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DeciShift v0.1.0

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@sauravsingla sauravsingla released this 24 Sep 07:09
· 91 commits to main since this release
v0.1.0
d24cde5

DeciShift v0.1.0 is the initial public release.

DeciShift is a CPU-first, local-first framework for explaining why individual decisions changed between versions of a machine-learning decision pipeline.

Added

  • CPU-first, local-first comparison of baseline and candidate ML decision pipelines.
  • Version-aware pipeline components for features, model, calibration, threshold/policy, and deterministic rules.
  • Record-level decision diffs, score deltas, threshold margins, and flip direction.
  • Exact Shapley counterfactual component attribution for small changed-component sets.
  • Deterministic permutation approximation for larger attribution problems.
  • Baseline-anchored pairwise interaction analysis, including interaction-only decision flips.
  • Predictions-only analysis with explicit insufficient-evidence handling for component attribution.
  • Numeric and categorical cohort analysis with minimum cohort-size controls.
  • Saved local runs and individual record explanations.
  • Terminal, JSON, and Markdown reporting.
  • Framework-agnostic model interface with optional scikit-learn, XGBoost, and LightGBM support.
  • Synthetic equipment-maintenance demonstration.
  • Reproducible CPU benchmark harness.
  • Regression tests and GitHub Actions CI.
  • Apache-2.0 licensing.
  • CITATION.cff metadata for software citation and Zenodo archival.

Install

After publication to PyPI:

pip install decishift

Then run:

decishift demo

Scientific scope

DeciShift reports observed decision differences and counterfactual attribution across executable software components. These results should not automatically be interpreted as real-world causal effects.

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