Example usage for weka.classifiers.functions LibSVM setEps

Introduction

In this page you can find the example usage for weka.classifiers.functions LibSVM setEps.

Prototype

public void setEps(double value) 

Source Link

Document

Sets tolerance of termination criterion (default 0.001).

Usage

From source file:org.jaqpot.algorithm.resource.WekaSVM.java

License:Open Source License

@POST
@Path("training")
public Response training(TrainingRequest request) {
    try {/*from  ww  w.ja  v  a2 s.co  m*/
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response.status(Response.Status.BAD_REQUEST).entity(
                    ErrorReportFactory.badRequest("Dataset is empty", "Cannot train model on empty dataset"))
                    .build();
        }
        List<String> features = request.getDataset().getDataEntry().stream().findFirst().get().getValues()
                .keySet().stream().collect(Collectors.toList());

        Instances data = InstanceUtils.createFromDataset(request.getDataset(), request.getPredictionFeature());
        Map<String, Object> parameters = request.getParameters() != null ? request.getParameters()
                : new HashMap<>();

        LibSVM regressor = new LibSVM();
        Double epsilon = Double.parseDouble(parameters.getOrDefault("epsilon", _epsilon).toString());
        Double cacheSize = Double.parseDouble(parameters.getOrDefault("cacheSize", _cacheSize).toString());
        Double gamma = Double.parseDouble(parameters.getOrDefault("gamma", _gamma).toString());
        Double coeff0 = Double.parseDouble(parameters.getOrDefault("coeff0", _coeff0).toString());
        Double cost = Double.parseDouble(parameters.getOrDefault("cost", _cost).toString());
        Double nu = Double.parseDouble(parameters.getOrDefault("nu", _nu).toString());
        Double loss = Double.parseDouble(parameters.getOrDefault("loss", _loss).toString());
        Integer degree = Integer.parseInt(parameters.getOrDefault("degree", _degree).toString());

        regressor.setEps(epsilon);
        regressor.setCacheSize(cacheSize);
        regressor.setDegree(degree);
        regressor.setCost(cost);
        regressor.setGamma(gamma);
        regressor.setCoef0(coeff0);
        regressor.setNu(nu);
        regressor.setLoss(loss);

        Integer svm_kernel = null;
        String kernel = parameters.getOrDefault("kernel", _kernel).toString();
        if (kernel.equalsIgnoreCase("rbf")) {
            svm_kernel = LibSVM.KERNELTYPE_RBF;
        } else if (kernel.equalsIgnoreCase("polynomial")) {
            svm_kernel = LibSVM.KERNELTYPE_POLYNOMIAL;
        } else if (kernel.equalsIgnoreCase("linear")) {
            svm_kernel = LibSVM.KERNELTYPE_LINEAR;
        } else if (kernel.equalsIgnoreCase("sigmoid")) {
            svm_kernel = LibSVM.KERNELTYPE_SIGMOID;
        }
        regressor.setKernelType(new SelectedTag(svm_kernel, LibSVM.TAGS_KERNELTYPE));

        Integer svm_type = null;
        String type = parameters.getOrDefault("type", _type).toString();
        if (type.equalsIgnoreCase("NU_SVR")) {
            svm_type = LibSVM.SVMTYPE_NU_SVR;
        } else if (type.equalsIgnoreCase("NU_SVC")) {
            svm_type = LibSVM.SVMTYPE_NU_SVC;
        } else if (type.equalsIgnoreCase("C_SVC")) {
            svm_type = LibSVM.SVMTYPE_C_SVC;
        } else if (type.equalsIgnoreCase("EPSILON_SVR")) {
            svm_type = LibSVM.SVMTYPE_EPSILON_SVR;
        } else if (type.equalsIgnoreCase("ONE_CLASS_SVM")) {
            svm_type = LibSVM.SVMTYPE_ONE_CLASS_SVM;
        }
        regressor.setSVMType(new SelectedTag(svm_type, LibSVM.TAGS_SVMTYPE));

        regressor.buildClassifier(data);

