def test_missing_queue_priority(self):
q = CustomPriorityQueue(sense=minimize, track_bound=True)
node = Node()
node.tree_depth = 0
node.bound = 0
assert node.queue_priority is None
with pytest.raises(ValueError):
q.put(node)
node.queue_priority = 1
q.put(node)
child = _new_child(node)
assert child.queue_priority is None
with pytest.raises(ValueError):
q.put(child)
def test_usage_maximize(self):
q = CustomPriorityQueue(maximize)
assert q.size() == 0
assert q.bound() is None
assert len(list(q.items())) == 0
assert q.get() is None
items = []
for i in range(1, 11):
node = Node(size=0)
node.bound = -i
node.queue_priority = i
assert q.put(node._data) == i - 1
assert node.queue_priority == i
items.append(node._data)
assert q.size() == i
assert q.bound() == -1
_check_items(q, items)
assert q.size() == 10
assert q.bound() == -1
removed = q.filter(lambda data: \
Node._extract_bound(data) <= -5)
assert q.size() == 6
assert len(removed) == 4
for data in removed:
assert Node._extract_bound(data) > -5
assert q.bound() == -5
for i in range(10, 4, -1):
node = Node(data_=q.get())
assert node.bound == -i
assert node.queue_priority == i
if i != 5:
assert q.bound() == -5
else:
assert q.bound() is None
assert q.size() == 0
node = Node(size=0)
node.bound = 0
node.queue_priority = 1
cnt_, data = q.put_get(node._data)
assert cnt_ == 10
assert data is node._data
assert node.queue_priority == 1
assert q.bound() is None
node.queue_priority = 2
cnt_ = q.put(node._data)
assert node.queue_priority == 2
assert cnt_ == 11
assert q.bound() == 0
node2 = Node(size=0)
node2.bound = 1
node2.queue_priority = 3
cnt_, data = q.put_get(node2._data)
assert cnt_ == 12
assert data is node2._data
assert node2.queue_priority == 3
assert q.bound() == 0
node2.queue_priority = 1
cnt_, data = q.put_get(node2._data)
assert node2.queue_priority == 1
assert cnt_ == 13
assert data is node._data
assert q.size() == 1
assert q.bound() == 1
def test_missing_queue_priority(self):
q = CustomPriorityQueue(minimize)
node = Node(size=0)
node.bound = 0
assert node.queue_priority is None
with pytest.raises(ValueError):
q.put(node._data)
with pytest.raises(ValueError):
q.put_get(node._data)
node.queue_priority = 1
q.put(node._data)
child = node.new_child()
assert child.queue_priority is None
with pytest.raises(ValueError):
q.put(child._data)
with pytest.raises(ValueError):
q.put_get(child._data)
def test_usage_maximize(self):
q = CustomPriorityQueue(sense=maximize, track_bound=True)
assert q.size() == 0
assert q.bound() is None
assert len(list(q.items())) == 0
assert q.get() is None
items = []
for i in range(1, 11):
node = Node()
node.bound = -i
node.queue_priority = i
assert q.put(node) == i - 1
assert node.queue_priority == i
items.append(node)
assert q.size() == i
assert q.bound() == -1
_check_items(q, items)
assert q.size() == 10
assert q.bound() == -1
removed = q.filter(lambda n_: n_.bound <= -5)
assert q.size() == 6
assert len(removed) == 4
for node_ in removed:
assert node_.bound > -5
assert q.bound() == -5
for i in range(10, 4, -1):
node = q.get()
assert node.bound == -i
assert node.queue_priority == i
if i != 5:
assert q.bound() == -5
else:
assert q.bound() is None
assert q.size() == 0
node = Node()
node.bound = 0
node.queue_priority = 1
cnt_ = q.put(node)
node_ = q.get()
assert cnt_ == 10
assert node_ is node
assert node.queue_priority == 1
assert q.bound() is None
node.queue_priority = 2
cnt_ = q.put(node)
assert node.queue_priority == 2
assert cnt_ == 11
assert q.bound() == 0
node2 = Node()
node2.bound = 1
node2.queue_priority = 3
cnt_ = q.put(node2)
node_ = q.get()
assert cnt_ == 12
assert node_ is node2
assert node2.queue_priority == 3
assert q.bound() == 0
node2.queue_priority = 1
cnt_ = q.put(node2)
node_ = q.get()
assert node2.queue_priority == 1
assert cnt_ == 13
assert node_ is node
assert q.size() == 1
assert q.bound() == 1
# no bound tracking
q = CustomPriorityQueue(sense=maximize, track_bound=False)
assert q.size() == 0
assert q.bound() is None
assert len(list(q.items())) == 0
assert q.get() is None
items = []
for i in range(1, 11):
node = Node()
node.bound = -i
node.queue_priority = i
assert q.put(node) == i - 1
assert node.queue_priority == i
items.append(node)
assert q.size() == i
assert q.bound() == inf
_check_items(q, items)
assert q.size() == 10
assert q.bound() == inf
removed = q.filter(lambda n_: n_.bound <= -5)
assert q.size() == 6
assert len(removed) == 4
for node_ in removed:
assert node_.bound > -5
assert q.bound() == inf
for i in range(10, 4, -1):
node = q.get()
assert node.bound == -i
assert node.queue_priority == i
if i != 5:
assert q.bound() == inf
else:
assert q.bound() is None
assert q.size() == 0
def initialize(self, best_objective, initialize_queue, queue_strategy,
converger, node_limit, time_limit, log,
log_interval_seconds, log_new_incumbent):
"""Initialize the dispatcher for a new solve.
