/*******************************************************************************
* 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 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.math.Math;
import smile.validation.CrossValidation;
import smile.validation.LOOCV;
import static org.junit.Assert.*;
/**
*
* @author Haifeng Li
*/
public class RidgeRegressionTest {
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
};
double[] residuals = {
-0.6008156, 1.5502732, 0.1032287, -1.2306486, -0.3355139, 0.2693345, 0.8776759,
0.1222429, -2.0086121, -0.4859826, 1.0663129, 1.2274906, -0.3835821, 0.2710215,
0.1978569, -0.6402823
};
public RidgeRegressionTest() {
}
@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 RidgeRegression.
*/
@Test
public void testLearn() {
System.out.println("learn");
RidgeRegression model = new RidgeRegression(longley, y, 0.0);
double rss = 0.0;
int n = longley.length;
for (int i = 0; i < n; i++) {
double r = y[i] - model.predict(longley[i]);
assertEquals(residuals[i], r, 1E-7);
rss += r * r;
}
System.out.println("Training MSE = " + rss/n);
model = new RidgeRegression(longley, y, 0.1);
assertEquals(-1.354007e+03, model.intercept(), 1E-3);
assertEquals(5.457700e-02, model.coefficients()[0], 1E-7);
assertEquals(1.198440e-02, model.coefficients()[1], 1E-7);
assertEquals(1.261978e-02, model.coefficients()[2], 1E-7);
assertEquals(-1.856041e-01, model.coefficients()[3], 1E-7);
assertEquals(7.218054e-01, model.coefficients()[4], 1E-7);
assertEquals(5.884884e-01, model.coefficients()[5], 1E-7);
LOOCV loocv = new LOOCV(n);
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]);
RidgeRegression ridge = new RidgeRegression(trainx, trainy, 0.1);
double r = y[loocv.test[i]] - ridge.predict(longley[loocv.test[i]]);
rss += r * r;
}
System.out.println("LOOCV MSE = " + rss/n);
}
/**
* Test of predict method, of class RidgeRegression.
*/
@Test
public void testPredict() {
System.out.println("predict");
for (int lambda = 0; lambda <= 20; lambda+=2) {
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]);
RidgeRegression ridge = new RidgeRegression(trainx, trainy, 0.01*lambda);
double r = y[loocv.test[i]] - ridge.predict(longley[loocv.test[i]]);
rss += r * r;
}
System.out.format("LOOCV MSE with lambda %.2f = %.3f%n", 0.01*lambda, rss/n);
}
}
/**
* Test of learn method, of class LinearRegression.
*/
@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[][] datax = data.toArray(new double[data.size()][]);
double[] datay = data.toArray(new double[data.size()]);
int n = datax.length;
int k = 10;
CrossValidation cv = new CrossValidation(n, k);
double rss = 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]);
RidgeRegression ridge = new RidgeRegression(trainx, trainy, 10.0);
for (int j = 0; j < testx.length; j++) {
double r = testy[j] - ridge.predict(testx[j]);
rss += r * r;
}
}
System.out.println("10-CV MSE = " + rss / n);
} catch (Exception ex) {
System.err.println(ex);
}
}
}
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