{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "7Tbk85xIbmXo", "outputId": "e6d83dec-d564-4641-fd95-a210bb9ff60f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: shap in /usr/local/lib/python3.9/dist-packages (0.41.0)\n", "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.9/dist-packages (from shap) (2.2.1)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from shap) (1.5.3)\n", "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.9/dist-packages (from shap) (0.0.7)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from shap) (1.10.1)\n", "Requirement already satisfied: numba in /usr/local/lib/python3.9/dist-packages (from shap) (0.56.4)\n", "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.9/dist-packages (from shap) (4.65.0)\n", "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.9/dist-packages (from shap) (23.0)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (from shap) (1.2.2)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from shap) (1.22.4)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from numba->shap) (67.6.1)\n", "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.9/dist-packages (from numba->shap) (0.39.1)\n", "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->shap) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->shap) (2022.7.1)\n", "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit-learn->shap) (1.2.0)\n", "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn->shap) (3.1.0)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (1.2.2)\n", "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.9/dist-packages (from scikit-learn) (1.22.4)\n", "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.9/dist-packages (from scikit-learn) (1.10.1)\n", "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn) (3.1.0)\n", "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit-learn) (1.2.0)\n", "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: optuna in /usr/local/lib/python3.9/dist-packages (3.1.1)\n", "Requirement already satisfied: cmaes>=0.9.1 in /usr/local/lib/python3.9/dist-packages (from optuna) (0.9.1)\n", "Requirement already satisfied: sqlalchemy>=1.3.0 in /usr/local/lib/python3.9/dist-packages (from optuna) (2.0.9)\n", "Requirement already satisfied: colorlog in /usr/local/lib/python3.9/dist-packages (from optuna) (6.7.0)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from optuna) (23.0)\n", "Requirement already satisfied: PyYAML in /usr/local/lib/python3.9/dist-packages (from optuna) (6.0)\n", "Requirement already satisfied: alembic>=1.5.0 in /usr/local/lib/python3.9/dist-packages (from optuna) (1.10.3)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from optuna) (4.65.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from optuna) (1.22.4)\n", "Requirement already satisfied: typing-extensions>=4 in /usr/local/lib/python3.9/dist-packages (from alembic>=1.5.0->optuna) (4.5.0)\n", "Requirement already satisfied: Mako in /usr/local/lib/python3.9/dist-packages (from alembic>=1.5.0->optuna) (1.2.4)\n", "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.9/dist-packages (from sqlalchemy>=1.3.0->optuna) (2.0.2)\n", "Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.9/dist-packages (from Mako->alembic>=1.5.0->optuna) (2.1.2)\n", "1460\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", "0 1 60 RL 65.0 8450 Pave NaN Reg \n", "1 2 20 RL 80.0 9600 Pave NaN Reg \n", "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", "\n", " LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold \\\n", "0 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n", "1 Lvl AllPub ... 0 NaN NaN NaN 0 5 \n", "2 Lvl AllPub ... 0 NaN NaN NaN 0 9 \n", "3 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n", "4 Lvl AllPub ... 0 NaN NaN NaN 0 12 \n", "\n", " YrSold SaleType SaleCondition SalePrice \n", "0 2008 WD Normal 208500 \n", "1 2007 WD Normal 181500 \n", "2 2008 WD Normal 223500 \n", "3 2006 WD Abnorml 140000 \n", "4 2008 WD Normal 250000 \n", "\n", "[5 rows x 81 columns]" ], "text/html": [ "\n", "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...PoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPub...0NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPub...0NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPub...0NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPub...0NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPub...0NaNNaNNaN0122008WDNormal250000
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5 rows × 81 columns

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\n", " " ] }, "metadata": {}, "execution_count": 9 } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import xgboost as xgb\n", "!pip install shap\n", "import shap\n", "shap.initjs()\n", "!pip install -U scikit-learn\n", "from sklearn.model_selection import train_test_split\n", "!pip install optuna\n", "import optuna\n", "path= \"/content/drive/MyDrive/train.csv\"\n", "data=pd.read_csv(path, header=0)\n", "print(len(data))\n", "data.head()" ] }, { "cell_type": "code", "source": [ "#Get Features\n", "y= data['SalePrice']\n", "x= data[[\"LotArea\",\"OverallQual\", \"OverallCond\", \"YearBuilt\",\"TotRmsAbvGrd\",\"GarageArea\"]]\n", "#Split the data using the scikit-learn library and having a train test split with validation sets\n", "x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.2, random_state=0)\n", "x_train.shape, x_val.shape, y_train.shape, y_val.shape\n", "# make a new regressor model \n", "regressor = xgb.XGBRegressor(objective ='reg:squarederror', \n", " colsample_bytree = 1, \n", " eta=0.3, \n", " learning_rate = 0.01,\n", " max_depth = 5, \n", " alpha = 10, \n", " n_estimators = 500)\n", "# fit on training set\n", "regressor.fit(x_train, y_train) \n", "# predict on validation set\n", "y_pred = regressor.predict(x_val)\n", "# calculate Root Mean Square Error\n", "from sklearn.metrics import mean_squared_error\n", "root_mean_squared_error = mean_squared_error(y_val, y_pred, squared=False)\n", "root_mean_squared_error" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X42ZpF0YcLBv", "outputId": "34646885-5aca-428b-d3a1-7b9535854945" }, "execution_count": 14, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "38378.44068863984" ] }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "code", "source": [ "def objective(n_trials):\n", " params = {\n", " \"n_estimators\": n_trials.suggest_int(\"n_estimators\", 100, 2000, step=100),\n", " \"learning_rate\": n_trials.suggest_float(\"learning_rate\", 1e-4, 0.3, log=True),\n", " \"max_depth\": n_trials.suggest_int(\"max_depth\", 3, 15),\n", " \"n_iter_no_change\": 50,\n", " }\n", " dtrain = xgb.DMatrix(data = x_train, label = y_train)\n", " dval = xgb.DMatrix(data = x_val, label = y_val)\n", "\n", " regressor = xgb.train(params, dtrain) \n", " y_pred = regressor.predict(dval)\n", " rmse = mean_squared_error(y_val, y_pred, squared=False)\n", "\n", " return rmse" ], "metadata": { "id": "QuMqCZDzi2fu" }, "execution_count": 19, "outputs": [] }, { "cell_type": "code", "source": [ "study = optuna.create_study(direction=\"minimize\")\n", "study.optimize(objective, n_trials=500)\n", "\n", "print(f\"Optimized RMSE: {study.best_value:.4f}\")\n", "print(\"Best params:\")\n", "for key, value in study.best_params.items():\n", " print(f\"\\t{key}: {value}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IxSTCUOTnrew", "outputId": "8d39b8b2-f4b6-4483-8f1c-5d5679927def" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:28,408]\u001b[0m A new study created in memory with name: no-name-cb8d0eec-258c-4cb6-9f89-ffd9e01eca92\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:28,527]\u001b[0m Trial 0 finished with value: 198943.67897469082 and parameters: {'n_estimators': 1400, 'learning_rate': 0.0002969536549477393, 'max_depth': 13}. Best is trial 0 with value: 198943.67897469082.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:28,570]\u001b[0m Trial 1 finished with value: 87823.91199760552 and parameters: {'n_estimators': 1200, 'learning_rate': 0.09138823223413792, 'max_depth': 4}. Best is trial 1 with value: 87823.91199760552.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:28] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:28] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:28] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:28,684]\u001b[0m Trial 2 finished with value: 177231.76870751628 and parameters: {'n_estimators': 1200, 'learning_rate': 0.01259678878419975, 'max_depth': 8}. Best is trial 1 with value: 87823.91199760552.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:28,739]\u001b[0m Trial 3 finished with value: 42147.24927997999 and parameters: {'n_estimators': 800, 'learning_rate': 0.24046670912332685, 'max_depth': 4}. Best is trial 3 with value: 42147.24927997999.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:28] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:28] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:29,973]\u001b[0m Trial 4 finished with value: 198493.0708843229 and parameters: {'n_estimators': 1200, 'learning_rate': 0.0005410500825128317, 'max_depth': 3}. Best is trial 3 with value: 42147.24927997999.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:30] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:30,663]\u001b[0m Trial 5 finished with value: 49559.493249812585 and parameters: {'n_estimators': 1200, 'learning_rate': 0.18210251533385016, 'max_depth': 6}. Best is trial 3 with value: 42147.24927997999.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:30] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:32,019]\u001b[0m Trial 6 finished with value: 198879.7879462171 and parameters: {'n_estimators': 1800, 'learning_rate': 0.00033101350644419584, 'max_depth': 13}. Best is trial 3 with value: 42147.24927997999.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,072]\u001b[0m Trial 7 finished with value: 185630.34572294302 and parameters: {'n_estimators': 1800, 'learning_rate': 0.007656543333687949, 'max_depth': 5}. Best is trial 3 with value: 42147.24927997999.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,100]\u001b[0m Trial 8 finished with value: 138217.74848183122 and parameters: {'n_estimators': 800, 'learning_rate': 0.03911088640019661, 'max_depth': 11}. Best is trial 3 with value: 42147.24927997999.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,133]\u001b[0m Trial 9 finished with value: 56500.93051100445 and parameters: {'n_estimators': 1200, 'learning_rate': 0.1607446226455004, 'max_depth': 14}. Best is trial 3 with value: 42147.24927997999.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,179]\u001b[0m Trial 10 finished with value: 196252.2173120141 and parameters: {'n_estimators': 100, 'learning_rate': 0.001741510829505106, 'max_depth': 8}. Best is trial 3 with value: 42147.24927997999.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,219]\u001b[0m Trial 11 finished with value: 39964.9690722631 and parameters: {'n_estimators': 600, 'learning_rate': 0.2963639040286593, 'max_depth': 6}. Best is trial 11 with value: 39964.9690722631.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:32,271]\u001b[0m Trial 12 finished with value: 39080.890279334846 and parameters: {'n_estimators': 600, 'learning_rate': 0.2924325870565413, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,315]\u001b[0m Trial 13 finished with value: 124981.45939804337 and parameters: {'n_estimators': 300, 'learning_rate': 0.05014108192467709, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,379]\u001b[0m Trial 14 finished with value: 40647.64471933146 and parameters: {'n_estimators': 600, 'learning_rate': 0.27073421663111485, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,418]\u001b[0m Trial 15 finished with value: 154783.81009606415 and parameters: {'n_estimators': 500, 'learning_rate': 0.02695342119948647, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:32,465]\u001b[0m Trial 16 finished with value: 88593.48551046038 and parameters: {'n_estimators': 800, 'learning_rate': 0.0897273673630594, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,507]\u001b[0m Trial 17 finished with value: 163495.25839386645 and parameters: {'n_estimators': 400, 'learning_rate': 0.021134138561122097, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,543]\u001b[0m Trial 18 finished with value: 193444.76088433893 and parameters: {'n_estimators': 200, 'learning_rate': 0.003289646897805105, 'max_depth': 3}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,581]\u001b[0m Trial 19 finished with value: 95467.86785435419 and parameters: {'n_estimators': 600, 'learning_rate': 0.08100347365643226, 'max_depth': 5}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,633]\u001b[0m Trial 20 finished with value: 39341.808840741745 and parameters: {'n_estimators': 900, 'learning_rate': 0.2890646117439157, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:32,700]\u001b[0m Trial 21 finished with value: 64892.96451367521 and parameters: {'n_estimators': 900, 'learning_rate': 0.1321420817394568, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,756]\u001b[0m Trial 22 finished with value: 39919.5408794358 and parameters: {'n_estimators': 1000, 'learning_rate': 0.29521950555952836, 'max_depth': 11}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,800]\u001b[0m Trial 23 finished with value: 115547.2678866634 and parameters: {'n_estimators': 1000, 'learning_rate': 0.058728722298502095, 'max_depth': 12}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,848]\u001b[0m Trial 24 finished with value: 67960.41607408407 and parameters: {'n_estimators': 1600, 'learning_rate': 0.12444622149728175, 'max_depth': 11}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:32,917]\u001b[0m Trial 25 finished with value: 43430.380632926775 and parameters: {'n_estimators': 1000, 'learning_rate': 0.2781729171316346, 'max_depth': 15}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:32,965]\u001b[0m Trial 26 finished with value: 121996.83901761193 and parameters: {'n_estimators': 