From e444a72889c92f1c45e51d2f32a816370aa78100 Mon Sep 17 00:00:00 2001 From: Celina Date: Wed, 10 Dec 2025 16:33:34 +0100 Subject: [PATCH] xgboost groupfold and autoencoder --- model_training/xgboost/xgboost.ipynb | 35 +- .../xgboost/xgboost_groupfold.ipynb | 3349 +++++++++++++++ model_training/xgboost/xgboost_with_AE.ipynb | 3595 +++++++++++++++++ 3 files changed, 6967 insertions(+), 12 deletions(-) create mode 100644 model_training/xgboost/xgboost_groupfold.ipynb create mode 100644 model_training/xgboost/xgboost_with_AE.ipynb diff --git a/model_training/xgboost/xgboost.ipynb b/model_training/xgboost/xgboost.ipynb index 6966d68..5bc7495 100644 --- a/model_training/xgboost/xgboost.ipynb +++ b/model_training/xgboost/xgboost.ipynb @@ -684,6 +684,29 @@ "model.save_model(\"xgb_model.json\") # als JSON (lesbar, portabel)\n", "model.save_model(\"xgb_model.bin\") # als Binärdatei (kompakt)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3195cc84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/jovyan'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.getcwd()\n", + "\n" + ] } ], "metadata": { @@ -691,18 +714,6 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.10" } }, "nbformat": 4, diff --git a/model_training/xgboost/xgboost_groupfold.ipynb b/model_training/xgboost/xgboost_groupfold.ipynb new file mode 100644 index 0000000..249adde --- /dev/null +++ b/model_training/xgboost/xgboost_groupfold.ipynb @@ -0,0 +1,3349 @@ +{ + "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 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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 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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 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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 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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 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\"use_label_encoder\" } are not used.\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, 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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": 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", 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", + "text/plain": [ + "
" + ] + }, + "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: \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: \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: \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: \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 +} diff --git a/model_training/xgboost/xgboost_with_AE.ipynb b/model_training/xgboost/xgboost_with_AE.ipynb new file mode 100644 index 0000000..fafa97a --- /dev/null +++ b/model_training/xgboost/xgboost_with_AE.ipynb @@ -0,0 +1,3595 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "e3be057e-8d2a-4d05-bd42-6b1dc75df5ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.17.1\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", + "import tensorflow as tf\n", + "print(tf.__version__)\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5aacaa32", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current working dir: /home/jovyan\n" + ] + }, + { + "data": { + "text/html": [ + "
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+ "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": 13, + "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": 14, + "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": 15, + "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": 16, + "id": "8e7fa0fa", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.model_selection import train_test_split,StratifiedKFold, GridSearchCV\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": 17, + "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": 18, + "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": 19, + "id": "960bb8c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3255, 26) (1219, 26) (2846, 26)\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_subs)]\n", + "val_df = data[data.subjectID.isin(val_subs)]\n", + "test_df = data[data.subjectID.isin(test_subjects)]\n", + "\n", + "print(train_df.shape, val_df.shape, test_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "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": 21, + "id": "289f6b89", + "metadata": {}, + "outputs": [], + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5df30e8d", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, y_train = train_scaled[au_columns].values, train_scaled[\"label\"].values\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": 23, + "id": "f4a1063e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1764515514.279010 1065 service.cc:146] XLA service 0x7fa034004d00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1764515514.279049 1065 service.cc:154] StreamExecutor device (0): NVIDIA A100 80GB PCIe MIG 3g.40gb, Compute Capability 8.0\n", + "2025-11-30 16:11:54.284023: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2025-11-30 16:11:54.324533: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m 57/102\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 903us/step " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1764515514.560254 1065 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n", + "\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n", + "\u001b[1m89/89\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n" + ] + } + ], + "source": [ + "X_train_encoded = encoder.predict(X_train)\n", + "X_val_encoded = encoder.predict(X_val)\n", + "X_test_encoded = encoder.predict(X_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:57] 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: [16:11:58] 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: [16:11:58] 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: [16:11:58] 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: [16:11:58] 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: [16:11:58] 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: [16:11:58] 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: [16:11:58] 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: [16:11:58] 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: [16:11:58] 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: [16:11:58] 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: [16:11:58] 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: [16:11:59] 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: [16:11:59] 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: [16:11:59] 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: [16:11:59] 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: [16:11:59] 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: [16:11:59] 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: [16:11:59] 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: [16:11:59] 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: [16:11:59] 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: [16:11:59] 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: [16:12:00] 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: [16:12:00] 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: [16:12:00] 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: [16:12:00] 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: [16:12:00] 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: [16:12:00] 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: [16:12:00] 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: [16:12:00] 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: [16:12:01] 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: [16:12:01] 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: [16:12:01] 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: [16:12:01] 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: [16:12:01] 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: [16:12:01] 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: [16:12:01] 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: [16:12:01] 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: [16:12:02] 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: [16:12:02] 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: [16:12:02] 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: [16:12:02] 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: [16:12:03] 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: [16:12:03] 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: [16:12:03] 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: [16:12:03] 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: [16:12:04] 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: [16:12:04] 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: [16:12:04] 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: [16:12:04] 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: [16:12:04] 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: [16:12:04] 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: [16:12:05] 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: [16:12:05] 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: [16:12:05] 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: [16:12:05] 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: [16:12:05] 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: [16:12:05] 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: [16:12:05] 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: [16:12:05] 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: [16:12:06] 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: [16:12:06] 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: [16:12:06] 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: [16:12:06] 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: [16:12:07] 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: [16:12:07] 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: [16:12:07] 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: [16:12:07] 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: [16:12:08] 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: [16:12:08] 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: [16:12:09] 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: [16:12:09] 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: [16:12:10] 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: [16:12:10] 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: [16:12:10] 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: [16:12:10] 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: [16:12:11] 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: [16:12:11] 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: [16:12:11] 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: [16:12:11] 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: [16:12:11] 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: [16:12:11] 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: [16:12:11] 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: [16:12:11] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:12] 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: [16:12:13] 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: [16:12:13] 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: [16:12:13] 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: [16:12:13] 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: [16:12:13] 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: [16:12:13] 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: [16:12:13] 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: [16:12:13] 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: [16:12:13] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:14] 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: [16:12:15] 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: [16:12:15] 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: [16:12:15] 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: [16:12:15] 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: [16:12:15] 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: [16:12:15] 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: [16:12:15] 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: [16:12:15] 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: [16:12:16] 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: [16:12:16] 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: [16:12:16] 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: [16:12:16] 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: [16:12:16] 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: [16:12:16] 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: [16:12:16] 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: [16:12:16] 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: [16:12:17] 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: [16:12:17] 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: [16:12:17] 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: [16:12:17] 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: [16:12:17] 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: [16:12:17] 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: [16:12:17] 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: [16:12:17] 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: [16:12:18] 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: [16:12:18] 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: [16:12:18] 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: [16:12:18] 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: [16:12:18] 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: [16:12:18] 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: [16:12:18] 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: [16:12:18] 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: [16:12:18] 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: [16:12:18] 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: [16:12:19] 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: [16:12:19] 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: [16:12:19] 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: [16:12:19] 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: [16:12:20] 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: [16:12:20] 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: [16:12:20] 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: [16:12:20] 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: [16:12:20] 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: [16:12:20] 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: [16:12:20] 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: [16:12:20] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:21] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:22] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:23] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:24] 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: [16:12:25] 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: [16:12:25] 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: [16:12:25] 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: [16:12:25] 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: [16:12:25] 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: [16:12:25] 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: [16:12:25] 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: [16:12:25] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:26] 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: [16:12:27] 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: [16:12:27] 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: [16:12:27] 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: [16:12:27] 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: [16:12:27] 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: [16:12:27] 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: [16:12:27] 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: [16:12:27] 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: [16:12:27] 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: [16:12:27] 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: [16:12:28] 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: [16:12:28] 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: [16:12:28] 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: [16:12:28] 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: [16:12:28] 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: [16:12:28] 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: [16:12:28] 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: [16:12:29] 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: [16:12:29] 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: [16:12:29] 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: [16:12:29] WARNING: /workspace/src/learner.cc:790: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] END 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[16:12:29] 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: [16:12:29] 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: [16:12:29] 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: [16:12:29] 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: [16:12:29] 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: [16:12:29] 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: [16:12:29] 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: [16:12:29] WARNING: /workspace/src/learner.cc:790: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n" + ] + }, + { + "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= 0.8s\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= 0.4s\n", + "[CV] END colsample_bytree=0.8, learning_rate=0.01, max_depth=4, n_estimators=500, subsample=1.0; total time= 0.4s\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, 