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Tutorial for Modular Interfaces

SHOGUN's "modular" interfaces to Python, Octave, Java, Lua, Ruby and C# give intuitive and easy access to the shogun's functionality. Compared to the static interfaces (Static Interfaces), the modular ones are much more flexible and allow for nearly unlimited extensibility.

If this is your first time using shogun, you've found the right place to start!

In this tutorial, we demonstrate how to use shogun to create a simple Gaussian kernel based Support Vector Machine (SVM) classifier, but first things first. Let's fire up python, octave, java, lua, ruby or C#, and load the modular shogun environment.

Starting SHOGUN

To load all of shogun's modules under octave start octave and issue

init_shogun

Under python we have to specify what we'd like to import. For this example, we will need features and labels to represent our input data, a classifier, a kernel to compare the similarity of features and some evaluation procedures.

from shogun.Features import *
from shogun.Kernel import *
from shogun.Classifier import *
from shogun.Evaluation import *

Under java we need import shogun package and jblas package.

import org.shogun.*;
import org.jblas.*;

Under lua We need set LUA_PATH and LUA_CPATH to find the *.lua lib and *.so lib that lua_modular requires. For instance, if current dir is examples/undocumented /lua_modular, we need set the path as the following when we want to test the examples:

export LUA_PATH=../../../src/interfaces/lua_modular/?.lua\;?.lua
export LUA_CPATH=../../../src/interfaces/lua_modular/?.so

Under ruby we need import shogun package and narray package.

require 'modshogun'
require 'narray'

Under c# we need import system package.

using System;

we need import shogun package, which includes all the sub package, such as kernel, classifier, distance and so on.

require('shogun')

we also have to specify what we'd like to load. For this example, we will need features and labels to represent our input data, a classifier, a kernel to compare the similarity of features

System.loadLibrary("Features");
System.loadLibrary("Classifier");
System.loadLibrary("Kernel");

We also need use the method of Features to init shogun as the following:

Features.init_shogun_with_defaults();

(Note here that the module shogun.Features contains the class Labels).

Getting Help

If you would like to get an overview of the classes in shogun or if you are looking for the documentation of a particular class, use the "Classes" tab above and browse through the class list. All classes are rather well documented (if documentation is not good enough in a particular class, please notify us). Note that all classes in the classes list (classes tab above) are all prefixed with a 'C' - that really is the only difference to using them from within the python or octave modular interfaces (where the C prefix is missing).

Alternatively, under python the same documentation is available via python help strings, so you may issue

help(<classname>)

for example

from shogun.Kernel import GaussianKernel
help(GaussianKernel)

or

import shogun.Kernel
help(shogun.Kernel)

Generating a toy dataset

To start with, we will generate a small toy dataset. In python, we need to import numpy to do this. We will generate real valued training (and testing) data drawn from Gaussians distribution. We will generate two Gauss bumps that are "dist" apart. The data is in matrix shape with each column describing an object and as many columns as we have examples. Additionally, we need a vector of labels (that is a vector of ones or minus ones) that indicates the class of each example.

from numpy import *
from numpy.random import randn
dist=0.5
traindata_real = concatenate((randn(2,100)-dist, randn(2,100)+dist), axis=1)
testdata_real = concatenate((randn(2,100)-dist, randn(2,100)+dist), axis=1)
train_labels = concatenate((-ones(100), ones(100)))
test_labels = concatenate((-ones(100), ones(100)))

In octave, this is is done as follows:

dist=0.5
traindata_real = [randn(2,100)-dist, randn(2,100)+dist];
testdata_real = [randn(2,100)-dist, randn(2,100)+dist];
train_labels = [-ones(1,100), ones(1,100)];
test_labels = [-ones(1,100), ones(1,100)];

In java, this is is done as follows:

dist=0.5
int num = 1000;
double dist = 1.0;

DoubleMatrix offs=ones(2, num).mmul(dist);
DoubleMatrix x = randn(2, num).sub(offs);
DoubleMatrix y = randn(2, num).add(offs);
DoubleMatrix traindata_real = concatHorizontally(x, y);

