[jira] [Assigned] (SPARK-17116) Allow params to be a {string, value} dict at fit time

2016-11-13 Thread Apache Spark (JIRA)

 [ 
https://issues.apache.org/jira/browse/SPARK-17116?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-17116:


Assignee: Apache Spark

> Allow params to be a {string, value} dict at fit time
> -
>
> Key: SPARK-17116
> URL: https://issues.apache.org/jira/browse/SPARK-17116
> Project: Spark
>  Issue Type: Improvement
>  Components: ML, PySpark
>Reporter: Manoj Kumar
>Assignee: Apache Spark
>Priority: Minor
>
> Currently, it is possible to override the default params set at constructor 
> time by supplying a ParamMap which is essentially a (Param: value) dict.
> Looking at the codebase, it should be trivial to extend this to a (string, 
> value) representation.
> {code}
> # This hints that the maxiter param of the lr instance is modified in-place
> lr = LogisticRegression(maxIter=10, regParam=0.01)
> lr.fit(dataset, {lr.maxIter: 20})
> # This seems more natural.
> lr.fit(dataset, {"maxIter": 20})
> {code}



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[jira] [Assigned] (SPARK-17116) Allow params to be a {string, value} dict at fit time

2016-11-13 Thread Apache Spark (JIRA)

 [ 
https://issues.apache.org/jira/browse/SPARK-17116?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-17116:


Assignee: (was: Apache Spark)

> Allow params to be a {string, value} dict at fit time
> -
>
> Key: SPARK-17116
> URL: https://issues.apache.org/jira/browse/SPARK-17116
> Project: Spark
>  Issue Type: Improvement
>  Components: ML, PySpark
>Reporter: Manoj Kumar
>Priority: Minor
>
> Currently, it is possible to override the default params set at constructor 
> time by supplying a ParamMap which is essentially a (Param: value) dict.
> Looking at the codebase, it should be trivial to extend this to a (string, 
> value) representation.
> {code}
> # This hints that the maxiter param of the lr instance is modified in-place
> lr = LogisticRegression(maxIter=10, regParam=0.01)
> lr.fit(dataset, {lr.maxIter: 20})
> # This seems more natural.
> lr.fit(dataset, {"maxIter": 20})
> {code}



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