utils_feature_selection.py 文件源码

def transform(self, X, y=None):

        if self.selector == 'KeepAll':
            return X

        if scipy.sparse.issparse(X):
            if X.getformat() == 'csr':
                # convert to a csc (column) matrix, rather than a csr (row) matrix
                X = X.tocsc()

            # Slice that column matrix to only get the relevant columns that we already calculated in fit:
            X = X[:, self.index_mask]

            # convert back to a csr matrix
            return X.tocsr()

        # If this is a dense matrix:
        else:
            pruned_X = [list(itertools.compress(row, self.support_mask)) for row in X]
            return pruned_X