List of usage examples for weka.attributeSelection Ranker setNumToSelect
@Override public void setNumToSelect(int n)
From source file:FeatureSelectionClass.java
public AttributeSelection withGainRatio(String path) throws Exception { int N;/*from w w w . j a va 2s .c o m*/ PreparingSteps pr = new PreparingSteps(); N = pr.getReadFileData(path).numAttributes(); Instances data = pr.getReadFileData(path); AttributeSelection selector = new AttributeSelection(); InfoGainAttributeEval evaluator = new InfoGainAttributeEval(); Ranker ranker = new Ranker(); ranker.setNumToSelect(Math.min(500, N - 1)); selector.setEvaluator(evaluator); selector.setSearch(ranker); selector.SelectAttributes(data); return selector; }
From source file:FeatureSelectionClass.java
public AttributeSelection withInfoGain(String path) throws Exception { int N;/*from w w w . j a v a 2 s .c o m*/ PreparingSteps pr = new PreparingSteps(); N = pr.getReadFileData(path).numAttributes(); Instances data = pr.getReadFileData(path); AttributeSelection selector = new AttributeSelection(); GainRatioAttributeEval evaluator = new GainRatioAttributeEval(); Ranker ranker = new Ranker(); ranker.setNumToSelect(Math.min(500, N - 1)); selector.setEvaluator(evaluator); selector.setSearch(ranker); selector.SelectAttributes(data); return selector; }
From source file:FeatureSelectionClass.java
public AttributeSelection withChiSquare(String path) throws Exception { int N;//from ww w. ja v a2 s . co m PreparingSteps pr = new PreparingSteps(); N = pr.getReadFileData(path).numAttributes(); Instances data = pr.getReadFileData(path); AttributeSelection selector = new AttributeSelection(); ChiSquaredAttributeEval evaluator = new ChiSquaredAttributeEval(); Ranker ranker = new Ranker(); ranker.setNumToSelect(Math.min(500, N - 1)); selector.setEvaluator(evaluator); selector.setSearch(ranker); selector.SelectAttributes(data); return selector; }
From source file:es.uvigo.ei.sing.gc.model.entities.GeneSelectionMetaData.java
License:Open Source License
public FeatureSelector createFeatureSelection() throws Exception { final Ranker ranker = new Ranker(); ranker.setNumToSelect(this.numGenes); return new WekaFeatureSelector(this.getASEvaluationBuilder().buildEvaluator(), ranker); }
From source file:etc.aloe.oilspill2010.FeatureGenerationImpl.java
License:Open Source License
protected Filter getFeatureSelectionFilter(ExampleSet examples) throws Exception { AttributeSelection filter = new AttributeSelection(); // package weka.filters.supervised.attribute! //CfsSubsetEval eval = new CfsSubsetEval(); //CorrelationAttributeEval eval = new CorrelationAttributeEval(); //InfoGainAttributeEval eval = new InfoGainAttributeEval(); ReliefFAttributeEval eval = new ReliefFAttributeEval(); //GreedyStepwise search = new GreedyStepwise(); //search.setNumToSelect(980); //search.setSearchBackwards(true); Ranker search = new Ranker(); search.setNumToSelect(980); filter.setEvaluator(eval);//from www. j a v a 2s .c om filter.setSearch(search); filter.setInputFormat(examples.getInstances()); Instances filtered = Filter.useFilter(examples.getInstances(), filter); examples.setInstances(filtered); return filter; }
From source file:it.poliba.sisinflab.simlib.featureSelection.methods.CHI.java
public void execute(String dataset) { try {/* www. j a v a 2s . co m*/ if (dataset.length() == 0) throw new IllegalArgumentException(); // Load input dataset. DataSource source = new DataSource(dataset); System.out.println("Reading instances..."); Instances data = source.getDataSet(); // Performs a principal components analysis. ChiSquaredAttributeEval chiEvaluator = new ChiSquaredAttributeEval(); // Ranking the attributes. Ranker ranker = new Ranker(); // Specify the number of attributes to select from the ranked list. /*ranker.setThreshold(-1.7976931348623157E308); ranker.setNumToSelect(-1); ranker.setGenerateRanking(true);*/ ranker.setNumToSelect(-1); AttributeSelection selector = new AttributeSelection(); System.out.println("Selecting attributes..."); selector.setSearch(ranker); selector.setEvaluator(chiEvaluator); selector.SelectAttributes(data); PrintStream o = new PrintStream(new File("data/" + "CHIResults" + ".txt")); System.setOut(o); System.out.println(Arrays.toString(selector.rankedAttributes())); System.out.println(Arrays.toString(selector.selectedAttributes())); //System.out.println(selector.CVResultsString()); System.out.println(selector.toResultsString()); System.out.println(); } catch (IllegalArgumentException e) { System.err.println("Error"); } catch (Exception e) { e.printStackTrace(); } }
From source file:it.poliba.sisinflab.simlib.featureSelection.methods.PCA.java
