o ci @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)XGBoostCheckpointzHA :py:class:`~ray.train.Checkpoint` with XGBoost-specific functionality.z model.jsonN) preprocessorpathboosterr rr returncCs\t|pt}|std||||j| |}|r,| ||S)aVCreate a :py:class:`~ray.train.Checkpoint` that stores an XGBoost model. Args: booster: The XGBoost 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:`XGBoostCheckpoint` containing the specified ``Estimator``. Examples: ... testcode:: import numpy as np import ray from ray.train.xgboost import XGBoostCheckpoint import xgboost train_X = np.array([[1, 2], [3, 4]]) train_y = np.array([0, 1]) model = xgboost.XGBClassifier().fit(train_X, train_y) checkpoint = XGBoostCheckpoint.from_model(model.get_booster()) z%`path` must be a directory, but got: ) rtempfilemkdtempis_dir ValueError save_modeljoinpathMODEL_FILENAMEas_posixfrom_directoryset_preprocessor)clsr r r checkpoint_path checkpointrX/home/ubuntu/.local/lib/python3.10/site-packages/ray/train/xgboost/xgboost_checkpoint.py from_models& zXGBoostCheckpoint.from_modelcCsN|}t}|t||j|WdS1s wYdS)z5Retrieve the XGBoost model stored in this checkpoint.N) as_directoryxgboostBooster load_modelrrr)selfrr rrr get_modelFs $zXGBoostCheckpoint.get_model) __name__ __module__ __qualname____doc__r classmethodr r!rstrrr$rrrrr s  1r )rpathlibrtypingrrr (ray.train._internal.framework_checkpointrray.util.annotationsrray.data.preprocessorrr rrrrs