https://github.com/kiudee/cs-ranking

Context-sensitive ranking and choice in Python with PyTorch
https://github.com/kiudee/cs-ranking

choice-model context-aware deep-learning discrete-choice learning-to-rank machine-learning neural-networks object-ranking pytorch ranking tensorflow

Last synced: 3 months ago
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Context-sensitive ranking and choice in Python with PyTorch

• Host: GitHub
• URL: https://github.com/kiudee/cs-ranking
• Owner: kiudee
• License: apache-2.0
• Created: 2018-02-09T19:21:15.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2023-02-11T00:36:22.000Z (over 1 year ago)
• Last Synced: 2024-06-06T18:16:48.683Z (5 months ago)
• Topics: choice-model, context-aware, deep-learning, discrete-choice, learning-to-rank, machine-learning, neural-networks, object-ranking, pytorch, ranking, tensorflow
• Language: Python
• Homepage: https://cs-ranking.readthedocs.io
• Size: 28.6 MB
• Stars: 67
• Watchers: 9
• Forks: 15
• Open Issues: 33
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE

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README

        
|Coverage| |Binder|

****

NOTE

****

This library has recently been migrated from tensorflow to PyTorch. The 2.0

version marks a breaking change. Some of the previous functionality is now

unavailable and some classes behave differently. You can use the latest 1.x

release if you are looking for the tensorflow based estimators.

*******

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

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 PyTorch, skorch, NumPy, SciPy, matplotlib, scikit-learn,

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 `_

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.. _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