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Buildbot: http://buildbot.shogun-toolbox.org/waterfall.

Quick links to this file:

• Introduction
• Interfaces
• Platforms
• Contents
• Applications
• Download
• License
• Contributions
• References

Other links that may be useful:

• See QUICKSTART for first steps on installation and running SHOGUN.
• See README_developer for the developer documentation.
• See README_data for how to download example data sets accompanying SHOGUN.
• See README_cmake for setting particular build options with SHOGUN and cmake.

The Shogun Machine learning toolbox provides a wide range of unified and efficient Machine Learning (ML) methods. The toolbox seamlessly allows to easily combine multiple data representations, algorithm classes, and general purpose tools. This enables both rapid prototyping of data pipelines and extensibility in terms of new algorithms. We combine modern software architecture in C++ with both efficient low-level computing backends and cutting edge algorithm implementations to solve large-scale Machine Learning problems (yet) on single machines.

One of Shogun's most exciting features is that you can use the toolbox through a unified interface from C++, Python, Octave, R, Java, Lua, C#, etc. This not just means that we are independent of trends in computing languages, but it also lets you use Shogun as a vehicle to expose your algorithm to multiple communities. We use SWIG to enable bidirectional communication between C++ and target languages. Shogun runs under Linux/Unix, MacOS, Windows.

Originally focussing on large-scale kernel methods and bioinformatics (for a list of scientific papers mentioning Shogun, see here), the toolbox saw massive extensions to other fields in recent years. It now offers features that span the whole space of Machine Learning methods, including many classical methods in classification, regression, dimensionality reduction, clustering, but also more advanced algorithm classes such as metric, multi-task, structured output, and online learning, as well as feature hashing, ensemble methods, and optimization, just to name a few. Shogun in addition contains a number of exclusive state-of-the art algorithms such as a wealth of efficient SVM implementations, Multiple Kernel Learning, kernel hypothesis testing, Krylov methods, etc. All algorithms are supported by a collection of general purpose methods for evaluation, parameter tuning, preprocessing, serialisation & I/O, etc; the resulting combinatorial possibilities are huge. See our feature list for more details.

The wealth of ML open-source software allows us to offer bindings to other sophisticated libraries including: LibSVM/LibLinear, SVMLight, LibOCAS, libqp, VowpalWabbit, Tapkee, SLEP, GPML and more. See our list of integrated external libraries.

Shogun got initiated in 1999 by Soeren Sonnenburg and Gunnar Raetsch (that's where the name ShoGun originates from). It is now developed by a much larger Team cf. website and AUTHORS, and would not have been possible without the patches and bug reports by various people. See CONTRIBUTIONS for a detailed list. Statistics on Shogun's development activity can be found on ohloh.

SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave, Java, C#, Ruby, Lua and Python.

The following table depicts the status of each interface available in SHOGUN:

Visit http://www.shogun-toolbox.org/doc/en/current for further information.

Debian GNU/Linux, Mac OSX and WIN32/CYGWIN are supported platforms (see the QUICKSTART file for generic installation instructions).

The following directories are found in the source distribution.

• src - source code.
• data - data sets (required for some examples / applications - these need to be downloaded separately via the download site or git submodule update --init from the root of the git checkout.
• doc - documentation (to be built using doxygen), ipython notebooks, and the PDF tutorial.
• examples - example files for all interfaces.
• applications - applications of SHOGUN.
• benchmarks - speed benchmarks.
• tests - unit and integration tests.
• cmake - cmake build scripts

We have successfully used this toolbox to tackle the following sequence analysis problems: Protein Super Family classification[6], Splice Site Prediction [8, 13, 14], Interpreting the SVM Classifier [11, 12], Splice Form Prediction [8], Alternative Splicing [9] and Promotor Prediction [15]. Some of them come with no less than 10 million training examples, others with 7 billion test examples.

SHOGUN can be downloaded from http://www.shogun-toolbox.org and GitHub at https://github.com/shogun-toolbox/shogun.

Except for the files classifier/svm/Optimizer.{cpp,h}, classifier/svm/SVM_light.{cpp,h}, regression/svr/SVR_light.{cpp,h} and the kernel caching functions in kernel/Kernel.{cpp,h} which are (C) Torsten Joachims and follow a different licensing scheme (cf. LICENSE_SVMlight) SHOGUN is generally licensed under the GPL version 3 or any later version (cf. LICENSE) with code borrowed from various GPL compatible libraries from various places (cf. CONTRIBUTIONS). See also LICENSE_msufsort and LICENSE_tapkee.

