Comparing 2 similar strings?

[William Park]

How do you compare 2 strings, and determine how much they are "close" to
each other? Eg.
aqwerty
qwertyb
are similar to each other, except for first/last char. But, how do I
quantify that?

I guess you can say for the above 2 strings that
- at max, 6 chars out of 7 are same sequence --> 85% max

But, for
qawerty
qwerbty
max correlation is
- 3 chars out of 7 are the same sequence --> 42% max

(Crossposted to 3 of my favourite newsgroup.)

 

[=?ISO-8859-1?Q?J=FCrgen_Kahrs?=]

Hello William,

How do you compare 2 strings, and determine how much they are "close" to
each other? Eg.
aqwerty
qwertyb
are similar to each other, except for first/last char. But, how do I
quantify that?

This is a classic problem of computer science.
Watch this:

http://odur.let.rug.nl/~kleiweg/lev/
http://www.levenshtein.net/

This solution has application in speech
recognition and also in DNA research.

 

[Ed Morton]

How do you compare 2 strings, and determine how much they are "close" to
each other? Eg.
aqwerty
qwertyb
are similar to each other, except for first/last char. But, how do I
quantify that?

I guess you can say for the above 2 strings that
- at max, 6 chars out of 7 are same sequence --> 85% max

But, for
qawerty
qwerbty
max correlation is
- 3 chars out of 7 are the same sequence --> 42% max

(Crossposted to 3 of my favourite newsgroup.)

"However you like" is probably the right answer, but one way might be to
compare their soundex encoding
(http://foldoc.doc.ic.ac.uk/foldoc/foldoc.cgi?soundex) and figure out
percentage difference based on comparing the numeric part.

Ed.

 

[John Machin]

"However you like" is probably the right answer, but one way might be to
compare their soundex encoding
(http://foldoc.doc.ic.ac.uk/foldoc/foldoc.cgi?soundex) and figure out
percentage difference based on comparing the numeric part.

Fantastic suggestion. Here's a tiny piece of real-life test data:

compare the surnames "Mousaferiadis" and "McPherson".

 

[Don]

How do you compare 2 strings, and determine how much they are "close" to
each other? Eg.
aqwerty
qwertyb
are similar to each other, except for first/last char. But, how do I
quantify that?

I guess you can say for the above 2 strings that
- at max, 6 chars out of 7 are same sequence --> 85% max

But, for
qawerty
qwerbty
max correlation is
- 3 chars out of 7 are the same sequence --> 42% max

(Crossposted to 3 of my favourite newsgroup.)

http://www.personal.psu.edu/staff/i/u/iua1/python/apse/
http://www.nist.gov/dads/HTML/editDistance.html

 

[John Machin]

How do you compare 2 strings, and determine how much they are "close" to
each other? Eg.
aqwerty
qwertyb
are similar to each other, except for first/last char. But, how do I
quantify that?

I guess you can say for the above 2 strings that
- at max, 6 chars out of 7 are same sequence --> 85% max

But, for
qawerty
qwerbty
max correlation is
- 3 chars out of 7 are the same sequence --> 42% max

1. Google for such topics as "fuzzy matching", "edit distance",
"approximate comparison".

2. Closer to home, look at the thread in comp.lang.python around
2004-11-18 -- search for "Pingel Hyyro" [and yes you do mean "hyyro",
not "hydro"!!]

3. Steadfastly ignore any (presumably) well-intentioned profferings of
soundex.


HTH,
John

 

[John Machin]

http://www.personal.psu.edu/staff/i/u/iua1/python/apse/

The above is broken, not meeting one of the elementary conditions for
a distance metric:

distance(a, b) == distance(b, a)

Quoting from its docs:
| Note: The definition of the goodness of an approximate match is
the
| number of steps required to bring the string pattern to a form
that is
| entirely contained in the string to which it is being matched.

