List of usage examples for weka.classifiers.trees J48 setOptions
@Override public void setOptions(String[] options) throws Exception
From source file:edu.uga.cs.fluxbuster.classification.Classifier.java
License:Open Source License
/** * Executes the classifier./*from w w w .j av a 2s . c om*/ * * @param prepfeatures the prepared features in arff format * @param modelfile the path to the serialized model * @param clusters the clusters to classify * @return a map of the classified clusters, the keys are the classes * and the values are lists of cluster id's belonging to those classes */ private Map<ClusterClass, List<StoredDomainCluster>> executeClassifier(String prepfeatures, String modelfile, List<StoredDomainCluster> clusters) { Map<ClusterClass, List<StoredDomainCluster>> retval = new HashMap<ClusterClass, List<StoredDomainCluster>>(); try { DataSource source = new DataSource(new ByteArrayInputStream(prepfeatures.getBytes())); Instances data = source.getDataSet(); if (data.classIndex() == -1) { data.setClassIndex(data.numAttributes() - 1); } String[] options = weka.core.Utils.splitOptions("-p 0"); J48 cls = (J48) weka.core.SerializationHelper.read(modelfile); cls.setOptions(options); for (int i = 0; i < data.numInstances(); i++) { double pred = cls.classifyInstance(data.instance(i)); ClusterClass clusClass = ClusterClass .valueOf(data.classAttribute().value((int) pred).toUpperCase()); if (!retval.containsKey(clusClass)) { retval.put(clusClass, new ArrayList<StoredDomainCluster>()); } retval.get(clusClass).add(clusters.get(i)); } } catch (Exception e) { if (log.isErrorEnabled()) { log.error("Error executing classifier.", e); } } return retval; }
From source file:examples.TrainerFrame.java
private void jButtonTrainActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButtonTrainActionPerformed //This is a temporary fix to make it appear like its finished pBar.setMaximum(7);//from ww w . j a v a 2 s . co m pBar.setValue(0); pBar.repaint(); jLabelTrainerStatus.setText("Extracting Target Features"); //Generate Target Features String featuresTarget = null; new Thread(new TrainerFrame.thread1()).start(); try { featuresTarget = GlobalData.getFeatures(jTextFieldCallDirectory.getText()); } catch (FileNotFoundException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(1); pBar.repaint(); jLabelTrainerStatus.setText("Extracting Other Features"); //Generate Non-targe features Features String featuresOther = null; new Thread(new TrainerFrame.thread1()).start(); try { featuresOther = GlobalData.getFeatures(jTextFieldOtherSoundDirectory.getText()); } catch (FileNotFoundException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(2); pBar.repaint(); jLabelTrainerStatus.setText("Parsing Features"); //Load Target Arrf File BufferedReader readerTarget; Instances dataTarget = null; try { readerTarget = new BufferedReader(new FileReader(featuresTarget)); dataTarget = new Instances(readerTarget); } catch (FileNotFoundException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } catch (IOException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(3); pBar.repaint(); //Load Other Arrf File BufferedReader readerOther; Instances dataOther = null; try { readerOther = new BufferedReader(new FileReader(featuresOther)); dataOther = new Instances(readerOther); } catch (FileNotFoundException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } catch (IOException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(4); pBar.repaint(); jLabelTrainerStatus.setText("Training Classifier"); Instances newData = new Instances(dataTarget); FastVector typeList = new FastVector() { }; typeList.add("target"); typeList.add("other"); newData.insertAttributeAt(new Attribute("NewNominal", (java.util.List<String>) typeList), newData.numAttributes()); for (Instance instance : newData) { instance.setValue(newData.numAttributes() - 1, "target"); } dataOther.insertAttributeAt(new Attribute("NewNominal", (java.util.List<String>) typeList), dataOther.numAttributes()); for (Instance instance : dataOther) { instance.setValue(newData.numAttributes() - 1, "other"); newData.add(instance); } newData.setClassIndex(newData.numAttributes() - 1); pBar.setValue(5); pBar.repaint(); ArffSaver saver = new ArffSaver(); saver.setInstances(newData); try { saver.setFile(new File("AnimalCallTrainingFile.arff")); } catch (IOException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } try { saver.writeBatch(); } catch (IOException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(6); pBar.repaint(); //Train a classifier String[] options = new String[1]; options[0] = "-U"; J48 tree = new J48(); try { tree.setOptions(options); } catch (Exception ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } try { tree.buildClassifier(newData); } catch (Exception ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } Debug.saveToFile("Classifiers/" + jTextFieldClassifierName.getText(), tree); System.out.println("classifier saved"); MyClassifier tempClass = new MyClassifier(jTextFieldClassifierName.getText()); GlobalData.classifierList.addElement(tempClass.name); pBar.setValue(7); pBar.repaint(); jLabelTrainerStatus.setText("Finished"); }
From source file:function.BuildClassifier.java
public static void buildClassifier(Instances inst) throws Exception { String[] options = new String[1]; options[0] = "-U"; J48 tree = new J48(); tree.setOptions(options); tree.buildClassifier(inst);// w w w .ja va2 s .c o m }
From source file:ia02classificacao.IA02Classificacao.java
/** * @param args the command line arguments */// w w w . j a v a2s .co m public static void main(String[] args) throws Exception { // abre o banco de dados arff e mostra a quantidade de instancias (linhas) DataSource arquivo = new DataSource("data/zoo.arff"); Instances dados = arquivo.getDataSet(); System.out.println("Instancias lidas: " + dados.numInstances()); // FILTER: remove o atributo nome do animal da classificao String[] parametros = new String[] { "-R", "1" }; Remove filtro = new Remove(); filtro.setOptions(parametros); filtro.setInputFormat(dados); dados = Filter.useFilter(dados, filtro); AttributeSelection selAtributo = new AttributeSelection(); InfoGainAttributeEval avaliador = new InfoGainAttributeEval(); Ranker busca = new Ranker(); selAtributo.setEvaluator(avaliador); selAtributo.setSearch(busca); selAtributo.SelectAttributes(dados); int[] indices = selAtributo.selectedAttributes(); System.out.println("Selected attributes: " + Utils.arrayToString(indices)); // Usa o algoritimo J48 e mostra a classificao dos dados em forma textual String[] opcoes = new String[1]; opcoes[0] = "-U"; J48 arvore = new J48(); arvore.setOptions(opcoes); arvore.buildClassifier(dados); System.out.println(arvore); // Usa o algoritimo J48 e mostra a classificao de dados em forma grafica /* TreeVisualizer tv = new TreeVisualizer(null, arvore.graph(), new PlaceNode2()); JFrame frame = new javax.swing.JFrame("?rvore de Conhecimento"); frame.setSize(800,500); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); frame.getContentPane().add(tv); frame.setVisible(true); tv.fitToScreen(); */ /* * Classificao de novos dados */ System.out.println("\n\nCLASSIFICAO DE NOVOS DADOS"); // criar atributos double[] vals = new double[dados.numAttributes()]; vals[0] = 1.0; // hair vals[1] = 0.0; // feathers vals[2] = 0.0; // eggs vals[3] = 1.0; // milk vals[4] = 1.0; // airborne vals[5] = 0.0; // aquatic vals[6] = 0.0; // predator vals[7] = 1.0; // toothed vals[8] = 1.0; // backbone vals[9] = 1.0; // breathes vals[10] = 0.0; // venomous vals[11] = 0.0; // fins vals[12] = 4.0; // legs vals[13] = 1.0; // tail vals[14] = 1.0; // domestic vals[15] = 1.0; // catsize // Criar uma instncia baseada nestes atributos Instance meuUnicornio = new DenseInstance(1.0, vals); // Adicionar a instncia nos dados meuUnicornio.setDataset(dados); // Classificar esta nova instncia double label = arvore.classifyInstance(meuUnicornio); // Imprimir o resultado da classificao System.out.println("Novo Animal: Unicrnio"); System.out.println("classificacao: " + dados.classAttribute().value((int) label)); /* * Avaliao e predio de erros de mtrica */ System.out.println("\n\nAVALIAO E PREDIO DE ERROS DE MTRICA"); Classifier cl = new J48(); Evaluation eval_roc = new Evaluation(dados); eval_roc.crossValidateModel(cl, dados, 10, new Random(1), new Object[] {}); System.out.println(eval_roc.toSummaryString()); /* * Matriz de confuso */ System.out.println("\n\nMATRIZ DE CONFUSO"); double[][] confusionMatrix = eval_roc.confusionMatrix(); System.out.println(eval_roc.toMatrixString()); }
From source file:ia03classificador.jFrClassificador.java
