{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e3be057e-8d2a-4d05-bd42-6b1dc75df5ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "13ad96f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path(r\"~/Fahrsimulator_MSY2526_AI/model_training/xgboost/output_windowed.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "95e1a351",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet(path=data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "68afd83e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  3  4  5  6  7  9 11 13 14 16 17 18 22 24 26 28 29]\n",
      "18\n",
      "11.88\n",
      "5.94\n"
     ]
    }
   ],
   "source": [
    "subjects = df['subjectID'].unique()\n",
    "print(subjects)\n",
    "print(len(subjects))\n",
    "print(len(subjects)*0.66)\n",
    "print(len(subjects)*0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "52dfd885",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "low all: (3080, 25)\n",
      "high n-back: (1031, 25)\n",
      "high k-drive: (3209, 25)\n",
      "high all: (4240, 25)\n"
     ]
    }
   ],
   "source": [
    "low_all = df[\n",
    "    ((df[\"PHASE\"] == \"baseline\") |\n",
    "     ((df[\"STUDY\"] == \"n-back\") & (df[\"PHASE\"] != \"baseline\") & (df[\"LEVEL\"].isin([1, 4]))))\n",
    "]\n",
    "print(f\"low all: {low_all.shape}\")\n",
    "\n",
    "high_nback = df[\n",
    "    (df[\"STUDY\"]==\"n-back\") &\n",
    "    (df[\"LEVEL\"].isin([2, 3, 5, 6])) &\n",
    "    (df[\"PHASE\"].isin([\"train\", \"test\"]))\n",
    "]\n",
    "print(f\"high n-back: {high_nback.shape}\")\n",
    "\n",
    "high_kdrive = df[\n",
    "    (df[\"STUDY\"] == \"k-drive\") & (df[\"PHASE\"] != \"baseline\")\n",
    "]\n",
    "print(f\"high k-drive: {high_kdrive.shape}\")\n",
    "\n",
    "high_all = pd.concat([high_nback, high_kdrive])\n",
    "print(f\"high all: {high_all.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8fba6edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_normalizer(train_data, au_columns, method='standard', scope='global'):\n",
    "    if method == 'standard':\n",
    "        Scaler = StandardScaler\n",
    "    elif method == 'minmax':\n",
    "        Scaler = MinMaxScaler\n",
    "    else:\n",
    "        raise ValueError(\"method must be 'standard' or 'minmax'\")\n",
    "    \n",
    "    scalers = {}\n",
    "    \n",
    "    if scope == 'subject':\n",
    "        for subject in train_data['subjectID'].unique():\n",
    "            subject_mask = train_data['subjectID'] == subject\n",
    "            scaler = Scaler()\n",
    "            scaler.fit(train_data.loc[subject_mask, au_columns])\n",
    "            scalers[subject] = scaler\n",
    "\n",
    "    elif scope == 'global':\n",
    "        scaler = Scaler()\n",
    "        scaler.fit(train_data[au_columns])\n",
    "        scalers['global'] = scaler\n",
    "\n",
    "    else:\n",
    "        raise ValueError(\"scope must be 'subject' or 'global'\")\n",
    "    \n",
    "    return {'scalers': scalers, 'method': method, 'scope': scope}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "24e3a77b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in /opt/conda/lib/python3.12/site-packages (3.1.2)\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.12/site-packages (from xgboost) (1.26.4)\n",
      "Requirement already satisfied: nvidia-nccl-cu12 in /opt/conda/lib/python3.12/site-packages (from xgboost) (2.19.3)\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.12/site-packages (from xgboost) (1.15.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8e7fa0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, GroupKFold\n",
    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, classification_report, confusion_matrix\n",
    "import xgboost as xgb\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "325ef71c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label distribution:\n",
      "label\n",
      "1    4240\n",
      "0    3080\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "low = low_all.copy()\n",
    "high = high_all.copy()\n",
    "\n",
    "low[\"label\"] = 0\n",
    "high[\"label\"] = 1\n",
    "\n",
    "data = pd.concat([low, high], ignore_index=True)\n",
    "data = data.drop_duplicates()\n",
    "\n",
    "print(\"Label distribution:\")\n",
    "print(data[\"label\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "67d70e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gefundene AU-Spalten: ['AU01_sum', 'AU02_sum', 'AU04_sum', 'AU05_sum', 'AU06_sum', 'AU07_sum', 'AU09_sum', 'AU10_sum', 'AU11_sum', 'AU12_sum', 'AU14_sum', 'AU15_sum', 'AU17_sum', 'AU20_sum', 'AU23_sum', 'AU24_sum', 'AU25_sum', 'AU26_sum', 'AU28_sum', 'AU43_sum']\n"
     ]
    }
   ],
   "source": [
    "au_columns = [col for col in data.columns if col.lower().startswith(\"au\")]\n",
    "print(\"Gefundene AU-Spalten:\", au_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "960bb8c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4470, 26) (2850, 26)\n",
      "[18  4  9 22  5  3 13 11 24  0 14]\n",
      "Index(['subjectID', 'start_time', 'STUDY', 'LEVEL', 'PHASE', 'AU01_sum',\n",
      "       'AU02_sum', 'AU04_sum', 'AU05_sum', 'AU06_sum', 'AU07_sum', 'AU09_sum',\n",
      "       'AU10_sum', 'AU11_sum', 'AU12_sum', 'AU14_sum', 'AU15_sum', 'AU17_sum',\n",
      "       'AU20_sum', 'AU23_sum', 'AU24_sum', 'AU25_sum', 'AU26_sum', 'AU28_sum',\n",
      "       'AU43_sum', 'label'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "subjects = np.random.permutation(data[\"subjectID\"].unique())\n",
    "\n",
    "n = len(subjects)\n",
    "n_train = int(n * 0.66)\n",
    "\n",
    "train_subjects = subjects[:n_train]\n",
    "test_subjects  = subjects[n_train:]\n",
    "# train_subs, val_subs = train_test_split(train_subjects, test_size=0.2, random_state=42)\n",
    "\n",
    "train_df = data[data.subjectID.isin(train_subjects)]\n",
    "#val_df   = data[data.subjectID.isin(val_subs)]\n",
    "test_df  = data[data.subjectID.isin(test_subjects)]\n",
    "\n",
    "print(train_df.shape, test_df.shape)\n",
    "print(train_subjects)\n",
    "print(train_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "802a45c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_normalizer(df_to_transform, normalizer_dict, au_columns):\n",
    "    scalers = normalizer_dict[\"scalers\"]\n",
    "    scope = normalizer_dict[\"scope\"]\n",
    "    df_out = df_to_transform.copy()\n",
    "\n",
    "    if scope == \"global\":\n",
    "        scaler = scalers[\"global\"]\n",
    "        df_out[au_columns] = scaler.transform(df_out[au_columns])\n",
    "\n",
    "    elif scope == \"subject\":\n",
    "        for subj, subdf in df_out.groupby(\"subjectID\"):\n",
    "            if subj in scalers:\n",
    "                df_out.loc[subdf.index, au_columns] = scalers[subj].transform(subdf[au_columns])\n",
    "            elif \"global\" in scalers:\n",
    "                df_out.loc[subdf.index, au_columns] = scalers[\"global\"].transform(subdf[au_columns])\n",
    "\n",
    "    return df_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "289f6b89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['subjectID', 'start_time', 'STUDY', 'LEVEL', 'PHASE', 'AU01_sum',\n",
