Citations#

If you use PyChemAuth in a publication, please cite the appropriate version (most recent is linked below).

PyChemAuth contains both original code and wrappers around other packages and thus relies on contributions from many other sources. If you use these tools be sure to cite the original authors.

Code#

If you use the Kennard-Stone features in PyChemAuth please cite the original authors:

@misc{kennard-stone,
title={kennard-stone},
author={yu9824},
year={2021},
howpublished={\url{https://github.com/yu9824/kennard_stone}},
}

If you use UMAP refer to the authors’ github repo for information about citation. At the very least, you should cite the manuscript associated with the software itself:

@article{mcinnes2018umap-software,
title={UMAP: Uniform Manifold Approximation and Projection},
author={McInnes, Leland and Healy, John and Saul, Nathaniel and Grossberger, Lukas},
journal={The Journal of Open Source Software},
volume={3},
number={29},
pages={861},
year={2018}
}

If you use PyOD be sure to cite:

@article{zhao2019pyod,
author  = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
title   = {PyOD: A Python Toolbox for Scalable Outlier Detection},
journal = {Journal of Machine Learning Research},
year    = {2019},
volume  = {20},
number  = {96},
pages   = {1-7},
url     = {http://jmlr.org/papers/v20/19-011.html}
}

Refer to several citations for SHAP on the authors’ website, but at a minimum be sure to cite:

@incollection{NIPS2017_7062,
title = {A Unified Approach to Interpreting Model Predictions},
author = {Lundberg, Scott M and Lee, Su-In},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {4765--4774},
year = {2017},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf}
}

The imbalanced-learn package should be cited as:

@article{JMLR:v18:16-365,
author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
journal = {Journal of Machine Learning Research},
year    = {2017},
volume  = {18},
number  = {17},
pages   = {1-5},
url     = {http://jmlr.org/papers/v18/16-365.html}
}

If you use any Keras models, be sure to cite:

@misc{chollet2015keras,
title={Keras},
author={Chollet, Fran\c{c}ois and others},
year={2015},
howpublished={\url{https://keras.io}},
}

PyChemAuth is configured to use the `tensorflow <>`_ backend of Keras, so if you use Keras please also cite:

@misc{tensorflow2015-whitepaper,
title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={https://www.tensorflow.org/},
note={Software available from tensorflow.org},
author={
   Mart\'{i}n~Abadi and
   Ashish~Agarwal and
   Paul~Barham and
   Eugene~Brevdo and
   Zhifeng~Chen and
   Craig~Citro and
   Greg~S.~Corrado and
   Andy~Davis and
   Jeffrey~Dean and
   Matthieu~Devin and
   Sanjay~Ghemawat and
   Ian~Goodfellow and
   Andrew~Harp and
   Geoffrey~Irving and
   Michael~Isard and
   Yangqing Jia and
   Rafal~Jozefowicz and
   Lukasz~Kaiser and
   Manjunath~Kudlur and
   Josh~Levenberg and
   Dandelion~Man\'{e} and
   Rajat~Monga and
   Sherry~Moore and
   Derek~Murray and
   Chris~Olah and
   Mike~Schuster and
   Jonathon~Shlens and
   Benoit~Steiner and
   Ilya~Sutskever and
   Kunal~Talwar and
   Paul~Tucker and
   Vincent~Vanhoucke and
   Vijay~Vasudevan and
   Fernanda~Vi\'{e}gas and
   Oriol~Vinyals and
   Pete~Warden and
   Martin~Wattenberg and
   Martin~Wicke and
   Yuan~Yu and
   Xiaoqiang~Zheng},
year={2015},
}

If you use “DIME” to perform out-of-distribution detection on a neural network model, please cite:

@misc{sjogren2021outofdistribution,
title = {Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embedding},
author = {Rickard Sjögren and Johan Trygg},
year = {2021},
eprint = {2108.10673},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}

If you use visualkeras to visualize any Keras models, please cite:

@misc{Gavrikov2020VisualKeras,
author = {Gavrikov, Paul},
title = {visualkeras},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/paulgavrikov/visualkeras}},
}

If you use pyts to “image” series, or in any other way, please cite:

@article{JMLR:v21:19-763,
author  = {Johann Faouzi and Hicham Janati},
title   = {pyts: A Python Package for Time Series Classification},
journal = {Journal of Machine Learning Research},
year    = {2020},
volume  = {21},
number  = {46},
pages   = {1-6},
url     = {http://jmlr.org/papers/v21/19-763.html}
}

Refer to the PU Learn website for citation and credit attribution for positive and unlabeled learning.

Refer to the sklearn-som website for citation and credit attribution for Kohonen Self-Organizing Maps.

Data#

Example data used in this repository comes from several sources; refer to the documentation for each data loader (e.g., load_pgaa()) for the appropriate citation(s).

Citations

Contents

Citations#

Code#

Data#