/*
* 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 opennlp.tools.languagemodel;
import java.io.InputStream;
import java.util.Arrays;
import java.util.List;
import org.apache.commons.io.IOUtils;
import org.junit.Assert;
import org.junit.Test;
import opennlp.tools.ngram.NGramGenerator;
import opennlp.tools.util.StringList;
/**
* Tests for {@link opennlp.tools.languagemodel.NGramLanguageModel}
*/
public class NgramLanguageModelTest {
@Test
public void testEmptyVocabularyProbability() throws Exception {
NGramLanguageModel model = new NGramLanguageModel();
Assert.assertEquals("probability with an empty vocabulary is always 0",
0d, model.calculateProbability(new StringList("")), 0d);
Assert.assertEquals("probability with an empty vocabulary is always 0",
0d, model.calculateProbability(new StringList("1", "2", "3")), 0d);
}
@Test
public void testRandomVocabularyAndSentence() throws Exception {
NGramLanguageModel model = new NGramLanguageModel();
for (StringList sentence : LanguageModelTestUtils.generateRandomVocabulary(10)) {
model.add(sentence, 1, 3);
}
double probability = model.calculateProbability(LanguageModelTestUtils.generateRandomSentence());
Assert.assertTrue("a probability measure should be between 0 and 1 [was "
+ probability + "]", probability >= 0 && probability <= 1);
}
@Test
public void testNgramModel() throws Exception {
NGramLanguageModel model = new NGramLanguageModel(4);
model.add(new StringList("I", "saw", "the", "fox"), 1, 4);
model.add(new StringList("the", "red", "house"), 1, 4);
model.add(new StringList("I", "saw", "something", "nice"), 1, 2);
double probability = model.calculateProbability(new StringList("I", "saw", "the", "red", "house"));
Assert.assertTrue("a probability measure should be between 0 and 1 [was "
+ probability + "]", probability >= 0 && probability <= 1);
StringList tokens = model.predictNextTokens(new StringList("I", "saw"));
Assert.assertNotNull(tokens);
Assert.assertEquals(new StringList("the", "fox"), tokens);
}
@Test
public void testBigramProbabilityNoSmoothing() throws Exception {
NGramLanguageModel model = new NGramLanguageModel(2);
model.add(new StringList("<s>", "I", "am", "Sam", "</s>"), 1, 2);
model.add(new StringList("<s>", "Sam", "I", "am", "</s>"), 1, 2);
model.add(new StringList("<s>", "I", "do", "not", "like", "green", "eggs", "and", "ham", "</s>"), 1, 2);
double probability = model.calculateProbability(new StringList("<s>", "I"));
Assert.assertEquals(0.666d, probability, 0.001);
probability = model.calculateProbability(new StringList("Sam", "</s>"));
Assert.assertEquals(0.5d, probability, 0.001);
probability = model.calculateProbability(new StringList("<s>", "Sam"));
Assert.assertEquals(0.333d, probability, 0.001);
probability = model.calculateProbability(new StringList("am", "Sam"));
Assert.assertEquals(0.5d, probability, 0.001);
probability = model.calculateProbability(new StringList("I", "am"));
Assert.assertEquals(0.666d, probability, 0.001);
probability = model.calculateProbability(new StringList("I", "do"));
Assert.assertEquals(0.333d, probability, 0.001);
probability = model.calculateProbability(new StringList("I", "am", "Sam"));
Assert.assertEquals(0.333d, probability, 0.001);
}
@Test
public void testTrigram() throws Exception {
NGramLanguageModel model = new NGramLanguageModel(3);
model.add(new StringList("I", "see", "the", "fox"), 1, 3);
model.add(new StringList("the", "red", "house"), 1, 3);
model.add(new StringList("I", "saw", "something", "nice"), 1, 3);
double probability = model.calculateProbability(new StringList("I", "saw", "the", "red", "house"));
Assert.assertTrue("a probability measure should be between 0 and 1 [was "
+ probability + "]", probability >= 0 && probability <= 1);
StringList tokens = model.predictNextTokens(new StringList("I", "saw"));
Assert.assertNotNull(tokens);
Assert.assertEquals(new StringList("something"), tokens);
}
@Test
public void testBigram() throws Exception {
NGramLanguageModel model = new NGramLanguageModel(2);
model.add(new StringList("I", "see", "the", "fox"), 1, 2);
model.add(new StringList("the", "red", "house"), 1, 2);
model.add(new StringList("I", "saw", "something", "nice"), 1, 2);
double probability = model.calculateProbability(new StringList("I", "saw", "the", "red", "house"));
Assert.assertTrue("a probability measure should be between 0 and 1 [was " + probability + "]",
probability >= 0 && probability <= 1);
StringList tokens = model.predictNextTokens(new StringList("I", "saw"));
Assert.assertNotNull(tokens);
Assert.assertEquals(new StringList("something"), tokens);
}
@Test
public void testSerializedNGramLanguageModel() throws Exception {
NGramLanguageModel languageModel = new NGramLanguageModel(getClass().getResourceAsStream(
"/opennlp/tools/ngram/ngram-model.xml"), 3);
double probability = languageModel.calculateProbability(new StringList("The", "brown", "fox", "jumped"));
Assert.assertTrue("a probability measure should be between 0 and 1 [was " + probability + "]",
probability >= 0 && probability <= 1);
StringList tokens = languageModel.predictNextTokens(new StringList("the","brown","fox"));
Assert.assertNotNull(tokens);
Assert.assertEquals(new StringList("jumped"), tokens);
}
@Test
public void testTrigramLanguageModelCreationFromText() throws Exception {
int ngramSize = 3;
NGramLanguageModel languageModel = new NGramLanguageModel(ngramSize);
InputStream stream = getClass().getResourceAsStream("/opennlp/tools/languagemodel/sentences.txt");
for (String line : IOUtils.readLines(stream)) {
String[] array = line.split(" ");
List<String> split = Arrays.asList(array);
List<String> generatedStrings = NGramGenerator.generate(split, ngramSize, " ");
for (String generatedString : generatedStrings) {
String[] tokens = generatedString.split(" ");
if (tokens.length > 0) {
languageModel.add(new StringList(tokens), 1, ngramSize);
}
}
}
StringList tokens = languageModel.predictNextTokens(new StringList("neural",
"network", "language"));
Assert.assertNotNull(tokens);
Assert.assertEquals(new StringList("models"), tokens);
double p1 = languageModel.calculateProbability(new StringList("neural", "network",
"language", "models"));
double p2 = languageModel.calculateProbability(new StringList("neural", "network",
"language", "model"));
Assert.assertTrue(p1 > p2);
}
}