When to use which fuzz function to compare 2 strings
Question:
I am learning fuzzywuzzy in Python.
I understand the concept of fuzz.ratio, fuzz.partial_ratio, fuzz.token_sort_ratio and fuzz.token_set_ratio. My question is when to use which function?
- Do I check the 2 strings’ length first, say if not similar, then rule
out fuzz.partial_ratio?
- If the 2 strings’ length are similar, I’ll use
fuzz.token_sort_ratio?
- Should I always use
fuzz.token_set_ratio?
Anyone knows what criteria SeatGeek uses?
I am trying to build a real estate website, thinking to use fuzzywuzzy to compare addresses.
Answers:
Great question.
I’m an engineer at SeatGeek, so I think I can help here. We have a great blog post that explains the differences quite well, but I can summarize and offer some insight into how we use the different types.
Overview
Under the hood each of the four methods calculate the edit distance between some ordering of the tokens in both input strings. This is done using the difflib.ratio function which will:
Return a measure of the sequences’ similarity (float in [0,1]).
Where T is the total number of elements in both sequences, and M is
the number of matches, this is 2.0*M / T. Note that this is 1 if the
sequences are identical, and 0 if they have nothing in common.
The four fuzzywuzzy methods call difflib.ratio on different combinations of the input strings.
fuzz.ratio
Simple. Just calls difflib.ratio on the two input strings (code).
fuzz.ratio("NEW YORK METS", "NEW YORK MEATS")
> 96
fuzz.partial_ratio
Attempts to account for partial string matches better. Calls ratio using the shortest string (length n) against all n-length substrings of the larger string and returns the highest score (code).
Notice here that “YANKEES” is the shortest string (length 7), and we run the ratio with “YANKEES” against all substrings of length 7 of “NEW YORK YANKEES” (which would include checking against “YANKEES”, a 100% match):
fuzz.ratio("YANKEES", "NEW YORK YANKEES")
> 60
fuzz.partial_ratio("YANKEES", "NEW YORK YANKEES")
> 100
fuzz.token_sort_ratio
Attempts to account for similar strings out of order. Calls ratio on both strings after sorting the tokens in each string (code). Notice here fuzz.ratio and fuzz.partial_ratio both fail, but once you sort the tokens it’s a 100% match:
fuzz.ratio("New York Mets vs Atlanta Braves", "Atlanta Braves vs New York Mets")
> 45
fuzz.partial_ratio("New York Mets vs Atlanta Braves", "Atlanta Braves vs New York Mets")
> 45
fuzz.token_sort_ratio("New York Mets vs Atlanta Braves", "Atlanta Braves vs New York Mets")
> 100
fuzz.token_set_ratio
Attempts to rule out differences in the strings. Calls ratio on three particular substring sets and returns the max (code):
- intersection-only and the intersection with remainder of string one
- intersection-only and the intersection with remainder of string two
- intersection with remainder of one and intersection with remainder of two
Notice that by splitting up the intersection and remainders of the two strings, we’re accounting for both how similar and different the two strings are:
fuzz.ratio("mariners vs angels", "los angeles angels of anaheim at seattle mariners")
> 36
fuzz.partial_ratio("mariners vs angels", "los angeles angels of anaheim at seattle mariners")
> 61
fuzz.token_sort_ratio("mariners vs angels", "los angeles angels of anaheim at seattle mariners")
> 51
fuzz.token_set_ratio("mariners vs angels", "los angeles angels of anaheim at seattle mariners")
> 91
Application
This is where the magic happens. At SeatGeek, essentially we create a vector score with each ratio for each data point (venue, event name, etc) and use that to inform programatic decisions of similarity that are specific to our problem domain.
That being said, truth by told it doesn’t sound like FuzzyWuzzy is useful for your use case. It will be tremendiously bad at determining if two addresses are similar. Consider two possible addresses for SeatGeek HQ: “235 Park Ave Floor 12” and “235 Park Ave S. Floor 12”:
fuzz.ratio("235 Park Ave Floor 12", "235 Park Ave S. Floor 12")
> 93
fuzz.partial_ratio("235 Park Ave Floor 12", "235 Park Ave S. Floor 12")
> 85
fuzz.token_sort_ratio("235 Park Ave Floor 12", "235 Park Ave S. Floor 12")
> 95
fuzz.token_set_ratio("235 Park Ave Floor 12", "235 Park Ave S. Floor 12")
> 100
FuzzyWuzzy gives these strings a high match score, but one address is our actual office near Union Square and the other is on the other side of Grand Central.
For your problem you would be better to use the Google Geocoding API.
