ray.tune.ExperimentAnalysis

class ray.tune.ExperimentAnalysis(experiment_checkpoint_path: str, trials: Optional[List[ray.tune.experiment.trial.Trial]] = None, default_metric: Optional[str] = None, default_mode: Optional[str] = None, remote_storage_path: Optional[str] = None, sync_config: Optional[ray.tune.syncer.SyncConfig] = None)

Bases: object

Analyze results from a Tune experiment.

To use this class, the experiment must be executed with the JsonLogger.

Parameters

• experiment_checkpoint_path – Path to a json file or directory representing an experiment state, or a directory containing multiple experiment states (a run’s local_dir). Corresponds to Experiment.local_dir/Experiment.name/ experiment_state.json

• trials – List of trials that can be accessed via analysis.trials.

• default_metric – Default metric for comparing results. Can be overwritten with the metric parameter in the respective functions.

• default_mode – Default mode for comparing results. Has to be one of [min, max]. Can be overwritten with the mode parameter in the respective functions.

Example

>>> from ray import tune
>>> tune.run( 
...     my_trainable, name="my_exp", local_dir="~/tune_results")
>>> analysis = ExperimentAnalysis( 
...     experiment_checkpoint_path="~/tune_results/my_exp/state.json")

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

Methods

dataframe([metric, mode])	Returns a pandas.DataFrame object constructed from the trials.
fetch_trial_dataframes()	Fetches trial dataframes from files.
get_all_configs([prefix])	Returns a list of all configurations.
get_best_checkpoint(trial[, metric, mode, ...])	Gets best persistent checkpoint path of provided trial.
get_best_config([metric, mode, scope])	Retrieve the best config corresponding to the trial.
get_best_logdir([metric, mode, scope])	Retrieve the logdir corresponding to the best trial.
get_best_trial([metric, mode, scope, ...])	Retrieve the best trial object.
get_last_checkpoint([trial, metric, mode])	Gets the last persistent checkpoint path of the provided trial, i.e., with the highest "training_iteration".
get_trial_checkpoints_paths(trial[, metric])	Gets paths and metrics of all persistent checkpoints of a trial.
runner_data()	Returns a dictionary of the TuneController data.
set_filetype([file_type])	Overrides the existing file type.
stats()	Returns a dictionary of the statistics of the experiment.

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_logdir	Get the logdir 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.