List of usage examples for weka.classifiers.functions SMO buildClassifier
public void buildClassifier(Instances insts) throws Exception
From source file:cs.man.ac.uk.predict.Predictor.java
License:Open Source License
public static void makePredictionsEnsembleNew(String trainPath, String testPath, String resultPath) { System.out.println("Training set: " + trainPath); System.out.println("Test set: " + testPath); /**/*from w w w . ja v a2s. co m*/ * The ensemble classifiers. This is a heterogeneous ensemble. */ J48 learner1 = new J48(); SMO learner2 = new SMO(); NaiveBayes learner3 = new NaiveBayes(); MultilayerPerceptron learner5 = new MultilayerPerceptron(); System.out.println("Training Ensemble."); long startTime = System.nanoTime(); try { BufferedReader reader = new BufferedReader(new FileReader(trainPath)); Instances data = new Instances(reader); data.setClassIndex(data.numAttributes() - 1); System.out.println("Training data length: " + data.numInstances()); learner1.buildClassifier(data); learner2.buildClassifier(data); learner3.buildClassifier(data); learner5.buildClassifier(data); long endTime = System.nanoTime(); long nanoseconds = endTime - startTime; double seconds = (double) nanoseconds / 1000000000.0; System.out.println("Training Ensemble completed in " + nanoseconds + " (ns) or " + seconds + " (s)."); } catch (IOException e) { System.out.println("Could not train Ensemble classifier IOException on training data file."); } catch (Exception e) { System.out.println("Could not train Ensemble classifier Exception building model."); } try { String line = ""; // Read the file and display it line by line. BufferedReader in = null; // Read in and store each positive prediction in the tree map. try { //open stream to file in = new BufferedReader(new FileReader(testPath)); while ((line = in.readLine()) != null) { if (line.toLowerCase().contains("@data")) break; } } catch (Exception e) { } // A different ARFF loader used here (compared to above) as // the ARFF file may be extremely large. In which case the whole // file cannot be read in. Instead it is read in incrementally. ArffLoader loader = new ArffLoader(); loader.setFile(new File(testPath)); Instances data = loader.getStructure(); data.setClassIndex(data.numAttributes() - 1); System.out.println("Ensemble Classifier is ready."); System.out.println("Testing on all instances avaialable."); startTime = System.nanoTime(); int instanceNumber = 0; // label instances Instance current; while ((current = loader.getNextInstance(data)) != null) { instanceNumber += 1; line = in.readLine(); double classification1 = learner1.classifyInstance(current); double classification2 = learner2.classifyInstance(current); double classification3 = learner3.classifyInstance(current); double classification5 = learner5.classifyInstance(current); // All classifiers must agree. This is a very primitive ensemble strategy! if (classification1 == 1 && classification2 == 1 && classification3 == 1 && classification5 == 1) { if (line != null) { //System.out.println("Instance: "+instanceNumber+"\t"+line); //System.in.read(); } Writer.append(resultPath, instanceNumber + "\n"); } } in.close(); System.out.println("Test set instances: " + instanceNumber); long endTime = System.nanoTime(); long duration = endTime - startTime; double seconds = (double) duration / 1000000000.0; System.out.println("Testing Ensemble completed in " + duration + " (ns) or " + seconds + " (s)."); } catch (Exception e) { System.out.println("Could not test Ensemble classifier due to an error."); } }
From source file:cs.man.ac.uk.predict.Predictor.java
License:Open Source License
