List of usage examples for weka.classifiers.trees RandomTree RandomTree
RandomTree
From source file:com.rokittech.ml.server.utils.MLUtils.java
License:Open Source License
public static Classifier getClassifier(String mlAlgorithm) { notEmpty(mlAlgorithm);/* w ww .ja va 2s.c o m*/ Classifier classifier; switch (mlAlgorithm.toUpperCase()) { case "J48": { classifier = new J48(); break; } case "IBK": { classifier = new IBk(); break; } case "NAIVE_BAYES": { classifier = new NaiveBayes(); break; } case "RANDOM_TREE": { classifier = new RandomTree(); break; } case "RANDOM_FOREST": { classifier = new RandomForest(); break; } case "BOOSTING": { classifier = new DecisionStump(); break; } case "BAGGING": { classifier = new Bagging(); break; } default: throw new UnsupportedOperationException("Classifier " + mlAlgorithm + " is not supported."); } return classifier; }
From source file:com.walmart.productgenome.matching.models.EMSRandomForest.java
License:Open Source License
/** * Returns default capabilities of the classifier. * * @return the capabilities of this classifier *//* w w w.j a va 2 s . c o m*/ public Capabilities getCapabilities() { return new RandomTree().getCapabilities(); }
From source file:com.walmart.productgenome.matching.models.EMSRandomForest.java
License:Open Source License
/** * Builds a classifier for a set of instances. * * @param data the instances to train the classifier with * @throws Exception if something goes wrong *///from w ww . j a va2s .com public void buildClassifier(Instances data) throws Exception { // can classifier handle the data? getCapabilities().testWithFail(data); // remove instances with missing class data = new Instances(data); data.deleteWithMissingClass(); m_bagger = new Bagging(); RandomTree rTree = new RandomTree(); // set up the random tree options m_KValue = m_numFeatures; if (m_KValue < 1) m_KValue = (int) Utils.log2(data.numAttributes()) + 1; rTree.setKValue(m_KValue); rTree.setMaxDepth(getMaxDepth()); // set up the bagger and build the forest m_bagger.setClassifier(rTree); m_bagger.setSeed(m_randomSeed); m_bagger.setNumIterations(m_numTrees); m_bagger.setCalcOutOfBag(true); m_bagger.setNumExecutionSlots(m_numExecutionSlots); m_bagger.buildClassifier(data); }
From source file:controller.MineroControler.java
public String clasificardorArbolAleat(String atributo) { BufferedReader breader = null; Instances datos = null;// w w w.j a va 2 s . co m breader = new BufferedReader(fuente_arff); try { datos = new Instances(breader); Attribute atr = datos.attribute(atributo); datos.setClass(atr); //datos.setClassIndex(0); } catch (IOException ex) { System.err.println("Problemas al intentar cargar los datos"); return null; } RandomTree arbol = new RandomTree(); // Class for constructing a tree that considers K randomly chosen attributes at each node. try { arbol.setNumFolds(100); arbol.setKValue(0); arbol.setMinNum(1); arbol.setMaxDepth(0); arbol.setSeed(1); arbol.buildClassifier(datos); } catch (Exception ex) { System.err.println("Problemas al ejecutar algorimo de clasificacion" + ex.getLocalizedMessage()); } return arbol.toString(); }
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 ww w .j a v 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:KFST.featureSelection.embedded.TreeBasedMethods.DecisionTreeBasedMethod.java
License:Open Source License
/** * {@inheritDoc }/*from w w w . jav a 2 s.com*/ */ @Override protected String buildClassifier(Instances dataTrain) { try { if (TREE_TYPE == TreeType.C45) { J48 decisionTreeC45 = new J48(); decisionTreeC45.setConfidenceFactor((float) confidenceValue); decisionTreeC45.setMinNumObj(minNumSampleInLeaf); decisionTreeC45.buildClassifier(dataTrain); return decisionTreeC45.toString(); } else if (TREE_TYPE == TreeType.RANDOM_TREE) { RandomTree decisionTreeRandomTree = new RandomTree(); decisionTreeRandomTree.setKValue(randomTreeKValue); decisionTreeRandomTree.setMaxDepth(randomTreeMaxDepth); decisionTreeRandomTree.setMinNum(randomTreeMinNum); decisionTreeRandomTree.setMinVarianceProp(randomTreeMinVarianceProp); decisionTreeRandomTree.buildClassifier(dataTrain); return decisionTreeRandomTree.toString(); } } catch (Exception ex) { Logger.getLogger(DecisionTreeBasedMethod.class.getName()).log(Level.SEVERE, null, ex); } return ""; }
From source file:learning.DMRandomForest.java
License:Open Source License
