{ "cells": [ { "cell_type": "code", "execution_count": 29, "id": "35268e29-6824-4e2d-983b-7e8259bd343f", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials\n", "from sklearn.model_selection import train_test_split\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 30, "id": "30a72889-e9da-45d3-bb03-cbe1b908a972", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AVFCCHNSOH/CO/CN/CRtHrdpTTar
025.5645.3254.6869.134.911.130.8124.020.8523070.2605960.01401130.010.00.26003.974958
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29.9235.1664.8480.254.660.910.5613.620.6968220.1272900.00972020.020.06.04006.452928
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49.9235.1664.8480.254.660.910.5613.620.6968220.1272900.00972020.020.06.050010.075968
\n", "
" ], "text/plain": [ " A V FC C H N S O H/C O/C \\\n", "0 25.56 45.32 54.68 69.13 4.91 1.13 0.81 24.02 0.852307 0.260596 \n", "1 15.26 45.11 54.89 63.80 5.11 1.56 0.88 28.65 0.961129 0.336795 \n", "2 9.92 35.16 64.84 80.25 4.66 0.91 0.56 13.62 0.696822 0.127290 \n", "3 9.92 35.16 64.84 80.25 4.66 0.91 0.56 13.62 0.696822 0.127290 \n", "4 9.92 35.16 64.84 80.25 4.66 0.91 0.56 13.62 0.696822 0.127290 \n", "\n", " N/C Rt Hr dp T Tar \n", "0 0.014011 30.0 10.0 0.2 600 3.974958 \n", "1 0.020958 30.0 10.0 0.2 600 4.629865 \n", "2 0.009720 20.0 20.0 6.0 400 6.452928 \n", "3 0.009720 20.0 20.0 6.0 450 8.724672 \n", "4 0.009720 20.0 20.0 6.0 500 10.075968 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_excel('/mnt/tanzk/mxx/rejie2.xlsx')\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 31, "id": "652c035d-937c-4b6b-bbe3-b5ed9ea2ca92", "metadata": {}, "outputs": [], "source": [ "out_cols = ['Tar']" ] }, { "cell_type": "code", "execution_count": 32, "id": "9f00267b-0cf5-4cfc-97ef-8c15cd8d262d", "metadata": {}, "outputs": [], "source": [ "feature_cols = [x for x in data.columns if x not in out_cols]" ] }, { "cell_type": "code", "execution_count": 33, "id": "676d77b7-605b-4a2a-acc0-8d625324e1c2", "metadata": {}, "outputs": [], "source": [ "train_data = data.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 34, "id": "7bc4dd3d-3285-4bea-bdd2-c55816674722", "metadata": {}, "outputs": [], "source": [ "import xgboost as xgb\n", "\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score" ] }, { "cell_type": "code", "execution_count": 35, "id": "52f19cb8-424e-47eb-a68d-f107a5d194bd", "metadata": {}, "outputs": [], "source": [ "# 定义超参数的搜索空间\n", "# space = {\n", "# 'eta': hp.loguniform('eta', -5, 0), # 学习率，搜索范围是 [1e-5, 1]\n", "# 'max_depth': hp.choice('max_depth', range(5, 30)), # 树的最大深度，搜索范围是 [1, 10]\n", "# 'min_child_weight': hp.uniform('min_child_weight', 0, 10), # 子节点最小的权重和\n", "# 'gamma': hp.loguniform('gamma', -5, 0), # 叶子节点分裂所需的最小损失减少\n", "# 'subsample': hp.uniform('subsample', 0.5, 1), # 训练集的采样率\n", "# 'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 1), # 特征的采样率\n", "# }\n", "space = {\n", " 'eta': hp.loguniform('eta', -4, 0.5), # 学习率，搜索范围是 [1e-5, 1]\n", " 'max_depth': hp.choice('max_depth', range(5, 50)), # 树的最大深度，搜索范围是 [1, 10]\n", " 'min_child_weight': hp.uniform('min_child_weight', 0, 20), # 子节点最小的权重和\n", " 'gamma': hp.loguniform('gamma', -3, 0.7), # 叶子节点分裂所需的最小损失减少\n", " 'subsample': hp.uniform('subsample', 0.5, 0.9), # 训练集的采样率\n", " 'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 0.9), # 特征的采样率\n", "}" ] }, { "cell_type": "code", "execution_count": 36, "id": "b4209d03-b608-4520-9e40-79e93b358c05", "metadata": {}, "outputs": [], "source": [ "# 划分训练集和测试集\n", "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], \n", " train_data[out_cols], \n", " test_size=0.1, random_state=42)" ] }, { "cell_type": "code", "execution_count": 37, "id": "e0e3e9fd-f85d-4370-8b7d-129556624721", "metadata": {}, "outputs": [], "source": [ "# 定义目标函数，用于评估模型的性能\n", "def objective(params):\n", " # 创建决策树分类器实例\n", " gbr = xgb.XGBRegressor(**params)\n", " # 训练模型\n", " gbr.fit(X_train, y_train)\n", " # 使用模型进行预测\n", " y_pred = gbr.predict(X_test)\n", " mae = mean_absolute_error(y_test, y_pred)\n", " return {'loss': mae, 'status': STATUS_OK}" ] }, { "cell_type": "code", "execution_count": 38, "id": "cc20c5e9-072f-4f6c-b74e-6042c54408ce", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:06<00:00, 15.37trial/s, best loss: 0.927005504531766]\n" ] } ], "source": [ "# 创建 Trials 对象来记录搜索历史\n", "trials = Trials()\n", "\n", "# 使用 fmin 函数进行超参数优化\n", "best_params = fmin(fn=objective, space=space, algo=tpe.suggest, max_evals=100, trials=trials)" ] }, { "cell_type": "code", "execution_count": 40, "id": "498cff26-e385-48ac-82ed-5057ccbc370c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'colsample_bytree': 0.7591178772740766, 'eta': 0.29006097172943296, 'gamma': 0.32020608660889016, 'max_depth': 21, 'min_child_weight': 5.912424330716954, 'subsample': 0.8011115810485918}\n" ] } ], "source": [ "print(best_params)\n" ] }, { "cell_type": "code", "execution_count": 41, "id": "5eeb907e-cdc1-434a-82d7-3b967287bbb3", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import KFold, train_test_split\n", "kf = KFold(n_splits=10, shuffle=True, random_state=42)" ] }, { "cell_type": "code", "execution_count": 42, "id": "e629cc0e-a9f8-44d7-9958-092a34a5e579", "metadata": {}, "outputs": [], "source": [ "num_boost_round = 1000" ] }, { "cell_type": "code", "execution_count": 43, "id": "8ddd834e-22b8-42dc-8387-b9983b52a71b", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 44, "id": "6e1074cd-88d1-41aa-8d6a-b11b3ae705e0", "metadata": {}, "outputs": [], "source": [ "plt.rcParams[\"font.sans-serif\"] = [\"WenQuanYi Micro Hei\"] # 设置字体\n", "plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号" ] }, { "cell_type": "code", "execution_count": 45, "id": "70490cbb-f9cf-4b3c-b476-37f3721d50e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE: 2.336, RMSE: 1.5284, MAE: 1.0563, MAPE: 17.41 %, R_2: 0.7993\n", "MSE: 2.1045, RMSE: 1.4507, MAE: 0.9808, MAPE: 14.64 %, R_2: 0.7211\n", "MSE: 1.7837, RMSE: 1.3356, MAE: 0.9841, MAPE: 12.31 %, R_2: 0.7866\n", "MSE: 3.7098, RMSE: 1.9261, MAE: 1.0768, MAPE: 21.11 %, R_2: 0.7009\n", "MSE: 1.5407, RMSE: 1.2412, MAE: 0.7912, MAPE: 13.12 %, R_2: 0.8225\n", "MSE: 2.5428, RMSE: 1.5946, MAE: 1.0797, MAPE: 15.42 %, R_2: 0.5668\n", "MSE: 1.6981, RMSE: 1.3031, MAE: 0.9132, MAPE: 13.13 %, R_2: 0.7728\n", "MSE: 1.6532, RMSE: 1.2858, MAE: 0.8018, MAPE: 9.68 %, R_2: 0.7663\n", "MSE: 2.2342, RMSE: 1.4947, MAE: 1.1072, MAPE: 16.14 %, R_2: 0.7479\n", "MSE: 1.6337, RMSE: 1.2782, MAE: 0.9285, MAPE: 12.27 %, R_2: 0.7293\n" ] } ], "source": [ "eva_list = list()\n", "eva_cols = ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']\n", "for (train_index, test_index) in kf.split(train_data):\n", " train = train_data.loc[train_index]\n", " valid = train_data.loc[test_index]\n", " X_train, Y_train = train[feature_cols], train[out_cols]\n", " X_valid, Y_valid = valid[feature_cols], valid[out_cols]\n", " dtrain = xgb.DMatrix(X_train, Y_train)\n", " dvalid = xgb.DMatrix(X_valid, Y_valid)\n", " watchlist = [(dvalid, 'eval')]\n", " gb_model = xgb.train(best_params, dtrain, num_boost_round, evals=watchlist,\n", " early_stopping_rounds=100, verbose_eval=False)\n", " y_pred = gb_model.predict(xgb.DMatrix(X_valid))\n", " y_true = Y_valid.values\n", " MSE = mean_squared_error(y_true, y_pred)\n", " RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", " MAE = mean_absolute_error(y_true, y_pred)\n", " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", " R_2 = r2_score(y_true, y_pred)\n", " print('MSE:', round(MSE, 4), end=', ')\n", " print('RMSE:', round(RMSE, 4), end=', ')\n", " print('MAE:', round(MAE, 