[SPARK-2852][MLLIB] API consistency for `mllib.feature`

This is part of SPARK-2828:

1. added a Java-friendly fit method to Word2Vec with tests
2. change DeveloperApi to Experimental for Normalizer & StandardScaler
3. change default feature dimension to 2^20 in HashingTF

Author: Xiangrui Meng <meng@databricks.com>

Closes #1807 from mengxr/feature-api-check and squashes the following commits:

773c1a9 [Xiangrui Meng] change default numFeatures to 2^20 in HashingTF change annotation from DeveloperApi to Experimental in Normalizer and StandardScaler
883e122 [Xiangrui Meng] add @Experimental to Word2VecModel add a Java-friendly method to Word2Vec.fit with tests

4 files changed, 25 insertions, 10 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
index 0f6d5809e0..c534758183 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
@@ -32,12 +32,12 @@ import org.apache.spark.util.Utils
  * :: Experimental ::
  * Maps a sequence of terms to their term frequencies using the hashing trick.
  *
- * @param numFeatures number of features (default: 1000000)
+ * @param numFeatures number of features (default: 2^20^)
  */
 @Experimental
 class HashingTF(val numFeatures: Int) extends Serializable {
 
-  def this() = this(1000000)
+  def this() = this(1 << 20)
 
   /**
    * Returns the index of the input term.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
index ea9fd0a80d..3afb477672 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
@@ -19,11 +19,11 @@ package org.apache.spark.mllib.feature
 
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
- * :: DeveloperApi ::
+ * :: Experimental ::
  * Normalizes samples individually to unit L^p^ norm
  *
  * For any 1 <= p < Double.PositiveInfinity, normalizes samples using
@@ -33,7 +33,7 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors}
  *
  * @param p Normalization in L^p^ space, p = 2 by default.
  */
-@DeveloperApi
+@Experimental
 class Normalizer(p: Double) extends VectorTransformer {
 
   def this() = this(2)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
index cc2d7579c2..e6c9f8f67d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
@@ -19,14 +19,14 @@ package org.apache.spark.mllib.feature
 
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
 
 /**
- * :: DeveloperApi ::
+ * :: Experimental ::
  * Standardizes features by removing the mean and scaling to unit variance using column summary
  * statistics on the samples in the training set.
  *
@@ -34,7 +34,7 @@ import org.apache.spark.rdd.RDD
  *                 dense output, so this does not work on sparse input and will raise an exception.
  * @param withStd True by default. Scales the data to unit standard deviation.
  */
-@DeveloperApi
+@Experimental
 class StandardScaler(withMean: Boolean, withStd: Boolean) extends VectorTransformer {
 
   def this() = this(false, true)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index 3bf44ad7c4..395037e1ec 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark.mllib.feature
 
+import java.lang.{Iterable => JavaIterable}
+
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -25,6 +28,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 import org.apache.spark.Logging
 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, Vectors}
 import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.rdd._
@@ -239,7 +243,7 @@ class Word2Vec extends Serializable with Logging {
       a += 1
     }
   }
-  
+
   /**
    * Computes the vector representation of each word in vocabulary.
    * @param dataset an RDD of words
@@ -369,11 +373,22 @@ class Word2Vec extends Serializable with Logging {
 
     new Word2VecModel(word2VecMap.toMap)
   }
+
+  /**
+   * Computes the vector representation of each word in vocabulary (Java version).
+   * @param dataset a JavaRDD of words
+   * @return a Word2VecModel
+   */
+  def fit[S <: JavaIterable[String]](dataset: JavaRDD[S]): Word2VecModel = {
+    fit(dataset.rdd.map(_.asScala))
+  }
 }
 
 /**
-* Word2Vec model
+ * :: Experimental ::
+ * Word2Vec model
  */
+@Experimental
 class Word2VecModel private[mllib] (
     private val model: Map[String, Array[Float]]) extends Serializable {