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https://issues.apache.org/jira/browse/FLINK-2116?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14600958#comment-14600958
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ASF GitHub Bot commented on FLINK-2116:
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Github user tillrohrmann commented on the pull request:
https://github.com/apache/flink/pull/772#issuecomment-115198124
Actually I wouldn't call it predictSomething, because then we're again
quite close to the former problem that we have a method whose semantics depend
on the provided type. And this only confuses users.
My concern is that the user does not really know what `predictLabeled`
means. Apparently it is something similar to `predict` but with a label. But
what is the label? Does it mean that I can apply `predict` on `T <: Vector` and
`predictLabeled` on `LabeledVector`? Does it mean that I get a labeled result
type? But don't I already get it with `predict`? Do I have to provide a type
with a label or can I also supply a vector?
IMO, the prediction which also returns the true label value deserves a more
distinguishable name than `predictSomething`, because it has different
semantics. I can't think of something better than `evaluate` at the moment. But
it makes it clear that the user has to provide some evaluation `DataSet`,
meaning some labeled data.
> Make pipeline extension require less coding
> -------------------------------------------
>
> Key: FLINK-2116
> URL: https://issues.apache.org/jira/browse/FLINK-2116
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Mikio Braun
> Assignee: Till Rohrmann
> Priority: Minor
>
> Right now, implementing methods from the pipelines for new types, or even
> adding new methods to pipelines requires many steps:
> 1) implementing methods for new types
> implement implicit of the corresponding class encapsulating the operation
> in the companion object
> 2) adding methods to the pipeline
> - adding a method
> - adding a trait for the operation
> - implement implicit in the companion object
> These are all objects which contain many generic parameters, so reducing the
> work would be great.
> The goal should be that you can really focus on the code to add, and have as
> little boilerplate code as possible.
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