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[SPAR-16328K][ML][MLLIB][PYSPARK] Add 'asML' and 'fromML' conversion
methods to PySpark linalg
The move to `ml.linalg` created `asML`/`fromML` utility methods in
Scala/Java for converting between
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Merging to branch-2.0.
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--- Diff: docs/mllib-guide.md ---
@@ -104,9 +104,102 @@ and the migration guide below will explain all
changes between releases.
## From 1.6 to
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--- Diff: python/pyspark/ml/tuning.py ---
@@ -266,7 +269,7 @@ class CrossValidatorModel(Model, ValidatorParams):
"""
.. note:: Experimental
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+/*
+ * Licensed to
Github user koeninger commented on the issue:
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I've got concerns about this, please don't merge these refactorings until I
get a chance to look at it today
On Jun 30, 2016 6:32 AM, "Tathagata Das" wrote:
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--- Diff: R/pkg/R/mllib.R ---
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#' @note KMeansModel since
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--- Diff:
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-/*
- * Licensed to the
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@@ -0,0 +1,217 @@
+/*
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--- Diff:
external/kafka-0-10/src/test/java/org/apache/spark/streaming/kafka010/JavaConsumerStrategySuite.java
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@@ -0,0 +1,84 @@
+/*
+ * Licensed
Github user tdas commented on the issue:
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@srowen yes, i noticed that.
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@koeninger @zsxwing
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Github user tdas commented on the issue:
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I played around with the API and I found a few issues
1. I mentioned above, case classes lead to problems in the public API. The
API could be simpler, and same for both Java and Scala users (dont
GitHub user tdas opened a pull request:
https://github.com/apache/spark/pull/13996
[SPARK-12177] Refactored some API in Kafka 0.10 to make public API simpler
## What changes were proposed in this pull request?
This refactoring make the public API simpler. Current version
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[MINOR] [BUILD] master branch should reference 2.1.0-SNAPSHOT now that
branch-2.0 exists
## What changes were proposed in this pull request?
`master` branch should reference
Github user dongjoon-hyun commented on a diff in the pull request:
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--- Diff:
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---
@@ -149,3 +149,42 @@ case class Explode(child:
Github user srowen commented on the issue:
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Yeah, note to self, `master` should be on `2.1.0-SNAPSHOT` now too...
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Github user dongjoon-hyun commented on the issue:
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@liancheng . Now, the PR is updated again.
- Use `AggregateFunction` instead of `DeclarativeAggregate`
- Add explicit comments about INTERSECT/EXCEPT/AGGREGATE/GENERATOR.
- Fix
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Github user kiszk commented on the issue:
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@cloud-fan , could you please review this again?
I added benchmark programs and their results, and addressed your review
comments.
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@cloud-fan, @hvanhovell I would appreciate it if you could review this
again.
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--- Diff:
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@@ -0,0 +1,173 @@
+/*
+ *
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@@ -0,0 +1,70 @@
+/*
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@@ -0,0 +1,173 @@
+/*
+ *
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+/*
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+/*
+ * Licensed to
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@@ -0,0 +1,70 @@
+/*
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GitHub user tdas opened a pull request:
https://github.com/apache/spark/pull/13994
[BUILD] Fix version in poms related to kafka-0-10
self explanatory
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/tdas/spark SPARK-12177-1
Github user dongjoon-hyun commented on a diff in the pull request:
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@@ -0,0 +1,70 @@
+/*
+ * Licensed
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+/*
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+/*
+ * Licensed to
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If I understand correctly, the remaining issue is `checkEvaluation`.
I'm sorry for this, but I'm still not sure how to use `checkEvaluation` for
the generators.
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@@ -191,6 +191,7 @@ private[hive] class
Github user dongjoon-hyun commented on the issue:
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Thank you for review, @rxin and @cloud-fan .
According to the comments, I removed the redundant `copy` operations and
added function detail description examples.
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Github user briuri closed the pull request at:
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Github user liancheng commented on the issue:
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Merging to master and branch-2.0. Thanks!
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--- Diff:
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---
@@ -44,6 +44,56 @@ case class
Github user liancheng commented on the issue:
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In general, I think reconstructing a DataFrame/Dataset or using `REFRESH
TABLE` may be a better approach to solve the problem this PR tries to solve.
Did I missed some context here?
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Github user dongjoon-hyun commented on the issue:
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Thank you so much, @liancheng and @cloud-fan !
I indeed focused on only `UnaryNode` plans before.
Now I've learned your view points, and reconsider `BinaryNode` logical
plans, mainly
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/13992
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Github user liancheng commented on the issue:
https://github.com/apache/spark/pull/13989
One concern of mine is that, analyzed plan, optimized plan, and executed
(physical) plan stored in `QueryExecution` are all lazy vals, which means that
they won't be re-optimized/planned
Github user krishnakalyan3 commented on a diff in the pull request:
https://github.com/apache/spark/pull/13894#discussion_r69103201
--- Diff: python/pyspark/ml/tuning.py ---
@@ -266,7 +269,7 @@ class CrossValidatorModel(Model, ValidatorParams):
"""
.. note::
Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/12972
ping @liancheng ...
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Github user lw-lin commented on the issue:
https://github.com/apache/spark/pull/13978
The programming guide is totally awesome, thanks @tdas!
Seems like there is one minor issue: we should also count this `12:11 dog`
into window `12:05-12:15`, right?
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/13987
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Github user janplus commented on a diff in the pull request:
https://github.com/apache/spark/pull/13966#discussion_r69098161
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
---
@@ -162,6 +163,46 @@ case class
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