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java.lang.Object cern.colt.PersistentObject hep.aida.bin.AbstractBin hep.aida.bin.AbstractBin1D hep.aida.bin.StaticBin1D hep.aida.bin.MightyStaticBin1D hep.aida.bin.QuantileBin1D
1dimensional nonrebinnable bin holding double elements with scalable quantile operations defined upon; Using little main memory, quickly computes approximate quantiles over very large data sequences with and even without apriori knowledge of the number of elements to be filled; Conceptually a strongly lossily compressed multiset (or bag); Guarantees to respect the worst case approximation error specified upon instance construction. First see the package summary and javadoc tree view to get the broad picture.
Motivation and Problem:
Intended to help scale applications requiring quantile computation.
Quantile computation on very large data sequences is problematic, for the following reasons:
Computing quantiles requires sorting the data sequence.
To sort a data sequence the entire data sequence needs to be available.
Thus, data cannot be thrown away during filling (as done by static bins like StaticBin1D
and MightyStaticBin1D
).
It needs to be kept, either in main memory or on disk.
There is often not enough main memory available.
Thus, during filling data needs to be streamed onto disk.
Sorting disk resident data is prohibitively time consuming.
As a consequence, traditional methods either need very large memories (like DynamicBin1D
) or time consuming disk based sorting.
This class proposes to efficiently solve the problem, at the expense of producing approximate rather than exact results. It can deal with infinitely many elements without resorting to disk. The main memory requirements are smaller than for any other known approximate technique by an order of magnitude. They get even smaller if an upper limit on the maximum number of elements ever to be added is known apriori.
Approximation error: The approximation guarantees are parametrizable and explicit but probabilistic, and apply for arbitrary value distributions and arrival distributions of the data sequence. In other words, this class guarantees to respect the worst case approximation error specified upon instance construction to a certain probability. Of course, if it is specified that the approximation error should not exceed some number very close to zero, this class will potentially consume just as much memory as any of the traditional exact techniques would do. However, for errors larger than 10^{5}, its memory requirements are modest, as shown by the table below.
Main memory requirements: Given in megabytes, assuming a single element (double) takes 8 byte. The number of elements required is then MB*1024*1024/8.
Parameters:
Required main memory
[MB]


#quantiles 
epsilon

delta  N unknown  N known  
N_{max}=inf  N_{max}=10^{6}  N_{max}=10^{7}  N_{max}=10^{8}  N_{max}=inf  N_{max}=10^{6}  N_{max}=10^{7}  N_{max}=10^{8}  
any 
0

any  infinity  7.6  76  762  infinity  7.6  76  762  
any 
10^{ 1}

0  infinity  0.003  0.005  0.006  0.03  0.003  0.005  0.006  
10^{ 2}

0.02  0.03  0.05  0.31  0.02  0.03  0.05  
10^{ 3}

0.12  0.2  0.3  2.7  0.12  0.2  0.3  
10^{ 4}

0.6  1.2  2.1  26.9  0.6  1.2  2.1  
10^{ 5}

2.5  6.4  11.6  205  2.5  6.4  11.6  
10^{ 6}

7.6  25.4  63.6  1758  7.6  25.4  63.6  
100 
10^{ 2}

10^{ 1}  0.033  0.021  0.03  0.03  0.020  0.020  0.020  0.020  
10^{ 5}  0.038  0.021  0.03  0.04  0.024  0.020  0.020  0.020  
10^{ 3}

10^{ 1}  0.48  0.12  0.2  0.3  0.32  0.12  0.2  0.3  
10^{ 5}  0.54  0.12  0.2  0.3  0.37  0.12  0.2  0.3  
10^{ 4}

10^{ 1}  6.6  0.6  1.2  2.1  4.6  0.6  1.2  2.1  
10^{ 5}  7.2  0.6  1.2  2.1  5.2  0.6  1.2  2.1  
10^{ 5}

10^{ 1}  86  2.5  6.4  11.6  63  2.5  6.4  11.6  
10^{ 5}  94  2.5  6.4  11.6  70  2.5  6.4  11.6  
10000 
10^{ 2}

10^{ 1}  0.04  0.02  0.03  0.04  0.02  0.02  0.02  0.02  
10^{ 5}  0.04  0.02  0.03  0.04  0.03  0.02  0.03  0.03  
10^{ 3}

10^{ 1}  0.52  0.12  0.21  0.3  0.35  0.12  0.21  0.3  
10^{ 5}  0.56  0.12  0.21  0.3  0.38  0.12  0.21  0.3  
10^{ 4}

