Skip to main content

Context-sensitive ranking

Project description

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:

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:

fate = cs.FATEChoiceFunction(n_object_features=2)
fate.fit(X_train, Y_train)

Predictions can then be obtained using:

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

History

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

csrank-1.1.0.tar.gz (124.2 kB view hashes)

Uploaded Source

Built Distribution

csrank-1.1.0-py2.py3-none-any.whl (216.0 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page