As of June 2017, fuzzywuzzy also includes some other comparison functions. Here is an overview of the ones missing from the accepted answer (taken from the source code):
fuzz.partial_token_sort_ratio
Same algorithm as in token_sort_ratio, but instead of applying ratio after sorting the tokens, uses partial_ratio.
fuzz.token_sort_ratio("New York Mets vs Braves", "Atlanta Braves vs New York Mets")
> 85
fuzz.partial_token_sort_ratio("New York Mets vs Braves", "Atlanta Braves vs New York Mets")
> 100
fuzz.token_sort_ratio("React.js framework", "React.js")
> 62
fuzz.partial_token_sort_ratio("React.js framework", "React.js")
> 100
fuzz.partial_token_set_ratio
Same algorithm as in token_set_ratio, but instead of applying ratio to the sets of tokens, uses partial_ratio.
fuzz.token_set_ratio("New York Mets vs Braves", "Atlanta vs New York Mets")
> 82
fuzz.partial_token_set_ratio("New York Mets vs Braves", "Atlanta vs New York Mets")
> 100
fuzz.token_set_ratio("React.js framework", "Reactjs")
> 40
fuzz.partial_token_set_ratio("React.js framework", "Reactjs")
> 71
fuzz.QRatio, fuzz.UQRatio
Just wrappers around fuzz.ratio with some validation and short-circuiting, included here for completeness.
UQRatio is a unicode version of QRatio.
fuzz.WRatio
An attempt to weight (the name stands for ‘Weighted Ratio’) results from different algorithms
to calculate the ‘best’ score.
Description from the source code:
1. Take the ratio of the two processed strings (fuzz.ratio)
2. Run checks to compare the length of the strings
* If one of the strings is more than 1.5 times as long as the other
use partial_ratio comparisons - scale partial results by 0.9
(this makes sure only full results can return 100)
* If one of the strings is over 8 times as long as the other
instead scale by 0.6
3. Run the other ratio functions
* if using partial ratio functions call partial_ratio,
partial_token_sort_ratio and partial_token_set_ratio
scale all of these by the ratio based on length
* otherwise call token_sort_ratio and token_set_ratio
* all token based comparisons are scaled by 0.95
(on top of any partial scalars)
4. Take the highest value from these results
round it and return it as an integer.
fuzz.UWRatio
Unicode version of WRatio.
I am learning fuzzywuzzy in Python.
I understand the concept of fuzz.ratio, fuzz.partial_ratio, fuzz.token_sort_ratio and fuzz.token_set_ratio. My question is when to use which function?
- Do I check the 2 strings’ length first, say if not similar, then rule
outfuzz.partial_ratio? - If the 2 strings’ length are similar, I’ll use
fuzz.token_sort_ratio? - Should I always use
fuzz.token_set_ratio?
Anyone knows what criteria SeatGeek uses?
I am trying to build a real estate website, thinking to use fuzzywuzzy to compare addresses.
Great question.
I’m an engineer at SeatGeek, so I think I can help here. We have a great blog post that explains the differences quite well, but I can summarize and offer some insight into how we use the different types.
Overview
Under the hood each of the four methods calculate the edit distance between some ordering of the tokens in both input strings. This is done using the difflib.ratio function which will:
Return a measure of the sequences’ similarity (float in [0,1]).
Where T is the total number of elements in both sequences, and M is
the number of matches, this is 2.0*M / T. Note that this is 1 if the
sequences are identical, and 0 if they have nothing in common.
The four fuzzywuzzy methods call difflib.ratio on different combinations of the input strings.
fuzz.ratio
Simple. Just calls difflib.ratio on the two input strings (code).
fuzz.ratio("NEW YORK METS", "NEW YORK MEATS")
> 96
fuzz.partial_ratio
Attempts to account for partial string matches better. Calls ratio using the shortest string (length n) against all n-length substrings of the larger string and returns the highest score (code).
