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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,
* 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.
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package opennlp.maxent.quasinewton;
import static opennlp.PrepAttachDataUtil.createTrainingStream;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNotNull;
import static org.junit.Assert.assertTrue;
import java.io.BufferedReader;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.DataInputStream;
import java.io.DataOutputStream;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.List;
import opennlp.model.AbstractModel;
import opennlp.model.BinaryFileDataReader;
import opennlp.model.DataIndexer;
import opennlp.model.Event;
import opennlp.model.GenericModelReader;
import opennlp.model.GenericModelWriter;
import opennlp.model.MaxentModel;
import opennlp.model.OnePassRealValueDataIndexer;
import opennlp.model.RealValueFileEventStream;
import opennlp.model.TwoPassDataIndexer;
import opennlp.perceptron.PerceptronPrepAttachTest;
import org.junit.Test;
public class QNTrainerTest {
@Test
public void testTrainModelReturnsAQNModel() throws Exception {
// given
RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
// when
QNModel trainedModel = new QNTrainer(false).trainModel(testDataIndexer);
// then
assertNotNull(trainedModel);
}
@Test
public void testInTinyDevSet() throws Exception {
// given
RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
// when
QNModel trainedModel = new QNTrainer(15, true).trainModel(testDataIndexer);
String[] features2Classify = new String[] {"feature2","feature3", "feature3", "feature3","feature3", "feature3", "feature3","feature3", "feature3", "feature3","feature3", "feature3"};
double[] eval = trainedModel.eval(features2Classify);
// then
assertNotNull(eval);
}
@Test
public void testInBigDevSet() throws IOException {
QNModel trainedModel = new QNTrainer(10, 1000, true).trainModel(new TwoPassDataIndexer(createTrainingStream()));
// then
testModel(trainedModel);
}
@Test
public void testModel() throws IOException {
// given
RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
// when
QNModel trainedModel = new QNTrainer(15, true).trainModel(testDataIndexer);
assertTrue(trainedModel.equals(trainedModel));
assertFalse(trainedModel.equals(null));
}
@Test
public void testSerdeModel() throws IOException {
// given
RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
// when
// QNModel trainedModel = new QNTrainer(5, 500, true).trainModel(new TwoPassDataIndexer(createTrainingStream()));
QNModel trainedModel = new QNTrainer(5, 700, true).trainModel(testDataIndexer);
ByteArrayOutputStream modelBytes = new ByteArrayOutputStream();
GenericModelWriter modelWriter = new GenericModelWriter(trainedModel, new DataOutputStream(modelBytes));
modelWriter.persist();
modelWriter.close();
GenericModelReader modelReader = new GenericModelReader(new BinaryFileDataReader(
new ByteArrayInputStream(modelBytes.toByteArray())));
AbstractModel readModel = modelReader.getModel();
QNModel deserModel = (QNModel) readModel;
assertTrue(trainedModel.equals(deserModel));
String[] features2Classify = new String[] {"feature2","feature3", "feature3", "feature3","feature3", "feature3", "feature3","feature3", "feature3", "feature3","feature3", "feature3"};
double[] eval01 = trainedModel.eval(features2Classify);
double[] eval02 = deserModel.eval(features2Classify);
assertEquals(eval01.length, eval02.length);
for (int i = 0; i < eval01.length; i++) {
assertEquals(eval01[i], eval02[i], 0.00000001);
}
}
public static void testModel(MaxentModel model) throws IOException {
List<Event> devEvents = readPpaFile("devset");
int total = 0;
int correct = 0;
for (Event ev: devEvents) {
String targetLabel = ev.getOutcome();
double[] ocs = model.eval(ev.getContext());
int best = 0;
for (int i=1; i<ocs.length; i++)
if (ocs[i] > ocs[best])
best = i;
String predictedLabel = model.getOutcome(best);
if (targetLabel.equals(predictedLabel))
correct++;
total++;
}
double accuracy = correct/(double)total;
System.out.println("Accuracy on PPA devset: (" + correct + "/" + total + ") " + accuracy);
}
private static List<Event> readPpaFile(String filename) throws IOException {
List<Event> events = new ArrayList<Event>();
InputStream in = PerceptronPrepAttachTest.class.getResourceAsStream("/data/ppa/" +
filename);
try {
BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
String line;
while ((line = reader.readLine()) != null) {
String[] items = line.split("\\s+");
String label = items[5];
String[] context = { "verb=" + items[1], "noun=" + items[2],
"prep=" + items[3], "prep_obj=" + items[4] };
events.add(new Event(label, context));
}
} finally {
in.close();
}
return events;
}
}