{ "cells": [ { "cell_type": "markdown", "source": [ "#Set Up Environment" ], "metadata": { "id": "sw90-qdlsfs2" } }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "5mJq1ix8Tera", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "b8c711eb-406a-4f19-b6de-ad329eb695a0" }, "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: kaggle in /usr/local/lib/python3.9/dist-packages (1.5.13)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from kaggle) (4.65.0)\n", "Requirement already satisfied: certifi in /usr/local/lib/python3.9/dist-packages (from kaggle) (2022.12.7)\n", "Requirement already satisfied: urllib3 in /usr/local/lib/python3.9/dist-packages (from kaggle) (1.26.15)\n", "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.9/dist-packages (from kaggle) (1.16.0)\n", "Requirement already satisfied: python-slugify in /usr/local/lib/python3.9/dist-packages (from kaggle) (8.0.1)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from kaggle) (2.27.1)\n", "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.9/dist-packages (from kaggle) (2.8.2)\n", "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.9/dist-packages (from python-slugify->kaggle) (1.3)\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->kaggle) (2.0.12)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->kaggle) (3.4)\n" ] } ], "source": [ "\n", "! pip install kaggle\n", "! pip install optuna\n", "! mkdir ~/.kaggle\n", "! cp kaggle.json ~/.kaggle/\n", "! chmod 600 ~/.kaggle/kaggle.json" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "ySlIx3PUUiVK", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ce2b51ef-85b5-4f4f-f57b-a6ec4d65c463" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading train.csv to /content\n", "\r 0% 0.00/450k [00:00\n", "
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MSSubClassMSZoningLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhood...OpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSaleTypeSaleCondition
254206840001101113...0000006201011
106660678370210119...40000005200911
63830687770110118...016400005200811
799506720001102119...026400006200711
380506500001101119...024200005201011
30320698000110216...0000007200612
86606119110310119...38000003200911
1385500543601101110...96000005201011
2652061209002101117...0000006200811
793206915801101122...130000006200740
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10 rows × 60 columns

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\n", " \n", " " ] }, "metadata": {}, "execution_count": 100 } ], "source": [ "y_hat = lgbm.predict(X_test)\n", "df = X_test.copy()\n", "df['SalePrice'] = y_hat\n", "print(df.head(10))\n", "X_train.head(10)\n" ] }, { "cell_type": "markdown", "source": [ "#Optuna Optimization" ], "metadata": { "id": "OSx5KOIjlzmA" } }, { "cell_type": "markdown", "source": [ "Here I used the Optuna framework to optimize the parameters of a new model, suggesting a starting point, and possible ranges for these features." ], "metadata": { "id": "f-ttjRcw6G0x" } }, { "cell_type": "code", "source": [ "import optuna # pip install optuna\n", "from sklearn.metrics import log_loss\n", "from sklearn.model_selection import StratifiedKFold\n", "from optuna.integration import LightGBMPruningCallback\n", "\n", "def objective(trial, X_train, y_train, X_test, y_test):\n", " param_grid = {\n", " # \"device_type\": trial.suggest_categorical(\"device_type\", ['gpu']),\n", " \"n_estimators\": trial.suggest_categorical(\"n_estimators\", [10000]),\n", " \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.3),\n", " \"num_leaves\": trial.suggest_int(\"num_leaves\", 1, 3000, step=1),\n", " \"max_depth\": trial.suggest_int(\"max_depth\", 1, 12),\n", " \"min_data_in_leaf\": trial.suggest_int(\"min_data_in_leaf\", 100, 10000, step=100),\n", " \"lambda_l1\": trial.suggest_int(\"lambda_l1\", 0, 100, step=5),\n", " \"lambda_l2\": trial.suggest_int(\"lambda_l2\", 0, 100, step=5),\n", " \"min_gain_to_split\": trial.suggest_float(\"min_gain_to_split\", 0, 15),\n", " \"bagging_fraction\": trial.suggest_float(\n", " \"bagging_fraction\", 0.2, 1.0, step=0.1\n", " ),\n", " \"bagging_freq\": trial.suggest_categorical(\"bagging_freq\", [1]),\n", " \"feature_fraction\": trial.suggest_float(\n", " \"feature_fraction\", 0.2, 1.0, step=0.1\n", " ),\n", " }\n", "\n", " model = lgb.LGBMRegressor(objective=\"regression\", **param_grid)\n", " model.fit(\n", " X_train,\n", " y_train,\n", " eval_set=[(X_test, y_test)],\n", " eval_metric=\"rmse\",\n", " callbacks=[\n", " LightGBMPruningCallback(trial, \"rmse\"),\n", " early_stopping(3000)\n", " ], # Add a pruning callback\n", " )\n", " y_pred = model.predict(X_test)\n", "\n", " error = mean_squared_error(y_test, y_pred)\n", "\n", " return error # An objective value linked with the Trial object." ], "metadata": { "id": "LMz2rsF5kFON" }, "execution_count": 105, "outputs": [] }, { "cell_type": "code", "source": [ "study = optuna.create_study(direction=\"minimize\", study_name=\"LGBM Regressor\")\n", "func = lambda trial: objective(trial, X_train, y_train, X_test, y_test)\n", "study.optimize(func, n_trials=40)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XyVbz7Ekl2eU", "outputId": "a3e4d183-e563-4285-f503-29a6c8a33092" }, "execution_count": 106, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:34:09,820]\u001b[0m A new study created in memory with name: LGBM Regressor\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[LightGBM] [Warning] lambda_l1 is set=35, reg_alpha=0.0 will be ignored. Current value: lambda_l1=35\n", "[LightGBM] [Warning] bagging_fraction is set=0.6000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=6.553586519724484, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=6.553586519724484\n", "[LightGBM] [Warning] lambda_l2 is set=100, reg_lambda=0.0 will be ignored. Current value: lambda_l2=100\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=6900, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6900\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:34:19,229]\u001b[0m Trial 0 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.07647008241828379, 'num_leaves': 423, 'max_depth': 8, 'min_data_in_leaf': 6900, 'lambda_l1': 35, 'lambda_l2': 100, 'min_gain_to_split': 6.553586519724484, 'bagging_fraction': 0.6000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=0, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0\n", "[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5\n", "[LightGBM] [Warning] min_gain_to_split is set=12.897968954825165, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=12.897968954825165\n", "[LightGBM] [Warning] lambda_l2 is set=80, reg_lambda=0.0 will be ignored. Current value: lambda_l2=80\n", "[LightGBM] [Warning] feature_fraction is set=0.6000000000000001, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6000000000000001\n", "[LightGBM] [Warning] min_data_in_leaf is set=2700, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=2700\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:34:28,687]\u001b[0m Trial 1 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.11504059859668476, 'num_leaves': 429, 'max_depth': 5, 'min_data_in_leaf': 2700, 'lambda_l1': 0, 'lambda_l2': 80, 'min_gain_to_split': 12.897968954825165, 'bagging_fraction': 0.5, 'bagging_freq': 1, 'feature_fraction': 0.6000000000000001}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=95, reg_alpha=0.0 will be ignored. Current value: lambda_l1=95\n", "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n", "[LightGBM] [Warning] min_gain_to_split is set=14.313864695190833, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=14.313864695190833\n", "[LightGBM] [Warning] lambda_l2 is set=75, reg_lambda=0.0 will be ignored. Current value: lambda_l2=75\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=1900, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=1900\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:34:38,462]\u001b[0m Trial 2 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.1151009425997454, 'num_leaves': 801, 'max_depth': 9, 'min_data_in_leaf': 1900, 'lambda_l1': 95, 'lambda_l2': 75, 'min_gain_to_split': 14.313864695190833, 'bagging_fraction': 1.0, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=30, reg_alpha=0.0 will be ignored. Current value: lambda_l1=30\n", "[LightGBM] [Warning] bagging_fraction is set=0.4, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4\n", "[LightGBM] [Warning] min_gain_to_split is set=5.960199375838675, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=5.960199375838675\n", "[LightGBM] [Warning] lambda_l2 is set=30, reg_lambda=0.0 will be ignored. Current value: lambda_l2=30\n", "[LightGBM] [Warning] feature_fraction is set=0.6000000000000001, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6000000000000001\n", "[LightGBM] [Warning] min_data_in_leaf is set=8300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=8300\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:34:45,924]\u001b[0m Trial 3 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.12014107758913735, 'num_leaves': 2401, 'max_depth': 8, 'min_data_in_leaf': 8300, 'lambda_l1': 30, 'lambda_l2': 30, 'min_gain_to_split': 5.960199375838675, 'bagging_fraction': 0.4, 'bagging_freq': 1, 'feature_fraction': 0.6000000000000001}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=70, reg_alpha=0.0 will be ignored. Current value: lambda_l1=70\n", "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n", "[LightGBM] [Warning] min_gain_to_split is set=4.43848158184257, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=4.43848158184257\n", "[LightGBM] [Warning] lambda_l2 is set=45, reg_lambda=0.0 will be ignored. Current value: lambda_l2=45\n", "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8\n", "[LightGBM] [Warning] min_data_in_leaf is set=8900, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=8900\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:34:55,384]\u001b[0m Trial 4 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.1307839513109744, 'num_leaves': 2953, 'max_depth': 12, 'min_data_in_leaf': 8900, 'lambda_l1': 70, 'lambda_l2': 45, 'min_gain_to_split': 4.43848158184257, 'bagging_fraction': 0.8, 'bagging_freq': 1, 'feature_fraction': 0.8}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=100, reg_alpha=0.0 will be ignored. Current value: lambda_l1=100\n", "[LightGBM] [Warning] bagging_fraction is set=0.30000000000000004, subsample=1.0 will be ignored. Current value: bagging_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_gain_to_split is set=6.109815650332015, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=6.109815650332015\n", "[LightGBM] [Warning] lambda_l2 is set=35, reg_lambda=0.0 will be ignored. Current value: lambda_l2=35\n", "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8\n", "[LightGBM] [Warning] min_data_in_leaf is set=9000, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=9000\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:35:07,378]\u001b[0m Trial 5 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.18517285387058852, 'num_leaves': 1909, 'max_depth': 8, 'min_data_in_leaf': 9000, 'lambda_l1': 100, 'lambda_l2': 35, 'min_gain_to_split': 6.109815650332015, 'bagging_fraction': 0.30000000000000004, 'bagging_freq': 1, 'feature_fraction': 0.8}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=50, reg_alpha=0.0 will be ignored. Current value: lambda_l1=50\n", "[LightGBM] [Warning] bagging_fraction is set=0.4, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4\n", "[LightGBM] [Warning] min_gain_to_split is set=10.899572557213567, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=10.899572557213567\n", "[LightGBM] [Warning] lambda_l2 is set=10, reg_lambda=0.0 will be ignored. Current value: lambda_l2=10\n", "[LightGBM] [Warning] feature_fraction is set=0.6000000000000001, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6000000000000001\n", "[LightGBM] [Warning] min_data_in_leaf is set=5200, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=5200\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:35:19,309]\u001b[0m Trial 6 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.1571250538717205, 'num_leaves': 2607, 'max_depth': 11, 'min_data_in_leaf': 5200, 'lambda_l1': 50, 'lambda_l2': 10, 'min_gain_to_split': 10.899572557213567, 'bagging_fraction': 0.4, 'bagging_freq': 1, 'feature_fraction': 0.6000000000000001}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=5, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5\n", "[LightGBM] [Warning] bagging_fraction is set=0.30000000000000004, subsample=1.0 will be ignored. Current value: bagging_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_gain_to_split is set=6.334952059577612, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=6.334952059577612\n", "[LightGBM] [Warning] lambda_l2 is set=30, reg_lambda=0.0 will be ignored. Current value: lambda_l2=30\n", "[LightGBM] [Warning] feature_fraction is set=0.2, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2\n", "[LightGBM] [Warning] min_data_in_leaf is set=4900, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=4900\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:35:31,164]\u001b[0m Trial 7 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.048739801770326135, 'num_leaves': 1405, 'max_depth': 10, 'min_data_in_leaf': 4900, 'lambda_l1': 5, 'lambda_l2': 30, 'min_gain_to_split': 6.334952059577612, 'bagging_fraction': 0.30000000000000004, 'bagging_freq': 1, 'feature_fraction': 0.2}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=50, reg_alpha=0.0 will be ignored. Current value: lambda_l1=50\n", "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n", "[LightGBM] [Warning] min_gain_to_split is set=3.890437542816328, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=3.890437542816328\n", "[LightGBM] [Warning] lambda_l2 is set=95, reg_lambda=0.0 will be ignored. Current value: lambda_l2=95\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=6900, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6900\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:35:42,868]\u001b[0m Trial 8 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.04187844624454774, 'num_leaves': 457, 'max_depth': 5, 'min_data_in_leaf': 6900, 'lambda_l1': 50, 'lambda_l2': 95, 'min_gain_to_split': 3.890437542816328, 'bagging_fraction': 1.0, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=20, reg_alpha=0.0 will be ignored. Current value: lambda_l1=20\n", "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n", "[LightGBM] [Warning] min_gain_to_split is set=6.679521462033761, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=6.679521462033761\n", "[LightGBM] [Warning] lambda_l2 is set=30, reg_lambda=0.0 will be ignored. Current value: lambda_l2=30\n", "[LightGBM] [Warning] feature_fraction is set=1.0, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=1.0\n", "[LightGBM] [Warning] min_data_in_leaf is set=5000, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=5000\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:35:55,015]\u001b[0m Trial 9 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.23567957313293242, 'num_leaves': 2644, 'max_depth': 1, 'min_data_in_leaf': 5000, 'lambda_l1': 20, 'lambda_l2': 30, 'min_gain_to_split': 6.679521462033761, 'bagging_fraction': 0.2, 'bagging_freq': 1, 'feature_fraction': 1.0}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=70, reg_alpha=0.0 will be ignored. Current value: lambda_l1=70\n", "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", "[LightGBM] [Warning] min_gain_to_split is set=0.0495655673939801, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.0495655673939801\n", "[LightGBM] [Warning] lambda_l2 is set=65, reg_lambda=0.0 will be ignored. Current value: lambda_l2=65\n", "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n", "[LightGBM] [Warning] min_data_in_leaf is set=6700, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6700\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:36:06,803]\u001b[0m Trial 10 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.2946061639514809, 'num_leaves': 21, 'max_depth': 2, 'min_data_in_leaf': 6700, 'lambda_l1': 70, 'lambda_l2': 65, 'min_gain_to_split': 0.0495655673939801, 'bagging_fraction': 0.7, 'bagging_freq': 1, 'feature_fraction': 0.4}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=5, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5\n", "[LightGBM] [Warning] bagging_fraction is set=0.6000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=10.236199545707844, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=10.236199545707844\n", "[LightGBM] [Warning] lambda_l2 