/*******************************************************************************
* 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.regression;
import org.junit.After;
import org.junit.AfterClass;
import org.junit.Before;
import org.junit.BeforeClass;
import org.junit.Test;
import smile.clustering.KMeans;
import smile.math.Math;
import smile.math.kernel.GaussianKernel;
import smile.data.AttributeDataset;
import smile.data.parser.ArffParser;
import smile.validation.CrossValidation;
import smile.validation.LOOCV;
/**
*
* @author Haifeng Li
*/
public class GaussianProcessRegressionTest {
public GaussianProcessRegressionTest() {
}
@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 RKHSRegression.
*/
@Test
public void testLearn() {
System.out.println("learn");
double[][] longley = {
{234.289, 235.6, 159.0, 107.608, 1947, 60.323},
{259.426, 232.5, 145.6, 108.632, 1948, 61.122},
{258.054, 368.2, 161.6, 109.773, 1949, 60.171},
{284.599, 335.1, 165.0, 110.929, 1950, 61.187},
{328.975, 209.9, 309.9, 112.075, 1951, 63.221},
{346.999, 193.2, 359.4, 113.270, 1952, 63.639},
{365.385, 187.0, 354.7, 115.094, 1953, 64.989},
{363.112, 357.8, 335.0, 116.219, 1954, 63.761},
{397.469, 290.4, 304.8, 117.388, 1955, 66.019},
{419.180, 282.2, 285.7, 118.734, 1956, 67.857},
{442.769, 293.6, 279.8, 120.445, 1957, 68.169},
{444.546, 468.1, 263.7, 121.950, 1958, 66.513},
{482.704, 381.3, 255.2, 123.366, 1959, 68.655},
{502.601, 393.1, 251.4, 125.368, 1960, 69.564},
{518.173, 480.6, 257.2, 127.852, 1961, 69.331},
{554.894, 400.7, 282.7, 130.081, 1962, 70.551}
};
double[] y = {
83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2,
104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9
};
Math.standardize(longley);
int n = longley.length;
LOOCV loocv = new LOOCV(n);
double rss = 0.0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(longley, loocv.train[i]);
double[] trainy = Math.slice(y, loocv.train[i]);
GaussianProcessRegression rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(8.0), 0.2);
double r = y[loocv.test[i]] - rkhs.predict(longley[loocv.test[i]]);
rss += r * r;
}
System.out.println("MSE = " + rss/n);
}
/**
* Test of learn method, of class GaussianProcessRegression.
*/
@Test
public void testCPU() {
System.out.println("CPU");
ArffParser parser = new ArffParser();
parser.setResponseIndex(6);
try {
AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
double[] datay = data.toArray(new double[data.size()]);
double[][] datax = data.toArray(new double[data.size()][]);
Math.standardize(datax);
int n = datax.length;
int k = 10;
CrossValidation cv = new CrossValidation(n, k);
double rss = 0.0;
double sparseRSS30 = 0.0;
double nystromRSS30 = 0.0;
for (int i = 0; i < k; i++) {
double[][] trainx = Math.slice(datax, cv.train[i]);
double[] trainy = Math.slice(datay, cv.train[i]);
double[][] testx = Math.slice(datax, cv.test[i]);
double[] testy = Math.slice(datay, cv.test[i]);
GaussianProcessRegression rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(47.02), 0.1);
KMeans kmeans = new KMeans(trainx, 30, 10);
double[][] centers = kmeans.centroids();
double r0 = 0.0;
for (int l = 0; l < centers.length; l++) {
for (int j = 0; j < l; j++) {
r0 += Math.distance(centers[l], centers[j]);
}
}
r0 /= (2 * centers.length);
System.out.println("Kernel width = " + r0);
GaussianProcessRegression sparse30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1);
GaussianProcessRegression nystrom30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1, true);
for (int j = 0; j < testx.length; j++) {
double r = testy[j] - rkhs.predict(testx[j]);
rss += r * r;
r = testy[j] - sparse30.predict(testx[j]);
sparseRSS30 += r * r;
r = testy[j] - nystrom30.predict(testx[j]);
nystromRSS30 += r * r;
}
}
System.out.println("Regular 10-CV MSE = " + rss / n);
System.out.println("Sparse (30) 10-CV MSE = " + sparseRSS30 / n);
System.out.println("Nystrom (30) 10-CV MSE = " + nystromRSS30 / n);
} catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* Test of learn method, of class GaussianProcessRegression.
