initial changes from JuliaFormatter

Author: Giovanni3A

docs/make.jl

@@ -4,13 +4,10 @@ push!(LOAD_PATH, "../src")
 using ApplicationDrivenLearning
 
 makedocs(;
-    modules=[ApplicationDrivenLearning],
-    doctest=false,
-    clean=true,
-    sitename="ApplicationDrivenLearning.jl",
-    authors="Giovanni Amorim, Joaquim Garcia",
-    pages=[
-        "Home" => "index.md",
-        "API Reference" => "reference.md"
-    ]
-)
+    modules = [ApplicationDrivenLearning],
+    doctest = false,
+    clean = true,
+    sitename = "ApplicationDrivenLearning.jl",
+    authors = "Giovanni Amorim, Joaquim Garcia",
+    pages = ["Home" => "index.md", "API Reference" => "reference.md"],
+)

src/ApplicationDrivenLearning.jl

@@ -22,7 +22,7 @@ struct Policy{T}
 end
 
 +(p1::Policy, p2::Policy) = Policy(p1.plan + p2.plan, p1.assess + p2.assess)
-*(c::Number, p::Policy) = Policy(c*p.plan, c*p.assess)
+*(c::Number, p::Policy) = Policy(c * p.plan, c * p.assess)
 
 """
     Forecast{T}
@@ -35,7 +35,7 @@ struct Forecast{T}
 end
 
 +(p1::Forecast, p2::Forecast) = Forecast(p1.plan + p2.plan, p1.assess + p2.assess)
-*(c::Number, p::Forecast) = Forecast(c*p.plan, c*p.assess)
+*(c::Number, p::Forecast) = Forecast(c * p.plan, c * p.assess)
 
 """
     Model <: JuMP.AbstractModel
@@ -46,7 +46,7 @@ missing forecast model and default settings.
 mutable struct Model <: JuMP.AbstractModel
     plan::JuMP.Model
     assess::JuMP.Model
-    forecast::Union{PredictiveModel, Nothing}
+    forecast::Union{PredictiveModel,Nothing}
 
     # variable arrays
     policy_vars::Vector{Policy}
@@ -62,9 +62,14 @@ mutable struct Model <: JuMP.AbstractModel
         assess = JuMP.Model()
 
         return new(
-            plan, assess, nothing,
-            Vector{Policy}(), Vector{Forecast}(), Vector{JuMP.VariableRef}(),
-            Dict{Symbol,Any}(), false
+            plan,
+            assess,
+            nothing,
+            Vector{Policy}(),
+            Vector{Forecast}(),
+            Vector{JuMP.VariableRef}(),
+            Dict{Symbol,Any}(),
+            false,
         )
     end
 end
@@ -102,8 +107,8 @@ Sets Chain, Dense or custom PredictiveModel object as
 forecast model.
 """
 function set_forecast_model(
-    model::Model, 
-    network::Union{PredictiveModel, Flux.Chain, Flux.Dense}
+    model::Model,
+    network::Union{PredictiveModel,Flux.Chain,Flux.Dense},
 )
     if typeof(network) == PredictiveModel
         forecast = network
@@ -131,13 +136,13 @@ function build_plan_model_forecast_params(model::Model)
     # adds parametrized forecast variables using MOI.Parameter
     forecast_size = size(model.forecast_vars)[1]
     model.plan_forecast_params = @variable(
-        model.plan, 
+        model.plan,
         _forecast[1:forecast_size] in MOI.Parameter.(zeros(forecast_size))
     )
     # fixes old and new prediction variables together
     @constraint(
-        model.plan, 
-        plan_forecast_fix, 
+        model.plan,
+        plan_forecast_fix,
         model.plan_forecast_params .== plan_forecast_vars(model)
     )
 end
@@ -146,11 +151,7 @@ end
 Creates new constraint to assess model that fixes policy variables.
 """
 function build_assess_model_policy_constraint(model::Model)
-    @constraint(
-        model.assess, 
-        assess_policy_fix, 
-        assess_policy_vars(model) .== 0
-    )
+    @constraint(model.assess, assess_policy_fix, assess_policy_vars(model) .== 0)
 end
 
 """
@@ -183,12 +184,7 @@ include("optimizers/bilevel.jl")
 
 Train model using given data and options.
 """
-function train!(
-    model::Model, 
-    X::Matrix{<:Real}, 
-    y::Matrix{<:Real}, 
-    options::Options,
-)
+function train!(model::Model, X::Matrix{<:Real}, y::Matrix{<:Real}, options::Options)
     if options.mode == NelderMeadMode
         return train_with_nelder_mead!(model, X, y, options.params)
     elseif options.mode == GradientMode
@@ -207,8 +203,14 @@ function train!(
     end
 end
 