is set=100, reg_lambda=0.0 will be ignored. Current value: lambda_l2=100\n", "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n", "[LightGBM] [Warning] min_data_in_leaf is set=1600, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=1600\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:36:18,662]\u001b[0m Trial 11 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.07654315003382442, 'num_leaves': 1107, 'max_depth': 5, 'min_data_in_leaf': 1600, 'lambda_l1': 5, 'lambda_l2': 100, 'min_gain_to_split': 10.236199545707844, 'bagging_fraction': 0.6000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.5}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=25, reg_alpha=0.0 will be ignored. Current value: lambda_l1=25\n", "[LightGBM] [Warning] bagging_fraction is set=0.6000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=14.77542754344958, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=14.77542754344958\n", "[LightGBM] [Warning] lambda_l2 is set=80, reg_lambda=0.0 will be ignored. Current value: lambda_l2=80\n", "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8\n", "[LightGBM] [Warning] min_data_in_leaf is set=500, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=500\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:36:29,582]\u001b[0m Trial 12 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.015759900691112827, 'num_leaves': 98, 'max_depth': 6, 'min_data_in_leaf': 500, 'lambda_l1': 25, 'lambda_l2': 80, 'min_gain_to_split': 14.77542754344958, 'bagging_fraction': 0.6000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.8}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=0, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0\n", "[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5\n", "[LightGBM] [Warning] min_gain_to_split is set=9.610990428540086, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=9.610990428540086\n", "[LightGBM] [Warning] lambda_l2 is set=85, reg_lambda=0.0 will be ignored. Current value: lambda_l2=85\n", "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n", "[LightGBM] [Warning] min_data_in_leaf is set=3300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=3300\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:36:40,590]\u001b[0m Trial 13 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.08702617243979525, 'num_leaves': 627, 'max_depth': 3, 'min_data_in_leaf': 3300, 'lambda_l1': 0, 'lambda_l2': 85, 'min_gain_to_split': 9.610990428540086, 'bagging_fraction': 0.5, 'bagging_freq': 1, 'feature_fraction': 0.4}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=35, reg_alpha=0.0 will be ignored. Current value: lambda_l1=35\n", "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n", "[LightGBM] [Warning] min_gain_to_split is set=12.424390908780744, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=12.424390908780744\n", "[LightGBM] [Warning] lambda_l2 is set=60, reg_lambda=0.0 will be ignored. Current value: lambda_l2=60\n", "[LightGBM] [Warning] feature_fraction is set=0.2, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2\n", "[LightGBM] [Warning] min_data_in_leaf is set=3500, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=3500\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:36:52,510]\u001b[0m Trial 14 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.09557487823238078, 'num_leaves': 1065, 'max_depth': 4, 'min_data_in_leaf': 3500, 'lambda_l1': 35, 'lambda_l2': 60, 'min_gain_to_split': 12.424390908780744, 'bagging_fraction': 0.8, 'bagging_freq': 1, 'feature_fraction': 0.2}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=15, reg_alpha=0.0 will be ignored. Current value: lambda_l1=15\n", "[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5\n", "[LightGBM] [Warning] min_gain_to_split is set=8.574127666768725, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=8.574127666768725\n", "[LightGBM] [Warning] lambda_l2 is set=90, reg_lambda=0.0 will be ignored. Current value: lambda_l2=90\n", "[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7\n", "[LightGBM] [Warning] min_data_in_leaf is set=7100, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=7100\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:37:04,575]\u001b[0m Trial 15 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.06595465709544801, 'num_leaves': 369, 'max_depth': 7, 'min_data_in_leaf': 7100, 'lambda_l1': 15, 'lambda_l2': 90, 'min_gain_to_split': 8.574127666768725, 'bagging_fraction': 0.5, 'bagging_freq': 1, 'feature_fraction': 0.7}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=40, reg_alpha=0.0 will be ignored. Current value: lambda_l1=40\n", "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", "[LightGBM] [Warning] min_gain_to_split is set=12.303673747764964, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=12.303673747764964\n", "[LightGBM] [Warning] lambda_l2 is set=70, reg_lambda=0.0 will be ignored. Current value: lambda_l2=70\n", "[LightGBM] [Warning] feature_fraction is set=1.0, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=1.0\n", "[LightGBM] [Warning] min_data_in_leaf is set=10000, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10000\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:37:16,632]\u001b[0m Trial 16 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.026996946500490157, 'num_leaves': 1675, 'max_depth': 6, 'min_data_in_leaf': 10000, 'lambda_l1': 40, 'lambda_l2': 70, 'min_gain_to_split': 12.303673747764964, 'bagging_fraction': 0.7, 'bagging_freq': 1, 'feature_fraction': 1.0}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=65, reg_alpha=0.0 will be ignored. Current value: lambda_l1=65\n", "[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5\n", "[LightGBM] [Warning] min_gain_to_split is set=8.77134204318839, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=8.77134204318839\n", "[LightGBM] [Warning] lambda_l2 is set=100, reg_lambda=0.0 will be ignored. Current value: lambda_l2=100\n", "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n", "[LightGBM] [Warning] min_data_in_leaf is set=3900, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=3900\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:37:28,670]\u001b[0m Trial 17 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.1553316048661736, 'num_leaves': 1037, 'max_depth': 8, 'min_data_in_leaf': 3900, 'lambda_l1': 65, 'lambda_l2': 100, 'min_gain_to_split': 8.77134204318839, 'bagging_fraction': 0.5, 'bagging_freq': 1, 'feature_fraction': 0.4}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=15, reg_alpha=0.0 will be ignored. Current value: lambda_l1=15\n", "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n", "[LightGBM] [Warning] min_gain_to_split is set=8.154359383408714, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=8.154359383408714\n", "[LightGBM] [Warning] lambda_l2 is set=55, reg_lambda=0.0 will be ignored. Current value: lambda_l2=55\n", "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n", "[LightGBM] [Warning] min_data_in_leaf is set=2200, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=2200\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:37:40,738]\u001b[0m Trial 18 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.06841404194619143, 'num_leaves': 297, 'max_depth': 4, 'min_data_in_leaf': 2200, 'lambda_l1': 15, 'lambda_l2': 55, 'min_gain_to_split': 8.154359383408714, 'bagging_fraction': 0.8, 'bagging_freq': 1, 'feature_fraction': 0.5}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=85, reg_alpha=0.0 will be ignored. Current value: lambda_l1=85\n", "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", "[LightGBM] [Warning] min_gain_to_split is set=11.52950845096658, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=11.52950845096658\n", "[LightGBM] [Warning] lambda_l2 is set=85, reg_lambda=0.0 will be ignored. Current value: lambda_l2=85\n", "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n", "[LightGBM] [Warning] min_data_in_leaf is set=6200, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6200\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:37:52,624]\u001b[0m Trial 19 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.011709431474256016, 'num_leaves': 813, 'max_depth': 7, 'min_data_in_leaf': 6200, 'lambda_l1': 85, 'lambda_l2': 85, 'min_gain_to_split': 11.52950845096658, 'bagging_fraction': 0.7, 'bagging_freq': 1, 'feature_fraction': 0.5}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=40, reg_alpha=0.0 will be ignored. Current value: lambda_l1=40\n", "[LightGBM] [Warning] bagging_fraction is set=0.4, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4\n", "[LightGBM] [Warning] min_gain_to_split is set=13.950871126172208, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=13.950871126172208\n", "[LightGBM] [Warning] lambda_l2 is set=5, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5\n", "[LightGBM] [Warning] feature_fraction is set=0.7, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7\n", "[LightGBM] [Warning] min_data_in_leaf is set=7700, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=7700\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:38:04,509]\u001b[0m Trial 20 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.054761961669109974, 'num_leaves': 1313, 'max_depth': 10, 'min_data_in_leaf': 7700, 'lambda_l1': 40, 'lambda_l2': 5, 'min_gain_to_split': 13.950871126172208, 'bagging_fraction': 0.4, 'bagging_freq': 1, 'feature_fraction': 0.7}. Best is trial 0 with value: 7677095207.783831.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=95, reg_alpha=0.0 will be ignored. Current value: lambda_l1=95\n", "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n", "[LightGBM] [Warning] min_gain_to_split is set=13.507670090004288, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=13.507670090004288\n", "[LightGBM] [Warning] lambda_l2 is set=75, reg_lambda=0.0 will be ignored. Current value: lambda_l2=75\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=400, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=400\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n", "Early stopping, best iteration is:\n", "[3323]\tvalid_0's rmse: 42812.7\tvalid_0's l2: 1.83292e+09\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:38:51,496]\u001b[0m Trial 21 finished with value: 1832924867.566719 and parameters: {'n_estimators': 10000, 'learning_rate': 0.10889427150094211, 'num_leaves': 720, 'max_depth': 9, 'min_data_in_leaf': 400, 'lambda_l1': 95, 'lambda_l2': 75, 'min_gain_to_split': 13.507670090004288, 'bagging_fraction': 1.0, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 21 with value: 1832924867.566719.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[LightGBM] [Warning] lambda_l1 is set=55, reg_alpha=0.0 will be ignored. Current value: lambda_l1=55\n", "[LightGBM] [Warning] bagging_fraction is set=0.9000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=13.398196528468796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=13.398196528468796\n", "[LightGBM] [Warning] lambda_l2 is set=75, reg_lambda=0.0 will be ignored. Current value: lambda_l2=75\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=100, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=100\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:39:11,604]\u001b[0m Trial 22 finished with value: 1107111591.7178745 and parameters: {'n_estimators': 10000, 'learning_rate': 0.09227174052619615, 'num_leaves': 634, 'max_depth': 9, 'min_data_in_leaf': 100, 'lambda_l1': 55, 'lambda_l2': 75, 'min_gain_to_split': 13.398196528468796, 'bagging_fraction': 0.9000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 22 with value: 1107111591.7178745.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[846]\tvalid_0's rmse: 33273.3\tvalid_0's l2: 1.10711e+09\n", "[LightGBM] [Warning] lambda_l1 is set=60, reg_alpha=0.0 will be ignored. Current value: lambda_l1=60\n", "[LightGBM] [Warning] bagging_fraction is set=0.9000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=13.762533690381279, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=13.762533690381279\n", "[LightGBM] [Warning] lambda_l2 is set=45, reg_lambda=0.0 will be ignored. Current value: lambda_l2=45\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n", "Early stopping, best iteration is:\n", "[5841]\tvalid_0's rmse: 36312.2\tvalid_0's l2: 1.31858e+09\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:40:47,411]\u001b[0m Trial 23 finished with value: 1318578467.2025561 and parameters: {'n_estimators': 10000, 'learning_rate': 0.0923827721243192, 'num_leaves': 736, 'max_depth': 9, 'min_data_in_leaf': 300, 'lambda_l1': 60, 'lambda_l2': 45, 'min_gain_to_split': 13.762533690381279, 'bagging_fraction': 0.9000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 22 with value: 1107111591.7178745.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[LightGBM] [Warning] lambda_l1 is set=85, reg_alpha=0.0 will be ignored. Current value: lambda_l1=85\n", "[LightGBM] [Warning] bagging_fraction is set=0.9000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=13.545820149509085, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=13.545820149509085\n", "[LightGBM] [Warning] lambda_l2 is set=45, reg_lambda=0.0 will be ignored. Current value: lambda_l2=45\n", "[LightGBM] [Warning] feature_fraction is set=0.2, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2\n", "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:41:00,385]\u001b[0m Trial 24 pruned. Trial was pruned at iteration 3001.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:41:12,586]\u001b[0m Trial 25 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.13458164230137284, 'num_leaves': 1704, 'max_depth': 12, 'min_data_in_leaf': 1100, 'lambda_l1': 60, 'lambda_l2': 50, 'min_gain_to_split': 13.338329697016016, 'bagging_fraction': 0.9000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 22 with value: 1107111591.7178745.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=85, reg_alpha=0.0 will be ignored. Current value: lambda_l1=85\n", "[LightGBM] [Warning] bagging_fraction is set=0.9000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=14.848214343688522, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=14.848214343688522\n", "[LightGBM] [Warning] lambda_l2 is set=65, reg_lambda=0.0 will be ignored. Current value: lambda_l2=65\n", "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n", "[LightGBM] [Warning] min_data_in_leaf is set=1000, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=1000\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:41:24,704]\u001b[0m Trial 26 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.10443947755166548, 'num_leaves': 966, 'max_depth': 9, 'min_data_in_leaf': 1000, 'lambda_l1': 85, 'lambda_l2': 65, 'min_gain_to_split': 14.848214343688522, 'bagging_fraction': 0.9000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.4}. Best is trial 22 with value: 1107111591.7178745.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=60, reg_alpha=0.0 will be ignored. Current value: lambda_l1=60\n", "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n", "[LightGBM] [Warning] min_gain_to_split is set=11.501706843464298, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=11.501706843464298\n", "[LightGBM] [Warning] lambda_l2 is set=20, reg_lambda=0.0 will be ignored. Current value: lambda_l2=20\n", "[LightGBM] [Warning] feature_fraction is set=0.2, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2\n", "[LightGBM] [Warning] min_data_in_leaf is set=400, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=400\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:41:37,063]\u001b[0m Trial 27 pruned. Trial was pruned at iteration 3001.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:41:49,029]\u001b[0m Trial 28 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.0772064072401324, 'num_leaves': 1276, 'max_depth': 9, 'min_data_in_leaf': 2600, 'lambda_l1': 75, 'lambda_l2': 40, 'min_gain_to_split': 12.902493334464323, 'bagging_fraction': 0.9000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 22 with value: 1107111591.7178745.