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

Example 36 with ExecutionContext

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

the class ReferenceColumnResorterNodeModel method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    return new StreamableOperator() {

        private String[] m_streamableOperatorOrder;

        /**
         * {@inheritDoc}
         */
        @Override
        public void runIntermediate(final PortInput[] inputs, final ExecutionContext exec) throws Exception {
            BufferedDataTable orderTable = (BufferedDataTable) ((PortObjectInput) inputs[1]).getPortObject();
            m_streamableOperatorOrder = readOrderFromTable(orderTable);
        }

        @Override
        public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
            RowInput dataInput = (RowInput) inputs[0];
            DataTableSpec dataInputSpec = dataInput.getDataTableSpec();
            ColumnRearranger rearranger = createColumnRearranger(dataInputSpec, m_streamableOperatorOrder);
            StreamableFunction streamableFunction = rearranger.createStreamableFunction();
            streamableFunction.runFinal(new PortInput[] { dataInput }, outputs, exec);
        }

        /**
         * {@inheritDoc}
         */
        @Override
        public StreamableOperatorInternals saveInternals() {
            return createStreamableOperatorInternalsFromOrder(m_streamableOperatorOrder);
        }

        /**
         * {@inheritDoc}
         */
        @Override
        public void loadInternals(final StreamableOperatorInternals internals) {
            m_streamableOperatorOrder = readOrderFromStreamableOperatorInternals(internals);
        }
    };
}

Example 37 with ExecutionContext

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

the class BoxplotCalculator method calculateMultiple.

/**
 * Calculates the necessary statistics for a non-conditional boxplot.
 * @param table the input data
 * @param numCol array of names of numeric columns to plot
 * @param exec Execution context to report progress to
 * @return LinkedHashMap with the column name as key and statistics as value
 * @throws CanceledExecutionException when the user cancels the execution
 */
public LinkedHashMap<String, BoxplotStatistics> calculateMultiple(final BufferedDataTable table, final String[] numCol, final ExecutionContext exec) throws CanceledExecutionException {
    DataTableSpec spec = table.getSpec();
    int[] numColIdxs = new int[numCol.length];
    for (int i = 0; i < numCol.length; i++) {
        numColIdxs[i] = spec.findColumnIndex(numCol[i]);
    }
    LinkedHashMap<String, DataContainer> containers = new LinkedHashMap<String, DataContainer>();
    for (int i = 0; i < numCol.length; i++) {
        containers.put(numCol[i], exec.createDataContainer(new DataTableSpec(new String[] { "col" }, new DataType[] { DoubleCell.TYPE })));
    }
    ExecutionContext subExec = exec.createSilentSubExecutionContext(0.7);
    long[] numMissValPerCol = new long[numCol.length];
    int count = 0;
    for (DataRow row : table) {
        exec.checkCanceled();
        subExec.setProgress((double) count++ / table.size());
        for (int i = 0; i < numCol.length; i++) {
            DataCell cell = row.getCell(numColIdxs[i]);
            if (!cell.isMissing()) {
                containers.get(numCol[i]).addRowToTable(new DefaultRow(row.getKey(), cell));
            } else {
                numMissValPerCol[i]++;
            }
        }
    }
    LinkedHashMap<String, BoxplotStatistics> statsMap = new LinkedHashMap<>();
    ExecutionContext subExec2 = exec.createSilentSubExecutionContext(1.0);
    count = 0;
    List<String> excludedDataColList = new ArrayList<String>();
    for (Entry<String, DataContainer> entry : containers.entrySet()) {
        exec.checkCanceled();
        subExec2.setProgress((double) count++ / containers.size());
        Set<Outlier> extremeOutliers = new HashSet<Outlier>();
        Set<Outlier> mildOutliers = new HashSet<Outlier>();
        entry.getValue().close();
        BufferedDataTable catTable = (BufferedDataTable) entry.getValue().getTable();
        if (catTable.size() == 0) {
            excludedDataColList.add(entry.getKey());
            continue;
        }
        SortedTable st = new SortedTable(catTable, new Comparator<DataRow>() {

