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* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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,
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* See the License for the specific language governing permissions and
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package org.apache.mahout.classifier.evaluation;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.jet.random.Normal;
import org.junit.Test;
import java.util.Random;
public class AucTest extends MahoutTestCase{
@Test
public void testAuc() {
Auc auc = new Auc();
Random gen = RandomUtils.getRandom();
auc.setProbabilityScore(false);
for (int i=0;i<100000;i++) {
auc.add(0, gen.nextGaussian());
auc.add(1, gen.nextGaussian() + 1);
}
assertEquals(0.76, auc.auc(), 0.01);
}
@Test
public void testTies() {
Auc auc = new Auc();
Random gen = RandomUtils.getRandom();
auc.setProbabilityScore(false);
for (int i=0;i<100000;i++) {
auc.add(0, gen.nextGaussian());
auc.add(1, gen.nextGaussian() + 1);
}
// ties outside the normal range could cause index out of range
auc.add(0, 5.0);
auc.add(0, 5.0);
auc.add(0, 5.0);
auc.add(0, 5.0);
auc.add(1, 5.0);
auc.add(1, 5.0);
auc.add(1, 5.0);
assertEquals(0.76, auc.auc(), 0.05);
}
@Test
public void testEntropy() {
Auc auc = new Auc();
Random gen = RandomUtils.getRandom();
Normal n0 = new Normal(-1, 1, gen);
Normal n1 = new Normal(1, 1, gen);
for (int i=0;i<100000;i++) {
double score = n0.nextDouble();
double p = n1.pdf(score) / (n0.pdf(score) + n1.pdf(score));
auc.add(0, p);
score = n1.nextDouble();
p = n1.pdf(score) / (n0.pdf(score) + n1.pdf(score));
auc.add(1, p);
}
Matrix m = auc.entropy();
assertEquals(-0.35, m.get(0, 0), 0.02);
assertEquals(-2.34, m.get(0, 1), 0.02);
assertEquals(-2.34, m.get(1, 0), 0.02);
assertEquals(-0.35, m.get(1, 1), 0.02);
}
}