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

Example 31 with ExecutionMonitor

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

the class AbstractBlobsInWorkflowTest method testSize.

public void testSize() throws Exception {
    m_flow.save(m_wfmDir, new ExecutionMonitor(), true);
    long size = calculateSize(m_wfmDir);
    double sizeInMB = size / 1024 / 1024.0;
    long roughlyExpectedSize = getApproximateSize();
    double roughlyExpectedSizeInMB = roughlyExpectedSize / 1024 / 1024.0;
    String error = "Size of workflow out of range, expected ~" + roughlyExpectedSizeInMB + "MB, actual " + sizeInMB + "MB";
    assertTrue(error, size > 0.8 * roughlyExpectedSize);
    assertTrue(error, size < 1.2 * roughlyExpectedSize);
    NodeLogger.getLogger(getClass()).info("Test succeeds: expected size (in MB) is " + sizeInMB + ", expected " + roughlyExpectedSizeInMB + " -- which is ok");
}

Example 32 with ExecutionMonitor

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

the class LogRegCoordinator method learn.

/**
 * Performs the learning task by creating the appropriate LogRegLearner and all other objects
 * necessary for a successful training.
 *
 * @param trainingData a DataTable that contains the data on which to learn the logistic regression model
 * @param exec the execution context of the corresponding KNIME node
 * @return the content of the logistic regression model
 * @throws InvalidSettingsException if the settings cause inconsistencies during training
 * @throws CanceledExecutionException if the training is canceled
 */
LogisticRegressionContent learn(final BufferedDataTable trainingData, final ExecutionContext exec) throws InvalidSettingsException, CanceledExecutionException {
    CheckUtils.checkArgument(trainingData.size() > 0, "The input table is empty. Please provide data to learn on.");
    CheckUtils.checkArgument(trainingData.size() <= Integer.MAX_VALUE, "The input table contains too many rows.");
    LogRegLearner learner;
    if (m_settings.getSolver() == Solver.IRLS) {
        learner = new IrlsLearner(m_settings.getMaxEpoch(), m_settings.getEpsilon(), m_settings.isCalcCovMatrix());
    } else {
        learner = new SagLogRegLearner(m_settings);
    }
    double calcDomainTime = 1.0 / (5.0 * 2.0 + 1.0);
    exec.setMessage("Analyzing categorical data");
    BufferedDataTable dataTable = recalcDomainForTargetAndLearningFields(trainingData, exec.createSubExecutionContext(calcDomainTime));
    checkConstantLearningFields(dataTable);
    exec.setMessage("Building logistic regression model");
    ExecutionMonitor trainExec = exec.createSubProgress(1.0 - calcDomainTime);
    LogRegLearnerResult result;
    TrainingRowBuilder<ClassificationTrainingRow> rowBuilder = new SparseClassificationTrainingRowBuilder(dataTable, m_pmmlOutSpec, m_settings.getTargetReferenceCategory(), m_settings.getSortTargetCategories(), m_settings.getSortIncludesCategories());
    TrainingData<ClassificationTrainingRow> data;
    Long seed = m_settings.getSeed();
    if (m_settings.isInMemory()) {
        data = new InMemoryData<ClassificationTrainingRow>(dataTable, seed, rowBuilder);
    } else {
        data = new DataTableTrainingData<ClassificationTrainingRow>(trainingData, seed, rowBuilder, m_settings.getChunkSize(), exec.createSilentSubExecutionContext(0.0));
    }
    checkShapeCompatibility(data);
    result = learner.learn(data, trainExec);
    LogisticRegressionContent content = createContentFromLearnerResult(result, rowBuilder, trainingData.getDataTableSpec());
    addToWarning(learner.getWarningMessage());
    return content;
}

Example 33 with ExecutionMonitor

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

the class HistogramNodeModel method createHistogramModel.

/**
 * {@inheritDoc}
 */
@Override
protected void createHistogramModel(final ExecutionContext exec, final int noOfRows, final BufferedDataTable data) throws CanceledExecutionException {
    LOGGER.debug("Entering createHistogramModel(exec, dataTable) " + "of class HistogramNodeModel.");
    if (noOfRows == 0) {
        m_model = null;
        return;
    }
    if (exec == null) {
        throw new NullPointerException("exec must not be null");
    }
    if (data == null) {
        throw new IllegalArgumentException("Table shouldn't be null");
    }
    ExecutionMonitor subExec = exec.createSubProgress(0.5);
    exec.setMessage("Adding rows to histogram model...");
    final DataArray dataArray = new DefaultDataArray(data, 1, noOfRows, subExec);
    exec.setMessage("Adding row color to histogram...");
    final SortedSet<Color> colorSet = new TreeSet<Color>(HSBColorComparator.getInstance());
    subExec = exec.createSubProgress(0.5);
    final double progressPerRow = 1.0 / noOfRows;
    double progress = 0.0;
    final CloseableRowIterator rowIterator = data.iterator();
    try {
        for (int i = 0; i < noOfRows && rowIterator.hasNext(); i++) {
            final DataRow row = rowIterator.next();
            final Color color = data.getDataTableSpec().getRowColor(row).getColor(false, false);
            if (!colorSet.contains(color)) {
                colorSet.add(color);
            }
            progress += progressPerRow;
            subExec.setProgress(progress, "Adding data rows to histogram...");
            subExec.checkCanceled();
        }
    } finally {
        if (rowIterator != null) {
            rowIterator.close();
        }
    }
    exec.setProgress(1.0, "Histogram finished.");
    m_model = new InteractiveHistogramDataModel(dataArray, new ArrayList<Color>(colorSet));
    LOGGER.debug("Exiting createHistogramModel(exec, dataTable) " + "of class HistogramNodeModel.");
}

