List of usage examples for org.apache.lucene.classification.utils ConfusionMatrixGenerator getConfusionMatrix
public static <T> ConfusionMatrix getConfusionMatrix(IndexReader reader, Classifier<T> classifier, String classFieldName, String textFieldName, long timeoutMilliseconds) throws IOException
From source file:com.github.tteofili.looseen.Test20NewsgroupsClassification.java
License:Apache License
private void testClassifier(final IndexReader ar, long startTime, IndexReader testReader, ExecutorService service, List<Future<String>> futures, Classifier<BytesRef> classifier) { futures.add(service.submit(() -> { ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix; if (split) { confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(testReader, classifier, CATEGORY_FIELD, BODY_FIELD, 60000 * 30); } else {/* ww w . j a v a2 s . c o m*/ confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(ar, classifier, CATEGORY_FIELD, BODY_FIELD, 60000 * 30); } final long endTime = System.currentTimeMillis(); final int elapse = (int) (endTime - startTime) / 1000; return " * " + classifier + " \n * accuracy = " + confusionMatrix.getAccuracy() + "\n * precision = " + confusionMatrix.getPrecision() + "\n * recall = " + confusionMatrix.getRecall() + "\n * f1-measure = " + confusionMatrix.getF1Measure() + "\n * avgClassificationTime = " + confusionMatrix.getAvgClassificationTime() + "\n * time = " + elapse + " (sec)\n "; })); }
From source file:com.github.tteofili.looseen.TestWikipediaClassification.java
License:Apache License
@Test public void testItalianWikipedia() throws Exception { String indexProperty = System.getProperty("index"); if (indexProperty != null) { try {/*w w w. j a v a2 s .c o m*/ index = Boolean.valueOf(indexProperty); } catch (Exception e) { // ignore } } String splitProperty = System.getProperty("split"); if (splitProperty != null) { try { split = Boolean.valueOf(splitProperty); } catch (Exception e) { // ignore } } Path mainIndexPath = Paths.get(INDEX + "/original"); Directory directory = FSDirectory.open(mainIndexPath); Path trainPath = Paths.get(INDEX + "/train"); Path testPath = Paths.get(INDEX + "/test"); Path cvPath = Paths.get(INDEX + "/cv"); FSDirectory cv = null; FSDirectory test = null; FSDirectory train = null; DirectoryReader testReader = null; if (split) { cv = FSDirectory.open(cvPath); test = FSDirectory.open(testPath); train = FSDirectory.open(trainPath); } if (index) { delete(mainIndexPath); if (split) { delete(trainPath, testPath, cvPath); } } IndexReader reader = null; try { Collection<String> stopWordsList = Arrays.asList("di", "a", "da", "in", "per", "tra", "fra", "il", "lo", "la", "i", "gli", "le"); CharArraySet stopWords = new CharArraySet(stopWordsList, true); Analyzer analyzer = new ItalianAnalyzer(stopWords); if (index) { System.out.format("Indexing Italian Wikipedia...%n"); long startIndex = System.currentTimeMillis(); IndexWriter indexWriter = new IndexWriter(directory, new IndexWriterConfig(analyzer)); importWikipedia(new File(PREFIX + "/itwiki/itwiki-20150405-pages-meta-current1.xml"), indexWriter); importWikipedia(new File(PREFIX + "/itwiki/itwiki-20150405-pages-meta-current2.xml"), indexWriter); importWikipedia(new File(PREFIX + "/itwiki/itwiki-20150405-pages-meta-current3.xml"), indexWriter); importWikipedia(new File(PREFIX + "/itwiki/itwiki-20150405-pages-meta-current4.xml"), indexWriter); long endIndex = System.currentTimeMillis(); System.out.format("Indexed %d pages in %ds %n", indexWriter.maxDoc(), (endIndex - startIndex) / 1000); indexWriter.close(); } if (split && !index) { reader = DirectoryReader.open(train); } else { reader = DirectoryReader.open(directory); } if (index && split) { // split the index System.out.format("Splitting the index...