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=55, reg_alpha=0.0 will be ignored. Current value: lambda_l1=55\n", "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n", "[LightGBM] [Warning] min_gain_to_split is set=13.827512822883651, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=13.827512822883651\n", "[LightGBM] [Warning] lambda_l2 is set=60, reg_lambda=0.0 will be ignored. Current value: lambda_l2=60\n", "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n", "[LightGBM] [Warning] min_data_in_leaf is set=100, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=100\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:42:07,039]\u001b[0m Trial 29 finished with value: 1079064009.7672784 and parameters: {'n_estimators': 10000, 'learning_rate': 0.08828308704850689, 'num_leaves': 256, 'max_depth': 11, 'min_data_in_leaf': 100, 'lambda_l1': 55, 'lambda_l2': 60, 'min_gain_to_split': 13.827512822883651, 'bagging_fraction': 1.0, 'bagging_freq': 1, 'feature_fraction': 0.4}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[574]\tvalid_0's rmse: 32849.1\tvalid_0's l2: 1.07906e+09\n", "[LightGBM] [Warning] lambda_l1 is set=50, reg_alpha=0.0 will be ignored. Current value: lambda_l1=50\n", "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n", "[LightGBM] [Warning] min_gain_to_split is set=11.694971746749147, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=11.694971746749147\n", "[LightGBM] [Warning] lambda_l2 is set=70, reg_lambda=0.0 will be ignored. Current value: lambda_l2=70\n", "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n", "[LightGBM] [Warning] min_data_in_leaf is set=1100, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=1100\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:42:19,203]\u001b[0m Trial 30 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.06442579015409629, 'num_leaves': 214, 'max_depth': 11, 'min_data_in_leaf': 1100, 'lambda_l1': 50, 'lambda_l2': 70, 'min_gain_to_split': 11.694971746749147, 'bagging_fraction': 0.8, 'bagging_freq': 1, 'feature_fraction': 0.4}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=55, reg_alpha=0.0 will be ignored. Current value: lambda_l1=55\n", "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n", "[LightGBM] [Warning] min_gain_to_split is set=14.946678494908936, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=14.946678494908936\n", "[LightGBM] [Warning] lambda_l2 is set=55, reg_lambda=0.0 will be ignored. Current value: lambda_l2=55\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=1500, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=1500\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:42:31,438]\u001b[0m Trial 31 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.08785820403511613, 'num_leaves': 547, 'max_depth': 9, 'min_data_in_leaf': 1500, 'lambda_l1': 55, 'lambda_l2': 55, 'min_gain_to_split': 14.946678494908936, 'bagging_fraction': 1.0, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=75, reg_alpha=0.0 will be ignored. Current value: lambda_l1=75\n", "[LightGBM] [Warning] bagging_fraction is set=0.9000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=13.417981622785735, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=13.417981622785735\n", "[LightGBM] [Warning] lambda_l2 is set=75, reg_lambda=0.0 will be ignored. Current value: lambda_l2=75\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=200, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=200\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n", "Early stopping, best iteration is:\n", "[2322]\tvalid_0's rmse: 34188.4\tvalid_0's l2: 1.16885e+09\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:43:08,448]\u001b[0m Trial 32 finished with value: 1168846213.1658614 and parameters: {'n_estimators': 10000, 'learning_rate': 0.1001120754241906, 'num_leaves': 194, 'max_depth': 11, 'min_data_in_leaf': 200, 'lambda_l1': 75, 'lambda_l2': 75, 'min_gain_to_split': 13.417981622785735, 'bagging_fraction': 0.9000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[LightGBM] [Warning] lambda_l1 is set=75, reg_alpha=0.0 will be ignored. Current value: lambda_l1=75\n", "[LightGBM] [Warning] bagging_fraction is set=0.9000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=14.181878655995135, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=14.181878655995135\n", "[LightGBM] [Warning] lambda_l2 is set=60, reg_lambda=0.0 will be ignored. Current value: lambda_l2=60\n", "[LightGBM] [Warning] feature_fraction is set=0.2, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2\n", "[LightGBM] [Warning] min_data_in_leaf is set=200, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=200\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:43:21,360]\u001b[0m Trial 33 pruned. Trial was pruned at iteration 3001.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:43:33,494]\u001b[0m Trial 34 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.1199631580462772, 'num_leaves': 436, 'max_depth': 11, 'min_data_in_leaf': 900, 'lambda_l1': 45, 'lambda_l2': 75, 'min_gain_to_split': 12.55177360084849, 'bagging_fraction': 0.9000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.5}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=60, reg_alpha=0.0 will be ignored. Current value: lambda_l1=60\n", "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n", "[LightGBM] [Warning] min_gain_to_split is set=13.944244965174844, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=13.944244965174844\n", "[LightGBM] [Warning] lambda_l2 is set=50, reg_lambda=0.0 will be ignored. Current value: lambda_l2=50\n", "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n", "[LightGBM] [Warning] min_data_in_leaf is set=2500, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=2500\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:43:45,540]\u001b[0m Trial 35 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.10316563838652494, 'num_leaves': 129, 'max_depth': 10, 'min_data_in_leaf': 2500, 'lambda_l1': 60, 'lambda_l2': 50, 'min_gain_to_split': 13.944244965174844, 'bagging_fraction': 0.8, 'bagging_freq': 1, 'feature_fraction': 0.4}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=80, reg_alpha=0.0 will be ignored. Current value: lambda_l1=80\n", "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n", "[LightGBM] [Warning] min_gain_to_split is set=12.982507743187444, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=12.982507743187444\n", "[LightGBM] [Warning] lambda_l2 is set=65, reg_lambda=0.0 will be ignored. Current value: lambda_l2=65\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=1800, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=1800\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:43:57,471]\u001b[0m Trial 36 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.05770960916919808, 'num_leaves': 858, 'max_depth': 12, 'min_data_in_leaf': 1800, 'lambda_l1': 80, 'lambda_l2': 65, 'min_gain_to_split': 12.982507743187444, 'bagging_fraction': 1.0, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=65, reg_alpha=0.0 will be ignored. Current value: lambda_l1=65\n", "[LightGBM] [Warning] bagging_fraction is set=0.9000000000000001, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9000000000000001\n", "[LightGBM] [Warning] min_gain_to_split is set=12.045373038720337, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=12.045373038720337\n", "[LightGBM] [Warning] lambda_l2 is set=80, reg_lambda=0.0 will be ignored. Current value: lambda_l2=80\n", "[LightGBM] [Warning] feature_fraction is set=0.30000000000000004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.30000000000000004\n", "[LightGBM] [Warning] min_data_in_leaf is set=100, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=100\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:44:17,643]\u001b[0m Trial 37 finished with value: 1101202442.7758107 and parameters: {'n_estimators': 10000, 'learning_rate': 0.12879999860826072, 'num_leaves': 2123, 'max_depth': 10, 'min_data_in_leaf': 100, 'lambda_l1': 65, 'lambda_l2': 80, 'min_gain_to_split': 12.045373038720337, 'bagging_fraction': 0.9000000000000001, 'bagging_freq': 1, 'feature_fraction': 0.30000000000000004}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[578]\tvalid_0's rmse: 33184.4\tvalid_0's l2: 1.1012e+09\n", "[LightGBM] [Warning] lambda_l1 is set=70, reg_alpha=0.0 will be ignored. Current value: lambda_l1=70\n", "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n", "[LightGBM] [Warning] min_gain_to_split is set=10.80010717058277, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=10.80010717058277\n", "[LightGBM] [Warning] lambda_l2 is set=90, reg_lambda=0.0 will be ignored. Current value: lambda_l2=90\n", "[LightGBM] [Warning] feature_fraction is set=0.2, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2\n", "[LightGBM] [Warning] min_data_in_leaf is set=1400, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=1400\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:44:29,611]\u001b[0m Trial 38 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.14294306750018326, 'num_leaves': 2348, 'max_depth': 11, 'min_data_in_leaf': 1400, 'lambda_l1': 70, 'lambda_l2': 90, 'min_gain_to_split': 10.80010717058277, 'bagging_fraction': 1.0, 'bagging_freq': 1, 'feature_fraction': 0.2}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n", "[LightGBM] [Warning] lambda_l1 is set=65, reg_alpha=0.0 will be ignored. Current value: lambda_l1=65\n", "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n", "[LightGBM] [Warning] min_gain_to_split is set=12.095118183555895, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=12.095118183555895\n", "[LightGBM] [Warning] lambda_l2 is set=80, reg_lambda=0.0 will be ignored. Current value: lambda_l2=80\n", "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n", "[LightGBM] [Warning] min_data_in_leaf is set=3100, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=3100\n", "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", "Training until validation scores don't improve for 3000 rounds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[32m[I 2023-04-17 01:44:41,634]\u001b[0m Trial 39 finished with value: 7677095207.783831 and parameters: {'n_estimators': 10000, 'learning_rate': 0.1253824923018295, 'num_leaves': 2117, 'max_depth': 10, 'min_data_in_leaf': 3100, 'lambda_l1': 65, 'lambda_l2': 80, 'min_gain_to_split': 12.095118183555895, 'bagging_fraction': 0.8, 'bagging_freq': 1, 'feature_fraction': 0.5}. Best is trial 29 with value: 1079064009.7672784.\u001b[0m\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Early stopping, best iteration is:\n", "[1]\tvalid_0's rmse: 203819\tvalid_0's l2: 4.15423e+10\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "print(f\"\\tBest value (rmse): {study.best_value:.5f}\")\n", "print(f\"\\tBest params:\")\n", "\n", "for key, value in study.best_params.items():\n", " print(f\"\\t\\t{key}: {value}\")\n", "\n", "print(\"Best trial: \")\n", "print(study.best_trial )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "h14dg44PsRZK", "outputId": "c8afdeb4-64e8-470c-b6fd-0a5329566d12" }, "execution_count": 109, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\tBest value (rmse): 1079064009.76728\n", "\tBest params:\n", "\t\tn_estimators: 10000\n", "\t\tlearning_rate: 0.08828308704850689\n", "\t\tnum_leaves: 256\n", "\t\tmax_depth: 11\n", "\t\tmin_data_in_leaf: 100\n", "\t\tlambda_l1: 55\n", "\t\tlambda_l2: 60\n", "\t\tmin_gain_to_split: 13.827512822883651\n", "\t\tbagging_fraction: 1.0\n", "\t\tbagging_freq: 1\n", "\t\tfeature_fraction: 0.4\n", "Best trial: \n", "FrozenTrial(number=29, state=TrialState.COMPLETE, values=[1079064009.7672784], datetime_start=datetime.datetime(2023, 4, 17, 1, 41, 49, 31820), datetime_complete=datetime.datetime(2023, 4, 17, 1, 42, 7, 20694), params={'n_estimators': 10000, 'learning_rate': 0.08828308704850689, 'num_leaves': 256, 'max_depth': 11, 'min_data_in_leaf': 100, 'lambda_l1': 55, 'lambda_l2': 60, 'min_gain_to_split': 13.827512822883651, 'bagging_fraction': 1.0, 'bagging_freq': 1, 'feature_fraction': 0.4}, user_attrs={}, system_attrs={}, intermediate_values={0: 84711.4188050922, 1: 82113.52771845245, 2: 79087.15153097108, 3: 76669.6362643588, 4: 74024.20304940095, 5: 71785.41804205524, 6: 69747.66431655476, 7: 67670.91745829608, 8: 65905.8746582046, 9: 64167.65644782391, 10: 62882.347115864504, 11: 61440.538246472395, 12: 59977.786554524326, 13: 58857.09541225565, 14: 57667.5724378964, 15: 56426.47129086571, 16: 55378.2453350199, 17: 54490.590370537866, 18: 53626.73504460004, 19: 52941.41960957028, 20: 52040.60014277087, 21: 51473.98465556567, 22: 50817.72445450128, 23: 50157.47558450927, 24: 49570.39802681609, 25: 49062.944502361366, 26: 48561.31060245953, 27: 48089.035273815585, 28: 47658.15226434289, 29: 47229.21935759269, 30: 46835.53365226038, 31: 46481.920817926446, 32: 46165.6544156111, 33: 45811.046180299854, 34: 45506.65784710256, 35: 45165.32061351448, 36: 44834.71308424177, 37: 44544.01853607058, 38: 44253.05332067695, 39: 43947.18212195761, 40: 43696.47050097573, 41: 43495.00367609887, 42: 43277.380573878334, 43: 43085.48746378272, 44: 42870.50462824421, 45: 42682.10025151194, 46: 42507.411486010365, 47: 42333.03443824501, 48: 42143.84702917458, 49: 41954.19694145964, 50: 41797.53628802923, 51: 41649.525806210855, 52: 41476.90598182128, 53: 41331.75071595733, 54: 41205.62074019448, 55: 41059.07571489963, 56: 40936.920871464674, 57: 40828.450833414, 58: 40726.14225089009, 59: 40600.69521994544, 60: 40471.82767199826, 61: 40355.34611680261, 62: 40233.963515647054, 63: 40144.23307296611, 64: 40056.48157267314, 65: 39964.718782256, 66: 39895.878561087186, 67: 39827.71677122123, 68: 39743.40446245671, 69: 39641.34524940661, 70: 39560.522183007226, 71: 39486.12791087255, 72: 39409.471127316894, 73: 39318.13951172387, 74: 39250.799429301434, 75: 39185.7877257607, 76: 39106.16071588017, 77: 39039.601671530705, 78: 38978.0812064675, 79: 38912.65429158117, 80: 38833.40199277891, 81: 38759.57739996201, 82: 38689.591563989394, 83: 38610.585320429556, 84: 38549.241391680735, 85: 38494.75137337186, 86: 38455.00938076722, 87: 38408.4281215161, 88: 38361.76454992371, 89: 38298.861139633445, 90: 38249.059146580024, 91: 38181.76556458771, 92: 38115.71055462305, 93: 38058.03374517953, 94: 37979.23688274108, 95: 37927.121075452735, 96: 37882.24908983378, 97: 37816.08112298688, 98: 37796.335566249734, 99: 37760.075015089, 100: 37730.52651943236, 101: 37689.853964167225, 102: 37651.147580593184, 103: 37519.45307653714, 104: 37485.47417761902, 105: 37434.209243648445, 106: 37399.17214048035, 107: 37355.06766257382, 108: 37321.223253137985, 109: 37276.39584006543, 110: 37252.997182252504, 111: 37217.61961547992, 112: 37177.472557165456, 113: 37151.556036783186, 114: 37135.74802865515, 115: 37100.54550986841, 116: 37061.311599466586, 117: 37025.868338525484, 118: 36965.48073627555, 119: 36944.673520066375, 120: 36899.46676701424, 121: 36871.47630110515, 122: 36851.10375708465, 123: 36813.28326364152, 124: 36756.82883342006, 125: 36720.09961092292, 126: 36674.136802879846, 127: 36647.48811265943, 128: 36589.73601712268, 129: 36563.44994388873, 130: 36537.22163632992, 131: 36473.85828441783, 132: 36456.13338506974, 133: 36374.5331506975, 134: 36339.407952289046, 135: 36314.99144398688, 136: 36266.61581002614, 137: 36242.59495457293, 138: 36166.58630108669, 139: 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33931.05903124452, 3322: 33932.12650968938, 3323: 33930.000549735916, 3324: 33930.426761510185, 3325: 33932.88366816673, 3326: 33932.65630620196, 3327: 33929.574932202566, 3328: 33927.99547269583, 3329: 33925.2766150912, 3330: 33924.65934473318, 3331: 33923.738853082155, 3332: 33920.06020327726, 3333: 33916.621674753565, 3334: 33915.74609806284, 3335: 33916.88302010726, 3336: 33915.97445598885, 3337: 33916.22092813421, 3338: 33914.67763641472, 3339: 33914.81418554382, 3340: 33917.85320368868, 3341: 33916.04481802345, 3342: 33915.50894292036, 3343: 33917.10144038709, 3344: 33916.265375092786, 3345: 33917.02748484863, 3346: 33917.734296385446, 3347: 33920.464663619074, 3348: 33919.048454921445, 3349: 33916.8986268918, 3350: 33913.564261184314, 3351: 33913.846850175236, 3352: 33919.245455784854, 3353: 33918.24167821827, 3354: 33918.95100275103, 3355: 33918.786342793945, 3356: 33919.88189427019, 3357: 33920.792621024186, 3358: 33920.27738423891, 3359: 33919.29703094799, 3360: 33918.09543472035, 3361: 33922.30527613371, 3362: 33924.262270331, 3363: 33924.97132368214, 3364: 33928.41905220658, 3365: 33929.47854673071, 3366: 33928.427544708495, 3367: 33928.76223387565, 3368: 33928.11768242816, 3369: 33925.388775276086, 3370: 33924.58097767135, 3371: 33923.79588514362, 3372: 33924.445896090685, 3373: 33923.864189167594, 3374: 33922.994774396204, 3375: 33923.43510659274, 3376: 33923.16980840166, 3377: 33920.75471681239, 3378: 33919.66968223756, 3379: 33921.87497749369, 3380: 33919.9690361289, 3381: 33920.70744588776, 3382: 33919.79593176932, 3383: 33918.53564132782, 3384: 33918.87336730688, 3385: 33918.78804286632, 