polyfuzz.models.RapidFuzz

Calculate the Edit Distance between lists of strings using RapidFuzz's process function

We are using RapidFuzz instead of FuzzyWuzzy since it is much faster and does not require the more restrictive GPL license

Parameters

Name Type Description Default
n_jobs int Nr of parallel processes, use -1 to use all cores 1
score_cutoff float The minimum similarity for which to return a good match. Should be between 0 and 1. 0
scorer Callable The scorer function to be used to calculate the edit distance Options: * fuzz.ratio * fuzz.partial_ratio * fuzz.token_sort_ratio * fuzz.partial_token_sort_ratio * fuzz.token_set_ratio * fuzz.partial_token_set_ratio * fuzz.token_ratio * fuzz.partial_token_ratio * fuzz.WRation * fuzz.QRatio See https://maxbachmann.github.io/rapidfuzz/usage/fuzz/ for an extensive description of the scoring methods. <cyfunction WRatio at 0x00000237A334D2B0>
model_id str The name of the particular instance, used when comparing models None

Usage:

from rapidfuzz import fuzz
model = RapidFuzz(n_jobs=-1, score_cutoff=0.5, scorer=fuzz.WRatio)

match(self, from_list, to_list=None, **kwargs)

Show source code in models\_rapidfuzz.py 
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    def match(self,
              from_list: List[str],
              to_list: List[str] = None,
              **kwargs) -> pd.DataFrame:
        """ Calculate the edit distances between two list of strings
        by parallelizing the calculation and passing the lists in
        batches.

        Arguments:
            from_list: The list from which you want mappings
            to_list: The list where you want to map to

        Returns:
            matches: The best matches between the lists of strings

        Usage:

        ```python
        from rapidfuzz import fuzz
        model = RapidFuzz(n_jobs=-1, score_cutoff=0.5, scorer=fuzz.WRatio)
        matches = model.match(["string_one", "string_two"],
                              ["string_three", "string_four"])
        ```
        """
        if to_list is None:
            self.equal_lists = True
            expected_iterations = int(len(from_list)/2)
            to_list = from_list.copy()
        else:
            expected_iterations = len(from_list)

        matches = Parallel(n_jobs=self.n_jobs)(delayed(self._calculate_edit_distance)
                                               (from_string, to_list)
                                               for from_string in tqdm(from_list, total=expected_iterations,
                                                                       disable=True))
        matches = pd.DataFrame(matches, columns=['From', "To", "Similarity"])
        return matches

Calculate the edit distances between two list of strings by parallelizing the calculation and passing the lists in batches.

Parameters

Name Type Description Default
from_list List[str] The list from which you want mappings required
to_list List[str] The list where you want to map to None

Returns

Type Description
DataFrame matches: The best matches between the lists of strings

Usage:

from rapidfuzz import fuzz
model = RapidFuzz(n_jobs=-1, score_cutoff=0.5, scorer=fuzz.WRatio)
matches = model.match(["string_one", "string_two"],
                      ["string_three", "string_four"])