Understanding the distance metric in company name matching using KNN - knn

Question

I am trying to understand the following code that I found for matching a messy list of company names to a list of clean list of company names. My question is what the 'Ratio' metric is calculated using. It appears that the ratio is from scorer = fuzz.token_sort_ratio which is I understand is part of the fuzzywuzzy package and therefore a levenschtein distance calculation correct? I'm trying to understand why the author uses this as the scorer rather than the distance output from KNN. When I try changing the metric inside NearestNeighbors, it doesn't appear to change the results. Does the metric in NearestNeighbors matter then?
Original article:
https://audhiaprilliant.medium.com/fuzzy-string-matching-optimization-using-tf-idf-and-knn-b07fce69b58f
def build_vectorizer(
clean: pd.Series,
analyzer: str = 'char',
ngram_range: Tuple[int, int] = (1, 4),
n_neighbors: int = 1,
**kwargs
) -> Tuple:
# Create vectorizer
vectorizer = TfidfVectorizer(analyzer = analyzer, ngram_range = ngram_range, **kwargs)
X = vectorizer.fit_transform(clean.values.astype('U'))
# Fit nearest neighbors corpus
nbrs = NearestNeighbors(n_neighbors = n_neighbors, metric = 'cosine').fit(X)
return vectorizer, nbrs
# String matching - KNN
def tfidf_nn(
messy,
clean,
n_neighbors = 1,
**kwargs
):
# Fit clean data and transform messy data
vectorizer, nbrs = build_vectorizer(clean, n_neighbors = n_neighbors, **kwargs)
input_vec = vectorizer.transform(messy)
# Determine best possible matches
distances, indices = nbrs.kneighbors(input_vec, n_neighbors = n_neighbors)
nearest_values = np.array(clean)[indices]
return nearest_values, distances
# String matching - match fuzzy
def find_matches_fuzzy(
row,
match_candidates,
limit = 5
):
row_matches = process.extract(
row, dict(enumerate(match_candidates)),
scorer = fuzz.token_sort_ratio,
limit = limit
)
result = [(row, match[0], match[1]) for match in row_matches]
return result
# String matching - TF-IDF
def fuzzy_nn_match(
messy,
clean,
column,
col,
n_neighbors = 100,
limit = 5, **kwargs):
nearest_values, _ = tfidf_nn(messy, clean, n_neighbors, **kwargs)
results = [find_matches_fuzzy(row, nearest_values[i], limit) for i, row in enumerate(messy)]
df = pd.DataFrame(itertools.chain.from_iterable(results),
columns = [column, col, 'Ratio']
)
return df
# String matching - Fuzzy
def fuzzy_tf_idf(
df: pd.DataFrame,
column: str,
clean: pd.Series,
mapping_df: pd.DataFrame,
col: str,
analyzer: str = 'char',
ngram_range: Tuple[int, int] = (1, 3)
) -> pd.Series:
# Create vectorizer
clean = clean.drop_duplicates().reset_index(drop = True)
messy_prep = df[column].drop_duplicates().dropna().reset_index(drop = True).astype(str)
messy = messy_prep.apply(preprocess_string)
result = fuzzy_nn_match(messy = messy, clean = clean, column = column, col = col, n_neighbors = 1)
# Map value from messy to clean
return result