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zhengruifeng updated SPARK-15291:
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Description:
{code}
val newVertices = g.vertices.mapValues(v => (v._1.toArray, v._2.toArray, v._3
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zhengruifeng updated SPARK-14174:
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Description:
The MiniBatchKMeans is a variant of the KMeans algorithm which uses
mini-batches to
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zhengruifeng resolved SPARK-34047.
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Fix Version/s: 3.2.0
Resolution: Fixed
Issue resolved by pull request 31090
[https://gi
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zhengruifeng reassigned SPARK-34047:
Assignee: zhengruifeng
> save tree models in single partition
> -
zhengruifeng created SPARK-34189:
Summary: w2v findSynonyms optimization
Key: SPARK-34189
URL: https://issues.apache.org/jira/browse/SPARK-34189
Project: Spark
Issue Type: Improvement
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zhengruifeng reassigned SPARK-34189:
Assignee: zhengruifeng
> w2v findSynonyms optimization
> -
>
zhengruifeng created SPARK-34220:
Summary: BucketedRandomProjectionLSH transform opt
Key: SPARK-34220
URL: https://issues.apache.org/jira/browse/SPARK-34220
Project: Spark
Issue Type: Improve
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zhengruifeng reassigned SPARK-34220:
Assignee: zhengruifeng
> BucketedRandomProjectionLSH transform opt
>
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zhengruifeng resolved SPARK-34220.
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Fix Version/s: 3.2.0
Resolution: Fixed
Issue resolved by pull request 31313
[https://gi
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zhengruifeng reassigned SPARK-34189:
Assignee: zhengruifeng (was: Apache Spark)
> w2v findSynonyms optimization
> ---
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zhengruifeng resolved SPARK-34189.
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Fix Version/s: 3.2.0
Resolution: Fixed
Issue resolved by pull request 31276
[https://gi
zhengruifeng created SPARK-34256:
Summary: VectorSlicer refine numFeatures checking and toString
method
Key: SPARK-34256
URL: https://issues.apache.org/jira/browse/SPARK-34256
Project: Spark
zhengruifeng created SPARK-34291:
Summary: LSH hashDistance optimization
Key: SPARK-34291
URL: https://issues.apache.org/jira/browse/SPARK-34291
Project: Spark
Issue Type: Improvement
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zhengruifeng reassigned SPARK-34256:
Assignee: zhengruifeng
> VectorSlicer refine numFeatures checking and toString method
> -
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zhengruifeng resolved SPARK-34256.
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Fix Version/s: 3.2.0
Resolution: Fixed
Issue resolved by pull request 31354
[https://gi
zhengruifeng created SPARK-34307:
Summary: TakeOrderedAndProjectExec avoid shuffle if input rdd has
single partition
Key: SPARK-34307
URL: https://issues.apache.org/jira/browse/SPARK-34307
Project: Sp
zhengruifeng created SPARK-34353:
Summary: CollectLimitExec avoid shuffle if input rdd has single
partition
Key: SPARK-34353
URL: https://issues.apache.org/jira/browse/SPARK-34353
Project: Spark
zhengruifeng created SPARK-34356:
Summary: OVR transform avoid potential column conflict
Key: SPARK-34356
URL: https://issues.apache.org/jira/browse/SPARK-34356
Project: Spark
Issue Type: Imp
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zhengruifeng reassigned SPARK-34356:
Assignee: zhengruifeng
> OVR transform avoid potential column conflict
>
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zhengruifeng updated SPARK-34356:
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Summary: OVR transform fix potential column conflict (was: OVR transform
avoid potential column
zhengruifeng created SPARK-34470:
Summary: VectorSlicer use ordering if possible
Key: SPARK-34470
URL: https://issues.apache.org/jira/browse/SPARK-34470
Project: Spark
Issue Type: Improvement
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zhengruifeng commented on SPARK-34448:
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[~srowen] Thanks for pinging me, I am going
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zhengruifeng commented on SPARK-34448:
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[~srowen] [~weichenxu123] [~ykerzhner]
M
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zhengruifeng edited comment on SPARK-34448 at 2/26/21, 4:29 AM:
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zhengruifeng edited comment on SPARK-34448 at 2/26/21, 4:30 AM:
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zhengruifeng edited comment on SPARK-34448 at 2/26/21, 4:33 AM:
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zhengruifeng edited comment on SPARK-34448 at 2/26/21, 4:41 AM:
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zhengruifeng commented on SPARK-34448:
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My test code and log is here
> Binary logi
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zhengruifeng edited comment on SPARK-34448 at 2/26/21, 4:56 AM:
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zhengruifeng edited comment on SPARK-34448 at 2/26/21, 4:58 AM:
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zhengruifeng commented on SPARK-34448:
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1, I just make a simple
impl(https://githu
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zhengruifeng commented on SPARK-34448:
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I am not sure to: 1, center the vector in e
zhengruifeng created SPARK-34765:
Summary: Linear Models standardization optimization
Key: SPARK-34765
URL: https://issues.apache.org/jira/browse/SPARK-34765
Project: Spark
Issue Type: Improv
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zhengruifeng updated SPARK-34765:
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Issue Type: Umbrella (was: Improvement)
> Linear Models standardization optimization
>
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zhengruifeng updated SPARK-34765:
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Description:
Existing impl of standardization in linear models does *NOT* center the vectors
by
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zhengruifeng resolved SPARK-32060.
