Develop branch build status:
Buildbot: http://buildbot.shogun-toolbox.org/waterfall.
Quick links to this file:
- Quickstart
- Introduction
- Interfaces
- Platforms
- Contents
- Applications
- Download
- License
- Contributions
- References
Other links that may be useful:
- See INSTALL 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 machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) [1]. It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art LibSVM [2] and SVMlight [3]. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved [4], Fischer [5], TOP [6], Spectrum [7], Weighted Degree Kernel (with shifts) [8, 9, 10]. For the latter the efficient LINADD [10] optimizations are implemented. Also SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the combined kernel which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning [11, 12, 16]. Currently SVM 2-class classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to train hidden markov models. The input feature-objects can be dense, sparse or strings, and of types int/short/double/char. In addition, they can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing.
Shogun got initiated by Soeren Sonnenburg and Gunnar Raetsch (thats where the name ShoGun originates from). It is now developed by a much larger Team cf. AUTHORS and would not have been possible without the patches and bug reports by various people and by the various authors of other machine learning packages that we utilize. See CONTRIBUTIONS for a detailled list.
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:
| Interface | Status |
|---|---|
| python_modular | mature (no known problems) |
| octave_modular | mature (no known problems) |
| java_modular | stable (no known problems; not all examples are ported) |
| ruby_modular | stable (no known problems; only few examples ported) |
| csharp_modular | stable (no known problems; not all examples are ported) |
| lua_modular | alpha (some examples work, string typemaps are unstable |
| perl_modular | pre-alpha (work in progress quality) |
| r_modular | pre-alpha (SWIG does not properly handle reference counting and thus only for the brave, --disable-reference-counting to get it to work, but beware that it will leak memory; disabled by default) |
| octave_static | mature (no known problems) |
| matlab_static | mature (no known problems) |
| python_static | mature (no known problems) |
| r_static | mature (no known problems) |
| libshogun_static | mature (no known problems) |
| cmdline_static | stable (but some data types incomplete) |
| elwms_static | this is the eierlegendewollmilchsau interface, a chimera that in one file interfaces with python, octave, r, matlab and provides the run_python command to run code in python using the in octave,r,matlab available variables, etc) |
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 INSTALL file for generic and platform specific 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 --initfrom 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.
[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.