Note: "entirely contained in", rather than "equal to". Now read on:


| The mathing
| is not commutative. The pattern that you instantiate the class
with will be
| matched against the input. For example the word "funky" can be
made to
| match the word "funnybone" with an edit distance of one. However,
using
| "funnybone" as a pattern that will be matched to "funky" the
distance
| will become five.
|
| Example:
|
| >>> from Apse import Approx
| >>> a = Approx("funky")
| >>> a.dist("funnybone")
| 1
| >>> a = Approx("funnybone")
| >>> a.dist("funky")
| 5

 

[Aahz]

[trimmed to c.l.py]

How do you compare 2 strings, and determine how much they are "close" to
each other? Eg.
aqwerty
qwertyb
are similar to each other, except for first/last char. But, how do I
quantify that?

I guess you can say for the above 2 strings that
- at max, 6 chars out of 7 are same sequence --> 85% max

But, for
qawerty
qwerbty
max correlation is
- 3 chars out of 7 are the same sequence --> 42% max

difflib -- use the string as the sequence

 

[Ed Morton]

Fantastic suggestion. Here's a tiny piece of real-life test data:

compare the surnames "Mousaferiadis" and "McPherson".

Fantastic test data set. I know how to pronounce McPherson but I'd never
have guessed that Mousaferiadis sounds like it. I suppose non-Celts
probably wouldn't be able to guess how Dalziell, Drumnadrochit, Culzean,
Ceilidh, or Concobarh are pronounced either.

I assume you were actually being facetious and trying to make the point
that names that don't look the same on paper can have the same soundex
encoding and that's obviously countered with the fact that soundex is
just a cheap and cheerful way to find names that probably sound similair
which can vary tremendously based on ethnicity or accent.

It's a reasonable approach to consider given the very loose requirements
presented.

Ed.

 

[John Machin]

Fantastic test data set. I know how to pronounce McPherson but I'd never
have guessed that Mousaferiadis sounds like it.

If you guessed "moose a ferry ah dis" i.e. phonetically you wouldn't
be far wrong. The point is that the two names neither look similar nor
sound similar. It is highly unlikely that one would be corrupted into
the other during either written or spoken communication. However they
get the same soundex code because the soundex method picks out MSFR
and MCPR and says in effect that S===C (sometimes) and F==P
(sometimes).

I assume you were actually being facetious
and trying to make the point
that names that don't look the same on paper can have the same soundex
encoding and that's obviously countered with the fact that soundex is
just a cheap and cheerful way to find names that probably sound similair
which can vary tremendously based on ethnicity or accent.

*If* you want phonetic similarity, there are methods that much better
than soundex, in the sense of fewer false positives and fewer false
negatives. Google for NYSIIS, dolby, metaphone, caverphone.

Cheap? You get what you pay for.

Cheerful? What's the relevance?

Someone who types "Mousaferiadis" into a customer search screen and
gets back several lines of McPherson and MacPherson is unlikely to be
cheerful -- even before we factor in the speed [soundex divides the
universe into a relative small number of buckets].

Someone who's looking for Erin when they should be looking for Aaron
(or vice versa) won't get much cheer out of soundex, either.

It's a reasonable approach to consider given the very loose requirements
presented.

Soundex is *NEVER* a reasonable approach to consider. Phonetic
variation is only one consideration. In any case, the OP didn't appear
to be concerned with phonetic variations.

 

[Ed Morton]

*If* you want phonetic similarity, there are methods that much better
than soundex, in the sense of fewer false positives and fewer false
negatives. Google for NYSIIS, dolby, metaphone, caverphone.

And I assume I'd find they all have pros and cons too, otherwise you'd
be referring to THE best one rather than a selection. It seems a bit
pointless to go browsing through the documentation on them when someone
who presumably already has can't just state the best one for the job.

Cheap? You get what you pay for.

Cheerful? What's the relevance?

"Cheap and cheerful" is a colloquial expression meaning cost-effective.

