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

Example 66 with ExecutionContext

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

the class ROCCalculator method calculateCurveData.

/**
 * Calculates the ROC curve.
 * @param table the table with the data
 * @param exec the execution context to use for reporting progress
 * @throws CanceledExecutionException when the user cancels the execution
 */
public void calculateCurveData(final BufferedDataTable table, final ExecutionContext exec) throws CanceledExecutionException {
    m_warningMessage = null;
    List<ROCCurve> curves = new ArrayList<ROCCurve>();
    int classIndex = table.getDataTableSpec().findColumnIndex(m_classCol);
    int curvesSize = m_curves.size();
    int size = table.getRowCount();
    if (size == 0) {
        m_warningMessage = "Input table contains no rows";
    }
    BufferedDataContainer outCont = exec.createDataContainer(OUT_SPEC);
    for (int i = 0; i < curvesSize; i++) {
        exec.checkCanceled();
        String c = m_curves.get(i);
        ExecutionContext subExec = exec.createSubExecutionContext(1.0 / curvesSize);
        SortedTable sortedTable = new SortedTable(table, Collections.singletonList(c), new boolean[] { false }, subExec);
        subExec.setProgress(1.0);
        int tp = 0, fp = 0;
        // these contain the coordinates for the plot
        double[] xValues = new double[size + 1];
        double[] yValues = new double[size + 1];
        int k = 0;
        final int scoreColIndex = sortedTable.getDataTableSpec().findColumnIndex(c);
        DataCell lastScore = null;
        for (DataRow row : sortedTable) {
            exec.checkCanceled();
            DataCell realClass = row.getCell(classIndex);
            if (realClass.isMissing() || row.getCell(scoreColIndex).isMissing()) {
                if (m_ignoreMissingValues) {
                    continue;
                } else {
                    m_warningMessage = "Table contains missing values.";
                }
            }
            if (realClass.toString().equals(m_posClass)) {
                tp++;
            } else {
                fp++;
            }
            // around ... the following lines circumvent this.
            if (!row.getCell(scoreColIndex).equals(lastScore)) {
                k++;
                lastScore = row.getCell(scoreColIndex);
            }
            xValues[k] = fp;
            yValues[k] = tp;
        }
        xValues = Arrays.copyOf(xValues, k + 1);
        yValues = Arrays.copyOf(yValues, k + 1);
        for (int j = 0; j <= k; j++) {
            xValues[j] /= fp;
            yValues[j] /= tp;
        }
        xValues[xValues.length - 1] = 1;
        yValues[yValues.length - 1] = 1;
        double area = 0;
        for (k = 1; k < xValues.length; k++) {
            if (xValues[k - 1] < xValues[k]) {
                // magical math: the rectangle + the triangle under
                // the segment xValues[k] to xValues[k - 1]
                area += 0.5 * (xValues[k] - xValues[k - 1]) * (yValues[k] + yValues[k - 1]);
            }
        }
        curves.add(new ROCCurve(c, xValues, yValues, area, m_maxPoints));
        outCont.addRowToTable(new DefaultRow(new RowKey(c.toString()), new DoubleCell(area)));
    }
    m_outCurves = curves;
    outCont.close();
    m_outTable = outCont.getTable();
}

Example 67 with ExecutionContext

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

the class SplitNodeModel2 method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    if (m_conf == null) {
        m_conf = createColFilterConf();
    }
    final DataTableSpec inSpec = (DataTableSpec) inSpecs[0];
    return new StreamableOperator() {

        @Override
        public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
            ColumnRearranger[] a = createColumnRearrangers(inSpec);
            StreamableFunction func1 = a[0].createStreamableFunction(0, 0);
            StreamableFunction func2 = a[1].createStreamableFunction(0, 1);
            // use both functions to actually do it
            RowInput rowInput = ((RowInput) inputs[0]);
            RowOutput rowOutput1 = ((RowOutput) outputs[0]);
            RowOutput rowOutput2 = ((RowOutput) outputs[1]);
            StreamableFunction.runFinalInterwoven(rowInput, func1, rowOutput1, func2, rowOutput2, exec);
        }
    };
}

Example 68 with ExecutionContext

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

the class RowKeyNodeModel2 method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    LOGGER.debug("Entering createStreamableOperator-method of class RowKeyNodeModel");
    if (m_replaceKey.getBooleanValue()) {
        DataTableSpec outSpec = configure((DataTableSpec) inSpecs[DATA_IN_PORT], true);
        return new StreamableOperator() {

