[SPARK-9661] [MLLIB] [ML] Java compatibility

I skimmed through the docs for various instance of Object and replaced them with Java compaible versions of the same.

1. Some methods in LDAModel.
2. runMiniBatchSGD
3. kolmogorovSmirnovTest

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8126 from MechCoder/java_incop.

2 files changed, 46 insertions, 0 deletions

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 d272a42c85..427be9430d 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
@@ -124,6 +124,10 @@ public class JavaLDASuite implements Serializable {
         }
     });
     assertEquals(topicDistributions.count(), nonEmptyCorpus.count());
+
+    // Check: javaTopTopicsPerDocuments
+    JavaRDD<scala.Tuple3<java.lang.Long, int[], java.lang.Double[]>> topTopics =
+      model.javaTopTopicsPerDocument(3);
   }
 
   @Test
@@ -160,11 +164,31 @@ public class JavaLDASuite implements Serializable {
     assertEquals(roundedLocalTopicSummary.length, k);
   }
 
+  @Test
+  public void localLdaMethods() {
+    JavaRDD<Tuple2<Long, Vector>> docs = sc.parallelize(toyData, 2);
+    JavaPairRDD<Long, Vector> pairedDocs = JavaPairRDD.fromJavaRDD(docs);
+
+    // check: topicDistributions
+    assertEquals(toyModel.topicDistributions(pairedDocs).count(), pairedDocs.count());
+
+    // check: logPerplexity
+    double logPerplexity = toyModel.logPerplexity(pairedDocs);
+
+    // check: logLikelihood.
+    ArrayList<Tuple2<Long, Vector>> docsSingleWord = new ArrayList<Tuple2<Long, Vector>>();
+    docsSingleWord.add(new Tuple2<Long, Vector>(Long.valueOf(0), Vectors.dense(1.0, 0.0, 0.0)));
+    JavaPairRDD<Long, Vector> single = JavaPairRDD.fromJavaRDD(sc.parallelize(docsSingleWord));
+    double logLikelihood = toyModel.logLikelihood(single);
+  }
+
   private static int tinyK = LDASuite$.MODULE$.tinyK();
   private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize();
   private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics();
   private static Tuple2<int[], double[]>[] tinyTopicDescription =
       LDASuite$.MODULE$.tinyTopicDescription();
   private JavaPairRDD<Long, Vector> corpus;
+  private LocalLDAModel toyModel = LDASuite$.MODULE$.toyModel();
+  private ArrayList<Tuple2<Long, Vector>> toyData = LDASuite$.MODULE$.javaToyData();
 
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
index 62f7f26b7c..eb4e369862 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
@@ -27,7 +27,12 @@ import org.junit.Test;
 import static org.junit.Assert.assertEquals;
 
 import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaDoubleRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.stat.test.ChiSqTestResult;
+import org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult;
 
 public class JavaStatisticsSuite implements Serializable {
   private transient JavaSparkContext sc;
@@ -53,4 +58,21 @@ public class JavaStatisticsSuite implements Serializable {
     // Check default method
     assertEquals(corr1, corr2);
   }
+
+  @Test
+  public void kolmogorovSmirnovTest() {
+    JavaDoubleRDD data = sc.parallelizeDoubles(Lists.newArrayList(0.2, 1.0, -1.0, 2.0));
+    KolmogorovSmirnovTestResult testResult1 = Statistics.kolmogorovSmirnovTest(data, "norm");
+    KolmogorovSmirnovTestResult testResult2 = Statistics.kolmogorovSmirnovTest(
+      data, "norm", 0.0, 1.0);
+  }
+
+  @Test
+  public void chiSqTest() {
+    JavaRDD<LabeledPoint> data = sc.parallelize(Lists.newArrayList(
+      new LabeledPoint(0.0, Vectors.dense(0.1, 2.3)),
+      new LabeledPoint(1.0, Vectors.dense(1.5, 5.1)),
+      new LabeledPoint(0.0, Vectors.dense(2.4, 8.1))));
+    ChiSqTestResult[] testResults = Statistics.chiSqTest(data);
+  }
 }