bencher.results.optuna_result
=============================

.. py:module:: bencher.results.optuna_result


Classes
-------

.. autoapisummary::

   bencher.results.optuna_result.OptunaResult


Functions
---------

.. autoapisummary::

   bencher.results.optuna_result._evaluate_over_non_optimized
   bencher.results.optuna_result._aggregate_non_optimized
   bencher.results.optuna_result._study_has_multiple_params


Module Contents
---------------

.. py:function:: _evaluate_over_non_optimized(worker, opt_kwargs, non_opt_vars, result_vars)

   Evaluate worker across all combinations of non-optimized vars and return mean results.


.. py:function:: _aggregate_non_optimized(df, opt_vars, non_opt_vars, target_names)

   Group DataFrame by optimized vars and average target columns over non-optimized vars.


.. py:function:: _study_has_multiple_params(study)

   True when the study has >1 trial parameter, making importance meaningful.


.. py:class:: OptunaResult(bench_cfg: bencher.bench_cfg.BenchCfg)

   Bases: :py:obj:`bencher.results.bench_result_base.BenchResultBase`


   .. py:method:: to_optuna_plots(**kwargs) -> list[panel.pane.panel]

      Create an optuna summary from the benchmark results

      :returns: A list of optuna plot summarising the benchmark process
      :rtype: list[pn.pane.panel]



   .. py:method:: to_optuna_from_results(worker, n_trials=100, extra_results: list[OptunaResult] | None = None, sampler=None)


   .. py:method:: bench_results_to_optuna_trials(include_meta: bool = True) -> list

      Convert an xarray dataset to optuna trials so optuna can further optimise or plot.

      :param include_meta: When True, include all variables (inputs + meta like repeat
                           and over_time) as trial parameters for importance analysis. When False, use
                           only input variables with partition/aggregation via the optimize flag.
      :type include_meta: bool

      :returns: Optuna trials derived from benchmark results.
      :rtype: list[optuna.trial.FrozenTrial]



   .. py:method:: bench_result_to_study(include_meta: bool) -> optuna.Study


   .. py:method:: get_best_trial_params(canonical=False)


   .. py:method:: get_pareto_front_params()


   .. py:method:: collect_optuna_plots(pareto_width: float | None = None, pareto_height: float | None = None) -> panel.pane.panel

      Use optuna to plot various summaries of the optimisation.

      :param pareto_width: Optional width for the pareto front plot.
      :param pareto_height: Optional height for the pareto front plot.

      :returns: A panel with optuna visualisations.
      :rtype: pn.pane.panel



   .. py:method:: deep() -> OptunaResult

      Return a deep copy of these results



   .. py:method:: get_best_holomap(name: str | None = None)