Make compatible with MIPLearn 5b3a56f0; reformat source code

Makefile

@@ -0,0 +1,25 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+JULIA := julia --color=yes --project=@.
+VERSION := 0.2
+
+build/sysimage.so: src/utils/sysimage.jl Project.toml Manifest.toml
+	mkdir -p build
+	$(JULIA) --trace-compile=build/precompile.jl test/runtests.jl
+	$(JULIA) src/utils/sysimage.jl
+
+clean:
+	rm -rf build/*
+
+test: build/sysimage.so
+	$(JULIA) --sysimage build/sysimage.so test/runtests.jl
+
+format:
+	julia -e 'using JuliaFormatter; format(["src", "test", "benchmark"], verbose=true);'
+
+install-deps:
+	julia -e 'using Pkg; Pkg.add(PackageSpec(name="JuliaFormatter", version="0.14.4"))'
+
+.PHONY: docs test format install-deps

Manifest.toml

@@ -361,6 +361,12 @@ git-tree-sha1 = "6a9967c4394858f38b7fc49787b983ba3847e73d"
 uuid = "7da25872-d9ce-5375-a4d3-7a845f58efdd"
 version = "0.108.6+2"
 
+[[PackageCompiler]]
+deps = ["Libdl", "Pkg", "UUIDs"]
+git-tree-sha1 = "bb40ed7cb3aac2b4cdf42f898c26a58ab797ac62"
+uuid = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
+version = "1.3.0"
+
 [[Parsers]]
 deps = ["Dates"]
 git-tree-sha1 = "c8abc88faa3f7a3950832ac5d6e690881590d6dc"

Project.toml

@@ -15,6 +15,7 @@ JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
+PackageCompiler = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
 TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"

src/MIPLearn.jl

@@ -44,13 +44,28 @@ function __init__()
     __init_PyFileInstance__()
     __init_PyJuMPInstance__()
     __init_JuMPSolver__()
+
+    py"""
+    import numpy as np
+
+    def to_str_array(values):
+        if values is None:
+            return None
+        return np.array(values, dtype="S")
+
+    def from_str_array(values):
+        return [v.decode() for v in values]
+    """
 end
 
+to_str_array(values) = py"to_str_array"(values)
+from_str_array(values) = py"from_str_array"(values)
+
 export DynamicLazyConstraintsComponent,
-       UserCutsComponent,
-       ObjectiveValueComponent,
-       PrimalSolutionComponent,
-       StaticLazyConstraintsComponent,
-       MinPrecisionThreshold
+    UserCutsComponent,
+    ObjectiveValueComponent,
+    PrimalSolutionComponent,
+    StaticLazyConstraintsComponent,
+    MinPrecisionThreshold
 
 end # module

src/instance/abstract.jl

@@ -2,7 +2,4 @@
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-abstract type Instance
-end
-
-
+abstract type Instance end

src/instance/file.jl

@@ -6,7 +6,7 @@ import Base: flush
 
 mutable struct FileInstance <: Instance
     py::Union{Nothing,PyCall.PyObject}
-    loaded::Union{Nothing, JuMPInstance}
+    loaded::Union{Nothing,JuMPInstance}
     filename::AbstractString
     h5::PyCall.PyObject
 
@@ -21,9 +21,14 @@ 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_variable_features(instance::FileInstance, names) =
+    get_variable_features(instance.loaded, names)
+get_variable_categories(instance::FileInstance, names) =
+    get_variable_categories(instance.loaded, names)
+get_constraint_features(instance::FileInstance, names) =
+    get_constraint_features(instance.loaded, names)
+get_constraint_categories(instance::FileInstance, names) =
+    get_constraint_categories(instance.loaded, names)
 
 function get_samples(instance::FileInstance)
     return [instance.h5]
@@ -33,10 +38,6 @@ function create_sample!(instance::FileInstance)
     return instance.h5
 end
 
-function get_constraint_categories(instance::FileInstance)
-    return get_constraint_categories(instance.loaded)
-end
-
 function load(instance::FileInstance)
     if instance.loaded === nothing
         instance.loaded = load_instance(instance.filename)
@@ -49,8 +50,7 @@ function free(instance::FileInstance)
     GC.gc()
 end
 