Notice here that “YANKEES” is the shortest string (length 7), and we run the ratio with “YANKEES” against all substrings of length 7 of “NEW YORK YANKEES” (which would include checking against “YANKEES”, a 100% match):
fuzz.ratio("YANKEES", "NEW YORK YANKEES")
> 60
fuzz.partial_ratio("YANKEES", "NEW YORK YANKEES")
> 100
fuzz.token_sort_ratio
Attempts to account for similar strings out of order. Calls ratio on both strings after sorting the tokens in each string (code). Notice here fuzz.ratio and fuzz.partial_ratio both fail, but once you sort the tokens it’s a 100% match:
fuzz.ratio("New York Mets vs Atlanta Braves", "Atlanta Braves vs New York Mets")
> 45
fuzz.partial_ratio("New York Mets vs Atlanta Braves", "Atlanta Braves vs New York Mets")
> 45
fuzz.token_sort_ratio("New York Mets vs Atlanta Braves", "Atlanta Braves vs New York Mets")
> 100
fuzz.token_set_ratio
Attempts to rule out differences in the strings. Calls ratio on three particular substring sets and returns the max (code):
- intersection-only and the intersection with remainder of string one
- intersection-only and the intersection with remainder of string two
- intersection with remainder of one and intersection with remainder of two
Notice that by splitting up the intersection and remainders of the two strings, we’re accounting for both how similar and different the two strings are:
fuzz.ratio("mariners vs angels", "los angeles angels of anaheim at seattle mariners")
> 36
fuzz.partial_ratio("mariners vs angels", "los angeles angels of anaheim at seattle mariners")
> 61
fuzz.token_sort_ratio("mariners vs angels", "los angeles angels of anaheim at seattle mariners")
> 51
fuzz.token_set_ratio("mariners vs angels", "los angeles angels of anaheim at seattle mariners")
> 91
Application
This is where the magic happens. At SeatGeek, essentially we create a vector score with each ratio for each data point (venue, event name, etc) and use that to inform programatic decisions of similarity that are specific to our problem domain.
That being said, truth by told it doesn’t sound like FuzzyWuzzy is useful for your use case. It will be tremendiously bad at determining if two addresses are similar. Consider two possible addresses for SeatGeek HQ: “235 Park Ave Floor 12” and “235 Park Ave S. Floor 12”:
fuzz.ratio("235 Park Ave Floor 12", "235 Park Ave S. Floor 12")
> 93
fuzz.partial_ratio("235 Park Ave Floor 12", "235 Park Ave S. Floor 12")
> 85
fuzz.token_sort_ratio("235 Park Ave Floor 12", "235 Park Ave S. Floor 12")
> 95
fuzz.token_set_ratio("235 Park Ave Floor 12", "235 Park Ave S. Floor 12")
> 100
FuzzyWuzzy gives these strings a high match score, but one address is our actual office near Union Square and the other is on the other side of Grand Central.
For your problem you would be better to use the Google Geocoding API.
As of June 2017, fuzzywuzzy also includes some other comparison functions. Here is an overview of the ones missing from the accepted answer (taken from the source code):
fuzz.partial_token_sort_ratio
Same algorithm as in token_sort_ratio, but instead of applying ratio after sorting the tokens, uses partial_ratio.
fuzz.token_sort_ratio("New York Mets vs Braves", "Atlanta Braves vs New York Mets")
> 85
fuzz.partial_token_sort_ratio("New York Mets vs Braves", "Atlanta Braves vs New York Mets")
> 100
fuzz.token_sort_ratio("React.js framework", "React.js")
> 62
fuzz.partial_token_sort_ratio("React.js framework", "React.js")
> 100
fuzz.partial_token_set_ratio
Same algorithm as in token_set_ratio, but instead of applying ratio to the sets of tokens, uses partial_ratio.
fuzz.token_set_ratio("New York Mets vs Braves", "Atlanta vs New York Mets")
> 82
fuzz.partial_token_set_ratio("New York Mets vs Braves", "Atlanta vs New York Mets")
> 100
fuzz.token_set_ratio("React.js framework", "Reactjs")
> 40
fuzz.partial_token_set_ratio("React.js framework", "Reactjs")
> 71
fuzz.QRatio, fuzz.UQRatio
Just wrappers around fuzz.ratio with some validation and short-circuiting, included here for completeness.
UQRatio is a unicode version of QRatio.
fuzz.WRatio
An attempt to weight (the name stands for ‘Weighted Ratio’) results from different algorithms
to calculate the ‘best’ score.
Description from the source code:
1. Take the ratio of the two processed strings (fuzz.ratio)
2. Run checks to compare the length of the strings
* If one of the strings is more than 1.5 times as long as the other
use partial_ratio comparisons - scale partial results by 0.9
(this makes sure only full results can return 100)
* If one of the strings is over 8 times as long as the other
instead scale by 0.6
3. Run the other ratio functions
* if using partial ratio functions call partial_ratio,
partial_token_sort_ratio and partial_token_set_ratio
scale all of these by the ratio based on length
* otherwise call token_sort_ratio and token_set_ratio
* all token based comparisons are scaled by 0.95
(on top of any partial scalars)
4. Take the highest value from these results
round it and return it as an integer.
fuzz.UWRatio
Unicode version of WRatio.