feat: add MinHash Jaccard similarity estimator#14973
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| from collections.abc import Iterable, Sequence | ||
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| def _token_hash(token: str, seed: int, permutation: int) -> int: |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/min_hash.py, please provide doctest for the function _token_hash
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@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| from collections.abc import Iterable, Sequence | ||
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| def _token_hash(token: str, seed: int, permutation: int) -> int: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/min_hash.py, please provide doctest for the function _token_hash
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Summary
Adds a dependency-free MinHash implementation for estimating Jaccard similarity from token sets. The implementation uses seeded BLAKE2b-derived permutations for deterministic signatures, validates inputs, and documents the algorithm with references.
MinHash is useful for near-duplicate detection, similarity search, and information-retrieval experiments where exact set comparisons are expensive.
Validation
python3 -m doctest -v machine_learning/min_hash.py— 7 tests passed.