List of usage examples for weka.filters.unsupervised.instance Randomize Randomize
Randomize
From source file:app.RunApp.java
License:Open Source License
/** * Preprocess dataset/*from w w w .java2 s .co m*/ * * @return Positive number if successfull and negative otherwise */ private int preprocess() { trainDatasets = new ArrayList(); testDatasets = new ArrayList(); Instances train, test; if (dataset == null) { JOptionPane.showMessageDialog(null, "You must load a dataset.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } MultiLabelInstances preprocessDataset = dataset.clone(); if (!radioNoIS.isSelected()) { //Do Instance Selection if (radioRandomIS.isSelected()) { int nInstances = Integer.parseInt(textRandomIS.getText()); if (nInstances < 1) { JOptionPane.showMessageDialog(null, "The number of instances must be a positive natural number.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } else if (nInstances > dataset.getNumInstances()) { JOptionPane.showMessageDialog(null, "The number of instances to select must be less than the original.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } Instances dataIS; try { Randomize randomize = new Randomize(); dataIS = dataset.getDataSet(); randomize.setInputFormat(dataIS); dataIS = Filter.useFilter(dataIS, randomize); randomize.batchFinished(); RemoveRange removeRange = new RemoveRange(); removeRange.setInputFormat(dataIS); removeRange.setInstancesIndices((nInstances + 1) + "-last"); dataIS = Filter.useFilter(dataIS, removeRange); removeRange.batchFinished(); preprocessDataset = dataset.reintegrateModifiedDataSet(dataIS); } catch (Exception ex) { Logger.getLogger(RunApp.class.getName()).log(Level.SEVERE, null, ex); } if (preprocessDataset == null) { JOptionPane.showMessageDialog(null, "Error when selecting instances.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } preprocessedDataset = preprocessDataset; } } if (!radioNoFS.isSelected()) { //FS_BR if (radioBRFS.isSelected()) { int nFeatures = Integer.parseInt(textBRFS.getText()); if (nFeatures < 1) { JOptionPane.showMessageDialog(null, "The number of features must be a positive natural number.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } else if (nFeatures > dataset.getFeatureIndices().length) { JOptionPane.showMessageDialog(null, "The number of features to select must be less than the original.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } String combination = jComboBoxBRFSComb.getSelectedItem().toString(); String normalization = jComboBoxBRFSNorm.getSelectedItem().toString(); String output = jComboBoxBRFSOut.getSelectedItem().toString(); FeatureSelector fs; if (radioNoIS.isSelected()) { fs = new FeatureSelector(dataset, nFeatures); } else { //If IS have been done fs = new FeatureSelector(preprocessDataset, nFeatures); } preprocessedDataset = fs.select(combination, normalization, output); if (preprocessedDataset == null) { JOptionPane.showMessageDialog(null, "Error when selecting features.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } preprocessDataset = preprocessedDataset; } else if (radioRandomFS.isSelected()) { int nFeatures = Integer.parseInt(textRandomFS.getText()); if (nFeatures < 1) { JOptionPane.showMessageDialog(null, "The number of features must be a positive natural number.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } else if (nFeatures > dataset.getFeatureIndices().length) { JOptionPane.showMessageDialog(null, "The number of features to select must be less than the original.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } FeatureSelector fs; if (radioNoIS.isSelected()) { fs = new FeatureSelector(dataset, nFeatures); } else { //If IS have been done fs = new FeatureSelector(preprocessDataset, nFeatures); } preprocessedDataset = fs.randomSelect(); if (preprocessedDataset == null) { JOptionPane.showMessageDialog(null, "Error when selecting features.