get_dataframe

ResultGrid.get_dataframe(filter_metric: str | None = None, filter_mode: str | None = None) → pandas.DataFrame

Return dataframe of all trials with their configs and reported results.

Per default, this returns the last reported results for each trial.

If filter_metric and filter_mode are set, the results from each trial are filtered for this metric and mode. For example, if filter_metric="some_metric" and filter_mode="max", for each trial, every received result is checked, and the one where some_metric is maximal is returned.

Examples

>>> import ray.tune
>>> def training_loop_per_worker(config):
...     ray.tune.report({"accuracy": 0.8})
>>> print("[ignore]"); result_grid = ray.tune.Tuner(
...     trainable=training_loop_per_worker,
...     run_config=ray.tune.RunConfig(name="my_tune_run")
... ).fit()
[ignore]...
>>> # Get last reported results per trial
>>> df = result_grid.get_dataframe()
>>> # Get best ever reported accuracy per trial
>>> df = result_grid.get_dataframe(
...     filter_metric="accuracy", filter_mode="max"
... )

Parameters:

• filter_metric (str | None) – Metric to filter best result for.

• filter_mode (str | None) – If filter_metric is given, one of ["min", "max"] to specify if we should find the minimum or maximum result.

Returns:

Pandas DataFrame with each trial as a row and their results as columns.

Return type:

pandas.DataFrame