Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15435#discussion_r133830499
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
---
@@ -199,15 +199,57 @@ class LogisticRegressionSuite
}
}
- test("empty probabilityCol") {
- val lr = new LogisticRegression().setProbabilityCol("")
- val model = lr.fit(smallBinaryDataset)
- assert(model.hasSummary)
- // Validate that we re-insert a probability column for evaluation
- val fieldNames = model.summary.predictions.schema.fieldNames
- assert(smallBinaryDataset.schema.fieldNames.toSet.subsetOf(
- fieldNames.toSet))
- assert(fieldNames.exists(s => s.startsWith("probability_")))
+ test("empty probabilityCol or predictionCol") {
+ val lr = new LogisticRegression().setMaxIter(1)
+ val datasetFieldNames = smallBinaryDataset.schema.fieldNames.toSet
+ def checkSummarySchema(model: LogisticRegressionModel, columns:
Seq[String]): Unit = {
+ val fieldNames = model.summary.predictions.schema.fieldNames
+ assert(model.hasSummary)
+ assert(datasetFieldNames.subsetOf(fieldNames.toSet))
+ columns.foreach { c => assert(fieldNames.exists(_.startsWith(c))) }
+ }
+ // check that the summary model adds the appropriate columns
+ Seq(("binomial", smallBinaryDataset), ("multinomial",
smallMultinomialDataset)).foreach {
+ case (family, dataset) =>
--- End diff --
You are not using "dataset" in this. However, this logic should be the
same for both binomial and multinomial families, so I'm OK if you just test the
binomial case here.
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