List of usage examples for weka.classifiers.bayes BayesNet BayesNet
BayesNet
From source file:com.daniel.convert.IncrementalClassifier.java
License:Open Source License
/** * Expects an ARFF file as first argument (class attribute is assumed to be * the last attribute)./*w w w .j av a 2 s. c o m*/ * * @param args * the commandline arguments * @throws Exception * if something goes wrong */ public static BayesNet treinar(String[] args) throws Exception { // load data ArffLoader loader = new ArffLoader(); loader.setFile(new File(args[0])); Instances structure = loader.getStructure(); structure.setClassIndex(structure.numAttributes() - 1); // train NaiveBayes BayesNet BayesNet = new BayesNet(); Instance current; while ((current = loader.getNextInstance(structure)) != null) { structure.add(current); } BayesNet.buildClassifier(structure); // output generated model // System.out.println(nb); // test set BayesNet BayesNetTest = new BayesNet(); // test the model Evaluation eTest = new Evaluation(structure); // eTest.evaluateModel(nb, structure); eTest.crossValidateModel(BayesNetTest, structure, 15, new Random(1)); // Print the result la Weka explorer: String strSummary = eTest.toSummaryString(); System.out.println(strSummary); return BayesNet; }
From source file:core.classification.Classifiers.java
License:Open Source License
/** * Private constructor for the <code>Classifiers</code> object * @param train/* w ww.j av a 2s . c o m*/ */ private Classifiers(boolean train) { SCA = new BayesNet(); SCB = new MultilayerPerceptron(); SCC1 = new MultilayerPerceptron(); SCC2 = new MultilayerPerceptron(); SCC3 = new MultilayerPerceptron(); RC = new CostSensitiveClassifier(); YNC = new J48(); if (train) { try { this.trainSC(); } catch (Exception e) { System.out.println("The system encountered the following error while training SC:"); e.printStackTrace(); } try { this.trainRC(); } catch (Exception e) { System.out.println("The system encountered the following error while training RC:"); e.printStackTrace(); } try { this.trainYNC(); } catch (Exception e) { System.out.println("The system encountered the following error while training YNC:"); e.printStackTrace(); } } else { try { readSC("SCA.model", "SCB.model", "SCC1.model", "SCC2.model", "SCC3.model"); readRC("RC.model"); readYNC("YNC.model"); } catch (Exception e) { System.out.println("Error while reading the classifiers: "); e.printStackTrace(); } } // Strutures Creations FastVector labels = new FastVector(); labels.addElement("0"); labels.addElement("1"); labels.addElement("2"); labels.addElement("3"); labels.addElement("4"); FastVector clabels = new FastVector(); clabels.addElement("1"); clabels.addElement("2"); clabels.addElement("3"); clabels.addElement("4"); FastVector clabels2 = new FastVector(); clabels2.addElement("0"); clabels2.addElement("1"); clabels2.addElement("2"); clabels2.addElement("3"); clabels2.addElement("4"); FastVector clabels3 = new FastVector(); clabels3.addElement("Y"); clabels3.addElement("N"); // Creating the structure for SC FastVector attrs = new FastVector(); attrs.addElement(new Attribute("RATIO")); attrs.addElement(new Attribute("CLASS", clabels)); dataStructSCA = new Instances("SCA-STRUCT", attrs, 0); dataStructSCA.setClassIndex(1); FastVector attrsB = new FastVector(); attrsB.addElement(new Attribute("H2")); attrsB.addElement(new Attribute("D2")); attrsB.addElement(new Attribute("DX")); attrsB.addElement(new Attribute("PCLASS", clabels)); attrsB.addElement(new Attribute("CLASS", clabels)); dataStructSCB = new Instances("SCB-STRUCT", attrsB, 0); dataStructSCB.setClassIndex(4); FastVector attrsC1 = new FastVector(); FastVector attrsC2 = new FastVector(); FastVector attrsC3 = new FastVector(); attrsC1.addElement(new Attribute("LH")); attrsC1.addElement(new Attribute("LD")); attrsC1.addElement(new Attribute("LDX")); attrsC1.addElement(new