Github user lionelfeng commented on the pull request:
https://github.com/apache/spark/pull/12265#issuecomment-207841507
Can I change the test a bit since the order of terms with same frequency
shouldn't matter in the vocabulary.
```scala
test("CountVectorizer common cases") {
val df = sqlContext.createDataFrame(Seq(
(0, split("a b c d d e"),
Vectors.sparse(5, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 2.0), (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)))))
).toDF("id", "words", "expected")
val cv = new CountVectorizer()
.setInputCol("words")
.setOutputCol("features")
.fit(df)
assert(cv.vocabulary === Array("a", "b", "c", "d", "e"))
cv.transform(df).select("features", "expected").collect().foreach {
case Row(features: Vector, expected: Vector) =>
assert(features ~== expected absTol 1e-14)
}
}
```
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