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https://issues.apache.org/jira/browse/SYSTEMML-983?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Janardhan updated SYSTEMML-983:
-------------------------------
Labels: beginner newbie starter (was: )
> Add mllearn and scala wrappers for GLM
> --------------------------------------
>
> Key: SYSTEMML-983
> URL: https://issues.apache.org/jira/browse/SYSTEMML-983
> Project: SystemML
> Issue Type: Task
> Components: APIs
> Reporter: Niketan Pansare
> Priority: Major
> Labels: beginner, newbie, starter
>
> See
> https://apache.github.io/incubator-systemml/algorithms-regression.html#generalized-linear-models
> for usage
> Since this is a starter task, I describe the steps to complete this task:
> 1. Implement a scala class (which inherits from BaseSystemMLRegressor)
> similar to
> https://github.com/apache/incubator-systemml/blob/master/src/main/scala/org/apache/sysml/api/ml/LinearRegression.scala
> 2. Modify getTrainingScript and getPredictionScript to specify the parameters
> used. See the algorithm documentation for these parameters.
> 3. Ensure that you implement appropriate traits to accept hyperparameters
> (eg: HasLaplace, HasIcpt, HasRegParam, HasTol, etc). These traits are
> available at
> https://github.com/apache/incubator-systemml/blob/master/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLClassifier.scala#L36
> 4. Implement a python class (that extends BaseSystemMLRegressor) with
> constructor similar to
> https://github.com/apache/incubator-systemml/blob/master/src/main/python/systemml/mllearn/estimators.py#L218
> which essentially accepts the hyperparameters and invokes the scala side
> methods (example: self.estimator.setLaplace(laplace))
> 5. Update the algorithm documentation by specifying the usage as well as
> examples.
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