Github user avulanov commented on a diff in the pull request:
https://github.com/apache/spark/pull/14597#discussion_r74570077
--- 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 --
@srowen I've checked our thread with @mengxr on that feature
https://github.com/apache/spark/pull/1484.
- We preserve the order of indexes to make the selection of features with
one loop (i.e. linear time complexity). Here is the code:
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala#L74.
The logic of feature selector, which is selection of N top features, does not
imply that it will sort the features by their Chi-square value. A parameter
must be introduced if it is required for some use-case.
- We were planning to include Chi-square values in the model later if
needed https://github.com/apache/spark/pull/1484#discussion_r23876952
@mpjlu It seems that FPR feature selection should not modify the code of
existing `ChiSqSelector`, because FPR feature selection works on top of a
scoring function rather than on top of another selector. Scoring function is a
parameter, and it might be Chi-square. For example, please refer to Sklearn's
`FPR` implementation mentioned. It uses ANOVA as a default scoring function
http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFpr.html#sklearn.feature_selection.SelectFpr.
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