ANL-CEEESA/MIPLearn.jl
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Replace tuples; make it work with plain JuMP models

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This commit is contained in:
Alinson S. Xavier
2021-05-20 12:02:29 -05:00
parent 241d1d4157
commit f94eb598c9
5 changed files with 99 additions and 76 deletions
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2
deps/build.jl vendored
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@@ -5,7 +5,7 @@ function install_miplearn()
Conda.update()
pip = joinpath(dirname(pyimport("sys").executable), "pip")
isfile(pip) || error("$pip: invalid path")
run(`$pip install miplearn==0.2.0.dev8`)
run(`$pip install miplearn==0.2.0.dev9`)
end
install_miplearn()

12
src/modeling/jump_instance.jl
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@@ -7,6 +7,7 @@ using JuMP
@pydef mutable struct PyJuMPInstance <: miplearn.Instance
function __init__(self, model)
init_miplearn_ext(model)
self.model = model
self.samples = []
end
@@ -22,25 +23,25 @@ using JuMP
function get_variable_features(self, var_name)
model = self.model
v = variable_by_name(model, var_name)
return model.ext[:miplearn][:variable_features][v]
return get(model.ext[:miplearn][:variable_features], v, [0.0])
end
function get_variable_category(self, var_name)
model = self.model
v = variable_by_name(model, var_name)
return model.ext[:miplearn][:variable_categories][v]
return get(model.ext[:miplearn][:variable_categories], v, "default")
end
function get_constraint_features(self, cname)
model = self.model
c = constraint_by_name(model, cname)
return model.ext[:miplearn][:constraint_features][c]
return get(model.ext[:miplearn][:constraint_features], c, [0.0])
end
function get_constraint_category(self, cname)
model = self.model
c = constraint_by_name(model, cname)
return model.ext[:miplearn][:constraint_categories][c]
return get(model.ext[:miplearn][:constraint_categories], c, "default")
end
end
@@ -50,7 +51,8 @@ struct JuMPInstance
end
function JuMPInstance(model::Model)
function JuMPInstance(model)
model isa Model || error("model should be a JuMP.Model. Found $(typeof(model)) instead.")
return JuMPInstance(PyJuMPInstance(model))
end