public void doClassificate() throws Exception { // Quando clicado, a variavel recebe 1, quando no clicado recebe 0 v00 = ((btn00.isSelected()) ? ((double) 1) : ((double) 0)); v01 = ((btn01.isSelected()) ? ((double) 1) : ((double) 0)); v02 = ((btn02.isSelected()) ? ((double) 1) : ((double) 0)); v03 = ((btn03.isSelected()) ? ((double) 1) : ((double) 0)); v04 = ((btn04.isSelected()) ? ((double) 1) : ((double) 0)); v05 = ((btn05.isSelected()) ? ((double) 1) : ((double) 0)); v06 = ((btn06.isSelected()) ? ((double) 1) : ((double) 0)); v07 = ((btn07.isSelected()) ? ((double) 1) : ((double) 0)); v08 = ((btn08.isSelected()) ? ((double) 1) : ((double) 0)); v09 = ((btn09.isSelected()) ? ((double) 1) : ((double) 0)); v10 = ((btn10.isSelected()) ? ((double) 1) : ((double) 0)); v11 = ((btn11.isSelected()) ? ((double) 1) : ((double) 0)); v13 = ((btn13.isSelected()) ? ((double) 1) : ((double) 0)); v14 = ((btn14.isSelected()) ? ((double) 1) : ((double) 0)); v15 = ((btn15.isSelected()) ? ((double) 1) : ((double) 0)); legs = txtLegs.getText();//w w w.j av a2 s . co m legs = ((legs == null || legs.trim().isEmpty() ? "2" : legs)); name = txtName.getText(); // abre o banco de dados arff e guarda os registros no objeto dados ConverterUtils.DataSource arquivo = new ConverterUtils.DataSource("data/zoo.arff"); Instances dados = arquivo.getDataSet(); // FILTER: remove o atributo nome do animal da classificao String[] parametros = new String[] { "-R", "1" }; Remove filtro = new Remove(); filtro.setOptions(parametros); filtro.setInputFormat(dados); dados = Filter.useFilter(dados, filtro); AttributeSelection selAtributo = new AttributeSelection(); InfoGainAttributeEval avaliador = new InfoGainAttributeEval(); Ranker busca = new Ranker(); selAtributo.setEvaluator(avaliador); selAtributo.setSearch(busca); selAtributo.SelectAttributes(dados); int[] indices = selAtributo.selectedAttributes(); //System.out.println("Selected attributes: " + Utils.arrayToString(indices)); // Usa o algoritimo J48 para montar a arvore de dados String[] opcoes = new String[1]; opcoes[0] = "-U"; J48 arvore = new J48(); arvore.setOptions(opcoes); arvore.buildClassifier(dados); // cria o novo elemento para comparao double[] vals = new double[dados.numAttributes()]; vals[0] = v00; // hair vals[1] = v01; // feathers vals[2] = v02; // eggs vals[3] = v03; // milk vals[4] = v04; // airborne vals[5] = v05; // aquatic vals[6] = v06; // predator vals[7] = v07; // toothed vals[8] = v08; // backbone vals[9] = v09; // breathes vals[10] = v10; // venomous vals[11] = v11; // fins vals[12] = Double.parseDouble(legs); // legs vals[13] = v13; // tail vals[14] = v14; // domestic vals[15] = v15; // catsize // Criar uma instncia baseada nestes atributos Instance newAnimal = new DenseInstance(1.0, vals); // Adicionar a instncia nos dados newAnimal.setDataset(dados); // Classificar esta nova instncia double label = arvore.classifyInstance(newAnimal); // Imprimir o resultado da classificao lblClassification.setText(dados.classAttribute().value((int) label)); }
From source file:org.dkpro.similarity.algorithms.ml.ClassifierSimilarityMeasure.java
License:Open Source License
public static Classifier getClassifier(WekaClassifier classifier) throws IllegalArgumentException { try {//from w w w .j a v a 2s . c o m switch (classifier) { case NAIVE_BAYES: return new NaiveBayes(); case J48: J48 j48 = new J48(); j48.setOptions(new String[] { "-C", "0.25", "-M", "2" }); return j48; case SMO: SMO smo = new SMO(); smo.setOptions(Utils.splitOptions( "-C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\"")); return smo; case LOGISTIC: Logistic logistic = new Logistic(); logistic.setOptions(Utils.splitOptions("-R 1.0E-8 -M -1")); return logistic; default: throw new IllegalArgumentException("Classifier " + classifier + " not found!"); } } catch (Exception e) { throw new IllegalArgumentException(e); } }
From source file:personality_prediction.Classifier.java
/** * @param args the command line arguments *//*from w w w . ja v a 2s.c o m*/ void run_classifier() { // TODO code application logic here try { //csv2arff(); System.out.println("Enter the class for which you want to classify"); System.out.println("1..Openness"); System.out.println("2..Neuroticism"); System.out.println("3..Agreeableness"); System.out.println("4..Conscientiousness"); System.out.println("5..Extraversion"); System.out.println(); Scanner sc = new Scanner(System.in); int choice = sc.nextInt(); String filename = ""; // BufferedReader reader=new BufferedReader(new FileReader("")); if (choice == 1) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_open.arff"; //reader = new BufferedReader(new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_open.arff")); } else