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Github user holdenk commented on the issue:
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I'm not sure if this is something that would still be considered since we
aren't doing new development for MLlib anymore. It might make more sense to
work on
Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/15371#discussion_r82467368
--- Diff: core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
---
@@ -344,15 +338,20 @@ private[spark] class BlockStatusesAccumulator
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Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/15089
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Github user yinxusen commented on the issue:
https://github.com/apache/spark/pull/14006
@holdenk I'll update a version ASAP
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/13794
@yinxusen - is this something you are still interested in? If so updating
it to master would be good as well as making sure the unit tests pass in
jenkins (a lot of reviwers just skip PRs which are
Github user zasdfgbnm commented on the issue:
https://github.com/apache/spark/pull/14198
Something to mention is, there is still one problem that I'm not sure
whether I solve it correctly: in python's array, unsigned types are supported,
but unsigned types are not supported in JVM.
Github user sethah commented on the issue:
https://github.com/apache/spark/pull/15394
cc @yanboliang @dbtsai
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Github user yinxusen commented on the issue:
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@dbtsai @sethah I updated the code. Now we check the equivalence of K when
setting initialModel if K is set previously. We also check the equivalence when
fitting a model.
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Github user rxin commented on the issue:
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LGTM - we should double check whether the synchronized block is sufficient,
but we can fix that in a follow-up pr if needed.
Merging in master.
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Github user holdenk commented on the issue:
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Thanks for working on this @bkpathak :) Are you still interested in working
on this? If so can you update this to the latest master and then we can try and
find a committer to take a more thorough
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Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15394#discussion_r82466110
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala ---
@@ -85,73 +101,193 @@ private[ml] class WeightedLeastSquares(
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GitHub user sethah opened a pull request:
https://github.com/apache/spark/pull/15394
[SPARK-17749][ML] One pass solver for Weighted Least Squares with ElasticNet
## What changes were proposed in this pull request?
1. Make a pluggable solver interface for
Github user zsxwing commented on the issue:
https://github.com/apache/spark/pull/15371
@seyfe I'm taking my words back. Yea, `BlockStatusesAccumulator.merge` will
be called in driver.
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Github user jkbradley commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala ---
@@ -196,8 +196,13 @@ final class OneVsRestModel private[ml] (
Github user mateiz commented on the issue:
https://github.com/apache/spark/pull/8318
BTW the other change now is that we don't make an assembly JAR by default
anymore, though we could build one for this. We just need a build script for
this that's solid, produces a
Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/14375#discussion_r82465422
--- Diff: python/pyspark/ml/stat/__init__.py ---
@@ -0,0 +1,27 @@
+
--- End diff --
Super minor but probably don't need this blank line.
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/14375
Ok so you are blocked on reviewers, but still active on the PR then yes?
You might want to try pinging (by using @[username] some of the reviewers from
your previous PR
Github user zasdfgbnm commented on the issue:
https://github.com/apache/spark/pull/14198
I'd love to help
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Github user mateiz commented on the issue:
https://github.com/apache/spark/pull/8318
Something like this would be great IMO. A few questions though:
* How will it work if users want to run a different version of PySpark from
a different version of Spark (maybe something they
Github user praveendareddy21 commented on the issue:
https://github.com/apache/spark/pull/14375
@holdenk
The work on this feature is done. This PR is reopen of PR on 2.0 branch.
refer https://github.com/apache/spark/pull/13248
I waiting for admins to verify and merge.
Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/14006
Hi @yinxusen thanks for working on this - can you update it against the
latest master? I'm all for switching our examples to be more include based and
the improved error messages during doc builds
Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/14198
Oh interesting - thanks for working on this @zasdfgbnm and sorry its sort
of fallen through the cracks. Is this something you are still working on? For
PRs to get in you generally need some form of
Github user tgravescs commented on the issue:
https://github.com/apache/spark/pull/15249
The question to me comes down to how many and how often do you expect
temporary resource issues. At some point if its just from that much skew you
should probably fix your configs and it would be
Github user holdenk commented on the issue:
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Is this something you are still working on?
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Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/14394#discussion_r82463086
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala ---
@@ -777,6 +777,10 @@ class DistributedLDAModel private[clustering] (
Github user falaki commented on a diff in the pull request:
https://github.com/apache/spark/pull/15375#discussion_r82462875
--- Diff: R/pkg/R/context.R ---
@@ -126,13 +126,13 @@ parallelize <- function(sc, coll, numSlices = 1) {
if (numSlices > length(coll))
Github user zsxwing commented on the issue:
https://github.com/apache/spark/pull/15384
@tdas since this one has not yet been merged, I just updated the PR to
remove the topic cleanup.