Attribute("LCLASS", clabels)); attrsC1.addElement(new Attribute("CLASS", clabels)); attrsC2.addElement(new Attribute("EH")); attrsC2.addElement(new Attribute("ED")); attrsC2.addElement(new Attribute("EDX")); attrsC2.addElement(new Attribute("ECLASS", clabels)); attrsC2.addElement(new Attribute("CLASS", clabels)); attrsC3.addElement(new Attribute("SH")); attrsC3.addElement(new Attribute("SD")); attrsC3.addElement(new Attribute("SDX")); attrsC3.addElement(new Attribute("SCLASS", clabels)); attrsC3.addElement(new Attribute("CLASS", clabels)); dataStructSCC1 = new Instances("SCC1-STRUCT", attrsC1, 0); dataStructSCC1.setClassIndex(4); dataStructSCC2 = new Instances("SCC2-STRUCT", attrsC2, 0); dataStructSCC2.setClassIndex(4); dataStructSCC3 = new Instances("SCC3-STRUCT", attrsC3, 0); dataStructSCC3.setClassIndex(4); FastVector attrs2 = new FastVector(); attrs2.addElement(new Attribute("H2")); attrs2.addElement(new Attribute("D2")); attrs2.addElement(new Attribute("DX")); attrs2.addElement(new Attribute("CLASS", clabels)); attrs2.addElement(new Attribute("PCLASS", clabels)); attrs2.addElement(new Attribute("RELID", clabels2)); dataStructRC = new Instances("RC-STRUCT", attrs2, 0); dataStructRC.setClassIndex(5); FastVector attrs3 = new FastVector(); attrs3.addElement(new Attribute("PCLASS", clabels)); attrs3.addElement(new Attribute("CCLASS", clabels)); attrs3.addElement(new Attribute("RAREA")); attrs3.addElement(new Attribute("H")); attrs3.addElement(new Attribute("D")); attrs3.addElement(new Attribute("V")); attrs3.addElement(new Attribute("YN", clabels3)); dataStructYC = new Instances("YC-STRUCT", attrs3, 0); dataStructYC.setClassIndex(6); }
From source file:de.fub.maps.project.detector.model.inference.impl.BayesNetworkInferenceModel.java
License:Open Source License
@Override protected Classifier createClassifier() { if (classifier == null) { classifier = new BayesNet(); } return classifier; }
From source file:de.tudarmstadt.ukp.alignment.framework.combined.WekaMachineLearning.java
License:Apache License
/** * * This method creates a serialized WEKA model file from an .arff file containing the annotated gold standard * * * @param gs_arff the annotated gold standard in an .arff file * @param model output file for the model * @param output_eval if true, the evaluation of the trained classifier is printed (10-fold cross validation) * @throws Exception/*from w w w.ja v a 2 s . c om*/ */ public static void createModelFromGoldstandard(String gs_arff, String model, boolean output_eval) throws Exception { DataSource source = new DataSource(gs_arff); Instances data = source.getDataSet(); if (data.classIndex() == -1) { data.setClassIndex(data.numAttributes() - 1); } Remove rm = new Remove(); rm.setAttributeIndices("1"); // remove ID attribute BayesNet bn = new BayesNet(); //Standard classifier; BNs proved most robust, but of course other classifiers are possible // meta-classifier FilteredClassifier fc = new FilteredClassifier(); fc.setFilter(rm); fc.setClassifier(bn); fc.buildClassifier(data); // build classifier SerializationHelper.write(model, fc); if (output_eval) { Evaluation eval = new Evaluation(data); eval.crossValidateModel(fc, data, 10, new Random(1)); System.out.println(eval.toSummaryString()); System.out.println(eval.toMatrixString()); System.out.println(eval.toClassDetailsString()); } }
From source file:development.CrossValidateShapelets.java
public static ArrayList<Classifier> setSingleClassifiers(ArrayList<String> names) { ArrayList<Classifier> sc = new ArrayList<>(); kNN n = new kNN(50); n.setCrossValidate(true);/*w w w.java2 s. c o m*/ sc.add(n); names.add("kNN"); sc.add(new J48()); names.add("C45"); sc.add(new NaiveBayes()); names.add("NB"); BayesNet bn = new BayesNet(); sc.add(bn); names.add("BayesNet"); RandomForest rf = new RandomForest(); rf.setNumTrees(200); sc.add(rf); names.add("RandForest"); RotationForest rot = new RotationForest(); rot.setNumIterations(30); sc.add(rf); names.add("RotForest"); SMO svmL = new SMO(); PolyKernel kernel = new PolyKernel(); kernel.setExponent(1); svmL.setKernel(kernel); sc.add(svmL); names.add("SVML"); kernel = new PolyKernel(); kernel.setExponent(2); SMO svmQ = new SMO(); svmQ.setKernel(kernel); sc.add(svmQ); names.add("SVMQ"); return sc; }
