Use optuna-fast-fanova if available

README.md

@@ -11,33 +11,23 @@ Code files were originally taken from [Goptuna](https://github.com/c-bata/goptun
 You can install optuna-dashboard via [PyPI](https://pypi.org/project/optuna-dashboard/) or [Anaconda Cloud](https://anaconda.org/conda-forge/optuna-dashboard).
 
 ```
-# PyPI
 $ pip install optuna-dashboard
 ```
 
-```
-# Anaconda Cloud
-$ conda install -c conda-forge optuna-dashboard
+And you can also install following optional dependencies to make optuna-dashboard faster.
+
+```console
+$ pip install optuna-fast-fanova gunicorn
 ```
 
 Then please execute `optuna-dashboard` command with Optuna storage URL.
 
 ```
 $ optuna-dashboard sqlite:///db.sqlite3
-Bottle v0.12.18 server starting up (using WSGIRefServer())...
 Listening on http://localhost:8080/
 Hit Ctrl-C to quit.
 ```
 
-Note that optuna-dashboard uses [wsgiref](https://docs.python.org/3/library/wsgiref.html) module by default.
-Although it requires no additional dependencies, it is NOT suitable for the production use.
-You can use [Gunicorn](https://gunicorn.org/) via "--server gunicorn" option.
-
-```console
-$ pip install gunicorn
-$ optuna-dashboard sqlite:///db.sqlite3 --server gunicorn
-```
-
 <details>
 
 <summary>More command line options</summary>
@@ -56,7 +46,7 @@ optional arguments:
   --port PORT           port number (default: 8080)
   --host HOST           hostname (default: 127.0.0.1)
   --server {wsgiref,gunicorn}
-                        server (default: wsgiref)
+                        server (default: auto)
   --version, -v         show program's version number and exit
   --quiet, -q           quiet
 ```

optuna_dashboard/_importance.py

@@ -3,17 +3,26 @@ from typing import Dict
 from typing import List
 from typing import Tuple
 
+from optuna.importance import BaseImportanceEvaluator
+from optuna.importance import FanovaImportanceEvaluator
+from optuna.importance import get_param_importances
+from optuna.storages import BaseStorage
+from optuna.study import Study
+from optuna.trial import FrozenTrial
+from optuna.trial import TrialState
+
 
 try:
     from typing import TypedDict
 except ImportError:
     from typing_extensions import TypedDict
 
-import optuna
-from optuna.storages import BaseStorage
-from optuna.study import Study
-from optuna.trial import FrozenTrial
-from optuna.trial import TrialState
+try:
+    from optuna_fast_fanova import (
+        FanovaImportanceEvaluator as FastFanovaImportanceEvaluator,
+    )
+except ImportError:
+    FastFanovaImportanceEvaluator = None  # type: ignore
 
 
 ImportanceItemType = TypedDict(
@@ -54,7 +63,8 @@ class StudyWrapper(Study):
 def get_param_importance_from_trials_cache(
     storage: BaseStorage, study_id: int, objective_id: int, trials: List[FrozenTrial]
 ) -> ImportanceType:
-    n_completed_trials = len([t for t in trials if t.state == TrialState.COMPLETE])
+    completed_trials = [t for t in trials if t.state == TrialState.COMPLETE]
+    n_completed_trials = len(completed_trials)
     if n_completed_trials == 0:
         return {"target_name": target_name, "param_importances": []}
 
@@ -67,8 +77,16 @@ def get_param_importance_from_trials_cache(
             return cache_importance
 
         study = StudyWrapper(storage, study_id, trials)
-        importance = optuna.importance.get_param_importances(
-            study, target=lambda t: t.values[objective_id]
+
+        evaluator: BaseImportanceEvaluator
+        if FastFanovaImportanceEvaluator is not None:
+            evaluator = FastFanovaImportanceEvaluator(completed_trials=completed_trials)
+        else:
+            evaluator = FanovaImportanceEvaluator()
+        importance = get_param_importances(
+            study,
+            target=lambda t: t.values[objective_id],
+            evaluator=evaluator,
         )
         converted = convert_to_importance_type(importance, trials)
         param_importance_cache[cache_key] = (n_completed_trials, converted)

optuna_dashboard/_serializer.py

@@ -80,11 +80,6 @@ def serialize_study_detail(
     if summary.datetime_start is not None:
         serialized["datetime_start"] = summary.datetime_start.isoformat()
 
-    if summary.best_trial is not None:
-        serialized["best_trial"] = serialize_frozen_trial(
-            summary._study_id, summary.best_trial
-        )
-
     serialized["trials"] = [
         serialize_frozen_trial(summary._study_id, trial) for trial in trials
     ]

setup.cfg

@@ -28,11 +28,11 @@ python_requires = >=3.6
 include_package_data = False
 packages = find:
 install_requires =
+    bottle
     optuna>=2.4
     packaging
-    bottle
-    typing-extensions;python_version<'3.8'
     scikit-learn
+    typing-extensions;python_version<'3.8'
 
 [options.entry_points]
 console_scripts =