-export Model, PredictiveModel, Plan, Assess,
-    Policy, Forecast,
-    set_forecast_model, forecast, 
-    compute_cost, train!
+export Model,
+    PredictiveModel,
+    Plan,
+    Assess,
+    Policy,
+    Forecast,
+    set_forecast_model,
+    forecast,
+    compute_cost,
+    train!
 end

src/flux_utils.jl

@@ -5,7 +5,7 @@ using Flux
 
 Extract the parameters of a Flux model (Flux.Chain or Flux.Dense) into a single vector.
 """
-function extract_flux_params(model::Union{Flux.Chain, Flux.Dense})
+function extract_flux_params(model::Union{Flux.Chain,Flux.Dense})
     θ = Flux.params(model)
     return reduce(vcat, [vec(p) for p in θ])
 end
@@ -15,7 +15,10 @@ end
 
 Return model after fixing the parameters from an adequate vector of parameters.
 """
-function fix_flux_params_single_model(model::Union{Flux.Chain, Flux.Dense}, θ::Vector{<:Real})
+function fix_flux_params_single_model(
+    model::Union{Flux.Chain,Flux.Dense},
+    θ::Vector{<:Real},
+)
     i = 1
     for p in Flux.params(model)
         psize = prod(size(p))
@@ -30,10 +33,7 @@ end
 
 Return iterable of models after fixing the parameters from an adequate vector of parameters.
 """
-function fix_flux_params_multi_model(
-    models, 
-    θ::Vector{<:Real}
-)
+function fix_flux_params_multi_model(models, θ::Vector{<:Real})
     i = 1
     for model in models
         for p in Flux.params(model)

src/jump.jl

@@ -11,29 +11,21 @@ function JuMP.build_variable(
     ::Type{Policy};
     kwargs...,
 )
-    return PolicyInfo(
-        info,
-        info,
-        kwargs
-    )
+    return PolicyInfo(info, info, kwargs)
 end
 
-function JuMP.add_variable(
-    model::Model, 
-    policy_info::PolicyInfo, 
-    name::String
-)
+function JuMP.add_variable(model::Model, policy_info::PolicyInfo, name::String)
     policy = Policy(
         JuMP.add_variable(
-            model.plan, 
-            JuMP.ScalarVariable(policy_info.plan), 
-            name * "_plan"
+            model.plan,
+            JuMP.ScalarVariable(policy_info.plan),
+            name * "_plan",
         ),
         JuMP.add_variable(
-            model.assess, 
-            JuMP.ScalarVariable(policy_info.assess), 
-            name * "_assess"
-        )
+            model.assess,
+            JuMP.ScalarVariable(policy_info.assess),
+            name * "_assess",
+        ),
     )
     push!(model.policy_vars, policy)
     return policy
@@ -52,29 +44,21 @@ function JuMP.build_variable(
     ::Type{Forecast};
     kwargs...,
 )
-    return ForecastInfo(
-        info,
-        info,
-        kwargs
-    )
+    return ForecastInfo(info, info, kwargs)
 end
 
-function JuMP.add_variable(
-    model::Model, 
-    forecast_info::ForecastInfo, 
-    name::String
-)
+function JuMP.add_variable(model::Model, forecast_info::ForecastInfo, name::String)
     forecast = Forecast(
         JuMP.add_variable(
-            model.plan, 
-            JuMP.ScalarVariable(forecast_info.plan), 
-            name * "_plan"
+            model.plan,
+            JuMP.ScalarVariable(forecast_info.plan),
+            name * "_plan",
         ),
         JuMP.add_variable(
-            model.assess, 
-            JuMP.ScalarVariable(forecast_info.assess), 
-            name * "_assess"
-        )
+            model.assess,
+            JuMP.ScalarVariable(forecast_info.assess),
+            name * "_assess",
+        ),
     )
     push!(model.forecast_vars, forecast)
     return forecast
@@ -124,12 +108,12 @@ end
 
 JuMP.object_dictionary(model::Model) = model.obj_dict
 
-function JuMP.set_optimizer(model::Model, builder, evaluate_duals::Bool=true)
+function JuMP.set_optimizer(model::Model, builder, evaluate_duals::Bool = true)
     # set diffopt optimizer for plan model
     new_diff_optimizer = DiffOpt.diff_optimizer(builder)
     JuMP.set_optimizer(
         model.plan,
-        () -> POI.Optimizer(new_diff_optimizer; evaluate_duals=evaluate_duals)
+        () -> POI.Optimizer(new_diff_optimizer; evaluate_duals = evaluate_duals),
     )
 
     # basic setting for assess model
@@ -160,4 +144,4 @@ function Base.print(io::IO, model::Model)
     else
         println(model.forecast.network)
     end
-end
+end