/*
* 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.doccat;
import java.io.IOException;
import java.util.Set;
import java.util.SortedMap;
import org.junit.Assert;
import org.junit.Test;
import opennlp.tools.ml.AbstractTrainer;
import opennlp.tools.ml.naivebayes.NaiveBayesTrainer;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.ObjectStreamUtils;
import opennlp.tools.util.TrainingParameters;
public class DocumentCategorizerNBTest {
@Test
public void testSimpleTraining() throws IOException {
ObjectStream<DocumentSample> samples = ObjectStreamUtils.createObjectStream(
new DocumentSample("1", new String[]{"a", "b", "c"}),
new DocumentSample("1", new String[]{"a", "b", "c", "1", "2"}),
new DocumentSample("1", new String[]{"a", "b", "c", "3", "4"}),
new DocumentSample("0", new String[]{"x", "y", "z"}),
new DocumentSample("0", new String[]{"x", "y", "z", "5", "6"}),
new DocumentSample("0", new String[]{"x", "y", "z", "7", "8"}));
TrainingParameters params = new TrainingParameters();
params.put(TrainingParameters.ITERATIONS_PARAM, 100);
params.put(TrainingParameters.CUTOFF_PARAM, 0);
params.put(AbstractTrainer.ALGORITHM_PARAM, NaiveBayesTrainer.NAIVE_BAYES_VALUE);
DoccatModel model = DocumentCategorizerME.train("x-unspecified", samples,
params, new DoccatFactory());
DocumentCategorizer doccat = new DocumentCategorizerME(model);
double[] aProbs = doccat.categorize(new String[]{"a"});
Assert.assertEquals("1", doccat.getBestCategory(aProbs));
double[] bProbs = doccat.categorize(new String[]{"x"});
Assert.assertEquals("0", doccat.getBestCategory(bProbs));
//test to make sure sorted map's last key is cat 1 because it has the highest score.
SortedMap<Double, Set<String>> sortedScoreMap = doccat.sortedScoreMap(new String[]{"a"});
Set<String> cat = sortedScoreMap.get(sortedScoreMap.lastKey());
Assert.assertEquals(1, cat.size());
}
}