List of usage examples for weka.classifiers.trees REPTree REPTree
REPTree
From source file:cezeri.feature.selection.FeatureSelectionInfluence.java
public static void main(String[] args) throws Exception { String filePath = "C:\\Users\\BAP1\\Google Drive\\DataSet\\Weka_Files\\dental_florisis\\kayac_dental_2.arff"; Classifier[] models = { new MultilayerPerceptron(), new Bagging(), new REPTree() }; Influence[] dFeature = getMostDiscriminativeFeature(filePath, models[2]); System.out.println("Most Disciriminative Features are"); for (int i = 0; i < dFeature.length; i++) { System.out.println(dFeature[i].attributeName + "=" + dFeature[i].infVal); }/*from w w w.j av a 2 s . co m*/ }
From source file:de.fub.maps.project.detector.model.inference.impl.REPTreeInferenceModel.java
License:Open Source License
@Override protected Classifier createClassifier() { repTree = new REPTree(); return repTree; }
From source file:de.ugoe.cs.cpdp.dataselection.DecisionTreeSelection.java
License:Apache License
@Override public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) { final Instances data = characteristicInstances(testdata, traindataSet); final ArrayList<String> attVals = new ArrayList<String>(); attVals.add("same"); attVals.add("more"); attVals.add("less"); final ArrayList<Attribute> atts = new ArrayList<Attribute>(); for (int j = 0; j < data.numAttributes(); j++) { atts.add(new Attribute(data.attribute(j).name(), attVals)); }//from www .j a v a 2 s . c o m atts.add(new Attribute("score")); Instances similarityData = new Instances("similarity", atts, 0); similarityData.setClassIndex(similarityData.numAttributes() - 1); try { Classifier classifier = new J48(); for (int i = 0; i < traindataSet.size(); i++) { classifier.buildClassifier(traindataSet.get(i)); for (int j = 0; j < traindataSet.size(); j++) { if (i != j) { double[] similarity = new double[data.numAttributes() + 1]; for (int k = 0; k < data.numAttributes(); k++) { if (0.9 * data.get(i + 1).value(k) > data.get(j + 1).value(k)) { similarity[k] = 2.0; } else if (1.1 * data.get(i + 1).value(k) < data.get(j + 1).value(k)) { similarity[k] = 1.0; } else { similarity[k] = 0.0; } } Evaluation eval = new Evaluation(traindataSet.get(j)); eval.evaluateModel(classifier, traindataSet.get(j)); similarity[data.numAttributes()] = eval.fMeasure(1); similarityData.add(new DenseInstance(1.0, similarity)); } } } REPTree repTree = new REPTree(); if (repTree.getNumFolds() > similarityData.size()) { repTree.setNumFolds(similarityData.size()); } repTree.setNumFolds(2); repTree.buildClassifier(similarityData); Instances testTrainSimilarity = new Instances(similarityData); testTrainSimilarity.clear(); for (int i = 0; i < traindataSet.size(); i++) { double[] similarity = new double[data.numAttributes() + 1]; for (int k = 0; k < data.numAttributes(); k++) { if (0.9 * data.get(0).value(k) > data.get(i + 1).value(k)) { similarity[k] = 2.0; } else if (1.1 * data.get(0).value(k) < data.get(i + 1).value(k)) { similarity[k] = 1.0; } else { similarity[k] = 0.0; } } testTrainSimilarity.add(new DenseInstance(1.0, similarity)); } int bestScoringProductIndex = -1; double maxScore = Double.MIN_VALUE; for (int i = 0; i < traindataSet.size(); i++) { double score = repTree.classifyInstance(testTrainSimilarity.get(i)); if (score > maxScore) { maxScore = score; bestScoringProductIndex = i; } } Instances bestScoringProduct = traindataSet.get(bestScoringProductIndex); traindataSet.clear(); traindataSet.add(bestScoringProduct); } catch (Exception e) { Console.printerr("failure during DecisionTreeSelection: " + e.getMessage()); throw new RuntimeException(e); } }
From source file:es.ubu.XRayDetector.modelo.Fachada.java
License:Open Source License
/** * Creates a model training a classifier using bagging. * /*from w ww . ja va 2s . c o m*/ * @param data Contains all the instances of the ARFF file * @param sizeWindow The size of the window */ public void createModel(Instances data, String sizeWindow) { // se crea, opciones, setiputformat Classifier cls = null; //String separator = System.getProperty("file.separator"); String path = prop.getPathModel(); int opcionClasificacion = prop.getTipoClasificacion(); switch (opcionClasificacion) { case 0: //CLASIFICADOR CLASES NOMINALES (TRUE,FALSE) Classifier base; base = new REPTree(); cls = new