[SPARK-13629][ML] Add binary toggle Param to CountVectorizer

## What changes were proposed in this pull request?

It would be handy to add a binary toggle Param to CountVectorizer, as in the scikit-learn one: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
If set, then all non-zero counts will be set to 1.

## How was this patch tested?

unit tests

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #11536 from hhbyyh/cvToggle.

1 files changed, 18 insertions, 1 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
index 9c99990173..04f165c5f1 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
@@ -157,7 +157,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext
       (3, split("e e e e e"), Vectors.sparse(4, Seq())))
     ).toDF("id", "words", "expected")
 
-    // minTF: count
+    // minTF: set frequency
     val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
       .setInputCol("words")
       .setOutputCol("features")
@@ -168,6 +168,23 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext
     }
   }
 
+  test("CountVectorizerModel with binary") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a a a b b c"), Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0)))),
+      (1, split("c c c"), Vectors.sparse(4, Seq((2, 1.0)))),
+      (2, split("a"), Vectors.sparse(4, Seq((0, 1.0))))
+    )).toDF("id", "words", "expected")
+
+    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setBinary(true)
+    cv.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+
   test("CountVectorizer read/write") {
     val t = new CountVectorizer()
       .setInputCol("myInputCol")