Rewrite FileInstance

src/instance/file.jl

@@ -2,72 +2,66 @@
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+import Base: flush
 
-function __init_PyFileInstance__()
-    @pydef mutable struct Class <: miplearn.Instance
-        function __init__(self, filename)
-            self.filename = filename
-            self.loaded = nothing
-            self.samples = nothing
-        end
-
-        function to_model(self)
-            return self.loaded.py.to_model()
-        end
-
-        function get_instance_features(self)
-            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
+mutable struct FileInstance <: Instance
+    py::Union{Nothing,PyCall.PyObject}
+    loaded::Union{Nothing, JuMPInstance}
+    filename::AbstractString
 
-        function get_constraint_category(self, cname)
-            return self.loaded.py.get_constraint_category(cname)
-        end
+    function FileInstance(filename::String)::FileInstance
+        instance = new(nothing, nothing, filename)
+        instance.py = PyFileInstance(instance)
+        return instance
+    end
+end
 
-        function load(self)
-            if self.loaded === nothing
-                self.loaded = load_instance(self.filename)
-                self.samples = self.loaded.py.samples
-            end
-        end
+to_model(instance::FileInstance) = to_model(instance.loaded)
+get_instance_features(instance::FileInstance) = get_instance_features(instance.loaded)
+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)
+push_sample!(instance::FileInstance, sample::PyCall.PyObject) = push_sample!(instance.loaded, sample)
 
-        function free(self)
-            self.loaded = nothing
-            self.samples = nothing
-        end
+function get_constraint_categories(instance::FileInstance)
+    return get_constraint_categories(instance.loaded)
+end
 
-        function flush(self)
-            self.loaded.py.samples = self.samples
-            save(self.filename, self.loaded)
-        end
+function load(instance::FileInstance)
+    if instance.loaded === nothing
+        instance.loaded = load_instance(instance.filename)
     end
-    copy!(PyFileInstance, Class)
 end
 
-struct FileInstance <: Instance
-    py::PyCall.PyObject
-    filename::AbstractString
+function free(instance::FileInstance)
+    instance.loaded.samples = []
+    instance.loaded = nothing
+    GC.gc()
 end
 
-
-function FileInstance(filename)::FileInstance
-    filename isa AbstractString || error("filename should be a string. Found $(typeof(filename)) instead.")
-    return FileInstance(
-        PyFileInstance(filename),
-        filename,
-    )
+function flush(instance::FileInstance)
+    save(instance.filename, instance.loaded)
 end
 
+function __init_PyFileInstance__()
+    @pydef mutable struct Class <: miplearn.Instance
+        function __init__(self, jl)
+            self.jl = jl
+        end
+        to_model(self) = to_model(self.jl)
+        get_instance_features(self) = get_instance_features(self.jl)
+        get_variable_features(self) = get_variable_features(self.jl)
+        get_variable_categories(self) = get_variable_categories(self.jl)
+        get_constraint_features(self) = get_constraint_features(self.jl)
+        get_constraint_categories(self) = get_constraint_categories(self.jl)
+        get_samples(self) = get_samples(self.jl)
+        push_sample(self, sample) = push_sample!(self.jl, sample)
+        load(self) = load(self.jl)
+        free(self) = free(self.jl)
+        flush(self) = flush(self.jl)
+    end
+    copy!(PyFileInstance, Class)
+end
 
 export FileInstance

src/instance/jump.jl

@@ -5,85 +5,88 @@
 using JuMP
 using JLD2
 
-function __init_PyJuMPInstance__()
-    @pydef mutable struct Class <: miplearn.Instance
-        function __init__(self, model)
-            init_miplearn_ext(model)
-            self.model = model
-            self.samples = []
-        end
-
-        function to_model(self)
-            return self.model
-        end
-
-        function get_instance_features(self)
-            return self.model.ext[:miplearn][:instance_features]
-        end
-
-        function get_variable_features(self, var_name)
-            model = self.model
-            return get(model.ext[:miplearn][:variable_features], var_name, nothing)
-        end
-
-        function get_variable_category(self, var_name)
-            model = self.model
-            return get(model.ext[:miplearn][:variable_categories], var_name, nothing)
-        end
-
-        function get_constraint_features(self, cname)
-            model = self.model
-            return get(model.ext[:miplearn][:constraint_features], cname, nothing)
-        end
+mutable struct JuMPInstance <: Instance
+    py::Union{Nothing,PyCall.PyObject}
+    model::Union{Nothing,JuMP.Model}
+    mps::Union{Nothing,Vector{UInt8}}
+    ext::AbstractDict
+    samples::Vector{PyCall.PyObject}
+
+    function JuMPInstance(model::JuMP.Model)
+        init_miplearn_ext(model)
+        instance = new(nothing, model, nothing, model.ext[:miplearn], [])
+        py = PyJuMPInstance(instance)
+        instance.py = py
+        return instance
+    end
 
