معرفی شرکت ها
dirty-cat-0.4.1
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Machine learning with dirty categories.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dirty-cat-0.4.1 |
| نام | dirty-cat |
| نسخه کتابخانه | 0.4.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Patricio Cerda |
| ایمیل نویسنده | patricio.cerda@inria.fr |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/dirty-cat/ |
| مجوز | BSD |
`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/main
.. |circleci| image:: https://img.shields.io/circleci/build/github/dirty-cat/dirty_cat/main?label=CircleCI
.. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
`dirty_cat <https://dirty-cat.github.io/>`_ is a Python library
that facilitates machine-learning on dirty categorical variables.
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]_.
If you like the package, please *spread the word*, and ⭐ `the repository <https://github.com/dirty-cat/dirty_cat/>`_!
What can `dirty_cat` do?
------------------------
`dirty_cat` provides tools (``TableVectorizer``, ``fuzzy_join``...) and
encoders (``GapEncoder``, ``MinHashEncoder``...) for **morphological similarities**,
for which we usually identify three common cases: **similarities, typos and variations**
`The first example notebook <https://dirty-cat.github.io/stable/auto_examples/01_dirty_categories.html>`_
goes in-depth on how to identify and deal with dirty data using the `dirty_cat` library.
What `dirty_cat` does not
~~~~~~~~~~~~~~~~~~~~~~~~~
`Semantic similarities <https://en.wikipedia.org/wiki/Semantic_similarity>`_
are currently not supported.
For example, the similarity between *car* and *automobile* is outside the reach
of the methods implemented here.
This kind of problem is tackled by
`Natural Language Processing <https://en.wikipedia.org/wiki/Natural_language_processing>`_
methods.
`dirty_cat` can still help with handling typos and variations in this kind of setting.
Installation
------------
dirty_cat can be easily installed via `pip`::
pip install dirty_cat
Dependencies
~~~~~~~~~~~~
Dependencies and minimal versions are listed in the `setup <https://github.com/dirty-cat/dirty_cat/blob/main/setup.cfg#L26>`_ file.
Related projects
----------------
Are listed on the `dirty_cat's website <https://dirty-cat.github.io/stable/#related-projects>`_
Contributing
------------
If you want to encourage development of `dirty_cat`,
the best thing to do is to *spread the word*!
If you encounter an issue while using `dirty_cat`, please
`open an issue <https://docs.github.com/en/issues/tracking-your-work-with-issues/creating-an-issue>`_ and/or
`submit a pull request <https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request>`_.
Don't hesitate, you're helping to make this project better for everyone!
Additional resources
--------------------
* `Introductory video (YouTube) <https://youtu.be/_GNaaeEI2tg>`_
* `Overview poster for EuroSciPy 2022 (Google Drive) <https://drive.google.com/file/d/1TtmJ3VjASy6rGlKe0txKacM-DdvJdIvB/view?usp=sharing>`_
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.
نیازمندی
| مقدار | نام |
|---|---|
| >=0.23.0 | scikit-learn |
| >=1.17.3 | numpy |
| >=1.4.0 | scipy |
| >=1.2.0 | pandas |
| - | requests |
| - | joblib |
| - | pyarrow |
| - | numpy |
| - | pandas |
| - | matplotlib |
| - | seaborn |
| - | tqdm |
| - | pytest |
| - | pytest-cov |
| - | pytest-xdist |
| - | pytest-xdist[psutil] |
| - | coverage |
| - | mypy |
| - | numpydoc |
| - | flake8 |
| - | openml |
| - | sphinx-gallery |
| - | sphinxext-opengraph |
| - | matplotlib |
| - | seaborn |
| - | statsmodels |
| ==1.0.2 | scikit-learn |
| ==1.21.3 | numpy |
| ==1.8.0 | scipy |
| ==1.3.5 | pandas |
| ==0.23.0 | scikit-learn |
| ==1.17.3 | numpy |
| ==1.4.0 | scipy |
| ==1.2.0 | pandas |
| ==0.24.0 | scikit-learn |
| ==1.19.3 | numpy |
| ==1.6.0 | scipy |
| ==1.2.0 | pandas |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl dirty-cat-0.4.1:
pip install dirty-cat-0.4.1.whl
نصب پکیج tar.gz dirty-cat-0.4.1:
pip install dirty-cat-0.4.1.tar.gz