econml/grf/_base_grf.py [413:463]:
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            return [subsample_random_state.choice(n_, ns_, replace=False)
                    for n_, ns_ in zip(self.n_samples_, self.n_samples_subsample_)]

    def feature_importances(self, max_depth=4, depth_decay_exponent=2.0):
        """
        The feature importances based on the amount of parameter heterogeneity they create.
        The higher, the more important the feature.
        The importance of a feature is computed as the (normalized) total heterogeneity that the feature
        creates. For each tree and for each split that the feature was chosen adds::

            parent_weight * (left_weight * right_weight)
                * mean((value_left[k] - value_right[k])**2) / parent_weight**2

        to the importance of the feature. Each such quantity is also weighted by the depth of the split.
        These importances are normalized at the tree level and then averaged across trees.

        Parameters
        ----------
        max_depth : int, default=4
            Splits of depth larger than `max_depth` are not used in this calculation
        depth_decay_exponent: double, default=2.0
            The contribution of each split to the total score is re-weighted by 1 / (1 + `depth`)**2.0.
        Returns
        -------
        feature_importances_ : ndarray of shape (n_features,)
            Normalized total parameter heterogeneity inducing importance of each feature
        """
        check_is_fitted(self)

        all_importances = Parallel(n_jobs=self.n_jobs, backend='threading')(
            delayed(tree.feature_importances)(
                max_depth=max_depth, depth_decay_exponent=depth_decay_exponent)
            for tree in self.estimators_ if tree.tree_.node_count > 1)

        if not all_importances:
            return np.zeros(self.n_features_, dtype=np.float64)

        all_importances = np.mean(all_importances,
                                  axis=0, dtype=np.float64)
        return all_importances / np.sum(all_importances)

    @property
    def feature_importances_(self):
        return self.feature_importances()

    def _validate_X_predict(self, X):
        """
        Validate X whenever one tries to predict, apply, and other predict methods."""
        check_is_fitted(self)

        return self.estimators_[0]._validate_X_predict(X, check_input=True)
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econml/policy/_forest/_forest.py [378:421]:
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        return [subsample_random_state.choice(n_, ns_, replace=False)
                for n_, ns_ in zip(self.n_samples_, self.n_samples_subsample_)]

    def feature_importances(self, max_depth=4, depth_decay_exponent=2.0):
        """
        The feature importances based on the amount of parameter heterogeneity they create.
        The higher, the more important the feature.

        Parameters
        ----------
        max_depth : int, default=4
            Splits of depth larger than `max_depth` are not used in this calculation
        depth_decay_exponent: double, default=2.0
            The contribution of each split to the total score is re-weighted by 1 / (1 + `depth`)**2.0.

        Returns
        -------
        feature_importances_ : ndarray of shape (n_features,)
            Normalized total parameter heterogeneity inducing importance of each feature
        """
        check_is_fitted(self)

        all_importances = Parallel(n_jobs=self.n_jobs, backend='threading')(
            delayed(tree.feature_importances)(
                max_depth=max_depth, depth_decay_exponent=depth_decay_exponent)
            for tree in self.estimators_ if tree.tree_.node_count > 1)

        if not all_importances:
            return np.zeros(self.n_features_, dtype=np.float64)

        all_importances = np.mean(all_importances,
                                  axis=0, dtype=np.float64)
        return all_importances / np.sum(all_importances)

    @property
    def feature_importances_(self):
        return self.feature_importances()

    def _validate_X_predict(self, X):
        """
        Validate X whenever one tries to predict, apply, and other predict methods."""
        check_is_fitted(self)

        return self.estimators_[0]._validate_X_predict(X, check_input=True)
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