Textual_metamorphic_testing.py

"""
Summary: Tests if the model prediction is invariant when the feature values are perturbed

Description: Test if the predicted classification label remains the same after
feature values perturbation.The test is passed when the percentage of unchanged
rows is higher than the threshold


Args:
    df(GiskardDataset):
        Dataset used to compute the test
    model(GiskardModel):
        Model used to compute the test
    perturbation_dict(dict):
        Dictionary of the perturbations. It provides the perturbed features as key
        and a perturbation lambda function as value
    threshold(float):
        Threshold of the ratio of invariant rows

Returns:
    actual_slices_size:
        total number of rows of actual dataset
    number_of_perturbed_rows:
        Number of perturbed rows
    metric:
        The ratio of unchanged rows over the perturbed rows
    passed:
        TRUE if metric > threshold
    output_df:
        Dataframe of rows where the prediction changes due to perturbation
"""

import nlpaug.augmenter.word as naw  
aug = naw.SynonymAug(aug_src='wordnet')


# Perturbation: all the words of the email are substituted by WordNet's synonym
perturbation = {
    "Content": lambda x: aug.augment(x["Content"])
}

tests.metamorphic.test_metamorphic_invariance(
    df=actual_ds,
    model=model,
    perturbation_dict=perturbation,  
    threshold=0.5
)