Description
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.
Attachments
Issue Links
- is duplicated by
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SPARK-2488 Model SerDe in MLlib
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- Closed
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- is related to
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SPARK-4604 Make MatrixFactorizationModel constructor public
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- Resolved
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- links to
