research/4_tune_catboost_300_feat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc134e5c-1ff1-42e0-8af7-b49db15ad8ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "79b76f48-32e1-4cfb-8863-e084bffb2488",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37d87f9f-7778-4eee-819b-085e6d510fb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/t3600/mambaforge/envs/boosting_mle/lib/python3.11/site-packages/paramiko/transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated and will be removed in a future release\n",
      "  \"class\": algorithms.Blowfish,\n",
      "/home/t3600/mambaforge/envs/boosting_mle/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor, Pool\n",
    "import joblib\n",
    "import json\n",
    "import mlflow\n",
    "import numpy as np\n",
    "import optuna\n",
    "import pickle\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f84b22e5-d1d3-4db3-9fca-ab733fb29541",
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append('../src/')\n",
    "import h5_utils\n",
    "from train import drop_target_nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dcdc02a-a6ee-46b3-9dfe-f4b2fea75db5",
   "metadata": {},
   "source": [
    "# Configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b3cb72bf-c8a0-498d-8e04-1053f1d627e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "settings_path = '../configs/settings.json'\n",
    "with open(settings_path, 'r') as f:\n",
    "    config = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c27f663c-8d7c-4b9c-b714-d03e7a571177",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_daily_h5 = f\"../{config['RAW_DATA_DIR']}/daily/\"\n",
    "\n",
    "metadata_filename = 'metadata.h5'\n",
    "metadata_filepath = f\"../{config['RAW_DATA_DIR']}/{metadata_filename}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b0b7895-4de1-497c-aa06-4e3a8c5005d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f\"../{config['MODEL_DIR']}/top_300_feats_nms.pkl\", 'rb') as f:\n",
    "    top_feature_names = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb632a50-94bc-4326-b972-01b609525e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "optuna_selected_feat_filename = 'optuna_selected_300.pkl'  #'optuna_selected_300_4_folds.pkl'\n",
    "optuna_selected_feat_filenpath = f\"../{config['RAW_DATA_DIR']}/{optuna_selected_feat_filename}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ef1f5ae-f79c-414b-9686-f5d53367c8be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train test split\n",
    "DAY_TEST_START = 400 # train size in day terms\n",
    "\n",
    "# MlFlow\n",
    "experiment_name = 'optiver_300_features'\n",
    "\n",
    "# features precision\n",
    "REDUCE_TO_FLOAT32 = True # guaranties < 9.3Gb train pool of 300 features "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca380b6a-1443-4a35-87c3-16dd1efe5943",
   "metadata": {},
   "source": [
    "# Daily load trick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3bbffe13-79db-45b7-a635-7eb5b7e54288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "481"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_ids = h5_utils.load_metadata(metadata_filepath)\n",
    "len(date_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a49868-80d6-4b8a-bfc6-ea6d5525d2a5",
   "metadata": {},
   "source": [
    "# Final optuna on Catboost with selected 300 features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc0b832c-d112-4ef6-b573-88ed6a8a5504",
   "metadata": {},
   "source": [
    "## Trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6dd6e6ad-5ff2-4a7a-98f4-87e18455f0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixed_params = {\n",
    "    \"loss_function\" : \"MAE\",\n",
    "    \"eval_metric\" : \"MAE\",\n",
    "    \"thread_count\" : -1, \n",
    "    \"random_seed\" : 42,\n",
    "    \"iterations\" : 1000,  \n",
    "    \"od_type\": \"Iter\",  # Activate CatBoost overfitting detector\n",
    "    \"od_wait\": 20\n",
    "    #\"used_ram_limit\": \"14gb\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4b75961-574c-4c50-a660-7ae32ffc6596",
   "metadata": {},
   "outputs": [],
   "source": [
    "def params_catboost(trial):\n",
    "    \n",
    "    param = {\n",
    "        \"depth\": trial.suggest_int(\"depth\", 5, 10),\n",
    "        \"l2_leaf_reg\": trial.suggest_int(\"l2_leaf_reg\", \n",
    "                                      3, 50),\n",
    "        \"bootstrap_type\": trial.suggest_categorical(\"bootstrap_type\", \n",
    "                             [\"Bayesian\", \"Bernoulli\", \"MVS\"])\n",
    "    }\n",
    "\n",
    "    if param[\"bootstrap_type\"] == \"Bayesian\":\n",
