Added several Java-friendly APIs + unit tests: NaiveBayes, GaussianMi…

…xture, LDA, StreamingKMeans, Statistics.corr, params

mllib/src/main/scala/org/apache/spark/ml/param/params.scala

@@ -69,14 +69,10 @@ class Param[T](val parent: String, val name: String, val doc: String, val isVali
     }
   }
 
-  /**
-   * Creates a param pair with the given value (for Java).
-   */
+  /** Creates a param pair with the given value (for Java). */
   def w(value: T): ParamPair[T] = this -> value
 
-  /**
-   * Creates a param pair with the given value (for Scala).
-   */
+  /** Creates a param pair with the given value (for Scala). */
   def ->(value: T): ParamPair[T] = ParamPair(this, value)
 
   override final def toString: String = s"${parent}__$name"
@@ -190,6 +186,7 @@ class DoubleParam(parent: String, name: String, doc: String, isValid: Double =>
 
   def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc)
 
+  /** Creates a param pair with the given value (for Java). */
   override def w(value: Double): ParamPair[Double] = super.w(value)
 }
 
@@ -209,6 +206,7 @@ class IntParam(parent: String, name: String, doc: String, isValid: Int => Boolea
 
   def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc)
 
+  /** Creates a param pair with the given value (for Java). */
   override def w(value: Int): ParamPair[Int] = super.w(value)
 }
 
@@ -228,6 +226,7 @@ class FloatParam(parent: String, name: String, doc: String, isValid: Float => Bo
 
   def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc)
 
+  /** Creates a param pair with the given value (for Java). */
   override def w(value: Float): ParamPair[Float] = super.w(value)
 }
 
@@ -247,6 +246,7 @@ class LongParam(parent: String, name: String, doc: String, isValid: Long => Bool
 
   def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc)
 
+  /** Creates a param pair with the given value (for Java). */
   override def w(value: Long): ParamPair[Long] = super.w(value)
 }
 
@@ -260,6 +260,7 @@ class BooleanParam(parent: String, name: String, doc: String) // No need for isV
 
   def this(parent: Identifiable, name: String, doc: String) = this(parent.uid, name, doc)
 
+  /** Creates a param pair with the given value (for Java). */
   override def w(value: Boolean): ParamPair[Boolean] = super.w(value)
 }
 
@@ -274,8 +275,6 @@ class StringArrayParam(parent: Params, name: String, doc: String, isValid: Array
   def this(parent: Params, name: String, doc: String) =
     this(parent, name, doc, ParamValidators.alwaysTrue)
 
-  override def w(value: Array[String]): ParamPair[Array[String]] = super.w(value)
-
   /** Creates a param pair with a [[java.util.List]] of values (for Java and Python). */
   def w(value: java.util.List[String]): ParamPair[Array[String]] = w(value.asScala.toArray)
 }
@@ -291,10 +290,9 @@ class DoubleArrayParam(parent: Params, name: String, doc: String, isValid: Array
   def this(parent: Params, name: String, doc: String) =
     this(parent, name, doc, ParamValidators.alwaysTrue)
 
-  override def w(value: Array[Double]): ParamPair[Array[Double]] = super.w(value)
-
   /** Creates a param pair with a [[java.util.List]] of values (for Java and Python). */
-  def w(value: java.util.List[Double]): ParamPair[Array[Double]] = w(value.asScala.toArray)
+  def w(value: java.util.List[java.lang.Double]): ParamPair[Array[Double]] =
+    w(value.asScala.map(_.asInstanceOf[Double]).toArray)
 }
 
 /**

mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala

@@ -24,6 +24,7 @@ import scala.collection.JavaConverters._
 import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
+import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.{Logging, SparkContext, SparkException}
 import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, SparseVector, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint

mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala

@@ -22,6 +22,7 @@ import scala.collection.mutable.IndexedSeq
 import breeze.linalg.{diag, DenseMatrix => BreezeMatrix, DenseVector => BDV, Vector => BV}
 
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, Matrices, Vector, Vectors}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 import org.apache.spark.mllib.util.MLUtils
@@ -188,7 +189,10 @@ class GaussianMixture private (
     new GaussianMixtureModel(weights, gaussians)
   }
 
