gmi_dual: Small fixes

src/Cuts/tableau/gmi_dual.jl

@@ -29,7 +29,7 @@ function collect_gmi_dual(
 
     @timeit "Read H5" begin
         h5_filename = replace(mps_filename, ".mps.gz" => ".h5")
-        h5 = H5File(h5_filename)
+        h5 = H5File(h5_filename, "r")
         sol_opt_dict = Dict(
             zip(
                 h5.get_array("static_var_names"),
@@ -255,7 +255,7 @@ end
 
 function ExpertDualGmiComponent_before_mip(test_h5, model, _)
     # Read cuts and optimal solution
-    h5 = H5File(test_h5)
+    h5 = H5File(test_h5, "r")
     sol_opt_dict = Dict(
         zip(
             h5.get_array("static_var_names"),
@@ -450,70 +450,69 @@ function _dualgmi_generate(train_h5, model)
     end
 
     @timeit "Collect cuts from H5 files" begin
-        cut_basis_vars = nothing
-        cut_basis_sizes = nothing
-        cut_rows = nothing
+        vars_to_unique_basis_offset = Dict()
+        unique_basis_vars = nothing
+        unique_basis_sizes = nothing
+        unique_basis_rows = nothing
+    
         for h5_filename in train_h5
-            h5 = H5File(h5_filename)
-            cut_basis_vars_sample = h5.get_array("cuts_basis_vars")
-            cut_basis_sizes_sample = h5.get_array("cuts_basis_sizes")
-            cut_rows_sample = h5.get_array("cuts_rows")
-            if cut_basis_vars === nothing
-                cut_basis_vars = cut_basis_vars_sample
-                cut_basis_sizes = cut_basis_sizes_sample
-                cut_rows = cut_rows_sample
-            else
-                cut_basis_vars = [cut_basis_vars; cut_basis_vars_sample]
-                cut_basis_sizes = [cut_basis_sizes; cut_basis_sizes_sample]
-                cut_rows = [cut_rows; cut_rows_sample]
+            h5 = H5File(h5_filename, "r")
+            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")
+            ncuts, nvars = size(cut_basis_vars)
+            if unique_basis_vars === nothing
+                unique_basis_vars = Matrix{Int}(undef, 0, nvars)
+                unique_basis_sizes = Matrix{Int}(undef, 0, 4)
+                unique_basis_rows = Dict{Int,Set{Int}}()                
             end
-            h5.close()
-        end
-        ncuts, nvars = size(cut_basis_vars)
-    end
-
-    @timeit "Group cuts by tableau basis" begin
-        vars_to_unique_basis_offset = Dict()
-        unique_basis_vars = Matrix{Int}(undef, 0, nvars)
-        unique_basis_sizes = Matrix{Int}(undef, 0, 4)
-        unique_basis_rows = Dict{Int,Set{Int}}()
-        for i in 1:ncuts
-            vars = cut_basis_vars[i, :]
-            sizes = cut_basis_sizes[i, :]
-            row = cut_rows[i]
-            if vars ∉ keys(vars_to_unique_basis_offset)
-                offset = size(unique_basis_vars)[1] + 1
-                vars_to_unique_basis_offset[vars] = offset
-                unique_basis_vars = [unique_basis_vars; vars']
-                unique_basis_sizes = [unique_basis_sizes; sizes']
-                unique_basis_rows[offset] = Set()
+            for i in 1:ncuts
+                vars = cut_basis_vars[i, :]
+                sizes = cut_basis_sizes[i, :]
+                row = cut_rows[i]
+                if vars ∉ keys(vars_to_unique_basis_offset)
+                    offset = size(unique_basis_vars)[1] + 1
+                    vars_to_unique_basis_offset[vars] = offset
+                    unique_basis_vars = [unique_basis_vars; vars']
+                    unique_basis_sizes = [unique_basis_sizes; sizes']
+                    unique_basis_rows[offset] = Set()
+                end
+                offset = vars_to_unique_basis_offset[vars]
+                push!(unique_basis_rows[offset], row)
             end
-            offset = vars_to_unique_basis_offset[vars]
-            push!(unique_basis_rows[offset], row)
+            h5.close()
         end
     end
 
