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

Example 56 with BufferedDataTable

use of org.knime.core.node.BufferedDataTable 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 };
}

Example 57 with BufferedDataTable

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

the class TreeEnsembleModelExtractorNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    TreeEnsembleModelPortObject treeEnsembleModel = (TreeEnsembleModelPortObject) inObjects[0];
    DataTableSpec outSpec = createOutSpec();
    BufferedDataContainer container = exec.createDataContainer(outSpec, false, 0);
    int nrModels = treeEnsembleModel.getEnsembleModel().getNrModels();
    for (int i = 0; i < nrModels; i++) {
        PMMLPortObject pmmlObject = treeEnsembleModel.createDecisionTreePMMLPortObject(i);
        DataCell cell = PMMLCellFactory.create(pmmlObject.getPMMLValue().toString());
        RowKey key = RowKey.createRowKey(i);
        container.addRowToTable(new DefaultRow(key, cell));
        exec.checkCanceled();
        exec.setProgress(i / (double) nrModels, "Exported model " + (i + 1) + "/" + nrModels);
    }
    container.close();
    return new BufferedDataTable[] { container.getTable() };
}

Example 58 with BufferedDataTable

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

the class DoubleToIntNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected BufferedDataTable[] execute(final BufferedDataTable[] inData, final ExecutionContext exec) throws Exception {
    List<String> inclcols = m_inclCols.getIncludeList();
    if (inclcols.size() == 0) {
        // nothing to convert, let's return the input table.
        setWarningMessage("No columns selected," + " returning input DataTable.");
        return new BufferedDataTable[] { inData[0] };
    }
    DataTableSpec inspec = inData[0].getDataTableSpec();
    WarningMessage warningMessage = new WarningMessage();
    int[] indices = findIndices(inspec, warningMessage);
    ConverterFactory converterFac;
    String calctype = m_calctype.getStringValue();
    if (calctype.equals(CFG_CEIL)) {
        converterFac = new CeilConverterFactory(indices, m_prodLong.getBooleanValue(), inspec, warningMessage);
    } else if (calctype.equals(CFG_FLOOR)) {
        converterFac = new FloorConverterFactory(indices, m_prodLong.getBooleanValue(), inspec, warningMessage);
    } else {
        converterFac = new ConverterFactory(indices, m_prodLong.getBooleanValue(), inspec, warningMessage);
    }
    ColumnRearranger colre = new ColumnRearranger(inspec);
    colre.replace(converterFac, indices);
    BufferedDataTable resultTable = exec.createColumnRearrangeTable(inData[0], colre, exec);
    if (warningMessage.get() != null) {
        setWarningMessage(warningMessage.get());
    }
    return new BufferedDataTable[] { resultTable };
}

Example 59 with BufferedDataTable

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

the class NormalizerNodeModel method calculate.