-        function get_constraint_category(self, cname)
-            model = self.model
-            return get(model.ext[:miplearn][:constraint_categories], cname, nothing)
-        end
+    function JuMPInstance(mps::Vector{UInt8}, ext::AbstractDict)
+        "instance_features" in keys(ext) || error("provided ext is not initialized")
+        instance = new(nothing, nothing, mps, ext, [])
+        instance.py = PyJuMPInstance(instance)
+        return instance
     end
-    copy!(PyJuMPInstance, Class)
 end
 
-
-struct JuMPInstance <: Instance
-    py::PyCall.PyObject
-    model::Model
+function to_model(instance::JuMPInstance)::JuMP.Model
+    if instance.model === nothing
+        mps_filename = "$(tempname()).mps.gz"
+        write(mps_filename, instance.mps)
+        instance.model = read_from_file(mps_filename)
+        instance.model.ext[:miplearn] = instance.ext
+    end
+    return instance.model
 end
 
+get_instance_features(instance::JuMPInstance) = instance.ext["instance_features"]
+get_variable_features(instance::JuMPInstance) = instance.ext["variable_features"]
+get_variable_categories(instance::JuMPInstance) = instance.ext["variable_categories"]
+get_constraint_features(instance::JuMPInstance) = instance.ext["constraint_features"]
+get_constraint_categories(instance::JuMPInstance) = instance.ext["constraint_categories"]
+get_samples(instance::JuMPInstance) = instance.samples
 
-function JuMPInstance(model)
-    model isa Model || error("model should be a JuMP.Model. Found $(typeof(model)) instead.")
-    return JuMPInstance(
-        PyJuMPInstance(model),
-        model,
-    )
+function push_sample!(instance::JuMPInstance, sample::PyCall.PyObject)
+    push!(instance.samples, sample)
 end
 
+function __init_PyJuMPInstance__()
+    @pydef mutable struct Class <: miplearn.Instance
+        function __init__(self, jl)
+            self.jl = jl
+        end
+        to_model(self) = to_model(self.jl)
+        get_instance_features(self) = get_instance_features(self.jl)
+        get_variable_features(self) = get_variable_features(self.jl)
+        get_variable_categories(self) = get_variable_categories(self.jl)
+        get_constraint_features(self,) = get_constraint_features(self.jl)
+        get_constraint_categories(self) = get_constraint_categories(self.jl)
+        get_samples(self) = get_samples(self.jl)
+        push_sample(self, sample) = push_sample!(self.jl, sample)
+    end
+    copy!(PyJuMPInstance, Class)
+end
 
 function save(filename::AbstractString, instance::JuMPInstance)::Nothing
-    @info "Writing: $filename"
-    time = @elapsed begin
-        # Convert JuMP model to MPS
-        mps_filename = "$(tempname()).mps.gz"
-        write_to_file(instance.model, mps_filename)
-        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, quiet=true)
-        py_samples = read(py_samples_filename)
-
-        # Generate JLD2 file
-        jldsave(
-            filename;
-            miplearn_version="0.2",
-            mps=mps,
-            ext=instance.model.ext[:miplearn],
-            py_samples=py_samples,
-        )
-    end
-    @info @sprintf("File written in %.2f seconds", time)
+    # Convert JuMP model to MPS
+    mps_filename = "$(tempname()).mps.gz"
+    model = instance.py.to_model()
+    write_to_file(model, mps_filename)
+    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.
+    samples_filename = tempname()
+    miplearn.write_pickle_gz(instance.samples, samples_filename)
+    samples = read(samples_filename)
+
+    # Generate JLD2 file
+    jldsave(
+        filename;
+        miplearn_version="0.2",
+        mps=mps,
+        ext=model.ext[:miplearn],
+        samples=samples,
+    )
     return
 end
 
