Optimize ALE and PD computation: 1.6-3.2x speedup#90
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ALE (compute_first_order_ale): - Replace DataFrame operations with numpy arrays throughout bootstrap loop - Batch both bin-edge predictions into single predict call (2 → 1 calls) - Replace pandas groupby with numpy bincount for mean effects - Eliminates 2 DataFrame copies per bootstrap iteration PD (compute_partial_dependence): - Vectorize grid-point loop: batch all n_bins points into single predict call instead of per-grid-point predict loop (20 → 1 calls per bootstrap) - Fix bug: predict was called inside feature loop instead of after all features assigned - Use numpy arrays instead of DataFrame throughout Benchmarks (2000 samples, 10 features, 50-tree RF): PD 1D (3 feat, 10 boot): 1.96s → 0.60s (3.2× faster) ALE 1D (all, 10 boot): 0.85s → 0.52s (1.6× faster) PD 1D (3 feat, 1 boot): 0.20s → 0.06s (3.3× faster) ALE 1D (all, 1 boot): 0.09s → 0.05s (1.7× faster) Add benchmark_suite.py for reproducible performance measurement. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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ALE (compute_first_order_ale):
PD (compute_partial_dependence):
Benchmarks (2000 samples, 10 features, 50-tree RF):
PD 1D (3 feat, 10 boot): 1.96s → 0.60s (3.2× faster)
ALE 1D (all, 10 boot): 0.85s → 0.52s (1.6× faster)
PD 1D (3 feat, 1 boot): 0.20s → 0.06s (3.3× faster)
ALE 1D (all, 1 boot): 0.09s → 0.05s (1.7× faster)
Add benchmark_suite.py for reproducible performance measurement.