%n"); long startSplit = System.currentTimeMillis(); DatasetSplitter datasetSplitter = new DatasetSplitter(0.1, 0); for (LeafReaderContext context : reader.leaves()) { datasetSplitter.split(context.reader(), train, test, cv, analyzer, false, CATEGORY_FIELD, TEXT_FIELD, CATEGORY_FIELD); } reader.close(); reader = DirectoryReader.open(train); // using the train index from now on long endSplit = System.currentTimeMillis(); System.out.format("Splitting done in %ds %n", (endSplit - startSplit) / 1000); } final long startTime = System.currentTimeMillis(); List<Classifier<BytesRef>> classifiers = new LinkedList<>(); classifiers.add(new KNearestNeighborClassifier(reader, new ClassicSimilarity(), analyzer, null, 1, 0, 0, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, new BM25Similarity(), analyzer, null, 1, 0, 0, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, null, analyzer, null, 1, 0, 0, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, new LMDirichletSimilarity(), analyzer, null, 3, 1, 1, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, new LMJelinekMercerSimilarity(0.3f), analyzer, null, 3, 1, 1, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, new ClassicSimilarity(), analyzer, null, 3, 0, 0, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, new ClassicSimilarity(), analyzer, null, 3, 1, 1, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, new DFRSimilarity(new BasicModelG(), new AfterEffectB(), new NormalizationH1()), analyzer, null, 3, 1, 1, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, new DFRSimilarity(new BasicModelP(), new AfterEffectL(), new NormalizationH3()), analyzer, null, 3, 1, 1, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, new IBSimilarity(new DistributionSPL(), new LambdaDF(), new Normalization.NoNormalization()), analyzer, null, 3, 1, 1, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestNeighborClassifier(reader, new IBSimilarity(new DistributionLL(), new LambdaTTF(), new NormalizationH1()), analyzer, null, 3, 1, 1, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 5, 1, 100)); classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 10, 1, 100)); classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 15, 1, 100)); classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 15, 3, 100)); classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 15, 3, 300)); classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 5, 3, 100)); classifiers.add(new KNearestFuzzyClassifier(reader, new ClassicSimilarity(), analyzer, null, 3, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestFuzzyClassifier(reader, new ClassicSimilarity(), analyzer, null, 1, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestFuzzyClassifier(reader, new BM25Similarity(), analyzer, null, 3, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new KNearestFuzzyClassifier(reader, new BM25Similarity(), analyzer, null, 1, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new BM25NBClassifier(reader, analyzer, null, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new CachingNaiveBayesClassifier(reader, analyzer, null, CATEGORY_FIELD, TEXT_FIELD)); classifiers.add(new SimpleNaiveBayesClassifier(reader, analyzer, null, CATEGORY_FIELD, TEXT_FIELD)); int maxdoc; if (split) { testReader = DirectoryReader.open(test); maxdoc = testReader.maxDoc(); } else { maxdoc = reader.maxDoc(); } System.out.format("Starting evaluation on %d docs...%n", maxdoc); ExecutorService service = Executors.newCachedThreadPool(); List<Future<String>> futures = new LinkedList<>(); for (Classifier<BytesRef> classifier : classifiers) { final IndexReader finalReader = reader; final DirectoryReader finalTestReader = testReader; futures.add(service.submit(() -> { ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix; if (split) { confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(finalTestReader, classifier, CATEGORY_FIELD, TEXT_FIELD, 60000 * 30); } else { confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(finalReader, classifier, CATEGORY_FIELD, TEXT_FIELD, 60000 * 30); } final long endTime = System.currentTimeMillis(); final int elapse = (int) (endTime - startTime) / 1000; return " * " + classifier + " \n * accuracy = " + confusionMatrix.getAccuracy() + "\n * precision = " + confusionMatrix.getPrecision() + "\n * recall = " + confusionMatrix.getRecall() + "\n * f1-measure = " + confusionMatrix.getF1Measure() + "\n * avgClassificationTime = " + confusionMatrix.getAvgClassificationTime() + "\n * time = " + elapse + " (sec)\n "; })); } for (Future<String> f : futures) { System.out.println(f.get()); } Thread.sleep(10000); service.shutdown(); } finally { try { if (reader != null) { reader.close(); } if (directory != null) { directory.close(); } if (test != null) { test.close(); } if (train != null) { train.close(); } if (cv != null) { cv.close(); } if (testReader != null) { testReader.close(); } } catch (Throwable e) { e.printStackTrace(); } } }