regexmodel 0.1.0

Package modeling structured strings with regex.

regexmodel

Regexmodel is a python package that uses a graph model to fit and synthesize structured strings. Structured strings are strings such as license plates, credit card numbers ip-addresses, and phone numbers. Regexmodel can infer a regex-like structure from a series of positive examples and create new samples (such as phone numbers etc.).

Features:

• Draw new synthetic values
• Only on the numpy and polar libraries (faker for benchmarks).
• Fast (on average < 1 second for about 500 positive examples).
• Can provide statistics on how good the regexmodel has fit your values using log likelihood.
• Can be serialized and can be modified by hand.

Installation

You can install regexmodel using pip:

pip install git+https://github.com/sodascience/regexmodel.git

If you want to run the benchmarks, you should also install the faker package:

pip install faker

Using regexmodel

Fitting the regexmodel is as simple as:

from regexmodel import RegexModel

model = RegexModel.fit(your_values_to_fit, count_thres=10)

The count_thres parameter changes how detailed and time consuming the fit is. A higher threshold means a shorter time to fit, but also a worse fit.

Then synthesizing a new value is done with:

model.draw()

Serialization

The regex model can be serialized so that it can be stored in for example a JSON file:

import json
with open(some_file, "w") as handle:
    json.dump(model.serialize(), handle)

And deserialized:

with open(some_file, "r") as handle:
    model = RegexModel(json.load(handle))