From source file:DiversifyQuery.DivTopK.java
public static void table4_5() throws Exception { // Initialise classifiers required for this experiment classifiers = new Classifier[8]; classifiers[0] = new ShapeletTreeClassifier("infoTree.txt"); classifiers[1] = new J48(); classifiers[2] = new IB1(); classifiers[3] = new NaiveBayes(); classifiers[4] = new BayesNet(); classifiers[5] = new RandomForest(); classifiers[6] = new RotationForest(); classifiers[7] = new SMO(); // Set up names for the classifiers - only used for output classifierNames = new String[8]; classifierNames[0] = "ShapeletTree"; classifierNames[1] = "C4.5"; classifierNames[2] = "1NN"; classifierNames[3] = "Naive Bayes"; classifierNames[4] = "Bayesian Network"; classifierNames[5] = "Random Forest"; classifierNames[6] = "Rotation Forest"; classifierNames[7] = "SVM (linear)"; // if ((classifierToProcessIndex < 1 || classifierToProcessIndex > classifiers.length) && classifierToProcessIndex != -1) { // throw new IOException("Invalid classifier identifier."); // } else { // if (classifierToProcessIndex != -1) { // classifierToProcessIndex--; // } // }//from ww w .java 2 s . c o m // Compute classifier accuracies for each classifier double accuracies[] = new double[classifiers.length]; for (int i = 1; i < classifiers.length; i++) { //if (i == classifierToProcessIndex || classifierToProcessIndex == -1) { accuracies[i] = classifierAccuracy(i, true, true); } // Write experiment output to file writeFileContent(accuracies); }
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 . ja v a 2 s. com //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:hero.unstable.util.classification.wekaClassifier.java
public wekaClassifier(String nameClassifier, String classifierOpt, int seed, int folds) throws Exception { String[] opts = classifierOpt.split(" "); this.seed = seed; this.folds = folds; // Create classifier if (nameClassifier.equals("AdaBoostM1")) { this.classifier = new AdaBoostM1(); } else if (nameClassifier.equals("J48")) { this.classifier = new AdaBoostM1(); } else if (nameClassifier.equals("RandomForest")) { this.classifier = new RandomForest(); } else if (nameClassifier.equals("Bayes")) { this.classifier = new BayesNet(); } else if (nameClassifier.equals("knn")) { this.classifier = new IBk(); } else if (nameClassifier.equals("ZeroR")) { this.classifier = new ZeroR(); } else if (nameClassifier.equals("NN")) { this.classifier = new MultilayerPerceptron(); } else {/*from www . j a va 2 s. co m*/ this.classifier = new ZeroR(); } this.nameClassifier = classifier.getClass().getName(); }
From source file:jjj.asap.sas.models1.job.BuildBasicModels.java
License:Open Source License
@Override protected void run() throws Exception { // validate args if (!Bucket.isBucket("datasets", inputBucket)) { throw new FileNotFoundException(inputBucket); }/*w w w .j a va 2s . co m*/ if (!Bucket.isBucket("models", outputBucket)) { throw new FileNotFoundException(outputBucket); } // create prototype classifiers Map<String, Classifier> prototypes = new HashMap<String, Classifier>(); // bayes BayesNet net = new BayesNet(); net.setEstimator(new BMAEstimator()); prototypes.put("BayesNet", net); prototypes.put("NaiveBayes", new NaiveBayes()); // functions prototypes.put("RBFNetwork", new RBFNetwork()); prototypes.put("SMO", new SMO()); // 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()) { // 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(AbstractClassifier.makeCopy(prototype.getValue())); 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(); }