Roadmap

This page is the high-level roadmap, summarising shipped milestones and planned work.

Released

v0.1 — Minimum viable

• ConceptGraph (tree, NetworkX-backed) with YAML / dict / NetworkX constructors.
• Metrics: feature_counts, importance_sum, utilization, auc_drop (3 strategies).
• Plots: sunburst, utilization_map, auc_drop_map.
• Adapters: from_shap_explanation, from_permutation_importance, from_feature_importances_.
• Tests, mypy strict, README quickstart, end-to-end notebook on Give Me Some Credit.

v0.2 — Bug-fix

• Fixed auc_drop_map rendering empty when skip_root=True (root feature_count was 0 with non-zero children, which Plotly silently dropped).

v0.3 — Concept-design diagnostics

• New metrics: feature_correlation, nullity_correlation, shap_correlation, joint_missing_rate, column_missing_rate, coherence_importance.
• New plots: correlation_block, joint_missing_map, coherence_importance_scatter, regulatory_tag_overlay.
• Cross-cutting decisions locked: switchable correlation method (Spearman default), joint_missing_rate is a standalone metric (no implicit fusion into auc_drop), shap stays an optional extra.

Planned

v0.4 — Direction, uncertainty, single-prediction

• concept_beeswarm — distribution of summed signed SHAP per concept.
• bootstrap_importance + signed_concept_bar — bar chart with bootstrap confidence intervals.
• ConceptPredictionExplainer.waterfall(row=...) — single-prediction waterfall rolled up to a chosen tree depth.

v0.5 — Interactions, cohort, drift

• concept_interaction_matrix + heatmap — concept × concept SHAP-interaction matrix.
• concept_sankey — three-tier SHAP flow diagram.
• segment_importance + segment_concept_heatmap — concept × cohort heatmap.
• concept_pareto — faceted Lorenz curves per cohort.
• attribution_drift + concept_drift_lines — multi-period attribution monitoring.
• concept_drift_sunburst — period-to-period delta sunburst.

v0.6 — Fairness

• concept_disparity + concept_disparity_heatmap — concept × protected-group disparity matrix.
• Protected-attribute API design pass before implementation.

v1.0 — DAG support

• Optional per-edge weights for multi-parent concepts.
• Sankey rendering for the DAG case.
• Backwards-compatible: tree users see no change.

Decision log

The four cross-cutting decisions locked during the v0.3 grooming session:

ID	Decision	Locked value
D1	Default correlation method	Switchable, default = Spearman
D2	auc_drop realism weight	Standalone joint_missing_rate metric, no implicit fusion
D3	SHAP dependency posture	Optional extra ([shap])
D4	Single-prediction surface	ConceptPredictionExplainer class