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Xiangrui Meng resolved SPARK-16107.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13820
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Xiangrui Meng resolved SPARK-16118.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13821
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Xiangrui Meng created SPARK-16118:
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Summary: getDropLast is missing in OneHotEncoder
Key: SPARK-16118
URL: https://issues.apache.org/jira/browse/SPARK-16118
Project: Spark
Issue Type: New Fea
Xiangrui Meng created SPARK-16117:
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Summary: Hide LibSVMFileFormat in public API docs
Key: SPARK-16117
URL: https://issues.apache.org/jira/browse/SPARK-16117
Project: Spark
Issue Type: Improv
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Xiangrui Meng closed SPARK-16113.
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Resolution: Not A Problem
> Deprecate (or remove) multiclass APIs in ml.LogisticRegression
>
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Xiangrui Meng commented on SPARK-16113:
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Just realized that `thresholds` was inheri
Xiangrui Meng created SPARK-16113:
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Summary: Deprecate (or remove) multiclass APIs in
ml.LogisticRegression
Key: SPARK-16113
URL: https://issues.apache.org/jira/browse/SPARK-16113
Project: Spark
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Xiangrui Meng edited comment on SPARK-16111 at 6/21/16 7:26 PM:
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Xiangrui Meng edited comment on SPARK-16111 at 6/21/16 7:26 PM:
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Xiangrui Meng commented on SPARK-16111:
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Ping [~ rbalamohan]
> Hide SparkOrcNewRec
Xiangrui Meng created SPARK-16111:
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Summary: Hide SparkOrcNewRecordReader in API docs
Key: SPARK-16111
URL: https://issues.apache.org/jira/browse/SPARK-16111
Project: Spark
Issue Type: Docume
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Xiangrui Meng updated SPARK-16086:
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Fix Version/s: 1.6.2
1.5.3
> Python UDF failed when there is no arguments
>
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Xiangrui Meng updated SPARK-15741:
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Assignee: Bryan Cutler
> PySpark Cleanup of _setDefault with seed=None
> --
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Xiangrui Meng updated SPARK-15741:
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Target Version/s: 2.0.0
> PySpark Cleanup of _setDefault with seed=None
> -
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Xiangrui Meng resolved SPARK-15741.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13672
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Xiangrui Meng updated SPARK-16107:
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Assignee: Junyang Qian
> Group GLM-related methods in generated doc
> -
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Xiangrui Meng updated SPARK-16107:
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Labels: starter (was: )
> Group GLM-related methods in generated doc
> ---
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Xiangrui Meng updated SPARK-16107:
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Description: Group API docs of spark.glm, glm, predict(GLM), summary(GLM),
read/write.ml(GLM) u
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Xiangrui Meng updated SPARK-16107:
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Description: spark.glm: spark.glm, glm, predict(GLM), summary(GLM),
read/write.ml(GLM)
> Group
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Xiangrui Meng commented on SPARK-16107:
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ping [~junyangq]
> Group GLM-related meth
Xiangrui Meng created SPARK-16107:
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Summary: Group GLM-related methods in generated doc
Key: SPARK-16107
URL: https://issues.apache.org/jira/browse/SPARK-16107
Project: Spark
Issue Type: Sub-
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Xiangrui Meng commented on SPARK-16090:
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I changed the issue type to umbrella since
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Xiangrui Meng updated SPARK-16090:
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Issue Type: Umbrella (was: Improvement)
> Improve method grouping in SparkR generated docs
> -
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Xiangrui Meng updated SPARK-15177:
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Description: Audit new public R APIs in mllib.R (was: Audit new public R
APIs in mllib.R.)
>
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Xiangrui Meng updated SPARK-15177:
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Summary: SparkR 2.0 QA: make SparkR model params and default values
consistent with MLlib (was
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Xiangrui Meng updated SPARK-15177:
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Shepherd: Xiangrui Meng
> SparkR 2.0 QA: New R APIs and API docs for mllib.R
>
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Xiangrui Meng updated SPARK-15177:
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Description: Make SparkR model params and default values consistent with
MLlib (was: Audit new
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Xiangrui Meng resolved SPARK-15177.
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Resolution: Fixed
Fix Version/s: 2.0.0
Target Version/s: 2.0.0
I marked this
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Xiangrui Meng updated SPARK-15177:
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Assignee: Yanbo Liang
> SparkR 2.0 QA: New R APIs and API docs for mllib.R
> --
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Xiangrui Meng commented on SPARK-16071:
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[~ding] This JIRA is not to solve this par
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Xiangrui Meng resolved SPARK-16045.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13375
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Xiangrui Meng updated SPARK-16045:
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Assignee: yuhao yang
> Spark 2.0 ML.feature: doc update for stopwords and binarizer
> -
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Xiangrui Meng updated SPARK-16045:
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Affects Version/s: 2.0.0
Target Version/s: 2.0.0
> Spark 2.0 ML.feature: doc update for st
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Xiangrui Meng resolved SPARK-7751.
