Examples with Kernel org.knime.base.node.mine.svm.kernel.Kernel used on opensource projects

Example 1 with Kernel

use of org.knime.base.node.mine.svm.kernel.Kernel in project knime-core by knime.

the class SVMLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable inTable = (BufferedDataTable) inData[0];
    DataTableSpec inSpec = inTable.getDataTableSpec();
    LearnColumnsAndColumnRearrangerTuple tuple = createTrainTableColumnRearranger(inSpec);
    // no progress needed as constant operation (column removal only)
    BufferedDataTable trainTable = exec.createColumnRearrangeTable(inTable, tuple.getTrainingRearranger(), exec.createSubProgress(0.0));
    DataTableSpec trainSpec = trainTable.getDataTableSpec();
    int classpos = trainSpec.findColumnIndex(m_classcol.getStringValue());
    CheckUtils.checkArgument(classpos >= 0, "Selected class column not found: " + m_classcol.getStringValue());
    // convert input data
    ArrayList<DoubleVector> inputData = new ArrayList<DoubleVector>();
    List<String> categories = new ArrayList<String>();
    StringValue classvalue = null;
    for (DataRow row : trainTable) {
        exec.checkCanceled();
        ArrayList<Double> values = new ArrayList<Double>();
        boolean add = true;
        for (int i = 0; i < row.getNumCells(); i++) {
            if (row.getCell(i).isMissing()) {
                add = false;
                break;
            }
            if (i != classpos) {
                DoubleValue cell = (DoubleValue) row.getCell(i);
                values.add(cell.getDoubleValue());
            } else {
                classvalue = (StringValue) row.getCell(classpos);
                if (!categories.contains(classvalue.getStringValue())) {
                    categories.add(classvalue.getStringValue());
                }
            }
        }
        if (add) {
            @SuppressWarnings("null") final String nonNullClassValue = classvalue.getStringValue();
            inputData.add(new DoubleVector(row.getKey(), values, nonNullClassValue));
        }
    }
    if (categories.isEmpty()) {
        throw new Exception("No categories found to train SVM. " + "Possibly an empty input table was provided.");
    }
    DoubleVector[] inputDataArr = new DoubleVector[inputData.size()];
    inputDataArr = inputData.toArray(inputDataArr);
    Kernel kernel = KernelFactory.getKernel(m_kernelType);
    Vector<SettingsModelDouble> kernelparams = m_kernelParameters.get(m_kernelType);
    for (int i = 0; i < kernel.getNumberParameters(); ++i) {
        kernel.setParameter(i, kernelparams.get(i).getDoubleValue());
    }
    final Svm[] svms = new Svm[categories.size()];
    exec.setMessage("Training SVM");
    final BinarySvmRunnable[] bst = new BinarySvmRunnable[categories.size()];
    for (int i = 0; i < categories.size(); i++) {
        bst[i] = new BinarySvmRunnable(inputDataArr, categories.get(i), kernel, m_paramC.getDoubleValue(), exec.createSubProgress((1.0 / categories.size())));
    }
    ThreadPool pool = KNIMEConstants.GLOBAL_THREAD_POOL;
    final Future<?>[] fut = new Future<?>[bst.length];
    KNIMETimer timer = KNIMETimer.getInstance();
    TimerTask timerTask = new TimerTask() {

        @Override
        public void run() {
            try {
                exec.checkCanceled();
            } catch (final CanceledExecutionException ce) {
                for (int i = 0; i < fut.length; i++) {
                    if (fut[i] != null) {
                        fut[i].cancel(true);
                    }
                }
                super.cancel();
            }
        }
    };
    timer.scheduleAtFixedRate(timerTask, 0, 3000);
    for (int i = 0; i < bst.length; i++) {
        fut[i] = pool.enqueue(bst[i]);
    }
    try {
        pool.runInvisible(new Callable<Void>() {

            @Override
            public Void call() throws Exception {
                for (int i = 0; i < fut.length; ++i) {
                    fut[i].get();
                    bst[i].ok();
                    if (bst[i].getWarning() != null) {
                        setWarningMessage(bst[i].getWarning());
                    }
                    svms[i] = bst[i].getSvm();
                }
                return null;
            }
        });
    } catch (Exception ex) {
        exec.checkCanceled();
        Throwable t = ex;
        if (ex instanceof ExecutionException) {
            t = ex.getCause();
        }
        if (t instanceof Exception) {
            throw (Exception) t;
        } else {
            throw new Exception(t);
        }
    } finally {
        for (int i = 0; i < fut.length; i++) {
            fut[i].cancel(true);
        }
        timerTask.cancel();
    }
    // the optional PMML input (can be null)
    PMMLPortObject inPMMLPort = m_pmmlInEnabled ? (PMMLPortObject) inData[1] : null;
    // create the outgoing PMML spec
    PMMLPortObjectSpecCreator specCreator = new PMMLPortObjectSpecCreator(inPMMLPort, inSpec);
    specCreator.setLearningCols(trainSpec);
    specCreator.setTargetCol(trainSpec.getColumnSpec(m_classcol.getStringValue()));
    // create the outgoing PMML port object
    PMMLPortObject outPMMLPort = new PMMLPortObject(specCreator.createSpec(), inPMMLPort, inSpec);
    outPMMLPort.addModelTranslater(new PMMLSVMTranslator(categories, Arrays.asList(svms), kernel));
    m_svms = svms;
    return new PortObject[] { outPMMLPort };
}

Example 2 with Kernel

use of org.knime.base.node.mine.svm.kernel.Kernel in project knime-core by knime.

the class SVMLearnerNodeModel method createKernelParams.

/**
 * creates the kernel parameter SettingsModels.
 *
 * @return HashMap containing the kernel and its assigned SettingsModels.
 */
static LinkedHashMap<KernelType, Vector<SettingsModelDouble>> createKernelParams() {
    LinkedHashMap<KernelType, Vector<SettingsModelDouble>> kernelParameters = new LinkedHashMap<KernelType, Vector<SettingsModelDouble>>();
    for (KernelType kerneltype : KernelType.values()) {
        Kernel kernel = KernelFactory.getKernel(kerneltype);
        Vector<SettingsModelDouble> settings = new Vector<SettingsModelDouble>();
        for (int i = 0; i < kernel.getNumberParameters(); i++) {
            settings.add(new SettingsModelDouble(CFG_KERNELPARAM + "_" + kernel.getParameterName(i), kernel.getDefaultParameter(i)));
        }
        kernelParameters.put(kerneltype, settings);
    }
    return kernelParameters;
}