Examples with DefaultDataArray org.knime.base.node.util.DefaultDataArray used on opensource projects

Example 1 with DefaultDataArray

use of org.knime.base.node.util.DefaultDataArray 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 2 with DefaultDataArray

use of org.knime.base.node.util.DefaultDataArray in project knime-core by knime.

the class LiftChartNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected BufferedDataTable[] execute(final BufferedDataTable[] inData, final ExecutionContext exec) throws Exception {
    ConvenienceMethods.checkTableSize(inData[0]);
    int predColIndex = inData[0].getDataTableSpec().findColumnIndex(m_responseColumn.getStringValue());
    List<String> inclList = new LinkedList<String>();
    inclList.add(m_probabilityColumn.getStringValue());
    boolean[] order = new boolean[] { false };
    SortedTable st = new SortedTable(inData[0], inclList, order, exec);
    long totalResponses = 0;
    double partWidth = Double.parseDouble(m_intervalWidth.getStringValue());
    int nrParts = (int) Math.ceil(100.0 / partWidth);
    List<Integer> positiveResponses = new LinkedList<Integer>();
    int rowIndex = 0;
    for (DataRow row : st) {
        if (row.getCell(predColIndex).isMissing()) {
            setWarningMessage("There are missing values." + " Please check your data.");
            continue;
        }
        String response = ((StringValue) row.getCell(predColIndex)).getStringValue().trim();
        if (response.equalsIgnoreCase(m_responseLabel.getStringValue())) {
            totalResponses++;
            positiveResponses.add(rowIndex);
        }
        rowIndex++;
    }
    int[] counter = new int[nrParts];
    int partWidthAbsolute = (int) Math.ceil(rowIndex / (double) nrParts);
    double avgResponse = (double) positiveResponses.size() / rowIndex;
    for (int rIndex : positiveResponses) {
        int index = rIndex / partWidthAbsolute;
        counter[index]++;
    }
    DataColumnSpec[] colSpec = new DataColumnSpec[3];
    colSpec[0] = new DataColumnSpecCreator("Lift", DoubleCell.TYPE).createSpec();
    colSpec[1] = new DataColumnSpecCreator("Baseline", DoubleCell.TYPE).createSpec();
    colSpec[2] = new DataColumnSpecCreator("Cumulative Lift", DoubleCell.TYPE).createSpec();
    DataTableSpec tableSpec = new DataTableSpec(colSpec);
    DataContainer cont = exec.createDataContainer(tableSpec);
    colSpec = new DataColumnSpec[2];
    colSpec[0] = new DataColumnSpecCreator("Actual", DoubleCell.TYPE).createSpec();
    colSpec[1] = new DataColumnSpecCreator("Baseline", DoubleCell.TYPE).createSpec();
    tableSpec = new DataTableSpec(colSpec);
    DataContainer responseCont = exec.createDataContainer(tableSpec);
    long cumulativeCounter = 0;
    responseCont.addRowToTable(new DefaultRow(new RowKey("0"), 0.0, 0.0));
    for (int i = 0; i < counter.length; i++) {
        cumulativeCounter += counter[i];
        double responseRate = (double) counter[i] / partWidthAbsolute;
        double lift = responseRate / avgResponse;
        double cumResponseRate = (double) cumulativeCounter / totalResponses;
        long number = partWidthAbsolute * (i + 1);
        // well.. rounding problems
        if (number > rowIndex) {
            number = rowIndex;
        }
        double cumulativeLift = // (double)cumulativeCounter / (partWidthAbsolute * (i + 1));
        (double) cumulativeCounter / number;
        cumulativeLift /= avgResponse;
        // cumulativeLift = lifts / (i+1);
        double rowKey = ((i + 1) * partWidth);
        if (rowKey > 100) {
            rowKey = 100;
        }
        cont.addRowToTable(new DefaultRow(new RowKey("" + rowKey), lift, 1.0, cumulativeLift));
        double cumBaseline = (i + 1) * partWidth;
        if (cumBaseline > 100) {
            cumBaseline = 100;
        }
        responseCont.addRowToTable(new DefaultRow(new RowKey("" + rowKey), cumResponseRate * 100, cumBaseline));
    }
    cont.close();
    responseCont.close();
    m_dataArray[0] = new DefaultDataArray(cont.getTable(), 1, (int) cont.size());
    m_dataArray[1] = new DefaultDataArray(responseCont.getTable(), 1, (int) responseCont.size());
    return new BufferedDataTable[] { st.getBufferedDataTable() };
}

Example 3 with DefaultDataArray

use of org.knime.base.node.util.DefaultDataArray in project knime-core by knime.

the class SotaNodeModel method loadInternals.

