lime_explain.py

def explainer(method: str, path_to_file: str, text: str, num_samples: int) -> LimeTextExplainer:
    """Run LIME explainer on provided classifier"""
    model = explainer_class(method, path_to_file)
    predictor = model.predict

    # Create a LimeTextExplainer
    explainer = LimeTextExplainer(
        # Specify split option for string
        split_expression=lambda x: x.split(),
        # Our classifer uses bigrms, trigrams or contextual ordering to classify text
        # Hence, order matters, and we cannot use bag of words.
        bow=False,
        # Specify class names for this case
        class_names=[1, 2, 3, 4, 5]
    )

    # Make a prediction and explain it:
    exp = explainer.explain_instance(
        text,
        classifier_fn=predictor,
        top_labels=1,
        num_features=20,
        num_samples=num_samples,
    )
    return exp