Disable precompilation

Project.toml

@@ -1,7 +1,7 @@
 name = "MIPLearn"
 uuid = "2b1277c3-b477-4c49-a15e-7ba350325c68"
 authors = ["Alinson S Xavier <git@axavier.org>"]
-version = "0.4.0"
+version = "0.4.2"
 
 [deps]
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
@@ -41,3 +41,5 @@ Requires = "1"
 Statistics = "1"
 TimerOutputs = "0.5"
 julia = "1"
+PrecompileTools = "1"
+SCIP = "0.12"

deps/build.jl

@@ -5,7 +5,7 @@ function install_miplearn()
     Conda.update()
     pip = joinpath(dirname(pyimport("sys").executable), "pip")
     isfile(pip) || error("$pip: invalid path")
-    run(`$pip install miplearn==0.4.0`)
+    run(`$pip install miplearn==0.4.2`)
 end
 
 install_miplearn()

src/BB/lp.jl

@@ -134,7 +134,11 @@ function _get_int_variables(
                 var_ub = constr.upper
                 MOI.delete(optimizer, _upper_bound_index(var))
             end
-            MOI.add_constraint(optimizer, var, MOI.Interval(var_lb, var_ub))
+            MOI.add_constraint(
+                optimizer,
+                MOI.VariableIndex(var.index),
+                MOI.Interval(var_lb, var_ub),
+            )
         end
         push!(vars, var)
         push!(lb, var_lb)

src/MIPLearn.jl

@@ -37,48 +37,48 @@ include("Cuts/Cuts.jl")
 # Precompilation
 # =============================================================================
 
-function __precompile_cuts__()
-    function build_model(mps_filename)
-        model = read_from_file(mps_filename)
-        set_optimizer(model, SCIP.Optimizer)
-        return JumpModel(model)
-    end
-    BASEDIR = dirname(@__FILE__)
-    mps_filename = "$BASEDIR/../test/fixtures/bell5.mps.gz"
-    h5_filename = "$BASEDIR/../test/fixtures/bell5.h5"
-    collect_gmi_dual(
-        mps_filename;
-        optimizer=HiGHS.Optimizer,
-        max_rounds = 10,
-        max_cuts_per_round = 500,
-    )
-    knn = KnnDualGmiComponent(
-        extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
-        k = 2,
-    )
-    knn.fit([h5_filename, h5_filename])
-    solver = LearningSolver(
-        components = [
-            ExpertPrimalComponent(action = SetWarmStart()),
-            knn,
-        ],
-        skip_lp = true,
-    )
-    solver.optimize(mps_filename, build_model)
-end
+# function __precompile_cuts__()
+#     function build_model(mps_filename)
+#         model = read_from_file(mps_filename)
+#         set_optimizer(model, SCIP.Optimizer)
+#         return JumpModel(model)
+#     end
+#     BASEDIR = dirname(@__FILE__)
+#     mps_filename = "$BASEDIR/../test/fixtures/bell5.mps.gz"
+#     h5_filename = "$BASEDIR/../test/fixtures/bell5.h5"
+#     collect_gmi_dual(
+#         mps_filename;
+#         optimizer=HiGHS.Optimizer,
+#         max_rounds = 10,
+#         max_cuts_per_round = 500,
+#     )
+#     knn = KnnDualGmiComponent(
+#         extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
+#         k = 2,
+#     )
+#     knn.fit([h5_filename, h5_filename])
+#     solver = LearningSolver(
+#         components = [
+#             ExpertPrimalComponent(action = SetWarmStart()),
+#             knn,
+#         ],
+#         skip_lp = true,
+#     )
+#     solver.optimize(mps_filename, build_model)
+# end
 
-@setup_workload begin
-    using SCIP
-    using HiGHS
-    using MIPLearn.Cuts
-    using PrecompileTools: @setup_workload, @compile_workload
+# @setup_workload begin
+#     using SCIP
+#     using HiGHS
+#     using MIPLearn.Cuts
+#     using PrecompileTools: @setup_workload, @compile_workload
 
-    __init__()
-    Cuts.__init__()
+#     __init__()
+#     Cuts.__init__()
 
-    @compile_workload begin
-        __precompile_cuts__()
-    end
-end
+#     @compile_workload begin
+#         __precompile_cuts__()
+#     end
+# end
 
 end # module