Example usage for org.apache.commons.math3.stat.descriptive.moment GeometricMean getResult

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.descriptive.moment GeometricMean getResult.

Prototype

@Override
public double getResult() 

Source Link

Usage

From source file:org.deidentifier.arx.aggregates.StatisticsBuilder.java

/**
 * Returns summary statistics for all attributes.
 * /*from ww  w. jav a  2  s . co m*/
 * @param listwiseDeletion A flag enabling list-wise deletion
 * @return
 */
@SuppressWarnings({ "unchecked", "rawtypes" })
public <T> Map<String, StatisticsSummary<?>> getSummaryStatistics(boolean listwiseDeletion) {

    // Reset stop flag
    interrupt.value = false;

    Map<String, DescriptiveStatistics> statistics = new HashMap<String, DescriptiveStatistics>();
    Map<String, StatisticsSummaryOrdinal> ordinal = new HashMap<String, StatisticsSummaryOrdinal>();
    Map<String, DataScale> scales = new HashMap<String, DataScale>();
    Map<String, GeometricMean> geomean = new HashMap<String, GeometricMean>();

    // Detect scales
    for (int col = 0; col < handle.getNumColumns(); col++) {

        // Meta
        String attribute = handle.getAttributeName(col);
        DataType<?> type = handle.getDataType(attribute);

        // Scale
        DataScale scale = type.getDescription().getScale();

        // Try to replace nominal scale with ordinal scale based on base data type
        if (scale == DataScale.NOMINAL && handle.getGeneralization(attribute) != 0) {
            if (!(handle.getBaseDataType(attribute) instanceof ARXString) && getHierarchy(col, true) != null) {
                scale = DataScale.ORDINAL;
            }
        }

        // Store
        scales.put(attribute, scale);
        statistics.put(attribute, new DescriptiveStatistics());
        geomean.put(attribute, new GeometricMean());
        ordinal.put(attribute, getSummaryStatisticsOrdinal(handle.getGeneralization(attribute),
                handle.getDataType(attribute), handle.getBaseDataType(attribute), getHierarchy(col, true)));
    }

    // Compute summary statistics
    for (int row = 0; row < handle.getNumRows(); row++) {

        // Check, if we should include this row
        boolean include = true;
        if (listwiseDeletion) {
            for (int col = 0; col < handle.getNumColumns(); col++) {
                if (handle.isOutlier(row) || DataType.isNull(handle.getValue(row, col))) {
                    include = false;
                    break;
                }
            }
        }

        // Check
        checkInterrupt();

        // If yes, add
        if (include) {

            // For each column
            for (int col = 0; col < handle.getNumColumns(); col++) {

                // Meta
                String value = handle.getValue(row, col);
                String attribute = handle.getAttributeName(col);
                DataType<?> type = handle.getDataType(attribute);

                // Analyze
                if (!DataType.isAny(value) && !DataType.isNull(value)) {
                    ordinal.get(attribute).addValue(value);
                    if (type instanceof DataTypeWithRatioScale) {
                        double doubleValue = ((DataTypeWithRatioScale) type).toDouble(type.parse(value));
                        statistics.get(attribute).addValue(doubleValue);
                        geomean.get(attribute).increment(doubleValue + 1d);
                    }
                }
            }
        }
    }

    // Convert
    Map<String, StatisticsSummary<?>> result = new HashMap<String, StatisticsSummary<?>>();
    for (int col = 0; col < handle.getNumColumns(); col++) {

        // Check
        checkInterrupt();

