Merge pull request optuna#4943 from cross32768/add_more_doc_for_fast_…

…fanova

Add introduction of `optuna-fast-fanova` in documents

docs/source/reference/importance.rst

@@ -5,6 +5,12 @@ optuna.importance
 
 The :mod:`~optuna.importance` module provides functionality for evaluating hyperparameter importances based on completed trials in a given study. The utility function :func:`~optuna.importance.get_param_importances` takes a :class:`~optuna.study.Study` and optional evaluator as two of its inputs. The evaluator must derive from :class:`~optuna.importance.BaseImportanceEvaluator`, and is initialized as a :class:`~optuna.importance.FanovaImportanceEvaluator` by default when not passed in. Users implementing custom evaluators should refer to either :class:`~optuna.importance.FanovaImportanceEvaluator` or :class:`~optuna.importance.MeanDecreaseImpurityImportanceEvaluator` as a guide, paying close attention to the format of the return value from the Evaluator's ``evaluate`` function.
 
+.. note::
+
+   :class:`~optuna.importance.FanovaImportanceEvaluator` takes over 1 minute when given a study that contains 1000+ trials.
+   We published `optuna-fast-fanova <https://github.com/optuna/optuna-fast-fanova>`_ library,
+   that is a Cython accelerated fANOVA implementation. By using it, you can get hyperparameter
+   importances within a few seconds.
 
 .. autosummary::
    :toctree: generated/

optuna/importance/__init__.py

@@ -63,6 +63,14 @@ def get_param_importances(
             assessment on.
             Defaults to
             :class:`~optuna.importance.FanovaImportanceEvaluator`.
+
+            .. note::
+                :class:`~optuna.importance.FanovaImportanceEvaluator` takes over 1 minute
+                when given a study that contains 1000+ trials. We published
+                `optuna-fast-fanova <https://github.com/optuna/optuna-fast-fanova>`_ library,
+                that is a Cython accelerated fANOVA implementation.
+                By using it, you can get hyperparameter importances within a few seconds.
+
         params:
             A list of names of parameters to assess.
             If :obj:`None`, all parameters that are present in all of the completed trials are

optuna/visualization/_param_importances.py

@@ -113,6 +113,14 @@ def objective(trial):
             assessment on.
             Defaults to
             :class:`~optuna.importance.FanovaImportanceEvaluator`.
+
+            .. note::
+                :class:`~optuna.importance.FanovaImportanceEvaluator` takes over 1 minute
+                when given a study that contains 1000+ trials. We published
+                `optuna-fast-fanova <https://github.com/optuna/optuna-fast-fanova>`_ library,
+                that is a Cython accelerated fANOVA implementation.
+                By using it, you can get hyperparameter importances within a few seconds.
+
         params:
             A list of names of parameters to assess.
             If :obj:`None`, all parameters that are present in all of the completed trials are