Examples with DataColumnDomainCreator org.knime.core.data.DataColumnDomainCreator used on opensource projects

Example 1 with DataColumnDomainCreator

use of org.knime.core.data.DataColumnDomainCreator in project knime-core by knime.

the class PMMLRuleEditorNodeModel method createRearranger.

/**
 * Creates the {@link ColumnRearranger} that can compute the new column.
 *
 * @param tableSpec The spec of the input table.
 * @param ruleSet The {@link RuleSet} xml object where the rules should be added.
 * @param parser The parser for the rules.
 * @return The {@link ColumnRearranger}.
 * @throws ParseException Problem during parsing.
 * @throws InvalidSettingsException if settings are invalid
 */
private ColumnRearranger createRearranger(final DataTableSpec tableSpec, final RuleSet ruleSet, final PMMLRuleParser parser) throws ParseException, InvalidSettingsException {
    if (m_settings.isAppendColumn() && m_settings.getNewColName().isEmpty()) {
        throw new InvalidSettingsException("No name for prediction column provided");
    }
    Set<String> outcomes = new LinkedHashSet<String>();
    List<DataType> outcomeTypes = new ArrayList<DataType>();
    int line = 0;
    final List<Pair<PMMLPredicate, Expression>> rules = new ArrayList<Pair<PMMLPredicate, Expression>>();
    for (String ruleText : m_settings.rules()) {
        ++line;
        if (RuleSupport.isComment(ruleText)) {
            continue;
        }
        try {
            ParseState state = new ParseState(ruleText);
            PMMLPredicate expression = parser.parseBooleanExpression(state);
            SimpleRule simpleRule = ruleSet.addNewSimpleRule();
            setCondition(simpleRule, expression);
            state.skipWS();
            state.consumeText("=>");
            state.skipWS();
            Expression outcome = parser.parseOutcomeOperand(state, null);
            // Only constants are allowed in the outcomes.
            assert outcome.isConstant() : outcome;
            rules.add(new Pair<PMMLPredicate, Expression>(expression, outcome));
            outcomeTypes.add(outcome.getOutputType());
            simpleRule.setScore(outcome.toString());
            // simpleRule.setConfidence(confidenceForRule(simpleRule, line, ruleText));
            simpleRule.setWeight(weightForRule(simpleRule, line, ruleText));
            outcomes.add(simpleRule.getScore());
        } catch (ParseException e) {
            throw Util.addContext(e, ruleText, line);
        }
    }
    DataType outcomeType = RuleEngineNodeModel.computeOutputType(outcomeTypes, true);
    ColumnRearranger rearranger = new ColumnRearranger(tableSpec);
    DataColumnSpecCreator specProto = new DataColumnSpecCreator(m_settings.isAppendColumn() ? DataTableSpec.getUniqueColumnName(tableSpec, m_settings.getNewColName()) : m_settings.getReplaceColumn(), outcomeType);
    specProto.setDomain(new DataColumnDomainCreator(toCells(outcomes, outcomeType)).createDomain());
    SingleCellFactory cellFactory = new SingleCellFactory(true, specProto.createSpec()) {

        @Override
        public DataCell getCell(final DataRow row) {
            for (Pair<PMMLPredicate, Expression> pair : rules) {
                if (pair.getFirst().evaluate(row, tableSpec) == Boolean.TRUE) {
                    return pair.getSecond().evaluate(row, null).getValue();
                }
            }
            return DataType.getMissingCell();
        }
    };
    if (m_settings.isAppendColumn()) {
        rearranger.append(cellFactory);
    } else {
        rearranger.replace(cellFactory, m_settings.getReplaceColumn());
    }
    return rearranger;
}

Example 2 with DataColumnDomainCreator

use of org.knime.core.data.DataColumnDomainCreator in project knime-core by knime.

the class PMMLRuleSetPredictorNodeModel method createRearranger.

