Examples with DefaultRow org.knime.core.data.def.DefaultRow used on opensource projects

Example 31 with DefaultRow

use of org.knime.core.data.def.DefaultRow 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 32 with DefaultRow

use of org.knime.core.data.def.DefaultRow in project knime-core by knime.

the class EnrichmentPlotterModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected BufferedDataTable[] execute(final BufferedDataTable[] inData, final ExecutionContext exec) throws Exception {
    final double rowCount = inData[0].size();
    final BufferedDataContainer areaOutCont = exec.createDataContainer(AREA_OUT_SPEC);
    final BufferedDataContainer discrateOutCont = exec.createDataContainer(DISCRATE_OUT_SPEC);
    for (int i = 0; i < m_settings.getCurveCount(); i++) {
        final ExecutionMonitor sexec = exec.createSubProgress(1.0 / m_settings.getCurveCount());
        exec.setMessage("Generating curve " + (i + 1));
        final Curve c = m_settings.getCurve(i);
        final Helper[] curve = new Helper[KnowsRowCountTable.checkRowCount(inData[0].size())];
        final int sortIndex = inData[0].getDataTableSpec().findColumnIndex(c.getSortColumn());
        final int actIndex = inData[0].getDataTableSpec().findColumnIndex(c.getActivityColumn());
        int k = 0, maxK = 0;
        for (DataRow row : inData[0]) {
            DataCell c1 = row.getCell(sortIndex);
            DataCell c2 = row.getCell(actIndex);
            if (k++ % 100 == 0) {
                sexec.checkCanceled();
                sexec.setProgress(k / rowCount);
            }
            if (c1.isMissing()) {
                continue;
            } else {
                curve[maxK] = new Helper(((DoubleValue) c1).getDoubleValue(), c2);
            }
            maxK++;
        }
        Arrays.sort(curve, 0, maxK);
        if (c.isSortDescending()) {
            for (int j = 0; j < maxK / 2; j++) {
                Helper h = curve[j];
                curve[j] = curve[maxK - j - 1];
                curve[maxK - j - 1] = h;
            }
        }
        // this is for down-sampling so that the view is faster;
        // plotting >100,000 points takes quite a long time
        final int size = Math.min(MAX_RESOLUTION, maxK);
        final double downSampleRate = maxK / (double) size;
        final double[] xValues = new double[size + 1];
        final double[] yValues = new double[size + 1];
        xValues[0] = 0;
        yValues[0] = 0;
        int lastK = 0;
        double y = 0, area = 0;
        int nextHitRatePoint = 0;
        final double[] hitRateValues = new double[DISCRATE_POINTS.length];
        final HashMap<DataCell, MutableInteger> clusters = new HashMap<DataCell, MutableInteger>();
        for (k = 1; k <= maxK; k++) {
            final Helper h = curve[k - 1];
            if (m_settings.plotMode() == PlotMode.PlotSum) {
                y += ((DoubleValue) h.b).getDoubleValue();
            } else if (m_settings.plotMode() == PlotMode.PlotHits) {
                if (!h.b.isMissing() && (((DoubleValue) h.b).getDoubleValue() >= m_settings.hitThreshold())) {
                    y++;
                }
            } else if (!h.b.isMissing()) {
                MutableInteger count = clusters.get(h.b);
                if (count == null) {
                    count = new MutableInteger(0);
                    clusters.put(h.b, count);
                }
                if (count.inc() == m_settings.minClusterMembers()) {
                    y++;
                }
            }
            area += y / maxK;
            if ((int) (k / downSampleRate) >= lastK + 1) {
                lastK++;
                xValues[lastK] = k;
                yValues[lastK] = y;
            }
            if ((nextHitRatePoint < DISCRATE_POINTS.length) && (k == (int) Math.floor(maxK * DISCRATE_POINTS[nextHitRatePoint] / 100))) {
                hitRateValues[nextHitRatePoint] = y;
                nextHitRatePoint++;
            }
        }
        xValues[xValues.length - 1] = maxK;
        yValues[yValues.length - 1] = y;
        area /= y;
        m_curves.add(new EnrichmentPlot(c.getSortColumn() + " vs " + c.getActivityColumn(), xValues, yValues, area));
        areaOutCont.addRowToTable(new DefaultRow(new RowKey(c.toString()), new DoubleCell(area)));
        for (int j = 0; j < hitRateValues.length; j++) {
            hitRateValues[j] /= y;
        }
        discrateOutCont.addRowToTable(new DefaultRow(new RowKey(c.toString()), hitRateValues));
    }
    areaOutCont.close();
    discrateOutCont.close();
    return new BufferedDataTable[] { areaOutCont.getTable(), discrateOutCont.getTable() };
}

