Github user mpjlu commented on a diff in the pull request:
https://github.com/apache/spark/pull/14597#discussion_r75289389
--- 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 --
Now, the user can use fit and transform only for consistent.
If the user wants to reuse ChiSqTestResult, call selectKBest,
SelectPercentile will help them. If we don't consider reuse ChiSqTestResult, we
can not expose this methods.
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