public class SamplingUtils extends Object
|Constructor and Description|
|Modifier and Type||Method and Description|
Returns a sampling rate that guarantees a sample of size >= sampleSizeLowerBound 99.99% of the time.
Reservoir sampling implementation that also returns the input size.
public static <T> scala.Tuple2<Object,Object> reservoirSampleAndCount(scala.collection.Iterator<T> input, int k, long seed, scala.reflect.ClassTag<T> evidence$1)
input- input size
k- reservoir size
seed- random seed
public static double computeFractionForSampleSize(int sampleSizeLowerBound, long total, boolean withReplacement)
How the sampling rate is determined: Let p = num / total, where num is the sample size and total is the total number of datapoints in the RDD. We're trying to compute q > p such that - when sampling with replacement, we're drawing each datapoint with prob_i ~ Pois(q), where we want to guarantee Pr[s < num] < 0.0001 for s = sum(prob_i for i from 0 to total), i.e. the failure rate of not having a sufficiently large sample < 0.0001. Setting q = p + 5 * sqrt(p/total) is sufficient to guarantee 0.9999 success rate for num > 12, but we need a slightly larger q (9 empirically determined). - when sampling without replacement, we're drawing each datapoint with prob_i ~ Binomial(total, fraction) and our choice of q guarantees 1-delta, or 0.9999 success rate, where success rate is defined the same as in sampling with replacement.
The smallest sampling rate supported is 1e-10 (in order to avoid running into the limit of the RNG's resolution).
sampleSizeLowerBound- sample size
total- size of RDD
withReplacement- whether sampling with replacement