1500, 'learning_rate': 0.0526641017076236, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,014]\u001b[0m Trial 27 finished with value: 61219.73793105805 and parameters: {'n_estimators': 700, 'learning_rate': 0.14090389484291524, 'max_depth': 11}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,057]\u001b[0m Trial 28 finished with value: 149726.3802269489 and parameters: {'n_estimators': 400, 'learning_rate': 0.03058627767968753, 'max_depth': 12}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,106]\u001b[0m Trial 29 finished with value: 86456.16858119742 and parameters: {'n_estimators': 1400, 'learning_rate': 0.09258860287609523, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:32] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:33,159]\u001b[0m Trial 30 finished with value: 54274.71256708668 and parameters: {'n_estimators': 2000, 'learning_rate': 0.16460632531808025, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,208]\u001b[0m Trial 31 finished with value: 41039.826367942645 and parameters: {'n_estimators': 600, 'learning_rate': 0.2531591453647617, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,263]\u001b[0m Trial 32 finished with value: 42486.20400760423 and parameters: {'n_estimators': 900, 'learning_rate': 0.2372859852477389, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,303]\u001b[0m Trial 33 finished with value: 95962.84480675156 and parameters: {'n_estimators': 1100, 'learning_rate': 0.08028301419096882, 'max_depth': 5}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,348]\u001b[0m Trial 34 finished with value: 67039.28448082336 and parameters: {'n_estimators': 900, 'learning_rate': 0.12522375300593075, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:33,403]\u001b[0m Trial 35 finished with value: 39907.27072015415 and parameters: {'n_estimators': 700, 'learning_rate': 0.24725978757199804, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,461]\u001b[0m Trial 36 finished with value: 50916.22828762896 and parameters: {'n_estimators': 700, 'learning_rate': 0.181933323966489, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,505]\u001b[0m Trial 37 finished with value: 172977.0187411539 and parameters: {'n_estimators': 1300, 'learning_rate': 0.015175344549002946, 'max_depth': 13}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,551]\u001b[0m Trial 38 finished with value: 105428.53924610029 and parameters: {'n_estimators': 1100, 'learning_rate': 0.0692652361532205, 'max_depth': 12}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,598]\u001b[0m Trial 39 finished with value: 49028.207638503605 and parameters: {'n_estimators': 700, 'learning_rate': 0.1907546562789119, 'max_depth': 4}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:33,654]\u001b[0m Trial 40 finished with value: 73624.00487657385 and parameters: {'n_estimators': 400, 'learning_rate': 0.11227136968954649, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,704]\u001b[0m Trial 41 finished with value: 39494.06679348176 and parameters: {'n_estimators': 500, 'learning_rate': 0.281847659231474, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,765]\u001b[0m Trial 42 finished with value: 48694.76886515337 and parameters: {'n_estimators': 500, 'learning_rate': 0.19537469360141685, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,822]\u001b[0m Trial 43 finished with value: 41540.73907240643 and parameters: {'n_estimators': 900, 'learning_rate': 0.28974323644544114, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,874]\u001b[0m Trial 44 finished with value: 49174.76346723035 and parameters: {'n_estimators': 800, 'learning_rate': 0.19132885928328566, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:33,926]\u001b[0m Trial 45 finished with value: 71317.6229241771 and parameters: {'n_estimators': 500, 'learning_rate': 0.11748874875785861, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:33,980]\u001b[0m Trial 46 finished with value: 41837.484157493025 and parameters: {'n_estimators': 300, 'learning_rate': 0.2984741515727143, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,026]\u001b[0m Trial 47 finished with value: 134882.30540004894 and parameters: {'n_estimators': 700, 'learning_rate': 0.041760362479718066, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,069]\u001b[0m Trial 48 finished with value: 50214.062413404 and parameters: {'n_estimators': 1000, 'learning_rate': 0.1782669517290495, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:33] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:34,121]\u001b[0m Trial 49 finished with value: 109708.74121365364 and parameters: {'n_estimators': 200, 'learning_rate': 0.06509861284586331, 'max_depth': 4}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,169]\u001b[0m Trial 50 finished with value: 83247.89225930415 and parameters: {'n_estimators': 1100, 'learning_rate': 0.09723478858875335, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,209]\u001b[0m Trial 51 finished with value: 40521.00520293694 and parameters: {'n_estimators': 600, 'learning_rate': 0.2876915817061607, 'max_depth': 5}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,254]\u001b[0m Trial 52 finished with value: 44249.267508307355 and parameters: {'n_estimators': 800, 'learning_rate': 0.21675361321337694, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,300]\u001b[0m Trial 53 finished with value: 57522.67092444571 and parameters: {'n_estimators': 500, 'learning_rate': 0.15457379507334276, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,342]\u001b[0m Trial 54 finished with value: 45182.419726172215 and parameters: {'n_estimators': 600, 'learning_rate': 0.21580957516345922, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:34,393]\u001b[0m Trial 55 finished with value: 79438.83647667873 and parameters: {'n_estimators': 800, 'learning_rate': 0.10282962553184158, 'max_depth': 11}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,440]\u001b[0m Trial 56 finished with value: 59984.13124101304 and parameters: {'n_estimators': 700, 'learning_rate': 0.1438544889477077, 'max_depth': 5}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,500]\u001b[0m Trial 57 finished with value: 39840.47019026054 and parameters: {'n_estimators': 300, 'learning_rate': 0.2941008466848309, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,552]\u001b[0m Trial 58 finished with value: 42886.483879145475 and parameters: {'n_estimators': 300, 'learning_rate': 0.22812814020884148, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:34,610]\u001b[0m Trial 59 finished with value: 59178.495417870705 and parameters: {'n_estimators': 100, 'learning_rate': 0.148940179234625, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,665]\u001b[0m Trial 60 finished with value: 39328.648203813704 and parameters: {'n_estimators': 200, 'learning_rate': 0.2956894994347374, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,723]\u001b[0m Trial 61 finished with value: 39583.09714604055 and parameters: {'n_estimators': 200, 'learning_rate': 0.29385023979462443, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:34,789]\u001b[0m Trial 62 finished with value: 45477.447492611354 and parameters: {'n_estimators': 200, 'learning_rate': 0.215065047000271, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,846]\u001b[0m Trial 63 finished with value: 54772.90137452703 and parameters: {'n_estimators': 400, 'learning_rate': 0.16370854159821618, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,897]\u001b[0m Trial 64 finished with value: 42271.45285809915 and parameters: {'n_estimators': 200, 'learning_rate': 0.2318998798575922, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,944]\u001b[0m Trial 65 finished with value: 101330.0572582894 and parameters: {'n_estimators': 100, 'learning_rate': 0.07366228537813713, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:34,989]\u001b[0m Trial 66 finished with value: 74660.68042223246 and parameters: {'n_estimators': 300, 'learning_rate': 0.11032732102699375, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:34] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:35,052]\u001b[0m Trial 67 finished with value: 40879.799006400695 and parameters: {'n_estimators': 400, 'learning_rate': 0.2880702629170375, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,103]\u001b[0m Trial 68 finished with value: 61235.61099207634 and parameters: {'n_estimators': 200, 'learning_rate': 0.14429665218993984, 'max_depth': 11}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,162]\u001b[0m Trial 69 finished with value: 44808.91293288233 and parameters: {'n_estimators': 300, 'learning_rate': 0.2204583559103831, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,208]\u001b[0m Trial 70 finished with value: 86915.73526588223 and parameters: {'n_estimators': 500, 'learning_rate': 0.09200233326508195, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:35,273]\u001b[0m Trial 71 finished with value: 40439.40798132122 and parameters: {'n_estimators': 100, 'learning_rate': 0.28776693376031565, 'max_depth': 12}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,334]\u001b[0m Trial 72 finished with value: 51290.664950282844 and parameters: {'n_estimators': 300, 'learning_rate': 0.17986076090825026, 'max_depth': 11}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,392]\u001b[0m Trial 73 finished with value: 42015.60790350888 and parameters: {'n_estimators': 400, 'learning_rate': 0.23313981952697407, 'max_depth': 11}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,440]\u001b[0m Trial 74 finished with value: 66151.14380308305 and parameters: {'n_estimators': 1000, 'learning_rate': 0.12967547098381196, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:35,506]\u001b[0m Trial 75 finished with value: 41197.482114896244 and parameters: {'n_estimators': 1200, 'learning_rate': 0.24949919668650344, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,557]\u001b[0m Trial 76 finished with value: 51868.146515381 and parameters: {'n_estimators': 500, 'learning_rate': 0.17449136214118455, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,618]\u001b[0m Trial 77 finished with value: 41200.60769848813 and parameters: {'n_estimators': 800, 'learning_rate': 0.29170163207126615, 'max_depth': 12}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,669]\u001b[0m Trial 78 finished with value: 67029.459760648 and parameters: {'n_estimators': 900, 'learning_rate': 0.12770471548477152, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:35,726]\u001b[0m Trial 79 finished with value: 50337.77620083129 and parameters: {'n_estimators': 700, 'learning_rate': 0.18893376757649322, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,805]\u001b[0m Trial 80 finished with value: 42066.23758512917 and parameters: {'n_estimators': 1300, 'learning_rate': 0.24409361876912325, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,850]\u001b[0m Trial 81 finished with value: 55948.69650244657 and parameters: {'n_estimators': 600, 'learning_rate': 0.1616163711303803, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,891]\u001b[0m Trial 82 finished with value: 41268.02810813567 and parameters: {'n_estimators': 600, 'learning_rate': 0.24297717806039892, 'max_depth': 5}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:35,948]\u001b[0m Trial 83 finished with value: 39449.53913077416 and parameters: {'n_estimators': 200, 'learning_rate': 0.290323157653214, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:35,994]\u001b[0m Trial 84 finished with value: 41093.1468671608 and parameters: {'n_estimators': 100, 'learning_rate': 0.2923085754662649, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,069]\u001b[0m Trial 85 finished with value: 45544.99145637636 and parameters: {'n_estimators': 200, 'learning_rate': 0.20972978985430676, 'max_depth': 11}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:35] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:36,146]\u001b[0m Trial 86 finished with value: 70947.62280909791 and parameters: {'n_estimators': 200, 'learning_rate': 0.11831126459699517, 'max_depth': 13}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,191]\u001b[0m Trial 87 finished with value: 46791.47407023258 and parameters: {'n_estimators': 300, 'learning_rate': 0.20087377907771276, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,233]\u001b[0m Trial 88 finished with value: 89407.51350614139 and parameters: {'n_estimators': 400, 'learning_rate': 0.08915334208438767, 'max_depth': 4}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,286]\u001b[0m Trial 89 finished with value: 41999.23363126114 and parameters: {'n_estimators': 100, 'learning_rate': 0.2990791489881827, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,336]\u001b[0m Trial 90 finished with value: 58484.55236994875 and parameters: {'n_estimators': 1000, 'learning_rate': 0.15240164166111247, 'max_depth': 10}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:36,389]\u001b[0m Trial 91 finished with value: 42045.07679976165 and parameters: {'n_estimators': 600, 'learning_rate': 0.2354165038737716, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,438]\u001b[0m Trial 92 finished with value: 50335.27098300319 and parameters: {'n_estimators': 500, 'learning_rate': 0.18158656033741288, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,483]\u001b[0m Trial 93 finished with value: 39489.34948682898 and parameters: {'n_estimators': 700, 'learning_rate': 0.25631141652595696, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,536]\u001b[0m Trial 94 finished with value: 40905.51536555017 and parameters: {'n_estimators': 700, 'learning_rate': 0.25253359453403723, 'max_depth': 5}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,585]\u001b[0m Trial 95 finished with value: 47380.07707641544 and parameters: {'n_estimators': 200, 'learning_rate': 0.19867923394525722, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:36,643]\u001b[0m Trial 96 finished with value: 54149.26469321809 and parameters: {'n_estimators': 800, 'learning_rate': 0.16270131863304085, 'max_depth': 8}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,697]\u001b[0m Trial 97 finished with value: 63309.79632797573 and parameters: {'n_estimators': 900, 'learning_rate': 0.1349367071542065, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,761]\u001b[0m Trial 98 finished with value: 40188.801397585616 and parameters: {'n_estimators': 1100, 'learning_rate': 0.24741233763952436, 'max_depth': 9}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,823]\u001b[0m Trial 99 finished with value: 46699.911221207854 and parameters: {'n_estimators': 300, 'learning_rate': 0.20149202714432052, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:36,890]\u001b[0m Trial 100 finished with value: 41089.94850353121 and parameters: {'n_estimators': 400, 'learning_rate': 0.25041970994073787, 'max_depth': 14}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,941]\u001b[0m Trial 101 finished with value: 40948.16804867185 and parameters: {'n_estimators': 700, 'learning_rate': 0.2991379487121858, 'max_depth': 6}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:36,988]\u001b[0m Trial 102 finished with value: 46807.170489973876 and parameters: {'n_estimators': 600, 'learning_rate': 0.20664949458701748, 'max_depth': 7}. Best is trial 12 with value: 39080.890279334846.