        WekaModel model = new WekaModel();
        model.setClassifier(regressor);

        Map<String, Double> options = new HashMap<>();
        options.put("gamma", gamma);
        options.put("coeff0", coeff0);
        options.put("degree", new Double(degree.toString()));

        Field modelField = LibSVM.class.getDeclaredField("m_Model");
        modelField.setAccessible(true);
        svm_model svmModel = (svm_model) modelField.get(regressor);
        double[][] coefs = svmModel.sv_coef;
        List<Double> coefsList = IntStream.range(0, coefs[0].length).mapToObj(i -> coefs[0][i])
                .collect(Collectors.toList());

        svm_node[][] nodes = svmModel.SV;

        List<Map<Integer, Double>> vectors = IntStream.range(0, nodes.length).mapToObj(i -> {
            Map<Integer, Double> node = new TreeMap<>();
            Arrays.stream(nodes[i]).forEach(n -> node.put(n.index, n.value));
            return node;
        }).collect(Collectors.toList());

        String pmml = PmmlUtils.createSVMModel(features, request.getPredictionFeature(), "SVM", kernel,
                svm_type, options, coefsList, vectors);
        TrainingResponse response = new TrainingResponse();
        ByteArrayOutputStream baos = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(baos);
        out.writeObject(model);
        String base64Model = Base64.getEncoder().encodeToString(baos.toByteArray());
        response.setRawModel(base64Model);
        List<String> independentFeatures = features.stream()
                .filter(feature -> !feature.equals(request.getPredictionFeature()))
                .collect(Collectors.toList());
        response.setIndependentFeatures(independentFeatures);
        response.setPmmlModel(pmml);
        response.setAdditionalInfo(request.getPredictionFeature());
        response.setPredictedFeatures(
                Arrays.asList("Weka SVM prediction of " + request.getPredictionFeature()));

        return Response.ok(response).build();
    } catch (Exception ex) {
        Logger.getLogger(WekaSVM.class.getName()).log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}

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From source file:org.jaqpot.algorithms.resource.WekaSVM.java

License:Open Source License

@POST
@Path("training")
public Response training(TrainingRequest request) {
    try {/*w w w .jav a2 s.  c  om*/
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response.status(Response.Status.BAD_REQUEST)
                    .entity("Dataset is empty. Cannot train model on empty dataset.").build();
        }
        List<String> features = request.getDataset().getDataEntry().stream().findFirst().get().getValues()
                .keySet().stream().collect(Collectors.toList());

        Instances data = InstanceUtils.createFromDataset(request.getDataset(), request.getPredictionFeature());
        Map<String, Object> parameters = request.getParameters() != null ? request.getParameters()
                : new HashMap<>();

        LibSVM regressor = new LibSVM();
        Double epsilon = Double.parseDouble(parameters.getOrDefault("epsilon", _epsilon).toString());
        Double cacheSize = Double.parseDouble(parameters.getOrDefault("cacheSize", _cacheSize).toString());
        Double gamma = Double.parseDouble(parameters.getOrDefault("gamma", _gamma).toString());
        Double coeff0 = Double.parseDouble(parameters.getOrDefault("coeff0", _coeff0).toString());
        Double cost = Double.parseDouble(parameters.getOrDefault("cost", _cost).toString());
        Double nu = Double.parseDouble(parameters.getOrDefault("nu", _nu).toString());
        Double loss = Double.parseDouble(parameters.getOrDefault("loss", _loss).toString());
        Integer degree = Integer.parseInt(parameters.getOrDefault("degree", _degree).toString());

        regressor.setEps(epsilon);
        regressor.setCacheSize(cacheSize);
        regressor.setDegree(degree);
        regressor.setCost(cost);
        regressor.setGamma(gamma);
        regressor.setCoef0(coeff0);
        regressor.setNu(nu);
        regressor.setLoss(loss);

