Github user yanboliang commented on a diff in the pull request: https://github.com/apache/spark/pull/13129#discussion_r63474760 --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala --- @@ -239,10 +239,7 @@ class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val } val familyAndLink = new FamilyAndLink(familyObj, linkObj) - val numFeatures = dataset.select(col($(featuresCol))).limit(1).rdd - .map { case Row(features: Vector) => - features.size - }.first() + val numFeatures = dataset.select(col($(featuresCol))).first().getAs[Vector](0).size --- End diff -- It looks like Spark does not provide encoder for Vector. If I change to use ```as[Vector]```, the compiler will complain: ``` Error:(244, 61) Unable to find encoder for type stored in a Dataset. Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._ Support for serializing other types will be added in future releases. val numFeatures = dataset.select(col($(featuresCol))).as[Vector].first().size ^ ```
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