⚡ Bolt: Vectorize BasicEstimator.predict#51
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Optimized `BasicEstimator.predict` by vectorizing the distance calculation using the squared Euclidean distance expansion formula. 💡 What: - Updated `fit` to pre-calculate squared norms of fitted embeddings. - Replaced the Python loop in `predict` with a vectorized matrix operation. - Added backward compatibility in `load` to handle models saved before this change. - Added a guard for empty input in `predict`. 🎯 Why: The original implementation looped over query embeddings and calculated distances one by one, which is inefficient for batch processing. 📊 Impact: Reduces execution time for 500 queries against 2000 fitted embeddings from ~0.23s to ~0.02s (~12x speedup). 🔬 Measurement: Ran `benchmark_estimator.py` (deleted after verification) comparing the original and optimized implementations. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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Vectorized the distance calculation in
BasicEstimator.predictfor significant performance gains.PR created automatically by Jules for task 10473050286438340270 started by @guesswh0