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,033]\u001b[0m Trial 103 finished with value: 38820.55017191217 and parameters: {'n_estimators': 700, 'learning_rate': 0.25902108628804354, 'max_depth': 6}. Best is trial 103 with value: 38820.55017191217.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,088]\u001b[0m Trial 104 finished with value: 53632.07956906222 and parameters: {'n_estimators': 800, 'learning_rate': 0.1659963086791974, 'max_depth': 6}. Best is trial 103 with value: 38820.55017191217.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:36] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:37,139]\u001b[0m Trial 105 finished with value: 39944.70388645487 and parameters: {'n_estimators': 900, 'learning_rate': 0.2504691317857929, 'max_depth': 5}. Best is trial 103 with value: 38820.55017191217.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,192]\u001b[0m Trial 106 finished with value: 78799.53475960283 and parameters: {'n_estimators': 500, 'learning_rate': 0.10375919889469944, 'max_depth': 10}. Best is trial 103 with value: 38820.55017191217.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,238]\u001b[0m Trial 107 finished with value: 60879.33062385189 and parameters: {'n_estimators': 700, 'learning_rate': 0.1410765120333037, 'max_depth': 7}. Best is trial 103 with value: 38820.55017191217.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,290]\u001b[0m Trial 108 finished with value: 38496.604981305536 and parameters: {'n_estimators': 800, 'learning_rate': 0.26427467675051897, 'max_depth': 5}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,331]\u001b[0m Trial 109 finished with value: 39983.09612386753 and parameters: {'n_estimators': 700, 'learning_rate': 0.25881147201517196, 'max_depth': 4}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:37,380]\u001b[0m Trial 110 finished with value: 49054.85198752612 and parameters: {'n_estimators': 800, 'learning_rate': 0.18729571322791028, 'max_depth': 5}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,426]\u001b[0m Trial 111 finished with value: 44615.09407498007 and parameters: {'n_estimators': 1000, 'learning_rate': 0.21197843737832905, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,474]\u001b[0m Trial 112 finished with value: 40702.79238445114 and parameters: {'n_estimators': 900, 'learning_rate': 0.2992295299752574, 'max_depth': 5}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,522]\u001b[0m Trial 113 finished with value: 38961.44587197785 and parameters: {'n_estimators': 700, 'learning_rate': 0.2583156408207672, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,573]\u001b[0m Trial 114 finished with value: 40152.557082183324 and parameters: {'n_estimators': 700, 'learning_rate': 0.255308873515212, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:37,633]\u001b[0m Trial 115 finished with value: 52090.72885848795 and parameters: {'n_estimators': 600, 'learning_rate': 0.16861318037336856, 'max_depth': 5}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,679]\u001b[0m Trial 116 finished with value: 44490.242064242215 and parameters: {'n_estimators': 800, 'learning_rate': 0.21965195572434007, 'max_depth': 3}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,729]\u001b[0m Trial 117 finished with value: 66050.31280348562 and parameters: {'n_estimators': 500, 'learning_rate': 0.12670494112802247, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,787]\u001b[0m Trial 118 finished with value: 59998.66798682677 and parameters: {'n_estimators': 200, 'learning_rate': 0.14663514289134094, 'max_depth': 7}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:37,860]\u001b[0m Trial 119 finished with value: 40169.32538594041 and parameters: {'n_estimators': 2000, 'learning_rate': 0.2617110175615475, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,928]\u001b[0m Trial 120 finished with value: 50357.47042007403 and parameters: {'n_estimators': 800, 'learning_rate': 0.17872308853983848, 'max_depth': 8}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:37,987]\u001b[0m Trial 121 finished with value: 45482.25235330388 and parameters: {'n_estimators': 900, 'learning_rate': 0.21891692533434756, 'max_depth': 10}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:37] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:38,036]\u001b[0m Trial 122 finished with value: 40710.405508980875 and parameters: {'n_estimators': 700, 'learning_rate': 0.29782604210410213, 'max_depth': 5}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,096]\u001b[0m Trial 123 finished with value: 39361.341451586086 and parameters: {'n_estimators': 100, 'learning_rate': 0.2636995746810408, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,149]\u001b[0m Trial 124 finished with value: 43330.60465555111 and parameters: {'n_estimators': 100, 'learning_rate': 0.2250666980944701, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,201]\u001b[0m Trial 125 finished with value: 39107.54256887916 and parameters: {'n_estimators': 200, 'learning_rate': 0.2608397410411993, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,257]\u001b[0m Trial 126 finished with value: 48913.30108776771 and parameters: {'n_estimators': 200, 'learning_rate': 0.1912767233163055, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:38,311]\u001b[0m Trial 127 finished with value: 73097.06808127328 and parameters: {'n_estimators': 300, 'learning_rate': 0.11320119073065979, 'max_depth': 8}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,359]\u001b[0m Trial 128 finished with value: 39446.16202041043 and parameters: {'n_estimators': 100, 'learning_rate': 0.2539380999090154, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,412]\u001b[0m Trial 129 finished with value: 58101.81241862241 and parameters: {'n_estimators': 100, 'learning_rate': 0.15219367021745975, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,460]\u001b[0m Trial 130 finished with value: 41318.480487682486 and parameters: {'n_estimators': 100, 'learning_rate': 0.24832002154669672, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:38,512]\u001b[0m Trial 131 finished with value: 42498.72807074778 and parameters: {'n_estimators': 200, 'learning_rate': 0.29716898235201616, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,572]\u001b[0m Trial 132 finished with value: 44876.034176107925 and parameters: {'n_estimators': 100, 'learning_rate': 0.21506567054574732, 'max_depth': 7}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,619]\u001b[0m Trial 133 finished with value: 40352.70381374221 and parameters: {'n_estimators': 200, 'learning_rate': 0.2601730672863565, 'max_depth': 5}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,668]\u001b[0m Trial 134 finished with value: 49620.43438794623 and parameters: {'n_estimators': 300, 'learning_rate': 0.18368827074968483, 'max_depth': 7}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:38,729]\u001b[0m Trial 135 finished with value: 42710.57785988656 and parameters: {'n_estimators': 100, 'learning_rate': 0.2993731397569816, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,782]\u001b[0m Trial 136 finished with value: 43791.60600941583 and parameters: {'n_estimators': 1700, 'learning_rate': 0.22565967331472303, 'max_depth': 10}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,838]\u001b[0m Trial 137 finished with value: 185050.76231311724 and parameters: {'n_estimators': 200, 'learning_rate': 0.007983483202576044, 'max_depth': 8}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:38,894]\u001b[0m Trial 138 finished with value: 53176.69365645314 and parameters: {'n_estimators': 300, 'learning_rate': 0.17051705126151057, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:38,962]\u001b[0m Trial 139 finished with value: 38849.409761372764 and parameters: {'n_estimators': 100, 'learning_rate': 0.2546634597534966, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,007]\u001b[0m Trial 140 finished with value: 47574.976148383445 and parameters: {'n_estimators': 100, 'learning_rate': 0.19982648363245997, 'max_depth': 4}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,063]\u001b[0m Trial 141 finished with value: 38963.934840236194 and parameters: {'n_estimators': 200, 'learning_rate': 0.2586018260074203, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,126]\u001b[0m Trial 142 finished with value: 39951.55902432996 and parameters: {'n_estimators': 200, 'learning_rate': 0.2512014357843843, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:38] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:39,186]\u001b[0m Trial 143 finished with value: 39201.88497040135 and parameters: {'n_estimators': 100, 'learning_rate': 0.26051336144258097, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,243]\u001b[0m Trial 144 finished with value: 45260.187814814766 and parameters: {'n_estimators': 100, 'learning_rate': 0.21047265904744183, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,300]\u001b[0m Trial 145 finished with value: 160248.13264697525 and parameters: {'n_estimators': 100, 'learning_rate': 0.023302408230722584, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,351]\u001b[0m Trial 146 finished with value: 124790.55101633571 and parameters: {'n_estimators': 200, 'learning_rate': 0.05033663140379654, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:39,412]\u001b[0m Trial 147 finished with value: 39041.248817038206 and parameters: {'n_estimators': 100, 'learning_rate': 0.2574329068799658, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,470]\u001b[0m Trial 148 finished with value: 56807.07039397066 and parameters: {'n_estimators': 100, 'learning_rate': 0.15962563635943375, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,528]\u001b[0m Trial 149 finished with value: 39798.59766112854 and parameters: {'n_estimators': 100, 'learning_rate': 0.24988915856517532, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:39,599]\u001b[0m Trial 150 finished with value: 63232.51063953214 and parameters: {'n_estimators': 100, 'learning_rate': 0.13514635436305458, 'max_depth': 10}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,658]\u001b[0m Trial 151 finished with value: 40244.41841480396 and parameters: {'n_estimators': 200, 'learning_rate': 0.26141305966762857, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,712]\u001b[0m Trial 152 finished with value: 47794.81749735232 and parameters: {'n_estimators': 100, 'learning_rate': 0.19631573952379003, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,775]\u001b[0m Trial 153 finished with value: 43491.334927996046 and parameters: {'n_estimators': 200, 'learning_rate': 0.2289138374183629, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,826]\u001b[0m Trial 154 finished with value: 41995.50779461595 and parameters: {'n_estimators': 100, 'learning_rate': 0.299229568221207, 'max_depth': 8}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:39,872]\u001b[0m Trial 155 finished with value: 50662.439025441614 and parameters: {'n_estimators': 200, 'learning_rate': 0.18066330746294806, 'max_depth': 5}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,946]\u001b[0m Trial 156 finished with value: 41321.95467400611 and parameters: {'n_estimators': 600, 'learning_rate': 0.2482674957416437, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:39,995]\u001b[0m Trial 157 finished with value: 146140.70007026166 and parameters: {'n_estimators': 800, 'learning_rate': 0.03321981375765517, 'max_depth': 10}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,047]\u001b[0m Trial 158 finished with value: 45697.98733726409 and parameters: {'n_estimators': 300, 'learning_rate': 0.214165414973786, 'max_depth': 8}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:39] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:40,099]\u001b[0m Trial 159 finished with value: 40411.45057269907 and parameters: {'n_estimators': 700, 'learning_rate': 0.25873110675080185, 'max_depth': 5}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,153]\u001b[0m Trial 160 finished with value: 109839.91320373303 and parameters: {'n_estimators': 200, 'learning_rate': 0.0644121439784623, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,212]\u001b[0m Trial 161 finished with value: 39936.91495291141 and parameters: {'n_estimators': 100, 'learning_rate': 0.2665024287570348, 'max_depth': 10}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,277]\u001b[0m Trial 162 finished with value: 44796.19768964945 and parameters: {'n_estimators': 200, 'learning_rate': 0.22193362712388148, 'max_depth': 10}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:40,339]\u001b[0m Trial 163 finished with value: 50789.752963937244 and parameters: {'n_estimators': 600, 'learning_rate': 0.1843173154363475, 'max_depth': 