[jira] [Assigned] (SPARK-17161) Add PySpark-ML JavaWrapper convenience function to create py4j JavaArrays
[
https://issues.apache.org/jira/browse/SPARK-17161?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
holdenk reassigned SPARK-17161:
---
Assignee: Bryan Cutler
Affects Version/s: 2.2.0
> Add PySpark-ML JavaWrapper convenience function to create py4j JavaArrays
> -
>
> Key: SPARK-17161
> URL: https://issues.apache.org/jira/browse/SPARK-17161
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
>Affects Versions: 2.2.0
>Reporter: Bryan Cutler
>Assignee: Bryan Cutler
>Priority: Minor
> Fix For: 2.2.0
>
>
> Often in Spark ML, there are classes that use a Scala Array in a constructor.
> In order to add the same API to Python, a Java-friendly alternate
> constructor needs to exist to be compatible with py4j when converting from a
> list. This is because the current conversion in PySpark _py2java creates a
> java.util.ArrayList, as shown in this error msg
> {noformat}
> Py4JError: An error occurred while calling
> None.org.apache.spark.ml.feature.CountVectorizerModel. Trace:
> py4j.Py4JException: Constructor
> org.apache.spark.ml.feature.CountVectorizerModel([class java.util.ArrayList])
> does not exist
> at
> py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:179)
> at
> py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:196)
> at py4j.Gateway.invoke(Gateway.java:235)
> {noformat}
> Creating an alternate constructor can be avoided by creating a py4j JavaArray
> using {{new_array}}. This type is compatible with the Scala Array currently
> used in classes like {{CountVectorizerModel}} and {{StringIndexerModel}}.
> Most of the boiler-plate Python code to do this can be put in a convenience
> function inside of ml.JavaWrapper to give a clean way of constructing ML
> objects without adding special constructors.
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[jira] [Assigned] (SPARK-17161) Add PySpark-ML JavaWrapper convenience function to create py4j JavaArrays
[
https://issues.apache.org/jira/browse/SPARK-17161?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-17161:
Assignee: (was: Apache Spark)
> Add PySpark-ML JavaWrapper convenience function to create py4j JavaArrays
> -
>
> Key: SPARK-17161
> URL: https://issues.apache.org/jira/browse/SPARK-17161
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
>Reporter: Bryan Cutler
>Priority: Minor
>
> Often in Spark ML, there are classes that use a Scala `Array` to construct.
> In order to add the same API to Python, a Java-friendly alternate constructor
> needs to exist to be compatible with py4j when converting from a list. This
> is because the current conversion in PySpark _py2java creates a
> java.util.ArrayList, as shown in this error msg
> {noformat}
> Py4JError: An error occurred while calling
> None.org.apache.spark.ml.feature.CountVectorizerModel. Trace:
> py4j.Py4JException: Constructor
> org.apache.spark.ml.feature.CountVectorizerModel([class java.util.ArrayList])
> does not exist
> at
> py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:179)
> at
> py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:196)
> at py4j.Gateway.invoke(Gateway.java:235)
> {noformat}
> Creating an alternate constructor can be avoided by creating a py4j JavaArray
> using {{new_array}}. This type is compatible with the Scala `Array`
> currently used in classes like {{CountVectorizerModel}} and
> {{StringIndexerModel}}.
> Most of the boiler-plate Python code to do this can be put in a convenience
> function inside of ml.JavaWrapper to give a clean way of constructing ML
> objects without adding special constructors.
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[jira] [Assigned] (SPARK-17161) Add PySpark-ML JavaWrapper convenience function to create py4j JavaArrays
[
https://issues.apache.org/jira/browse/SPARK-17161?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-17161:
Assignee: Apache Spark
> Add PySpark-ML JavaWrapper convenience function to create py4j JavaArrays
> -
>
> Key: SPARK-17161
> URL: https://issues.apache.org/jira/browse/SPARK-17161
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
>Reporter: Bryan Cutler
>Assignee: Apache Spark
>Priority: Minor
>
> Often in Spark ML, there are classes that use a Scala `Array` to construct.
> In order to add the same API to Python, a Java-friendly alternate constructor
> needs to exist to be compatible with py4j when converting from a list. This
> is because the current conversion in PySpark _py2java creates a
> java.util.ArrayList, as shown in this error msg
> {noformat}
> Py4JError: An error occurred while calling
> None.org.apache.spark.ml.feature.CountVectorizerModel. Trace:
> py4j.Py4JException: Constructor
> org.apache.spark.ml.feature.CountVectorizerModel([class java.util.ArrayList])
> does not exist
> at
> py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:179)
> at
> py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:196)
> at py4j.Gateway.invoke(Gateway.java:235)
> {noformat}
> Creating an alternate constructor can be avoided by creating a py4j JavaArray
> using {{new_array}}. This type is compatible with the Scala `Array`
> currently used in classes like {{CountVectorizerModel}} and
> {{StringIndexerModel}}.
> Most of the boiler-plate Python code to do this can be put in a convenience
> function inside of ml.JavaWrapper to give a clean way of constructing ML
> objects without adding special constructors.
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