@@ -97,32 +100,15 @@ function _check_miplearn_version(file)
     )
 end
 
-
 function load_instance(filename::AbstractString)::JuMPInstance
-    @info "Reading: $filename"
-    instance = nothing
-    time = @elapsed begin
-        jldopen(filename, "r") do file
-            _check_miplearn_version(file)
-
-            # Convert MPS to JuMP
-            mps_filename = "$(tempname()).mps.gz"
-            write(mps_filename, file["mps"])
-            model = read_from_file(mps_filename)
-            model.ext[:miplearn] = file["ext"]
-
-            # 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)
-
-            instance = JuMPInstance(model)
-            instance.py.samples = py_samples
-        end
+    jldopen(filename, "r") do file
+        _check_miplearn_version(file)
+        instance = JuMPInstance(file["mps"], file["ext"])
+        samples_filename = tempname()
+        write(samples_filename, file["samples"])
+        @time instance.samples = miplearn.read_pickle_gz(samples_filename)
+        return instance
     end
-    @info @sprintf("File read in %.2f seconds", time)
-    return instance
 end
 
-
 export JuMPInstance, save, load_instance

src/solvers/jump.jl

@@ -41,6 +41,8 @@ function _optimize_and_capture_output!(model; tee::Bool=false)
     rm(logname)
     if tee
         println(log)
+        flush(stdout)
+        Base.Libc.flush_cstdio()
     end
     return log
 end
@@ -150,7 +152,7 @@ function build_test_instance_knapsack()
     @objective(model, Max, sum(x[i-1] * prices[i] for i in 1:n))
     @constraint(model, eq_capacity, sum(x[i-1] * weights[i] for i in 1:n) - z == 0)
 
-    return PyJuMPInstance(model)
+    return JuMPInstance(model).py
 end
 
 
@@ -159,7 +161,7 @@ function build_test_instance_infeasible()
     @variable(model, x, Bin)
     @objective(model, Max, x)
     @constraint(model, x >= 2)
-    return PyJuMPInstance(model)
+    return JuMPInstance(model).py
 end
 
 
@@ -389,6 +391,8 @@ end
 function get_constraints(
     data::JuMPSolverData;
     with_static::Bool,
+    with_sa::Bool,
+    with_lhs::Bool,
 )
     names = []
     senses, lhs, rhs = nothing, nothing, nothing
@@ -420,24 +424,26 @@ function get_constraints(
 
             if with_static
                 if ftype == JuMP.AffExpr
-                    push!(
-                        lhs,
-                        [
-                            (
-                                MOI.get(
+                    if with_lhs
+                        push!(
+                            lhs,
+                            [
+                                (
+                                    MOI.get(
+                                        constr.model.moi_backend,
+                                        MOI.VariableName(),
+                                        term.variable_index
+                                    ),
+                                    term.coefficient,
+                                )
+                                for term in MOI.get(
                                     constr.model.moi_backend,
-                                    MOI.VariableName(),
-                                    term.variable_index
-                                ),
-                                term.coefficient,
-                            )
-                            for term in MOI.get(
-                                constr.model.moi_backend,
-                                MOI.ConstraintFunction(),
-                                constr.index,
-                            ).terms
-                        ]
-                    )
+                                    MOI.ConstraintFunction(),
+                                    constr.index,
+                                ).terms
+                            ]
+                        )
+                    end
                     if stype == MOI.EqualTo{Float64}
                         push!(senses, "=")
                         push!(rhs, cset.value)
@@ -535,6 +541,8 @@ function __init_JuMPSolver__()
         ) = get_constraints(
             self.data,
             with_static=with_static,
+            with_sa=with_sa,
+            with_lhs=with_lhs,
         )
 
         get_constraint_attrs(self) = [

src/solvers/learning.jl

@@ -17,6 +17,8 @@ function LearningSolver(
     mode::AbstractString = "exact",
     simulate_perfect::Bool = false,
     solve_lp::Bool = true,
+    extract_sa::Bool = true,
+    extract_lhs::Bool = true,
 )::LearningSolver
     return LearningSolver(
         miplearn.LearningSolver(
@@ -25,6 +27,8 @@ function LearningSolver(
             solve_lp=solve_lp,
             simulate_perfect=simulate_perfect,
             components=components,
+            extract_lhs=extract_lhs,
+            extract_sa=extract_sa,
         ),
         optimizer_factory,
     )
@@ -47,66 +51,83 @@ end
 
 function fit!(solver::LearningSolver, instances::Vector{<:Instance})
     @python_call solver.py.fit([instance.py for instance in instances])
+    return
+end
+
+
+function _solve(
+    solver_filename,
+    instance_filename;
+    discard_output::Bool,
+)
+    @info "solve $instance_filename"
+    solver = load_solver(solver_filename)
+    solver.py._silence_miplearn_logger()
+    stats = solve!(
+        solver, 
+        FileInstance(instance_filename),
+        discard_output = discard_output,
+    )
+    solver.py._restore_miplearn_logger()
+    GC.gc()
+    @info "solve $instance_filename [done]"
+    return stats
 end
 
