[SPARK-21806][MLLIB] BinaryClassificationMetrics pr(): first point (0.0, 1.0) is misleading #19038
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srowen
commented
Aug 24, 2017
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| private def assertSequencesMatch(left: Seq[Double], right: Seq[Double]): Unit = { | ||
| assert(left.zip(right).forall(areWithinEpsilon)) | ||
| private def assertSequencesMatch(actual: Seq[Double], expected: Seq[Double]): Unit = { |
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90% of the changes in this file were to improve the error log message and clean up the code a bit.
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Test build #81078 has finished for PR 19038 at commit
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srowen
commented
Aug 25, 2017
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I think this is a good fix -- minimal change, replaces a value that is maybe the only one that can't be justified with one that can. Only thing I can't decide is whether to change the Precision "computer" class which defines 0/0 precision to be 1, even though that logic is never actually used.
| @@ -111,7 +109,7 @@ class BinaryClassificationMetricsSuite extends SparkFunSuite with MLlibTestSpark | |||
| val fpr = Seq(1.0) | |||
| 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 | |||
| val prCurve = Seq((0.0, 0.0)) ++ pr | |||
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(This was the only actual test change)
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Merged to master |
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What changes were proposed in this pull request?
Prepend (0,p) to precision-recall curve not (0,1) where p matches lowest recall point
How was this patch tested?
Updated tests.