improve coverage

Author: BatyLeo

test/dynamic_assortment.jl

@@ -1,4 +1,4 @@
-@testitem "DVSP" begin
+@testitem "dynamic Assortment" begin
     using DecisionFocusedLearningBenchmarks
     using Statistics: mean
 
@@ -18,8 +18,15 @@
     expert = policies[1]
     greedy = policies[2]
 
-    r_expert, _ = evaluate_policy!(expert, environments)
+    r_expert, d = evaluate_policy!(expert, environments)
     r_greedy, _ = evaluate_policy!(greedy, environments)
 
     @test mean(r_expert) >= mean(r_greedy)
+
+    model = generate_statistical_model(b)
+    maximizer = generate_maximizer(b)
+    sample = d[1]
+    x = sample.x
+    θ = model(x)
+    y = maximizer(θ)
 end

test/utils.jl

@@ -46,6 +46,74 @@ end
     show(io, sample)
     @test String(take!(io)) ==
         "DataSample(x=$(sample.x), θ_true=$(sample.θ_true), y_true=$(sample.y_true), instance=$(sample.instance))"
+
+    # Test StatsBase methods
+    using StatsBase:
+        ZScoreTransform,
+        UnitRangeTransform,
+        fit,
+        transform,
+        transform!,
+        reconstruct,
+        reconstruct!
+
+    # Create a dataset for testing
+    N = 5
+    dataset = [random_sample() for _ in 1:N]
+
+    # Test fit with ZScoreTransform
+    zt = fit(ZScoreTransform, dataset)
+    @test zt isa ZScoreTransform
+
+    # Test fit with UnitRangeTransform
+    ut = fit(UnitRangeTransform, dataset)
+    @test ut isa UnitRangeTransform
+
+    # Test transform (non-mutating)
+    dataset_zt = transform(zt, dataset)
+    @test length(dataset_zt) == length(dataset)
+    @test all(d -> d isa DataSample, dataset_zt)
+
+    # Check that other fields are preserved
+    for i in 1:N
+        @test dataset_zt[i].θ_true == dataset[i].θ_true
+        @test dataset_zt[i].y_true == dataset[i].y_true
+        @test dataset_zt[i].instance == dataset[i].instance
+    end
+
+    # Check that features are actually transformed
+    @test dataset_zt[1].x != dataset[1].x
+
+    # Test transform! (mutating)
+    dataset_copy = deepcopy(dataset)
+    original_x = copy(dataset_copy[1].x)
+    transform!(ut, dataset_copy)
+    @test dataset_copy[1].x != original_x
+
+    # Check that other fields remain unchanged after transform!
+    for i in 1:N
+        @test dataset_copy[i].θ_true == dataset[i].θ_true
+        @test dataset_copy[i].y_true == dataset[i].y_true
+        @test dataset_copy[i].instance == dataset[i].instance
+    end
+
+    # Test reconstruct (non-mutating)
+    dataset_reconstructed = reconstruct(zt, dataset_zt)
+    @test length(dataset_reconstructed) == length(dataset)
+
+    # Test round-trip consistency (should be close to original)
+    for i in 1:N
+        @test dataset_reconstructed[i].x ≈ dataset[i].x atol = 1e-10
+        @test dataset_reconstructed[i].θ_true == dataset[i].θ_true
+        @test dataset_reconstructed[i].y_true == dataset[i].y_true
+        @test dataset_reconstructed[i].instance == dataset[i].instance
+    end
+
+    # Test reconstruct! (mutating)
+    reconstruct!(zt, dataset_zt)
+    for i in 1:N
+        @test dataset_zt[i].x ≈ dataset[i].x atol = 1e-10
+    end
 end
 
 @testitem "Maximizers" begin