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
@@ -0,0 +1,143 @@
+# coding: utf-8
+from pathlib import Path
+
+import numpy as np
+from sklearn.datasets import load_svmlight_file
+
+import lightgbm as lgb
+
+EXAMPLES_DIR = Path(__file__).absolute().parents[2] / "examples"
+
+
+class FileLoader:
+    def __init__(self, directory, prefix, config_file="train.conf"):
+        self.directory = directory
+        self.prefix = prefix
+        self.params = {"gpu_use_dp": True}
+        with open(self.directory / config_file, "r") as f:
+            for line in f.readlines():
+                line = line.strip()
+                if line and not line.startswith("#"):
+                    key, value = [token.strip() for token in line.split("=")]
+                    if "early_stopping" not in key:  # disable early_stopping
+                        self.params[key] = value if key not in {"num_trees", "num_threads"} else int(value)
+
+    def load_dataset(self, suffix, is_sparse=False):
+        filename = str(self.path(suffix))
+        if is_sparse:
+            X, Y = load_svmlight_file(filename, dtype=np.float64, zero_based=True)
+            return X, Y, filename
+        else:
+            mat = np.loadtxt(filename, dtype=np.float64)
+            return mat[:, 1:], mat[:, 0], filename
+
+    def load_field(self, suffix):
+        return np.loadtxt(str(self.directory / f"{self.prefix}{suffix}"))
+
+    def load_cpp_result(self, result_file="LightGBM_predict_result.txt"):
+        return np.loadtxt(str(self.directory / result_file))
+
+    def train_predict_check(self, lgb_train, X_test, X_test_fn, sk_pred):
+        params = dict(self.params)
+        params["force_row_wise"] = True
+        gbm = lgb.train(params, lgb_train)
+        y_pred = gbm.predict(X_test)
+        cpp_pred = gbm.predict(X_test_fn)
+        np.testing.assert_allclose(y_pred, cpp_pred)
+        np.testing.assert_allclose(y_pred, sk_pred)
+
+    def file_load_check(self, lgb_train, name):
+        lgb_train_f = lgb.Dataset(self.path(name), params=self.params).construct()
+        for f in ("num_data", "num_feature", "get_label", "get_weight", "get_init_score", "get_group"):
+            a = getattr(lgb_train, f)()
+            b = getattr(lgb_train_f, f)()
+            if a is None and b is None:
+                pass
+            elif a is None:
+                assert np.all(b == 1), f
+            elif isinstance(b, (list, np.ndarray)):
+                np.testing.assert_allclose(a, b)
+            else:
+                assert a == b, f
+
+    def path(self, suffix):
+        return self.directory / f"{self.prefix}{suffix}"
+
+
+def test_binary():
+    fd = FileLoader(EXAMPLES_DIR / "binary_classification", "binary")
+    X_train, y_train, _ = fd.load_dataset(".train")
+    X_test, _, X_test_fn = fd.load_dataset(".test")
+    weight_train = fd.load_field(".train.weight")
+    lgb_train = lgb.Dataset(X_train, y_train, params=fd.params, weight=weight_train)
+    gbm = lgb.LGBMClassifier(**fd.params)
+    gbm.fit(X_train, y_train, sample_weight=weight_train)
+    sk_pred = gbm.predict_proba(X_test)[:, 1]
+    fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred)
+    fd.file_load_check(lgb_train, ".train")
+
+
+def test_binary_linear():
+    fd = FileLoader(EXAMPLES_DIR / "binary_classification", "binary", "train_linear.conf")
+    X_train, y_train, _ = fd.load_dataset(".train")
+    X_test, _, X_test_fn = fd.load_dataset(".test")
+    weight_train = fd.load_field(".train.weight")
+    lgb_train = lgb.Dataset(X_train, y_train, params=fd.params, weight=weight_train)
+    gbm = lgb.LGBMClassifier(**fd.params)
+    gbm.fit(X_train, y_train, sample_weight=weight_train)
+    sk_pred = gbm.predict_proba(X_test)[:, 1]
+    fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred)
+    fd.file_load_check(lgb_train, ".train")
+
+
+def test_multiclass():
+    fd = FileLoader(EXAMPLES_DIR / "multiclass_classification", "multiclass")
+    X_train, y_train, _ = fd.load_dataset(".train")
+    X_test, _, X_test_fn = fd.load_dataset(".test")
+    lgb_train = lgb.Dataset(X_train, y_train)
+    gbm = lgb.LGBMClassifier(**fd.params)
+    gbm.fit(X_train, y_train)
+    sk_pred = gbm.predict_proba(X_test)
+    fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred)
+    fd.file_load_check(lgb_train, ".train")
+
+
+def test_regression():
+    fd = FileLoader(EXAMPLES_DIR / "regression", "regression")
+    X_train, y_train, _ = fd.load_dataset(".train")
+    X_test, _, X_test_fn = fd.load_dataset(".test")
+    init_score_train = fd.load_field(".train.init")
+    lgb_train = lgb.Dataset(X_train, y_train, init_score=init_score_train)
+    gbm = lgb.LGBMRegressor(**fd.params)
+    gbm.fit(X_train, y_train, init_score=init_score_train)
+    sk_pred = gbm.predict(X_test)
+    fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred)
+    fd.file_load_check(lgb_train, ".train")
+
+
+def test_lambdarank():
+    fd = FileLoader(EXAMPLES_DIR / "lambdarank", "rank")
+    X_train, y_train, _ = fd.load_dataset(".train", is_sparse=True)
+    X_test, _, X_test_fn = fd.load_dataset(".test", is_sparse=True)
+    group_train = fd.load_field(".train.query")
+    lgb_train = lgb.Dataset(X_train, y_train, group=group_train)
+    params = dict(fd.params)
+    params["force_col_wise"] = True
+    gbm = lgb.LGBMRanker(**params)
+    gbm.fit(X_train, y_train, group=group_train)
+    sk_pred = gbm.predict(X_test)
+    fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred)
+    fd.file_load_check(lgb_train, ".train")
+
+
+def test_xendcg():
+    fd = FileLoader(EXAMPLES_DIR / "xendcg", "rank")
+    X_train, y_train, _ = fd.load_dataset(".train", is_sparse=True)
+    X_test, _, X_test_fn = fd.load_dataset(".test", is_sparse=True)
+    group_train = fd.load_field(".train.query")
+    lgb_train = lgb.Dataset(X_train, y_train, group=group_train)
+    gbm = lgb.LGBMRanker(**fd.params)
+    gbm.fit(X_train, y_train, group=group_train)
+    sk_pred = gbm.predict(X_test)
+    fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred)
+    fd.file_load_check(lgb_train, ".train")