 
-function parallel_solve!(solver::LearningSolver, instances::Vector{FileInstance})
-    filenames = [instance.filename for instance in instances]
+function parallel_solve!(
+    solver::LearningSolver,
+    instances::Vector{FileInstance};
+    discard_output::Bool = false,
+)
+    instance_filenames = [instance.filename for instance in instances]
     solver_filename = tempname()
     save(solver_filename, solver)
-    @sync @distributed for filename in filenames
-        s = load_solver(solver_filename)
-        solve!(s, FileInstance(filename))
-        nothing
-    end
+    return pmap(
+        instance_filename -> _solve(
+            solver_filename,
+            instance_filename,
+            discard_output = discard_output,
+        ),
+        instance_filenames,
+        on_error=identity,
+    )
 end
 
 
 function save(filename::AbstractString, solver::LearningSolver)
-    @info "Writing: $filename"
-    time = @elapsed begin
-        # Pickle solver.py
-        internal_solver = solver.py.internal_solver
-        internal_solver_prototype = solver.py.internal_solver_prototype
-        solver.py.internal_solver = nothing
-        solver.py.internal_solver_prototype = nothing
-        solver_py_filename = tempname()
-        miplearn.write_pickle_gz(solver.py, solver_py_filename, quiet=true)
-        solver_py = read(solver_py_filename)
-        solver.py.internal_solver = internal_solver
-        solver.py.internal_solver_prototype = internal_solver_prototype
-
-        jldsave(
-            filename;
-            miplearn_version="0.2",
-            solver_py=solver_py,
-            optimizer_factory=solver.optimizer_factory,
-        )
-    end
-    @info @sprintf("File written in %.2f seconds", time)
+    internal_solver = solver.py.internal_solver
+    internal_solver_prototype = solver.py.internal_solver_prototype
+    solver.py.internal_solver = nothing
+    solver.py.internal_solver_prototype = nothing
+    solver_py_filename = tempname()
+    miplearn.write_pickle_gz(solver.py, solver_py_filename)
+    solver_py = read(solver_py_filename)
+    solver.py.internal_solver = internal_solver
+    solver.py.internal_solver_prototype = internal_solver_prototype
+    jldsave(
+        filename;
+        miplearn_version="0.2",
+        solver_py=solver_py,
+        optimizer_factory=solver.optimizer_factory,
+    )
     return
 end
 
 
 function load_solver(filename::AbstractString)::LearningSolver
-    @info "Reading: $filename"
-    solver = nothing
-    time = @elapsed begin
-        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, quiet=true)
-            internal_solver = JuMPSolver(file["optimizer_factory"])
-            solver_py.internal_solver_prototype = internal_solver
-            solver = LearningSolver(
-                solver_py,
-                file["optimizer_factory"],
-            )
-        end
+    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)
+        internal_solver = JuMPSolver(file["optimizer_factory"])
+        solver_py.internal_solver_prototype = internal_solver
+        return LearningSolver(
+            solver_py,
+            file["optimizer_factory"],
+        )
     end
-    @info @sprintf("File read in %.2f seconds", time)
-    return solver
 end
 
 

src/solvers/macros.jl

@@ -4,12 +4,12 @@
 
 function init_miplearn_ext(model)::Dict
     if :miplearn ∉ keys(model.ext)
-        model.ext[:miplearn] = Dict{Symbol, Any}()
-        model.ext[:miplearn][:instance_features] = [0.0]
-        model.ext[:miplearn][:variable_features] = Dict{AbstractString, Vector{Float64}}()
-        model.ext[:miplearn][:variable_categories] = Dict{AbstractString, String}()
-        model.ext[:miplearn][:constraint_features] = Dict{AbstractString, Vector{Float64}}()
-        model.ext[:miplearn][:constraint_categories] = Dict{AbstractString, String}()
+        model.ext[:miplearn] = Dict()
+        model.ext[:miplearn]["instance_features"] = [0.0]
+        model.ext[:miplearn]["variable_features"] = Dict{AbstractString, Vector{Float64}}()
+        model.ext[:miplearn]["variable_categories"] = Dict{AbstractString, String}()
+        model.ext[:miplearn]["constraint_features"] = Dict{AbstractString, Vector{Float64}}()
+        model.ext[:miplearn]["constraint_categories"] = Dict{AbstractString, String}()
     end
     return model.ext[:miplearn]
 end
@@ -17,7 +17,7 @@ end
 
 function set_features!(m::Model, f::Array{Float64})::Nothing
     ext = init_miplearn_ext(m)
-    ext[:instance_features] = f
+    ext["instance_features"] = f
     return
 end
 
