def __init__(self,
problem,
node_limit=None,
time_limit=5,
queue_limit=None,
track_bound=True,
queue_strategy="depth"):
# we import from pybnb.solver here to avoid a
# circular import reference
from pybnb.solver import Solver
self._problem = problem
self._node_limit = node_limit
self._time_limit = time_limit
self._queue_limit = queue_limit
self._track_bound = track_bound
self._queue_strategy = queue_strategy
self._solver = Solver(comm=None)
self._log = logging.Logger(None, level=logging.WARNING)
self._convergence_checker = None
self._best_objective = None
self._best_node = None
self._current_node = None
self._results = None
self._queue = None
super(NestedSolver, self).__init__()
def test_bad_dispatcher_rank(comm):
with pytest.raises(ValueError):
Solver(comm=comm, dispatcher_rank=-1)
with pytest.raises(ValueError):
Solver(comm=comm, dispatcher_rank=comm.size)
with pytest.raises(ValueError):
Solver(comm=comm, dispatcher_rank=comm.size - 1.1)
Solver(comm=comm, dispatcher_rank=comm.size - 1)
def test_bad_dispatcher_rank(self):
with pytest.raises(ValueError):
Solver(comm=None, dispatcher_rank=-1)
with pytest.raises(ValueError):
Solver(comm=None, dispatcher_rank=1)
with pytest.raises(ValueError):
Solver(comm=None, dispatcher_rank=1.1)
Solver(comm=None, dispatcher_rank=0)
Solver(comm=None)
def _logging_redirect_check(comm):
opt = Solver(comm=comm)
p = DummyProblem()
log = logging.Logger(None, level=logging.WARNING)
log.addHandler(_RedirectHandler(opt._disp))
if opt.is_dispatcher:
assert (comm is None) or (comm.rank == 0)
root = Node()
p.save_state(root)
root.objective = p.infeasible_objective()
root.bound = p.unbounded_objective()
initialize_queue = DispatcherQueueData(nodes=[root],
worst_terminal_bound=None,
sense=p.sense())
out = StringIO()
formatter = logging.Formatter("[%(levelname)s] %(message)s")
opt._disp.initialize(
p.infeasible_objective(),
None,
initialize_queue,
"bound",
ConvergenceChecker(p.sense()),
None,
None,
None,
True,
get_simple_logger(console=True,
stream=out,
level=logging.DEBUG,
formatter=formatter),
0.0,
True,
)
log.debug("0: debug")
log.info("0: info")
log.warning("0: warning")
log.error("0: error")
log.critical("0: critical")
if (comm is not None) and (comm.size > 1):
opt._disp.serve()
else:
assert comm is not None
if comm.size > 1:
for i in range(1, comm.size):
if comm.rank == i:
log.debug(str(comm.rank) + ": debug")
log.info(str(comm.rank) + ": info")
log.warning(str(comm.rank) + ": warning")
log.error(str(comm.rank) + ": error")
log.critical(str(comm.rank) + ": critical")
opt.worker_comm.Barrier()
if opt.worker_comm.rank == 0:
opt._disp.stop_listen()
if comm is not None:
comm.Barrier()
if opt.is_dispatcher:
assert ("\n".join(
out.getvalue().splitlines()[7:])) == _get_logging_baseline(
comm.size if comm is not None else 1)
def _execute_single_test(problem, baseline, solver=None, comm=None, **kwds):
if solver is None:
solver = Solver(comm=comm)
else:
assert comm is None
if solver.comm is not None:
if solver.comm.rank == 0:
pass
elif solver.comm.rank == 1:
pass
elif solver.comm.rank == 3:
pass
orig = pybnb.node.Node()
problem.save_state(orig)
results = solver.solve(problem, **kwds)
current = pybnb.node.Node()
problem.save_state(current)
assert len(current.state) == len(orig.state)
for i in range(len(current.state)):
assert current.state[i] == orig.state[i]
assert len(vars(results)) > 0
assert len(vars(results)) == len(vars(baseline))
for name in sorted(sorted(list(vars(results).keys()))):
if getattr(baseline, name) is _ignore_value_:
continue
if (name == 'nodes') and \
(solver.comm is not None) and \
(solver.comm.size > 2):
pass
else:
assert getattr(results, name) == getattr(baseline, name), \
("value for '"+str(name)+"' ("+
str(getattr(results, name))+") does "
"not match baseline ("+
