» pip install fuzzywuzzy
Is there a search algorithm?
Given your task your comparing 70k strings with each other using fuzz.WRatio, so your having a total of 4,900,000,000 comparisions, with each of these comparisions using the levenshtein distance inside fuzzywuzzy which is a O(N*M) operation. fuzz.WRatio is a combination of multiple different string matching ratios that have different weights. It then selects the best ratio among them. Therefore it even has to calculate the Levenshtein distance multiple times. So one goal should be to reduce the search space by excluding some possibilities using a way faster matching algorithm. Another issue is that the strings are preprocessed to remove punctuation and to lowercase the strings. While this is required for the matching (so e.g. a uppercased word becomes equal to a lowercased one) we can do this ahead of time. So we only have to preprocess the 70k strings once. I will use RapidFuzz instead of FuzzyWuzzy here, since it is quite a bit faster (I am the author).
The following version performs more than 10 times as fast as your previous solution in my experiments and applies the following improvements:
it preprocesses the strings ahead of time
it passes a score_cutoff to extractOne so it can skip calculations where it already knows they can not reach this ratio
import pandas as pd, numpy as np
from rapidfuzz import process, utils
org_list = df['org_name']
processed_orgs = [utils.default_process(org) for org in org_list]
for (i, processed_query) in enumerate(processed_orgs):
# None is skipped by extractOne, so we set the current element to None an
# revert this change after the comparision
processed_orgs[i] = None
match = process.extractOne(processed_query, processed_orgs, processor=None, score_cutoff=93)
processed_orgs[i] = processed_query
if match:
df.loc[i, 'fuzzy_match'] = org_list[match[2]]
df.loc[i, 'fuzzy_match_score'] = match[1]
Here is a list of the most relevant improvements of RapidFuzz to make it faster than FuzzyWuzzy in this example:
It is implemented fully in C++ while a big part of FuzzyWuzzy is implemented in Python
When calculating the levenshtein distance it takes into account the
score_cutoffto choose an optimized implementation based. E.g. when the length difference between the strings is to big it can exit in O(1).FuzzyWuzzy uses Python-Levenshtein to calculate the similarity between two strings, which uses a weightened Levenshtein distance with a weight of 2 for substitutions. This is implemented using Wagner-Fischer. RapidFuzz on the other hand uses a bitparallel implementation for this based on BitPal, which is faster
fuzz.WRatiois combining the results of multiple other string matching algorithms likefuzz.ratio,fuzz.token_sort_ratioandfuzz.token_set_ratioand takes the maximum result after weighting them. So whilefuzz.ratiohas a weighting of 1fuzz.token_sort_ratioandfuzz.token_set_ratiohave one of 0.95. When thescore_cutoffis bigger than 95fuzz.token_sort_ratioandfuzz.token_set_ratioare not calculated anymore, since the results are guaranteed to be smaller than thescore_cutoffIn process.extractOne RapidFuzz avoids calls through Python whenever possible and preprocesses the query once ahead of time. E.g. the BitPal algorithm requires one of the two strings which are compared to be stored into a bitvector which takes a big part of the algorithms runtime. In process.extractOne the query is stored into this bitvector only once and the bitvector is reused afterwards making the algorithm a lot faster.
since extractOne only searches for the best match it uses the ratio of the current best match as
score_cutofffor the next elements. This way it can quickly discard more elements by using the improvements to the levenshtein distance calculation from 2) in many cases. When it finds a element with a similarity of 100 it exits early since there can't be a better match afterwards.
This solution leverages apply() and should demonstrate reasonable performance improvements. Feel free to play around with the scorer and change the threshold to meet your needs:
import pandas as pd, numpy as np
from fuzzywuzzy import process, fuzz
df = pd.DataFrame([['cliftonlarsonallen llp minneapolis MN'],
['loeb and troper llp newyork NY'],
["dauby o'connor and zaleski llc carmel IN"],
['wegner cpas llp madison WI']],
columns=['org_name'])
org_list = df['org_name']
threshold = 40
def find_match(x):
match = process.extract(x, org_list, limit=2, scorer=fuzz.partial_token_sort_ratio)[1]
match = match if match[1]>threshold else np.nan
return match
df['match found'] = df['org_name'].apply(find_match)
Returns:
org_name match found
0 cliftonlarsonallen llp minneapolis MN (wegner cpas llp madison WI, 50, 3)
1 loeb and troper llp newyork NY (wegner cpas llp madison WI, 46, 3)
2 dauby o'connor and zaleski llc carmel IN NaN
3 wegner cpas llp madison WI (cliftonlarsonallen llp minneapolis MN, 50, 0)
If you would just like to return the matching string itself, then you can modify as follows:
match = match[0] if match[1]>threshold else np.nan
I've added @user3483203's comment pertaining to a list comprehension here as an alternative option as well:
df['match found'] = [find_match(row) for row in df['org_name']]
Note that process.extract() is designed to handle a single query string and apply the passed scoring algorithm to that query and the supplied match options. For that reason, you will have to evaluate that query against all 70,000 match options (the way you currently have your code setup). So therefore, you will be evaluating len(match_options)**2 (or 4,900,000,000) string comparisons. Therefore, I think the best performance improvements could be achieved by limiting the potential match options via more extensive logic in the find_match() function, e.g. enforcing that the match options start with the same letter as the query, etc.