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c0ec20dc3a
Rolling out next deprecation cycle: - DeprecationWarnings that were `warnings.warn` or `logger.warn` before are now raised errors - Raised Deprecation warnings are now removed - Notably, this involves deprecating the TrialCheckpoint functionality and associated cloud tests - Added annotations to deprecation warning for when to fully remove
47 lines
1.3 KiB
ReStructuredText
47 lines
1.3 KiB
ReStructuredText
.. _tune-analysis-docs:
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Analysis (tune.analysis)
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========================
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You can use the ``ExperimentAnalysis`` object for analyzing results.
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It is returned automatically when calling ``tune.run``.
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.. code-block:: python
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analysis = tune.run(
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trainable,
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name="example-experiment",
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num_samples=10,
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)
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Here are some example operations for obtaining a summary of your experiment:
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.. code-block:: python
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# Get a dataframe for the last reported results of all of the trials
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df = analysis.results_df
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# Get a dataframe for the max accuracy seen for each trial
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df = analysis.dataframe(metric="mean_accuracy", mode="max")
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# Get a dict mapping {trial logdir -> dataframes} for all trials in the experiment.
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all_dataframes = analysis.trial_dataframes
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# Get a list of trials
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trials = analysis.trials
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You may want to get a summary of multiple experiments that point to the same ``local_dir``.
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This is also supported by the ``ExperimentAnalysis`` class.
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.. code-block:: python
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from ray.tune import ExperimentAnalysis
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analysis = ExperimentAnalysis("~/ray_results/example-experiment")
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.. _exp-analysis-docstring:
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ExperimentAnalysis (tune.ExperimentAnalysis)
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--------------------------------------------
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.. autoclass:: ray.tune.ExperimentAnalysis
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:members:
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