-function flush(instance::FileInstance)
-end
+function flush(instance::FileInstance) end
 
 function __init_PyFileInstance__()
     @pydef mutable struct Class <: miplearn.Instance
@@ -59,10 +59,14 @@ function __init_PyFileInstance__()
         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_variable_features(self, names) =
+            get_variable_features(self.jl, from_str_array(names))
+        get_variable_categories(self, names) =
+            to_str_array(get_variable_categories(self.jl, from_str_array(names)))
+        get_constraint_features(self, names) =
+            get_constraint_features(self.jl, from_str_array(names))
+        get_constraint_categories(self, names) =
+            to_str_array(get_constraint_categories(self.jl, from_str_array(names)))
         get_samples(self) = get_samples(self.jl)
         create_sample(self) = create_sample!(self.jl)
         load(self) = load(self.jl)

src/instance/jump.jl

@@ -38,11 +38,44 @@ function to_model(instance::JuMPInstance)::JuMP.Model
     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"]
+function get_instance_features(instance::JuMPInstance)::Union{Vector{Float64},Nothing}
+    return instance.ext["instance_features"]
+end
+
+function _concat_features(dict, names)::Matrix{Float64}
+    if isempty(dict)
+        return zeros(length(names), 1)
+    end
+    ncols = length(first(dict).second)
+    return vcat([n in keys(dict) ? dict[n]' : zeros(ncols) for n in names]...)
+end
+
+function _concat_categories(dict, names)::Vector{String}
+    return String[n in keys(dict) ? dict[n] : n for n in names]
+end
+
+function get_variable_features(
+    instance::JuMPInstance,
+    names::Vector{String},
+)::Matrix{Float64}
+    return _concat_features(instance.ext["variable_features"], names)
+end
+
+function get_variable_categories(instance::JuMPInstance, names::Vector{String})
+    return _concat_categories(instance.ext["variable_categories"], names)
+end
+
+function get_constraint_features(
+    instance::JuMPInstance,
+    names::Vector{String},
+)::Matrix{Float64}
+    return _concat_features(instance.ext["constraint_features"], names)
+end
+
+function get_constraint_categories(instance::JuMPInstance, names::Vector{String})
+    return _concat_categories(instance.ext["constraint_categories"], names)
+end
+
 get_samples(instance::JuMPInstance) = instance.samples
 
 function create_sample!(instance::JuMPInstance)
@@ -58,10 +91,14 @@ function __init_PyJuMPInstance__()
         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_variable_features(self, names) =
+            get_variable_features(self.jl, from_str_array(names))
+        get_variable_categories(self, names) =
+            to_str_array(get_variable_categories(self.jl, from_str_array(names)))
+        get_constraint_features(self, names) =
+            get_constraint_features(self.jl, from_str_array(names))
+        get_constraint_categories(self, names) =
+            to_str_array(get_constraint_categories(self.jl, from_str_array(names)))
         get_samples(self) = get_samples(self.jl)
         create_sample(self) = create_sample!(self.jl)
     end
@@ -76,7 +113,7 @@ function save(filename::AbstractString, instance::JuMPInstance)::Nothing
     mps = read(mps_filename)
 
     # Generate HDF5
-    h5 = Hdf5Sample(filename, mode="w")
+    h5 = Hdf5Sample(filename, mode = "w")
     h5.put_scalar("miplearn_version", "0002")
     h5.put_bytes("mps", mps)
     h5.put_scalar("jump_ext", JSON.json(model.ext[:miplearn]))
@@ -89,7 +126,7 @@ function _check_miplearn_version(h5)
         "The file you are trying to load has been generated by " *
         "MIPLearn $(v) and you are currently running MIPLearn 0002 " *
         "Reading files generated by different versions of MIPLearn is " *
-        "not currently supported."
+        "not currently supported.",
     )
 end
 

src/solvers/jump.jl

@@ -9,9 +9,8 @@ using MathOptInterface
 using TimerOutputs
 const MOI = MathOptInterface
 
-
 mutable struct JuMPSolverData
-    optimizer_factory
+    optimizer_factory::Any
     varname_to_var::Dict{String,VariableRef}
     cname_to_constr::Dict{String,JuMP.ConstraintRef}
     instance::Union{Nothing,PyObject}
@@ -29,7 +28,7 @@ end
 Optimizes a given JuMP model while capturing the solver log, then returns that log.
 If tee=true, prints the solver log to the standard output as the optimization takes place.
 """
-function _optimize_and_capture_output!(model; tee::Bool=false)
+function _optimize_and_capture_output!(model; tee::Bool = false)
     logname = tempname()
     logfile = open(logname, "w")
     redirect_stdout(logfile) do
@@ -50,10 +49,7 @@ end
 
 function _update_solution!(data::JuMPSolverData)
     vars = JuMP.all_variables(data.model)
-    data.solution = Dict(
-        var => JuMP.value(var)
-        for var in vars
-    )
+    data.solution = Dict(var => JuMP.value(var) for var in vars)
 