We include numerous lines of code from various standalone free libraries including the following:

• LibSVM by Chih-Chung Chang and Chih-Jen Lin
• LibLinear by Xiang-Rui Wang and Chih-Jen Lin
• SLEP by J. Liu, S. Ji and J. Ye
• MALSAR by J. Zhou, J. Chen and J. Y
• LaRank by A. Bordes
• GPBT by T. Serafini, L. Zanni and G. Zanghirati
• LibOCAS by V. Franc and S. Sonnenburg
• SVMLin by V. Sindhwani
• SGDSVM by Leon Bottou
• Vowpal Wabbit by John Langford
• Cover Tree for Nearest Neighbour calculation by John Langford
• GPML by Carl Edward Rasmussen and Hannes Nickisch

as well as take inspiration from many other libraries:

• LMNN by Kilian Q. Weinberger
• Hidden Markov Support Vector Machines by Georg Zeller, Gunnar Raetsch and Pramod Mudrakarta
• Matlab Toolbox for Dimensionality Reduction by Laurens van der Maaten

Please let us know if we missed your name in this page, we will do our best to acknowledge your contributions.

Papers including "Shogun Machine Learning Toolbox"

[1] C. Cortes and V.N. Vapnik. Support-vector networks. Machine Learning, 20(3):273--297, 1995.

[2] J. Liu, S. Ji, and J. Ye. SLEP: Sparse Learning with Efficient Projections. Arizona State University, 2009. http://www.public.asu.edu/~jye02/Software/SLEP.

[3] C.C. Chang and C.J. Lin. Libsvm: Introduction and benchmarks. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 2000.

[4] T. Joachims. Making large-scale SVM learning practical. In B.~Schoelkopf, C.J.C. Burges, and A.J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169--184, Cambridge, MA, 1999. MIT Press.

[5] A.Zien, G.Raetsch, S.Mika, B.Schoelkopf, T.Lengauer, and K.-R. Mueller. Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites. Bioinformatics, 16(9):799-807, September 2000.

[6] T.S. Jaakkola and D.Haussler.Exploiting generative models in discriminative classifiers. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Advances in Neural Information Processing Systems, volume 11, pages 487-493, 1999.

[7] K.Tsuda, M.Kawanabe, G.Raetsch, S.Sonnenburg, and K.R. Mueller. A new discriminative kernel from probabilistic models. Neural Computation, 14:2397--2414, 2002.

[8] C.Leslie, E.Eskin, and W.S. Noble. The spectrum kernel: A string kernel for SVM protein classification. In R.B. Altman, A.K. Dunker, L.Hunter, K.Lauderdale, and T.E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing, pages 564-575, Kaua'i, Hawaii, 2002.

[9] G.Raetsch and S.Sonnenburg. Accurate Splice Site Prediction for Caenorhabditis Elegans, pages 277-298. MIT Press series on Computational Molecular Biology. MIT Press, 2004.

[10] G.Raetsch, S.Sonnenburg, and B.Schoelkopf. RASE: recognition of alternatively spliced exons in c. elegans. Bioinformatics, 21:i369--i377, June 2005.

[11] S.Sonnenburg, G.Raetsch, and B.Schoelkopf. Large scale genomic sequence SVM classifiers. In Proceedings of the 22nd International Machine Learning Conference. ACM Press, 2005.

[12] S.Sonnenburg, G.Raetsch, and C.Schaefer. Learning interpretable SVMs for biological sequence classification. In RECOMB 2005, LNBI 3500, pages 389-407. Springer-Verlag Berlin Heidelberg, 2005.

[13] G.Raetsch, S.Sonnenburg, and C.Schaefer. Learning Interpretable SVMs for Biological Sequence Classification. BMC Bioinformatics, Special Issue from NIPS workshop on New Problems and Methods in Computational Biology Whistler, Canada, 18 December 2004, 7:(Suppl. 1):S9, March 2006.

[14] S. Sonnenburg. New methods for splice site recognition. Master's thesis, Humboldt University, 2002. Supervised by K.R. Mueller H.D. Burkhard and G. Raetsch.

[15] S. Sonnenburg, G. Raetsch, A. Jagota, and K.R. Mueller. New methods for splice-site recognition. In Proceedings of the International Conference on Artifical Neural Networks, 2002. Copyright by Springer.

[16] S. Sonnenburg, A. Zien, and G. Raetsch. ARTS: Accurate Recognition of Transcription Starts in Human. 2006.

[17] S. Sonnenburg, G. Raetsch, C.Schaefer, and B.Schoelkopf, Large Scale Multiple Kernel Learning, Journal of Machine Learning Research, 2006, K.Bennett and E.P. Hernandez Editors.