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From smar...@apache.org
Subject [37/50] [abbrv] mahout git commit: MAHOUT-1818 workaround and test cleanup for Flink release closes apache/mahout#218
Date Mon, 11 Apr 2016 08:10:04 GMT
MAHOUT-1818 workaround and test cleanup for Flink release closes apache/mahout#218


Project: http://git-wip-us.apache.org/repos/asf/mahout/repo
Commit: http://git-wip-us.apache.org/repos/asf/mahout/commit/472438bc
Tree: http://git-wip-us.apache.org/repos/asf/mahout/tree/472438bc
Diff: http://git-wip-us.apache.org/repos/asf/mahout/diff/472438bc

Branch: refs/heads/master
Commit: 472438bc83a51bcc08518e44daff4ec5f3bf81e9
Parents: 681d30e
Author: Andrew Palumbo <apalumbo@apache.org>
Authored: Sun Apr 10 19:10:44 2016 -0400
Committer: Andrew Palumbo <apalumbo@apache.org>
Committed: Sun Apr 10 19:11:31 2016 -0400

----------------------------------------------------------------------
 .../flinkbindings/FailingTestsSuite.scala       | 272 -------------------
 .../DistributedDecompositionsSuite.scala        |  31 ---
 .../FlinkDistributedDecompositionsSuite.scala   | 221 +++++++++++++++
 3 files changed, 221 insertions(+), 303 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/mahout/blob/472438bc/flink/src/test/scala/org/apache/mahout/flinkbindings/FailingTestsSuite.scala
----------------------------------------------------------------------
diff --git a/flink/src/test/scala/org/apache/mahout/flinkbindings/FailingTestsSuite.scala
b/flink/src/test/scala/org/apache/mahout/flinkbindings/FailingTestsSuite.scala
deleted file mode 100644
index 8186e2d..0000000
--- a/flink/src/test/scala/org/apache/mahout/flinkbindings/FailingTestsSuite.scala
+++ /dev/null
@@ -1,272 +0,0 @@
-/**
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements. See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership. The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied. See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-package org.apache.mahout.flinkbindings
-
-import org.apache.flink.api.common.functions.MapFunction
-import org.apache.flink.api.scala.DataSet
-import org.apache.flink.api.scala.hadoop.mapreduce.HadoopOutputFormat
-import org.apache.mahout.common.RandomUtils
-
-import scala.collection.immutable.List
-
-//import org.apache.flink.api.scala.hadoop.mapreduce.HadoopOutputFormat
-import org.apache.hadoop.io.IntWritable
-import org.apache.hadoop.mapreduce.Job
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat, SequenceFileOutputFormat}
-import org.apache.mahout.math.scalabindings._
-import org.apache.mahout.math._
-import org.apache.mahout.math.drm._
-import RLikeDrmOps._
-import RLikeOps._
-import math._
-
-import org.apache.mahout.math.decompositions._
-import org.scalatest.{FunSuite, Matchers}
-
-
-import scala.reflect.ClassTag
-import org.apache.flink.api.scala._
-
-
-
-class FailingTestsSuite extends FunSuite with DistributedFlinkSuite with Matchers {
-
-// // passing now
-//  test("Simple DataSet to IntWritable") {
-//    val path = TmpDir + "flinkOutput"
-//
-//    implicit val typeInfo = createTypeInformation[(Int,Int)]
-//    val ds = env.fromElements[(Int,Int)]((1,2),(3,4),(5,6),(7,8))
-//   // val job = new JobConf
-//
-//
-//    val writableDataset : DataSet[(IntWritable,IntWritable)] =
-//      ds.map( tuple =>
-//        (new IntWritable(tuple._1.asInstanceOf[Int]), new IntWritable(tuple._2.asInstanceOf[Int]))
-//    )
-//
-//    val job: Job = new Job()
-//
-//    job.setOutputKeyClass(classOf[IntWritable])
-//    job.setOutputValueClass(classOf[IntWritable])
-//
-//    // setup sink for IntWritable
-//    val sequenceFormat = new SequenceFileOutputFormat[IntWritable, IntWritable]
-//    val hadoopOutput  = new HadoopOutputFormat[IntWritable,IntWritable](sequenceFormat,
job)
-//    FileOutputFormat.setOutputPath(job, new org.apache.hadoop.fs.Path(path))
-//
-//    writableDataset.output(hadoopOutput)
-//
-//    env.execute(s"dfsWrite($path)")
-//
-//  }
-
-
-//  test("C = A + B, identically partitioned") {
-//
-//    val inCoreA = dense((1, 2, 3), (3, 4, 5), (5, 6, 7))
-//
-//    val A = drmParallelize(inCoreA, numPartitions = 2)
-//
-//     //   printf("A.nrow=%d.\n", A.rdd.count())
-//
-//    // Create B which would be identically partitioned to A. mapBlock() by default will
do the trick.