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"/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [16:12:29] 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: [16:12:29] 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: [16:12:29] WARNING: /workspace/src/learner.cc:790: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n" + ] + }, + { + "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= 0.8s\n", + "[CV] END colsample_bytree=0.8, 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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= 0.4s\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= 0.5s\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=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=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" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [16:12:30] 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: [16:12:30] 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: [16:12:30] 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: [16:12:30] 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: [16:12:30] 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: [16:12:30] 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: [16:12:30] 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: [16:12:30] WARNING: /workspace/src/learner.cc:790: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n" + ] + }, + { + "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= 0.8s\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 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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= 0.4s\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= 0.5s\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\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" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [16:12:31] 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: [16:12:31] 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: [16:12:31] WARNING: /workspace/src/learner.cc:790: \n", + "Parameters: { \"use_label_encoder\" } are not 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"Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/opt/conda/lib/python3.12/site-packages/xgboost/training.py:199: UserWarning: [16:12:32] 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: [16:12:32] 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: [16:12:32] 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: [16:12:32] 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: [16:12:32] 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: [16:12:32] 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: [16:12:32] 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: [16:12:33] 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: [16:12:33] 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: [16:12:33] 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: [16:12:33] 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: [16:12:34] 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: [16:12:34] 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: [16:12:34] 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: [16:12:34] 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: [16:12:34] 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: [16:12:35] 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: [16:12:35] 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: [16:12:35] 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: [16:12:36] 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: [16:12:36] 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: [16:12:36] 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: [16:12:36] 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: [16:12:37] 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: [16:12:37] 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: [16:12:37] 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: [16:12:37] 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: [16:12:37] 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: [16:12:38] 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: [16:12:38] 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: [16:12:38] 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: [16:12:38] 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: [16:12:38] 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: [16:12:38] 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: [16:12:38] 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: [16:12:38] 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: [16:12:39] 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: [16:12:40] 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: [16:12:40] 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: [16:12:40] 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: [16:12:40] 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: [16:12:41] 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: [16:12:41] 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: [16:12:41] 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: [16:12:41] 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: [16:12:42] 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: [16:12:42] 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: [16:12:43] 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: [16:12:43] 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: [16:12:44] 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: [16:12:44] 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: [16:12:44] 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: [16:12:45] 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: [16:12:45] 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: [16:12:45] 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: [16:12:45] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:46] 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: [16:12:47] 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: [16:12:47] 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: [16:12:47] 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: [16:12:47] 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: [16:12:47] 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: [16:12:47] 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: [16:12:47] 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: [16:12:47] 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: [16:12:47] 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: [16:12:48] 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: [16:12:48] 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: [16:12:48] 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: [16:12:48] 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: [16:12:48] 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: [16:12:48] 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: [16:12:48] 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: [16:12:48] 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: [16:12:48] 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: [16:12:48] 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: [16:12:49] 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: [16:12:49] 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: [16:12:49] 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: [16:12:49] 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: [16:12:49] 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: [16:12:49] 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: [16:12:49] 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: [16:12:49] 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: [16:12:49] 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: [16:12:49] 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: [16:12:50] 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: [16:12:50] 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: [16:12:50] 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: [16:12:50] 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: [16:12:50] 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: [16:12:50] 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: [16:12:51] 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: [16:12:51] 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: [16:12:51] 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: [16:12:51] 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: [16:12:51] 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: [16:12:51] 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: [16:12:51] 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: [16:12:52] 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: [16:12:52] 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: [16:12:52] 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: [16:12:52] 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: [16:12:52] 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: [16:12:52] 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: [16:12:52] 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: [16:12:53] 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: [16:12:53] 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: [16:12:53] 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: [16:12:53] 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: [16:12:53] 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: [16:12:53] 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: [16:12:53] 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: [16:12:53] 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: [16:12:54] 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: [16:12:54] 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: [16:12:54] 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: [16:12:54] 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: [16:12:54] 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: [16:12:54] 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: [16:12:55] 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: [16:12:55] 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: [16:12:55] 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: [16:12:55] 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: [16:12:55] 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: [16:12:55] 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: [16:12:56] 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: [16:12:56] 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: [16:12:56] 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: [16:12:56] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:57] 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: [16:12:58] 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: [16:12:58] 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: [16:12:58] 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: [16:12:58] 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: [16:12:58] 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: [16:12:58] 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: [16:12:58] 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: [16:12:58] 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: [16:12:58] 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: [16:12:58] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:12:59] 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: [16:13:00] 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: [16:13:00] 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: [16:13:00] 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: [16:13:00] 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: [16:13:00] 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: [16:13:00] 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: [16:13:00] 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: [16:13:00] 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: [16:13:00] 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: [16:13:01] 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: [16:13:01] 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: [16:13:01] 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: [16:13:01] 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: [16:13:01] 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: [16:13:01] 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: [16:13:01] 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: [16:13:01] 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: [16:13:01] 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: [16:13:02] 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: [16:13:02] 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: [16:13:02] 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: [16:13:02] 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: [16:13:02] 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: [16:13:02] 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: [16:13:02] 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: [16:13:02] 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: [16:13:02] 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: [16:13:02] 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: [16:13:02] 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: [16:13:03] 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: [16:13:03] 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: [16:13:03] 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: [16:13:03] 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: [16:13:03] 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: [16:13:03] 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: [16:13:03] 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: [16:13:03] 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: [16:13:03] 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: [16:13:04] 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: [16:13:04] 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: [16:13:04] 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: [16:13:04] 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: [16:13:04] 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: [16:13:04] 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: [16:13:04] 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: [16:13:04] 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: [16:13:05] 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: [16:13:05] 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: [16:13:05] 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: [16:13:05] 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: [16:13:06] WARNING: /workspace/src/learner.cc:790: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beste Parameter: {'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_depth': 8, 'n_estimators': 800, 'subsample': 1.0}\n", + "Bestes AUC: 0.9991301455961723\n" + ] + } + ], + "source": [ + "# Basis-Modell\n", + "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", + "# K-Fold Cross Validation\n", + "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\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\n", + "grid_search.fit(X_train, y_train)\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": 25, + "id": "09a8cd21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN:\n", + "Accuracy: 1.0\n", + "F1: 1.0\n", + "AUC: 1.0\n", + "Confusion:\n", + " [[1367 0]\n", + " [ 0 1888]]\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1367\n", + " 1 1.00 1.00 1.00 1888\n", + "\n", + " accuracy 1.00 3255\n", + " macro avg 1.00 1.00 1.00 3255\n", + "weighted avg 1.00 1.00 1.00 3255\n", + "\n", + "VAL:\n", + "Accuracy: 0.7013945857260049\n", + "F1: 0.7602108036890646\n", + "AUC: 0.679595392320903\n", + "Confusion:\n", + " [[278 235]\n", + " [129 577]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.68 0.54 0.60 513\n", + " 1 0.71 0.82 0.76 706\n", + "\n", + " accuracy 0.70 1219\n", + " macro avg 0.70 0.68 0.68 1219\n", + "weighted avg 0.70 0.70 0.69 1219\n", + "\n", + "TEST:\n", + "Accuracy: 0.5758959943780745\n", + "F1: 0.6303215926493109\n", + "AUC: 0.5667426083434588\n", + "Confusion:\n", + " [[ 610 590]\n", + " [ 617 1029]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.50 0.51 0.50 1200\n", + " 1 0.64 0.63 0.63 1646\n", + "\n", + " accuracy 0.58 2846\n", + " macro avg 0.57 0.57 0.57 2846\n", + "weighted avg 0.58 0.58 0.58 2846\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "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", + " #cm = confusion_matrix(y, preds)\n", + " plot_confusion_matrix(y,preds, label_names=['Low','High'])\n", + " # plt.figure(figsize=(5,4))\n", + " # sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False,\n", + " # xticklabels=[\"Predicted low\", \"Predicted high\"],\n", + " # yticklabels=[\"Actual low\", \"Actual high\"])\n", + " # plt.title(f\"Confusion Matrix - {title}\")\n", + " # plt.ylabel(\"True label\")\n", + " # plt.xlabel(\"Predicted label\")\n", + " # plt.show()\n", + "\n", + "# Aufrufen für Train/Val/Test\n", + "print(\"TRAIN:\")\n", + "evaluate(model, X_train, 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": 26, + "id": "c43b0c80", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model gespeichert.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.12/site-packages/xgboost/sklearn.py:1116: UserWarning: [16:13:07] WARNING: /workspace/src/c_api/c_api.cc:1575: Saving model in the UBJSON format as default. You can use a file extension: `json` or `ubj` to choose between formats.\n", + " self.get_booster().save_model(fname)\n" + ] + } + ], + "source": [ + "joblib.dump(model, \"xgb_model.joblib\")\n", + "joblib.dump(normalizer, \"normalizer.