Metadata-Version: 1.2
Name: dirty_cat
Version: 0.3.0
Summary: Machine learning with dirty categories.
Home-page: http://dirty-cat.github.io/
Author: Patricio Cerda
Author-email: patricio.cerda@inria.fr
License: BSD
Description: dirty_cat
        =========
        
        .. image:: https://dirty-cat.github.io/stable/_static/dirty_cat.svg
           :align: center
           :alt: dirty_cat logo
        
        
        |py_ver| |pypi_var| |pypi_dl| |codecov| |circleci| |Black|
        
        .. |py_ver| image:: https://img.shields.io/pypi/pyversions/dirty_cat
        .. |pypi_var| image:: https://img.shields.io/pypi/v/dirty_cat?color=informational
        .. |pypi_dl| image:: https://img.shields.io/pypi/dm/dirty_cat
        .. |codecov| image:: https://img.shields.io/codecov/c/github/dirty-cat/dirty_cat/master
        .. |circleci| image:: https://img.shields.io/circleci/build/github/dirty-cat/dirty_cat/master?label=CircleCI
        .. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
        
        dirty_cat is a Python module for machine-learning on dirty categorical variables.
        
        Website: https://dirty-cat.github.io/
        
        dirty_cat's SuperVectorizer automatically turns pandas data frames into
        numerical arrays suitable for learning.
        
        For a detailed description of the problem of encoding dirty categorical data,
        see `Similarity encoding for learning with dirty categorical variables
        <https://hal.inria.fr/hal-01806175>`_ [1]_ and `Encoding high-cardinality string categorical variables
        <https://hal.inria.fr/hal-02171256v4>`_ [2]_.
        
        Installation
        ------------
        
        Dependencies
        ~~~~~~~~~~~~
        
        dirty_cat requires:
        
        - Python (>= 3.8)
        - NumPy (>= 1.17.3)
        - SciPy (>= 1.4.0)
        - scikit-learn (>= 0.23.0)
        - pandas (>= 1.2.0)
        
        User installation
        ~~~~~~~~~~~~~~~~~
        
        If you already have a working installation of NumPy and SciPy,
        the easiest way to install dirty_cat is using ``pip`` ::
        
            pip install -U --user dirty_cat
        
        Other implementations
        ~~~~~~~~~~~~~~~~~~~~~~
        
        -  Spark ML: https://github.com/rakutentech/spark-dirty-cat
        
        
        References
        ~~~~~~~~~~
        
        .. [1] Patricio Cerda, Gaël Varoquaux, Balázs Kégl. Similarity encoding for learning with dirty categorical variables. 2018. Machine Learning journal, Springer.
        .. [2] Patricio Cerda, Gaël Varoquaux. Encoding high-cardinality string categorical variables. 2020. IEEE Transactions on Knowledge & Data Engineering.
        
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.8
