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.
MIPLearn/tests/test_features.py

137 lines
4.1 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,
)
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),
),
),
),
)