Bagging(); ((Bagging) cls).setNumIterations(25); ((Bagging) cls).setBagSizePercent(100); ((Bagging) cls).setNumExecutionSlots(Runtime.getRuntime().availableProcessors()); ((Bagging) cls).setClassifier(base); break; case 1: //REGRESIN LINEAL (CLASES NUMRICAS, 1,0) cls = new REPTree(); break; } ObjectOutputStream oos = null; try { data.setClassIndex(data.numAttributes() - 1); cls.buildClassifier(data); /*if (arffName.contains("mejores")) oos = new ObjectOutputStream(new FileOutputStream((path + separator + "Modelos" + separator + "Bagging_" + "mejores_" + sizeWindow + ".model"))); if (arffName.contains("todas"))*/ oos = new ObjectOutputStream(new FileOutputStream((path + "todas_" + sizeWindow + ".model"))); oos.writeObject(cls); oos.flush(); oos.close(); } catch (Exception e) { throw new RuntimeException(e); } }
From source file:fr.unice.i3s.rockflows.experiments.main.IntermediateExecutor.java
private List<InfoClassifier> inputClassifier(Dataset original) throws Exception { List<InfoClassifier> cls = new ArrayList<>(); int id = 0;/*from w w w . j av a 2s. c o m*/ //LogisticRegression: InfoClassifier ic1 = new InfoClassifier(id++); ic1.classifier = new Logistic(); ic1.name = "Logistic Regression"; ic1.properties.requireNumericDataset = true; cls.add(ic1); //SVM: InfoClassifier ic2 = new InfoClassifier(id++); LibSVM ccc = new LibSVM(); //disable ccc.setOptions(new String[] { "-J", //Turn off nominal to binary conversion. "-V" //Turn off missing value replacement }); //ccc.setSVMType(new SelectedTag(LibSVM.SVMTYPE_C_SVC, LibSVM.TAGS_SVMTYPE)); //ccc.setKernelType(new SelectedTag(LibSVM.KERNELTYPE_RBF, LibSVM.TAGS_KERNELTYPE)); //ccc.setEps(0.001); //tolerance ic2.classifier = ccc; ic2.name = "Svm"; ic2.properties.requireNumericDataset = true; cls.add(ic2); //J48: InfoClassifier ic3 = new InfoClassifier(id++); ic3.classifier = new J48(); ic3.name = "J48"; ic3.properties.manageMissingValues = true; cls.add(ic3); //NBTree: InfoClassifier ic4 = new InfoClassifier(id++); ic4.classifier = new NBTree(); ic4.name = "NBTree"; ic4.properties.manageMissingValues = true; cls.add(ic4); //RandomForest: InfoClassifier ic5 = new InfoClassifier(id++); RandomForest ccc2 = new RandomForest(); ccc2.setNumTrees(500); ccc2.setMaxDepth(0); ic5.classifier = ccc2; ic5.name = "Random Forest"; ic5.properties.manageMissingValues = true; cls.add(ic5); //Logistic Model Trees (LMT): InfoClassifier ic6 = new InfoClassifier(id++); ic6.classifier = new LMT(); ic6.name = "Logistic Model Tree"; ic6.properties.manageMissingValues = true; cls.add(ic6); //Alternating Decision Trees (ADTree): InfoClassifier ic7 = new InfoClassifier(id++); if (original.trainingSet.numClasses() > 2) { MultiClassClassifier mc = new MultiClassClassifier(); mc.setOptions(new String[] { "-M", "3" }); //1 vs 1 mc.setClassifier(new ADTree()); ic7.classifier = mc; ic7.name = "1-vs-1 Alternating Decision Tree"; } else { ic7.classifier = new ADTree(); ic7.name = "Alternating Decision Tree"; } ic7.properties.manageMultiClass = false; ic7.properties.manageMissingValues = true; cls.add(ic7); //Naive Bayes: InfoClassifier ic8 = new InfoClassifier(id++); ic8.classifier = new NaiveBayes(); ic8.name = "Naive Bayes"; ic8.properties.manageMissingValues = true; cls.add(ic8); //Bayesian Networks: /* All Bayes network algorithms implemented in Weka assume the following for the data set: all variables are discrete finite variables. If you have a data set with continuous variables, you can use the following filter to discretize them: weka.filters.unsupervised.attribute.Discretize no instances have missing values. If there are missing values in the data set, values are filled in using the following filter: weka.filters.unsupervised.attribute.ReplaceMissingValues The first step performed by buildClassifier is checking if the data set fulfills those assumptions. If those assumptions are not met, the data set is automatically filtered and a warning is written to STDERR.2 */ InfoClassifier ic9 = new InfoClassifier(id++); ic9.classifier = new BayesNet(); ic9.name = "Bayesian Network"; ic9.properties.requireNominalDataset = true; cls.add(ic9); //IBK InfoClassifier ic10 = new InfoClassifier(id++); ic10.classifier = new IBk(); ic10.name = "IBk"; ic10.properties.manageMissingValues = true; cls.add(ic10); //JRip: InfoClassifier ic11 = new InfoClassifier(id++); ic11.classifier = new JRip(); ic11.name = "JRip"; ic11.properties.manageMissingValues = true; cls.add(ic11); //MultilayerPerceptron(MLP): InfoClassifier ic12 = new InfoClassifier(id++); ic12.classifier = new MultilayerPerceptron(); ic12.name = "Multillayer Perceptron"; ic12.properties.requireNumericDataset = true; cls.add(ic12); //Bagging RepTree: InfoClassifier ic14 = new InfoClassifier(id++); REPTree base3 = new REPTree(); Bagging ccc4 = new Bagging(); ccc4.setClassifier(base3); ic14.classifier = ccc4; ic14.name = "Bagging RepTree"; ic14.properties.manageMissingValues = true; cls.add(ic14); //Bagging J48 InfoClassifier ic15 = new InfoClassifier(id++); Bagging ccc5 = new Bagging(); ccc5.setClassifier(new J48()); ic15.classifier = ccc5; ic15.name = "Bagging J48"; ic15.properties.manageMissingValues = true; cls.add(ic15); //Bagging NBTree InfoClassifier ic16 = new InfoClassifier(id++); Bagging ccc6 = new Bagging(); ccc6.setClassifier(new NBTree()); ic16.classifier = ccc6; ic16.name = "Bagging NBTree"; ic16.properties.manageMissingValues = true; cls.add(ic16); //Bagging OneR: InfoClassifier ic17 = new InfoClassifier(id++); Bagging ccc7 = new Bagging(); ccc7.setClassifier(new OneR()); ic17.classifier = ccc7; ic17.name = "Bagging OneR"; ic17.properties.requireNominalDataset = true; ic17.properties.manageMissingValues = true; cls.add(ic17); //Bagging Jrip InfoClassifier ic18 = new InfoClassifier(id++); Bagging ccc8 = new Bagging(); ccc8.setClassifier(new JRip()); ic18.classifier = ccc8; ic18.name = "Bagging JRip"; ic18.properties.manageMissingValues = true; cls.add(ic18); //MultiboostAB DecisionStump InfoClassifier ic24 = new InfoClassifier(id++); MultiBoostAB ccc14 = new MultiBoostAB(); ccc14.setClassifier(new DecisionStump()); ic24.classifier = ccc14; ic24.name = "MultiboostAB DecisionStump"; ic24.properties.manageMissingValues = true; cls.add(ic24); //MultiboostAB OneR InfoClassifier ic25 = new InfoClassifier(id++); MultiBoostAB ccc15 = new MultiBoostAB(); ccc15.setClassifier(new OneR()); ic25.classifier = ccc15; ic25.name = "MultiboostAB OneR"; ic25.properties.requireNominalDataset = true; cls.add(ic25); //MultiboostAB J48 InfoClassifier ic27 = new InfoClassifier(id++); MultiBoostAB ccc17 = new MultiBoostAB(); ccc17.setClassifier(new J48()); ic27.classifier = ccc17; ic27.name = "MultiboostAB J48"; ic27.properties.manageMissingValues = true; cls.add(ic27); //MultiboostAB Jrip InfoClassifier ic28 = new InfoClassifier(id++); MultiBoostAB ccc18 = new MultiBoostAB(); ccc18.setClassifier(new JRip()); ic28.classifier = ccc18; ic28.name = "MultiboostAB JRip"; cls.add(ic28); //MultiboostAB NBTree InfoClassifier ic29 = new InfoClassifier(id++); MultiBoostAB ccc19 = new MultiBoostAB(); ccc19.setClassifier(new NBTree()); ic29.classifier = ccc19; ic29.name = "MultiboostAB NBTree"; ic29.properties.manageMissingValues = true; cls.add(ic29); //RotationForest RandomTree InfoClassifier ic32 = new InfoClassifier(id++); RotationForest ccc21 = new RotationForest(); RandomTree rtr5 = new RandomTree(); rtr5.setMinNum(2); rtr5.setAllowUnclassifiedInstances(true); ccc21.setClassifier(rtr5); ic32.classifier = ccc21; ic32.name = "RotationForest RandomTree"; ic32.properties.manageMissingValues = true; cls.add(ic32); //RotationForest J48: InfoClassifier ic33 = new InfoClassifier(id++); J48 base6 = new J48(); RotationForest ccc22 = new RotationForest(); ccc22.setClassifier(base6); ic33.classifier = ccc22; ic33.name = "RotationForest J48"; ic33.properties.manageMissingValues = true; cls.add(ic33); //RandomCommittee RandomTree: InfoClassifier ic34 = new InfoClassifier(id++); RandomTree rtr4 = new RandomTree(); rtr4.setMinNum(2); rtr4.setAllowUnclassifiedInstances(true); RandomCommittee ccc23 = new RandomCommittee(); ccc23.setClassifier(rtr4); ic34.classifier = ccc23; ic34.name = "RandomComittee RandomTree"; ic34.properties.manageMissingValues = true; cls.add(ic34); //Class via Clustering: SimpleKMeans //N.B: it can't handle date attributes InfoClassifier ic35 = new InfoClassifier(id++); ClassificationViaClustering ccc24 = new ClassificationViaClustering(); SimpleKMeans km = new SimpleKMeans(); km.setNumClusters(original.trainingSet.numClasses()); ccc24.setClusterer(km); ic35.classifier = ccc24; ic35.name = "Classification via Clustering: KMeans"; ic35.properties.requireNumericDataset = true; cls.add(ic35); //Class via Clustering: FarthestFirst InfoClassifier ic36 = new InfoClassifier(id++); ClassificationViaClustering ccc25 = new