Someone who types "Mousaferiadis" into a customer search screen and
gets back several lines of McPherson and MacPherson is unlikely to be
cheerful -- even before we factor in the speed [soundex divides the
universe into a relative small number of buckets].

Someone who's looking for Erin when they should be looking for Aaron
(or vice versa) won't get much cheer out of soundex, either.

That goes back to accent. In [some parts at least of] the USA Erin
sounds very much like Aaron wheras in the UK the 2 are very dissimilar.
I assume since you apparently consider them similair that you live in
the USA and so would consider soundex as providing a "false negative" by
saying they don't match. Perhaps one of the other approaches you suggest
would report that they do match but that wouldn't make it clearly a
better choice to everyone.

Soundex is *NEVER* a reasonable approach to consider. Phonetic
variation is only one consideration. In any case, the OP didn't appear
to be concerned with phonetic variations.

The OP didn't say what the application was at all, but you're right that
from his example he does SEEM more interested in character matches than
phonetic ones so he'd presumably quickly discard phonetic comparisons if
that's really not what he wants.

Ed.

 

[John Machin]

And I assume I'd find they all have pros and cons too, otherwise you'd
be referring to THE best one rather than a selection.

*ALL* approximate matching methods have pros and cons -- and all
others have fewer than soundex.

It seems a bit
pointless to go browsing through the documentation on them when someone
who presumably already has can't just state the best one for the job.

They were listed in roughly increasing order of general rough
effectiveness. It depends on the job. It depends on the language. None
of them would work well with your O Muirchaitaeioughs

"Cheap and cheerful" is a colloquial expression meaning cost-effective.

Grossly misapplied to soundex.

Someone who types "Mousaferiadis" into a customer search screen and
gets back several lines of McPherson and MacPherson is unlikely to be
cheerful -- even before we factor in the speed [soundex divides the
universe into a relative small number of buckets].

Someone who's looking for Erin when they should be looking for Aaron
(or vice versa) won't get much cheer out of soundex, either.

That goes back to accent. In [some parts at least of] the USA Erin
sounds very much like Aaron wheras in the UK the 2 are very dissimilar.
I assume since you apparently consider them similair that you live in
the USA

You assume incorrectly. In any case my whereabouts are of sublime
irrelevance. What matters is that some people will as you say think
that Aaron and Erin sound similar in the best of circumstances; they
are prone to be mistaken one for the other by (say) a tired call
centre operative especially when the caller and the callee are from
different backgrounds.

and so would consider soundex as providing a "false negative" by
saying they don't match. Perhaps one of the other approaches you suggest
would report that they do match but that wouldn't make it clearly a
better choice to everyone.

None of the other approaches make the mistake of preserving the first
letter -- this alone is almost enough reason for jettisoning soundex.

 

[Scott David Daniels]

How do you compare 2 strings, and determine how much they are "close" to
each other?

Here's a really weird idea: Measure the size difference between the
pair of strings compressed together and compressed separately.

--Scott David Daniels
(e-mail address removed)

 

[Dennis Lee Bieber]

Fantastic test data set. I know how to pronounce McPherson but I'd never
have guessed that Mousaferiadis sounds like it. I suppose non-Celts
probably wouldn't be able to guess how Dalziell, Drumnadrochit, Culzean,
Ceilidh, or Concobarh are pronounced either.

Since "soundex" is initial letter (consonant?) and a code for
the next three syllables (or close to it), really long multi-syllabic
names are effectively truncated...

Howe'er... When Maire Brennan releases an album as "Moya",
following sister's "Enya" (Eithne, as I seem to recall reading)... I'd
not attempt to pronounce most of the names you supply... "Dalziell"
doesn't look Celtic... "Culzean" almost looks Aztec/Mayan... "Ceilidh"
=> kay-lee?

