Sorting out issue with calling predict on matrix

[pat-alt]

Closes #218

[pat-alt]

I've updated the scitype declarations for regression (here and here) and for classification (here). For testing, I thought I'd just rerun basic tests with a matrix input (see here). Unfortunately, I can't immediately make sense of the error I'm getting.

[ablaom]

Thanks for persisting with this.

This error usually means we're trying to grab the schema of an object that doesn't have one. I believe the culprit is here. We could add a second method dispatching on matrices to address. There are similar issues for shape in regressor.jl.

[pat-alt]

Thanks! I believe that's fixed now, though I did still get some warnings like this one:

Warning: The number and/or types of data arguments do not match what the specified model
│ supports. Suppress this type check by specifying `scitype_check_level=0`.
│ 
│ Run `@doc MLJFlux.NeuralNetworkRegressor` to learn more about your model's requirements.
│ 
│ Commonly, but non exclusively, supervised models are constructed using the syntax
│ `machine(model, X, y)` or `machine(model, X, y, w)` while most other models are
│ constructed with `machine(model, X)`.  Here `X` are features, `y` a target, and `w`
│ sample or class weights.
│ 
│ In general, data in `machine(model, data...)` is expected to satisfy
│ 
│     scitype(data) <: MLJ.fit_data_scitype(model)
│ 
│ In the present case:
│ 
│ scitype(data) = Tuple{Table{AbstractVector{Continuous}}, AbstractVector{Continuous}}
│ 
│ fit_data_scitype(model) = Tuple{Union{Table{<:AbstractVector{<:Continuous}}, AbstractMatrix{Continuous}}, AbstractVector{<:Finite}}

Edit: As for docstrings, I've updated for the classifier and regressors as follows:

- `X` is either a `Matrix` or any table of input features (eg, a `DataFrame`) whose columns are of scitype
  `Continuous`; check column scitypes with `schema(X)`. If `X` is a `Matrix`, it is assumed to have columns corresponding to features and rows corresponding to observations.

[ablaom]

Thanks! I believe that's fixed now, though I did still get some warnings like this one:

According to the warning you are providing the regressor with a Continuous target, rather than a Finite one (categorical vector).

[ablaom]

Sorry, rather the scitype declaration for the regressor is wrong. You need Continuous in the target_scitype where you appear to have Finite.

[codecov-commenter]

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[pat-alt]

Sorry, rather the scitype declaration for the regressor is wrong. You need Continuous in the target_scitype where you appear to have Finite.

You're right, thanks. Fixed that now, but I still get some broken tests (e.g. here. Is this expected?

[ablaom]

Fixed that now, but I still get some broken tests (e.g. here. Is this expected?

Yes. Some of the broken tests are because you are looking at CPU tests, where some tests are excluded and get marked as broken.

The remaining broken tests are explained here https://github.com/FluxML/MLJFlux.jl/issues/87 .

So no further action on your part needed here. I will finish my review shortly, thanks.

[pat-alt]

Thanks, good idea! I've made the necessary adjustments and updated tests accordingly. Had to make one additional small modification to the collate function. I've also updated the docstring.