ClassificationViaClustering(); FarthestFirst ff = new FarthestFirst(); ff.setNumClusters(original.trainingSet.numClasses()); ccc25.setClusterer(ff); ic36.classifier = ccc25; ic36.name = "Classification via Clustering: FarthestFirst"; ic36.properties.requireNumericDataset = true; cls.add(ic36); //SMO InfoClassifier ic37 = new InfoClassifier(id++); ic37.classifier = new SMO(); ic37.properties.requireNumericDataset = true; ic37.properties.manageMultiClass = false; ic37.name = "Smo"; cls.add(ic37); //Random Subspace InfoClassifier ic38 = new InfoClassifier(id++); RandomSubSpace sub = new RandomSubSpace(); sub.setClassifier(new REPTree()); ic38.classifier = sub; ic38.name = "Random Subspaces of RepTree"; ic38.properties.manageMissingValues = true; cls.add(ic38); //PART rule based InfoClassifier ic39 = new InfoClassifier(id++); PART p39 = new PART(); p39.setOptions(new String[] { "-C", "0.5" }); ic39.classifier = new PART(); ic39.name = "PART"; ic39.properties.manageMissingValues = true; cls.add(ic39); //Decision-Table / Naive Bayes InfoClassifier ic40 = new InfoClassifier(id++); ic40.classifier = new DTNB(); ic40.name = "DTNB"; ic40.properties.manageMissingValues = true; cls.add(ic40); //Ridor Rule based InfoClassifier ic41 = new InfoClassifier(id++); ic41.classifier = new Ridor(); ic41.name = "Ridor"; ic41.properties.manageMissingValues = true; cls.add(ic41); //Decision Table InfoClassifier ic42 = new InfoClassifier(id++); ic42.classifier = new DecisionTable(); ic42.name = "Decision Table"; ic42.properties.manageMissingValues = true; cls.add(ic42); //Conjunctive Rule InfoClassifier ic43 = new InfoClassifier(id++); ic43.classifier = new ConjunctiveRule(); ic43.name = "Conjunctive Rule"; ic43.properties.manageMissingValues = true; cls.add(ic43); //LogitBoost Decision Stump InfoClassifier ic44 = new InfoClassifier(id++); LogitBoost lb = new LogitBoost(); lb.setOptions(new String[] { "-L", "1.79" }); lb.setClassifier(new DecisionStump()); ic44.classifier = lb; ic44.name = "LogitBoost Decision Stump"; ic44.properties.manageMissingValues = true; cls.add(ic44); //Raced Incremental Logit Boost, Decision Stump InfoClassifier ic45 = new InfoClassifier(id++); RacedIncrementalLogitBoost rlb = new RacedIncrementalLogitBoost(); rlb.setClassifier(new DecisionStump()); ic45.classifier = rlb; ic45.name = "Raced Incremental Logit Boost, Decision Stumps"; ic45.properties.manageMissingValues = true; cls.add(ic45); //AdaboostM1 decision stump InfoClassifier ic46 = new InfoClassifier(id++); AdaBoostM1 adm = new AdaBoostM1(); adm.setClassifier(new DecisionStump()); ic46.classifier = adm; ic46.name = "AdaboostM1, Decision Stumps"; ic46.properties.manageMissingValues = true; cls.add(ic46); //AdaboostM1 J48 InfoClassifier ic47 = new InfoClassifier(id++); AdaBoostM1 adm2 = new AdaBoostM1(); adm2.setClassifier(new J48()); ic47.classifier = adm2; ic47.name = "AdaboostM1, J48"; ic47.properties.manageMissingValues = true; cls.add(ic47); //MultiboostAb Decision Table InfoClassifier ic48 = new InfoClassifier(id++); MultiBoostAB mba = new MultiBoostAB(); mba.setClassifier(new DecisionTable()); ic48.classifier = mba; ic48.name = "MultiboostAB, Decision Table"; ic48.properties.manageMissingValues = true; cls.add(ic48); //Multiboost NaiveBayes InfoClassifier ic49 = new InfoClassifier(id++); MultiBoostAB mba2 = new MultiBoostAB(); mba2.setClassifier(new NaiveBayes()); ic49.classifier = mba2; ic49.name = "MultiboostAB, Naive Bayes"; ic49.properties.manageMissingValues = true; cls.add(ic49); //Multiboost PART InfoClassifier ic50 = new InfoClassifier(id++); MultiBoostAB mba3 = new MultiBoostAB(); mba3.setClassifier(new PART()); ic50.classifier = mba3; ic50.name = "MultiboostAB, PART"; ic50.properties.manageMissingValues = true; cls.add(ic50); //Multiboost Random Tree InfoClassifier ic51 = new InfoClassifier(id++); MultiBoostAB mba4 = new MultiBoostAB(); RandomTree rtr3 = new RandomTree(); rtr3.setMinNum(2); rtr3.setAllowUnclassifiedInstances(true); mba4.setClassifier(rtr3); ic51.classifier = mba4; ic51.name = "MultiboostAB, RandomTree"; ic51.properties.manageMissingValues = true; cls.add(ic51); //Multiboost Rep Tree InfoClassifier ic52 = new InfoClassifier(id++); MultiBoostAB mba5 = new MultiBoostAB(); mba5.setClassifier(new REPTree()); ic52.classifier = mba5; ic52.name = "MultiboostAB, RepTree"; ic52.properties.manageMissingValues = true; cls.add(ic52); //Bagging