add gradient mode test

Author: Giovanni3A

test/runtests.jl

@@ -14,3 +14,4 @@ Random.seed!(123)
 include("utils.jl")
 include("test_predictive_model.jl")
 include("test_newsvendor.jl")
+include("test_gradient.jl")

test/test_gradient.jl

@@ -0,0 +1,41 @@
+# basic model for testing gradient mode
+X = Float32.(ones(1, 1))
+Y = Float32.(ones(1, 1))
+model = ApplicationDrivenLearning.Model()
+@variables(model, begin
+    x >= 0, ApplicationDrivenLearning.Policy
+    d, ApplicationDrivenLearning.Forecast
+end)
+@objective(ApplicationDrivenLearning.Plan(model), Min, x.plan)
+@objective(ApplicationDrivenLearning.Assess(model), Min, x.assess)
+set_optimizer(model, HiGHS.Optimizer)
+set_silent(model)
+ApplicationDrivenLearning.set_forecast_model(model, Chain(Dense(1 => 1)))
+
+@testset "GradientMode Stop Rules" begin
+    # epochs
+    opt = ApplicationDrivenLearning.Options(
+        ApplicationDrivenLearning.GradientMode,
+        epochs = 0,
+    )
+    sol = ApplicationDrivenLearning.train!(model, X, Y, opt)
+    @test initial_sol == sol.params
+
+    # time_limit
+    initial_sol = ApplicationDrivenLearning.extract_params(model.forecast)
+    opt = ApplicationDrivenLearning.Options(
+        ApplicationDrivenLearning.GradientMode,
+        time_limit = 0,
+    )
+    sol = ApplicationDrivenLearning.train!(model, X, Y, opt)
+    @test initial_sol == sol.params
+
+    # gradient norm
+    initial_sol = ApplicationDrivenLearning.extract_params(model.forecast)
+    opt = ApplicationDrivenLearning.Options(
+        ApplicationDrivenLearning.GradientMode,
+        g_tol = Inf,
+    )
+    sol = ApplicationDrivenLearning.train!(model, X, Y, opt)
+    @test initial_sol == sol.params
+end