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

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This commit is contained in:
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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121
miplearn/problems/tsp.py
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@@ -13,41 +13,44 @@ import random
class ChallengeA:
def __init__(self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.generator = TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=350, high=351),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True,
round=True,
)
self.generator = TravelingSalesmanGenerator(
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=350, high=351),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True,
round=True,
)
np.random.seed(seed + 1)
self.training_instances = self.generator.generate(n_training_instances)
np.random.seed(seed + 2)
self.test_instances = self.generator.generate(n_test_instances)
self.test_instances = self.generator.generate(n_test_instances)
class TravelingSalesmanGenerator:
"""Random generator for the Traveling Salesman Problem."""
def __init__(self,
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=1.0, scale=0.0),
fix_cities=True,
round=True,
):
def __init__(
self,
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=1.0, scale=0.0),
fix_cities=True,
round=True,
):
"""Initializes the problem generator.
Initially, the generator creates n cities (x_1,y_1),...,(x_n,y_n) where n, x_i and y_i are
sampled independently from the provided probability distributions `n`, `x` and `y`. For each
(unordered) pair of cities (i,j), the distance d[i,j] between them is set to:
@@ -58,7 +61,7 @@ class TravelingSalesmanGenerator:
If fix_cities=True, the list of cities is kept the same for all generated instances. The
gamma values, and therefore also the distances, are still different.
By default, all distances d[i,j] are rounded to the nearest integer. If `round=False`
is provided, this rounding will be disabled.
@@ -79,19 +82,22 @@ class TravelingSalesmanGenerator:
assert isinstance(x, rv_frozen), "x should be a SciPy probability distribution"
assert isinstance(y, rv_frozen), "y should be a SciPy probability distribution"
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
assert isinstance(gamma, rv_frozen), "gamma should be a SciPy probability distribution"
assert isinstance(
gamma,
rv_frozen,
), "gamma should be a SciPy probability distribution"
self.x = x
self.y = y
self.n = n
self.gamma = gamma
self.round = round
if fix_cities:
self.fixed_n, self.fixed_cities = self._generate_cities()
else:
self.fixed_n = None
self.fixed_cities = None
def generate(self, n_samples):
def _sample():
if self.fixed_cities is not None:
@@ -103,54 +109,62 @@ class TravelingSalesmanGenerator:
if self.round:
distances = distances.round()
return TravelingSalesmanInstance(n, distances)
return [_sample() for _ in range(n_samples)]
def _generate_cities(self):
n = self.n.rvs()
cities = np.array([(self.x.rvs(), self.y.rvs()) for _ in range(n)])
return n, cities
class TravelingSalesmanInstance(Instance):
"""An instance ot the Traveling Salesman Problem.
Given a list of cities and the distance between each pair of cities, the problem asks for the
shortest route starting at the first city, visiting each other city exactly once, then
returning to the first city. This problem is a generalization of the Hamiltonian path problem,
one of Karp's 21 NP-complete problems.
"""
def __init__(self, n_cities, distances):
assert isinstance(distances, np.ndarray)
assert distances.shape == (n_cities, n_cities)
self.n_cities = n_cities
self.distances = distances
def to_model(self):
model = pe.ConcreteModel()
model.edges = edges = [(i,j)
for i in range(self.n_cities)
for j in range(i+1, self.n_cities)]
model.edges = edges = [
(i, j) for i in range(self.n_cities) for j in range(i + 1, self.n_cities)
]
model.x = pe.Var(edges, domain=pe.Binary)
model.obj = pe.Objective(expr=sum(model.x[i,j] * self.distances[i,j]
for (i,j) in edges),
sense=pe.minimize)
model.obj = pe.Objective(
expr=sum(model.x[i, j] * self.distances[i, j] for (i, j) in edges),
sense=pe.minimize,
)
model.eq_degree = pe.ConstraintList()
model.eq_subtour = pe.ConstraintList()
for i in range(self.n_cities):
model.eq_degree.add(sum(model.x[min(i,j), max(i,j)]
for j in range(self.n_cities) if i != j) == 2)
model.eq_degree.add(
sum(
model.x[min(i, j), max(i, j)]
for j in range(self.n_cities)
if i != j
)
== 2
)
return model
def get_instance_features(self):
return np.array([1])
def get_variable_features(self, var_name, index):
return np.array([1])
def get_variable_category(self, var_name, index):
return index
def find_violated_lazy_constraints(self, model):
selected_edges = [e for e in model.edges if model.x[e].value > 0.5]
graph = nx.Graph()
@@ -161,15 +175,18 @@ class TravelingSalesmanInstance(Instance):
if len(c) < self.n_cities:
violations += [c]
return violations
def build_lazy_constraint(self, model, component):
cut_edges = [e for e in model.edges
if (e[0] in component and e[1] not in component) or
(e[0] not in component and e[1] in component)]
cut_edges = [
e
for e in model.edges
if (e[0] in component and e[1] not in component)
or (e[0] not in component and e[1] in component)
]
return model.eq_subtour.add(sum(model.x[e] for e in cut_edges) >= 2)
def find_violated_user_cuts(self, model):
return self.find_violated_lazy_constraints(model)
def build_user_cut(self, model, violation):
return self.build_lazy_constraint(model, violation)
return self.build_lazy_constraint(model, violation)