/**
* Returns a frequency distribution for the values in the given column. The order for string data
* items is derived from the provided hierarchy
*
* @param column The column
* @param hierarchy The hierarchy, may be null
* @return
*/
public StatisticsFrequencyDistribution getFrequencyDistribution(int column, Hierarchy hierarchy) {
// Reset stop flag
interrupt = false;
// Init
String[] values = getDistinctValuesOrdered(column, hierarchy);
double[] frequencies = new double[values.length];
// Create map of indexes
Map<String, Integer> indexes = new HashMap<String, Integer>();
for (int i = 0; i < values.length; i++) {
checkInterrupt();
indexes.put(values[i], i);
}
// Count frequencies
for (int row = 0; row < handle.getNumRows(); row++) {
checkInterrupt();
String value = handle.getValue(row, column);
frequencies[indexes.get(value)]++;
}
// Divide by count
int count = handle.getNumRows();
for (int i = 0; i < frequencies.length; i++) {
checkInterrupt();
frequencies[i] /= (double) count;
}
// Return
return new StatisticsFrequencyDistribution(values, frequencies, count);
}
/**
* Returns summary statistics for all attributes.
*
* @param listwiseDeletion A flag enabling list-wise deletion
* @return
*/
@SuppressWarnings({"unchecked", "rawtypes"})
public <T> Map<String, StatisticsSummary<?>> getSummaryStatistics(boolean listwiseDeletion) {
Map<String, DescriptiveStatistics> statistics = new HashMap<String, DescriptiveStatistics>();
Map<String, StatisticsSummaryOrdinal> ordinal = new HashMap<String, StatisticsSummaryOrdinal>();
Map<String, DataScale> scales = new HashMap<String, DataScale>();
// 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());
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.isSuppressed(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 (!value.equals(handle.getSuppressionString()) && !DataType.isNull(value)) {
ordinal.get(attribute).addValue(value);
if (type instanceof DataTypeWithRatioScale) {
statistics
.get(attribute)
.addValue(((DataTypeWithRatioScale) type).toDouble(type.parse(value)));
}
}
}
}
}
// 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);
// 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, stats2.getGeometricMean(), false, false),
toValue(type, stats2.getGeometricMean()),
stats2.getGeometricMean()));
}
}
return result;
}
/**
* Returns a contingency table for the given columns. The order for string data items is derived
* from the provided hierarchies
*
* @param column1 The first column
* @param hierarchy1 The hierarchy for the first column, may be null
* @param column2 The second column
* @param hierarchy2 The hierarchy for the second column, may be null
* @return
*/
public StatisticsContingencyTable getContingencyTable(
int column1, Hierarchy hierarchy1, int column2, Hierarchy hierarchy2) {
// Reset stop flag
interrupt = false;
// Init
String[] values1 = getDistinctValuesOrdered(column1, hierarchy1);
String[] values2 = getDistinctValuesOrdered(column2, hierarchy2);
// Create maps of indexes
Map<String, Integer> indexes1 = new HashMap<String, Integer>();
for (int i = 0; i < values1.length; i++) {
checkInterrupt();
indexes1.put(values1[i], i);
}
Map<String, Integer> indexes2 = new HashMap<String, Integer>();
for (int i = 0; i < values2.length; i++) {
checkInterrupt();
indexes2.put(values2[i], i);
}
// Create entry set
int max = Integer.MIN_VALUE;
final Map<Entry, Integer> entries = new HashMap<Entry, Integer>();
for (int row = 0; row < handle.getNumRows(); row++) {
checkInterrupt();
int index1 = indexes1.get(handle.getValue(row, column1));
int index2 = indexes2.get(handle.getValue(row, column2));
Entry entry = new Entry(index1, index2);
Integer previous = entries.get(entry);
int value = previous != null ? previous + 1 : 1;
max = Math.max(max, value);
entries.put(entry, value);
}
// Create iterator
final int count = handle.getNumRows();
final Iterator<Entry> internal = entries.keySet().iterator();
final Iterator<Entry> iterator =
new Iterator<Entry>() {
private Map<Entry, Integer> _entries = entries;
private Iterator<Entry> _internal = internal;
@Override
public boolean hasNext() {
if (_internal == null) return false;
boolean result = _internal.hasNext();
// Try to release resources as early as possible
if (!result) {
_internal = null;
_entries = null;
}
return result;
}
@Override
public Entry next() {
if (_internal == null) return null;
Entry e = _internal.next();
e.frequency = (double) _entries.get(e) / (double) count;
return e;
}
@Override
public void remove() {
throw new UnsupportedOperationException();
}
};
// Result result
return new StatisticsContingencyTable(
values1, values2, count, (double) max / (double) count, iterator);
}
