Merge pull request #8 from LAMPSPUC/fix/formatter

Apply JuliaFormatter

Author: Giovanni3A

.JuliaFormatter.toml

@@ -0,0 +1,9 @@
+# Configuration file for JuliaFormatter.jl
+# For more information, see: https://domluna.github.io/JuliaFormatter.jl/stable/config/
+
+indent = 4
+always_use_return = true
+margin = 80
+remove_extra_newlines = true
+short_to_long_function_def = true
+format_docstrings = true

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

@@ -1,13 +1,11 @@
 module ApplicationDrivenLearning
 
-
 using Flux
 using JuMP
 using DiffOpt
 import ParametricOptInterface as POI
 import Base.*, Base.+
 
-
 include("flux_utils.jl")
 include("predictive_model.jl")
 
@@ -22,7 +20,7 @@ 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}
@@ -34,19 +32,21 @@ struct Forecast{T}
     assess::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)
+function +(p1::Forecast, p2::Forecast)
+    return Forecast(p1.plan + p2.plan, p1.assess + p2.assess)
+end
+*(c::Number, p::Forecast) = Forecast(c * p.plan, c * p.assess)
 
 """
     Model <: JuMP.AbstractModel
 
-Create an empty ApplicationDrivenLearning.Model with empty plan and assess models,
-missing forecast model and default settings.
+Create an empty ApplicationDrivenLearning.Model with empty plan and assess
+models, 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
@@ -102,16 +107,16 @@ 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
     else
         forecast = PredictiveModel(network)
     end
     @assert forecast.output_size == size(model.forecast_vars, 1)
-    model.forecast = forecast
+    return model.forecast = forecast
 end
 
 """
@@ -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
@@ -147,8 +152,8 @@ Creates new constraint to assess model that fixes policy variables.
 """
 function build_assess_model_policy_constraint(model::Model)
     @constraint(
-        model.assess, 
-        assess_policy_fix, 
+        model.assess,
+        assess_policy_fix,
         assess_policy_vars(model) .== 0
     )
 end
@@ -165,7 +170,7 @@ function build(model::Model)
 
     # build plan model
     build_plan_model_forecast_params(model)
-    build_assess_model_policy_constraint(model)
+    return build_assess_model_policy_constraint(model)
 end
 
 include("jump.jl")
@@ -184,9 +189,9 @@ include("optimizers/bilevel.jl")
 Train model using given data and options.
 """
 function train!(
-    model::Model, 
-    X::Matrix{<:Real}, 
-    y::Matrix{<:Real}, 
+    model::Model,
+    X::Matrix{<:Real},
+    y::Matrix{<:Real},
     options::Options,
 )
     if options.mode == NelderMeadMode
@@ -198,17 +203,23 @@ function train!(
     elseif options.mode == GradientMPIMode
         return train_with_gradient_mpi!(model, X, y, options.params)
     elseif options.mode == BilevelMode
-        assert_msg = "BilevelMode not implemented for multiple forecasting models"
-        @assert length(model.forecast.networks) == 1 assert_msg
+        asr_msg = "BilevelMode not implemented for multiple forecasting models"
+        @assert length(model.forecast.networks) == 1 asr_msg
         return solve_bilevel(model, X, y, options.params)
     else
         # should never get here
         throw(ArgumentError("Invalid optimization method"))
     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

@@ -3,9 +3,10 @@ using Flux
 """
     extract_flux_params(model)
 
-Extract the parameters of a Flux model (Flux.Chain or Flux.Dense) into a single vector.
+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 +16,10 @@ 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))
@@ -28,12 +32,10 @@ end
 """
     fix_flux_params_multi_model(models, θ)
 
-Return iterable of models after fixing the parameters from an adequate vector of parameters.
+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)
@@ -55,7 +57,7 @@ function has_params(layer)
         # Attempt to get parameters; if it works and isn't empty, return true
         return !isempty(Flux.params(layer))
     catch e
-        # If there is an error (e.g., method not matching), assume no parameters
+        # If there is an error (e.g. method not matching), assume no parameters
         return false
     end
 end

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 +44,25 @@ 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
+    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
@@ -100,11 +88,14 @@ end
 
 # jump functions
 function JuMP.objective_sense(model::Model)
-    @assert JuMP.objective_sense(model.plan) == JuMP.objective_sense(model.assess)
+    @assert JuMP.objective_sense(model.plan) ==
+            JuMP.objective_sense(model.assess)
     return JuMP.objective_sense(model.plan)
 end
 
-JuMP.num_variables(m::Model) = JuMP.num_variables(m.plan) + JuMP.num_variables(m.assess)
+function JuMP.num_variables(m::Model)
+    return JuMP.num_variables(m.plan) + JuMP.num_variables(m.assess)
+end
 
 function JuMP.show_constraints_summary(io::IO, model::Model)
     println("Plan Model:")
@@ -124,21 +115,22 @@ 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
-    JuMP.set_optimizer(model.assess, builder)
+    return JuMP.set_optimizer(model.assess, builder)
 end
 
 function JuMP.set_silent(model::Model)
     MOI.set(model.plan, MOI.Silent(), true)
-    MOI.set(model.assess, MOI.Silent(), true)
+    return MOI.set(model.assess, MOI.Silent(), true)
 end
 
 function JuMP.num_variables(model::Model)
@@ -160,4 +152,4 @@ function Base.print(io::IO, model::Model)
     else
         println(model.forecast.network)
     end
-end
+end