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.
54 lines
1.8 KiB
54 lines
1.8 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 networkx as nx
|
|
import numpy as np
|
|
from scipy.stats import uniform, randint
|
|
|
|
from miplearn.problems.stab import MaxWeightStableSetInstance
|
|
from miplearn.solvers.learning import LearningSolver
|
|
|
|
|
|
def test_stab():
|
|
graph = nx.cycle_graph(5)
|
|
weights = [1.0, 1.0, 1.0, 1.0, 1.0]
|
|
instance = MaxWeightStableSetInstance(graph, weights)
|
|
solver = LearningSolver()
|
|
stats = solver.solve(instance)
|
|
assert stats["Lower bound"] == 2.0
|
|
|
|
|
|
def test_stab_generator_fixed_graph():
|
|
np.random.seed(42)
|
|
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
|
|
|
gen = MaxWeightStableSetGenerator(
|
|
w=uniform(loc=50.0, scale=10.0),
|
|
n=randint(low=10, high=11),
|
|
p=uniform(loc=0.05, scale=0.0),
|
|
fix_graph=True,
|
|
)
|
|
instances = gen.generate(1_000)
|
|
weights = np.array([instance.weights for instance in instances])
|
|
weights_avg_actual = np.round(np.average(weights, axis=0))
|
|
weights_avg_expected = [55.0] * 10
|
|
assert list(weights_avg_actual) == weights_avg_expected
|
|
|
|
|
|
def test_stab_generator_random_graph():
|
|
np.random.seed(42)
|
|
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
|
|
|
gen = MaxWeightStableSetGenerator(
|
|
w=uniform(loc=50.0, scale=10.0),
|
|
n=randint(low=30, high=41),
|
|
p=uniform(loc=0.5, scale=0.0),
|
|
fix_graph=False,
|
|
)
|
|
instances = gen.generate(1_000)
|
|
n_nodes = [instance.graph.number_of_nodes() for instance in instances]
|
|
n_edges = [instance.graph.number_of_edges() for instance in instances]
|
|
assert np.round(np.mean(n_nodes)) == 35.0
|
|
assert np.round(np.mean(n_edges), -1) == 300.0
|