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

Example 41 with PortObject

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

the class PCAComputeNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    if (!(inData[DATA_INPORT] instanceof BufferedDataTable)) {
        throw new IllegalArgumentException("Datatable as input expected");
    }
    final BufferedDataTable dataTable = (BufferedDataTable) inData[DATA_INPORT];
    if (dataTable.size() == 0) {
        throw new IllegalArgumentException("Input table is empty!");
    }
    final double[] meanVector = PCANodeModel.getMeanVector(dataTable, m_inputColumnIndices, m_failOnMissingValues.getBooleanValue(), exec.createSubExecutionContext(0.4));
    final double[][] m = new double[m_inputColumnIndices.length][m_inputColumnIndices.length];
    exec.checkCanceled();
    final int missingValues = PCANodeModel.getCovarianceMatrix(exec.createSubExecutionContext(0.4), dataTable, m_inputColumnIndices, meanVector, m);
    if (missingValues > 0) {
        if (m_failOnMissingValues.getBooleanValue()) {
            throw new IllegalArgumentException("missing, infinite or impossible values in table");
        }
        setWarningMessage(missingValues + " rows ignored because of missing, " + "infinite or impossible values");
    }
    exec.checkCanceled();
    final Matrix covarianceMatrix = new Matrix(m);
    exec.setProgress("calculation of spectral decomposition");
    final EigenvalueDecomposition evd = covarianceMatrix.eig();
    exec.checkCanceled();
    exec.setProgress(0.9);
    final Matrix d = evd.getD();
    final double[] evs = new double[d.getRowDimension()];
    for (int i = 0; i < evs.length; i++) {
        evs[i] = d.get(i, i);
    }
    exec.checkCanceled();
    return new PortObject[] { PCANodeModel.createCovarianceTable(exec, m, m_inputColumnNames), PCANodeModel.createDecompositionOutputTable(exec.createSubExecutionContext(0.1), evd, m_inputColumnNames), new PCAModelPortObject(evd.getV().getArray(), evs, m_inputColumnNames, meanVector) };
}

Example 42 with PortObject

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

the class PCAReverseNodeModel method execute.

/**
 * Performs the PCA.
 *
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    final PCAModelPortObject model = (PCAModelPortObject) inData[MODEL_INPORT];
    final Matrix eigenvectors = EigenValue.getSortedEigenVectors(model.getEigenVectors(), model.getEigenvalues(), m_inputColumnIndices.length);
    if (m_failOnMissingValues.getBooleanValue()) {
        for (final DataRow row : (DataTable) inData[DATA_INPORT]) {
            for (int i = 0; i < m_inputColumnIndices.length; i++) {
                if (row.getCell(m_inputColumnIndices[i]).isMissing()) {
                    throw new IllegalArgumentException("data table contains missing values");
                }
            }
        }
    }
    final String[] origColumnNames = ((PCAModelPortObjectSpec) ((PCAModelPortObject) inData[MODEL_INPORT]).getSpec()).getColumnNames();
    final DataColumnSpec[] specs = createAddTableSpec((DataTableSpec) inData[DATA_INPORT].getSpec(), origColumnNames);
    final CellFactory fac = new CellFactory() {

        @Override
        public DataCell[] getCells(final DataRow row) {
            return convertInputRow(eigenvectors, row, model.getCenter(), m_inputColumnIndices, origColumnNames.length);
        }

        @Override
        public DataColumnSpec[] getColumnSpecs() {
            return specs;
        }

        @Override
        public void setProgress(final int curRowNr, final int rowCount, final RowKey lastKey, final ExecutionMonitor texec) {
            texec.setProgress((double) curRowNr / rowCount);
        }
    };
    final ColumnRearranger cr = new ColumnRearranger((DataTableSpec) inData[DATA_INPORT].getSpec());
    cr.append(fac);
    if (m_removePCACols.getBooleanValue()) {
        cr.remove(m_inputColumnIndices);
    }
    final BufferedDataTable result = exec.createColumnRearrangeTable((BufferedDataTable) inData[DATA_INPORT], cr, exec);
    final PortObject[] out = { result };
    return out;
}

