30631c5a179a705125f40b1ec97b8ca67993788b,pyinterpret/core/global_interpretation/partial_dependence.py,,_compute_pd,#Any#Any#Any#Any#Any#,23

Before Change

        mean_prediction = np.array([mean_prediction])
        std_prediction = np.array([std_prediction])

    for feature_idx, feature_id in enumerate(feature_ids):
        val_col = feature_columns[feature_idx]
        pd_dict[val_col] = new_row[feature_idx]

    number_of_classes = len(class_columns)
    assert number_of_classes == mean_prediction.shape[0], "Mismatch between prediction dimension" \
                                                          "and number of class names" \
                                                          "Prediction dimension: {} " \

After Change

                                                          "n class_names: {}".format(*[mean_prediction.shape,
                                                                                       number_of_classes])

    pd_dict = {column: new_row[idx] for idx, column in enumerate(feature_columns)}
    if number_of_classes == 2:
        class_column = class_columns[1]
        pd_dict[class_column] = mean_prediction[1]
        pd_dict["sd"] = std_prediction[0]

Instances