HMNI

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.

Model	Accuracy	Precision	Recall	F1-Score
HMNI-Latin	0.9393	0.9255	0.7548	0.8315

Installation

pip install hmni

Quick Usage Guide

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']

Requirements

• Python >=3.9
• numpy
• pandas
• torch
• joblib
• unidecode
• fuzzywuzzy
• editdistance
• abydos

License

MIT