Project details for SHOGUN

Screenshot SHOGUN 0.7.0

by sonne - February 20, 2009, 10:41:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It comes with a generic interface for SVMs, features several SVM and kernel implementations, includes LinAdd optimizations and also Multiple Kernel Learning algorithms. SHOGUN also implements a number of linear methods. It allows the input feature-objects to be dense, sparse or strings and of type int/short/double/char.

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,
  • Fischer,
  • TOP,
  • Spectrum,
  • Weighted Degree Kernel (with shifts).

For the latter the efficient LINADD 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. 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 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.

Changes to previous version:

This release contains major feature enhancements and bugfixes:

  • Implement DotFeatures and CombinedDotFeatures. DotFeatures need to provide dot-product and similar operations (hence the name). This enables training of linear methods with mixed datatypes (sparse and dense and other even the newly implemented string based SpecFeatures and WDFeatures).
  • MKL now does not require CPLEX any longer.
  • Add q-norm MKL support based on internal Newton implementation.
  • Add 1-norm MKL support based on GLPK.
  • Add multiclass MKL support based on the GLPK and the GMNP svm solver.
  • Implement Tensor Product Pair Kernel (TPPK).
  • Support compilation on the iPhone :)
  • Add an option to set wds kernel position weights.
  • Build static libshogun.a for libshogun target.
  • Testsuite can also test the modular R interface, added test for OligoKernel.
  • Ocas and WDOcas can be used with a bias feature now.
  • Update to LibSVM 2.88.
  • Enable parallelized HMM code by default.
BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Cygwin, Linux, Macosx
Data Formats: Plain Ascii, Svmlight
Tags: Bioinformatics, Large Scale, String Kernel, Kernel, Kernelmachine, Lda, Lpm, Matlab, Mkl, Octave, Python, R, Svm
Archive: download here


Soeren Sonnenburg (on September 12, 2008, 16:14:36)
In case you find bugs, feel free to report them at [](
Tom Fawcett (on January 3, 2011, 03:20:48)
You say, "Some of them come with no less than 10 million training examples, others with 7 billion test examples." I'm not sure what this means. I have problems with mixed symbolic/numeric attributes and the training example sets don't fit in memory. Does SHOGUN require that training examples fit in memory?
Soeren Sonnenburg (on January 14, 2011, 18:12:01)
Shogun does not necessarily require examples to be in memory (if you use any of the FileFeatures). However, most algorithms within shogun are batch type - so using the non in-memory FileFeatures would probably be very slow. This does not matter for doing predictions of course, even though the 7 billion test examples above referred to predicting gene starts on the whole human genome (in memory ~3.5GB and a context window of 1200nt was shifted around in that string). In addition one can compute features (or feature space) on-the-fly potentially saving lots of memory. Not sure how big your problem is but I guess this is better discussed on the shogun mailinglist.
Yuri Hoffmann (on September 14, 2013, 17:12:16)
cannot use the java interface in cygwin (already reported on github) nor in debian.

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