GALE is a Python library used to assess the similarity of local explanations from methods such as LIME, SHAP or generalized additive models (GAMs). To do so, GALE models the relationship between the explanation space and the model predictions as a scalar function. Then, we compute the topological skeleton of this function. This topological skeleton acts as a signature, which we use to compare outputs from different explanation methods.
GALE is easy to install and use. Simply run
pip install gale-topoand you're good to go!
You can measure the similarity between sets of explanations in just a few lines of code, shown below
from gale import create_mapper, bottleneck_distance
model_one_mapper = create_mapper(explanations_one, predictions_one, resolution=10, gain=0.3, dist_thresh=0.5)
model_two_mapper = create_mapper(explanations_two, predictions_two, resolution=10, gain=0.3, dist_thresh=0.5)
bottleneck_distance(model_one_mapper, model_two_mapper) # This returns a float which represents the distance between the two Mapper outputsNeed help? Open up an issue.
GALE was published at TAGML 2022, a workshop at ICML 2022. If you use GALE in your work, please cite the following
latex citation goes here