ray.tune.ExperimentAnalysis#

class ray.tune.ExperimentAnalysis(experiment_checkpoint_path: str | PathLike, *, storage_filesystem: pyarrow.fs.FileSystem | None = None, trials: List[Trial] | None = None, default_metric: str | None = None, default_mode: str | None = None)[source]#

Bases: object

Analyze results from a Ray Train/Tune experiment.

To use this class, the run must store the history of reported metrics in log files (e.g., result.json and progress.csv). This is the default behavior, unless default loggers are explicitly excluded with the TUNE_DISABLE_AUTO_CALLBACK_LOGGERS=1 environment variable.

PublicAPI (beta): This API is in beta and may change before becoming stable.

Methods

`__init__`	Initialize an `ExperimentAnalysis`.
`dataframe`	Returns a pandas.DataFrame object constructed from the trials.
`get_all_configs`	Returns all trial hyperparameter configurations.
`get_best_checkpoint`	Gets best persistent checkpoint path of provided trial.
`get_best_config`	Retrieve the best config corresponding to the trial.
`get_best_trial`	Retrieve the best trial object.
`get_last_checkpoint`	Gets the last checkpoint of the provided trial, i.e., with the highest "training_iteration".

Attributes

`best_checkpoint`	Get the checkpoint path of the best trial of the experiment
`best_config`	Get the config of the best trial of the experiment
`best_dataframe`	Get the full result dataframe of the best trial of the experiment
`best_result`	Get the last result of the best trial of the experiment
`best_result_df`	Get the best result of the experiment as a pandas dataframe.
`best_trial`	Get the best trial of the experiment
`experiment_path`	Path pointing to the experiment directory on persistent storage.
`results`	Get the last result of the all trials of the experiment
`results_df`	Get all the last results as a pandas dataframe.
`trial_dataframes`	List of all dataframes of the trials.