Kernel Density Estimation (KDE) is a very powerful, non-parametric method to extract a empirical continuous probability density function from a dataset. At its core the KDE is a smooth approximation of an histogram.
For a set of observations y, and given a kernel function K and a bandwidth, the estimation of the density function f, can be expressed as.
For a set of observations y, and given a kernel function K and a bandwidth, the estimation of the density function f, can be expressed as.
The implementation of the kernel density estimation in the current version of Apache Spark MLlib library, 2.3.1, org.apache.spark.mllib.stats.KernelDensity has two important limitations:
- It is a univariate estimation
- The estimation is performed on a sequence of observations, not an RDD or dataset, putting computation load on the Spark driver.
val sample = sparkSession.sparkContext.parallelize(data)
val kd = new KernelDensity()
.setSample(sample)
.setBandwidth(3.0)
val densities = kd.estimate(Array(-2.0, 5.0))
The method setSample specifies the training set but the KDE is actually trained when the method estimate is invoked on the driver.
Multivariate KDE
The purpose of this post is to extend the current functionality of the KDE by supporting multi-dimensional features and allows the developers to apply the estimation to a dataset. This implementation is restricted to the Normal distribution although it can easily be extended to other kernel functions.
We assume
- The reference to the current Spark session is implicit (line 1)
- The encoding of a row for serialization of the task is provided (line 1)
- TrainingDS training dataset (line 9)
- Validation validation set (line 10)
- bandwidth size of the Parzen window
The training set is passed to each partitions as iterator through a mapPartitions (line 17). The probability densities and count are computed through a Scala aggregate method with a zero function of type, (Array[Double], Long) (line 23). The sequence operator invokes the multinomial normal distribution (line 29).
The combiner (3rd argument of the aggregate) relies on the BLAS vectorization z = <- a.x+y dxapy (line 38). BLAS library has 3 levels (1D, 2D and 3D arrays). Blas library
The vector of densities is scaled with invCount using the decal BLAS level 1 method (line 45).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | final class KDE(implicit sparkSession: SparkSession, encoder: Encoder[Row]) { /** * Applied the trained KDE to a set of validation data * @param trainingDS Training data sets * @param validationRdd Validation data sets * @return Datasets of probability densities */ def estimate( trainingDS: Dataset[Obs], validationDS: Dataset[Obs], bandwidth: Double = 1.0): Dataset[Double] = { import math._, sparkSession.implicits._ val validation_brdcast = sparkSession.sparkContext .broadcast[Array[Obs]](validationDS.collect) trainingDS.mapPartitions((iter: Iterator[Obs]) => { val seqObs = iter.toArray val scale = 0.5 * seqObs.size* log(2 * Pi) val validation = validation_brdcast.value val (densities, count) = seqObs.aggregate( (new Array[Double](validation.length), 0L) ) ( { // seqOp (U, T) => U case ((x, z), y) => { var i = 0 while (i < validation.length) { // Call the pdf function for the normal distribution x(i) += multiNorm(y, bandwidth, scale, validation(i)) i += 1 } (x, z + 1)// Update count & validation values } }, { // combOp: (U, U) => U case ((u, z), (v, t)) => { // Combiner calls vectorization z <- a.x + y blas.daxpy(validation.length, 1.0, v, 1, u, 1) (u, z + t) } } ) val invCount: Double = 1.0 / count blas.dscal(validation.length, invCount, densities, 1) // Rescale the density using LINPACK z <- a.x densities.iterator }) } } |
The companion singleton is used to define the multinomial normal distribution (line 5). The type of observations (feature) is Array[Double].
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | final object KDE { import math._ type Obs = Array[Double] @throws(classOf[IllegalArgumentException]) def multiNorm( means: Obs, bandWidth: Double, scale: Double, x: Obs): Double = { require(x.length == means.length, "Dimension of means and observations differs") exp( -scale - (0 until means.length).map(n => { val sx = (means(n) - x(n)) / bandWidth -0.5 * sx * sx }).sum ) } } |
Driver
This simple application requires that the spark context (SparkSession) to be defined as well as an explicit encoding of Row using Kryo serializer. The implicit conversion are made available by importing sparkSession.implicits.
The training set is a sequence of key-value pairs (lines 3-14). The validation set is synthetically generated by multiplying the data in the training value with 2.0 (line 17).
The training set is a sequence of key-value pairs (lines 3-14). The validation set is synthetically generated by multiplying the data in the training value with 2.0 (line 17).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | implicit val sparkSession: SparkSession = confToSessionFromFile(LocalSparkConf) implicit val encoder = Encoders.kryo[Row] import sparkSession.implicits._ val trainingData = Seq[(String, Array[Double])]( ("A", Array[Double](1.0, 0.6)), ("B", Array[Double](2.0, 0.6)), ("C", Array[Double](1.5, 9.5)), ("D", Array[Double](4.5, 0.7)), ("E", Array[Double](0.4, 1.5)), ("F", Array[Double](2.1, 0.6)), ("G", Array[Double](0.5, 6.3)), ("H", Array[Double](1.5, 0.1)), ("I", Array[Double](1.2, 3.0)), ("B", Array[Double](3.1, 1.1)) ).toDS val validationData = trainingData .map { case (key, values) => values.map(_ *2.0) } val kde = new KDE val result = kde.estimate(trainingData.map(_._2),validationData) println(s"result: ${result.collect.mkString(", ")}") sparkSession.close val data = Seq[Double](1.0, 5.6) val sample = sparkSession.sparkContext.parallelize(data) val kd = new KernelDensity() .setSample(sample) .setBandwidth(3.0) val densities = kd.estimate(Array(-2.0, 5.0)) |
Environment
Scala: 2.11.8
Java JDK 1.8
Apache Spark 2.3.1
OpenBLAS 0.3.4
References