            @Override
            public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
                RowInput rowInput = (RowInput) inputs[DATA_IN_PORT];
                RowOutput rowOutput = (RowOutput) outputs[DATA_OUT_PORT];
                replaceKey(rowInput, rowOutput, outSpec.getNumColumns(), -1, exec);
            }
        };
    } else if (m_appendRowKey.getBooleanValue()) {
        LOGGER.debug("The user only wants to append a new column with " + "name " + m_newColumnName);
        // the user wants only a column with the given name which
        // contains the rowkey as value
        final DataTableSpec tableSpec = (DataTableSpec) inSpecs[DATA_IN_PORT];
        final String newColumnName = m_newColumnName.getStringValue();
        final ColumnRearranger c = RowKeyUtil2.createColumnRearranger(tableSpec, newColumnName, StringCell.TYPE);
        return c.createStreamableFunction();
    } else {
        // the given data
        return new StreamableFunction() {

            @Override
            public DataRow compute(final DataRow input) throws Exception {
                return input;
            }
        };
    }
}

Example 69 with ExecutionContext

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

the class BoxPlotNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected BufferedDataTable[] execute(final BufferedDataTable[] inData, final ExecutionContext exec) throws Exception {
    if (inData[0] == null) {
        return new BufferedDataTable[] {};
    }
    BufferedDataTable table = inData[0];
    m_statistics = new LinkedHashMap<DataColumnSpec, double[]>();
    m_mildOutliers = new LinkedHashMap<String, Map<Double, Set<RowKey>>>();
    m_extremeOutliers = new LinkedHashMap<String, Map<Double, Set<RowKey>>>();
    int colIdx = 0;
    List<DataColumnSpec> outputColSpecs = new ArrayList<DataColumnSpec>();
    double subProgress = 1.0 / getNumNumericColumns(table.getDataTableSpec());
    for (DataColumnSpec colSpec : table.getDataTableSpec()) {
        ExecutionContext colExec = exec.createSubExecutionContext(subProgress);
        exec.checkCanceled();
        if (colSpec.getType().isCompatible(DoubleValue.class)) {
            double[] statistic = new double[SIZE];
            outputColSpecs.add(colSpec);
            List<String> col = new ArrayList<String>();
            col.add(colSpec.getName());
            ExecutionContext sortExec = colExec.createSubExecutionContext(0.75);
            ExecutionContext findExec = colExec.createSubExecutionContext(0.25);
            SortedTable sorted = new SortedTable(table, col, new boolean[] { true }, sortExec);
            long currRowAbsolute = 0;
            int currCountingRow = 1;
            double lastValue = 1;
            long nrOfRows = table.size();
            boolean first = true;
            for (DataRow row : sorted) {
                exec.checkCanceled();
                double rowProgress = currRowAbsolute / (double) table.size();
                findExec.setProgress(rowProgress, "determining statistics for: " + table.getDataTableSpec().getColumnSpec(colIdx).getName());
                if (row.getCell(colIdx).isMissing()) {
                    // asserts that the missing values are sorted at
                    // the top of the table
                    currRowAbsolute++;
                    nrOfRows--;
                    continue;
                }
                // get the first value = actually observed minimum
                if (first) {
                    statistic[MIN] = ((DoubleValue) row.getCell(colIdx)).getDoubleValue();
                    // initialize the statistics with first value
                    // if the table is large enough it will be overriden
                    // this is just for the case of tables with < 5 rows
                    statistic[MEDIAN] = statistic[MIN];
                    statistic[LOWER_QUARTILE] = statistic[MIN];
                    statistic[UPPER_QUARTILE] = statistic[MIN];
                    first = false;
                }
                // get the last value = actually observed maximum
                if (currRowAbsolute == table.size() - 1) {
                    statistic[MAX] = ((DoubleValue) row.getCell(colIdx)).getDoubleValue();
                }
                float medianPos = nrOfRows * 0.5f;
                float lowerQuartilePos = nrOfRows * 0.25f;
                float upperQuartilePos = nrOfRows * 0.75f;
                if (currCountingRow == (int) Math.floor(lowerQuartilePos) + 1) {
                    if (lowerQuartilePos % 1 != 0) {
                        // get the row's value
                        statistic[LOWER_QUARTILE] = ((DoubleValue) row.getCell(colIdx)).getDoubleValue();
                    } else {
                        // calculate the mean between row and last row
                        double value = ((DoubleValue) row.getCell(colIdx)).getDoubleValue();
                        statistic[LOWER_QUARTILE] = (value + lastValue) / 2;
                    }
                }
                if (currCountingRow == (int) Math.floor(medianPos) + 1) {
                    if (medianPos % 1 != 0) {
                        // get the row's value
                        statistic[MEDIAN] = ((DoubleValue) row.getCell(colIdx)).getDoubleValue();
                    } else {
                        // calculate the mean between row and last row
                        double value = ((DoubleValue) row.getCell(colIdx)).getDoubleValue();
                        statistic[MEDIAN] = (value + lastValue) / 2;
                    }
                }
                if (currCountingRow == (int) Math.floor(upperQuartilePos) + 1) {
                    if (upperQuartilePos % 1 != 0) {
                        // get the row's value
                        statistic[UPPER_QUARTILE] = ((DoubleValue) row.getCell(colIdx)).getDoubleValue();
                    } else {
                        // calculate the mean between row and last row
                        double value = ((DoubleValue) row.getCell(colIdx)).getDoubleValue();
                        statistic[UPPER_QUARTILE] = (value + lastValue) / 2;
                    }
                }
                lastValue = ((DoubleValue) row.getCell(colIdx)).getDoubleValue();
                currRowAbsolute++;
                currCountingRow++;
            }
            double iqr = statistic[UPPER_QUARTILE] - statistic[LOWER_QUARTILE];
            Map<Double, Set<RowKey>> mild = new LinkedHashMap<Double, Set<RowKey>>();
            Map<Double, Set<RowKey>> extreme = new LinkedHashMap<Double, Set<RowKey>>();
            // per default the whiskers are at min and max
            double[] whiskers = new double[] { statistic[MIN], statistic[MAX] };
            if (statistic[MIN] < (statistic[LOWER_QUARTILE] - (1.5 * iqr)) || statistic[MAX] > statistic[UPPER_QUARTILE] + (1.5 * iqr)) {
                detectOutliers(sorted, iqr, new double[] { statistic[LOWER_QUARTILE], statistic[UPPER_QUARTILE] }, mild, extreme, whiskers, colIdx);
            }
            statistic[LOWER_WHISKER] = whiskers[0];
            statistic[UPPER_WHISKER] = whiskers[1];
            m_mildOutliers.put(colSpec.getName(), mild);
            m_extremeOutliers.put(colSpec.getName(), extreme);
            m_statistics.put(colSpec, statistic);
        }
        colIdx++;
    }
    DataContainer container = createOutputTable(exec, outputColSpecs);
    // return a data array with just one row but with the data table spec
    // for the column selection panel
    m_array = new DefaultDataArray(table, 1, 2);
    return new BufferedDataTable[] { exec.createBufferedDataTable(container.getTable(), exec) };
}

