| /* |
| * 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.addons.mallet; |
| |
| import java.io.IOException; |
| import java.util.ArrayList; |
| import java.util.Collection; |
| import java.util.Map; |
| |
| import opennlp.tools.ml.AbstractEventTrainer; |
| import opennlp.tools.ml.model.DataIndexer; |
| import opennlp.tools.ml.model.MaxentModel; |
| import cc.mallet.classify.C45Trainer; |
| import cc.mallet.classify.Classifier; |
| import cc.mallet.classify.MaxEntGETrainer; |
| import cc.mallet.classify.MaxEntL1Trainer; |
| import cc.mallet.classify.MaxEntPRTrainer; |
| import cc.mallet.classify.MaxEntTrainer; |
| import cc.mallet.classify.NaiveBayes; |
| import cc.mallet.classify.NaiveBayesEMTrainer; |
| import cc.mallet.classify.NaiveBayesTrainer; |
| import cc.mallet.optimize.LimitedMemoryBFGS; |
| import cc.mallet.optimize.Optimizer; |
| import cc.mallet.types.Alphabet; |
| import cc.mallet.types.FeatureVector; |
| import cc.mallet.types.Instance; |
| import cc.mallet.types.InstanceList; |
| import cc.mallet.types.LabelAlphabet; |
| |
| public class MaxentTrainer extends AbstractEventTrainer { |
| |
| @Override |
| public boolean isSortAndMerge() { |
| return true; |
| } |
| |
| @Override |
| public MaxentModel doTrain(DataIndexer indexer) throws IOException { |
| |
| int numFeatures = indexer.getPredLabels().length; |
| |
| Alphabet dataAlphabet = new Alphabet(numFeatures); |
| LabelAlphabet targetAlphabet = new LabelAlphabet(); |
| |
| Collection<Instance> instances = new ArrayList<>(); |
| |
| String predLabels[] = indexer.getPredLabels(); |
| |
| int outcomes[] = indexer.getOutcomeList(); |
| for (int contextIndex = 0; contextIndex < indexer.getContexts().length; contextIndex++) { |
| |
| int malletFeatures[] = new int[indexer.getContexts()[contextIndex].length]; |
| double weights[] = new double[indexer.getContexts()[contextIndex].length]; |
| |
| for (int featureIndex = 0; featureIndex < malletFeatures.length; featureIndex++) { |
| malletFeatures[featureIndex] = dataAlphabet.lookupIndex( |
| predLabels[indexer.getContexts()[contextIndex][featureIndex]], true); |
| |
| weights[featureIndex] = indexer.getNumTimesEventsSeen()[contextIndex]; |
| } |
| |
| FeatureVector fv = new FeatureVector(dataAlphabet, malletFeatures, weights); |
| Instance inst = new Instance(fv, targetAlphabet.lookupLabel( |
| indexer.getOutcomeLabels()[outcomes[contextIndex]], true), "fid:" + contextIndex, |
| "data-indexer"); |
| instances.add(inst); |
| } |
| |
| InstanceList trainingData = new InstanceList(dataAlphabet, targetAlphabet); |
| |
| trainingData.addAll(instances); |
| |
| MaxEntTrainer trainer = new MaxEntTrainer(); |
| // trainer.setGaussianPriorVariance(1d); |
| // trainer.setNumIterations(100); |
| |
| Classifier classifier = trainer.train(trainingData); |
| |
| return new ClassifierModel(classifier); |
| } |
| } |