Source code for optuna.importance._fanova._evaluator

from typing import Callable
from typing import Dict
from typing import List
from typing import Optional

import numpy as np

from optuna._transform import _SearchSpaceTransform
from optuna.importance._base import _get_distributions
from optuna.importance._base import _get_filtered_trials
from optuna.importance._base import _get_target_values
from optuna.importance._base import _get_trans_params
from optuna.importance._base import _param_importances_to_dict
from optuna.importance._base import _sort_dict_by_importance
from optuna.importance._base import BaseImportanceEvaluator
from optuna.importance._fanova._fanova import _Fanova
from optuna.study import Study
from optuna.trial import FrozenTrial


[docs] class FanovaImportanceEvaluator(BaseImportanceEvaluator): """fANOVA importance evaluator. Implements the fANOVA hyperparameter importance evaluation algorithm in `An Efficient Approach for Assessing Hyperparameter Importance <http://proceedings.mlr.press/v32/hutter14.html>`_. fANOVA fits a random forest regression model that predicts the objective values of :class:`~optuna.trial.TrialState.COMPLETE` trials given their parameter configurations. The more accurate this model is, the more reliable the importances assessed by this class are. .. note:: This class 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. .. note:: Requires the `sklearn <https://github.com/scikit-learn/scikit-learn>`_ Python package. .. note:: The performance of fANOVA depends on the prediction performance of the underlying random forest model. In order to obtain high prediction performance, it is necessary to cover a wide range of the hyperparameter search space. It is recommended to use an exploration-oriented sampler such as :class:`~optuna.samplers.RandomSampler`. .. note:: For how to cite the original work, please refer to https://automl.github.io/fanova/cite.html. Args: n_trees: The number of trees in the forest. max_depth: The maximum depth of the trees in the forest. seed: Controls the randomness of the forest. For deterministic behavior, specify a value other than :obj:`None`. """ def __init__( self, *, n_trees: int = 64, max_depth: int = 64, seed: Optional[int] = None ) -> None: self._evaluator = _Fanova( n_trees=n_trees, max_depth=max_depth, min_samples_split=2, min_samples_leaf=1, seed=seed, )
[docs] def evaluate( self, study: Study, params: Optional[List[str]] = None, *, target: Optional[Callable[[FrozenTrial], float]] = None, ) -> Dict[str, float]: if target is None and study._is_multi_objective(): raise ValueError( "If the `study` is being used for multi-objective optimization, " "please specify the `target`. For example, use " "`target=lambda t: t.values[0]` for the first objective value." ) distributions = _get_distributions(study, params=params) if params is None: params = list(distributions.keys()) assert params is not None # fANOVA does not support parameter distributions with a single value. # However, there is no reason to calculate parameter importance in such case anyway, # since it will always be 0 as the parameter is constant in the objective function. non_single_distributions = { name: dist for name, dist in distributions.items() if not dist.single() } single_distributions = { name: dist for name, dist in distributions.items() if dist.single() } if len(non_single_distributions) == 0: return {} trials: List[FrozenTrial] = _get_filtered_trials(study, params=params, target=target) trans = _SearchSpaceTransform( non_single_distributions, transform_log=False, transform_step=False ) trans_params: np.ndarray = _get_trans_params(trials, trans) target_values: np.ndarray = _get_target_values(trials, target) evaluator = self._evaluator evaluator.fit( X=trans_params, y=target_values, search_spaces=trans.bounds, column_to_encoded_columns=trans.column_to_encoded_columns, ) param_importances = np.array( [evaluator.get_importance(i)[0] for i in range(len(non_single_distributions))] ) return _sort_dict_by_importance( { **_param_importances_to_dict(non_single_distributions.keys(), param_importances), **_param_importances_to_dict(single_distributions.keys(), 0.0), } )