10}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,394]\u001b[0m Trial 164 finished with value: 40009.810582894876 and parameters: {'n_estimators': 200, 'learning_rate': 0.2904432402855049, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,466]\u001b[0m Trial 165 finished with value: 43220.21487826459 and parameters: {'n_estimators': 100, 'learning_rate': 0.2992402217744419, 'max_depth': 11}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:40,530]\u001b[0m Trial 166 finished with value: 43706.985521430164 and parameters: {'n_estimators': 300, 'learning_rate': 0.225196961558913, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,593]\u001b[0m Trial 167 finished with value: 57170.78078456128 and parameters: {'n_estimators': 700, 'learning_rate': 0.15540764612633345, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,664]\u001b[0m Trial 168 finished with value: 47821.2646593011 and parameters: {'n_estimators': 200, 'learning_rate': 0.19614895334524932, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,722]\u001b[0m Trial 169 finished with value: 40832.41301691631 and parameters: {'n_estimators': 100, 'learning_rate': 0.2498802345335422, 'max_depth': 7}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:40,791]\u001b[0m Trial 170 finished with value: 54881.15627423422 and parameters: {'n_estimators': 800, 'learning_rate': 0.16781697622454084, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,848]\u001b[0m Trial 171 finished with value: 38654.196575147944 and parameters: {'n_estimators': 100, 'learning_rate': 0.2548420842192987, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,902]\u001b[0m Trial 172 finished with value: 39917.70946648478 and parameters: {'n_estimators': 100, 'learning_rate': 0.25171793613926813, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:40,972]\u001b[0m Trial 173 finished with value: 44955.91551663381 and parameters: {'n_estimators': 200, 'learning_rate': 0.21188820635933964, 'max_depth': 10}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:40] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:41,042]\u001b[0m Trial 174 finished with value: 39107.79337454158 and parameters: {'n_estimators': 100, 'learning_rate': 0.26452651102449926, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,096]\u001b[0m Trial 175 finished with value: 41999.362530512924 and parameters: {'n_estimators': 100, 'learning_rate': 0.29907469677636017, 'max_depth': 8}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,150]\u001b[0m Trial 176 finished with value: 43250.84945675358 and parameters: {'n_estimators': 100, 'learning_rate': 0.22718027567611263, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,214]\u001b[0m Trial 177 finished with value: 47427.28057458764 and parameters: {'n_estimators': 100, 'learning_rate': 0.19166878441279575, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,268]\u001b[0m Trial 178 finished with value: 195035.42472705356 and parameters: {'n_estimators': 600, 'learning_rate': 0.0024020005372535815, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:41,327]\u001b[0m Trial 179 finished with value: 197669.55893189146 and parameters: {'n_estimators': 100, 'learning_rate': 0.0009783752254714651, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,389]\u001b[0m Trial 180 finished with value: 130727.14362129995 and parameters: {'n_estimators': 700, 'learning_rate': 0.04516597276366771, 'max_depth': 6}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,449]\u001b[0m Trial 181 finished with value: 39622.21698206594 and parameters: {'n_estimators': 200, 'learning_rate': 0.2583464365952619, 'max_depth': 10}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:41,524]\u001b[0m Trial 182 finished with value: 39110.03575957275 and parameters: {'n_estimators': 200, 'learning_rate': 0.26079916428457955, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,575]\u001b[0m Trial 183 finished with value: 175708.82226014294 and parameters: {'n_estimators': 100, 'learning_rate': 0.013518206665748668, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,635]\u001b[0m Trial 184 finished with value: 39055.51972786577 and parameters: {'n_estimators': 200, 'learning_rate': 0.25722825504286667, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,697]\u001b[0m Trial 185 finished with value: 41770.15631828906 and parameters: {'n_estimators': 200, 'learning_rate': 0.2349308945979238, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,751]\u001b[0m Trial 186 finished with value: 96168.92148251449 and parameters: {'n_estimators': 300, 'learning_rate': 0.0794520959884378, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:41,810]\u001b[0m Trial 187 finished with value: 46982.789327190505 and parameters: {'n_estimators': 200, 'learning_rate': 0.2051186439598286, 'max_depth': 8}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,871]\u001b[0m Trial 188 finished with value: 38600.41415655861 and parameters: {'n_estimators': 100, 'learning_rate': 0.262725738665155, 'max_depth': 9}. Best is trial 108 with value: 38496.604981305536.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:41,929]\u001b[0m Trial 189 finished with value: 37633.06408026974 and parameters: {'n_estimators': 100, 'learning_rate': 0.2631629457241946, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:41] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:42,008]\u001b[0m Trial 190 finished with value: 51680.92622670183 and parameters: {'n_estimators': 100, 'learning_rate': 0.17056613634639728, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:42,088]\u001b[0m Trial 191 finished with value: 42428.02346623968 and parameters: {'n_estimators': 100, 'learning_rate': 0.29833162057257323, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:42,158]\u001b[0m Trial 192 finished with value: 41082.813026031145 and parameters: {'n_estimators': 100, 'learning_rate': 0.2561869484643119, 'max_depth': 3}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:42] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:42] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:42] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:42,283]\u001b[0m Trial 193 finished with value: 44626.14554335176 and parameters: {'n_estimators': 200, 'learning_rate': 0.22317092069648276, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:42] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:42,929]\u001b[0m Trial 194 finished with value: 39104.4757535831 and parameters: {'n_estimators': 100, 'learning_rate': 0.2610167263894733, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:43] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:43,572]\u001b[0m Trial 195 finished with value: 169247.0194223342 and parameters: {'n_estimators': 100, 'learning_rate': 0.017506151373163463, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:43] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:45,294]\u001b[0m Trial 196 finished with value: 45918.080856215245 and parameters: {'n_estimators': 100, 'learning_rate': 0.199783016379459, 'max_depth': 8}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:45,534]\u001b[0m Trial 197 finished with value: 143110.95495500235 and parameters: {'n_estimators': 100, 'learning_rate': 0.03532553590632379, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:45] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:45,686]\u001b[0m Trial 198 finished with value: 38911.570003784414 and parameters: {'n_estimators': 200, 'learning_rate': 0.25797346767030205, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:45,744]\u001b[0m Trial 199 finished with value: 41975.028954257694 and parameters: {'n_estimators': 200, 'learning_rate': 0.23274684993741065, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:45,797]\u001b[0m Trial 200 finished with value: 61677.053304489615 and parameters: {'n_estimators': 300, 'learning_rate': 0.1432845139440841, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:45] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:45] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:45] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:45,859]\u001b[0m Trial 201 finished with value: 39500.70715118098 and parameters: {'n_estimators': 100, 'learning_rate': 0.25869017673738803, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:45,924]\u001b[0m Trial 202 finished with value: 38797.15352394253 and parameters: {'n_estimators': 200, 'learning_rate': 0.2659984898467621, 'max_depth': 8}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:45,981]\u001b[0m Trial 203 finished with value: 39469.43973586086 and parameters: {'n_estimators': 200, 'learning_rate': 0.2674340166164901, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:45] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:45] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:45] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:46,045]\u001b[0m Trial 204 finished with value: 41994.26429496072 and parameters: {'n_estimators': 200, 'learning_rate': 0.2992741124705862, 'max_depth': 8}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,107]\u001b[0m Trial 205 finished with value: 43943.77243451573 and parameters: {'n_estimators': 300, 'learning_rate': 0.21319091547299837, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,167]\u001b[0m Trial 206 finished with value: 157280.55186731883 and parameters: {'n_estimators': 200, 'learning_rate': 0.025330770077878286, 'max_depth': 8}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,236]\u001b[0m Trial 207 finished with value: 51141.84979391918 and parameters: {'n_estimators': 200, 'learning_rate': 0.1859509231766958, 'max_depth': 10}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:46,302]\u001b[0m Trial 208 finished with value: 43254.61516324554 and parameters: {'n_estimators': 100, 'learning_rate': 0.2271461150759613, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,364]\u001b[0m Trial 209 finished with value: 40068.76186541486 and parameters: {'n_estimators': 100, 'learning_rate': 0.26152928404395925, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,434]\u001b[0m Trial 210 finished with value: 42694.54578328854 and parameters: {'n_estimators': 200, 'learning_rate': 0.2993924911855828, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,495]\u001b[0m Trial 211 finished with value: 40651.1041312931 and parameters: {'n_estimators': 100, 'learning_rate': 0.24050264446002367, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:46,557]\u001b[0m Trial 212 finished with value: 114449.5067957649 and parameters: {'n_estimators': 100, 'learning_rate': 0.05992908224349568, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,613]\u001b[0m Trial 213 finished with value: 191368.41891076136 and parameters: {'n_estimators': 100, 'learning_rate': 0.004416477626596088, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,663]\u001b[0m Trial 214 finished with value: 199135.94223034175 and parameters: {'n_estimators': 200, 'learning_rate': 0.0001945255839989158, 'max_depth': 10}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,723]\u001b[0m Trial 215 finished with value: 39220.97948821273 and parameters: {'n_estimators': 1900, 'learning_rate': 0.25717857428671, 'max_depth': 8}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,788]\u001b[0m Trial 216 finished with value: 47068.2402641389 and parameters: {'n_estimators': 1500, 'learning_rate': 0.20351461826061426, 'max_depth': 8}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:46,857]\u001b[0m Trial 217 finished with value: 43467.727051890564 and parameters: {'n_estimators': 1300, 'learning_rate': 0.22995060219647812, 'max_depth': 8}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,919]\u001b[0m Trial 218 finished with value: 39072.83751697544 and parameters: {'n_estimators': 1200, 'learning_rate': 0.2621102151800624, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:46,978]\u001b[0m Trial 219 finished with value: 50799.77789299215 and parameters: {'n_estimators': 1200, 'learning_rate': 0.18718796662972964, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:46] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:47,034]\u001b[0m Trial 220 finished with value: 184650.8378732131 and parameters: {'n_estimators': 1000, 'learning_rate': 0.00822315284436165, 'max_depth': 8}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,101]\u001b[0m Trial 221 finished with value: 39078.853029116515 and parameters: {'n_estimators': 1800, 'learning_rate': 0.2620077076797459, 'max_depth': 9}. Best is trial 189 with value: 37633.06408026974.