Use HDF5 for instance files

Project.toml

@@ -11,6 +11,7 @@ Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"

README.md

@@ -147,14 +147,14 @@ for i in 1:600
     @feature(...)
     @category(...)
     
-    # Save instances to a file
+    # Save instances to file
     instance = JuMPInstance(m)
-    save("instance-$i.bin", instance)
+    save("instance-$i.h5", instance)
 end
 
 # Initialize training and test instances
-training_instances = [FileInstance("instance-$i.bin") for i in 1:500]
+training_instances = [FileInstance("instance-$i.h5") for i in 1:500]
-test_instances = [FileInstance("instance-$i.bin") for i in 501:600]
+test_instances = [FileInstance("instance-$i.h5") for i in 501:600]
 
 # Initialize solver
 solver = LearningSolver(Cbc.Optimizer)

src/instance/file.jl

@@ -8,10 +8,13 @@ mutable struct FileInstance <: Instance
     py::Union{Nothing,PyCall.PyObject}
     loaded::Union{Nothing, JuMPInstance}
     filename::AbstractString
+    h5::PyCall.PyObject
 
-    function FileInstance(filename::String)::FileInstance
+    function FileInstance(filename::AbstractString)::FileInstance
         instance = new(nothing, nothing, filename)
         instance.py = PyFileInstance(instance)
+        instance.h5 = Hdf5Sample(filename)
+        instance.filename = filename
         return instance
     end
 end
@@ -21,8 +24,14 @@ get_instance_features(instance::FileInstance) = get_instance_features(instance.l
 get_variable_features(instance::FileInstance) = get_variable_features(instance.loaded)
 get_variable_categories(instance::FileInstance) = get_variable_categories(instance.loaded)
 get_constraint_features(instance::FileInstance) = get_constraint_features(instance.loaded)
-get_samples(instance::FileInstance) = get_samples(instance.loaded)
+
-create_sample!(instance::FileInstance) = create_sample!(instance.loaded)
+function get_samples(instance::FileInstance)
+    return [instance.h5]
+end
+
+function create_sample!(instance::FileInstance)
+    return instance.h5
+end
 
 function get_constraint_categories(instance::FileInstance)
     return get_constraint_categories(instance.loaded)
@@ -41,7 +50,6 @@ function free(instance::FileInstance)
 end
 
 function flush(instance::FileInstance)
-    save(instance.filename, instance.loaded)
 end
 
 function __init_PyFileInstance__()

src/instance/jump.jl

@@ -3,7 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 using JuMP
-using JLD2
+import JSON
 
 mutable struct JuMPInstance <: Instance
     py::Union{Nothing,PyCall.PyObject}
@@ -75,42 +75,31 @@ function save(filename::AbstractString, instance::JuMPInstance)::Nothing
     write_to_file(model, mps_filename)
     mps = read(mps_filename)
 
-    # Pickle instance.py.samples. Ideally, we would use dumps and loads, but this
+    # Generate HDF5
-    # causes some issues with PyCall, probably due to automatic type conversions.
+    h5 = Hdf5Sample(filename, mode="w")
-    samples_filename = tempname()
+    h5.put_scalar("miplearn_version", "0002")
-    miplearn.write_pickle_gz(instance.samples, samples_filename)
+    h5.put_bytes("mps", mps)
-    samples = read(samples_filename)
+    h5.put_scalar("jump_ext", JSON.json(model.ext[:miplearn]))
-
-    # Generate JLD2 file
-    jldsave(
-        filename;
-        miplearn_version="0.2",
-        mps=mps,
-        ext=model.ext[:miplearn],
-        samples=samples,
-    )
     return
 end
 
-function _check_miplearn_version(file)
+function _check_miplearn_version(h5)
-    v = file["miplearn_version"]
+    v = h5.get_scalar("miplearn_version")
-    v == "0.2" || error(
+    v == "0002" || error(
         "The file you are trying to load has been generated by " *
-        "MIPLearn $(v) and you are currently running MIPLearn 0.2. " *
+        "MIPLearn $(v) and you are currently running MIPLearn 0002 " *
         "Reading files generated by different versions of MIPLearn is " *
         "not currently supported."
     )
 end
 
 function load_instance(filename::AbstractString)::JuMPInstance
-    jldopen(filename, "r") do file
+    h5 = Hdf5Sample(filename)
-        _check_miplearn_version(file)
+    _check_miplearn_version(h5)
-        instance = JuMPInstance(file["mps"], file["ext"])
+    mps = h5.get_bytes("mps")
-        samples_filename = tempname()
+    ext = h5.get_scalar("jump_ext")
-        write(samples_filename, file["samples"])
+    instance = JuMPInstance(Vector{UInt8}(mps), JSON.parse(ext))
-        instance.samples = miplearn.read_pickle_gz(samples_filename)
     return instance
-    end
 end
 
 export JuMPInstance, save, load_instance

src/solvers/learning.jl

@@ -3,6 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 using Distributed
+using JLD2
 
 
 struct LearningSolver
@@ -117,7 +118,6 @@ end
 
 function load_solver(filename::AbstractString)::LearningSolver
     jldopen(filename, "r") do file
-        _check_miplearn_version(file)
         solve_py_filename = tempname()
         write(solve_py_filename, file["solver_py"])
         solver_py = miplearn.read_pickle_gz(solve_py_filename)

test/instance/file_test.jl

@@ -14,11 +14,15 @@ using Cbc
         filename = tempname()
         save(filename, instance)
 
+        h5 = MIPLearn.Hdf5Sample(filename)
+        @test h5.get_scalar("miplearn_version") == "0002"
+        @test length(h5.get_bytes("mps")) > 0
+        @test length(h5.get_scalar("jump_ext")) > 0
+
         file_instance = FileInstance(filename)
         solver = LearningSolver(Cbc.Optimizer)
         solve!(solver, file_instance)
 
-        loaded = load_instance(filename)
+        @test length(h5.get_vector("mip_var_values")) == 3
-        @test length(loaded.samples) == 1
     end    
 end

test/runtests.jl

@@ -9,8 +9,8 @@ MIPLearn.setup_logger()
 
 @testset "MIPLearn" begin
     include("fixtures/knapsack.jl")
+    include("instance/file_test.jl")
     include("solvers/jump_test.jl")
     include("solvers/learning_test.jl")
-    include("instance/file_test.jl")
     include("utils/benchmark_test.jl")
 end