     # Reduced costs
     if has_duals(data.model)
@@ -89,7 +85,7 @@ end
 
 function add_constraints(
     data::JuMPSolverData;
-    lhs::Vector{Vector{Tuple{String, Float64}}},
+    lhs::Vector{Vector{Tuple{String,Float64}}},
     rhs::Vector{Float64},
     senses::Vector{String},
     names::Vector{String},
@@ -104,8 +100,10 @@ function add_constraints(
             constr = @constraint(data.model, lhs_expr <= rhs[i])
         elseif sense == ">"
             constr = @constraint(data.model, lhs_expr >= rhs[i])
-        else
+        elseif sense == "="
             constr = @constraint(data.model, lhs_expr == rhs[i])
+        else
+            error("unknown sense: $(sense)")
         end
         set_name(constr, names[i])
         data.cname_to_constr[names[i]] = constr
@@ -116,10 +114,10 @@ end
 
 function are_constraints_satisfied(
     data::JuMPSolverData;
-    lhs::Vector{Vector{Tuple{String, Float64}}},
+    lhs::Vector{Vector{Tuple{String,Float64}}},
     rhs::Vector{Float64},
     senses::Vector{String},
-    tol::Float64=1e-5,
+    tol::Float64 = 1e-5,
 )::Vector{Bool}
     result = []
     for (i, sense) in enumerate(senses)
@@ -132,8 +130,10 @@ function are_constraints_satisfied(
             push!(result, lhs_value <= rhs[i] + tol)
         elseif sense == ">"
             push!(result, lhs_value >= rhs[i] - tol)
-        else
+        elseif sense == "="
             push!(result, abs(lhs_value - rhs[i]) <= tol)
+        else
+            error("unknown sense: $(sense)")
         end
     end
     return result
@@ -148,9 +148,9 @@ function build_test_instance_knapsack()
     model = Model()
     n = length(weights)
     @variable(model, x[0:n-1], Bin)
-    @variable(model, z, lower_bound=0.0, upper_bound=capacity)
-    @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)
+    @variable(model, z, lower_bound = 0.0, upper_bound = capacity)
+    @objective(model, Max, sum(x[i-1] * prices[i] for i = 1:n))
+    @constraint(model, eq_capacity, sum(x[i-1] * weights[i] for i = 1:n) - z == 0)
 
     return JuMPInstance(model).py
 end
@@ -165,10 +165,7 @@ function build_test_instance_infeasible()
 end
 
 
-function remove_constraints(
-    data::JuMPSolverData,
-    names::Vector{String},
-)::Nothing
+function remove_constraints(data::JuMPSolverData, names::Vector{String})::Nothing
     for name in names
         constr = data.cname_to_constr[name]
         delete(data.model, constr)
@@ -178,17 +175,13 @@ function remove_constraints(
 end
 
 
-function solve(
-    data::JuMPSolverData;
-    tee::Bool=false,
-    iteration_cb=nothing,
-)
+function solve(data::JuMPSolverData; tee::Bool = false, iteration_cb = nothing)
     model = data.model
     wallclock_time = 0
     log = ""
     while true
         wallclock_time += @elapsed begin
-            log *= _optimize_and_capture_output!(model, tee=tee)
+            log *= _optimize_and_capture_output!(model, tee = tee)
         end
         if iteration_cb !== nothing
             iteration_cb() || break
@@ -215,18 +208,18 @@ function solve(
         upper_bound = dual_bound
     end
     return miplearn.solvers.internal.MIPSolveStats(
-        mip_lower_bound=lower_bound,
-        mip_upper_bound=upper_bound,
-        mip_sense=sense,
-        mip_wallclock_time=wallclock_time,
-        mip_nodes=1,
-        mip_log=log,
-        mip_warm_start_value=nothing,
+        mip_lower_bound = lower_bound,
+        mip_upper_bound = upper_bound,
+        mip_sense = sense,
+        mip_wallclock_time = wallclock_time,
+        mip_nodes = 1,
+        mip_log = log,
+        mip_warm_start_value = nothing,
     )
 end
 
 
-function solve_lp(data::JuMPSolverData; tee::Bool=false)
+function solve_lp(data::JuMPSolverData; tee::Bool = false)
     model, bin_vars = data.model, data.bin_vars
     for var in bin_vars
         ~is_fixed(var) || continue
@@ -242,7 +235,7 @@ function solve_lp(data::JuMPSolverData; tee::Bool=false)
         set_optimizer(model, Clp.Optimizer)
     end
     wallclock_time = @elapsed begin
-        log = _optimize_and_capture_output!(model, tee=tee)
+        log = _optimize_and_capture_output!(model, tee = tee)
     end
     if is_infeasible(data)
         data.solution = Dict()
@@ -259,9 +252,9 @@ function solve_lp(data::JuMPSolverData; tee::Bool=false)
         set_binary(var)
     end
     return miplearn.solvers.internal.LPSolveStats(
-        lp_value=obj_value,
-        lp_log=log,
-        lp_wallclock_time=wallclock_time,
+        lp_value = obj_value,
+        lp_log = log,
+        lp_wallclock_time = wallclock_time,
     )
 end
 
