Summary of methods

The compulsory and optional methods to be implemented for each concrete type SomeSupervisedModel <: MMI.Supervised are summarized below.

An = indicates the return value for a fallback version of the method.

Compulsory:

MMI.fit(model::SomeSupervisedModel, verbosity, X, y) -> fitresult, cache, report
MMI.predict(model::SomeSupervisedModel, fitresult, Xnew) -> yhat

Optional, to check and correct invalid hyperparameter values:

MMI.clean!(model::SomeSupervisedModel) = ""

Optional, to return user-friendly form of fitted parameters:

MMI.fitted_params(model::SomeSupervisedModel, fitresult) = fitresult

Optional, to avoid redundant calculations when re-fitting machines associated with a model:

MMI.update(model::SomeSupervisedModel, verbosity, old_fitresult, old_cache, X, y) =
   MMI.fit(model, verbosity, X, y)

Optional, to specify default hyperparameter ranges (for use in tuning):

MMI.hyperparameter_ranges(T::Type) = Tuple(fill(nothing, length(fieldnames(T))))

Optional, if SomeSupervisedModel <: Probabilistic:

MMI.predict_mode(model::SomeSupervisedModel, fitresult, Xnew) =
    mode.(predict(model, fitresult, Xnew))
MMI.predict_mean(model::SomeSupervisedModel, fitresult, Xnew) =
    mean.(predict(model, fitresult, Xnew))
MMI.predict_median(model::SomeSupervisedModel, fitresult, Xnew) =
    median.(predict(model, fitresult, Xnew))

Required, if the model is to be registered (findable by general users):

MMI.load_path(::Type{<:SomeSupervisedModel})    = ""
MMI.package_name(::Type{<:SomeSupervisedModel}) = "Unknown"
MMI.package_uuid(::Type{<:SomeSupervisedModel}) = "Unknown"

MMI.input_scitype(::Type{<:SomeSupervisedModel}) = Unknown

Strongly recommended, to constrain the form of target data passed to fit:

MMI.target_scitype(::Type{<:SomeSupervisedModel}) = Unknown

Optional but recommended:

MMI.package_url(::Type{<:SomeSupervisedModel})  = "unknown"
MMI.is_pure_julia(::Type{<:SomeSupervisedModel}) = false
MMI.package_license(::Type{<:SomeSupervisedModel}) = "unknown"

If SomeSupervisedModel supports sample weights or class weights, then instead of the fit above, one implements

MMI.fit(model::SomeSupervisedModel, verbosity, X, y, w=nothing) -> fitresult, cache, report

and, if appropriate

MMI.update(model::SomeSupervisedModel, verbosity, old_fitresult, old_cache, X, y, w=nothing) =
   MMI.fit(model, verbosity, X, y, w)

Additionally, if SomeSupervisedModel supports sample weights, one must declare

MMI.supports_weights(model::Type{<:SomeSupervisedModel}) = true

Optionally, an implementation may add a data front-end, for transforming user data (such as a table) into some model-specific format (such as a matrix), and/or add methods to specify how reformatted data is resampled. This alters the interpretation of the data arguments of fit, update and predict, whose number may also change. See Implementing a data front-end for details). A data front-end provides the MLJ user certain performance advantages when retraining a machine.

Third-party packages that interact directly with models using the MLJModelInterface.jl API, rather than through the machine interface, will also need to understand how the data front-end works, so they incorporate reformat into their fit/update/predict calls. See also this issue.

MLJModelInterface.reformat(model::SomeSupervisedModel, args...) = args
MLJModelInterface.selectrows(model::SomeSupervisedModel, I, data...) = data

Optionally, to customized support for serialization of machines (see Serialization), overload

MMI.save(filename, model::SomeModel, fitresult; kwargs...) = fitresult

and possibly

MMI.restore(filename, model::SomeModel, serializable_fitresult) -> serializable_fitresult

These last two are unlikely to be needed if wrapping pure Julia code.