[SPARK-13702][CORE][SQL][MLLIB] Use diamond operator for generic instance creation in Java code.

## What changes were proposed in this pull request?

In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator.

```
-    final ArrayList<Product2<Object, Object>> dataToWrite =
-      new ArrayList<Product2<Object, Object>>();
+    final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>();
```

Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this.

## How was this patch tested?

Manual.
Pass the existing tests.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11541 from dongjoon-hyun/SPARK-13702.

8 files changed, 12 insertions, 12 deletions

diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java
index 60f25e5cce..40b9c35adc 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java
@@ -56,7 +56,7 @@ public class JavaDecisionTreeClassifierSuite implements Serializable {
 
     JavaRDD<LabeledPoint> data = sc.parallelize(
       LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
-    Map<Integer, Integer> categoricalFeatures = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeatures = new HashMap<>();
     DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2);
 
     // This tests setters. Training with various options is tested in Scala.
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java
index 3c69467fa1..59b6fba7a9 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java
@@ -56,7 +56,7 @@ public class JavaGBTClassifierSuite implements Serializable {
 
     JavaRDD<LabeledPoint> data = sc.parallelize(
       LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
-    Map<Integer, Integer> categoricalFeatures = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeatures = new HashMap<>();
     DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2);
 
     // This tests setters. Training with various options is tested in Scala.
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
index a66a1e1292..5485fcbf01 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
@@ -57,7 +57,7 @@ public class JavaRandomForestClassifierSuite implements Serializable {
 
     JavaRDD<LabeledPoint> data = sc.parallelize(
       LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
-    Map<Integer, Integer> categoricalFeatures = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeatures = new HashMap<>();
     DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2);
 
     // This tests setters. Training with various options is tested in Scala.
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java
index ebe800e749..d5c9d120c5 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java
@@ -56,7 +56,7 @@ public class JavaDecisionTreeRegressorSuite implements Serializable {
 
     JavaRDD<LabeledPoint> data = sc.parallelize(
       LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
-    Map<Integer, Integer> categoricalFeatures = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeatures = new HashMap<>();
     DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0);
 
     // This tests setters. Training with various options is tested in Scala.
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java
index fc8c13db07..38d15dc2b7 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java
@@ -56,7 +56,7 @@ public class JavaGBTRegressorSuite implements Serializable {
 
     JavaRDD<LabeledPoint> data = sc.parallelize(
       LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
-    Map<Integer, Integer> categoricalFeatures = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeatures = new HashMap<>();
     DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0);
 
     GBTRegressor rf = new GBTRegressor()
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
index a00ce5e249..31be8880c2 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
@@ -57,7 +57,7 @@ public class JavaRandomForestRegressorSuite implements Serializable {
 
     JavaRDD<LabeledPoint> data = sc.parallelize(
       LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
-    Map<Integer, Integer> categoricalFeatures = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeatures = new HashMap<>();
     DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0);
 
     // This tests setters. Training with various options is tested in Scala.
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index 225a216270..db19b309f6 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -45,9 +45,9 @@ public class JavaLDASuite implements Serializable {
   @Before
   public void setUp() {
     sc = new JavaSparkContext("local", "JavaLDA");
-    ArrayList<Tuple2<Long, Vector>> tinyCorpus = new ArrayList<Tuple2<Long, Vector>>();
+    ArrayList<Tuple2<Long, Vector>> tinyCorpus = new ArrayList<>();
     for (int i = 0; i < LDASuite.tinyCorpus().length; i++) {
-      tinyCorpus.add(new Tuple2<Long, Vector>((Long)LDASuite.tinyCorpus()[i]._1(),
+      tinyCorpus.add(new Tuple2<>((Long)LDASuite.tinyCorpus()[i]._1(),
           LDASuite.tinyCorpus()[i]._2()));
     }
     JavaRDD<Tuple2<Long, Vector>> tmpCorpus = sc.parallelize(tinyCorpus, 2);
@@ -189,8 +189,8 @@ public class JavaLDASuite implements Serializable {
     double logPerplexity = toyModel.logPerplexity(pairedDocs);
 
     // check: logLikelihood.
-    ArrayList<Tuple2<Long, Vector>> docsSingleWord = new ArrayList<Tuple2<Long, Vector>>();
-    docsSingleWord.add(new Tuple2<Long, Vector>(0L, Vectors.dense(1.0, 0.0, 0.0)));
+    ArrayList<Tuple2<Long, Vector>> docsSingleWord = new ArrayList<>();
+    docsSingleWord.add(new Tuple2<>(0L, Vectors.dense(1.0, 0.0, 0.0)));
     JavaPairRDD<Long, Vector> single = JavaPairRDD.fromJavaRDD(sc.parallelize(docsSingleWord));
     double logLikelihood = toyModel.logLikelihood(single);
   }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java b/mllib/src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java
index 9925aae441..8dd29061da 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java
@@ -64,7 +64,7 @@ public class JavaDecisionTreeSuite implements Serializable {
   public void runDTUsingConstructor() {
     List<LabeledPoint> arr = DecisionTreeSuite.generateCategoricalDataPointsAsJavaList();
     JavaRDD<LabeledPoint> rdd = sc.parallelize(arr);
-    HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
     categoricalFeaturesInfo.put(1, 2); // feature 1 has 2 categories
 
     int maxDepth = 4;
@@ -84,7 +84,7 @@ public class JavaDecisionTreeSuite implements Serializable {
   public void runDTUsingStaticMethods() {
     List<LabeledPoint> arr = DecisionTreeSuite.generateCategoricalDataPointsAsJavaList();
     JavaRDD<LabeledPoint> rdd = sc.parallelize(arr);
-    HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
     categoricalFeaturesInfo.put(1, 2); // feature 1 has 2 categories
 
     int maxDepth = 4;