/**
 * Constructs the {@link ColumnRearranger} for computing the new columns.
 *
 * @param obj The {@link PMMLPortObject} of the preprocessing model.
 * @param spec The {@link DataTableSpec} of the table.
 * @param replaceColumn Should replace the {@code outputColumnName}?
 * @param outputColumnName The output column name (which might be an existing).
 * @param addConfidence Should add the confidence values to a column?
 * @param confidenceColumnName The name of the confidence column.
 * @param validationColumnIdx Index of the validation column, {@code -1} if not specified.
 * @param processConcurrently Should be {@code false} when the statistics are to be computed.
 * @return The {@link ColumnRearranger} computing the result.
 * @throws InvalidSettingsException Problem with rules.
 */
private static ColumnRearranger createRearranger(final PMMLPortObject obj, final DataTableSpec spec, final boolean replaceColumn, final String outputColumnName, final boolean addConfidence, final String confidenceColumnName, final int validationColumnIdx, final boolean processConcurrently) throws InvalidSettingsException {
    List<Node> models = obj.getPMMLValue().getModels(PMMLModelType.RuleSetModel);
    if (models.size() != 1) {
        throw new InvalidSettingsException("Expected exactly on RuleSetModel, but got: " + models.size());
    }
    final PMMLRuleTranslator translator = new PMMLRuleTranslator();
    obj.initializeModelTranslator(translator);
    if (!translator.isScorable()) {
        throw new UnsupportedOperationException("The model is not scorable.");
    }
    final List<PMMLRuleTranslator.Rule> rules = translator.getRules();
    ColumnRearranger ret = new ColumnRearranger(spec);
    final List<DataColumnSpec> targetCols = obj.getSpec().getTargetCols();
    final DataType dataType = targetCols.isEmpty() ? StringCell.TYPE : targetCols.get(0).getType();
    DataColumnSpecCreator specCreator = new DataColumnSpecCreator(outputColumnName, dataType);
    Set<DataCell> outcomes = new LinkedHashSet<>();
    for (Rule rule : rules) {
        DataCell outcome;
        if (dataType.equals(BooleanCell.TYPE)) {
            outcome = BooleanCellFactory.create(rule.getOutcome());
        } else if (dataType.equals(StringCell.TYPE)) {
            outcome = new StringCell(rule.getOutcome());
        } else if (dataType.equals(DoubleCell.TYPE)) {
            try {
                outcome = new DoubleCell(Double.parseDouble(rule.getOutcome()));
            } catch (NumberFormatException e) {
                // ignore
                continue;
            }
        } else if (dataType.equals(IntCell.TYPE)) {
            try {
                outcome = new IntCell(Integer.parseInt(rule.getOutcome()));
            } catch (NumberFormatException e) {
                // ignore
                continue;
            }
        } else if (dataType.equals(LongCell.TYPE)) {
            try {
                outcome = new LongCell(Long.parseLong(rule.getOutcome()));
            } catch (NumberFormatException e) {
                // ignore
                continue;
            }
        } else {
            throw new UnsupportedOperationException("Unknown outcome type: " + dataType);
        }
        outcomes.add(outcome);
    }
    specCreator.setDomain(new DataColumnDomainCreator(outcomes).createDomain());
    DataColumnSpec colSpec = specCreator.createSpec();
    final RuleSelectionMethod ruleSelectionMethod = translator.getSelectionMethodList().get(0);
    final String defaultScore = translator.getDefaultScore();
    final Double defaultConfidence = translator.getDefaultConfidence();
    final DataColumnSpec[] specs;
    if (addConfidence) {
        specs = new DataColumnSpec[] { new DataColumnSpecCreator(DataTableSpec.getUniqueColumnName(ret.createSpec(), confidenceColumnName), DoubleCell.TYPE).createSpec(), colSpec };
    } else {
        specs = new DataColumnSpec[] { colSpec };
    }
    final int oldColumnIndex = replaceColumn ? ret.indexOf(outputColumnName) : -1;
    ret.append(new AbstractCellFactory(processConcurrently, specs) {

        private final List<String> m_values;

        {
            Map<String, List<String>> dd = translator.getDataDictionary();
            m_values = dd.get(targetCols.get(0).getName());
        }

        /**
         * {@inheritDoc}
         */
        @Override
        public DataCell[] getCells(final DataRow row) {
            // See http://www.dmg.org/v4-1/RuleSet.html#Rule
            switch(ruleSelectionMethod.getCriterion().intValue()) {
                case RuleSelectionMethod.Criterion.INT_FIRST_HIT:
                    {
                        Pair<DataCell, Double> resultAndConfidence = selectFirstHit(row);
                        return toCells(resultAndConfidence);
                    }
                case RuleSelectionMethod.Criterion.INT_WEIGHTED_MAX:
                    {
                        Pair<DataCell, Double> resultAndConfidence = selectWeightedMax(row);
                        return toCells(resultAndConfidence);
                    }
                case RuleSelectionMethod.Criterion.INT_WEIGHTED_SUM:
                    {
                        Pair<DataCell, Double> resultAndConfidence = selectWeightedSum(row);
                        return toCells(resultAndConfidence);
                    }
                default:
                    throw new UnsupportedOperationException(ruleSelectionMethod.getCriterion().toString());
            }
        }