DoubleMatrix m = randn(2, num).sub(offs);
DoubleMatrix n = randn(2, num).add(offs);
DoubleMatrix testdata_real = concatHorizontally(m, n);

DoubleMatrix o = ones(1,num);
DoubleMatrix trainlab = concatHorizontally(o.neg(), o);
DoubleMatrix testlab = concatHorizontarily(o.neg(), o)

In lua, this is is done as follows:

require 'shogun'
require 'load'

function concatenate(...)
    local result = ...
    for _,t in ipairs{select(2, ...)} do
        for row,rowdata in ipairs(t) do
            for col,coldata in ipairs(rowdata) do
                table.insert(result[row], coldata)
            end
        end
    end
    return result
end

function rand_matrix(rows, cols, dist)
  local matrix = {}
    for i = 1, rows do
        matrix[i] = {}
        for j = 1, cols do
            matrix[i][j] = math.random() + dist
        end
    end
    return matrix
end

function ones(num)
    r={}
    for i=1,num do
        r[i]=1
    end
    return r
end


num=1000
dist=1
width=2.1
C=1

traindata_real=concatenate(rand_matrix(2,num, -dist),rand_matrix(2,num,dist))
testdata_real=concatenate(rand_matrix(2,num,-dist), rand_matrix(2,num, dist))
trainlab={}
for i = 1, num do
    trainlab[i] = -1
    trainlab[i + num] = 1
end

testlab={}
for i = 1, num do
    testlab[i] = -1
    testlab[i + num] = 1
end

In ruby, this is is done as follows:

@num = 1000
@dist = 1
@width = 2.1
C = 1

def gen_rand_ary
  ary = [[],[]]
  ary.each do |p|
    p << ary_fill( @dist ) + ary_fill( -@dist )
    p.flatten!
  end
  return ary
end

def gen_ones_vec
  ary = []
  @num.times do
    ary << -1
  end
  @num.times do
    ary << 1
  end
  return ary
end

puts "generating training data"
traindata_real = gen_rand_ary
testdata_real = gen_rand_ary

puts "generating labels"
trainlab = gen_ones_vec
testlab = gen_ones_vec

In C#, this is is done as follows: int num = 1000; double dist = 1.0; double width = 2.1; double C = 1.0;

Random RandomNumber = new Random();

double[,] traindata_real = new double[2, num * 2]; for (int i = 0; i < num; i ++) { traindata_real[0, i] = RandomNumber.NextDouble() - dist; traindata_real[0, i + num] = RandomNumber.NextDouble() + dist; traindata_real[1, i] = RandomNumber.NextDouble() - dist; traindata_real[1, i + num] = RandomNumber.NextDouble() + dist; }

double[,] testdata_real = new double[2, num * 2]; for (int i = 0; i < num; i ++) { testdata_real[0, i] = RandomNumber.NextDouble() - dist; testdata_real[0, i + num] = RandomNumber.NextDouble() + dist; testdata_real[1, i] = RandomNumber.NextDouble() - dist; testdata_real[1, i + num] = RandomNumber.NextDouble() + dist; }

double[] trainlab = new double[num * 2]; for (int i = 0; i < num; i ++) { trainlab[i] = -1; trainlab[i + num] = 1; }

double[] testlab = new double[num * 2]; for (int i = 0; i < num; i ++) { testlab[i] = -1; testlab[i + num] = 1; }

The rest of this tutorial below will now work the same (identical syntax) for python, octave (when using a trailing semicolon for each command, which is optional in python), lua(we use colon to get the method of an object), ruby and C#

For java, we use DoubleMatrix to represent most of the types. If shogun C++ class need a int Vector/Matrix, we convert the DoubleMatrix into int Vector/Matrix and when returned from the function, we convert the int Vector/Matrix into DoubleMatrix again.

