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

Example 1 with IntCell

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

the class TSAverageHandler method getMean.

private DataCell getMean() {
    if (m_previous instanceof IntValue) {
        // get an int, create an int
        double mean = (((DoubleValue) m_previous).getDoubleValue() + ((DoubleValue) m_next).getDoubleValue()) * 0.5;
        return new IntCell((int) Math.round(mean));
    }
    if (m_previous instanceof LongValue) {
        // get an int, create an int
        double mean = (((DoubleValue) m_previous).getDoubleValue() + ((DoubleValue) m_next).getDoubleValue()) * 0.5;
        return new LongCell(Math.round(mean));
    }
    if (m_previous instanceof DoubleValue) {
        // get an double, create an double
        double mean = (((DoubleValue) m_previous).getDoubleValue() + ((DoubleValue) m_next).getDoubleValue()) * 0.5;
        return new DoubleCell(mean);
    }
    if (m_previous instanceof DateAndTimeValue) {
        // get an int, create an int
        DateAndTimeValue dataCell1 = (DateAndTimeValue) m_previous;
        DateAndTimeValue dataCell2 = ((DateAndTimeValue) m_next);
        boolean hasDate = dataCell1.hasDate() | dataCell2.hasDate();
        boolean hasTime = dataCell1.hasTime() | dataCell2.hasTime();
        boolean hasMilis = dataCell1.hasMillis() | dataCell2.hasMillis();
        double d = dataCell1.getUTCTimeInMillis() + dataCell2.getUTCTimeInMillis();
        d *= 0.5;
        return new DateAndTimeCell((long) d, hasDate, hasTime, hasMilis);
    }
    return DataType.getMissingCell();
}

Example 2 with IntCell

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

the class TSLinearHandler method getLinearInterpolation.

private DataCell getLinearInterpolation(final double stepnumber, final double stepcount) {
    if (m_previous instanceof DoubleValue || m_previous instanceof IntValue || m_previous instanceof LongValue) {
        double prev = ((DoubleValue) m_previous).getDoubleValue();
        double next = ((DoubleValue) m_next).getDoubleValue();
        double lin = prev + 1.0 * stepnumber / (1.0 * stepcount) * (next - prev);
        if (m_previous instanceof IntValue) {
            // get an int, create an int
            return new IntCell((int) Math.round(lin));
        }
        if (m_previous instanceof LongValue) {
            // get an long, create an long
            return new LongCell(Math.round(lin));
        }
        return new DoubleCell(lin);
    }
    if (m_previous instanceof DateAndTimeValue) {
        // get an int, create an int
        DateAndTimeValue dataCell1 = (DateAndTimeValue) m_previous;
        DateAndTimeValue dataCell2 = ((DateAndTimeValue) m_next);
        boolean hasDate = dataCell1.hasDate() | dataCell2.hasDate();
        boolean hasTime = dataCell1.hasTime() | dataCell2.hasTime();
        boolean hasMilis = dataCell1.hasMillis() | dataCell2.hasMillis();
        double prev = dataCell1.getUTCTimeInMillis();
        double next = dataCell2.getUTCTimeInMillis();
        double d = prev + stepnumber / stepcount * (next - prev);
        return new DateAndTimeCell((long) d, hasDate, hasTime, hasMilis);
    }
    return DataType.getMissingCell();
}

Example 3 with IntCell

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

the class RuleEngineNodeModel method getRulesOutcome.

/**
 * @param outType
 * @param row
 * @param r
 * @param isDisallowlongOutputForCompatibility TODO
 * @param variableProvider TODO
 * @return
 * @noreference This method is not intended to be referenced by clients.
 */
public static final DataCell getRulesOutcome(final DataType outType, final DataRow row, final List<Rule> rules, final boolean isDisallowLongOutputForCompatibility, final VariableProvider variableProvider) {
    for (Rule r : rules) {
        if (r.getCondition().matches(row, variableProvider).getOutcome() == MatchState.matchedAndStop) {
            Outcome outcome2 = r.getOutcome();
            // r.getSideEffect().perform(row, this);
            DataCell cell = (DataCell) outcome2.getComputedResult(row, variableProvider);
            // ... don't want Booleans (also implementing Long), for instance)
            if (cell instanceof LongCell && isDisallowLongOutputForCompatibility) {
                long l = ((LongValue) cell).getLongValue();
                if (l > Integer.MAX_VALUE) {
                    throw new RuntimeException("Values larger than " + Integer.MAX_VALUE + " not supported in old instances of the node -- recreate the node " + "(node was created using an KNIME version < 3.2");
                }
                cell = new IntCell((int) l);
            }
            if (outType.equals(StringCell.TYPE) && !cell.isMissing() && !cell.getType().equals(StringCell.TYPE)) {
                return new StringCell(cell.toString());
            } else {
                return cell;
            }
        }
    }
    return DataType.getMissingCell();
}

Example 4 with IntCell

use of org.knime.core.data.def.IntCell 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 5 with IntCell

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

the class TwoSampleTTestStatistics method getGroupStatistics.

/**
 * Get descriptive statistics for the given Group.
 * @param group the group
 * @return the
 */
public List<DataCell> getGroupStatistics(final Group group) {
    List<DataCell> cells = new ArrayList<DataCell>();
    cells.add(new StringCell(m_column));
    cells.add(new StringCell(m_groups.get(group)));
    SummaryStatistics stats = m_gstats.get(group);
    cells.add(new IntCell((int) stats.getN()));
    cells.add(new IntCell(m_missing.get(group).intValue()));
    cells.add(new IntCell(m_missingGroup.intValue()));
    cells.add(new IntCell(m_ignoredGroup.intValue()));
    cells.add(new DoubleCell(stats.getMean()));
    cells.add(new DoubleCell(stats.getStandardDeviation()));
    cells.add(new DoubleCell(StatsUtil.getStandardError(stats)));
    return cells;
}