kurzti88066/Fahrsimulator_MSY2526_AI
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Fahrsimulator_MSY2526_AI/model_training/xgboost/xgboost_groupfold.ipynb

3350 lines
275 KiB
Plaintext
Raw Blame History

{
"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": "iVBORw0KGgoAAAANSUhEUgAAAhsAAAGwCAYAAAAAFKcNAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAARQxJREFUeJzt3XlclXXe//H3YUeEI6CAGO7mSmq4pHYn3m5pblMzVpplkdq4RS5ZY5nNKKR3qaWTmeMd3i5ZU+lUU6aWUGZuKJpKOpkZLoQVsoms5/eHw/nNETgB1zlsvZ4+rsfMua7vdfG5yAd8/Hy+3+syWSwWiwAAAJzEpaYDAAAA9RvJBgAAcCqSDQAA4FQkGwAAwKlINgAAgFORbAAAAKci2QAAAE7lVtMB1FbFxcW6ePGifH19ZTKZajocAEAlWSwWZWVlKTQ0VC4uzvu39bVr15Sfn2/4Oh4eHvLy8nJARLUPyUY5Ll68qLCwsJoOAwBgUEpKim666SanXPvatWvy9g2UCq8avlZISIjOnj1bLxMOko1y+Pr6SpI+2ntSPg19azgawDn++a+0mg4BcJq8q9l6eUJ/689zZ8jPz5cKr8qz00OSq0fVL1SUr9ST65Wfn0+y8VtS0jrxaeirhr5+NRwN4ByePsb/NQbUdtXSCnfzkslAsmEx1e8plCQbAAAYZZJkJKmp51MDSTYAADDK5HJ9M3J+PVa/7w4AANQ4KhsAABhlMhlso9TvPgrJBgAARtFGsat+3x0AAKhxVDYAADCKNopdJBsAABhmsI1SzxsN9fvuAABAjaOyAQCAUbRR7CLZAADAKFaj2FW/7w4AANQ4KhsAABhFG8Uukg0AAIyijWIXyQYAAEZR2bCrfqdSAACgxlHZAADAKNoodpFsAABglMlkMNmgjQIAAFBlVDYAADDKxXR9M3J+PUayAQCAUczZsKt+3x0AAKhxJBsAABhV8pwNI1slfP755xo5cqRCQ0NlMpm0bdu2csdOmTJFJpNJK1assNmfl5enGTNmqHHjxvLx8dGoUaN0/vx5mzHp6emaMGGCzGazzGazJkyYoCtXrlQqVolkAwAA40raKEa2SsjJyVHXrl21atUqu+O2bdum/fv3KzQ0tNSx6Ohobd26VVu2bNGePXuUnZ2tESNGqKioyDpm3LhxSkpK0vbt27V9+3YlJSVpwoQJlYpVYs4GAAB1zrBhwzRs2DC7Yy5cuKDp06frk08+0V133WVzLCMjQ+vWrdOGDRs0aNAgSdLGjRsVFhamXbt2aejQoUpOTtb27du1b98+9e7dW5K0du1a9enTR6dOnVL79u0rHC+VDQAAjHJQGyUzM9Nmy8vLq1I4xcXFmjBhgubOnavOnTuXOp6YmKiCggINGTLEui80NFRdunTR3r17JUlfffWVzGazNdGQpNtuu01ms9k6pqJINgAAMMpBbZSwsDDr/Aiz2azY2NgqhbNkyRK5ublp5syZZR5PTU2Vh4eH/P39bfYHBwcrNTXVOiYoKKjUuUFBQdYxFUUbBQAAoxz0IraUlBT5+flZd3t6elb6UomJiXr55Zd1+PBhmSoZk8VisTmnrPNvHFMRVDYAAKgl/Pz8bLaqJBtffPGF0tLS1Lx5c7m5ucnNzU3nzp3T7Nmz1bJlS0lSSEiI8vPzlZ6ebnNuWlqagoODrWN+/PHHUte/fPmydUxFkWwAAGBUNa9GsWfChAk6duyYkpKSrFtoaKjmzp2rTz75RJIUEREhd3d37dy503repUuXdPz4cfXt21eS1KdPH2VkZOjAgQPWMfv371dGRoZ1TEXRRgEAwCgHtVEqKjs7W99++63189mzZ5WUlKSAgAA1b95cgYGBNuPd3d0VEhJiXUFiNpsVFRWl2bNnKzAwUAEBAZozZ47Cw8Otq1M6duyoO++8U5MmTdKaNWskSZMnT9aIESMqtRJFItkAAKDOOXTokAYMGGD9PGvWLEnSQw89pLi4uApdY/ny5XJzc9PYsWOVm5urgQMHKi4uTq6urtYxmzZt0syZM62rVkaNGvWrz/YoC8kGAACGGW2FVO7cyMhIWSyWCo///vvvS+3z8vLSypUrtXLlynLPCwgI0MaNGysVW1lINgAAMKqa2yh1DRNEAQCAU1HZAADAKJPJ4Cvm63dlg2QDAACjjC5fdeDS19qoft8dAACocVQ2AAAwigmidpFsAABgFG0Uu0g2AAAwisqGXfU7lQIAADWOygYAAEbRRrGLZAMAAKNoo9hVv1MpAABQ46hsAABgkMlkkonKRrlINgAAMIhkwz7aKAAAwKmobAAAYJTp35uR8+sxkg0AAAyijWIfbRQAAOBUVDYAADCIyoZ9JBsAABhEsmEfyQYAAAaRbNjHnA0AAOBUVDYAADCKpa92kWwAAGAQbRT7aKMAAACnorIBAIBB198wb6Sy4bhYaiOSDQAADDLJYBulnmcbtFEAAIBTUdkAAMAgJojaR7IBAIBRLH21izYKAABwKiobAAAYZbCNYqGNAgAA7DE6Z8PYSpbaj2QDAACDSDbsY84GAABwKiobAAAYxWoUu0g2AAAwiDaKfbRRAACAU1HZAADAICob9pFsAABgEMmGfbRRAACAU1HZAADAICob9pFsAABgFEtf7aKNAgAAnIpkAwAAg0raKEa2yvj88881cuRIhYaGymQyadu2bdZjBQUFmjdvnsLDw+Xj46PQ0FA9+OCDunjxos018vLyNGPGDDVu3Fg+Pj4aNWqUzp8/bzMmPT1dEyZMkNlsltls1oQJE3TlypVKf39INgAAMKi6k42cnBx17dpVq1atKnXs6tWrOnz4sJ599lkdPnxY7733nk6fPq1Ro0bZjIuOjtbWrVu1ZcsW7dmzR9nZ2RoxYoSKioqsY8aNG6ekpCRt375d27dvV1JSkiZMmFDp7w9zNgAAMMhRE0QzMzNt9nt6esrT07PU+GHDhmnYsGFlXstsNmvnzp02+1auXKlevXrphx9+UPPmzZWRkaF169Zpw4YNGjRokCRp48aNCgsL065duzR06FAlJydr+/bt2rdvn3r37i1JWrt2rfr06aNTp06pffv2Fb4/KhsAANQSYWFh1paF2WxWbGysQ66bkZEhk8mkRo0aSZISExNVUFCgIUOGWMeEhoaqS5cu2rt3ryTpq6++ktlstiYaknTbbbfJbDZbx1QUlQ0AAIxy0GqUlJQU+fn5WXeXVdWorGvXrumpp57SuHHjrNdOTU2Vh4eH/P39bcYGBwcrNTXVOiYoKKjU9YKCgqxjKopkAwAAgxzVRvHz87NJNowqKCjQfffdp+LiYr366qu/Ot5isdjcR1n3dOOYiqCNAgBAPVRQUKCxY8fq7Nmz2rlzp00SExISovz8fKWnp9uck5aWpuDgYOuYH3/8sdR1L1++bB1TUVQ2UG3+7914rdm4Q38Y0VfRUSMkSeu27NKuPceU9lOG3N1c1b5NM00eP0Sdbw6znrd09VYdPHpGP6VnqoGXh7q0b6GpDw5Vi5tKl/eA6rTmhTeUeSWr1P5ut4Vr8JgBkqSf035RwsdfKuW7C7JYLGocHKBR44fLr5GvJGnLmneVcvaCzfkdbmmnkePKnvyH2qm2PUG0JNH417/+pd27dyswMNDmeEREhNzd3bVz506NHTtWknTp0iUdP35cS5culST16dNHGRkZOnDggHr16iVJ2r9/vzIyMtS3b99KxUOygWqR/K/zen/HQbVtGWKzPyy0sWZNGqXQ4ADl5RforQ++1BPP/6/eenW2/M0NJUnt2zTTkDu6KbhJI2VmXdW6tz7VE8+/ob+/NleurhTnUHMmTL9XxRaL9fNPqT/r7+u2qX14O0lS+s9XtPm1dxTeo5P6DeotTy9P/Xz5F7m6udpc55ZendVv8G3Wz+7u/Giua0wymGxUcsJHdna2vv32W+vns2fPKikpSQEBAQoNDdXvf/97HT58WB9++KGKioqscywCAgLk4eEhs9msqKgozZ49W4GBgQoICNCcOXMUHh5uXZ3SsWNH3XnnnZo0aZLWrFkjSZo8ebJGjBhRqZUoUi1LNiZOnKgrV67YPJwEdd/V3Dw9v/wtzZv6O63/+26bY0Pu6GbzeebDw/XhrkM6cy5VPW5pK0kaPaSX9XjTIH9NHjdYDz2xUpfS0nVTU9tsHahODRo2sPl8ID5RjQLNCmvdTJK055Ov1Lp9C0UOv906plGgudR13N3d1dDXx7nBol45dOiQBgwYYP08a9YsSdJDDz2khQsX6v3335ckdevWzea83bt3KzIyUpK0fPlyubm5aezYscrNzdXAgQMVFxcnV9f/nwxv2rRJM2fOtK5aGTVqVJnP9vg1tSrZQP300uvvq0+PDurZtW2pZOM/FRQU6h87DqphAy+1bdm0zDG51/L1z88OKzTYX8GNS//QBmpKUWGRTh75Rj3+q7tMJpMsxRad+eZ79eofob+v26a0i5dlDvBT78geate5jc25J5O+0ckj36hBwwZq1b6F+g3qLQ9Pjxq6E1RFdbdRIiMjZfmPqtqN7B0r4eXlpZUrV2rlypXljgkICNDGjRsrFVtZ6kwNOiEhQb169ZKnp6eaNm2qp556SoWFhZKkDz74QI0aNVJxcbEkKSkpSSaTSXPnzrWeP2XKFN1///01Evtv2a4vjur0dxf12ANDyh3z5cFvNOj+hRpw73N664MvtWLhI2rkZ/uvvPc+3qdB9y/UoPsXav/h01r+3COUmlGr/OvkGV27lqcuER0lSTk5V1WQX6AD8YfU6uYW+n3UGLXr3EbbNv5TKd/9/0dCd+zeXiPuu1P3Tr5HfQb20r+On9G2Df+sqdtAVZkcsNVjdSLZuHDhgoYPH66ePXvq6NGjWr16tdatW6dFixZJku644w5lZWXpyJEjkq4nJo0bN1ZCQoL1GvHx8erfv3+5XyMvL0+ZmZk2G4z58acrWrHuQy2IHitPD/dyx90a3lpxy2botdgpuq17Oz374ptKv5JtM2bIHd30xkvT9ddFk3RTaKAWvPim8vILnH0LQIV9ffCkWt/cQg39rs810r//Zdm2U2v1+K/uCg5tot6RPdSmQysl7T9uPa9rry5q2a65moQEqmPXmzVq/HCd+zZFP15Iq4nbAJyiTiQbr776qsLCwrRq1Sp16NBBY8aM0fPPP6+XXnpJxcXFMpvN6tatm+Lj4yVdTyyeeOIJHT16VFlZWUpNTdXp06etfaqyxMbG2jy1LSwsrNyxqJhTZy4qPSNHUXP+qjvueUZ33POMjpw4q3f++ZXuuOcZFRVdr0R5e3nopqaB6tK+uZ6efo9cXV30waeHbK7V0MdLYaGN1a1zKy2eO07nLlzW5/tP1sRtAaVkpGfq3LcpCu/Z2brPu4G3XFxcFBgUYDM2MCigzBUsJYKbNZGLq4vSf7rirHDhBNX9bpS6pk7UoZOTk9WnTx+b/xj9+vVTdna2zp8/r+bNmysyMlLx8fGaNWuWvvjiCy1atEjvvvuu9uzZoytXrig4OFgdOnQo92s8/fTT1gk20vXn05NwGBNxSxttWDHTZt/iVe+qRbMmeuB3d5S7ksRisaigoNDutS0WKf9XxgDV5fihk2rQ0FttOrSy7nN1c1XITUH65Sfb5xj8cjld5n8vey3LTz/+ouKiYvkwYbROqW1LX2ubOpFslPW0spLJLyX7IyMjtW7dOh09elQuLi7q1KmT+vfvr4SEBKWnp9ttoUjlv+wGVefj7anWLWyXunp7esjPt4FatwhR7rV8rX9nt27v2VGN/X2VkXVV723fr8s/Z2pA33BJ0oXUX/Tpl8fUq1s7NfLz0U8/Z2rj1gR5erip762VW3