Creating an SVM classifier

To process the above toy data in shogun, we need to create a shogun feature object, here RealFeatures (for dense real valued feature matrices, see also shogun::CSimpleFeatures) like this

feats_train = RealFeatures(traindata_real);
feats_test = RealFeatures(testdata_real);

Using the above feature object we can now create a kernel object. Here, we create a Gaussian kernel of a certain width (see also shogun::CGaussianKernel) based on our training features

width = 2;
kernel = GaussianKernel(feats_train, feats_train, width);

and can now compute the kernel matrix

km = kernel.get_kernel_matrix();

To train an SVM, we need labeled examples, which is a vector of ones and minus ones, such as the one we have previously stored in the variable train_labels. We now create a shogun label object from it (the same goes for our test labels, which we'll use later):

labels = Labels(train_labels);
labels_test = Labels(test_labels);

Given the labels object and the kernel all that is left to do is to specify a cost parameter C (used to control generalization performance) and we can construct an SVM object. To start the training, we simply invoke the train method of the SVM object. Quiet easy, isn't it?

C = 1.0;
svm = LibSVM(C, kernel, labels);
svm.train();

To apply the SVM to unseen test data, we simply need to pass a feature object to the SVM's apply method, which returns a shogun Label object (note that we could alternatively initialize the kernel object with the train and test data manually and then call apply without arguments, which is done in some of the other example scripts). If we would like to analyze the outputs directly in python/octave, we can obtain the vector of outputs in native python/octave representation via get_labels().

output = svm.apply(feats_test);
output_vector = output.get_labels();

Given the output and the test labels, we can now assess the prediction performance. For this, we create an instance of the class PerformanceMeasures, which provides a convenient way of obtaining various performance measures, such as accuracy (acc), area under the receiver operator characteristic curve (auROC), F-score and others:

pm = PerformanceMeasures(labels_test, output);
acc = pm.get_accuracy();
roc = pm.get_auROC();
fms = pm.get_fmeasure();

That's really it. For any of the advanced topics please have a look one of the many self-explanatory examples in

A full, working example similar to the one presented above is shown below:

For octave:

% In this example a two-class support vector machine classifier is trained on a
% 2-dimensional randomly generated data set and the trained classifier is used to
% predict labels of test examples. As training algorithm the LIBSVM solver is used
% with SVM regularization parameter C=1 and a Gaussian kernel of width 2.1.
% 
% For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/

modshogun

num=1000;
dist=1;
width=2.1;
C=1;

traindata_real=[randn(2,num)-dist, randn(2,num)+dist];
testdata_real=[randn(2,num)-dist, randn(2,num)+dist];

trainlab=[-ones(1,num), ones(1,num)];
testlab=[-ones(1,num), ones(1,num)];

feats_train=RealFeatures(traindata_real);
feats_test=RealFeatures(testdata_real);
kernel=GaussianKernel(feats_train, feats_train, width);

labels=BinaryLabels(trainlab);
svm=LibSVM(C, kernel, labels);
svm.parallel.set_num_threads(8);
svm.train();
kernel.init(feats_train, feats_test);
out=svm.apply().get_labels();
testerr=mean(sign(out)~=testlab)

For java:

// In this example a two-class support vector machine classifier is trained on a
// 2-dimensional randomly generated data set and the trained classifier is used to
// predict labels of test examples. As training algorithm the LIBSVM solver is used
// with SVM regularization parameter C=1 and a Gaussian kernel of width 2.1.
// 
// For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/

import org.shogun.*;
import org.jblas.*;
import static org.jblas.MatrixFunctions.signum;
import static org.jblas.DoubleMatrix.concatHorizontally;
import static org.jblas.DoubleMatrix.ones;
import static org.jblas.DoubleMatrix.randn;

import static org.shogun.LabelsFactory.to_binary;

public class classifier_libsvm_minimal_modular {
	static {
		System.loadLibrary("modshogun");
	}

	public static void main(String argv[]) {
		modshogun.init_shogun_with_defaults();

		int num = 1000;
		double dist = 1.0;
		double width = 2.1;
		double C = 1.0;

		DoubleMatrix offs=ones(2, num).mmul(dist);
		DoubleMatrix x = randn(2, num).sub(offs);
		DoubleMatrix y = randn(2, num).add(offs);
		DoubleMatrix traindata_real = concatHorizontally(x, y);