public static void makePredictionsEnsembleStream(String trainPath, String testPath, String resultPath) { System.out.println("Training set: " + trainPath); System.out.println("Test set: " + testPath); /**// ww w . j ava 2s .c om * The ensemble classifiers. This is a heterogeneous ensemble. */ J48 learner1 = new J48(); SMO learner2 = new SMO(); NaiveBayes learner3 = new NaiveBayes(); MultilayerPerceptron learner5 = new MultilayerPerceptron(); System.out.println("Training Ensemble."); long startTime = System.nanoTime(); try { BufferedReader reader = new BufferedReader(new FileReader(trainPath)); Instances data = new Instances(reader); data.setClassIndex(data.numAttributes() - 1); System.out.println("Training data length: " + data.numInstances()); learner1.buildClassifier(data); learner2.buildClassifier(data); learner3.buildClassifier(data); learner5.buildClassifier(data); long endTime = System.nanoTime(); long nanoseconds = endTime - startTime; double seconds = (double) nanoseconds / 1000000000.0; System.out.println("Training Ensemble completed in " + nanoseconds + " (ns) or " + seconds + " (s)."); } catch (IOException e) { System.out.println("Could not train Ensemble classifier IOException on training data file."); } catch (Exception e) { System.out.println("Could not train Ensemble classifier Exception building model."); } try { // A different ARFF loader used here (compared to above) as // the ARFF file may be extremely large. In which case the whole // file cannot be read in. Instead it is read in incrementally. ArffLoader loader = new ArffLoader(); loader.setFile(new File(testPath)); Instances data = loader.getStructure(); data.setClassIndex(data.numAttributes() - 1); System.out.println("Ensemble Classifier is ready."); System.out.println("Testing on all instances avaialable."); startTime = System.nanoTime(); int instanceNumber = 0; // label instances Instance current; while ((current = loader.getNextInstance(data)) != null) { instanceNumber += 1; double classification1 = learner1.classifyInstance(current); double classification2 = learner2.classifyInstance(current); double classification3 = learner3.classifyInstance(current); double classification5 = learner5.classifyInstance(current); // All classifiers must agree. This is a very primitive ensemble strategy! if (classification1 == 1 && classification2 == 1 && classification3 == 1 && classification5 == 1) { Writer.append(resultPath, instanceNumber + "\n"); } } System.out.println("Test set instances: " + instanceNumber); long endTime = System.nanoTime(); long duration = endTime - startTime; double seconds = (double) duration / 1000000000.0; System.out.println("Testing Ensemble completed in " + duration + " (ns) or " + seconds + " (s)."); } catch (Exception e) { System.out.println("Could not test Ensemble classifier due to an error."); } }
From source file:farm_ads.MyClassifier.java
public Classifier classifierSMO(Instances instances) throws Exception { SMO classifier = new SMO(); classifier.setOptions(weka.core.Utils.splitOptions( "-C 1.0 -L 0.0010 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\"")); classifier.buildClassifier(instances); return classifier; }
From source file:kfst.classifier.WekaClassifier.java
License:Open Source License
/** * This method builds and evaluates the support vector machine(SVM) * classifier. The SMO are used as the SVM classifier implemented in the * Weka software.//from w w w . j av a 2 s . c o m * * @param pathTrainData the path of the train set * @param pathTestData the path of the test set * @param svmKernel the kernel to use * * @return the classification accuracy */ public static double SVM(String pathTrainData, String pathTestData, String svmKernel) { double resultValue = 0; try { BufferedReader readerTrain = new BufferedReader(new FileReader(pathTrainData)); Instances dataTrain = new Instances(readerTrain); readerTrain.close(); dataTrain.setClassIndex(dataTrain.numAttributes() - 1); BufferedReader readerTest = new BufferedReader(new FileReader(pathTestData)); Instances dataTest = new Instances(readerTest); readerTest.close(); dataTest.setClassIndex(dataTest.numAttributes() - 1); SMO svm = new SMO(); if (svmKernel.equals("Polynomial kernel")) { svm.setKernel(weka.classifiers.functions.supportVector.PolyKernel.class.newInstance()); } else if (svmKernel.equals("RBF kernel")) { svm.setKernel(weka.classifiers.functions.supportVector.RBFKernel.class.newInstance()); } else { svm.setKernel(weka.classifiers.functions.supportVector.Puk.class.newInstance()); } svm.buildClassifier(dataTrain); Evaluation eval = new Evaluation(dataTest); eval.evaluateModel(svm, dataTest); resultValue = 100 - (eval.errorRate() * 100); } catch (Exception ex) { Logger.getLogger(WekaClassifier.class.getName()).log(Level.SEVERE, null, ex); } return resultValue; }
