[SPARK-4614][MLLIB] Slight API changes in Matrix and Matrices
Before we have a full picture of the operators we want to add, it might be safer to hide `Matrix.transposeMultiply` in 1.2.0. Another update we want to change is `Matrix.randn` and `Matrix.rand`, both of which should take a `Random` implementation. Otherwise, it is very likely to produce inconsistent RDDs. I also added some unit tests for matrix factory methods. All APIs are new in 1.2, so there is no incompatible changes. brkyvz Author: Xiangrui Meng <meng@databricks.com> Closes #3468 from mengxr/SPARK-4614 and squashes the following commits: 3b0e4e2 [Xiangrui Meng] add mima excludes 6bfd8a4 [Xiangrui Meng] hide transposeMultiply; add rng to rand and randn; add unit tests
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@ -17,12 +17,10 @@
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package org.apache.spark.mllib.linalg
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import java.util.Arrays
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import java.util.{Random, Arrays}
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import breeze.linalg.{Matrix => BM, DenseMatrix => BDM, CSCMatrix => BSM}
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import org.apache.spark.util.random.XORShiftRandom
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/**
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* Trait for a local matrix.
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*/
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@ -67,14 +65,14 @@ sealed trait Matrix extends Serializable {
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}
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/** Convenience method for `Matrix`^T^-`DenseMatrix` multiplication. */
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def transposeMultiply(y: DenseMatrix): DenseMatrix = {
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private[mllib] def transposeMultiply(y: DenseMatrix): DenseMatrix = {
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val C: DenseMatrix = Matrices.zeros(numCols, y.numCols).asInstanceOf[DenseMatrix]
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BLAS.gemm(true, false, 1.0, this, y, 0.0, C)
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C
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}
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/** Convenience method for `Matrix`^T^-`DenseVector` multiplication. */
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def transposeMultiply(y: DenseVector): DenseVector = {
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private[mllib] def transposeMultiply(y: DenseVector): DenseVector = {
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val output = new DenseVector(new Array[Double](numCols))
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BLAS.gemv(true, 1.0, this, y, 0.0, output)
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output
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@ -291,22 +289,22 @@ object Matrices {
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* Generate a `DenseMatrix` consisting of i.i.d. uniform random numbers.
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* @param numRows number of rows of the matrix
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* @param numCols number of columns of the matrix
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* @param rng a random number generator
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* @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
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*/
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def rand(numRows: Int, numCols: Int): Matrix = {
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val rand = new XORShiftRandom
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new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(rand.nextDouble()))
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def rand(numRows: Int, numCols: Int, rng: Random): Matrix = {
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new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(rng.nextDouble()))
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}
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/**
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* Generate a `DenseMatrix` consisting of i.i.d. gaussian random numbers.
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* @param numRows number of rows of the matrix
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* @param numCols number of columns of the matrix
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* @param rng a random number generator
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* @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
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*/
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def randn(numRows: Int, numCols: Int): Matrix = {
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val rand = new XORShiftRandom
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new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(rand.nextGaussian()))
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def randn(numRows: Int, numCols: Int, rng: Random): Matrix = {
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new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(rng.nextGaussian()))
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}
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/**
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@ -17,7 +17,11 @@
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package org.apache.spark.mllib.linalg
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import java.util.Random
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import org.mockito.Mockito.when
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import org.scalatest.FunSuite
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import org.scalatest.mock.MockitoSugar._
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class MatricesSuite extends FunSuite {
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test("dense matrix construction") {
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@ -112,4 +116,50 @@ class MatricesSuite extends FunSuite {
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assert(sparseMat(0, 1) === 10.0)
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assert(sparseMat.values(2) === 10.0)
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}
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test("zeros") {
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val mat = Matrices.zeros(2, 3).asInstanceOf[DenseMatrix]
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assert(mat.numRows === 2)
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assert(mat.numCols === 3)
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assert(mat.values.forall(_ == 0.0))
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}
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test("ones") {
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val mat = Matrices.ones(2, 3).asInstanceOf[DenseMatrix]
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assert(mat.numRows === 2)
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assert(mat.numCols === 3)
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assert(mat.values.forall(_ == 1.0))
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}
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test("eye") {
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val mat = Matrices.eye(2).asInstanceOf[DenseMatrix]
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assert(mat.numCols === 2)
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assert(mat.numCols === 2)
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assert(mat.values.toSeq === Seq(1.0, 0.0, 0.0, 1.0))
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}
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test("rand") {
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val rng = mock[Random]
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when(rng.nextDouble()).thenReturn(1.0, 2.0, 3.0, 4.0)
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val mat = Matrices.rand(2, 2, rng).asInstanceOf[DenseMatrix]
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assert(mat.numRows === 2)
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assert(mat.numCols === 2)
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assert(mat.values.toSeq === Seq(1.0, 2.0, 3.0, 4.0))
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}
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test("randn") {
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val rng = mock[Random]
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when(rng.nextGaussian()).thenReturn(1.0, 2.0, 3.0, 4.0)
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val mat = Matrices.randn(2, 2, rng).asInstanceOf[DenseMatrix]
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assert(mat.numRows === 2)
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assert(mat.numCols === 2)
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assert(mat.values.toSeq === Seq(1.0, 2.0, 3.0, 4.0))
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}
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test("diag") {
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val mat = Matrices.diag(Vectors.dense(1.0, 2.0)).asInstanceOf[DenseMatrix]
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assert(mat.numRows === 2)
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assert(mat.numCols === 2)
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assert(mat.values.toSeq === Seq(1.0, 0.0, 0.0, 2.0))
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}
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}
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@ -47,6 +47,12 @@ object MimaExcludes {
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"org.apache.spark.SparkStageInfoImpl.this"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.SparkStageInfo.submissionTime")
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) ++ Seq(
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// SPARK-4614
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.mllib.linalg.Matrices.randn"),
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ProblemFilters.exclude[MissingMethodProblem](
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"org.apache.spark.mllib.linalg.Matrices.rand")
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)
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case v if v.startsWith("1.2") =>
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