    "        param[\"bagging_temperature\"] = trial.suggest_float(\"bagging_temperature\", \n",
    "                                                 1, 20)\n",
    "        \n",
    "    elif param[\"bootstrap_type\"] == \"Bernoulli\":\n",
    "        param[\"subsample\"] = trial.suggest_float(\"subsample\", \n",
    "                                         0.5, 1)\n",
    "    return param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "90572616-ecf2-4950-9cbc-0912b662b4aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mae_of_fit_model(train_pair, test_pair,\n",
    "              param):\n",
    "    train_data, train_labels = train_pair\n",
    "    \n",
    "    train_pool = Pool(train_data, \n",
    "                label=train_labels)\n",
    "    \n",
    "    model = CatBoostRegressor(**param)\n",
    "\n",
    "    test_data, test_labels = test_pair\n",
    "    test_pool = Pool(test_data, label=test_labels)\n",
    "    \n",
    "    model.fit(X=train_pool, \n",
    "              eval_set=test_pool,\n",
    "              verbose=1)\n",
    "\n",
    "    # Score\n",
    "    ## Load validation data\n",
    "\n",
    "    valid_preds = model.predict(test_data)\n",
    "    ## Calculate metric\n",
    "    mae = mean_absolute_error(test_labels, valid_preds)\n",
    "\n",
    "    return mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d05d5ffc-4cea-4bce-bc68-b75b874e9a44",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def objective_hard(trial, n_splits=4):\n",
    "    tscv = TimeSeriesSplit(n_splits=n_splits)\n",
    "    fold_scores = []\n",
    "\n",
    "    for fold, (train_index, test_index) in enumerate(tscv.split(date_ids)):\n",
    "        train_ids = date_ids[train_index]\n",
    "        test_ids = date_ids[test_index]\n",
    "\n",
    "        param = params_catboost(trial)\n",
    "        model = fit_model(train_ids, param)\n",
    "\n",
    "        valid_data, valid_labels = h5_utils.stacked_daily_data(test_ids,\n",
    "                                                               folder_daily_h5,\n",
    "                                                               top_feature_names,\n",
    "                                                               float32=REDUCE_TO_FLOAT32)\n",
    "        valid_data, valid_labels = drop_target_nan(valid_data, valid_labels)\n",
    "        valid_preds = model.predict(valid_data)\n",
    "\n",
    "        mae = mean_absolute_error(valid_labels, valid_preds)\n",
    "        fold_scores.append(mae)\n",
    "\n",
    "        # MLflow part\n",
    "        with mlflow.start_run():\n",
    "            mlflow.log_param(\"fold\", fold)\n",
    "            mlflow.log_params(param)\n",
    "            mlflow.log_metric(\"mae\", mae)\n",
    "\n",
    "    return np.mean(fold_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f09f866c-7d76-4e19-8a0c-a8fa641b819c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_process(data_ids):\n",
    "    data_features, data_labels = h5_utils.stacked_daily_data(data_ids,\n",
    "                                                       folder_daily_h5,\n",
    "                                                       top_feature_names,\n",
    "                                                       float32=REDUCE_TO_FLOAT32)\n",
    "    data_features, data_labels = drop_target_nan(data_features, \n",
    "                                               data_labels)\n",
    "\n",
    "    return data_features, data_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22bdad98-0ed1-4830-bd40-0522f3f37c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(trial):\n",
    "    # CV\n",
    "    train_ids = date_ids[:DAY_TEST_START]\n",
    "    test_ids = date_ids[DAY_TEST_START:]\n",
    "    \n",
    "    param = params_catboost(trial)\n",
    "\n",
    "    # Data\n",
    "    train_data, train_labels = load_and_process(train_ids) \n",
    "    ## NaN in target is not scored by the rules\n",
    "    valid_data, valid_labels = load_and_process(test_ids)\n",
    "\n",
    "    train_pair = train_data, train_labels\n",
    "    test_pair = valid_data, valid_labels\n",
    "    # Train\n",
    "    mae = fit_model(train_pair, test_pair, \n",
    "                    {**param, **fixed_params})\n",
    "\n",
    "    # MlFLow part\n",
    "    with mlflow.start_run():\n",
    "        mlflow.log_params(param)\n",
    "        mlflow.log_params(fixed_params)\n",
    "        mlflow.log_metric(\"mae\", mae)\n",
    "    \n",
    "    return mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58e83d00-52e6-4218-bf57-8b05ec5334d1",