Example 33 with DefaultRow

use of org.knime.core.data.def.DefaultRow in project knime-core by knime.

the class LogisticRegressionContent method createTablePortObject.

/**
 * Creates a BufferedDataTable with the
 * @param exec The execution context
 * @return a port object
 */
public BufferedDataTable createTablePortObject(final ExecutionContext exec) {
    DataTableSpec tableOutSpec = new DataTableSpec("Coefficients and Statistics", new String[] { "Logit", "Variable", "Coeff.", "Std. Err.", "z-score", "P>|z|" }, new DataType[] { StringCell.TYPE, StringCell.TYPE, DoubleCell.TYPE, DoubleCell.TYPE, DoubleCell.TYPE, DoubleCell.TYPE });
    BufferedDataContainer dc = exec.createDataContainer(tableOutSpec);
    List<DataCell> logits = this.getLogits();
    List<String> parameters = this.getParameters();
    int c = 0;
    for (DataCell logit : logits) {
        Map<String, Double> coefficients = this.getCoefficients(logit);
        Map<String, Double> stdErrs = this.getStandardErrors(logit);
        Map<String, Double> zScores = this.getZScores(logit);
        Map<String, Double> pValues = this.getPValues(logit);
        for (String parameter : parameters) {
            List<DataCell> cells = new ArrayList<DataCell>();
            cells.add(new StringCell(logit.toString()));
            cells.add(new StringCell(parameter));
            cells.add(new DoubleCell(coefficients.get(parameter)));
            cells.add(new DoubleCell(stdErrs.get(parameter)));
            cells.add(new DoubleCell(zScores.get(parameter)));
            cells.add(new DoubleCell(pValues.get(parameter)));
            c++;
            dc.addRowToTable(new DefaultRow("Row" + c, cells));
        }
        List<DataCell> cells = new ArrayList<DataCell>();
        cells.add(new StringCell(logit.toString()));
        cells.add(new StringCell("Constant"));
        cells.add(new DoubleCell(this.getIntercept(logit)));
        cells.add(new DoubleCell(this.getInterceptStdErr(logit)));
        cells.add(new DoubleCell(this.getInterceptZScore(logit)));
        cells.add(new DoubleCell(this.getInterceptPValue(logit)));
        c++;
        dc.addRowToTable(new DefaultRow("Row" + c, cells));
    }
    dc.close();
    return dc.getTable();
}

Example 34 with DefaultRow

use of org.knime.core.data.def.DefaultRow in project knime-core by knime.

the class LogisticRegressionContent method createTablePortObject.

