ptype 0.2.17
Probabilistic type inference
License: MIT
Requires Python: >=3.6.0


.. image:: https://github.com/alan-turing-institute/ptype/workflows/build-publish/badge.svg?branch=release
    :target: https://github.com/alan-turing-institute/ptype/actions?query=workflow%3Abuild-publish+branch%3Arelease
    :alt: build-publish on release

.. image:: https://github.com/alan-turing-institute/ptype/workflows/build/badge.svg?branch=develop
    :target: https://github.com/alan-turing-institute/ptype/actions?query=workflow%3Abuild+branch%3Adevelop
    :alt: build on develop

.. image:: https://badge.fury.io/py/ptype.svg
    :target: https://badge.fury.io/py/ptype
    :alt: PyPI version

.. image:: https://readthedocs.org/projects/ptype/badge/?version=stable
    :target: https://ptype.readthedocs.io/en/stable/
    :alt: Documentation status

.. image:: https://pepy.tech/badge/ptype
    :target: https://pepy.tech/project/ptype
    :alt: Downloads

.. image:: https://mybinder.org/badge_logo.svg
    :target: https://mybinder.org/v2/gh/alan-turing-institute/ptype/release?filepath=notebooks
    :alt: Binder

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

.. sectnum::

.. contents::

This repository provides the source code of a Python package for ptype and its extension ptype-cat.

+++++++++++++++
ptype
+++++++++++++++
ptype is a probabilistic approach to *type inference*, which is the task of identifying the data type (e.g. Boolean, date, integer or string) of a given column of data.

Existing approaches often fail on type inference for messy datasets where data is missing or anomalous. With ptype_, our goal is to develop a robust method that can deal with such data.

.. figure:: https://raw.githubusercontent.com/alan-turing-institute/ptype/release/notes/motivation.png
    :width: 400

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

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

ptype uses `Probabilistic Finite-State Machines`_ (PFSMs) to model known data types, missing and anomalous data. Given a column of data, we can infer a plausible column type, and also identify any values which (conditional on that type) are deemed missing or anomalous. In contrast to more familiar finite-state machines, such as regular expressions, that either accept or reject a given data value, PFSMs assign probabilities to different values. They therefore offer the advantage of generating weighted predictions when a column of messy data is consistent with more than one type assignment.

.. _`Probabilistic Finite-State Machines`: https://en.wikipedia.org/wiki/Probabilistic_automaton

If you use this package, please cite the `ptype paper`_, using the following BibTeX entry:

.. _`ptype paper`: http://doi.org/10.1007/s10618-020-00680-1

::

    @article{ceritli2020ptype,
      title={ptype: probabilistic type inference},
      author={Ceritli, Taha and Williams, Christopher K I 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},
    }

+++++++++++++++
ptype-cat
+++++++++++++++
A weakness of ptype is that it does not handle well type inference for
categorical variables which are non-Boolean.  For example, most
existing methods including ptype treat the "Class Name" and "Rating"
columns in the example below as string and integer types respectively,
rather than categoricals. Therefore the user needs to manually
convert their assigned types.

.. figure:: https://raw.githubusercontent.com/alan-turing-institute/ptype/release/notes/motivation-ptype-cat.png
    :width: 600

    The data on the left-hand side are sampled from `a dataset about clothing`_.

To (semi-)automate this manual task, we introduce ptype-cat, which is
an extension of ptype to enable detection of the general categorical
type, including the non-Boolean categorical variables. ptype-cat
combines the output of ptype with additional features such as the
number of unique values in a column, and runs a Logistic Regression
classifier to determine whether a column denotes a categorical
variable or not when a column is labeled with the integer or string
type by ptype.

Please see the `ptype-cat paper`_ for the details of ptype-cat, for which you can use
the following BibTeX entry to cite:

.. _`ptype-cat paper`: https://www.google.com/url?q=https%3A%2F%2Fupvedues-my.sharepoint.com%2F%3Ab%3A%2Fg%2Fpersonal%2Fjorallo_upv_edu_es%2FER3GUrAraHRJmzdvbnJ75LgBB1fgbwHlWAbdcxjxMij-vQ%3Fe%3DfUOZ9K&sa=D&sntz=1&usg=AFQjCNH-zU7Vk-_I5M6MFaXL6-nn72zYFQ
.. _`a dataset about clothing`: https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews

::

    @inproceedings{ptype-cat,
      title={ptype-cat: Inferring the Type and Values of Categorical Variables},
      author={Ceritli, Taha and Williams, Christopher K I},
      booktitle={21st ECML-PKDD Automating Data Science Workshop},
      year={2021},
    }

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

You can simply install ptype from PyPI:

.. code:: bash

    pip install ptype

=====
Usage
=====

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

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