/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreemnets. 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 opennlp.tools.util;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
import static org.junit.Assert.assertNotSame;
import java.util.HashMap;
import java.util.Map;
import opennlp.model.MaxentModel;
import org.junit.Test;
public class BeamSearchTest {
static class IdentityFeatureGenerator implements BeamSearchContextGenerator<String> {
private String[] outcomeSequence;
IdentityFeatureGenerator(String outcomeSequence[]) {
this.outcomeSequence = outcomeSequence;
}
public String[] getContext(int index, String[] sequence,
String[] priorDecisions, Object[] additionalContext) {
return new String[] {outcomeSequence[index]};
}
}
static class IdentityModel implements MaxentModel {
private String[] outcomes;
private Map<String, Integer> outcomeIndexMap = new HashMap<String, Integer>();
private double bestOutcomeProb = 0.8d;
private double otherOutcomeProb;
IdentityModel(String outcomes[]) {
this.outcomes = outcomes;
for (int i = 0; i < outcomes.length; i++) {
outcomeIndexMap.put(outcomes[i], i);
}
otherOutcomeProb = 0.2d / (outcomes.length - 1);
}
public double[] eval(String[] context) {
double probs[] = new double[outcomes.length];
for (int i = 0; i < probs.length; i++) {
if (outcomes[i].equals(context[0])) {
probs[i] = bestOutcomeProb;
}
else {
probs[i] = otherOutcomeProb;
}
}
return probs;
}
public double[] eval(String[] context, double[] probs) {
return eval(context);
}
public double[] eval(String[] context, float[] values) {
return eval(context);
}
public String getAllOutcomes(double[] outcomes) {
return null;
}
public String getBestOutcome(double[] outcomes) {
return null;
}
public Object[] getDataStructures() {
return null;
}
public int getIndex(String outcome) {
return 0;
}
public int getNumOutcomes() {
return outcomes.length;
}
public String getOutcome(int i) {
return outcomes[i];
}
}
/**
* Tests that beam search does not fail to detect an empty sequence.
*/
@Test
public void testBestSequenceZeroLengthInput() {
String sequence[] = new String[0];
BeamSearchContextGenerator<String> cg = new IdentityFeatureGenerator(sequence);
String outcomes[] = new String[] {"1", "2", "3"};
MaxentModel model = new IdentityModel(outcomes);
BeamSearch<String> bs = new BeamSearch<String>(3, cg, model);
Sequence seq = bs.bestSequence(sequence, null);
assertNotNull(seq);
assertEquals(sequence.length, seq.getOutcomes().size());
}
/**
* Tests finding a sequence of length one.
*/
@Test
public void testBestSequenceOneElementInput() {
String sequence[] = {"1"};
BeamSearchContextGenerator<String> cg = new IdentityFeatureGenerator(sequence);
String outcomes[] = new String[] {"1", "2", "3"};
MaxentModel model = new IdentityModel(outcomes);
BeamSearch<String> bs = new BeamSearch<String>(3, cg, model);
Sequence seq = bs.bestSequence(sequence, null);
assertNotNull(seq);
assertEquals(sequence.length, seq.getOutcomes().size());
assertEquals("1", seq.getOutcomes().get(0));
}
/**
* Tests finding the best sequence on a short input sequence.
*/
@Test
public void testBestSequence() {
String sequence[] = {"1", "2", "3", "2", "1"};
BeamSearchContextGenerator<String> cg = new IdentityFeatureGenerator(sequence);
String outcomes[] = new String[] {"1", "2", "3"};
MaxentModel model = new IdentityModel(outcomes);
BeamSearch<String> bs = new BeamSearch<String>(2, cg, model);
Sequence seq = bs.bestSequence(sequence, null);
assertNotNull(seq);
assertEquals(sequence.length, seq.getOutcomes().size());
assertEquals("1", seq.getOutcomes().get(0));
assertEquals("2", seq.getOutcomes().get(1));
assertEquals("3", seq.getOutcomes().get(2));
assertEquals("2", seq.getOutcomes().get(3));
assertEquals("1", seq.getOutcomes().get(4));
}
/**
* Tests finding the best sequence on a short input sequence.
*/
@Test
public void testBestSequenceWithValidator() {
String sequence[] = {"1", "2", "3", "2", "1"};
BeamSearchContextGenerator<String> cg = new IdentityFeatureGenerator(sequence);
String outcomes[] = new String[] {"1", "2", "3"};
MaxentModel model = new IdentityModel(outcomes);
BeamSearch<String> bs = new BeamSearch<String>(2, cg, model, new SequenceValidator<String>(){
public boolean validSequence(int i, String[] inputSequence,
String[] outcomesSequence, String outcome) {
return !"2".equals(outcome);
}}, 0);
Sequence seq = bs.bestSequence(sequence, null);
assertNotNull(seq);
assertEquals(sequence.length, seq.getOutcomes().size());
assertEquals("1", seq.getOutcomes().get(0));
assertNotSame("2", seq.getOutcomes().get(1));
assertEquals("3", seq.getOutcomes().get(2));
assertNotSame("2", seq.getOutcomes().get(3));
assertEquals("1", seq.getOutcomes().get(4));
}
}