Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14597#discussion_r74741127
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala ---
@@ -197,3 +197,28 @@ class ChiSqSelector @Since("1.3.0") (
new ChiSqSelectorModel(indices)
}
}
+
+/**
+ * Creates a ChiSquared feature selector by False Positive Rate (FPR) test.
+ * @param alpha the highest p-value for features to be kept
+ */
+@Since("2.1.0")
+class ChiSqSelectorByFpr @Since("2.1.0") (
+ @Since("2.1.0") val alpha: Double) extends Serializable {
+
+ /**
+ * Returns a ChiSquared feature selector by FPR.
+ *
+ * @param data an `RDD[LabeledPoint]` containing the labeled dataset
with categorical features.
+ * Real-valued features will be treated as categorical for
each distinct value.
+ * Apply feature discretizer before using this function.
+ */
+ @Since("2.1.0")
+ def fit(data: RDD[LabeledPoint]): ChiSqSelectorModel = {
+ val indices = Statistics.chiSqTest(data)
+ .zipWithIndex.filter { case (res, _) => res.pValue < alpha }
--- End diff --
Yeah, I suppose I _really_ thought it would be nice for the model to expose
the p values to the caller as a side benefit, since they're the essence of the
result. Exposing this isn't strictly required here though so I would not say I
feel strongly about it. But that was the motive.
OK, then we're back to mostly what you're already proposing. What about
"fixing" the model so that it sorts the indices internally, while we're here,
then? also not strictly required but might make this a little cleaner.
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