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Resolution: Fixed
Fix Version/s: 2.0.0
Mark this umbrella as resolved since all sub-tasks
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Xiangrui Meng updated SPARK-10258:
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Shepherd: Nick Pentreath
> Add @Since annotation to ml.feature
> --
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Xiangrui Meng resolved SPARK-10258.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13641
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Xiangrui Meng commented on SPARK-16086:
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Reverted the changes in master and branch-
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Xiangrui Meng reopened SPARK-16086:
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> Python UDF failed when there is no arguments
>
>
>
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Xiangrui Meng updated SPARK-16086:
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Fix Version/s: (was: 2.0.0)
> Python UDF failed when there is no arguments
> --
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Xiangrui Meng commented on SPARK-16090:
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For ML methods, I'd like to propose the fo
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Xiangrui Meng updated SPARK-16090:
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Description: This JIRA follows the discussion on
https://github.com/apache/spark/pull/13109 to
Xiangrui Meng created SPARK-16090:
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Summary: Improve method grouping in SparkR generated docs
Key: SPARK-16090
URL: https://issues.apache.org/jira/browse/SPARK-16090
Project: Spark
Issue Type
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https://issues.apache.org/jira/browse/SPARK-16079?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-16079.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13787
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Xiangrui Meng updated SPARK-16079:
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Assignee: Bryan Cutler
> PySpark ML classification missing import of DecisionTreeRegressionMode
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Xiangrui Meng updated SPARK-16079:
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Affects Version/s: 2.0.0
> PySpark ML classification missing import of DecisionTreeRegressionMo
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Xiangrui Meng commented on SPARK-16074:
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Picked option 2) because we don't have any
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Xiangrui Meng reassigned SPARK-16074:
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Assignee: Xiangrui Meng
> Expose VectorUDT/MatrixUDT in a public API
> -
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Xiangrui Meng commented on SPARK-16075:
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I'm not sure whether we should make this c
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Xiangrui Meng updated SPARK-16075:
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Description:
Both VectorUDT and MatrixUDT are implemented as normal classes and their could
be
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Xiangrui Meng updated SPARK-16074:
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Description:
Both VectorUDT and MatrixUDT are private APIs, because UserDefinedType itself
is
Xiangrui Meng created SPARK-16075:
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Summary: Make VectorUDT/MatrixUDT singleton under spark.ml package
Key: SPARK-16075
URL: https://issues.apache.org/jira/browse/SPARK-16075
Project: Spark
I
Xiangrui Meng created SPARK-16074:
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Summary: Expose VectorUDT/MatrixUDT in a public API
Key: SPARK-16074
URL: https://issues.apache.org/jira/browse/SPARK-16074
Project: Spark
Issue Type: New
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https://issues.apache.org/jira/browse/SPARK-16073?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-16073:
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Description:
Spark supports both uncompressed and compressed (snappy, gzip, lzo) Parquet
data.
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Xiangrui Meng updated SPARK-16073:
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Description:
Spark supports both uncompressed and compressed (snappy, gzip, lzo) Parquet
data.
Xiangrui Meng created SPARK-16073:
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Summary: Performance of Parquet encodings on saving primitive
arrays
Key: SPARK-16073
URL: https://issues.apache.org/jira/browse/SPARK-16073
Project: Spark
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Xiangrui Meng updated SPARK-16070:
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Description:
I created this umbrella JIRA to track DataFrame/Parquet issues with primitive
arr
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Xiangrui Meng updated SPARK-16071:
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Description:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
i
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Xiangrui Meng updated SPARK-16070:
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Description:
I created this umbrella JIRA to track DataFrame/Parquet issues with primitive
arr
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https://issues.apache.org/jira/browse/SPARK-16071?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-16071:
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Description:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
i
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Xiangrui Meng updated SPARK-16071:
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Description:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
i
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Xiangrui Meng updated SPARK-16071:
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Description:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
i
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Xiangrui Meng updated SPARK-16071:
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Description:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
i
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Xiangrui Meng updated SPARK-16071:
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Description:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
i
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Xiangrui Meng updated SPARK-16071:
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Description:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
i
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Xiangrui Meng updated SPARK-16071:
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Assignee: Yin Huai
> Not sufficient array size checks to avoid integer overflows in Tungsten
>
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Xiangrui Meng updated SPARK-16071:
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Description:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
i
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Xiangrui Meng updated SPARK-16071:
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Summary: Not sufficient array size checks to avoid integer overflows in
Tungsten (was: Not suf
Xiangrui Meng created SPARK-16071:
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Summary: Not sufficient size checks to avoid integer overflows in
Tungsten
Key: SPARK-16071
URL: https://issues.apache.org/jira/browse/SPARK-16071
Project: Spark
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Xiangrui Meng updated SPARK-16070:
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Description:
I created this umbrella JIRA to track DataFrame/Parquet issues with primitive
arr
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Xiangrui Meng updated SPARK-16070:
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Description:
I created this umbrella JIRA to track DataFrame/Parquet issues with primitive
arr
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Xiangrui Meng updated SPARK-16070:
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Description:
I created this umbrella JIRA to track DataFrame/Parquet issues with primitive
arr
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https://issues.apache.org/jira/browse/SPARK-16070?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-16070:
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Description:
I created this umbrella JIRA to track DataFrame/Parquet issues with primitive
arr
Xiangrui Meng created SPARK-16070:
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Summary: DataFrame/Parquet issues with primitive arrays
Key: SPARK-16070
URL: https://issues.apache.org/jira/browse/SPARK-16070
Project: Spark
Issue Type:
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Xiangrui Meng updated SPARK-16035:
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Assignee: Andrea Pasqua
> The SparseVector parser fails checking for valid end parenthesis
> --
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Xiangrui Meng resolved SPARK-16035.
