Java tutorial
/* * Copyright (c) 2010 the original author or authors. * * 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 org.mahout.example.classifier.naivebayes; import java.io.BufferedReader; import java.io.FileReader; import java.io.StringReader; import java.util.HashMap; import java.util.Map; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.lucene.analysis.Analyzer; import org.apache.lucene.analysis.TokenStream; import org.apache.lucene.analysis.standard.StandardAnalyzer; import org.apache.lucene.analysis.tokenattributes.CharTermAttribute; import org.apache.lucene.util.Version; import org.apache.mahout.classifier.naivebayes.BayesUtils; import org.apache.mahout.classifier.naivebayes.NaiveBayesModel; import org.apache.mahout.classifier.naivebayes.StandardNaiveBayesClassifier; import org.apache.mahout.common.Pair; import org.apache.mahout.common.iterator.sequencefile.SequenceFileIterable; import org.apache.mahout.math.RandomAccessSparseVector; import org.apache.mahout.math.Vector; import org.apache.mahout.math.Vector.Element; import org.apache.mahout.vectorizer.TFIDF; import com.google.common.collect.ConcurrentHashMultiset; import com.google.common.collect.Multiset; public class Classifier { public static Map<String, Integer> readDictionnary(Configuration conf, Path dictionnaryPath) { Map<String, Integer> dictionnary = new HashMap<String, Integer>(); for (Pair<Text, IntWritable> pair : new SequenceFileIterable<Text, IntWritable>(dictionnaryPath, true, conf)) { dictionnary.put(pair.getFirst().toString(), pair.getSecond().get()); } return dictionnary; } public static Map<Integer, Long> readDocumentFrequency(Configuration conf, Path documentFrequencyPath) { Map<Integer, Long> documentFrequency = new HashMap<Integer, Long>(); for (Pair<IntWritable, LongWritable> pair : new SequenceFileIterable<IntWritable, LongWritable>( documentFrequencyPath, true, conf)) { documentFrequency.put(pair.getFirst().get(), pair.getSecond().get()); } return documentFrequency; } public static void main(String[] args) throws Exception { if (args.length < 5) { System.out.println("Arguments: [model] [label index] [dictionnary] [document frequency] [tweet file]"); return; } String modelPath = args[0]; String labelIndexPath = args[1]; String dictionaryPath = args[2]; String documentFrequencyPath = args[3]; String tweetsPath = args[4]; Configuration configuration = new Configuration(); // model is a matrix (wordId, labelId) => probability score NaiveBayesModel model = NaiveBayesModel.materialize(new Path(modelPath), configuration); StandardNaiveBayesClassifier classifier = new StandardNaiveBayesClassifier(model); // labels is a map label => classId Map<Integer, String> labels = BayesUtils.readLabelIndex(configuration, new Path(labelIndexPath)); Map<String, Integer> dictionary = readDictionnary(configuration, new Path(dictionaryPath)); Map<Integer, Long> documentFrequency = readDocumentFrequency(configuration, new Path(documentFrequencyPath)); // analyzer used to extract word from tweet Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_43); int labelCount = labels.size(); int documentCount = documentFrequency.get(-1).intValue(); System.out.println("Number of labels: " + labelCount); System.out.println("Number of documents in training set: " + documentCount); BufferedReader reader = new BufferedReader(new FileReader(tweetsPath)); while (true) { String line = reader.readLine(); if (line == null) { break; } String[] tokens = line.split("\t", 2); String tweetId = tokens[0]; String tweet = tokens[1]; System.out.println("Tweet: " + tweetId + "\t" + tweet); Multiset<String> words = ConcurrentHashMultiset.create(); // extract words from tweet TokenStream ts = analyzer.tokenStream("text", new StringReader(tweet)); CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class); ts.reset(); int wordCount = 0; while (ts.incrementToken()) { if (termAtt.length() > 0) { String word = ts.getAttribute(CharTermAttribute.class).toString(); Integer wordId = dictionary.get(word); // if the word is not in the dictionary, skip it if (wordId != null) { words.add(word); wordCount++; } } } // create vector wordId => weight using tfidf Vector vector = new RandomAccessSparseVector(10000); TFIDF tfidf = new TFIDF(); for (Multiset.Entry<String> entry : words.entrySet()) { String word = entry.getElement(); int count = entry.getCount(); Integer wordId = dictionary.get(word); Long freq = documentFrequency.get(wordId); double tfIdfValue = tfidf.calculate(count, freq.intValue(), wordCount, documentCount); vector.setQuick(wordId, tfIdfValue); } // With the classifier, we get one score for each label // The label with the highest score is the one the tweet is more likely to // be associated to Vector resultVector = classifier.classifyFull(vector); double bestScore = -Double.MAX_VALUE; int bestCategoryId = -1; for (Element element : resultVector.all()) { int categoryId = element.index(); double score = element.get(); if (score > bestScore) { bestScore = score; bestCategoryId = categoryId; } System.out.print(" " + labels.get(categoryId) + ": " + score); } System.out.println(" => " + labels.get(bestCategoryId)); } analyzer.close(); reader.close(); } }