Make compatible with MIPLearn 5b3a56f0; reformat source code

test/fixtures/knapsack.jl

@@ -15,14 +15,14 @@ function build_knapsack_model()
 
     n = length(weights)
     @variable(model, x[1:n], Bin)
-    @objective(model, Max, sum(x[i] * prices[i] for i in 1:n))
-    @constraint(model, c1, sum(x[i] * weights[i] for i in 1:n) <= capacity)
+    @objective(model, Max, sum(x[i] * prices[i] for i = 1:n))
+    @constraint(model, c1, sum(x[i] * weights[i] for i = 1:n) <= capacity)
 
     # Add ML information to the model
     @feature(model, [5.0])
     @feature(c1, [1.0, 2.0, 3.0])
     @category(c1, "c1")
-    for i in 1:n
+    for i = 1:n
         @feature(x[i], [weights[i]; prices[i]])
         @category(x[i], "type-$i")
     end
@@ -36,7 +36,7 @@ function build_knapsack_model()
     @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]["instance_features"] == [5.0]
 
     return model
 end

test/instance/file_test.jl

@@ -23,6 +23,6 @@ using Cbc
         solver = LearningSolver(Cbc.Optimizer)
         solve!(solver, file_instance)
 
-        @test length(h5.get_vector("mip_var_values")) == 3
-    end    
+        @test length(h5.get_array("mip_var_values")) == 3
+    end
 end

test/solvers/learning_test.jl

@@ -39,7 +39,7 @@ using MIPLearn
     @testset "Discard output" begin
         instance = build_knapsack_file_instance()
         solver = LearningSolver(Cbc.Optimizer)
-        solve!(solver, instance, discard_output=true)
+        solve!(solver, instance, discard_output = true)
         loaded = load_instance(instance.filename)
         @test length(loaded.samples) == 0
     end

test/utils/benchmark_test.jl

@@ -9,38 +9,24 @@ using DataFrames
 
 @testset "BenchmarkRunner" begin
     @info "Building training data..."
-    instances = [
-        build_knapsack_file_instance(),
-        build_knapsack_file_instance(),
-    ]
-    stats = parallel_solve!(
-        LearningSolver(Cbc.Optimizer),
-        instances,
-    )
+    instances = [build_knapsack_file_instance(), build_knapsack_file_instance()]
+    stats = parallel_solve!(LearningSolver(Cbc.Optimizer), instances)
     @test length(stats) == 2
     @test stats[1] !== nothing
     @test stats[2] !== nothing
 
     benchmark = BenchmarkRunner(
-        solvers=Dict(
-            "baseline" => LearningSolver(
-                Cbc.Optimizer,
-                components=[],
-            ),
-            "ml-exact" => LearningSolver(
-                Cbc.Optimizer,
-            ),
-            "ml-heur" => LearningSolver(
-                Cbc.Optimizer,
-                mode="heuristic",
-            ),
+        solvers = Dict(
+            "baseline" => LearningSolver(Cbc.Optimizer, components = []),
+            "ml-exact" => LearningSolver(Cbc.Optimizer),
+            "ml-heur" => LearningSolver(Cbc.Optimizer, mode = "heuristic"),
         ),
     )
     @info "Fitting..."
     fit!(benchmark, instances)
 
     @info "Benchmarking..."
-    parallel_solve!(benchmark, instances, n_trials=2)
+    parallel_solve!(benchmark, instances, n_trials = 2)
 
     csv_filename = tempname()
     write_csv!(benchmark, csv_filename)