HyukjinKwon commented on a change in pull request #32673:
URL: https://github.com/apache/spark/pull/32673#discussion_r640329780
##########
File path:
mllib/src/test/scala/org/apache/spark/ml/optim/aggregator/DifferentiableLossAggregatorSuite.scala
##########
@@ -149,10 +152,43 @@ object DifferentiableLossAggregatorSuite {
override def add(instance: Instance): TestAggregator = {
val error = instance.label - BLAS.dot(coefficients, instance.features)
- weightSum += instance.weight
- lossSum += instance.weight * error * error / 2.0
- (0 until dim).foreach { j =>
- gradientSumArray(j) += instance.weight * error * instance.features(j)
+ val mathCls = Utils.classForName("java.lang.Math")
Review comment:
@sarutak, I think we can just fix the test with tolerance e.g.)
`assert(agg.weight ~== weightSum relTol 1e-5)` like the above one or
https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/ml/tuning/RandomRangesSuite.scala#L37
Up to my knowledge, this kind of precision diff is a common problem in ML
side. cc @srowen, @WeichenXu123 @zhengruifeng FYI
##########
File path:
mllib/src/test/scala/org/apache/spark/ml/optim/aggregator/DifferentiableLossAggregatorSuite.scala
##########
@@ -149,10 +152,43 @@ object DifferentiableLossAggregatorSuite {
override def add(instance: Instance): TestAggregator = {
val error = instance.label - BLAS.dot(coefficients, instance.features)
- weightSum += instance.weight
- lossSum += instance.weight * error * error / 2.0
- (0 until dim).foreach { j =>
- gradientSumArray(j) += instance.weight * error * instance.features(j)
+ val mathCls = Utils.classForName("java.lang.Math")
Review comment:
@sarutak, I think we can just fix the test with tolerance at the
assertion e.g.) `assert(agg.weight ~== weightSum relTol 1e-5)` like the above
one or
https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/ml/tuning/RandomRangesSuite.scala#L37
Up to my knowledge, this kind of precision diff is a common problem in ML
side. cc @srowen, @WeichenXu123 @zhengruifeng FYI
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