-//    val B = A.mapBlock() {
-//      case (keys, block) =>
-//        val bBlock = block.like() := { (r, c, v) => util.Random.nextDouble()}
-//        keys -> bBlock
-//    }
-//      // Prevent repeated computation non-determinism
-//      // flink problem is here... checkpoint is not doing what it should
-//      // ie. greate a physical plan w/o side effects
-//      .checkpoint()
-//
-//    val inCoreB = B.collect
-//
-//    printf("A=\n%s\n", inCoreA)
-//    printf("B=\n%s\n", inCoreB)
-//
-//    val C = A + B
-//
-//    val inCoreC = C.collect
-//
-//    printf("C=\n%s\n", inCoreC)
-//
-//    // Actual
-//    val inCoreCControl = inCoreA + inCoreB
-//
-//    (inCoreC - inCoreCControl).norm should be < 1E-10
-//  }
-//// Passing now.
-//  test("C = inCoreA %*%: B") {
-//
-//    val inCoreA = dense((1, 2, 3), (3, 4, 5), (4, 5, 6), (5, 6, 7))
-//    val inCoreB = dense((3, 5, 7, 10), (4, 6, 9, 10), (5, 6, 7, 7))
-//
-//    val B = drmParallelize(inCoreB, numPartitions = 2)
-//    val C = inCoreA %*%: B
-//
-//    val inCoreC = C.collect
-//    val inCoreCControl = inCoreA %*% inCoreB
-//
-//    println(inCoreC)
-//    (inCoreC - inCoreCControl).norm should be < 1E-10
-//
-//  }
-
-  test("dsqDist(X,Y)") {
-    val m = 100
-    val n = 300
-    val d = 7
-    val mxX = Matrices.symmetricUniformView(m, d, 12345).cloned -= 5
-    val mxY = Matrices.symmetricUniformView(n, d, 1234).cloned += 10
-    val (drmX, drmY) = (drmParallelize(mxX, 3), drmParallelize(mxY, 4))
-
-    val mxDsq = dsqDist(drmX, drmY).collect
-    val mxDsqControl = new DenseMatrix(m, n) := { (r, c, _) ⇒ (mxX(r, ::) - mxY(c, ::))
^= 2 sum }
-    (mxDsq - mxDsqControl).norm should be < 1e-7
-  }
-
-  test("dsqDist(X)") {
-    val m = 100
-    val d = 7
-    val mxX = Matrices.symmetricUniformView(m, d, 12345).cloned -= 5
-    val drmX = drmParallelize(mxX, 3)
-
-    val mxDsq = dsqDist(drmX).collect
-    val mxDsqControl = sqDist(drmX)
-    (mxDsq - mxDsqControl).norm should be < 1e-7
-  }
-
-//// passing now
-//  test("DRM DFS i/o (local)") {
-//
-//    val uploadPath = TmpDir + "UploadedDRM"
-//
-//    val inCoreA = dense((1, 2, 3), (3, 4, 5))
-//    val drmA = drmParallelize(inCoreA)
-//
-//    drmA.dfsWrite(path = uploadPath)
-//
-//    println(inCoreA)
-//
-//    // Load back from hdfs
-//    val drmB = drmDfsRead(path = uploadPath)
-//
-//    // Make sure keys are correctly identified as ints
-//    drmB.checkpoint(CacheHint.NONE).keyClassTag shouldBe ClassTag.Int
-//
-//    // Collect back into in-core
-//    val inCoreB = drmB.collect
-//
-//    // Print out to see what it is we collected:
-//    println(inCoreB)
-//
-//    (inCoreA - inCoreB).norm should be < 1e-7
-//  }
-
-
-
-//  test("dspca") {
-//
-//    val rnd = RandomUtils.getRandom
-//
-//    // Number of points
-//    val m = 500
-//    // Length of actual spectrum
-//    val spectrumLen = 40
-//
-//    val spectrum = dvec((0 until spectrumLen).map(x => 300.0 * exp(-x) max 1e-3))
-//    printf("spectrum:%s\n", spectrum)
-//
-//    val (u, _) = qr(new SparseRowMatrix(m, spectrumLen) :=
-//      ((r, c, v) => if (rnd.nextDouble() < 0.2) 0 else rnd.nextDouble() + 5.0))
-//
-//    // PCA Rotation matrix -- should also be orthonormal.
