Implement lazy callbacks

test/instance/jump_test.jl

@@ -2,29 +2,62 @@
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+using Cbc
+using JuMP
+using MathOptInterface
 using MIPLearn
+const MOI = MathOptInterface
 
+function find_lazy(model::Model, cb_data)::Vector{String}
+    x = variable_by_name(model, "x")
+    y = variable_by_name(model, "y")
+    x_val = value(cb_data, x)
+    y_val = value(cb_data, y)
+    if x_val + y_val > 1 + 1e-6
+        return ["con"]
+    end
+    return []
+end
 
-@testset "JuMPInstance" begin
-    @testset "Save and load" begin
-        # Build instance and solve
-        model = model = build_knapsack_model()
+function enforce_lazy(model::Model, cb_data, violation::String)::Nothing
+    if violation == "con"
+        x = variable_by_name(model, "x")
+        y = variable_by_name(model, "y")
+        con = @build_constraint(x + y <= 1)
+        submit(cb_data, con)
+    end
+    return
+end
+
+function build_model()
+    model = Model()
+    @variable(model, x, Bin)
+    @variable(model, y, Bin)
+    @objective(model, Max, 2 * x + y)
+    @constraint(model, c1, x + y <= 2)
+    @lazycb(model, find_lazy, enforce_lazy)
+    return model
+end
+
+@testset "Lazy callback" begin
+    @testset "JuMPInstance" begin
+        model = build_model()
         instance = JuMPInstance(model)
-        solver = LearningSolver(Gurobi.Optimizer)
-        stats = solve!(solver, instance)
-        @test length(instance.py.samples) == 1
+        solver = LearningSolver(Cbc.Optimizer)
+        solve!(solver, instance)
+        @test value(model[:x]) == 1.0
+        @test value(model[:y]) == 0.0
+    end
 
-        # Save model to file
+    @testset "FileInstance" begin
+        model = build_model()
+        instance = JuMPInstance(model)
         filename = tempname()
         save(filename, instance)
-        @test isfile(filename)
-
-        # Read model from file
-        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 loaded.model.ext[:miplearn][:instance_features] == [5.0]
-        @test length(loaded.py.samples) == 1
+        file_instance = FileInstance(filename, lazycb = (find_lazy, enforce_lazy))
+        solver = LearningSolver(Cbc.Optimizer)
+        solve!(solver, file_instance)
+        h5 = MIPLearn.Hdf5Sample(filename)
+        @test h5.get_array("mip_var_values") == [1.0, 0.0]
     end
-end
+end