Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14597#discussion_r75451038
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala ---
@@ -189,11 +228,35 @@ class ChiSqSelector @Since("1.3.0") (
*/
@Since("1.3.0")
def fit(data: RDD[LabeledPoint]): ChiSqSelectorModel = {
- val indices = Statistics.chiSqTest(data)
- .zipWithIndex.sortBy { case (res, _) => -res.statistic }
- .take(numTopFeatures)
- .map { case (_, indices) => indices }
- .sorted
+ chiSqTestResult = Statistics.chiSqTest(data)
+ selectorType match {
+ case ChiSqSelectorType.KBest => selectKBest(numTopFeatures)
+ case ChiSqSelectorType.Percentile => selectPercentile(percentile)
+ case ChiSqSelectorType.Fpr => selectFpr(alpha)
+ case _ => throw new Exception("Unknown ChiSqSelector Type")
+ }
+ }
+
+ @Since("2.1.0")
+ def selectKBest(value: Int): ChiSqSelectorModel = {
--- End diff --
I agree with your second paragraph. I don't think it's very common that
someone would perform different chi-squared selections on the same values but
with different criteria. Therefore I'm not concerned about recomputing
different models for different criteria. So I think indeed the model does not
need parameters, and then it's consistent. Does that match your idea?
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