Example 43 with PortObject

use of org.knime.core.node.port.PortObject 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 44 with PortObject

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

the class PolyRegLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable inTable = (BufferedDataTable) inData[0];
    DataTableSpec inSpec = inTable.getDataTableSpec();
    final int colCount = inSpec.getNumColumns();
    String[] selectedCols = computeSelectedColumns(inSpec);
    Set<String> hash = new HashSet<String>(Arrays.asList(selectedCols));
    m_colSelected = new boolean[colCount];
    for (int i = 0; i < colCount; i++) {
        m_colSelected[i] = hash.contains(inTable.getDataTableSpec().getColumnSpec(i).getName());
    }
    final int rowCount = inTable.getRowCount();
    String[] temp = new String[m_columnNames.length + 1];
    System.arraycopy(m_columnNames, 0, temp, 0, m_columnNames.length);
    temp[temp.length - 1] = m_settings.getTargetColumn();
    FilterColumnTable filteredTable = new FilterColumnTable(inTable, temp);
    final DataArray rowContainer = new DefaultDataArray(filteredTable, 1, m_settings.getMaxRowsForView());
    // handle the optional PMML input
    PMMLPortObject inPMMLPort = m_pmmlInEnabled ? (PMMLPortObject) inData[1] : null;
    PortObjectSpec[] outputSpec = configure((inPMMLPort == null) ? new PortObjectSpec[] { inData[0].getSpec(), null } : new PortObjectSpec[] { inData[0].getSpec(), inPMMLPort.getSpec() });
    Learner learner = new Learner((PMMLPortObjectSpec) outputSpec[0], 0d, m_settings.getMissingValueHandling() == MissingValueHandling.fail, m_settings.getDegree());
    try {
        PolyRegContent polyRegContent = learner.perform(inTable, exec);
        m_betas = fillBeta(polyRegContent);
        m_meanValues = polyRegContent.getMeans();
        ColumnRearranger crea = new ColumnRearranger(inTable.getDataTableSpec());
        crea.append(getCellFactory(inTable.getDataTableSpec().findColumnIndex(m_settings.getTargetColumn())));
        PortObject[] bdt = new PortObject[] { createPMMLModel(inPMMLPort, inSpec), exec.createColumnRearrangeTable(inTable, crea, exec.createSilentSubExecutionContext(.2)), polyRegContent.createTablePortObject(exec.createSubExecutionContext(0.2)) };
        m_squaredError /= rowCount;
        if (polyRegContent.getWarningMessage() != null) {
            setWarningMessage(polyRegContent.getWarningMessage());
        }
        double[] stdErrors = PolyRegViewData.mapToArray(polyRegContent.getStandardErrors(), m_columnNames, m_settings.getDegree(), polyRegContent.getInterceptStdErr());
        double[] tValues = PolyRegViewData.mapToArray(polyRegContent.getTValues(), m_columnNames, m_settings.getDegree(), polyRegContent.getInterceptTValue());
        double[] pValues = PolyRegViewData.mapToArray(polyRegContent.getPValues(), m_columnNames, m_settings.getDegree(), polyRegContent.getInterceptPValue());
        m_viewData = new PolyRegViewData(m_meanValues, m_betas, stdErrors, tValues, pValues, m_squaredError, polyRegContent.getAdjustedRSquared(), m_columnNames, m_settings.getDegree(), m_settings.getTargetColumn(), rowContainer);
        return bdt;
    } catch (ModelSpecificationException e) {
        final String origWarning = getWarningMessage();
        final String warning = (origWarning != null && !origWarning.isEmpty()) ? (origWarning + "\n") : "" + e.getMessage();
        setWarningMessage(warning);
        final ExecutionContext subExec = exec.createSubExecutionContext(.1);
        final BufferedDataContainer empty = subExec.createDataContainer(STATS_SPEC);
        int rowIdx = 1;
        for (final String column : m_columnNames) {
            for (int d = 1; d <= m_settings.getDegree(); ++d) {
                empty.addRowToTable(new DefaultRow("Row" + rowIdx++, new StringCell(column), new IntCell(d), new DoubleCell(0.0d), DataType.getMissingCell(), DataType.getMissingCell(), DataType.getMissingCell()));
            }
        }
        empty.addRowToTable(new DefaultRow("Row" + rowIdx, new StringCell("Intercept"), new IntCell(0), new DoubleCell(0.0d), DataType.getMissingCell(), DataType.getMissingCell(), DataType.getMissingCell()));
        double[] nans = new double[m_columnNames.length * m_settings.getDegree() + 1];
        Arrays.fill(nans, Double.NaN);
        m_betas = new double[nans.length];
        // Mean only for the linear tags
        m_meanValues = new double[nans.length / m_settings.getDegree()];
        m_viewData = new PolyRegViewData(m_meanValues, m_betas, nans, nans, nans, m_squaredError, Double.NaN, m_columnNames, m_settings.getDegree(), m_settings.getTargetColumn(), rowContainer);
        empty.close();
        ColumnRearranger crea = new ColumnRearranger(inTable.getDataTableSpec());
        crea.append(getCellFactory(inTable.getDataTableSpec().findColumnIndex(m_settings.getTargetColumn())));
        BufferedDataTable rearrangerTable = exec.createColumnRearrangeTable(inTable, crea, exec.createSubProgress(0.6));
        PMMLPortObject model = createPMMLModel(inPMMLPort, inTable.getDataTableSpec());
        PortObject[] bdt = new PortObject[] { model, rearrangerTable, empty.getTable() };
        return bdt;
    }
}

