Examples with PortObject org.knime.core.node.port.PortObject used on opensource projects

Example 26 with PortObject

use of org.knime.core.node.port.PortObject in project knime-core by knime.

the class FileNodePersistor method loadPorts.

/**
 * @noreference
 * @nooverride
 */
void loadPorts(final Node node, final ExecutionMonitor exec, final NodeSettingsRO settings, final Map<Integer, BufferedDataTable> loadTblRep, final HashMap<Integer, ContainerTable> tblRep, final FileStoreHandlerRepository fileStoreHandlerRepository) throws IOException, InvalidSettingsException, CanceledExecutionException {
    final int nrOutPorts = node.getNrOutPorts();
    if (getLoadVersion().isOlderThan(FileWorkflowPersistor.LoadVersion.V200)) {
        // skip flow variables port (introduced in v2.2)
        for (int i = 1; i < nrOutPorts; i++) {
            int oldIndex = getOldPortIndex(i);
            ExecutionMonitor execPort = exec.createSubProgress(1.0 / nrOutPorts);
            exec.setMessage("Port " + oldIndex);
            PortType type = node.getOutputType(i);
            boolean isDataPort = BufferedDataTable.class.isAssignableFrom(type.getPortObjectClass());
            if (m_isConfigured) {
                PortObjectSpec spec = loadPortObjectSpec(node, settings, oldIndex);
                setPortObjectSpec(i, spec);
            }
            if (m_isExecuted) {
                PortObject object;
                if (isDataPort) {
                    object = loadBufferedDataTable(node, settings, execPort, loadTblRep, oldIndex, tblRep, fileStoreHandlerRepository);
                } else {
                    throw new IOException("Can't restore model ports of " + "old 1.x workflows. Execute node again.");
                }
                String summary = object != null ? object.getSummary() : null;
                setPortObject(i, object);
                setPortObjectSummary(i, summary);
            }
            execPort.setProgress(1.0);
        }
    } else {
        if (nrOutPorts == 1) {
            // only the mandatory flow variable port
            return;
        }
        NodeSettingsRO portsSettings = loadPortsSettings(settings);
        exec.setMessage("Reading outport data");
        for (String key : portsSettings.keySet()) {
            NodeSettingsRO singlePortSetting = portsSettings.getNodeSettings(key);
            ExecutionMonitor subProgress = exec.createSubProgress(1 / (double) nrOutPorts);
            int index = loadPortIndex(singlePortSetting);
            if (index < 0 || index >= nrOutPorts) {
                throw new InvalidSettingsException("Invalid outport index in settings: " + index);
            }
            String portDirN = singlePortSetting.getString("port_dir_location");
            if (portDirN != null) {
                ReferencedFile portDir = new ReferencedFile(getNodeDirectory(), portDirN);
                subProgress.setMessage("Port " + index);
                loadPort(node, portDir, singlePortSetting, subProgress, index, loadTblRep, tblRep, fileStoreHandlerRepository);
            }
            subProgress.setProgress(1.0);
        }
    }
}

Example 27 with PortObject

use of org.knime.core.node.port.PortObject in project knime-core by knime.

the class RandomForestClassificationLearnerNodeModel 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 = new TreeEnsembleModelPortObject(ensembleSpec, model);
    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 28 with PortObject

use of org.knime.core.node.port.PortObject 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 = new TreeEnsembleModelPortObject(ensembleSpec, model);
    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 29 with PortObject

use of org.knime.core.node.port.PortObject 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 = new TreeEnsembleModelPortObject(ensembleSpec, model);
    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 30 with PortObject

use of org.knime.core.node.port.PortObject in project knime-core by knime.

the class TreeEnsembleShrinkerNodeModel method execute.

@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    TreeEnsembleModel treeEnsemble = ((TreeEnsembleModelPortObject) inObjects[0]).getEnsembleModel();
    TreeEnsembleModelPortObject resultEnsemble;
    int resultSize = m_config.getResultSize(treeEnsemble.getNrModels());
    boolean shrink = true;
    if (!m_config.isResultSizeAutomatic()) {
        // Check if result size is valid
        if (resultSize < 1) {
            // Result size is to small, use 1
            setWarningMessage("The configured result size is smaller than 1, defaulting to 1");
            resultSize = 1;
        } else if (resultSize > treeEnsemble.getNrModels()) {
            // Result size is to big, just keep current ensemble
            setWarningMessage("The configured result size is bigger than the size of the input ensemble, defaulting to the input ensembles size");
            shrink = false;
        } else if (resultSize == treeEnsemble.getNrModels()) {
            // Result size is ensemble size -> we don't need to shrink
            shrink = false;
        }
    }
    // If our result size is not smaller than the current ensemble we don't have to do the following and therefore can save time
    if (shrink) {
        BufferedDataTable inData = (BufferedDataTable) inObjects[1];
        // Create shrinker
        TreeEnsembleShrinker shrinker = new TreeEnsembleShrinker(treeEnsemble, inData, m_config.getTargetColumn(), exec);
        // Shrink ensemble
        if (m_config.isResultSizeAutomatic()) {
            shrinker.autoShrink();
        } else {
            shrinker.shrinkTo(resultSize);
        }
        // Get shrunk ensemble
        TreeEnsembleModel newEnsemble = shrinker.getModel();
        // Push flow variable with archived accuracy
        pushFlowVariableDouble("Tree Ensemble Shrinker Prediction Accuracy", shrinker.getAccuracy());
        // Create port object for tree ensemble
        resultEnsemble = new TreeEnsembleModelPortObject(((TreeEnsembleModelPortObject) inObjects[0]).getSpec(), newEnsemble);
    } else {
        // We did not need to shrink just use input tree ensemble port object
        resultEnsemble = (TreeEnsembleModelPortObject) inObjects[0];
    }
    // Convert tree ensemble port object to PMML
    PMMLPortObject pmmlEnsemble = convertToPmmlEnsemble(resultEnsemble, exec);
    return new PortObject[] { pmmlEnsemble };
}