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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3195cc84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/jovyan'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\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\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=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=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=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= 1.1s\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= 1.2s\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=800, subsample=0.8; total time= 1.7s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time= 1.7s\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=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= 0.3s\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= 0.3s\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=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= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time= 0.7s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time= 0.7s\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=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= 0.4s\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= 0.7s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; 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= 0.8s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time= 0.8s\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=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=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= 0.4s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=0.8; total time= 0.4s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time= 0.4s\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= 0.2s\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=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= 0.4s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time= 0.5s\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= 0.3s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; 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= 0.4s\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=1.0; total time= 0.6s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Exception ignored in: \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=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= 0.4s\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=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= 1.3s\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= 0.5s\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= 1.2s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time= 1.7s\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=0.01, max_depth=8, n_estimators=800, subsample=1.0; total time= 1.6s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=4, n_estimators=500, subsample=0.8; 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= 0.3s\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= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time= 0.7s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time= 0.7s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time= 0.7s\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= 0.4s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time= 0.6s\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= 0.7s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=0.8; total time= 0.8s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time= 0.8s\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= 0.1s\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= 0.4s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time= 0.4s\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= 0.2s\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= 0.4s\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= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time= 0.5s\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= 0.3s\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=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= 0.5s\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=1.0; total time= 0.6s\n", + "\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=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= 0.4s\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=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=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= 1.1s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=200, subsample=0.8; total time= 0.5s\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= 1.2s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=500, subsample=0.8; total time= 1.1s\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=800, subsample=0.8; total time= 1.7s\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= 0.2s\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=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= 0.5s\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=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= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=0.8; total time= 0.5s\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= 0.7s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=1.0; total time= 0.7s\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= 0.4s\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= 0.7s\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= 0.8s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, 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=800, subsample=1.0; total time= 0.8s\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=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=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= 0.4s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, 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=1.0; total time= 0.4s\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=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= 0.4s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=0.8; total time= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, 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=800, subsample=1.0; total time= 0.5s\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= 0.3s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=8, n_estimators=500, subsample=0.8; total time= 0.4s\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= 0.6s\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: \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", + "Exception ignored in: \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=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=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= 0.8s\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= 1.2s\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=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= 0.5s\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= 1.2s\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= 1.2s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=8, n_estimators=800, subsample=0.8; total time= 1.7s\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= 0.2s\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= 0.1s\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= 0.3s\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= 0.5s\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=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= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=500, subsample=1.0; total time= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=6, n_estimators=800, subsample=0.8; total time= 0.7s\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= 0.4s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=500, subsample=0.8; total time= 0.6s\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=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= 0.8s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.05, max_depth=8, n_estimators=800, subsample=1.0; total time= 0.8s\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= 0.1s\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= 0.4s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=4, n_estimators=800, subsample=1.0; total time= 0.4s\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=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= 0.2s\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= 0.4s\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= 0.5s\n", + "[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=6, n_estimators=800, subsample=1.0; total time= 0.5s\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= 0.3s\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=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= 0.5s\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", + "[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: \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": [ + "import os\n", + "os.getcwd()\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}