3386: 33921.00469313751, 3387: 33920.21664360838, 3388: 33921.13419879517, 3389: 33918.819366504875, 3390: 33916.94002949115, 3391: 33920.00009027976, 3392: 33921.37027085951, 3393: 33920.11841831088, 3394: 33915.08470469233, 3395: 33912.152349107266, 3396: 33914.22718050195, 3397: 33913.2158141887, 3398: 33912.307190287036, 3399: 33912.73107163422, 3400: 33911.548819884425, 3401: 33915.6180986815, 3402: 33915.42858862289, 3403: 33915.18804752104, 3404: 33917.18415808347, 3405: 33920.50207032077, 3406: 33919.79222621069, 3407: 33921.58054936525, 3408: 33924.71463007617, 3409: 33924.11666908861, 3410: 33924.062440843525, 3411: 33925.072489783655, 3412: 33922.39118956385, 3413: 33924.88637937282, 3414: 33925.585048172834, 3415: 33926.35126525266, 3416: 33924.399220790845, 3417: 33924.67679307373, 3418: 33920.15225664642, 3419: 33919.774529838694, 3420: 33921.2890205124, 3421: 33920.80472809777, 3422: 33919.24545749244, 3423: 33921.86469421649, 3424: 33919.073766930174, 3425: 33920.09780889614, 3426: 33917.48700022527, 3427: 33917.99728958074, 3428: 33919.06634523343, 3429: 33916.34953748519, 3430: 33915.138486640455, 3431: 33914.759701734765, 3432: 33914.14049147861, 3433: 33914.82734025919, 3434: 33917.282255963786, 3435: 33917.1942522434, 3436: 33916.66663604088, 3437: 33918.77859106882, 3438: 33917.88072839694, 3439: 33920.78837267092, 3440: 33923.15747568435, 3441: 33921.44763054334, 3442: 33918.892585796544, 3443: 33919.395914661894, 3444: 33921.34976974181, 3445: 33923.55143023698, 3446: 33923.4170524071, 3447: 33924.77657546516, 3448: 33925.720719851, 3449: 33927.401598526834, 3450: 33926.68083951936, 3451: 33927.54941114538, 3452: 33929.71242571446, 3453: 33934.58331609021, 3454: 33933.547150390434, 3455: 33933.591766972255, 3456: 33930.95287755881, 3457: 33931.429705960225, 3458: 33931.61261241509, 3459: 33933.036972939284, 3460: 33933.753902865195, 3461: 33934.669590761514, 3462: 33935.77051015451, 3463: 33935.46545998604, 3464: 33935.02196173636, 3465: 33935.84783083856, 3466: 33936.27398015846, 3467: 33937.54589099501, 3468: 33936.01417040012, 3469: 33939.09858416115, 3470: 33940.22488120763, 3471: 33938.85075955462, 3472: 33941.45677630884, 3473: 33941.4731729418, 3474: 33941.256298310705, 3475: 33941.56497433836, 3476: 33943.07420130474, 3477: 33942.91789310471, 3478: 33944.092235886266, 3479: 33944.64757411142, 3480: 33945.76129222439, 3481: 33943.96930193282, 3482: 33946.136764353076, 3483: 33948.78171438248, 3484: 33947.06822230418, 3485: 33947.28099969446, 3486: 33948.30762455855, 3487: 33947.04523054277, 3488: 33946.71060281215, 3489: 33947.660477083686, 3490: 33947.45726691891, 3491: 33949.21423069849, 3492: 33947.969528003945, 3493: 33948.69554128644, 3494: 33949.81411333831, 3495: 33948.06134160667, 3496: 33948.36311861087, 3497: 33950.17619028168, 3498: 33949.779682572356, 3499: 33945.08865563393, 3500: 33945.03978007504, 3501: 33943.78442257179, 3502: 33942.41957834672, 3503: 33947.949169540865, 3504: 33948.33792887824, 3505: 33949.30110453043, 3506: 33948.92800818781, 3507: 33952.553049954986, 3508: 33954.04608993284, 3509: 33954.337118822485, 3510: 33951.440104936264, 3511: 33950.93346561069, 3512: 33950.00806731217, 3513: 33949.92470936138, 3514: 33949.97414381509, 3515: 33950.009710989776, 3516: 33950.8963135069, 3517: 33951.05502536899, 3518: 33950.948270905596, 3519: 33955.81681561589, 3520: 33959.058456005136, 3521: 33960.15747866811, 3522: 33962.19761445131, 3523: 33960.54475246431, 3524: 33961.456073927366, 3525: 33963.10347660363, 3526: 33962.45208030286, 3527: 33961.64635090804, 3528: 33961.325794129334, 3529: 33961.50193420062, 3530: 33962.24602108129, 3531: 33959.773792546395, 3532: 33960.431684707815, 3533: 33960.707585910524, 3534: 33959.41397255385, 3535: 33960.644014762045, 3536: 33960.19341739399, 3537: 33957.3580179977, 3538: 33958.71484703312, 3539: 33957.38468150798, 3540: 33959.43725439939, 3541: 33960.51068360742, 3542: 33959.06458536927, 3543: 33958.8637238442, 3544: 33955.94557853273, 3545: 33956.37913778651, 3546: 33956.45517318175, 3547: 33957.50530972573, 3548: 33957.372417655104, 3549: 33958.169453310504, 3550: 33955.321723978566, 3551: 33953.88995790262, 3552: 33956.96304096495, 3553: 33956.50515034199, 3554: 33959.394720703815, 3555: 33959.65299626205, 3556: 33961.21625303985, 3557: 33960.25114591756, 3558: 33961.714059920734, 3559: 33960.828903084235, 3560: 33959.86507615624, 3561: 33959.647051072614, 3562: 33960.45305117093, 3563: 33959.16471344539, 3564: 33960.04016544366, 3565: 33965.383887498974, 3566: 33965.238715889885, 3567: 33967.59416609486, 3568: 33968.32978014328, 3569: 33968.51755152689, 3570: 33968.595442077196, 3571: 33968.15819116927, 3572: 33970.069050958606, 3573: 33970.259414816945}, distributions={'n_estimators': CategoricalDistribution(choices=(10000,)), 'learning_rate': FloatDistribution(high=0.3, log=False, low=0.01, step=None), 'num_leaves': IntDistribution(high=3000, log=False, low=1, step=1), 'max_depth': IntDistribution(high=12, log=False, low=1, step=1), 'min_data_in_leaf': IntDistribution(high=10000, log=False, low=100, step=100), 'lambda_l1': IntDistribution(high=100, log=False, low=0, step=5), 'lambda_l2': IntDistribution(high=100, log=False, low=0, step=5), 'min_gain_to_split': FloatDistribution(high=15.0, log=False, low=0.0, step=None), 'bagging_fraction': FloatDistribution(high=1.0, log=False, low=0.2, step=0.1), 'bagging_freq': CategoricalDistribution(choices=(1,)), 'feature_fraction': FloatDistribution(high=1.0, log=False, low=0.2, step=0.1)}, trial_id=29, value=None)\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "params = study.best_params\n", "params[\"objective\"] = \"regression\"\n", "model = lgb.train(params,\n", " training_data,\n", " valid_sets=valid_data,\n", " early_stopping_rounds = 300\n", " )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7jFHKzi1uju6", "outputId": "dff10185-1030-4fa8-84d9-37f7546af203" }, "execution_count": 110, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Found `n_estimators` in params. Will use it instead of argument\n", "'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000364 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 2639\n", "[LightGBM] [Info] Number of data points in the train set: 1168, number of used features: 43\n", "[LightGBM] [Info] Start training from score 181441.541952\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[1]\tvalid_0's l2: 7.17602e+09\n", "Training until validation scores don't improve for 300 rounds\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[2]\tvalid_0's l2: 6.74263e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[3]\tvalid_0's l2: 6.25478e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[4]\tvalid_0's l2: 5.87823e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[5]\tvalid_0's l2: 5.47958e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[6]\tvalid_0's l2: 5.15315e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[7]\tvalid_0's l2: 4.86474e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[8]\tvalid_0's l2: 4.57935e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[9]\tvalid_0's l2: 4.34358e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[10]\tvalid_0's l2: 4.11749e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[11]\tvalid_0's l2: 3.95419e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[12]\tvalid_0's l2: 3.77494e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[13]\tvalid_0's l2: 3.59733e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[14]\tvalid_0's l2: 3.46416e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[15]\tvalid_0's l2: 3.32555e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[16]\tvalid_0's l2: 3.18395e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[17]\tvalid_0's l2: 3.06675e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[18]\tvalid_0's l2: 2.96922e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[19]\tvalid_0's l2: 2.87583e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[20]\tvalid_0's l2: 2.80279e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[21]\tvalid_0's l2: 2.70822e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[22]\tvalid_0's l2: 2.64957e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[23]\tvalid_0's l2: 2.58244e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[24]\tvalid_0's l2: 2.51577e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[25]\tvalid_0's l2: 2.45722e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[26]\tvalid_0's l2: 2.40717e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[27]\tvalid_0's l2: 2.3582e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[28]\tvalid_0's l2: 2.31256e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[29]\tvalid_0's l2: 2.2713e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[30]\tvalid_0's l2: 2.2306e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[31]\tvalid_0's l2: 2.19357e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[32]\tvalid_0's l2: 2.16057e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[33]\tvalid_0's l2: 2.13127e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[34]\tvalid_0's l2: 2.09865e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[35]\tvalid_0's l2: 2.07086e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[36]\tvalid_0's l2: 2.03991e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[37]\tvalid_0's l2: 2.01015e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[38]\tvalid_0's l2: 1.98417e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[39]\tvalid_0's l2: 1.95833e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[40]\tvalid_0's l2: 1.93135e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[41]\tvalid_0's l2: 1.90938e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[42]\tvalid_0's l2: 1.89182e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[43]\tvalid_0's l2: 1.87293e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[44]\tvalid_0's l2: 1.85636e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[45]\tvalid_0's l2: 1.83788e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[46]\tvalid_0's l2: 1.82176e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[47]\tvalid_0's l2: 1.80688e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[48]\tvalid_0's l2: 1.79209e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[49]\tvalid_0's l2: 1.7761e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[50]\tvalid_0's l2: 1.76015e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[51]\tvalid_0's l2: 1.74703e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[52]\tvalid_0's l2: 1.73468e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[53]\tvalid_0's l2: 1.72033e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[54]\tvalid_0's l2: 1.70831e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[55]\tvalid_0's l2: 1.6979e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[56]\tvalid_0's l2: 1.68585e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[57]\tvalid_0's l2: 1.67583e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[58]\tvalid_0's l2: 1.66696e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[59]\tvalid_0's l2: 1.65862e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[60]\tvalid_0's l2: 1.64842e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[61]\tvalid_0's l2: 1.63797e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[62]\tvalid_0's l2: 1.62855e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[63]\tvalid_0's l2: 1.61877e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[64]\tvalid_0's l2: 1.61156e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[65]\tvalid_0's l2: 1.60452e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[66]\tvalid_0's l2: 1.59718e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[67]\tvalid_0's l2: 1.59168e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[68]\tvalid_0's l2: 1.58625e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[69]\tvalid_0's l2: 1.57954e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[70]\tvalid_0's l2: 1.57144e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[71]\tvalid_0's l2: 1.56503e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[72]\tvalid_0's l2: 1.55915e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[73]\tvalid_0's l2: 1.55311e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[74]\tvalid_0's l2: 1.54592e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[75]\tvalid_0's l2: 1.54063e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[76]\tvalid_0's l2: 1.53553e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[77]\tvalid_0's l2: 1.52929e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[78]\tvalid_0's l2: 1.52409e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[79]\tvalid_0's l2: 1.51929e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[80]\tvalid_0's l2: 1.51419e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[81]\tvalid_0's l2: 1.50803e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[82]\tvalid_0's l2: 1.5023e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[83]\tvalid_0's l2: 1.49688e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[84]\tvalid_0's l2: 1.49078e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[85]\tvalid_0's l2: 1.48604e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[86]\tvalid_0's l2: 1.48185e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[87]\tvalid_0's l2: 1.47879e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[88]\tvalid_0's l2: 1.47521e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[89]\tvalid_0's l2: 1.47162e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[90]\tvalid_0's l2: 1.4668e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[91]\tvalid_0's l2: 1.46299e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[92]\tvalid_0's l2: 1.45785e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[93]\tvalid_0's l2: 1.45281e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[94]\tvalid_0's l2: 1.44841e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[95]\tvalid_0's l2: 1.44242e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[96]\tvalid_0's l2: 1.43847e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[97]\tvalid_0's l2: 1.43506e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[98]\tvalid_0's l2: 1.43006e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[99]\tvalid_0's l2: 1.42856e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[100]\tvalid_0's l2: 1.42582e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[101]\tvalid_0's l2: 1.42359e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[102]\tvalid_0's l2: 1.42053e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[103]\tvalid_0's l2: 1.41761e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[104]\tvalid_0's l2: 1.40771e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[105]\tvalid_0's l2: 1.40516e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[106]\tvalid_0's l2: 1.40132e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[107]\tvalid_0's l2: 1.3987e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[108]\tvalid_0's l2: 1.3954e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[109]\tvalid_0's l2: 1.39287e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[110]\tvalid_0's l2: 1.38953e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[111]\tvalid_0's l2: 1.38779e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[112]\tvalid_0's l2: 1.38515e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[113]\tvalid_0's l2: 1.38216e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[114]\tvalid_0's l2: 1.38024e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[115]\tvalid_0's l2: 1.37906e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[116]\tvalid_0's l2: 1.37645e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[117]\tvalid_0's l2: 1.37354e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[118]\tvalid_0's l2: 1.37091e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[119]\tvalid_0's l2: 1.36645e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[120]\tvalid_0's l2: 1.36491e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[121]\tvalid_0's l2: 1.36157e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[122]\tvalid_0's l2: 1.35951e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[123]\tvalid_0's l2: 1.358e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[124]\tvalid_0's l2: 1.35522e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[125]\tvalid_0's l2: 1.35106e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[126]\tvalid_0's