⚡ Bolt: Vectorize BasicEstimator.predict#38
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Vectorized the predict method of BasicEstimator using NumPy matrix operations and pre-calculated norms. This significantly improves performance when making predictions for batches of embeddings. - Pre-calculate squared norms of fitted embeddings in `fit()` - Use the expansion formula for squared Euclidean distance calculation - Add guard for empty input embeddings - Ensure backward compatibility for loaded models lacking `fitted_norms_sq` - Speedup: ~5.8x for 500 input embs vs 2000 fitted embs Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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💡 What: Vectorized the
predictmethod ofBasicEstimatorusing NumPy matrix operations and pre-calculated norms.🎯 Why: The previous implementation used a Python loop over input embeddings, which was slow for batch processing.
📊 Impact: Achieved a ~5.8x speedup (0.12s down to 0.02s) for 500 input embeddings against 2000 fitted embeddings.
🔬 Measurement: Verified using a benchmark script that compared the original and vectorized implementations for correctness and performance.
PR created automatically by Jules for task 17903989983958903667 started by @guesswh0