select_percentile_classification.py 文件源码

def transform(self, X):
        import scipy.sparse
        import sklearn.feature_selection

        # Because the pipeline guarantees that each feature is positive,
        # clip all values below zero to zero
        if self.score_func == sklearn.feature_selection.chi2:
            if scipy.sparse.issparse(X):
                X.data[X.data < 0] = 0.0
            else:
                X[X < 0] = 0.0

        if self.preprocessor is None:
            raise NotImplementedError()
        Xt = self.preprocessor.transform(X)
        if Xt.shape[1] == 0:
            raise ValueError(
                "%s removed all features." % self.__class__.__name__)
        return Xt