doc updates

Author: mengxr

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

@@ -151,12 +151,13 @@ trait Params extends Identifiable with Serializable {
   private[ml] def getParam(paramName: String): Param[Any] = {
     val m = this.getClass.getMethod(paramName)
     assert(Modifier.isPublic(m.getModifiers) &&
-      classOf[Param[_]].isAssignableFrom(m.getReturnType))
+      classOf[Param[_]].isAssignableFrom(m.getReturnType) &&
+      m.getParameterTypes.isEmpty)
     m.invoke(this).asInstanceOf[Param[Any]]
   }
 
   /**
-   * Sets a parameter in the own parameter map.
+   * Sets a parameter in the embedded param map.
    */
   private[ml] def set[T](param: Param[T], value: T): this.type = {
     require(param.parent.eq(this))
@@ -165,7 +166,7 @@ trait Params extends Identifiable with Serializable {
   }
 
   /**
-   * Gets the value of a parameter.
+   * Gets the value of a parameter in the embedded param map.
    */
   private[ml] def get[T](param: Param[T]): T = {
     require(param.parent.eq(this))
@@ -180,11 +181,6 @@ trait Params extends Identifiable with Serializable {
 
 private[ml] object Params {
 
-  /**
-   * Returns an empty Params implementation without any params.
-   */
-  val empty: Params = new Params {}
-
   /**
    * Copies parameter values from the parent estimator to the child model it produced.
    * @param paramMap the param map that holds parameters of the parent
@@ -224,7 +220,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten
   }
 
   /**
-   * Puts a param pair (overwrites if the input param exists).
+   * Puts a list of param pairs (overwrites if the input params exists).
    */
   def put(paramPairs: ParamPair[_]*): this.type = {
     paramPairs.foreach { p =>

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

@@ -33,15 +33,15 @@ class ParamGridBuilder {
   private val paramGrid = mutable.Map.empty[Param[_], Iterable[_]]
 
   /**
-   * Builds base on parameters with fixed values.
+   * Sets the given parameters in this grid to fixed values.
    */
   def baseOn(paramMap: ParamMap): this.type = {
     baseOn(paramMap.toSeq: _*)
     this
   }
 
   /**
-   * Builds base on parameters with fixed values.
+   * Sets the given parameters in this grid to fixed values.
    */
   @varargs
   def baseOn(paramPairs: ParamPair[_]*): this.type = {
@@ -59,7 +59,7 @@ class ParamGridBuilder {
     this
   }
 
-  // specialized versions of addMulti for Java.
+  // specialized versions of addGrid for Java.
 
   /**
    * Adds a double param with multiple values.

mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java

@@ -28,7 +28,6 @@
 import org.apache.spark.ml.feature.StandardScaler;
 import org.apache.spark.sql.api.java.JavaSQLContext;
 import org.apache.spark.sql.api.java.JavaSchemaRDD;
-import org.apache.spark.sql.api.java.Row;
 import static org.apache.spark.mllib.classification.LogisticRegressionSuite
   .generateLogisticInputAsList;
 

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

@@ -24,7 +24,6 @@
 import org.junit.Before;
 import org.junit.Test;
 
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.api.java.JavaSQLContext;

mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java

@@ -43,7 +43,7 @@ public class JavaCrossValidatorSuite implements Serializable {
 
   @Before
   public void setUp() {
-    jsc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
+    jsc = new JavaSparkContext("local", "JavaCrossValidatorSuite");
     jsql = new JavaSQLContext(jsc);
     List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42);
     dataset = jsql.applySchema(jsc.parallelize(points, 2), LabeledPoint.class);