-//    val (tr, _) = qr(Matrices.symmetricUniformView(spectrumLen, spectrumLen, rnd.nextInt)
- 10.0)
-//
-//    val input = (u %*%: diagv(spectrum)) %*% tr.t
-//    val drmInput = drmParallelize(m = input, numPartitions = 2)
-//
-//    // Calculate just first 10 principal factors and reduce dimensionality.
-//    // Since we assert just validity of the s-pca, not stochastic error, we bump p parameter
to
-//    // ensure to zero stochastic error and assert only functional correctness of the method's
pca-
-//    // specific additions.
-//    val k = 10
-//
-//    // Calculate just first 10 principal factors and reduce dimensionality.
-//    var (drmPCA, _, s) = dspca(drmA = drmInput, k = 10, p = spectrumLen, q = 1)
-//    // Un-normalized pca data:
-//    drmPCA = drmPCA %*% diagv(s)
-//
-//    val pca = drmPCA.checkpoint(CacheHint.NONE).collect
-//
-//    // Of course, once we calculated the pca, the spectrum is going to be different since
our originally
-//    // generated input was not centered. So here, we'd just brute-solve pca to verify
-//    val xi = input.colMeans()
-//    for (r <- 0 until input.nrow) input(r, ::) -= xi
-//    var (pcaControl, _, sControl) = svd(m = input)
-//    pcaControl = (pcaControl %*%: diagv(sControl))(::, 0 until k)
-//
-//    printf("pca:\n%s\n", pca(0 until 10, 0 until 10))
-//    printf("pcaControl:\n%s\n", pcaControl(0 until 10, 0 until 10))
-//
-//    (pca(0 until 10, 0 until 10).norm - pcaControl(0 until 10, 0 until 10).norm).abs should
be < 1E-5
-//
-//  }
-
-  test("dals") {
-
-    val rnd = RandomUtils.getRandom
-
-    // Number of points
-    val m = 500
-    val n = 500
-
-    // Length of actual spectrum
-    val spectrumLen = 40
-
-    // Create singluar values with decay
-    val spectrum = dvec((0 until spectrumLen).map(x => 300.0 * exp(-x) max 1e-3))
-    printf("spectrum:%s\n", spectrum)
-
-    // Create A as an ideal input
-    val inCoreA = (qr(Matrices.symmetricUniformView(m, spectrumLen, 1234))._1 %*%: diagv(spectrum))
%*%
-      qr(Matrices.symmetricUniformView(n, spectrumLen, 2345))._1.t
-    val drmA = drmParallelize(inCoreA, numPartitions = 2)
-
-    // Decompose using ALS
-    val (drmU, drmV, rmse) = dals(drmA = drmA, k = 20).toTuple
-    val inCoreU = drmU.collect
-    val inCoreV = drmV.collect
-
-    val predict = inCoreU %*% inCoreV.t
-
-    printf("Control block:\n%s\n", inCoreA(0 until 3, 0 until 3))
-    printf("ALS factorized approximation block:\n%s\n", predict(0 until 3, 0 until 3))
-
-    val err = (inCoreA - predict).norm
-    printf("norm of residuals %f\n", err)
-    printf("train iteration rmses: %s\n", rmse)
-
-    err should be < 15e-2
-
-  }
-
-}
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/mahout/blob/472438bc/flink/src/test/scala/org/apache/mahout/flinkbindings/standard/DistributedDecompositionsSuite.scala
----------------------------------------------------------------------
diff --git a/flink/src/test/scala/org/apache/mahout/flinkbindings/standard/DistributedDecompositionsSuite.scala
b/flink/src/test/scala/org/apache/mahout/flinkbindings/standard/DistributedDecompositionsSuite.scala
deleted file mode 100644
index 031553e..0000000
--- a/flink/src/test/scala/org/apache/mahout/flinkbindings/standard/DistributedDecompositionsSuite.scala
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