        /**
         * Converts the pair to a {@link DataCell} array.
         *
         * @param resultAndConfidence The {@link Pair}.
         * @return The result and possibly the confidence.
         */
        private DataCell[] toCells(final Pair<DataCell, Double> resultAndConfidence) {
            if (!addConfidence) {
                return new DataCell[] { resultAndConfidence.getFirst() };
            }
            if (resultAndConfidence.getSecond() == null) {
                return new DataCell[] { DataType.getMissingCell(), resultAndConfidence.getFirst() };
            }
            return new DataCell[] { new DoubleCell(resultAndConfidence.getSecond()), resultAndConfidence.getFirst() };
        }

        /**
         * Computes the result and the confidence using the weighted sum method.
         *
         * @param row A {@link DataRow}
         * @return The result and the confidence.
         */
        private Pair<DataCell, Double> selectWeightedSum(final DataRow row) {
            final Map<String, Double> scoreToSumWeight = new LinkedHashMap<String, Double>();
            for (String val : m_values) {
                scoreToSumWeight.put(val, 0.0);
            }
            int matchedRuleCount = 0;
            for (final PMMLRuleTranslator.Rule rule : rules) {
                if (rule.getCondition().evaluate(row, spec) == Boolean.TRUE) {
                    ++matchedRuleCount;
                    Double sumWeight = scoreToSumWeight.get(rule.getOutcome());
                    if (sumWeight == null) {
                        throw new IllegalStateException("The score value: " + rule.getOutcome() + " is not in the data dictionary.");
                    }
                    final Double wRaw = rule.getWeight();
                    final double w = wRaw == null ? 0.0 : wRaw.doubleValue();
                    scoreToSumWeight.put(rule.getOutcome(), sumWeight + w);
                }
            }
            double maxSumWeight = Double.NEGATIVE_INFINITY;
            String bestScore = null;
            for (Entry<String, Double> entry : scoreToSumWeight.entrySet()) {
                final double d = entry.getValue().doubleValue();
                if (d > maxSumWeight) {
                    maxSumWeight = d;
                    bestScore = entry.getKey();
                }
            }
            if (bestScore == null || matchedRuleCount == 0) {
                return pair(result(defaultScore), defaultConfidence);
            }
            return pair(result(bestScore), maxSumWeight / matchedRuleCount);
        }

        /**
         * Helper method to create {@link Pair}s.
         *
         * @param f The first element.
         * @param s The second element.
         * @return The new pair.
         */
        private <F, S> Pair<F, S> pair(final F f, final S s) {
            return new Pair<F, S>(f, s);
        }

        /**
         * Computes the result and the confidence using the weighted max method.
         *
         * @param row A {@link DataRow}
         * @return The result and the confidence.
         */
        private Pair<DataCell, Double> selectWeightedMax(final DataRow row) {
            double maxWeight = Double.NEGATIVE_INFINITY;
            PMMLRuleTranslator.Rule bestRule = null;
            for (final PMMLRuleTranslator.Rule rule : rules) {
                if (rule.getCondition().evaluate(row, spec) == Boolean.TRUE) {
                    if (rule.getWeight() > maxWeight) {
                        maxWeight = rule.getWeight();
                        bestRule = rule;
                    }
                }
            }
            if (bestRule == null) {
                return pair(result(defaultScore), defaultConfidence);
            }
            bestRule.setRecordCount(bestRule.getRecordCount() + 1);
            DataCell result = result(bestRule);
            if (validationColumnIdx >= 0) {
                if (row.getCell(validationColumnIdx).equals(result)) {
                    bestRule.setNbCorrect(bestRule.getNbCorrect() + 1);
                }
            }
            Double confidence = bestRule.getConfidence();
            return pair(result, confidence == null ? defaultConfidence : confidence);
        }

        /**
         * Selects the outcome of the rule and converts it to the proper outcome type.
         *
         * @param rule A {@link Rule}.
         * @return The {@link DataCell} representing the result. (May be missing.)
         */
        private DataCell result(final PMMLRuleTranslator.Rule rule) {
            String outcome = rule.getOutcome();
            return result(outcome);
        }

