clean up

Author: mengxr

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

@@ -60,34 +60,34 @@ class Param[T] (
 
 // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ...
 
-/** Specialized version of [[Param[Double]] for Java. */
+/** Specialized version of [[Param[Double]]] for Java. */
 class DoubleParam(parent: Params, name: String, doc: String, default: Option[Double] = None)
     extends Param[Double](parent, name, doc, default) {
-  override def w(value: Double): ParamPair[Double] = ParamPair(this, value)
+  override def w(value: Double): ParamPair[Double] = super.w(value)
 }
 
-/** Specialized version of [[Param[Int]] for Java. */
+/** Specialized version of [[Param[Int]]] for Java. */
 class IntParam(parent: Params, name: String, doc: String, default: Option[Int] = None)
     extends Param[Int](parent, name, doc, default) {
-  override def w(value: Int): ParamPair[Int] = ParamPair(this, value)
+  override def w(value: Int): ParamPair[Int] = super.w(value)
 }
 
-/** Specialized version of [[Param[Float]] for Java. */
+/** Specialized version of [[Param[Float]]] for Java. */
 class FloatParam(parent: Params, name: String, doc: String, default: Option[Float] = None)
     extends Param[Float](parent, name, doc, default) {
-  override def w(value: Float): ParamPair[Float] = ParamPair(this, value)
+  override def w(value: Float): ParamPair[Float] = super.w(value)
 }
 
-/** Specialized version of [[Param[Long]] for Java. */
+/** Specialized version of [[Param[Long]]] for Java. */
 class LongParam(parent: Params, name: String, doc: String, default: Option[Long] = None)
     extends Param[Long](parent, name, doc, default) {
-  override def w(value: Long): ParamPair[Long] = ParamPair(this, value)
+  override def w(value: Long): ParamPair[Long] = super.w(value)
 }
 
-/** Specilized version of [[Param[Boolean]] for Java. */
+/** Specialized version of [[Param[Boolean]]] for Java. */
 class BooleanParam(parent: Params, name: String, doc: String, default: Option[Boolean] = None)
     extends Param[Boolean](parent, name, doc, default) {
-  override def w(value: Boolean): ParamPair[Boolean] = ParamPair(this, value)
+  override def w(value: Boolean): ParamPair[Boolean] = super.w(value)
 }
 
 /**

mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala

@@ -28,7 +28,7 @@ import org.apache.spark.sql.{SchemaRDD, StructType}
 /**
  * Params for [[CrossValidator]] and [[CrossValidatorModel]].
  */
-trait CrossValidatorParams extends Params {
+private[ml] trait CrossValidatorParams extends Params {
   /** param for the estimator to be cross-validated */
   val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection")
   def getEstimator: Estimator[_] = get(estimator)

mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala

@@ -25,25 +25,14 @@ import org.apache.spark.sql.test.TestSQLContext._
 
 class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
 
-  var dataset: SchemaRDD = _
-
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    dataset = sparkContext.parallelize(generateLogisticInput(1.0, 1.0, 1000, 42), 2)
-  }
-
-  override def afterAll(): Unit = {
-    dataset = null
-    super.afterAll()
-  }
+  var dataset: SchemaRDD = sparkContext.parallelize(generateLogisticInput(1.0, 1.0, 1000, 42), 2)
 
   test("logistic regression") {
     val lr = new LogisticRegression
     val model = lr.fit(dataset)
     model.transform(dataset)
       .select('label, 'prediction)
       .collect()
-      .foreach(println)
   }
 
   test("logistic regression with setters") {
@@ -52,15 +41,15 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
       .setRegParam(1.0)
     val model = lr.fit(dataset)
     model.transform(dataset, model.threshold -> 0.8) // overwrite threshold
-      .select('label, 'score, 'prediction).collect()
-      .foreach(println)
+      .select('label, 'score, 'prediction)
+      .collect()
   }
 
   test("logistic regression fit and transform with varargs") {
     val lr = new LogisticRegression
     val model = lr.fit(dataset, lr.maxIter -> 10, lr.regParam -> 1.0)
     model.transform(dataset, model.threshold -> 0.8, model.scoreCol -> "probability")
       .select('label, 'probability, 'prediction)
-      .foreach(println)
+      .collect()
   }
 }