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author | Xiangrui Meng <meng@databricks.com> | 2015-05-20 23:38:58 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-05-20 23:38:58 -0700 |
commit | 947ea1cf5f6986aa687631d6cf9f0fb974ee7caf (patch) | |
tree | ef03c5cff0c9f2c964be4034f842ad00263312c7 | |
parent | 8ddcb25b3990ec691463f87d4071e7425f4909a9 (diff) | |
download | spark-947ea1cf5f6986aa687631d6cf9f0fb974ee7caf.tar.gz spark-947ea1cf5f6986aa687631d6cf9f0fb974ee7caf.tar.bz2 spark-947ea1cf5f6986aa687631d6cf9f0fb974ee7caf.zip |
[SPARK-7753] [MLLIB] Update KernelDensity API
Update `KernelDensity` API to make it extensible to different kernels in the future. `bandwidth` is used instead of `standardDeviation`. The static `kernelDensity` method is removed from `Statistics`. The implementation is updated using BLAS, while the algorithm remains the same. sryza srowen
Author: Xiangrui Meng <meng@databricks.com>
Closes #6279 from mengxr/SPARK-7753 and squashes the following commits:
4cdfadc [Xiangrui Meng] add example code in the doc
767fd5a [Xiangrui Meng] update KernelDensity API
3 files changed, 82 insertions, 48 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala index 79747cc5d7..a6bfe26e1e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala @@ -17,52 +17,101 @@ package org.apache.spark.mllib.stat +import com.github.fommil.netlib.BLAS.{getInstance => blas} + +import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD import org.apache.spark.rdd.RDD -private[stat] object KernelDensity { +/** + * :: Experimental :: + * Kernel density estimation. Given a sample from a population, estimate its probability density + * function at each of the given evaluation points using kernels. Only Gaussian kernel is supported. + * + * Scala example: + * + * {{{ + * val sample = sc.parallelize(Seq(0.0, 1.0, 4.0, 4.0)) + * val kd = new KernelDensity() + * .setSample(sample) + * .setBandwidth(3.0) + * val densities = kd.estimate(Array(-1.0, 2.0, 5.0)) + * }}} + */ +@Experimental +class KernelDensity extends Serializable { + + import KernelDensity._ + + /** Bandwidth of the kernel function. */ + private var bandwidth: Double = 1.0 + + /** A sample from a population. */ + private var sample: RDD[Double] = _ + /** - * Given a set of samples from a distribution, estimates its density at the set of given points. - * Uses a Gaussian kernel with the given standard deviation. + * Sets the bandwidth (standard deviation) of the Gaussian kernel (default: `1.0`). */ - def estimate(samples: RDD[Double], standardDeviation: Double, - evaluationPoints: Array[Double]): Array[Double] = { - if (standardDeviation <= 0.0) { - throw new IllegalArgumentException("Standard deviation must be positive") - } + def setBandwidth(bandwidth: Double): this.type = { + require(bandwidth > 0, s"Bandwidth must be positive, but got $bandwidth.") + this.bandwidth = bandwidth + this + } - // This gets used in each Gaussian PDF computation, so compute it up front - val logStandardDeviationPlusHalfLog2Pi = - math.log(standardDeviation) + 0.5 * math.log(2 * math.Pi) + /** + * Sets the sample to use for density estimation. + */ + def setSample(sample: RDD[Double]): this.type = { + this.sample = sample + this + } + + /** + * Sets the sample to use for density estimation (for Java users). + */ + def setSample(sample: JavaRDD[java.lang.Double]): this.type = { + this.sample = sample.rdd.asInstanceOf[RDD[Double]] + this + } + + /** + * Estimates probability density function at the given array of points. + */ + def estimate(points: Array[Double]): Array[Double] = { + val sample = this.sample + val bandwidth = this.bandwidth + + require(sample != null, "Must set sample before calling estimate.") - val (points, count) = samples.aggregate((new