Decision Stump InfoClassifier ic53 = new InfoClassifier(id++); Bagging bag = new Bagging(); bag.setClassifier(new DecisionStump()); ic53.classifier = bag; ic53.name = "Bagging Decision Stump"; ic53.properties.manageMissingValues = true; cls.add(ic53); //Bagging Decision Table InfoClassifier ic54 = new InfoClassifier(id++); Bagging bag1 = new Bagging(); bag1.setClassifier(new DecisionTable()); ic54.classifier = bag1; ic54.name = "Bagging Decision Table"; ic54.properties.manageMissingValues = true; cls.add(ic54); //Bagging HyperPipes InfoClassifier ic55 = new InfoClassifier(id++); Bagging bag2 = new Bagging(); bag2.setClassifier(new HyperPipes()); ic55.classifier = bag2; ic55.name = "Bagging Hyper Pipes"; cls.add(ic55); //Bagging Naive Bayes InfoClassifier ic56 = new InfoClassifier(id++); Bagging bag3 = new Bagging(); bag3.setClassifier(new NaiveBayes()); ic56.classifier = bag3; ic56.name = "Bagging Naive Bayes"; ic56.properties.manageMissingValues = true; cls.add(ic56); //Bagging PART InfoClassifier ic57 = new InfoClassifier(id++); Bagging bag4 = new Bagging(); bag4.setClassifier(new PART()); ic57.classifier = bag4; ic57.name = "Bagging PART"; ic57.properties.manageMissingValues = true; cls.add(ic57); //Bagging RandomTree InfoClassifier ic58 = new InfoClassifier(id++); Bagging bag5 = new Bagging(); RandomTree rtr2 = new RandomTree(); rtr2.setMinNum(2); rtr2.setAllowUnclassifiedInstances(true); bag5.setClassifier(rtr2); ic58.classifier = bag5; ic58.name = "Bagging RandomTree"; ic58.properties.manageMissingValues = true; cls.add(ic58); //NNge InfoClassifier ic59 = new InfoClassifier(id++); NNge nng = new NNge(); nng.setNumFoldersMIOption(1); nng.setNumAttemptsOfGeneOption(5); ic59.classifier = nng; ic59.name = "NNge"; cls.add(ic59); //OrdinalClassClassifier J48 InfoClassifier ic60 = new InfoClassifier(id++); OrdinalClassClassifier occ = new OrdinalClassClassifier(); occ.setClassifier(new J48()); ic60.classifier = occ; ic60.name = "OrdinalClassClassifier J48"; ic60.properties.manageMissingValues = true; cls.add(ic60); //Hyper Pipes InfoClassifier ic61 = new InfoClassifier(id++); ic61.classifier = new HyperPipes(); ic61.name = "Hyper Pipes"; cls.add(ic61); //Classification via Regression, M5P used by default InfoClassifier ic62 = new InfoClassifier(id++); ic62.classifier = new ClassificationViaRegression(); ic62.name = "Classification ViaRegression, M5P"; ic62.properties.requireNumericDataset = true; cls.add(ic62); //RBF Network InfoClassifier ic64 = new InfoClassifier(id++); RBFNetwork rbf = new RBFNetwork(); rbf.setRidge(0.00000001); //10^-8 rbf.setNumClusters(original.trainingSet.numAttributes() / 2); ic64.classifier = rbf; ic64.name = "RBF Network"; ic64.properties.requireNumericDataset = true; if (!original.properties.isStandardized) { ic64.properties.compatibleWithDataset = false; } cls.add(ic64); //RandomTree InfoClassifier ic66 = new InfoClassifier(id++); RandomTree rtr = new RandomTree(); rtr.setMinNum(2); rtr.setAllowUnclassifiedInstances(true); ic66.classifier = rtr; ic66.name = "Random Tree"; ic66.properties.manageMissingValues = true; cls.add(ic66); //RepTree InfoClassifier ic67 = new InfoClassifier(id++); REPTree rept = new REPTree(); ic67.classifier = rept; ic67.name = "Rep Tree"; ic67.properties.manageMissingValues = true; cls.add(ic67); //Decision Stump InfoClassifier ic68 = new InfoClassifier(id++); ic68.classifier = new DecisionStump(); ic68.name = "Decision Stump"; ic68.properties.manageMissingValues = true; cls.add(ic68); //OneR InfoClassifier ic69 = new InfoClassifier(id++); ic69.classifier = new OneR(); ic69.name = "OneR"; ic69.properties.requireNominalDataset = true; ic69.properties.manageMissingValues = true; cls.add(ic69); //LWL InfoClassifier ic71 = new InfoClassifier(id++); ic71.classifier = new LWL(); ic71.name = "LWL"; ic71.properties.manageMissingValues = true; cls.add(ic71); //Bagging LWL InfoClassifier ic72 = new InfoClassifier(id++); Bagging bg72 = new Bagging(); bg72.setClassifier(new LWL()); ic72.classifier = bg72; ic72.name = "Bagging LWL"; ic72.properties.manageMissingValues = true; cls.add(ic72); //Decorate InfoClassifier ic73 = new InfoClassifier(id++); ic73.classifier = new Decorate(); ic73.name = "Decorate"; ic73.properties.manageMissingValues = true; ic73.properties.minNumTrainingInstances = 15; this.indexDecorate = id - 1; cls.add(ic73); //Dagging InfoClassifier