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An automated Docker build for Shogun
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The SHOGUN machine learning toolbox


Develop branch build status:


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

Quick links to this file:

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.

Introduction


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.

Interfaces


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, <br/> --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.

Platforms


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

Contents


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

Applications


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.

License


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.

Download


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

References


[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.

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