Hands-On Design Patterns and Best Practices with Julia

Example 2 – the ScikitLearn.jl package

Let's also take a look at the ScikitLearn.jl package, which defines a consistent API for fitting machine learning models and doing prediction. 

The following is how the FitBit type is defined:

""" `FitBit(model)` will behave just like `model`, but also supports`isfit(fb)`, which returns true IFF `fit!(model, ...)` has been called """mutable struct FitBit    model    isfit::Bool    FitBit(model) = new(model, false)endfunction fit!(fb::FitBit, args...; kwargs...)    fit!(fb.model, args...; kwargs...)    fb.isfit = true    fbendisfit(fb::FitBit) = fb.isfit

Here, we can see that the FitBit object contains a model object and that it adds a new functionality that keeps track of whether a model has been fitted or not:

@forward ...