oFOIOl2KLjicnqfGtHudyQQPe8I0IfvPmxbmrVTM1b36Szp8/pzDdndd/keyRdXxqbfOSUWndoKe8G3vo57Rft/ucXCgptomblTJJG7WQyXd+MnF+f1Ylko1OnTnr33Xdtko69e/fK19dXzZpdX2JWMm9jxYoV6t+/v0wmk/r376/Y2Filp6fr8ccfr8lbQBlcXEw6d/6yPt59RBmZOfLzbaCObW/Sq4snq3Xz60+n8/Bw09GT3+vtD75UVs41BZgbqmvnlnrthcfk36hhDd8BIH3/7Q/KvJKl8B6dSh27uUsbDRkzQPviD+mz9xPk38Rfo8cP100tQyVJrq6uOncmRYl7j6ogL1++jXzVun1L9R3UWy4udaLLDVSIyVKR9THVZOLEiTp37pyWL19us9/f31+dOnXSww8/rOnTp+vUqVN69NFHNW3aNC1cuNA6LiIiQkePHtXLL7+sadOmKT09XcHBwSooKNCJEyfUqVPpHwblyczMlNlsVsKxFDX0ddxz6oHaZNupyr1MCahL8nKytfSeCGVkZDj0fSP/qeR3ResZ78jFs+qtr+K8HH238vdOjbUm1brKRnx8vLp3726z76GHHtJHH32kuXPnqmvXrgoICFBUVJSeeeYZm3EDBgzQ4cOHrRNBS5KUixcvqmPHjtV1CwCA3xqDbZT6vvS1VlU2ahMqG/gtoLKB+qxaKxsz35GrgcpGUV6OvnuFygYAACgHq1HsI9kAAMAgVqPYx3RnAADgVFQ2AAAwyMXFJBeXqpcnLAbOrQtINgAAMIg2in20UQAAgFNR2QAAwCBWo9hHsgEAgEG0Uewj2QAAwCAqG/YxZwMAADgVlQ0AAAyismEfyQYAAAYxZ8M+2igAAMCpqGwAAGCQSQbbKPX8HfMkGwAAGEQbxT7aKAAAwKmobAAAYBCrUewj2QAAwCDaKPbRRgEAAE5FZQMAAINoo9hHsgEAgEG0Uewj2QAAwCAqG/YxZwMAADgVlQ0AAIwy2Eap5w8QJdkAAMAo2ij20UYBAABORWUDAACDWI1iH8kGAAAG0UaxjzYKAABwKiobAAAYRBvFPpINAAAMoo1iH20UAADgVFQ2AAAwiMqGfSQbAAAYxJwN+2ijAABgUEllw8hWGZ9//rlGjhyp0NBQmUwmbdu2zea4xWLRwoULFRoaKm9vb0VGRurEiRM2Y/Ly8jRjxgw1btxYPj4+GjVqlM6fP28zJj09XRMmTJDZbJbZbNaECRN05cqVSn9/SDYAAKhjcnJy1LVrV61atarM40uXLtWyZcu0atUqHTx4UCEhIRo8eLCysrKsY6Kjo7V161Zt2bJFe/bsUXZ2tkaMGKGioiLrmHHjxikpKUnbt2/X9u3blZSUpAkTJlQ6XtooAAAYVN1tlGHDhmnYsGFlHrNYLFqxYoXmz5+vu+++W5K0fv16BQcHa/PmzZoyZYoyMjK0bt06bdiwQYMGDZIkbdy4UWFhYdq1a5eGDh2q5ORkbd++Xfv27VPv3r0lSWvXrlWfPn106tQptW/fvsLxUtkAAMAgR7VRMjMzbba8vLxKx3L27FmlpqZqyJAh1n2enp7q37+/9u7dK0lKTExUQUGBzZjQ0FB16dLFOuarr76S2Wy2JhqSdNttt8lsNlvHVBTJBgAAtURYWJh1foTZbFZsbGylr5GamipJCg4OttkfHBxsPZaamioPDw/5+/vbHRMUFFTq+kFBQdYxFUUbBQAAg0wy2Eb59/+mpKTIz8/Put/T07Pq17whIIvF8qsTUW8cU9b4ilznRlQ2AAAwyMVkMrxJkp+fn81WlWQjJCREkkpVH9LS0qzVjpCQEOXn5ys9Pd3umB9//LHU9S9fvlyqavJrSDYAAKhHWrVqpZCQEO3cudO6Lz8/XwkJCerbt68kKSIiQu7u7jZjLl26pOPHj1vH9OnTRxkZGTpw4IB1zP79+5WRkWEdU1G0UQAAMKi6V6NkZ2fr22+/tX4+e/askpKSFBAQoObNmys6OloxMTFq166d2rVrp5iYGDVo0EDjxo2TJJnNZkVFRWn27NkKDAxUQECA5syZo/DwcOvqlI4dO+rOO+/UpEmTtGbNGknS5MmTNWLEiEqtRJFINgAAMKy6H1d+6NAhDRgwwPp51qxZkqSHHnpIcXFxevLJJ5Wbm6upU6cqPT1dvXv31o4dO+Tr62s9Z/ny5XJzc9PYsWOVm5urgQMHKi4uTq6urtYxmzZt0syZM62rVkaNGlXusz3s3p/FYrFU+qzfgMzMTJnNZiUcS1FDX79fPwGog7adqtyMcqAuycvJ1tJ7IpSRkWEz6dKRSn5XDHrpU7l5+1T5OoW5Odo1e6BTY61JzNkAAABORRsFAACjTAbf3FrPX8RGsgEAgEG89dU+2igAAMCpqGwAAGCQ6d9/jJxfn5FsAABgkIvp+mbk/PqMNgoAAHAqKhsAABhU3Q/1qmtINgAAMIjVKPZVKNl45ZVXKnzBmTNnVjkYAABQ/1Qo2Vi+fHmFLmYymUg2AAC/Of/5mviqnl+fVSjZOHv2rLPjAACgzqKNYl+VV6Pk5+fr1KlTKiwsdGQ8AADUOSUTRI1s9Vmlk42rV68qKipKDRo0UOfOnfXDDz9Iuj5X44UXXnB4gAAAoG6rdLLx9NNP6+jRo4qPj5eXl5d1/6BBg/TWW285NDgAAOqCkjaKka0+q/TS123btumtt97SbbfdZlP26dSpk86cOePQ4AAAqAuYIGpfpSsbly9fVlBQUKn9OTk59b7nBAAAKq/SyUbPnj31z3/+0/q5JMFYu3at+vTp47jIAACoI0wO2OqzSrdRYmNjdeedd+rkyZMqLCzUyy+/rBMnTuirr75SQkKCM2IEAKBW43Hl9lW6stG3b199+eWXunr1qtq0aaMdO3YoODhYX331lSIiIpwRIwAAqMOq9G6U8PBwrV+/3tGxAABQJ/GKefuqlGwUFRVp69atSk5OlslkUseOHTV69Gi5ufFeNwDAbw9tFPsqnR0cP35co0ePVmpqqtq3