ic74 = new InfoClassifier(id++); Dagging dng = new Dagging(); dng.setClassifier(new SMO()); dng.setNumFolds(4); ic74.classifier = dng; ic74.properties.requireNumericDataset = true; ic74.properties.manageMultiClass = false; ic74.name = "Dagging SMO"; cls.add(ic74); //IB1 InfoClassifier ic75 = new InfoClassifier(id++); ic75.classifier = new IB1(); ic75.properties.manageMissingValues = true; ic75.name = "IB1"; cls.add(ic75); //Simple Logistic InfoClassifier ic76 = new InfoClassifier(id++); ic76.classifier = new SimpleLogistic(); ic76.properties.requireNumericDataset = true; ic76.name = "Simple Logistic"; cls.add(ic76); //VFI InfoClassifier ic77 = new InfoClassifier(id++); ic77.classifier = new VFI(); ic77.properties.manageMissingValues = true; ic77.name = "VFI"; cls.add(ic77); //check if classifier satisfies the constraints of min #instances checkMinNumInstanes(cls, original.trainingSet); return cls; }
From source file:jjj.asap.sas.models1.job.BuildBasicMetaCostModels.java
License:Open Source License
@Override protected void run() throws Exception { // validate args if (!Bucket.isBucket("datasets", inputBucket)) { throw new FileNotFoundException(inputBucket); }/*from w ww .j a v a 2s . c o m*/ if (!Bucket.isBucket("models", outputBucket)) { throw new FileNotFoundException(outputBucket); } // create prototype classifiers Map<String, Classifier> prototypes = new HashMap<String, Classifier>(); // Bagged REPTrees Bagging baggedTrees = new Bagging(); baggedTrees.setNumExecutionSlots(1); baggedTrees.setNumIterations(100); baggedTrees.setClassifier(new REPTree()); baggedTrees.setCalcOutOfBag(false); prototypes.put("Bagged-REPTrees", baggedTrees); // Bagged SMO Bagging baggedSVM = new Bagging(); baggedSVM.setNumExecutionSlots(1); baggedSVM.setNumIterations(100); baggedSVM.setClassifier(new SMO()); baggedSVM.setCalcOutOfBag(false); prototypes.put("Bagged-SMO", baggedSVM); // Meta Cost model for Naive Bayes Bagging bagging = new Bagging(); bagging.setNumExecutionSlots(1); bagging.setNumIterations(100); bagging.setClassifier(new NaiveBayes()); CostSensitiveClassifier meta = new CostSensitiveClassifier(); meta.setClassifier(bagging); meta.setMinimizeExpectedCost(true); prototypes.put("CostSensitive-MinimizeExpectedCost-NaiveBayes", bagging); // init multi-threading Job.startService(); final Queue<Future<Object>> queue = new LinkedList<Future<Object>>(); // get the input from the bucket List<String> names = Bucket.getBucketItems("datasets", this.inputBucket); for (String dsn : names) { // for each prototype classifier for (Map.Entry<String, Classifier> prototype : prototypes.entrySet()) { // // speical logic for meta cost // Classifier alg = AbstractClassifier.makeCopy(prototype.getValue()); if (alg instanceof CostSensitiveClassifier) { int essaySet = Contest.getEssaySet(dsn); String matrix = Contest.getRubrics(essaySet).size() == 3 ? "cost3.txt" : "cost4.txt"; ((CostSensitiveClassifier) alg) .setCostMatrix(new CostMatrix(new FileReader("/asap/sas/trunk/" + matrix))); } // use InfoGain to discard useless attributes AttributeSelectedClassifier classifier = new AttributeSelectedClassifier(); classifier.setEvaluator(new InfoGainAttributeEval()); Ranker ranker = new Ranker(); ranker.setThreshold(0.0001); classifier.setSearch(ranker); classifier.setClassifier(alg); queue.add(Job.submit( new ModelBuilder(dsn, "InfoGain-" + prototype.getKey(), classifier, this.outputBucket))); } } // wait on complete Progress progress = new Progress(queue.size(), this.getClass().getSimpleName()); while (!queue.isEmpty()) { try { queue.remove().get(); } catch (Exception e) { Job.log("ERROR", e.toString()); } progress.tick(); } progress.done(); Job.stopService(); }
From source file:jjj.asap.sas.models1.job.BuildRegressionModels.java
License:Open Source License
