Finally green

appveyor.yml

@@ -3,7 +3,7 @@ environment:
     - JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x64/1.0/julia-1.0-latest-win64.exe"
       GAMS_URL: "https://d37drm4t2jghv5.cloudfront.net/distributions/25.1.3/windows/windows_x64_64.exe"
 
-    - JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x64/1.1/julia-1.3-latest-win64.exe"
+    - JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x64/1.3/julia-1.3-latest-win64.exe"
       GAMS_URL: "https://d37drm4t2jghv5.cloudfront.net/distributions/25.1.3/windows/windows_x64_64.exe"
 
   PATH: 'C:\projects\gams;%PATH%'

examples/ovf_loss_equil.jl

@@ -6,11 +6,21 @@ if !isdef(:ovf_formulations); ovf_formulations = ["equilibrium"] end
 
 @testset "Equilibrium loss test: penalty = $penalty_name; ovf_formulation = $ovf_formulation" for penalty_name in penalty_names, ovf_formulation in ovf_formulations
 
+if ovf_formulation == "dual"
+  if penalty_name == "hubnik"
+    n_MP = 2
+  else
+    n_MP = 3
+  end
+else
+  n_MP = 5
+end
+
 m = direct_model(ReSHOP.Optimizer(;ovf_formulation=ovf_formulation))
 
 fit_model = EMP.Model(m)
 
-mps = [MathPrgm(fit_model) for i=1:5]
+mps = [MathPrgm(fit_model) for i=1:n_MP]
 
 EquilibriumProblem(fit_model, mps)
 
@@ -46,6 +56,7 @@ addovf!(fit_model, loss_var2, fit2, penalty_name, all_params[penalty_name])
 
 @objectiveMP(mps[2], Min, loss_var2)
 
+if n_MP >= 3
 # primary unknonwn
 @variableMP(mps[3], c3[j=1:1])
 @variableMP(mps[3], d3)
@@ -62,37 +73,39 @@ addovf!(fit_model, loss_var3, fit3, penalty_name, all_params[penalty_name])
 
 @objectiveMP(mps[3], Min, loss_var3)
 
-# primary unknonwn
-@variableMP(mps[4], c4[j=1:1])
-@variableMP(mps[4], d4)
+elseif n_MP >= 4
+  # primary unknonwn
+  @variableMP(mps[4], c4[j=1:1])
+  @variableMP(mps[4], d4)
 
-# OVF variable
-@variableMP(mps[4], loss_var4)
+  # OVF variable
+  @variableMP(mps[4], loss_var4)
 
-# OVF arguments
-@variableMP(mps[4], fit4[i=1:N]);
+  # OVF arguments
+  @variableMP(mps[4], fit4[i=1:N]);
 
-@NLconstraintMP(mps[4], fiteqn4[i=1:N], fit4[i] == log(exp((y[i] - sum(c4[j]*x[i, j] for j=1:M) - d4) / sigma_y[i])));
+  @NLconstraintMP(mps[4], fiteqn4[i=1:N], fit4[i] == log(exp((y[i] - sum(c4[j]*x[i, j] for j=1:M) - d4) / sigma_y[i])));
 
-addovf!(fit_model, loss_var4, fit4, penalty_name, all_params[penalty_name])
+  addovf!(fit_model, loss_var4, fit4, penalty_name, all_params[penalty_name])
 
-@objectiveMP(mps[4], Min, loss_var4)
+  @objectiveMP(mps[4], Min, loss_var4)
 
 # primary unknonwn
-@variableMP(mps[5], c5[j=1:1])
-@variableMP(mps[5], d5)
+  @variableMP(mps[5], c5[j=1:1])
+  @variableMP(mps[5], d5)
 
-# OVF variable
-@variableMP(mps[5], loss_var5)
+  # OVF variable
+  @variableMP(mps[5], loss_var5)
 
-# OVF arguments
-@variableMP(mps[5], fit5[i=1:N]);
+  # OVF arguments
+  @variableMP(mps[5], fit5[i=1:N]);
 