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } preprocessDataset = preprocessedDataset; } } if (!radioNoSplit.isSelected()) { //Random Holdout if (radioRandomHoldout.isSelected()) { String split = textRandomHoldout.getText(); double percentage = Double.parseDouble(split); if ((percentage <= 0) || (percentage >= 100)) { JOptionPane.showMessageDialog(null, "The percentage must be a number in the range (0, 100).", "alert", JOptionPane.ERROR_MESSAGE); return -1; } try { RandomTrainTest pre = new RandomTrainTest(); MultiLabelInstances[] partitions = pre.split(preprocessDataset, percentage); trainDataset = partitions[0]; testDataset = partitions[1]; } catch (InvalidDataFormatException ex) { Logger.getLogger(RunApp.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(RunApp.class.getName()).log(Level.SEVERE, null, ex); } } //Random CV else if (radioRandomCV.isSelected()) { String split = textRandomCV.getText(); if (split.equals("")) { JOptionPane.showMessageDialog(null, "You must enter the number of folds.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } int nFolds; try { nFolds = Integer.parseInt(split); } catch (Exception e) { JOptionPane.showMessageDialog(null, "Introduce a correct number of folds.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } if (nFolds < 2) { JOptionPane.showMessageDialog(null, "The number of folds must be greater or equal to 2.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } else if (nFolds > preprocessDataset.getNumInstances()) { JOptionPane.showMessageDialog(null, "The number of folds can not be greater than the number of instances.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } try { MultiLabelInstances temp = preprocessDataset.clone(); Instances dataTemp = temp.getDataSet(); int seed = (int) (Math.random() * 100) + 100; Random rand = new Random(seed); dataTemp.randomize(rand); Instances[] foldsCV = new Instances[nFolds]; for (int i = 0; i < nFolds; i++) { foldsCV[i] = new Instances(dataTemp); foldsCV[i].clear(); } for (int i = 0; i < dataTemp.numInstances(); i++) { foldsCV[i % nFolds].add(dataTemp.get(i)); } train = new Instances(dataTemp); test = new Instances(dataTemp); for (int i = 0; i < nFolds; i++) { train.clear(); test.clear(); for (int j = 0; j < nFolds; j++) { if (i != j) { System.out.println("Add fold " + j + " to train"); train.addAll(foldsCV[j]); } } System.out.println("Add fold " + i + " to test"); test.addAll(foldsCV[i]); System.out.println(train.get(0).toString()); System.out.println(test.get(0).toString()); trainDatasets.add(new MultiLabelInstances(new Instances(train), preprocessDataset.getLabelsMetaData())); testDatasets.add(new MultiLabelInstances(new Instances(test), preprocessDataset.getLabelsMetaData())); System.out.println(trainDatasets.get(i).getDataSet().get(0).toString()); System.out.println(testDatasets.get(i).getDataSet().get(0).toString()); System.out.println("---"); } } catch (Exception ex) { Logger.getLogger(RunApp.class.getName()).log(Level.SEVERE, null, ex); } } //Iterative stratified holdout else if (radioIterativeStratifiedHoldout.isSelected()) { String split = textIterativeStratifiedHoldout.getText(); double percentage = Double.parseDouble(split); if ((percentage <= 0) || (percentage >= 100)) { JOptionPane.showMessageDialog(null, "The percentage must be a number in the range (0, 100).", "alert", JOptionPane.ERROR_MESSAGE); return -1; } try { IterativeTrainTest pre = new IterativeTrainTest(); MultiLabelInstances[] partitions = pre.split(preprocessDataset, percentage); trainDataset = partitions[0]; testDataset = partitions[1]; } catch (Exception ex) { Logger.getLogger(RunApp.class.getName()).log(Level.SEVERE, null, ex); } } //Iterative stratified CV else if (radioIterativeStratifiedCV.isSelected()) { String split = textIterativeStratifiedCV.getText(); if (split.equals("")) { JOptionPane.showMessageDialog(null, "You must enter the number of folds.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } int nFolds = 0; try { nFolds = Integer.parseInt(split); } catch (Exception e) { JOptionPane.showMessageDialog(null, "Introduce a correct number of folds.