Implement expert and knn dual gmi component

test/src/Cuts/tableau/test_gmi_dual.jl

@@ -2,9 +2,10 @@
 #  Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+using SCIP
 using HiGHS
 
-function test_cuts_tableau_gmi_dual()
+function test_cuts_tableau_gmi_dual_collect()
     mps_filename = "$BASEDIR/../fixtures/bell5.mps.gz"
     h5_filename = "$BASEDIR/../fixtures/bell5.h5"
     stats = collect_gmi_dual(mps_filename, optimizer = HiGHS.Optimizer)
@@ -14,9 +15,56 @@ function test_cuts_tableau_gmi_dual()
         cuts_basis_sizes = h5.get_array("cuts_basis_sizes")
         cuts_rows = h5.get_array("cuts_rows")
         @test size(cuts_basis_vars) == (15, 402)
-        @test size(cuts_basis_sizes) == (15,4)
+        @test size(cuts_basis_sizes) == (15, 4)
         @test size(cuts_rows) == (15,)
     finally
         h5.close()
     end
 end
+
+function test_cuts_tableau_gmi_dual_usage()
+    function build_model(mps_filename)
+        model = read_from_file(mps_filename)
+        set_optimizer(model, SCIP.Optimizer)
+        return JumpModel(model)
+    end
+
+    mps_filename = "$BASEDIR/../fixtures/bell5.mps.gz"
+    h5_filename = "$BASEDIR/../fixtures/bell5.h5"
+    # rm(h5_filename, force=true)
+
+    # # Run basic collector
+    # bc = BasicCollector(write_mps = false, skip_lp = true)
+    # bc.collect([mps_filename], build_model)
+
+    # # Run dual GMI collector
+    # @info "Running dual GMI collector..."
+    # collect_gmi_dual(mps_filename, optimizer = HiGHS.Optimizer)
+
+    # # Test expert component
+    # solver = LearningSolver(
+    #     components = [
+    #         ExpertPrimalComponent(action = SetWarmStart()),
+    #         ExpertDualGmiComponent(),
+    #     ],
+    #     skip_lp = true,
+    # )
+    # solver.optimize(mps_filename, build_model)
+
+    # Test kNN component
+    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)
+
+    return
+end