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Resolution: Resolved
> Huber loss Convergence
> --
>
> Key
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zhengruifeng resolved SPARK-31783.
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Resolution: Resolved
> Performance test on dense and sparse datasets
> ---
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zhengruifeng resolved SPARK-31714.
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Resolution: Resolved
> Performance test on java vectorization vs dot vs gemv vs gemm
> ---
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zhengruifeng resolved SPARK-32061.
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Resolution: Resolved
> potential regression if use memoryUsage instead of numRows
> --
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zhengruifeng reassigned SPARK-31661:
Assignee: zhengruifeng
> Document usage of blockSize
> ---
>
>
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zhengruifeng reassigned SPARK-31976:
Assignee: zhengruifeng
> use MemoryUsage to control the size of block
> -
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zhengruifeng updated SPARK-34765:
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Issue Type: Improvement (was: Umbrella)
> Linear Models standardization optimization
>
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zhengruifeng updated SPARK-34765:
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Parent: SPARK-30641
Issue Type: Sub-task (was: Improvement)
> Linear Models standardiza
zhengruifeng created SPARK-34797:
Summary: Refactor Logistic Aggregator
Key: SPARK-34797
URL: https://issues.apache.org/jira/browse/SPARK-34797
Project: Spark
Issue Type: Sub-task
C
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zhengruifeng updated SPARK-34797:
-
Description:
1, add BinaryLogisticBlockAggregator and MultinomialLogisticBlockAggregator and
re
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zhengruifeng updated SPARK-34797:
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Summary: Refactor Logistic Aggregator - support virtual centering (was:
Refactor Logistic Aggre
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zhengruifeng updated SPARK-30641:
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Affects Version/s: 3.2.0
> ML algs blockify input vectors
> --
>
>
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zhengruifeng reassigned SPARK-34470:
Assignee: zhengruifeng
> VectorSlicer use ordering if possible
>
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zhengruifeng resolved SPARK-34470.
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Resolution: Resolved
> VectorSlicer use ordering if possible
> ---
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zhengruifeng updated SPARK-30641:
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Summary: Project Matrix: Linear Models revisit and refactor (was: ML algs
blockify input vector
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
-
Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
-
Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
-
Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
-
Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
-
Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng updated SPARK-30641:
-
Description:
We had been refactoring linear models for a long time, and there still are some
wo
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https://issues.apache.org/jira/browse/SPARK-30641?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
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zhengruifeng reassigned SPARK-30641:
Assignee: (was: zhengruifeng)
> Project Matrix: Linear Models revisit and refactor
>
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zhengruifeng updated SPARK-30641:
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Description:
We had been refactoring linear models for a long time, and there still are some
wo
zhengruifeng created SPARK-34858:
Summary: Binary Logistic Regression with intercept support
centering
Key: SPARK-34858
URL: https://issues.apache.org/jira/browse/SPARK-34858
Project: Spark
zhengruifeng created SPARK-34860:
Summary: Multinomial Logistic Regression with intercept support
centering
Key: SPARK-34860
URL: https://issues.apache.org/jira/browse/SPARK-34860
Project: Spark
zhengruifeng created SPARK-36956:
Summary: model prediction in .mllib avoid conversion to breeze
vector
Key: SPARK-36956
URL: https://issues.apache.org/jira/browse/SPARK-36956
Project: Spark
zhengruifeng created SPARK-36963:
Summary: Add max_by/min_by to sql.functions
Key: SPARK-36963
URL: https://issues.apache.org/jira/browse/SPARK-36963
Project: Spark
Issue Type: Improvement
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zhengruifeng updated SPARK-37099:
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Description:
in JD, we found that more than 80% usage of window function follows this
pattern:
zhengruifeng created SPARK-37099:
Summary: Impl a rank-based filter to optimize top-k computation
Key: SPARK-37099
URL: https://issues.apache.org/jira/browse/SPARK-37099
Project: Spark
Issue
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zhengruifeng updated SPARK-37099:
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Description:
in JD, we found that more than 80% usage of window function follows this
pattern:
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zhengruifeng updated SPARK-37099:
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Description:
in JD, we found that more than 80% usage of window function follows this
pattern:
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zhengruifeng updated SPARK-37099:
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Attachment: skewed_window.png
> Impl a rank-based filter to optimize top-k computation
> ---
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zhengruifeng updated SPARK-37099:
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Description:
in JD, we found that more than 90% usage of window function follows this
pattern:
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zhengruifeng updated SPARK-37099:
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Description:
in JD, we found that more than 90% usage of window function follows this
pattern:
zhengruifeng created SPARK-37597:
Summary: Deduplicate the right side of left-semi join and
left-anti join
Key: SPARK-37597
URL: https://issues.apache.org/jira/browse/SPARK-37597
Project: Spark
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zhengruifeng resolved SPARK-37597.
