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Alinson S. Xavier
2020-03-05 17:58:56 -06:00
parent 37795fe013
commit 7765d1f822
50 changed files with 168 additions and 11 deletions
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4
miplearn/problems/tests/__init__.py
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.

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miplearn/problems/tests/test_knapsack.py
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn import LearningSolver
from miplearn.problems.knapsack import MultiKnapsackGenerator, MultiKnapsackInstance
from scipy.stats import uniform, randint
import numpy as np
def test_knapsack_generator():
gen = MultiKnapsackGenerator(n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=501),
u=uniform(loc=1.0, scale=1.0),
alpha=uniform(loc=0.50, scale=0.0),
)
instances = gen.generate(100)
w_sum = sum(instance.weights for instance in instances) / len(instances)
p_sum = sum(instance.prices for instance in instances) / len(instances)
b_sum = sum(instance.capacities for instance in instances) / len(instances)
assert round(np.mean(w_sum), -1) == 500.
assert round(np.mean(p_sum), -1) == 1250.
assert round(np.mean(b_sum), -3) == 25000.

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miplearn/problems/tests/test_stab.py
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn import LearningSolver
from miplearn.problems.stab import MaxWeightStableSetInstance
from miplearn.problems.stab import MaxWeightStableSetGenerator
import networkx as nx
import numpy as np
from scipy.stats import uniform, randint
def test_stab():
graph = nx.cycle_graph(5)
weights = [1., 1., 1., 1., 1.]
instance = MaxWeightStableSetInstance(graph, weights)
solver = LearningSolver()
solver.solve(instance)
assert instance.model.OBJ() == 2.
def test_stab_generator_fixed_graph():
from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
n=randint(low=10, high=11),
p=uniform(loc=0.05, scale=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():
from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
n=randint(low=30, high=41),
p=uniform(loc=0.5, scale=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.
assert np.round(np.mean(n_edges), -1) == 300.

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miplearn/problems/tests/test_tsp.py
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn import LearningSolver
from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
import numpy as np
from numpy.linalg import norm
from scipy.spatial.distance import pdist, squareform
from scipy.stats import uniform, randint
def test_generator():
instances = TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=100, high=101),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True).generate(100)
assert len(instances) == 100
assert instances[0].n_cities == 100
assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
d = [instance.distances[0,1] for instance in instances]
assert np.std(d) > 0
def test_instance():
n_cities = 4
distances = np.array([
[0., 1., 2., 1.],
[1., 0., 1., 2.],
[2., 1., 0., 1.],
[1., 2., 1., 0.],
])
instance = TravelingSalesmanInstance(n_cities, distances)
for solver_name in ['gurobi', 'cplex']:
solver = LearningSolver(solver=solver_name)
solver.solve(instance)
x = instance.solution["x"]
assert x[0,1] == 1.0
assert x[0,2] == 0.0
assert x[0,3] == 1.0
assert x[1,2] == 1.0
assert x[1,3] == 0.0
assert x[2,3] == 1.0
assert instance.lower_bound == 4.0
assert instance.upper_bound == 4.0
def test_subtour():
n_cities = 6
cities = np.array([
[0., 0.],
[1., 0.],
[2., 0.],
[3., 0.],
[0., 1.],
[3., 1.],
])
distances = squareform(pdist(cities))
instance = TravelingSalesmanInstance(n_cities, distances)
for solver_name in ['gurobi', 'cplex']:
solver = LearningSolver(solver=solver_name)
solver.solve(instance)
x = instance.solution["x"]
assert x[0,1] == 1.0
assert x[0,4] == 1.0
assert x[1,2] == 1.0
assert x[2,3] == 1.0
assert x[3,5] == 1.0
assert x[4,5] == 1.0