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Implement iteration_cb for LearningSolver; reactivate TSP

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This commit is contained in:
Alinson S. Xavier
2020-09-23 17:46:18 -05:00
parent 5390a5b656
commit e527e75481
8 changed files with 91 additions and 60 deletions
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3
miplearn/components/component.py
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@@ -21,3 +21,6 @@ class Component(ABC):
@abstractmethod
def fit(self, training_instances):
pass
def after_iteration(self, solver, instance, model):
return False

12
miplearn/components/lazy_dynamic.py
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@@ -38,6 +38,18 @@ class DynamicLazyConstraintsComponent(Component):
cut = instance.build_lazy_constraint(model, v)
solver.internal_solver.add_constraint(cut)
def after_iteration(self, solver, instance, model):
logger.debug("Finding violated (dynamic) lazy constraints...")
violations = instance.find_violated_lazy_constraints(model)
if len(violations) == 0:
return False
instance.found_violated_lazy_constraints += violations
logger.debug(" %d violations found" % len(violations))
for v in violations:
cut = instance.build_lazy_constraint(model, v)
solver.internal_solver.add_constraint(cut)
return True
def after_solve(self, solver, instance, model, results):
pass

11
miplearn/components/lazy_static.py
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@@ -35,13 +35,20 @@ class StaticLazyConstraintsComponent(Component):
def after_solve(self, solver, instance, model, results):
pass
def on_callback(self, solver, instance, model):
print(self.pool)
def after_iteration(self, solver, instance, model):
logger.debug("Finding violated (static) lazy constraints...")
n_added = 0
for c in self.pool:
if not solver.internal_solver.is_constraint_satisfied(c.obj):
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
instance.found_violated_lazy_constraints += [c.cid]
n_added += 1
if n_added > 0:
logger.debug(" %d violations found" % n_added)
return True
else:
return False
def fit(self, training_instances):
logger.debug("Extracting x and y...")

10
miplearn/components/tests/test_lazy_static.py
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@@ -95,8 +95,9 @@ def test_usage_with_solver():
])
internal.add_constraint.reset_mock()
# LearningSolver calls callback (first time)
component.on_callback(solver, instance, None)
# LearningSolver calls after_iteration (first time)
should_repeat = component.after_iteration(solver, instance, None)
assert should_repeat
# Should ask internal solver to verify if constraints in the pool are
# satisfied and add the ones that are not
@@ -105,8 +106,9 @@ def test_usage_with_solver():
internal.add_constraint.assert_called_once_with("<c2>")
internal.add_constraint.reset_mock()
# LearningSolver calls callback (second time)
component.on_callback(solver, instance, None)
# LearningSolver calls after_iteration (second time)
should_repeat = component.after_iteration(solver, instance, None)
assert not should_repeat
# The lazy constraint pool should be empty by now, so no calls should be made
internal.is_constraint_satisfied.assert_not_called()

100
miplearn/problems/tests/test_tsp.py
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@@ -19,56 +19,56 @@ def test_generator():
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]
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)
# assert hasattr(instance, "found_violated_lazy_constraints")
# assert hasattr(instance, "found_violated_user_cuts")
# 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
# solver.fit([instance])
# solver.solve(instance)
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)
assert hasattr(instance, "found_violated_lazy_constraints")
assert hasattr(instance, "found_violated_user_cuts")
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
solver.fit([instance])
solver.solve(instance)

2
miplearn/solvers/internal.py
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@@ -126,7 +126,7 @@ class InternalSolver(ABC):
Parameters
----------
iteration_cb: function
iteration_cb: () -> Bool
By default, InternalSolver makes a single call to the native `solve`
method and returns the result. If an iteration callback is provided
instead, InternalSolver enters a loop, where `solve` and `iteration_cb`

9
miplearn/solvers/learning.py
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@@ -171,8 +171,15 @@ class LearningSolver:
if relaxation_only:
return results
def iteration_cb():
should_repeat = False
for component in self.components.values():
if component.after_iteration(self, instance, model):
should_repeat = True
return should_repeat
logger.info("Solving MILP...")
results = self.internal_solver.solve(tee=tee)
results = self.internal_solver.solve(tee=tee, iteration_cb=iteration_cb)
results["LP value"] = instance.lp_value
# Read MIP solution and bounds

2
miplearn/solvers/tests/test_learning_solver.py
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@@ -64,4 +64,4 @@ def test_add_components():
solver.add(DynamicLazyConstraintsComponent())
solver.add(DynamicLazyConstraintsComponent())
assert len(solver.components) == 1
assert "LazyConstraintsComponent" in solver.components
assert "DynamicLazyConstraintsComponent" in solver.components