laubeee / polyfuzz

Fuzzy string matching, grouping, and evaluation.

PolyFuzz performs fuzzy string matching, string grouping, and contains extensive evaluation functions. PolyFuzz is meant to bring fuzzy string matching techniques together within a single framework.

Currently, methods include a variety of edit distance measures, a character-based n-gram TF-IDF, word embedding techniques such as FastText and GloVe, and 🤗 transformers embeddings.

Corresponding medium post can be found here.

Installation

You can install PolyFuzz via pip:

pip install polyfuzz

You may want to install more depending on the transformers and language backends that you will be using. The possible installations are:

pip install polyfuzz[sbert]
pip install polyfuzz[flair]
pip install polyfuzz[gensim]
pip install polyfuzz[spacy]
pip install polyfuzz[use]

If you want to speed up the cosine similarity comparison and decrease memory usage when using embedding models, you can use sparse_dot_topn which is installed via:

pip install polyfuzz[fast]

Installation Issues

You might run into installation issues with sparse_dot_topn. If so, one solution that has worked for many is by installing it via conda first before installing PolyFuzz:

conda install -c conda-forge sparse_dot_topn

If that does not work, I would advise you to look through their issues](https://github.com/ing-bank/sparse_dot_topn/issues) page or continue to use PolyFuzz without sparse_dot_topn.

Getting Started

For an in-depth overview of the possibilities of PolyFuzz you can check the full documentation here or you can follow along with the notebook here.

Quick Start

The main goal of PolyFuzz is to allow the user to perform different methods for matching strings. We start by defining two lists, one to map from and one to map to. We are going to be using TF-IDF to create n-grams on a character level in order to compare similarity between strings. Then, we calculate the similarity between strings by calculating the cosine similarity between vector representations.

We only have to instantiate PolyFuzz with TF-IDF and match the lists:

from polyfuzz import PolyFuzz

from_list = ["apple", "apples", "appl", "recal", "house", "similarity"]
to_list = ["apple", "apples", "mouse"]

model = PolyFuzz("TF-IDF")
model.match(from_list, to_list)

The resulting matches can be accessed through model.get_matches():

>>> model.get_matches()
         From      To    Similarity
0       apple   apple    1.000000
1      apples  apples    1.000000
2        appl   apple    0.783751
3       recal    None    0.000000
4       house   mouse    0.587927
5  similarity    None    0.000000

NOTE 1: If you want to compare distances within a single list, you can simply pass that list as such: model.match(from_list)

NOTE 2: When instantiating PolyFuzz we also could have used "EditDistance" or "Embeddings" to quickly access Levenshtein and FastText (English) respectively.

Production

The .match function allows you to quickly extract similar strings. However, after selecting the right models to be used, you may want to use PolyFuzz in production to match incoming strings. To do so, we can make use of the familiar fit, transform, and fit_transform functions.

Let's say that we have a list of words that we know to be correct called train_words. We want to any incoming word to mapped to one of the words in train_words. In other words, we fit on train_words and we use transform on any incoming words:

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from polyfuzz import PolyFuzz

train_words = ["apple", "apples", "appl", "recal", "house", "similarity"]
unseen_words = ["apple", "apples", "mouse"]

# Fit
model = PolyFuzz("TF-IDF")
model.fit(train_words)

# Transform
results = model.transform(unseen_words)

In the above example, we are using fit on train_words to calculate the TF-IDF representation of those words which are saved to be used again in transform. This speeds up transform quite a bit since all TF-IDF representations are stored when applying fit.

Then, we apply save and load the model as follows to be used in production:

# Save the model
model.save("my_model")

# Load the model
loaded_model = PolyFuzz.load("my_model")

Group Matches

We can group the matches To as there might be significant overlap in strings in our to_list. To do this, we calculate the similarity within strings in to_list and use single linkage to then group the strings with a high similarity.

When we extract the new matches, we can see an additional column Group in which all the To matches were grouped to:

>>> model.group(link_min_similarity=0.75)
>>> model.get_matches()
	      From	To		Similarity	Group
0	     apple	apple	1.000000	apples
1	    apples	apples	1.000000	apples
2	      appl	apple	0.783751	apples
3	     recal	None	0.000000	None
4	     house	mouse	0.587927	mouse
5	similarity	None	0.000000	None

As can be seen above, we grouped apple and apples together to apple such that when a string is mapped to apple it will fall in the cluster of [apples, apple] and will be mapped to the first instance in the cluster which is apples.

