SWIFT

SHAP-Weighted Impact Feature Testing for Model-Aware Distribution Monitoring

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SWIFT detects feature drift by comparing SHAP-transformed distributions between reference and monitoring data. Unlike traditional drift detection that treats features independently of the model, SWIFT weights distribution changes by their impact on model predictions — flagging only the shifts that affect model behavior.

Why SWIFT?

Traditional distribution monitoring methods (KS test, PSI, etc.) detect any statistical shift, regardless of whether it matters to the model. SWIFT solves this by:

• Using SHAP values to weight each feature region by its impact on predictions
• Focusing on model-relevant drift — ignoring benign distribution changes
• Providing statistical rigor with permutation tests and multiple testing correction
• Integrating with scikit-learn via the familiar fit / transform / score API

Quick Example

from swift import SWIFTMonitor

# Create monitor with a trained tree-ensemble model
monitor = SWIFTMonitor(model=lgb_model, n_permutations=200)

# Fit on reference data (stages 1-3)
monitor.fit(X_ref)

# Test monitoring data for drift (stages 4-5)
result = monitor.test(X_mon)
print(result.drifted_features)

Example Output

SWIFT scores per feature — drifted features highlighted in red

Bucket profile showing SHAP response curve and density shift for a drifted feature

The Pipeline at a Glance

Stage	Operation	Description
1	Extraction	Extract decision points (split thresholds) from the trained model
2	Bucketing	Partition each feature's domain into buckets based on decision points
3	SHAP Normalization	Compute bucket-level mean SHAP values on reference data
4	Wasserstein Distance	Measure distance between SHAP-transformed distributions
5	Permutation Test + MTC	Estimate p-values and apply multiple testing correction

Stages 1-3 run during fit(). Stages 4-5 run during test().

Learn more about the pipeline :material-arrow-right:

Project Structure

swift/
├── src/swift/
│   ├── __init__.py          # Public API exports
│   ├── pipeline.py          # SWIFTMonitor (main entry point)
│   ├── plotting.py          # Visualization functions
│   ├── extraction.py        # Stage 1: Decision point extraction
│   ├── bucketing.py         # Stage 2: Bucket construction
│   ├── normalization.py     # Stage 3: SHAP normalization
│   ├── distance.py          # Stage 4: Wasserstein distance
│   ├── threshold.py         # Stage 5: Permutation test + MTC
│   ├── aggregation.py       # Model-level score aggregation
│   └── types.py             # Data types (BucketSet, SWIFTResult, etc.)
├── tests/                   # Test suite
├── experiments/             # Experiment runners
└── pyproject.toml

Citation

@article{swift2025,
  title={SWIFT: SHAP-Weighted Impact Feature Testing for Model-Aware Distribution Monitoring},
  year={2025}
}