From 96028e36b4d08427fdd94df55595849c2346ead4 Mon Sep 17 00:00:00 2001
From: WeichenXu <weichen.xu@databricks.com>
Date: Thu, 31 Aug 2017 16:22:40 -0700
Subject: [PATCH] [SPARK-17139][ML][FOLLOW-UP] Add convenient method `asBinary`
for casting to BinaryLogisticRegressionSummary
## What changes were proposed in this pull request?
add an "asBinary" method to LogisticRegressionSummary for convenient casting to BinaryLogisticRegressionSummary.
## How was this patch tested?
Testcase updated.
Author: WeichenXu <weichen.xu@databricks.com>
Closes #19072 from WeichenXu123/mlor_summary_as_binary.
---
.../spark/ml/classification/LogisticRegression.scala | 11 +++++++++++
.../ml/classification/LogisticRegressionSuite.scala | 6 ++++++
project/MimaExcludes.scala | 1 +
3 files changed, 18 insertions(+)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 1869d51af7..f491a679b2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -1473,6 +1473,17 @@ sealed trait LogisticRegressionSummary extends Serializable {
/** Returns weighted averaged f1-measure. */
@Since("2.3.0")
def weightedFMeasure: Double = multiclassMetrics.weightedFMeasure(1.0)
+
+ /**
+ * Convenient method for casting to binary logistic regression summary.
+ * This method will throws an Exception if the summary is not a binary summary.
+ */
+ @Since("2.3.0")
+ def asBinary: BinaryLogisticRegressionSummary = this match {
+ case b: BinaryLogisticRegressionSummary => b
+ case _ =>
+ throw new RuntimeException("Cannot cast to a binary summary.")
+ }
}
/**
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 6649fa4025..6bf1253b71 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -256,6 +256,7 @@ class LogisticRegressionSuite
val blorModel = lr.fit(smallBinaryDataset)
assert(blorModel.summary.isInstanceOf[BinaryLogisticRegressionTrainingSummary])
+ assert(blorModel.summary.asBinary.isInstanceOf[BinaryLogisticRegressionSummary])
assert(blorModel.binarySummary.isInstanceOf[BinaryLogisticRegressionTrainingSummary])
val mlorModel = lr.setFamily("multinomial").fit(smallMultinomialDataset)
@@ -265,6 +266,11 @@ class LogisticRegressionSuite
mlorModel.binarySummary
}
}
+ withClue("cannot cast summary to binary summary multiclass model") {
+ intercept[RuntimeException] {
+ mlorModel.summary.asBinary
+ }
+ }
val mlorBinaryModel = lr.setFamily("multinomial").fit(smallBinaryDataset)
assert(mlorBinaryModel.summary.isInstanceOf[BinaryLogisticRegressionTrainingSummary])
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index eecda26abb..27e4183550 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -62,6 +62,7 @@ object MimaExcludes {
ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedRecall"),
ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedPrecision"),
ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedFMeasure"),
+ ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.asBinary"),
ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.org$apache$spark$ml$classification$LogisticRegressionSummary$$multiclassMetrics"),
ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.org$apache$spark$ml$classification$LogisticRegressionSummary$_setter_$org$apache$spark$ml$classification$LogisticRegressionSummary$$multiclassMetrics_=")
)
--
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