78
src/modeling/jump_solver.jl
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@@ -316,49 +316,49 @@ function get_variables(
values, rc = nothing, nothing
# Variable names
names = Tuple(JuMP.name.(vars))
names = JuMP.name.(vars)
# Primal values
if !isempty(data.solution)
values = Tuple([data.solution[v] for v in vars])
values = [data.solution[v] for v in vars]
end
if with_static
# Lower bounds
lb = Tuple(
lb = [
JuMP.is_binary(v) ? 0.0 :
JuMP.has_lower_bound(v) ? JuMP.lower_bound(v) :
-Inf
for v in vars
)
]
# Upper bounds
ub = Tuple(
ub = [
JuMP.is_binary(v) ? 1.0 :
JuMP.has_upper_bound(v) ? JuMP.upper_bound(v) :
Inf
for v in vars
)
]
# Variable types
types = Tuple(
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 = Tuple(
obj_coeffs = [
v ∈ keys(obj.terms) ? obj.terms[v] : 0.0
for v in vars
)
]
end
rc = isempty(data.reduced_costs) ? nothing : Tuple(data.reduced_costs)
rc = isempty(data.reduced_costs) ? nothing : data.reduced_costs
return miplearn.features.VariableFeatures(
vf = miplearn.features.VariableFeatures(
names=names,
lower_bounds=lb,
upper_bounds=ub,
@@ -367,6 +367,7 @@ function get_variables(
reduced_costs=rc,
values=values,
)
return vf
end
@@ -406,7 +407,7 @@ function get_constraints(
if ftype == JuMP.AffExpr
push!(
lhs,
Tuple(
[
(
MOI.get(
constr.model.moi_backend,
@@ -420,7 +421,7 @@ function get_constraints(
MOI.ConstraintFunction(),
constr.index,
).terms
)
]
)
if stype == MOI.EqualTo{Float64}
push!(senses, "=")
@@ -441,17 +442,12 @@ function get_constraints(
end
end
function to_tuple(x)
x !== nothing || return nothing
return Tuple(x)
end
return miplearn.features.ConstraintFeatures(
names=to_tuple(names),
senses=to_tuple(senses),
lhs=to_tuple(lhs),
rhs=to_tuple(rhs),
dual_values=to_tuple(dual_values),
names=names,
senses=senses,
lhs=lhs,
rhs=rhs,
dual_values=dual_values,
)
end
@@ -471,23 +467,35 @@ end
)
end
add_constraints(self, cf) =
function add_constraints(self, cf)
lhs = cf.lhs
if lhs isa Matrix
# Undo incorrect automatic conversion performed by PyCall
lhs = [col[:] for col in eachcol(lhs)]
end
add_constraints(
self.data,
lhs=[[term for term in constr] for constr in cf.lhs],
rhs=[r for r in cf.rhs],
senses=[s for s in cf.senses],
names=[n for n in cf.names],
lhs=lhs,
rhs=cf.rhs,
senses=cf.senses,
names=cf.names,
)
end
are_constraints_satisfied(self, cf; tol=1e-5) =
tuple(are_constraints_satisfied(
function are_constraints_satisfied(self, cf; tol=1e-5)
lhs = cf.lhs
if lhs isa Matrix
# Undo incorrect automatic conversion performed by PyCall
lhs = [col[:] for col in eachcol(lhs)]
end
return are_constraints_satisfied(
self.data,
lhs=[[term for term in constr] for constr in cf.lhs],
rhs=[r for r in cf.rhs],
senses=[s for s in cf.senses],
lhs=lhs,
rhs=cf.rhs,
senses=cf.senses,
tol=tol,
)...)
)
end
build_test_instance_infeasible(self) =
build_test_instance_infeasible()

1
src/modeling/macros.jl
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@@ -5,6 +5,7 @@
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{VariableRef, Vector{Float64}}()
model.ext[:miplearn][:variable_categories] = Dict{VariableRef, String}()
model.ext[:miplearn][:constraint_features] = Dict{ConstraintRef, Vector{Float64}}()

82
test/modeling/learning_solver_test.jl
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@@ -6,43 +6,55 @@ using JuMP
using MIPLearn
using Gurobi
@testset "macros" begin
weights = [1.0, 2.0, 3.0]
prices = [5.0, 6.0, 7.0]
capacity = 3.0
@testset "LearningSolver" begin
@testset "model with annotations" begin
# Create standard JuMP model
weights = [1.0, 2.0, 3.0]
prices = [5.0, 6.0, 7.0]
capacity = 3.0
model = Model()
# Create standard JuMP model
model = 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)
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)
# 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
@feature(x[i], [weights[i]; prices[i]])
@category(x[i], "type-$i")
# Add ML information to the model
@feature(model, [5.0])
@feature(c1, [1.0, 2.0, 3.0])
@category(c1, "c1")
for i in 1:n
@feature(x[i], [weights[i]; prices[i]])
@category(x[i], "type-$i")
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]
solver = LearningSolver(Gurobi.Optimizer)
instance = JuMPInstance(model)
stats = solve!(solver, instance)
@test stats["mip_lower_bound"] == 11.0
@test length(instance.py.samples) == 1
fit!(solver, [instance])
solve!(solver, instance)
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]
solver = LearningSolver(Gurobi.Optimizer)
instance = JuMPInstance(model)
stats = solve!(solver, instance)
@test stats["mip_lower_bound"] == 11.0
@test length(instance.py.samples) == 1
fit!(solver, [instance])
solve!(solver, instance)
@testset "plain model" begin
model = Model()
@variable(model, x, Bin)
@variable(model, y, Bin)
@objective(model, Max, x + y)
solver = LearningSolver(Gurobi.Optimizer)
instance = JuMPInstance(model)
stats = solve!(solver, instance)
end
end