Java tutorial
/* * Copyright 2016 * Ubiquitous Knowledge Processing (UKP) Lab * Technische Universitt 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.experiments.argumentation.sequence.feature.lda; import cc.mallet.pipe.*; import cc.mallet.topics.ParallelTopicModel; import cc.mallet.topics.TopicInferencer; import cc.mallet.types.Instance; import cc.mallet.types.InstanceList; import de.tudarmstadt.ukp.experiments.argumentation.sequence.feature.AbstractUnitSentenceFeatureGenerator; import de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Sentence; import de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Token; import de.tudarmstadt.ukp.dkpro.tc.api.exception.TextClassificationException; import de.tudarmstadt.ukp.dkpro.tc.api.features.Feature; import org.apache.commons.io.IOUtils; import org.apache.commons.lang.StringUtils; import org.apache.uima.fit.descriptor.ConfigurationParameter; import org.apache.uima.fit.util.JCasUtil; import org.apache.uima.jcas.JCas; import org.apache.uima.resource.ResourceInitializationException; import org.apache.uima.resource.ResourceSpecifier; import java.io.IOException; import java.io.InputStream; import java.io.ObjectInputStream; import java.net.URL; import java.util.ArrayList; import java.util.List; import java.util.Map; import java.util.regex.Pattern; import static de.tudarmstadt.ukp.dkpro.core.api.resources.ResourceUtils.resolveLocation; /** * Use pre-trained LDA model with k topics created externally on a large corpus; extract k features, * each represent weight of k-th topic on the document. The topic distribution on document is * computed using Gibbs sampling (see * {@link TopicInferencer#getSampledDistribution(Instance, int, int, int)}). * <p/> * This extractor is based on lemma; thus both the pre-trained LDA and the annotated jCas must * contain lemmas. * * @author Ivan Habernal */ public class LDATopicsFeature extends AbstractUnitSentenceFeatureGenerator { public static final String PARAM_LDA_MODEL_FILE = "ldaModelFile"; @ConfigurationParameter(name = PARAM_LDA_MODEL_FILE, mandatory = true) private String ldaModelFile; private Object[][] topWords; @Override public boolean initialize(ResourceSpecifier aSpecifier, Map<String, Object> aAdditionalParams) throws ResourceInitializationException { if (!super.initialize(aSpecifier, aAdditionalParams)) { return false; } try { URL source = resolveLocation(ldaModelFile); // load(source.openStream()); // FileInputStream fis = new FileInputStream(ldaModelFile); InputStream stream = source.openStream(); ObjectInputStream ois = new ObjectInputStream(stream); this.model = (ParallelTopicModel) ois.readObject(); IOUtils.closeQuietly(stream); this.pipes = createPipes(); this.pipes.setDataAlphabet(model.getAlphabet()); for (Pipe pipe : this.pipes.pipes()) { pipe.setDataAlphabet(this.model.getAlphabet()); } // extract top words for feature naming topWords = this.model.getTopWords(10); } catch (IOException | ClassNotFoundException ex) { throw new ResourceInitializationException(ex); } return true; } // public static int repeatSimComputing = 50; public static int inferIteration = 20; // public static int wordsPerTopic = 30; // public static int numTrainIter = 500; // public static int numTopics = 100; // public static float alpha = 1.0f; // public static float beta = 0.01f; protected ParallelTopicModel model; private SerialPipes pipes; protected static SerialPipes createPipes() { ArrayList<Pipe> pipeList = new ArrayList<>(); // Pipes: lowercase, tokenize, remove stopwords, map to features pipeList.add(new CharSequenceLowercase()); pipeList.add(new CharSequence2TokenSequence(Pattern.compile("\\p{L}[\\p{L}\\p{P}]+\\p{L}"))); pipeList.add(new TokenSequenceRemoveStopwords()); pipeList.add(new TokenSequence2FeatureSequence()); return new SerialPipes(pipeList); } public double[] getVector(String text) { InstanceList instances = new InstanceList(this.pipes); instances.addThruPipe(new Instance(text, "X", "doc1", null)); TopicInferencer inference = model.getInferencer(); return inference.getSampledDistribution(instances.get(0), inferIteration, 1, 5); } @Override protected List<Feature> extract(JCas jCas, Sentence sentence, String sentencePrefix) throws TextClassificationException { StringBuilder sb = new StringBuilder(); for (Token token : JCasUtil.selectCovered(Token.class, sentence)) { String lemma = token.getLemma().getValue().toLowerCase(); sb.append(lemma); sb.append(" "); } double[] topicDistribution = getVector(sb.toString()); List<Feature> features = new ArrayList<>(topicDistribution.length); for (int i = 0; i < topicDistribution.length; i++) { double value = topicDistribution[i]; String name = getKthTopicDescription(i); features.add(new Feature(sentencePrefix + name, value)); } return features; } private String getKthTopicDescription(int k) { Object[] words = this.topWords[k]; return StringUtils.join(words, "_"); } }