        // Depending on scale
        String attribute = handle.getAttributeName(col);
        DataScale scale = scales.get(attribute);
        DataType<T> type = (DataType<T>) handle.getDataType(attribute);
        ordinal.get(attribute).analyze();
        if (scale == DataScale.NOMINAL) {
            StatisticsSummaryOrdinal stats = ordinal.get(attribute);
            result.put(attribute, new StatisticsSummary<T>(DataScale.NOMINAL, stats.getNumberOfMeasures(),
                    stats.getMode(), type.parse(stats.getMode())));
        } else if (scale == DataScale.ORDINAL) {
            StatisticsSummaryOrdinal stats = ordinal.get(attribute);
            result.put(attribute,
                    new StatisticsSummary<T>(DataScale.ORDINAL, stats.getNumberOfMeasures(), stats.getMode(),
                            type.parse(stats.getMode()), stats.getMedian(), type.parse(stats.getMedian()),
                            stats.getMin(), type.parse(stats.getMin()), stats.getMax(),
                            type.parse(stats.getMax())));
        } else if (scale == DataScale.INTERVAL) {
            StatisticsSummaryOrdinal stats = ordinal.get(attribute);
            DescriptiveStatistics stats2 = statistics.get(attribute);
            boolean isPeriod = type.getDescription().getWrappedClass() == Date.class;

            // TODO: Something is wrong with commons math's kurtosis
            double kurtosis = stats2.getKurtosis();
            kurtosis = kurtosis < 0d ? Double.NaN : kurtosis;
            double range = stats2.getMax() - stats2.getMin();
            double stddev = Math.sqrt(stats2.getVariance());

            result.put(attribute, new StatisticsSummary<T>(DataScale.INTERVAL, stats.getNumberOfMeasures(),
                    stats.getMode(), type.parse(stats.getMode()), stats.getMedian(),
                    type.parse(stats.getMedian()), stats.getMin(), type.parse(stats.getMin()), stats.getMax(),
                    type.parse(stats.getMax()), toString(type, stats2.getMean(), false, false),
                    toValue(type, stats2.getMean()), stats2.getMean(),
                    toString(type, stats2.getVariance(), isPeriod, true), toValue(type, stats2.getVariance()),
                    stats2.getVariance(), toString(type, stats2.getPopulationVariance(), isPeriod, true),
                    toValue(type, stats2.getPopulationVariance()), stats2.getPopulationVariance(),
                    toString(type, stddev, isPeriod, false), toValue(type, stddev), stddev,
                    toString(type, range, isPeriod, false), toValue(type, range),
                    stats2.getMax() - stats2.getMin(), toString(type, kurtosis, isPeriod, false),
                    toValue(type, kurtosis), kurtosis));
        } else if (scale == DataScale.RATIO) {
            StatisticsSummaryOrdinal stats = ordinal.get(attribute);
            DescriptiveStatistics stats2 = statistics.get(attribute);
            GeometricMean geo = geomean.get(attribute);

            // TODO: Something is wrong with commons math's kurtosis
            double kurtosis = stats2.getKurtosis();
            kurtosis = kurtosis < 0d ? Double.NaN : kurtosis;
            double range = stats2.getMax() - stats2.getMin();
            double stddev = Math.sqrt(stats2.getVariance());

            result.put(attribute, new StatisticsSummary<T>(DataScale.RATIO, stats.getNumberOfMeasures(),
                    stats.getMode(), type.parse(stats.getMode()), stats.getMedian(),
                    type.parse(stats.getMedian()), stats.getMin(), type.parse(stats.getMin()), stats.getMax(),
                    type.parse(stats.getMax()), toString(type, stats2.getMean(), false, false),
                    toValue(type, stats2.getMean()), stats2.getMean(),
                    toString(type, stats2.getVariance(), false, false), toValue(type, stats2.getVariance()),
                    stats2.getVariance(), toString(type, stats2.getPopulationVariance(), false, false),
                    toValue(type, stats2.getPopulationVariance()), stats2.getPopulationVariance(),
                    toString(type, stddev, false, false), toValue(type, stddev), stddev,
                    toString(type, range, false, false), toValue(type, range), range,
                    toString(type, kurtosis, false, false), toValue(type, kurtosis), kurtosis,
                    toString(type, geo.getResult() - 1d, false, false), toValue(type, geo.getResult() - 1d),
                    stats2.getGeometricMean()));
        }
    }

    return result;
}

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