@@ -25,7 +25,7 @@ end
 function set_features!(v::VariableRef, f::Array{Float64})::Nothing
     ext = init_miplearn_ext(v.model)
     n = _get_and_check_name(v)
-    ext[:variable_features][n] = f
+    ext["variable_features"][n] = f
     return
 end
 
@@ -33,7 +33,7 @@ end
 function set_category!(v::VariableRef, category::String)::Nothing
     ext = init_miplearn_ext(v.model)
     n = _get_and_check_name(v)
-    ext[:variable_categories][n] = category
+    ext["variable_categories"][n] = category
     return
 end
 
@@ -41,7 +41,7 @@ end
 function set_features!(c::ConstraintRef, f::Array{Float64})::Nothing
     ext = init_miplearn_ext(c.model)
     n = _get_and_check_name(c)
-    ext[:constraint_features][n] = f
+    ext["constraint_features"][n] = f
     return
 end
 
@@ -49,7 +49,7 @@ end
 function set_category!(c::ConstraintRef, category::String)::Nothing
     ext = init_miplearn_ext(c.model)
     n = _get_and_check_name(c)
-    ext[:constraint_categories][n] = category
+    ext["constraint_categories"][n] = category
     return
 end
 

src/utils/benchmark.jl

@@ -9,61 +9,60 @@ using DataFrames
 mutable struct BenchmarkRunner
     solvers::Dict
     results::Union{Nothing,DataFrame}
-end
-
+    py::PyCall.PyObject
 
-function BenchmarkRunner(; solvers::Dict)
-    return BenchmarkRunner(
-        solvers,
-        nothing,  # results
-    )
+    function BenchmarkRunner(; solvers::Dict)
+        return new(
+            solvers,
+            nothing,  # results
+            miplearn.BenchmarkRunner(
+                Dict(
+                    sname => solver.py
+                    for (sname, solver) in solvers
+                )
+            )
+        )
+    end
 end
 
-
 function parallel_solve!(
     runner::BenchmarkRunner,
     instances::Vector{FileInstance};
     n_trials::Int = 3,
 )::Nothing
+    instances = repeat(instances, n_trials)
     for (solver_name, solver) in runner.solvers
-        @info "Benchmarking: $solver_name"
-        for i in 1:n_trials
-            for instance in instances
-                stats = solve!(solver, instance, discard_output=true)
-                instance.py.free()
-                stats["Solver"] = solver_name
-                stats = Dict(k => isnothing(v) ? missing : v for (k, v) in stats)
-                if runner.results === nothing
-                    runner.results = DataFrame(stats)
-                else
-                    push!(runner.results, stats, cols=:union)
-                end
+        @info "benchmark $solver_name"
+        stats = parallel_solve!(solver, instances, discard_output=true)
+        for (i, s) in enumerate(stats)
+            s["Solver"] = solver_name
+            s["Instance"] = instances[i].filename
+            s = Dict(k => isnothing(v) ? missing : v for (k, v) in s)
+            if runner.results === nothing
+                runner.results = DataFrame(s)
+            else
+                push!(runner.results, s, cols=:union)
             end
         end
+        @info "benchmark $solver_name [done]"
     end
 end
 
-
 function fit!(
     runner::BenchmarkRunner,
     instances::Vector{FileInstance}
 )::Nothing
-    for (solver_name, solver) in runner.solvers
-        fit!(solver, instances)
-    end
+    @python_call runner.py.fit([instance.py for instance in instances])
 end
 
-
 function write_csv!(
     runner::BenchmarkRunner,
     filename::AbstractString,
 )::Nothing
-    @info "Writing: $filename"
     CSV.write(filename, runner.results)
     return
 end
 
-
 export BenchmarkRunner,
        parallel_solve!,
        fit!,

src/utils/log.jl

@@ -44,16 +44,22 @@ function handle_message(logger::TimeLogger,
         color = :light_green
     end
     