str(getattr(baseline, name))+")")
if solver.is_dispatcher:
q = solver.save_dispatcher_queue()
assert len(q) == 2
assert q.next_tree_id >= 0
assert len(q.nodes) == solver._disp.queue.size()
def test_no_mpi(self):
b = Solver(comm=None)
assert b.comm is None
assert b.worker_comm is None
assert b.is_worker == True
assert b.is_dispatcher == True
assert b.worker_count == 1
b._reset_local_solve_stats()
stats = b.collect_worker_statistics()
assert len(stats) == 13
assert stats['rank'] == [0]
assert stats['wall_time'] == [0]
assert stats['queue_time'] == [0]
assert stats['queue_call_count'] == [0]
assert stats['objective_time'] == [0]
assert stats['objective_call_count'] == [0]
assert stats['bound_time'] == [0]
assert stats['bound_call_count'] == [0]
assert stats['branch_time'] == [0]
assert stats['branch_call_count'] == [0]
assert stats['load_state_time'] == [0]
assert stats['load_state_call_count'] == [0]
assert stats['explored_nodes_count'] == [0]
out = \
"""Number of Workers: 1
Load Imbalance: 0.00%
Average Worker Timing:
- queue: 0.00% [avg time: 0.0 s , count: 0]
- load_state: 0.00% [avg time: 0.0 s , count: 0]
- bound: 0.00% [avg time: 0.0 s , count: 0]
- objective: 0.00% [avg time: 0.0 s , count: 0]
- branch: 0.00% [avg time: 0.0 s , count: 0]
- other: 0.00% [avg time: 0.0 s , count: 0]
"""
tmp = StringIO()
summarize_worker_statistics(stats, stream=tmp)
assert tmp.getvalue() == out
def _execute_tests(comm, problem, baseline, **kwds):
assert 'log_interval_second' not in kwds
assert 'log' not in kwds
_execute_single_test(problem, baseline, comm=comm, **kwds)
kwds['log_interval_seconds'] = 0.0
_execute_single_test(problem, baseline, comm=comm, **kwds)
kwds['log_interval_seconds'] = 100.0
_execute_single_test(problem, baseline, comm=comm, **kwds)
kwds['log_interval_seconds'] = 0.0
kwds['log'] = None
_execute_single_test(problem, baseline, comm=comm, **kwds)
kwds['log_interval_seconds'] = 100.0
_execute_single_test(problem, baseline, comm=comm, **kwds)
solver = Solver(comm=comm)
_execute_single_test(problem, baseline, solver=solver, **kwds)
_execute_single_test(problem, baseline, solver=solver, **kwds)
kwds['log'] = get_simple_logger(level="WARNING")
_execute_single_test(problem, baseline, solver=solver, **kwds)
def _test_delayed_unbounded_min(comm):
solver = Solver(comm=comm)
baseline = SolverResults()
baseline.solution_status = "unbounded"
baseline.termination_condition = "no_nodes"
baseline.objective = -inf
baseline.bound = -inf
baseline.nodes = _ignore_value_
baseline.wall_time = _ignore_value_
problem = delayed_unbounded_min.DelayedUnboundedMin()
_execute_single_test(problem, baseline, solver=solver)
for queue_strategy in sorted(pybnb.QueueStrategy):
if queue_strategy == "custom":
continue
_execute_single_test(problem,
baseline,
solver=solver,
queue_strategy=queue_strategy)
def _test_root_infeasible_min(comm):
solver = Solver(comm=comm)
baseline = SolverResults()
baseline.solution_status = "infeasible"
baseline.termination_condition = "optimality"
baseline.objective = inf
baseline.bound = inf
baseline.nodes = 1
baseline.wall_time = _ignore_value_
problem = root_infeasible_min.RootInfeasibleMin()
_execute_single_test(problem, baseline, solver=solver)
for queue_strategy in sorted(pybnb.QueueStrategy):
if queue_strategy == "custom":
continue
_execute_single_test(problem,
baseline,
solver=solver,
queue_strategy=queue_strategy)
def test_solver_comm(comm):
solver = Solver(comm=comm, dispatcher_rank=0)
if comm.size > 1:
if comm.rank == 0:
assert solver.is_dispatcher
assert not solver.is_worker
assert solver.comm is comm
assert solver.worker_comm is None
else:
assert not solver.is_dispatcher
assert solver.is_worker
assert solver.comm is comm
assert solver.worker_comm is not None
else:
assert solver.is_dispatcher
assert solver.is_worker