   "metadata": {},
   "source": [
    "## Mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "688c296b-1d22-4597-851e-e73dfcaea1ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "if mlflow.__version__<='1.26.1':\n",
    "    from mlflow.tracking import MlflowClient\n",
    "else:\n",
    "    from mlflow import MlflowClient\n",
    "\n",
    "client = MlflowClient()\n",
    "\n",
    "experiment_info = client.get_experiment_by_name(experiment_name)\n",
    "\n",
    "if experiment_info is None:\n",
    "    mlflow.create_experiment(name=experiment_name) \n",
    "experiment_id = client.get_experiment_by_name(experiment_name).experiment_id\n",
    "print(experiment_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c248ca41-dfae-44f1-b1a5-c644236736e1",
   "metadata": {},
   "source": [
    "## Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c912ada5-34ca-45fc-b827-930d92ea562c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-07-20 04:25:32,265] A new study created in memory with name: no-name-9c2680dc-8ffe-4db8-9032-8c514866e4e0\n"
     ]
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      "250:\tlearn: 2.0707461\ttest: 1.7997188\tbest: 1.7997188 (250)\ttotal: 14m 22s\tremaining: 42m 54s\n",
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      "255:\tlearn: 2.0700016\ttest: 1.7989788\tbest: 1.7989788 (255)\ttotal: 14m 41s\tremaining: 42m 41s\n",
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      "270:\tlearn: 2.0432341\ttest: 1.7740737\tbest: 1.7740737 (270)\ttotal: 15m 36s\tremaining: 41m 59s\n",
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      "298:\tlearn: 2.0253491\ttest: 1.7577446\tbest: 1.7577446 (298)\ttotal: 17m 19s\tremaining: 40m 36s\n",
      "299:\tlearn: 2.0253354\ttest: 1.7577353\tbest: 1.7577353 (299)\ttotal: 17m 22s\tremaining: 40m 32s\n",
      "300:\tlearn: 2.0215289\ttest: 1.7544065\tbest: 1.7544065 (300)\ttotal: 17m 25s\tremaining: 40m 28s\n",
      "301:\tlearn: 2.0215214\ttest: 1.7544014\tbest: 1.7544014 (301)\ttotal: 17m 29s\tremaining: 40m 24s\n",
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      "303:\tlearn: 2.0213963\ttest: 1.7542790\tbest: 1.7542790 (303)\ttotal: 17m 36s\tremaining: 40m 18s\n",
      "304:\tlearn: 2.0210352\ttest: 1.7539685\tbest: 1.7539685 (304)\ttotal: 17m 39s\tremaining: 40m 15s\n",
      "305:\tlearn: 2.0210287\ttest: 1.7539714\tbest: 1.7539685 (304)\ttotal: 17m 43s\tremaining: 40m 11s\n",
      "306:\tlearn: 2.0210233\ttest: 1.7539682\tbest: 1.7539682 (306)\ttotal: 17m 46s\tremaining: 40m 7s\n",
      "307:\tlearn: 2.0206606\ttest: 1.7535693\tbest: 1.7535693 (307)\ttotal: 17m 50s\tremaining: 40m 4s\n",
      "308:\tlearn: 2.0205685\ttest: 1.7534775\tbest: 1.7534775 (308)\ttotal: 17m 54s\tremaining: 40m 3s\n",
      "309:\tlearn: 2.0205453\ttest: 1.7534600\tbest: 1.7534600 (309)\ttotal: 17m 58s\tremaining: 40m\n",
      "310:\tlearn: 2.0205374\ttest: 1.7534553\tbest: 1.7534553 (310)\ttotal: 18m 1s\tremaining: 39m 56s\n",
      "311:\tlearn: 2.0205318\ttest: 1.7534522\tbest: 1.7534522 (311)\ttotal: 18m 5s\tremaining: 39m 53s\n",
      "312:\tlearn: 2.0205260\ttest: 1.7534501\tbest: 1.7534501 (312)\ttotal: 18m 9s\tremaining: 39m 52s\n",
      "313:\tlearn: 2.0200702\ttest: 1.7530625\tbest: 1.7530625 (313)\ttotal: 18m 14s\tremaining: 39m 50s\n",
      "314:\tlearn: 2.0200663\ttest: 1.7530608\tbest: 1.7530608 (314)\ttotal: 18m 18s\tremaining: 39m 48s\n",
      "315:\tlearn: 2.0196706\ttest: 1.7526736\tbest: 1.7526736 (315)\ttotal: 18m 22s\tremaining: 39m 45s\n",
      "316:\tlearn: 2.0196631\ttest: 1.7526691\tbest: 1.7526691 (316)\ttotal: 18m 26s\tremaining: 39m 43s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "hours = 7\n",
    "\n",
    "study = optuna.create_study(direction='minimize',\n",
    "                pruner=optuna.pruners.MedianPruner(n_warmup_steps=5))\n",
    "study.optimize(objective, \n",
    "               n_trials=120,\n",
    "               timeout = 60*60*hours\n",
    "              )\n",
    "print('Number of finished trials:', len(study.trials))\n",
    "print('Best trial:', study.best_trial.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee57746e-0216-40c8-813e-e070c6fd0774",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Best trial: score {}, params {}\".format(study.best_trial.value, \n",
    "\t\t\t\t\t       study.best_trial.params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3d39911-055e-41bc-a3a0-66f6ab846da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(study.trials)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c86eac9-cb93-4dc3-a5d4-ab7c9a90ef6c",
   "metadata": {},
   "source": [
