⚡ Bolt: vectorize BasicEstimator prediction#41
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💡 What: Replaced the row-wise loop in `BasicEstimator.predict` with a vectorized matrix operation using the squared Euclidean distance expansion formula: ||a-b||^2 = ||a||^2 + ||b||^2 - 2ab. 🎯 Why: The original implementation performed distance calculations in a Python loop, incurring significant overhead for each query. Vectorization allows NumPy to use optimized BLAS routines, drastically improving throughput for batch predictions. 📊 Impact: Benchmarked a ~2.4x speedup (from 0.22s to 0.09s) for a batch of 500 queries against 2,000 fitted samples. 🔬 Measurement: Verified with a benchmark script using random embeddings and ensured correctness via the existing `unittest` suite (specifically `TestBasicEstimator`). backward compatibility for pickled models was also implemented and verified. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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Implemented a vectorized version of the
BasicEstimator.predictmethod using the squared distance expansion formula. This optimization pre-calculates squared norms of fitted embeddings and uses matrix multiplication to compute distances for all query embeddings at once, significantly reducing Python interpreter overhead and leveraging optimized BLAS routines.Key changes:
BasicEstimator.fitto pre-calculate and store squared norms of fitted embeddings.BasicEstimator.predictto handle batch queries efficiently.predict.BasicEstimator.loadto recalculate missing norms for backward compatibility with older pickled states..jules/bolt.md.PR created automatically by Jules for task 10318935965228248926 started by @guesswh0