@Override protected void run() throws Exception { // validate args if (!Bucket.isBucket("datasets", inputBucket)) { throw new FileNotFoundException(inputBucket); }/*from ww w . ja va 2s . c o m*/ if (!Bucket.isBucket("models", outputBucket)) { throw new FileNotFoundException(outputBucket); } // create prototype classifiers List<Classifier> models = new ArrayList<Classifier>(); LinearRegression m5 = new LinearRegression(); m5.setAttributeSelectionMethod(M5); LinearRegression lr = new LinearRegression(); lr.setAttributeSelectionMethod(NONE); RandomSubSpace rss = new RandomSubSpace(); rss.setClassifier(lr); rss.setNumIterations(30); AdditiveRegression boostedStumps = new AdditiveRegression(); boostedStumps.setClassifier(new DecisionStump()); boostedStumps.setNumIterations(1000); AdditiveRegression boostedTrees = new AdditiveRegression(); boostedTrees.setClassifier(new REPTree()); boostedTrees.setNumIterations(100); models.add(m5); models.add(boostedStumps); models.add(boostedTrees); models.add(rss); // init multi-threading Job.startService(); final Queue<Future<Object>> queue = new LinkedList<Future<Object>>(); // get the input from the bucket List<String> names = Bucket.getBucketItems("datasets", this.inputBucket); for (String dsn : names) { for (Classifier model : models) { String tag = null; if (model instanceof SingleClassifierEnhancer) { tag = model.getClass().getSimpleName() + "-" + ((SingleClassifierEnhancer) model).getClassifier().getClass().getSimpleName(); } else { tag = model.getClass().getSimpleName(); } queue.add(Job.submit(new RegressionModelBuilder(dsn, tag, AbstractClassifier.makeCopy(model), this.outputBucket))); } } // wait on complete Progress progress = new Progress(queue.size(), this.getClass().getSimpleName()); while (!queue.isEmpty()) { try { queue.remove().get(); } catch (Exception e) { Job.log("ERROR", e.toString()); } progress.tick(); } progress.done(); Job.stopService(); }
From source file:jjj.asap.sas.models1.job.RGramModels.java
License:Open Source License
@Override protected void run() throws Exception { // validate args if (!Bucket.isBucket("datasets", inputBucket)) { throw new FileNotFoundException(inputBucket); }//from www.j a va2s .c o m if (!Bucket.isBucket("models", outputBucket)) { throw new FileNotFoundException(outputBucket); } // create prototype classifiers List<Classifier> models = new ArrayList<Classifier>(); //SGD sgd = new SGD(); //sgd.setDontNormalize(true); //sgd.setLossFunction(new SelectedTag(SGD.SQUAREDLOSS,SGD.TAGS_SELECTION)); LinearRegression m5 = new LinearRegression(); m5.setAttributeSelectionMethod(M5); //models.add(sgd); models.add(m5); LinearRegression lr = new LinearRegression(); lr.setAttributeSelectionMethod(NONE); RandomSubSpace rss = new RandomSubSpace(); rss.setClassifier(lr); rss.setNumIterations(30); models.add(rss); AdditiveRegression boostedStumps = new AdditiveRegression(); boostedStumps.setClassifier(new DecisionStump()); boostedStumps.setNumIterations(1000); AdditiveRegression boostedTrees = new AdditiveRegression(); boostedTrees.setClassifier(new REPTree()); boostedTrees.setNumIterations(100); models.add(boostedStumps); models.add(boostedTrees); models.add(new PLSClassifier()); // init multi-threading Job.startService(); final Queue<Future<Object>> queue = new LinkedList<Future<Object>>(); // get the input from the bucket List<String> names = Bucket.getBucketItems("datasets", this.inputBucket); for (String dsn : names) { for (Classifier model : models) { String tag = null; if (model instanceof SingleClassifierEnhancer) { tag = model.getClass().getSimpleName() + "-" + ((SingleClassifierEnhancer) model).getClassifier().getClass().getSimpleName(); } else { tag = model.getClass().getSimpleName(); } queue.add(Job.submit(new RegressionModelBuilder(dsn, tag, AbstractClassifier.makeCopy(model), this.outputBucket))); } } // wait on complete Progress progress = new Progress(queue.size(), this.getClass().getSimpleName()); while (!queue.isEmpty()) { try { queue.remove().get(); } catch (Exception e) { Job.log("ERROR", e.toString()); } progress.tick(); } progress.done(); Job.stopService(); }
From source file:lu.lippmann.cdb.datasetview.tabs.RegressionTreeTabView.java
License:Open Source License
/** * {@inheritDoc}//from w w w. j a v a2s . c o m */ @SuppressWarnings("unchecked") @Override public void update0(final Instances dataSet) throws Exception { this.panel.removeAll(); //final Object[] attrNames=WekaDataStatsUtil.getNumericAttributesNames(dataSet).toArray(); final Object[] attrNames = WekaDataStatsUtil.getAttributeNames(dataSet).toArray(); final JComboBox xCombo = new JComboBox(attrNames); xCombo.setBorder(new TitledBorder("Attribute to evaluate")); final JXPanel comboPanel = new JXPanel(); comboPanel.setLayout(new GridLayout(1, 2)); comboPanel.add(xCombo); final JXButton jxb = new JXButton("Compute"); comboPanel.add(jxb); this.panel.add(comboPanel, BorderLayout.NORTH); jxb.