visualize_as_dataframe(show_only_changes=True) does not work when categorical data is composed of numbers

[mimicarina]

DiCE/dice_ml/data_interfaces/private_data_interface.py

Line 336 in e9e7147

levels.append(self.categorical_levels[cat_feature])

When categorical columns contain numerical levels (e.g. yes - 1, no - 0) visualize_as_dataframe(show_only_changes=True) (and also visualize_as_list()) does not work, as it encodes the string values to numerical.

Example dataset: https://archive.ics.uci.edu/ml/machine-learning-databases/00573/SouthGermanCredit.zip.

During data prep, categorical values are encoded as 'category' data type (see query instance below). The counterfactual uses numeric representation; hence it will show as 'changed' value even though it is the same category (e.g. '2' vs 2).

Query instance (original outcome : 1)
['1', 21, '2', '2', 3599, '1', '4', '1', '2', '1', '4', '3', '3', '1', '1', '2', '2', '1', '2', '1', 1]

Diverse Counterfactual set (new outcome: 0.0)
[1, '-', 2, 2, 17507, 1, 4, 1, 2, 1, 4, 3, 3, 1, 1, 2, 2, '2', 2, 1, 0]
[1, '-', '0', 2, '-', 1, '-', 1, 2, 1, 4, 3, 3, 1, 1, 2, 2, 1, 2, 1, 0]

This is happening because train_data[cat_feature].cat.categories.tolist() returns integer and not categories/strings; for sample dataset above the categories and levels are:

['credit_history', 'foreign_worker', 'housing', 'other_debtors', 'other_installment_plans', 'people_liable', 'personal_status_sex', 'purpose', 'savings', 'status', 'telephone', 'employment_duration', 'installment_rate', 'job', 'number_credits', 'present_residence', 'property']
[[0, 1, 2, 3, 4], [1, 2], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2], [1, 2, 3, 4], [0, 1, 2, 3, 4, 5, 6, 8, 9, 10], [1, 2, 3, 4, 5], [1, 2, 3, 4], [1, 2], [1, 2, 3, 4, 5], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]]