Cuts: Store variable names

src/Cuts/BlackBox/cplex.jl

@@ -80,6 +80,11 @@ function collect(
     ncuts = length([cr for cr in constraints if select(cr)])
     cuts_lhs = spzeros(ncuts, nvars)
     cuts_rhs = Float64[]
+    cuts_var_names = String[]
+
+    for i in 0:(nvars-1)
+        push!(cuts_var_names, name(VariableRef(model, MOI.VariableIndex(i))))
+    end
 
     offset = 1
     for conRef in constraints
@@ -98,6 +103,7 @@ function collect(
     h5 = Hdf5Sample(h5_filename)
     h5.put_sparse("cuts_cpx_lhs", cuts_lhs)
     h5.put_array("cuts_cpx_rhs", cuts_rhs)
+    h5.put_array("cuts_cpx_var_names", to_str_array(cuts_var_names))
     h5.file.close()
 
     return

src/MIPLearn.jl

@@ -43,7 +43,8 @@ function PyObject(m::SparseMatrixCSC)
     ).tocoo()
 end
 
-
 include("Cuts/BlackBox/cplex.jl")
 
+export Hdf5Sample
+
 end # module

test/Cuts/BlackBox/test_cplex.jl

@@ -3,6 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 using HDF5
+using MIPLearn
 
 function test_cuts_blackbox_cplex()
     # Prepare filenames
@@ -13,10 +14,10 @@ function test_cuts_blackbox_cplex()
     MIPLearn.collect(mps_filename, CplexBlackBoxCuts())
 
     # Read HDF5 file
-    h5open(h5_filename, "r+") do h5
+    h5 = Hdf5Sample(h5_filename)
-        rhs = h5["cuts_cpx_rhs"]
+    lhs = h5.get_sparse("cuts_cpx_lhs")
-        lhs = h5["cuts_cpx_lhs"]
+    rhs = h5.get_array("cuts_cpx_rhs")
-        ncuts = length(rhs)
+    h5.file.close()
-        @test size(lhs) == (ncuts, 104)
+    @test lhs.shape == (22, 100)
-    end
+    @test length(rhs) == 22
 end

test/Project.toml

@@ -1,5 +1,6 @@
 [deps]
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
+JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 MIPLearn = "2b1277c3-b477-4c49-a15e-7ba350325c68"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"