Implement expert and knn dual gmi component

src/Cuts/tableau/gmi_dual.jl

@@ -4,6 +4,17 @@
 
 using Printf
 using JuMP
+using HiGHS
+
+global ExpertDualGmiComponent = PyNULL()
+global KnnDualGmiComponent = PyNULL()
+
+Base.@kwdef mutable struct _KnnDualGmiData
+    k = nothing
+    extractor = nothing
+    train_h5 = nothing
+    model = nothing
+end
 
 Base.@kwdef mutable struct ConstraintSet_v2
     lhs::SparseMatrixCSC
@@ -106,7 +117,10 @@ function collect_gmi_dual(
 
         if round == 1
             # Assert standard form problem has same value as original
-            assert_eq(obj, obj_lp)
+            if obj_lp !== nothing
+                assert_eq(obj, obj_lp)
+            end
+            obj_lp = obj
             push!(stats_obj, obj)
             push!(stats_gap, gap(obj))
             push!(stats_ncuts, 0)
@@ -128,7 +142,7 @@ function collect_gmi_dual(
 
         # Compute selected tableau rows
         stats_time_tableau += @elapsed begin
-            tableau = compute_tableau(data_s, basis, sol_frac, rows = selected_rows)
+            tableau = compute_tableau(data_s, basis, x = sol_frac, rows = selected_rows)
 
             # Assert tableau rows have been computed correctly
             assert_eq(tableau.lhs * sol_frac, tableau.rhs)
@@ -147,7 +161,7 @@ function collect_gmi_dual(
             # Abort if no cuts are left
             if length(cuts_s.lb) == 0
                 @info "No cuts generated. Aborting."
-                continue
+                break
             end
         end
 
@@ -194,7 +208,7 @@ function collect_gmi_dual(
             set_optimizer(model, optimizer)
             optimize!(model)
             n_obj = objective_value(model)
-            @assert obj ≈ n_obj
+            assert_eq(obj, n_obj, atol = 0.01)
         end
         undo_relax()
     end
@@ -240,26 +254,26 @@ function collect_gmi_dual(
     end
     basis = original_basis
 
-    cut_sizezz = length(all_cuts_v2.Bv)
-    var_totall =
-        length(basis.var_basic) +
-        length(basis.var_nonbasic) +
-        length(basis.constr_basic) +
-        length(basis.constr_nonbasic)
-    bm_size = Array{Int64,2}(undef, cut_sizezz, 4)
-    basis_matrix = Array{Int64,2}(undef, cut_sizezz, var_totall)
-
-    for ii = 1:cut_sizezz
-        vb = all_cuts_v2.Bss[ii].var_basic
-        vn = all_cuts_v2.Bss[ii].var_nonbasic
-        cb = all_cuts_v2.Bss[ii].constr_basic
-        cn = all_cuts_v2.Bss[ii].constr_nonbasic
-        bm_size[ii, :] = [length(vb) length(vn) length(cb) length(cn)]
-        basis_matrix[ii, :] = [vb' vn' cb' cn']
-    end
-
-    # Store cuts
     if all_cuts !== nothing
+        cut_sizezz = length(all_cuts_v2.Bv)
+        var_totall =
+            length(basis.var_basic) +
+            length(basis.var_nonbasic) +
+            length(basis.constr_basic) +
+            length(basis.constr_nonbasic)
+        bm_size = Array{Int64,2}(undef, cut_sizezz, 4)
+        basis_matrix = Array{Int64,2}(undef, cut_sizezz, var_totall)
+
+        for ii = 1:cut_sizezz
+            vb = all_cuts_v2.Bss[ii].var_basic
+            vn = all_cuts_v2.Bss[ii].var_nonbasic
+            cb = all_cuts_v2.Bss[ii].constr_basic
+            cn = all_cuts_v2.Bss[ii].constr_nonbasic
+            bm_size[ii, :] = [length(vb) length(vn) length(cb) length(cn)]
+            basis_matrix[ii, :] = [vb' vn' cb' cn']
+        end
+
+        # Store cuts
         @info "Storing $(length(all_cuts.ub)) GMI cuts..."
         h5 = H5File(h5_filename)
         h5.put_sparse("cuts_lhs", all_cuts.lhs)
@@ -287,7 +301,101 @@ function collect_gmi_dual(
         "stats_gap" => stats_gap,
         "stats_ncuts" => length(keep),
     )
+end
 
