Handling category, float and int type features while using LIME for model interpretation

Question

I am using Lime (Local Interpretable Model-agnostic Explanations) with mixed feature types in order to evaluate my model predictions for classification task. Does anyone know how to specify binary features in lime.lime_tabular.LimeTabularExplainer() method. How actually LIME handles these types of features (more features with only 1's and 0's)?

Answer 1

I think your should declare your binary features as categorical features in order to allow your Lime explainer to use its sampling mechanism efficiently when performing local perturbation around the studied sample.

You can do it using the categorical_features keyword parameter in the LimeTabularExplainer constructor.

my_binary_feature_column_index = 0 # put your column index here    

explainer = LimeTabularExplainer(my_data, categorical_features=[my_binary_feature_column_index], categorical_name={my_binary_feature_column_index: ["foo", "bar", "baz"]})

• categorical_features is a list of categorical column indexes, and
• categorical_name is a dictionary containing a map of column index and list of category names.

As it is mentionned in the LIME code :

Explains predictions on tabular (i.e. matrix) data. For numerical features, perturb them by sampling from a Normal(0,1) and doing the inverse operation of mean-centering and scaling, according to the means and stds in the training data. For categorical features, perturb by sampling according to the training distribution, and making a binary feature that is 1 when the value is the same as the instance being explained.

So, categorical features are one hot encoded under the hood and the value 0 or 1 is used according to the feature distribution in your training dataset (unless you chose to use a LabelEncoder, which will result in LIME processing the feature as a continuous variable).

A good tutorial is available in the LIME project: https://github.com/marcotcr/lime/blob/master/doc/notebooks/Tutorial%20-%20continuous%20and%20categorical%20features.ipynb