New tests

Author: BatyLeo

src/Utils/interface.jl

@@ -276,13 +276,7 @@ The instance and other solver-relevant fields are spread from the sample context
     y          = solver(scenario; sample.context...)  # stochastic
     trajectory = solver(scenario; sample.context...)  # dynamic
 """
-function generate_anticipative_solver(bench::AbstractStochasticBenchmark{true})
-    return (scenario; kwargs...) -> error(
-        "`generate_anticipative_solver` is not implemented for $(typeof(bench)). " *
-        "Implement `generate_anticipative_solver(::$(typeof(bench))) -> (scenario; kwargs...) -> y` " *
-        "to use it.",
-    )
-end
+function generate_anticipative_solver end
 
 """
     generate_parametric_anticipative_solver(::AbstractStochasticBenchmark{true}) -> callable

test/dynamic_assortment.jl

@@ -310,6 +310,28 @@ end
     @test sum(greedy_action) == DAP.assortment_size(env)
 end
 
+@testset "DynamicAssortment - generate_dataset with environments (exogenous)" begin
+    b = DynamicAssortmentBenchmark(; N=4, d=2, K=2, max_steps=10, exogenous=true)
+    envs = generate_environments(b, 4; seed=0)
+    policies = generate_baseline_policies(b)
+    expert = policies[1]
+
+    # target_policy: env -> Vector{DataSample} (full trajectory)
+    target_policy = env -> evaluate_policy!(expert, env)[2]
+
+    # vector-of-environments overload
+    dataset = generate_dataset(b, envs; target_policy=target_policy)
+    @test dataset isa Vector{DataSample}
+    @test !isempty(dataset)
+    @test all(!isnothing(s.x) for s in dataset)
+    @test all(!isnothing(s.y) for s in dataset)
+
+    # count-based wrapper
+    dataset2 = generate_dataset(b, 3; seed=7, target_policy=target_policy)
+    @test dataset2 isa Vector{DataSample}
+    @test !isempty(dataset2)
+end
+
 @testset "DynamicAssortment - Model and Maximizer Integration" begin
     b = DynamicAssortmentBenchmark(; N=4, d=3, K=2)
 

test/dynamic_vsp.jl

@@ -51,3 +51,32 @@
     @test isapprox(cost, anticipative_value; atol=1e-5)
     @test isapprox(cost, cost2; atol=1e-5)
 end
+
+@testset "DVSP - generate_dataset with environments" begin
+    using DecisionFocusedLearningBenchmarks.DynamicVehicleScheduling
+
+    b = DynamicVehicleSchedulingBenchmark(; two_dimensional_features=true)
+    envs = generate_environments(b, 5; seed=0)
+    policies = generate_baseline_policies(b)
+    lazy = policies[1]
+
+    # target_policy takes env -> Vector{DataSample} (full trajectory)
+    target_policy = env -> evaluate_policy!(lazy, env)[2]
+
+    # vector-of-environments overload
+    dataset = generate_dataset(b, envs; target_policy=target_policy)
+    @test dataset isa Vector{DataSample}
+    @test !isempty(dataset)
+    @test all(!isnothing(s.x) for s in dataset)
+    @test all(!isnothing(s.y) for s in dataset)
+
+    # count-based wrapper
+    dataset2 = generate_dataset(b, 3; seed=1, target_policy=target_policy)
+    @test dataset2 isa Vector{DataSample}
+    @test !isempty(dataset2)
+
+    # seed keyword is forwarded: same seed → same dataset
+    dataset3a = generate_dataset(b, 3; seed=42, target_policy=target_policy)
+    dataset3b = generate_dataset(b, 3; seed=42, target_policy=target_policy)
+    @test length(dataset3a) == length(dataset3b)
+end