Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15435#discussion_r94638095
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
---
@@ -149,15 +149,34 @@ class LogisticRegressionSuite
assert(copiedModel.hasSummary)
}
- test("empty probabilityCol") {
- val lr = new LogisticRegression().setProbabilityCol("")
- val model = lr.fit(smallBinaryDataset)
+ test("empty probabilityCol or predictionCol") {
+ var lr = new LogisticRegression().setProbabilityCol("")
--- End diff --
I think we can get rid of the vars and shorten this up. Also, we should
probably test both binary and multiclass. How does this look?:
````scala
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))) }
}
Seq("binomial", "multinomial").foreach { family =>
lr.setProbabilityCol("")
val modelNoProb = lr.fit(smallBinaryDataset)
checkSummarySchema(modelNoProb, Seq("probability_"))
lr.setProbabilityCol("probability").setPredictionCol("")
val modelNoPred = lr.fit(smallBinaryDataset)
checkSummarySchema(modelNoPred, Seq("prediction_"))
lr.setProbabilityCol("").setPredictionCol("")
val modelNoPredNoProb = lr.fit(smallBinaryDataset)
checkSummarySchema(modelNoPredNoProb, Seq("prediction_",
"probability_"))
}
````
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