@@ -276,15 +269,8 @@ function set_instance!(
         model = instance.to_model()
     end
     data.model = model
-    data.bin_vars = [
-        var
-        for var in JuMP.all_variables(model)
-        if JuMP.is_binary(var)
-    ]
-    data.varname_to_var = Dict(
-        JuMP.name(var) => var
-        for var in JuMP.all_variables(model)
-    )
+    data.bin_vars = [var for var in JuMP.all_variables(model) if JuMP.is_binary(var)]
+    data.varname_to_var = Dict(JuMP.name(var) => var for var in JuMP.all_variables(model))
     JuMP.set_optimizer(model, data.optimizer_factory)
     data.cname_to_constr = Dict()
     for (ftype, stype) in JuMP.list_of_constraint_types(model)
@@ -302,7 +288,7 @@ function fix!(data::JuMPSolverData, solution)
     for (varname, value) in solution
         value !== nothing || continue
         var = data.varname_to_var[varname]
-        JuMP.fix(var, value, force=true)
+        JuMP.fix(var, value, force = true)
     end
 end
 
@@ -317,17 +303,12 @@ end
 
 
 function is_infeasible(data::JuMPSolverData)
-    return JuMP.termination_status(data.model) in [
-        MOI.INFEASIBLE,
-        MOI.INFEASIBLE_OR_UNBOUNDED,
-    ]
+    return JuMP.termination_status(data.model) in
+           [MOI.INFEASIBLE, MOI.INFEASIBLE_OR_UNBOUNDED]
 end
 
 
-function get_variables(
-    data::JuMPSolverData;
-    with_static::Bool,
-)
+function get_variables(data::JuMPSolverData; with_static::Bool)
     vars = JuMP.all_variables(data.model)
     lb, ub, types, obj_coeffs = nothing, nothing, nothing, nothing
     values, rc = nothing, nothing
@@ -343,46 +324,32 @@ function get_variables(
     if with_static
         # Lower bounds
         lb = [
-            JuMP.is_binary(v) ? 0.0 :
-                JuMP.has_lower_bound(v) ? JuMP.lower_bound(v) :
-                    -Inf
-            for v in vars
+            JuMP.is_binary(v) ? 0.0 : JuMP.has_lower_bound(v) ? JuMP.lower_bound(v) : -Inf for v in vars
         ]
 
         # Upper bounds
         ub = [
-            JuMP.is_binary(v) ? 1.0 :
-                JuMP.has_upper_bound(v) ? JuMP.upper_bound(v) :
-                    Inf
-            for v in vars
+            JuMP.is_binary(v) ? 1.0 : JuMP.has_upper_bound(v) ? JuMP.upper_bound(v) : Inf for v in vars
         ]
 
         # Variable types
-        types = [
-            JuMP.is_binary(v) ? "B" :
-                JuMP.is_integer(v) ? "I" :
-                    "C"
-            for v in vars
-        ]
+        types = [JuMP.is_binary(v) ? "B" : JuMP.is_integer(v) ? "I" : "C" for v in vars]
 
         # Objective function coefficients
         obj = objective_function(data.model)
-        obj_coeffs = [
-            v ∈ keys(obj.terms) ? obj.terms[v] : 0.0
-            for v in vars
-        ]
+        obj_coeffs = [v ∈ keys(obj.terms) ? obj.terms[v] : 0.0 for v in vars]
     end
 
     rc = isempty(data.reduced_costs) ? nothing : data.reduced_costs
 
     vf = miplearn.solvers.internal.Variables(
-        names=names,
-        lower_bounds=lb,
-        upper_bounds=ub,
-        types=types,
-        obj_coeffs=obj_coeffs,
-        reduced_costs=rc,
-        values=values,
+        names = to_str_array(names),
+        lower_bounds = lb,
+        upper_bounds = ub,
+        types = to_str_array(types),
+        obj_coeffs = obj_coeffs,
+        reduced_costs = rc,
+        values = values,
     )
     return vf
 end
@@ -394,26 +361,23 @@ function get_constraints(
     with_sa::Bool,
     with_lhs::Bool,
 )
-    names = []
+    names = String[]
     senses, lhs, rhs = nothing, nothing, nothing
     dual_values = nothing
 
     if !isempty(data.dual_values)
-        dual_values = []
+        dual_values = Float64[]
     end
 
