BenchmarkRunner: Ensure outputs are discarded

src/instance/file.jl

@@ -36,7 +36,7 @@
 
     function load(self)
         if self.loaded === nothing
-            self.loaded = load_jump_instance(self.filename)
+            self.loaded = load_instance(self.filename)
             self.samples = self.loaded.py.samples
         end
     end

src/instance/jump.jl

@@ -111,7 +111,7 @@ function _check_miplearn_version(file)
 end
 
 
-function load_jump_instance(filename::AbstractString)::JuMPInstance
+function load_instance(filename::AbstractString)::JuMPInstance
     @info "Reading: $filename"
     instance = nothing
     time = @elapsed begin
@@ -155,4 +155,4 @@ function load_jump_instance(filename::AbstractString)::JuMPInstance
 end
 
 
-export JuMPInstance, save, load_jump_instance
+export JuMPInstance, save, load_instance

src/solvers/jump.jl

@@ -224,9 +224,10 @@ end
 function solve_lp(data::JuMPSolverData; tee::Bool=false)
     model, bin_vars = data.model, data.bin_vars
     for var in bin_vars
-        JuMP.unset_binary(var)
+        ~is_fixed(var) || continue
-        JuMP.set_upper_bound(var, 1.0)
+        unset_binary(var)
-        JuMP.set_lower_bound(var, 0.0)
+        set_upper_bound(var, 1.0)
+        set_lower_bound(var, 0.0)
     end
     wallclock_time = @elapsed begin
         log = _optimize_and_capture_output!(model, tee=tee)
@@ -236,10 +237,11 @@ function solve_lp(data::JuMPSolverData; tee::Bool=false)
         obj_value = nothing
     else
         _update_solution!(data)
-        obj_value = JuMP.objective_value(model)
+        obj_value = objective_value(model)
     end
     for var in bin_vars
-        JuMP.set_binary(var)
+        ~is_fixed(var) || continue
+        set_binary(var)
     end
     return miplearn.solvers.internal.LPSolveStats(
         lp_value=obj_value,

src/solvers/learning.jl

@@ -35,8 +35,13 @@ function solve!(
     solver::LearningSolver,
     instance::Instance;
     tee::Bool = false,
+    discard_output::Bool = false,
 )
-    return @python_call solver.py.solve(instance.py, tee=tee)
+    return @python_call solver.py.solve(
+        instance.py,
+        tee=tee,
+        discard_output=discard_output,
+    )
 end
 
 

src/utils/benchmark.jl

@@ -26,9 +26,11 @@ function parallel_solve!(
     n_trials::Int = 3,
 )::Nothing
     for (solver_name, solver) in runner.solvers
+        @info "Benchmarking: $solver_name"
         for i in 1:n_trials
             for instance in instances
-                stats = solve!(solver, instance)
+                stats = solve!(solver, instance, discard_output=true)
+                instance.py.free()
                 stats["Solver"] = solver_name
                 stats = Dict(k => isnothing(v) ? missing : v for (k, v) in stats)
                 if runner.results === nothing

test/instance/file_test.jl

@@ -18,7 +18,7 @@ using Gurobi
         solver = LearningSolver(Gurobi.Optimizer)
         solve!(solver, file_instance)
 
-        loaded = load_jump_instance(filename)
+        loaded = load_instance(filename)
         @test length(loaded.py.samples) == 1
     end    
 end

test/instance/jump_test.jl

@@ -20,7 +20,7 @@ using MIPLearn
         @test isfile(filename)
 
         # Read model from file
-        loaded = load_jump_instance(filename)
+        loaded = load_instance(filename)
         x = variable_by_name(loaded.model, "x")
         @test loaded.model.ext[:miplearn][:variable_features][x] == [1.0]
         @test loaded.model.ext[:miplearn][:variable_categories][x] == "cat1"

test/solvers/learning_test.jl

@@ -35,4 +35,12 @@ using Gurobi
         loaded = load_solver(filename)
         @test loaded.py.components == "Placeholder"
     end
+
+    @testset "Discard output" begin
+        instance = build_knapsack_file_instance()
+        solver = LearningSolver(Gurobi.Optimizer)
+        solve!(solver, instance, discard_output=true)
+        loaded = load_instance(instance.filename)
+        @test length(loaded.py.samples) == 0
+    end
 end

test/utils/benchmark_test.jl

@@ -8,24 +8,39 @@ using Gurobi
 
 
 @testset "BenchmarkRunner" begin
-    # Configure benchmark suite
+    # Initialie instances and generate training data
-    benchmark = BenchmarkRunner(
-        solvers=Dict(
-            "Baseline" => LearningSolver(Gurobi.Optimizer, components=[]),
-            "Proposed" => LearningSolver(Gurobi.Optimizer),
-        ),
-    )
-
-    # Solve instances in parallel
     instances = [
         build_knapsack_file_instance(),
         build_knapsack_file_instance(),
     ]
-    parallel_solve!(benchmark, instances)
+    parallel_solve!(
+        LearningSolver(Gurobi.Optimizer),
+        instances,
+    )
+
+    # Fit and benchmark
+    benchmark = BenchmarkRunner(
+        solvers=Dict(
+            "baseline" => LearningSolver(
+                Gurobi.Optimizer,
+                components=[],
+            ),
+            "ml-exact" => LearningSolver(
+                Gurobi.Optimizer,
+            ),
+            "ml-heur" => LearningSolver(
+                Gurobi.Optimizer,
+                mode="heuristic",
+            ),
+        ),
+    )
+    fit!(benchmark, instances)
+    parallel_solve!(benchmark, instances, n_trials=1)
 
     # Write CSV
     csv_filename = tempname()
     write_csv!(benchmark, csv_filename)
     @test isfile(csv_filename)
     csv = DataFrame(CSV.File(csv_filename))
+    @test size(csv)[1] == 6
 end