assert solver.comm is comm
assert solver.worker_comm is comm
def _logging_check(comm):
opt = Solver(comm=comm)
p = DummyProblem()
if opt.is_dispatcher:
assert (comm is None) or (comm.rank == 0)
root = Node()
p.save_state(root)
root.objective = p.infeasible_objective()
root.bound = p.unbounded_objective()
assert root.tree_id is None
Node._insert_tree_id(root._data, 0)
initialize_queue = DispatcherQueueData(nodes=[root], next_tree_id=1)
out = StringIO()
formatter = logging.Formatter("[%(levelname)s] %(message)s")
opt._disp.initialize(
p.infeasible_objective(), initialize_queue, "bound",
ConvergenceChecker(p.sense()), None, None,
get_simple_logger(console=True,
stream=out,
level=logging.DEBUG,
formatter=formatter), 0.0, True)
opt._disp.log_debug("0: debug")
opt._disp.log_info("0: info")
opt._disp.log_warning("0: warning")
opt._disp.log_error("0: error")
if (comm is not None) and (comm.size > 1):
opt._disp.serve()
else:
assert comm is not None
if comm.size > 1:
for i in range(1, comm.size):
if comm.rank == i:
opt._disp.log_debug(str(comm.rank) + ": debug")
opt._disp.log_info(str(comm.rank) + ": info")
opt._disp.log_warning(str(comm.rank) + ": warning")
opt._disp.log_error(str(comm.rank) + ": error")
opt.worker_comm.Barrier()
if opt.worker_comm.rank == 0:
opt._disp.stop_listen()
if comm is not None:
comm.Barrier()
if opt.is_dispatcher:
assert ('\n'.join(out.getvalue().splitlines()[8:])) == \
_get_logging_baseline(comm.size if comm is not None else 1)
def _test_infeasible_max(comm):
solver = None
if comm is not None:
solver = Solver(comm=comm)
baseline = SolverResults()
baseline.solution_status = "infeasible"
baseline.termination_condition = "optimality"
baseline.objective = -inf
baseline.bound = -inf
baseline.nodes = 255
baseline.wall_time = _ignore_value_
problem = infeasible_max.InfeasibleMax()
if comm is None:
_execute_tests(comm, problem, baseline)
else:
_execute_single_test(problem, baseline, solver=solver)
baseline = SolverResults()
baseline.solution_status = "unknown"
baseline.termination_condition = "no_nodes"
baseline.objective = -inf
baseline.bound = -16
baseline.nodes = 31
baseline.wall_time = _ignore_value_
problem = infeasible_max.InfeasibleMax(branching_abstol=0.1)
if comm is None:
_execute_tests(comm, problem, baseline)
else:
_execute_single_test(problem, baseline, solver=solver)
baseline = SolverResults()
baseline.solution_status = "unknown"
baseline.termination_condition = "objective_limit"
baseline.objective = -inf
baseline.bound = -16
baseline.nodes = 31
baseline.wall_time = _ignore_value_
problem = infeasible_max.InfeasibleMax(branching_abstol=0.1)
if comm is None:
_execute_tests(comm, problem, baseline, bound_stop=-15)
else:
_execute_single_test(problem, baseline, solver=solver, bound_stop=-15)
baseline = SolverResults()
baseline.solution_status = "feasible"
baseline.termination_condition = "objective_limit"
baseline.objective = -20
baseline.bound = -16
baseline.absolute_gap = 4
baseline.relative_gap = 0.2
baseline.nodes = 31
baseline.wall_time = _ignore_value_
problem = infeasible_max.InfeasibleMax(branching_abstol=0.1,
fixed_objective=-20)
if comm is None:
_execute_tests(comm, problem, baseline, bound_stop=-15)
else:
_execute_single_test(problem, baseline, solver=solver, bound_stop=-15)
baseline = SolverResults()
baseline.solution_status = "unknown"
baseline.termination_condition = "node_limit"
baseline.objective = -inf
baseline.bound = -16
baseline.nodes = 31
baseline.wall_time = _ignore_value_
problem = infeasible_max.InfeasibleMax()
if comm is None:
_execute_tests(comm, problem, baseline, node_limit=31)
elif comm.size <= 2:
# skip for larger comm sizes
# as the node_limit can lead to
# a number of different outcomes
_execute_single_test(problem, baseline, solver=solver, node_limit=31)
