Merge pull request #70 from jmejia8/develop

Fix low value from codecov

src/DecisionMaking/ROI.jl

@@ -78,16 +78,12 @@ end
 
 function roiarchiving(population, w, parameters::ROIArchiving; verbose=true)
 
-    if isempty(population) || isempty(w)
-        # nothing to do
-        return Int[]
-    end
+    (isempty(population) || isempty(w)) && (return Int[]) # nothing to do
 
     feasible_sols = Metaheuristics.is_feasible.(population)
-    if !any(feasible_sols)
-        # we cannot work on infeasible solutions
-        return Int[]
-    end
+
+    # we cannot work on infeasible solutions
+    !any(feasible_sols) && (return Int[])
 
     δ_w = parameters.δ_w_transformed
 

src/common/gen_initial_state.jl

@@ -51,7 +51,7 @@ function _complete_population!(status,problem,parameters,information,options)
         # increase population if necessary
         parameters.N = length(status.population)
         # TODO: use this options.debug == true to show the message?
-        @warn("Population size increased to $(parameters.N) due to initial solutions.")
+        options.debug && @warn("Population size increased to $(parameters.N) due to initial solutions.")
         return
     end
 

src/common/set_user_solutions.jl

@@ -1,7 +1,7 @@
 include("gen_initial_state.jl")
 
 """
-    set_user_solutions!(optimizer, x, fx)
+    set_user_solutions!(optimizer, x, fx;verbose=true)
 
 Provide initial solutions to the `optimizer`.
 - `x` can be a `Vector` and `fx` a function or `fx = f(x)`
@@ -37,7 +37,7 @@ stop reason: Small difference of objective function values.
 ```
 
 """
-function set_user_solutions!(algo::AbstractAlgorithm, solution::AbstractSolution)
+function set_user_solutions!(algo::AbstractAlgorithm, solution::AbstractSolution;kargs...)
     status = algo.status
     if !isnothing(status.best_sol) && !isempty(status.population)
         push!(status.population, solution)
@@ -48,23 +48,23 @@ function set_user_solutions!(algo::AbstractAlgorithm, solution::AbstractSolution
 end
 
 
-function set_user_solutions!(algo::AbstractAlgorithm, x::AbstractVector, fx)
-    set_user_solutions!(algo, create_child(x, fx))
+function set_user_solutions!(algo::AbstractAlgorithm, x::AbstractVector, fx;kargs...)
+    set_user_solutions!(algo, create_child(x, fx); kargs...)
 end
 
-function set_user_solutions!(algo::AbstractAlgorithm, x::AbstractVector, f::Function)
-    set_user_solutions!(algo, x, f(x))
+function set_user_solutions!(algo::AbstractAlgorithm, x::AbstractVector, f::Function;kargs...)
+    set_user_solutions!(algo, x, f(x); kargs...)
 end
 
 
-function set_user_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, fX::AbstractVector)
+function set_user_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, fX::AbstractVector;verbose=true)
     n = size(X, 1)
     m = length(fX)
 
     if n != m
-        @warn "$(n) decision vectors provided but $(m) objective values."
+        verbose && @warn "$(n) decision vectors provided but $(m) objective values."
         n = min(m, n)
-        println("Taking ", n, " as the number of initial solutions.")
+        verbose && println("Taking ", n, " as the number of initial solutions.")
     end
 
     # nothing to do due to it is necessary the objective value
@@ -80,7 +80,7 @@ function set_user_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, fX::Abs
     algo
 end
 
-function set_user_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, f::Function)
-    set_user_solutions!(algo, X, [f(X[i,:]) for i in 1:size(X,1)])
+function set_user_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, f::Function;kargs...)
+    set_user_solutions!(algo, X, [f(X[i,:]) for i in 1:size(X,1)];kargs...)
 end
 

src/core/stop_status_codes.jl

@@ -1,6 +1,6 @@
 """
     TerminationStatusCode
-An Enum of possible return values for [`State`](@ref).
+An Enum of possible => values for [`State`](@ref).
 Possible values:
 
 - `ITERATION_LIMIT`
@@ -46,24 +46,20 @@ julia> termination_status_message(ECA())
 ```
 """
 function termination_status_message(status_code::TerminationStatusCode)
-    if status_code == ITERATION_LIMIT
-        return "Maximum number of iterations exceeded."
-    elseif status_code == TIME_LIMIT
-        return "Maximum time exceeded."
-    elseif status_code == EVALUATIONS_LIMIT
-        return "Maximum objective function calls exceeded."
-    elseif status_code == ACCURACY_LIMIT
-        return "The desired accuracy was obtained."
-    elseif status_code == OBJECTIVE_VARIANCE_LIMIT
-        return "Small variance of the objective function."
-    elseif status_code == OBJECTIVE_DIFFERENCE_LIMIT
-        return "Small difference of objective function values."
-    elseif status_code == OTHER_LIMIT
-        return "Other stopping criteria."
-    elseif status_code == UNKNOWN_STOP_REASON
-        return "Unknown stop reason."
+    codes = Dict(
+                 ITERATION_LIMIT => "Maximum number of iterations exceeded.",
+                 TIME_LIMIT => "Maximum time exceeded.",
+                 EVALUATIONS_LIMIT => "Maximum objective function calls exceeded.",
+                 ACCURACY_LIMIT => "The desired accuracy was obtained.",
+                 OBJECTIVE_VARIANCE_LIMIT => "Small variance of the objective function.",
+                 OBJECTIVE_DIFFERENCE_LIMIT =>"Small difference of objective function values.",
+                 OTHER_LIMIT => "Other stopping criteria.",
+                 UNKNOWN_STOP_REASON => "Unknown stop reason."
+    )
+    try
+        return codes[status_code]
+    catch
+        throw("Illegal value $status_code")
     end
-
-    throw("Illegal value $status_code")
 end
 

test/initial_solution.jl

@@ -20,11 +20,19 @@
         for method in methods
             for N in [5, 10, 20] # vary the population size
                 algo = method(N = N, options = options, information = information)
-                # initial solutions
-                X = rand(10, size(bounds, 2))
-                X[1,:] = x_optimum #zeros(size(bounds, 2))
+                # one solution
+                set_user_solutions!(algo, x_optimum, f)
+                # multiple solutions
+                X = rand(5, size(bounds, 2))
                 set_user_solutions!(algo, X, f)
 
+                # check if size of x and f is defferent
+                X = ones(4, size(bounds, 2))
+                set_user_solutions!(algo,
+                                    X,
+                                    f.([ones(size(bounds, 2)) for i in 1:5]),
+                                    verbose = false)
+
                 res = optimize(f, bounds, algo)
 
                 @test length(res.population) == algo.parameters.N