     if with_static
-        senses, lhs, rhs = [], [], []
+        senses, lhs, rhs = String[], [], Float64[]
     end
 
     for (ftype, stype) in JuMP.list_of_constraint_types(data.model)
-        ftype in [JuMP.AffExpr, JuMP.VariableRef] || error("Unsupported constraint type: ($ftype, $stype)")
+        ftype in [JuMP.AffExpr, JuMP.VariableRef] ||
+            error("Unsupported constraint type: ($ftype, $stype)")
         for constr in JuMP.all_constraints(data.model, ftype, stype)
-            cset = MOI.get(
-                constr.model.moi_backend,
-                MOI.ConstraintSet(),
-                constr.index,
-            )
+            cset = MOI.get(constr.model.moi_backend, MOI.ConstraintSet(), constr.index)
             name = JuMP.name(constr)
             length(name) > 0 || continue
             push!(names, name)
@@ -429,19 +393,21 @@ function get_constraints(
                             lhs,
                             [
                                 (
-                                    MOI.get(
-                                        constr.model.moi_backend,
-                                        MOI.VariableName(),
-                                        term.variable_index
+                                    pybytes(
+                                        MOI.get(
+                                            constr.model.moi_backend,
+                                            MOI.VariableName(),
+                                            term.variable_index,
+                                        ),
                                     ),
                                     term.coefficient,
-                                )
-                                for term in MOI.get(
+                                ) for term in
+                                MOI.get(
                                     constr.model.moi_backend,
                                     MOI.ConstraintFunction(),
                                     constr.index,
                                 ).terms
-                            ]
+                            ],
                         )
                     end
                     if stype == MOI.EqualTo{Float64}
@@ -464,11 +430,11 @@ function get_constraints(
     end
 
     return miplearn.solvers.internal.Constraints(
-        names=names,
-        senses=senses,
-        lhs=lhs,
-        rhs=rhs,
-        dual_values=dual_values,
+        names = to_str_array(names),
+        senses = to_str_array(senses),
+        lhs = lhs,
+        rhs = rhs,
+        dual_values = dual_values,
     )
 end
 
@@ -497,14 +463,14 @@ function __init_JuMPSolver__()
             end
             add_constraints(
                 self.data,
-                lhs=lhs,
-                rhs=cf.rhs,
-                senses=cf.senses,
-                names=cf.names,
+                lhs = lhs,
+                rhs = cf.rhs,
+                senses = from_str_array(cf.senses),
+                names = from_str_array(cf.names),
             )
         end
 
-        function are_constraints_satisfied(self, cf; tol=1e-5)
+        function are_constraints_satisfied(self, cf; tol = 1e-5)
             lhs = cf.lhs
             if lhs isa Matrix
                 # Undo incorrect automatic conversion performed by PyCall
@@ -512,38 +478,30 @@ function __init_JuMPSolver__()
             end
             return are_constraints_satisfied(
                 self.data,
-                lhs=lhs,
-                rhs=cf.rhs,
-                senses=cf.senses,
-                tol=tol,
+                lhs = lhs,
+                rhs = cf.rhs,
+                senses = from_str_array(cf.senses),
+                tol = tol,
             )
         end
 
-        build_test_instance_infeasible(self) =
-            build_test_instance_infeasible()
+        build_test_instance_infeasible(self) = build_test_instance_infeasible()
 
-        build_test_instance_knapsack(self) =
-            build_test_instance_knapsack()
+        build_test_instance_knapsack(self) = build_test_instance_knapsack()
 
         clone(self) = JuMPSolver(self.data.optimizer_factory)
 
-        fix(self, solution) =
-            fix!(self.data, solution)
+        fix(self, solution) = fix!(self.data, solution)
 
-        get_solution(self) =
-            isempty(self.data.solution) ? nothing : self.data.solution
+        get_solution(self) = isempty(self.data.solution) ? nothing : self.data.solution
 
-        get_constraints(
-            self;
-            with_static=true,
-            with_sa=true,
-            with_lhs=true,
-        ) = get_constraints(
-            self.data,
-            with_static=with_static,
-            with_sa=with_sa,
-            with_lhs=with_lhs,
-        )
+        get_constraints(self; with_static = true, with_sa = true, with_lhs = true) =
+            get_constraints(
+                self.data,
+                with_static = with_static,
+                with_sa = with_sa,
+                with_lhs = with_lhs,
+            )
 
         get_constraint_attrs(self) = [
             # "basis_status",
@@ -560,13 +518,10 @@ function __init_JuMPSolver__()
             "user_features",
         ]
 
-        get_variables(
-            self;
-            with_static=true,
-            with_sa=true,
-        ) = get_variables(self.data; with_static=with_static)
+        get_variables(self; with_static = true, with_sa = true) =
+            get_variables(self.data; with_static = with_static)
 
