/*
* 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.postag;
import java.io.File;
import java.io.IOException;
import java.util.Map;
import opennlp.tools.dictionary.Dictionary;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.TrainingParameters;
import opennlp.tools.util.eval.CrossValidationPartitioner;
import opennlp.tools.util.eval.Mean;
public class POSTaggerCrossValidator {
private final String languageCode;
private final TrainingParameters params;
private byte[] featureGeneratorBytes;
private Map<String, Object> resources;
private Mean wordAccuracy = new Mean();
private POSTaggerEvaluationMonitor[] listeners;
/* this will be used to load the factory after the ngram dictionary was created */
private String factoryClassName;
/* user can also send a ready to use factory */
private POSTaggerFactory factory;
private Integer tagdicCutoff = null;
private File tagDictionaryFile;
/**
* Creates a {@link POSTaggerCrossValidator} that builds a ngram dictionary
* dynamically. It instantiates a sub-class of {@link POSTaggerFactory} using
* the tag and the ngram dictionaries.
*/
public POSTaggerCrossValidator(String languageCode,
TrainingParameters trainParam, File tagDictionary,
byte[] featureGeneratorBytes, Map<String, Object> resources,
Integer tagdicCutoff, String factoryClass,
POSTaggerEvaluationMonitor... listeners) {
this.languageCode = languageCode;
this.params = trainParam;
this.featureGeneratorBytes = featureGeneratorBytes;
this.resources = resources;
this.listeners = listeners;
this.factoryClassName = factoryClass;
this.tagdicCutoff = tagdicCutoff;
this.tagDictionaryFile = tagDictionary;
}
/**
* Creates a {@link POSTaggerCrossValidator} using the given
* {@link POSTaggerFactory}.
*/
public POSTaggerCrossValidator(String languageCode,
TrainingParameters trainParam, POSTaggerFactory factory,
POSTaggerEvaluationMonitor... listeners) {
this.languageCode = languageCode;
this.params = trainParam;
this.listeners = listeners;
this.factory = factory;
this.tagdicCutoff = null;
}
/**
* Starts the evaluation.
*
* @param samples
* the data to train and test
* @param nFolds
* number of folds
*
* @throws IOException
*/
public void evaluate(ObjectStream<POSSample> samples, int nFolds) throws IOException {
CrossValidationPartitioner<POSSample> partitioner = new CrossValidationPartitioner<>(
samples, nFolds);
while (partitioner.hasNext()) {
CrossValidationPartitioner.TrainingSampleStream<POSSample> trainingSampleStream = partitioner
.next();
if (this.tagDictionaryFile != null
&& this.factory.getTagDictionary() == null) {
this.factory.setTagDictionary(this.factory
.createTagDictionary(tagDictionaryFile));
}
TagDictionary dict = null;
if (this.tagdicCutoff != null) {
dict = this.factory.getTagDictionary();
if (dict == null) {
dict = this.factory.createEmptyTagDictionary();
}
if (dict instanceof MutableTagDictionary) {
POSTaggerME.populatePOSDictionary(trainingSampleStream, (MutableTagDictionary)dict,
this.tagdicCutoff);
} else {
throw new IllegalArgumentException(
"Can't extend a TagDictionary that does not implement MutableTagDictionary.");
}
trainingSampleStream.reset();
}
if (this.factory == null) {
this.factory = POSTaggerFactory.create(this.factoryClassName, null, null);
}
factory.init(featureGeneratorBytes, resources, dict);
POSModel model = POSTaggerME.train(languageCode, trainingSampleStream,
params, this.factory);
POSEvaluator evaluator = new POSEvaluator(new POSTaggerME(model), listeners);
evaluator.evaluate(trainingSampleStream.getTestSampleStream());
wordAccuracy.add(evaluator.getWordAccuracy(), evaluator.getWordCount());
if (this.tagdicCutoff != null) {
this.factory.setTagDictionary(null);
}
}
}
/**
* Retrieves the accuracy for all iterations.
*
* @return the word accuracy
*/
public double getWordAccuracy() {
return wordAccuracy.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 getWordCount() {
return wordAccuracy.count();
}
private static POSTaggerFactory create(Dictionary ngram, TagDictionary pos) {
return new POSTaggerFactory(ngram, pos);
}
}