SparkMLlib之二Basic Stastics

Summary statistics

We provide column summary statistics for RDD[Vector] through the function colStats available in Statistics.

import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics}

val observations: RDD[Vector] = ... // an RDD of Vectors

// 计算列统计值
val summary: MultivariateStatisticalSummary = Statistics.colStats(observations)
println(summary.mean) //包含每列均值的dense vector
println(summary.variance) // column-wise variance
println(summary.numNonzeros) // number of nonzeros in each column

另外还有

bstract Value Members
abstract def
count: Long
Sample size.
abstract def
max: Vector
Maximum value of each column.
abstract def
mean: Vector
Sample mean vector.
abstract def
min: Vector
Minimum value of each column.
abstract def
normL1: Vector
L1 norm of each column
abstract def
normL2: Vector
Euclidean magnitude of each column
abstract def
numNonzeros: Vector
Number of nonzero elements (including explicitly presented zero values) in each column.
abstract def
variance: Vector
Sample variance vector.

Correlation

import org.apache.spark.SparkContext
import org.apache.spark.mllib.linalg._
import org.apache.spark.mllib.stat.Statistics

val sc: SparkContext = ...

val seriesX: RDD[Double] = ... // a series
val seriesY: RDD[Double] = ... // must have the same number of partitions and cardinality as seriesX

// compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. If a 
// method is not specified, Pearson's method will be used by default. val correlation: Double = Statistics.corr(seriesX, seriesY, "pearson") val data: RDD[Vector] = ... // note that each Vector is a row and not a column // calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's method. // If a method is not specified, Pearson's method will be used by default. 
val correlMatrix: Matrix = Statistics.corr(data, "pearson")

Stratified sampling分层抽样

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.rdd.PairRDDFunctions

val sc: SparkContext = ...

val data = ... // an RDD[(K, V)] of any key value pairs
val fractions: Map[K, Double] = ... // specify the exact fraction desired from each key

// Get an exact sample from each stratum
val approxSample = data.sampleByKey(withReplacement = false, fractions)
val exactSample = data.sampleByKeyExact(withReplacement = false, fractions)

假设检验

import org.apache.spark.SparkContext
import org.apache.spark.mllib.linalg._
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.stat.Statistics._

val sc: SparkContext = ...

val vec: Vector = ... // a vector composed of the frequencies of events

// compute the goodness of fit. If a second vector to test against is not supplied as a parameter, 
// the test runs against a uniform distribution.  
val goodnessOfFitTestResult = Statistics.chiSqTest(vec)
println(goodnessOfFitTestResult) // summary of the test including the p-value, degrees of freedom, 
                                 // test statistic, the method used, and the null hypothesis.

val mat: Matrix = ... // a contingency matrix

// conduct Pearson's independence test on the input contingency matrix val independenceTestResult = Statistics.chiSqTest(mat) println(independenceTestResult) // summary of the test including the p-value, degrees of freedom... val obs: RDD[LabeledPoint] = ... // (feature, label) pairs. // The contingency table is constructed from the raw (feature, label) pairs and used to conduct // the independence test. Returns an array containing the ChiSquaredTestResult for every feature // against the label. val featureTestResults: Array[ChiSqTestResult] = Statistics.chiSqTest(obs) var i = 1 featureTestResults.foreach { result => println(s"Column $i:\n$result") i += 1 } // summary of the test

Statistics provides methods to run a 1-sample, 2-sided Kolmogorov-Smirnov test

import org.apache.spark.mllib.stat.Statistics

val data: RDD[Double] = ... // an RDD of sample data

// run a KS test for the sample versus a standard normal distribution
val testResult = Statistics.kolmogorovSmirnovTest(data, "norm", 0, 1)
println(testResult) // summary of the test including the p-value, test statistic,
                    // and null hypothesis
                    // if our p-value indicates significance, we can reject the null hypothesis

// perform a KS test using a cumulative distribution function of our making
val myCDF: Double => Double = ...
val testResult2 = Statistics.kolmogorovSmirnovTest(data, myCDF)

随机数生成

import org.apache.spark.SparkContext
import org.apache.spark.mllib.random.RandomRDDs._

val sc: SparkContext = ...

// Generate a random double RDD that contains 1 million i.i.d. values drawn from the
// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
val u = normalRDD(sc, 1000000L, 10)
// Apply a transform to get a random double RDD following `N(1, 4)`.
val v = u.map(x => 1.0 + 2.0 * x)

核密度估计

import org.apache.spark.mllib.stat.KernelDensity
import org.apache.spark.rdd.RDD

val data: RDD[Double] = ... // an RDD of sample data

// Construct the density estimator with the sample data and a standard deviation for the Gaussian
// kernels
val kd = new KernelDensity()
  .setSample(data)
  .setBandwidth(3.0)

// Find density estimates for the given values
val densities = kd.estimate(Array(-1.0, 2.0, 5.0))