opennlp.tools.chunker.ChunkerCrossValidator Maven / Gradle / Ivy
/*
* 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.chunker;
import java.io.IOException;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.TrainingParameters;
import opennlp.tools.util.eval.CrossValidationPartitioner;
import opennlp.tools.util.eval.FMeasure;
public class ChunkerCrossValidator {
private final String languageCode;
private final TrainingParameters params;
private FMeasure fmeasure = new FMeasure();
private ChunkerEvaluationMonitor[] listeners;
private ChunkerFactory chunkerFactory;
public ChunkerCrossValidator(String languageCode, TrainingParameters params,
ChunkerFactory factory, ChunkerEvaluationMonitor... listeners) {
this.chunkerFactory = factory;
this.languageCode = languageCode;
this.params = params;
this.listeners = listeners;
}
/**
* Starts the evaluation.
*
* @param samples
* the data to train and test
* @param nFolds
* number of folds
*
* @throws IOException
*/
public void evaluate(ObjectStream samples, int nFolds)
throws IOException {
CrossValidationPartitioner partitioner = new CrossValidationPartitioner<>(
samples, nFolds);
while (partitioner.hasNext()) {
CrossValidationPartitioner.TrainingSampleStream trainingSampleStream = partitioner
.next();
ChunkerModel model = ChunkerME.train(languageCode, trainingSampleStream,
params, chunkerFactory);
// do testing
ChunkerEvaluator evaluator = new ChunkerEvaluator(new ChunkerME(model), listeners);
evaluator.evaluate(trainingSampleStream.getTestSampleStream());
fmeasure.mergeInto(evaluator.getFMeasure());
}
}
public FMeasure getFMeasure() {
return fmeasure;
}
}