Retrieval and similarity#

Levenshtein facade#

scikitplot.levenshtein is intentionally independent from Corpus and is always importable. The automatic backend preference is:

bundled scikitplot.cexternals._editdistance
    -> RapidFuzz when installed
    -> dependency-free Python fallback

The separately distributed Levenshtein/python-Levenshtein backend is supported only when explicitly requested; it is not silently selected by the automatic chain.

from scikitplot.levenshtein import distance, rank

distance("kitten", "sitting")
# 3

rank(
    "colour",
    ["color", "collar", "cloud"],
    score_cutoff=0.60,
)

Corpus compatibility is lazy:

from scikitplot.levenshtein import make_corpus_scorer
from scikitplot.corpus import CustomRetrievalIndex

index = CustomRetrievalIndex(
    custom_scorer_fn=make_corpus_scorer(
        backend="auto",
        score_cutoff=0.70,
    )
)

Importing scikitplot.levenshtein alone does not import Corpus.

score_cutoff uses the same normalized similarity scale [0, 1] for every backend, so switching between bundled, RapidFuzz and pure-Python implementations does not change the filtering contract. top_k is therefore an upper bound: a cutoff can intentionally return fewer hits when the remaining candidates are too dissimilar.

Choosing a metric#

Levenshtein distance is a character/sequence edit metric, not a semantic embedding model. It is useful for spelling variants, OCR noise, identifiers, near-duplicate labels and fuzzy lexical matching. Do not present edit-distance similarity as semantic equivalence.