Examples with ExecutionMonitor org.knime.core.node.ExecutionMonitor used on opensource projects

Example 6 with ExecutionMonitor

use of org.knime.core.node.ExecutionMonitor in project knime-core by knime.

the class PathProximity method calculatePathProximities.

public ProximityMatrix calculatePathProximities(final ExecutionContext exec) throws InterruptedException, CanceledExecutionException {
    final ThreadPool tp = KNIMEConstants.GLOBAL_THREAD_POOL;
    final int procCount = 3 * Runtime.getRuntime().availableProcessors() / 2;
    final Semaphore semaphore = new Semaphore(procCount);
    final AtomicReference<Throwable> proxThrowableRef = new AtomicReference<Throwable>();
    // The path proximity matrix is not symmetric if applied for a single table
    // therefore we have to use the two table approach even it is only a single table
    ProximityMatrix proximityMatrix = new TwoTablesProximityMatrix(m_tables[0], m_tables[1]);
    final int nrTrees = m_modelPO.getEnsembleModel().getNrModels();
    final Future<?>[] calcFutures = new Future<?>[nrTrees];
    exec.setProgress(0, "Starting proximity calculation per tree.");
    for (int i = 0; i < nrTrees; i++) {
        semaphore.acquire();
        finishedTree(i, exec, nrTrees);
        checkThrowable(proxThrowableRef);
        ExecutionMonitor subExec = exec.createSubProgress(0.0);
        calcFutures[i] = tp.enqueue(new PathProximityCalcRunnable(i, proximityMatrix, semaphore, proxThrowableRef, subExec));
    }
    for (int i = 0; i < procCount; i++) {
        semaphore.acquire();
        finishedTree(nrTrees - procCount + i, exec, nrTrees);
    }
    for (Future<?> future : calcFutures) {
        try {
            future.get();
        } catch (Exception e) {
            proxThrowableRef.compareAndSet(null, e);
        }
    }
    checkThrowable(proxThrowableRef);
    proximityMatrix.normalize(1.0 / nrTrees);
    return proximityMatrix;
}

Example 7 with ExecutionMonitor

use of org.knime.core.node.ExecutionMonitor in project knime-core by knime.

the class RandomForestClassificationLearnerNodeModel method saveInternals.

/**
 * {@inheritDoc}
 */
@Override
protected void saveInternals(final File nodeInternDir, final ExecutionMonitor exec) throws IOException, CanceledExecutionException {
    File file;
    ExecutionMonitor sub;
    if (m_oldStyleEnsembleModel_deprecated != null) {
        // old workflow (<2.10) loaded and saved ...
        file = new File(nodeInternDir, INTERNAL_TREES_FILE);
        OutputStream out = new GZIPOutputStream(new FileOutputStream(file));
        sub = exec.createSubProgress(0.2);
        m_oldStyleEnsembleModel_deprecated.save(out, sub);
        out.close();
    }
    if (m_hiliteRowSample != null) {
        file = new File(nodeInternDir, INTERNAL_DATASAMPLE_FILE);
        sub = exec.createSubProgress(0.2);
        DataContainer.writeToZip(m_hiliteRowSample, file, sub);
    }
    if (m_viewMessage != null) {
        file = new File(nodeInternDir, INTERNAL_INFO_FILE);
        NodeSettings sets = new NodeSettings("ensembleData");
        sets.addString("view_warning", m_viewMessage);
        sets.saveToXML(new FileOutputStream(file));
    }
}

Example 8 with ExecutionMonitor

use of org.knime.core.node.ExecutionMonitor in project knime-core by knime.

the class RandomForestRegressionLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inObjects[0];
    DataTableSpec spec = t.getDataTableSpec();
    final FilterLearnColumnRearranger learnRearranger = m_configuration.filterLearnColumns(spec);
    String warn = learnRearranger.getWarning();
    BufferedDataTable learnTable = exec.createColumnRearrangeTable(t, learnRearranger, exec.createSubProgress(0.0));
    DataTableSpec learnSpec = learnTable.getDataTableSpec();
    TreeEnsembleModelPortObjectSpec ensembleSpec = m_configuration.createPortObjectSpec(learnSpec);
    ExecutionMonitor readInExec = exec.createSubProgress(0.1);
    ExecutionMonitor learnExec = exec.createSubProgress(0.8);
    ExecutionMonitor outOfBagExec = exec.createSubProgress(0.1);
    TreeDataCreator dataCreator = new TreeDataCreator(m_configuration, learnSpec, learnTable.getRowCount());
    exec.setProgress("Reading data into memory");
    TreeData data = dataCreator.readData(learnTable, m_configuration, readInExec);
    m_hiliteRowSample = dataCreator.getDataRowsForHilite();
    m_viewMessage = dataCreator.getViewMessage();
    String dataCreationWarning = dataCreator.getAndClearWarningMessage();
    if (dataCreationWarning != null) {
        if (warn == null) {
            warn = dataCreationWarning;
        } else {
            warn = warn + "\n" + dataCreationWarning;
        }
    }
    readInExec.setProgress(1.0);
    exec.setMessage("Learning trees");
    TreeEnsembleLearner learner = new TreeEnsembleLearner(m_configuration, data);
    TreeEnsembleModel model;
    try {
        model = learner.learnEnsemble(learnExec);
    } catch (ExecutionException e) {
        Throwable cause = e.getCause();
        if (cause instanceof Exception) {
            throw (Exception) cause;
        }
        throw e;
    }
    TreeEnsembleModelPortObject modelPortObject = TreeEnsembleModelPortObject.createPortObject(ensembleSpec, model, exec.createFileStore("TreeEnsemble"));
    learnExec.setProgress(1.0);
    exec.setMessage("Out of bag prediction");
    TreeEnsemblePredictor outOfBagPredictor = createOutOfBagPredictor(ensembleSpec, modelPortObject, spec);
    outOfBagPredictor.setOutofBagFilter(learner.getRowSamples(), data.getTargetColumn());
    ColumnRearranger outOfBagRearranger = outOfBagPredictor.getPredictionRearranger();
    BufferedDataTable outOfBagTable = exec.createColumnRearrangeTable(t, outOfBagRearranger, outOfBagExec);
    BufferedDataTable colStatsTable = learner.createColumnStatisticTable(exec.createSubExecutionContext(0.0));
    m_ensembleModelPortObject = modelPortObject;
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { outOfBagTable, colStatsTable, modelPortObject };
}

