Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14597#discussion_r77505385
--- Diff: python/pyspark/mllib/feature.py ---
@@ -305,7 +350,12 @@ def fit(self, data):
treated as categorical for each distinct value.
Apply feature discretizer before using this function.
"""
- jmodel = callMLlibFunc("fitChiSqSelector", self.numTopFeatures,
data)
+ if self.selectorType == ChiSqSelectorType.KBest:
+ jmodel = callMLlibFunc("fitChiSqSelectorKBest",
self.numTopFeatures, data)
+ elif self.selectorType == ChiSqSelectorType.Percentile:
+ jmodel = callMLlibFunc("fitChiSqSelectorPercentile",
self.percentile, data)
+ elif self.selectorType == ChiSqSelectorType.FPR:
--- End diff --
I'm not sure what the convention in on the Pyspark side for args checking.
For example here it doesn't check that the type is one of the supported types.
I guess follow the same convention you find elsewhere.
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