-  * Licensed to the Apache Software Foundation (ASF) under one
-  * or more contributor license agreements. See the NOTICE file
-  * distributed with this work for additional information
-  * regarding copyright ownership. The ASF licenses this file
-  * to you under the Apache License, Version 2.0 (the
-  * "License"); you may not use this file except in compliance
-  * with the License. You may obtain a copy of the License at
-  *
-  * http://www.apache.org/licenses/LICENSE-2.0
-  *
-  * Unless required by applicable law or agreed to in writing,
-  * software distributed under the License is distributed on an
-  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-  * KIND, either express or implied. See the License for the
-  * specific language governing permissions and limitations
-  * under the License.
-  */
-package org.apache.mahout.flinkbindings.standard
-
-import org.apache.mahout.flinkbindings._
-import org.apache.mahout.math.decompositions.DistributedDecompositionsSuiteBase
-import org.junit.runner.RunWith
-import org.scalatest.FunSuite
-import org.scalatest.junit.JUnitRunner
-
-@RunWith(classOf[JUnitRunner])
-class DistributedDecompositionsSuite extends FunSuite with DistributedFlinkSuite
-      with DistributedDecompositionsSuiteBase {
-
-}
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/mahout/blob/472438bc/flink/src/test/scala/org/apache/mahout/flinkbindings/standard/FlinkDistributedDecompositionsSuite.scala
----------------------------------------------------------------------
diff --git a/flink/src/test/scala/org/apache/mahout/flinkbindings/standard/FlinkDistributedDecompositionsSuite.scala
b/flink/src/test/scala/org/apache/mahout/flinkbindings/standard/FlinkDistributedDecompositionsSuite.scala
new file mode 100644
index 0000000..a1054af
--- /dev/null
+++ b/flink/src/test/scala/org/apache/mahout/flinkbindings/standard/FlinkDistributedDecompositionsSuite.scala
@@ -0,0 +1,221 @@
+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements. See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership. The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License. You may obtain a copy of the License at
+  *
+  * http://www.apache.org/licenses/LICENSE-2.0
+  *
+  * Unless required by applicable law or agreed to in writing,
+  * software distributed under the License is distributed on an
+  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+  * KIND, either express or implied. See the License for the
+  * specific language governing permissions and limitations
+  * under the License.
+  */
+package org.apache.mahout.flinkbindings.standard
+
+import org.apache.mahout.common.RandomUtils
+import org.apache.mahout.flinkbindings._
+import org.apache.mahout.math.{Matrices, SparseRowMatrix}
+import org.apache.mahout.math.decompositions._
+import org.apache.mahout.math.drm.{CacheHint, _}
+import org.scalatest.{FunSuite, Matchers}
+import org.apache.mahout.math._
+import scalabindings._
+import RLikeOps._
+import RLikeDrmOps._
+
+import scala.math._
+
+// Exact copy of the DistributedDecompositionsSuiteBase trait with the exception of the
+// matrix size in the dals test which has been lowered to 350 x 350 from 500 x 500
+// due to some Flink serialization issues.
