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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