def _transform_col()

in causalml/features.py [0:0]

• 12 lines of code
• 5 McCabe index (conditional complexity)

    def _transform_col(self, x, i):
        """Encode one categorical column into sparse matrix with one-hot-encoding.

        Args:
            x (pandas.Series): a categorical column to encode
            i (int): column index

        Returns:
            X (scipy.sparse.coo_matrix): sparse matrix encoding a categorical
                                         variable into dummy variables
        """

        labels = self.label_encoder._transform_col(x, i)
        label_max = self.label_encoder.label_maxes[i]

        # build row and column index for non-zero values of a sparse matrix
        index = np.array(range(len(labels)))
        i = index[labels > 0]
        j = labels[labels > 0] - 1  # column index starts from 0

        if len(i) > 0:
            return sparse.coo_matrix(
                (np.ones_like(i), (i, j)), shape=(x.shape[0], label_max)
            )
        else:
            # if there is no non-zero value, return no matrix
            return None