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ok to test
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add to whitelist
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https://github.com/apache/spark/pull/8567
[SPARK-9723][ML] params getordefault should throw more useful error
Params.getOrDefault should throw a more meaningful exception than what you
get from a bad key lookup.
You can merge this pull
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Github user liancheng commented on the pull request:
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This PR was originally part of the closed PR #7679, which aimed to refactor
Parquet write path for better interoperability. I put too many things into that
one and
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Opened #8568 for the same purpose but against branch-1.5.
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I'd like to trigger the build multiple times to make sure it works.
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Move visualization above explain output and hide explain by default
New screenshots after this fix:
Github user witgo commented on the pull request:
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I think it is necessary to merge the PR into master.
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--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -278,7 +278,8 @@ class DenseMatrix @Since("1.3.0") (
}
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/param/params.scala ---
@@ -501,7 +501,13 @@ trait Params extends Identifiable with Serializable {
GitHub user noel-smith opened a pull request:
https://github.com/apache/spark/pull/8571
[SPARK-10415][PySpark] Enhance Navigation Sidebar in PySpark API
These are CSS/JavaScript changes to add classes/functions + a few other
tweaks to make navigation in the PySpark API a bit
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Jenkins, retest this please.
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Github user srowen commented on the pull request:
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My snap reaction is that this is a lot of new code to support something
that's not that Spark-specific; you could imagine a general REST API for YARN
that doesn't need
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Github user srowen commented on the pull request:
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Yes, do you mind closing this PR? this is not quite getting at what needs
to happen for SPARK-4449
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Github user JoshRosen commented on a diff in the pull request:
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--- Diff: core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala ---
@@ -29,7 +29,7 @@ import org.apache.spark.annotation.DeveloperApi
class
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Jenkins, retest this please.
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/param/params.scala ---
@@ -501,7 +501,13 @@ trait Params extends Identifiable with Serializable {
*/
Github user srowen commented on the pull request:
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(Is GraphX active anymore? I haven't seen activity or responses on these
issues in quite a while)
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cc @andrewor14
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@andrewor14 my short summary of the problem and/or appeal of a change is
that, right now, if you set a port to P, then Spark will always try ports P to
P+(N-1), where N
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I have little knowledge of the metrics system. I can appreciate the
overhead of yet another config flag, and can also see how you might want to
monitor apps by instance
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--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -278,7 +278,8 @@ class DenseMatrix @Since("1.3.0") (
}
Github user mwws commented on the pull request:
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@andrewor14 I am working on it now, here is the latest design doc based on
Saisai's previous work.
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cc @yhuai
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--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -278,7 +278,8 @@ class DenseMatrix @Since("1.3.0") (
}
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Can one of the admins verify this patch?
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Github user harishreedharan commented on the pull request:
https://github.com/apache/spark/pull/2994#issuecomment-136950364
Sure, if there is interest in this. I have a version of this on the
cloudera github (which I pushed there, since I didn't see much interest in
having this in
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Jenkins, retest this please.
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GitHub user holdenk opened a pull request:
https://github.com/apache/spark/pull/8564
[SPARK-9718][ML] linear regression training summary all columns
LinearRegression training summary: The transformed dataset should hold all
columns, not just selected ones like prediction and label.
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Github user tijoparacka commented on a diff in the pull request:
https://github.com/apache/spark/pull/8554#discussion_r38502967
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
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@@ -31,15 +31,18 @@ import
Github user jameszhouyi commented on the pull request:
https://github.com/apache/spark/pull/8476#issuecomment-13695
This is real-world case using Spark SQL and hopefully it can be
fixed/merged in Spark 1.5.0.Thanks in advance !
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