-@constraintMP(mps[5], fiteqn5[i=1:N], pi*(y[i] - sum(c5[j]*x[i, j] for j=1:M) - d5) / sigma_y[i] == pi*fit5[i]);
+  @constraintMP(mps[5], fiteqn5[i=1:N], pi*(y[i] - sum(c5[j]*x[i, j] for j=1:M) - d5) / sigma_y[i] == pi*fit5[i]);
 
-addovf!(fit_model, loss_var5, fit5, penalty_name, all_params[penalty_name])
+  addovf!(fit_model, loss_var5, fit5, penalty_name, all_params[penalty_name])
 
-@objectiveMP(mps[5], Min, loss_var5)
+  @objectiveMP(mps[5], Min, loss_var5)
+end
 
 
 cmp_dir = joinpath(dirname(@__FILE__), "test_res")
@@ -114,19 +127,22 @@ occursin("hinge", penalty_name) && continue
 @test isapprox(value.(fit2), file_ref[1:N], rtol=1e-4)
 @test isapprox(value(loss_var2), unknown_ref[3], rtol=1e-4)
 
+if n_MP >= 3
 @test isapprox(value.(c3), [unknown_ref[1]], rtol=1e-4)
 @test isapprox(value(d3), unknown_ref[2], rtol=1e-4)
 @test isapprox(value.(fit3), file_ref[1:N], rtol=1e-4)
 @test isapprox(value(loss_var3), unknown_ref[3], rtol=1e-4)
 
-@test isapprox(value.(c4), [unknown_ref[1]], rtol=1e-4)
-@test isapprox(value(d4), unknown_ref[2], rtol=1e-4)
-@test isapprox(value.(fit4), file_ref[1:N], rtol=1e-4)
-@test isapprox(value(loss_var4), unknown_ref[3], rtol=1e-4)
+elseif n_MP >= 4
+  @test isapprox(value.(c4), [unknown_ref[1]], rtol=1e-4)
+  @test isapprox(value(d4), unknown_ref[2], rtol=1e-4)
+  @test isapprox(value.(fit4), file_ref[1:N], rtol=1e-4)
+  @test isapprox(value(loss_var4), unknown_ref[3], rtol=1e-4)
 
-@test isapprox(value.(c5), [unknown_ref[1]], rtol=1e-4)
-@test isapprox(value(d5), unknown_ref[2], rtol=1e-4)
-@test isapprox(value.(fit5), file_ref[1:N], rtol=1e-4)
-@test isapprox(value(loss_var5), unknown_ref[3], rtol=1e-4)
+  @test isapprox(value.(c5), [unknown_ref[1]], rtol=1e-4)
+  @test isapprox(value(d5), unknown_ref[2], rtol=1e-4)
+  @test isapprox(value.(fit5), file_ref[1:N], rtol=1e-4)
+  @test isapprox(value(loss_var5), unknown_ref[3], rtol=1e-4)
+end
 
 end

test/gnep_river_basin.jl

@@ -1,5 +1,5 @@
 using JuMP, ReSHOP, EMP
-using Compat.Test
+using Test
 
 @testset "gnep river basin" begin
 

test/ovf_loss_equil.jl

@@ -1,5 +1,5 @@
 using JuMP, EMP, ReSHOP
-using Compat, Compat.Test
+using Compat, Test
 
 # Data is from  ``Data analysis recipes: Fitting a model to data'' by Hogg, Bovy & Lang
 # available on https://arxiv.org/abs/1008.4686.

test/ovf_loss_nl.jl

@@ -1,5 +1,5 @@
 using JuMP, EMP, ReSHOP
-using Compat.Test
+using Test
 
 # Data is from  ``Data analysis recipes: Fitting a model to data'' by Hogg, Bovy & Lang
 # available on https://arxiv.org/abs/1008.4686.

test/runtests.jl

@@ -1,6 +1,6 @@
 #for undef and occursin
 using Compat
-using Compat.Test
+using Test
 
 #examples_path = joinpath(dirname(dirname(@__FILE__)), "examples")
 #for example in ["simple_mopec2.jl"]

test/simple_mopec2.jl

@@ -1,5 +1,5 @@
 using JuMP, ReSHOP, EMP
-using Compat.Test
+using Test
 
 @testset "simple mopec" begin