if (choice == 2) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_neur.arff"; // reader = new BufferedReader(new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_neur.arff")); } else if (choice == 3) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_agr.arff"; // reader = new BufferedReader(new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_agr.arff")); } else if (choice == 4) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_con.arff"; // reader = new BufferedReader(new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_con.arff")); } else if (choice == 5) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_extr.arff"; // reader = new BufferedReader(new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\Training_data_extr.arff")); } //BufferedReader reader = new BufferedReader(new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Training dataset\\")); // DataSource source = new DataSource("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_Dataset\\Features_value.arff"); //Instances data = source.getDataSet(); BufferedReader reader = new BufferedReader(new FileReader(filename)); Instances data = new Instances(reader); reader.close(); //******88setting class attribute************ data.setClassIndex(data.numAttributes() - 1); // OptionsToCode option=new OptionsToCode(); // String options[]={"java","ExperimentDemo","-classifier weka.classifiers.trees.M5P","-exptype regression","-splittype randomsplit","-runs 10", //"-percentage 66","-result /some/where/results.arff","-t bolts.arff","-t bodyfat.arff"}; // String[] options={"weka.classifiers.functions.SMO"}; //String[] options={"weka.classifiers.trees.M5P"}; //option.convert(options); //*******************building a classifier********************* String[] options = new String[1]; options[0] = "-U"; // unpruned tree J48 tree = new J48(); // new instance of tree tree.setOptions(options); // set the options tree.buildClassifier(data); // build classifier if (choice == 1) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_open.arff"; //fr=new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_open.arff"); } else if (choice == 2) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_neur.arff"; //fr=new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_neur.arff"); } else if (choice == 3) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_agr.arff"; // fr=new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_agr.arff"); } else if (choice == 4) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_con.arff"; //fr=new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_con.arff"); } else if (choice == 5) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_extr.arff"; //fr=new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_extr.arff"); } FileReader fr = new FileReader(filename); BufferedReader br = new BufferedReader(fr); Instances unlabeled = new Instances(br); /// Instances unlabeled = new Instances( // new BufferedReader( // new FileReader("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_Dataset\\experiment\\test_data_unlabelled.arff"))); // set class attribute unlabeled.setClassIndex(unlabeled.numAttributes() - 1); // create copy Instances labeled = new Instances(unlabeled); // label instances for (int i = 0; i < unlabeled.numInstances(); i++) { double clsLabel = tree.classifyInstance(unlabeled.instance(i)); labeled.instance(i).setClassValue(clsLabel); } // save labeled data if (choice == 1) { filename = "C:\\Users\\divya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_open_labelled.arff"; // fr1=new FileWriter("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_open123.arff"); } else if (choice == 2) { // fr1=new FileWriter("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_neur_labelled.arff"); } else if (choice == 3) { // fr1=new FileWriter("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_agr_labelled.arff"); } else if (choice == 4) { //fr1=new FileWriter("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_con_labelled.arff"); } else if (choice == 5) { // fr1=new FileWriter("C:\\Users\\somya\\Desktop\\Personality Mining\\WEKA_DataSet\\Labelling\\Testing_data_extr_labelled.arff"); } FileWriter fr1 = new FileWriter(filename); BufferedWriter writer = new BufferedWriter(fr1); writer.write(labeled.toString()); writer.newLine(); writer.flush(); writer.close(); } catch (Exception e) { System.out.println(e.getLocalizedMessage()); } }