*/
@Test
public void test2DPlanes() {
System.out.println("2dplanes");
ArffParser parser = new ArffParser();
parser.setResponseIndex(10);
try {
AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/2dplanes.arff"));
double[][] x = data.toArray(new double[data.size()][]);
double[] y = data.toArray(new double[data.size()]);
int[] perm = Math.permutate(x.length);
double[][] datax = new double[4000][];
double[] datay = new double[datax.length];
for (int i = 0; i < datax.length; i++) {
datax[i] = x[perm[i]];
datay[i] = y[perm[i]];
}
int n = datax.length;
int k = 10;
CrossValidation cv = new CrossValidation(n, k);
double rss = 0.0;
double sparseRSS30 = 0.0;
for (int i = 0; i < k; i++) {
double[][] trainx = Math.slice(datax, cv.train[i]);
double[] trainy = Math.slice(datay, cv.train[i]);
double[][] testx = Math.slice(datax, cv.test[i]);
double[] testy = Math.slice(datay, cv.test[i]);
GaussianProcessRegression rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(34.866), 0.1);
KMeans kmeans = new KMeans(trainx, 30, 10);
double[][] centers = kmeans.centroids();
double r0 = 0.0;
for (int l = 0; l < centers.length; l++) {
for (int j = 0; j < l; j++) {
r0 += Math.distance(centers[l], centers[j]);
}
}
r0 /= (2 * centers.length);
System.out.println("Kernel width = " + r0);
GaussianProcessRegression sparse30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1);
for (int j = 0; j < testx.length; j++) {
double r = testy[j] - rkhs.predict(testx[j]);
rss += r * r;
r = testy[j] - sparse30.predict(testx[j]);
sparseRSS30 += r * r;
}
}
System.out.println("Regular 10-CV MSE = " + rss / n);
System.out.println("Sparse (30) 10-CV MSE = " + sparseRSS30 / n);
} catch (Exception ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class GaussianProcessRegression.
*/
@Test
public void testAilerons() {
System.out.println("ailerons");
ArffParser parser = new ArffParser();
parser.setResponseIndex(40);
try {
AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/ailerons.arff"));
double[][] x = data.toArray(new double[data.size()][]);
Math.standardize(x);
double[] y = data.toArray(new double[data.size()]);
for (int i = 0; i < y.length; i++) {
y[i] *= 10000;
}
int[] perm = Math.permutate(x.length);
double[][] datax = new double[4000][];
double[] datay = new double[datax.length];
for (int i = 0; i < datax.length; i++) {
datax[i] = x[perm[i]];
datay[i] = y[perm[i]];
}
int n = datax.length;
int k = 10;
CrossValidation cv = new CrossValidation(n, k);
double rss = 0.0;
double sparseRSS30 = 0.0;
for (int i = 0; i < k; i++) {
double[][] trainx = Math.slice(datax, cv.train[i]);
double[] trainy = Math.slice(datay, cv.train[i]);
double[][] testx = Math.slice(datax, cv.test[i]);
double[] testy = Math.slice(datay, cv.test[i]);
GaussianProcessRegression rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(183.96), 0.1);
KMeans kmeans = new KMeans(trainx, 30, 10);
double[][] centers = kmeans.centroids();
double r0 = 0.0;
for (int l = 0; l < centers.length; l++) {
for (int j = 0; j < l; j++) {
r0 += Math.distance(centers[l], centers[j]);
}
}
r0 /= (2 * centers.length);
System.out.println("Kernel width = " + r0);
GaussianProcessRegression sparse30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1);
for (int j = 0; j < testx.length; j++) {
double r = testy[j] - rkhs.predict(testx[j]);
rss += r * r;
r = testy[j] - sparse30.predict(testx[j]);
sparseRSS30 += r * r;
}
}
System.out.println("Regular 10-CV MSE = " + rss / n);
System.out.println("Sparse (30) 10-CV MSE = " + sparseRSS30 / n);
} catch (Exception ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class GaussianProcessRegression.
*/
@Test
public void testBank32nh() {
System.out.println("bank32nh");
ArffParser parser = new ArffParser();
parser.setResponseIndex(32);
try {
AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/bank32nh.arff"));
double[] y = data.toArray(new double[data.size()]);
double[][] x = data.toArray(new double[data.size()][]);
Math.standardize(x);
int[] perm = Math.permutate(x.length);
double[][] datax = new double[4000][];
double[] datay = new double[datax.length];
for (int i = 0; i < datax.length; i++) {
datax[i] = x[perm[i]];
datay[i] = y[perm[i]];
}
int n = datax.length;
int k = 10;
CrossValidation cv = new CrossValidation(n, k);
double rss = 0.0;
double sparseRSS30 = 0.0;
for (int i = 0; i < k; i++) {
double[][] trainx = Math.slice(datax, cv.train[i]);
double[] trainy = Math.slice(datay, cv.train[i]);
double[][] testx = Math.slice(datax, cv.test[i]);
double[] testy = Math.slice(datay, cv.test[i]);
GaussianProcessRegression rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(55.3), 0.1);
KMeans kmeans = new KMeans(trainx, 30, 10);
double[][] centers = kmeans.centroids();
double r0 = 0.0;
for (int l = 0; l < centers.length; l++) {
for (int j = 0; j < l; j++) {
r0 += Math.distance(centers[l], centers[j]);
}
}
r0 /= (2 * centers.length);
System.out.println("Kernel width = " + r0);
GaussianProcessRegression sparse30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1);
for (int j = 0; j < testx.length; j++) {
double r = testy[j] - rkhs.predict(testx[j]);
rss += r * r;
r = testy[j] - sparse30.predict(testx[j]);
sparseRSS30 += r * r;
}
}
System.out.println("Regular 10-CV MSE = " + rss / n);
System.out.println("Sparse (30) 10-CV MSE = " + sparseRSS30 / n);
} catch (Exception ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class GaussianProcessRegression.