      "       'AU02_sum', 'AU04_sum', 'AU05_sum', 'AU06_sum', 'AU07_sum', 'AU09_sum',\n",
      "       'AU10_sum', 'AU11_sum', 'AU12_sum', 'AU14_sum', 'AU15_sum', 'AU17_sum',\n",
      "       'AU20_sum', 'AU23_sum', 'AU24_sum', 'AU25_sum', 'AU26_sum', 'AU28_sum',\n",
      "       'AU43_sum', 'label'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "normalizer = fit_normalizer(train_df, au_columns, method=\"standard\", scope=\"global\")\n",
    "\n",
    "train_scaled = apply_normalizer(train_df, normalizer, au_columns)\n",
    "#val_scaled   = apply_normalizer(val_df, normalizer, au_columns)\n",
    "test_scaled  = apply_normalizer(test_df, normalizer, au_columns)\n",
    "\n",
    "print(test_scaled.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5df30e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Neue Feature-Liste: AU-Spalten + subjectID\n",
    "feature_columns = au_columns + [\"subjectID\"]\n",
    "\n",
    "# X und y erstellen\n",
    "X_train = train_scaled[feature_columns].values\n",
    "y_train = train_scaled[\"label\"].values\n",
    "\n",
    "#X_val,   y_val   = val_scaled[au_columns].values, val_scaled[\"label\"].values\n",
    "X_test,  y_test  = test_scaled[au_columns].values, test_scaled[\"label\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6fb7c86a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:15] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:15] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:15] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:15] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:16] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:16] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:16] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:16] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:22] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:22] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:25] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:25] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:25] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:27] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:28] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:29] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:29] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:29] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:29] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:29] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:29] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:29] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:29] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:29] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:30] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:30] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:30] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:30] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:30] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:30] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:30] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:30] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:31] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:31] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:31] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:31] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:31] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:31] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:32] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:32] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:32] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:32] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:32] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:32] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:32] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:32] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:33] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:33] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:33] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:33] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:33] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:33] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:33] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:33] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:34] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:34] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:34] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:34] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:34] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:34] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:34] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:35] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:35] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:35] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:35] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:35] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:36] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:36] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:36] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:36] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:37] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:38] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:39] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:40] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:41] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:41] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:41] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:41] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:41] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:41] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:41] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:42] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:43] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:44] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:44] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:44] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:44] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:44] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:44] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:44] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:44] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:45] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:45] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:45] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:45] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:45] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:45] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.6s[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:46] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:47] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:47] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:47] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:47] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:47] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:47] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.4s[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.6s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:47] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:47] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:48] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:48] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:48] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:48] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:48] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:48] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:49] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:50] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:50] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:50] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:50] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:51] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:51] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:51] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:51] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:52] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:52] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:52] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:52] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:53] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:53] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:53] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:53] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:54] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:54] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:54] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:55] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:55] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:55] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:55] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:55] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:55] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:55] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:56] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:56] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:56] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:56] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:57] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:57] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:57] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:57] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:58] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:58] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:58] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:19:59] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:00] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:00] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:00] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:01] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:02] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:02] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:02] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:03] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:03] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:03] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:04] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:05] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:06] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:06] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:06] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:06] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:06] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:06] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:06] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:06] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:07] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:08] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:08] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:08] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:08] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:08] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:09] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:09] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:09] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:09] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:09] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:09] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:09] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:10] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:11] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:12] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:13] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:14] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:15] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:15] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:15] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:15] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:16] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:16] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:16] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:16] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:16] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:17] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:18] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:19] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:20] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:21] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:22] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:22] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:22] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:22] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:22] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:22] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:22] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:23] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:24] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:25] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:25] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:25] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:25] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:25] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:25] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.9s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.9s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.7s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beste Parameter: {'colsample_bytree': 1.0, 'learning_rate': 0.01, 'max_depth': 4, 'n_estimators': 200, 'subsample': 0.8}\n",
      "Bestes AUC: 0.7129603264527704\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [17:20:26] WARNING: /workspace/src/learner.cc:790: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n"