baseline = SolverResults()
baseline.solution_status = "feasible"
baseline.termination_condition = "node_limit"
baseline.objective = -17
baseline.bound = -16
baseline.absolute_gap = 1.0
baseline.relative_gap = 1.0 / 17
baseline.nodes = 31
baseline.wall_time = _ignore_value_
problem = infeasible_max.InfeasibleMax(fixed_objective=-17)
if comm is None:
_execute_tests(comm, problem, baseline, node_limit=31)
elif comm.size <= 2:
# skip for larger comm sizes
# as the node_limit can lead to
# a number of different outcomes
_execute_single_test(problem, baseline, solver=solver, node_limit=31)
baseline = SolverResults()
baseline.solution_status = "unknown"
baseline.termination_condition = "time_limit"
baseline.objective = -inf
baseline.bound = inf
baseline.nodes = 0
baseline.wall_time = _ignore_value_
problem = infeasible_max.InfeasibleMax()
if comm is None:
_execute_tests(comm, problem, baseline, time_limit=0)
else:
_execute_single_test(problem, baseline, solver=solver, time_limit=0)
if solver is None:
solver = Solver(comm=comm)
baseline = SolverResults()
baseline.solution_status = "infeasible"
baseline.termination_condition = "optimality"
baseline.objective = -inf
baseline.bound = -inf
baseline.nodes = 255
baseline.wall_time = _ignore_value_
problem = infeasible_max.InfeasibleMax()
for queue_strategy in sorted(pybnb.QueueStrategy):
if queue_strategy == "custom":
continue
_execute_single_test(problem,
baseline,
solver=solver,
queue_strategy=queue_strategy)
def _test_zero_objective_min(comm):
solver = Solver(comm=comm)
baseline = SolverResults()
baseline.solution_status = "optimal"
baseline.termination_condition = "optimality"
baseline.objective = 0.0
baseline.bound = -0.0078125
baseline.absolute_gap = 0.0078125
baseline.relative_gap = 0.0078125
baseline.nodes = 255
baseline.wall_time = _ignore_value_
problem = zero_objective_min.ZeroObjectiveMin()
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.01,
absolute_gap=0.01)
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.0,
absolute_gap=0.01)
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.01,
absolute_gap=0.0)
baseline = SolverResults()
baseline.solution_status = "optimal"
baseline.termination_condition = "optimality"
baseline.objective = 0.0
baseline.bound = -0.0009765625
baseline.absolute_gap = 0.0009765625
baseline.relative_gap = 0.0009765625
baseline.nodes = 2047
baseline.wall_time = _ignore_value_
problem = zero_objective_min.ZeroObjectiveMin()
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.001,
absolute_gap=0.001)
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.0,
absolute_gap=0.001)
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.001,
absolute_gap=0.0)
baseline = SolverResults()
baseline.solution_status = "feasible"
baseline.termination_condition = "no_nodes"
baseline.objective = 0.0
baseline.bound = -0.0009765625
baseline.absolute_gap = 0.0009765625
baseline.relative_gap = 0.0009765625
baseline.nodes = 2047
baseline.wall_time = _ignore_value_
problem = zero_objective_min.ZeroObjectiveMin()
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.0001,
absolute_gap=0.0001)
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.0,
absolute_gap=0.0001)
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.0001,
absolute_gap=0.0)
baseline = SolverResults()
baseline.solution_status = "optimal"
baseline.termination_condition = "optimality"
baseline.objective = 0.0
baseline.bound = -0.0078125
baseline.absolute_gap = 0.0078125
baseline.relative_gap = 0.0078125
baseline.nodes = None
baseline.wall_time = _ignore_value_
problem = zero_objective_min.ZeroObjectiveMin()
for queue_strategy in sorted(pybnb.QueueStrategy):
if queue_strategy == "custom":
continue
if queue_strategy == "depth":
baseline.nodes = 2033
elif queue_strategy == "random":