+
+class FlinkDistributedDecompositionsSuite extends FunSuite with DistributedFlinkSuite
+      with Matchers {this:FunSuite =>
+
+
+  test("thin distributed qr") {
+
+    val inCoreA = dense(
+      (1, 2, 3, 4),
+      (2, 3, 4, 5),
+      (3, -4, 5, 6),
+      (4, 5, 6, 7),
+      (8, 6, 7, 8)
+    )
+
+    val drmA = drmParallelize(inCoreA, numPartitions = 2)
+    val (drmQ, inCoreR) = dqrThin(drmA, checkRankDeficiency = false)
+
+    // Assert optimizer still knows Q and A are identically partitioned
+    drmQ.partitioningTag should equal(drmA.partitioningTag)
+
+    //    drmQ.rdd.partitions.size should be(A.rdd.partitions.size)
+    //
+    //    // Should also be zippable
+    //    drmQ.rdd.zip(other = A.rdd)
+
+    val inCoreQ = drmQ.collect
+
+    printf("A=\n%s\n", inCoreA)
+    printf("Q=\n%s\n", inCoreQ)
+    printf("R=\n%s\n", inCoreR)
+
+    val (qControl, rControl) = qr(inCoreA)
+    printf("qControl=\n%s\n", qControl)
+    printf("rControl=\n%s\n", rControl)
+
+    // Validate with Cholesky
+    val ch = chol(inCoreA.t %*% inCoreA)
+    printf("A'A=\n%s\n", inCoreA.t %*% inCoreA)
+    printf("L:\n%s\n", ch.getL)
+
+    val rControl2 = (ch.getL cloned).t
+    val qControl2 = ch.solveRight(inCoreA)
+    printf("qControl2=\n%s\n", qControl2)
+    printf("rControl2=\n%s\n", rControl2)
+
+    // Householder approach seems to be a little bit more stable
+    (rControl - inCoreR).norm should be < 1E-5
+    (qControl - inCoreQ).norm should be < 1E-5
+
+    // Assert identicity with in-core Cholesky-based -- this should be tighter.
+    (rControl2 - inCoreR).norm should be < 1E-10
+    (qControl2 - inCoreQ).norm should be < 1E-10
+
+    // Assert orthogonality:
+    // (a) Q[,j] dot Q[,j] == 1.0 for all j
+    // (b) Q[,i] dot Q[,j] == 0.0 for all i != j
+    for (col <- 0 until inCoreQ.ncol)
+      ((inCoreQ(::, col) dot inCoreQ(::, col)) - 1.0).abs should be < 1e-10
+    for (col1 <- 0 until inCoreQ.ncol - 1; col2 <- col1 + 1 until inCoreQ.ncol)
+      (inCoreQ(::, col1) dot inCoreQ(::, col2)).abs should be < 1e-10
+
+
+  }
+
+  test("dssvd - the naive-est - q=0") {
+    dssvdNaive(q = 0)
+  }
+
+  test("ddsvd - naive - q=1") {
+    dssvdNaive(q = 1)
+  }
+
+  test("ddsvd - naive - q=2") {
+    dssvdNaive(q = 2)
+  }
+
+
+  def dssvdNaive(q: Int) {
+    val inCoreA = dense(
+      (1, 2, 3, 4),
+      (2, 3, 4, 5),
+      (3, -4, 5, 6),
+      (4, 5, 6, 7),
+      (8, 6, 7, 8)
+    )
+    val drmA = drmParallelize(inCoreA, numPartitions = 2)
+
+    val (drmU, drmV, s) = dssvd(drmA, k = 4, q = q)
+    val (inCoreU, inCoreV) = (drmU.collect, drmV.collect)
+
+    printf("U:\n%s\n", inCoreU)
+    printf("V:\n%s\n", inCoreV)
+    printf("Sigma:\n%s\n", s)
+
+    (inCoreA - (inCoreU %*%: diagv(s)) %*% inCoreV.t).norm should be < 1E-5
+  }
+
+  test("dspca") {
+
+    val rnd = RandomUtils.getRandom
+
+    // Number of points
+    val m = 500
+    // Length of actual spectrum
+    val spectrumLen = 40
+
+    val spectrum = dvec((0 until spectrumLen).map(x => 300.0 * exp(-x) max 1e-3))
+    printf("spectrum:%s\n", spectrum)
+
+    val (u, _) = qr(new SparseRowMatrix(m, spectrumLen) :=
+      ((r, c, v) => if (rnd.nextDouble() < 0.2) 0 else rnd.nextDouble() + 5.0))
+
+    // PCA Rotation matrix -- should also be orthonormal.