+function ExpertDualGmiComponent_before_mip(test_h5, model, stats)
+    # Read cuts and optimal solution
+    h5 = H5File(test_h5)
+    sol_opt_dict = Dict(
+        zip(
+            h5.get_array("static_var_names"),
+            convert(Array{Float64}, h5.get_array("mip_var_values")),
+        ),
+    )
+    cut_basis_vars = h5.get_array("cuts_basis_vars")
+    cut_basis_sizes = h5.get_array("cuts_basis_sizes")
+    cut_rows = h5.get_array("cuts_rows")
+    obj_mip = h5.get_scalar("mip_lower_bound")
+    if obj_mip === nothing
+        obj_mip = h5.get_scalar("mip_obj_value")
+    end
+    h5.close()
+
+    # Initialize stats
+    stats_time_convert = 0
+    stats_time_tableau = 0
+    stats_time_gmi = 0
+    all_cuts = []
+
+    stats_time_convert = @elapsed begin
+        # Extract problem data
+        data = ProblemData(model)
+
+        # Construct optimal solution vector (with correct variable sequence)
+        sol_opt = [sol_opt_dict[n] for n in data.var_names]
+
+        # Assert optimal solution is feasible for the original problem
+        assert_leq(data.constr_lb, data.constr_lhs * sol_opt)
+        assert_leq(data.constr_lhs * sol_opt, data.constr_ub)
+
+        # Convert to standard form
+        data_s, transforms = convert_to_standard_form(data)
+        model_s = to_model(data_s)
+        set_optimizer(model_s, HiGHS.Optimizer)
+        relax_integrality(model_s)
+
+        # Convert optimal solution to standard form
+        sol_opt_s = forward(transforms, sol_opt)
+
+        # Assert converted solution is feasible for standard form problem
+        assert_eq(data_s.constr_lhs * sol_opt_s, data_s.constr_lb)
+
+    end
+
+    current_basis = nothing
+    for (r, row) in enumerate(cut_rows)
+        stats_time_tableau += @elapsed begin
+            if r == 1 || cut_basis_vars[r, :] != cut_basis_vars[r-1, :]
+                vbb, vnn, cbb, cnn = cut_basis_sizes[r, :]
+                current_basis = Basis(;
+                    var_basic = cut_basis_vars[r, 1:vbb],
+                    var_nonbasic = cut_basis_vars[r, vbb+1:vbb+vnn],
+                    constr_basic = cut_basis_vars[r, vbb+vnn+1:vbb+vnn+cbb],
+                    constr_nonbasic = cut_basis_vars[r, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
+                )
+            end
+            tableau = compute_tableau(data_s, current_basis, rows = [row])
+            assert_eq(tableau.lhs * sol_opt_s, tableau.rhs)
+        end
+        stats_time_gmi += @elapsed begin
+            cuts_s = compute_gmi(data_s, tableau)
+            assert_does_not_cut_off(cuts_s, sol_opt_s)
+        end
+        cuts = backwards(transforms, cuts_s)
+        assert_does_not_cut_off(cuts, sol_opt)
+        push!(all_cuts, cuts)
+    end
+
+    function cut_callback(cb_data)
+        if all_cuts !== nothing
+            @info "Enforcing dual GMI cuts..."
+            for cuts in all_cuts
+                constrs = build_constraints(model, cuts)
+                for c in constrs
+                    MOI.submit(model, MOI.UserCut(cb_data), c)
+                end
+            end
+            all_cuts = nothing
+        end
+    end
+
+    # Set up cut callback
+    set_attribute(model, MOI.UserCutCallback(), cut_callback)
+
+    stats["gmi_time_convert"] = stats_time_convert
+    stats["gmi_time_tableau"] = stats_time_tableau
+    stats["gmi_time_gmi"] = stats_time_gmi
+    return
 end
 
 function add_constraint_set_dual_v2(model::JuMP.Model, cs::ConstraintSet)
@@ -320,4 +428,106 @@ function add_constraint_set_dual_v2(model::JuMP.Model, cs::ConstraintSet)
     return constrs, gmi_exps
 end
 
