From 0dc4310b470c7e4355c0da67ca3373c3013cc9dd Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sat, 30 Jul 2016 04:42:38 -0700
Subject: [SPARK-16694][CORE] Use for/foreach rather than map for Unit
 expressions whose side effects are required

## What changes were proposed in this pull request?

Use foreach/for instead of map where operation requires execution of body, not actually defining a transformation

## How was this patch tested?

Jenkins

Author: Sean Owen <sowen@cloudera.com>

Closes #14332 from srowen/SPARK-16694.
---
 mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala | 2 +-
 .../src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala  | 4 ++--
 .../org/apache/spark/mllib/classification/NaiveBayesSuite.scala     | 2 +-
 .../org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala    | 6 +++---
 4 files changed, 7 insertions(+), 7 deletions(-)

(limited to 'mllib/src')

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 871b1c7d21..9a3d64fca5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -268,7 +268,7 @@ class KMeans private (
 
     val iterationStartTime = System.nanoTime()
 
-    instr.map(_.logNumFeatures(centers(0)(0).vector.size))
+    instr.foreach(_.logNumFeatures(centers(0)(0).vector.size))
 
     // Execute iterations of Lloyd's algorithm until all runs have converged
     while (iteration < maxIterations && !activeRuns.isEmpty) {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
index 16c74f6785..0b441f8b80 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
@@ -138,8 +138,8 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul
       case Row(w: String, sim: Double) => (w, sim)
     }.collect().unzip
 
-    assert(synonyms.toArray === Array("b", "c"))
-    expectedSimilarity.zip(similarity).map {
+    assert(synonyms === Array("b", "c"))
+    expectedSimilarity.zip(similarity).foreach {
       case (expected, actual) => assert(math.abs((expected - actual) / expected) < 1E-5)
     }
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
index 0c0aefc52b..5ec4c15387 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
@@ -307,7 +307,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
     val tempDir = Utils.createTempDir()
     val path = tempDir.toURI.toString
 
-    Seq(NaiveBayesSuite.binaryBernoulliModel, NaiveBayesSuite.binaryMultinomialModel).map {
+    Seq(NaiveBayesSuite.binaryBernoulliModel, NaiveBayesSuite.binaryMultinomialModel).foreach {
       model =>
         // Save model, load it back, and compare.
         try {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
index 8416771552..e30ad15967 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
@@ -80,7 +80,7 @@ class RandomDataGeneratorSuite extends SparkFunSuite {
   }
 
   test("LogNormalGenerator") {
-    List((0.0, 1.0), (0.0, 2.0), (2.0, 1.0), (2.0, 2.0)).map {
+    List((0.0, 1.0), (0.0, 2.0), (2.0, 1.0), (2.0, 2.0)).foreach {
       case (mean: Double, vari: Double) =>
         val normal = new LogNormalGenerator(mean, math.sqrt(vari))
         apiChecks(normal)
@@ -125,7 +125,7 @@ class RandomDataGeneratorSuite extends SparkFunSuite {
 
   test("GammaGenerator") {
     // mean = 0.0 will not pass the API checks since 0.0 is always deterministically produced.
-    List((1.0, 2.0), (2.0, 2.0), (3.0, 2.0), (5.0, 1.0), (9.0, 0.5)).map {
+    List((1.0, 2.0), (2.0, 2.0), (3.0, 2.0), (5.0, 1.0), (9.0, 0.5)).foreach {
       case (shape: Double, scale: Double) =>
         val gamma = new GammaGenerator(shape, scale)
         apiChecks(gamma)
@@ -138,7 +138,7 @@ class RandomDataGeneratorSuite extends SparkFunSuite {
   }
 
   test("WeibullGenerator") {
-    List((1.0, 2.0), (2.0, 3.0), (2.5, 3.5), (10.4, 2.222)).map {
+    List((1.0, 2.0), (2.0, 3.0), (2.5, 3.5), (10.4, 2.222)).foreach {
       case (alpha: Double, beta: Double) =>
         val weibull = new WeibullGenerator(alpha, beta)
         apiChecks(weibull)
-- 
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