/**
 * New normalized {@link org.knime.core.data.DataTable} is created depending
 * on the mode.
 */
/**
 * @param inData The input data.
 * @param exec For BufferedDataTable creation and progress.
 * @return the result of the calculation
 * @throws Exception If the node calculation fails for any reason.
 */
protected CalculationResult calculate(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable inTable = (BufferedDataTable) inData[0];
    DataTableSpec inSpec = inTable.getSpec();
    // extract selected numeric columns
    updateNumericColumnSelection(inSpec);
    Normalizer ntable = new Normalizer(inTable, m_columns);
    long rowcount = inTable.size();
    ExecutionMonitor prepareExec = exec.createSubProgress(0.3);
    AffineTransTable outTable;
    boolean fixDomainBounds = false;
    switch(m_mode) {
        case NONORM_MODE:
            return new CalculationResult(inTable, new DataTableSpec(), new AffineTransConfiguration());
        case MINMAX_MODE:
            fixDomainBounds = true;
            outTable = ntable.doMinMaxNorm(m_max, m_min, prepareExec);
            break;
        case ZSCORE_MODE:
            outTable = ntable.doZScoreNorm(prepareExec);
            break;
        case DECIMALSCALING_MODE:
            outTable = ntable.doDecimalScaling(prepareExec);
            break;
        default:
            throw new Exception("No mode set");
    }
    if (outTable.getErrorMessage() != null) {
        // something went wrong, report and throw an exception
        throw new Exception(outTable.getErrorMessage());
    }
    if (ntable.getErrorMessage() != null) {
        // something went wrong during initialization, report.
        setWarningMessage(ntable.getErrorMessage());
    }
    DataTableSpec modelSpec = FilterColumnTable.createFilterTableSpec(inSpec, m_columns);
    AffineTransConfiguration configuration = outTable.getConfiguration();
    DataTableSpec spec = outTable.getDataTableSpec();
    // the same transformation, which is not guaranteed to snap to min/max)
    if (fixDomainBounds) {
        DataColumnSpec[] newColSpecs = new DataColumnSpec[spec.getNumColumns()];
        for (int i = 0; i < newColSpecs.length; i++) {
            newColSpecs[i] = spec.getColumnSpec(i);
        }
        for (int i = 0; i < m_columns.length; i++) {
            int index = spec.findColumnIndex(m_columns[i]);
            DataColumnSpecCreator creator = new DataColumnSpecCreator(newColSpecs[index]);
            DataColumnDomainCreator domCreator = new DataColumnDomainCreator(newColSpecs[index].getDomain());
            domCreator.setLowerBound(new DoubleCell(m_min));
            domCreator.setUpperBound(new DoubleCell(m_max));
            creator.setDomain(domCreator.createDomain());
            newColSpecs[index] = creator.createSpec();
        }
        spec = new DataTableSpec(spec.getName(), newColSpecs);
    }
    ExecutionMonitor normExec = exec.createSubProgress(.7);
    BufferedDataContainer container = exec.createDataContainer(spec);
    long count = 1;
    for (DataRow row : outTable) {
        normExec.checkCanceled();
        normExec.setProgress(count / (double) rowcount, "Normalizing row no. " + count + " of " + rowcount + " (\"" + row.getKey() + "\")");
        container.addRowToTable(row);
        count++;
    }
    container.close();
    return new CalculationResult(container.getTable(), modelSpec, configuration);
}

Example 60 with BufferedDataTable

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

the class Many2OneColPMMLNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws CanceledExecutionException, Exception {
    BufferedDataTable inData = (BufferedDataTable) inObjects[0];
    AbstractMany2OneCellFactory cellFactory = getCellFactory(inData.getDataTableSpec());
    BufferedDataTable outData = exec.createColumnRearrangeTable(inData, createRearranger(inData.getDataTableSpec(), cellFactory), exec);
    if (m_pmmlEnabled) {
        if (IncludeMethod.valueOf(m_includeMethod.getStringValue()) == IncludeMethod.RegExpPattern) {
            setWarningMessage("Regular Expressions are not supported in PMML. " + "The generated PMML document is invalid.");
        }
        // the optional PMML in port (can be null)
        PMMLPortObject inPMMLPort = (PMMLPortObject) inObjects[1];
        /*
             PMMLOne2ManyTranslator trans = new PMMLOne2ManyTranslator(
                    cellFactory.getColumnMapping(),
                    new DerivedFieldMapper(inPMMLPort));
            */
        int[] sourceColIndices = cellFactory.getIncludedColIndices();
        String[] sourceColNames = new String[sourceColIndices.length];
        for (int i = 0; i < sourceColIndices.length; i++) {
            sourceColNames[i] = inData.getDataTableSpec().getColumnSpec(sourceColIndices[i]).getName();
        }
        PMMLMany2OneTranslator trans = new PMMLMany2OneTranslator(cellFactory.getAppendedColumnName(), sourceColNames, IncludeMethod.valueOf(m_includeMethod.getStringValue()));
        PMMLPortObjectSpecCreator creator = new PMMLPortObjectSpecCreator(inPMMLPort, outData.getDataTableSpec());
        PMMLPortObject outPMMLPort = new PMMLPortObject(creator.createSpec(), inPMMLPort);
        outPMMLPort.addGlobalTransformations(trans.exportToTransDict());
        return new PortObject[] { outData, outPMMLPort };
    } else {
        return new PortObject[] { outData };
    }
}