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } if (nFolds < 2) { JOptionPane.showMessageDialog(null, "The number of folds must be greater or equal to 2.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } else if (nFolds > preprocessDataset.getNumInstances()) { JOptionPane.showMessageDialog(null, "The number of folds can not be greater than the number of instances.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } IterativeStratification strat = new IterativeStratification(); MultiLabelInstances folds[] = strat.stratify(preprocessDataset, nFolds); for (int i = 0; i < nFolds; i++) { try { int trainSize = 0, testSize = 0; for (int j = 0; j < nFolds; j++) { if (i != j) { trainSize += folds[j].getNumInstances(); } } testSize += folds[i].getNumInstances(); train = new Instances(preprocessDataset.getDataSet(), trainSize); test = new Instances(preprocessDataset.getDataSet(), testSize); for (int j = 0; j < nFolds; j++) { if (i != j) { train.addAll(folds[j].getDataSet()); } } test.addAll(folds[i].getDataSet()); trainDatasets.add(new MultiLabelInstances(train, preprocessDataset.getLabelsMetaData())); testDatasets.add(new MultiLabelInstances(test, preprocessDataset.getLabelsMetaData())); } catch (InvalidDataFormatException ex) { Logger.getLogger(RunApp.class.getName()).log(Level.SEVERE, null, ex); } } } //LP stratified holdout else if (radioLPStratifiedHoldout.isSelected()) { String split = textLPStratifiedHoldout.getText(); double percentage = Double.parseDouble(split); if ((percentage <= 0) || (percentage >= 100)) { JOptionPane.showMessageDialog(null, "The percentage must be a number in the range (0, 100).", "alert", JOptionPane.ERROR_MESSAGE); return -1; } try { IterativeTrainTest pre = new IterativeTrainTest(); MultiLabelInstances[] partitions = pre.split(preprocessDataset, percentage); trainDataset = partitions[0]; testDataset = partitions[1]; } catch (Exception ex) { Logger.getLogger(RunApp.class.getName()).log(Level.SEVERE, null, ex); } } //LP stratified CV else if (radioLPStratifiedCV.isSelected()) { String split = textLPStratifiedCV.getText(); if (split.equals("")) { JOptionPane.showMessageDialog(null, "You must enter the number of folds.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } int nFolds = 0; try { nFolds = Integer.parseInt(split); } catch (Exception e) { JOptionPane.showMessageDialog(null, "Introduce a correct number of folds.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } if (nFolds < 2) { JOptionPane.showMessageDialog(null, "The number of folds must be greater or equal to 2.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } else if (nFolds > preprocessDataset.getNumInstances()) { JOptionPane.showMessageDialog(null, "The number of folds can not be greater than the number of instances.", "alert", JOptionPane.ERROR_MESSAGE); return -1; } LabelPowersetTrainTest strat = new LabelPowersetTrainTest(); MultiLabelInstances folds[] = strat.stratify(preprocessDataset, nFolds); for (int i = 0; i < nFolds; i++) { try { train = new Instances(preprocessDataset.getDataSet(), 0); test = new Instances(preprocessDataset.getDataSet(), 0); for (int j = 0; j < nFolds; j++) { if (i != j) { train.addAll(folds[j].getDataSet()); } } test.addAll(folds[i].getDataSet()); trainDatasets.add(new MultiLabelInstances(train, preprocessDataset.getLabelsMetaData())); testDatasets.add(new MultiLabelInstances(test, preprocessDataset.getLabelsMetaData())); } catch (InvalidDataFormatException ex) { Logger.getLogger(RunApp.class.getName()).log(Level.SEVERE, null, ex); } } } } jButtonSaveDatasets.setEnabled(true); jComboBoxSaveFormat.setEnabled(true); return 1; }
From source file:clases.Preproceso.java
public static Instances randomize(Instances data) throws Exception { Randomize ran = new Randomize(); ran.setRandomSeed(42);//w w w.j a v a 2 s .c o m ran.setInputFormat(data); return Filter.useFilter(data, ran); }