Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15435#discussion_r106726296
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -780,19 +788,33 @@ class LogisticRegressionModel private[spark] (
throw new SparkException("No training summary available for this
LogisticRegressionModel")
}
+ @Since("2.2.0")
+ def binarySummary: BinaryLogisticRegressionTrainingSummary = summary
match {
--- End diff --
The behavior we are implementing is non-trivial. We need to add tests to
ensure that everything happens as expected. This is an example:
````scala
test("binary and multiclass summary") {
val lr = new LogisticRegression()
.setFamily("binomial")
val blorModel = lr.fit(smallBinaryDataset)
assert(blorModel.summary.isInstanceOf[BinaryLogisticRegressionTrainingSummaryImpl])
assert(blorModel.binarySummary.isInstanceOf[BinaryLogisticRegressionTrainingSummaryImpl])
val mlorModel = lr.setFamily("multinomial").fit(smallMultinomialDataset)
assert(mlorModel.summary.isInstanceOf[LogisticRegressionTrainingSummaryImpl])
withClue("cannot get binary summary for multiclass model") {
intercept[RuntimeException] {
mlorModel.binarySummary
}
}
val blorSummary = blorModel.evaluate(smallBinaryDataset)
val mlorSummary = mlorModel.evaluate(smallMultinomialDataset)
assert(blorSummary.isInstanceOf[BinaryLogisticRegressionSummaryImpl])
assert(mlorSummary.isInstanceOf[LogisticRegressionSummaryImpl])
}
````
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