    "## Store study (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98d38e9f-a884-40d0-b5c8-76eec4f4165a",
   "metadata": {},
   "outputs": [],
   "source": [
    "optuna_selected_feat_filenpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6354b9f6-15c1-45c3-b385-12ddb69ccdc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(study, optuna_selected_feat_filenpath)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56826685-c472-40a4-8807-7f800d96143d",
   "metadata": {},
   "source": [
    "## Store best on selected 300 features for submission initial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a40a54be-483d-412c-93e6-5a34bd040fd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = study.best_trial.params.copy()\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ef1876d-51ce-4530-a9ba-244d7598cd23",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = {'depth': 9,\n",
    " 'l2_leaf_reg': 18,\n",
    " 'bootstrap_type': 'Bayesian',\n",
    " 'bagging_temperature': 2.855446277852597}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "add2157a-734d-4fd8-98d3-816013a05049",
   "metadata": {},
   "outputs": [],
   "source": [
    "del study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1770aec3-1ef2-4258-8f06-b61761c683b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc \n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b77fd427-6150-4d3c-9d13-d23de22a8d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'depth': 9,\n",
       " 'l2_leaf_reg': 18,\n",
       " 'bootstrap_type': 'Bayesian',\n",
       " 'bagging_temperature': 2.855446277852597}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fb6791f9-7b2c-4d9b-8e06-df0cef1e5c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss_function': 'MAE',\n",
       " 'eval_metric': 'MAE',\n",
       " 'thread_count': -1,\n",
       " 'random_seed': 42,\n",
       " 'iterations': 1000,\n",
       " 'od_type': 'Iter',\n",
       " 'od_wait': 20}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixed_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbe85e72-be83-470d-8963-a00fb04b4374",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "data_whole = load_and_process(date_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "00f51e53-5cb4-4563-9c5c-4b712a0f7443",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 6.2867236\ttotal: 6.79s\tremaining: 1h 53m 3s\n",
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      "987:\tlearn: 0.7428178\ttotal: 55m 10s\tremaining: 40.2s\n",
      "988:\tlearn: 0.7428159\ttotal: 55m 13s\tremaining: 36.8s\n",
      "989:\tlearn: 0.7428017\ttotal: 55m 15s\tremaining: 33.5s\n",
      "990:\tlearn: 0.7427624\ttotal: 55m 18s\tremaining: 30.1s\n",
      "991:\tlearn: 0.7427550\ttotal: 55m 21s\tremaining: 26.8s\n",
      "992:\tlearn: 0.7427462\ttotal: 55m 24s\tremaining: 23.4s\n",
      "993:\tlearn: 0.7427373\ttotal: 55m 28s\tremaining: 20.1s\n",
      "994:\tlearn: 0.7427305\ttotal: 55m 32s\tremaining: 16.7s\n",
      "995:\tlearn: 0.7427284\ttotal: 55m 36s\tremaining: 13.4s\n",
      "996:\tlearn: 0.7427181\ttotal: 55m 39s\tremaining: 10s\n",
      "997:\tlearn: 0.7427051\ttotal: 55m 43s\tremaining: 6.7s\n",
      "998:\tlearn: 0.7426971\ttotal: 55m 47s\tremaining: 3.35s\n",
      "999:\tlearn: 0.7423174\ttotal: 55m 50s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0xac4d7e0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pool = Pool(data_whole[0], \n",
    "                  label=data_whole[1])\n",
    "\n",
    "best_model = CatBoostRegressor(**best_params, \n",
    "                          **fixed_params)\n",
    "\n",
    "best_model.fit(X=train_pool, \n",
    "          #eval_set=test_pool,\n",
    "          verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d4396eac-2a66-4ccd-9bc3-ffc6f9c41880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.69 ms, sys: 8 ms, total: 11.7 ms\n",
      "Wall time: 45.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "start_day = date_ids[0]\n",
    "end_day = date_ids[-1]\n",
    "\n",
    "#path = f\"../{config['MODEL_DIR']}/catboost_best_daya_{start_day}_{end_day}_4_folds\"\n",
    "path = f\"../{config['MODEL_DIR']}/catboost_best_daya_{start_day}_{end_day}\"\n",
    "\n",
    "best_model.save_model(path,\n",
    "           format=\"cbm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62ff16e6-28e2-496b-b206-72723f331302",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10:30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3e887f0-52a8-4ea5-9d7a-a2ed5a7b6e7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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