Example 34 with ExecutionMonitor

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

the class TableNodeView method writeToCSV.

/**
 * Called by the JMenu item "Write to CVS", it write the table as shown in
 * table view to a CSV file.
 *
 * @param file the file to write to
 */
private void writeToCSV(final File file) {
    // CSV Writer supports ExecutionMonitor. Some table may be big.
    DefaultNodeProgressMonitor progMon = new DefaultNodeProgressMonitor();
    ExecutionMonitor e = new ExecutionMonitor(progMon);
    // Frame of m_tableView (if any)
    Frame f = (Frame) SwingUtilities.getAncestorOfClass(Frame.class, m_tableView);
    final NodeProgressMonitorView progView = new NodeProgressMonitorView(f, progMon);
    // CSV Writer does not support 1-100 progress (unknown row count)
    progView.setShowProgress(false);
    // Writing is done in a thread (allows repainting of GUI)
    final CSVWriterThread t = new CSVWriterThread(file, e);
    t.start();
    // A thread that waits for t to finish and then disposes the prog view
    new Thread(new Runnable() {

        @Override
        public void run() {
            try {
                t.join();
            } catch (InterruptedException ie) {
            // do nothing. Only dispose the view
            } finally {
                progView.dispose();
            }
        }
    }).start();
    progView.pack();
    progView.setLocationRelativeTo(m_tableView);
    progView.setVisible(true);
}

Example 35 with ExecutionMonitor

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

the class ExpandVectorNodeModel method createRearranger.

/**
 * Creates the {@link ColumnRearranger} to perform the computation.
 *
 * @param table The input table.
 * @param exec An {@link ExecutionMonitor}.
 * @return The {@link ColumnRearranger}.
 * @throws CanceledExecutionException Execution cancelled.
 */
private ColumnRearranger createRearranger(final BufferedDataTable table, final ExecutionMonitor exec) throws CanceledExecutionException {
    DataTableSpec spec = table.getSpec();
    final ColumnRearranger ret = new ColumnRearranger(spec);
    ExecutionMonitor subRead = exec.createSubProgress(.25);
    int maxOutput = 0;
    String inputColumnName = findInputColumnName(spec);
    final int colIndex = spec.findColumnIndex(inputColumnName);
    for (DataRow dataRow : table) {
        subRead.checkCanceled();
        DataCell cell = dataRow.getCell(colIndex);
        long length = getCellLength(cell);
        if (length > m_maxNewColumns.getIntValue()) {
            length = m_maxNewColumns.getIntValue();
        }
        maxOutput = Math.max(maxOutput, (int) length);
    }
    final String[] colNames = new String[maxOutput];
    final DataColumnSpec inputSpec = spec.getColumnSpec(inputColumnName);
    final int namedColumns;
    if (useNames()) {
        String[] origNames = inputSpec.getElementNames().toArray(new String[0]);
        System.arraycopy(origNames, 0, colNames, 0, Math.min(maxOutput, origNames.length));
        namedColumns = origNames.length;
    } else {
        namedColumns = 0;
    }
    for (int i = namedColumns; i < maxOutput; ++i) {
        colNames[i] = m_outputPrefix.getStringValue() + (i - namedColumns + m_startIndex.getIntValue());
    }
    DataColumnSpec[] outputColumns = new DataColumnSpec[maxOutput];
    DataColumnSpecCreator dataColumnSpecCreator = new DataColumnSpecCreator("Dummy", IntCell.TYPE);
    for (int idx = 0; idx < colNames.length; ++idx) {
        dataColumnSpecCreator.setName(DataTableSpec.getUniqueColumnName(spec, colNames[idx]));
        outputColumns[idx] = dataColumnSpecCreator.createSpec();
    }
    ret.append(createCellFactory(colNames, outputColumns, colIndex));
    if (m_removeOriginal.getBooleanValue()) {
        ret.remove(inputColumnName);
    }
    return ret;
}