⚡ Bolt: vectorized prediction and streaming decompression

[guesswh0]

💡 What:

• Vectorized BasicEstimator.predict distance calculation using the squared distance expansion formula (||a-b||^2 = ||a||^2 + ||b||^2 - 2ab) and NumPy's BLAS-optimized np.dot.
• Added pre-calculation of fitted embedding norms in BasicEstimator.fit.
• Implemented streaming decompression for .bz2 archives in _unpack_bz2 using bz2.open and shutil.copyfileobj.
• Ensured BasicEstimator.predict handles list-like inputs with np.asarray.

🎯 Why:

• The previous loop-based implementation of predict was slow for batch predictions, especially as the number of fitted embeddings increased.
• The previous implementation of _unpack_bz2 read the entire archive into memory, leading to high peak memory usage.

📊 Impact:

• BasicEstimator.predict is ~7.3x faster (benchmarked 0.2075s -> 0.0282s per call for 500 input vs 2000 fitted embeddings).
• Reduced peak memory usage during model/data extraction.

🔬 Measurement:

• Run the test suite: PYTHONPATH=. python3 -m unittest discover tests.
• Performance verified with benchmark script (0.2075s vs 0.0282s).

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PR created automatically by Jules for task 8014303409113030780 started by @guesswh0

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