l2: 1.34837e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[127]\tvalid_0's l2: 1.34499e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[128]\tvalid_0's l2: 1.34304e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[129]\tvalid_0's l2: 1.33881e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[130]\tvalid_0's l2: 1.33689e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[131]\tvalid_0's l2: 1.33497e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[132]\tvalid_0's l2: 1.33034e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[133]\tvalid_0's l2: 1.32905e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[134]\tvalid_0's l2: 1.32311e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[135]\tvalid_0's l2: 1.32055e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[136]\tvalid_0's l2: 1.31878e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[137]\tvalid_0's l2: 1.31527e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[138]\tvalid_0's l2: 1.31353e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[139]\tvalid_0's l2: 1.30802e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[140]\tvalid_0's l2: 1.3071e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[141]\tvalid_0's l2: 1.30491e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[142]\tvalid_0's l2: 1.30262e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[143]\tvalid_0's l2: 1.30368e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[144]\tvalid_0's l2: 1.30237e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[145]\tvalid_0's l2: 1.30004e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[146]\tvalid_0's l2: 1.29584e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[147]\tvalid_0's l2: 1.29443e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[148]\tvalid_0's l2: 1.29111e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[149]\tvalid_0's l2: 1.28856e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[150]\tvalid_0's l2: 1.28452e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[151]\tvalid_0's l2: 1.28389e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[152]\tvalid_0's l2: 1.28142e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[153]\tvalid_0's l2: 1.27818e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[154]\tvalid_0's l2: 1.27666e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[155]\tvalid_0's l2: 1.27456e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[156]\tvalid_0's l2: 1.27209e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[157]\tvalid_0's l2: 1.26794e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[158]\tvalid_0's l2: 1.26541e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[159]\tvalid_0's l2: 1.26469e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[160]\tvalid_0's l2: 1.26328e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[161]\tvalid_0's l2: 1.26555e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[162]\tvalid_0's l2: 1.26526e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[163]\tvalid_0's l2: 1.26402e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[164]\tvalid_0's l2: 1.26476e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[165]\tvalid_0's l2: 1.26397e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[166]\tvalid_0's l2: 1.26298e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[167]\tvalid_0's l2: 1.26131e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[168]\tvalid_0's l2: 1.25924e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[169]\tvalid_0's l2: 1.25604e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[170]\tvalid_0's l2: 1.25359e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[171]\tvalid_0's l2: 1.2518e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[172]\tvalid_0's l2: 1.2515e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[173]\tvalid_0's l2: 1.2486e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[174]\tvalid_0's l2: 1.24641e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[175]\tvalid_0's l2: 1.24436e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[176]\tvalid_0's l2: 1.24352e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[177]\tvalid_0's l2: 1.24142e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[178]\tvalid_0's l2: 1.2418e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[179]\tvalid_0's l2: 1.24143e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[180]\tvalid_0's l2: 1.2408e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[181]\tvalid_0's l2: 1.2413e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[182]\tvalid_0's l2: 1.24046e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[183]\tvalid_0's l2: 1.23946e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[184]\tvalid_0's l2: 1.2375e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[185]\tvalid_0's l2: 1.23562e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[186]\tvalid_0's l2: 1.23488e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[187]\tvalid_0's l2: 1.23464e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[188]\tvalid_0's l2: 1.23376e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[189]\tvalid_0's l2: 1.23246e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[190]\tvalid_0's l2: 1.23337e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[191]\tvalid_0's l2: 1.23109e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[192]\tvalid_0's l2: 1.22799e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[193]\tvalid_0's l2: 1.22736e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[194]\tvalid_0's l2: 1.22492e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[195]\tvalid_0's l2: 1.2218e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[196]\tvalid_0's l2: 1.21831e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[197]\tvalid_0's l2: 1.21752e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[198]\tvalid_0's l2: 1.2186e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[199]\tvalid_0's l2: 1.21727e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[200]\tvalid_0's l2: 1.21645e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[201]\tvalid_0's l2: 1.21456e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[202]\tvalid_0's l2: 1.21348e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[203]\tvalid_0's l2: 1.21275e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[204]\tvalid_0's l2: 1.21186e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[205]\tvalid_0's l2: 1.209e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[206]\tvalid_0's l2: 1.20576e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[207]\tvalid_0's l2: 1.2067e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[208]\tvalid_0's l2: 1.20905e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[209]\tvalid_0's l2: 1.20631e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[210]\tvalid_0's l2: 1.20545e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[211]\tvalid_0's l2: 1.2035e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[212]\tvalid_0's l2: 1.20192e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[213]\tvalid_0's l2: 1.20048e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[214]\tvalid_0's l2: 1.20126e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[215]\tvalid_0's l2: 1.19956e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[216]\tvalid_0's l2: 1.19907e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[217]\tvalid_0's l2: 1.19851e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[218]\tvalid_0's l2: 1.19605e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[219]\tvalid_0's l2: 1.19586e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[220]\tvalid_0's l2: 1.19442e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[221]\tvalid_0's l2: 1.19171e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[222]\tvalid_0's l2: 1.19102e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[223]\tvalid_0's l2: 1.18816e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[224]\tvalid_0's l2: 1.18585e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[225]\tvalid_0's l2: 1.18734e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[226]\tvalid_0's l2: 1.18657e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[227]\tvalid_0's l2: 1.18665e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[228]\tvalid_0's l2: 1.18573e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[229]\tvalid_0's l2: 1.18433e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[230]\tvalid_0's l2: 1.18277e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[231]\tvalid_0's l2: 1.18308e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[232]\tvalid_0's l2: 1.18264e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[233]\tvalid_0's l2: 1.18081e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[234]\tvalid_0's l2: 1.179e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[235]\tvalid_0's l2: 1.17789e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[236]\tvalid_0's l2: 1.17822e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[237]\tvalid_0's l2: 1.17779e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[238]\tvalid_0's l2: 1.1767e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[239]\tvalid_0's l2: 1.17701e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[240]\tvalid_0's l2: 1.17533e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[241]\tvalid_0's l2: 1.17308e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[242]\tvalid_0's l2: 1.17377e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[243]\tvalid_0's l2: 1.17186e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[244]\tvalid_0's l2: 1.17214e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[245]\tvalid_0's l2: 1.17014e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[246]\tvalid_0's l2: 1.17046e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[247]\tvalid_0's l2: 1.16997e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[248]\tvalid_0's l2: 1.1695e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[249]\tvalid_0's l2: 1.16931e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[250]\tvalid_0's l2: 1.16781e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[251]\tvalid_0's l2: 1.16599e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[252]\tvalid_0's l2: 1.16534e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[253]\tvalid_0's l2: 1.16296e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[254]\tvalid_0's l2: 1.16358e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[255]\tvalid_0's l2: 1.16261e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[256]\tvalid_0's l2: 1.16369e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[257]\tvalid_0's l2: 1.16237e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[258]\tvalid_0's l2: 1.16205e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[259]\tvalid_0's l2: 1.16188e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[260]\tvalid_0's l2: 1.16067e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[261]\tvalid_0's l2: 1.15984e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[262]\tvalid_0's l2: 1.15926e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[263]\tvalid_0's l2: 1.15874e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[264]\tvalid_0's l2: 1.1583e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[265]\tvalid_0's l2: 1.15756e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[266]\tvalid_0's l2: 1.15891e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[267]\tvalid_0's l2: 1.15741e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[268]\tvalid_0's l2: 1.15613e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[269]\tvalid_0's l2: 1.15595e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[270]\tvalid_0's l2: 1.15602e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[271]\tvalid_0's l2: 1.15692e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[272]\tvalid_0's l2: 1.15605e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[273]\tvalid_0's l2: 1.15527e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[274]\tvalid_0's l2: 1.15445e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[275]\tvalid_0's l2: 1.15405e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[276]\tvalid_0's l2: 1.15312e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[277]\tvalid_0's l2: 1.15327e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[278]\tvalid_0's l2: 1.15281e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[279]\tvalid_0's l2: 1.15395e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[280]\tvalid_0's l2: 1.15307e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[281]\tvalid_0's l2: 1.15254e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[282]\tvalid_0's l2: 1.15168e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[283]\tvalid_0's l2: 1.15107e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[284]\tvalid_0's l2: 1.1506e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[285]\tvalid_0's l2: 1.14911e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[286]\tvalid_0's l2: 1.14834e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[287]\tvalid_0's l2: 1.14605e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[288]\tvalid_0's l2: 1.14554e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[289]\tvalid_0's l2: 1.14522e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[290]\tvalid_0's l2: 1.14347e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[291]\tvalid_0's l2: 1.14164e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[292]\tvalid_0's l2: 1.14071e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[293]\tvalid_0's l2: 1.13924e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[294]\tvalid_0's l2: 1.13853e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[295]\tvalid_0's l2: 1.13759e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[296]\tvalid_0's l2: 1.13663e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[297]\tvalid_0's l2: 1.1355e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[298]\tvalid_0's l2: 1.13415e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[299]\tvalid_0's l2: 1.13576e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[300]\tvalid_0's l2: 1.13572e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[301]\tvalid_0's l2: 1.13501e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[302]\tvalid_0's l2: 1.13445e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[303]\tvalid_0's l2: 1.13413e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[304]\tvalid_0's l2: 1.1349e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[305]\tvalid_0's l2: 1.13423e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[306]\tvalid_0's l2: 1.13346e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[307]\tvalid_0's l2: 1.1334e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[308]\tvalid_0's l2: 1.13306e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[309]\tvalid_0's l2: 1.13229e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[310]\tvalid_0's l2: 1.1319e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[311]\tvalid_0's l2: 1.13111e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[312]\tvalid_0's l2: 1.1305e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[313]\tvalid_0's l2: 1.12953e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[314]\tvalid_0's l2: 1.13062e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[315]\tvalid_0's l2: 1.13055e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[316]\tvalid_0's l2: 1.13082e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[317]\tvalid_0's l2: 1.12992e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[318]\tvalid_0's l2: 1.12937e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[319]\tvalid_0's l2: 1.12903e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[320]\tvalid_0's l2: 1.12888e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[321]\tvalid_0's l2: 1.12865e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[322]\tvalid_0's l2: 1.12774e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[323]\tvalid_0's l2: 1.12671e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[324]\tvalid_0's l2: 1.12598e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[325]\tvalid_0's l2: 1.12523e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[326]\tvalid_0's l2: 1.12493e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[327]\tvalid_0's l2: 1.12514e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[328]\tvalid_0's l2: 1.12496e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[329]\tvalid_0's