Example 45 with PortObject

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

the class FuzzyClusterNodeModel method execute.

/**
 * Generate new clustering based on InputDataTable and specified number of
 * clusters. In the output table, you will find the datarow with
 * supplementary information about the membership to each cluster center.
 * OUTPORT = original datarows with cluster membership information
 *
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable indata = (BufferedDataTable) inData[0];
    m_clusters = null;
    m_betweenClusterVariation = Double.NaN;
    m_withinClusterVariation = null;
    if (m_noise) {
        if (m_calculateDelta) {
            if (m_memory) {
                m_fcmAlgo = new FCMAlgorithmMemory(m_nrClusters, m_fuzzifier, m_calculateDelta, m_lambda);
            } else {
                m_fcmAlgo = new FCMAlgorithm(m_nrClusters, m_fuzzifier, m_calculateDelta, m_lambda);
            }
        } else {
            if (m_memory) {
                m_fcmAlgo = new FCMAlgorithmMemory(m_nrClusters, m_fuzzifier, m_calculateDelta, m_delta);
            } else {
                m_fcmAlgo = new FCMAlgorithm(m_nrClusters, m_fuzzifier, m_calculateDelta, m_delta);
            }
        }
    } else {
        if (m_memory) {
            m_fcmAlgo = new FCMAlgorithmMemory(m_nrClusters, m_fuzzifier);
        } else {
            m_fcmAlgo = new FCMAlgorithm(m_nrClusters, m_fuzzifier);
        }
    }
    int nrRows = indata.getRowCount();
    DataTableSpec spec = indata.getDataTableSpec();
    int nrCols = spec.getNumColumns();
    List<String> learningCols = new LinkedList<String>();
    List<String> ignoreCols = new LinkedList<String>();
    // counter for included columns
    int z = 0;
    final int[] columns = new int[m_list.size()];
    for (int i = 0; i < nrCols; i++) {
        // if include does contain current column name
        String colname = spec.getColumnSpec(i).getName();
        if (m_list.contains(colname)) {
            columns[z] = i;
            z++;
            learningCols.add(colname);
        } else {
            ignoreCols.add(colname);
        }
    }
    ColumnRearranger colre = new ColumnRearranger(spec);
    colre.keepOnly(columns);
    BufferedDataTable filteredtable = exec.createColumnRearrangeTable(indata, colre, exec);
    // get dimension of feature space
    int dimension = filteredtable.getDataTableSpec().getNumColumns();
    Random random = new Random();
    if (m_useRandomSeed) {
        random.setSeed(m_randomSeed);
    }
    m_fcmAlgo.init(nrRows, dimension, filteredtable, random);
    // main loop - until clusters stop changing or maxNrIterations reached
    int currentIteration = 0;
