[jira] [Created] (SYSTEMML-990) Add mllearn and scala wrappers for stepwise GLM

["Niketan Pansare (JIRA)" <ji...@apache.org>]

Niketan Pansare created SYSTEMML-990:
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             Summary: Add mllearn and scala wrappers for stepwise GLM
                 Key: SYSTEMML-990
                 URL: https://issues.apache.org/jira/browse/SYSTEMML-990
             Project: SystemML
          Issue Type: Task
          Components: APIs
            Reporter: Niketan Pansare


See https://apache.github.io/incubator-systemml/algorithms-regression.html#stepwise-generalized-linear-regression 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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