⚡ Bolt: vectorize BasicEstimator.predict#22
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💡 What: Vectorized the distance calculation in `BasicEstimator.predict` using the squared Euclidean distance expansion formula and pre-calculated norms in `fit`. 🎯 Why: The previous implementation used a Python loop over query embeddings, calling `np.linalg.norm` for each one, which is inefficient for large batches of query or fitted embeddings. 📊 Impact: Improved prediction performance by ~2.4x (from 0.28s to 0.11s) for 500 query embeddings against 2000 fitted embeddings in the test environment. 🔬 Measurement: Verified numerical consistency with iterative implementation using `extra/verify_optimization.py` and measured speedup with `benchmark_predict.py`. Verified backward compatibility for models fitted with older versions. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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Vectorized the distance calculation in
BasicEstimator.predictusing the squared Euclidean distance expansion formula and pre-calculated norms infit.Impact: Improved prediction performance by ~2.4x (from 0.28s to 0.11s) for 500 query embeddings against 2000 fitted embeddings in the test environment.
PR created automatically by Jules for task 1557230087967395033 started by @guesswh0