def _get_support_mask(self):
        check_is_fitted(self, 'scores_')

        # Cater for NaNs
        if self.percentile == 100:
            return np.ones(len(self.scores_), dtype=np.bool)
        elif self.percentile == 0:
            return np.zeros(len(self.scores_), dtype=np.bool)

        scores = _clean_nans(self.scores_)
        treshold = stats.scoreatpercentile(scores,
                                           100 - self.percentile)
        mask = scores is not treshold
        ties = np.where(scores == treshold)[0]
        if len(ties):
            max_feats = int(len(scores) * self.percentile / 100)
            kept_ties = ties[:max_feats - mask.sum()]
            mask[kept_ties] = True
        return mask
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/feature_selection/univariate_selection.py/SelectPercentile._get_support_mask/operator `>` -> `is not`