معرفی شرکت ها
csrank-2.0.0rc2
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
Context-sensitive ranking
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | csrank-2.0.0rc2 |
| نام | csrank |
| نسخه کتابخانه | 2.0.0rc2 |
| نگهدارنده | ['Karlson Pfannschmidt'] |
| ایمیل نگهدارنده | ['kiudee@mail.upb.de'] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/kiudee/cs-ranking |
| آدرس اینترنتی | https://pypi.org/project/csrank/ |
| مجوز | - |
|Build Status| |Coverage| |Binder|
*******
CS-Rank
*******
CS-Rank is a Python package for context-sensitive ranking and choice
algorithms.
We implement the following new object ranking/choice architectures:
* FATE (First aggregate then evaluate)
* FETA (First evaluate then aggregate)
In addition, we also implement these algorithms for choice functions:
* RankNetChoiceFunction
* GeneralizedLinearModel
* PairwiseSVMChoiceFunction
These are the state-of-the-art approaches implemented for the discrete choice
setting:
* GeneralizedNestedLogitModel
* MixedLogitModel
* NestedLogitModel
* PairedCombinatorialLogit
* RankNetDiscreteChoiceFunction
* PairwiseSVMDiscreteChoiceFunction
Check out our `interactive notebooks`_ to quickly find out what our package can
do.
Getting started
===============
As a simple "Hello World!"-example we will try to learn the Pareto problem:
.. code-block:: python
import csrank as cs
from csrank import ChoiceDatasetGenerator
gen = ChoiceDatasetGenerator(dataset_type='pareto',
n_objects=30,
n_features=2)
X_train, Y_train, X_test, Y_test = gen.get_single_train_test_split()
All our learning algorithms are implemented using the scikit-learn estimator
API. Fitting our FATENet architecture is as simple as calling the ``fit``
method:
.. code-block:: python
fate = cs.FATEChoiceFunction()
fate.fit(X_train, Y_train)
Predictions can then be obtained using:
.. code-block:: python
fate.predict(X_test)
Installation
------------
The latest release version of CS-Rank can be installed from Github as follows::
pip install git+https://github.com/kiudee/cs-ranking.git
Another option is to clone the repository and install CS-Rank using::
python setup.py install
Dependencies
------------
CS-Rank depends on Tensorflow, Keras, NumPy, SciPy, matplotlib, scikit-learn,
scikit-optimize, joblib and tqdm. For data processing and generation you will
also need PyGMO, H5Py and pandas.
Citing CS-Rank
----------------
You can cite our `arXiv papers`_::
@article{csrank2019,
author = {Karlson Pfannschmidt and
Pritha Gupta and
Eyke H{\"{u}}llermeier},
title = {Learning Choice Functions: Concepts and Architectures },
journal = {CoRR},
volume = {abs/1901.10860},
year = {2019}
}
@article{csrank2018,
author = {Karlson Pfannschmidt and
Pritha Gupta and
Eyke H{\"{u}}llermeier},
title = {Deep architectures for learning context-dependent ranking functions},
journal = {CoRR},
volume = {abs/1803.05796},
year = {2018}
}
License
--------
`Apache License, Version 2.0 <https://github.com/kiudee/cs-ranking/blob/master/LICENSE>`_
.. |Binder| image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/kiudee/cs-ranking/master?filepath=docs%2Fnotebooks
.. |Coverage| image:: https://codecov.io/gh/kiudee/cs-ranking/branch/master/graph/badge.svg
:target: https://codecov.io/gh/kiudee/cs-ranking
.. |Build Status| image:: https://travis-ci.org/kiudee/cs-ranking.svg?branch=master
:target: https://travis-ci.org/kiudee/cs-ranking
.. _interactive notebooks: https://mybinder.org/v2/gh/kiudee/cs-ranking/master?filepath=docs%2Fnotebooks
.. _arXiv papers: https://arxiv.org/search/cs?searchtype=author&query=Pfannschmidt%2C+K
=======
History
=======
Unreleased
------------------
No changes yet.
1.2.1 (2020-06-08)
------------------
* Make all our optional dependencies mandatory to work around a bug in our
optional imports code. Without this, an exception is raised on import.
A proper fix will follow.
1.2.0 (2020-06-05)
------------------
* Change public interface of the learners to be more in line with the
scikit-learn interface (ongoing). As part of these changes, it is no longer
required to explicitly pass the data dimensionality to the learners on
initialization.
* Rewrite and document normalized discounted cumulative gain (ndcg) metric to
fix numerical issues.
See `#32 <https://github.com/kiudee/cs-ranking/issues/32>`__ for details.
* Fix passing fit keyword arguments on to the core network in
``FATEChoiceFunction``.
* Fix arguments for ``AllPositive`` baseline.
* Raise ValueError rather than silently using a default value for unknown
passed arguments.
* Internal efforts to increase code quality and make use of linting
(``black``, ``flake8``, ``doc8``).
* Remove old experimental code.
1.1.0 (2020-03-19)
------------------
* Add the expected reciprocal rank (ERR) metric.
* Fix bug in callbacks causing the wrong learning rate schedule to be applied.
* Make csrank easier to install by making some dependencies optional.
* Add guidelines for how to contribute to the project.
1.0.2 (2020-02-12)
------------------
* Fix deployment to GH-pages
1.0.1 (2020-02-03)
------------------
* Add ``HISTORY.rst`` file to track changes over time
* Set up travis-ci for deployment to PyPi
1.0.0 (2018-03-05)
------------------
* Initial release
نیازمندی
| مقدار | نام |
|---|---|
| >=2.7 | h5py |
| >=1.12.1 | numpy |
| >=0.19.0 | scipy |
| >=0.18.2 | scikit-learn |
| >=0.4 | scikit-optimize |
| >=0.22 | pandas |
| >=2.7 | h5py |
| >=0.6.0 | docopt |
| >=0.9.4 | joblib |
| >=4.11.2 | tqdm |
| >=2.3 | keras |
| <2.0,>=1.5 | tensorflow |
| >=2.7 | psycopg2-binary |
| >=2.7 | pygmo |
| >=3.8 | pymc3 |
| >=1.0 | theano |
| >=2.7 | psycopg2-binary |
| >=0.22 | pandas |
| >=2.7 | pygmo |
| >=3.8 | pymc3 |
| >=1.0 | theano |
نحوه نصب
نصب پکیج whl csrank-2.0.0rc2:
pip install csrank-2.0.0rc2.whl
نصب پکیج tar.gz csrank-2.0.0rc2:
pip install csrank-2.0.0rc2.tar.gz