Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14597#discussion_r74738464
--- 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 --
Hm, would it be more consistent to add the parameter controlling what type
of feature selection is done to `ChiSqSelector`? it would actually compute the
same thing in all cases, and pass along the p-values as well as what type of
selection it is supposed to do, to the `ChiSqModelSelector` instance, which
would then behave accordingly.
This might be more consistent with other models and at the same time
flexible. Then the model itself doesn't have a configurable parameter, but
still contains all the raw information required to answer additional questions,
like "what are the p-values for the features?"
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