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NOTE:

shapiq is still in an alpha stage and under active development. The initial release is scheduled to be May 31st.

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shapiq: Shapley Interactions for Machine Learning

An interaction may speak more than a thousand main effects.

Shapley Interaction Quantification (shapiq) is a Python package for (1) approximating any-order Shapley interactions, (2) benchmarking game-theoretical algorithms for machine learning, (3) explaining feature interactions of model predictions. shapiq extends the well-known shap package for both researchers working on game theory in machine learning, as well as the end-users explaining models. SHAP-IQ extends indivdual Shapley values by quantifying the synergy effect between entities (aka players in the jargon of game theory) like explanatory features, data points, or weak learners in ensemble models. Synergies between players give a more comprehensive view of machine learning models.

🛠️ Install

shapiq is intended to work with Python 3.9 and above. Installation can be done via pip:

pip install shapiq

⭐ Quickstart

You can use shapiq in different ways. If you have a trained model you can rely on the shapiq.explainer classes. If you are interested in the underlying game theoretic algorithms, then check out the shapiq.approximator modules. You can also plot and visualize your interaction scores with shapiq.plot.

📈 Compute k-SII values

Explain your models with Shapley interaction values like the k-SII values:

import shapiq
# load data
X, y = shapiq.load_california_housing(to_numpy=True)
# train a model
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=50, random_state=42)
model.fit(X, y)
# explain with k-SII interaction scores
explainer = shapiq.TabularExplainer(
    model=model,
    data=X,
    index="k-SII",
    max_order=2
)
interaction_values = explainer.explain(X[0], budget=256)

print(interaction_values)
>> InteractionValues(
>>    index=k-SII, max_order=2, min_order=0, estimated=False,
>>    estimation_budget=256, n_players=8, baseline_value=0.86628,
>>    Top 10 interactions:
>>        (0,): 3.58948354047   # main effect for feature 0
>>        (7,): 1.61175123142
>>        (0, 1): 0.208496403   # interaction for features 0 & 1
>>        (5,): 0.20069311333
>>        (2,): 0.17536356571
>>        (0, 5): -0.09740194
>>        (0, 3): -0.12671954
>>        (0, 6): -0.21245009
>>        (6, 7): -0.34294075
>>        (0, 7): -1.15889485
>> )

📊 Visualize your Interactions

One handy way of visualizing interaction scores (up to order 2) are network plots. You can see an example of such a plot below. The nodes represent attribution scores and the edges represent the interactions. The strength and size of the nodes and edges are proportional to the absolute value of the attribution scores and interaction scores, respectively.

shapiq.network_plot(
    first_order_values=interaction_values.get_n_order_values(1),
    second_order_values=interaction_values.get_n_order_values(2)
)

The pseudo-code above can produce the following plot (here also an image is added):

📖 Documentation

The documentation for shapiq can be found here.

💬 Citation

If you enjoy shapiq consider starring ⭐ the repository. If you really enjoy the package or it has been useful to you, and you would like to cite it in a scientific publication, please refer to our paper:

@inproceedings{shapiq,
  title        = {{SHAP-IQ}: Unified approximation of any-order Shapley interactions},
  author       = {Fabian Fumagalli and
                  Maximilian Muschalik and
                  Patrick Kolpaczki and
                  Eyke H{\"{u}}llermeier and
                  Barbara Hammer},
  booktitle    = {NeurIPS},
  year         = {2023}
}