            @Override
            public int compare(final DataRow o1, final DataRow o2) {
                DataCell c1 = o1.getCell(0);
                DataCell c2 = o2.getCell(0);
                double d1 = ((DoubleValue) c1).getDoubleValue();
                double d2 = ((DoubleValue) c2).getDoubleValue();
                if (d1 == d2) {
                    return 0;
                } else {
                    return d1 < d2 ? -1 : 1;
                }
            }
        }, false, exec);
        double min = 0, max = 0, q1 = 0, q3 = 0, median = 0;
        boolean dq1 = catTable.size() % 4 == 0;
        long q1Idx = catTable.size() / 4;
        boolean dq3 = 3 * catTable.size() % 4 == 0;
        long q3Idx = 3 * catTable.size() / 4;
        boolean dMedian = catTable.size() % 2 == 0;
        long medianIdx = catTable.size() / 2;
        int counter = 0;
        for (DataRow row : st) {
            double val = ((DoubleValue) row.getCell(0)).getDoubleValue();
            if (counter == 0) {
                min = val;
            }
            if (counter == catTable.size() - 1) {
                max = val;
            }
            if (counter == q1Idx - 1 && dq1) {
                q1 = val;
            }
            if (counter == q1Idx || (counter == 0 && st.size() <= 3)) {
                if (dq1) {
                    q1 = (q1 + val) / 2.0;
                } else {
                    q1 = val;
                }
            }
            if (counter == medianIdx - 1 && dMedian) {
                median = val;
            }
            if (counter == medianIdx) {
                if (dMedian) {
                    median = (median + val) / 2;
                } else {
                    median = val;
                }
            }
            if (counter == q3Idx - 1 && dq3) {
                q3 = val;
            }
            if (counter == q3Idx || (counter == st.size() - 1 && st.size() <= 3)) {
                if (dq3) {
                    q3 = (q3 + val) / 2.0;
                } else {
                    q3 = val;
                }
            }
            counter++;
        }
        double iqr = q3 - q1;
        double lowerWhisker = min;
        double upperWhisker = max;
        double upperWhiskerFence = q3 + (1.5 * iqr);
        double lowerWhiskerFence = q1 - (1.5 * iqr);
        double lowerFence = q1 - (3 * iqr);
        double upperFence = q3 + (3 * iqr);
        for (DataRow row : st) {
            double value = ((DoubleValue) row.getCell(0)).getDoubleValue();
            String rowKey = row.getKey().getString();
            if (value < lowerFence) {
                extremeOutliers.add(new Outlier(value, rowKey));
            } else if (value < lowerWhiskerFence) {
                mildOutliers.add(new Outlier(value, rowKey));
            } else if (lowerWhisker < lowerWhiskerFence && value >= lowerWhiskerFence) {
                lowerWhisker = value;
            } else if (value <= upperWhiskerFence) {
                upperWhisker = value;
            } else if (value > upperFence) {
                extremeOutliers.add(new Outlier(value, rowKey));
            } else if (value > upperWhiskerFence) {
                mildOutliers.add(new Outlier(value, rowKey));
            }
        }
        statsMap.put(entry.getKey(), new BoxplotStatistics(mildOutliers, extremeOutliers, min, max, lowerWhisker, q1, median, q3, upperWhisker));
    }
    // missing values part
    m_excludedDataCols = excludedDataColList.toArray(new String[excludedDataColList.size()]);
    m_numMissValPerCol = new LinkedHashMap<String, Long>();
    for (int i = 0; i < numCol.length; i++) {
        if (numMissValPerCol[i] > 0 && !excludedDataColList.contains(numCol[i])) {
            m_numMissValPerCol.put(numCol[i], numMissValPerCol[i]);
        }
    }
    return statsMap;
}

Example 38 with ExecutionContext

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

the class Normalizer3NodeModel 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
    String[] includedColumns = getIncludedComlumns(inSpec);
    Normalizer2 ntable = new Normalizer2(inTable, includedColumns);
    long rowcount = inTable.size();
    ExecutionContext prepareExec = exec.createSubExecutionContext(0.3);
    AffineTransTable outTable;
    boolean fixDomainBounds = false;
    switch(m_config.getMode()) {
        case MINMAX:
            fixDomainBounds = true;
            outTable = ntable.doMinMaxNorm(m_config.getMax(), m_config.getMin(), prepareExec);
            break;
        case Z_SCORE:
            outTable = ntable.doZScoreNorm(prepareExec);
            break;
        case DECIMALSCALING:
            outTable = ntable.doDecimalScaling(prepareExec);
            break;
        default:
            throw new InvalidSettingsException("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, includedColumns);
    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 < includedColumns.length; i++) {
            int index = spec.findColumnIndex(includedColumns[i]);
            DataColumnSpecCreator creator = new DataColumnSpecCreator(newColSpecs[index]);
            DataColumnDomainCreator domCreator = new DataColumnDomainCreator(newColSpecs[index].getDomain());
            domCreator.setLowerBound(new DoubleCell(m_config.getMin()));
            domCreator.setUpperBound(new DoubleCell(m_config.getMax()));
            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 39 with ExecutionContext

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

the class DataColumnSpecFilterPMMLNodeModel method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    final ColumnRearranger corea = createColumnRearranger((DataTableSpec) inSpecs[0]);
    final StreamableFunction fct = corea.createStreamableFunction();
    return new StreamableOperator() {

        @Override
        public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
            // execute streamable in- and outport (0)
            fct.init(exec);
            fct.runFinal(inputs, outputs, exec);
            fct.finish();
            DataTableSpec outSpec = corea.createSpec();
            // pmml ports
            PMMLPortObject pmmlIn = (PMMLPortObject) ((PortObjectInput) inputs[1]).getPortObject();
            final PMMLPortObject pmmlOut = createPMMLOut(pmmlIn, outSpec, getFilterResult((DataTableSpec) inSpecs[0]));
            ((PortObjectOutput) outputs[1]).setPortObject(pmmlOut);
        }
    };
}

Example 40 with ExecutionContext

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

the class AbstractColumnRefNodeModel method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    return new StreamableOperator() {

        @Override
        public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
            ColumnRearranger[] cr = createRearranger((DataTableSpec) inSpecs[0], (DataTableSpec) inSpecs[1]);
            StreamableFunction func1 = cr[0].createStreamableFunction(0, 0);
            if (m_isSplitter) {
                StreamableFunction func2 = cr[1].createStreamableFunction(0, 1);
                RowInput rowInput = ((RowInput) inputs[0]);
                RowOutput rowOutput1 = ((RowOutput) outputs[0]);
                RowOutput rowOutput2 = ((RowOutput) outputs[1]);
                StreamableFunction.runFinalInterwoven(rowInput, func1, rowOutput1, func2, rowOutput2, exec);
            } else {
                func1.runFinal(inputs, outputs, exec);
            }
        }
    };
}