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Resolution: Fixed
Fix Version/s: 1.6.2
2.0.0
Issue resolved by pull
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Xiangrui Meng resolved SPARK-15129.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13285
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Xiangrui Meng updated SPARK-15892:
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Fix Version/s: 2.0.0
> Incorrectly merged AFTAggregator with zero total count
> ---
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Xiangrui Meng resolved SPARK-15892.
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Resolution: Fixed
Fix Version/s: (was: 2.0.0)
1.6.2
Issue resol
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Xiangrui Meng resolved SPARK-15603.
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Resolution: Fixed
Fix Version/s: 2.0.0
> Replace SQLContext with SparkSession in ML/ML
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Xiangrui Meng resolved SPARK-16008.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13729
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Xiangrui Meng updated SPARK-16000:
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Assignee: yuhao yang
> Make model loading backward compatible with saved models using old vecto
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Xiangrui Meng commented on SPARK-16000:
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That's great! Please let me know if you wa
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Xiangrui Meng closed SPARK-15947.
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> Make pipeline components backward compatible with old vector columns in
> Scala/Java
> ---
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Xiangrui Meng reassigned SPARK-15946:
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Assignee: Xiangrui Meng
> Wrap the conversion utils in Python
>
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Xiangrui Meng updated SPARK-15947:
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Summary: Make pipeline components backward compatible with old vector
columns in Scala/Java (w
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https://issues.apache.org/jira/browse/SPARK-15948?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng closed SPARK-15948.
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Resolution: Won't Fix
> Make pipeline components backward compatible with old vector columns in P
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Xiangrui Meng resolved SPARK-15947.
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Resolution: Won't Fix
> Make pipeline components backward compatible with old vector columns
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Xiangrui Meng commented on SPARK-15948:
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Marked this as "Won't Do". See SPARK-15947
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https://issues.apache.org/jira/browse/SPARK-15643?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-15643:
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Assignee: Yanbo Liang
> ML 2.0 QA: migration guide update
> -
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Xiangrui Meng commented on SPARK-15643:
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[~yanboliang] Please include a paragraph t
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Xiangrui Meng edited comment on SPARK-15947 at 6/16/16 9:30 PM:
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Xiangrui Meng commented on SPARK-15947:
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Had an offline discussion with [~josephkb]
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https://issues.apache.org/jira/browse/SPARK-16000?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-16000:
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Description: To help users migrate from Spark 1.6. to 2.0, we should make
model loading backwar
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Xiangrui Meng updated SPARK-15947:
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Description:
After SPARK-15945, we should make ALL pipeline components accept old vector
colum
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https://issues.apache.org/jira/browse/SPARK-16000?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-16000:
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Summary: Make model loading backward compatible with saved models using old
vector columns (wa
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https://issues.apache.org/jira/browse/SPARK-15947?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-15947:
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Description:
After SPARK-15945, we should make ALL pipeline components accept old vector
colum
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https://issues.apache.org/jira/browse/SPARK-15947?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-15947:
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Summary: Make pipeline components backward compatible with old vector
columns (was: Make pipel
Xiangrui Meng created SPARK-16000:
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Summary: Make model loading backward compatible with saved models
using old vector columns
Key: SPARK-16000
URL: https://issues.apache.org/jira/browse/SPARK-16000
P
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Xiangrui Meng updated SPARK-16000:
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Summary: Make model loading backward compatible with saved models using old
vector columns in S
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Xiangrui Meng reassigned SPARK-15947:
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Assignee: Xiangrui Meng
> Make pipeline components backward compatible with old vector c
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