Java tutorial
/** * Copyright 2013 BigML * Licensed under the Apache License, Version 2.0 * http://www.apache.org/licenses/LICENSE-2.0 */ package org.streaminer.stream.histogram.spdt; import java.text.DecimalFormat; import java.util.ArrayList; import java.util.List; import java.util.Arrays; import java.util.Collection; import java.util.HashMap; import java.util.Map.Entry; import java.util.TreeMap; import org.json.simple.JSONArray; /** * Implements a Histogram as defined by the <a * href="http://jmlr.csail.mit.edu/papers/v11/ben-haim10a.html"> * Streaming Parallel Decision Tree (SPDT)</a> algorithm. <p>The * Histogram consumes numeric points and maintains a running * approximation of the dataset using the given number of bins. The * methods <code>insert</code>, <code>sum</code>, and * <code>uniform</code> are described in detail in the SPDT paper. * * <p>The histogram has an <code>insert</code> method which uses two * parameters and an <code>extendedSum</code> method which add the * capabilities described in <a * href="http://research.engineering.wustl.edu/~tyrees/Publications_files/fr819-tyreeA.pdf"> * Tyree's paper</a>. Along with Tyree's extension this histogram * supports inserts with categorical targets. * * @author Adam Ashenfelter (ashenfelter@bigml.com) */ public class Histogram<T extends Target> { public static final String DEFAULT_FORMAT_STRING = "#.#####"; public static final int RESERVOIR_THRESHOLD = 256; /** * Creates an empty Histogram with the defined number of bins. * * @param maxBins the maximum number of bins for this histogram * @param countWeightedGaps true if count weighted gaps are desired * @param categories if the histogram uses categorical targets * then a collection of the possible category targets may be * provided to increase performance * @param groups if the histogram uses a group target * then a collection group target types may be provided * @param freezeThreshold after this # of inserts, bin locations * will 'freeze', increasing the performance of future inserts * @param reservoirType selects the bin reservoir implementation, * defaults to 'array' when # bins < 256 and 'tree' otherwise */ public Histogram(int maxBins, boolean countWeightedGaps, Collection<Object> categories, Collection<TargetType> groupTypes, Long freezeThreshold, BinReservoirType reservoirType) { if (reservoirType == BinReservoirType.tree || (reservoirType == null && maxBins > RESERVOIR_THRESHOLD)) { _bins = new TreeBinReservoir<T>(maxBins, countWeightedGaps, freezeThreshold); } else { _bins = new ArrayBinReservoir<T>(maxBins, countWeightedGaps, freezeThreshold); } _decimalFormat = new DecimalFormat(DEFAULT_FORMAT_STRING); _missingCount = 0; _minimum = null; _maximum = null; if (categories != null && !categories.isEmpty()) { _targetType = TargetType.categorical; _groupTypes = null; _indexMap = new HashMap<Object, Integer>(); for (Object category : categories) { if (_indexMap.get(category) == null) { _indexMap.put(category, _indexMap.size()); } } } else if (groupTypes != null && !groupTypes.isEmpty()) { _targetType = TargetType.group; _groupTypes = new ArrayList<TargetType>(groupTypes); } else { _groupTypes = null; _indexMap = null; } } /** * Creates an empty Histogram with the defined number of bins. * * @param maxBins the maximum number of bins for this histogram * @param countWeightedGaps true if count weighted gaps are desired */ public Histogram(int maxBins, boolean countWeightedGaps) { this(maxBins, countWeightedGaps, null, null, null, null); } /** * Creates an empty Histogram with the defined number of bins. * * @param maxBins the maximum number of bins for this histogram */ public Histogram(int maxBins) { this(maxBins, false); } /** * Inserts a new point into the histogram. * The histogram returns itself after modification. * * @param point the new point */ public Histogram<T> insert(Double point) throws MixedInsertException { checkType(TargetType.none); processPointTarget(point, SimpleTarget.TARGET); return this; } /** * Inserts a new point with a numeric target into the histogram. * The histogram returns itself after modification. * * @param point the new point * @param target the numeric target */ public Histogram<T> insert(Double point, double target) throws MixedInsertException { return insertNumeric(point, target); } /** * Inserts a new point with a categorical target into the histogram. * The histogram returns itself after modification. * * @param