python中搜索字符串和字符串列表之间最高百分比Levenshtein距离的最快方法是什么？

Question

I'm writing a program that compares a smaller list of game titles to a master list of many games to see which games in the smaller list more closely match with the titles of the games in the master list than others. In order to do this, I've been checking the Levenshtein distance (in percent form) between each game in the smaller list and every game in the master list and taking the maximum of all of these values (the lower the maximum percentage, the more unique the game has to be) using both the difflib and the fuzzywuzzy modules. The problem that I'm having is that a typical search using either process.extractOne() or difflib.get_close_matches() takes about 5+ seconds per game (with 38000+ strings in the master list), and I have about 4500 games to search through (5 * 4500 is about 6 hours and 15 minutes, which I don't have time for).

In hopes of finding a better and faster method of searching through a list of strings, I'm asking here what the fastest method in python of searching for the highest percent Levenshtein distance between a string and a list of strings is. If there is no better way than by using the two functions above or writing some other looping code, then please say so.

The two functions I used in specific to search for the highest distance are these:

metric = process.extractOne(name, master_names)[1] / 100
metric = fuzz.ratio(name, difflib.get_close_matches(name, master_names, 1, 0)[0]) / 100

Answer 1

Through experimentation and further research I discovered that the fastest method of checking the Levenshtein ratio is through the python-Levenshtein library itself. The function Levenshtein.ratio() is significantly faster (for one game the entire search takes only 0.05 seconds on average) compared to using any function in fuzzywuzzy or difflib, likely because of its simplicity and C implementation. I used this function in a for loop iterating over every name in the master list to get the best answer:

from Levenshtein import ratio

metric = 0
for master_name in master_names:
    new_metric = ratio(name, master_name)
    if (new_metric > metric):
        metric = new_metric

In conclusion I say that the fastest method of searching for the highest percent Levenshtein distance between a string and a list of strings is to iterate over the list of strings, use Levenshtein.ratio() to get the ratio of each string compared with the first string, and then check for the highest value ratio on each iteration.