        /**
         * Constructs the {@link DataCell} from its {@link String} representation ({@code outcome}) and its type.
         *
         * @param dataType The expected {@link DataType}
         * @param outcome The {@link String} representation.
         * @return The {@link DataCell}.
         */
        private DataCell result(final String outcome) {
            if (outcome == null) {
                return DataType.getMissingCell();
            }
            try {
                if (dataType.isCompatible(BooleanValue.class)) {
                    return BooleanCellFactory.create(outcome);
                }
                if (IntCell.TYPE.isASuperTypeOf(dataType)) {
                    return new IntCell(Integer.parseInt(outcome));
                }
                if (LongCell.TYPE.isASuperTypeOf(dataType)) {
                    return new LongCell(Long.parseLong(outcome));
                }
                if (DoubleCell.TYPE.isASuperTypeOf(dataType)) {
                    return new DoubleCell(Double.parseDouble(outcome));
                }
                return new StringCell(outcome);
            } catch (NumberFormatException e) {
                return new MissingCell(outcome + "\n" + e.getMessage());
            }
        }

        /**
         * Selects the first rule that matches and computes the confidence and result for the {@code row}.
         *
         * @param row A {@link DataRow}.
         * @return The result and the confidence.
         */
        private Pair<DataCell, Double> selectFirstHit(final DataRow row) {
            for (final PMMLRuleTranslator.Rule rule : rules) {
                Boolean eval = rule.getCondition().evaluate(row, spec);
                if (eval == Boolean.TRUE) {
                    rule.setRecordCount(rule.getRecordCount() + 1);
                    DataCell result = result(rule);
                    if (validationColumnIdx >= 0) {
                        if (row.getCell(validationColumnIdx).equals(result)) {
                            rule.setNbCorrect(rule.getNbCorrect() + 1);
                        }
                    }
                    Double confidence = rule.getConfidence();
                    return pair(result, confidence == null ? defaultConfidence : confidence);
                }
            }
            return pair(result(defaultScore), defaultConfidence);
        }

        /**
         * {@inheritDoc}
         */
        @Override
        public void afterProcessing() {
            super.afterProcessing();
            obj.getPMMLValue();
            RuleSetModel ruleSet = translator.getOriginalRuleSetModel();
            assert rules.size() == ruleSet.getRuleSet().getSimpleRuleList().size() + ruleSet.getRuleSet().getCompoundRuleList().size();
            if (ruleSet.getRuleSet().getSimpleRuleList().size() == rules.size()) {
                for (int i = 0; i < rules.size(); ++i) {
                    Rule rule = rules.get(i);
                    final SimpleRule simpleRuleArray = ruleSet.getRuleSet().getSimpleRuleArray(i);
                    synchronized (simpleRuleArray) /*synchronized fixes AP-6766 */
                    {
                        simpleRuleArray.setRecordCount(rule.getRecordCount());
                        if (validationColumnIdx >= 0) {
                            simpleRuleArray.setNbCorrect(rule.getNbCorrect());
                        } else if (simpleRuleArray.isSetNbCorrect()) {
                            simpleRuleArray.unsetNbCorrect();
                        }
                    }
                }
            }
        }
    });
    if (replaceColumn) {
        ret.remove(outputColumnName);
        ret.move(ret.getColumnCount() - 1 - (addConfidence ? 1 : 0), oldColumnIndex);
    }
    return ret;
}

Example 3 with DataColumnDomainCreator

use of org.knime.core.data.DataColumnDomainCreator in project knime-core by knime.

the class StatisticsTable method calculateAllMoments.