     @timeit "Compute tableaus and cuts" begin
         all_cuts = nothing
         for (offset, rows) in unique_basis_rows
-            vbb, vnn, cbb, cnn = unique_basis_sizes[offset, :]
-            current_basis = Basis(;
-                var_basic = unique_basis_vars[offset, 1:vbb],
-                var_nonbasic = unique_basis_vars[offset, vbb+1:vbb+vnn],
-                constr_basic = unique_basis_vars[offset, vbb+vnn+1:vbb+vnn+cbb],
-                constr_nonbasic = unique_basis_vars[offset, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
-            )
-
-            tableau = compute_tableau(data_s, current_basis; rows=collect(rows))
-            cuts_s = compute_gmi(data_s, tableau)
-            cuts = backwards(transforms, cuts_s)
+            try
+                vbb, vnn, cbb, cnn = unique_basis_sizes[offset, :]
+                current_basis = Basis(;
+                    var_basic = unique_basis_vars[offset, 1:vbb],
+                    var_nonbasic = unique_basis_vars[offset, vbb+1:vbb+vnn],
+                    constr_basic = unique_basis_vars[offset, vbb+vnn+1:vbb+vnn+cbb],
+                    constr_nonbasic = unique_basis_vars[offset, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
+                )
 
-            if all_cuts === nothing
-                all_cuts = cuts
-            else
-                all_cuts.lhs = [all_cuts.lhs; cuts.lhs]
-                all_cuts.lb = [all_cuts.lb; cuts.lb]
-                all_cuts.ub = [all_cuts.ub; cuts.ub]
+                tableau = compute_tableau(data_s, current_basis; rows=collect(rows))
+                cuts_s = compute_gmi(data_s, tableau)
+                cuts = backwards(transforms, cuts_s)
+                if all_cuts === nothing
+                    all_cuts = cuts
+                else
+                    all_cuts.lhs = [all_cuts.lhs; cuts.lhs]
+                    all_cuts.lb = [all_cuts.lb; cuts.lb]
+                    all_cuts.ub = [all_cuts.ub; cuts.ub]
+                end
+            catch e
+                if isa(e, AssertionError)
+                    @warn "Numerical error detected. Skipping cuts from current tableau."
+                    continue
+                else
+                    rethrow(e)
+                end
             end
         end
     end
@@ -555,13 +554,14 @@ function KnnDualGmiComponent_before_mip(data::_KnnDualGmiData, test_h5, model, _
         neigh_dist, neigh_ind = data.model.kneighbors(x, return_distance = true)
         neigh_ind = neigh_ind .+ 1
         N = length(neigh_dist)
+        k = min(N, data.k)
 
         if data.strategy == "near"
-            selected = collect(1:(data.k))
+            selected = collect(1:k)
         elseif data.strategy == "far"
-            selected = collect((N - data.k + 1) : N)
+            selected = collect((N - k + 1) : N)
         elseif data.strategy == "rand"
-            selected = shuffle(collect(1:N))[1:(data.k)]
+            selected = shuffle(collect(1:N))[1:k]
         else
             error("unknown strategy: $(data.strategy)")
         end
@@ -569,7 +569,7 @@ function KnnDualGmiComponent_before_mip(data::_KnnDualGmiData, test_h5, model, _
         @info "Dual GMI: Selected neighbors ($(data.strategy)):"
         neigh_dist = neigh_dist[selected]
         neigh_ind = neigh_ind[selected]
-        for i in 1:data.k
+        for i in 1:k
             h5_filename = data.train_h5[neigh_ind[i]]
             dist = neigh_dist[i]
             @info "    $(h5_filename) dist=$(dist)"
@@ -591,7 +591,7 @@ function KnnDualGmiComponent_before_mip(data::_KnnDualGmiData, test_h5, model, _
     print_timer()
 
     stats = Dict()
-    stats["KnnDualGmi: k"] = data.k
+    stats["KnnDualGmi: k"] = k
     stats["KnnDualGmi: strategy"] = data.strategy
     stats["KnnDualGmi: cuts"] = length(cuts.lb)
     stats["KnnDualGmi: time generate"] = time_generate