-        get_variable_attrs(self) =  [
+        get_variable_attrs(self) = [
             "names",
             # "basis_status",
             "categories",
@@ -585,35 +540,24 @@ function __init_JuMPSolver__()
             "values",
         ]
 
-        is_infeasible(self) =
-            is_infeasible(self.data)
+        is_infeasible(self) = is_infeasible(self.data)
 
-        remove_constraints(self, names) =
-            remove_constraints(
-                self.data,
-                [n for n in names],
-            )
+        remove_constraints(self, names) = remove_constraints(self.data, [n for n in names])
 
-        set_instance(self, instance, model=nothing) =
-            set_instance!(self.data, instance, model=model)
+        set_instance(self, instance, model = nothing) =
+            set_instance!(self.data, instance, model = model)
 
-        set_warm_start(self, solution) =
-            set_warm_start!(self.data, solution)
+        set_warm_start(self, solution) = set_warm_start!(self.data, solution)
 
         solve(
             self;
-            tee=false,
-            iteration_cb=nothing,
-            lazy_cb=nothing,
-            user_cut_cb=nothing,
-        ) = solve(
-            self.data,
-            tee=tee,
-            iteration_cb=iteration_cb,
-        )
-        
-        solve_lp(self; tee=false) =
-            solve_lp(self.data, tee=tee)
+            tee = false,
+            iteration_cb = nothing,
+            lazy_cb = nothing,
+            user_cut_cb = nothing,
+        ) = solve(self.data, tee = tee, iteration_cb = iteration_cb)
+
+        solve_lp(self; tee = false) = solve_lp(self.data, tee = tee)
     end
     copy!(JuMPSolver, Class)
 end

src/solvers/learning.jl

@@ -8,7 +8,7 @@ using JLD2
 
 struct LearningSolver
     py::PyCall.PyObject
-    optimizer_factory
+    optimizer_factory::Any
 end
 
 
@@ -23,13 +23,13 @@ function LearningSolver(
 )::LearningSolver
     return LearningSolver(
         miplearn.LearningSolver(
-            solver=JuMPSolver(optimizer_factory),
-            mode=mode,
-            solve_lp=solve_lp,
-            simulate_perfect=simulate_perfect,
-            components=components,
-            extract_lhs=extract_lhs,
-            extract_sa=extract_sa,
+            solver = JuMPSolver(optimizer_factory),
+            mode = mode,
+            solve_lp = solve_lp,
+            simulate_perfect = simulate_perfect,
+            components = components,
+            extract_lhs = extract_lhs,
+            extract_sa = extract_sa,
         ),
         optimizer_factory,
     )
@@ -44,8 +44,8 @@ function solve!(
 )
     return @python_call solver.py.solve(
         instance.py,
-        tee=tee,
-        discard_output=discard_output,
+        tee = tee,
+        discard_output = discard_output,
     )
 end
 
@@ -56,19 +56,11 @@ function fit!(solver::LearningSolver, instances::Vector{<:Instance})
 end
 
 
-function _solve(
-    solver_filename,
-    instance_filename;
-    discard_output::Bool,
-)
+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,
-    )
+    stats = solve!(solver, FileInstance(instance_filename), discard_output = discard_output)
     solver.py._restore_miplearn_logger()
     GC.gc()
     @info "solve $instance_filename [done]"
@@ -85,13 +77,10 @@ function parallel_solve!(
     solver_filename = tempname()
     save(solver_filename, solver)
     return pmap(
-        instance_filename -> _solve(
-            solver_filename,
-            instance_filename,
-            discard_output = discard_output,
-        ),
+        instance_filename ->
+            _solve(solver_filename, instance_filename, discard_output = discard_output),
         instance_filenames,
-        on_error=identity,
+        on_error = identity,
     )
 end
 
@@ -108,9 +97,9 @@ function save(filename::AbstractString, solver::LearningSolver)
     solver.py.internal_solver_prototype = internal_solver_prototype
     jldsave(
         filename;
-        miplearn_version="0.2",
-        solver_py=solver_py,
-        optimizer_factory=solver.optimizer_factory,
+        miplearn_version = "0.2",
+        solver_py = solver_py,
+        optimizer_factory = solver.optimizer_factory,
     )
     return
 end
@@ -123,18 +112,9 @@ function load_solver(filename::AbstractString)::LearningSolver
         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"],
-        )
+        return LearningSolver(solver_py, file["optimizer_factory"])
     end
 end
 