-export collect_gmi_dual
+function _dualgmi_features(h5_filename, extractor)
+    h5 = H5File(h5_filename, "r")
+    try
+        return extractor.get_instance_features(h5)
+    finally
+        h5.close()
+    end
+end
+
+function _dualgmi_generate(train_h5, model)
+    data = ProblemData(model)
+    data_s, transforms = convert_to_standard_form(data)
+    all_cuts = []
+    for h5_filename in train_h5
+        h5 = H5File(h5_filename)
+        cut_basis_vars = h5.get_array("cuts_basis_vars")
+        cut_basis_sizes = h5.get_array("cuts_basis_sizes")
+        cut_rows = h5.get_array("cuts_rows")
+        h5.close()
+        current_basis = nothing
+        for (r, row) in enumerate(cut_rows)
+            if r == 1 || cut_basis_vars[r, :] != cut_basis_vars[r-1, :]
+                vbb, vnn, cbb, cnn = cut_basis_sizes[r, :]
+                current_basis = Basis(;
+                    var_basic = cut_basis_vars[r, 1:vbb],
+                    var_nonbasic = cut_basis_vars[r, vbb+1:vbb+vnn],
+                    constr_basic = cut_basis_vars[r, vbb+vnn+1:vbb+vnn+cbb],
+                    constr_nonbasic = cut_basis_vars[r, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
+                )
+            end
+            tableau = compute_tableau(data_s, current_basis, rows = [row])
+            cuts_s = compute_gmi(data_s, tableau)
+            cuts = backwards(transforms, cuts_s)
+            push!(all_cuts, cuts)
+        end
+    end
+    return all_cuts
+end
+
+function _dualgmi_set_callback(model, all_cuts)
+    function cut_callback(cb_data)
+        if all_cuts !== nothing
+            @info "Dual GMI: Submitting cuts..."
+            for cuts in all_cuts
+                constrs = build_constraints(model, cuts)
+                for c in constrs
+                    MOI.submit(model, MOI.UserCut(cb_data), c)
+                end
+            end
+            all_cuts = nothing
+        end
+    end
+    set_attribute(model, MOI.UserCutCallback(), cut_callback)
+end
+
+function KnnDualGmiComponent_fit(data::_KnnDualGmiData, train_h5)
+    x = hcat([
+        _dualgmi_features(filename, data.extractor)
+        for filename in train_h5
+    ]...)'
+    model = pyimport("sklearn.neighbors").NearestNeighbors(n_neighbors=data.k)
+    model.fit(x)
+    data.model = model
+    data.train_h5 = train_h5
+end
+
+
+function KnnDualGmiComponent_before_mip(data::_KnnDualGmiData, test_h5, model, stats)
+    x = _dualgmi_features(test_h5, data.extractor)
+    x = reshape(x, 1, length(x))
+    selected = vec(data.model.kneighbors(x, return_distance=false)) .+ 1
+    @info "Dual GMI: Nearest neighbors:"
+    for h5_filename in data.train_h5[selected]
+        @info "    $(h5_filename)"
+    end
+    cuts = _dualgmi_generate(data.train_h5[selected], model)
+    _dualgmi_set_callback(model, cuts)
+end
+
+function __init_gmi_dual__()
+    @pydef mutable struct Class1
+        function fit(_, _) end
+        function before_mip(self, test_h5, model, stats)
+            ExpertDualGmiComponent_before_mip(test_h5, model.inner, stats)
+        end
+    end
+    copy!(ExpertDualGmiComponent, Class1)
+
+    @pydef mutable struct Class2
+        function __init__(self; extractor, k = 3)
+            self.data = _KnnDualGmiData(; extractor, k)
+        end
+        function fit(self, train_h5)
+            KnnDualGmiComponent_fit(self.data, train_h5)
+        end
+        function before_mip(self, test_h5, model, stats)
+            KnnDualGmiComponent_before_mip(self.data, test_h5, model.inner, stats)
+        end
+    end
+    copy!(KnnDualGmiComponent, Class2)
+end
+
+export collect_gmi_dual, expert_gmi_dual, ExpertDualGmiComponent, KnnDualGmiComponent