Example 9 with ExecutionMonitor

use of org.knime.core.node.ExecutionMonitor in project knime-core by knime.

the class RandomForestRegressionLearnerNodeModel method saveInternals.

/**
 * {@inheritDoc}
 */
@Override
protected void saveInternals(final File nodeInternDir, final ExecutionMonitor exec) throws IOException, CanceledExecutionException {
    File file;
    ExecutionMonitor sub;
    if (m_oldStyleEnsembleModel_deprecated != null) {
        // old workflow (<2.10) loaded and saved ...
        file = new File(nodeInternDir, INTERNAL_TREES_FILE);
        OutputStream out = new GZIPOutputStream(new FileOutputStream(file));
        sub = exec.createSubProgress(0.2);
        m_oldStyleEnsembleModel_deprecated.save(out, sub);
        out.close();
    }
    if (m_hiliteRowSample != null) {
        file = new File(nodeInternDir, INTERNAL_DATASAMPLE_FILE);
        sub = exec.createSubProgress(0.2);
        DataContainer.writeToZip(m_hiliteRowSample, file, sub);
    }
    if (m_viewMessage != null) {
        file = new File(nodeInternDir, INTERNAL_INFO_FILE);
        NodeSettings sets = new NodeSettings("ensembleData");
        sets.addString("view_warning", m_viewMessage);
        sets.saveToXML(new FileOutputStream(file));
    }
}

Example 10 with ExecutionMonitor

use of org.knime.core.node.ExecutionMonitor in project knime-core by knime.

the class TreeEnsembleClassificationLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inObjects[0];
    DataTableSpec spec = t.getDataTableSpec();
    final FilterLearnColumnRearranger learnRearranger = m_configuration.filterLearnColumns(spec);
    String warn = learnRearranger.getWarning();
    BufferedDataTable learnTable = exec.createColumnRearrangeTable(t, learnRearranger, exec.createSubProgress(0.0));
    DataTableSpec learnSpec = learnTable.getDataTableSpec();
    TreeEnsembleModelPortObjectSpec ensembleSpec = m_configuration.createPortObjectSpec(learnSpec);
    Map<String, DataCell> targetValueMap = ensembleSpec.getTargetColumnPossibleValueMap();
    if (targetValueMap == null) {
        throw new InvalidSettingsException("The target column does not " + "have possible values assigned. Most likely it " + "has too many different distinct values (learning an ID " + "column?) Fix it by preprocessing the table using " + "a \"Domain Calculator\".");
    }
    ExecutionMonitor readInExec = exec.createSubProgress(0.1);
    ExecutionMonitor learnExec = exec.createSubProgress(0.8);
    ExecutionMonitor outOfBagExec = exec.createSubProgress(0.1);
    TreeDataCreator dataCreator = new TreeDataCreator(m_configuration, learnSpec, learnTable.getRowCount());
    exec.setProgress("Reading data into memory");
    TreeData data = dataCreator.readData(learnTable, m_configuration, readInExec);
    m_hiliteRowSample = dataCreator.getDataRowsForHilite();
    m_viewMessage = dataCreator.getViewMessage();
    String dataCreationWarning = dataCreator.getAndClearWarningMessage();
    if (dataCreationWarning != null) {
        if (warn == null) {
            warn = dataCreationWarning;
        } else {
            warn = warn + "\n" + dataCreationWarning;
        }
    }
    readInExec.setProgress(1.0);
    exec.setMessage("Learning trees");
    TreeEnsembleLearner learner = new TreeEnsembleLearner(m_configuration, data);
    TreeEnsembleModel model;
    try {
        model = learner.learnEnsemble(learnExec);
    } catch (ExecutionException e) {
        Throwable cause = e.getCause();
        if (cause instanceof Exception) {
            throw (Exception) cause;
        }
        throw e;
    }
    TreeEnsembleModelPortObject modelPortObject = TreeEnsembleModelPortObject.createPortObject(ensembleSpec, model, exec.createFileStore(UUID.randomUUID().toString() + ""));
    learnExec.setProgress(1.0);
    exec.setMessage("Out of bag prediction");
    TreeEnsemblePredictor outOfBagPredictor = createOutOfBagPredictor(ensembleSpec, modelPortObject, spec);
    outOfBagPredictor.setOutofBagFilter(learner.getRowSamples(), data.getTargetColumn());
    ColumnRearranger outOfBagRearranger = outOfBagPredictor.getPredictionRearranger();
    BufferedDataTable outOfBagTable = exec.createColumnRearrangeTable(t, outOfBagRearranger, outOfBagExec);
    BufferedDataTable colStatsTable = learner.createColumnStatisticTable(exec.createSubExecutionContext(0.0));
    m_ensembleModelPortObject = modelPortObject;
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { outOfBagTable, colStatsTable, modelPortObject };
}