l2: 1.12541e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[330]\tvalid_0's l2: 1.12601e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[331]\tvalid_0's l2: 1.12583e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[332]\tvalid_0's l2: 1.12644e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[333]\tvalid_0's l2: 1.1265e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[334]\tvalid_0's l2: 1.12475e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[335]\tvalid_0's l2: 1.12356e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[336]\tvalid_0's l2: 1.12184e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[337]\tvalid_0's l2: 1.1218e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[338]\tvalid_0's l2: 1.12118e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[339]\tvalid_0's l2: 1.12275e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[340]\tvalid_0's l2: 1.12233e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[341]\tvalid_0's l2: 1.12188e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[342]\tvalid_0's l2: 1.1234e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[343]\tvalid_0's l2: 1.12209e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[344]\tvalid_0's l2: 1.12182e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[345]\tvalid_0's l2: 1.12142e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[346]\tvalid_0's l2: 1.12108e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[347]\tvalid_0's l2: 1.1209e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[348]\tvalid_0's l2: 1.11963e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[349]\tvalid_0's l2: 1.11919e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[350]\tvalid_0's l2: 1.11852e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[351]\tvalid_0's l2: 1.11851e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[352]\tvalid_0's l2: 1.11775e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[353]\tvalid_0's l2: 1.11878e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[354]\tvalid_0's l2: 1.11845e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[355]\tvalid_0's l2: 1.11881e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[356]\tvalid_0's l2: 1.11762e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[357]\tvalid_0's l2: 1.11665e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[358]\tvalid_0's l2: 1.11608e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[359]\tvalid_0's l2: 1.11565e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[360]\tvalid_0's l2: 1.11564e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[361]\tvalid_0's l2: 1.11554e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[362]\tvalid_0's l2: 1.11438e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[363]\tvalid_0's l2: 1.11503e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[364]\tvalid_0's l2: 1.11429e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[365]\tvalid_0's l2: 1.11307e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[366]\tvalid_0's l2: 1.11407e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[367]\tvalid_0's l2: 1.11264e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[368]\tvalid_0's l2: 1.11176e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[369]\tvalid_0's l2: 1.11151e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[370]\tvalid_0's l2: 1.11103e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[371]\tvalid_0's l2: 1.11005e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[372]\tvalid_0's l2: 1.10937e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[373]\tvalid_0's l2: 1.10948e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[374]\tvalid_0's l2: 1.10812e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[375]\tvalid_0's l2: 1.10765e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[376]\tvalid_0's l2: 1.10863e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[377]\tvalid_0's l2: 1.10836e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[378]\tvalid_0's l2: 1.10794e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[379]\tvalid_0's l2: 1.10731e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[380]\tvalid_0's l2: 1.10647e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[381]\tvalid_0's l2: 1.10499e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[382]\tvalid_0's l2: 1.10359e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[383]\tvalid_0's l2: 1.1037e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[384]\tvalid_0's l2: 1.10353e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[385]\tvalid_0's l2: 1.10357e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[386]\tvalid_0's l2: 1.10318e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[387]\tvalid_0's l2: 1.10322e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[388]\tvalid_0's l2: 1.10254e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[389]\tvalid_0's l2: 1.10166e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[390]\tvalid_0's l2: 1.10135e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[391]\tvalid_0's l2: 1.10076e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[392]\tvalid_0's l2: 1.10061e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[393]\tvalid_0's l2: 1.10008e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[394]\tvalid_0's l2: 1.09999e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[395]\tvalid_0's l2: 1.09948e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[396]\tvalid_0's l2: 1.09894e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[397]\tvalid_0's l2: 1.09816e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[398]\tvalid_0's l2: 1.098e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[399]\tvalid_0's l2: 1.09833e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[400]\tvalid_0's l2: 1.09829e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[401]\tvalid_0's l2: 1.09706e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[402]\tvalid_0's l2: 1.09627e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[403]\tvalid_0's l2: 1.09568e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[404]\tvalid_0's l2: 1.09642e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[405]\tvalid_0's l2: 1.09602e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[406]\tvalid_0's l2: 1.09831e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[407]\tvalid_0's l2: 1.0982e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[408]\tvalid_0's l2: 1.09773e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[409]\tvalid_0's l2: 1.09722e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[410]\tvalid_0's l2: 1.09705e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[411]\tvalid_0's l2: 1.09776e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[412]\tvalid_0's l2: 1.09768e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[413]\tvalid_0's l2: 1.09794e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[414]\tvalid_0's l2: 1.09716e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[415]\tvalid_0's l2: 1.09731e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[416]\tvalid_0's l2: 1.09868e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[417]\tvalid_0's l2: 1.09866e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[418]\tvalid_0's l2: 1.09934e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[419]\tvalid_0's l2: 1.09977e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[420]\tvalid_0's l2: 1.09911e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[421]\tvalid_0's l2: 1.09959e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[422]\tvalid_0's l2: 1.10083e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[423]\tvalid_0's l2: 1.10034e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[424]\tvalid_0's l2: 1.09967e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[425]\tvalid_0's l2: 1.09983e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[426]\tvalid_0's l2: 1.09998e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[427]\tvalid_0's l2: 1.10054e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[428]\tvalid_0's l2: 1.09956e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[429]\tvalid_0's l2: 1.09892e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[430]\tvalid_0's l2: 1.09874e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[431]\tvalid_0's l2: 1.09837e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[432]\tvalid_0's l2: 1.09779e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[433]\tvalid_0's l2: 1.09737e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[434]\tvalid_0's l2: 1.0985e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[435]\tvalid_0's l2: 1.09886e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[436]\tvalid_0's l2: 1.09839e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[437]\tvalid_0's l2: 1.0976e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[438]\tvalid_0's l2: 1.09825e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[439]\tvalid_0's l2: 1.09848e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[440]\tvalid_0's l2: 1.09836e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[441]\tvalid_0's l2: 1.09861e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[442]\tvalid_0's l2: 1.09914e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[443]\tvalid_0's l2: 1.10018e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[444]\tvalid_0's l2: 1.09999e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[445]\tvalid_0's l2: 1.09989e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[446]\tvalid_0's l2: 1.09959e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[447]\tvalid_0's l2: 1.09873e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[448]\tvalid_0's l2: 1.09886e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[449]\tvalid_0's l2: 1.09907e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[450]\tvalid_0's l2: 1.09871e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[451]\tvalid_0's l2: 1.09791e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[452]\tvalid_0's l2: 1.09735e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[453]\tvalid_0's l2: 1.09744e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[454]\tvalid_0's l2: 1.09687e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[455]\tvalid_0's l2: 1.09671e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[456]\tvalid_0's l2: 1.09656e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[457]\tvalid_0's l2: 1.09605e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[458]\tvalid_0's l2: 1.09812e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[459]\tvalid_0's l2: 1.09718e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[460]\tvalid_0's l2: 1.09674e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[461]\tvalid_0's l2: 1.09621e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[462]\tvalid_0's l2: 1.09621e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[463]\tvalid_0's l2: 1.09491e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[464]\tvalid_0's l2: 1.09483e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[465]\tvalid_0's l2: 1.09487e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[466]\tvalid_0's l2: 1.09571e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[467]\tvalid_0's l2: 1.09551e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[468]\tvalid_0's l2: 1.09533e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[469]\tvalid_0's l2: 1.09516e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[470]\tvalid_0's l2: 1.09555e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[471]\tvalid_0's l2: 1.09628e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[472]\tvalid_0's l2: 1.09607e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[473]\tvalid_0's l2: 1.09456e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[474]\tvalid_0's l2: 1.09312e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[475]\tvalid_0's l2: 1.09125e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[476]\tvalid_0's l2: 1.09193e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[477]\tvalid_0's l2: 1.09255e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[478]\tvalid_0's l2: 1.0924e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[479]\tvalid_0's l2: 1.09261e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[480]\tvalid_0's l2: 1.09236e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[481]\tvalid_0's l2: 1.09159e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[482]\tvalid_0's l2: 1.09189e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[483]\tvalid_0's l2: 1.09222e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[484]\tvalid_0's l2: 1.09177e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[485]\tvalid_0's l2: 1.09122e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[486]\tvalid_0's l2: 1.09034e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[487]\tvalid_0's l2: 1.08907e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[488]\tvalid_0's l2: 1.08937e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[489]\tvalid_0's l2: 1.08797e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[490]\tvalid_0's l2: 1.08713e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[491]\tvalid_0's l2: 1.08655e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[492]\tvalid_0's l2: 1.08711e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[493]\tvalid_0's l2: 1.08748e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[494]\tvalid_0's l2: 1.08852e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[495]\tvalid_0's l2: 1.08872e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[496]\tvalid_0's l2: 1.088e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[497]\tvalid_0's l2: 1.08789e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[498]\tvalid_0's l2: 1.08917e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[499]\tvalid_0's l2: 1.08889e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[500]\tvalid_0's l2: 1.08887e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[501]\tvalid_0's l2: 1.08834e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[502]\tvalid_0's l2: 1.08745e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[503]\tvalid_0's l2: 1.08578e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[504]\tvalid_0's l2: 1.08534e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[505]\tvalid_0's l2: 1.08528e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[506]\tvalid_0's l2: 1.08492e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[507]\tvalid_0's l2: 1.08449e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[508]\tvalid_0's l2: 1.08505e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[509]\tvalid_0's l2: 1.08467e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[510]\tvalid_0's l2: 1.08477e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[511]\tvalid_0's l2: 1.08504e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[512]\tvalid_0's l2: 1.08608e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[513]\tvalid_0's l2: 1.08663e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[514]\tvalid_0's l2: 1.08579e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[515]\tvalid_0's l2: 1.08677e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[516]\tvalid_0's l2: 1.08657e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[517]\tvalid_0's l2: 1.08625e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[518]\tvalid_0's l2: 1.08645e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[519]\tvalid_0's l2: 1.08641e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[520]\tvalid_0's l2: 1.08638e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[521]\tvalid_0's l2: 1.08592e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[522]\tvalid_0's l2: 1.0851e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[523]\tvalid_0's l2: 1.08488e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[524]\tvalid_0's l2: 1.08414e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[525]\tvalid_0's l2: 1.0836e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[526]\tvalid_0's l2: 1.08493e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[527]\tvalid_0's l2: 1.08464e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[528]\tvalid_0's l2: 1.08466e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[529]\tvalid_0's l2: 1.0845e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[530]\tvalid_0's l2: 1.08393e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[531]\tvalid_0's l2: 