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,189]\u001b[0m Trial 222 finished with value: 37591.26290793679 and parameters: {'n_estimators': 1900, 'learning_rate': 0.26335499333391776, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,249]\u001b[0m Trial 223 finished with value: 39097.427514998046 and parameters: {'n_estimators': 1800, 'learning_rate': 0.264703153599739, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:47,315]\u001b[0m Trial 224 finished with value: 40518.84431244421 and parameters: {'n_estimators': 1800, 'learning_rate': 0.25212193453372905, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,374]\u001b[0m Trial 225 finished with value: 42713.79830996325 and parameters: {'n_estimators': 1700, 'learning_rate': 0.29969486786918964, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,439]\u001b[0m Trial 226 finished with value: 45300.360441163386 and parameters: {'n_estimators': 2000, 'learning_rate': 0.21618968328127006, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:47,514]\u001b[0m Trial 227 finished with value: 39148.89431728849 and parameters: {'n_estimators': 1900, 'learning_rate': 0.25607370423073067, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,574]\u001b[0m Trial 228 finished with value: 51991.940908169796 and parameters: {'n_estimators': 1800, 'learning_rate': 0.16982996643183654, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,638]\u001b[0m Trial 229 finished with value: 44869.53590628439 and parameters: {'n_estimators': 1800, 'learning_rate': 0.2178817136156727, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,709]\u001b[0m Trial 230 finished with value: 39303.13479622807 and parameters: {'n_estimators': 1900, 'learning_rate': 0.2596752109814831, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:47,774]\u001b[0m Trial 231 finished with value: 150919.59791840232 and parameters: {'n_estimators': 1900, 'learning_rate': 0.02977538474439465, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,849]\u001b[0m Trial 232 finished with value: 38577.01437420992 and parameters: {'n_estimators': 1900, 'learning_rate': 0.2537041754776736, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,911]\u001b[0m Trial 233 finished with value: 42514.207802890596 and parameters: {'n_estimators': 2000, 'learning_rate': 0.2275605775533244, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:47,970]\u001b[0m Trial 234 finished with value: 42172.1514974547 and parameters: {'n_estimators': 1800, 'learning_rate': 0.2987035257330816, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:47] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:48,037]\u001b[0m Trial 235 finished with value: 48841.69638952905 and parameters: {'n_estimators': 2000, 'learning_rate': 0.1924555837912114, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,096]\u001b[0m Trial 236 finished with value: 128968.11676119138 and parameters: {'n_estimators': 1900, 'learning_rate': 0.04677040648206524, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,154]\u001b[0m Trial 237 finished with value: 39133.28249993186 and parameters: {'n_estimators': 1700, 'learning_rate': 0.2566509077327803, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,234]\u001b[0m Trial 238 finished with value: 42110.54000730715 and parameters: {'n_estimators': 1900, 'learning_rate': 0.23329936679681368, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:48,296]\u001b[0m Trial 239 finished with value: 138325.004895974 and parameters: {'n_estimators': 1800, 'learning_rate': 0.039031234734204445, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,360]\u001b[0m Trial 240 finished with value: 50706.009535861835 and parameters: {'n_estimators': 1700, 'learning_rate': 0.18948759030871046, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,412]\u001b[0m Trial 241 finished with value: 180337.554815579 and parameters: {'n_estimators': 1900, 'learning_rate': 0.01073818886134328, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,470]\u001b[0m Trial 242 finished with value: 38975.453173210335 and parameters: {'n_estimators': 1900, 'learning_rate': 0.2584268493404686, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:48,544]\u001b[0m Trial 243 finished with value: 39402.439571571056 and parameters: {'n_estimators': 1900, 'learning_rate': 0.2617462349584277, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,607]\u001b[0m Trial 244 finished with value: 41752.81391281406 and parameters: {'n_estimators': 1900, 'learning_rate': 0.23510976934254388, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,674]\u001b[0m Trial 245 finished with value: 169442.20444933575 and parameters: {'n_estimators': 1600, 'learning_rate': 0.017383172504275006, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,728]\u001b[0m Trial 246 finished with value: 121601.93977343761 and parameters: {'n_estimators': 1700, 'learning_rate': 0.053014493406072706, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:48,798]\u001b[0m Trial 247 finished with value: 42593.546423123254 and parameters: {'n_estimators': 1800, 'learning_rate': 0.2976480743661574, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,862]\u001b[0m Trial 248 finished with value: 45751.74464579747 and parameters: {'n_estimators': 1900, 'learning_rate': 0.21494951871343734, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,924]\u001b[0m Trial 249 finished with value: 39041.03213253651 and parameters: {'n_estimators': 2000, 'learning_rate': 0.2574361504234921, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:48,987]\u001b[0m Trial 250 finished with value: 81832.49025663805 and parameters: {'n_estimators': 2000, 'learning_rate': 0.10016853107752545, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:48] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:49,053]\u001b[0m Trial 251 finished with value: 40186.24391555508 and parameters: {'n_estimators': 2000, 'learning_rate': 0.2649887483299557, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,113]\u001b[0m Trial 252 finished with value: 44601.08124021763 and parameters: {'n_estimators': 1100, 'learning_rate': 0.2126443026674747, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,160]\u001b[0m Trial 253 finished with value: 40119.10843672639 and parameters: {'n_estimators': 1800, 'learning_rate': 0.2998527610876501, 'max_depth': 4}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,244]\u001b[0m Trial 254 finished with value: 52136.89849666958 and parameters: {'n_estimators': 1400, 'learning_rate': 0.173351868005178, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:49,311]\u001b[0m Trial 255 finished with value: 90655.01090308982 and parameters: {'n_estimators': 1800, 'learning_rate': 0.08665210816564416, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,370]\u001b[0m Trial 256 finished with value: 41830.730492859206 and parameters: {'n_estimators': 2000, 'learning_rate': 0.2347283301148063, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,429]\u001b[0m Trial 257 finished with value: 46053.144528576304 and parameters: {'n_estimators': 1700, 'learning_rate': 0.202040945781668, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,487]\u001b[0m Trial 258 finished with value: 39121.58841001792 and parameters: {'n_estimators': 1600, 'learning_rate': 0.26064788711148923, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:49,554]\u001b[0m Trial 259 finished with value: 38300.063763739 and parameters: {'n_estimators': 1600, 'learning_rate': 0.2614672478070513, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,623]\u001b[0m Trial 260 finished with value: 56484.70646694724 and parameters: {'n_estimators': 1500, 'learning_rate': 0.160439535117247, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,682]\u001b[0m Trial 261 finished with value: 43737.790116297074 and parameters: {'n_estimators': 1600, 'learning_rate': 0.22074013096351477, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,740]\u001b[0m Trial 262 finished with value: 41467.82153229156 and parameters: {'n_estimators': 2000, 'learning_rate': 0.2997760957021886, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:49,812]\u001b[0m Trial 263 finished with value: 48157.67126652329 and parameters: {'n_estimators': 200, 'learning_rate': 0.19624065590176537, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,871]\u001b[0m Trial 264 finished with value: 40732.571008807725 and parameters: {'n_estimators': 1900, 'learning_rate': 0.24104287061559077, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,940]\u001b[0m Trial 265 finished with value: 38696.159447375656 and parameters: {'n_estimators': 1900, 'learning_rate': 0.2616683106497657, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:49,997]\u001b[0m Trial 266 finished with value: 72511.11377205743 and parameters: {'n_estimators': 1900, 'learning_rate': 0.11417224917246328, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:49] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:50,069]\u001b[0m Trial 267 finished with value: 42787.13201285495 and parameters: {'n_estimators': 1900, 'learning_rate': 0.29878301453749617, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,130]\u001b[0m Trial 268 finished with value: 192049.73114987012 and parameters: {'n_estimators': 2000, 'learning_rate': 0.004039392028542517, 'max_depth': 15}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,191]\u001b[0m Trial 269 finished with value: 45539.62870422405 and parameters: {'n_estimators': 1900, 'learning_rate': 0.21805352926620936, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,270]\u001b[0m Trial 270 finished with value: 188573.33858221804 and parameters: {'n_estimators': 1400, 'learning_rate': 0.0059832551006671255, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:50,332]\u001b[0m Trial 271 finished with value: 197995.99130059665 and parameters: {'n_estimators': 1800, 'learning_rate': 0.0008032883609653727, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,387]\u001b[0m Trial 272 finished with value: 96820.09724316913 and parameters: {'n_estimators': 1800, 'learning_rate': 0.07867553415366284, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,456]\u001b[0m Trial 273 finished with value: 50472.4324280162 and parameters: {'n_estimators': 1900, 'learning_rate': 0.18101933317454366, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,516]\u001b[0m Trial 274 finished with value: 62085.84807180083 and parameters: {'n_estimators': 400, 'learning_rate': 0.13952919655488968, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:50,596]\u001b[0m Trial 275 finished with value: 38976.602049907255 and parameters: {'n_estimators': 1300, 'learning_rate': 0.25838985732922465, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,652]\u001b[0m Trial 276 finished with value: 156697.37084439053 and parameters: {'n_estimators': 1300, 'learning_rate': 0.0257280681203022, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,707]\u001b[0m Trial 277 finished with value: 42619.77701667686 and parameters: {'n_estimators': 1200, 'learning_rate': 0.23677076810427414, 'max_depth': 5}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,769]\u001b[0m Trial 278 finished with value: 46188.72132017419 and parameters: {'n_estimators': 1200, 'learning_rate': 0.2012010802839517, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:50,867]\u001b[0m Trial 279 finished with value: 38931.61355450408 and parameters: {'n_estimators': 1100, 'learning_rate': 0.26235681617303674, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,932]\u001b[0m Trial 280 finished with value: 42956.59203662629 and parameters: {'n_estimators': 1100, 'learning_rate': 0.29902174231113865, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:50,993]\u001b[0m Trial 281 finished with value: 52129.2497561834 and parameters: {'n_estimators': 1100, 'learning_rate': 0.17319879942737706, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,050]\u001b[0m Trial 282 finished with value: 118013.20183188206 and parameters: {'n_estimators': 2000, 'learning_rate': 0.05647201824249767, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:50] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:51,129]\u001b[0m Trial 283 finished with value: 41994.47368522898 and parameters: {'n_estimators': 1100, 'learning_rate': 0.2329249756357463, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,188]\u001b[0m Trial 284 finished with value: 46914.34494509866 and parameters: {'n_estimators': 1300, 'learning_rate': 0.205859967229031, 'max_depth': 7}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,262]\u001b[0m Trial 285 finished with value: 40141.912533093775 and parameters: {'n_estimators': 1300, 'learning_rate': 0.25691455974871646, 'max_depth': 11}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,330]\u001b[0m Trial 286 finished with value: 194788.6927236481 and parameters: {'n_estimators': 1100, 'learning_rate': 0.00253640014964996, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:51,399]\u001b[0m Trial 287 finished with value: 40004.063933642545 and parameters: {'n_estimators': 1200, 'learning_rate': 0.2614636866017125, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,448]\u001b[0m Trial 288 finished with value: 55682.73196881878 and parameters: {'n_estimators': 1000, 'learning_rate': 0.1581190878263581, 'max_depth': 4}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,508]\u001b[0m Trial 289 finished with value: 43293.637011699124 and parameters: {'n_estimators': 1900, 'learning_rate': 0.22611279294033382, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,570]\u001b[0m Trial 290 finished with value: 42166.36928543777 and parameters: {'n_estimators': 1200, 'learning_rate': 0.2988954197751415, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:51,647]\u001b[0m Trial 291 finished with value: 46657.92616312257 and parameters: {'n_estimators': 900, 'learning_rate': 0.20207233008727088, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,708]\u001b[0m Trial 292 finished with value: 39188.86896418513 and parameters: {'n_estimators': 1000, 'learning_rate': 0.25571187559897146, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,765]\u001b[0m Trial 293 finished with value: 198821.12987381924 and parameters: {'n_estimators': 1800, 'learning_rate': 0.0003622933629987968, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,827]\u001b[0m Trial 294 finished with value: 177878.17810587186 and parameters: {'n_estimators': 800, 'learning_rate': 0.01220916355142994, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:51,897]\u001b[0m Trial 295 finished with value: 51118.67415819986 and parameters: {'n_estimators': 2000, 'learning_rate': 0.18538053062078486, 'max_depth': 11}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:51,951]\u001b[0m Trial 296 finished with value: 135259.4827307967 and parameters: {'n_estimators': 1900, 'learning_rate': 0.0415167283421649, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,021]\u001b[0m Trial 297 finished with value: 43436.06875746437 and parameters: {'n_estimators': 1200, 'learning_rate': 0.229504706723464, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,079]\u001b[0m Trial 298 finished with value: 67093.66987036975 and parameters: {'n_estimators': 600, 'learning_rate': 0.12535311890790365, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:51] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:52,156]\u001b[0m Trial 299 finished with value: 42170.41428122388 and parameters: {'n_estimators': 100, 'learning_rate': 