+    flush(stdout)
+    flush(stderr)
+    Base.Libc.flush_cstdio()
     if level >= logger.screen_log_level
         printstyled(time_string, color=color)
         println(message)
     end
-    if logger.file != nothing && level >= logger.io_log_level
+    if logger.file !== nothing && level >= logger.io_log_level
         write(logger.file, time_string)
         write(logger.file, message)
         write(logger.file, "\n")
         flush(logger.file)
     end
+    flush(stdout)
+    flush(stderr)
+    Base.Libc.flush_cstdio()
 end
 
 function setup_logger()

test/fixtures/knapsack.jl

@@ -28,15 +28,15 @@ function build_knapsack_model()
     end
 
     # Should store ML information
-    @test model.ext[:miplearn][:variable_features]["x[1]"] == [1.0, 5.0]
-    @test model.ext[:miplearn][:variable_features]["x[2]"] == [2.0, 6.0]
-    @test model.ext[:miplearn][:variable_features]["x[3]"] == [3.0, 7.0]
-    @test model.ext[:miplearn][:variable_categories]["x[1]"] == "type-1"
-    @test model.ext[:miplearn][:variable_categories]["x[2]"] == "type-2"
-    @test model.ext[:miplearn][:variable_categories]["x[3]"] == "type-3"
-    @test model.ext[:miplearn][:constraint_features]["c1"] == [1.0, 2.0, 3.0]
-    @test model.ext[:miplearn][:constraint_categories]["c1"] == "c1"
-    @test model.ext[:miplearn][:instance_features] == [5.0]    
+    @test model.ext[:miplearn]["variable_features"]["x[1]"] == [1.0, 5.0]
+    @test model.ext[:miplearn]["variable_features"]["x[2]"] == [2.0, 6.0]
+    @test model.ext[:miplearn]["variable_features"]["x[3]"] == [3.0, 7.0]
+    @test model.ext[:miplearn]["variable_categories"]["x[1]"] == "type-1"
+    @test model.ext[:miplearn]["variable_categories"]["x[2]"] == "type-2"
+    @test model.ext[:miplearn]["variable_categories"]["x[3]"] == "type-3"
+    @test model.ext[:miplearn]["constraint_features"]["c1"] == [1.0, 2.0, 3.0]
+    @test model.ext[:miplearn]["constraint_categories"]["c1"] == "c1"
+    @test model.ext[:miplearn]["instance_features"] == [5.0]    
 
     return model
 end

test/instance/file_test.jl

@@ -19,6 +19,6 @@ using Cbc
         solve!(solver, file_instance)
 
         loaded = load_instance(filename)
-        @test length(loaded.py.samples) == 1
+        @test length(loaded.samples) == 1
     end    
 end

test/solvers/learning_test.jl

@@ -13,7 +13,7 @@ using MIPLearn
         instance = JuMPInstance(model)
         stats = solve!(solver, instance)
         @test stats["mip_lower_bound"] == 11.0
-        @test length(instance.py.samples) == 1
+        @test length(instance.samples) == 1
         fit!(solver, [instance])
         solve!(solver, instance)
     end
@@ -41,6 +41,6 @@ using MIPLearn
         solver = LearningSolver(Cbc.Optimizer)
         solve!(solver, instance, discard_output=true)
         loaded = load_instance(instance.filename)
-        @test length(loaded.py.samples) == 0
+        @test length(loaded.samples) == 0
     end
 end

test/utils/benchmark_test.jl

@@ -8,17 +8,19 @@ using DataFrames
 
 
 @testset "BenchmarkRunner" begin
-    # Initialize instances and generate training data
+    @info "Building training data..."
     instances = [
         build_knapsack_file_instance(),
         build_knapsack_file_instance(),
     ]
-    parallel_solve!(
+    stats = parallel_solve!(
         LearningSolver(Cbc.Optimizer),
         instances,
     )
+    @test length(stats) == 2
+    @test stats[1] !== nothing
+    @test stats[2] !== nothing
 
-    # Fit and benchmark
     benchmark = BenchmarkRunner(
         solvers=Dict(
             "baseline" => LearningSolver(
@@ -34,13 +36,15 @@ using DataFrames
             ),
         ),
     )
+    @info "Fitting..."
     fit!(benchmark, instances)
-    parallel_solve!(benchmark, instances, n_trials=1)
 
-    # Write CSV
+    @info "Benchmarking..."
+    parallel_solve!(benchmark, instances, n_trials=2)
+
     csv_filename = tempname()
     write_csv!(benchmark, csv_filename)
     @test isfile(csv_filename)
     csv = DataFrame(CSV.File(csv_filename))
-    @test size(csv)[1] == 6
+    @test size(csv)[1] == 12
 end