Source code for ray.train.lightgbm._lightgbm_utils

import tempfile
from contextlib import contextmanager
from pathlib import Path
from typing import Callable, Dict, List, Optional, Union

from lightgbm.basic import Booster
from lightgbm.callback import CallbackEnv

import ray.train
from ray.train import Checkpoint
from ray.tune.utils import flatten_dict
from ray.util.annotations import PublicAPI

[docs]@PublicAPI(stability="beta") class RayTrainReportCallback: """Creates a callback that reports metrics and checkpoints model. Args: metrics: Metrics to report. If this is a list, each item should be a metric key reported by LightGBM, and it will be reported to Ray Train/Tune under the same name. This can also be a dict of {<key-to-report>: <lightgbm-metric-key>}, which can be used to rename LightGBM default metrics. filename: Customize the saved checkpoint file type by passing a filename. Defaults to "model.txt". frequency: How often to save checkpoints, in terms of iterations. Defaults to 0 (no checkpoints are saved during training). checkpoint_at_end: Whether or not to save a checkpoint at the end of training. results_postprocessing_fn: An optional Callable that takes in the metrics dict that will be reported (after it has been flattened) and returns a modified dict. Examples -------- Reporting checkpoints and metrics to Ray Tune when running many independent xgboost trials (without data parallelism within a trial). .. testcode:: :skipif: True import lightgbm from ray.train.lightgbm import RayTrainReportCallback config = { # ... "metric": ["binary_logloss", "binary_error"], } # Report only log loss to Tune after each validation epoch. bst = lightgbm.train( ..., callbacks=[ RayTrainReportCallback( metrics={"loss": "eval-binary_logloss"}, frequency=1 ) ], ) Loading a model from a checkpoint reported by this callback. .. testcode:: :skipif: True from ray.train.lightgbm import RayTrainReportCallback # Get a `Checkpoint` object that is saved by the callback during training. result = booster = RayTrainReportCallback.get_model(result.checkpoint) """ CHECKPOINT_NAME = "model.txt" def __init__( self, metrics: Optional[Union[str, List[str], Dict[str, str]]] = None, filename: str = CHECKPOINT_NAME, frequency: int = 0, checkpoint_at_end: bool = True, results_postprocessing_fn: Optional[ Callable[[Dict[str, Union[float, List[float]]]], Dict[str, float]] ] = None, ): if isinstance(metrics, str): metrics = [metrics] self._metrics = metrics self._filename = filename self._frequency = frequency self._checkpoint_at_end = checkpoint_at_end self._results_postprocessing_fn = results_postprocessing_fn
[docs] @classmethod def get_model( cls, checkpoint: Checkpoint, filename: str = CHECKPOINT_NAME ) -> Booster: """Retrieve the model stored in a checkpoint reported by this callback. Args: checkpoint: The checkpoint object returned by a training run. The checkpoint should be saved by an instance of this callback. filename: The filename to load the model from, which should match the filename used when creating the callback. """ with checkpoint.as_directory() as checkpoint_path: return Booster(model_file=Path(checkpoint_path, filename).as_posix())
def _get_report_dict(self, evals_log: Dict[str, Dict[str, list]]) -> dict: result_dict = flatten_dict(evals_log, delimiter="-") if not self._metrics: report_dict = result_dict else: report_dict = {} for key in self._metrics: if isinstance(self._metrics, dict): metric = self._metrics[key] else: metric = key report_dict[key] = result_dict[metric] if self._results_postprocessing_fn: report_dict = self._results_postprocessing_fn(report_dict) return report_dict def _get_eval_result(self, env: CallbackEnv) -> dict: eval_result = {} for entry in env.evaluation_result_list: data_name, eval_name, result = entry[0:3] if len(entry) > 4: stdv = entry[4] suffix = "-mean" else: stdv = None suffix = "" if data_name not in eval_result: eval_result[data_name] = {} eval_result[data_name][eval_name + suffix] = result if stdv is not None: eval_result[data_name][eval_name + "-stdv"] = stdv return eval_result @contextmanager def _get_checkpoint(self, model: Booster) -> Optional[Checkpoint]: if ray.train.get_context().get_world_rank() in (0, None): with tempfile.TemporaryDirectory() as temp_checkpoint_dir: model.save_model(Path(temp_checkpoint_dir, self._filename).as_posix()) yield Checkpoint.from_directory(temp_checkpoint_dir) else: yield None def __call__(self, env: CallbackEnv) -> None: eval_result = self._get_eval_result(env) report_dict = self._get_report_dict(eval_result) # Ex: if frequency=2, checkpoint_at_end=True and num_boost_rounds=11, # you will checkpoint at iterations 1, 3, 5, ..., 9, and 10 (checkpoint_at_end) # (iterations count from 0) on_last_iter = env.iteration == env.end_iteration - 1 should_checkpoint_at_end = on_last_iter and self._checkpoint_at_end should_checkpoint_with_frequency = ( self._frequency != 0 and (env.iteration + 1) % self._frequency == 0 ) should_checkpoint = should_checkpoint_at_end or should_checkpoint_with_frequency if should_checkpoint: with self._get_checkpoint(model=env.model) as checkpoint:, checkpoint=checkpoint) else: