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This commit optimizes the `BasicEstimator.predict` method by replacing the iterative distance calculation with a vectorized approach using NumPy. By using the squared distance expansion formula (||a-b||^2 = ||a||^2 + ||b||^2 - 2ab), we can compute distances for an entire batch of embeddings simultaneously, leveraging optimized linear algebra routines. Key changes: - Updated `BasicEstimator.fit` to ensure fitted embeddings are stored as a NumPy array. - Re-implemented `BasicEstimator.predict` with vectorized distance calculation. - Added handling for empty input embedding lists. - Added a benchmark script `extra/benchmark_basic_estimator.py` to verify performance gains. Performance impact: - Benchmark shows a ~2.7x speedup for large datasets (10,000 fitted embeddings, 1,000 predict embeddings). - Measurable speedup even for smaller batches. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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⚡ Bolt: Vectorized BasicEstimator.predict
💡 What:
Optimized the
BasicEstimator.predictmethod by vectorizing the distance calculation using NumPy and the squared distance expansion formula.🎯 Why:
The previous implementation used an iterative Python loop to calculate distances for each input embedding against all fitted embeddings, which was a significant bottleneck in face recognition tasks.
📊 Impact:
🔬 Measurement:
Verified using
extra/benchmark_basic_estimator.py, which compares the execution time of the original iterative implementation against the new vectorized version and ensures the results are identical.✅ Verification:
python3 -m unittest discover tests- All relevant tests passed.PR created automatically by Jules for task 601678828355604908 started by @guesswh0