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Github user rxin commented on the issue:
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LGTM, pending the new test run.
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Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/15074#discussion_r82461977
--- Diff: python/pyspark/mllib/linalg/__init__.py ---
@@ -1296,9 +1296,19 @@ def asML(self):
return newlinalg.SparseMatrix(self.numRows,
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Github user holdenk commented on the issue:
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cc @mateiz are you interested in seeing something like this move forward?
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--- Diff: python/pyspark/ml/clustering.py ---
@@ -201,7 +203,74 @@ def computeCost(self, dataset):
"""
return
Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/13557#discussion_r82460090
--- Diff: python/pyspark/ml/clustering.py ---
@@ -201,7 +203,74 @@ def computeCost(self, dataset):
"""
return
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--- Diff: python/pyspark/ml/clustering.py ---
@@ -201,7 +203,74 @@ def computeCost(self, dataset):
"""
return
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/13440
Is this something your still working on? If so it would be good to merge in
the latest master. We can also check with @jkbradley to see if he has some
review bandwidth.
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/13780
so just following up because I know there is some other loosely blocked on
this. Do @rxin @marmbrus @davies @gatorsmile have any comments?
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Github user holdenk commented on the issue:
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Just a quick ping @sethah - I know your pretty busy but I'm assuming this
is still active. One minor note is it seems there is another new addition to
types.py which maybe should also be changed.
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Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/15393
Yep. I knew. What I'm asking is we don't need to explain 'contains' usage
here in this PR.
I prefer not to advertise `contains` for `Option[Boolean]`.
```
scala> val o:
Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/12398
@buckhx Just following up to see if this is something you are still
interested in working on?
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Github user HyukjinKwon commented on the issue:
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@dongjoon-hyun I already did :)
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Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/15393
BTW, could you update the PR description, @HyukjinKwon ? Maybe, removing
`exists` part?
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/15113
@yanboliang : I've certainly seen even Spark developers be tripped up with
the handling of `None` by the Py4J bridge so I wouldn't be surprised if this is
a more common problem than we might
Github user wgtmac commented on the issue:
https://github.com/apache/spark/pull/14215
@HyukjinKwon yep, keep each PR as small as possible is a good idea. BTW,
may I know the target version of your non-vectorize fix? Our production job is
in need of this fix.
Separating
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Github user holdenk commented on the issue:
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I think this is a pretty reasonable simple docstirng improvement - maybe
@srowen or @davies can take a look?
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Github user holdenk commented on the issue:
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@zjffdu so is the intent of this to allow people to use PySpark from Scala
Spark applications?
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/15159
Just a heads up this has conflicts with master so it might be good to
update (since I know a lot of reviewers use the spark-pr dashboard and maybe
skip PRs which aren't mergable).
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/15209
Thanks for looking to get involved @frankfqchen :)
I think this PR definitely needs a JIRA and maybe some more description
about what the intent of the change is rather than just the
Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/15074
Maybe it would be good to add some more tests for this?
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Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/15074#discussion_r82455939
--- Diff: python/pyspark/mllib/linalg/__init__.py ---
@@ -1296,9 +1296,19 @@ def asML(self):
return newlinalg.SparseMatrix(self.numRows,
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--- Diff: python/pyspark/mllib/linalg/__init__.py ---
@@ -1296,9 +1296,19 @@ def asML(self):
return newlinalg.SparseMatrix(self.numRows,
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Github user mtustin-handy commented on the issue:
https://github.com/apache/spark/pull/12004
I don't see any downsides to this. At present working with s3 isn't super
painful, but I do see why one would want support to be better and smoother.
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/14547
Just a heads up there is a merge conflict with the excludes you might want
to update for so that jenkins can run its tests on this PR :)
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/10307
@Donderia it seems like some of the files have changed since 1.6 so this
won't apply cleanly against 1.6
@zjffdu if your still working on this can you update this against the
latest master?
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Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/15393
I apologize and will definitely be careful as well.
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/14861
@jpiper are you still working on this PR? If so you can merge in the latest
version of master so we can continue to review? (If not interested that's ok
just let us know :)).
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/14233
Thanks for adding it to the documentation files, looks reasonable pending
@sethah's suggestions and jenkins :)
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Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/15393
Actually, I hit this before. Sorry, I did not catch it when I merging the
PR. Next time, I will be more careful about it.
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Github user marmbrus commented on the issue:
https://github.com/apache/spark/pull/9766
+1 to this functionality, but also to the request to add more tests and
documentation. It would also to be good to comment on the idea of using SQL as
a more general way to implement this.
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Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/15393
LGTM pending tests
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Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/15377
Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/66506/
Test PASSed.
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Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/15377
Merged build finished. Test PASSed.
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