Array[Double](evaluationPoints.length), 0))( + val n = points.length + // This gets used in each Gaussian PDF computation, so compute it up front + val logStandardDeviationPlusHalfLog2Pi = math.log(bandwidth) + 0.5 * math.log(2 * math.Pi) + val (densities, count) = sample.aggregate((new Array[Double](n), 0L))( (x, y) => { var i = 0 - while (i < evaluationPoints.length) { - x._1(i) += normPdf(y, standardDeviation, logStandardDeviationPlusHalfLog2Pi, - evaluationPoints(i)) + while (i < n) { + x._1(i) += normPdf(y, bandwidth, logStandardDeviationPlusHalfLog2Pi, points(i)) i += 1 } - (x._1, i) + (x._1, n) }, (x, y) => { - var i = 0 - while (i < evaluationPoints.length) { - x._1(i) += y._1(i) - i += 1 - } + blas.daxpy(n, 1.0, y._1, 1, x._1, 1) (x._1, x._2 + y._2) }) - - var i = 0 - while (i < points.length) { - points(i) /= count - i += 1 - } - points + blas.dscal(n, 1.0 / count, densities, 1) + densities } +} + +private object KernelDensity { - private def normPdf(mean: Double, standardDeviation: Double, - logStandardDeviationPlusHalfLog2Pi: Double, x: Double): Double = { + /** Evaluates the PDF of a normal distribution. */ + def normPdf( + mean: Double, + standardDeviation: Double, + logStandardDeviationPlusHalfLog2Pi: Double, + x: Double): Double = { val x0 = x - mean val x1 = x0 / standardDeviation val logDensity = -0.5 * x1 * x1 - logStandardDeviationPlusHalfLog2Pi diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala index 32561620ac..b3fad0c52d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala @@ -149,18 +149,4 @@ object Statistics { def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = { ChiSqTest.chiSquaredFeatures(data) } - - /** - * Given an empirical distribution defined by the input RDD of samples, estimate its density at - * each of the given evaluation points using a Gaussian kernel. - * - * @param samples The samples RDD used to define the empirical distribution. - * @param standardDeviation The standard deviation of the kernel Gaussians. - * @param evaluationPoints The points at which to estimate densities. - * @return An array the same size as evaluationPoints with the density at each point. - */ - def kernelDensity(samples: RDD[Double], standardDeviation: Double, - evaluationPoints: Iterable[Double]): Array[Double] = { - KernelDensity.estimate(samples, standardDeviation, evaluationPoints.toArray) - } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala index 16ecae23dd..14bb1cebf0 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala @@ -17,9 +17,8 @@ package org.apache.spark.mllib.stat -import org.scalatest.FunSuite - import org.apache.commons.math3.distribution.NormalDistribution +import org.scalatest.FunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext @@ -27,7 +26,7 @@ class KernelDensitySuite extends FunSuite with MLlibTestSparkContext { test("kernel density single sample") { val rdd = sc.parallelize(Array(5.0)) val evaluationPoints = Array(5.0, 6.0) - val densities = KernelDensity.estimate(rdd, 3.0, evaluationPoints) + val densities = new KernelDensity().setSample(rdd).setBandwidth(3.0).estimate(evaluationPoints) val normal = new NormalDistribution(5.0, 3.0) val acceptableErr = 1e-6 assert(densities(0) - normal.density(5.0) < acceptableErr) @@ -37,7 +36,7 @@ class KernelDensitySuite extends FunSuite with MLlibTestSparkContext { test("kernel density multiple samples") { val rdd = sc.parallelize(Array(5.0, 10.0)) val evaluationPoints = Array(5.0, 6.0) - val densities = KernelDensity.estimate(rdd, 3.0, evaluationPoints) + val densities = new KernelDensity().setSample(rdd).setBandwidth(3.0).estimate(evaluationPoints) val normal1 = new NormalDistribution(5.0, 3.0) val normal2 = new NormalDistribution(10.0, 3.0) val acceptableErr = 1e-6 |