 
-export Instance,
-       LearningSolver,
-       solve!,
-       fit!,
-       parallel_solve!,
-       save,
-       load_solver
+export Instance, LearningSolver, solve!, fit!, parallel_solve!, save, load_solver

src/solvers/macros.jl

@@ -6,10 +6,10 @@ function init_miplearn_ext(model)::Dict
     if :miplearn ∉ keys(model.ext)
         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}()
+        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
@@ -71,11 +71,10 @@ function _get_and_check_name(obj)
     n = name(obj)
     length(n) > 0 || error(
         "Features and categories can only be assigned to variables and " *
-        "constraints that have names. Unnamed model element detected."
+        "constraints that have names. Unnamed model element detected.",
     )
     return n
 end
 
 
-export @feature,
-       @category
+export @feature, @category

src/utils/benchmark.jl

@@ -16,11 +16,8 @@ mutable struct BenchmarkRunner
             solvers,
             nothing,  # results
             miplearn.BenchmarkRunner(
-                Dict(
-                    sname => solver.py
-                    for (sname, solver) in solvers
-                )
-            )
+                Dict(sname => solver.py for (sname, solver) in solvers),
+            ),
         )
     end
 end
@@ -33,7 +30,7 @@ function parallel_solve!(
     instances = repeat(instances, n_trials)
     for (solver_name, solver) in runner.solvers
         @info "benchmark $solver_name"
-        stats = parallel_solve!(solver, instances, discard_output=true)
+        stats = parallel_solve!(solver, instances, discard_output = true)
         for (i, s) in enumerate(stats)
             s["Solver"] = solver_name
             s["Instance"] = instances[i].filename
@@ -41,29 +38,20 @@ function parallel_solve!(
             if runner.results === nothing
                 runner.results = DataFrame(s)
             else
-                push!(runner.results, s, cols=:union)
+                push!(runner.results, s, cols = :union)
             end
         end
         @info "benchmark $solver_name [done]"
     end
 end
 
-function fit!(
-    runner::BenchmarkRunner,
-    instances::Vector{FileInstance}
-)::Nothing
+function fit!(runner::BenchmarkRunner, instances::Vector{FileInstance})::Nothing
     @python_call runner.py.fit([instance.py for instance in instances])
 end
 
-function write_csv!(
-    runner::BenchmarkRunner,
-    filename::AbstractString,
-)::Nothing
+function write_csv!(runner::BenchmarkRunner, filename::AbstractString)::Nothing
     CSV.write(filename, runner.results)
     return
 end
 
-export BenchmarkRunner,
-       parallel_solve!,
-       fit!,
-       write_csv!
+export BenchmarkRunner, parallel_solve!, fit!, write_csv!

src/utils/exceptions.jl

@@ -11,7 +11,7 @@ macro python_call(expr)
             return $(esc(expr))
         catch e
             if isa(e, PyCall.PyError)
-                printstyled("Uncaught Python exception:\n", bold=true, color=:red)
+                printstyled("Uncaught Python exception:\n", bold = true, color = :red)
                 traceback.print_exception(e.T, e.val, e.traceback)
             end
             rethrow()

src/utils/log.jl

@@ -7,32 +7,34 @@ using Base.CoreLogging, Logging, Printf
 
 struct TimeLogger <: AbstractLogger
     initial_time::Float64
-    file::Union{Nothing, IOStream}
-    screen_log_level
-    io_log_level
+    file::Union{Nothing,IOStream}
+    screen_log_level::Any
+    io_log_level::Any
 end
 
 function TimeLogger(;
-                    initial_time::Float64,
-                    file::Union{Nothing, IOStream} = nothing,
-                    screen_log_level = CoreLogging.Info,
-                    io_log_level = CoreLogging.Info,
-                   ) :: TimeLogger
+    initial_time::Float64,
+    file::Union{Nothing,IOStream} = nothing,
+    screen_log_level = CoreLogging.Info,
+    io_log_level = CoreLogging.Info,
+)::TimeLogger
     return TimeLogger(initial_time, file, screen_log_level, io_log_level)
 end
 
 min_enabled_level(logger::TimeLogger) = logger.io_log_level
 shouldlog(logger::TimeLogger, level, _module, group, id) = true
 
-function handle_message(logger::TimeLogger,
-                        level,
-                        message,
-                        _module,
-                        group,
-                        id,
-                        filepath,
-                        line;
-                        kwargs...)
+function handle_message(
+    logger::TimeLogger,
+    level,
+    message,
+    _module,
+    group,
+    id,
+    filepath,
+    line;
+    kwargs...,
+)
     elapsed_time = time() - logger.initial_time
     time_string = @sprintf("[%12.3f] ", elapsed_time)
 
