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https://issues.apache.org/jira/browse/SPARK-10560?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14944305#comment-14944305
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Bryan Cutler commented on SPARK-10560:
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Hi [~yanboliang]], I just want to make sure I'm on the same page as to what we
need to do here.
Here are the differences I see between the Python and Scala APIs for
StreamingLogisticRegressionWithSGD:
-
* The documentation for Python is missing the default parameter values, also
the same for StreamingLinearRegressionWithSGD
* In Python StreamingLogisticRegressionWithSGD the regularization defaults to
0.01 while the Scala version defaults to 0.
I believe other SGD implementations default to non-zero, so maybe there is some
reason to turn it off in Streaming implementations?
In any case, these ones should probably default to the same value
* The Scala StreamingLogisticRegressionWithSGD is missing a method to set
convergence tolerance, it is in the Python one
* StreamingLogisticRegressionWithSGD for Scala and Python are missing ability
to set regularization parameter
* Python Streaming**RegressionWithSGD are missing API methods to set
parameters, i.e. setStepSize
How about for this JIRA, I fix the documentation to include default parameters
and then I will make JIRAs for the other items?
> Make StreamingLogisticRegressionWithSGD Python API equals with Scala one
> ------------------------------------------------------------------------
>
> Key: SPARK-10560
> URL: https://issues.apache.org/jira/browse/SPARK-10560
> Project: Spark
> Issue Type: Sub-task
> Components: MLlib, PySpark
> Reporter: Yanbo Liang
> Priority: Minor
>
> StreamingLogisticRegressionWithSGD Python API lacks of some parameters
> compared with Scala one, here we make them equality.
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