Implement FileInstance

src/MIPLearn.jl

@@ -16,9 +16,10 @@ miplearn = pyimport("miplearn")
 
 include("utils/log.jl")
 include("utils/exceptions.jl")
-include("instance/jump.jl")
 include("solvers/jump.jl")
 include("solvers/learning.jl")
 include("solvers/macros.jl")
+include("instance/jump.jl")
+include("instance/file.jl")
 
 end # module

src/instance/file.jl

@@ -2,30 +2,66 @@
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-struct FileInstance
-    filename::AbstractString
-    loaded::Union{Nothing,JuMPInstance}
-end
 
+@pydef mutable struct PyFileInstance <: miplearn.Instance
+    function __init__(self, filename)
+        self.filename = filename
+        self.loaded = nothing
+        self.samples = nothing
+    end
 
-function FileInstance(filename::AbstractString)::FileInstance
+    function to_model(self)
-    return FileInstance(
+        return self.loaded.py.to_model()
-        filename,
+    end
-        nothing,
+
-    )
+    function get_instance_features(self)
-end
+        return self.loaded.py.get_instance_features()
+    end
+
+    function get_variable_features(self, var_name)
+        return self.loaded.py.get_variable_features(var_name)
+    end
+
+    function get_variable_category(self, var_name)
+        return self.loaded.py.get_variable_category(var_name)
+    end
 
+    function get_constraint_features(self, cname)
+        return self.loaded.py.get_constraint_features(cname)
+    end
 
-function load!(instance::FileInstance)
+    function get_constraint_category(self, cname)
-    instance.loaded = load_jump_instance(instance.filename)
+        return self.loaded.py.get_constraint_category(cname)
+    end
+
+    function load(self)
+        if self.loaded === nothing
+            self.loaded = load_jump_instance(self.filename)
+            self.samples = self.loaded.py.samples
+        end
+    end
+
+    function free(self)
+        self.loaded = nothing
+        self.samples = nothing
+    end
+
+    function flush(self)
+        self.loaded.py.samples = self.samples
+        save(self.filename, self.loaded)
+    end
 end
 
 
-function free!(instance::FileInstance)
+struct FileInstance <: Instance
-    instance.loaded = nothing
+    py::PyCall.PyObject
 end
 
 
-function flush!(instance::FileInstance)
+function FileInstance(filename)::FileInstance
-    save(instance.filename, instance.loaded)
+    filename isa AbstractString || error("filename should be a string. Found $(typeof(filename)) instead.")
+    return FileInstance(PyFileInstance(filename))
 end
+
+
+export FileInstance

src/instance/jump.jl

@@ -47,7 +47,7 @@ using JLD2
 end
 
 
-struct JuMPInstance
+struct JuMPInstance <: Instance
     py::PyCall.PyObject
     model::Model
 end
@@ -63,80 +63,88 @@ end
 
 
 function save(filename::AbstractString, instance::JuMPInstance)::Nothing
-    # Convert JuMP model to MPS
+    @info "Writing: $filename"
-    mps_filename = "$(tempname()).mps.gz"
+    time = @elapsed begin
-    write_to_file(instance.model, mps_filename)
+        # Convert JuMP model to MPS
-    mps = read(mps_filename)
-
-    # Pickle instance.py.samples. Ideally, we would use dumps and loads, but this
-    # causes some issues with PyCall, probably due to automatic type conversions.
-    py_samples_filename = tempname()
-    miplearn.write_pickle_gz(instance.py.samples, py_samples_filename)
-    py_samples = read(py_samples_filename)
-
-    # Replace variable/constraint refs by names
-    _to_names(d) = Dict(name(var) => value for (var, value) in d)
-    ext_original = instance.model.ext[:miplearn]
-    ext_names = Dict(
-        :variable_features => _to_names(ext_original[:variable_features]),
-        :variable_categories => _to_names(ext_original[:variable_categories]),
-        :constraint_features => _to_names(ext_original[:constraint_features]),
-        :constraint_categories => _to_names(ext_original[:constraint_categories]),
-        :instance_features => ext_original[:instance_features],
-    )
-
-    # Generate JLD2 file
-    jldsave(
-        filename;
-        miplearn_version=0.2,
-        mps=mps,
-        ext=ext_names,
-        py_samples=py_samples,
-    )
-    return
-end
-
-
-function load_jump_instance(filename::AbstractString)::JuMPInstance
-    jldopen(filename, "r") do file
-        file["miplearn_version"] == 0.2 || error(
-            "MIPLearn version 0.2 cannot read instance files generated by " *
-            "version $(file["miplearn_version"])."
-        )
-
-        # Convert MPS to JuMP
         mps_filename = "$(tempname()).mps.gz"
-        write(mps_filename, file["mps"])
+        write_to_file(instance.model, mps_filename)
-        model = read_from_file(mps_filename)
+        mps = read(mps_filename)
 
