def levenshtein(string1, string2):
if string1 is None:
string1 = ""
if string2 is None:
string2 = ""
string_matcher = StringMatcher(seq1=string1.lower(), seq2=string2.lower())
return string_matcher.distance()
def levenshtein(string1, string2):
if string1 is None:
string1 = ""
if string2 is None:
string2 = ""
string_matcher = StringMatcher(seq1=string1.lower(), seq2=string2.lower())
return string_matcher.distance()
def get_match_score(phrase, words, min_distance=2):
score = 0
phrase_len = len(''.join(phrase))
for p in phrase:
matcher = StringMatcher(seq1=p)
for w in words:
matcher.set_seq2(w)
match_distance = matcher.distance()
if match_distance <= min_distance:
score += max(0, len(p) - match_distance) / phrase_len
return score
def get_loosly_matching_keyword(self, term):
splitted_terms = self.tokenize_text(term)
max_nb_commun = 0
most_common_keys = []
for key, key_tokens in self.tokenized_keys_.items():
nb_words_common = 0
for potential_word in splitted_terms:
if potential_word in key_tokens:
nb_words_common += 1
if nb_words_common > max_nb_commun:
max_nb_commun = nb_words_common
most_common_keys = []
if nb_words_common == max_nb_commun:
most_common_keys.append(key)
min_distance = 9999999
result = None
for key in most_common_keys:
match = StringMatcher(seq1=key, seq2=term)
distance = match.distance()
if distance < min_distance:
min_distance = distance
result = key
return result
def is_typo(word, word_from_dict):
sm = StringMatcher()
sm.set_seq1(word)
sm.set_seq2(word_from_dict)
dist = sm.distance()
return dist == 1 or (dist == 2 and fl(word, word_from_dict))
def levenshtein_ratio(s1, s2):
m = StringMatcher(None, s1, s2)
return truncate(m.ratio(), 2), m.distance()