1.08321e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[532]\tvalid_0's l2: 1.08367e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[533]\tvalid_0's l2: 1.08329e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[534]\tvalid_0's l2: 1.08306e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[535]\tvalid_0's l2: 1.08363e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[536]\tvalid_0's l2: 1.08299e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[537]\tvalid_0's l2: 1.08244e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[538]\tvalid_0's l2: 1.08295e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[539]\tvalid_0's l2: 1.08241e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[540]\tvalid_0's l2: 1.0816e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[541]\tvalid_0's l2: 1.08156e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[542]\tvalid_0's l2: 1.08152e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[543]\tvalid_0's l2: 1.08107e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[544]\tvalid_0's l2: 1.08139e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[545]\tvalid_0's l2: 1.0815e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[546]\tvalid_0's l2: 1.08149e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[547]\tvalid_0's l2: 1.0818e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[548]\tvalid_0's l2: 1.08218e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[549]\tvalid_0's l2: 1.08211e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[550]\tvalid_0's l2: 1.08176e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[551]\tvalid_0's l2: 1.08146e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[552]\tvalid_0's l2: 1.08099e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[553]\tvalid_0's l2: 1.08043e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[554]\tvalid_0's l2: 1.08048e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[555]\tvalid_0's l2: 1.08021e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[556]\tvalid_0's l2: 1.08082e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[557]\tvalid_0's l2: 1.08039e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[558]\tvalid_0's l2: 1.07962e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[559]\tvalid_0's l2: 1.08027e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[560]\tvalid_0's l2: 1.08013e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[561]\tvalid_0's l2: 1.08165e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[562]\tvalid_0's l2: 1.08134e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[563]\tvalid_0's l2: 1.08074e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[564]\tvalid_0's l2: 1.0812e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[565]\tvalid_0's l2: 1.08043e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[566]\tvalid_0's l2: 1.08132e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[567]\tvalid_0's l2: 1.08018e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[568]\tvalid_0's l2: 1.08051e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[569]\tvalid_0's l2: 1.0802e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[570]\tvalid_0's l2: 1.07953e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[571]\tvalid_0's l2: 1.07948e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[572]\tvalid_0's l2: 1.07933e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[573]\tvalid_0's l2: 1.07916e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[574]\tvalid_0's l2: 1.07906e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[575]\tvalid_0's l2: 1.08006e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[576]\tvalid_0's l2: 1.08135e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[577]\tvalid_0's l2: 1.08169e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[578]\tvalid_0's l2: 1.08057e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[579]\tvalid_0's l2: 1.08107e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[580]\tvalid_0's l2: 1.0815e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[581]\tvalid_0's l2: 1.08194e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[582]\tvalid_0's l2: 1.08151e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[583]\tvalid_0's l2: 1.08264e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[584]\tvalid_0's l2: 1.08274e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[585]\tvalid_0's l2: 1.08298e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[586]\tvalid_0's l2: 1.08265e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[587]\tvalid_0's l2: 1.08272e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[588]\tvalid_0's l2: 1.08364e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[589]\tvalid_0's l2: 1.08417e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[590]\tvalid_0's l2: 1.0844e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[591]\tvalid_0's l2: 1.08414e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[592]\tvalid_0's l2: 1.08362e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[593]\tvalid_0's l2: 1.08371e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[594]\tvalid_0's l2: 1.08316e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[595]\tvalid_0's l2: 1.0839e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[596]\tvalid_0's l2: 1.08303e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[597]\tvalid_0's l2: 1.08351e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[598]\tvalid_0's l2: 1.08406e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[599]\tvalid_0's l2: 1.08384e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[600]\tvalid_0's l2: 1.08405e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[601]\tvalid_0's l2: 1.08369e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[602]\tvalid_0's l2: 1.08378e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[603]\tvalid_0's l2: 1.08402e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[604]\tvalid_0's l2: 1.08438e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[605]\tvalid_0's l2: 1.08415e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[606]\tvalid_0's l2: 1.08316e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[607]\tvalid_0's l2: 1.08325e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[608]\tvalid_0's l2: 1.08388e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[609]\tvalid_0's l2: 1.08412e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[610]\tvalid_0's l2: 1.08334e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[611]\tvalid_0's l2: 1.08312e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[612]\tvalid_0's l2: 1.0839e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[613]\tvalid_0's l2: 1.08364e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[614]\tvalid_0's l2: 1.08301e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[615]\tvalid_0's l2: 1.0827e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[616]\tvalid_0's l2: 1.08325e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[617]\tvalid_0's l2: 1.08303e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[618]\tvalid_0's l2: 1.08242e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[619]\tvalid_0's l2: 1.08206e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[620]\tvalid_0's l2: 1.08293e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[621]\tvalid_0's l2: 1.0831e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[622]\tvalid_0's l2: 1.08354e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[623]\tvalid_0's l2: 1.08392e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[624]\tvalid_0's l2: 1.084e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[625]\tvalid_0's l2: 1.08402e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[626]\tvalid_0's l2: 1.08389e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[627]\tvalid_0's l2: 1.08322e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[628]\tvalid_0's l2: 1.08212e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[629]\tvalid_0's l2: 1.08259e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[630]\tvalid_0's l2: 1.08239e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[631]\tvalid_0's l2: 1.08301e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[632]\tvalid_0's l2: 1.0834e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[633]\tvalid_0's l2: 1.0835e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[634]\tvalid_0's l2: 1.08397e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[635]\tvalid_0's l2: 1.08414e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[636]\tvalid_0's l2: 1.08394e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[637]\tvalid_0's l2: 1.08374e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[638]\tvalid_0's l2: 1.08358e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[639]\tvalid_0's l2: 1.08295e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[640]\tvalid_0's l2: 1.08187e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[641]\tvalid_0's l2: 1.08147e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[642]\tvalid_0's l2: 1.08241e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[643]\tvalid_0's l2: 1.08271e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[644]\tvalid_0's l2: 1.08267e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[645]\tvalid_0's l2: 1.08222e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[646]\tvalid_0's l2: 1.08176e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[647]\tvalid_0's l2: 1.0828e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[648]\tvalid_0's l2: 1.08274e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[649]\tvalid_0's l2: 1.08258e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[650]\tvalid_0's l2: 1.08276e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[651]\tvalid_0's l2: 1.0828e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[652]\tvalid_0's l2: 1.08234e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[653]\tvalid_0's l2: 1.08363e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[654]\tvalid_0's l2: 1.08409e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[655]\tvalid_0's l2: 1.08515e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[656]\tvalid_0's l2: 1.08482e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[657]\tvalid_0's l2: 1.08521e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[658]\tvalid_0's l2: 1.08494e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[659]\tvalid_0's l2: 1.08535e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[660]\tvalid_0's l2: 1.08559e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[661]\tvalid_0's l2: 1.08578e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[662]\tvalid_0's l2: 1.08665e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[663]\tvalid_0's l2: 1.0864e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[664]\tvalid_0's l2: 1.08627e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[665]\tvalid_0's l2: 1.08657e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[666]\tvalid_0's l2: 1.08661e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[667]\tvalid_0's l2: 1.08735e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[668]\tvalid_0's l2: 1.08736e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[669]\tvalid_0's l2: 1.08749e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[670]\tvalid_0's l2: 1.08718e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[671]\tvalid_0's l2: 1.08739e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[672]\tvalid_0's l2: 1.08693e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[673]\tvalid_0's l2: 1.08642e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[674]\tvalid_0's l2: 1.08613e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[675]\tvalid_0's l2: 1.08647e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[676]\tvalid_0's l2: 1.08582e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[677]\tvalid_0's l2: 1.08551e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[678]\tvalid_0's l2: 1.08541e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[679]\tvalid_0's l2: 1.08569e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[680]\tvalid_0's l2: 1.08534e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[681]\tvalid_0's l2: 1.0851e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[682]\tvalid_0's l2: 1.08479e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[683]\tvalid_0's l2: 1.08483e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[684]\tvalid_0's l2: 1.08479e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[685]\tvalid_0's l2: 1.0844e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[686]\tvalid_0's l2: 1.08454e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[687]\tvalid_0's l2: 1.08483e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[688]\tvalid_0's l2: 1.08547e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[689]\tvalid_0's l2: 1.0857e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[690]\tvalid_0's l2: 1.08608e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[691]\tvalid_0's l2: 1.08647e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[692]\tvalid_0's l2: 1.08672e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[693]\tvalid_0's l2: 1.08662e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[694]\tvalid_0's l2: 1.08619e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[695]\tvalid_0's l2: 1.08553e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[696]\tvalid_0's l2: 1.08506e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[697]\tvalid_0's l2: 1.08528e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[698]\tvalid_0's l2: 1.08432e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[699]\tvalid_0's l2: 1.08513e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[700]\tvalid_0's l2: 1.08565e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[701]\tvalid_0's l2: 1.08534e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[702]\tvalid_0's l2: 1.08682e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[703]\tvalid_0's l2: 1.08678e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[704]\tvalid_0's l2: 1.0866e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[705]\tvalid_0's l2: 1.08635e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[706]\tvalid_0's l2: 1.08634e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[707]\tvalid_0's l2: 1.08586e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[708]\tvalid_0's l2: 1.08603e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[709]\tvalid_0's l2: 1.08482e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[710]\tvalid_0's l2: 1.0847e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[711]\tvalid_0's l2: 1.08512e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[712]\tvalid_0's l2: 1.08548e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[713]\tvalid_0's l2: 1.08608e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[714]\tvalid_0's l2: 1.08633e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[715]\tvalid_0's l2: 1.08701e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[716]\tvalid_0's l2: 1.08702e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[717]\tvalid_0's l2: 1.08695e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[718]\tvalid_0's l2: 1.08652e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[719]\tvalid_0's l2: 1.08638e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[720]\tvalid_0's l2: 1.08667e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[721]\tvalid_0's l2: 1.08668e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[722]\tvalid_0's l2: 1.08655e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[723]\tvalid_0's l2: 1.08665e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[724]\tvalid_0's l2: 1.08595e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[725]\tvalid_0's l2: 1.08623e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[726]\tvalid_0's l2: 1.08591e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[727]\tvalid_0's l2: 1.08538e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[728]\tvalid_0's l2: 1.08646e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[729]\tvalid_0's l2: 1.08579e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[730]\tvalid_0's l2: 1.08584e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[731]\tvalid_0's l2: 1.08655e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[732]\tvalid_0's l2: 1.08697e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[733]\tvalid_0's l2: 1.08723e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[734]\tvalid_0's l2: 