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hmni
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hmni

📛 Fuzzy Name Matching with Machine Learning

by Christopher Thornton

0.1.8 (see all)License:MIT
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HMNI

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Fuzzy name matching with machine learning. Perform common fuzzy name matching tasks including similarity scoring, record linkage, deduplication and normalization.

HMNI is trained on an internationally-transliterated Latin firstname dataset, where precision is afforded priority.

ModelAccuracyPrecisionRecallF1-Score
HMNI-Latin0.93930.92550.75480.8315

For an introduction to the methodology and research behind HMNI, please refer to my blog post.

Requirements

Python 3.5–3.8

  • tensorflow
  • scikit-learn
  • fuzzywuzzy
  • abydos
  • unidecode

QUICK USAGE GUIDE

Installation

Using PIP via PyPI

pip install hmni

Initialize a Matcher Object

import hmni
matcher = hmni.Matcher(model='latin')

Single Pair Similarity

matcher.similarity('Alan', 'Al')
# 0.6838303319889133

matcher.similarity('Alan', 'Al', prob=False)
# 1

matcher.similarity('Alan Turing', 'Al Turing', surname_first=False)
# 0.6838303319889133

Record Linkage

import pandas as pd

df1 = pd.DataFrame({'name': ['Al', 'Mark', 'James', 'Harold']})
df2 = pd.DataFrame({'name': ['Mark', 'Alan', 'James', 'Harold']})

merged = matcher.fuzzymerge(df1, df2, how='left', on='name')

Name Deduplication and Normalization

names_list = ['Alan', 'Al', 'Al', 'James']

matcher.dedupe(names_list, keep='longest')
# ['Alan', 'James']

matcher.dedupe(names_list, keep='frequent')
# ['Al, 'James']

matcher.dedupe(names_list, keep='longest', replace=True)
# ['Alan, 'Alan', 'Alan', 'James']

Matcher Parameters

hmni.Matcher(model='latin', prefilter=True, allow_alt_surname=True, allow_initials=True, allow_missing_components=True)

  • model (str) -- HMNI statistical model (latin by default)
  • prefilter (bool) -- Should the matcher prefilter unlikely candidates (True by default)
  • allow_alt_surname (bool) -- Should the matcher consider phonetic matching surnames e.g. Smith, Schmidt (True by default)
  • allow_initials (bool) -- Should the matcher consider names with initials (True by default)
  • allow_missing_components (bool) -- Should the matcher consider names with missing components (True by default)

Matcher Methods

similarity(name_a, name_b, prob=True, surname_first=False)

  • name_a (str) -- First name for comparison
  • name_b (str) -- Second name for comparison
  • prob (bool) -- If True return a predicted probability, else binary class label
  • threshold (float) -- Prediction probability threshold for positive match (0.5 by default)
  • surname_first (bool) -- If name strings start with surname (False by default)

fuzzymerge(df1, df2, how='inner', on=None, left_on=None, right_on=None, indicator=False, limit=1, threshold=0.5, allow_exact_matches=True, surname_first=False)

  • df1 (pandas DataFrame or named Series) -- First/Left object to merge with
  • df2 (pandas DataFrame or named Series) -- Second/Right object to merge with
  • how (str) -- Type of merge to be performed
    • inner (default): Use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys
    • left: Use only keys from left frame, similar to a SQL left outer join; preserve key order
    • right: Use only keys from right frame, similar to a SQL right outer join; preserve key order
    • outer: Use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically
  • on (label or list) -- Column or index level names to join on. These must be found in both DataFrames
  • left_on (label or list) -- Column or index level names to join on in the left DataFrame
  • right_on (label or list) -- Column or index level names to join on in the right DataFrame
  • indicator (bool) -- If True, adds a column to output DataFrame called “_merge” with information on the source of each row (False by default)
  • limit (int) -- Top number of name matches to consider (1 by default)
  • threshold (float) -- Prediction probability threshold for positive match (0.5 by default)
  • allow_exact_matches (bool) -- If True allow merging on exact name matches, else do not consider exact matches (True by default)
  • surname_first (bool) -- If name strings start with surname (False by default)

dedupe(names, threshold=0.5, keep='longest', reverse=True, limit=3, replace=False, surname_first=False)

  • names (list) -- List of names to dedupe
  • threshold (float) -- Prediction probability threshold for positive match (0.5 by default)
  • keep (str) -- Specifies method for keeping one of multiple alternative names
    • longest (default): Keeps longest name
    • frequent: Keeps most frequent name in names list
  • reverse (bool) -- If True will sort matches descending order, else ascending (True by default)
  • limit (int) -- Top number of name matches to consider (3 by default)
  • replace (bool) -- If True return normalized name list, else return deduplicated name list (False by default)
  • surname_first (bool) -- If name strings start with surname (False by default)

assign_similarity(name_a, name_b, score)

  • name_a (str) -- First name for similarity score assignment
  • name_b (str) -- Second name for similarity score assignment
  • score (float) -- Assigned similarity score for pair of names

Contributing

Pull requests are welcome. For developers wishing to build a model using Latin or non-Latin writing systems (Chinese, Cyrillic, Arabic), jupyter notebooks are shared in the dev folder to build models using similar methods.

License

MIT

GitHub Stars

137

LAST COMMIT

1yr ago

MAINTAINERS

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CONTRIBUTORS

1

OPEN ISSUES

3

OPEN PRs

1
VersionTagPublished
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2yrs ago
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