/**
* 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,
* 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 org.apache.mahout.math.stats;
import org.apache.mahout.classifier.evaluation.Auc;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.common.RandomUtils;
import org.junit.Test;
import java.util.Random;
public final class OnlineAucTest extends MahoutTestCase {
@Test
public void testBinaryCase() {
Random gen = RandomUtils.getRandom();
OnlineSummarizer[] stats = new OnlineSummarizer[4];
for (int i = 0; i < 4; i++) {
stats[i] = new OnlineSummarizer();
}
for (int i = 0; i < 100; i++) {
OnlineAuc a1 = new GlobalOnlineAuc();
a1.setPolicy(GlobalOnlineAuc.ReplacementPolicy.FAIR);
OnlineAuc a2 = new GlobalOnlineAuc();
a2.setPolicy(GlobalOnlineAuc.ReplacementPolicy.FIFO);
OnlineAuc a3 = new GlobalOnlineAuc();
a3.setPolicy(GlobalOnlineAuc.ReplacementPolicy.RANDOM);
Auc a4 = new Auc();
for (int j = 0; j < 10000; j++) {
double x = gen.nextGaussian();
a1.addSample(0, x);
a2.addSample(0, x);
a3.addSample(0, x);
a4.add(0, x);
x = gen.nextGaussian() + 1;
a1.addSample(1, x);
a2.addSample(1, x);
a3.addSample(1, x);
a4.add(1, x);
}
stats[0].add(a1.auc());
stats[1].add(a2.auc());
stats[2].add(a3.auc());
stats[3].add(a4.auc());
}
int i = 0;
for (GlobalOnlineAuc.ReplacementPolicy policy : new GlobalOnlineAuc.ReplacementPolicy[] {
GlobalOnlineAuc.ReplacementPolicy.FAIR,
GlobalOnlineAuc.ReplacementPolicy.FIFO,
GlobalOnlineAuc.ReplacementPolicy.RANDOM,
null}) {
OnlineSummarizer summary = stats[i++];
System.out.printf("%s,%.4f (min = %.4f, 25%%-ile=%.4f, 75%%-ile=%.4f, max=%.4f)\n", policy, summary.getMean(),
summary.getQuartile(0), summary.getQuartile(1), summary.getQuartile(2), summary.getQuartile(3));
}
// FAIR policy isn't so accurate
assertEquals(0.7603, stats[0].getMean(), 0.03);
assertEquals(0.7603, stats[0].getQuartile(1), 0.03);
assertEquals(0.7603, stats[0].getQuartile(3), 0.03);
// FIFO policy seems best
assertEquals(0.7603, stats[1].getMean(), 0.001);
assertEquals(0.7603, stats[1].getQuartile(1), 0.006);
assertEquals(0.7603, stats[1].getQuartile(3), 0.006);
// RANDOM policy is nearly the same as FIFO
assertEquals(0.7603, stats[2].getMean(), 0.001);
assertEquals(0.7603, stats[2].getQuartile(1), 0.006);
assertEquals(0.7603, stats[2].getQuartile(1), 0.006);
}
@Test(expected=UnsupportedOperationException.class)
public void mustNotOmitGroup() {
OnlineAuc x = new GroupedOnlineAuc();
x.addSample(0, 3.14);
}
@Test
public void groupedAuc() {
Random gen = RandomUtils.getRandom();
OnlineAuc x = new GroupedOnlineAuc();
OnlineAuc y = new GlobalOnlineAuc();
for (int i = 0; i < 10000; i++) {
x.addSample(0, "a", gen.nextGaussian());
x.addSample(1, "a", gen.nextGaussian() + 1);
x.addSample(0, "b", gen.nextGaussian() + 10);
x.addSample(1, "b", gen.nextGaussian() + 11);
y.addSample(0, "a", gen.nextGaussian());
y.addSample(1, "a", gen.nextGaussian() + 1);
y.addSample(0, "b", gen.nextGaussian() + 10);
y.addSample(1, "b", gen.nextGaussian() + 11);
}
assertEquals(0.7603, x.auc(), 0.01);
assertEquals((0.7603 + 0.5) / 2, y.auc(), 0.02);
}
}