Description:

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 several cleanups and enhancements:

Features:

• Support all data types from python_modular: dense, scipy-sparse csc_sparse matrices and strings of type bool, char, (u)int{8,16,32,64}, float{32,64,96}. In addition, individual vectors/strings can now be obtained and even changed. See examples/python_modular/features_*.py for examples.
• AUC maximization now works with arbitrary kernel SVMs.
• Documentation updates, many examples have been polished.
• Slightly speedup Oligo kernel.

Bugfixes:

• Fix reading strings from directory (f.load_from_directory()).
• Update copyright to 2009.

Cleanup and API Changes:

• Remove {Char,Short,Word,Int,Real}Features and only ever use the templated SimpleFeatures.
• Split up examples in examples/python_modular to separate files.
• Now use s.set_features(strs) instead of s.set_string_features(strs) to set string features.
• The meaning of the width parameter for the Oligo Kernel changed, the OligoKernel has been renamed to OligoStringKernel.

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

Comments

Soeren Sonnenburg (on September 12, 2008, 16:14:36)

In case you find bugs, feel free to report them at [http://trac.tuebingen.mpg.de/shogun](http://trac.tuebingen.mpg.de/shogun).

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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