You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
157 lines
4.6 KiB
157 lines
4.6 KiB
# 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.
|
|
|
|
from miplearn.features import (
|
|
FeaturesExtractor,
|
|
InstanceFeatures,
|
|
VariableFeatures,
|
|
ConstraintFeatures,
|
|
)
|
|
from miplearn.solvers.gurobi import GurobiSolver
|
|
from miplearn.solvers.tests import (
|
|
assert_equals,
|
|
_round,
|
|
)
|
|
import numpy as np
|
|
|
|
inf = float("inf")
|
|
|
|
|
|
def test_knapsack() -> None:
|
|
solver = GurobiSolver()
|
|
instance = solver.build_test_instance_knapsack()
|
|
model = instance.to_model()
|
|
solver.set_instance(instance, model)
|
|
solver.solve_lp()
|
|
|
|
features = FeaturesExtractor().extract(instance, solver)
|
|
assert features.variables is not None
|
|
assert features.instance is not None
|
|
|
|
assert_equals(
|
|
_round(features.variables),
|
|
VariableFeatures(
|
|
names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
|
|
basis_status=["U", "B", "U", "L", "U"],
|
|
categories=["default", "default", "default", "default", None],
|
|
lower_bounds=[0.0, 0.0, 0.0, 0.0, 0.0],
|
|
obj_coeffs=[505.0, 352.0, 458.0, 220.0, 0.0],
|
|
reduced_costs=[193.615385, 0.0, 187.230769, -23.692308, 13.538462],
|
|
sa_lb_down=[-inf, -inf, -inf, -0.111111, -inf],
|
|
sa_lb_up=[1.0, 0.923077, 1.0, 1.0, 67.0],
|
|
sa_obj_down=[311.384615, 317.777778, 270.769231, -inf, -13.538462],
|
|
sa_obj_up=[inf, 570.869565, inf, 243.692308, inf],
|
|
sa_ub_down=[0.913043, 0.923077, 0.9, 0.0, 43.0],
|
|
sa_ub_up=[2.043478, inf, 2.2, inf, 69.0],
|
|
types=["B", "B", "B", "B", "C"],
|
|
upper_bounds=[1.0, 1.0, 1.0, 1.0, 67.0],
|
|
user_features=[
|
|
[23.0, 505.0],
|
|
[26.0, 352.0],
|
|
[20.0, 458.0],
|
|
[18.0, 220.0],
|
|
None,
|
|
],
|
|
values=[1.0, 0.923077, 1.0, 0.0, 67.0],
|
|
alvarez_2017=[
|
|
[1.0, 0.32899, 0.0, 0.0, 1.0, 1.0, 5.265874, 46.051702],
|
|
[1.0, 0.229316, 0.0, 0.076923, 1.0, 1.0, 3.532875, 5.388476],
|
|
[1.0, 0.298371, 0.0, 0.0, 1.0, 1.0, 5.232342, 46.051702],
|
|
[1.0, 0.143322, 0.0, 0.0, 1.0, -1.0, 46.051702, 3.16515],
|
|
[0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0],
|
|
],
|
|
),
|
|
)
|
|
assert_equals(
|
|
_round(features.constraints),
|
|
ConstraintFeatures(
|
|
basis_status=["N"],
|
|
categories=["eq_capacity"],
|
|
dual_values=[13.538462],
|
|
names=["eq_capacity"],
|
|
lazy=[False],
|
|
lhs=[
|
|
[
|
|
("x[0]", 23.0),
|
|
("x[1]", 26.0),
|
|
("x[2]", 20.0),
|
|
("x[3]", 18.0),
|
|
("z", -1.0),
|
|
],
|
|
],
|
|
rhs=[0.0],
|
|
sa_rhs_down=[-24.0],
|
|
sa_rhs_up=[2.0],
|
|
senses=["="],
|
|
slacks=[0.0],
|
|
user_features=[[0.0]],
|
|
),
|
|
)
|
|
assert_equals(
|
|
features.instance,
|
|
InstanceFeatures(
|
|
user_features=[67.0, 21.75],
|
|
lazy_constraint_count=0,
|
|
),
|
|
)
|
|
|
|
|
|
def test_constraint_getindex() -> None:
|
|
cf = ConstraintFeatures(
|
|
names=["c1", "c2", "c3"],
|
|
rhs=[1.0, 2.0, 3.0],
|
|
senses=["=", "<", ">"],
|
|
lhs=[
|
|
[
|
|
("x1", 1.0),
|
|
("x2", 1.0),
|
|
],
|
|
[
|
|
("x2", 2.0),
|
|
("x3", 2.0),
|
|
],
|
|
[
|
|
("x3", 3.0),
|
|
("x4", 3.0),
|
|
],
|
|
],
|
|
)
|
|
assert_equals(
|
|
cf[[True, False, True]],
|
|
ConstraintFeatures(
|
|
names=["c1", "c3"],
|
|
rhs=[1.0, 3.0],
|
|
senses=["=", ">"],
|
|
lhs=[
|
|
[
|
|
("x1", 1.0),
|
|
("x2", 1.0),
|
|
],
|
|
[
|
|
("x3", 3.0),
|
|
("x4", 3.0),
|
|
],
|
|
],
|
|
),
|
|
)
|
|
|
|
|
|
def test_assert_equals() -> None:
|
|
assert_equals("hello", "hello")
|
|
assert_equals([1.0, 2.0], [1.0, 2.0])
|
|
assert_equals(
|
|
np.array([1.0, 2.0]),
|
|
np.array([1.0, 2.0]),
|
|
)
|
|
assert_equals(
|
|
np.array([[1.0, 2.0], [3.0, 4.0]]),
|
|
np.array([[1.0, 2.0], [3.0, 4.0]]),
|
|
)
|
|
assert_equals(
|
|
VariableFeatures(values=np.array([1.0, 2.0])), # type: ignore
|
|
VariableFeatures(values=np.array([1.0, 2.0])), # type: ignore
|
|
)
|
|
assert_equals((1.0,), (1.0,))
|
|
assert_equals({"x": 10}, {"x": 10})
|