/** * Builds a classifier for a set of instances. * * @param data the instances to train the classifier with * @throws Exception if something goes wrong *///from w w w . j a va 2 s . co m public void buildClassifier(Instances data) throws Exception { // can classifier handle the data? getCapabilities().testWithFail(data); // remove instances with missing class data = new Instances(data); data.deleteWithMissingClass(); m_bagger = new DMBagging(); RandomTree rTree = new RandomTree(); // set up the random tree options m_KValue = m_numFeatures; if (m_KValue < 1) m_KValue = (int) Utils.log2(data.numAttributes()) + 1; rTree.setKValue(m_KValue); rTree.setMaxDepth(getMaxDepth()); // set up the bagger and build the forest m_bagger.setClassifier(rTree); m_bagger.setSeed(m_randomSeed); m_bagger.setNumIterations(m_numTrees); m_bagger.setCalcOutOfBag(true); m_bagger.buildClassifier(data); }
From source file:org.uclab.mm.kcl.ddkat.modellearner.ModelLearner.java
License:Apache License
/** * Method to compute the classification accuracy. * * @param algo the algorithm name//from ww w . j a va 2s . c om * @param data the data instances * @param datanature the dataset nature (i.e. original or processed data) * @throws Exception the exception */ protected String[] modelAccuracy(String algo, Instances data, String datanature) throws Exception { String modelResultSet[] = new String[4]; String modelStr = ""; Classifier classifier = null; // setting class attribute if the data format does not provide this information if (data.classIndex() == -1) data.setClassIndex(data.numAttributes() - 1); String decisionAttribute = data.attribute(data.numAttributes() - 1).toString(); String res[] = decisionAttribute.split("\\s+"); decisionAttribute = res[1]; if (algo.equals("BFTree")) { // Use BFTree classifiers BFTree BFTreeclassifier = new BFTree(); BFTreeclassifier.buildClassifier(data); modelStr = BFTreeclassifier.toString(); classifier = BFTreeclassifier; } else if (algo.equals("FT")) { // Use FT classifiers FT FTclassifier = new FT(); FTclassifier.buildClassifier(data); modelStr = FTclassifier.toString(); classifier = FTclassifier; } else if (algo.equals("J48")) { // Use J48 classifiers J48 J48classifier = new J48(); J48classifier.buildClassifier(data); modelStr = J48classifier.toString(); classifier = J48classifier; System.out.println("Model String: " + modelStr); } else if (algo.equals("J48graft")) { // Use J48graft classifiers J48graft J48graftclassifier = new J48graft(); J48graftclassifier.buildClassifier(data); modelStr = J48graftclassifier.toString(); classifier = J48graftclassifier; } else if (algo.equals("RandomTree")) { // Use RandomTree classifiers RandomTree RandomTreeclassifier = new RandomTree(); RandomTreeclassifier.buildClassifier(data); modelStr = RandomTreeclassifier.toString(); classifier = RandomTreeclassifier; } else if (algo.equals("REPTree")) { // Use REPTree classifiers REPTree REPTreeclassifier = new REPTree(); REPTreeclassifier.buildClassifier(data); modelStr = REPTreeclassifier.toString(); classifier = REPTreeclassifier; } else if (algo.equals("SimpleCart")) { // Use SimpleCart classifiers SimpleCart SimpleCartclassifier = new SimpleCart(); SimpleCartclassifier.buildClassifier(data); modelStr = SimpleCartclassifier.toString(); classifier = SimpleCartclassifier; } modelResultSet[0] = algo; modelResultSet[1] = decisionAttribute; modelResultSet[2] = modelStr; // Collect every group of predictions for J48 model in a FastVector FastVector predictions = new FastVector(); Evaluation evaluation = new Evaluation(data); int folds = 10; // cross fold validation = 10 evaluation.crossValidateModel(classifier, data, folds, new Random(1)); // System.out.println("Evaluatuion"+evaluation.toSummaryString()); System.out.println("\n\n" + datanature + " Evaluatuion " + evaluation.toMatrixString()); // ArrayList<Prediction> predictions = evaluation.predictions(); predictions.appendElements(evaluation.predictions()); System.out.println("\n\n 11111"); // Calculate overall accuracy of current classifier on all splits double correct = 0; for (int i = 0; i < predictions.size(); i++) { NominalPrediction np = (NominalPrediction) predictions.elementAt(i); if (np.predicted() == np.actual()) { correct++; } } System.out.println("\n\n 22222"); double accuracy = 100 * correct / predictions.size(); String accString = String.format("%.2f%%", accuracy); modelResultSet[3] = accString; System.out.println(datanature + " Accuracy " + accString); String modelFileName = algo + "-DDKA.model"; System.out.println("\n\n 33333"); ObjectOutputStream oos = new ObjectOutputStream( new FileOutputStream("D:\\DDKAResources\\" + modelFileName)); oos.writeObject(classifier); oos.flush(); oos.close(); return modelResultSet; }