public void execute(String dataset) { try {/*ww w . j a v a 2 s . c o m*/ if (dataset.length() == 0) throw new IllegalArgumentException(); // Load input dataset. DataSource source = new DataSource(dataset); Instances data = source.getDataSet(); // Performs a principal components analysis. PrincipalComponents pcaEvaluator = new PrincipalComponents(); // Sets the amount of variance to account for when retaining principal // components. pcaEvaluator.setVarianceCovered(1.0); // Sets maximum number of attributes to include in transformed attribute // names. pcaEvaluator.setMaximumAttributeNames(-1); // Scaled X such that the variance of each feature is 1. boolean scale = true; if (scale) { pcaEvaluator.setCenterData(true); } else { pcaEvaluator.setCenterData(false); } // Ranking the attributes. Ranker ranker = new Ranker(); ranker.setNumToSelect(-1); AttributeSelection selector = new AttributeSelection(); selector.setSearch(ranker); selector.setEvaluator(pcaEvaluator); selector.SelectAttributes(data); // Transform data into eigenvector basis. Instances transformedData = selector.reduceDimensionality(data); PrintStream o = new PrintStream(new File("data/" + "PCAResults" + ".txt")); System.setOut(o); System.out.println(Arrays.toString(selector.rankedAttributes())); System.out.println(Arrays.toString(selector.selectedAttributes())); //System.out.println(selector.CVResultsString()); System.out.println(selector.toResultsString()); System.out.println(); } catch (IllegalArgumentException e) { System.err.println("Error"); } catch (Exception e) { e.printStackTrace(); } }
From source file:moa.reduction.core.ReductionClassifier.java
License:Open Source License
private Instance performFS(Instance rinst) { // Feature selection process performed before weka.core.Instance winst = new weka.core.DenseInstance(rinst.weight(), rinst.toDoubleArray()); if (fselector != null) { if (fselector.isUpdated() && totalCount % winSizeOption.getValue() == 0) { fselector.applySelection();// w ww .j a v a 2s. c o m selector = new AttributeSelection(); Ranker ranker = new Ranker(); ranker.setNumToSelect(Math.min(numFeaturesOption.getValue(), winst.numAttributes() - 1)); selector.setEvaluator((ASEvaluation) fselector); selector.setSearch(ranker); ArrayList<Attribute> list = new ArrayList<Attribute>(); //ArrayList<Attribute> list = Collections.list(winst.enumerateAttributes()); //list.add(winst.classAttribute()); for (int i = 0; i < rinst.numAttributes(); i++) list.add(new Attribute(rinst.attribute(i).name(), i)); //ArrayList<Attribute> list = Collections.list(winst.enumerateAttributes()); //list.add(winst.classAttribute()); weka.core.Instances single = new weka.core.Instances("single", list, 1); single.setClassIndex(rinst.classIndex()); single.add(winst); try { selector.SelectAttributes(single); System.out.println("Selected features: " + selector.toResultsString()); selectedFeatures.clear(); for (int att : selector.selectedAttributes()) selectedFeatures.add(att); WekaToSamoaInstanceConverter convWS = new WekaToSamoaInstanceConverter(); return convWS.samoaInstance(selector.reduceDimensionality(winst)); } catch (Exception e) { // TODO Auto-generated catch block e.printStackTrace(); } } } return rinst; }
From source file:mulan.classifier.transformation.MultiLabelStacking.java
License:Open Source License
/** * Initializes all the parameters used in the meta-level. * Calculates the correlated labels if meta-level pruning is applied. * * @param dataSet// ww w .j av a 2 s .c om * @param metaClassifier * @param includeAttrs * @param metaPercentage * @param eval * @throws Exception */ public void initializeMetaLevel(MultiLabelInstances dataSet, Classifier metaClassifier, boolean includeAttrs, double metaPercentage, ASEvaluation eval) throws Exception { this.metaClassifier = metaClassifier; metaLevelEnsemble = AbstractClassifier.makeCopies(metaClassifier, numLabels); metaLevelData = new Instances[numLabels]; metaLevelFilteredEnsemble = new FilteredClassifier[numLabels]; this.includeAttrs = includeAttrs; // calculate the number of correlated labels that corresponds to the // given percentage topkCorrelated = (int) Math.floor(metaPercentage * numLabels); if (topkCorrelated < 1) { debug("Too small percentage, selecting k=1"); topkCorrelated = 1; } if (topkCorrelated < numLabels) {// pruning should be applied selectedAttributes = new int[numLabels][]; if (eval == null) {// calculate the PhiCoefficient Statistics phi = new Statistics(); phi.calculatePhi(dataSet); for (int i = 0; i < numLabels; i++) { selectedAttributes[i] = phi.topPhiCorrelatedLabels(i, topkCorrelated); } } else {// apply feature selection AttributeSelection attsel = new AttributeSelection(); Ranker rankingMethod = new Ranker(); rankingMethod.setNumToSelect(topkCorrelated); attsel.setEvaluator(eval); attsel.setSearch(rankingMethod); // create a dataset consisting of all the classes of each // instance plus the class