        Integer svm_kernel = null;
        String kernel = parameters.getOrDefault("kernel", _kernel).toString();
        if (kernel.equalsIgnoreCase("rbf")) {
            svm_kernel = LibSVM.KERNELTYPE_RBF;
        } else if (kernel.equalsIgnoreCase("polynomial")) {
            svm_kernel = LibSVM.KERNELTYPE_POLYNOMIAL;
        } else if (kernel.equalsIgnoreCase("linear")) {
            svm_kernel = LibSVM.KERNELTYPE_LINEAR;
        } else if (kernel.equalsIgnoreCase("sigmoid")) {
            svm_kernel = LibSVM.KERNELTYPE_SIGMOID;
        }
        regressor.setKernelType(new SelectedTag(svm_kernel, LibSVM.TAGS_KERNELTYPE));

        Integer svm_type = null;
        String type = parameters.getOrDefault("type", _type).toString();
        if (type.equalsIgnoreCase("NU_SVR")) {
            svm_type = LibSVM.SVMTYPE_NU_SVR;
        } else if (type.equalsIgnoreCase("NU_SVC")) {
            svm_type = LibSVM.SVMTYPE_NU_SVC;
        } else if (type.equalsIgnoreCase("C_SVC")) {
            svm_type = LibSVM.SVMTYPE_C_SVC;
        } else if (type.equalsIgnoreCase("EPSILON_SVR")) {
            svm_type = LibSVM.SVMTYPE_EPSILON_SVR;
        } else if (type.equalsIgnoreCase("ONE_CLASS_SVM")) {
            svm_type = LibSVM.SVMTYPE_ONE_CLASS_SVM;
        }
        regressor.setSVMType(new SelectedTag(svm_type, LibSVM.TAGS_SVMTYPE));

        regressor.buildClassifier(data);

        WekaModel model = new WekaModel();
        model.setClassifier(regressor);

        Map<String, Double> options = new HashMap<>();
        options.put("gamma", gamma);
        options.put("coeff0", coeff0);
        options.put("degree", new Double(degree.toString()));

        Field modelField = LibSVM.class.getDeclaredField("m_Model");
        modelField.setAccessible(true);
        svm_model svmModel = (svm_model) modelField.get(regressor);
        double[][] coefs = svmModel.sv_coef;
        List<Double> coefsList = IntStream.range(0, coefs[0].length).mapToObj(i -> coefs[0][i])
                .collect(Collectors.toList());

        svm_node[][] nodes = svmModel.SV;

        List<Map<Integer, Double>> vectors = IntStream.range(0, nodes.length).mapToObj(i -> {
            Map<Integer, Double> node = new TreeMap<>();
            Arrays.stream(nodes[i]).forEach(n -> node.put(n.index, n.value));
            return node;
        }).collect(Collectors.toList());

        String pmml = PmmlUtils.createSVMModel(features, request.getPredictionFeature(), "SVM", kernel,
                svm_type, options, coefsList, vectors);
        TrainingResponse response = new TrainingResponse();
        ByteArrayOutputStream baos = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(baos);
        out.writeObject(model);
        String base64Model = Base64.getEncoder().encodeToString(baos.toByteArray());
        response.setRawModel(base64Model);
        List<String> independentFeatures = features.stream()
                .filter(feature -> !feature.equals(request.getPredictionFeature()))
                .collect(Collectors.toList());
        response.setIndependentFeatures(independentFeatures);
        response.setPmmlModel(pmml);
        response.setAdditionalInfo(request.getPredictionFeature());
        response.setPredictedFeatures(
                Arrays.asList("Weka SVM prediction of " + request.getPredictionFeature()));

        return Response.ok(response).build();
    } catch (Exception ex) {
        Logger.getLogger(WekaSVM.class.getName()).log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}

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