Coverage alignment heuristic #76
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SkBlaz
commented
Aug 16, 2024
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| def max_pair_coverage(array1: np.array, array2: np.array) -> float: | ||
| def hash_pair(el1, el2): | ||
| return el1 * 17 - el2 |
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this is around 100% faster to hash(el1) someOp hash(el2) and is fine for this use case col wise
jasnaurbancic
approved these changes
Aug 21, 2024
miha-jenko
approved these changes
Aug 22, 2024
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| def max_pair_coverage(array1: npt.NDArray[np.int32], array2: npt.NDArray[np.int32]) -> float: | ||
| def hash_pair(el1: np.int32, el2: np.int32): |
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Missing output type hint :)
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Will add with improved pre-commit hooks, as that should have picked it up
| import numpy.typing as npt | ||
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| np.random.seed(123) | ||
| max_size = 10**6 |
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qq, can max_size be estimated depending on input vector?
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It can, but this is directly related to batch sizes, where 1mil is a very very safe bound (many things go wrong before this is reached)
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Adding a simple heuristic that enables computing proportion of aligned values. Makes pairwise comparisons breezy
https://jira.outbrain.com/browse/REF-51623