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 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
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()
fate.fit(X_train, Y_train)
Predictions can then be obtained using:
fate.predict(X_test)
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
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.
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}
}