point the new point * @param target the categorical target */ public Histogram<T> insert(Double point, String target) throws MixedInsertException { return insertCategorical(point, target); } /** * Inserts a new point with a group of targets into the histogram. * A null group target is _not_ allowed. * The histogram returns itself after modification. * * @param point the new point * @param target the group targets */ public Histogram<T> insert(Double point, Collection<Object> group) throws MixedInsertException { return insertGroup(point, group); } /** * Inserts a new point with a categorical target into the histogram. * Null target values are allowed. * The histogram returns itself after modification. * * @param point the new point * @param target the categorical target */ public Histogram<T> insertCategorical(Double point, Object target) throws MixedInsertException { checkType(TargetType.categorical); Target catTarget; if (_indexMap == null) { catTarget = new MapCategoricalTarget(target); } else { catTarget = new ArrayCategoricalTarget(_indexMap, target); } processPointTarget(point, catTarget); return this; } /** * Inserts a new point with a numeric target into the histogram. * Null target values are allowed. * The histogram returns itself after modification. * * @param point the new point * @param target the categorical target */ public Histogram<T> insertNumeric(Double point, Double target) throws MixedInsertException { checkType(TargetType.numeric); processPointTarget(point, new NumericTarget(target)); return this; } /** * Inserts a new point with a group target into the histogram. * A null group target is _not_ allowed. * The histogram returns itself after modification. * * @param point the new point * @param target the categorical target */ public Histogram<T> insertGroup(Double point, Collection<Object> group) throws MixedInsertException { checkType(TargetType.group); if (group == null) { throw new MixedInsertException(); } GroupTarget groupTarget = new GroupTarget(group, _groupTypes); if (_groupTypes == null) { _groupTypes = new ArrayList<TargetType>(); for (Target t : groupTarget.getGroupTarget()) { _groupTypes.add(t.getTargetType()); } } processPointTarget(point, groupTarget); return this; } /** * Inserts a new bin into the histogram. * The histogram returns itself after modification. * * @param bin the new bin */ public Histogram<T> insertBin(Bin<T> bin) { if (_minimum == null || _minimum > bin.getMean()) { _minimum = bin.getMean(); } if (_maximum == null || _maximum < bin.getMean()) { _maximum = bin.getMean(); } clearCacheMaps(); _bins.insert(bin); _bins.merge(); return this; } /** * Returns the target type for the histogram */ public TargetType getTargetType() { return _targetType; } /** * Returns the target types for a group histogram */ public ArrayList<TargetType> getGroupTypes() { return _groupTypes; } /** * Returns the maximum number of allowed bins. */ public int getMaxBins() { return _bins.getMaxBins(); } /** * Returns the freeze threshold. */ public Long getFreezeThreshold() { return _bins.getFreezeThreshold(); } /** * Returns whether gaps are count weighted. */ public boolean isCountWeightedGaps() { return _bins.isWeightGaps(); } /** * Returns the categories for an array-backed * categorical histogram */ public List<Object> getTargetCategories() { List<Object> categories = null; if (_indexMap != null) { Object[] catArray = new Object[_indexMap.size()]; for (Entry<Object, Integer> entry : _indexMap.entrySet()) { catArray[entry.getValue()] = entry.getKey(); } categories = Arrays.asList(catArray); } return categories; } /** * Returns the approximate number of points less than * <code>p</code>. * * @param p the sum point */ public double sum(double p) throws SumOutOfRangeException { return extendedSum(p).getCount(); } /** * Returns a <code>SumResult</code> object which contains the * approximate number of points less than <code>p</code> along * with the sum of their targets. * * @param p the sum point */ public SumResult<T> extendedSum(double p) throws SumOutOfRangeException { SumResult<T> result; if (_bins.getBins().isEmpty()) { throw new SumOutOfRangeException("Cannot sum with an empty histogram."); } if (Double.isNaN(p)) { throw new SumOutOfRangeException("Cannot compute a histogram sum for NaN"); } double binMax = _bins.last().getMean(); if (p < _minimum) { result = new SumResult<T>(0, (T) _bins.first().getTarget().init()); } else if (p >= _maximum) { result = new SumResult<T>(getTotalCount(), getTotalTargetSum()); } else if (p == binMax) { Bin<T> lastBin = _bins.last(); double totalCount = this.getTotalCount(); double count = totalCount - (lastBin.getCount() / 2d); T targetSum = (T) getTotalTargetSum().sum(lastBin.getTarget().clone().mult(-0.5d)); result = new SumResult<T>(count, targetSum); } else { T emptyTarget = (T) _bins.first().getTarget().init(); Bin<T> bin_i = _bins.floor(p); if (bin_i == null) { bin_i = new Bin(_minimum, 0, emptyTarget.clone()); } Bin<T> bin_i1 = _bins.higher(p); if (bin_i1 == null) { bin_i1 = new Bin(_maximum, 0, emptyTarget.clone()); } double prevCount; T prevTargetSum; if (bin_i.getMean() == _minimum) { prevCount = _bins.first().getCount() / 2; prevTargetSum = (T) _bins.first().getTarget().clone().mult(0.5); } else { SumResult<T> prevSumResult = getPointToSumMap().get(bin_i.getMean()); prevCount = prevSumResult.getCount(); prevTargetSum = prevSumResult.getTargetSum(); } double bDiff = p - bin_i.getMean(); double pDiff = bin_i1.getMean() - bin_i.getMean(); double bpRatio = bDiff / pDiff; NumericTarget countTarget = (NumericTarget) computeSum(bpRatio, new NumericTarget(prevCount), new NumericTarget(bin_i.getCount()), new NumericTarget(bin_i1.getCount())); double countSum = countTarget.getSum(); T targetSum = (T) computeSum(bpRatio, prevTargetSum, bin_i.getTarget(), bin_i1.getTarget()); result = new SumResult<T>(countSum, targetSum); } return result; } /** * Returns the density estimate at point * <code>p</code>. * * @param p the density estimate point */ public double density(double p) { return extendedDensity(p).getCount(); } /** * Returns a <code>SumResult</code> object which contains the * density estimate at the point <code>p</code> along * with the density for the targets. * * @param p the density estimate point */ public SumResult<T> extendedDensity(double p) { T emptyTarget = (T) _bins.first().getTarget().init(); double countDensity; T targetDensity; Bin<T> exact = _bins.get(p); if (p < _minimum || p > _maximum) { countDensity = 0; targetDensity = (T) emptyTarget.clone(); } else if (p == _minimum && p == _maximum) { countDensity = Double.POSITIVE_INFINITY; targetDensity = emptyTarget; } else if (exact != null) { double higher = Math.nextAfter(p, Double.POSITIVE_INFINITY); double lower = Math.nextAfter(p, Double.NEGATIVE_INFINITY); SumResult<T> lowerResult = extendedDensity(lower); SumResult<T> higherResult = extendedDensity(higher); countDensity = (lowerResult.getCount() + higherResult.getCount()) / 2; targetDensity = (T) lowerResult.getTargetSum().clone().sum(higherResult.getTargetSum()).mult(0.5); } else { Bin<T> lowerBin = _bins.lower(p); if (lowerBin == null) { lowerBin = new Bin(_minimum, 0, emptyTarget.clone()); } Bin<T> higherBin = _bins.higher(p); if (higherBin == null) { higherBin = new Bin(_maximum, 0, emptyTarget.clone()); } double bDiff = p - lowerBin.getMean(); double pDiff = higherBin.getMean() - lowerBin.getMean(); double bpRatio = bDiff / pDiff; NumericTarget countTarget = (NumericTarget) computeDensity(bpRatio, lowerBin.getMean(), higherBin.getMean(), new NumericTarget(lowerBin.getCount()), new NumericTarget(higherBin.getCount())); countDensity = countTarget.getSum(); targetDensity = (T) computeDensity(bpRatio, lowerBin.getMean(), higherBin.getMean(), lowerBin.getTarget(), higherBin.getTarget()); } return new SumResult<T>(countDensity, targetDensity); } /** * Returns a <code>Target</code> object representing the * average (or expected) target value for point <code>p</code>. * * @param p the density estimate point */ public T averageTarget(double p) { SumResult<T> density = extendedDensity(p); return (T) density.getTargetSum().mult(1 / density.getCount()); } /** * Returns a list containing split points that form bins with * uniform membership. * * @param numberOfBins the desired number of uniform bins */ public ArrayList<Double> uniform(int numberOfBins) { ArrayList<Double> uniformBinSplits = new ArrayList<Double>(); double totalCount = getTotalCount(); if (totalCount > 0) { double gapSize = totalCount / (double) numberOfBins; double minGapSize = Math.max(_bins.first().getCount(), _bins.last().getCount()) / 2; int splits = numberOfBins; if (gapSize < minGapSize) { splits = (int) (totalCount / minGapSize); gapSize = totalCount / (double) splits; } for (int i = 1; i < splits; i++) { double targetSum = (double) i * gapSize; double binSplit = findPointForSum(targetSum); uniformBinSplits.add(binSplit); } } return uniformBinSplits; } /** * Returns a map of percentiles and their associated locations. * * @param percentiles the desired percentiles */ public HashMap<Double, Double> percentiles(Double... percentiles) { HashMap<Double, Double> results = new HashMap<Double, Double>(); double totalCount = getTotalCount(); if (totalCount > 0) { for (double percentile : percentiles) { double targetSum = (double) percentile * totalCount; results.put(percentile, findPointForSum(targetSum)); } } return results; } /** * Merges a histogram into the current histogram. * The histogram returns itself after modification. * * @param histogram the histogram to be merged */ public Histogram merge(Histogram<T> histogram) throws MixedInsertException { if (_indexMap == null && histogram._indexMap != null) { if (getBins().isEmpty()) { _indexMap = histogram._indexMap; } else { throw new MixedInsertException(); } } if (_indexMap != null && !_indexMap.equals(histogram._indexMap)) { throw new MixedInsertException(); } else if (!histogram.getBins().isEmpty()) { checkType(histogram.getTargetType()); for (Bin<T> bin : histogram.getBins()) { Bin<T> newBin = new Bin<T>(bin); if (_indexMap != null) { ((ArrayCategoricalTarget) newBin.getTarget()).setIndexMap(_indexMap); } _bins.insert(new Bin<T>(bin)); } _bins.merge(); } if (_minimum == null) { _minimum = histogram.getMinimum(); } else if (histogram.getMinimum() != null) { _minimum = Math.min(_minimum, histogram.getMinimum()); } if (_maximum == null) { _maximum = histogram.getMaximum(); } else if (histogram.getMaximum() != null) { _maximum = Math.max(_maximum, histogram.getMaximum()); } if (_missingTarget == null) { _missingTarget = (T) histogram.getMissingTarget(); } else if (histogram.getMissingTarget() != null) { _missingTarget.sum(histogram.getMissingTarget()); } _missingCount += histogram.getMissingCount(); return this; } /** * Returns the total number of points in the histogram. */ public double getTotalCount() { return _bins.getTotalCount(); } /** * Returns the collection of bins that form the histogram. */ public Collection<Bin<T>> getBins() { return _bins.getBins(); } public JSONArray toJSON(DecimalFormat format) { JSONArray bins = new JSONArray(); for (Bin<T> bin : getBins()) { bins.add(bin.toJSON(format)); } return bins; } public String toJSONString(DecimalFormat format) { return toJSON(format).toJSONString(); } @Override public String toString() { return toJSONString(_decimalFormat); } /* * Returns the total sum of the targets for each bin, * returns nil if there are no bins in the histogram. */ public T getTotalTargetSum() { if (_bins.getBins().isEmpty()) { return null; } else { return getPointToSumMap().get(_maximum).getTargetSum(); } } public long getMissingCount() { return _missingCount; } public T getMissingTarget() { return _missingTarget; } /** * Inserts count and target information for missing inputs. * The histogram returns itself after modification. * * @param count the number of missing values * @param count the target sum for the missing values */ public Histogram<T> insertMissing(long count, T target) { if (_missingTarget == null) { _missingTarget = (T) target; } else { _missingTarget.sum(target); } _missingCount += count; return this; } /** * Returns the minimum value inserted into the histogram. */ public Double getMinimum() { return _minimum; } /** * Returns the maximum value inserted into the histogram. */ public Double getMaximum() { return _maximum; } /** * Sets the minimum input value for the histogram. This * method should only be used for histograms created * by inserting pre-existing bins. * * @param minimum the minimum value observed by the histogram */ public Histogram setMinimum(Double minimum) { _minimum = minimum; return this; } /** * Sets the maximum input value for the histogram. This * method should only be used for histograms created * by inserting pre-existing bins. * * @param maximum the maximum value observed by the histogram */ public Histogram setMaximum(Double maximum) { _maximum = maximum; return this; } private void checkType(TargetType newType) throws MixedInsertException { if (_targetType == null) { _targetType = newType; } else if (_targetType != newType || newType == null) { throw new MixedInsertException(); } } private void processPointTarget(Double point, Target target) { if (point == null) { insertMissing(1, (T) target); } else { insertBin(new Bin(point, 1, target)); } } private void clearCacheMaps() { _sumToBinMap = null; _pointToSumMap = null; } private void refreshCacheMaps() { T emptyTarget = (T) _bins.first().getTarget().init(); _pointToSumMap = new TreeMap<Double, SumResult<T>>(); _pointToSumMap.put(_minimum, new SumResult<T>(0d, emptyTarget)); _sumToBinMap = new TreeMap<Double, Bin<T>>(); Bin<T> minBin = new Bin(_minimum, 0d, emptyTarget); Bin<T> maxBin = new Bin(_maximum, 0d, emptyTarget); _sumToBinMap.put(0d, minBin); _sumToBinMap.put((double) getTotalCount(), maxBin); SumResult<T> sum = new SumResult<T>(0d, (T) emptyTarget.init()); Bin<T> lastBin = minBin; for (Bin<T> bin : getBins()) { sum = new SumResult<T>(sum.getCount() + (bin.getCount() + lastBin.getCount()) / 2, (T) sum.getTargetSum().clone().sum(bin.getTarget().clone().sum(lastBin.getTarget()).mult(0.5))); _sumToBinMap.put(sum.getCount(), bin); _pointToSumMap.put(bin.getMean(), sum); lastBin = bin; } SumResult<T> lastSumResult = new SumResult<T>(sum.getCount() + lastBin.getCount() / 2, (T) sum.getTargetSum().clone().sum(lastBin.getTarget().clone().mult(0.5))); _pointToSumMap.put(_maximum, lastSumResult); } private TreeMap<Double, Bin<T>> getSumToBinMap() { if (_sumToBinMap == null) { refreshCacheMaps(); } return _sumToBinMap; } private TreeMap<Double, SumResult<T>> getPointToSumMap() { if (_pointToSumMap == null) { refreshCacheMaps(); } return _pointToSumMap; } /* * Deriving the sum in terms of p, r, i, and i1 * starting from the Ben-Haim paper: * m = i + (i1 - i) * r * s = p + i/2 + (m + i) * r/2 * p' = p + i/2 (our prev value includes i/2) * s = p' + (i + (i1 - i) * r + i) * r/2 * s = p' + (i + r*i1 - r*i + i) * r/2 * s = p' + r/2*i + r^2/2*i1 - r^2/2*i + r/2*i * s = p' + r/2*i + r/2*i - r^2/2*i + r^2/2*i1 * s = p' + r*i - r^2/2*i + r^2/2*i1 * s = p' + (r - r^2/2)*i + r^2/2*i1 */ private <U extends Target> Target computeSum(double r, U p, U i, U i1) { double i1Term = 0.5 * r * r; double iTerm = r - i1Term; return (U) p.clone().sum(i.clone().mult(iTerm)).sum(i1.clone().mult(i1Term)); } /* * Finding the density starting from the sum * s = p + (1/2 + r - r^2/2)*i + r^2/2*i1 * r = (x - m) / (m1 - m) * s_dx = i - (i1 - i) * (x - m) / (m1 - m) */ private <U extends Target> Target computeDensity(double r, double m, double m1, U i, U i1) { return i.clone().sum(i1.clone().sum(i.clone().mult(-1)).mult(r)).mult(1 / (m1 - m)); } private double findPointForSum(double s) { double result; if (s <= 0) { result = _minimum; } else if (s >= _bins.getTotalCount()) { result = _maximum; } else { Entry<Double, Bin<T>> sumEntry = getSumToBinMap().floorEntry(s); double sumP_i = sumEntry.getKey(); Bin<T> bin_i = sumEntry.getValue(); double p_i = bin_i.getMean(); double m_i = bin_i.getCount(); Double sumP_i1 = getSumToBinMap().navigableKeySet().higher(sumP_i); Bin<T> bin_i1 = getSumToBinMap().get(sumP_i1); double p_i1 = bin_i1.getMean(); double m_i1 = bin_i1.getCount(); double d = s - sumP_i; double a = m_i1 - m_i; double u; if (a == 0) { double offset = d / ((m_i + m_i1) / 2); u = p_i + (offset * (p_i1 - p_i)); } else { double b = 2 * m_i; double c = -2 * d; double z = findZ(a, b, c); u = (p_i + (p_i1 - p_i) * z); } result = u; } return result; } private static Double findZ(double a, double b, double c) { Double resultRoot = null; ArrayList<Double> candidateRoots = solveQuadratic(a, b, c); for (Double candidateRoot : candidateRoots) { if (candidateRoot >= 0 && candidateRoot <= 1) { resultRoot = candidateRoot; break; } } return resultRoot; } /* * Simple quadratic solver - doesn't handle edge cases */ private static ArrayList<Double> solveQuadratic(double a, double b, double c) { double discriminantSquareRoot = Math.sqrt(Math.pow(b, 2) - (4 * a * c)); ArrayList<Double> roots = new ArrayList<Double>(); roots.add((-b + discriminantSquareRoot) / (2 * a)); roots.add((-b - discriminantSquareRoot) / (2 * a)); return roots; } public enum BinReservoirType { tree, array }; public enum TargetType { none, numeric, categorical, group, histogram }; private TargetType _targetType; private final BinReservoir<T> _bins; private final DecimalFormat _decimalFormat; private ArrayList<TargetType> _groupTypes; private HashMap<Object, Integer> _indexMap; private long _missingCount; private T _missingTarget; private Double _minimum; private Double _maximum; private TreeMap<Double, Bin<T>> _sumToBinMap; private TreeMap<Double, SumResult<T>> _pointToSumMap; }