		DoubleMatrix m = randn(2, num).sub(offs);
		DoubleMatrix n = randn(2, num).add(offs);
		DoubleMatrix testdata_real = concatHorizontally(m, n);

		DoubleMatrix o = ones(1,num);
		DoubleMatrix trainlab = concatHorizontally(o.neg(), o);
		DoubleMatrix testlab = concatHorizontally(o.neg(), o);

		RealFeatures feats_train = new RealFeatures(traindata_real);
		RealFeatures feats_test = new RealFeatures(testdata_real);
		GaussianKernel kernel = new GaussianKernel(feats_train, feats_train, width);
		BinaryLabels labels = new BinaryLabels(trainlab);
		LibSVM svm = new LibSVM(C, kernel, labels);
		svm.train();

		DoubleMatrix out = to_binary(svm.apply(feats_test)).get_labels();

		System.out.println("Mean Error = " + signum(out).ne(testlab).mean());
	}
}

For lua:

--  In this example a two-class support vector machine classifier is trained on a
--  2-dimensional randomly generated data set and the trained classifier is used to
--  predict labels of test examples. As training algorithm the LIBSVM solver is used
--  with SVM regularization parameter C=1 and a Gaussian kernel of width 2.1.
--  
--  For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/

require 'modshogun'
require 'load'

function concatenate(...)
	local result = ...
	for _,t in ipairs{select(2, ...)} do
		for row,rowdata in ipairs(t) do
			for col,coldata in ipairs(rowdata) do
				table.insert(result[row], coldata)
			end
		end
	end
	return result
end

function rand_matrix(rows, cols, dist)
  local matrix = {}
	for i = 1, rows do
		matrix[i] = {}
		for j = 1, cols do
			matrix[i][j] = math.random() + dist
		end
	end
	return matrix
end

function ones(num)
	r={}
	for i=1,num do
		r[i]=1
	end
	return r
end


num=1000
dist=1
width=2.1
C=1

traindata_real=concatenate(rand_matrix(2,num, -dist),rand_matrix(2,num,dist))
testdata_real=concatenate(rand_matrix(2,num,-dist), rand_matrix(2,num, dist))

trainlab={}
for i = 1, num do
	trainlab[i] = -1
	trainlab[i + num] = 1
end

testlab={}
for i = 1, num do
	testlab[i] = -1
	testlab[i + num] = 1
end

feats_train=modshogun.RealFeatures(traindata_real)
feats_test=modshogun.RealFeatures(testdata_real)
kernel=modshogun.GaussianKernel(feats_train, feats_train, width)

labels=modshogun.BinaryLabels(trainlab)
svm=modshogun.LibSVM(C, kernel, labels)
svm:train()

kernel:init(feats_train, feats_test)
out=svm:apply():get_labels()

err_num = 0
for i = 1, num do
	if out[i] > 0 then
		err_num = err_num+1
	end
	if out[i+num] < 0 then
		err_num = err_num+1
	end
end

testerr=err_num/(2*num)
print(testerr)

For ruby:

# In this example a two-class support vector machine classifier is trained on a
# 2-dimensional randomly generated data set and the trained classifier is used to
# predict labels of test examples. As training algorithm the LIBSVM solver is used
# with SVM regularization parameter C=1 and a Gaussian kernel of width 2.1.
# 
# For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/

require 'modshogun'
require 'load'
require 'narray'

@num = 1000
@dist = 1
@width = 2.1
C = 1

puts "generating training data"
traindata_real = gen_rand_ary @num
testdata_real = gen_rand_ary @num

puts "generating labels"
trainlab = gen_ones_vec @num
testlab = gen_ones_vec @num

puts "doing feature stuff"
feats_train = Modshogun::RealFeatures.new
feats_train.set_feature_matrix traindata_real
feats_test = Modshogun::RealFeatures.new
feats_test.set_feature_matrix testdata_real
kernel = Modshogun::GaussianKernel.new feats_train, feats_train, @width

puts "labeling stuff"
labels = Modshogun::BinaryLabels.new
labels.set_labels trainlab
svm = Modshogun::LibSVM.new C, kernel, labels
svm.train

puts "the grand finale"
kernel.init feats_train, feats_test
out = svm.apply.get_labels
testerr = mean out.sign.eql_items? testlab
puts testerr

