srowen commented on a change in pull request #17084: [SPARK-24103][ML][MLLIB]
ML Evaluators should use weight column - added weight column for binary
classification evaluator
URL: https://github.com/apache/spark/pull/17084#discussion_r259648266
##########
File path:
mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
##########
@@ -82,6 +82,34 @@ class BinaryClassificationMetricsSuite extends
SparkFunSuite with MLlibTestSpark
validateMetrics(metrics, thresholds, rocCurve, prCurve, f1, f2,
precisions, recalls)
}
+ test("binary evaluation metrics with weights") {
+ val w1 = 1.5
+ val w2 = 0.7
+ val w3 = 0.4
+ val scoreAndLabelsWithWeights = sc.parallelize(
+ Seq((0.1, 0.0, w1), (0.1, 1.0, w2), (0.4, 0.0, w1), (0.6, 0.0, w3),
+ (0.6, 1.0, w2), (0.6, 1.0, w2), (0.8, 1.0, w1)), 2)
+ val metrics = new BinaryClassificationMetrics(scoreAndLabelsWithWeights, 0)
+ val thresholds = Seq(0.8, 0.6, 0.4, 0.1)
+ val numTruePositives =
+ Seq(1.0 * w1, 1.0 * w1 + 2.0 * w2, 1.0 * w1 + 2.0 * w2, 3.0 * w2 + 1.0 *
w1)
+ val numFalsePositives = Seq(0.0, 1.0 * w3, 1.0 * w1 + 1.0 * w3, 1.0 * w3 +
2.0 * w1)
+ val numPositives = 3 * w2 + 1 * w1
+ val numNegatives = 2 * w1 + w3
+ val precisions = numTruePositives.zip(numFalsePositives).map { case (t, f)
=>
+ t.toDouble / (t + f)
+ }
+ val recalls = numTruePositives.map(t => t / numPositives)
+ val fpr = numFalsePositives.map(f => f / numNegatives)
+ val rocCurve = Seq((0.0, 0.0)) ++ fpr.zip(recalls) ++ Seq((1.0, 1.0))
+ val pr = recalls.zip(precisions)
+ val prCurve = Seq((0.0, 1.0)) ++ pr
Review comment:
You don't need to change this, just an aside, but you can write in cases
like this `(0.0, 1.0) +: pr` I know the other code is written like this so
don't change it.
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