o $i @srddlZddlmZddlmZmZddlZddlmZddl m Z er*ddl m Z e ddGd d d eZ dS) N)Path) TYPE_CHECKINGOptional)FrameworkCheckpoint) PublicAPI) Preprocessorbeta) stabilityc @sTeZdZdZdZeddddejdeddee d dfd d Z d ejfd d Z dS)LightGBMCheckpointzIA :py:class:`~ray.train.Checkpoint` with LightGBM-specific functionality.z model.txtN) preprocessorpathboosterr rr returncCs\t|pt}|std||||j| |}|r,| ||S)a3Create 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_) z%`path` must be a directory, but got: ) rtempfilemkdtempis_dir ValueError save_modeljoinpathMODEL_FILENAMEas_posixfrom_directoryset_preprocessor)clsr r r checkpoint_path checkpointrc/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/ray/train/lightgbm/lightgbm_checkpoint.py from_models" zLightGBMCheckpoint.from_modelcCsD|}tjt||jdWdS1swYdS)z6Retrieve the LightGBM model stored in this checkpoint.) model_fileN) as_directorylightgbmBoosterrrr)selfrrrr get_modelCs $zLightGBMCheckpoint.get_model) __name__ __module__ __qualname____doc__r classmethodr!r"rstrrr$rrrrr s  .r )rpathlibrtypingrrr!(ray.train._internal.framework_checkpointrray.util.annotationsrray.data.preprocessorrr rrrrs