we want to select attributes from for (int i = 0; i < numLabels; i++) { ArrayList<Attribute> attributes = new ArrayList<Attribute>(); for (int j = 0; j < numLabels; j++) { attributes.add(train.attribute(labelIndices[j])); } attributes.add(train.attribute(labelIndices[i]).copy("meta")); Instances iporesult = new Instances("Meta format", attributes, 0); iporesult.setClassIndex(numLabels); for (int k = 0; k < train.numInstances(); k++) { double[] values = new double[numLabels + 1]; for (int m = 0; m < numLabels; m++) { values[m] = Double.parseDouble(train.attribute(labelIndices[m]) .value((int) train.instance(k).value(labelIndices[m]))); } values[numLabels] = Double.parseDouble(train.attribute(labelIndices[i]) .value((int) train.instance(k).value(labelIndices[i]))); Instance metaInstance = DataUtils.createInstance(train.instance(k), 1, values); metaInstance.setDataset(iporesult); iporesult.add(metaInstance); } attsel.SelectAttributes(iporesult); selectedAttributes[i] = attsel.selectedAttributes(); iporesult.delete(); } } } }
From source file:net.semanticmetadata.lire.classifiers.HashingSearchBasedClassifierMod.java
License:Open Source License
private static HashMap<String, Double> calculateInformationGain(String wekaFileLocation, double[] featureInformationGain, int featureSpace[], HashMap<String, Integer> featureSpaceHashMap, ArrayList<String> featureOrder, HashMap<String, Double> featureInformationGainHashMap) { Instances data = null;// w w w.j a v a2s. co m try { data = new Instances(new BufferedReader(new FileReader(wekaFileLocation))); } catch (IOException e) { e.printStackTrace(); //To change body of catch statement use File | Settings | File Templates. } AttributeSelection attsel = new AttributeSelection(); // package weka.attributeSelection! InfoGainAttributeEval eval = new InfoGainAttributeEval(); Ranker search = new Ranker(); search.setThreshold(-1.7976931348623157E308); search.setNumToSelect(-1); search.setGenerateRanking(true); attsel.setEvaluator(eval); attsel.setSearch(search); try { attsel.SelectAttributes(data); } catch (Exception e) { e.printStackTrace(); //To change body of catch statement use File | Settings | File Templates. } // obtain the attribute indices that were selected int[] indices = new int[0]; double[][] rankedAttribuesArray = new double[0][0]; try { rankedAttribuesArray = attsel.rankedAttributes(); } catch (Exception e) { e.printStackTrace(); //To change body of catch statement use File | Settings | File Templates. } try { indices = attsel.selectedAttributes(); } catch (Exception e) { e.printStackTrace(); //To change body of catch statement use File | Settings | File Templates. } for (int i = 0; i < rankedAttribuesArray.length; i++) { int currentFeature = Integer.parseInt(data.attribute((int) rankedAttribuesArray[i][0]).name() .substring(0, data.attribute((int) rankedAttribuesArray[i][0]).name().indexOf("_"))); //System.out.println("DDDDDDDDDDDDDD"+currentFeature); // System.out.print(data.attribute((int) rankedAttribuesArray[i][0]).name() + "/" + rankedAttribuesArray[i][0] + "/"); // System.out.println(rankedAttribuesArray[i][1]); // data.attribute((int) rankedAttribuesArray[i][0]).name().substring(0,data.attribute((int) rankedAttribuesArray[i][0]).name().indexOf("_")); // featureInformationGain[currentFeature] = featureInformationGain[currentFeature] + rankedAttribuesArray[i][1]; featureInformationGainHashMap.put(featureOrder.get(currentFeature), featureInformationGainHashMap.get(featureOrder.get(currentFeature)) + rankedAttribuesArray[i][1]); } //Caalculate the mean of the information gain (better comparable) // for (int i = 0; i < featureInformationGain.length; i++) { // featureInformationGain[i] = (featureInformationGain[i] / featureSpace[i]) * 100; // } //Calculate the mean of the information gain (better comparable) for (int i = 0; i < featureOrder.size(); i++) { // featureInformationGainHashMap.put(featureOrder.get(i), (featureInformationGainHashMap.get(featureOrder.get(i)) / featureSpaceHashMap.get(featureOrder.get(i))) * 100); featureInformationGainHashMap.put(featureOrder.get(i), (featureInformationGainHashMap.get(featureOrder.get(i)))); } // for(int i=0;i<0;i++){ // System.out.println(data.attribute(indices[i]).toString()); // } System.out.println("Scoring finished, starting with classification! Scores: "); for (int i = 0; i < featureOrder.size(); i++) { System.out.println(featureOrder.get(i) + " " + featureInformationGainHashMap.get(featureOrder.get(i))); // featureInformationGainHashMap.put(featureOrder.get(i),(featureInformationGainHashMap.get(featureOrder.get(i))/featureSpaceHashMap.get(featureOrder.get(i)))*100); } // return featureInformationGain; File deleteFile = new File(wekaFileLocation); deleteFile.delete(); return featureInformationGainHashMap; }