by9JOn36tJo0aaL3339f4eHhDg8SAADUXZWes/Hoo4+qc+fOOn/+vA4fPqzDhw8rJSVFt9xyiyZPnuyMGAEAqPV4oFf5Kl3ZOHr0qA4dOiR/f3/rPn9/fy1evFg9e/Z0aHAAANQFtFHsq3Rlo3379vrxxx9L7U9LS1Pbtm0dEhQAAHVJyQRRI1t9VqFkIzMz07rFxMRo5syZeuedd3T+/HmdP39e77zzjqKjo7VkyRJnxwsAAOqYCrVRGjVqZFPisVgsGjt2rHWfxWKRJI0cOVJFRUVOCBMAgNqLNop9FUo2du/e7ew4AACos4w+crx+pxoVTDb69+/v7DgAAEA9VeWncF29elU//PCD8vPzbfbfcssthoMCAKAu4RXz9lU62bh8+bIefvhhffzxx2UeZ84GAOC3xujzMup5rlH5pa/R0dFKT0/Xvn375O3tre3bt2v9+vVq166d3n//fWfECAAA6rBKVzY+++wz/eMf/1DPnj3l4uKiFi1aaPDgwfLz81NsbKzuuusuZ8QJAECtxWoU+ypd2cjJyVFQUJAkKSAgQJcvX5Z0/U2whw8fdmx0AADUAUYeVf5beGR5lZ4geurUKUlSt27dtGbNGl24cEGvvfaamjZt6vAAAQBA3VbpNkp0dLQuXbokSXruuec0dOhQbdq0SR4eHoqLi3N0fAAA1HqsRrGv0snG+PHjrf+/e/fu+v777/XNN9+oefPmaty4sUODAwCgLmA1in1Vfs5GiQYNGujWW291RCwAANRJTBC1r0LJxqxZsyp8wWXLllU5GAAAUP9UKNk4cuRIhS5WHzOzDs385OfnV9NhAE7R73d/qukQAKexFOX/+iAHcVEVVlzccH59xovYAAAwiDaKffU9mQIAADWMZAMAAINMJsnFwFbZwkZhYaGeeeYZtWrVSt7e3mrdurX+/Oc/q7i42DrGYrFo4cKFCg0Nlbe3tyIjI3XixAmb6+Tl5WnGjBlq3LixfHx8NGrUKJ0/f94R3xIbJBsAABhkJNEo2SpjyZIleu2117Rq1SolJydr6dKl+p//+R+tXLnSOmbp0qVatmyZVq1apYMHDyokJESDBw9WVlaWdUx0dLS2bt2qLVu2aM+ePcrOztaIESMc/lJVw0tfAQBA9frqq680evRo6/vIWrZsqTfffFOHDh2SdL2qsWLFCs2fP1933323JGn9+vUKDg7W5s2bNWXKFGVkZGjdunXasGGDBg0aJEnauHGjwsLCtGvXLg0dOtRh8VLZAADAoJIJokY2ScrMzLTZ8vLyyvx6t99+uz799FOdPn1aknT06FHt2bNHw4cPlySdPXtWqampGjJkiPUcT09P9e/fX3v37pUkJSYmqqCgwGZMaGiounTpYh3jKFVKNjZs2KB+/fopNDRU586dkyStWLFC//jHPxwaHAAAdYGj2ihhYWEym83WLTY2tsyvN2/ePN1///3q0KGD3N3d1b17d0VHR+v++++XJKWmpkqSgoODbc4LDg62HktNTZWHh4f8/f3LHeMolU42Vq9erVmzZmn48OG6cuWKta/TqFEjrVixwqHBAQDwW5KSkqKMjAzr9vTTT5c57q233tLGjRu1efNmHT58WOvXr9eLL76o9evX24y7cUmtxWL51WW2FRlTWZVONlauXKm1a9dq/vz5cnV1te7v0aOHvv76a4cGBwBAXeCoV8z7+fnZbJ6enmV+vblz5+qpp57Sfffdp/DwcE2YMEFPPPGEtRISEhIiSaUqFGlpadZqR0hIiPLz85Wenl7uGEepdLJx9uxZde/evdR+T09P5eTkOCQoAADqkpK3vhrZKuPq1atycbH9Fe7q6mpd+tqqVSuFhIRo586d1uP5+flKSEhQ3759JUkRERFyd3e3GXPp0iUdP37cOsZRKr0apVWrVkpKSlKLFi1s9n/88cfq1KmTwwIDAKCuqO7HlY8cOVKLFy9W8+bN1blzZx05ckTLli3TI488Iul6+yQ6OloxMTFq166d2rVrp5iYGDVo0EDjxo2TJJnNZkVFRWn27NkKDAxUQECA5syZo/DwcOvqFEepdLIxd+5cTZs2TdeuXZPFYtGBAwf05ptvKjY2Vn/7298cGhwAACht5cqVevbZZzV16lSlpaUpNDRUU6ZM0YIFC6xjnnzySeXm5mrq1KlKT09X7969tWPHDvn6+lrHLF++XG5ubho7dqxyc3M1cOBAxcXF2UyTcASTxWKxVPaktWvXatGiRUpJSZEkNWvWTAsXLlRUVJRDg6tJmZmZMpvN+vHnDF7EhnrLv+f0mg4BcBpLUb7yvl6rjAzn/Rwv+V0x+51EeTZoWOXr5F3N1ku/j3BqrDWpSg/1mjRpkiZNmqSffvpJxcXFCgoKcnRcAADUGS6q/LyLG8+vzww9QbRx48aOigMAANRTVZogam/97XfffWcoIAAA6pr/XL5a1fPrs0onG9HR0TafCwoKdOTIEW3fvl1z5851VFwAANQZVXmZ2o3n12eVTjYef/zxMvf/9a9/tb4ABgAAoITDXsQ2bNgwvfvuu466HAAAdYbJZOzBXrRRKuidd95RQECAoy4HAECdwZwN+yqdbHTv3t1mgqjFYlFqaqouX76sV1991aHBAQCAuq/SycaYMWNsPru4uKhJkyaKjIxUhw4dHBUXAAB1BhNE7atUslFYWKiWLVtq6NCh1jfKAQDwW2f69x8j59dnlZog6ubmpj/+8Y/Ky8tzVjwAANQ5JZUNI1t9VunVKL1799aRI0ecEQsAAKiHKj1nY+rUqZo9e7bOnz+viIgI+fj42By/5ZZbHBYcAAB1AXM27KtwsvHII49oxYoVuvfeeyVJM2fOtB4zmUyyWCwymUwqKipyfJQAANRiJpPJ7qs8KnJ+fVbhZGP9+vV64YUXdPbsWWfGAwAA6pkKJxsWi0WS1KJFC6cFAwBAXUQbxb5Kzdmo72UeAACqgieI2lepZOPmm2/+1YTjl19+MRQQAACoXyqVbDz//PMym83OigUAgDqp5IVqRs6vzyqVbNx3330KCgpyViwAANRJzNmwr8IP9WK+BgAAqIpKr0YBAAA3MDhBtJ6/GqXiyUZxcbEz4wAAoM5ykUkuBjIGI+fWBZV+XDkAALDF0lf7Kv0iNgAAgMqgsgEAgEGsRrGPZAMAAIN4zoZ9tFEAAIBTUdkAAMAgJojaR7IBAIBBLjLYRqnnS19powAAAKeisgEAgEG0Uewj2QAAwCAXGWsV1Pc2Q32/PwAAUMOobAAAYJDJZDL0dvT6/mZ1kg0AAAwyydiLW+t3qkGyAQCAYTxB1D7mbAAAAKeisgEAgAPU79qEMSQbAAAYxHM27KONAgAAnIrKBgAABrH01T6SDQAADOIJovbV9/sDAAA1jGQDAACDStooRrbKunDhgh544AEFBgaqQYMG6tatmxITE63HLRaLFi5cqNDQUHl7eysyMlInTpywuUZeXp5mzJihxo0by8fHR6NGjdL58+cNfz9uRLIBAIBBJgdslZGenq5+/frJ3d1dH3/8sU6ePKmXXnpJjRo1so5ZunSpli1bplWrVungwYMKCQnR4MGDlZWVZR0THR2trVu3asuWLdqzZ4+ys7M1YsQIFRUVVe0bUQ7mbAAAUEtkZmbafPb09JSnp2epcUuWLFFYWJjeeOMN676WLVta/7/FYtGKFSs0f/583X333ZKk9evXKzg4WJs3b9aUKVOUkZGhdevWacOGDRo0aJAkaePGjQoLC9OuXbs0dOhQh90XlQ0AAAxyVBslLCxMZrPZusXGxpb59d5//3316NFDf/jDHxQUFKTu3btr7dq11uNnz55VamqqhgwZYt3n6emp/v37a+/evZKkxMREFRQU2IwJDQ1Vly5drGMchcoGAAAGOWo1SkpKivz8/Kz7y6pqSNJ3332n1atXa9asWfrTn/6kAwcOaObMmfL09NSDDz6o1NRUSVJwcLDNecHBwTp37pwkKTU1VR4eHvL39y81puR8RyHZAADAIEc9Z8PPz88m2ShPcXGxevTooZiYGElS9+7ddeLECa1evVoPPvhgqeuWsFgsvxpnRcZUFm0UAADqmKZNm6pTp042+zp27KgffvhBkhQSEiJJpSoUaWlp1mpHSEiI8vPzlZ6eXu4YRyHZAADAoOpejdKvXz+dOnXKZt/p06fVokULSVKrVq0UEhKinTt3Wo/n5+crISFBffv2lSRFRETI3d3dZsylS5d0/Phx6xhHoY0CAIBB1f0itieeeEJ9+/ZVTEyMxo4dqwMHDuj111/X66+//u/rmRQdHa2YmBi1a9dO7dq1U0xMjBo0aKBx48ZJksxms6KiojR79mwFBgYqICBAc+bMUXh4uHV1iqOQbAAAUMf07NlTW7du1dNPP60///nPatWqlVasWKHx48dbxzz55JPKzc3V1KlTlZ6ert69e2vHjh3y9fW1jlm+fLnc3Nw0duxY5ebmauDAgYqLi5Orq6tD4zVZLBaLQ69YT2RmZspsNuvHnzMqNFkHqIv8e06v6RAAp7EU5Svv67XKyHDez/GS3xVb9v5LDRr6/voJ5bianaX7+rZzaqw1icoGAAAGVXcbpa5hgigAAHAqKhsAABhk+vcfI+fXZyQbAAAYRBvFPtooAADAqahsAABgkEkmudBGKRfJBgAABtFGsY9kAwAAg0g27GPOBgAAcCoqGwAAGMTSV/tINgAAMMjFdH0zcn59RhsFAAA4FZUNAAAMoo1iH8kGAAAGsRrFPtooAADAqahsAABgkEnGWiH1vLBBsgEAgFGsRrGPNgoAAHAqKhtwqi8Pf6uVG3bp6Dc/KPWnTG38n0m6K7JrmWOjY97U+q1fKuaJe/THcQNsjh049p0Wrf5Qice/l5ubq8Jvbqa/vzxV3l4e1XEbgCSpb/c2mjFhkLp2aK6mTcwaP+d1fZRwzHr8r889oHEjbrM55+DXZzXkkZesnz3c3fSXx3+ne4ZGyMvTXZ8fPK05S97SxbQrNucN6ddZcx8dps5tQ3X1Wr72HvlWDz75N6feH6qO1Sj21fpkIy4uTtHR0bpy5UqFz5k4caKuXLmibdu2OS0uVMzV3Dx1ubmZxo+8TQ/OK/8H5T/jjyrx+Pdq2sRc6tiBY9/p9zNf1RMTh2jJnD/Iw91Vx/91QS71ve6IWqeBt6eOn76gTR/s04alk8ocs2vvCU3780br5/yCIpvjsbPu0dD/6qKo+W/olys5WhT9O21Z/pgiJyxRcbFFkjRyQDe9PP9+/eXVD/T5odMymaRObUKdd2MwjNUo9tVoslFeUhAfH68BAwYoPT1d9957r4YPH14zAcKwwf06a3C/znbHXEy7oif/5+9655VpuveJ1aWOz1/+nqbcG6knJg6x7mvTPMjhsQK/Ztfek9q196TdMXn5hUr7OavMY34+XnpgdB899tz/KeHAKUnSlAX/p+Mf/kWRvTros33JcnV1Uezse7TglW3a+P5X1nO/PZfmuBuBw5lkbJJnPc81av+cDW9vbwUF8YulviouLtZjz/2fZjwwUB3bNC11/PIvWTp0/Hs1CWioIY+8pJuHPq27Jq/QV0lnaiBa4NfdHtFOpz+J1cF3FmjF/PvV2L+h9VjXjs3l4e6mz/YlW/el/pSh5DMX1euWVtfHtA9Ts2B/FVssStg4T8kfL9bfX/6jOrQOqfZ7ARyl1icbcXFxatSokc2+RYsWKSgoSL6+vnr00Uf11FNPqVu3bqXOffHFF9W0aVMFBgZq2rRpKigoKPfr5OXlKTMz02aD861Yv1Nuri6acl9kmce/v/CTJOmFtR/poTF99c4rU9W1Q5jGTF2pMz/wLz3ULrv2ntTkZ9dr9NRX9OzL7+nWTi30/uqZ8nC/XkQODvRTXn6BMrJybc5L+yVLwYF+kqSWzRpLkp6aNFwvrvtE9z3xmq5k5urDNdFq5Negem8IFeYik1xMBrZ6Xtuo9cnGjTZt2qTFixdryZIlSkxMVPPmzbV6denS++7du3XmzBnt3r1b69evV1xcnOLi4sq9bmxsrMxms3ULCwtz4l1AkpKSf