⚡ Bolt: Vectorize BasicEstimator.predict for ~15x speedup#47
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Vectorized the Euclidean distance calculation in BasicEstimator.predict using the squared distance expansion formula. Added pre-calculation of norms in fit and load for further efficiency. Measured a ~15x speedup. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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💡 What: Vectorized the Euclidean distance calculation in
BasicEstimator.predictusing the squared distance expansion formula (||a-b||² = ||a||² + ||b||² - 2ab). Added pre-calculation of norms infitandloadfor further efficiency.🎯 Why: The original implementation used a Python loop over query embeddings, calculating distances one-by-one, which was a significant bottleneck for large datasets or high-frequency predictions.
📊 Impact: Measured a ~15x speedup (from ~0.215s down to ~0.014s for 500 queries against 2000 fitted embeddings).
🔬 Measurement: Verified using benchmark scripts and the existing test suite.
PR created automatically by Jules for task 3637823378371246650 started by @guesswh0