/**
 * Calculates <b>all the statistical moments in one pass </b>. After the
 * call of this operation, the statistical moments can be obtained very fast
 * from all the other methods.
 *
 * @param rowCount Row count of table for progress, may be NaN if unknown.
 * @param exec object to check with if user canceled the operation
 * @throws CanceledExecutionException if user canceled
 * @throws IllegalArgumentException if rowCount argument < 0
 */
protected void calculateAllMoments(final double rowCount, final ExecutionMonitor exec) throws CanceledExecutionException {
    if (rowCount < 0.0) {
        throw new IllegalArgumentException("rowCount argument must not < 0: " + rowCount);
    }
    DataTableSpec origSpec = m_table.getDataTableSpec();
    int numOfCols = origSpec.getNumColumns();
    // the number of non-missing cells in each column
    int[] validCount = new int[numOfCols];
    double[] sumsquare = new double[numOfCols];
    final DataValueComparator[] comp = new DataValueComparator[numOfCols];
    for (int i = 0; i < numOfCols; i++) {
        sumsquare[i] = 0.0;
        validCount[i] = 0;
        comp[i] = origSpec.getColumnSpec(i).getType().getComparator();
        assert comp[i] != null;
    }
    int nrRows = 0;
    for (RowIterator rowIt = m_table.iterator(); rowIt.hasNext(); nrRows++) {
        DataRow row = rowIt.next();
        if (exec != null) {
            double prog = Double.isNaN(rowCount) ? 0.0 : nrRows / rowCount;
            exec.setProgress(prog, "Calculating statistics, processing row " + (nrRows + 1) + " (\"" + row.getKey() + "\")");
            // throws exception if user canceled
            exec.checkCanceled();
        }
        for (int c = 0; c < numOfCols; c++) {
            final DataCell cell = row.getCell(c);
            if (!(cell.isMissing())) {
                // keep the min and max for each column
                if ((m_minValues[c] == null) || (comp[c].compare(cell, m_minValues[c]) < 0)) {
                    m_minValues[c] = cell;
                }
                if ((m_maxValues[c] == null) || (comp[c].compare(m_maxValues[c], cell) < 0)) {
                    m_maxValues[c] = cell;
                }
                // for double columns we calc the sum (for the mean calc)
                DataType type = origSpec.getColumnSpec(c).getType();
                if (type.isCompatible(DoubleValue.class)) {
                    double d = ((DoubleValue) cell).getDoubleValue();
                    if (Double.isNaN(m_sum[c])) {
                        m_sum[c] = d;
                    } else {
                        m_sum[c] += d;
                    }
                    sumsquare[c] += d * d;
                    validCount[c]++;
                }
            } else {
                m_missingValueCnt[c]++;
            }
        }
        calculateMomentInSubClass(row);
    }
    m_nrRows = nrRows;
    for (int j = 0; j < numOfCols; j++) {
        // missing values
        if (validCount[j] == 0 || m_minValues[j] == null) {
            DataCell mc = DataType.getMissingCell();
            m_minValues[j] = mc;
            m_maxValues[j] = mc;
            m_meanValues[j] = Double.NaN;
            m_varianceValues[j] = Double.NaN;
        } else {
            m_meanValues[j] = m_sum[j] / validCount[j];
            if (validCount[j] > 1) {
                m_varianceValues[j] = (sumsquare[j] - ((m_sum[j] * m_sum[j]) / validCount[j])) / (validCount[j] - 1);
            } else {
                m_varianceValues[j] = 0.0;
            }
            // round-off errors resulting in negative variance values
            if (m_varianceValues[j] < 0.0 && m_varianceValues[j] > -1.0E8) {
                m_varianceValues[j] = 0.0;
            }
            assert m_varianceValues[j] >= 0.0 : "Variance cannot be negative (column \"" + origSpec.getColumnSpec(j).getName() + "\": " + m_varianceValues[j];
        }
    }
    // compute resulting table spec
    int nrCols = m_table.getDataTableSpec().getNumColumns();
    DataColumnSpec[] cSpec = new DataColumnSpec[nrCols];
    for (int c = 0; c < nrCols; c++) {
        DataColumnSpec s = m_table.getDataTableSpec().getColumnSpec(c);
        // we create domains with our bounds.
        Set<DataCell> values = (s.getDomain() == null ? null : s.getDomain().getValues());
        DataColumnDomain newDomain = new DataColumnDomainCreator(values, (m_minValues[c] == null || m_minValues[c].isMissing()) ? null : m_minValues[c], (m_maxValues[c] == null || m_maxValues[c].isMissing()) ? null : m_maxValues[c]).createDomain();
        DataColumnSpecCreator creator = new DataColumnSpecCreator(s);
        creator.setDomain(newDomain);
        cSpec[c] = creator.createSpec();
    }
    m_tSpec = new DataTableSpec(cSpec);
}

Example 4 with DataColumnDomainCreator

use of org.knime.core.data.DataColumnDomainCreator in project knime-core by knime.

the class DecTreePredictorNodeModel method createOutTableSpec.

