fc4917ae8a7320fc9a258b50d82a177ed2124a91,category_encoders/backward_difference.py,BackwardDifferenceEncoder,fit_backward_difference_coding,#,216

Before Change

        if len(values) < 2:
            return pd.DataFrame()

        backwards_difference_matrix = Diff().code_without_intercept(values.get_values())
        df = pd.DataFrame(data=backwards_difference_matrix.matrix, columns=backwards_difference_matrix.column_suffixes)
        df.index += 1

        if handle_unknown == "return_nan":
            df.loc[-1] = np.nan
        elif handle_unknown == "value":
            df.loc[-1] = np.zeros(len(values) - 1)

        if handle_missing == "return_nan":
            df.loc[values.loc[np.nan]] = np.nan
        elif handle_missing == "value":
            df.loc[-2] = np.zeros(len(values) - 1)

        return df

    @staticmethod

After Change

        if handle_missing == "value":
            values = values[values > 0]

        values_to_encode = values.get_values()

        if len(values) < 2:
            return pd.DataFrame(index=values_to_encode)

        if handle_unknown == "indicator":
            values_to_encode = np.append(values_to_encode, -1)

        backwards_difference_matrix = Diff().code_without_intercept(values_to_encode)
        df = pd.DataFrame(data=backwards_difference_matrix.matrix, index=values_to_encode,
                          columns=[str(col) + "_%d" % (i, ) for i in range(len(backwards_difference_matrix.column_suffixes))])

        if handle_unknown == "return_nan":
            df.loc[-1] = np.nan
        elif handle_unknown == "value":
            df.loc[-1] = np.zeros(len(values_to_encode) - 1)

        if handle_missing == "return_nan":
            df.loc[values.loc[np.nan]] = np.nan
        elif handle_missing == "value":
            df.loc[-2] = np.zeros(len(values_to_encode) - 1)

        return df

    @staticmethod

Instances