/*
* Copyright 2016
* Ubiquitous Knowledge Processing (UKP) Lab
* Technische Universität Darmstadt
*
* Licensed 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 de.tudarmstadt.ukp.dkpro.core.opennlp;
import static org.apache.uima.fit.factory.AnalysisEngineFactory.createEngineDescription;
import static org.apache.uima.fit.factory.CollectionReaderFactory.createReaderDescription;
import static org.apache.uima.fit.pipeline.SimplePipeline.iteratePipeline;
import static org.junit.Assert.assertEquals;
import java.io.File;
import java.util.List;
import org.apache.uima.analysis_engine.AnalysisEngineDescription;
import org.apache.uima.collection.CollectionReaderDescription;
import org.apache.uima.fit.factory.ConfigurationParameterFactory;
import org.apache.uima.fit.pipeline.SimplePipeline;
import org.junit.Rule;
import org.junit.Test;
import de.tudarmstadt.ukp.dkpro.core.api.datasets.Dataset;
import de.tudarmstadt.ukp.dkpro.core.api.datasets.DatasetFactory;
import de.tudarmstadt.ukp.dkpro.core.api.datasets.Split;
import de.tudarmstadt.ukp.dkpro.core.api.lexmorph.type.pos.POS;
import de.tudarmstadt.ukp.dkpro.core.eval.EvalUtil;
import de.tudarmstadt.ukp.dkpro.core.eval.model.Span;
import de.tudarmstadt.ukp.dkpro.core.eval.report.Result;
import de.tudarmstadt.ukp.dkpro.core.io.conll.Conll2006Reader;
import de.tudarmstadt.ukp.dkpro.core.testing.DkproTestContext;
public class OpenNlpPosTaggerTrainerTest
{
@Test
public void test()
throws Exception
{
File cache = DkproTestContext.getCacheFolder();
File targetFolder = testContext.getTestOutputFolder();
// NOTE: This file contains Asciidoc markers for partial inclusion of this file in the
// documentation. Do not remove these tags!
// tag::datasets[]
// Obtain dataset
DatasetFactory loader = new DatasetFactory(cache);
Dataset ds = loader.load("gum-en-conll-2.3.2");
Split split = ds.getSplit(0.8);
// Train model
System.out.println("Training model from training data");
CollectionReaderDescription trainReader = createReaderDescription(
Conll2006Reader.class,
Conll2006Reader.PARAM_PATTERNS, split.getTrainingFiles(),
Conll2006Reader.PARAM_USE_CPOS_AS_POS, true,
Conll2006Reader.PARAM_LANGUAGE, ds.getLanguage());
// end::datasets[]
AnalysisEngineDescription trainer = createEngineDescription(
OpenNlpPosTaggerTrainer.class,
OpenNlpPosTaggerTrainer.PARAM_TARGET_LOCATION, new File(targetFolder, "model.bin"),
OpenNlpPosTaggerTrainer.PARAM_LANGUAGE, ds.getLanguage(),
OpenNlpPosTaggerTrainer.PARAM_ITERATIONS, 10);
SimplePipeline.runPipeline(trainReader, trainer);
// Apply model and collect labels
System.out.println("Applying model to test data");
CollectionReaderDescription testReader = createReaderDescription(
Conll2006Reader.class,
Conll2006Reader.PARAM_PATTERNS, split.getTestFiles(),
Conll2006Reader.PARAM_READ_POS, false,
Conll2006Reader.PARAM_LANGUAGE, ds.getLanguage());
AnalysisEngineDescription postagger = createEngineDescription(
OpenNlpPosTagger.class,
OpenNlpPosTagger.PARAM_PRINT_TAGSET, true,
OpenNlpPosTagger.PARAM_MODEL_LOCATION, new File(targetFolder, "model.bin"));
List<Span<String>> actual = EvalUtil.loadSamples(iteratePipeline(testReader, postagger),
POS.class, pos -> {
return pos.getPosValue();
});
System.out.printf("Actual samples: %d%n", actual.size());
// Read reference data collect labels
ConfigurationParameterFactory.setParameter(testReader,
Conll2006Reader.PARAM_READ_POS, true);
List<Span<String>> expected = EvalUtil.loadSamples(testReader, POS.class, pos -> {
return pos.getPosValue();
});
System.out.printf("Expected samples: %d%n", expected.size());
Result results = EvalUtil.dumpResults(targetFolder, expected, actual);
assertEquals(0.646981, results.getFscore(), 0.0001);
assertEquals(0.646981, results.getPrecision(), 0.0001);
assertEquals(0.646981, results.getRecall(), 0.0001);
}
@Rule
public DkproTestContext testContext = new DkproTestContext();
}