huaxingao commented on a change in pull request #27841: [SPARK-31077][ML]
Remove ChiSqSelector dependency on mllib.ChiSqSelectorModel
URL: https://github.com/apache/spark/pull/27841#discussion_r390092960
##########
File path: mllib/src/main/scala/org/apache/spark/ml/stat/ChiSquareTest.scala
##########
@@ -75,4 +77,31 @@ object ChiSquareTest {
val statistics: Vector = Vectors.dense(testResults.map(_.statistic))
spark.createDataFrame(Seq(ChiSquareResult(pValues, degreesOfFreedom,
statistics)))
}
+
+ /**
+ * @param dataset DataFrame of categorical labels and categorical features.
+ * Real-valued features will be treated as categorical for
each distinct value.
+ * @param featuresCol Name of features column in dataset, of type `Vector`
(`VectorUDT`)
+ * @param labelCol Name of label column in dataset, of any numerical type
+ * @return Array containing the SelectionTestResult for every feature
against the label.
+ */
+ @Since("3.1.0")
+ def testChiSquare(
+ dataset: Dataset[_],
+ featuresCol: String,
+ labelCol: String): Array[SelectionTestResult] = {
Review comment:
I will need to have an abstract method in abstract class ```Selector``` in
next subtask
```
def getSelectionTestResult(dataset: Dataset[_]): Array[SelectionTestResult]
```
so I prefer to return ```Array[SelectionTestResult]``` here.
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