From source file:KFST.featureSelection.embedded.SVMBasedMethods.MSVM_RFE.java
License:Open Source License
/** * generates binary classifiers (SVM by applying k-fold cross validation * resampling strategy) using input data and based on selected feature * subset./*from w w w. jav a2s . c o m*/ * * @param selectedFeature an array of indices of the selected feature subset * * @return an array of the weights of features */ protected double[][] buildSVM_KFoldCrossValidation(int[] selectedFeature) { double[][] weights = new double[numRun * kFoldValue][selectedFeature.length]; int classifier = 0; for (int i = 0; i < numRun; i++) { double[][] copyTrainSet = ArraysFunc.copyDoubleArray2D(trainSet); //shuffles the train set MathFunc.randomize(copyTrainSet); int numSampleInFold = copyTrainSet.length / kFoldValue; int remainder = copyTrainSet.length % kFoldValue; int indexStart = 0; for (int k = 0; k < kFoldValue; k++) { int indexEnd = indexStart + numSampleInFold; if (k < remainder) { indexEnd++; } double[][] subTrainSet = ArraysFunc.copyDoubleArray2D(copyTrainSet, indexStart, indexEnd); String nameDataCSV = TEMP_PATH + "dataCSV[" + i + "-" + k + "].csv"; String nameDataARFF = TEMP_PATH + "dataARFF[" + i + "-" + k + "].arff"; FileFunc.createCSVFile(subTrainSet, selectedFeature, nameDataCSV, nameFeatures, classLabel); FileFunc.convertCSVtoARFF(nameDataCSV, nameDataARFF, TEMP_PATH, selectedFeature.length, numFeatures, nameFeatures, numClass, classLabel); try { BufferedReader readerTrain = new BufferedReader(new FileReader(nameDataARFF)); Instances dataTrain = new Instances(readerTrain); readerTrain.close(); dataTrain.setClassIndex(dataTrain.numAttributes() - 1); SMO svm = new SMO(); svm.setC(parameterC); svm.setKernel(WekaSVMKernel.parse(kernelType)); svm.buildClassifier(dataTrain); double[] weightsSparse = svm.sparseWeights()[0][1]; int[] indicesSparse = svm.sparseIndices()[0][1]; for (int m = 0; m < weightsSparse.length; m++) { weights[classifier][indicesSparse[m]] = weightsSparse[m]; } } catch (Exception ex) { Logger.getLogger(MSVM_RFE.class.getName()).log(Level.SEVERE, null, ex); } indexStart = indexEnd; classifier++; } } return weights; }
From source file:KFST.featureSelection.embedded.SVMBasedMethods.SVMBasedMethods.java
License:Open Source License
/** * generates binary classifiers (SVM) using input data and based on selected * feature subset, and finally returns the weights of features. * One-Versus-One strategy is used to construct classifiers in multiclass * classification./*w ww. ja v a 2 s .c o m*/ * * @param selectedFeature an array of indices of the selected feature subset * * @return an array of the weights of features */ protected double[][][] buildSVM_OneAgainstOne(int[] selectedFeature) { String nameDataCSV = TEMP_PATH + "dataCSV.csv"; String nameDataARFF = TEMP_PATH + "dataARFF.arff"; double[][][] weights = new double[numClass][numClass][selectedFeature.length]; FileFunc.createCSVFile(trainSet, selectedFeature, nameDataCSV, nameFeatures, classLabel); FileFunc.convertCSVtoARFF(nameDataCSV, nameDataARFF, TEMP_PATH, selectedFeature.length, numFeatures, nameFeatures, numClass, classLabel); try { BufferedReader readerTrain = new BufferedReader(new FileReader(nameDataARFF)); Instances dataTrain = new Instances(readerTrain); readerTrain.close(); dataTrain.setClassIndex(dataTrain.numAttributes() - 1); SMO svm = new SMO(); svm.setC(parameterC); svm.setKernel(WekaSVMKernel.parse(kernelType)); svm.buildClassifier(dataTrain); for (int i = 0; i < numClass; i++) { for (int j = i + 1; j < numClass; j++) { double[] weightsSparse = svm.sparseWeights()[i][j]; int[] indicesSparse = svm.sparseIndices()[i][j]; for (int k = 0; k < weightsSparse.length; k++) { weights[i][j][indicesSparse[k]] = weightsSparse[k]; } } } } catch (Exception ex) { Logger.getLogger(SVMBasedMethods.class.getName()).log(Level.SEVERE, null, ex); } return weights; }