0.29905049775626474, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,217]\u001b[0m Trial 300 finished with value: 39123.217350658735 and parameters: {'n_estimators': 1900, 'learning_rate': 0.25679986713949854, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,282]\u001b[0m Trial 301 finished with value: 44880.210741048904 and parameters: {'n_estimators': 1400, 'learning_rate': 0.20708341933061336, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,359]\u001b[0m Trial 302 finished with value: 59796.19567357602 and parameters: {'n_estimators': 1500, 'learning_rate': 0.1476572301203122, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:52,428]\u001b[0m Trial 303 finished with value: 42104.36490221916 and parameters: {'n_estimators': 700, 'learning_rate': 0.2326933112784734, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,495]\u001b[0m Trial 304 finished with value: 51120.888983455276 and parameters: {'n_estimators': 1800, 'learning_rate': 0.1755269280038163, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,550]\u001b[0m Trial 305 finished with value: 139397.50973992125 and parameters: {'n_estimators': 2000, 'learning_rate': 0.038337728807407295, 'max_depth': 5}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:52,620]\u001b[0m Trial 306 finished with value: 108749.08798300567 and parameters: {'n_estimators': 100, 'learning_rate': 0.06566743773513162, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,686]\u001b[0m Trial 307 finished with value: 39508.39324272189 and parameters: {'n_estimators': 300, 'learning_rate': 0.26223429981074003, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,742]\u001b[0m Trial 308 finished with value: 107462.71119467354 and parameters: {'n_estimators': 100, 'learning_rate': 0.06697702749200951, 'max_depth': 7}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,800]\u001b[0m Trial 309 finished with value: 44298.07919693795 and parameters: {'n_estimators': 1300, 'learning_rate': 0.22145358286061906, 'max_depth': 3}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,858]\u001b[0m Trial 310 finished with value: 199311.70685450317 and parameters: {'n_estimators': 1700, 'learning_rate': 0.00010097383074112626, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:52,928]\u001b[0m Trial 311 finished with value: 40249.80781867514 and parameters: {'n_estimators': 600, 'learning_rate': 0.26181117181153707, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:52,988]\u001b[0m Trial 312 finished with value: 197164.99338152134 and parameters: {'n_estimators': 1800, 'learning_rate': 0.0012495514479776634, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,048]\u001b[0m Trial 313 finished with value: 48556.69454069699 and parameters: {'n_estimators': 1500, 'learning_rate': 0.19426394785760648, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:52] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:53,122]\u001b[0m Trial 314 finished with value: 154268.52619993323 and parameters: {'n_estimators': 1900, 'learning_rate': 0.02735097093493295, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,180]\u001b[0m Trial 315 finished with value: 183318.14290295963 and parameters: {'n_estimators': 200, 'learning_rate': 0.008979735862637223, 'max_depth': 7}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,236]\u001b[0m Trial 316 finished with value: 189261.70391980227 and parameters: {'n_estimators': 500, 'learning_rate': 0.0055960792637263865, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,307]\u001b[0m Trial 317 finished with value: 39583.32532834744 and parameters: {'n_estimators': 100, 'learning_rate': 0.2627933606470232, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:53,392]\u001b[0m Trial 318 finished with value: 43078.9827754115 and parameters: {'n_estimators': 800, 'learning_rate': 0.2999812435604582, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,450]\u001b[0m Trial 319 finished with value: 174732.96057334254 and parameters: {'n_estimators': 1600, 'learning_rate': 0.014102348138886521, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,514]\u001b[0m Trial 320 finished with value: 43680.88725690632 and parameters: {'n_estimators': 2000, 'learning_rate': 0.22590043593590434, 'max_depth': 6}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,571]\u001b[0m Trial 321 finished with value: 145396.1210857718 and parameters: {'n_estimators': 100, 'learning_rate': 0.03372288413245492, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:53,632]\u001b[0m Trial 322 finished with value: 87781.38374953065 and parameters: {'n_estimators': 900, 'learning_rate': 0.09084960220761043, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,696]\u001b[0m Trial 323 finished with value: 54248.50186063326 and parameters: {'n_estimators': 1100, 'learning_rate': 0.1695582423043835, 'max_depth': 8}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,763]\u001b[0m Trial 324 finished with value: 47931.39319698086 and parameters: {'n_estimators': 1200, 'learning_rate': 0.19863254014282067, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,827]\u001b[0m Trial 325 finished with value: 39091.41505044204 and parameters: {'n_estimators': 700, 'learning_rate': 0.2618524872522512, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:53,887]\u001b[0m Trial 326 finished with value: 42487.11576293604 and parameters: {'n_estimators': 700, 'learning_rate': 0.23146867208672092, 'max_depth': 5}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:53,938]\u001b[0m Trial 327 finished with value: 180835.03708694357 and parameters: {'n_estimators': 700, 'learning_rate': 0.010473767654577928, 'max_depth': 4}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,007]\u001b[0m Trial 328 finished with value: 57814.26152961197 and parameters: {'n_estimators': 700, 'learning_rate': 0.15419680139807834, 'max_depth': 9}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,065]\u001b[0m Trial 329 finished with value: 195982.71070174495 and parameters: {'n_estimators': 1900, 'learning_rate': 0.0018872290389946077, 'max_depth': 14}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:53] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:54,139]\u001b[0m Trial 330 finished with value: 43045.57865477164 and parameters: {'n_estimators': 800, 'learning_rate': 0.2994634228626886, 'max_depth': 10}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,209]\u001b[0m Trial 331 finished with value: 43608.122622058036 and parameters: {'n_estimators': 600, 'learning_rate': 0.22813701505702352, 'max_depth': 11}. Best is trial 222 with value: 37591.26290793679.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,273]\u001b[0m Trial 332 finished with value: 37574.28472651776 and parameters: {'n_estimators': 700, 'learning_rate': 0.26364097356592897, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,344]\u001b[0m Trial 333 finished with value: 99899.66105541211 and parameters: {'n_estimators': 700, 'learning_rate': 0.07526608792693482, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:54,429]\u001b[0m Trial 334 finished with value: 48404.09216418666 and parameters: {'n_estimators': 700, 'learning_rate': 0.19541686532024163, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,499]\u001b[0m Trial 335 finished with value: 38557.799085621264 and parameters: {'n_estimators': 800, 'learning_rate': 0.2539719794320131, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,560]\u001b[0m Trial 336 finished with value: 170602.43178398526 and parameters: {'n_estimators': 800, 'learning_rate': 0.016649175754659604, 'max_depth': 12}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:54,625]\u001b[0m Trial 337 finished with value: 165911.36850972156 and parameters: {'n_estimators': 900, 'learning_rate': 0.019587512263850308, 'max_depth': 6}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,696]\u001b[0m Trial 338 finished with value: 44891.9419311641 and parameters: {'n_estimators': 800, 'learning_rate': 0.21763070308411175, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,760]\u001b[0m Trial 339 finished with value: 78824.1190584338 and parameters: {'n_estimators': 400, 'learning_rate': 0.10371359406681534, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:54,846]\u001b[0m Trial 340 finished with value: 66814.05668001641 and parameters: {'n_estimators': 1000, 'learning_rate': 0.12849542860936944, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,912]\u001b[0m Trial 341 finished with value: 50688.43457187709 and parameters: {'n_estimators': 800, 'learning_rate': 0.1816216479260366, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:54,981]\u001b[0m Trial 342 finished with value: 39727.33952942417 and parameters: {'n_estimators': 300, 'learning_rate': 0.26599572614721495, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:54] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:55,064]\u001b[0m Trial 343 finished with value: 43085.20684144136 and parameters: {'n_estimators': 200, 'learning_rate': 0.2997786475965734, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:55,133]\u001b[0m Trial 344 finished with value: 43563.18888577349 and parameters: {'n_estimators': 2000, 'learning_rate': 0.22952848413827182, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:55,204]\u001b[0m Trial 345 finished with value: 48925.90047471531 and parameters: {'n_estimators': 1400, 'learning_rate': 0.1964198295601318, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:55,290]\u001b[0m Trial 346 finished with value: 41751.894575698665 and parameters: {'n_estimators': 900, 'learning_rate': 0.2998616786083981, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:55,349]\u001b[0m Trial 347 finished with value: 57912.00035911192 and parameters: {'n_estimators': 600, 'learning_rate': 0.15362300493555198, 'max_depth': 7}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:55,418]\u001b[0m Trial 348 finished with value: 42189.18549452137 and parameters: {'n_estimators': 1100, 'learning_rate': 0.23958882809131374, 'max_depth': 6}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:55,489]\u001b[0m Trial 349 finished with value: 38555.62908777818 and parameters: {'n_estimators': 200, 'learning_rate': 0.25400516281270213, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:55,559]\u001b[0m Trial 350 finished with value: 52154.717369310434 and parameters: {'n_estimators': 200, 'learning_rate': 0.17418738647541304, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:55,621]\u001b[0m Trial 351 finished with value: 192548.69269561113 and parameters: {'n_estimators': 200, 'learning_rate': 0.00376294869327289, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:55,710]\u001b[0m Trial 352 finished with value: 45708.596367311045 and parameters: {'n_estimators': 200, 'learning_rate': 0.20880601028342086, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:55] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:58,544]\u001b[0m Trial 353 finished with value: 39504.36854382124 and parameters: {'n_estimators': 300, 'learning_rate': 0.2570900526265465, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:58] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:59,103]\u001b[0m Trial 354 finished with value: 42536.118640429515 and parameters: {'n_estimators': 100, 'learning_rate': 0.23054791570236938, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,174]\u001b[0m Trial 355 finished with value: 48930.89694004134 and parameters: {'n_estimators': 1200, 'learning_rate': 0.19391334946328748, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,238]\u001b[0m Trial 356 finished with value: 39314.89281984703 and parameters: {'n_estimators': 200, 'learning_rate': 0.2573660004640636, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,309]\u001b[0m Trial 357 finished with value: 44851.45849448534 and parameters: {'n_estimators': 1900, 'learning_rate': 0.22009944824050906, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:59,376]\u001b[0m Trial 358 finished with value: 179017.5622597368 and parameters: {'n_estimators': 100, 'learning_rate': 0.011518643058582417, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,441]\u001b[0m Trial 359 finished with value: 190916.32409638382 and parameters: {'n_estimators': 2000, 'learning_rate': 0.004667495193676816, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,500]\u001b[0m Trial 360 finished with value: 187440.33447429602 and parameters: {'n_estimators': 200, 'learning_rate': 0.006623567732204302, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:59,594]\u001b[0m Trial 361 finished with value: 50727.46099536184 and parameters: {'n_estimators': 200, 'learning_rate': 0.17736803731608655, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,657]\u001b[0m Trial 362 finished with value: 148597.20423179207 and parameters: {'n_estimators': 300, 'learning_rate': 0.03139413435074341, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,715]\u001b[0m Trial 363 finished with value: 128318.66996915209 and parameters: {'n_estimators': 100, 'learning_rate': 0.04725488724929525, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:17:59,791]\u001b[0m Trial 364 finished with value: 62422.01359780641 and parameters: {'n_estimators': 1000, 'learning_rate': 0.13837247430699115, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,862]\u001b[0m Trial 365 finished with value: 174503.45241250895 and parameters: {'n_estimators': 1900, 'learning_rate': 0.014242400489786979, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,926]\u001b[0m Trial 366 finished with value: 39097.92537157571 and parameters: {'n_estimators': 1300, 'learning_rate': 0.26226736076841156, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:17:59,987]\u001b[0m Trial 367 finished with value: 44034.84516100793 and parameters: {'n_estimators': 100, 'learning_rate': 0.2976023096916034, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:17:59] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:00,063]\u001b[0m Trial 368 finished with value: 154617.7881525995 and parameters: {'n_estimators': 200, 'learning_rate': 