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Resolution: Duplicate
> Deduplicate the right side of left-semi join and left-anti join
>
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zhengruifeng updated SPARK-38588:
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Summary: Validate input dataset of ml.classification (was: Validate input
dataset of LinearSVC)
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zhengruifeng updated SPARK-38588:
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Fix Version/s: 3.4.0
> Validate input dataset of ml.classification
> ---
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zhengruifeng resolved SPARK-38588.
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Resolution: Resolved
> Validate input dataset of ml.classification
> -
zhengruifeng created SPARK-38643:
Summary: Validate input dataset of ml.regression
Key: SPARK-38643
URL: https://issues.apache.org/jira/browse/SPARK-38643
Project: Spark
Issue Type: Sub-task
zhengruifeng created SPARK-38669:
Summary: Validate input dataset of ml.clustering
Key: SPARK-38669
URL: https://issues.apache.org/jira/browse/SPARK-38669
Project: Spark
Issue Type: Sub-task
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zhengruifeng updated SPARK-37099:
-
Description:
in JD, we found that more than 90% usage of window function follows this
pattern:
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zhengruifeng updated SPARK-37099:
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Summary: Introduce a rank-based filter to optimize top-k computation (was:
Impl a rank-based fi
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zhengruifeng updated SPARK-37099:
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Affects Version/s: 3.4.0
(was: 3.3.0)
> Introduce a rank-based filter
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zhengruifeng updated SPARK-36638:
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Affects Version/s: 3.4.0
(was: 3.3.0)
> Generalize OptimizeSkewedJoin
zhengruifeng created SPARK-38774:
Summary: impl Series.autocorr
Key: SPARK-38774
URL: https://issues.apache.org/jira/browse/SPARK-38774
Project: Spark
Issue Type: Sub-task
Component
zhengruifeng created SPARK-38775:
Summary: cleanup validation functions
Key: SPARK-38775
URL: https://issues.apache.org/jira/browse/SPARK-38775
Project: Spark
Issue Type: Sub-task
C
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https://issues.apache.org/jira/browse/SPARK-38775?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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zhengruifeng reassigned SPARK-38775:
Assignee: zhengruifeng
> cleanup validation functions
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>
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zhengruifeng created SPARK-38785:
Summary: impl Series.ewm and DataFrame.ewm
Key: SPARK-38785
URL: https://issues.apache.org/jira/browse/SPARK-38785
Project: Spark
Issue Type: Sub-task
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https://issues.apache.org/jira/browse/SPARK-38785?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17516846#comment-17516846
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zhengruifeng commented on SPARK-38785:
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h1. Pandas API on Spark:
[EWM|https://pand
zhengruifeng created SPARK-38844:
Summary: impl Series.interpolate and DataFrame.interpolate
Key: SPARK-38844
URL: https://issues.apache.org/jira/browse/SPARK-38844
Project: Spark
Issue Type:
zhengruifeng created SPARK-38907:
Summary: Impl DataFrame.corrwith
Key: SPARK-38907
URL: https://issues.apache.org/jira/browse/SPARK-38907
Project: Spark
Issue Type: Sub-task
Compon
zhengruifeng created SPARK-38937:
Summary: interpolate support param `limit_direction`
Key: SPARK-38937
URL: https://issues.apache.org/jira/browse/SPARK-38937
Project: Spark
Issue Type: Impro
zhengruifeng created SPARK-38943:
Summary: EWM support ignore_na
Key: SPARK-38943
URL: https://issues.apache.org/jira/browse/SPARK-38943
Project: Spark
Issue Type: Improvement
Compo
zhengruifeng created SPARK-38993:
Summary: Impl DataFrame.boxplot and DataFrame.plot.box
Key: SPARK-38993
URL: https://issues.apache.org/jira/browse/SPARK-38993
Project: Spark
Issue Type: Sub
zhengruifeng created SPARK-39081:
Summary: Impl DataFrame.resample and Series.resample
Key: SPARK-39081
URL: https://issues.apache.org/jira/browse/SPARK-39081
Project: Spark
Issue Type: Sub-t
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