4), end=', ')\n", " print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n", " print('R_2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n", " eva_list.append([MSE, RMSE, MAE, MAPE, R_2])" ] }, { "cell_type": "code", "execution_count": 46, "id": "10886d74-7543-49a6-b4f1-a31a3110fb99", "metadata": {}, "outputs": [], "source": [ "eva_df = pd.DataFrame.from_records(eva_list, columns=eva_cols)" ] }, { "cell_type": "code", "execution_count": 47, "id": "87718c37-27a4-44b4-b5ec-fbc6fdbc14fe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MSE 2.123660\n", "RMSE 1.443832\n", "MAE 0.971950\n", "MAPE 0.145207\n", "R2 0.741365\n", "dtype: float64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eva_df.mean()" ] }, { "cell_type": "code", "execution_count": 48, "id": "ef6508a2-ca8a-4fd1-8a8b-0e8a91d01d87", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1.861425600429374,\n", " 1.3643407200656932,\n", " 0.9369589057201204,\n", " 0.14918013653153422,\n", " 0.8400893043071764)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xgboost as xgb\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Extract best parameters\n", "best_params = {'colsample_bytree': 0.7591178772740766, 'eta': 0.29006097172943296, 'gamma': 0.32020608660889016, \n", " 'max_depth': 21, 'min_child_weight': 5.912424330716954, 'subsample': 0.8011115810485918}\n", "\n", "# Re-train the model with the best parameters and evaluate\n", "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], train_data[out_cols], test_size=0.1, random_state=42)\n", "gbr = xgb.XGBRegressor(**best_params, n_estimators=1500)\n", "gbr.fit(X_train, y_train)\n", "\n", "# Predict and evaluate\n", "y_pred = gbr.predict(X_test)\n", "mse = mean_squared_error(y_test, y_pred)\n", "rmse = np.sqrt(mse)\n", "mae = mean_absolute_error(y_test, y_pred)\n", "mape = mean_absolute_percentage_error(y_test, y_pred)\n", "r2 = r2_score(y_test, y_pred)\n", "\n", "mse, rmse, mae, mape, r2\n" ] }, { "cell_type": "code", "execution_count": 49, "id": "f74ccd89-e1b0-44e8-913f-6b33e8115250", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1.78639546955746,\n", " 1.3365610609162082,\n", " 0.9437850044721565,\n", " 0.1579769923858331,\n", " 0.846534966396966)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xgboost as xgb\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Extract best parameters\n", "best_params = {'colsample_bytree': 0.8182688124328266, 'eta': 0.39669872117044186, 'gamma': 0.67893237292294242, \n", " 'max_depth': 23, 'min_child_weight': 7.274037788798998, 'subsample': 0.6957233806783182}\n", "\n", "# Re-train the model with the best parameters and evaluate\n", "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], train_data[out_cols], test_size=0.1, random_state=42)\n", "gbr = xgb.XGBRegressor(**best_params, n_estimators=1500)\n", "gbr.fit(X_train, y_train)\n", "\n", "# Predict and evaluate\n", "y_pred = gbr.predict(X_test)\n", "mse = mean_squared_error(y_test, y_pred)\n", "rmse = np.sqrt(mse)\n", "mae = mean_absolute_error(y_test, y_pred)\n", "mape = mean_absolute_percentage_error(y_test, y_pred)\n", "r2 = r2_score(y_test, y_pred)\n", "\n", "mse, rmse, mae, mape, r2" ] }, { "cell_type": "code", "execution_count": 50, "id": "8e223a2f-676b-4255-bd7e-b6ea8857a7e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1.5784385533129173,\n", " 1.256359245324727,\n", " 0.8012875783803378,\n", " 0.12426418160928662,\n", " 0.8644000560052363)" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xgboost as xgb\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Extract best parameters\n", "best_params = {'colsample_bytree': 0.7756001484050402, 'eta': 0.31927224345318256, 'gamma': 0.5049174573053737, \n", " 'max_depth': 42, 'min_child_weight': 6.449650970113468, 'subsample': 0.7873416063207794}\n", "\n", "# Re-train the model with the best parameters and evaluate\n", "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], train_data[out_cols], test_size=0.1, random_state=42)\n", "gbr = xgb.XGBRegressor(**best_params, n_estimators=1500)\n", "gbr.fit(X_train, y_train)\n", "\n", "# Predict and evaluate\n", "y_pred = gbr.predict(X_test)\n", "mse = mean_squared_error(y_test, y_pred)\n", "rmse = np.sqrt(mse)\n", "mae = mean_absolute_error(y_test, y_pred)\n", "mape = mean_absolute_percentage_error(y_test, y_pred)\n", "r2 = r2_score(y_test, y_pred)\n", "\n", "mse, rmse, mae, mape, r2\n" ] }, { "cell_type": "code", "execution_count": 52, "id": "92bf1d55-a20d-4337-b8cf-9f96cb53e71e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\tvalidation_0-rmse:2.94022\n", "[1]\tvalidation_0-rmse:2.32601\n", "[2]\tvalidation_0-rmse:2.15546\n", "[3]\tvalidation_0-rmse:1.97899\n", "[4]\tvalidation_0-rmse:1.76293\n", "[5]\tvalidation_0-rmse:1.66392\n", "[6]\tvalidation_0-rmse:1.56808\n", "[7]\tvalidation_0-rmse:1.57516\n", "[8]\tvalidation_0-rmse:1.55810\n", "[9]\tvalidation_0-rmse:1.49683\n", "[10]\tvalidation_0-rmse:1.42146\n", "[11]\tvalidation_0-rmse:1.38196\n", "[12]\tvalidation_0-rmse:1.33497\n", "[13]\tvalidation_0-rmse:1.29656\n", 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}, { "name": "stderr", "output_type": "stream", "text": [ "/root/miniconda3/envs/python38/lib/python3.8/site-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "(1.6418860457242266,\n", " 1.2813610130342763,\n", " 0.8241156918341648,\n", " 0.13793177033015197,\n", " 0.8589494311459256)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xgboost as xgb\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Extract best parameters\n", "best_params = {'colsample_bytree': 0.8997619766112827, 'eta': 0.48900376453173927, 'gamma': 0.09568323449358279, \n", " 'max_depth': 29, 'min_child_weight': 2.0607020689885673, 'subsample': 0.5621662915587151}\n", "\n", "# Re-train the model with the best parameters and evaluate\n", "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], train_data[out_cols], test_size=0.1, random_state=42)\n", "gbr = xgb.XGBRegressor(**best_params, n_estimators=1500)\n", "# gbr.fit(X_train, y_train)\n", "gbr.fit(X_train, y_train, early_stopping_rounds=50, eval_set=[(X_test, y_test)], verbose=True)\n", "\n", "# Predict and evaluate\n", "y_pred = gbr.predict(X_test)\n", "mse = mean_squared_error(y_test, y_pred)\n", "rmse = np.sqrt(mse)\n", "mae = mean_absolute_error(y_test, y_pred)\n", "mape = mean_absolute_percentage_error(y_test, y_pred)\n", "r2 = r2_score(y_test, y_pred)\n", "\n", "mse, rmse, mae, mape, r2" ] }, { "cell_type": "code", "execution_count": 53, "id": "097fba6f-a6af-4529-9d09-589ccf84b991", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\tvalidation_0-rmse:3.03131\n", 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0.8199210432780543,\n", " 0.1256990216917289,\n", " 0.8716922073374574)" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xgboost as xgb\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Extract best parameters\n", "best_params = {'colsample_bytree': 0.7756001484050402, 'eta': 0.31927224345318256, 'gamma': 0.5049174573053737, \n", " 'max_depth': 42, 'min_child_weight': 6.449650970113468, 'subsample': 0.7873416063207794}\n", "\n", "# Re-train the model with the best parameters and evaluate\n", "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], train_data[out_cols], test_size=0.1, random_state=42)\n", "gbr = xgb.XGBRegressor(**best_params, n_estimators=1500)\n", "# gbr.fit(X_train, y_train)\n", "gbr.fit(X_train, y_train, early_stopping_rounds=50, eval_set=[(X_test, y_test)], verbose=True)\n", "# Predict and evaluate\n", "y_pred = gbr.predict(X_test)\n", "mse = mean_squared_error(y_test, y_pred)\n", "rmse = np.sqrt(mse)\n", "mae = mean_absolute_error(y_test, y_pred)\n", "mape = mean_absolute_percentage_error(y_test, y_pred)\n", "r2 = r2_score(y_test, y_pred)\n", "\n", "mse, rmse, mae, mape, r2" ] }, { "cell_type": "code", "execution_count": 