chore: import upstream snapshot with attribution

python/ray/train/lightgbm/lightgbm_checkpoint.py

@@ -0,0 +1,72 @@
+import tempfile
+from pathlib import Path
+from typing import TYPE_CHECKING, Optional
+
+import lightgbm
+
+from ray.train._internal.framework_checkpoint import FrameworkCheckpoint
+from ray.util.annotations import PublicAPI
+
+if TYPE_CHECKING:
+    from ray.data.preprocessor import Preprocessor
+
+
+@PublicAPI(stability="beta")
+class LightGBMCheckpoint(FrameworkCheckpoint):
+    """A :py:class:`~ray.train.Checkpoint` with LightGBM-specific functionality."""
+
+    MODEL_FILENAME = "model.txt"
+
+    @classmethod
+    def from_model(
+        cls,
+        booster: lightgbm.Booster,
+        *,
+        preprocessor: Optional["Preprocessor"] = None,
+        path: Optional[str] = None,
+    ) -> "LightGBMCheckpoint":
+        """Create a :py:class:`~ray.train.Checkpoint` that stores a LightGBM model.
+
+        Args:
+            booster: The LightGBM model to store in the checkpoint.
+            preprocessor: A fitted preprocessor to be applied before inference.
+            path: The path to the directory where the checkpoint file will be saved.
+                This should start as an empty directory, since the *entire*
+                directory will be treated as the checkpoint when reported.
+                By default, a temporary directory will be created.
+
+        Returns:
+            An :py:class:`LightGBMCheckpoint` containing the specified ``Estimator``.
+
+        Examples:
+            .. testcode::
+
+                import lightgbm
+                import numpy as np
+                from ray.train.lightgbm import LightGBMCheckpoint
+
+                train_X = np.array([[1, 2], [3, 4]])
+                train_y = np.array([0, 1])
+
+                model = lightgbm.LGBMClassifier().fit(train_X, train_y)
+                checkpoint = LightGBMCheckpoint.from_model(model.booster_)
+        """
+        checkpoint_path = Path(path or tempfile.mkdtemp())
+
+        if not checkpoint_path.is_dir():
+            raise ValueError(f"`path` must be a directory, but got: {checkpoint_path}")
+
+        booster.save_model(checkpoint_path.joinpath(cls.MODEL_FILENAME).as_posix())
+
+        checkpoint = cls.from_directory(checkpoint_path.as_posix())
+        if preprocessor:
+            checkpoint.set_preprocessor(preprocessor)
+
+        return checkpoint
+
+    def get_model(self) -> lightgbm.Booster:
+        """Retrieve the LightGBM model stored in this checkpoint."""
+        with self.as_directory() as checkpoint_path:
+            return lightgbm.Booster(
+                model_file=Path(checkpoint_path, self.MODEL_FILENAME).as_posix()
+            )