For csharp:

// In this example a two-class support vector machine classifier is trained on a
// 2-dimensional randomly generated data set and the trained classifier is used to
// predict labels of test examples. As training algorithm the LIBSVM solver is used
// with SVM regularization parameter C=1 and a Gaussian kernel of width 2.1.
// 
// For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/

using System;


public class classifier_libsvm_minimal_modular {
	public static void Main() {
		modshogun.init_shogun_with_defaults();

		int num = 1000;
		double dist = 1.0;
		double width = 2.1;
		double C = 1.0;

		Random RandomNumber = new Random();

		double[,] traindata_real = new double[2, num * 2];
		for (int i = 0; i < num; i ++) {
			traindata_real[0, i] = RandomNumber.NextDouble() - dist;
			traindata_real[0, i + num] = RandomNumber.NextDouble() + dist;
			traindata_real[1, i] = RandomNumber.NextDouble() - dist;
			traindata_real[1, i + num] = RandomNumber.NextDouble() + dist;
		}

		double[,] testdata_real = new double[2, num * 2];
		for (int i = 0; i < num; i ++) {
			testdata_real[0, i] = RandomNumber.NextDouble() - dist;
			testdata_real[0, i + num] = RandomNumber.NextDouble() + dist;
			testdata_real[1, i] = RandomNumber.NextDouble() - dist;
			testdata_real[1, i + num] = RandomNumber.NextDouble() + dist;
		}

		double[] trainlab = new double[num * 2];
		for (int i = 0; i < num; i ++) {
			trainlab[i] = -1;
			trainlab[i + num] = 1;
		}

		double[] testlab = new double[num * 2];
		for (int i = 0; i < num; i ++) {
			testlab[i] = -1;
			testlab[i + num] = 1;
		}

		RealFeatures feats_train = new RealFeatures(traindata_real);
		RealFeatures feats_test = new RealFeatures(testdata_real);
		GaussianKernel kernel = new GaussianKernel(feats_train, feats_train, width);
		BinaryLabels labels = new BinaryLabels(trainlab);
		LibSVM svm = new LibSVM(C, kernel, labels);
		svm.train();

		double[] result = LabelsFactory.to_binary(svm.apply(feats_test)).get_labels();

		int err_num = 0;
		for (int i = 0; i < num; i++) {
			if (result[i] > 0) {
				err_num += 1;
			}
			if (result[i+num] < 0) {
				err_num += 1;
			}
		}

		double testerr=err_num/(2*num);
		Console.WriteLine(testerr);
	}
}

For python:

# In this example a two-class support vector machine classifier is trained on a
# 2-dimensional randomly generated data set and the trained classifier is used to
# predict labels of test examples. As training algorithm the LIBSVM solver is used
# with SVM regularization parameter C=1 and a Gaussian kernel of width 2.1.
# 
# For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/

#!/usr/bin/env python
traindat = '../data/fm_train_real.dat'
testdat = '../data/fm_test_real.dat'
label_traindat = '../data/label_train_twoclass.dat'

parameter_list = [[traindat,testdat,label_traindat,2.1,1]]

def classifier_libsvm_minimal_modular (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,width=2.1,C=1):
	from modshogun import RealFeatures, BinaryLabels
	from modshogun import LibSVM, GaussianKernel, CSVFile
	from modshogun import ErrorRateMeasure

	feats_train=RealFeatures(CSVFile(train_fname))
	feats_test=RealFeatures(CSVFile(test_fname))
	labels=BinaryLabels(CSVFile(label_fname))
	kernel=GaussianKernel(feats_train, feats_train, width);

	svm=LibSVM(C, kernel, labels);
	svm.train();

	out=svm.apply(feats_train);
	evaluator = ErrorRateMeasure()
	testerr = evaluator.evaluate(out,labels)
	#print(testerr)

if __name__=='__main__':
	print('LibSVM Minimal')
	classifier_libsvm_minimal_modular(*parameter_list[0])

SHOGUN Machine Learning Toolbox - Documentation