10^{ 1}  7.0  0.64  1.2  2.1  5.0  0.64  1.2  2.1  
10^{ 5}  7.5  0.64  1.2  2.1  5.4  0.64  1.2  2.1  
10^{ 5}

10^{ 1}  90  2.5  6.4  11.6  67  2.5  6.4  11.6  
10^{ 5}  96  2.5  6.4  11.6  71  2.5  6.4  11.6  
#quantiles  epsilon  delta  N_{max}=inf  N_{max}=10^{6}  N_{max}=10^{7}  N_{max}=10^{8}  N_{max}=inf  N_{max}=10^{6}  N_{max}=10^{7}  N_{max}=10^{8}  
N unknown  N known  
Required main memory [MB] 
Implementation:
After: Gurmeet Singh Manku, Sridhar Rajagopalan and Bruce G. Lindsay, Random Sampling Techniques for Space Efficient Online Computation of Order Statistics of Large Datasets. Proc. of the 1999 ACM SIGMOD Int. Conf. on Management of Data, Paper available here.
and
Gurmeet Singh Manku, Sridhar Rajagopalan and Bruce G. Lindsay, Approximate Medians and other Quantiles in One Pass and with Limited Memory, Proc. of the 1998 ACM SIGMOD Int. Conf. on Management of Data, Paper available here.
The broad picture is as follows. Two concepts are used: Shrinking and Sampling. Shrinking takes a data sequence, sorts it and produces a shrinked data sequence by picking every kth element and throwing away all the rest. The shrinked data sequence is an approximation to the original data sequence.
Imagine a large data sequence (residing on disk or being generated in memory on the fly) and a main memory block of n=b*k elements (b is the number of buffers, k is the number of elements per buffer). Fill elements from the data sequence into the block until it is full or the data sequence is exhausted. When the block (or a subset of buffers) is full and the data sequence is not exhausted, apply shrinking to lossily compress a number of buffers into one single buffer. Repeat these steps until all elements of the data sequence have been consumed. Now the block is a shrinked approximation of the original data sequence. Treating it as if it would be the original data sequence, we can determine quantiles in main memory.
Now, the whole thing boils down to the question of: Can we choose b and k (the number of buffers and the buffer size) such that b*k is minimized, yet quantiles determined upon the block are guaranteed to be away from the true quantiles no more than some epsilon? It turns out, we can. It also turns out that the required main memory block size n=b*k is usually moderate (see the table above).
The theme can be combined with random sampling to further reduce main memory requirements, at the expense of probabilistic guarantees. Sampling filters the data sequence and feeds only selected elements to the algorithm outlined above. Sampling is turned on or off, depending on the parametrization.
This quick overview does not go into important details, such as assigning proper weights to buffers, how to choose subsets of buffers to shrink, etc. For more information consult the papers cited above.
Time Performance:
Performance  
Quantiles  Epsilon  Delta  Filling
[#elements/sec] 
Quantile
computation [#quantiles/sec] 

N
unknown, N_{max}=inf 
N
known, N_{max}=10^{7} 
N
unknown, N_{max}=inf 
N
known, N_{max}=10^{7} 

10^{4}  10 ^{ 1}  10 ^{ 1} 
1600000 
1300000  250000  130000  
10 ^{ 2}  360000  1200000  50000  20000  
10 ^{ 3}  150000  200000  3600  3000  
10 ^{ 4}  120000  170000  80  1000 
cern.jet.stat.quantile
,
Serialized FormField Summary 
Fields inherited from class cern.colt.PersistentObject 
serialVersionUID 
Constructor Summary  
QuantileBin1D(boolean known_N,
long N,
double epsilon,
double delta,
int quantiles,
RandomEngine randomGenerator)
Equivalent to new QuantileBin1D(known_N, N, epsilon, delta, quantiles, randomGenerator, false, false, 2). 

QuantileBin1D(boolean known_N,
long N,
double epsilon,
double delta,
int quantiles,
RandomEngine randomGenerator,
boolean hasSumOfLogarithms,
boolean hasSumOfInversions,
int maxOrderForSumOfPowers)
Constructs and returns an empty bin that, under the given constraints, minimizes the amount of memory needed. 