54, "id": "9479b1af-a005-4abb-9664-9d3d4837133f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\tvalidation_0-rmse:2.94796\n", "[1]\tvalidation_0-rmse:2.47933\n", "[2]\tvalidation_0-rmse:2.32980\n", "[3]\tvalidation_0-rmse:2.10618\n", "[4]\tvalidation_0-rmse:1.97724\n", "[5]\tvalidation_0-rmse:1.95411\n", "[6]\tvalidation_0-rmse:1.84506\n", "[7]\tvalidation_0-rmse:1.84115\n", "[8]\tvalidation_0-rmse:1.82857\n", "[9]\tvalidation_0-rmse:1.76881\n", "[10]\tvalidation_0-rmse:1.66718\n", "[11]\tvalidation_0-rmse:1.65162\n", "[12]\tvalidation_0-rmse:1.61560\n", 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"/root/miniconda3/envs/python38/lib/python3.8/site-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "(1.5880184359288727,\n", " 1.2601660350639803,\n", " 0.9411508613501586,\n", " 0.1599000195341022,\n", " 0.8635770708193553)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xgboost as xgb\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Extract best parameters\n", "best_params = {'colsample_bytree': 0.8182688124328266, 'eta': 0.39669872117044186, 'gamma': 0.67893237292294242, \n", " 'max_depth': 23, 'min_child_weight': 7.274037788798998, 'subsample': 0.6957233806783182}\n", "\n", "# Re-train the model with the best parameters and evaluate\n", "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], train_data[out_cols], test_size=0.1, random_state=42)\n", "gbr = xgb.XGBRegressor(**best_params, n_estimators=1500)\n", "gbr.fit(X_train, y_train,early_stopping_rounds=50, eval_set=[(X_test, y_test)], verbose=True)\n", "\n", "# Predict and evaluate\n", "y_pred = gbr.predict(X_test)\n", "mse = mean_squared_error(y_test, y_pred)\n", "rmse = np.sqrt(mse)\n", "mae = mean_absolute_error(y_test, y_pred)\n", "mape = mean_absolute_percentage_error(y_test, y_pred)\n", "r2 = r2_score(y_test, y_pred)\n", "\n", "mse, rmse, mae, mape, r2" ] }, { "cell_type": "code", "execution_count": 55, "id": "ae2c8a8a-1333-487e-9c2a-fee46bb9c934", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\tvalidation_0-rmse:2.99087\n", "[1]\tvalidation_0-rmse:2.66375\n", "[2]\tvalidation_0-rmse:2.40788\n", "[3]\tvalidation_0-rmse:2.21494\n", 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"[111]\tvalidation_0-rmse:1.36646\n", "[112]\tvalidation_0-rmse:1.38288\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/root/miniconda3/envs/python38/lib/python3.8/site-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "(1.7612323261291336,\n", " 1.3271142852554687,\n", " 0.9281123720415317,\n", " 0.14669084489239353,\n", " 0.8486966728710329)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xgboost as xgb\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Extract best parameters\n", "best_params = {'colsample_bytree': 0.7591178772740766, 'eta': 0.29006097172943296, 'gamma': 0.32020608660889016, \n", " 'max_depth': 21, 'min_child_weight': 5.912424330716954, 'subsample': 0.8011115810485918}\n", "\n", "# Re-train the model with the best parameters and evaluate\n", "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], train_data[out_cols], test_size=0.1, random_state=42)\n", "gbr = xgb.XGBRegressor(**best_params, n_estimators=1500)\n", "gbr.fit(X_train, y_train,early_stopping_rounds=50, eval_set=[(X_test, y_test)], verbose=True)\n", "\n", "# Predict and evaluate\n", "y_pred = gbr.predict(X_test)\n", "mse = mean_squared_error(y_test, y_pred)\n", "rmse = np.sqrt(mse)\n", "mae = mean_absolute_error(y_test, y_pred)\n", "mape = mean_absolute_percentage_error(y_test, y_pred)\n", "r2 = r2_score(y_test, y_pred)\n", "\n", "mse, rmse, mae, mape, r2" ] }, { "cell_type": "code", "execution_count": null, "id": "27880e3b-0149-4611-abda-9e06442704e4", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { 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