Parameters
----------
best_objective : float
The assumed best objective to start with.
initialize_queue : :class:`pybnb.dispatcher.DispatcherQueueData`
The initial queue.
queue_strategy : :class:`QueueStrategy <pybnb.common.QueueStrategy>`
Sets the strategy for prioritizing nodes in the
central dispatcher queue. See the
:class:`QueueStrategy <pybnb.common.QueueStrategy>`
enum for the list of acceptable values.
converger : :class:`pybnb.convergence_checker.ConvergenceChecker`
The branch-and-bound convergence checker object.
node_limit : int or None
An integer representing the maximum number of
nodes to processes before beginning to terminate
the solve. If None, no node limit will be
enforced.
time_limit : float or None
The maximum amount of time to spend processing
nodes before beginning to terminate the
solve. If None, no time limit will be enforced.
log : ``logging.Logger``
A log object where solver output should be sent.
log_interval_seconds : float
The approximate maximum time (in seconds)
between solver log updates. More time may pass
between log updates if no updates have been
received from any workers, and less time may
pass if a new incumbent is found.
log_new_incumbent : bool
Controls whether updates to the best objective
are logged immediately (overriding the log
interval). Setting this to false can be useful
when frequent updates to the incumbent are
expected and the additional logging slows down
the dispatcher.
"""
assert (node_limit is None) or \
((node_limit > 0) and \
(node_limit == int(node_limit)))
assert (time_limit is None) or \
(time_limit >= 0)
self.start_time = self.clock()
self.initialized = True
self.log_new_incumbent = log_new_incumbent
self.termination_condition = None
self.converger = converger
self.last_global_bound = self.converger.unbounded_objective
self.best_objective = best_objective
if queue_strategy == "bound":
self.queue = WorstBoundFirstPriorityQueue(self.converger.sense)
elif queue_strategy == "custom":
self.queue = CustomPriorityQueue(self.converger.sense)
elif queue_strategy == "objective":
self.queue = BestObjectiveFirstPriorityQueue(self.converger.sense)
elif queue_strategy == "fifo":
self.queue = FIFOQueue(self.converger.sense)
elif queue_strategy == "breadth":
self.queue = BreadthFirstPriorityQueue(self.converger.sense)
elif queue_strategy == "depth":
self.queue = DepthFirstPriorityQueue(self.converger.sense)
elif queue_strategy == "local_gap":
self.queue = LocalGapPriorityQueue(self.converger.sense)
elif queue_strategy == "random":
self.queue = RandomPriorityQueue(self.converger.sense)
else:
raise ValueError("'queue_strategy' must be one of: %s" %
(str([v.value for v in QueueStrategy])))
self.node_limit = None
if node_limit is not None:
self.node_limit = int(node_limit)
self.time_limit = None
if time_limit is not None:
self.time_limit = float(time_limit)
self.served_nodes_count = 0
self.worst_terminal_bound = None
self.next_tree_id = initialize_queue.next_tree_id
self.journalist = None
if (log is not None) and (not log.disabled):
self.journalist = StatusPrinter(
self, log, log_interval_seconds=log_interval_seconds)
if len(initialize_queue.nodes):
self._check_update_best_objective(
self.converger.best_objective(
node.objective for node in initialize_queue.nodes))
for node in initialize_queue.nodes:
assert node.tree_id is not None
self._add_work_to_queue(node._data, set_tree_id=False)