Source code for optuna.importance._mean_decrease_impurity

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

import numpy as np

from optuna._imports import try_import
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.study import Study
from optuna.trial import FrozenTrial


with try_import() as _imports:
    from sklearn.ensemble import RandomForestRegressor


[docs] class MeanDecreaseImpurityImportanceEvaluator(BaseImportanceEvaluator): """Mean Decrease Impurity (MDI) parameter importance evaluator. This evaluator fits fits a random forest regression model that predicts the objective values of :class:`~optuna.trial.TrialState.COMPLETE` trials given their parameter configurations. Feature importances are then computed using MDI. .. note:: This evaluator requires the `sklearn <https://scikit-learn.org/stable/>`_ Python package and is based on `sklearn.ensemble.RandomForestClassifier.feature_importances_ <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.feature_importances_>`_. Args: n_trees: Number of trees in the random forest. max_depth: The maximum depth of each tree in the random forest. seed: Seed for the random forest. """ def __init__( self, *, n_trees: int = 64, max_depth: int = 64, seed: Optional[int] = None ) -> None: _imports.check() self._forest = RandomForestRegressor( n_estimators=n_trees, max_depth=max_depth, min_samples_split=2, min_samples_leaf=1, random_state=seed, ) self._trans_params = np.empty(0) self._trans_values = np.empty(0) self._param_names: List[str] = list()
[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 if len(params) == 0: return {} trials: List[FrozenTrial] = _get_filtered_trials(study, params=params, target=target) trans = _SearchSpaceTransform(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) forest = self._forest forest.fit(X=trans_params, y=target_values) feature_importances = forest.feature_importances_ # Untransform feature importances to param importances # by adding up relevant feature importances. param_importances = np.zeros(len(params)) np.add.at(param_importances, trans.encoded_column_to_column, feature_importances) return _sort_dict_by_importance(_param_importances_to_dict(params, param_importances))