Match - Probabilistic Entity Detection and Matching in Python

• Free software: MIT license
• Documentation: https://match.readthedocs.io.

Match brings common-sense entity detection and matching to python. Match is:

• Dead-simple to use
• Fast
• Lightweight (no heavy dependencies)
• Magic!

Installation

• TODO

Usage

Auto-detect common entity types

>>> import match


>>> match.detect_type('608-555-5555')
(1, 'phonenumber')

>>> match.detect_type('joe.van.gogh@example.com')
(1, 'email')

>>> match.detect_type('John R. Smith')
(.95, 'fullname')

>>> match.detect_type('Hi, how are you?')
(1, 'string')

>>> match.score_types('@squaredloss: match v0.2.0 is out!')
[(0, 'email'), (.05, 'fullname'), (0, 'phonenumber'), (1, 'string'), (0, 'datetime'), ...

Intelligently score similarity based on detected type

>>> match.score_similarity('Jonathan R. Smith', 'john r smith')
(.82, 'fullname') # Similar, but common name

>>> match.score_similarity('Jayden R. Smith', 'jayden r smith')
(.93, 'fullname') # Similar, but uncommon name, so higher match probability

>>> match.score_similarity('123 easy st, NY, NY', '123 Easy Street, New York City')
(.98, 'address')

>>> match.score_similarity('223 easy st, NY, NY', '123 easy st, NY, NY')
(.6, 'address') # Locations are close but unlikely to be the same physical place (barring a typo)

>>> match.score_similarity('Hi, how are you Joe?', 'hi how are you doing joe?')
(.81, 'string')

>>> match.score_similarity('608-555-5555', '608-555-5554', as_type='phonenumber')
.0

>>> match.score_similarity('608-555-5555', '608-555-5554', as_type='string')
.9

Parse normalized entity representations

# As string
>>> match.parse('(608) 555-5555')
('+1 608 555 5555', 'phonenumber')

>>> match.parse('6085555555')
('+1 608 555 5555', 'phonenumber')

# As object
>>> match.parse(' march 3rd, 1997', to_object=True)
(datetime.datetime(1997, 3, 3), 'datetime')

>>> match.parse_as(' march 3rd, 1997', 'email')
None

Probabilistic similarities, based on frequencies in a given corpus.

>>> from match import similarities
>>> import random


# Build similarity model from weighted random corpus of a's, b's, c's, and d's
>>> corpus = [''.join(random.sample('a'*10000 + ' '*10000 + 'b'*1000 + 'c'*100 + 'd'*10, k=10)) for _ in range(1000)]
>>> model = match.build_similarity_model(corpus, model_type='tfidf', tokenizer='2grams')
>>> model.similarity('ab ba c', 'ab ba d')
.6  # Lower similarity since 'a' is common

>>> model.similarity('db bd c', 'db bd a')
.8  # Higher similarity since 'd' is rare

# Use in high-level api
>>> match.score_similarity('db bd c', 'db bd a', similarity_measure=model)
.8


# Efficient similarity lookups with indexing (requires numpy and pandas, optional requirements)
>>> model.build_index() # Requires O(n*k) space, where n is number of docs and k is average doc length
>>> len(model.get_all_similar('db bd c', measure='overlap', threshold=.6))
48 # O(k) similarity search

Custom type detection and scoring

>>> from match.similarity import ProbabilisticDiceCoefficient


# Build similarity model from custom corpus
>>> corpus = ['cheddar', 'brie', 'guyere', 'mozzarella', 'parmesian', 'jack', 'colby']
>>> model = match.build_similarity_model(corpus, model_type='dice', tokenizer='3grams')
>>> match.add_type('cheese', similarity_model=model)
>>> match.detect_type('colby jack')
(.8, 'cheese')

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.