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bof-0.3.5
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مشاهده بیشترتوضیحات
Bag of Factors allow you to analyze a corpus from its self_factors.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bof-0.3.5 |
| نام | bof |
| نسخه کتابخانه | 0.3.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Fabien Mathieu |
| ایمیل نویسنده | loufab@gmail.com |
| آدرس صفحه اصلی | https://github.com/balouf/bof |
| آدرس اینترنتی | https://pypi.org/project/bof/ |
| مجوز | GNU General Public License v3 |
==============
Bag of Factors
==============
.. image:: https://img.shields.io/pypi/v/bof.svg
:target: https://pypi.python.org/pypi/bof
:alt: PyPI Status
.. image:: https://github.com/balouf/bof/workflows/build/badge.svg?branch=master
:target: https://github.com/balouf/bof/actions?query=workflow%3Abuild
:alt: Build Status
.. image:: https://github.com/balouf/bof/workflows/docs/badge.svg?branch=master
:target: https://github.com/balouf/bof/actions?query=workflow%3Adocs
:alt: Documentation Status
.. image:: https://codecov.io/gh/balouf/bof/branch/master/graphs/badge.svg
:target: https://codecov.io/gh/balouf/bof/branch/master/graphs
:alt: Code Coverage
Bag of Factors allow you to analyze a corpus from its factors.
* Free software: GNU General Public License v3
* Documentation: https://balouf.github.io/bof/.
--------
Features
--------
Feature Extraction
-------------------
The `feature_extraction` module mimicks the module https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text
with a focus on character-based extraction.
The main differences are:
- it is slightly faster;
- the features can be incrementally updated;
- it is possible to fit only a random sample of factors to reduce space and computation time.
The main entry point for this module is the `CountVectorizer` class, which mimicks
its *scikit-learn* counterpart (also named `CountVectorizer`).
It is in fact very similar to sklearn's `CountVectorizer` using `char` or
`char_wb` analyzer option from that module.
Fuzz
--------
The `fuzz` module mimicks the fuzzywuzzy-like packages like
- fuzzywuzzy (https://github.com/seatgeek/fuzzywuzzy)
- rapidfuzz (https://github.com/maxbachmann/rapidfuzz)
The main difference is that the Levenshtein distance is replaced by the Joint Complexity distance. The API is also
slightly change to enable new features:
- The list of possible choices can be pre-trained (`fit`) to accelerate the computation in
the case a stream of queries is sent against the same list of choices.
- Instead of one single query, a list of queries can be used. Computations will be parallelized.
The main `fuzz` entry point is the `Process` class.
----------------
Getting Started
----------------
Look at examples from the reference_ section.
-------
Credits
-------
This package was created with Cookiecutter_ and the `francois-durand/package_helper_2`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`francois-durand/package_helper_2`: https://github.com/francois-durand/package_helper_2
.. _reference: https://balouf.github.io/bof/reference/index.html
=======
History
=======
---------------------------------------------------
0.3.5 (2021-04-08): ARM64
---------------------------------------------------
* Attempt to update PyPi with Mac M1 compatible wheels.
---------------------------------------------------
0.3.4 (2021-01-05): Cleaning
---------------------------------------------------
* Renaming process.py to fuzz.py to emphasize that the module aims at being an alternative to the fuzzywuzzy package.
* Removed modules FactorTree and JC. What they did is now essentially covered by the feature_extraction and fuzz
modules.
* General cleaning / rewriting of the documentation.
---------------------------------------------------
0.3.3 (2021-01-01): Cython/Numba balanced
---------------------------------------------------
* All core CountVectorizer methods ported to Cython. Roughly 2.5X faster than sklearn counterpart (mainly because some features like min_df/max_df are not implemented).
* Process numba methods NOT converted to Cython as Numba seems to be 20% faster for csr manipulation.
* Numba functions are cached to avoid compilation lag.
---------------------------------------------------
0.3.2 (2020-12-30): Going Cython
---------------------------------------------------
* First attempt to use Cython
* Right now only the fit_transform method of CountVectorizer has been cythonized, for testing wheels.
* If all goes well, numba will probably be abandoned and all the heavy-lifting will be in Cython.
-----------------------------------------------------
0.3.1 (2020-12-28): Simplification of core algorithm
-----------------------------------------------------
* Attributes of the CountVectorizer have been reduced to the minimum: one dict!
* Now faster than sklearn counterpart! (The reason been only one case is considered here so we can ditch a lot of checks and attributes).
---------------------------------------------------
0.3.0 (2020-12-15): CountVectorizer and Process
---------------------------------------------------
* The core is now the CountVectorizer class. Lighter and faster. Only features are kept inside.
* New process module inspired by fuzzywuzzy!
---------------------------------
0.2.0 (2020-12-15): Fit/Transform
---------------------------------
* Full refactoring to make the package fit/transform compliant.
* Add a fit_sampling method that allows to fit only a (random) subset of factors
---------------------------------
0.1.1 (2020-12-12): Upgrades
---------------------------------
* Docstrings added
* Common module (feat. save/load capabilities)
* Joint Complexity module
---------------------------------
0.1.0 (2020-12-12): First release
---------------------------------
* First release on PyPI.
* Core FactorTree class added.
نیازمندی
| مقدار | نام |
|---|---|
| - | dill |
| - | numba |
| - | numpy |
| - | scipy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl bof-0.3.5:
pip install bof-0.3.5.whl
نصب پکیج tar.gz bof-0.3.5:
pip install bof-0.3.5.tar.gz