0.14.2 — predict 1.35–1.63× faster, bit-identical

Cuts the 0.14.2 release. The diff here is just the version bump and CHANGELOG
date stamp -the changes being released are already on main (merge a5810fa).

What's in this release

Predict is 1.35–1.63× faster end-to-end, with predictions unchanged to the
last bit. Two changes:

1. Row-major fused predict kernels (_predict_forest_rm,
   _predict_forest_linear_rm) — predict consumes the binner's row-major
   output directly, so each sample's bins sit in one or two cache lines for
   the whole forest walk and the per-predict feature-major transpose copy is
   gone. Fit-side kernels keep the feature-major layout (histograms want it);
   SHAP keeps the feature-major kernels for its background walk.
2. No more whole-matrix copy for all-numeric input —
   FeaturePreprocessor extracted the numeric block as
   X[:, num_features_] even when every column is numeric, a full
   fancy-index copy on each transform (~18% of end-to-end predict on large
   batches). Plain asarray now covers that case; mixed/categorical inputs
   keep the gather.

Numbers (fit 200k / predict 2M×30 / 200 trees / 12 threads, min-of-5)

config	0.14.1	0.14.2	speedup
classifier, linear leaves (default)	2.140 s	1.588 s	1.35×
classifier, constant leaves	1.244 s	0.764 s	1.63×
regressor	1.236 s	0.757 s	1.63×

Against the field on the same benchmark (benchmarks/scaling_predict.py):
default binary predict is now 1.26 Mrows/s — 1.30× LightGBM, 3.0×
sklearn-HGB, 0.50× XGBoost; constant-leaf paths reach ~2.6 Mrows/s, on par
with XGBoost. CatBoost's SIMD-fused C++ inference remains ~10× faster.

Why "bit-identical" is safe to claim

• Exact-equality kernel test (test_rowmajor_predict_kernels_match_feature_major_exactly):
  row-major vs feature-major kernels compared with np.array_equal across
  depth-0 trees, constant and linear leaf blocks, NaN center bins, n ∈ {0, 1, many}.
• Regenerated golden baseline changed only machine-relative timing ratios —
  every accuracy metric field is byte-identical.
• Full suite: 378 passed. warmup() compiles the new kernels automatically
  (same numba argument types), so the 0.14.1 fresh-worker JIT fix carries over.

After merging

• git tag v0.14.2 && git push origin v0.14.2
• build + upload to PyPI

🤖 Generated with Claude Code