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author | MechCoder <manojkumarsivaraj334@gmail.com> | 2015-02-10 14:05:55 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-02-10 14:05:55 -0800 |
commit | fd2c032f95bbee342ca539df9e44927482981659 (patch) | |
tree | d62c6a533c9ae2d06c8d8888d5197f481955969f /mllib/src | |
parent | f98707c043f1be9569ec774796edb783132773a8 (diff) | |
download | spark-fd2c032f95bbee342ca539df9e44927482981659.tar.gz spark-fd2c032f95bbee342ca539df9e44927482981659.tar.bz2 spark-fd2c032f95bbee342ca539df9e44927482981659.zip |
[SPARK-5021] [MLlib] Gaussian Mixture now supports Sparse Input
Following discussion in the Jira.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes #4459 from MechCoder/sparse_gmm and squashes the following commits:
1b18dab [MechCoder] Rewrite syr for sparse matrices
e579041 [MechCoder] Add test for covariance matrix
5cb370b [MechCoder] Separate tests for sparse data
5e096bd [MechCoder] Alphabetize and correct error message
e180f4c [MechCoder] [SPARK-5021] Gaussian Mixture now supports Sparse Input
Diffstat (limited to 'mllib/src')
5 files changed, 125 insertions, 26 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala index 0be3014de8..80584ef5e5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala @@ -19,10 +19,12 @@ package org.apache.spark.mllib.clustering import scala.collection.mutable.IndexedSeq -import breeze.linalg.{DenseMatrix => BreezeMatrix, DenseVector => BreezeVector, Transpose, diag} +import breeze.linalg.{diag, DenseMatrix => BreezeMatrix, DenseVector => BDV, SparseVector => BSV, + Transpose, Vector => BV} import org.apache.spark.annotation.Experimental -import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, Matrices, Vector, Vectors} +import org.apache.spark.mllib.linalg.{BLAS, DenseVector, DenseMatrix, Matrices, + SparseVector, Vector, Vectors} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD @@ -130,7 +132,7 @@ class GaussianMixture private ( val sc = data.sparkContext // we will operate on the data as breeze data - val breezeData = data.map(u => u.toBreeze.toDenseVector).cache() + val breezeData = data.map(_.toBreeze).cache() // Get length of the input vectors val d = breezeData.first().length @@ -148,7 +150,7 @@ class GaussianMixture private ( (Array.fill(k)(1.0 / k), Array.tabulate(k) { i => val slice = samples.view(i * nSamples, (i + 1) * nSamples) new MultivariateGaussian(vectorMean(slice), initCovariance(slice)) - }) + }) } } @@ -169,7 +171,7 @@ class GaussianMixture private ( var i = 0 while (i < k) { val mu = sums.means(i) / sums.weights(i) - BLAS.syr(-sums.weights(i), Vectors.fromBreeze(mu).asInstanceOf[DenseVector], + BLAS.syr(-sums.weights(i), Vectors.fromBreeze(mu), Matrices.fromBreeze(sums.sigmas(i)).asInstanceOf[DenseMatrix]) weights(i) = sums.weights(i) / sumWeights gaussians(i) = new MultivariateGaussian(mu, sums.sigmas(i) / sums.weights(i)) @@ -185,8 +187,8 @@ class GaussianMixture private ( } /** Average of dense breeze vectors */ - private def vectorMean(x: IndexedSeq[BreezeVector[Double]]): BreezeVector[Double] = { - val v = BreezeVector.zeros[Double](x(0).length) + private def vectorMean(x: IndexedSeq[BV[Double]]): BDV[Double] = { + val v = BDV.zeros[Double](x(0).length) x.foreach(xi => v += xi) v / x.length.toDouble } @@ -195,10 +197,10 @@ class GaussianMixture private ( * Construct matrix where diagonal entries are element-wise * variance of input vectors (computes biased variance) */ - private def initCovariance(x: IndexedSeq[BreezeVector[Double]]): BreezeMatrix[Double] = { + private def initCovariance(x: IndexedSeq[BV[Double]]): BreezeMatrix[Double] = { val mu = vectorMean(x) - val ss = BreezeVector.zeros[Double](x(0).length) - x.map(xi => (xi - mu) :^ 2.0).foreach(u => ss += u) + val ss = BDV.zeros[Double](x(0).length) + x.foreach(xi => ss += (xi - mu) :^ 2.0) diag(ss / x.length.toDouble) } } @@ -207,7 +209,7 @@ class GaussianMixture private ( private object ExpectationSum { def zero(k: Int, d: Int): ExpectationSum = { new ExpectationSum(0.0, Array.fill(k)(0.0), - Array.fill(k)(BreezeVector.zeros(d)), Array.fill(k)(BreezeMatrix.zeros(d,d))) + Array.fill(k)(BDV.zeros(d)), Array.fill(k)(BreezeMatrix.zeros(d,d))) } // compute cluster contributions for each input point @@ -215,19 +217,18 @@ private object ExpectationSum { def add( weights: Array[Double], dists: Array[MultivariateGaussian]) - (sums: ExpectationSum, x: BreezeVector[Double]): ExpectationSum = { + (sums: ExpectationSum, x: BV[Double]): ExpectationSum = { val p = weights.zip(dists).map { case (weight, dist) => MLUtils.EPSILON + weight * dist.pdf(x) } val pSum = p.sum sums.logLikelihood += math.log(pSum) - val xxt = x * new Transpose(x) var i = 0 while (i < sums.k) { p(i) /= pSum sums.weights(i) += p(i) sums.means(i) += x * p(i) - BLAS.syr(p(i), Vectors.fromBreeze(x).asInstanceOf[DenseVector], + BLAS.syr(p(i), Vectors.fromBreeze(x), Matrices.fromBreeze(sums.sigmas(i)).asInstanceOf[DenseMatrix]) i = i + 1 } @@ -239,7 +240,7 @@ private object ExpectationSum { private class ExpectationSum( var logLikelihood: Double, val weights: Array[Double], - val means: Array[BreezeVector[Double]], + val means: Array[BDV[Double]], val sigmas: Array[BreezeMatrix[Double]]) extends Serializable { val k = weights.length diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala index 079f7ca564..87052e1ba8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala @@ -235,12 +235,24 @@ private[spark] object BLAS extends Serializable with Logging { * @param x the vector x that contains the n elements. * @param A the symmetric matrix A. Size of n x n. */ - def syr(alpha: Double, x: DenseVector, A: DenseMatrix) { + def syr(alpha: Double, x: Vector, A: DenseMatrix) { val mA = A.numRows val nA = A.numCols - require(mA == nA, s"A is not a symmetric matrix. A: $mA x $nA") + require(mA == nA, s"A is not a square matrix (and hence is not symmetric). A: $mA x $nA") require(mA == x.size, s"The size of x doesn't match the rank of A. A: $mA x $nA, x: ${x.size}") + x match { + case dv: DenseVector => syr(alpha, dv, A) + case sv: SparseVector => syr(alpha, sv, A) + case _ => + throw new IllegalArgumentException(s"syr doesn't support vector type ${x.getClass}.") + } + } + + private def syr(alpha: Double, x: DenseVector, A: DenseMatrix) { + val nA = A.numRows + val mA = A.numCols + nativeBLAS.dsyr("U", x.size, alpha, x.values, 1, A.values, nA) // Fill lower triangular part of A @@ -255,6 +267,26 @@ private[spark] object BLAS extends Serializable with Logging { } } + private def syr(alpha: Double, x: SparseVector, A: DenseMatrix) { + val mA = A.numCols + val xIndices = x.indices + val xValues = x.values + val nnz = xValues.length + val Avalues = A.values + + var i = 0 + while (i < nnz) { + val multiplier = alpha * xValues(i) + val offset = xIndices(i) * mA + var j = 0 + while (j < nnz) { + Avalues(xIndices(j) + offset) += multiplier * xValues(j) + j += 1 + } + i += 1 + } + } + /** * C := alpha * A * B + beta * C * @param alpha a scalar to scale the multiplication A * B. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala index fd186b5ee6..cd6add9d60 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala @@ -17,7 +17,7 @@ package org.apache.spark.mllib.stat.distribution -import breeze.linalg.{DenseVector => DBV, DenseMatrix => DBM, diag, max, eigSym} +import breeze.linalg.{DenseVector => DBV, DenseMatrix => DBM, diag, max, eigSym, Vector => BV} import org.apache.spark.annotation.DeveloperApi; import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix} @@ -62,21 +62,21 @@ class MultivariateGaussian ( /** Returns density of this multivariate Gaussian at given point, x */ def pdf(x: Vector): Double = { - pdf(x.toBreeze.toDenseVector) + pdf(x.toBreeze) } /** Returns the log-density of this multivariate Gaussian at given point, x */ def logpdf(x: Vector): Double = { - logpdf(x.toBreeze.toDenseVector) + logpdf(x.toBreeze) } /** Returns density of this multivariate Gaussian at given point, x */ - private[mllib] def pdf(x: DBV[Double]): Double = { + private[mllib] def pdf(x: BV[Double]): Double = { math.exp(logpdf(x)) } /** Returns the log-density of this multivariate Gaussian at given point, x */ - private[mllib] def logpdf(x: DBV[Double]): Double = { + private[mllib] def logpdf(x: BV[Double]): Double = { val delta = x - breezeMu val v = rootSigmaInv * delta u + v.t * v * -0.5 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala index c2cd56ea40..1b46a4012d 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala @@ -31,7 +31,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { Vectors.dense(5.0, 10.0), Vectors.dense(4.0, 11.0) )) - + // expectations val Ew = 1.0 val Emu = Vectors.dense(5.0, 10.0) @@ -44,6 +44,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { assert(gmm.gaussians(0).mu ~== Emu absTol 1E-5) assert(gmm.gaussians(0).sigma ~== Esigma absTol 1E-5) } + } test("two clusters") { @@ -54,7 +55,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { Vectors.dense( 5.7048), Vectors.dense( 4.6567), Vectors.dense( 5.5026), Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734) )) - + // we set an initial gaussian to induce expected results val initialGmm = new GaussianMixtureModel( Array(0.5, 0.5), @@ -63,7 +64,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { new MultivariateGaussian(Vectors.dense(1.0), Matrices.dense(1, 1, Array(1.0))) ) ) - + val Ew = Array(1.0 / 3.0, 2.0 / 3.0) val Emu = Array(Vectors.dense(-4.3673), Vectors.dense(5.1604)) val Esigma = Array(Matrices.dense(1, 1, Array(1.1098)), Matrices.dense(1, 1, Array(0.86644))) @@ -72,7 +73,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { .setK(2) .setInitialModel(initialGmm) .run(data) - + assert(gmm.weights(0) ~== Ew(0) absTol 1E-3) assert(gmm.weights(1) ~== Ew(1) absTol 1E-3) assert(gmm.gaussians(0).mu ~== Emu(0) absTol 1E-3) @@ -80,4 +81,61 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { assert(gmm.gaussians(0).sigma ~== Esigma(0) absTol 1E-3) assert(gmm.gaussians(1).sigma ~== Esigma(1) absTol 1E-3) } + + test("single cluster with sparse data") { + val data = sc.parallelize(Array( + Vectors.sparse(3, Array(0, 2), Array(4.0, 2.0)), + Vectors.sparse(3, Array(0, 2), Array(2.0, 4.0)), + Vectors.sparse(3, Array(1), Array(6.0)) + )) + + val Ew = 1.0 + val Emu = Vectors.dense(2.0, 2.0, 2.0) + val Esigma = Matrices.dense(3, 3, + Array(8.0 / 3.0, -4.0, 4.0 / 3.0, -4.0, 8.0, -4.0, 4.0 / 3.0, -4.0, 8.0 / 3.0) + ) + + val seeds = Array(42, 1994, 27, 11, 0) + seeds.foreach { seed => + val gmm = new GaussianMixture().setK(1).setSeed(seed).run(data) + assert(gmm.weights(0) ~== Ew absTol 1E-5) + assert(gmm.gaussians(0).mu ~== Emu absTol 1E-5) + assert(gmm.gaussians(0).sigma ~== Esigma absTol 1E-5) + } + } + + test("two clusters with sparse data") { + val data = sc.parallelize(Array( + Vectors.dense(-5.1971), Vectors.dense(-2.5359), Vectors.dense(-3.8220), + Vectors.dense(-5.2211), Vectors.dense(-5.0602), Vectors.dense( 4.7118), + Vectors.dense( 6.8989), Vectors.dense( 3.4592), Vectors.dense( 4.6322), + Vectors.dense( 5.7048), Vectors.dense( 4.6567), Vectors.dense( 5.5026), + Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734) + )) + + val sparseData = data.map(point => Vectors.sparse(1, Array(0), point.toArray)) + // we set an initial gaussian to induce expected results + val initialGmm = new GaussianMixtureModel( + Array(0.5, 0.5), + Array( + new MultivariateGaussian(Vectors.dense(-1.0), Matrices.dense(1, 1, Array(1.0))), + new MultivariateGaussian(Vectors.dense(1.0), Matrices.dense(1, 1, Array(1.0))) + ) + ) + val Ew = Array(1.0 / 3.0, 2.0 / 3.0) + val Emu = Array(Vectors.dense(-4.3673), Vectors.dense(5.1604)) + val Esigma = Array(Matrices.dense(1, 1, Array(1.1098)), Matrices.dense(1, 1, Array(0.86644))) + + val sparseGMM = new GaussianMixture() + .setK(2) + .setInitialModel(initialGmm) + .run(data) + + assert(sparseGMM.weights(0) ~== Ew(0) absTol 1E-3) + assert(sparseGMM.weights(1) ~== Ew(1) absTol 1E-3) + assert(sparseGMM.gaussians(0).mu ~== Emu(0) absTol 1E-3) + assert(sparseGMM.gaussians(1).mu ~== Emu(1) absTol 1E-3) + assert(sparseGMM.gaussians(0).sigma ~== Esigma(0) absTol 1E-3) + assert(sparseGMM.gaussians(1).sigma ~== Esigma(1) absTol 1E-3) + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala index b0b78acd6d..002cb25386 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala @@ -166,6 +166,14 @@ class BLASSuite extends FunSuite { syr(alpha, y, dA) } } + + val xSparse = new SparseVector(4, Array(0, 2, 3), Array(1.0, 3.0, 4.0)) + val dD = new DenseMatrix(4, 4, + Array(0.0, 1.2, 2.2, 3.1, 1.2, 3.2, 5.3, 4.6, 2.2, 5.3, 1.8, 3.0, 3.1, 4.6, 3.0, 0.8)) + syr(0.1, xSparse, dD) + val expectedSparse = new DenseMatrix(4, 4, + Array(0.1, 1.2, 2.5, 3.5, 1.2, 3.2, 5.3, 4.6, 2.5, 5.3, 2.7, 4.2, 3.5, 4.6, 4.2, 2.4)) + assert(dD ~== expectedSparse absTol 1e-15) } test("gemm") { |