goldenfuzz
Fast, byte-identical-to-rapidfuzz fuzzy-string scorers, plus a one-vs-many
extract / cdist / BatchComparator API. A thin PyO3 wheel over the pyo3-free
goldenfuzz-core Rust crate.
import goldenfuzz as gf
gf.jaro_winkler("jonathan", "jonathon") # -> 0.95...
gf.levenshtein("kitten", "sitting") # normalized similarity in [0, 1]
gf.indel("fuzzy wuzzy", "wuzzy fuzzy")
# one-vs-many top-k (query bitmap built once)
gf.extract("jonathan smith",
["jon smith", "jane doe", "jonathan smith"],
scorer="jaro_winkler", score_cutoff=0.7, limit=2)
# -> [(2, 1.0), (0, 0.9...)]
# reuse a prepared query across many choices
bc = gf.BatchComparator("acme corporation")
[bc.jaro_winkler(c) for c in choices]
Scorers: jaro_winkler | levenshtein | indel, each returning normalized
similarity in [0, 1], byte-identical to the corresponding rapidfuzz metric
(proven by an oracle fuzz in goldenfuzz-core). On short strings (names,
addresses) goldenfuzz is faster than rapidfuzz; on documents it matches/beats on
jaro-winkler and levenshtein.
Part of the golden suite.
1 maintainer
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