Learning graph in mach

src/composition/learning_networks/machines.jl

@@ -449,7 +449,7 @@ method exists for the composite model. This can be removed in the future.
 function fit_(mach::Machine{<:Composite}, resampled_data...; verbosity=0, kwargs...)
     if applicable(learning_network, mach, resampled_data...)
         signature = learning_network(mach, resampled_data...; verbosity=verbosity, kwargs...)
-        return finalize(mach, signature; verbosity=verbosity, kwargs...)
+        return finalize!(mach, signature; verbosity=verbosity, kwargs...)
     else
         return fit(mach.model, verbosity, resampled_data...)
     end
@@ -490,15 +490,17 @@ function update_(mach::Machine{<:Composite}, resampled_data...; verbosity=0, kwa
 
 end
 
-function finalize(mach::Machine{<:Composite}, signature; kwargs...)
+function finalize!(mach::Machine{T}, signature; acceleration=CPU1(), kwargs...) where T <:Composite
     check_signature(signature)
     # Build composite Fitresult
     fitresult = CompositeFitresult(signature)
     fitresult.network_model_names = network_model_names(mach.model, fitresult)
 
     # Fit all machines in the learning network
     glb_node = glb(fitresult)
-    fit!(glb_node; kwargs...)
+    acceleration = hasfield(T, :acceleration) && getfield(mach.model, :acceleration) !== nothing ? 
+        getfield(mach.model, :acceleration) : acceleration
+    fit!(glb_node; acceleration=acceleration, kwargs...)
 
     # Build report
     report_additions_ = _call(_report_part(MLJBase.signature(fitresult)))

src/composition/models/stacking.jl

@@ -31,9 +31,10 @@ mutable struct DeterministicStack{modelnames, inp_scitype, tg_scitype} <: Determ
    metalearner::Deterministic
    resampling
    measures::Union{Nothing,AbstractVector}
-   function DeterministicStack(modelnames, models, metalearner, resampling, measures)
+   acceleration::Union{AbstractResource, Nothing}
+   function DeterministicStack(modelnames, models, metalearner, resampling, measures, acceleration)
         inp_scitype, tg_scitype = input_target_scitypes(models, metalearner)
-        return new{modelnames, inp_scitype, tg_scitype}(models, metalearner, resampling, measures)
+        return new{modelnames, inp_scitype, tg_scitype}(models, metalearner, resampling, measures, acceleration)
    end
 end
 
@@ -42,9 +43,10 @@ mutable struct ProbabilisticStack{modelnames, inp_scitype, tg_scitype} <: Probab
     metalearner::Probabilistic
     resampling
     measures::Union{Nothing,AbstractVector}
-    function ProbabilisticStack(modelnames, models, metalearner, resampling, measures)
+    acceleration::Union{AbstractResource, Nothing}
+    function ProbabilisticStack(modelnames, models, metalearner, resampling, measures, acceleration)
         inp_scitype, tg_scitype = input_target_scitypes(models, metalearner)
-        return new{modelnames, inp_scitype, tg_scitype}(models, metalearner, resampling, measures)
+        return new{modelnames, inp_scitype, tg_scitype}(models, metalearner, resampling, measures, acceleration)
     end
  end
 
@@ -147,7 +149,7 @@ report(mach).cv_report
 ```
 
 """
-function Stack(;metalearner=nothing, resampling=CV(), measure=nothing, measures=measure, named_models...)
+function Stack(;metalearner=nothing, resampling=CV(), measure=nothing, measures=measure, acceleration=nothing, named_models...)
     metalearner === nothing &&
         throw(ArgumentError("No metalearner specified. Use Stack(metalearner=...)"))
 
@@ -159,9 +161,9 @@ function Stack(;metalearner=nothing, resampling=CV(), measure=nothing, measures=
     end
 
     if metalearner isa Deterministic
-        stack =  DeterministicStack(modelnames, models, metalearner, resampling, measures)
+        stack =  DeterministicStack(modelnames, models, metalearner, resampling, measures, acceleration)
     elseif metalearner isa Probabilistic
-        stack = ProbabilisticStack(modelnames, models, metalearner, resampling, measures)
+        stack = ProbabilisticStack(modelnames, models, metalearner, resampling, measures, acceleration)
     else
         throw(ArgumentError("The metalearner should be a subtype
                     of $(Union{Deterministic, Probabilistic})"))

test/composition/models/stacking.jl

@@ -226,7 +226,7 @@ end
     metalearner = DeterministicConstantRegressor()
     resampling = CV()
 
-    MLJBase.DeterministicStack(modelnames, models, metalearner, resampling, nothing)
+    MLJBase.DeterministicStack(modelnames, models, metalearner, resampling, nothing, nothing)
 
     # Test input_target_scitypes with non matching target_scitypes
     models = [KNNRegressor()]