Precision-Recall Curve

Next, we would like to see how well our model is doing on our data. We express our results as precision and recall where precision is defined as the minimum similarity score before a match is correct and recall the percentage of matches found at a certain minimum similarity score.

Creating the visualizations is as simple as:

model.visualize_precision_recall()

Models

Currently, the following models are implemented in PolyFuzz:

• TF-IDF
• EditDistance (you can use any distance measure, see documentation)
• FastText and GloVe
• 🤗 Transformers

With Flair, we can use all 🤗 Transformers models. We simply have to instantiate any Flair WordEmbedding method and pass it through PolyFuzzy.

All models listed above can be found in polyfuzz.models and can be used to create and compare different models:

from polyfuzz.models import EditDistance, TFIDF, Embeddings
from flair.embeddings import TransformerWordEmbeddings

embeddings = TransformerWordEmbeddings('bert-base-multilingual-cased')
bert = Embeddings(embeddings, min_similarity=0, model_id="BERT")
tfidf = TFIDF(min_similarity=0)
edit = EditDistance()

string_models = [bert, tfidf, edit]
model = PolyFuzz(string_models)
model.match(from_list, to_list)

To access the results, we again can call get_matches but since we have multiple models we get back a dictionary of dataframes back.

In order to access the results of a specific model, call get_matches with the correct id:

>>> model.get_matches("BERT")
        From	    To          Similarity
0	apple	    apple	1.000000
1	apples	    apples	1.000000
2	appl	    apple	0.928045
3	recal	    apples	0.825268
4	house	    mouse	0.887524
5	similarity  mouse	0.791548

Finally, visualize the results to compare the models:

model.visualize_precision_recall(kde=True)

Custom Grouper

We can even use one of the polyfuzz.models to be used as the grouper in case you would like to use something else than the standard TF-IDF model:

model = PolyFuzz("TF-IDF")
model.match(from_list, to_list)

edit_grouper = EditDistance(n_jobs=1)
model.group(edit_grouper)

Custom Models

Although the options above are a great solution for comparing different models, what if you have developed your own? If you follow the structure of PolyFuzz's BaseMatcher
you can quickly implement any model you would like.

Below, we are implementing the ratio similarity measure from RapidFuzz.

import numpy as np
import pandas as pd
from rapidfuzz import fuzz
from polyfuzz.models import BaseMatcher


class MyModel(BaseMatcher):
    def match(self, from_list, to_list, **kwargs):
        # Calculate distances
        matches = [[fuzz.ratio(from_string, to_string) / 100 for to_string in to_list] 
                    for from_string in from_list]
        
        # Get best matches
        mappings = [to_list[index] for index in np.argmax(matches, axis=1)]
        scores = np.max(matches, axis=1)
        
        # Prepare dataframe
        matches = pd.DataFrame({'From': from_list,'To': mappings, 'Similarity': scores})
        return matches

Then, we can simply create an instance of MyModel and pass it through PolyFuzz:

custom_model = MyModel()
model = PolyFuzz(custom_model)

Citation

To cite PolyFuzz in your work, please use the following bibtex reference:

@misc{grootendorst2020polyfuzz,
  author       = {Maarten Grootendorst},
  title        = {PolyFuzz: Fuzzy string matching, grouping, and evaluation.},
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v0.2.2},
  doi          = {10.5281/zenodo.4461050},
  url          = {https://doi.org/10.5281/zenodo.4461050}
}

References

Below, you can find several resources that were used for or inspired by when developing PolyFuzz:

Edit distance algorithms:
These algorithms focus primarily on edit distance measures and can be used in polyfuzz.models.EditDistance:

• https://github.com/jamesturk/jellyfish
• https://github.com/ztane/python-Levenshtein
• https://github.com/seatgeek/fuzzywuzzy
• https://github.com/maxbachmann/rapidfuzz
• https://github.com/roy-ht/editdistance

Other interesting repos:

• https://github.com/ing-bank/sparse_dot_topn
  • Used in PolyFuzz for fast cosine similarity between sparse matrices