1.0864e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[735]\tvalid_0's l2: 1.0862e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[736]\tvalid_0's l2: 1.08639e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[737]\tvalid_0's l2: 1.08666e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[738]\tvalid_0's l2: 1.08668e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[739]\tvalid_0's l2: 1.0866e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[740]\tvalid_0's l2: 1.08724e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[741]\tvalid_0's l2: 1.08739e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[742]\tvalid_0's l2: 1.08715e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[743]\tvalid_0's l2: 1.08665e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[744]\tvalid_0's l2: 1.08672e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[745]\tvalid_0's l2: 1.08593e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[746]\tvalid_0's l2: 1.08691e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[747]\tvalid_0's l2: 1.08657e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[748]\tvalid_0's l2: 1.08647e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[749]\tvalid_0's l2: 1.08592e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[750]\tvalid_0's l2: 1.08607e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[751]\tvalid_0's l2: 1.08608e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[752]\tvalid_0's l2: 1.0854e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[753]\tvalid_0's l2: 1.08564e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[754]\tvalid_0's l2: 1.08554e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[755]\tvalid_0's l2: 1.08539e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[756]\tvalid_0's l2: 1.08575e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[757]\tvalid_0's l2: 1.08545e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[758]\tvalid_0's l2: 1.08572e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[759]\tvalid_0's l2: 1.08564e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[760]\tvalid_0's l2: 1.08539e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[761]\tvalid_0's l2: 1.08433e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[762]\tvalid_0's l2: 1.08392e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[763]\tvalid_0's l2: 1.08426e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[764]\tvalid_0's l2: 1.0843e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[765]\tvalid_0's l2: 1.08402e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[766]\tvalid_0's l2: 1.08335e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[767]\tvalid_0's l2: 1.08397e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[768]\tvalid_0's l2: 1.08409e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[769]\tvalid_0's l2: 1.08322e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[770]\tvalid_0's l2: 1.08336e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[771]\tvalid_0's l2: 1.08357e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[772]\tvalid_0's l2: 1.08356e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[773]\tvalid_0's l2: 1.08327e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[774]\tvalid_0's l2: 1.08347e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[775]\tvalid_0's l2: 1.08314e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[776]\tvalid_0's l2: 1.08306e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[777]\tvalid_0's l2: 1.08326e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[778]\tvalid_0's l2: 1.08328e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[779]\tvalid_0's l2: 1.08315e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[780]\tvalid_0's l2: 1.08338e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[781]\tvalid_0's l2: 1.08309e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[782]\tvalid_0's l2: 1.0829e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[783]\tvalid_0's l2: 1.0838e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[784]\tvalid_0's l2: 1.08349e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[785]\tvalid_0's l2: 1.08396e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[786]\tvalid_0's l2: 1.08386e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[787]\tvalid_0's l2: 1.0841e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[788]\tvalid_0's l2: 1.08487e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[789]\tvalid_0's l2: 1.08466e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[790]\tvalid_0's l2: 1.08513e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[791]\tvalid_0's l2: 1.08469e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[792]\tvalid_0's l2: 1.08446e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[793]\tvalid_0's l2: 1.08416e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[794]\tvalid_0's l2: 1.08432e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[795]\tvalid_0's l2: 1.08362e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[796]\tvalid_0's l2: 1.08361e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[797]\tvalid_0's l2: 1.08339e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[798]\tvalid_0's l2: 1.08232e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[799]\tvalid_0's l2: 1.08204e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[800]\tvalid_0's l2: 1.08204e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[801]\tvalid_0's l2: 1.08153e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[802]\tvalid_0's l2: 1.08155e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[803]\tvalid_0's l2: 1.08158e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[804]\tvalid_0's l2: 1.08196e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[805]\tvalid_0's l2: 1.08139e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[806]\tvalid_0's l2: 1.08164e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[807]\tvalid_0's l2: 1.08146e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[808]\tvalid_0's l2: 1.0817e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[809]\tvalid_0's l2: 1.08224e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[810]\tvalid_0's l2: 1.08242e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[811]\tvalid_0's l2: 1.08287e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[812]\tvalid_0's l2: 1.08311e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[813]\tvalid_0's l2: 1.08277e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[814]\tvalid_0's l2: 1.08249e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[815]\tvalid_0's l2: 1.08316e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[816]\tvalid_0's l2: 1.08382e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[817]\tvalid_0's l2: 1.08299e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[818]\tvalid_0's l2: 1.08441e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[819]\tvalid_0's l2: 1.08468e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[820]\tvalid_0's l2: 1.08466e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[821]\tvalid_0's l2: 1.08415e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[822]\tvalid_0's l2: 1.08398e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[823]\tvalid_0's l2: 1.08444e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[824]\tvalid_0's l2: 1.08489e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[825]\tvalid_0's l2: 1.08474e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[826]\tvalid_0's l2: 1.08437e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[827]\tvalid_0's l2: 1.08489e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[828]\tvalid_0's l2: 1.08477e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[829]\tvalid_0's l2: 1.08537e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[830]\tvalid_0's l2: 1.08599e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[831]\tvalid_0's l2: 1.08583e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[832]\tvalid_0's l2: 1.08563e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[833]\tvalid_0's l2: 1.08509e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[834]\tvalid_0's l2: 1.08484e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[835]\tvalid_0's l2: 1.08515e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[836]\tvalid_0's l2: 1.08478e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[837]\tvalid_0's l2: 1.08443e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[838]\tvalid_0's l2: 1.08437e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[839]\tvalid_0's l2: 1.08449e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[840]\tvalid_0's l2: 1.08443e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[841]\tvalid_0's l2: 1.0842e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[842]\tvalid_0's l2: 1.08382e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[843]\tvalid_0's l2: 1.08427e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[844]\tvalid_0's l2: 1.0841e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[845]\tvalid_0's l2: 1.08391e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[846]\tvalid_0's l2: 1.08361e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[847]\tvalid_0's l2: 1.08346e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[848]\tvalid_0's l2: 1.08444e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[849]\tvalid_0's l2: 1.08429e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[850]\tvalid_0's l2: 1.08493e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[851]\tvalid_0's l2: 1.08525e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[852]\tvalid_0's l2: 1.08483e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[853]\tvalid_0's l2: 1.08461e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[854]\tvalid_0's l2: 1.08519e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[855]\tvalid_0's l2: 1.08474e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[856]\tvalid_0's l2: 1.08458e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[857]\tvalid_0's l2: 1.08431e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[858]\tvalid_0's l2: 1.08423e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[859]\tvalid_0's l2: 1.08507e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[860]\tvalid_0's l2: 1.085e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[861]\tvalid_0's l2: 1.08481e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[862]\tvalid_0's l2: 1.08455e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[863]\tvalid_0's l2: 1.08529e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[864]\tvalid_0's l2: 1.0858e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[865]\tvalid_0's l2: 1.08626e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[866]\tvalid_0's l2: 1.08702e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[867]\tvalid_0's l2: 1.08669e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[868]\tvalid_0's l2: 1.08699e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[869]\tvalid_0's l2: 1.08685e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[870]\tvalid_0's l2: 1.08672e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[871]\tvalid_0's l2: 1.08627e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[872]\tvalid_0's l2: 1.08644e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[873]\tvalid_0's l2: 1.08729e+09\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[874]\tvalid_0's l2: 1.08704e+09\n", "Early stopping, best iteration is:\n", "[574]\tvalid_0's l2: 1.07906e+09\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "imVX-quCm-Ci" }, "source": [ "#SHAP ANALYSIS\n", "Below, I make use of the Shap module to plot the most influential features in the decisions made by the model, as well as the interaction_value matrix. \n", "\n", "The force plots display the overall weight of the features in making predictions, as well as the weight of certain key features. The most important feature was \"GrLivArea: Above grade (ground) living area square feet\". Additionally, \"OverallQual\" mattered the second-most. These two features being key makes sense and are the usual features marketed by real estate agents." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "SW-1Fi4pMx3q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "4aa17b89-3be9-4d17-e59c-83817893ba4a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting shap\n", " Downloading shap-0.41.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (572 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m572.4/572.4 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from shap) (1.22.4)\n", "Collecting slicer==0.0.7\n", " Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (from shap) (1.2.2)\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: pandas in /usr/local/lib/python3.9/dist-packages (from shap) (1.5.3)\n", "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.9/dist-packages (from shap) (2.2.1)\n", "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.9/dist-packages (from shap) (23.0)\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: 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: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->shap) (2022.7.1)\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: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn->shap) (3.1.0)\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: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n", "Installing collected packages: slicer, shap\n", "Successfully installed shap-0.41.0 slicer-0.0.7\n" ] } ], "source": [ "! pip install shap" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 411 }, "id": "0Bo19U2vZ8g3", "outputId": "9d2fd092-1583-4112-afc6-61077da2ad12" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security. If you are using\n", " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", "
\n", " " ] }, "metadata": {}, "execution_count": 111 } ], "source": [ "import shap\n", "shap.initjs()\n", "\n", "explainer = shap.TreeExplainer(model)\n", "shap_values = explainer.shap_values(X_test)\n", "shap.force_plot(explainer.expected_value, shap_values=shap_values, feature_names=X_test.columns)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 411 }, "id": "v0MKxr_Hnj_B", "outputId": "45abeb6d-fb6e-4848-df69-a58562e81696" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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\n", " " ] }, "metadata": {}, "execution_count": 112 } ], "source": [ "shap.initjs()\n", "shap.force_plot(explainer.expected_value, shap_values=shap_values, feature_names=X_test.columns)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 411 }, "id": "Qm-ibJaknkbk", "outputId": "ed314efe-93ca-43be-b31d-06039543a248" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security. If you are using\n", " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", "
\n", " " ] }, "metadata": {}, "execution_count": 113 } ], "source": [ "shap.initjs()\n", "shap.force_plot(explainer.expected_value, shap_values=shap_values, feature_names=X_test.columns)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 841 }, "id": "MAzL1Ic_fUx8", "outputId": "adc65de0-83f9-4131-f63a-1df360beeb2f" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ], "source": [ "shap.decision_plot(explainer.expected_value, shap_values, feature_names=np.array(X_test.columns))" ] }, { "cell_type": "markdown", "metadata": { "id": "UFYfPnadqMEE" }, "source": [ "**For the first prediction, we see the Shapley Interaction values and are able to see the effects of each feature as they are added alone, or in succession to one another. **" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "fMuCoKr9fWfb", "outputId": "08ff183d-791c-4d65-e729-2c5e94994694" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 ... \\\n", "0 -473.15 -12.77 51.46 0.0 -11.63 0.0 0.0 0.00 0.0 -4.93 ... \n", "1 -12.77 336.63 18.08 0.0 0.00 0.0 0.0 -1.85 0.0 2.73 ... \n", "2 51.46 18.08 -1546.88 0.0 -1.64 0.0 0.0 22.90 0.0 204.41 ... \n", "3 0.00 0.00 0.00 0.0 0.00 0.0 0.0 0.00 0.0 0.00 ... \n", "4 -11.63 0.00 -1.64 0.0 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60 rows × 60 columns

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\n", " " ] }, "metadata": {}, "execution_count": 115 } ], "source": [ "interaction_values = explainer.shap_interaction_values(X_test)\n", "interaction_values[0].round(2)\n", "pd.DataFrame(interaction_values[0].round(2)).head(60)" ] } ], "metadata": { "colab": { "provenance": [], "toc_visible": true }, "gpuClass": "standard", "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }