⚡ Bolt: vectorize BasicEstimator.predict#20
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Optimized `BasicEstimator.predict` by replacing the iterative loop with a vectorized distance calculation using the squared distance expansion formula. - Pre-calculates squared norms in `fit` to avoid redundant work. - Uses matrix multiplication (BLAS-optimized) for distances. - Includes backward compatibility for models fitted with older versions. - Handles empty input embeddings gracefully. Benchmarks show a significant performance improvement: - Small batch (500 queries, 2000 samples): ~2.5x speedup. - Larger batches can see up to ~12x speedup. Numerical correctness verified with `np.allclose(rtol=1e-4)`. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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💡 What: Optimized the$||a - b||^2 = ||a||^2 + ||b||^2 - 2ab^T$ ). Also pre-calculates squared norms in the
BasicEstimator.predictmethod by vectorizing the distance calculation logic using the squared distance expansion formula (fitmethod.🎯 Why: The original implementation used a Python loop to calculate distances for each input embedding one-by-one, which is slow for large datasets or multiple face detections.
📊 Impact: Provides a ~2.5x to 12x speedup for the prediction phase depending on the number of embeddings and training samples.
🔬 Measurement: Verified with a benchmark script comparing the original iterative logic vs. the new vectorized implementation, and ran existing unit tests to ensure no regressions.
PR created automatically by Jules for task 16753551081782039115 started by @guesswh0