/**
 * {@inheritDoc}
 */
@Override
protected void loadInternals(final File internDir, final ExecutionMonitor exec) throws IOException {
    File file = new File(internDir, TREE_FILE);
    FileInputStream fis = new FileInputStream(file);
    ModelContentRO modelContent = ModelContent.loadFromXML(fis);
    // Load settings
    int inDataSize = 0;
    int origDataSize = 0;
    try {
        m_sota.setUseHierarchicalFuzzyData(modelContent.getBoolean(SotaPortObject.CFG_KEY_USE_FUZZY_HIERARCHY));
        m_sota.setMaxHierarchicalLevel(modelContent.getInt(SotaPortObject.CFG_KEY_MAX_FUZZY_LEVEL));
        inDataSize = modelContent.getInt(SotaPortObject.CFG_KEY_INDATA_SIZE);
        origDataSize = modelContent.getInt(SotaPortObject.CFG_KEY_ORIGDATA_SIZE);
    } catch (InvalidSettingsException e1) {
        IOException ioe = new IOException("Could not load settings," + "due to invalid settings in model content !");
        ioe.initCause(e1);
        fis.close();
        throw ioe;
    }
    // Load in data
    DataTable table = DataContainer.readFromZip(new File(internDir, IN_DATA_FILE));
    final DataArray inData = new DefaultDataArray(table, 1, inDataSize);
    m_sota.setInData(inData);
    // Load orig data
    table = DataContainer.readFromZip(new File(internDir, ORIG_DATA_FILE));
    final DataArray origData = new DefaultDataArray(table, 1, origDataSize);
    m_sota.setOriginalData(origData);
    // Load tree
    SotaTreeCell root = new SotaTreeCell(0, false);
    try {
        root.loadFrom(modelContent, 0, null, false);
    } catch (InvalidSettingsException e) {
        IOException ioe = new IOException("Could not load tree cells," + "due to invalid settings in model content !");
        ioe.initCause(e);
        fis.close();
        throw ioe;
    }
    m_sota.setRoot(root);
    fis.close();
}

Example 4 with DefaultDataArray

use of org.knime.base.node.util.DefaultDataArray in project knime-core by knime.

the class LinReg2LearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    final BufferedDataTable data = (BufferedDataTable) inObjects[0];
    // cache the entire table as otherwise the color information
    // may be lost (filtering out the "colored" column)
    m_rowContainer = new DefaultDataArray(data, m_settings.getScatterPlotFirstRow(), m_settings.getScatterPlotRowCount());
    DataTableSpec tableSpec = data.getDataTableSpec();
    // handle the optional PMML input
    PMMLPortObject inPMMLPort = m_pmmlInEnabled ? (PMMLPortObject) inObjects[1] : null;
    PMMLPortObjectSpec inPMMLSpec = null;
    if (inPMMLPort != null) {
        inPMMLSpec = inPMMLPort.getSpec();
    } else {
        PMMLPortObjectSpecCreator creator = new PMMLPortObjectSpecCreator(tableSpec);
        inPMMLSpec = creator.createSpec();
        inPMMLPort = new PMMLPortObject(inPMMLSpec);
    }
    LinReg2Learner learner = new LinReg2Learner(new PortObjectSpec[] { tableSpec, inPMMLSpec }, m_pmmlInEnabled, m_settings);
    m_content = learner.execute(new PortObject[] { data, inPMMLPort }, exec);
    if (learner.getWarningMessage() != null && learner.getWarningMessage().length() > 0) {
        setWarningMessage(learner.getWarningMessage());
    }
    // third argument is ignored since we provide a port
    PMMLPortObject outPMMLPort = new PMMLPortObject((PMMLPortObjectSpec) learner.getOutputSpec()[0], inPMMLPort, null);
    PMMLGeneralRegressionTranslator trans = new PMMLGeneralRegressionTranslator(m_content.createGeneralRegressionContent());
    outPMMLPort.addModelTranslater(trans);
    final String warningMessage = m_content.getWarningMessage();
    if (warningMessage != null) {
        setWarningMessage(getWarningMessage() == null ? warningMessage : (getWarningMessage() + "\n" + warningMessage));
    }
    return new PortObject[] { outPMMLPort, m_content.createTablePortObject(exec) };
}

Example 5 with DefaultDataArray

use of org.knime.base.node.util.DefaultDataArray in project knime-core by knime.

the class PolyRegLearnerNodeModel method loadInternals.

/**
 * {@inheritDoc}
 */
@Override
protected void loadInternals(final File nodeInternDir, final ExecutionMonitor exec) throws IOException, CanceledExecutionException {
    File f = new File(nodeInternDir, "data.zip");
    final DataArray rowContainer;
    if (f.exists()) {
        ContainerTable t = DataContainer.readFromZip(f);
        int rowCount = t.getRowCount();
        rowContainer = new DefaultDataArray(t, 1, rowCount, exec);
    } else {
        throw new FileNotFoundException("Internals do not exist");
    }
    f = new File(nodeInternDir, "internals.xml");
    if (f.exists()) {
        NodeSettingsRO internals = NodeSettings.loadFromXML(new BufferedInputStream(new FileInputStream(f)));
        try {
            double[] betas = internals.getDoubleArray("betas");
            String[] columnNames = internals.getStringArray("columnNames");
            double squaredError = internals.getDouble("squaredError");
            double adjustedR2 = internals.getDouble("adjustedSquaredError", Double.NaN);
            double[] meanValues = internals.getDoubleArray("meanValues");
            double[] emptyArray = new double[betas.length];
            Arrays.fill(emptyArray, Double.NaN);
            double[] stdErrs = internals.getDoubleArray("stdErrors", emptyArray);
            double[] tValues = internals.getDoubleArray("tValues", emptyArray);
            double[] pValues = internals.getDoubleArray("pValues", emptyArray);
            m_viewData = new PolyRegViewData(meanValues, betas, stdErrs, tValues, pValues, squaredError, adjustedR2, columnNames, m_settings.getDegree(), m_settings.getTargetColumn(), rowContainer);
        } catch (InvalidSettingsException ex) {
            throw new IOException("Old or corrupt internals", ex);
        }
    } else {
        throw new FileNotFoundException("Internals do not exist");
    }
}