opennlp.tools.doccat.DoccatCrossValidator 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.doccat;
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.Mean;
/**
* Cross validator for document categorization
*/
public class DoccatCrossValidator {
private final String languageCode;
private final TrainingParameters params;
private Mean documentAccuracy = new Mean();
private DoccatEvaluationMonitor[] listeners;
private DoccatFactory factory;
/**
* Creates a {@link DoccatCrossValidator} with the given
* {@link FeatureGenerator}s.
*/
public DoccatCrossValidator(String languageCode, TrainingParameters mlParams,
DoccatFactory factory, DoccatEvaluationMonitor ... listeners) {
this.languageCode = languageCode;
this.params = mlParams;
this.listeners = listeners;
this.factory = factory;
}
/**
* 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();
DoccatModel model = DocumentCategorizerME.train(languageCode,
trainingSampleStream, params, factory);
DocumentCategorizerEvaluator evaluator = new DocumentCategorizerEvaluator(
new DocumentCategorizerME(model), listeners);
evaluator.evaluate(trainingSampleStream.getTestSampleStream());
documentAccuracy.add(evaluator.getAccuracy(),
evaluator.getDocumentCount());
}
}
/**
* Retrieves the accuracy for all iterations.
*
* @return the word accuracy
*/
public double getDocumentAccuracy() {
return documentAccuracy.mean();
}
/**
* Retrieves the number of words which where validated over all iterations.
* The result is the amount of folds multiplied by the total number of words.
*
* @return the word count
*/
public long getDocumentCount() {
return documentAccuracy.count();
}
}