@@ -48,7 +50,7 @@ function handle_message(logger::TimeLogger,
     flush(stderr)
     Base.Libc.flush_cstdio()
     if level >= logger.screen_log_level
-        printstyled(time_string, color=color)
+        printstyled(time_string, color = color)
         println(message)
     end
     if logger.file !== nothing && level >= logger.io_log_level
@@ -64,7 +66,7 @@ end
 
 function setup_logger()
     initial_time = time()
-    global_logger(TimeLogger(initial_time=initial_time))
+    global_logger(TimeLogger(initial_time = initial_time))
     miplearn = pyimport("miplearn")
     miplearn.setup_logger(initial_time)
 end

src/utils/sysimage.jl

@@ -0,0 +1,44 @@
+#  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 PackageCompiler
+
+using CSV
+using Cbc
+using Clp
+using Conda
+using DataFrames
+using Distributed
+using JLD2
+using JSON
+using JuMP
+using Logging
+using MathOptInterface
+using Printf
+using PyCall
+using TimerOutputs
+
+pkg = [
+    :CSV
+    :Cbc
+    :Clp
+    :Conda
+    :DataFrames
+    :Distributed
+    :JLD2
+    :JSON
+    :JuMP
+    :Logging
+    :MathOptInterface
+    :Printf
+    :PyCall
+    :TimerOutputs
+]
+
+@info "Building system image..."
+create_sysimage(
+    pkg,
+    precompile_statements_file = "build/precompile.jl",
+    sysimage_path = "build/sysimage.so",
+)

test/fixtures/knapsack.jl

@@ -15,14 +15,14 @@ function build_knapsack_model()
 
     n = length(weights)
     @variable(model, x[1:n], Bin)
-    @objective(model, Max, sum(x[i] * prices[i] for i in 1:n))
-    @constraint(model, c1, sum(x[i] * weights[i] for i in 1:n) <= capacity)
+    @objective(model, Max, sum(x[i] * prices[i] for i = 1:n))
+    @constraint(model, c1, sum(x[i] * weights[i] for i = 1:n) <= capacity)
 
     # Add ML information to the model
     @feature(model, [5.0])
     @feature(c1, [1.0, 2.0, 3.0])
     @category(c1, "c1")
-    for i in 1:n
+    for i = 1:n
         @feature(x[i], [weights[i]; prices[i]])
         @category(x[i], "type-$i")
     end

test/instance/file_test.jl

@@ -23,6 +23,6 @@ using Cbc
         solver = LearningSolver(Cbc.Optimizer)
         solve!(solver, file_instance)
 
-        @test length(h5.get_vector("mip_var_values")) == 3
+        @test length(h5.get_array("mip_var_values")) == 3
     end
 end

test/solvers/learning_test.jl

@@ -39,7 +39,7 @@ using MIPLearn
     @testset "Discard output" begin
         instance = build_knapsack_file_instance()
         solver = LearningSolver(Cbc.Optimizer)
-        solve!(solver, instance, discard_output=true)
+        solve!(solver, instance, discard_output = true)
         loaded = load_instance(instance.filename)
         @test length(loaded.samples) == 0
     end

test/utils/benchmark_test.jl

@@ -9,38 +9,24 @@ using DataFrames
 
 @testset "BenchmarkRunner" begin
     @info "Building training data..."
-    instances = [
-        build_knapsack_file_instance(),
-        build_knapsack_file_instance(),
-    ]
-    stats = parallel_solve!(
-        LearningSolver(Cbc.Optimizer),
-        instances,
-    )
+    instances = [build_knapsack_file_instance(), build_knapsack_file_instance()]
+    stats = parallel_solve!(LearningSolver(Cbc.Optimizer), instances)
     @test length(stats) == 2
     @test stats[1] !== nothing
     @test stats[2] !== nothing
 
     benchmark = BenchmarkRunner(
-        solvers=Dict(
-            "baseline" => LearningSolver(
-                Cbc.Optimizer,
-                components=[],
-            ),
-            "ml-exact" => LearningSolver(
-                Cbc.Optimizer,
-            ),
-            "ml-heur" => LearningSolver(
-                Cbc.Optimizer,
-                mode="heuristic",
-            ),
+        solvers = Dict(
+            "baseline" => LearningSolver(Cbc.Optimizer, components = []),
+            "ml-exact" => LearningSolver(Cbc.Optimizer),
+            "ml-heur" => LearningSolver(Cbc.Optimizer, mode = "heuristic"),
         ),
     )
     @info "Fitting..."
     fit!(benchmark, instances)
 
     @info "Benchmarking..."
-    parallel_solve!(benchmark, instances, n_trials=2)
+    parallel_solve!(benchmark, instances, n_trials = 2)
 
     csv_filename = tempname()
     write_csv!(benchmark, csv_filename)