[SPARK-10763][ML][JAVA][TEST] Update Java MLLIB/ML tests to use simplified dataframe construction

mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java

@@ -19,6 +19,7 @@
 
 import java.io.Serializable;
 import java.util.Arrays;
+import java.util.List;
 
 import org.junit.After;
 import org.junit.Before;
@@ -75,21 +76,20 @@ public void naiveBayesDefaultParams() {
 
   @Test
   public void testNaiveBayes() {
-    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+    List<Row> data = Arrays.asList(
       RowFactory.create(0.0, Vectors.dense(1.0, 0.0, 0.0)),
       RowFactory.create(0.0, Vectors.dense(2.0, 0.0, 0.0)),
       RowFactory.create(1.0, Vectors.dense(0.0, 1.0, 0.0)),
       RowFactory.create(1.0, Vectors.dense(0.0, 2.0, 0.0)),
       RowFactory.create(2.0, Vectors.dense(0.0, 0.0, 1.0)),
-      RowFactory.create(2.0, Vectors.dense(0.0, 0.0, 2.0))
-    ));
+      RowFactory.create(2.0, Vectors.dense(0.0, 0.0, 2.0)));
 
     StructType schema = new StructType(new StructField[]{
       new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
       new StructField("features", new VectorUDT(), false, Metadata.empty())
     });
 
-    DataFrame dataset = jsql.createDataFrame(jrdd, schema);
+    DataFrame dataset = jsql.createDataFrame(data, schema);
     NaiveBayes nb = new NaiveBayes().setSmoothing(0.5).setModelType("multinomial");
     NaiveBayesModel model = nb.fit(dataset);
 

mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java

@@ -55,16 +55,16 @@ public void tearDown() {
   public void bucketizerTest() {
     double[] splits = {-0.5, 0.0, 0.5};
 
-    JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
-      RowFactory.create(-0.5),
-      RowFactory.create(-0.3),
-      RowFactory.create(0.0),
-      RowFactory.create(0.2)
-    ));
     StructType schema = new StructType(new StructField[] {
       new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
     });
-    DataFrame dataset = jsql.createDataFrame(data, schema);
+    DataFrame dataset = jsql.createDataFrame(
+      Arrays.asList(
+        RowFactory.create(-0.5),
+        RowFactory.create(-0.3),
+        RowFactory.create(0.0),
+        RowFactory.create(0.2)),
+      schema);
 
     Bucketizer bucketizer = new Bucketizer()
       .setInputCol("feature")

mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java

@@ -57,12 +57,11 @@ public void tearDown() {
   @Test
   public void javaCompatibilityTest() {
     double[] input = new double[] {1D, 2D, 3D, 4D};
-    JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
-      RowFactory.create(Vectors.dense(input))
-    ));
-    DataFrame dataset = jsql.createDataFrame(data, new StructType(new StructField[]{
-      new StructField("vec", (new VectorUDT()), false, Metadata.empty())
-    }));
+    DataFrame dataset = jsql.createDataFrame(
+      Arrays.asList(RowFactory.create(Vectors.dense(input))),
+      new StructType(new StructField[]{
+        new StructField("vec", (new VectorUDT()), false, Metadata.empty())
+      }));
 
     double[] expectedResult = input.clone();
     (new DoubleDCT_1D(input.length)).forward(expectedResult, true);

mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java

@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature;
 
 import java.util.Arrays;
+import java.util.List;
 
 import org.junit.After;
 import org.junit.Assert;
@@ -55,17 +56,17 @@ public void tearDown() {
 
   @Test
   public void hashingTF() {
-    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+    List<Row> data = Arrays.asList(
       RowFactory.create(0.0, "Hi I heard about Spark"),
       RowFactory.create(0.0, "I wish Java could use case classes"),
       RowFactory.create(1.0, "Logistic regression models are neat")
-    ));
+    );
     StructType schema = new StructType(new StructField[]{
       new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
       new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
     });
 
-    DataFrame sentenceData = jsql.createDataFrame(jrdd, schema);
+    DataFrame sentenceData = jsql.createDataFrame(data, schema);
     Tokenizer tokenizer = new Tokenizer()
       .setInputCol("sentence")
       .setOutputCol("words");

mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java

@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature;
 
 import java.util.Arrays;
+import java.util.List;
 
 import org.junit.After;
 import org.junit.Assert;
@@ -60,7 +61,7 @@ public void polynomialExpansionTest() {
       .setOutputCol("polyFeatures")
       .setDegree(3);
 
