest: 0.0866357 (3)\ttotal: 2.65s\tremaining: 5m 28s\n",
      "8:\tlearn: 0.0277071\ttest: 0.0933238\tbest: 0.0866357 (3)\ttotal: 2.98s\tremaining: 5m 28s\n",
      "9:\tlearn: 0.0274124\ttest: 0.0960445\tbest: 0.0866357 (3)\ttotal: 3.33s\tremaining: 5m 29s\n",
      "10:\tlearn: 0.0271396\ttest: 0.0995381\tbest: 0.0866357 (3)\ttotal: 3.66s\tremaining: 5m 28s\n",
      "11:\tlearn: 0.0268569\ttest: 0.1029000\tbest: 0.0866357 (3)\ttotal: 4s\tremaining: 5m 28s\n",
      "12:\tlearn: 0.0266114\ttest: 0.1064072\tbest: 0.0866357 (3)\ttotal: 4.36s\tremaining: 5m 30s\n",
      "13:\tlearn: 0.0263768\ttest: 0.1111566\tbest: 0.0866357 (3)\ttotal: 4.69s\tremaining: 5m 30s\n",
      "14:\tlearn: 0.0261545\ttest: 0.1157987\tbest: 0.0866357 (3)\ttotal: 5.03s\tremaining: 5m 30s\n",
      "15:\tlearn: 0.0259404\ttest: 0.1202845\tbest: 0.0866357 (3)\ttotal: 5.3s\tremaining: 5m 26s\n",
      "16:\tlearn: 0.0257147\ttest: 0.1259486\tbest: 0.0866357 (3)\ttotal: 5.59s\tremaining: 5m 23s\n",
      "17:\tlearn: 0.0255080\ttest: 0.1315592\tbest: 0.0866357 (3)\ttotal: 5.94s\tremaining: 5m 24s\n",
      "18:\tlearn: 0.0253219\ttest: 0.1371013\tbest: 0.0866357 (3)\ttotal: 6.21s\tremaining: 5m 20s\n",
      "19:\tlearn: 0.0251337\ttest: 0.1438164\tbest: 0.0866357 (3)\ttotal: 6.58s\tremaining: 5m 22s\n",
      "20:\tlearn: 0.0249537\ttest: 0.1515877\tbest: 0.0866357 (3)\ttotal: 6.95s\tremaining: 5m 24s\n",
      "21:\tlearn: 0.0247774\ttest: 0.1590226\tbest: 0.0866357 (3)\ttotal: 7.27s\tremaining: 5m 23s\n",
      "22:\tlearn: 0.0245857\ttest: 0.1669982\tbest: 0.0866357 (3)\ttotal: 7.63s\tremaining: 5m 24s\n",
      "23:\tlearn: 0.0244288\ttest: 0.1754181\tbest: 0.0866357 (3)\ttotal: 7.91s\tremaining: 5m 21s\n",
      "24:\tlearn: 0.0242672\ttest: 0.1831472\tbest: 0.0866357 (3)\ttotal: 8.18s\tremaining: 5m 18s\n",
      "25:\tlearn: 0.0240861\ttest: 0.1922630\tbest: 0.0866357 (3)\ttotal: 8.51s\tremaining: 5m 18s\n",
      "26:\tlearn: 0.0239490\ttest: 0.2022749\tbest: 0.0866357 (3)\ttotal: 8.87s\tremaining: 5m 19s\n",
      "27:\tlearn: 0.0237799\ttest: 0.2102707\tbest: 0.0866357 (3)\ttotal: 9.24s\tremaining: 5m 20s\n",
      "28:\tlearn: 0.0236459\ttest: 0.2198809\tbest: 0.0866357 (3)\ttotal: 9.6s\tremaining: 5m 21s\n",
      "29:\tlearn: 0.0235210\ttest: 0.2299704\tbest: 0.0866357 (3)\ttotal: 9.99s\tremaining: 5m 23s\n",
      "30:\tlearn: 0.0233730\ttest: 0.2402027\tbest: 0.0866357 (3)\ttotal: 10.3s\tremaining: 5m 22s\n",
      "31:\tlearn: 0.0232443\ttest: 0.2468154\tbest: 0.0866357 (3)\ttotal: 10.7s\tremaining: 5m 23s\n",
      "32:\tlearn: 0.0231130\ttest: 0.2579710\tbest: 0.0866357 (3)\ttotal: 11s\tremaining: 5m 21s\n",
      "33:\tlearn: 0.0229940\ttest: 0.2671424\tbest: 0.0866357 (3)\ttotal: 11.2s\tremaining: 5m 19s\n",
      "34:\tlearn: 0.0228831\ttest: 0.2725495\tbest: 0.0866357 (3)\ttotal: 11.6s\tremaining: 5m 18s\n",
      "35:\tlearn: 0.0227791\ttest: 0.2816950\tbest: 0.0866357 (3)\ttotal: 11.8s\tremaining: 5m 16s\n",