9CaLfH663MPyFROw7Kkhz3xd7dr/Kg+uqV9mGJm3aO2LYJsSsxAbbB152Ht+PKEks9c0vYvjusPM19Vm+ZBGnK7/VaiyWSS5fpfdetcpJfe+EQf7E7S0W9SNO3PG2WxWDRmYHdn3wKqyOSArT6r8QmiH374oRo2bGizr6ioqJzR0sqVKxUVFaWHH35YkrRgwQLt2LFD2dnZNuP8/f21atUqubq6qkOHDrrrrrv06aefatKksid1Pf3005o1a5b1c2ZmJgmHk3115Iwup2crfOQC676iomI98/J7Wr1lt469/2eFNL7+r732rWxLyO1bhuh8anq1xgtU1o8/Zyrl0i9qE9bE+tnTw11mX2+b6kYT/4Y6cOw7SdfbKpJ06rtL1uP5BYX6/sLPuikkoBqjBxynxpONAQMGlKpM7N+/Xw888ECZ40+dOqWpU6fa7OvVq5c+++wzm32dO3eWq6ur9XPTpk319ddflxuHp6enPD09Kxs+DLh3eE/179XeZt/vZ/5VY4f10viR15cPNg8NVNMm5lKT4779IU2D+naqtliBqvA3+6hZsL9Sf7relj2a/IPyCwo1oHcHbdt1RNL11krHNqF6buU/ro/5JkXX8grUtkWw9h29noC4ubqoedMApaT+UjM3gl/HDFG7ajzZ8PHxUdu2bW32nT9/3u45N5bcLSX1x//g7u5e6pzi4uIqRomqyr6ap7Mpl62fz138WV+fOq9G5gYKCwlQQCPbqpabm6uCA/3UrmWwpOv/3WY8MEixr/9TXW5upvCbb9KbH+7Xv879qPVLoqr1XgAfbw+1+neVQpJahAaqy83NdCXjqtIzczRv8l364LMkpf6UoeZNA7Vg2kj9fCVb/4w/KknKzLmmjf/4Soui79YvGTlKz7iqv0T/TifPXFT8gW8kSVk51/TGe3v01OThuvBjulJSf9GMBwZJkrbtOlz9N40K4Tkb9tV4slFZ7du314EDBzRhwgTrvkOHDtVgRLAnKfmcRj72ivXz/OXvSZLuv6u3Xl04obzTbPxx3ABdyy/Qn5a9qyuZV9W5XTO9t2q6Wt3U5NdPBhyoW8cW+nDN49bPMbPukSRt/nCfZr/wljq1CdV9w3vJ7OutH3/K1BeJp/XIn/5X2VfzrOf8afm7Kiwq1hsxUfLyctfnB0/p/uc3WOcnSdKCl7eqsKhYrz3/oLw83ZV44pxGT32l1MRSoK6oc8nGjBkzNGnSJPXo0UN9+/bVW2+9pWPHjql169Y1HRrKcHvEzUo/uKrC44+9/+cy9z8xcYjNczaAmvDl4X/Jv+f0co//fuZff/UaefmFmvfi3zXvxb+XO6awqFgLXt6qBS9vrVKcqAEGH+pVzwsbdS/ZGD9+vL777jvNmTNH165d09ixYzVx4kQdOHCgpkMDAPxGMWXDPpOlrAkPdczgwYMVEhKiDRs2OOyamZmZMpvN+vHnDPn5+TnsukBtYu9f6UBdZynKV97Xa5WR4byf4yW/Kz5L+kENfav+NbKzMvXf3Zo7NdaaVOcqG1evXtVrr72moUOHytXVVW+++aZ27dqlnTt31nRoAIDfKkobdtW5ZMNkMumjjz7SokWLlJeXp/bt2+vdd9/VoEGDajo0AMBvFKtR7KtzyYa3t7d27dpV02EAAGDFW1/tq3OPKwcAAHVLnatsAABQ2zBlwz6SDQAAjCLbsIs2CgAAcCoqGwAAGMRqFPtINgAAMIjVKPbRRgEAAE5FZQMAAIOYH2ofyQYAAEaRbdhFGwUAgDosNjZWJpNJ0dHR1n0Wi0ULFy5UaGiovL29FRkZqRMnTticl5eXpxkzZqhx48by8fHRqFGjdP78eafESLIBAIBBJgf8qYqDBw/q9ddf1y233GKzf+nSpVq2bJlWrVqlgwcPKiQkRIMHD1ZWVpZ1THR0tLZu3aotW7Zoz549ys7O1ogRI1RUVGToe1EWkg0AAAwqWY1iZKus7OxsjR8/XmvXrpW/v791v8Vi0YoVKzR//nzdfffd6tKli9avX6+rV69q8+bNkqSMjAytW7dOL730kgYNGqTu3btr48aN+vrrr53y/jGSDQAADDI5YJOkzMxMmy0vL6/crzlt2jTdddddpd56fvbsWaWmpmrIkCHWfZ6enurfv7/27t0rSUpMTFRBQYHNmNDQUHXp0sU6xpFINgAAqCXCwsJkNputW2xsbJnjtmzZosOHD5d5PDU1VZIUHBxssz84ONh6LDU1VR4eHjYVkRvHOBKrUQAAMMpBq1FSUlLk5+dn3e3p6VlqaEpKih5//HHt2LFDXl5e5V/yht6MxWIpte9GFRlTFVQ2AAAwyFETRP38/Gy2spKNxMREpaWlKSIiQm5ubnJzc1NCQoJeeeUVubm5WSsaN1Yo0tLSrMdCQkKUn5+v9PT0csc4EskGAAB1yMCBA/X1118rKSnJuvXo0UPjx49XUlKSWrdurZCQEO3cudN6Tn5+vhISEtS3b19JUkREhNzd3W3GXLp0ScePH7eOcSTaKAAAGFSd70bx9fVVly5dbPb5+PgoMDDQuj86OloxMTFq166d2rVrp5iYGDVo0EDjxo2TJJnNZkVFRWn27NkKDAxUQECA5syZo/Dw8FITTh2BZAMAAINq2wNEn3zySeXm5mrq1KlKT09X7969tWPHDvn6+lrHLF++XG5ubho7dqxyc3M1cOBAxcXFydXV1cHRSCaLxWJx+FXrgczMTJnNZv34c4bNZB2gPvHvOb2mQwCcxlKUr7yv1yojw3k/x0t+Vxz45qIa+lb9a2RnZapXh1CnxlqTqGwAAGBUbStt1DIkGwAAGGTkkeMl59dnrEYBAABORWUDAACDqnM1Sl1EsgEAgEFM2bCPZAMAAKPINuxizgYAAHAqKhsAABjEahT7SDYAADDK4ATRep5r0EYBAADORWUDAACDmB9qH8kGAABGkW3YRRsFAAA4FZUNAAAMYjWKfSQbAAAYxOPK7aONAgAAnIrKBgAABjE/1D6SDQAAjCLbsItkAwAAg5ggah9