shapiq 0.0.4

SHAPley Interaction Quantification (SHAP-IQ) for Explainable AI

SHAP-IQ: SHAP Interaction Quantification

An interaction may speak more than a thousand main effects.

SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions to the equation. Shapley interactions extend on indivdual Shapley values by quantifying the synergy effect between machine learning entities such as features, data points, or weak learners in ensemble models. Synergies between these entities (also called players in game theory jargon) allows for a more intricate evaluation of your black-box models!

🛠️ Install

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

pip install shapiq

⭐ Quickstart

📈 Compute n-SII values

📊 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.

from shapiq.plot import network_plot

network_plot(
    first_order_values=n_sii_first_order,  # first order n-SII values
    second_order_values=n_sii_second_order # second order n-SII values
)

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

📖 Documentation

The documentation for shapiq can be found here.