Example 70 with ExecutionContext

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

the class BoxplotCalculator method calculateMultipleConditional.

/**
 * Calculates statistics for a conditional box plot.
 * @param table the data table
 * @param catCol the column with the category values
 * @param numCol the numeric column
 * @param exec an execution context
 * @return A linked hash map with BoxplotStatistics for each category
 * @throws CanceledExecutionException when the user cancels the execution
 * @throws InvalidSettingsException when the category column has no domain values
 */
public LinkedHashMap<String, LinkedHashMap<String, BoxplotStatistics>> calculateMultipleConditional(final BufferedDataTable table, final String catCol, final String[] numCol, final ExecutionContext exec) throws CanceledExecutionException, InvalidSettingsException {
    DataTableSpec spec = table.getSpec();
    int catColIdx = spec.findColumnIndex(catCol);
    int[] numColIdxs = new int[numCol.length];
    for (int i = 0; i < numCol.length; i++) {
        numColIdxs[i] = spec.findColumnIndex(numCol[i]);
    }
    Set<DataCell> valuesSet = spec.getColumnSpec(catColIdx).getDomain().getValues();
    if (valuesSet == null) {
        throw new InvalidSettingsException("Selected category column has no domain values");
    }
    ArrayList<DataCell> vals = new ArrayList<>(valuesSet);
    Collections.sort(vals, new Comparator<DataCell>() {

