[SPARK-7573] [ML] OneVsRest cleanups

Minor cleanups discussed with [~mengxr]:
* move OneVsRest from reduction to classification sub-package
* make model constructor private

Some doc cleanups too

CC: harsha2010  Could you please verify this looks OK?  Thanks!

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #6097 from jkbradley/onevsrest-cleanup and squashes the following commits:

4ecd48d [Joseph K. Bradley] org imports
430b065 [Joseph K. Bradley] moved OneVsRest from reduction subpackage to classification.  small java doc style fixes
9f8b9b9 [Joseph K. Bradley] Small cleanups to OneVsRest.  Made model constructor private to ml package.

2 files changed, 8 insertions, 14 deletions

diff --git a/mllib/src/test/java/org/apache/spark/ml/reduction/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
index 40a90ae9de..a1ee554152 100644
--- a/mllib/src/test/java/org/apache/spark/ml/reduction/JavaOneVsRestSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
@@ -15,21 +15,20 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.reduction;
+package org.apache.spark.ml.classification;
 
 import java.io.Serializable;
 import java.util.List;
 
+import static scala.collection.JavaConversions.seqAsJavaList;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
-import static scala.collection.JavaConversions.seqAsJavaList;
-
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.ml.classification.LogisticRegression;
 import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateMultinomialLogisticInput;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.DataFrame;
@@ -48,10 +47,8 @@ public class JavaOneVsRestSuite implements Serializable {
         jsql = new SQLContext(jsc);
         int nPoints = 3;
 
-        /**
-         * The following weights and xMean/xVariance are computed from iris dataset with lambda = 0.2.
-         * As a result, we are actually drawing samples from probability distribution of built model.
-         */
+        // The following weights and xMean/xVariance are computed from iris dataset with lambda=0.2.
+        // As a result, we are drawing samples from probability distribution of an actual model.
         double[] weights = {
                 -0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
                 -0.16624, -0.84355, -0.048509, -0.301789, 4.170682 };
diff --git a/mllib/src/test/scala/org/apache/spark/ml/reduction/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
index ebec7c68e8..e65ffae918 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/reduction/OneVsRestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
@@ -15,12 +15,11 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.reduction
+package org.apache.spark.ml.classification
 
 import org.scalatest.FunSuite
 
 import org.apache.spark.ml.attribute.NominalAttribute
-import org.apache.spark.ml.classification.{LogisticRegressionModel, LogisticRegression}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.classification.LogisticRegressionSuite._
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
@@ -42,10 +41,8 @@ class OneVsRestSuite extends FunSuite with MLlibTestSparkContext {
     sqlContext = new SQLContext(sc)
     val nPoints = 1000
 
-    /**
-     * The following weights and xMean/xVariance are computed from iris dataset with lambda = 0.2.
-     * As a result, we are actually drawing samples from probability distribution of built model.
-     */
+    // The following weights and xMean/xVariance are computed from iris dataset with lambda=0.2.
+    // As a result, we are drawing samples from probability distribution of an actual model.
     val weights = Array(
       -0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
       -0.16624, -0.84355, -0.048509, -0.301789, 4.170682)