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Reformat source code with Black; add pre-commit hooks and CI checks

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Alinson S. Xavier
2020-12-05 10:59:33 -06:00
parent 3823931382
commit d99600f101
49 changed files with 1291 additions and 972 deletions
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1
miplearn/problems/tests/__init__.py
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@@ -1,4 +1,3 @@
# 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.

19
miplearn/problems/tests/test_knapsack.py
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@@ -9,17 +9,18 @@ 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),
)
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(w_sum), -1) == 500.0
# assert round(np.mean(p_sum), -1) == 1200. # flaky
assert round(np.mean(b_sum), -3) == 25000.
assert round(np.mean(b_sum), -3) == 25000.0

38
miplearn/problems/tests/test_stab.py
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@@ -11,36 +11,42 @@ from scipy.stats import uniform, randint
def test_stab():
graph = nx.cycle_graph(5)
weights = [1., 1., 1., 1., 1.]
weights = [1.0, 1.0, 1.0, 1.0, 1.0]
instance = MaxWeightStableSetInstance(graph, weights)
solver = LearningSolver()
solver.solve(instance)
assert instance.lower_bound == 2.
assert instance.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., scale=10.),
n=randint(low=10, high=11),
p=uniform(loc=0.05, scale=0.),
fix_graph=True)
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., scale=10.),
n=randint(low=30, high=41),
p=uniform(loc=0.5, scale=0.),
fix_graph=False)
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.
assert np.round(np.mean(n_edges), -1) == 300.
assert np.round(np.mean(n_nodes)) == 35.0
assert np.round(np.mean(n_edges), -1) == 300.0

48
miplearn/problems/tests/test_tsp.py
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@@ -11,11 +11,13 @@ 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)
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
@@ -25,14 +27,16 @@ def test_generator():
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.],
])
distances = np.array(
[
[0.0, 1.0, 2.0, 1.0],
[1.0, 0.0, 1.0, 2.0],
[2.0, 1.0, 0.0, 1.0],
[1.0, 2.0, 1.0, 0.0],
]
)
instance = TravelingSalesmanInstance(n_cities, distances)
for solver_name in ['gurobi']:
for solver_name in ["gurobi"]:
solver = LearningSolver(solver=solver_name)
solver.solve(instance)
x = instance.solution["x"]
@@ -48,17 +52,19 @@ def test_instance():
def test_subtour():
n_cities = 6
cities = np.array([
[0., 0.],
[1., 0.],
[2., 0.],
[3., 0.],
[0., 1.],
[3., 1.],
])
cities = np.array(
[
[0.0, 0.0],
[1.0, 0.0],
[2.0, 0.0],
[3.0, 0.0],
[0.0, 1.0],
[3.0, 1.0],
]
)
distances = squareform(pdist(cities))
instance = TravelingSalesmanInstance(n_cities, distances)
for solver_name in ['gurobi']:
for solver_name in ["gurobi"]:
solver = LearningSolver(solver=solver_name)
solver.solve(instance)
assert hasattr(instance, "found_violated_lazy_constraints")