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https://issues.apache.org/jira/browse/SPARK-2495?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14224894#comment-14224894
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Tamas Jambor commented on SPARK-2495:
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hi all,
what's the reason MatrixFactorizationModel is still private?
I am building a workflow similar to what Alexander described above and run into
the same problem with MatrixFactorizationModel. There should be a point where
it is accessible to store and recreate the model.
> Ability to re-create ML models
> ------------------------------
>
> Key: SPARK-2495
> URL: https://issues.apache.org/jira/browse/SPARK-2495
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.0.1
> Reporter: Alexander Albul
> Assignee: Alexander Albul
> Fix For: 1.1.0
>
>
> Hi everyone.
> Previously (prior to Spark 1.0) we was working with MLib like this:
> 1) Calculate model (costly operation)
> 2) Take model and collect it's fields like weights, intercept e.t.c.
> 3) Store model somewhere in our format
> 4) Do predictions by loading model attributes, creating new model and
> predicting using it.
> Now i see that model's constructors have *private* modifier and cannot be
> created from outside.
> If you want to hide implementation details and keep this constructor as
> "developer api", why not to create at least method, which will take weights,
> intercept (for example) an materialize that model?
> A good example of model that i am talking about is: *LinearRegressionModel*
> I know that *LinearRegressionWithSGD* class have *createModel* method but the
> problem is that it have *protected* modifier as well.
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