        @Override
        public int compare(final DataCell o1, final DataCell o2) {
            return o1.toString().compareTo(o2.toString());
        }
    });
    // add Missing values class as it is never in specification
    vals.add(new MissingCell(null));
    // we need to have clear names, otherwise Missing values class will be taken as "?"
    ArrayList<String> catNames = new ArrayList<>(vals.size());
    for (DataCell cell : vals) {
        catNames.add(cell.isMissing() ? MISSING_VALUES_CLASS : cell.toString());
    }
    LinkedHashMap<String, LinkedHashMap<String, DataContainer>> containers = new LinkedHashMap<>();
    m_ignoredMissVals = new LinkedHashMap<>();
    for (int i = 0; i < numCol.length; i++) {
        LinkedHashMap<String, DataContainer> map = new LinkedHashMap<>();
        LinkedHashMap<String, Long> missValMap = new LinkedHashMap<>();
        for (DataCell c : vals) {
            String name = c.isMissing() ? MISSING_VALUES_CLASS : c.toString();
            map.put(name, exec.createDataContainer(new DataTableSpec(new String[] { "col" }, new DataType[] { DoubleCell.TYPE })));
            missValMap.put(name, 0L);
        }
        containers.put(numCol[i], map);
        m_ignoredMissVals.put(numCol[i], missValMap);
    }
    ExecutionContext subExec = exec.createSubExecutionContext(0.7);
    // long[][] ignoredMissVals = new long[numCol.length][vals.size()];  // count missing values per data col per class
    long count = 0;
    final long numOfRows = table.size();
    for (DataRow row : table) {
        exec.checkCanceled();
        subExec.setProgress(count++ / (double) numOfRows);
        DataCell catCell = row.getCell(catColIdx);
        String catName = catCell.isMissing() ? MISSING_VALUES_CLASS : catCell.toString();
        for (int i = 0; i < numCol.length; i++) {
            DataCell cell = row.getCell(numColIdxs[i]);
            if (!cell.isMissing()) {
                containers.get(numCol[i]).get(catName).addRowToTable(new DefaultRow(row.getKey(), cell));
            } else {
                // increment missing values
                LinkedHashMap<String, Long> missValMap = m_ignoredMissVals.get(numCol[i]);
                missValMap.replace(catName, missValMap.get(catName) + 1);
            }
        }
    }
    LinkedHashMap<String, LinkedHashMap<String, BoxplotStatistics>> statsMap = new LinkedHashMap<>();
    excludedClasses = new LinkedHashMap<>();
    List<String> colList = Arrays.asList(numCol);
    ExecutionContext subExec2 = exec.createSubExecutionContext(1.0);
    int count2 = 0;
    for (Entry<String, LinkedHashMap<String, DataContainer>> entry : containers.entrySet()) {
        exec.checkCanceled();
        subExec2.setProgress(count2++ / (double) containers.size());
        LinkedHashMap<String, DataContainer> containers2 = entry.getValue();
        LinkedHashMap<String, BoxplotStatistics> colStats = new LinkedHashMap<String, BoxplotStatistics>();
        String colName = entry.getKey();
        List<String> excludedColClassesList = new ArrayList<>();
        LinkedHashMap<String, Long> ignoredColMissVals = new LinkedHashMap<>();
        for (Entry<String, DataContainer> entry2 : containers2.entrySet()) {
            Set<Outlier> extremeOutliers = new HashSet<Outlier>();
            Set<Outlier> mildOutliers = new HashSet<Outlier>();
            entry2.getValue().close();
            String catName = entry2.getKey();
            BufferedDataTable catTable = (BufferedDataTable) entry2.getValue().getTable();
            LinkedHashMap<String, Long> missValMap = m_ignoredMissVals.get(colName);
            if (catTable.size() == 0) {
                if (!(catName.equals(MISSING_VALUES_CLASS) && missValMap.get(catName) == 0)) {
                    // we should add missing values to this list, only if they were there
                    excludedColClassesList.add(catName);
                }
                missValMap.remove(catName);
                continue;
            } else {
                if (missValMap.get(catName) == 0) {
                    missValMap.remove(catName);
                }
            }
            SortedTable st = new SortedTable(catTable, new Comparator<DataRow>() {

                @Override
                public int compare(final DataRow o1, final DataRow o2) {
                    double d1 = ((DoubleValue) o1.getCell(0)).getDoubleValue();
                    double d2 = ((DoubleValue) o2.getCell(0)).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));
                }
            }
            colStats.put(catName, new BoxplotStatistics(mildOutliers, extremeOutliers, min, max, lowerWhisker, q1, median, q3, upperWhisker));
        }
        statsMap.put(colName, colStats);
        // missing values part
        String[] excludedColClasses = excludedColClassesList.toArray(new String[excludedColClassesList.size()]);
        excludedClasses.put(colName, excludedColClasses);
    }
    return statsMap;
}