regime-shap 0.1.0

Quantify SHAP feature importance stability across structural breaks in time-series data

regime-shap

Quantify SHAP feature importance stability across structural breaks in time-series data.

Install with pip install regime-shap. The package follows Semantic Versioning; during the 0.x series the public API may still change.

Documentation: https://faithcodes-lab.github.io/regime-shap/

regime-shap extends SHAP feature importance analysis to time-series with structural breaks. Given a pre-trained tree model, a feature matrix, and a set of regimes, it quantifies how the model's feature importance rankings change across regimes, surfacing instability that may matter for trustworthy interpretation.

Development status

Built so far:

• breaks: turn regime specifications into per-row regime labels (plus optional break detection).
• compare: per-regime SHAP feature importance and rankings, with small-sample flagging.
• stability: pairwise Spearman stability matrix, Akoglu (2018) bands, and bootstrap confidence intervals.
• plots: global and per-regime importance figures, and the banded stability heatmap.
• report: self-contained HTML report plus dict and CSV export.
• RegimeSHAPAnalyzer: a single high-level entry point that ties the modules together.

Usage

from regime_shap import RegimeSHAPAnalyzer

# model is a fitted tree model, X is the feature matrix, regimes is one label per row
analyzer = RegimeSHAPAnalyzer(model, X, regimes)

analyzer.stability_matrix()      # regime-by-regime Spearman correlation of importance rankings
analyzer.stability_classified()  # each regime pair with its Akoglu (2018) stability band
analyzer.plot_stability()        # the banded stability heatmap
analyzer.to_html(title="...")    # a self-contained report of every result table

The building-block functions in regime_shap.breaks, regime_shap.compare, and regime_shap.stability are also available directly if you want finer control.

Supported models

regime-shap currently supports tree-based models (for example XGBoost, LightGBM, and scikit-learn tree ensembles) through SHAP's TreeExplainer. This is a deliberate choice: TreeExplainer computes exact SHAP values quickly, which is what makes the bootstrap confidence intervals feasible, since they recompute SHAP up to a thousand times per regime.

The stability methodology itself is model-agnostic. The comparison, stability, plotting, and report steps operate on SHAP values alone and never inspect the model, so only the SHAP computation is tree-specific.

Support for other model types through pluggable explainers (for example KernelExplainer for arbitrary models, or LinearExplainer for linear ones) is a possible future extension, tracked in issue #8. The honest caveat is that the bootstrap confidence intervals become expensive and approximate for non-tree models, because general explainers are slower and introduce sampling variance.

Installation

pip install regime-shap

For development, install from source:

git clone https://github.com/faithcodes-lab/regime-shap.git
cd regime-shap
pip install -e ".[dev]"

Origin

This package generalises a SHAP stability methodology, first developed for analysing UK economic regimes (the Global Financial Crisis, Brexit, and COVID-19), into a standalone, domain-agnostic tool. Its examples span finance and energy as well as macroeconomics.

If you use regime-shap in research, please cite:

@software{olan_george_regime_shap,
  author = {Olan-George, Faith},
  title = {regime-shap: Quantifying SHAP Feature Importance Stability Across Structural Breaks},
  year = {2026},
  url = {https://github.com/faithcodes-lab/regime-shap},
}

Licence

MIT, see LICENSE for details.