You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "zhengruifeng (Jira)" <ji...@apache.org> on 2019/09/17 09:42:00 UTC

[jira] [Updated] (SPARK-29118) Avoid redundant computation in GMM.transform && GLR.transform

     [ https://issues.apache.org/jira/browse/SPARK-29118?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

zhengruifeng updated SPARK-29118:
---------------------------------
    Description: 
In SPARK-27944, the computation for output columns with empty name is skipped.

Now, I find that we can furthermore optimize

1, GMM.transform by directly obtaining the prediction(double) from its probabilty prediction(vector), like what ProbabilisticClassificationModel and ClassificationModel do.

2, GLR.transform by obtaining the prediction(double) from its link prediction(double)

  was:
In SPARK-27944, the computation for output columns with empty name is skipped.

Now, I find that we can furthermore optimize GMM.transform by directly obtaining the prediction(double) from its probabilty prediction(vector), like what ProbabilisticClassificationModel and ClassificationModel do.


> Avoid redundant computation in GMM.transform && GLR.transform
> -------------------------------------------------------------
>
>                 Key: SPARK-29118
>                 URL: https://issues.apache.org/jira/browse/SPARK-29118
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 3.0.0
>            Reporter: zhengruifeng
>            Priority: Minor
>
> In SPARK-27944, the computation for output columns with empty name is skipped.
> Now, I find that we can furthermore optimize
> 1, GMM.transform by directly obtaining the prediction(double) from its probabilty prediction(vector), like what ProbabilisticClassificationModel and ClassificationModel do.
> 2, GLR.transform by obtaining the prediction(double) from its link prediction(double)



--
This message was sent by Atlassian Jira
(v8.3.2#803003)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org