-        # Unpickle instance.py.samples
+        # Pickle instance.py.samples. Ideally, we would use dumps and loads, but this
+        # causes some issues with PyCall, probably due to automatic type conversions.
         py_samples_filename = tempname()
-        write(py_samples_filename, file["py_samples"])
+        miplearn.write_pickle_gz(instance.py.samples, py_samples_filename, quiet=true)
-        py_samples = miplearn.read_pickle_gz(py_samples_filename)
+        py_samples = read(py_samples_filename)
-
+
-        # Replace variable/constraint names by refs
+        # Replace variable/constraint refs by names
-        _to_var(model, d) = Dict(
+        _to_names(d) = Dict(name(var) => value for (var, value) in d)
-            variable_by_name(model, varname) => value
+        ext_original = instance.model.ext[:miplearn]
-            for (varname, value) in d
+        ext_names = Dict(
+            :variable_features => _to_names(ext_original[:variable_features]),
+            :variable_categories => _to_names(ext_original[:variable_categories]),
+            :constraint_features => _to_names(ext_original[:constraint_features]),
+            :constraint_categories => _to_names(ext_original[:constraint_categories]),
+            :instance_features => ext_original[:instance_features],
         )
-        _to_constr(model, d) = Dict(
+
-            constraint_by_name(model, cname) => value
+        # Generate JLD2 file
-            for (cname, value) in d
+        jldsave(
-        )
+            filename;
-        ext = file["ext"]
+            miplearn_version=0.2,
-        model.ext[:miplearn] = Dict(
+            mps=mps,
-            :variable_features => _to_var(model, ext[:variable_features]),
+            ext=ext_names,
-            :variable_categories => _to_var(model, ext[:variable_categories]),
+            py_samples=py_samples,
-            :constraint_features => _to_constr(model, ext[:constraint_features]),
-            :constraint_categories => _to_constr(model, ext[:constraint_categories]),
-            :instance_features => ext[:instance_features],
         )
+    end
+    @info @sprintf("File written in %.2f seconds", time)
+    return
+end
 
-        instance = JuMPInstance(model)
-        instance.py.samples = py_samples
 
-        return instance
+function load_jump_instance(filename::AbstractString)::JuMPInstance
+    @info "Reading: $filename"
+    instance = nothing
+    time = @elapsed begin
+        jldopen(filename, "r") do file
+            file["miplearn_version"] == 0.2 || error(
+                "MIPLearn version 0.2 cannot read instance files generated by " *
+                "version $(file["miplearn_version"])."
+            )
+
+            # Convert MPS to JuMP
+            mps_filename = "$(tempname()).mps.gz"
+            write(mps_filename, file["mps"])
+            model = read_from_file(mps_filename)
+
+            # Unpickle instance.py.samples
+            py_samples_filename = tempname()
+            write(py_samples_filename, file["py_samples"])
+            py_samples = miplearn.read_pickle_gz(py_samples_filename, quiet=true)
+
+            # Replace variable/constraint names by refs
+            _to_var(model, d) = Dict(
+                variable_by_name(model, varname) => value
+                for (varname, value) in d
+            )
+            _to_constr(model, d) = Dict(
+                constraint_by_name(model, cname) => value
+                for (cname, value) in d
+            )
+            ext = file["ext"]
+            model.ext[:miplearn] = Dict(
+                :variable_features => _to_var(model, ext[:variable_features]),
+                :variable_categories => _to_var(model, ext[:variable_categories]),
+                :constraint_features => _to_constr(model, ext[:constraint_features]),
+                :constraint_categories => _to_constr(model, ext[:constraint_categories]),
+                :instance_features => ext[:instance_features],
+            )
+
+            instance = JuMPInstance(model)
+            instance.py.samples = py_samples
+        end
     end
+    @info @sprintf("File read in %.2f seconds", time)
+    return instance
 end
 
