Source code for eli5.catboost

from __future__ import absolute_import, division

import numpy as np  # type: ignore
import catboost # type: ignore

from eli5.explain import explain_weights
from eli5._feature_importances import get_feature_importance_explanation

DESCRIPTION_CATBOOST = """CatBoost feature importances; 
values are numbers 0 <= x <= 1; all values sum to 1."""

[docs]@explain_weights.register(catboost.CatBoost) @explain_weights.register(catboost.CatBoostClassifier) @explain_weights.register(catboost.CatBoostRegressor) def explain_weights_catboost(catb, vec=None, top=20, importance_type='PredictionValuesChange', feature_names=None, pool=None ): """ Return an explanation of an CatBoost estimator (CatBoostClassifier, CatBoost, CatBoostRegressor) as feature importances. See :func:`eli5.explain_weights` for description of ``top``, ``feature_names``, ``feature_re`` and ``feature_filter`` parameters. ``target_names`` and ``targets`` parameters are ignored. Parameters ---------- :param 'importance_type' : str, optional A way to get feature importance. Possible values are: - 'PredictionValuesChange' (default) - The individual importance values for each of the input features. - 'LossFunctionChange' - The individual importance values for each of the input features for ranking metrics (requires training data to be passed or a similar dataset with Pool) :param 'pool' : catboost.Pool, optional To be passed if explain_weights_catboost has importance_type set to LossFunctionChange. The catboost feature_importances uses the Pool datatype to calculate the parameter for the specific importance_type. """ is_regression = _is_regression(catb) catb_feature_names = catb.feature_names_ coef = _catb_feature_importance(catb, importance_type=importance_type, pool=pool) return get_feature_importance_explanation(catb, vec, coef, feature_names=feature_names, estimator_feature_names=catb_feature_names, feature_filter=None, feature_re=None, top=top, description=DESCRIPTION_CATBOOST, is_regression=is_regression, num_features=coef.shape[-1] )
def _is_regression(catb): return isinstance(catb, catboost.CatBoostRegressor) def _catb_feature_importance(catb, importance_type, pool=None): if importance_type == "PredictionValuesChange": fs = catb.get_feature_importance(type=importance_type) elif importance_type == "LossFunctionChange": if isinstance(pool, catboost.Pool): fs = catb.get_feature_importance(data=pool, type=importance_type) else: raise ValueError( 'importance_type: "LossFunctionChange" requires catboost.Pool ' 'datatype to be passed with parameter pool to calculate ' 'metric. Either no datatype or invalid datatype was passed' ) else: raise ValueError( 'Only two importance_type "PredictionValuesChange" ' 'and "LossFunctionChange" are supported. Invalid Parameter ' '{} for importance_type'.format(importance_type)) all_features = np.array(fs, dtype=np.float32) return all_features/all_features.sum()