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.
204 lines
6.7 KiB
204 lines
6.7 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.
|
|
|
|
import sys
|
|
import time
|
|
from typing import Any
|
|
|
|
import numpy as np
|
|
import gurobipy as gp
|
|
|
|
from miplearn.features.extractor import FeaturesExtractor
|
|
from miplearn.features.sample import MemorySample, Hdf5Sample
|
|
from miplearn.instance.base import Instance
|
|
from miplearn.solvers.gurobi import GurobiSolver
|
|
from miplearn.solvers.internal import Variables, Constraints
|
|
from miplearn.solvers.tests import assert_equals
|
|
import cProfile
|
|
|
|
inf = float("inf")
|
|
|
|
|
|
def test_knapsack() -> None:
|
|
solver = GurobiSolver()
|
|
instance = solver.build_test_instance_knapsack()
|
|
model = instance.to_model()
|
|
solver.set_instance(instance, model)
|
|
extractor = FeaturesExtractor()
|
|
sample = MemorySample()
|
|
|
|
# after-load
|
|
# -------------------------------------------------------
|
|
extractor.extract_after_load_features(instance, solver, sample)
|
|
assert_equals(
|
|
sample.get_vector("static_var_names"),
|
|
np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"),
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("static_var_lower_bounds"), [0.0, 0.0, 0.0, 0.0, 0.0]
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("static_var_obj_coeffs"), [505.0, 352.0, 458.0, 220.0, 0.0]
|
|
)
|
|
assert_equals(
|
|
sample.get_array("static_var_types"),
|
|
np.array(["B", "B", "B", "B", "C"], dtype="S"),
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("static_var_upper_bounds"), [1.0, 1.0, 1.0, 1.0, 67.0]
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("static_var_categories"),
|
|
["default", "default", "default", "default", None],
|
|
)
|
|
assert sample.get_vector_list("static_var_features") is not None
|
|
assert_equals(sample.get_vector("static_constr_names"), ["eq_capacity"])
|
|
# assert_equals(
|
|
# sample.get_vector("static_constr_lhs"),
|
|
# [
|
|
# [
|
|
# ("x[0]", 23.0),
|
|
# ("x[1]", 26.0),
|
|
# ("x[2]", 20.0),
|
|
# ("x[3]", 18.0),
|
|
# ("z", -1.0),
|
|
# ],
|
|
# ],
|
|
# )
|
|
assert_equals(sample.get_vector("static_constr_rhs"), [0.0])
|
|
assert_equals(sample.get_vector("static_constr_senses"), ["="])
|
|
assert_equals(sample.get_vector("static_constr_features"), [None])
|
|
assert_equals(sample.get_vector("static_constr_categories"), ["eq_capacity"])
|
|
assert_equals(sample.get_vector("static_constr_lazy"), [False])
|
|
assert_equals(sample.get_vector("static_instance_features"), [67.0, 21.75])
|
|
assert_equals(sample.get_scalar("static_constr_lazy_count"), 0)
|
|
|
|
# after-lp
|
|
# -------------------------------------------------------
|
|
solver.solve_lp()
|
|
extractor.extract_after_lp_features(solver, sample)
|
|
assert_equals(
|
|
sample.get_array("lp_var_basis_status"),
|
|
np.array(["U", "B", "U", "L", "U"], dtype="S"),
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("lp_var_reduced_costs"),
|
|
[193.615385, 0.0, 187.230769, -23.692308, 13.538462],
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("lp_var_sa_lb_down"),
|
|
[-inf, -inf, -inf, -0.111111, -inf],
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("lp_var_sa_lb_up"),
|
|
[1.0, 0.923077, 1.0, 1.0, 67.0],
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("lp_var_sa_obj_down"),
|
|
[311.384615, 317.777778, 270.769231, -inf, -13.538462],
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("lp_var_sa_obj_up"),
|
|
[inf, 570.869565, inf, 243.692308, inf],
|
|
)
|
|
assert_equals(
|
|
sample.get_vector("lp_var_sa_ub_down"), [0.913043, 0.923077, 0.9, 0.0, 43.0]
|
|
)
|
|
assert_equals(sample.get_vector("lp_var_sa_ub_up"), [2.043478, inf, 2.2, inf, 69.0])
|
|
assert_equals(sample.get_vector("lp_var_values"), [1.0, 0.923077, 1.0, 0.0, 67.0])
|
|
assert sample.get_vector_list("lp_var_features") is not None
|
|
assert_equals(sample.get_vector("lp_constr_basis_status"), ["N"])
|
|
assert_equals(sample.get_vector("lp_constr_dual_values"), [13.538462])
|
|
assert_equals(sample.get_vector("lp_constr_sa_rhs_down"), [-24.0])
|
|
assert_equals(sample.get_vector("lp_constr_sa_rhs_up"), [2.0])
|
|
assert_equals(sample.get_vector("lp_constr_slacks"), [0.0])
|
|
|
|
# after-mip
|
|
# -------------------------------------------------------
|
|
solver.solve()
|
|
extractor.extract_after_mip_features(solver, sample)
|
|
assert_equals(sample.get_vector("mip_var_values"), [1.0, 0.0, 1.0, 1.0, 61.0])
|
|
assert_equals(sample.get_vector("mip_constr_slacks"), [0.0])
|
|
|
|
|
|
def test_constraint_getindex() -> None:
|
|
cf = Constraints(
|
|
names=["c1", "c2", "c3"],
|
|
rhs=np.array([1.0, 2.0, 3.0]),
|
|
senses=["=", "<", ">"],
|
|
lhs=[
|
|
[
|
|
(b"x1", 1.0),
|
|
(b"x2", 1.0),
|
|
],
|
|
[
|
|
(b"x2", 2.0),
|
|
(b"x3", 2.0),
|
|
],
|
|
[
|
|
(b"x3", 3.0),
|
|
(b"x4", 3.0),
|
|
],
|
|
],
|
|
)
|
|
assert_equals(
|
|
cf[[True, False, True]],
|
|
Constraints(
|
|
names=["c1", "c3"],
|
|
rhs=np.array([1.0, 3.0]),
|
|
senses=["=", ">"],
|
|
lhs=[
|
|
[
|
|
(b"x1", 1.0),
|
|
(b"x2", 1.0),
|
|
],
|
|
[
|
|
(b"x3", 3.0),
|
|
(b"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(
|
|
Variables(values=np.array([1.0, 2.0])), # type: ignore
|
|
Variables(values=np.array([1.0, 2.0])), # type: ignore
|
|
)
|
|
assert_equals(np.array([True, True]), [True, True])
|
|
assert_equals((1.0,), (1.0,))
|
|
assert_equals({"x": 10}, {"x": 10})
|
|
|
|
|
|
class MpsInstance(Instance):
|
|
def __init__(self, filename: str) -> None:
|
|
super().__init__()
|
|
self.filename = filename
|
|
|
|
def to_model(self) -> Any:
|
|
return gp.read(self.filename)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
solver = GurobiSolver()
|
|
instance = MpsInstance(sys.argv[1])
|
|
solver.set_instance(instance)
|
|
solver.solve_lp(tee=True)
|
|
extractor = FeaturesExtractor(with_lhs=False)
|
|
sample = Hdf5Sample("tmp/prof.h5", mode="w")
|
|
|
|
def run() -> None:
|
|
extractor.extract_after_load_features(instance, solver, sample)
|
|
extractor.extract_after_lp_features(solver, sample)
|
|
|
|
cProfile.run("run()", filename="tmp/prof")
|