private DataTableSpec createOutTableSpec(final PortObjectSpec[] inSpecs) {
    LinkedList<DataCell> predValues = null;
    if (m_showDistribution.getBooleanValue()) {
        predValues = getPredictionValues((PMMLPortObjectSpec) inSpecs[INMODELPORT]);
        if (predValues == null) {
            // no out spec can be determined
            return null;
        }
    }
    int numCols = (predValues == null ? 0 : predValues.size()) + 1;
    DataTableSpec inSpec = (DataTableSpec) inSpecs[INDATAPORT];
    UniqueNameGenerator nameGenerator = new UniqueNameGenerator(inSpec);
    DataColumnSpec[] newCols = new DataColumnSpec[numCols];
    /* Set bar renderer and domain [0,1] as default for the double cells
         * containing the distribution */
    // DataColumnProperties propsRendering = new DataColumnProperties(
    // Collections.singletonMap(
    // DataValueRenderer.PROPERTY_PREFERRED_RENDERER,
    // DoubleBarRenderer.DESCRIPTION));
    DataColumnDomain domain = new DataColumnDomainCreator(new DoubleCell(0.0), new DoubleCell(1.0)).createDomain();
    // add all distribution columns
    for (int i = 0; i < numCols - 1; i++) {
        DataColumnSpecCreator colSpecCreator = nameGenerator.newCreator(predValues.get(i).toString(), DoubleCell.TYPE);
        // colSpecCreator.setProperties(propsRendering);
        colSpecCreator.setDomain(domain);
        newCols[i] = colSpecCreator.createSpec();
    }
    // add the prediction column
    newCols[numCols - 1] = nameGenerator.newColumn("Prediction (DecTree)", StringCell.TYPE);
    DataTableSpec newColSpec = new DataTableSpec(newCols);
    return new DataTableSpec(inSpec, newColSpec);
}

Example 5 with DataColumnDomainCreator

use of org.knime.core.data.DataColumnDomainCreator in project knime-core by knime.

the class AutoBinner method calcDomainBoundsIfNeccessary.

/**
 * Determines the per column min/max values of the given data if not already
 * present in the domain.
 * @param data the data
 * @param exec the execution context
 * @param recalcValuesFor The columns
 * @return The data with extended domain information
 * @throws InvalidSettingsException
 * @throws CanceledExecutionException
 */
public BufferedDataTable calcDomainBoundsIfNeccessary(final BufferedDataTable data, final ExecutionContext exec, final List<String> recalcValuesFor) throws InvalidSettingsException, CanceledExecutionException {
    if (null == recalcValuesFor || recalcValuesFor.isEmpty()) {
        return data;
    }
    List<Integer> valuesI = new ArrayList<Integer>();
    for (String colName : recalcValuesFor) {
        DataColumnSpec colSpec = data.getDataTableSpec().getColumnSpec(colName);
        if (!colSpec.getType().isCompatible(DoubleValue.class)) {
            throw new InvalidSettingsException("Can only process numeric " + "data. The column \"" + colSpec.getName() + "\" is not numeric.");
        }
        if (recalcValuesFor.contains(colName) && !colSpec.getDomain().hasBounds()) {
            valuesI.add(data.getDataTableSpec().findColumnIndex(colName));
        }
    }
    if (valuesI.isEmpty()) {
        return data;
    }
    Map<Integer, Double> min = new HashMap<Integer, Double>();
    Map<Integer, Double> max = new HashMap<Integer, Double>();
    for (int col : valuesI) {
        min.put(col, Double.MAX_VALUE);
        max.put(col, Double.MIN_VALUE);
    }
    int c = 0;
    for (DataRow row : data) {
        c++;
        exec.checkCanceled();
        exec.setProgress(c / (double) data.getRowCount());
        for (int col : valuesI) {
            double val = ((DoubleValue) row.getCell(col)).getDoubleValue();
            if (min.get(col) > val) {
                min.put(col, val);
            }
            if (max.get(col) < val) {
                min.put(col, val);
            }
        }
    }
    List<DataColumnSpec> newColSpecList = new ArrayList<DataColumnSpec>();
    int cc = 0;
    for (DataColumnSpec columnSpec : data.getDataTableSpec()) {
        if (recalcValuesFor.contains(columnSpec.getName())) {
            DataColumnSpecCreator specCreator = new DataColumnSpecCreator(columnSpec);
            DataColumnDomainCreator domainCreator = new DataColumnDomainCreator(new DoubleCell(min.get(cc)), new DoubleCell(max.get(cc)));
            specCreator.setDomain(domainCreator.createDomain());
            DataColumnSpec newColSpec = specCreator.createSpec();
            newColSpecList.add(newColSpec);
        } else {
            newColSpecList.add(columnSpec);
        }
        cc++;
    }
    DataTableSpec spec = new DataTableSpec(newColSpecList.toArray(new DataColumnSpec[0]));
    BufferedDataTable newDataTable = exec.createSpecReplacerTable(data, spec);
    return newDataTable;
}