Okay, I think I can manage bain sidhe and uisge (after too much
of the latter, I'll be seeing the former)

--

 

[chrisconnett]

Could hit a few snags. Quick out-of-the-library compression using
standards like zlib will have headers that will dilute the difference
on short strings, and on long strings block compression (zlib, bzip2)
will not pick up similarities because the similarities will be in
different blocks. With blocks of around 100k-1M in these algos by
default (IIRC), this could work well for strings between oh say
1k-50k.

But I need to underscore Aahz's posting above:

***Check out difflib, it's in the library.*** Perfect package for what
the OP wants AFAICT.

 

[Chris Croughton]

Fantastic suggestion. Here's a tiny piece of real-life test data:

compare the surnames "Mousaferiadis" and "McPherson".

I remember a word processor, MultiMate, which used Soundex to do
matching for its suggestions for spelling correction. One of my
cow-orkers typed the word 'agains' -- it was supposed to be 'against'
but 'again' would also have been a sensible suggestion. MultiMate,
however, suggested neither of those reasonable words, it did suggest
"iguanas" amd "Utahns"...

(I wonder what it does with "Talliafero" and "Tolliver", and with
"Featherstone-Howe" and "Fanshaw"...)

The answer to the OP, which someone just pointed out to me on
comp.programming, is "edit distance" or "Levenshtein Distance"[1]. A
google search on either produces some good descriptions in the first few
links, including http://www.merriampark.com/ld.htm which has not only a
description of the algorithm but also source code in Java, C++ and
Visual Basic (no Awk, thought there are links to pages with
implementations in Perl, Python, Delphi and many more)...

[1] I would have spelt it 'Levenstein', and pronounced it 'Levenshtein'
in Schwaebisch (south German) fashion, but apparently the author of the
algorithm was one Vladimir I. Levenshtein and that is how he is credited
on the IEEE site. There are also a number of Google hits under the
'stein' spelling, though...

Chris C

 

[Steven D'Aprano]

The above is broken, not meeting one of the elementary conditions for
a distance metric:

distance(a, b) == distance(b, a)

I agree that this makes the edit distance broken in the context of text
strings, but you should be aware that distance is only commutative if
there are no constraints on travel. If you've ever driven around cities
like Sydney that use lots of one-way streets, you will know that the
distance from point A to point B is not necessarily the same as the
distance from B back to A.

 

[Steven D'Aprano]

None of the other approaches make the mistake of preserving the first
letter -- this alone is almost enough reason for jettisoning soundex.

Off-topic now, but you've made me curious.

Why is this a bad idea?

How would you handle the case of "barow" and "marow"? (Barrow and
marrow, naturally.) Without the first letter, they sound identical. Why is
throwing that information away a good thing?

Thanks,

 

[Chris Croughton]

Off-topic now, but you've made me curious.

Why is this a bad idea?

Why is the first letter any more important than any other?

How would you handle the case of "barow" and "marow"? (Barrow and
marrow, naturally.) Without the first letter, they sound identical. Why is
throwing that information away a good thing?

Well, Soundex will quite possibly throw the information away anyway,
certainly it regards several letters as the same. But why is the
difference between barrow and marrow more important than that between
help and held? Or between hatter and hammer?

Regarding 'agains' as similar to 'iguanas' and 'Utahns', but not to
'again' or 'against', is silly...

Chris C

 

[Dan Bishop]

soundex.

Off-topic now, but you've made me curious.

Why is this a bad idea?

Because of situations like, for example, my mother's last name, which
originally started with "Y" but got anglicized to a name beginning with
"E". Same name, different Soundex codes, and the problem occurs only
occurs because of Soundex's preservation of the exact initial letter.

How would you handle the case of "barow" and "marow"? (Barrow and
marrow, naturally.) Without the first letter, they sound identical. Why is
throwing that information away a good thing?

No one's suggesting throwing away the first letter's information, just
removing the special treatment for it. "Barow" becomes 1600 and
"Marow" becomes 5600.