[SPARK-14497][ML] Use top instead of sortBy() to get top N frequent words as...

[SPARK-14497][ML] Use top instead of sortBy() to get top N frequent words as dict in ConutVectorizer

## What changes were proposed in this pull request?

Replace sortBy() with top() to calculate the top N frequent words as dictionary.

## How was this patch tested?
existing unit tests.  The terms with same TF would be sorted in descending order. The test would fail if hardcode the terms with same TF the dictionary like "c", "d"...

Author: fwang1 <desperado.wf@gmail.com>

Closes #12265 from lionelfeng/master.

mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala

@@ -170,16 +170,10 @@ class CountVectorizer(override val uid: String)
       (word, count)
     }.cache()
     val fullVocabSize = wordCounts.count()
-    val vocab: Array[String] = {
-      val tmpSortedWC: Array[(String, Long)] = if (fullVocabSize <= vocSize) {
-        // Use all terms
-        wordCounts.collect().sortBy(-_._2)
-      } else {
-        // Sort terms to select vocab
-        wordCounts.sortBy(_._2, ascending = false).take(vocSize)
-      }
-      tmpSortedWC.map(_._1)
-    }
+
+    val vocab = wordCounts
+      .top(math.min(fullVocabSize, vocSize).toInt)(Ordering.by(_._2))
+      .map(_._1)
 
     require(vocab.length > 0, "The vocabulary size should be > 0. Lower minDF as necessary.")
     copyValues(new CountVectorizerModel(uid, vocab).setParent(this))

mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala

@@ -59,14 +59,15 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext
       (0, split("a b c d e"),
         Vectors.sparse(5, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0), (4, 1.0)))),
       (1, split("a a a a a a"), Vectors.sparse(5, Seq((0, 6.0)))),
-      (2, split("c"), Vectors.sparse(5, Seq((2, 1.0)))),
-      (3, split("b b b b b"), Vectors.sparse(5, Seq((1, 5.0)))))
+      (2, split("c c"), Vectors.sparse(5, Seq((2, 2.0)))),
+      (3, split("d"), Vectors.sparse(5, Seq((3, 1.0)))),
+      (4, split("b b b b b"), Vectors.sparse(5, Seq((1, 5.0)))))
     ).toDF("id", "words", "expected")
     val cv = new CountVectorizer()
       .setInputCol("words")
       .setOutputCol("features")
       .fit(df)
-    assert(cv.vocabulary === Array("a", "b", "c", "d", "e"))
+    assert(cv.vocabulary.toSet === Set("a", "b", "c", "d", "e"))
 
     cv.transform(df).select("features", "expected").collect().foreach {
       case Row(features: Vector, expected: Vector) =>