Github user NarineK closed the pull request at:
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Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/9667#issuecomment-173381317
I've pushed all the suggestions and changes.
@jkbradley I have a concern with respect to: weight: Vector and
lossHistory: Array[Double] which is originally being
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Github user NarineK commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
---
@@ -133,7 +133,7 @@ class GradientDescent private[spark]
Github user NarineK commented on a diff in the pull request:
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--- Diff:
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@@ -122,8 +122,8 @@ class GradientDescent private[spark]
Github user NarineK commented on a diff in the pull request:
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--- Diff:
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@@ -122,8 +122,8 @@ class GradientDescent private[spark]
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--- Diff:
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@@ -122,8 +122,8 @@ class GradientDescent private[spark]
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--- Diff:
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@@ -178,7 +178,7 @@ object GradientDescent extends Logging {
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Github user jkbradley commented on the pull request:
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@NarineK Apologies for the lack of response here! We've had limited review
bandwidth. I like providing this information to users. However, we have some
of it
Github user jkbradley commented on the pull request:
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Yes, that's the main issue, especially since these features are already in
spark.ml.
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Github user avulanov commented on the pull request:
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@jkbradley Thanks for explanation! With regards to this particular PR, Do I
understand correct, that you don't want to add a new feature to the
soon-to-be-deprecated
Github user jkbradley commented on the pull request:
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There has been a lot of confusion about the spark.ml vs spark.mllib APIs,
so we want to achieve feature parity in spark.ml. There are no set plans to
deprecate
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/9667#issuecomment-169178623
Originally, I needed that information for the neuronal network. I extended
it so that the ANN can expose it too.
I think in general it would be good to have the
Github user avulanov commented on the pull request:
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@jkbradley Could you suggest why do you think that it does not worth having
the output from `mlllib` optimizers? Are planning to deprecate all optimizers
from `mllib`
Github user NarineK commented on the pull request:
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Thanks for your feedback!
@jkbradley : Which package do you exactly mean with DataFrame's spark.ml ?
I can see
Github user jkbradley commented on the pull request:
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Maybe I misunderstood the need here. Do you want to use the optimizers for
your own custom algorithms? Or do you want to get info back from MLlib
algorithms like
Github user jkbradley commented on the pull request:
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I see. Adding this seems reasonable since some spark.ml algorithms depend
on these APIs. However, I want to avoid breaking the public optimization APIs
in
Github user jkbradley commented on a diff in the pull request:
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--- Diff:
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--- Diff:
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Github user jkbradley commented on a diff in the pull request:
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Hi there,
If we want to have many output variables, we'll need to add wrapper class
e.g. OptimizeResult which will embrace all those output variables.
Since
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Github user NarineK commented on the pull request:
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It seems that there is a problem in java for many output parameters.
Introducing a Result output object would be probably better.
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@mengxr , @avulanov
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GitHub user NarineK opened a pull request:
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[SPARK-11696] [ML, MLlib] Optimization: Extend optimizer output
Hi there,
I've added two simple parameter for the start:
1. the number of iterations
2. The loss after the last
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Github user NarineK commented on the pull request:
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Here is an example from scipy:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult
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