Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/14112#discussion_r70335191
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala ---
@@ -574,18 +578,28 @@ object LocalLDAModel extends
MLReadable[LocalLDAModel] {
val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
val dataPath = new Path(path, "data").toString
val data = sparkSession.read.parquet(dataPath)
- .select("vocabSize", "topicsMatrix", "docConcentration",
"topicConcentration",
- "gammaShape")
- .head()
- val vocabSize = data.getAs[Int](0)
- val topicsMatrix = data.getAs[Matrix](1)
- val docConcentration = data.getAs[Vector](2)
- val topicConcentration = data.getAs[Double](3)
- val gammaShape = data.getAs[Double](4)
+ val vectorConverted = MLUtils.convertVectorColumnsToML(data,
"docConcentration")
+ val Row(vocabSize: Int, topicsMatrix: Matrix, docConcentration:
Vector,
+ topicConcentration: Double, gammaShape: Double) =
MLUtils.convertMatrixColumnsToML(
--- End diff --
style: indent 4 spaces
You could also simplify this with Datasets by using
```(DataFrame).as[Data]``` if you make ```LocalLDAModelWriter.Data```
accessible here (by moving it to sit under ```object LocalLDAModel```).
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