chore: import upstream snapshot with attribution

python/ray/tune/examples/lightgbm_example.py

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+import lightgbm as lgb
+import sklearn.datasets
+import sklearn.metrics
+from sklearn.model_selection import train_test_split
+
+from ray import tune
+from ray.tune.integration.lightgbm import TuneReportCheckpointCallback
+from ray.tune.schedulers import ASHAScheduler
+
+
+def train_breast_cancer(config: dict):
+    # This is a simple training function to be passed into Tune
+
+    # Load dataset
+    data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
+
+    # Split into train and test set
+    train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.25)
+
+    # Build input Datasets for LightGBM
+    train_set = lgb.Dataset(train_x, label=train_y)
+    test_set = lgb.Dataset(test_x, label=test_y)
+
+    # Train the classifier, using the Tune callback
+    lgb.train(
+        config,
+        train_set,
+        valid_sets=[test_set],
+        valid_names=["eval"],
+        callbacks=[
+            TuneReportCheckpointCallback(
+                {
+                    "binary_error": "eval-binary_error",
+                    "binary_logloss": "eval-binary_logloss",
+                }
+            )
+        ],
+    )
+
+
+def train_breast_cancer_cv(config: dict):
+    # This is a simple training function to be passed into Tune, using
+    # lightgbm's cross validation functionality
+
+    # Load dataset
+    data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
+
+    train_set = lgb.Dataset(data, label=target)
+
+    # Run CV, using the Tune callback
+    lgb.cv(
+        config,
+        train_set,
+        stratified=True,
+        # Checkpointing is not supported for CV
+        # LightGBM aggregates metrics over folds automatically
+        # with the cv_agg key. Both mean and standard deviation
+        # are provided.
+        callbacks=[
+            TuneReportCheckpointCallback(
+                {
+                    "binary_error": "valid-binary_error-mean",
+                    "binary_logloss": "valid-binary_logloss-mean",
+                    "binary_error_stdv": "valid-binary_error-stdv",
+                    "binary_logloss_stdv": "valid-binary_logloss-stdv",
+                },
+                frequency=0,
+            )
+        ],
+    )
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--use-cv", action="store_true", help="Use `lgb.cv` instead of `lgb.train`."
+    )
+    args, _ = parser.parse_known_args()
+
+    config = {
+        "objective": "binary",
+        "metric": ["binary_error", "binary_logloss"],
+        "verbose": -1,
+        "boosting_type": tune.grid_search(["gbdt", "dart"]),
+        "num_leaves": tune.randint(10, 1000),
+        "learning_rate": tune.loguniform(1e-8, 1e-1),
+    }
+
+    tuner = tune.Tuner(
+        train_breast_cancer if not args.use_cv else train_breast_cancer_cv,
+        tune_config=tune.TuneConfig(
+            metric="binary_error",
+            mode="min",
+            num_samples=2,
+            scheduler=ASHAScheduler(),
+        ),
+        param_space=config,
+    )
+    results = tuner.fit()
+
+    print("Best hyperparameters found were: ", results.get_best_result().config)