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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