mindis/cs-ranking

Context-sensitive ranking and choice in Python with Tensorflow

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 paper:

@ARTICLE{csrank2018,
       author = {{Pfannschmidt}, K. and {Gupta}, P. and {H{\"u}llermeier}, E.},
        title = "{Deep architectures for learning context-dependent ranking functions}",
      journal = {ArXiv e-prints},
archivePrefix = "arXiv",
       eprint = {1803.05796},
 primaryClass = "stat.ML",
     keywords = {Statistics - Machine Learning, Computer Science - Information Retrieval, Computer Science - Learning, Computer Science - Neural and Evolutionary Computing},
         year = 2018,
        month = mar,
       adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180305796P},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

License

Apache License, Version 2.0