/*******************************************************************************
* 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 smile.validation.LOOCV;
import smile.data.AttributeDataset;
import smile.data.parser.ArffParser;
import org.junit.After;
import org.junit.AfterClass;
import org.junit.Before;
import org.junit.BeforeClass;
import org.junit.Test;
import smile.data.NominalAttribute;
import smile.data.parser.DelimitedTextParser;
import smile.math.Math;
import smile.sort.QuickSort;
/**
*
* @author Haifeng Li
*/
public class GradientTreeBoostTest {
public GradientTreeBoostTest() {
}
@BeforeClass
public static void setUpClass() throws Exception {
}
@AfterClass
public static void tearDownClass() throws Exception {
}
@Before
public void setUp() {
}
@After
public void tearDown() {
}
/**
* Test of predict method, of class GradientTreeBoost.
*/
@Test
public void testIris2() {
System.out.println("Iris binary");
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()]);
for (int i = 0; i < y.length; i++) {
if (y[i] == 2) {
y[i] = 1;
} else {
y[i] = 0;
}
}
int n = x.length;
LOOCV loocv = new LOOCV(n);
int error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
GradientTreeBoost boost = new GradientTreeBoost(iris.attributes(), trainx, trainy, 100);
if (y[loocv.test[i]] != boost.predict(x[loocv.test[i]]))
error++;
}
System.out.println("Gradient Tree Boost error = " + error);
//assertEquals(6, error);
} catch (Exception ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class GradientTreeBoost.
*/
@Test
public void testIris() {
System.out.println("Iris");
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()]);
int n = x.length;
LOOCV loocv = new LOOCV(n);
int error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
GradientTreeBoost boost = new GradientTreeBoost(iris.attributes(), trainx, trainy, 100);
if (y[loocv.test[i]] != boost.predict(x[loocv.test[i]]))
error++;
}
System.out.println("Gradient Tree Boost error = " + error);
//assertEquals(6, error);
} catch (Exception ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class GradientTreeBoost.
*/
@Test
public void testSegment() {
System.out.println("Segment");
ArffParser arffParser = new ArffParser();
arffParser.setResponseIndex(19);
try {
AttributeDataset train = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-challenge.arff"));
AttributeDataset test = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-test.arff"));
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()]);
GradientTreeBoost boost = new GradientTreeBoost(train.attributes(), x, y, 100);
int error = 0;
for (int i = 0; i < testx.length; i++) {
if (boost.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("Gradient Tree Boost error rate = %.2f%%%n", 100.0 * error / testx.length);
//assertEquals(28, error);
} catch (Exception ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class GradientTreeBoost.
*/
@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()]);
GradientTreeBoost boost = new GradientTreeBoost(train.attributes(), x, y, 100);
int error = 0;
for (int i = 0; i < testx.length; i++) {
if (boost.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("Gradient Tree Boost error rate = %.2f%%%n", 100.0 * error / testx.length);
double[] accuracy = boost.test(testx, testy);
for (int i = 1; i <= accuracy.length; i++) {
System.out.format("%d trees accuracy = %.2f%%%n", i, 100.0 * accuracy[i-1]);
}
double[] importance = boost.importance();
int[] index = QuickSort.sort(importance);
for (int i = importance.length; i-- > 0; ) {
System.out.format("%s importance is %.4f%n", train.attributes()[index[i]], importance[i]);
}
} catch (Exception ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class GradientTreeBoost.
*/
@Test
public void testUSPS2() {
System.out.println("USPS 2 classes");
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()]);
for (int i = 0; i < y.length; i++) {
if (y[i] != 0) {
y[i] = 1;
}
}
for (int i = 0; i < testy.length; i++) {
if (testy[i] != 0) {
testy[i] = 1;
}
}
GradientTreeBoost boost = new GradientTreeBoost(train.attributes(), x, y, 100);
int error = 0;
for (int i = 0; i < testx.length; i++) {
if (boost.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("Gradient Tree Boost error rate = %.2f%%%n", 100.0 * error / testx.length);
double[] accuracy = boost.test(testx, testy);
for (int i = 1; i <= accuracy.length; i++) {
System.out.format("%d trees accuracy = %.2f%%%n", i, 100.0 * accuracy[i-1]);
}
double[] importance = boost.importance();
int[] index = QuickSort.sort(importance);
for (int i = importance.length; i-- > 0; ) {
System.out.format("%s importance is %.4f%n", train.attributes()[index[i]], importance[i]);
}
} catch (Exception ex) {
System.err.println(ex);
}
}
}