chore: import upstream snapshot with attribution

2026-07-13 13:27:18 +08:00
commit d72d1a58f0
553 changed files with 214565 additions and 0 deletions
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+# coding: utf-8
+from pathlib import Path
+
+import pandas as pd
+from sklearn.metrics import mean_squared_error
+
+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)
+
+# create dataset for lightgbm
+lgb_train = lgb.Dataset(X_train, y_train)
+lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
+
+# specify your configurations as a dict
+params = {
+    "boosting_type": "gbdt",
+    "objective": "regression",
+    "metric": {"l2", "l1"},
+    "num_leaves": 31,
+    "learning_rate": 0.05,
+    "feature_fraction": 0.9,
+    "bagging_fraction": 0.8,
+    "bagging_freq": 5,
+    "verbose": 0,
+}
+
+print("Starting training...")
+# train
+gbm = lgb.train(
+    params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.early_stopping(stopping_rounds=5)]
+)
+
+print("Saving model...")
+# save model to file
+gbm.save_model("model.txt")
+
+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}")