QuantileBin1D(double epsilon)
Equivalent to new QuantileBin1D(false, Long.MAX_VALUE, epsilon, 0.001, 10000, new cern.jet.random.engine.DRand(new java.util.Date()). 
Method Summary  
void 
addAllOfFromTo(DoubleArrayList list,
int from,
int to)
Adds the part of the specified list between indexes from (inclusive) and to (inclusive) to the receiver. 
void 
clear()
Removes all elements from the receiver. 
Object 
clone()
Returns a deep copy of the receiver. 
String 
compareWith(AbstractBin1D other)
Computes the deviations from the receiver's measures to another bin's measures. 
double 
median()
Returns the median. 
double 
quantile(double phi)
Computes and returns the phiquantile. 
double 
quantileInverse(double element)
Returns how many percent of the elements contained in the receiver are <= element. 
DoubleArrayList 
quantiles(DoubleArrayList phis)
Returns the quantiles of the specified percentages. 
int 
sizeOfRange(double minElement,
double maxElement)
Returns how many elements are contained in the range [minElement,maxElement]. 
MightyStaticBin1D[] 
splitApproximately(DoubleArrayList percentages,
int k)
Divides (rebins) a copy of the receiver at the given percentage boundaries into bins and returns these bins, such that each bin approximately reflects the data elements of its range. 
MightyStaticBin1D[] 
splitApproximately(IAxis axis,
int k)
Divides (rebins) a copy of the receiver at the given interval boundaries into bins and returns these bins, such that each bin approximately reflects the data elements of its range. 
String 
toString()
Returns a String representation of the receiver. 
Methods inherited from class hep.aida.bin.MightyStaticBin1D 
geometricMean, getMaxOrderForSumOfPowers, getMinOrderForSumOfPowers, harmonicMean, hasSumOfInversions, hasSumOfLogarithms, hasSumOfPowers, kurtosis, moment, product, skew, sumOfInversions, sumOfLogarithms, sumOfPowers 
Methods inherited from class hep.aida.bin.StaticBin1D 
add, isRebinnable, max, min, size, sum, sumOfSquares 
Methods inherited from class hep.aida.bin.AbstractBin1D 
addAllOf, buffered, equals, mean, rms, standardDeviation, standardError, trimToSize, variance 
Methods inherited from class hep.aida.bin.AbstractBin 
center, center, error, error, offset, offset, value, value 
Methods inherited from class java.lang.Object 
getClass, hashCode, notify, notifyAll, wait, wait, wait 
Constructor Detail 
public QuantileBin1D(double epsilon)
public QuantileBin1D(boolean known_N, long N, double epsilon, double delta, int quantiles, RandomEngine randomGenerator)
public QuantileBin1D(boolean known_N, long N, double epsilon, double delta, int quantiles, RandomEngine randomGenerator, boolean hasSumOfLogarithms, boolean hasSumOfInversions, int maxOrderForSumOfPowers)
known_N
 specifies whether the number of elements over which quantiles are to be computed is known or not.
N
 if known_N==true, the number of elements over which quantiles are to be computed.
if known_N==false, the upper limit on the number of elements over which quantiles are to be computed.
In other words, the maximum number of elements ever to be added.
If such an upper limit is apriori unknown, then set N = Long.MAX_VALUE.
epsilon
 the approximation error which is guaranteed not to be exceeded (e.g. 0.001) (0 <= epsilon <= 1).
To get exact rather than approximate quantiles, set epsilon=0.0;
delta
 the allowed probability that the actual approximation error exceeds epsilon (e.g. 0.0001) (0 <= delta <= 1).
To avoid probabilistic answers, set delta=0.0.
For example, delta = 0.0001 is equivalent to a confidence of 99.99%.
quantiles
 the number of quantiles to be computed (e.g. 100) (quantiles >= 1).
If unknown in advance, set this number large, e.g. quantiles >= 10000.
hasSumOfLogarithms
 Tells whether MightyStaticBin1D.sumOfLogarithms()
can return meaningful results.
Set this parameter to false if measures of sum of logarithms, geometric mean and product are not required.
hasSumOfInversions
 Tells whether MightyStaticBin1D.sumOfInversions()
can return meaningful results.
Set this parameter to false if measures of sum of inversions, harmonic mean and sumOfPowers(1) are not required.
maxOrderForSumOfPowers
 The maximum order k for which MightyStaticBin1D.sumOfPowers(int)
can return meaningful results.
Set this parameter to at least 3 if the skew is required, to at least 4 if the kurtosis is required.
In general, if moments are required set this parameter at least as large as the largest required moment.
This method always substitutes Math.max(2,maxOrderForSumOfPowers) for the parameter passed in.
Thus, sumOfPowers(0..2) always returns meaningful results.Method Detail 
public void addAllOfFromTo(DoubleArrayList list, int from, int to)
addAllOfFromTo
in class MightyStaticBin1D
list
 the list of which elements shall be added.from
 the index of the first element to be added (inclusive).to
 the index of the last element to be added (inclusive).
IndexOutOfBoundsException