From source file:KFST.featureSelection.embedded.SVMBasedMethods.SVMBasedMethods.java
License:Open Source License
/** * generates binary classifiers (SVM) using input data and based on selected * feature subset, and finally returns the weights of features. * One-Versus-All strategy is used to construct classifiers in multiclass * classification.//w w w. j a va 2s . co m * * @param selectedFeature an array of indices of the selected feature subset * * @return an array of the weights of features */ protected double[][] buildSVM_OneAgainstRest(int[] selectedFeature) { double[][] weights = new double[numClass][selectedFeature.length]; String[] tempClassLabel = new String[] { "c1", "c2" }; for (int indexClass = 0; indexClass < numClass; indexClass++) { double[][] copyTrainSet = ArraysFunc.copyDoubleArray2D(trainSet); String nameDataCSV = TEMP_PATH + "dataCSV" + indexClass + ".csv"; String nameDataARFF = TEMP_PATH + "dataARFF" + indexClass + ".arff"; for (double[] dataRow : copyTrainSet) { if (dataRow[numFeatures] == classLabelInTrainSet[indexClass]) { dataRow[numFeatures] = 0; } else { dataRow[numFeatures] = 1; } } FileFunc.createCSVFile(copyTrainSet, selectedFeature, nameDataCSV, nameFeatures, tempClassLabel); FileFunc.convertCSVtoARFF(nameDataCSV, nameDataARFF, TEMP_PATH, selectedFeature.length, numFeatures, nameFeatures, tempClassLabel.length, tempClassLabel); try { BufferedReader readerTrain = new BufferedReader(new FileReader(nameDataARFF)); Instances dataTrain = new Instances(readerTrain); readerTrain.close(); dataTrain.setClassIndex(dataTrain.numAttributes() - 1); SMO svm = new SMO(); svm.setC(parameterC); svm.setKernel(WekaSVMKernel.parse(kernelType)); svm.buildClassifier(dataTrain); double[] weightsSparse = svm.sparseWeights()[0][1]; int[] indicesSparse = svm.sparseIndices()[0][1]; for (int k = 0; k < weightsSparse.length; k++) { weights[indexClass][indicesSparse[k]] = weightsSparse[k]; } } catch (Exception ex) { Logger.getLogger(SVMBasedMethods.class.getName()).log(Level.SEVERE, null, ex); } } return weights; }
From source file:machinelearningproject.MachineLearningProject.java
/** * @param args the command line arguments *///w ww . j a va 2s. c om public static void main(String[] args) throws Exception { // TODO code application logic here DataSource source = new DataSource("D:\\spambase.arff"); // DataSource source = new DataSource("D:\\weather-nominal.arff"); Instances instances = source.getDataSet(); int numAttr = instances.numAttributes(); instances.setClassIndex(instances.numAttributes() - 1); int runs = 5; int seed = 15; for (int i = 0; i < runs; i++) { //randomize data seed = seed + 1; // the seed for randomizing the data Random rand = new Random(seed); // create seeded number generator Instances randData = new Instances(instances); // create copy of original data Collections.shuffle(randData); Evaluation evalDTree = new Evaluation(randData); Evaluation evalRF = new Evaluation(randData); Evaluation evalSVM = new Evaluation(randData); int folds = 10; for (int n = 0; n < folds; n++) { Instances train = randData.trainCV(folds, n, rand); Instances test = randData.testCV(folds, n); //instantiate classifiers DecisionTree dtree = new DecisionTree(); RandomForest rf = new RandomForest(100); SMO svm = new SMO(); RBFKernel rbfKernel = new RBFKernel(); double gamma = 0.70; rbfKernel.setGamma(gamma); dtree.buildClassifier(train); rf.buildClassifier(train); svm.buildClassifier(train); evalDTree.evaluateModel(dtree, test); evalRF.evaluateModel(rf, test); evalSVM.evaluateModel(svm, test); } System.out.println("=== Decision Tree Evaluation ==="); System.out.println(evalDTree.toSummaryString()); System.out.println(evalDTree.toClassDetailsString()); System.out.println(evalDTree.toMatrixString()); System.out.println("=== Random Forest Evaluation ==="); System.out.println(evalRF.toSummaryString()); System.out.println(evalRF.toClassDetailsString()); System.out.println(evalRF.toMatrixString()); System.out.println("=== SVM Evaluation ==="); System.out.println(evalSVM.toSummaryString()); System.out.println(evalSVM.toClassDetailsString()); System.out.println(evalSVM.toMatrixString()); } }