⚡ Bolt: Vectorize BasicEstimator.predict#52
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💡 What: Replaced the Python loop in `BasicEstimator.predict` with a vectorized Euclidean distance calculation using the expansion formula ||a-b||² = ||a||² + ||b||² - 2ab. Pre-calculated and cached squared norms of fitted embeddings to further speed up the process. 🎯 Why: The previous implementation performed a manual loop over query embeddings and used `np.linalg.norm` for each, which is slow for larger batches of queries or high-dimensional embeddings. 📊 Impact: Provides a ~13x speedup for the prediction step. Benchmarked 500 queries vs 2000 embeddings (128-dim): - Before: ~0.26s - After: ~0.02s 🔬 Measurement: Verified by running `PYTHONPATH=. python3 benchmark_basic_estimator.py` (script used during development) and ensuring all existing tests in `tests/test_face_engine_models.py` pass. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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Vectorized the
BasicEstimator.predictmethod to improve performance. The new implementation uses matrix operations instead of a Python loop for distance calculations, leveraging the squared distance expansion formula. It also pre-calculates squared norms of the fitted embeddings to avoid redundant work. Backward compatibility for serialized models is maintained by lazy-reconstructing the norms if missing.PR created automatically by Jules for task 11667060105484873819 started by @guesswh0