Metadata-Version: 2.1
Name: ptype
Version: 0.2.2
Summary: Probabilistic type inference
Home-page: https://github.com/alan-turing-institute/ptype-dmkd
Author: Taha Ceritli, Christopher K. I. Williams, James Geddes, Roly Perera
Author-email: t.y.ceritli@sms.ed.ac.uk, ckiw@inf.ed.ac.uk, jgeddes@turing.ac.uk, rperera@turing.ac.uk
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: greenery
Requires-Dist: clevercsv

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============
Introduction
============

.. sectnum::

.. contents::

Type inference refers to the task of inferring the data type (e.g., Boolean, date, integer and string) of a given column of data, which becomes challenging in the presence of missing data and anomalies.

.. figure:: ../notes/motivation.png
    :width: 400

    Normal, missing and anomalous values are denoted by green, yellow and red, respectively in the right hand figure.

ptype_ is a probabilistic type inference model for tabular data, which aims to robustly infer the data type for each column in a table of data. By taking into account missing data and anomalies, ptype improves over the existing type inference methods. This repository provides an implementation of ptype in Python.

.. _ptype: https://link.springer.com/content/pdf/10.1007/s10618-020-00680-1.pdf

If you use this package, please cite ptype with the following BibTeX entry:

::

    @article{ceritli2020ptype,
      title={ptype: probabilistic type inference},
      author={Ceritli, Taha and Williams, Christopher KI and Geddes, James},
      journal={Data Mining and Knowledge Discovery},
      year={2020},
      volume = {34},
      number = {3},
      pages={870–-904},
      doi = {10.1007/s10618-020-00680-1},
    }

====================
Install requirements
====================

.. code:: bash

    pip install -r requirements.txt

=====
Usage
=====

See demo notebooks in ``notebooks`` folder. View them online via Binder_.

.. _Binder: https://mybinder.org/v2/gh/alan-turing-institute/ptype-dmkd/release?filepath=notebooks


