/*******************************************************************************
* Copyright (c) 2010 Haifeng Li
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
package smile.classification;
import org.junit.After;
import org.junit.AfterClass;
import org.junit.Before;
import org.junit.BeforeClass;
import org.junit.Test;
import static org.junit.Assert.*;
import smile.data.AttributeDataset;
import smile.data.NominalAttribute;
import smile.data.parser.ArffParser;
import smile.data.parser.DelimitedTextParser;
import smile.math.Math;
import smile.math.kernel.GaussianKernel;
import smile.math.kernel.LinearKernel;
import smile.math.kernel.PolynomialKernel;
/**
*
* @author Haifeng Li
*/
public class SVMTest {
public SVMTest() {
}
@BeforeClass
public static void setUpClass() throws Exception {
}
@AfterClass
public static void tearDownClass() throws Exception {
}
@Before
public void setUp() {
}
@After
public void tearDown() {
}
/**
* Test of learn method, of class SVM.
*/
@Test
public void testLearn() {
System.out.println("learn");
ArffParser arffParser = new ArffParser();
arffParser.setResponseIndex(4);
try {
AttributeDataset iris = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/iris.arff"));
double[][] x = iris.toArray(new double[iris.size()][]);
int[] y = iris.toArray(new int[iris.size()]);
SVM<double[]> svm = new SVM<>(new LinearKernel(), 10.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
svm.learn(x, y);
svm.learn(x, y);
svm.finish();
int error = 0;
for (int i = 0; i < x.length; i++) {
if (svm.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("Linear ONE vs. ALL error = " + error);
assertTrue(error <= 10);
svm = new SVM<>(new GaussianKernel(1), 1.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
svm.learn(x, y);
svm.learn(x, y);
svm.finish();
svm.trainPlattScaling(x, y);
error = 0;
for (int i = 0; i < x.length; i++) {
if (svm.predict(x[i]) != y[i]) {
error++;
}
double[] prob = new double[3];
int yp = svm.predict(x[i], prob);
//System.out.format("%d %d %.2f, %.2f %.2f\n", y[i], yp, prob[0], prob[1], prob[2]);
}
System.out.println("Gaussian ONE vs. ALL error = " + error);
assertTrue(error <= 5);
svm = new SVM<>(new GaussianKernel(1), 1.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ONE);
svm.learn(x, y);
svm.learn(x, y);
svm.finish();
assertTrue(!svm.hasPlattScaling());
svm.trainPlattScaling(x, y);
assertTrue(svm.hasPlattScaling());
error = 0;
for (int i = 0; i < x.length; i++) {
if (svm.predict(x[i]) != y[i]) {
error++;
}
double[] prob = new double[3];
int yp = svm.predict(x[i], prob);
//System.out.format("%d %d %.2f, %.2f %.2f\n", y[i], yp, prob[0], prob[1], prob[2]);
}
System.out.println("Gaussian ONE vs. ONE error = " + error);
assertTrue(error <= 5);
svm = new SVM<>(new PolynomialKernel(2), 1.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
svm.learn(x, y);
svm.learn(x, y);
svm.finish();
error = 0;
for (int i = 0; i < x.length; i++) {
if (svm.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("Polynomial ONE vs. ALL error = " + error);
assertTrue(error <= 5);
} catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* Test of learn method, of class SVM.
*/
@Test
public void testSegment() {
System.out.println("Segment");
ArffParser parser = new ArffParser();
parser.setResponseIndex(19);
try {
AttributeDataset train = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-challenge.arff"));
AttributeDataset test = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-test.arff"));
System.out.println(train.size() + " " + test.size());
double[][] x = train.toArray(new double[0][]);
int[] y = train.toArray(new int[0]);
double[][] testx = test.toArray(new double[0][]);
int[] testy = test.toArray(new int[0]);
SVM<double[]> svm = new SVM<>(new GaussianKernel(8.0), 5.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
svm.learn(x, y);
svm.finish();
int error = 0;
for (int i = 0; i < testx.length; i++) {
if (svm.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("Segment error rate = %.2f%%%n", 100.0 * error / testx.length);
assertTrue(error < 70);
} catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* Test of learn method, of class SVM.
*/
@Test
public void testUSPS() {
System.out.println("USPS");
DelimitedTextParser parser = new DelimitedTextParser();
parser.setResponseIndex(new NominalAttribute("class"), 0);
try {
AttributeDataset train = parser.parse("USPS Train", smile.data.parser.IOUtils.getTestDataFile("usps/zip.train"));
AttributeDataset test = parser.parse("USPS Test", smile.data.parser.IOUtils.getTestDataFile("usps/zip.test"));
double[][] x = train.toArray(new double[train.size()][]);
int[] y = train.toArray(new int[train.size()]);
double[][] testx = test.toArray(new double[test.size()][]);
int[] testy = test.toArray(new int[test.size()]);
SVM<double[]> svm = new SVM<>(new GaussianKernel(8.0), 5.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ONE);
svm.learn(x, y);
svm.finish();
int error = 0;
for (int i = 0; i < testx.length; i++) {
if (svm.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
assertTrue(error < 95);
System.out.println("USPS one more epoch...");
for (int i = 0; i < x.length; i++) {
int j = Math.randomInt(x.length);
svm.learn(x[j], y[j]);
}
svm.finish();
error = 0;
for (int i = 0; i < testx.length; i++) {
if (svm.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
assertTrue(error < 95);
} catch (Exception ex) {
ex.printStackTrace();
}
}
}