 

src/solvers/learning.jl

@@ -7,20 +7,31 @@ struct LearningSolver
 end
 
 
+abstract type Instance
+end
+
+
 function LearningSolver(optimizer_factory)::LearningSolver
     py = miplearn.LearningSolver(solver=JuMPSolver(optimizer_factory))
     return LearningSolver(py)
 end
 
 
-function solve!(solver::LearningSolver, instance::JuMPInstance)
+function solve!(
-    return @python_call solver.py.solve(instance.py)
+    solver::LearningSolver,
+    instance::Instance;
+    tee::Bool = false,
+)
+    return @python_call solver.py.solve(instance.py, tee=tee)
 end
 
 
-function fit!(solver::LearningSolver, instances::Vector{JuMPInstance})
+function fit!(solver::LearningSolver, instances::Vector{<:Instance})
     @python_call solver.py.fit([instance.py for instance in instances])
 end
 
 
-export LearningSolver, solve!, fit!
+export Instance,
+       LearningSolver,
+       solve!,
+       fit!

test/instance/file_test.jl

@@ -0,0 +1,26 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using JuMP
+using MIPLearn
+using Gurobi
+
+@testset "FileInstance" begin
+    @testset "solve" begin
+        model = Model()
+        @variable(model, x, Bin)
+        @variable(model, y, Bin)
+        @objective(model, Max, x + y)
+        instance = JuMPInstance(model)
+        filename = tempname()
+        save(filename, instance)
+
+        file_instance = FileInstance(filename)
+        solver = LearningSolver(Gurobi.Optimizer)
+        solve!(solver, file_instance)
+
+        loaded = load_jump_instance(filename)
+        @test length(loaded.py.samples) == 1
+    end    
+end

test/instance/jump_test.jl

@@ -0,0 +1,35 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+@testset "JuMPInstance" begin
+    @testset "save and load" begin
+        # Create basic model
+        model = Model()
+        @variable(model, x, Bin)
+        @variable(model, y, Bin)
+        @objective(model, Max, x + y)
+        @feature(x, [1.0])
+        @category(x, "cat1")
+        @feature(model, [5.0])
+
+        # Solve
+        instance = JuMPInstance(model)
+        solver = LearningSolver(Gurobi.Optimizer)
+        stats = solve!(solver, instance)
+        @test length(instance.py.samples) == 1
+
+        # Save model to file
+        filename = tempname()
+        save(filename, instance)
+        @test isfile(filename)
+
+        # Read model from file
+        loaded = load_jump_instance(filename)
+        x = variable_by_name(loaded.model, "x")
+        @test loaded.model.ext[:miplearn][:variable_features][x] == [1.0]
+        @test loaded.model.ext[:miplearn][:variable_categories][x] == "cat1"
+        @test loaded.model.ext[:miplearn][:instance_features] == [5.0]
+        @test length(loaded.py.samples) == 1
+    end
+end

test/runtests.jl

@@ -8,6 +8,7 @@ using MIPLearn
 MIPLearn.setup_logger()
 
 @testset "MIPLearn" begin
-    include("solvers/jump.jl")
+    include("solvers/jump_test.jl")
-    include("solvers/learning.jl")
+    include("solvers/learning_test.jl")
+    include("instance/file_test.jl")
 end

test/solvers/learning_test.jl

@@ -58,33 +58,4 @@ using Gurobi
         stats = solve!(solver, instance)
     end
 
-    @testset "file model" begin
-        # Create basic model
-        model = Model()
-        @variable(model, x, Bin)
-        @variable(model, y, Bin)
-        @objective(model, Max, x + y)
-        @feature(x, [1.0])
-        @category(x, "cat1")
-        @feature(model, [5.0])
-
-        # Solve
-        instance = JuMPInstance(model)
-        solver = LearningSolver(Gurobi.Optimizer)
-        stats = solve!(solver, instance)
-        @test length(instance.py.samples) == 1
-
-        # Save model to file
-        filename = tempname()
-        save(filename, instance)
-        @test isfile(filename)
-
-        # Read model from file
-        loaded = load_jump_instance(filename)
-        x = variable_by_name(loaded.model, "x")
-        @test loaded.model.ext[:miplearn][:variable_features][x] == [1.0]
-        @test loaded.model.ext[:miplearn][:variable_categories][x] == "cat1"
-        @test loaded.model.ext[:miplearn][:instance_features] == [5.0]
-        @test length(loaded.py.samples) == 1
-    end
 end