- TreeSHAP implementation (XGBoost, LightGBM JSON)
- PermutationSHAP with antithetic sampling
- SamplingSHAP (Monte Carlo)
- LinearSHAP - exact closed-form SHAP for linear models
- KernelSHAP - model-agnostic weighted linear regression (validated against Python SHAP)
- ExactSHAP - brute-force exact Shapley values (O(n*2^n), validated against mathematical derivations)
- ONNX Runtime integration
- Render package (ChartIR visualizations)
- MkDocs documentation site
- CI/CD workflows (tests, lint, docs deployment)
- Benchmarks
- Fix masker panics (return errors instead)
- Add ONNX tests
- Python validation documentation - testing methodology docs
- Explainers overview page - comparison and decision guide
- Add internal/rand tests
- Update README to mark KernelSHAP and ExactSHAP as complete
- DeepSHAP - neural network explanations using DeepLIFT rescale rule
- GradientSHAP - expected gradients using numerical differentiation
- PartitionSHAP - hierarchical Owen values for feature groupings
- AdditiveSHAP - exact SHAP for Generalized Additive Models (GAMs)
- ONNX graph parsing and ActivationSession for intermediate layer capture
- TreeSHAP interaction values - feature interaction computation
- CatBoost model support
- LightGBM text format parser - parse
.txtmodel files - ONNX-ML TreeEnsemble parser - native ONNX tree model support
- Batch explanation API - generic parallel wrapper for any explainer
- Batched model predictions -
WithBatchedPredictions()option for KernelSHAP, ExactSHAP, PartitionSHAP - Confidence intervals - uncertainty bounds for sampling methods
- Improved model/onnx test coverage (15% -> 33%)
- Background data selection guide - best practices for reference data
- API reference improvements - comprehensive docs for all explainers
- Examples (deepshap, gradientshap, multiclass, onnx_basic)
- Expected output documentation for all examples
- Udemy-style LMS presentation
- Coverage badge in README (80% coverage)
- Performance profiling and optimization
- Memory usage optimization for large models
| Package | Coverage |
|---|---|
| background | 90.2% |
| explainer | 58.6% |
| explainer/additive | 89.7% |
| explainer/deepshap | 60.7% |
| explainer/exact | 90.9% |
| explainer/gradient | 87.9% |
| explainer/kernel | 88.5% |
| explainer/linear | 96.6% |
| explainer/partition | 91.7% |
| explainer/permutation | 91.4% |
| explainer/sampling | 93.3% |
| explainer/tree | 84.4% |
| explanation | 77.8% |
| internal/rand | 100.0% |
| masker | 98.0% |
| model | 100.0% |
| model/onnx | 33.2% |
| render | 90.9% |
| Total | 80.6% |