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* 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.tools.eval;
import java.io.ByteArrayOutputStream;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.math.BigInteger;
import java.net.URISyntaxException;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.nio.file.StandardCopyOption;
import java.security.MessageDigest;
import java.util.Map;
import org.junit.Assert;
import org.junit.BeforeClass;
import org.junit.Test;
import opennlp.tools.cmdline.namefind.TokenNameFinderTrainerTool;
import opennlp.tools.formats.DirectorySampleStream;
import opennlp.tools.formats.convert.FileToStringSampleStream;
import opennlp.tools.formats.ontonotes.OntoNotesNameSampleStream;
import opennlp.tools.namefind.NameSample;
import opennlp.tools.namefind.NameSampleTypeFilter;
import opennlp.tools.namefind.TokenNameFinderCrossValidator;
import opennlp.tools.namefind.TokenNameFinderFactory;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.TrainingParameters;
import opennlp.tools.util.model.ModelUtil;
public class OntoNotes4NameFinderEval {
private static ObjectStream<NameSample> createNameSampleStream() throws IOException {
ObjectStream<File> documentStream = new DirectorySampleStream(new File(
EvalUtil.getOpennlpDataDir(), "ontonotes4/data/files/data/english"),
file -> {
if (file.isFile()) {
return file.getName().endsWith(".name");
}
return file.isDirectory();
}, true);
return new OntoNotesNameSampleStream(new FileToStringSampleStream(
documentStream, StandardCharsets.UTF_8));
}
private static void crossEval(TrainingParameters params, String type, double expectedScore)
throws IOException {
try (ObjectStream<NameSample> samples = createNameSampleStream()) {
TokenNameFinderCrossValidator cv = new TokenNameFinderCrossValidator("en", null,
params, new TokenNameFinderFactory());
ObjectStream<NameSample> filteredSamples;
if (type != null) {
filteredSamples = new NameSampleTypeFilter(new String[] {type}, samples);
}
else {
filteredSamples = samples;
}
cv.evaluate(filteredSamples, 5);
Assert.assertEquals(expectedScore, cv.getFMeasure().getFMeasure(), 0.001d);
}
}
@BeforeClass
public static void verifyTrainingData() throws IOException {
MessageDigest digest = EvalUtil.createDigest();
try (ObjectStream<NameSample> samples = createNameSampleStream()) {
NameSample sample;
while ((sample = samples.read()) != null) {
digest.update(sample.toString().getBytes(StandardCharsets.UTF_8));
}
Assert.assertEquals(new BigInteger("168206908604555450993491898907821588182"),
new BigInteger(1, digest.digest()));
}
}
@Test
public void evalEnglishPersonNameFinder() throws IOException {
TrainingParameters params = ModelUtil.createDefaultTrainingParameters();
params.put("Threads", "4");
crossEval(params, "person", 0.822014580552418d);
}
@Test
public void evalEnglishDateNameFinder() throws IOException {
TrainingParameters params = ModelUtil.createDefaultTrainingParameters();
params.put("Threads", "4");
crossEval(params, "date", 0.8043873255040994d);
}
@Test
public void evalAllTypesNameFinder() throws IOException {
TrainingParameters params = ModelUtil.createDefaultTrainingParameters();
params.put("Threads", "4");
crossEval(params, null, 0.8014054850253551d);
}
@Test
public void evalAllTypesWithPOSNameFinder() throws IOException, URISyntaxException {
TrainingParameters params = ModelUtil.createDefaultTrainingParameters();
params.put("Threads", "4");
// load the feature generator
ByteArrayOutputStream bytes = new ByteArrayOutputStream();
try (InputStream in = this.getClass().getResourceAsStream(
"ner-en_pos-features.xml")) {
byte[] buf = new byte[1024];
int len;
while ((len = in.read(buf)) > 0) {
bytes.write(buf, 0, len);
}
}
byte[] featureGen = bytes.toByteArray();
// create a temp resource folder and copy the pos model there
Path resourcesPath = Files.createTempDirectory("opennlp_resources");
Files.copy(new File(EvalUtil.getOpennlpDataDir(), "models-sf/en-pos-perceptron.bin").toPath(),
new File(resourcesPath.toFile(), "en-pos-perceptron.bin").toPath(),
StandardCopyOption.REPLACE_EXISTING);
Map<String, Object> resources = TokenNameFinderTrainerTool.loadResources(resourcesPath.toFile(),
Paths.get(this.getClass().getResource("ner-en_pos-features.xml").toURI()).toFile());
try (ObjectStream<NameSample> samples = createNameSampleStream()) {
TokenNameFinderCrossValidator cv = new TokenNameFinderCrossValidator("en", null,
params, featureGen, resources);
ObjectStream<NameSample> filteredSamples;
filteredSamples = samples;
cv.evaluate(filteredSamples, 5);
Assert.assertEquals(0.8070226153653437d, cv.getFMeasure().getFMeasure(), 0.001d);
}
}
}