+    val (tr, _) = qr(Matrices.symmetricUniformView(spectrumLen, spectrumLen, rnd.nextInt)
- 10.0)
+
+    val input = (u %*%: diagv(spectrum)) %*% tr.t
+    val drmInput = drmParallelize(m = input, numPartitions = 2)
+
+    // Calculate just first 10 principal factors and reduce dimensionality.
+    // Since we assert just validity of the s-pca, not stochastic error, we bump p parameter
to
+    // ensure to zero stochastic error and assert only functional correctness of the method's
pca-
+    // specific additions.
+    val k = 10
+
+    // Calculate just first 10 principal factors and reduce dimensionality.
+    var (drmPCA, _, s) = dspca(drmA = drmInput, k = 10, p = spectrumLen, q = 1)
+    // Un-normalized pca data:
+    drmPCA = drmPCA %*% diagv(s)
+
+    val pca = drmPCA.checkpoint(CacheHint.NONE).collect
+
+    // Of course, once we calculated the pca, the spectrum is going to be different since
our originally
+    // generated input was not centered. So here, we'd just brute-solve pca to verify
+    val xi = input.colMeans()
+    for (r <- 0 until input.nrow) input(r, ::) -= xi
+    var (pcaControl, _, sControl) = svd(m = input)
+    pcaControl = (pcaControl %*%: diagv(sControl))(::, 0 until k)
+
+    printf("pca:\n%s\n", pca(0 until 10, 0 until 10))
+    printf("pcaControl:\n%s\n", pcaControl(0 until 10, 0 until 10))
+
+    (pca(0 until 10, 0 until 10).norm - pcaControl(0 until 10, 0 until 10).norm).abs should
be < 1E-5
+
+  }
+
+  test("dals") {
+
+    val rnd = RandomUtils.getRandom
+
+    // Number of points
+    val m = 350
+    val n = 350
+
+    // Length of actual spectrum
+    val spectrumLen = 40
+
+    // Create singluar values with decay
+    val spectrum = dvec((0 until spectrumLen).map(x => 300.0 * exp(-x) max 1e-3))
+    printf("spectrum:%s\n", spectrum)
+
+    // Create A as an ideal input
+    val inCoreA = (qr(Matrices.symmetricUniformView(m, spectrumLen, 1234))._1 %*%: diagv(spectrum))
%*%
+      qr(Matrices.symmetricUniformView(n, spectrumLen, 2345))._1.t
+    val drmA = drmParallelize(inCoreA, numPartitions = 2)
+
+    // Decompose using ALS
+    val (drmU, drmV, rmse) = dals(drmA = drmA, k = 20).toTuple
+    val inCoreU = drmU.collect
+    val inCoreV = drmV.collect
+
+    val predict = inCoreU %*% inCoreV.t
+
+    printf("Control block:\n%s\n", inCoreA(0 until 3, 0 until 3))
+    printf("ALS factorized approximation block:\n%s\n", predict(0 until 3, 0 until 3))
+
+    val err = (inCoreA - predict).norm
+    printf("norm of residuals %f\n", err)
+    printf("train iteration rmses: %s\n", rmse)
+
+    err should be < 15e-2
+
+  }
+
+}
\ No newline at end of file


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