Rewrite FileInstance

test/fixtures/knapsack.jl

@@ -28,15 +28,15 @@ function build_knapsack_model()
     end
 
     # Should store ML information
-    @test model.ext[:miplearn][:variable_features]["x[1]"] == [1.0, 5.0]
-    @test model.ext[:miplearn][:variable_features]["x[2]"] == [2.0, 6.0]
-    @test model.ext[:miplearn][:variable_features]["x[3]"] == [3.0, 7.0]
-    @test model.ext[:miplearn][:variable_categories]["x[1]"] == "type-1"
-    @test model.ext[:miplearn][:variable_categories]["x[2]"] == "type-2"
-    @test model.ext[:miplearn][:variable_categories]["x[3]"] == "type-3"
-    @test model.ext[:miplearn][:constraint_features]["c1"] == [1.0, 2.0, 3.0]
-    @test model.ext[:miplearn][:constraint_categories]["c1"] == "c1"
-    @test model.ext[:miplearn][:instance_features] == [5.0]    
+    @test model.ext[:miplearn]["variable_features"]["x[1]"] == [1.0, 5.0]
+    @test model.ext[:miplearn]["variable_features"]["x[2]"] == [2.0, 6.0]
+    @test model.ext[:miplearn]["variable_features"]["x[3]"] == [3.0, 7.0]
+    @test model.ext[:miplearn]["variable_categories"]["x[1]"] == "type-1"
+    @test model.ext[:miplearn]["variable_categories"]["x[2]"] == "type-2"
+    @test model.ext[:miplearn]["variable_categories"]["x[3]"] == "type-3"
+    @test model.ext[:miplearn]["constraint_features"]["c1"] == [1.0, 2.0, 3.0]
+    @test model.ext[:miplearn]["constraint_categories"]["c1"] == "c1"
+    @test model.ext[:miplearn]["instance_features"] == [5.0]    
 
     return model
 end

test/instance/file_test.jl

@@ -19,6 +19,6 @@ using Cbc
         solve!(solver, file_instance)
 
         loaded = load_instance(filename)
-        @test length(loaded.py.samples) == 1
+        @test length(loaded.samples) == 1
     end    
 end

test/solvers/learning_test.jl

@@ -13,7 +13,7 @@ using MIPLearn
         instance = JuMPInstance(model)
         stats = solve!(solver, instance)
         @test stats["mip_lower_bound"] == 11.0
-        @test length(instance.py.samples) == 1
+        @test length(instance.samples) == 1
         fit!(solver, [instance])
         solve!(solver, instance)
     end
@@ -41,6 +41,6 @@ using MIPLearn
         solver = LearningSolver(Cbc.Optimizer)
         solve!(solver, instance, discard_output=true)
         loaded = load_instance(instance.filename)
-        @test length(loaded.py.samples) == 0
+        @test length(loaded.samples) == 0
     end
 end

test/utils/benchmark_test.jl

@@ -8,17 +8,19 @@ using DataFrames
 
 
 @testset "BenchmarkRunner" begin
-    # Initialize instances and generate training data
+    @info "Building training data..."
     instances = [
         build_knapsack_file_instance(),
         build_knapsack_file_instance(),
     ]
-    parallel_solve!(
+    stats = parallel_solve!(
         LearningSolver(Cbc.Optimizer),
         instances,
     )
+    @test length(stats) == 2
+    @test stats[1] !== nothing
+    @test stats[2] !== nothing
 
-    # Fit and benchmark
     benchmark = BenchmarkRunner(
         solvers=Dict(
             "baseline" => LearningSolver(
@@ -34,13 +36,15 @@ using DataFrames
             ),
         ),
     )
+    @info "Fitting..."
     fit!(benchmark, instances)
-    parallel_solve!(benchmark, instances, n_trials=1)
 
-    # Write CSV
+    @info "Benchmarking..."
+    parallel_solve!(benchmark, instances, n_trials=2)
+
     csv_filename = tempname()
     write_csv!(benchmark, csv_filename)
     @test isfile(csv_filename)
     csv = DataFrame(CSV.File(csv_filename))
-    @test size(csv)[1] == 6
+    @test size(csv)[1] == 12
 end