baseline.nodes = _ignore_value_
else:
baseline.nodes = 255
_execute_single_test(problem,
baseline,
solver=solver,
relative_gap=0.01,
absolute_gap=0.01,
queue_strategy=queue_strategy)
class NestedSolver(_ProblemWithSolveInfoCollection):
"""A class for creating a nested branch-and-bound solve
strategy. An instance of this class (wrapped around a
standard problem) can be passed to the solver as the
problem argument.
Parameters
----------
problem : :class:`pybnb.Problem <pybnb.problem.Problem>`
An object defining a branch-and-bound problem.
node_limit : int, optional
The same as the standard solver option, but applied
to the nested solver to limit the number of nodes to
explore when processing a work item. (default: None)
time_limit : float, optional
The same as the standard solver option, but applied
to the nested solver to limit the amount of time
spent processing a work item. (default: 5)
queue_limit : int, optional
The same as the standard solver option, but applied
to the nested solver to limit the size of the queue.
(default: None)
track_bound : bool, optional
The same as the standard solver option, but applied
to the nested solver control bound tracking.
(default: True)
queue_strategy : :class:`QueueStrategy <pybnb.common.QueueStrategy>` or tuple
The same as the standard solver option, but applied
to the nested solver to control the queue strategy
used when processing a work item. (default: 'depth')
"""
def __init__(self,
problem,
node_limit=None,
time_limit=5,
queue_limit=None,
track_bound=True,
queue_strategy="depth"):
# we import from pybnb.solver here to avoid a
# circular import reference
from pybnb.solver import Solver
self._problem = problem
self._node_limit = node_limit
self._time_limit = time_limit
self._queue_limit = queue_limit
self._track_bound = track_bound
self._queue_strategy = queue_strategy
self._solver = Solver(comm=None)
self._log = logging.Logger(None, level=logging.WARNING)
self._convergence_checker = None
self._best_objective = None
self._best_node = None
self._current_node = None
self._results = None
self._queue = None
super(NestedSolver, self).__init__()
def _initialize(self, dispatcher, best_objective, disable_objective_call):
assert best_objective is not None
self._best_objective = best_objective
self._disable_objective_call = disable_objective_call
self._log.addHandler(_RedirectHandler(dispatcher))
def _solve(self):
bound_stop = None
if not math.isinf(self._best_objective):
bound_stop = self._best_objective
# shallow copy
root = copy.copy(self._current_node)
init_queue = DispatcherQueueData(nodes=[root],
worst_terminal_bound=None,
sense=self._convergence_checker.sense)
self._results = self._solver.solve(
self._problem,
best_objective=self._best_objective,
best_node=self._best_node,
bound_stop=bound_stop,
initialize_queue=init_queue,
log=self._log,
absolute_gap=\
self._convergence_checker.absolute_gap,
relative_gap=\
self._convergence_checker.relative_gap,
scale_function=\
self._convergence_checker.scale_function,
queue_tolerance=\
self._convergence_checker.queue_tolerance,
branch_tolerance=\
self._convergence_checker.branch_tolerance,
comparison_tolerance=\
self._convergence_checker.comparison_tolerance,
objective_stop=\
self._convergence_checker.objective_stop,
disable_objective_call=self._disable_objective_call,
node_limit=self._node_limit,
time_limit=self._time_limit,
queue_limit=self._queue_limit,
track_bound=self._track_bound,
queue_strategy=self._queue_strategy,
disable_signal_handlers=True)
self._queue = self._solver.save_dispatcher_queue()
self._solve_info.add_from(self._solver._global_solve_info)
#
# Ducktype a partial Problem interface
#
def objective(self): #pragma:nocover
"""The solver does not call this method when it sees the
problem implements a nested solve."""