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent e) { try { if (gv != null) panel.remove((Component) gv); dataSet.setClassIndex(xCombo.getSelectedIndex()); final REPTree rt = new REPTree(); rt.setNoPruning(true); //rt.setMaxDepth(3); rt.buildClassifier(dataSet); /*final M5P rt=new M5P(); rt.buildClassifier(dataSet);*/ final Evaluation eval = new Evaluation(dataSet); double[] d = eval.evaluateModel(rt, dataSet); System.out.println("PREDICTED -> " + FormatterUtil.buildStringFromArrayOfDoubles(d)); System.out.println(eval.errorRate()); System.out.println(eval.sizeOfPredictedRegions()); System.out.println(eval.toSummaryString("", true)); final GraphWithOperations gwo = GraphUtil .buildGraphWithOperationsFromWekaRegressionString(rt.graph()); final DecisionTree dt = new DecisionTree(gwo, eval.errorRate()); gv = DecisionTreeToGraphViewHelper.buildGraphView(dt, eventPublisher, commandDispatcher); gv.addMetaInfo("Size=" + dt.getSize(), ""); gv.addMetaInfo("Depth=" + dt.getDepth(), ""); gv.addMetaInfo("MAE=" + FormatterUtil.DECIMAL_FORMAT.format(eval.meanAbsoluteError()) + "", ""); gv.addMetaInfo("RMSE=" + FormatterUtil.DECIMAL_FORMAT.format(eval.rootMeanSquaredError()) + "", ""); final JCheckBox toggleDecisionTreeDetails = new JCheckBox("Toggle details"); toggleDecisionTreeDetails.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent e) { if (!tweakedGraph) { final Object[] mapRep = WekaDataStatsUtil .buildNodeAndEdgeRepartitionMap(dt.getGraphWithOperations(), dataSet); gv.updateVertexShapeTransformer((Map<CNode, Map<Object, Integer>>) mapRep[0]); gv.updateEdgeShapeRenderer((Map<CEdge, Float>) mapRep[1]); } else { gv.resetVertexAndEdgeShape(); } tweakedGraph = !tweakedGraph; } }); gv.addMetaInfoComponent(toggleDecisionTreeDetails); /*final JButton openInEditorButton = new JButton("Open in editor"); openInEditorButton.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent e) { GraphUtil.importDecisionTreeInEditor(dtFactory, dataSet, applicationContext, eventPublisher, commandDispatcher); } }); this.gv.addMetaInfoComponent(openInEditorButton);*/ final JButton showTextButton = new JButton("In text"); showTextButton.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent e) { JOptionPane.showMessageDialog(null, graphDsl.getDslString(dt.getGraphWithOperations())); } }); gv.addMetaInfoComponent(showTextButton); panel.add(gv.asComponent(), BorderLayout.CENTER); } catch (Exception e1) { e1.printStackTrace(); panel.add(new JXLabel("Error during computation: " + e1.getMessage()), BorderLayout.CENTER); } } }); }
From source file:lu.lippmann.cdb.dt.RegressionTreeFactory.java
License:Open Source License
/** * Main method.//from www . ja va 2s .c o m * @param args command line arguments */ public static void main(final String[] args) { try { final String f = "./samples/csv/uci/winequality-red.csv"; //final String f="./samples/arff/UCI/crimepredict.arff"; final Instances dataSet = WekaDataAccessUtil.loadInstancesFromARFFOrCSVFile(new File(f)); System.out.println(dataSet.classAttribute().isNumeric()); final REPTree rt = new REPTree(); rt.setMaxDepth(3); rt.buildClassifier(dataSet); System.out.println(rt); //System.out.println(rt.graph()); final GraphWithOperations gwo = GraphUtil.buildGraphWithOperationsFromWekaRegressionString(rt.graph()); System.out.println(gwo); System.out.println(new ASCIIGraphDsl().getDslString(gwo)); final Evaluation eval = new Evaluation(dataSet); /*Field privateStringField = Evaluation.class.getDeclaredField("m_CoverageStatisticsAvailable"); privateStringField.setAccessible(true); //privateStringField.get boolean fieldValue = privateStringField.getBoolean(eval); System.out.println("fieldValue = " + fieldValue);*/ double[] d = eval.evaluateModel(rt, dataSet); System.out.println("PREDICTED -> " + FormatterUtil.buildStringFromArrayOfDoubles(d)); System.out.println(eval.errorRate()); System.out.println(eval.sizeOfPredictedRegions()); System.out.println(eval.toSummaryString("", true)); /*final String f2="./samples/csv/salary.csv"; final Instances dataSet2=WekaDataAccessUtil.loadInstancesFromARFFOrCSVFile(new File(f2)); final J48 j48=new J48(); j48.buildClassifier(dataSet2); System.out.println(j48.graph()); final GraphWithOperations gwo2=GraphUtil.buildGraphWithOperationsFromWekaString(j48.graph(),false); System.out.println(gwo2);*/ System.out.println(new DecisionTree(gwo, eval.errorRate())); } catch (Exception e) { e.printStackTrace(); } }