⚡ Bolt: vectorize BasicEstimator.predict#25
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Optimized the `BasicEstimator.predict` method by replacing the iterative loop with a vectorized NumPy implementation using the squared distance expansion formula. Key changes: - Pre-calculate squared norms of fitted embeddings in `fit()` to avoid redundant computation during prediction. - Use `np.dot` and matrix operations in `predict()` to calculate distances for all input embeddings simultaneously. - Added numerical stability check (`np.maximum(dists_sq, 0)`) to handle floating-point noise. - Maintained backward compatibility for models missing `norms_sq` using `getattr`. Performance impact: - ~12x speedup observed for 500 input embeddings against 2000 fitted embeddings (0.28s -> 0.02s). Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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💡 What: Optimized
BasicEstimator.predictby vectorizing the distance calculation.🎯 Why: The original implementation used a Python loop over input embeddings, which was slow for large batches of predictions.
📊 Impact: Reduces prediction time by ~12x for typical batch sizes.
🔬 Measurement: Run a benchmark script comparing the original iterative approach with the new vectorized one using
np.randomembeddings.PR created automatically by Jules for task 18070330578237288375 started by @guesswh0