raise NotImplementedError()
def bound(self): #pragma:nocover
"""The solver does not call this method when it sees the
problem implements a nested solve."""
raise NotImplementedError()
def branch(self): #pragma:nocover
"""The solver does not call this method when it sees the
problem implements a nested solve."""
raise NotImplementedError()
def sense(self):
"""Calls the sense() method on the user-provided
problem."""
return self._problem.sense()
def save_state(self, node):
"""Calls the save_state() method on the user-provided
problem."""
self._problem.save_state(node)
def load_state(self, node):
"""Calls the load_state() method on the user-provided
problem and prepares for a nested solve."""
self._problem.load_state(node)
self._results = None
self._children = None
self._current_node = node
self._solve_found_best_node = False
def notify_solve_begins(self, comm, worker_comm, convergence_checker):
"""Calls the notify_solve_begins() method on the
user-provided problem and prepares for a solve."""
self._best_objective = None
self._best_node = None
self._convergence_checker = convergence_checker
self._current_node = None
self._results = None
self._queue = None
self._problem.notify_solve_begins(comm, worker_comm,
convergence_checker)
def notify_new_best_node(self, node, current):
"""Calls the notify_new_best_node() method on the
user-provided problem and stores the best node for
use in the next nested solve."""
self._best_objective = self._convergence_checker.\
best_objective(self._best_objective,
node.objective)
self._best_node = node
self._problem.notify_new_best_node(node, current)
def notify_solve_finished(self, comm, worker_comm, results):
"""Calls the notify_solve_finished() method on the
user-provided problem and does some final
cleanup."""
while len(self._log.handlers) > 0:
self._log.removeHandler(self._log.handlers[0])
self._problem.notify_solve_finished(comm, worker_comm, results)
def _test_initialize_queue(comm):
solver = Solver(comm=comm)
# no initial queue
for sense in (minimize, maximize):
problem = DummyProblem(sense)
results = solver.solve(problem, queue_limit=0)
assert results.solution_status == "unknown"
assert results.termination_condition == "queue_limit"
assert results.objective == (inf if (sense == minimize) else -inf)
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap is None
assert results.relative_gap is None
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
assert solver._local_solve_info.explored_nodes_count == 0
problem = DummyProblem(sense)
results = solver.solve(problem)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node is not None
assert results.best_node.objective == results.objective
assert results.best_node.tree_depth == 0
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
problem = DummyProblem(sense)
results = solver.solve(
problem, best_objective=(1 if (sense == minimize) else -1)
)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node is not None
assert results.best_node.objective == results.objective
assert results.best_node.tree_depth == 0
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
problem = DummyProblem(sense)
results = solver.solve(
problem,
best_objective=(1 if (sense == minimize) else -1),
disable_objective_call=True,
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == (1 if (sense == minimize) else -1)
assert results.bound == 0
assert results.absolute_gap == 1
assert results.relative_gap == 1
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
best_node_ = Node()
best_node_.objective = 1 if (sense == minimize) else -1
best_node_._uuid = "abcd"
problem = DummyProblem(sense)
results = solver.solve(
problem, best_node=best_node_, disable_objective_call=True
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == best_node_.objective
assert results.bound == 0
assert results.absolute_gap == 1
assert results.relative_gap == 1
assert results.nodes == 1
assert results.best_node._uuid == best_node_._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is best_node_
assert results.best_node.objective == results.objective
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert not problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
best_node_ = Node()
best_node_.objective = 1 if (sense == minimize) else -1
best_node_._uuid = "abcd"
problem = DummyProblem(sense)
results = solver.solve(problem, best_node=best_node_)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node._uuid != best_node_._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is not best_node_
assert results.best_node.objective == results.objective