/**
 * Creates a BufferedDataTable with the
 * @param exec The execution context
 * @return a port object
 */
public BufferedDataTable createTablePortObject(final ExecutionContext exec) {
    DataTableSpec tableOutSpec = new DataTableSpec("Coefficients and Statistics", new String[] { "Logit", "Variable", "Coeff.", "Std. Err.", "z-score", "P>|z|" }, new DataType[] { StringCell.TYPE, StringCell.TYPE, DoubleCell.TYPE, DoubleCell.TYPE, DoubleCell.TYPE, DoubleCell.TYPE });
    BufferedDataContainer dc = exec.createDataContainer(tableOutSpec);
    List<DataCell> logits = this.getLogits();
    List<String> parameters = this.getParameters();
    int c = 0;
    for (DataCell logit : logits) {
        Map<String, Double> coefficients = this.getCoefficients(logit);
        Map<String, Double> stdErrs = this.getStandardErrors(logit);
        Map<String, Double> zScores = this.getZScores(logit);
        Map<String, Double> pValues = this.getPValues(logit);
        for (String parameter : parameters) {
            List<DataCell> cells = new ArrayList<DataCell>();
            cells.add(new StringCell(logit.toString()));
            cells.add(new StringCell(parameter));
            cells.add(new DoubleCell(coefficients.get(parameter)));
            cells.add(new DoubleCell(stdErrs.get(parameter)));
            cells.add(new DoubleCell(zScores.get(parameter)));
            cells.add(new DoubleCell(pValues.get(parameter)));
            c++;
            dc.addRowToTable(new DefaultRow("Row" + c, cells));
        }
        List<DataCell> cells = new ArrayList<DataCell>();
        cells.add(new StringCell(logit.toString()));
        cells.add(new StringCell("Constant"));
        cells.add(new DoubleCell(this.getIntercept(logit)));
        cells.add(new DoubleCell(this.getInterceptStdErr(logit)));
        cells.add(new DoubleCell(this.getInterceptZScore(logit)));
        cells.add(new DoubleCell(this.getInterceptPValue(logit)));
        c++;
        dc.addRowToTable(new DefaultRow("Row" + c, cells));
    }
    dc.close();
    return dc.getTable();
}

Example 35 with DefaultRow

use of org.knime.core.data.def.DefaultRow in project knime-core by knime.

the class AbstractTreeEnsembleModel method createLearnAttributeRow.

public DataRow createLearnAttributeRow(final DataRow learnRow, final DataTableSpec learnSpec) {
    final TreeType type = getType();
    final DataCell c = learnRow.getCell(0);
    final int nrAttributes = getMetaData().getNrAttributes();
    switch(type) {
        case Ordinary:
            return learnRow;
        case BitVector:
            if (c.isMissing()) {
                return null;
            }
            BitVectorValue bv = (BitVectorValue) c;
            final long length = bv.length();
            if (length != nrAttributes) {
                // TODO indicate error message
                return null;
            }
            DataCell trueCell = new StringCell("1");
            DataCell falseCell = new StringCell("0");
            DataCell[] cells = new DataCell[nrAttributes];
            for (int i = 0; i < nrAttributes; i++) {
                cells[i] = bv.get(i) ? trueCell : falseCell;
            }
            return new DefaultRow(learnRow.getKey(), cells);
        case ByteVector:
            if (c.isMissing()) {
                return null;
            }
            ByteVectorValue byteVector = (ByteVectorValue) c;
            final long bvLength = byteVector.length();
            if (bvLength != nrAttributes) {
                return null;
            }
            DataCell[] bvCells = new DataCell[nrAttributes];
            for (int i = 0; i < nrAttributes; i++) {
                bvCells[i] = new IntCell(byteVector.get(i));
            }
            return new DefaultRow(learnRow.getKey(), bvCells);
        case DoubleVector:
            if (c.isMissing()) {
                return null;
            }
            DoubleVectorValue doubleVector = (DoubleVectorValue) c;
            final int dvLength = doubleVector.getLength();
            if (dvLength != nrAttributes) {
                return null;
            }
            DataCell[] dvCells = new DataCell[nrAttributes];
            for (int i = 0; i < nrAttributes; i++) {
                dvCells[i] = new DoubleCell(doubleVector.getValue(i));
            }
            return new DefaultRow(learnRow.getKey(), dvCells);
        default:
            throw new IllegalStateException("Type unknown (not implemented): " + type);
    }
}