 if list.size()>0 && (from<0  from>to  to>=list.size()).public void clear()
clear
in class StaticBin1D
public Object clone()
clone
in class MightyStaticBin1D
public String compareWith(AbstractBin1D other)
compareWith
in class MightyStaticBin1D
other
 the other bin to compare with
public double median()
public double quantile(double phi)
phi
 the percentage for which the quantile is to be computed.
phi must be in the interval (0.0,1.0].
public double quantileInverse(double element)
public DoubleArrayList quantiles(DoubleArrayList phis)
quantile(double)
various times.
public int sizeOfRange(double minElement, double maxElement)
minElement
 the minimum element to search for.maxElement
 the maximum element to search for.
public MightyStaticBin1D[] splitApproximately(DoubleArrayList percentages, int k)
The split(...) methods are particularly well suited for realtime interactive rebinning (the famous "scrolling slider" effect).
Passing equidistant percentages like (0.0, 0.2, 0.4, 0.6, 0.8, 1.0) into this method will yield bins of an equidepth histogram, i.e. a histogram with bin boundaries adjusted such that each bin contains the same number of elements, in this case 20% each. Equidepth histograms can be useful if, for example, not enough properties of the data to be captured are known apriori to be able to define reasonable bin boundaries (partitions). For example, when guesses about minimas and maximas are strongly unreliable. Or when chances are that by focussing too much on one particular area other important areas and characters of a data set may be missed.
Implementation:
The receiver is divided into s = percentages.size()1 intervals (bins). For each interval I, its minimum and maximum elements are determined based upon quantile computation. Further, each interval I is split into k equipercentdistant subintervals (subbins). In other words, an interval is split into subintervals such that each subinterval contains the same number of elements.
For each subinterval S, its minimum and maximum are determined, again, based upon quantile computation. They yield an approximate arithmetic mean am = (min+max)/2 of the subinterval. A subinterval is treated as if it would contain only elements equal to the mean am. Thus, if the subinterval contains, say, n elements, it is assumed to consist of n mean elements (am,am,...,am). A subinterval's sum of elements, sum of squared elements, sum of inversions, etc. are then approximated using such a sequence of mean elements.
Finally, the statistics measures of an interval I are computed by summing up (integrating) the measures of its subintervals.
Accuracy:
Depending on the accuracy of quantile computation and the number of subintervals per interval (the resolution).
Objects of this class compute exact or approximate quantiles, depending on the parameters used upon instance construction.
Objects of subclasses may always compute exact quantiles, as is the case for DynamicBin1D
.
Most importantly for this class QuantileBin1D, a reasonably small epsilon (e.g. 0.01, perhaps 0.001) should be used upon instance construction.
The confidence parameter delta is less important, you may find delta=0.00001 appropriate.
The larger the resolution, the smaller the approximation error, up to some limit.
Integrating over only a few subintervals per interval will yield very crude approximations.
If the resolution is set to a reasonably large number, say 10..100, more small subintervals are integrated, resulting in more accurate results.
Note that for good accuracy, the number of quantiles computable with the given approximation guarantees should upon instance construction be specified, so as to satisfy
quantiles > resolution * (percentages.size()1)
Example:
resolution=2, percentList = (0.0, 0.1, 0.2, 0.5, 0.9, 1.0) means the receiver is to be split into 5 bins:
The statistics measures for each bin are to be computed at a resolution of 2 subbins per bin. Thus, the statistics measures of a bin are the integrated measures over 2 subbins, each containing the same amount of elements:
Lets concentrate on the subbins of bin 0.
Assume the entire data set consists of N=100 elements.
Finally, the statistics measures of bin 0 are computed by summing up (integrating) the measures of its subintervals: Bin 0 has a size of N*(10%0%)=10 elements (we knew that already), sum of 1625+2250=3875, sum of squares of 528125+1012500=1540625, sum of inversions of 0.015+0.01=0.025, etc. From these follow other measures such as mean=3875/10=387.5, rms = sqrt(1540625 / 10)=392.5, etc. The other bins are computes analogously.
percentages
 the percentage boundaries at which the receiver shall be split.public MightyStaticBin1D[] splitApproximately(IAxis axis, int k)
splitApproximately(DoubleArrayList,int)
do the real work.
axis
 an axis defining interval boundaries.public String toString()
toString
in class MightyStaticBin1D

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