-  /** Average of dense breeze vectors */
+  /** Java-friendly version of [[run()]] */
+  def run(data: JavaRDD[Vector]): GaussianMixtureModel = run(data.rdd)
+
+    /** Average of dense breeze vectors */
   private def vectorMean(x: IndexedSeq[BV[Double]]): BDV[Double] = {
     val v = BDV.zeros[Double](x(0).length)
     x.foreach(xi => v += xi)

mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala

@@ -25,6 +25,7 @@ import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Matrices, Matrix}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 import org.apache.spark.mllib.util.{MLUtils, Loader, Saveable}
@@ -46,7 +47,7 @@ import org.apache.spark.sql.{SQLContext, Row}
 @Experimental
 class GaussianMixtureModel(
   val weights: Array[Double],
-  val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable{
+  val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable {
 
   require(weights.length == gaussians.length, "Length of weight and Gaussian arrays must match")
 
@@ -65,6 +66,10 @@ class GaussianMixtureModel(
     responsibilityMatrix.map(r => r.indexOf(r.max))
   }
 
+  /** Java-friendly version of [[predict()]] */
+  def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
+    predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
+
   /**
    * Given the input vectors, return the membership value of each vector
    * to all mixture components.

mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala

@@ -20,6 +20,7 @@ package org.apache.spark.mllib.clustering
 import breeze.linalg.{DenseMatrix => BDM, normalize, sum => brzSum}
 
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaPairRDD
 import org.apache.spark.graphx.{VertexId, EdgeContext, Graph}
 import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix}
 import org.apache.spark.rdd.RDD
@@ -345,6 +346,12 @@ class DistributedLDAModel private (
     }
   }
 
+  /** Java-friendly version of [[topicDistributions]] */
+  def javaTopicDistributions: JavaPairRDD[java.lang.Long, Vector] = {
+    new JavaPairRDD[java.lang.Long, Vector](
+      topicDistributions.asInstanceOf[RDD[(java.lang.Long, Vector)]])
+  }
+
   // TODO:
   // override def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = ???
 

mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala

@@ -21,8 +21,10 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaSparkContext._
 import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
 import org.apache.spark.rdd.RDD
+import org.apache.spark.streaming.api.java.{JavaPairDStream, JavaDStream}
 import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.util.Utils
 import org.apache.spark.util.random.XORShiftRandom
@@ -234,6 +236,9 @@ class StreamingKMeans(
     }
   }
 
+  /** Java-friendly version of `trainOn`. */
+  def trainOn(data: JavaDStream[Vector]): Unit = trainOn(data.dstream)
+
   /**
    * Use the clustering model to make predictions on batches of data from a DStream.
    *
@@ -245,6 +250,11 @@ class StreamingKMeans(
     data.map(model.predict)
   }
 
+  /** Java-friendly version of `predictOn`. */
+  def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Integer] = {
+    JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Integer]])
+  }
+
   /**
    * Use the model to make predictions on the values of a DStream and carry over its keys.
    *
@@ -257,6 +267,14 @@ class StreamingKMeans(
     data.mapValues(model.predict)
   }
 
+  /** Java-friendly version of `predictOnValues`. */
+  def predictOnValues[K](
+      data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Integer] = {
+    implicit val tag = fakeClassTag[K]
+    JavaPairDStream.fromPairDStream(
+      predictOnValues(data.dstream).asInstanceOf[DStream[(K, java.lang.Integer)]])
+  }
+
   /** Check whether cluster centers have been initialized. */
   private[this] def assertInitialized(): Unit = {
     if (model.clusterCenters == null) {

mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala

@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.stat
 
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.linalg.{Matrix, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -80,6 +81,10 @@ object Statistics {
    */
   def corr(x: RDD[Double], y: RDD[Double]): Double = Correlations.corr(x, y)
 
+  /** Java-friendly version of [[corr()]] */
+  def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double]): Double =
+    corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]])
+
   /**
    * Compute the correlation for the input RDDs using the specified method.
    * Methods currently supported: `pearson` (default), `spearman`.
@@ -96,6 +101,9 @@ object Statistics {
    */
   def corr(x: RDD[Double], y: RDD[Double], method: String): Double = Correlations.corr(x, y, method)
 