      "36:\tlearn: 0.0226717\ttest: 0.2892261\tbest: 0.0866357 (3)\ttotal: 12.1s\tremaining: 5m 15s\n",
      "37:\tlearn: 0.0225640\ttest: 0.2986388\tbest: 0.0866357 (3)\ttotal: 12.4s\tremaining: 5m 14s\n",
      "38:\tlearn: 0.0224842\ttest: 0.3067563\tbest: 0.0866357 (3)\ttotal: 12.7s\tremaining: 5m 11s\n",
      "39:\tlearn: 0.0223895\ttest: 0.3145136\tbest: 0.0866357 (3)\ttotal: 13s\tremaining: 5m 12s\n",
      "40:\tlearn: 0.0222870\ttest: 0.3246437\tbest: 0.0866357 (3)\ttotal: 13.4s\tremaining: 5m 12s\n",
      "41:\tlearn: 0.0221852\ttest: 0.3360653\tbest: 0.0866357 (3)\ttotal: 13.6s\tremaining: 5m 10s\n",
      "42:\tlearn: 0.0220946\ttest: 0.3503909\tbest: 0.0866357 (3)\ttotal: 13.9s\tremaining: 5m 10s\n",
      "43:\tlearn: 0.0220128\ttest: 0.3603398\tbest: 0.0866357 (3)\ttotal: 14.2s\tremaining: 5m 7s\n",
      "44:\tlearn: 0.0219256\ttest: 0.3711526\tbest: 0.0866357 (3)\ttotal: 14.5s\tremaining: 5m 7s\n",
      "45:\tlearn: 0.0218497\ttest: 0.3780915\tbest: 0.0866357 (3)\ttotal: 14.8s\tremaining: 5m 6s\n",
      "46:\tlearn: 0.0217661\ttest: 0.3865747\tbest: 0.0866357 (3)\ttotal: 15.2s\tremaining: 5m 7s\n",
      "47:\tlearn: 0.0216972\ttest: 0.3982867\tbest: 0.0866357 (3)\ttotal: 15.4s\tremaining: 5m 6s\n",
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      "945:\tlearn: 0.0162080\ttest: 0.9859210\tbest: 0.0866357 (3)\ttotal: 4m 53s\tremaining: 16.7s\n",
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      "948:\tlearn: 0.0162016\ttest: 0.9863576\tbest: 0.0866357 (3)\ttotal: 4m 54s\tremaining: 15.8s\n",
      "949:\tlearn: 0.0162007\ttest: 0.9868936\tbest: 0.0866357 (3)\ttotal: 4m 54s\tremaining: 15.5s\n",
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      "955:\tlearn: 0.0161861\ttest: 0.9873285\tbest: 0.0866357 (3)\ttotal: 4m 56s\tremaining: 13.6s\n",
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      "989:\tlearn: 0.0161181\ttest: 0.9934285\tbest: 0.0866357 (3)\ttotal: 5m 7s\tremaining: 3.1s\n",
      "990:\tlearn: 0.0161169\ttest: 0.9934863\tbest: 0.0866357 (3)\ttotal: 5m 7s\tremaining: 2.79s\n",
      "991:\tlearn: 0.0161156\ttest: 0.9935749\tbest: 0.0866357 (3)\ttotal: 5m 7s\tremaining: 2.48s\n",
      "992:\tlearn: 0.0161137\ttest: 0.9934796\tbest: 0.0866357 (3)\ttotal: 5m 7s\tremaining: 2.17s\n",
      "993:\tlearn: 0.0161113\ttest: 0.9935062\tbest: 0.0866357 (3)\ttotal: 5m 8s\tremaining: 1.86s\n",
      "994:\tlearn: 0.0161101\ttest: 0.9934793\tbest: 0.0866357 (3)\ttotal: 5m 8s\tremaining: 1.55s\n",
      "995:\tlearn: 0.0161078\ttest: 0.9939157\tbest: 0.0866357 (3)\ttotal: 5m 8s\tremaining: 1.24s\n",
      "996:\tlearn: 0.0161067\ttest: 0.9944019\tbest: 0.0866357 (3)\ttotal: 5m 9s\tremaining: 931ms\n",
      "997:\tlearn: 0.0161031\ttest: 0.9919607\tbest: 0.0866357 (3)\ttotal: 5m 9s\tremaining: 620ms\n",