assert results.best_node.tree_depth == 0
if solver.is_worker:
assert problem._notify_new_best_node_call_count >= 1
assert problem._notify_new_best_node_args[0] is results.best_node
if problem._notify_new_best_node_call_count == 2:
assert problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_args[0]
else:
assert problem._notify_new_best_node_call_count == 0
# empty initial queue
queue = DispatcherQueueData(nodes=[], worst_terminal_bound=None, sense=minimize)
for sense in (minimize, maximize):
queue.sense = sense
problem = DummyProblem(sense)
results = solver.solve(problem, initialize_queue=queue)
assert results.solution_status == "unknown"
assert results.termination_condition == "queue_empty"
assert results.objective == (inf if (sense == minimize) else -inf)
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap is None
assert results.relative_gap is None
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
problem = DummyProblem(sense)
results = solver.solve(problem, initialize_queue=queue, best_objective=0)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == 0
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap == inf
assert results.relative_gap == inf
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=0,
disable_objective_call=True,
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == 0
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap == inf
assert results.relative_gap == inf
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
best_node_ = Node()
best_node_.objective = 1 if (sense == minimize) else -1
best_node_._uuid = "abcd"
problem = DummyProblem(sense)
results = solver.solve(
problem, initialize_queue=queue, best_objective=0, best_node=best_node_
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == 0
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap == inf
assert results.relative_gap == inf
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node._uuid == best_node_._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is best_node_
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
best_node_ = Node()
best_node_.objective = 1 if (sense == minimize) else -1
best_node_._uuid = "abcd"
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=(2 if (sense == minimize) else -2),
best_node=best_node_,
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == (1 if (sense == minimize) else -1)
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap == inf
assert results.relative_gap == inf
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node._uuid == best_node_._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is best_node_
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
# non-empty initial queue
root = Node()
root._uuid = "abcd"
root.tree_depth = 0
root.objective = 0
root.bound = 0
queue = DispatcherQueueData(nodes=[root], worst_terminal_bound=None, sense=minimize)
orig_objective = queue.nodes[0].objective
for sense in (minimize, maximize):
queue.sense = sense
queue.nodes[0].objective = orig_objective
problem = DummyProblem(sense)
results = solver.solve(problem, initialize_queue=queue)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node._uuid == root._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is root
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
queue.nodes[0].objective = orig_objective
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=(1 if (sense == minimize) else -1),
)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node._uuid == root._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is root
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
queue.nodes[0].objective = inf if (sense == minimize) else -inf
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=(1 if (sense == minimize) else -1),
)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node._uuid == root._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is root
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
queue.nodes[0].objective = inf if (sense == minimize) else -inf
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=(1 if (sense == minimize) else -1),
disable_objective_call=True,
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == (1 if (sense == minimize) else -1)
assert results.bound == 0
assert results.absolute_gap == 1
assert results.relative_gap == 1
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
queue.nodes[0].objective = orig_objective
def test_solver_nocomm():
solver = Solver(comm=None)
assert solver.is_worker
assert solver.is_dispatcher
assert solver.comm is None
assert solver.worker_comm is None
