chore: import upstream snapshot with attribution

examples/python-guide/sklearn_example.py

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+# coding: utf-8
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import GridSearchCV
+
+import lightgbm as lgb
+
+print("Loading data...")
+# load or create your dataset
+regression_example_dir = Path(__file__).absolute().parents[1] / "regression"
+df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t")
+df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t")
+
+y_train = df_train[0]
+y_test = df_test[0]
+X_train = df_train.drop(0, axis=1)
+X_test = df_test.drop(0, axis=1)
+
+print("Starting training...")
+# train
+gbm = lgb.LGBMRegressor(num_leaves=31, learning_rate=0.05, n_estimators=20)
+gbm.fit(X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric="l1", callbacks=[lgb.early_stopping(5)])
+
+print("Starting predicting...")
+# predict
+y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
+# eval
+rmse_test = mean_squared_error(y_test, y_pred) ** 0.5
+print(f"The RMSE of prediction is: {rmse_test}")
+
+# feature importances
+print(f"Feature importances: {list(gbm.feature_importances_)}")
+
+
+# self-defined eval metric
+# f(y_true: array, y_pred: array) -> metric_name: str, metric_value: float, maximize: bool
+# Root Mean Squared Logarithmic Error (RMSLE)
+def rmsle(y_true, y_pred):
+    return "RMSLE", np.sqrt(np.mean(np.power(np.log1p(y_pred) - np.log1p(y_true), 2))), False
+
+
+print("Starting training with custom eval function...")
+# train
+gbm.fit(X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric=rmsle, callbacks=[lgb.early_stopping(5)])
+
+
+# another self-defined eval metric
+# f(y_true: array, y_pred: array) -> metric_name: str, metric_value: float, maximize: bool
+# Relative Absolute Error (RAE)
+def rae(y_true, y_pred):
+    return "RAE", np.sum(np.abs(y_pred - y_true)) / np.sum(np.abs(np.mean(y_true) - y_true)), False
+
+
+print("Starting training with multiple custom eval functions...")
+# train
+gbm.fit(
+    X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric=[rmsle, rae], callbacks=[lgb.early_stopping(5)]
+)
+
+print("Starting predicting...")
+# predict
+y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
+# eval
+rmsle_test = rmsle(y_test, y_pred)[1]
+rae_test = rae(y_test, y_pred)[1]
+print(f"The RMSLE of prediction is: {rmsle_test}")
+print(f"The RAE of prediction is: {rae_test}")
+
+# other scikit-learn modules
+estimator = lgb.LGBMRegressor(num_leaves=31)
+
+param_grid = {"learning_rate": [0.01, 0.1, 1], "n_estimators": [20, 40]}
+
+gbm = GridSearchCV(estimator, param_grid, cv=3)
+gbm.fit(X_train, y_train)
+
+print(f"Best parameters found by grid search are: {gbm.best_params_}")