0.027114751853903354, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:00,134]\u001b[0m Trial 369 finished with value: 43606.840024954465 and parameters: {'n_estimators': 2000, 'learning_rate': 0.22792761144192342, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:00,191]\u001b[0m Trial 370 finished with value: 46504.7848924097 and parameters: {'n_estimators': 1900, 'learning_rate': 0.20533597736278977, 'max_depth': 4}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:00,257]\u001b[0m Trial 371 finished with value: 39124.21114231327 and parameters: {'n_estimators': 100, 'learning_rate': 0.2606053919236248, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:00,346]\u001b[0m Trial 372 finished with value: 75665.01314942307 and parameters: {'n_estimators': 200, 'learning_rate': 0.10887654073531496, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:00,414]\u001b[0m Trial 373 finished with value: 166120.63540320747 and parameters: {'n_estimators': 300, 'learning_rate': 0.01947552761043993, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:00,493]\u001b[0m Trial 374 finished with value: 56481.99390258949 and parameters: {'n_estimators': 500, 'learning_rate': 0.16163513359160092, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:00,563]\u001b[0m Trial 375 finished with value: 170879.12721138383 and parameters: {'n_estimators': 100, 'learning_rate': 0.01647638886410853, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:00,654]\u001b[0m Trial 376 finished with value: 114679.4608199225 and parameters: {'n_estimators': 1200, 'learning_rate': 0.05966256612909042, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:00,715]\u001b[0m Trial 377 finished with value: 185263.4858891723 and parameters: {'n_estimators': 1900, 'learning_rate': 0.00786688651123064, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:00,775]\u001b[0m Trial 378 finished with value: 40686.4897778615 and parameters: {'n_estimators': 1800, 'learning_rate': 0.2998517402977455, 'max_depth': 5}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:00,852]\u001b[0m Trial 379 finished with value: 198146.10276488768 and parameters: {'n_estimators': 100, 'learning_rate': 0.0007229243111391781, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:00,915]\u001b[0m Trial 380 finished with value: 42525.21067818549 and parameters: {'n_estimators': 1100, 'learning_rate': 0.230260855201408, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:00] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:01,035]\u001b[0m Trial 381 finished with value: 48410.617964194804 and parameters: {'n_estimators': 200, 'learning_rate': 0.193326683818495, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:01,103]\u001b[0m Trial 382 finished with value: 38396.49263848588 and parameters: {'n_estimators': 300, 'learning_rate': 0.25421791900183943, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:01,174]\u001b[0m Trial 383 finished with value: 41577.77788709888 and parameters: {'n_estimators': 300, 'learning_rate': 0.24172335205772583, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:01,234]\u001b[0m Trial 384 finished with value: 123375.51481607991 and parameters: {'n_estimators': 200, 'learning_rate': 0.051546098301510766, 'max_depth': 11}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:01,317]\u001b[0m Trial 385 finished with value: 43089.39137343103 and parameters: {'n_estimators': 800, 'learning_rate': 0.2996188926443448, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:01,382]\u001b[0m Trial 386 finished with value: 88673.53089886635 and parameters: {'n_estimators': 200, 'learning_rate': 0.08961966796613659, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:01,449]\u001b[0m Trial 387 finished with value: 45884.72668316292 and parameters: {'n_estimators': 400, 'learning_rate': 0.20897720647897836, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:01,522]\u001b[0m Trial 388 finished with value: 165688.3542523479 and parameters: {'n_estimators': 100, 'learning_rate': 0.019762362342746635, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:01,597]\u001b[0m Trial 389 finished with value: 50905.98097931091 and parameters: {'n_estimators': 300, 'learning_rate': 0.17739405363190122, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:01,670]\u001b[0m Trial 390 finished with value: 197048.49531081357 and parameters: {'n_estimators': 200, 'learning_rate': 0.0013122398675541208, 'max_depth': 11}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:01,746]\u001b[0m Trial 391 finished with value: 40445.66968961675 and parameters: {'n_estimators': 900, 'learning_rate': 0.2563119519637983, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:01,823]\u001b[0m Trial 392 finished with value: 58779.217271829504 and parameters: {'n_estimators': 700, 'learning_rate': 0.15207328911659665, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:01,888]\u001b[0m Trial 393 finished with value: 190429.5950041514 and parameters: {'n_estimators': 100, 'learning_rate': 0.004938403229509954, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:01,961]\u001b[0m Trial 394 finished with value: 44172.223395521265 and parameters: {'n_estimators': 200, 'learning_rate': 0.2247618267110009, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,024]\u001b[0m Trial 395 finished with value: 195242.41878329846 and parameters: {'n_estimators': 100, 'learning_rate': 0.002288906164282872, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,093]\u001b[0m Trial 396 finished with value: 39260.35533536683 and parameters: {'n_estimators': 1000, 'learning_rate': 0.25938518756397333, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:01] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:02,163]\u001b[0m Trial 397 finished with value: 48018.28806297698 and parameters: {'n_estimators': 100, 'learning_rate': 0.19670943752258582, 'max_depth': 7}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,246]\u001b[0m Trial 398 finished with value: 41559.7567057365 and parameters: {'n_estimators': 300, 'learning_rate': 0.232848546671487, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,309]\u001b[0m Trial 399 finished with value: 64827.70723896998 and parameters: {'n_estimators': 1400, 'learning_rate': 0.1324590648784898, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,380]\u001b[0m Trial 400 finished with value: 38707.93281262971 and parameters: {'n_estimators': 800, 'learning_rate': 0.2647638910391068, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:02,450]\u001b[0m Trial 401 finished with value: 41469.22495197698 and parameters: {'n_estimators': 900, 'learning_rate': 0.29972022974514473, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,508]\u001b[0m Trial 402 finished with value: 143725.79153303834 and parameters: {'n_estimators': 800, 'learning_rate': 0.0349065955215423, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,578]\u001b[0m Trial 403 finished with value: 50846.5901521596 and parameters: {'n_estimators': 800, 'learning_rate': 0.18190523583639476, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:02,676]\u001b[0m Trial 404 finished with value: 45579.440233938134 and parameters: {'n_estimators': 900, 'learning_rate': 0.21460979571253538, 'max_depth': 7}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,730]\u001b[0m Trial 405 finished with value: 198523.9143831045 and parameters: {'n_estimators': 800, 'learning_rate': 0.0005217839602207597, 'max_depth': 5}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,794]\u001b[0m Trial 406 finished with value: 39612.19157405189 and parameters: {'n_estimators': 700, 'learning_rate': 0.2566336624548122, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:02,875]\u001b[0m Trial 407 finished with value: 54471.59003618456 and parameters: {'n_estimators': 800, 'learning_rate': 0.16768984981150617, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:02,952]\u001b[0m Trial 408 finished with value: 41152.235993336864 and parameters: {'n_estimators': 1600, 'learning_rate': 0.29958321674785005, 'max_depth': 12}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:03,034]\u001b[0m Trial 409 finished with value: 104082.60075106118 and parameters: {'n_estimators': 600, 'learning_rate': 0.07071815609799886, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:02] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:03,106]\u001b[0m Trial 410 finished with value: 71436.88779135102 and parameters: {'n_estimators': 800, 'learning_rate': 0.11741395924209047, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:03,190]\u001b[0m Trial 411 finished with value: 45913.50374108369 and parameters: {'n_estimators': 200, 'learning_rate': 0.21457975706979432, 'max_depth': 11}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:03,256]\u001b[0m Trial 412 finished with value: 39935.65457935505 and parameters: {'n_estimators': 700, 'learning_rate': 0.2507823695037066, 'max_depth': 7}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:03,335]\u001b[0m Trial 413 finished with value: 163067.5051483109 and parameters: {'n_estimators': 100, 'learning_rate': 0.021452985096468404, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:03,401]\u001b[0m Trial 414 finished with value: 190038.7554331994 and parameters: {'n_estimators': 700, 'learning_rate': 0.005155698405055556, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:03,471]\u001b[0m Trial 415 finished with value: 47211.16719948733 and parameters: {'n_estimators': 300, 'learning_rate': 0.2041805315731841, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:03,553]\u001b[0m Trial 416 finished with value: 181661.6198268882 and parameters: {'n_estimators': 100, 'learning_rate': 0.009960151497505183, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:03,613]\u001b[0m Trial 417 finished with value: 39902.651908749984 and parameters: {'n_estimators': 200, 'learning_rate': 0.2602130346364145, 'max_depth': 4}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:03,697]\u001b[0m Trial 418 finished with value: 117267.01708599464 and parameters: {'n_estimators': 800, 'learning_rate': 0.05710860237717256, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:03,774]\u001b[0m Trial 419 finished with value: 42710.12157790487 and parameters: {'n_estimators': 500, 'learning_rate': 0.23035027320408727, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:03,841]\u001b[0m Trial 420 finished with value: 57248.96908911349 and parameters: {'n_estimators': 1500, 'learning_rate': 0.15691844834456856, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:03,912]\u001b[0m Trial 421 finished with value: 148904.02030622802 and parameters: {'n_estimators': 2000, 'learning_rate': 0.031157959687121897, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:03,973]\u001b[0m Trial 422 finished with value: 196587.71368357108 and parameters: {'n_estimators': 200, 'learning_rate': 0.001570590223255445, 'max_depth': 3}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,038]\u001b[0m Trial 423 finished with value: 50807.78375064869 and parameters: {'n_estimators': 100, 'learning_rate': 0.1871445970542862, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,103]\u001b[0m Trial 424 finished with value: 40072.58386737847 and parameters: {'n_estimators': 200, 'learning_rate': 0.26146588485769023, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:03] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:04,182]\u001b[0m Trial 425 finished with value: 42784.794443774816 and parameters: {'n_estimators': 400, 'learning_rate': 0.2988797613394198, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,248]\u001b[0m Trial 426 finished with value: 44687.78675507346 and parameters: {'n_estimators': 700, 'learning_rate': 0.2232971097693733, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,309]\u001b[0m Trial 427 finished with value: 178141.1903024403 and parameters: {'n_estimators': 900, 'learning_rate': 0.012051680835827257, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,382]\u001b[0m Trial 428 finished with value: 193332.07626413248 and parameters: {'n_estimators': 100, 'learning_rate': 0.0033322433038919124, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:04,458]\u001b[0m Trial 429 finished with value: 40047.70996672368 and parameters: {'n_estimators': 200, 'learning_rate': 0.250046539862088, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,522]\u001b[0m Trial 430 finished with value: 47965.28521507515 and parameters: {'n_estimators': 1900, 'learning_rate': 0.19820540846964335, 'max_depth': 5}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,585]\u001b[0m Trial 431 finished with value: 186835.47114062263 and parameters: {'n_estimators': 800, 'learning_rate': 0.006956670487962179, 'max_depth': 7}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:04,673]\u001b[0m Trial 432 finished with value: 42948.84050712798 and parameters: {'n_estimators': 2000, 'learning_rate': 0.2998202219600922, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,750]\u001b[0m Trial 433 finished with value: 41863.29917521655 and parameters: {'n_estimators': 100, 'learning_rate': 0.23422202279866058, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,824]\u001b[0m Trial 434 finished with value: 52213.61698403258 and parameters: {'n_estimators': 300, 'learning_rate': 0.17163463568369255, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:04,908]\u001b[0m Trial 435 finished with value: 39402.33188378736 and parameters: {'n_estimators': 600, 'learning_rate': 0.26174802531365743, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:04,994]\u001b[0m Trial 436 finished with value: 61214.44608050026 and parameters: {'n_estimators': 100, 'learning_rate': 0.14421795930091005, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:05,064]\u001b[0m Trial 437 finished with value: 80057.7113958735 and parameters: {'n_estimators': 200, 'learning_rate': 0.10179326971177388, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:04] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:05,136]\u001b[0m Trial 438 finished with