Model interface design

[fkiraly]

This is in reference to the "adding new models" interface design document, and in reaction to earlier request for comments.

Personally, I like:

• the struct-dispatch architecture (probably has to be like that in Julia)
• learners being parametric structs
• the fit/predict methods

Things I like less or would like to highlight:

• I think we need user interaction studies for major use cases: fitting/prediction, and benchmarking; and any minor use cases. What would we expect a user to type in the console?
• there is no abstract hyper-parameter interface. This makes abstract higher-order operations such as tuning difficult.
• Interface splits into X and y and seems to implicitly assume these are arrays. Should we: (a) pass only a single object plus a target specifier instead? (b) assume a data frame like structure instead? I.e., with typed and type-aware columns.
• learners are parametric with intended variable type being a matrix/vector. I would instead go with specifying "allowed input/output column types" for column-typed data containers.
• output type of predict_proba needs clarification. We should avoid sth hacky such as multiplying columns, per class, and end up case-dependent interfaces such as mlr. Suggestion: output row with simplex/pmf object
• I still don't understand the use case of fit2, or why this should be a single interface point. Would suggest to split into (i) inference (ii) updateParams (iii) updateData interface points. These are not the same use case!
• Also, if fit2 (or whatever) is supposed to be an efficient interface point for altering hyper-parameters in a fitted model, or get post-fitting functionality, wouldn't one need to pass a fitresult struct rather than a ConcreteModel?
• in any case, fit2 doesn't seem to be necessary for MVP, so park and revisit?

[vollmersj]

I think the data container is likely to be something like https://github.com/queryverse/IterableTables.jl. For the hyperparameter interface it can be as simple as old mlj or involved as in mathematical optimisation

http://www.juliaopt.org/MathOptInterface.jl/stable/apimanual.html#Variables-1

[ablaom]

Closed, as duplicate of JuliaAI/MLJ.jl#16