/*******************************************************************************
* 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.math.matrix.Matrix;
import smile.math.matrix.DenseMatrix;
import smile.validation.CrossValidation;
import smile.validation.LOOCV;
import static org.junit.Assert.*;
/**
*
* @author Haifeng Li
*/
public class LASSOTest {
public LASSOTest() {
}
@BeforeClass
public static void setUpClass() throws Exception {
}
@AfterClass
public static void tearDownClass() throws Exception {
}
@Before
public void setUp() {
}
@After
public void tearDown() {
}
/*
@Test
public void testToy() {
double[][] A = {
{1, 0, 0, 0.5},
{0, 1, 0.2, 0.3},
{0, 0.1, 1, 0.2}
};
double[] x0 = {1, 0, 1, 0}; // original signal
double[] y = new double[A.length];
Math.ax(A, x0, y); // measurements with no noise
LASSO lasso = new LASSO(A, y, 0.01, 0.01, 500);
double rss = 0.0;
int n = A.length;
for (int i = 0; i < n; i++) {
double r = y[i] - lasso.predict(A[i]);
rss += r * r;
}
System.out.println("MSE = " + rss / n);
assertEquals(0.0, lasso.intercept(), 1E-4);
double[] w = {0.9930, 0.0004, 0.9941, 0.0040};
for (int i = 0; i < w.length; i++) {
assertEquals(w[i], lasso.coefficients()[i], 1E-4);
}
}
*/
@Test
public void testToy2() {
double[][] A = {
{1, 0, 0, 0.5},
{0, 1, 0.2, 0.3},
{1, 0.5, 0.2, 0.3},
{0, 0.1, 0, 0.2},
{0, 0.1, 1, 0.2}
};
double[] x0 = {1, 0, 1, 0}; // original signal
double[] y = new double[A.length];
DenseMatrix a = Matrix.newInstance(A);
a.ax(x0, y); // measurements with no noise
for (int i = 0; i < y.length; i++) {
y[i] += 5;
}
LASSO lasso = new LASSO(A, y, 0.1, 0.001, 500);
double rss = 0.0;
int n = A.length;
for (int i = 0; i < n; i++) {
double r = y[i] - lasso.predict(A[i]);
rss += r * r;
}
System.out.println("MSE = " + rss / n);
assertEquals(5.0259443688265355, lasso.intercept(), 1E-7);
double[] w = {0.9659945126777854, -3.7147706312985876E-4, 0.9553629503697613, 9.416740009376934E-4};
for (int i = 0; i < w.length; i++) {
assertEquals(w[i], lasso.coefficients()[i], 1E-5);
}
}
/**
* Test of learn method, of class RidgeRegression.
*/
@Test
public void testLongley() {
System.out.println("longley");
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 rss = 0.0;
int n = longley.length;
LOOCV loocv = new LOOCV(n);
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(longley, loocv.train[i]);
double[] trainy = Math.slice(y, loocv.train[i]);
LASSO lasso = new LASSO(trainx, trainy, 0.1);
double r = y[loocv.test[i]] - lasso.predict(longley[loocv.test[i]]);
rss += r * r;
}
System.out.println("LOOCV MSE = " + rss / n);
assertEquals(2.0012529348358212, rss/n, 1E-4);
}
/**
* 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]);
LASSO lasso = new LASSO(trainx, trainy, 50.0);
for (int j = 0; j < testx.length; j++) {
double r = testy[j] - lasso.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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