Github user wangmiao1981 commented on a diff in the pull request:
https://github.com/apache/spark/pull/12200#discussion_r58746316
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala ---
@@ -183,6 +183,26 @@ class CountVectorizerSuite extends SparkFunSuite with
MLlibTestSparkContext
case Row(features: Vector, expected: Vector) =>
assert(features ~== expected absTol 1e-14)
}
+
+ // CountVectorizer test
+ val df2 = sqlContext.createDataFrame(Seq(
--- End diff --
@BryanCutler If I understand correctly,
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)
has different expectation if I use CountVectorizer to get the vocabulary,
since the CountVectorizerModel(Array("a", "b", "c", "d")) takes a different
dictionary.
So I can't reuse the DF. Am I right?
Thanks!
Miao
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