zNgAAgFNR2QAAwCCTDK5GcVgktRPJBgAABjFlwz7aKAAAwKmobAAAYBAP9bKPZAMAAMNopNhDGwUAADgVlQ0AAAyijWIfyQYAAAbRRLGPNgoAAHAqKhsAABhEG8U+kg0AAAzi3Sj2kWwAAGAUkzbsYs4GAABwKiobAAAYRGHDPpINAAAMYoKofbRRAACAU1HZAADAIFaj2EeyAQCAUUzasIs2CgAAcCoqGwAAGERhwz6SDQAADGI1in20UQAAgFNR2QAAwDBjq1HqeyOFygYAAAaVtFGMbJURGxurnj17ytfXV0FBQRozZoxOnTplM8ZisWjhwoUKDQ2Vt7e3IiMjdeLECZsxeXl5mjFjhho3biwfHx+NGjVK58+fN/rtKIVkAwCAOiYhIUHTpk3Tvn37tHPnThUWFmrIkCHKycmxjlm6dKmWLVumVatW6eDBgwoJCdHgwYOVlZVlHRMdHa2tW7dqy5Yt2rNnj7KzszVixAgVFRU5NF6TxWKxOPSK9URmZqbMZrN+/DlDfn5+NR0O4BT+PafXdAiA01iK8pX39VplZDjv53jJ74rvL/1i6GtkZmaqZdOAKsd6+fJlBQUFKSEhQXfccYcsFotCQ0MVHR2tefPmSbpexQgODtaSJUs0ZcoUZWRkqEmTJtqwYYPuvfdeSdLFixcVFhamjz76SEOHDq3y/dyIygYAAAY5qo2SmZlps+Xl5VXo62dkZEiSAgICJElnz55VamqqhgwZYh3j6emp/v37a+/evZKkxMREFRQU2IwJDQ1Vly5drGMchWQDAACDTA74I0lhYWEym83WLTY29le/tsVi0axZs3T77berS5cukqTU1FRJUnBwsM3Y4OBg67HU1FR5eHjI39+/3DGOwmoUAABqiZSUFJs2iqen56+eM336dB07dkx79uwpdcx0w8xTi8VSat+NKjKmsqhsAABgkKPaKH5+fjbbryUbM2bM0Pvvv6/du3frpptusu4PCQmRpFIVirS0NGu1IyQkRPn5+UpPTy93jKOQbAAAYJDJAVtlWCwWTZ8+Xe+9954+++wztWrVyuZ4q1atFBISop07d1r35efnKyEhQX379pUkRUREyN3d3WbMpUuXdPz4cesYR6GNAgBAHTNt2jRt3rxZ//jHP+Tr62utYJjNZnl7e8tkMik6OloxMTFq166d2rVrp5iYGDVo0EDjxo2zjo2KitLs2bMVGBiogIAAzZkzR+Hh4Ro0aJBD4yXZAADAqGp+E9vq1aslSZGRkTb733jjDU2cOFGS9OSTTyo3N1dTp05Venq6evfurR07dsjX19c6fvny5XJzc9PYsWOVm5urgQMHKi4uTq6urgZupjSes1EOnrOB3wKes4H6rDqfs3Eh7Yrh52w0C2rk1FhrEnM2AACAU9FGAQDAIF4xbx/JBgAABlXzlI06h2QDAACjyDbsYs4GAABwKiobAAAY9J/vN6nq+fUZyQYAAAYxQdQ+ko1ylDx+JCszs4YjAZzHUpRf0yEATlPy97s6HieVafB3hdHzazuSjXJkZWVJktq2CqvhSAAARmRlZclsNjvl2h4eHgoJCVE7B/yuCAkJkYeHhwOiqn14gmg5iouLdfHiRfn6+jr8VbsoW2ZmpsLCwkq9YhmoD/j7Xf0sFouysrIUGhoqFxfnrYe4du2a8vONVwk9PDzk5eXlgIhqHyob5XBxcbF5XS+qT8mrlYH6iL/f1ctZFY3/5OXlVW+TBEdh6SsAAHAqkg0AAOBUJBuoNTw9PfXcc8/J09OzpkMBHI6/3/gtY4IoAABwKiobAADAqUg2AACAU5FsAAAApyLZAAAniIuLU6NGjSp1zsSJEzVmzBinxAPUJJINOA0/OFFflfd3Oz4+XiaTSVeuXNG9996r06dPV39wQC3EE0QBwAm8vb3l7e1d02EAtQKVDdSIhIQE9erVS56enmratKmeeuopFRYWSpI++OADNWrUSMXFxZKkpKQkmUwmzZ0713r+lClTdP/999dI7EBFlNVGWbRokYKCguTr66tHH31UTz31lLp161bq3BdffFFNmzZVYGCgpk2bpoKCguoJGnASkg1UuwsXLmj48OHq2bOnjh49qtWrV2vdunVatGiRJOmOO+5QVlaWjhw5Iul6YtK4cWMlJCRYrxEfH6/+/fvXSPxAVWzatEmLFy/WkiVLlJiYqObNm2v16tWlxu3evVtnzpzR7t27tX79esXFxSkuLq76AwYciDYKqt2rr76qsLAwrVq1SiaTSR06dNDFixc1b948LViwQGazWd26dVN8fLwiIiIUHx+vJ554Qs8//7yysrKUk5Oj06dPKzIysqZvBb9hH374oRo2bGizr6ioqNzxK1euVFRUlB5++GFJ0oIFC7Rjxw5lZ2fbjPP399eqVavk6uqqDh066K677tKnn36qSZMmOf4mgGpCZQPVLjk5WX369JHJZLLu69evn7Kzs3X+/HlJUmRkpOLj42WxWPTFF19o9OjR6tKli/bs2aPdu3crODhYHTp0qKlbADRgwAAlJSXZbH/729/KHX/q1Cn16tXLZt+NnyWpc+fOcnV1tX5u2rSp0tLSHBc4UAOobKDaWSwWm0SjZJ8k6/7IyEitW7dOR48elYuLizp16qT+/fsrISFB6enptFBQ43x8fNS2bVubfSXJcnnK+3v/n9zd3UudUzJ/CairqGyg2nXq1El79+61+UG7d+9e+fr6qlmzZpL+/7yNFStWqH///jKZTOrfv7/i4+OZr4E6qX379jpw4IDNvkOHDtVQNED1orIBp8rIyFBSUpLNvsmTJ2vFihWaMWOGpk+frlOnTum5557TrFmz5OJyPf8tmbexceNGvfzyy5KuJyB/+MMfVFBQwHwN1DkzZszQpEmT1KNHD/Xt21dvvfWWjh07ptatW9d0aIDTkWzAqeLj49W9e3ebfQ899JA++ugjzZ07V127dlVAQICioqL0zDPP2IwbMGCADh8+bE0s/P391alTJ128eFEdO3asrlsAHGL8+PH67rvvNGfOHF27dk1jx47VxIkTS1U7gPqIV8wDQA0ZPHiwQkJCtGHDhpoOBXAqKhsAUA2uXr2q1157TUOHDpWrq6vefPNN7dq1Szt37qzp0ACno7IBANUgNzdXI0eO1OHDh5WXl6f27dvrmWee0d13313ToQFOR7IBAACciqWvAADAqUg2AACAU5FsAAAApyLZAAAATkWyAQAAnIpkA6jFFi5cqG7dulk/T5w4UWPGjKn2OL7//nuZTKZSj57/Ty1bttSKFSsqfM24uDg1atTIcGwmk0nbtm0zfB0AzkOyAVTSxIkTZTKZZDKZ5O7urtatW2vOnDnKyclx+td++eWXFRcXV6GxFUkQAKA68ARRoAruvPNOvfHGGyooKNAXX3yhRx99VDk5OVq9enWpsQUFBaVeG15VZrPZIdcBgOpEZQOoAk9PT4WEhCgsLEzjxo3T+PHjraX8ktbH//7v/6p169by9PSUxWJRRkaGJk+erKCgIPn5+em///u/dfToUZvrvvDCCwoODpavr6+ioqJ07do1m+M3tlGKi4u1ZMkStW3bVp6enmrevLkWL14sSWrVqpUkqXv37jKZTDZvyn3jjTfUsWNHeXl5qUOHDnr11Vdtvs6BAwfUvXt3eXl5qUePHjpy5Eilv0fLli1TeHi4fHx8FBYWpqlTpyo7O7vUuG3btunmm2+Wl5eXBg8erJSUFJvjH3zwgSIiIuTl5aXWrVvr+eefV2FhYaXjAVBzSDYAB/D29lZBQYH187fffqu3335b7777rrWNcddddyk1NVUfffSREhMTdeutt2rgwIH65ZdfJElvv/22nnvuOS1evFiHDh1S06ZNSyUBN3r66ae1ZMkSPfvsszp58qQ2b96s4OBgSbK+TXTXrl26dOmS3nvvPUnS2rVrNX/+fC1evFjJycmKiYnRs88+q/Xr10uScnJyNGLECLVv316JiYlauHCh5syZU+nviYuLi1555RUdP35c69ev12effaYnn3zSZszVq1e1ePFirV+/Xl9++aUyMzN13333WY9/8skneuCBBzRz5kydPHlSa9asUVxcnDWhAlBHWABUykMPPWQZPXq09fP+/fstgYGBlrFjx1osFovlueees7i7u1vS0tKsYz799FOLn5+f5dq1azbXatOmjWXNmjUWi8Vi6dOnj+Wxxx6zOd67d29L165dy/zamZmZFk9PT8vatWvLjPPs2bMWSZYjR47Y7A8LC7Ns3rzZZt9f/vIXS58+fSwWi8WyZs0aS0BAgCUnJ8d6fPXq1WVe6z+1aNHCsnz58nKPv/3225bAwEDr5zfeeMMiybJv3z7rvuTkZIsky/79+y0Wi8XyX//1X5aYmBib62zYsMHStGlT62dJlq1bt5b7dQHUPOZsAFXw4YcfqmHDhiosLFRBQYFGjx6tlStXWo+3aNFCTZo0sX5OTExUdna2AgMDba6Tm5urM2fOSJKSk5P12GOP2Rzv06ePdu/eXWYMycnJysvL08CBAysc9+XLl5WSkqKoqChNmjTJur+wsNA6HyQ5OVldu3ZVgwYNbOKorN27dysmJkYnT55UZmamCgsLde3aNeXk5MjHx0eS5Obmph49eljP6dChgxo1aqTk5GT16tVLiYmJOnjwoE0lo6ioSNeuXdPVq1dtYgRQe5FsAFUwYMAArV69Wu7u7goNDS01AbTkl2mJ4uJiNW3aVPHx8aWuVdXln97e3pU+p7i4WNL1Vkrv3r1tjrm6ukqSLA54N+O5c+c0fPhwPfbYY/rLX/6igIAA7dmzR1FRUTbtJun60tUblewrLi7W888/X+abUb28vAzHCaB6kGwAVeDj46O2bdtWePytt96q1NRUubm5qWXLlmWO6dixo/bt26cHH3zQum/fvn3lXrNdu3by9vbWp59+qkcffbTUcQ8PD0nXKwElgoOD1axZM3333XcaP358mdft1KmTNmzYoNzcXGtCYy+Oshw6dEiFhYV66aWX5OJyfWrY22+/XWpcYWGhDh06pF69ekmSTp06pStXrqhDhw6Srn/fTp06VanvNYDah2QDqAaDBg1Snz59NGbMGC1ZskTt27fXxYsX9dFHH2nMmDHq0aOHHn/8cT300EPq0aOHbr/9dm3atEknTpxQ69aty7yml5eX5s2bpyeffFIeHh7q16+fLl++rBMnTigqKkpBQUHy9vbW9u3bddNNN8nLy0tms1kLFy7UzJkz5efnp2HDhikvL0+HDh1Senq6Zs2apXHjxmn+/PmKiorSM888o++//14vvvhipe63TZs2Kiws1MqVKzVy5Eh9+eWXeu2110qNc3d314wZM/TKK6/I3d1d06dP12233WZNPhYsWKARI0YoLCxMf/jDH+Ti4qJjx47p66+/1qJFiyr/HwJAjWA1ClANTCaTPvroI91xxx165JFHdPPNN+u+++7T999/b109cu+992rBggWaN2+eIiIidO7cOf3xj3+0e91nn31Ws2fP1oIFC9SxY0fde++9SktLk3R9PsQrr7yiNWvWKDQ0VKNHj5YkPfroo/rb3/6muLg4hYeHq3///oqLi7MulW3YsKE++OADnTx5Ut27d9f8+fO1ZMmSSt1vt27dtGzZMi1ZskRdunTRpk2bFBsbW2pcgwYNNG/ePI0bN059+vSRt7e3tmzZYj0+dOhQffjhh9q5c6d69uyp2267TcuWLVOLFi0qFQ+AmmWyOKJBCwAAUA4qGwAAwKlINgAAgFORbAAAAKci2QAAAE5FsgEAAJyKZAMAADgVyQYAAHAqkg0AAOBUJBsAAMCpSDYAAIBTkWwAAACn+n9cPr6xVOxtMgAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"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)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue View Git Blame Copy Permalink