use of org.knime.base.node.mine.svm.PMMLSVMTranslator 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 };
}
use of org.knime.base.node.mine.svm.PMMLSVMTranslator in project knime-core by knime.
the class SVMPredictorNodeModel method createColumnRearranger.
private ColumnRearranger createColumnRearranger(final PMMLPortObject pmmlModel, final DataTableSpec inSpec) throws InvalidSettingsException {
List<Node> models = pmmlModel.getPMMLValue().getModels(PMMLModelType.SupportVectorMachineModel);
if (models.isEmpty()) {
String msg = "SVM evaluation failed: " + "No support vector machine model found.";
LOGGER.error(msg);
throw new RuntimeException(msg);
}
PMMLSVMTranslator trans = new PMMLSVMTranslator();
pmmlModel.initializeModelTranslator(trans);
List<Svm> svms = trans.getSVMs();
m_svms = svms.toArray(new Svm[svms.size()]);
if (m_addProbabilities.getBooleanValue() == pmmlModel.getSpec().getTargetCols().size() > 0) {
adjustOrder(pmmlModel.getSpec().getTargetCols().get(0));
}
DataTableSpec testSpec = inSpec;
PMMLPortObjectSpec pmmlSpec = pmmlModel.getSpec();
DataTableSpec trainingSpec = pmmlSpec.getDataTableSpec();
// try to find all columns (except the class column)
Vector<Integer> colindices = new Vector<Integer>();
for (DataColumnSpec colspec : trainingSpec) {
if (colspec.getType().isCompatible(DoubleValue.class)) {
int colindex = testSpec.findColumnIndex(colspec.getName());
if (colindex < 0) {
throw new InvalidSettingsException("Column " + "\'" + colspec.getName() + "\' not found" + " in test data");
}
colindices.add(colindex);
}
}
m_colindices = new int[colindices.size()];
for (int i = 0; i < m_colindices.length; i++) {
m_colindices[i] = colindices.get(i);
}
final PredictorHelper predictorHelper = PredictorHelper.getInstance();
final String targetCol = pmmlSpec.getTargetFields().iterator().next();
SVMPredictor svmpredict = new SVMPredictor(targetCol, m_svms, m_colindices, predictorHelper.computePredictionColumnName(m_predictionColumn.getStringValue(), m_overridePrediction.getBooleanValue(), targetCol), m_addProbabilities.getBooleanValue(), m_suffix.getStringValue());
ColumnRearranger colre = new ColumnRearranger(testSpec);
colre.append(svmpredict);
return colre;
}
use of org.knime.base.node.mine.svm.PMMLSVMTranslator in project knime-core by knime.
the class SVMPredictorNodeModel method execute.
/**
* {@inheritDoc}
*/
@Override
public PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
PMMLPortObject port = (PMMLPortObject) inData[0];
List<Node> models = port.getPMMLValue().getModels(PMMLModelType.SupportVectorMachineModel);
if (models.isEmpty()) {
String msg = "SVM evaluation failed: " + "No support vector machine model found.";
LOGGER.error(msg);
throw new RuntimeException(msg);
}
PMMLSVMTranslator trans = new PMMLSVMTranslator();
port.initializeModelTranslator(trans);
List<Svm> svms = trans.getSVMs();
m_svms = svms.toArray(new Svm[svms.size()]);
DataTableSpec testSpec = ((BufferedDataTable) inData[1]).getDataTableSpec();
DataTableSpec trainingSpec = ((PMMLPortObject) inData[0]).getSpec().getDataTableSpec();
// try to find all columns (except the class column)
Vector<Integer> colindices = new Vector<Integer>();
for (DataColumnSpec colspec : trainingSpec) {
if (colspec.getType().isCompatible(DoubleValue.class)) {
int colindex = testSpec.findColumnIndex(colspec.getName());
if (colindex < 0) {
throw new InvalidSettingsException("Column " + "\'" + colspec.getName() + "\' not found" + " in test data");
}
colindices.add(colindex);
}
}
m_colindices = new int[colindices.size()];
for (int i = 0; i < m_colindices.length; i++) {
m_colindices[i] = colindices.get(i);
}
SVMPredictor svmpredict = new SVMPredictor(m_svms, m_colindices);
BufferedDataTable testData = (BufferedDataTable) inData[1];
ColumnRearranger colre = new ColumnRearranger(testData.getDataTableSpec());
colre.append(svmpredict);
BufferedDataTable result = exec.createColumnRearrangeTable(testData, colre, exec);
return new BufferedDataTable[] { result };
}
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