    double totalchange = Double.MAX_VALUE;
    while ((totalchange > 1e-7) && (currentIteration < m_maxNrIterations)) {
        exec.checkCanceled();
        exec.setProgress((double) currentIteration / (double) m_maxNrIterations, "Iteration " + currentIteration + " Total change of prototypes: " + totalchange);
        totalchange = m_fcmAlgo.doOneIteration(exec);
        currentIteration++;
    }
    if (m_measures) {
        double[][] data = null;
        if (m_fcmAlgo instanceof FCMAlgorithmMemory) {
            data = ((FCMAlgorithmMemory) m_fcmAlgo).getConvertedData();
        } else {
            data = new double[nrRows][m_fcmAlgo.getDimension()];
            int curRow = 0;
            for (DataRow dRow : filteredtable) {
                for (int j = 0; j < dRow.getNumCells(); j++) {
                    if (!(dRow.getCell(j).isMissing())) {
                        DoubleValue dv = (DoubleValue) dRow.getCell(j);
                        data[curRow][j] = dv.getDoubleValue();
                    } else {
                        data[curRow][j] = 0;
                    }
                }
                curRow++;
            }
        }
        m_fcmmeasures = new FCMQualityMeasures(m_fcmAlgo.getClusterCentres(), m_fcmAlgo.getweightMatrix(), data, m_fuzzifier);
    }
    ColumnRearranger colRearranger = new ColumnRearranger(spec);
    CellFactory membershipFac = new ClusterMembershipFactory(m_fcmAlgo);
    colRearranger.append(membershipFac);
    BufferedDataTable result = exec.createColumnRearrangeTable(indata, colRearranger, exec);
    // don't write out the noise cluster!
    double[][] clustercentres = m_fcmAlgo.getClusterCentres();
    if (m_noise) {
        double[][] cleaned = new double[clustercentres.length - 1][];
        for (int i = 0; i < cleaned.length; i++) {
            cleaned[i] = new double[clustercentres[i].length];
            System.arraycopy(clustercentres[i], 0, cleaned[i], 0, clustercentres[i].length);
        }
        clustercentres = cleaned;
    }
    exec.setMessage("Creating PMML cluster model...");
    // handle the optional PMML input
    PMMLPortObject inPMMLPort = m_enablePMMLInput ? (PMMLPortObject) inData[1] : null;
    PMMLPortObjectSpec inPMMLSpec = null;
    if (inPMMLPort != null) {
        inPMMLSpec = inPMMLPort.getSpec();
    }
    PMMLPortObjectSpec pmmlOutSpec = createPMMLPortObjectSpec(inPMMLSpec, spec, learningCols);
    PMMLPortObject outPMMLPort = new PMMLPortObject(pmmlOutSpec, inPMMLPort, spec);
    outPMMLPort.addModelTranslater(new PMMLClusterTranslator(ComparisonMeasure.squaredEuclidean, m_nrClusters, clustercentres, null, new LinkedHashSet<String>(pmmlOutSpec.getLearningFields())));
    return new PortObject[] { result, outPMMLPort };
}