ray.tune.ExperimentAnalysis.get_best_config#

ExperimentAnalysis.get_best_config(metric: str | None = None, mode: str | None = None, scope: str = 'last') → Dict | None[source]#

Retrieve the best config corresponding to the trial.

Compares all trials’ scores on metric. If metric is not specified, self.default_metric will be used. If mode is not specified, self.default_mode will be used. These values are usually initialized by passing the metric and mode parameters to tune.run().

Parameters:

metric – Key for trial info to order on. Defaults to self.default_metric.
mode – One of [min, max]. Defaults to self.default_mode.
scope – One of [all, last, avg, last-5-avg, last-10-avg]. If scope=last, only look at each trial’s final step for metric, and compare across trials based on mode=[min,max]. If scope=avg, consider the simple average over all steps for metric and compare across trials based on mode=[min,max]. If scope=last-5-avg or scope=last-10-avg, consider the simple average over the last 5 or 10 steps for metric and compare across trials based on mode=[min,max]. If scope=all, find each trial’s min/max score for metric based on mode, and compare trials based on mode=[min,max].