Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/12200#discussion_r58749870
--- 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 --
if you create a model from a given vocab, I believe the expected transform
would be the same as a model that is fit, as long as same tokens in the 2
vocabs have the same indicies - so using a vocab with an added "d" token should
be fine.
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