value: 137813.48837812027 and parameters: {'n_estimators': 1900, 'learning_rate': 0.03954101417387522, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:05,214]\u001b[0m Trial 439 finished with value: 45885.77603287197 and parameters: {'n_estimators': 300, 'learning_rate': 0.20702243058158973, 'max_depth': 13}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:05,287]\u001b[0m Trial 440 finished with value: 96246.17179498148 and parameters: {'n_estimators': 100, 'learning_rate': 0.07933108960094412, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:05,369]\u001b[0m Trial 441 finished with value: 39429.98968611158 and parameters: {'n_estimators': 200, 'learning_rate': 0.2598155260609544, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:05,436]\u001b[0m Trial 442 finished with value: 42555.439413347485 and parameters: {'n_estimators': 200, 'learning_rate': 0.22740269249764417, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:05,506]\u001b[0m Trial 443 finished with value: 188176.70372798987 and parameters: {'n_estimators': 800, 'learning_rate': 0.006206959340373267, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:05,583]\u001b[0m Trial 444 finished with value: 174226.61281080198 and parameters: {'n_estimators': 2000, 'learning_rate': 0.014411584075462943, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:05,654]\u001b[0m Trial 445 finished with value: 50558.13559529252 and parameters: {'n_estimators': 900, 'learning_rate': 0.17863290516519315, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:05,727]\u001b[0m Trial 446 finished with value: 188903.37749205297 and parameters: {'n_estimators': 100, 'learning_rate': 0.005797395885514381, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:05,799]\u001b[0m Trial 447 finished with value: 186570.32135011783 and parameters: {'n_estimators': 700, 'learning_rate': 0.007118941429346852, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:05,871]\u001b[0m Trial 448 finished with value: 182143.26425832752 and parameters: {'n_estimators': 1700, 'learning_rate': 0.009678205653590632, 'max_depth': 11}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:05,936]\u001b[0m Trial 449 finished with value: 182850.840867715 and parameters: {'n_estimators': 600, 'learning_rate': 0.009265834874231308, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:05] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:06,022]\u001b[0m Trial 450 finished with value: 42694.325082370706 and parameters: {'n_estimators': 1400, 'learning_rate': 0.29939914053324224, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:06,094]\u001b[0m Trial 451 finished with value: 46972.70180487777 and parameters: {'n_estimators': 1900, 'learning_rate': 0.20519015535359494, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:06,166]\u001b[0m Trial 452 finished with value: 39718.708822219625 and parameters: {'n_estimators': 100, 'learning_rate': 0.26040949674809855, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:06,231]\u001b[0m Trial 453 finished with value: 43681.637297166024 and parameters: {'n_estimators': 700, 'learning_rate': 0.2250347901119113, 'max_depth': 7}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:06,330]\u001b[0m Trial 454 finished with value: 137052.99230757513 and parameters: {'n_estimators': 1000, 'learning_rate': 0.04011023299336056, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:06,395]\u001b[0m Trial 455 finished with value: 191177.76806973238 and parameters: {'n_estimators': 1800, 'learning_rate': 0.0045392253493023475, 'max_depth': 4}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:06,465]\u001b[0m Trial 456 finished with value: 39403.12628701672 and parameters: {'n_estimators': 200, 'learning_rate': 0.26173496085496956, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:06,541]\u001b[0m Trial 457 finished with value: 193545.24572748676 and parameters: {'n_estimators': 100, 'learning_rate': 0.003215335579029212, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:06,614]\u001b[0m Trial 458 finished with value: 157886.05587185913 and parameters: {'n_estimators': 300, 'learning_rate': 0.02492774458915288, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:06,678]\u001b[0m Trial 459 finished with value: 185600.43919112455 and parameters: {'n_estimators': 1600, 'learning_rate': 0.007673728637100484, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:06,776]\u001b[0m Trial 460 finished with value: 47831.760418151156 and parameters: {'n_estimators': 200, 'learning_rate': 0.1960829063710649, 'max_depth': 6}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:06,844]\u001b[0m Trial 461 finished with value: 56607.31474187186 and parameters: {'n_estimators': 1300, 'learning_rate': 0.15918088790532597, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:06,915]\u001b[0m Trial 462 finished with value: 177657.682221293 and parameters: {'n_estimators': 1900, 'learning_rate': 0.01234103022779609, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:07,023]\u001b[0m Trial 463 finished with value: 40705.88024884723 and parameters: {'n_estimators': 100, 'learning_rate': 0.2986963371385678, 'max_depth': 5}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:07,095]\u001b[0m Trial 464 finished with value: 44562.13471115712 and parameters: {'n_estimators': 2000, 'learning_rate': 0.2223223145949435, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:07,160]\u001b[0m Trial 465 finished with value: 121605.91657084995 and parameters: {'n_estimators': 900, 'learning_rate': 0.05301338485596302, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:06] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:07,237]\u001b[0m Trial 466 finished with value: 41676.2921075011 and parameters: {'n_estimators': 800, 'learning_rate': 0.24210234951504236, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:07,306]\u001b[0m Trial 467 finished with value: 50838.001860231394 and parameters: {'n_estimators': 200, 'learning_rate': 0.18523554098614992, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:07,398]\u001b[0m Trial 468 finished with value: 157040.6346324716 and parameters: {'n_estimators': 300, 'learning_rate': 0.025494165915108526, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:07,477]\u001b[0m Trial 469 finished with value: 40186.98064782405 and parameters: {'n_estimators': 700, 'learning_rate': 0.26497465094432554, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:07,557]\u001b[0m Trial 470 finished with value: 194189.02621047047 and parameters: {'n_estimators': 100, 'learning_rate': 0.00286259263073395, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:07,625]\u001b[0m Trial 471 finished with value: 63687.34150649253 and parameters: {'n_estimators': 200, 'learning_rate': 0.13266784194995293, 'max_depth': 7}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:07,698]\u001b[0m Trial 472 finished with value: 197783.51514630395 and parameters: {'n_estimators': 800, 'learning_rate': 0.000917253459534698, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:07,762]\u001b[0m Trial 473 finished with value: 166297.52937432178 and parameters: {'n_estimators': 400, 'learning_rate': 0.019371990965903342, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:07,851]\u001b[0m Trial 474 finished with value: 43087.350986949285 and parameters: {'n_estimators': 1900, 'learning_rate': 0.2992848435444514, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:07,932]\u001b[0m Trial 475 finished with value: 45752.039861918696 and parameters: {'n_estimators': 100, 'learning_rate': 0.21857543121441464, 'max_depth': 11}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,005]\u001b[0m Trial 476 finished with value: 95063.9314877084 and parameters: {'n_estimators': 1800, 'learning_rate': 0.08100560546891629, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,076]\u001b[0m Trial 477 finished with value: 42062.92746102228 and parameters: {'n_estimators': 100, 'learning_rate': 0.23380616779128283, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:07] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:08,149]\u001b[0m Trial 478 finished with value: 199026.95601645808 and parameters: {'n_estimators': 2000, 'learning_rate': 0.0002525757074921575, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,223]\u001b[0m Trial 479 finished with value: 50878.219855591116 and parameters: {'n_estimators': 200, 'learning_rate': 0.18636313260683157, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,293]\u001b[0m Trial 480 finished with value: 40966.09858898253 and parameters: {'n_estimators': 800, 'learning_rate': 0.25365450319019556, 'max_depth': 5}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:08,368]\u001b[0m Trial 481 finished with value: 148190.4474116002 and parameters: {'n_estimators': 100, 'learning_rate': 0.031688422962913826, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,432]\u001b[0m Trial 482 finished with value: 195734.62644761597 and parameters: {'n_estimators': 600, 'learning_rate': 0.002021485245505789, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,501]\u001b[0m Trial 483 finished with value: 194686.9759578961 and parameters: {'n_estimators': 700, 'learning_rate': 0.002591819617916842, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:08,586]\u001b[0m Trial 484 finished with value: 56894.83447705343 and parameters: {'n_estimators': 200, 'learning_rate': 0.15982639047373737, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,655]\u001b[0m Trial 485 finished with value: 183659.81961527624 and parameters: {'n_estimators': 500, 'learning_rate': 0.00879634709733939, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,728]\u001b[0m Trial 486 finished with value: 46778.83063279629 and parameters: {'n_estimators': 1700, 'learning_rate': 0.20454750122227036, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:08,818]\u001b[0m Trial 487 finished with value: 198580.0451510845 and parameters: {'n_estimators': 1900, 'learning_rate': 0.0004909508987477076, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,890]\u001b[0m Trial 488 finished with value: 39192.57103742445 and parameters: {'n_estimators': 1000, 'learning_rate': 0.267200568168654, 'max_depth': 6}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:08,961]\u001b[0m Trial 489 finished with value: 42397.33068724781 and parameters: {'n_estimators': 200, 'learning_rate': 0.23340038170089722, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:08] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:09,039]\u001b[0m Trial 490 finished with value: 133449.30113036028 and parameters: {'n_estimators': 100, 'learning_rate': 0.042920722487127305, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:09,156]\u001b[0m Trial 491 finished with value: 71432.35130705987 and parameters: {'n_estimators': 300, 'learning_rate': 0.11737122892505586, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:09] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:09] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:09,289]\u001b[0m Trial 492 finished with value: 198189.25068488216 and parameters: {'n_estimators': 1500, 'learning_rate': 0.0006998358302510063, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:09] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:09] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:10,022]\u001b[0m Trial 493 finished with value: 43189.18225476868 and parameters: {'n_estimators': 900, 'learning_rate': 0.29910254664809205, 'max_depth': 10}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:10] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:11,975]\u001b[0m Trial 494 finished with value: 45549.1133490262 and parameters: {'n_estimators': 200, 'learning_rate': 0.2015144043677493, 'max_depth': 7}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:12] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:12,685]\u001b[0m Trial 495 finished with value: 39290.77478246897 and parameters: {'n_estimators': 2000, 'learning_rate': 0.25775848959649966, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:12,759]\u001b[0m Trial 496 finished with value: 199289.0485490794 and parameters: {'n_estimators': 100, 'learning_rate': 0.00011307183867062966, 'max_depth': 6}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:12,832]\u001b[0m Trial 497 finished with value: 54558.15943062692 and parameters: {'n_estimators': 700, 'learning_rate': 0.16885151485350006, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n", "\u001b[32m[I 2023-04-16 20:18:12,904]\u001b[0m Trial 498 finished with value: 131052.83691128207 and parameters: {'n_estimators': 1600, 'learning_rate': 0.04483998848585322, 'max_depth': 8}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:12] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:12] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "[20:18:12] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-16 20:18:13,008]\u001b[0m Trial 499 finished with value: 41779.176777447385 and parameters: {'n_estimators': 1900, 'learning_rate': 0.23483326097631244, 'max_depth': 9}. Best is trial 332 with value: 37574.28472651776.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[20:18:12] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"n_estimators\", \"n_iter_no_change\" } are not used.\n", "\n", "Optimized RMSE: 37574.2847\n", "Best params:\n", "\tn_estimators: 700\n", "\tlearning_rate: 0.26364097356592897\n", "\tmax_depth: 9\n" ] } ] }, { "cell_type": "code", "source": [ "from optuna.visualization.matplotlib import plot_optimization_history\n", "plot_optimization_history(study)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 512 }, "id": "aR5RQXLH1reT", "outputId": "e6fe892d-6dd0-42d5-b3be-66aa1fbeeed1" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "plot_optimization_history is experimental (supported from v2.2.0). The interface can change in the future.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 21 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" 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