API Reference¶
Auto-generated from source-code docstrings via mkdocstrings.
Core¶
| Module | Description |
|---|---|
ConceptGraph |
The tree-shaped concept graph. Constructors from dict / YAML / NetworkX; deterministic DFS traversal. |
Adapters |
Convert SHAP, sklearn permutation results, and model.feature_importances_ into the canonical (values, feature_names) tuple. |
Metrics¶
Each function returns a tidy pandas.DataFrame indexed by concept-path.
| Module | Function(s) | Question answered |
|---|---|---|
Counts |
feature_counts |
How many features under each concept? |
Importance |
importance_sum |
How much importance does each concept aggregate? |
Utilization |
utilization |
Which concepts does the model actually use? |
Ablation |
auc_drop (3 strategies) |
How much performance is lost when a concept's data is missing? |
Correlation |
feature_correlation, nullity_correlation, shap_correlation |
Are concepts internally coherent? Do they go missing together? Do features look substitutable to the model? |
Missingness |
column_missing_rate, joint_missing_rate |
How often does a feature / a whole concept go missing? |
Coherence |
coherence_importance |
Are concepts well-designed (coherent + important)? |
Plotting¶
All plots return plotly.graph_objects.Figure. Static PNG via the [png] extra (kaleido==0.2.1).
| Module | Functions |
|---|---|
Plotting |
sunburst, utilization_map, auc_drop_map, correlation_block, joint_missing_map, coherence_importance_scatter, regulatory_tag_overlay |