Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/6785#discussion_r37819138
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala ---
@@ -99,6 +107,66 @@ class ChiSqSelectorModel (val selectedFeatures:
Array[Int]) extends VectorTransf
s"Only sparse and dense vectors are supported but got
${other.getClass}.")
}
}
+
+ override def save(sc: SparkContext, path: String): Unit = {
+ ChiSqSelectorModel.SaveLoadV1_0.save(sc, this, path)
+ }
+
+ override protected def formatVersion: String = "1.0"
+}
+
+object ChiSqSelectorModel extends Loader[ChiSqSelectorModel] {
+ override def load(sc: SparkContext, path: String): ChiSqSelectorModel = {
+ ChiSqSelectorModel.SaveLoadV1_0.load(sc, path)
+ }
+
+ private[feature]
+ object SaveLoadV1_0 {
+
+ private val thisFormatVersion = "1.0"
+
+ /** Model data for import/export */
+ case class Data(feature: Int)
--- End diff --
This is fine for now since it's being used in other places (e.g.
[LDAModel](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala)).
However, inferring a schema from `case class`es [uses
reflection](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala#L361)so
it would be better to just specify the schema for the data frame directly.
I'll create some JIRAs for that.
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