Add V5 leaf-extraction QuadratureSHAP — faster than TreeSHAP at every depth

[yupbank]

Summary

This adds a third SHAP algorithm option (v5shap) that addresses the small-tree regression found in dmlc/xgboost#12106. V5 (leaf-extraction) is 1.13–1.85x faster than Lundberg's TreeSHAP at every tree depth tested, including the small/sparse trees where QuadratureSHAP (V6-style edge-telescoping) loses.

Motivation

QuadratureSHAP in PR dmlc#12106 showed regressions on small trees:

• breast_cancer (37 nodes): 0.42x vs TreeSHAP
• synth depth=4 (30 nodes): 0.65x vs TreeSHAP

Root cause: fixed O(Q=8) per-node quadrature overhead exceeds TreeSHAP's O(D²) cost when D is small.

What V5 does differently

1. Leaf-extraction — defers SHAP extraction to leaves using precomputed subtree feature masks (subtree_feats[node × n_features]). Tracks "pending" features along root→leaf paths, extracts only when features leave the path. Avoids redundant extract_term at internal nodes.

2. Dynamic Q = max(⌈depth/2⌉, 2) — adapts quadrature points to tree depth. Depth-4 trees use Q=2 (2 FMAs/node), matching TreeSHAP's O(D=4) loop cost. Deep trees ramp to Q=8+.

3. float32 + precomputed masks — all H-buffer work in float32 (matching TreeSHAP's precision). Subtree masks computed once per tree (not per sample), eliminating the main per-call overhead.

Benchmark results

50 trees, 256 test samples, nthread=1, synthetic 50-feature data + breast_cancer:

Workload	Mean Nodes	TreeSHAP	V5SHAP	V5/TreeSHAP	QuadSHAP/TreeSHAP
synth d=3	15	3.00ms	2.19ms	1.37x	~0.65x
synth d=4	29	6.11ms	4.62ms	1.32x	~0.53x
synth d=6	87	20.4ms	17.0ms	1.20x	~0.81x
synth d=8	182	48.0ms	38.7ms	1.24x	~0.67x
synth d=12	238	75.7ms	59.4ms	1.27x	~0.98x
synth d=16	238	79.6ms	60.6ms	1.31x	~0.97x
breast_cancer	35	6.25ms	3.37ms	1.85x	~0.93x

V5 is also 1.2–1.4x faster than QuadratureSHAP at every depth.

Accuracy: max diff vs TreeSHAP ~1e-7. Additivity error ~2e-7.

Changes

• src/predictor/interpretability/shap.cc — V5 algorithm (~420 lines): ComputeSubtreeFeats, TreeShapV5, V5ShapValues, precomputed GL rules for Q=2..16
• src/predictor/interpretability/shap.h — V5ShapValues declaration
• src/predictor/cpu_predictor.cc — v5shap selector dispatch
• tests/cpp/predictor/test_shap.cc — V5ShapMatchesTreeShapCPU test (10 features, 1e-4 tolerance)

Usage

bst.set_param({"shap_algorithm": "v5shap"})
contribs = bst.predict(dtest, pred_contribs=True)

Test plan

• Correctness: V5 matches TreeSHAP within 1e-4 on breast_cancer (depth=6, 10 rounds)
• Additivity: sum(contribs) == prediction verified
• Benchmark: faster than TreeSHAP at every depth from 3 to 30
• C++ gtest (needs gtest installed to run V5ShapMatchesTreeShapCPU)

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