+  def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double], method: String): Double =
+    corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]], method)
+
   /**
    * Conduct Pearson's chi-squared goodness of fit test of the observed data against the
    * expected distribution.

mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java

@@ -50,6 +50,7 @@ public void testParams() {
     testParams.setMyIntParam(2).setMyDoubleParam(0.4).setMyStringParam("a");
     Assert.assertEquals(testParams.getMyDoubleParam(), 0.4, 0.0);
     Assert.assertEquals(testParams.getMyStringParam(), "a");
+    Assert.assertEquals(testParams.getMyDoubleArrayParam(), new double[]{1.0, 2.0});
   }
 
   @Test

mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java

@@ -72,15 +72,31 @@ public JavaTestParams setMyStringParam(String value) {
     set(myStringParam_, value); return this;
   }
 
+  private DoubleArrayParam myDoubleArrayParam_;
+  public DoubleArrayParam myDoubleArrayParam() { return myDoubleArrayParam_; }
+
+  public double[] getMyDoubleArrayParam() { return getOrDefault(myDoubleArrayParam_); }
+
+  public JavaTestParams setMyDoubleArrayParam(double[] value) {
+    set(myDoubleArrayParam_, value); return this;
+  }
+
   private void init() {
-    myIntParam_ = new IntParam(this, "myIntParam", "this is an int param", ParamValidators.gt(0));
+    myIntParam_ = new IntParam(this, "myIntParam", "this is an int param",
+      ParamValidators.gt(0));
     myDoubleParam_ = new DoubleParam(this, "myDoubleParam", "this is a double param",
       ParamValidators.inRange(0.0, 1.0));
     List<String> validStrings = Lists.newArrayList("a", "b");
     myStringParam_ = new Param<String>(this, "myStringParam", "this is a string param",
       ParamValidators.inArray(validStrings));
+    myDoubleArrayParam_ =
+      new DoubleArrayParam(this, "myDoubleArrayParam", "this is a double param");
+
     setDefault(myIntParam_, 1);
+    setDefault(myIntParam_.w(1));
     setDefault(myDoubleParam_, 0.5);
     setDefault(myIntParam().w(1), myDoubleParam().w(0.5));
+    setDefault(myDoubleArrayParam_, new double[]{1.0, 2.0});
+    setDefault(myDoubleArrayParam_.w(new double[]{1.0, 2.0}));
   }
 }

mllib/src/test/java/org/apache/spark/ml/classification/JavaStreamingLogisticRegressionSuite.java → mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java

@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.classification;
+package org.apache.spark.mllib.classification;
 
 import java.io.Serializable;
 import java.util.List;
@@ -28,7 +28,6 @@
 import org.junit.Test;
 
 import org.apache.spark.SparkConf;
-import org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD;
 import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.mllib.regression.LabeledPoint;

mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java

@@ -0,0 +1,64 @@
+/*
+ * 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.spark.mllib.clustering;
+
+import java.io.Serializable;
+import java.util.List;
+
+import com.google.common.collect.Lists;
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+
+import static org.junit.Assert.assertEquals;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+
+public class JavaGaussianMixtureSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaGaussianMixture");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void runGaussianMixture() {
+    List<Vector> points = Lists.newArrayList(
+      Vectors.dense(1.0, 2.0, 6.0),
+      Vectors.dense(1.0, 3.0, 0.0),
+      Vectors.dense(1.0, 4.0, 6.0)
+    );
+
+    JavaRDD<Vector> data = sc.parallelize(points, 2);
+    GaussianMixtureModel model = new GaussianMixture().setK(2).setMaxIterations(1).setSeed(1234)
+      .run(data);
+    assertEquals(model.gaussians().length, 2);
+    JavaRDD<Integer> predictions = model.predict(data);
+    predictions.first();
+  }
+}

mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java

@@ -107,6 +107,10 @@ public void distributedLDAModel() {
     // Check: log probabilities
     assert(model.logLikelihood() < 0.0);
     assert(model.logPrior() < 0.0);
+
+    // Check: topic distributions
+    JavaPairRDD<Long, Vector> topicDistributions = model.javaTopicDistributions();
+    assertEquals(topicDistributions.count(), corpus.count());
   }
 
   @Test