      "998:\tlearn: 0.0160998\ttest: 0.9920297\tbest: 0.0866357 (3)\ttotal: 5m 9s\tremaining: 310ms\n",
      "999:\tlearn: 0.0160996\ttest: 0.9920040\tbest: 0.0866357 (3)\ttotal: 5m 10s\tremaining: 0us\n",
      "bestTest = 0.08663574564\n",
      "bestIteration = 3\n",
      "Shrink model to first 4 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7fac70fb35e0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_FU_final_G=CatBoostClassifier(task_type = 'GPU')\n",
    "model_FU_final_G.fit(train_data_FU, eval_set=val_data_FU, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "73ab13dd-ca02-43ab-80c4-69c2c53043fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8452781371280724"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(train_Y_FU, model_FU_final_G.predict(train_data_FU))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f06b36c0-eb07-4356-8a9b-2518873f53f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0.8453535750480877"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(train_Y_FU, model_FU_final.predict(train_data_FU))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c94332b-7e91-478e-b5b7-5e28aa9a4454",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c45db3f-b5da-4250-affc-be8fc32111d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f17f3a3f-af1f-41a3-ad2a-81252a368c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0.9473684210526316"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(val_Y_AF, model_AF.predict(val_X_AF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "022deff1-3ea1-40d4-b7e6-f37531e2bf49",
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(val_Y_AF, model_AF.predict(val_X_AF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b9cc57c-cd3e-442d-af4e-7fb8db127847",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e0a6bb6-f517-42a4-898e-90acf66752f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1594533029612756"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(val_Y_UBF, model_UBF.predict(val_X_UBF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc86f0a7-6227-46a9-8f16-dfe8bd9c06f3",
   "metadata": {},
   "outputs": [],
   "source": []
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   "id": "bfd9424c-1821-4a18-9547-727fb3c6edde",
   "metadata": {},
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  {
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   "execution_count": null,
   "id": "9f4ce1a1-e5f9-4f6b-a2a8-080292dfecef",
   "metadata": {},
   "outputs": [],
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   "id": "e9d5901c-4b71-45ed-a53b-bd919c1a87a4",
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   "id": "301b82a3-8566-4c98-9509-cee0cb753515",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
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   "execution_count": null,
   "id": "256c9bad-7380-4c62-8c94-0d1b3116d0d2",
   "metadata": {},
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