Spectral Explainer: Scalable Feature Interaction Attribution

Installation • Quickstart • Examples • Citation

Installation

To install the core spectralexplain package via PyPI, run:

pip install spectralexplain

To replicate the experiments in this repository, you need to install additional dependencies. To install spectralexplain with these optional dependencies, run:

git clone git@github.com:basics-lab/spectral-explain.git
cd spectral-explain
pip install -e .[dev]

Quickstart

spectralexplain can be used to quickly compute feature interactions for your models and datasets. Simply define a value_function which takes in a matrix of masking patterns and returns the model's outputs to masked inputs.

Upon passing this function to the Explainer class, alongside the number of features in your dataset, spectralexplain will discover feature interactions.

Calling explainer.interactions, alongside a choice of interaction index, will return an Interactions object for any of the following interaction types:

• fbii Faith-Banzhaf Interaction Index, fsii Faith-Shapley Interaction Index, stii Shapley-Taylor Interaction Index, bii Banzhaf Interaction Index, sii Shapley Interaction Index, fourier Fourier Interactions, mobius Mobius Interactions

import spectralexplain as spex

# X is a (num_samples x num_features) binary masking matrix
def value_function(X):
    return ...

explainer = spex.Explainer(
    value_function=value_function,
    features=num_features,
)

print(explainer.interactions(index="fbii"))

Examples

Tabular

import spectralexplain as spex
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_breast_cancer

data, target = load_breast_cancer(return_X_y=True)
test_point, data, target = data[0], data[1:], target[1:]

model = RandomForestRegressor().fit(data, target)

def tabular_masking(X):
    return model.predict(np.where(X, test_point, data.mean(axis=0)))

explainer = spex.Explainer(
    value_function=tabular_masking,
    features=range(len(test_point)),
    sample_budget=1000
)

print(explainer.interactions(index="fbii"))

>> Interactions(
>>   index=FBII, max_order=4, baseline_value=0.626
>>   sample_budget=1000, num_features=30,
>>   Top Interactions:
>>     (27,): -0.295
>>     (22,): -0.189
>>     (3, 6, 8, 22): 0.188
>>     (6, 10, 14, 28): 0.176
>>     (23,): -0.145
>> )

Sentiment Analysis

import spectralexplain as spex
from transformers import pipeline

review = "Her acting never fails to impress".split()
sentiment_pipeline = pipeline("sentiment-analysis")

def sentiment_masking(X):
    masked_reviews = [" ".join([review[i] if x[i] == 1 else "[MASK]" for i in range(len(review))]) for x in X]
    return [outputs['score'] if outputs['label'] == 'POSITIVE' else 1-outputs['score'] for outputs in sentiment_pipeline(masked_reviews)]

explainer = spex.Explainer(value_function=sentiment_masking,
                           features=review,
                           sample_budget=1000)

print(explainer.interactions(index="stii"))

>> Interactions(
>>   index=STII, max_order=5, baseline_value=-0.63
>>   sample_budget=1000, num_features=6,
>>   Top Interactions:
>>     ('never', 'fails'): 2.173
>>     ('fails', 'impress'): -1.615
>>     ('never', 'fails', 'impress'): 1.592
>>     ('fails', 'to'): -1.505
>>     ('impress',): 1.436
>> )

Citation

@misc{kang2025spex,
      title={SPEX: Scaling Feature Interaction Explanations for LLMs}, 
      author={Justin Singh Kang and Landon Butler and Abhineet Agarwal and Yigit Efe Erginbas and Ramtin Pedarsani and Kannan Ramchandran and Bin Yu},
      year={2025},
      eprint={2502.13870},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2502.13870}, 
}