*/
@Test
public void testPuma8nh() {
System.out.println("puma8nh");
ArffParser parser = new ArffParser();
parser.setResponseIndex(8);
try {
AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/puma8nh.arff"));
double[] y = data.toArray(new double[data.size()]);
double[][] x = data.toArray(new double[data.size()][]);
int[] perm = Math.permutate(x.length);
double[][] datax = new double[4000][];
double[] datay = new double[datax.length];
for (int i = 0; i < datax.length; i++) {
datax[i] = x[perm[i]];
datay[i] = y[perm[i]];
}
int n = datax.length;
int k = 10;
CrossValidation cv = new CrossValidation(n, k);
double rss = 0.0;
double sparseRSS30 = 0.0;
for (int i = 0; i < k; i++) {
double[][] trainx = Math.slice(datax, cv.train[i]);
double[] trainy = Math.slice(datay, cv.train[i]);
double[][] testx = Math.slice(datax, cv.test[i]);
double[] testy = Math.slice(datay, cv.test[i]);
GaussianProcessRegression rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(38.63), 0.1);
KMeans kmeans = new KMeans(trainx, 30, 10);
double[][] centers = kmeans.centroids();
double r0 = 0.0;
for (int l = 0; l < centers.length; l++) {
for (int j = 0; j < l; j++) {
r0 += Math.distance(centers[l], centers[j]);
}
}
r0 /= (2 * centers.length);
System.out.println("Kernel width = " + r0);
GaussianProcessRegression sparse30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1);
for (int j = 0; j < testx.length; j++) {
double r = testy[j] - rkhs.predict(testx[j]);
rss += r * r;
r = testy[j] - sparse30.predict(testx[j]);
sparseRSS30 += r * r;
}
}
System.out.println("Regular 10-CV MSE = " + rss / n);
System.out.println("Sparse (30) 10-CV MSE = " + sparseRSS30 / n);
} catch (Exception ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class GaussianProcessRegression.
*/
@Test
public void testKin8nm() {
System.out.println("kin8nm");
ArffParser parser = new ArffParser();
parser.setResponseIndex(8);
try {
AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/kin8nm.arff"));
double[] y = data.toArray(new double[data.size()]);
double[][] x = data.toArray(new double[data.size()][]);
int[] perm = Math.permutate(x.length);
double[][] datax = new double[4000][];
double[] datay = new double[datax.length];
for (int i = 0; i < datax.length; i++) {
datax[i] = x[perm[i]];
datay[i] = y[perm[i]];
}
int n = datax.length;
int k = 10;
CrossValidation cv = new CrossValidation(n, k);
double rss = 0.0;
double sparseRSS30 = 0.0;
for (int i = 0; i < k; i++) {
double[][] trainx = Math.slice(datax, cv.train[i]);
double[] trainy = Math.slice(datay, cv.train[i]);
double[][] testx = Math.slice(datax, cv.test[i]);
double[] testy = Math.slice(datay, cv.test[i]);
GaussianProcessRegression rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(34.97), 0.1);
KMeans kmeans = new KMeans(trainx, 30, 10);
double[][] centers = kmeans.centroids();
double r0 = 0.0;
for (int l = 0; l < centers.length; l++) {
for (int j = 0; j < l; j++) {
r0 += Math.distance(centers[l], centers[j]);
}
}
r0 /= (2 * centers.length);
System.out.println("Kernel width = " + r0);
GaussianProcessRegression sparse30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1);
for (int j = 0; j < testx.length; j++) {
double r = testy[j] - rkhs.predict(testx[j]);
rss += r * r;
r = testy[j] - sparse30.predict(testx[j]);
sparseRSS30 += r * r;
}
}
System.out.println("Regular 10-CV MSE = " + rss / n);
System.out.println("Sparse (30) 10-CV MSE = " + sparseRSS30 / n);
} catch (Exception ex) {
System.err.println(ex);
}
}
}
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