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Jaccard algorithms for comparing two strings and return similarity score, Port and refactor https://github.com/aceakash/string-similarity to python
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def ngram_string(string, n=3, remove_space=False): | |
if remove_space: | |
string = string.replace(' ', '') | |
if len(string) < n: | |
return {string: 1} | |
ngrams = dict() | |
for i in range(len(string)-n+1): | |
ngram = string[i:i+n] | |
ngrams.setdefault(ngram, 0) | |
ngrams[ngram] += 1 | |
return ngrams | |
def jaccard_ngrams(first, second): | |
intersection = 0 | |
for ngram in first.keys(): | |
intersection += min(first.get(ngram, 0), second.get(ngram, 0)) | |
union = sum(first.values()) + sum(second.values()) | |
return (2.0 * intersection) / union | |
def compare_two_strings(first, second, remove_space=False): | |
first_ngrams = ngram_string(first, remove_space=remove_space) | |
second_ngrams = ngram_string(second, remove_space=remove_space) | |
return jaccard_ngrams(first_ngrams, second_ngrams) | |
def find_best_match(main_string, target_strings): | |
ratings = [compare_two_strings(main_string, target_string) for target_string in target_strings] | |
best_match_index = target_strings.index(max(ratings)) | |
return target_strings[best_match_index], ratings[best_match_index] |
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