⚡ Bolt: Vectorize BasicEstimator.predict#49
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Optimized `BasicEstimator.predict` by replacing the per-query Python loop with a vectorized NumPy matrix operation using the squared distance expansion formula: $||a-b||^2 = ||a||^2 + ||b||^2 - 2ab$. - Pre-calculated squared norms of fitted embeddings in `fit` to avoid redundant computations. - Implemented vectorized prediction logic for significant performance gains on batch inputs. - Ensured backward compatibility in `load` by recalculating norms if missing. - Handled potential floating-point precision errors with `np.maximum(dists_sq, 0)`. - Handled empty input batches. Performance impact: Reduces execution time for 500 queries against 2000 fitted samples from ~0.23s to ~0.03s (~7x speedup). Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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💡 What: Optimized
BasicEstimator.predictusing vectorized NumPy operations and pre-calculated squared norms.🎯 Why: The original implementation used a Python loop over query embeddings, which was inefficient for large batches.
📊 Impact: ~7x speedup for batch predictions (e.g., 500 queries vs 2000 samples improved from ~0.23s to ~0.03s).
🔬 Measurement: Verified with a benchmark script comparing loop-based vs vectorized performance and a verification script ensuring numerical correctness (matching the original implementation).
PR created automatically by Jules for task 4875092788767067941 started by @guesswh0