A small package that enables super-fast TF-IDF based string matching.
Project description
tfidf_matcher
is a package for fuzzymatching large datasets together.
Most fuzzy matching libraries like fuzzywuzzy
get great results, but
don't scale well due to their O(n^2) complexity.
How does it work?
This package provides two functions:
ngrams()
: Simple ngram generator.matcher()
: Matches a list of strings against a reference corpus. Does this by:- Vectorizing the reference corpus using TF-IDF into a term-document matrix.
- Fitting a K-NearestNeighbours model to the sparse matrix.
- Vectorizing the list of strings to be matched and passing it in
to the KNN model to calculate the cosine distance (the OOTB
cosine_similarity
function in sklearn is very memory-inefficient for our use case). - Some data manipulation to emit
k_matches
closest matches.
Yeah ok, but how do I use it?
Define two lists; your original list (list you want matches for) and
your lookup list (list you want to match against). Typically your
lookup list will be much longer than your original list. Pass them into
the matcher
function along with the number of matches you want to
display from the lookup list using the k_matches
argument. The
result will be a pandas DataFrame containing 1 row per item in your
original list, along with k_matches
columns containing the closest
match from the lookup list, and a match score for the closest match
(which is 1 - the cosine distance between the matches normalised to
[0,1])
Simply import with import tfidf_matcher as tm
, and call the matcher
function with tm.matcher()
. It takes the following arguments:
original
: List of strings you want to match.lookup
: List of strings you want to match against.k_matches
: Number of the closest results fromlookup
to return (1 per column).ngram_length
: Length ofngrams
used in the algorithm. Anecdotal testing shows 2 or 3 to be optimal, but feel free to tinker.
Strengths and Weaknesses
- Quick. Very quick.
- Can emit however many closest matches you want. I found that 3 worked best.
- Not very well tested so potentially unstable results. Worked well for 640 company names matched against a lookup corpus of >700,000 company names.
Who do I thank?
For the method, thank Josh Taylor and Chris van den Berg. I wanted to adapt the methods to work nicely on a company mathcing problem I was having, and decided to build out my resultant code into a package for two reasons:
- Package building experience.
- Utility for future projects which may require large-domain fuzzy matching.
I understand the algorithms behind k-Nearest Neighbours & TF-IDF Vectorisation, but it was through implementing the ideas in the blogs linked that I was able to build this project out.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file tfidf_matcher-0.3.0.tar.gz
.
File metadata
- Download URL: tfidf_matcher-0.3.0.tar.gz
- Upload date:
- Size: 8.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 26939ab3f7f12c6c7421af83ab974c1707aa9724eb99de2f66f00bd5d5d5b468 |
|
MD5 | d72520ea9ad44d7443ff12b318580b2f |
|
BLAKE2b-256 | cccdaf378c05b1f199879f2deed7c31abc4175dad73052f08832a4b4752cdbf6 |
File details
Details for the file tfidf_matcher-0.3.0-py3-none-any.whl
.
File metadata
- Download URL: tfidf_matcher-0.3.0-py3-none-any.whl
- Upload date:
- Size: 8.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1f5ba14465a9d6a3fbb244d77319240908110ab81cd453bbb26ab99fff687210 |
|
MD5 | 38399ed465677d876f0a5ff4d6c57fd6 |
|
BLAKE2b-256 | 02bf9c086937798418e7eb283b97638e478ec39333bbed646b85c98024fd4b8c |