Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1425#discussion_r14988046
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
---
@@ -20,8 +20,20 @@ package org.apache.spark.mllib.evaluation
import org.scalatest.FunSuite
import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
class BinaryClassificationMetricsSuite extends FunSuite with
LocalSparkContext {
+
+ implicit class SeqDoubleWithAlmostEquals(val x: Seq[Double]) {
+ def almostEquals(y: Seq[Double], eps: Double = 1E-6): Boolean =
--- End diff --
1.0e-6 is way bigger than an ulp for a double; 1.0e-12 is more like it. I
understand a complex calculation might legitimately vary by significantly more
than an ulp depending on the implementation. As @mengxr says where you mean to
allow significantly more than machine precision worth of noise, that's probably
good to do with an explicitly larger epsilon. But this is certainly a good step
forward already.
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