Shogun - A Large Scale Machine Learning Toolbox

This is the official homepage of the SHOGUN machine learning toolbox.

SHOGUN Logo

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 OCAS [21], Liblinear [20], LibSVM [2], SVMLight, [3] SVMLin [4] and GPDT [5]. 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 [6], Fischer [7], TOP [8], Spectrum [9], Weighted Degree Kernel (with shifts) [10] [11] [12]. For the latter the efficient LINADD [12] optimizations are implemented. For linear SVMs the COFFIN framework [22][23] allows for on-demand computing feature spaces on-the-fly, even allowing to mix sparse, dense and other data types. Furthermore, 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 [13] [14] [18] [19]. Currently SVM one-class, 2-class and multiclass 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 type int/short/double/char and 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 is implemented in C++ and interfaces to Matlab(tm), R, Octave and Python and is proudly released as Machine Learning Open Source Software.

Screenshots

As everyone likes screenshots, we have produced one for each interface: SHOGUN with Octave, Matlab, Python and R. Click on the link for higher resolution images.

Octave Demo Matlab Demo Python Demo R Demo

Getting Started

Applications

We have successfully used this toolbox to tackle the following sequence analysis problems: Protein Super Family classification, Splice Site Prediction [10] [15] [16], Interpreting the SVM Classifier [13] [14], Splice Form Prediction [10], Alternative Splicing [11] and Promotor Prediction [17]. Some of them come with no less than 10 million training examples, others with 7 billion test examples.

Licensing Information

Except for SVMLight which is (C) Torsten Joachims and follows a different licensing scheme (cf. LICENSE.SVMLight in the tar achive) SHOGUN is licensed under the GPL version 3 or any later version (cf. LICENSE). GPLv3 Logo

Cite us

If you use SHOGUN in your research you are kindly asked to cite the following paper:

Soeren Sonnenburg, Gunnar Raetsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien,
Fabio de Bona, Alexander Binder, Christian Gehl, and Vojtech Franc.
The SHOGUN Machine Learning Toolbox. Journal of Machine Learning Research, 11:1799-1802, June 2010.

Download Releases

SHOGUN Version 0.9.3 (libshogun 8.0, libshogunui 5.0)

(updated 31.05.2010) Older Versions

This release contains several enhancements, cleanups and bugfixes:

  • Features:
    • Experimental lp-norm MCMKL
    • New Kernels: SpectrumRBFKernelRBF, SpectrumMismatchRBFKernel, WeightedDegreeRBFKernel
    • WDK kernel supports amino acids
    • String Features now support append operations (and creation of
    • python-dbg support
    • Allow floats as input for custom kernel (and matrices > 4GB in size)
  • Bugfixes:
    • Static linking fix.
    • Fix sparse linear kernel's add_to_normal
  • Cleanup and API Changes:
    • Remove init() function in Performance Measures
    • Adjust .so suffix for python and use python distutils to figure out install paths

Documentation and Examples

We use Doxygen for both user and developer documentation which may be read online here. More than 600 documented examples for the interfaces python_modular, octave_modular, r_modular, static python, static matlab and octave, static r, static command line and C++ libshogun developer interface can be found in the online documentation. In addition, examples are shipped in the examples/(un)documented/[interface] directory in the source code (where interface is one of r, octave, matlab, python, python_modular, r_modular, octave_modular, cmdline, libshogun).

English

Chinese
Note that documentation for python-modular is most complete and also that python's help function will show the documentation when working interactively:

$ python
Python 2.4.4 (#2, Jan  3 2008, 13:36:28) 
[GCC 4.2.3 20071123 (prerelease) (Debian 4.2.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from shogun.Classifier import SVM
>>> help(SVM)

class SVM(CSVM)
 |  Method resolution order:
 |      SVM
 |      CSVM
 |      CKernelMachine
 |      Classifier
 |      SGObject
 |      __builtin__.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, kernel, alphas, support_vectors, b)
[...]
Below we provide some of the (in the meantime outdated) examples that were used to carry out experiments for a number of publications. Note that more than 600 examples and updated versions of all of these can also be found in the source code and in the online documentation.

Click on the corresponding link to see classification and regression examples for Matlab(tm), R, Octave or Python:

Below one finds some Bioinformatics examples (for octave and matlab) as presented at BOSC 2006:

Multiple Kernel Learning examples (JMLR 2006 paper "Large Scale Multiple Kernel Learning"):

Bug-Reports, Mailinglist and Contact

In case you find bugs or have feature requests, file them using the SHOGUN-TRAC bug tracking system. We are coordinating development (milestones, roadmap) using trac. Also if you would like to browse syntax hilighted source from svn, just have a look.

In case of comments, problems, questions, bug-reports etc. please use the mailing list (subscription required)

In case you need to directly get in touch with us, feel free to contact

Developer Information

Want to contribute ? We maintain SHOGUNs source code via SVN

Acknowlegements

The authors gratefully acknowledge the support of DFG grant MU 987/2-1, MU 987/6-1, RA-1894/1-1 and the PASCAL Network of Excellence.

References

[1]C.Cortes and V.N. Vapnik. Support-vector networks. Machine Learning, 20(3):273--297, 1995.
[2]C.-C. Chang and C.-J. Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
[3]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.
[4] V. Sindhwani, S. S. Keerthi. Large Scale Semi-supervised Linear SVMs. SIGIR, 2006.
[5] L. Zanni, T. Serafini, G. Zanghirati. Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems. JMLR 7(Jul), 1467-1492, 2006.
[6]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.
[7]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.
[8]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.
[9]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.
[10](1, 2, 3) 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.
[11](1, 2) G.Raetsch, S.Sonnenburg, and B.Schoelkopf. RASE: recognition of alternatively spliced exons in c. elegans. Bioinformatics, 21:i369--i377, June 2005.
[12](1, 2) 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.
[13](1, 2) 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.
[14](1, 2) 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.
[15]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.
[16]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.
[17]S.Sonnenburg, A.Zien, and G.Raetsch. ARTS: Accurate Recognition of Transcription Starts in Human. 2006. (accepted).
[18]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
[19] M.Kloft, U.Brefeldt, S.Sonnenburg, A.Zien, P.Laskov, K.-R. Mueller, Efficient and Accurate Lp-Norm Multiple Kernel Learning, Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA,2009
[20] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A Library for Large Linear Classification, Journal of Machine Learning Research 9(2008), 1871-1874. Software available at http://www.csie.ntu.edu.tw/~cjlin/liblinear
[21] V. Franc, S. Sonnenburg. Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization, Journal of Machine Learning Research 10(2009), 2157--2192, Software available at http://jmlr.csail.mit.edu/papers/v10/franc09a.html
[22] S. Sonnenburg, V. Franc. COFFIN: A Computational Framework for Linear SVMs, Research Report, Center for Machine Perception, K13133 FEE Czech Technical University, 2009
[23] S. Sonnenburg, V. Franc. COFFIN: A Computational Framework for Linear SVMs. Proceedings of the 27nd International Machine Learning Conference, 2010.