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This release is a pre-release and may not be stable for production use.

Unified IG

Unified IG provides one small, SHAP-like API for Integrated Gradients across model families. The first implementation supports closed-form attributions for scikit-learn linear models and multilayer perceptrons.

import unifiedig as uig

explainer = uig.Explainer(model, baseline)
explanation = explainer(X)

For numerical backends, the quadrature resolution is configurable:

explainer = uig.Explainer(model, baseline, n_steps=128)

Explanation is lightweight and has SHAP-compatible fields. SHAP remains an optional dependency; call explanation.to_shap() to use its plotting tools.

Installation

During development, install the project and its test dependencies with:

python -m pip install -e ".[test]"

Output semantics

For regression, attributions sum to the difference between the prediction and the baseline prediction. Binary classifiers are explained on their decision-score (logit) scale; probability attributions are not part of V1. See docs/semantics.md for the complete array-shape and output contract.

Numerical explanations expose their observed completeness residual:

explanation.completeness_error
explanation.max_abs_completeness_error

Unified IG warns when this error exceeds the configured tolerance. Increasing n_steps usually improves it. See the examples/ directory for complete linear, logistic, MLP regression, and MLP classification programs.

Supported models

sklearn.linear_model.LinearRegression (closed form)
Binary sklearn.linear_model.LogisticRegression (closed form)
sklearn.neural_network.MLPRegressor (analytic gradients and quadrature)
Binary sklearn.neural_network.MLPClassifier (analytic logit gradients and quadrature)
PyTorch modules with one raw scalar output per sample (Captum, optional)

Install PyTorch support separately so the core package remains lightweight:

pip install "unifiedig[torch]"

PyTorch models may accept tabular or structured single-tensor inputs. Unified IG preserves the model's device and floating-point dtype, temporarily evaluates the model in inference mode, and restores every module's prior training state. V1 expects one raw scalar output per sample. For binary classification that output must be the logit, not a sigmoid probability.

Development

Run the tests and validate distribution artifacts with:

pytest
python -m build
python -m twine check dist/*

See CONTRIBUTING.md for the development workflow.

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