     ]
    }
   ],
   "source": [
    "xgb_clf = xgb.XGBClassifier(\n",
    "    objective=\"binary:logistic\",\n",
    "    eval_metric=\"auc\",\n",
    "    use_label_encoder=False,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "# Parameter-Raster\n",
    "param_grid = {\n",
    "    \"learning_rate\": [0.01, 0.05, 0.1],\n",
    "    \"max_depth\": [4, 6, 8],\n",
    "    \"n_estimators\": [200, 500, 800],\n",
    "    \"subsample\": [0.8, 1.0],\n",
    "    \"colsample_bytree\": [0.8, 1.0]\n",
    "}\n",
    "\n",
    "# GroupKFold (keine shuffle/random_state Parameter!)\n",
    "cv = GroupKFold(n_splits=5)\n",
    "\n",
    "# Grid Search Setup\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=xgb_clf,\n",
    "    param_grid=param_grid,\n",
    "    scoring=\"roc_auc\",\n",
    "    n_jobs=-1,\n",
    "    cv=cv,\n",
    "    verbose=2\n",
    ")\n",
    "\n",
    "# Training mit Cross Validation, Gruppen übergeben\n",
    "X_train = train_scaled[au_columns].values\n",
    "y_train = train_scaled[\"label\"].values\n",
    "groups  = train_scaled[\"subjectID\"].values\n",
    "\n",
    "grid_search.fit(X_train, y_train, groups=groups)\n",
    "\n",
    "\n",
    "print(\"Beste Parameter:\", grid_search.best_params_)\n",
    "print(\"Bestes AUC:\", grid_search.best_score_)\n",
    "\n",
    "# Bestes Modell extrahieren\n",
    "model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "09a8cd21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN:\n",
      "VAL:\n",
      "TEST:\n",
      "Accuracy: 0.6803508771929825\n",
      "F1: 0.7677797603874585\n",
      "AUC: 0.6365290011197031\n",
      "Confusion:\n",
      " [[ 433  765]\n",
      " [ 146 1506]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.36      0.49      1198\n",
      "           1       0.66      0.91      0.77      1652\n",
      "\n",
      "    accuracy                           0.68      2850\n",
      "   macro avg       0.71      0.64      0.63      2850\n",
      "weighted avg       0.70      0.68      0.65      2850\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 23768 characters]",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 23328 characters]",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_auc_score, classification_report, ConfusionMatrixDisplay\n",
    "\n",
    "def evaluate(model, X, y, title=\"Evaluation\"):\n",
    "    # Vorhersagen\n",
    "    preds_proba = model.predict_proba(X)[:, 1]\n",
    "    preds = (preds_proba > 0.5).astype(int)\n",
    "\n",
    "    # Metriken ausgeben\n",
    "    print(\"Accuracy:\", accuracy_score(y, preds))\n",
    "    print(\"F1:\", f1_score(y, preds))\n",
    "    print(\"AUC:\", roc_auc_score(y, preds))\n",
    "    print(\"Confusion:\\n\", confusion_matrix(y, preds))\n",
    "    print(classification_report(y, preds))\n",
    "\n",
    "    # Confusion Matrix plotten\n",
    "    def plot_confusion_matrix(true_labels, predictions, label_names):\n",
    "        for normalize in [None, 'true']:\n",
    "            cm = confusion_matrix(true_labels, predictions, normalize=normalize)\n",
    "            cm_disp = ConfusionMatrixDisplay(cm,  display_labels=label_names)\n",
    "            cm_disp.plot(cmap=\"Blues\")\n",
    "    plot_confusion_matrix(y,preds, label_names=['Low','High'])\n",
    "\n",
    "\n",
    "# Aufrufen für Train/Val/Test\n",
    "print(\"TRAIN:\")\n",
    "#evaluate(model, X_train[au_columns].values, y_train, title=\"Train\")\n",
    "\n",
    "print(\"VAL:\")\n",
    "#evaluate(model, X_val, y_val, title=\"Validation\")\n",
    "\n",
    "print(\"TEST:\")\n",
    "evaluate(model, X_test, y_test, title=\"Test\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c43b0c80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model gespeichert.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.1s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   1.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   1.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   2.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.9s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   2.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.9s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function ResourceTracker.__del__ at 0x7f2b26cb3d80>\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 77, in __del__\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 86, in _stop\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 111, in _stop_locked\n",
      "ChildProcessError: [Errno 10] No child processes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function ResourceTracker.__del__ at 0x7ff884f4bd80>\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 77, in __del__\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 86, in _stop\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 111, in _stop_locked\n",
      "ChildProcessError: [Errno 10] No child processes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   1.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.9s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.9s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.9s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function ResourceTracker.__del__ at 0x7f89d9447d80>\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 77, in __del__\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 86, in _stop\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 111, in _stop_locked\n",
      "ChildProcessError: [Errno 10] No child processes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=0.8; total time=   1.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=6, n_estimators=800, subsample=1.0; total time=   1.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time=   1.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=1.0; total time=   1.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   2.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.9s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.9s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=500, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=500, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=800, subsample=1.0; total time=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function ResourceTracker.__del__ at 0x7f52cb243d80>\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 77, in __del__\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 86, in _stop\n",
      "  File \"/opt/conda/lib/python3.12/multiprocessing/resource_tracker.py\", line 111, in _stop_locked\n",
      "ChildProcessError: [Errno 10] No child processes\n"
     ]
    }
   ],
   "source": [
    "joblib.dump(model, \"xgb_model_3_groupK.joblib\")\n",
    "joblib.dump(normalizer, \"normalizer_3_groupK.joblib\")\n",
    "print(\"Model gespeichert.\")\n",
    "\n",
    "#model.save_model(\"xgb_model.json\")   # als JSON (lesbar, portabel)\n",
    "#model.save_model(\"xgb_model.bin\")    # als Binärdatei (kompakt)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
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