-    JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
+    List<Row> data = Arrays.asList(
       RowFactory.create(
         Vectors.dense(-2.0, 2.3),
         Vectors.dense(-2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17)
@@ -70,7 +71,7 @@ public void polynomialExpansionTest() {
         Vectors.dense(0.6, -1.1),
         Vectors.dense(0.6, 0.36, 0.216, -1.1, -0.66, -0.396, 1.21, 0.726, -1.331)
       )
-    ));
+    );
 
     StructType schema = new StructType(new StructField[] {
       new StructField("features", new VectorUDT(), false, Metadata.empty()),

mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java

@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature;
 
 import java.util.Arrays;
+import java.util.List;
 
 import org.junit.After;
 import org.junit.Before;
@@ -58,14 +59,14 @@ public void javaCompatibilityTest() {
       .setInputCol("raw")
       .setOutputCol("filtered");
 
-    JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
+    List<Row> data = Arrays.asList(
       RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
       RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb"))
-    ));
+    );
     StructType schema = new StructType(new StructField[] {
       new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
     });
-    DataFrame dataset = jsql.createDataFrame(rdd, schema);
+    DataFrame dataset = jsql.createDataFrame(data, schema);
 
     remover.transform(dataset).collect();
   }

mllib/src/test/java/org/apache/spark/ml/feature/JavaStringIndexerSuite.java

@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature;
 
 import java.util.Arrays;
+import java.util.List;
 
 import org.junit.After;
 import org.junit.Assert;
@@ -56,9 +57,9 @@ public void testStringIndexer() {
       createStructField("id", IntegerType, false),
       createStructField("label", StringType, false)
     });
-    JavaRDD<Row> rdd = jsc.parallelize(
-      Arrays.asList(c(0, "a"), c(1, "b"), c(2, "c"), c(3, "a"), c(4, "a"), c(5, "c")));
-    DataFrame dataset = sqlContext.createDataFrame(rdd, schema);
+    List<Row> data = Arrays.asList(
+      c(0, "a"), c(1, "b"), c(2, "c"), c(3, "a"), c(4, "a"), c(5, "c"));
+    DataFrame dataset = sqlContext.createDataFrame(data, schema);
 
     StringIndexer indexer = new StringIndexer()
       .setInputCol("label")

mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorAssemblerSuite.java

@@ -65,8 +65,7 @@ public void testVectorAssembler() {
     Row row = RowFactory.create(
       0, 0.0, Vectors.dense(1.0, 2.0), "a",
       Vectors.sparse(2, new int[] {1}, new double[] {3.0}), 10L);
-    JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(row));
-    DataFrame dataset = sqlContext.createDataFrame(rdd, schema);
+    DataFrame dataset = sqlContext.createDataFrame(Arrays.asList(row), schema);
     VectorAssembler assembler = new VectorAssembler()
       .setInputCols(new String[] {"x", "y", "z", "n"})
       .setOutputCol("features");

mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java

@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature;
 
 import java.util.Arrays;
+import java.util.List;
 
 import org.junit.After;
 import org.junit.Assert;
@@ -63,12 +64,12 @@ public void vectorSlice() {
     };
     AttributeGroup group = new AttributeGroup("userFeatures", attrs);
 
-    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+    List<Row> data = Arrays.asList(
       RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})),
       RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0))
-    ));
+    );
 
-    DataFrame dataset = jsql.createDataFrame(jrdd, (new StructType()).add(group.toStructField()));
+    DataFrame dataset = jsql.createDataFrame(data, (new StructType()).add(group.toStructField()));
 
     VectorSlicer vectorSlicer = new VectorSlicer()
       .setInputCol("userFeatures").setOutputCol("features");

mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java

@@ -51,15 +51,15 @@ public void tearDown() {
 
   @Test
   public void testJavaWord2Vec() {
-    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
-      RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
-      RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
-      RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))
-    ));
     StructType schema = new StructType(new StructField[]{
       new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
     });
-    DataFrame documentDF = sqlContext.createDataFrame(jrdd, schema);
+    DataFrame documentDF = sqlContext.createDataFrame(
+      Arrays.asList(
+        RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
+        RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
+        RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))),
+      schema);
 
     Word2Vec word2Vec = new Word2Vec()
       .setInputCol("text")