def __function_type(node,cpt):
type(cpt) is FunctionType
# functions that specify cpts cannot have free variables
freevars = cpt.__code__.co_freevars
u.check(not freevars,
f'cpt function for node {node.name} has free variables, {u.unpack(freevars)}',
f'specifying TBN cpt using python code')
# let's find out whether this is a functional or constrained cpt
parameters = signature(cpt).parameters
free_parameter_count = 0
has_star_args = False
for p in parameters.values():
sp = str(p)
assert '**' not in sp
if '*' in sp:
assert free_parameter_count == 0 # *args must be first
has_star_args = True
elif '=' not in sp: free_parameter_count += 1
parent_count = len(node.parents)
if (not has_star_args and free_parameter_count == parent_count) or \
(has_star_args and free_parameter_count==0):
return 'function'
assert (not has_star_args and free_parameter_count == parent_count + 1) or \
(has_star_args and free_parameter_count == 1)
return 'constraint'
def expand(node,cpt,cpt_type):
normalized = lambda cpt: np.allclose(1.,np.sum(cpt,axis=-1))
tabular = True # whether original cpt is a list or np array
if type(cpt) is list:
cpt = np.array(cpt)
elif type(cpt) is FunctionType:
tabular = False
fn_type = __function_type(node,cpt)
if fn_type == 'function':
cpt = __expand_fcpt(node,cpt,node.parents)
else:
assert fn_type == 'constraint'
cpt = __expand_ccpt(node,cpt,node.parents)
else:
u.check(type(cpt) is np.array,
f'{cpt_type} of node {node.name} is not a list, np array or python function:\n {cpt}',
f'specifying TBN cpt')
assert type(cpt) is np.ndarray
assert cpt.shape == node.shape()
u.check(normalized(cpt),
f'{cpt_type} of node {node.name} is not normalized:\n {cpt}',
f'specifying TBN cpt')
cpt.flags.writeable = False # read only
return cpt, tabular # np array
def train(self, model: nn.Module,
data_dict: Dict[str, BaseModel.Dataset]) -> NoReturn:
main_metric_results, dev_results, test_results = list(), list(), list()
self._check_time(start=True)
try:
for epoch in range(self.epoch):
# Fit
self._check_time()
loss = self.fit(model, data_dict['train'], epoch=epoch + 1)
training_time = self._check_time()
# Observe selected tensors
if len(
model.check_list
) > 0 and self.check_epoch > 0 and epoch % self.check_epoch == 0:
utils.check(model.check_list)
# Record dev and test results
dev_result = self.evaluate(model, data_dict['dev'],
self.topk[:1], self.metrics)
test_result = self.evaluate(model, data_dict['test'],
self.topk[:1], self.metrics)
testing_time = self._check_time()
dev_results.append(dev_result)
test_results.append(test_result)
main_metric_results.append(dev_result[self.main_metric])
logging.info(
"Epoch {:<5} loss={:<.4f} [{:<.1f} s]\t dev=({}) test=({}) [{:<.1f} s] "
.format(epoch + 1, loss, training_time,
utils.format_metric(dev_result),
utils.format_metric(test_result), testing_time))
# Save model and early stop
if max(main_metric_results) == main_metric_results[-1] or \
(hasattr(model, 'stage') and model.stage == 1):
model.save_model()
if self.early_stop and self.eval_termination(
main_metric_results):
logging.info("Early stop at %d based on dev result." %
(epoch + 1))
break
except KeyboardInterrupt:
logging.info("Early stop manually")
exit_here = input(
"Exit completely without evaluation? (y/n) (default n):")
if exit_here.lower().startswith('y'):
logging.info(os.linesep + '-' * 45 + ' END: ' +
utils.get_time() + ' ' + '-' * 45)
exit(1)
# Find the best dev result across iterations
best_epoch = main_metric_results.index(max(main_metric_results))
logging.info(
os.linesep +
"Best Iter(dev)={:>5}\t dev=({}) test=({}) [{:<.1f} s] ".format(
best_epoch + 1, utils.format_metric(dev_results[best_epoch]),
utils.format_metric(test_results[best_epoch]), self.time[1] -
self.time[0]))
model.load_model()
def __prepare_for_inference(self):
# the following attributes are updated in decouple.py, which replicates
# functional cpts and handles nodes with hard evidence, creating clones
# of nodes in the process (clones are added to another 'decoupled' network)
self._original = None # tbn node cloned by this one
self._master = None # exactly one clone is declared as master
self._clamped = False # whether tbn node has hard evidence
# the following attributes with _cpt, _cpt1, _cpt2 are updated in cpt.y
self._values_org = self.values # original node values before pruning
self._card_org = self.card # original node cardinality before pruning
self._values_idx = None # indices of unpruned values, if pruning happens
# -process node and its cpts
# -prune node values & parents and expand/prune cpts into tabular form
tbn.cpt.set_cpts(self)
# the following attributes will be updated next
self._all01_cpt = None # whether cpt is 0/1 (not applicable for testing nodes)
self._cpt_label = None # for saving to file (updated when processing cpts)
# identify 0/1 cpts
if self.testing:
# selected cpt is not necessarily all zero-one even if cpt1 and cpt2 are
self._all01_cpt = False
else:
self._all01_cpt = np.all(
np.logical_or(self.cpt == 0, self.cpt == 1))
u.check(
not (self.fixed_cpt and self._functional) or self._all01_cpt,
f'node {self.name} is declared functional but its fixed cpt is not functional',
f'specifying TBN node')
# -pruning node values or parents changes the shape of cpt for node
# -a set of tied cpts may end up having different shapes due to pruning
# -we create refined ties between groups that continue to have the same shape
""" this is not really proper and needs to be updated """
if self.cpt_tie is not None:
# s = '.'.join([str(hash(n.values)) for n in self.family])
self._cpt_tie = f'{self.cpt_tie}__{self.shape()}'
self.__set_cpt_labels()
# we need to sort parents & family and also adjust the cpt accordingly
# this must be done after processing cpts which may prune parents
self.__sort()
assert u.sorted(u.map('id', self.parents))
assert u.sorted(u.map('id', self.family))
def elm_order(self, solver, wait):
u.show(f' calling {solver}...', end='')
graph_fname = 'decompose/tmp/graph.gr'
tree_fname = 'decompose/tmp/tree.td'
if solver == 'flow cutter':
program = 'flow_cutter_pace17'
cmd = [f'./decompose/solvers/{program}']
online = True
elif solver == 'tamaki heuristic':
program = 'tamaki/tw-heuristic'
cmd = [f'./decompose/solvers/{program}']
online = True
elif solver == 'tamaki exact':
program = 'tamaki/tw-exact'
cmd = [f'./decompose/solvers/{program}']
online = False
# write graph to file
self.write(graph_fname)
# call tree decomposition program
with open(f'{graph_fname}', "r") as input, open(f'{tree_fname}',
"w") as output:
process = subprocess.Popen(cmd, stdin=input, stdout=output)
if online:
u.show(f'waiting {wait} sec...', end='', flush=True)
sleep(wait)
process.send_signal(signal.SIGTERM)
else:
process.wait() # blocks python until process returns
code = process.returncode
_, error = process.communicate()
process.kill()
u.check(code != 0, f'failed to execute {solver} because\n {error}',
f'using treewidth solver')
u.show('done')
# read decomposition tree from file
tree = TreeD(tree_fname)
# convert decomposition tree to elimination order (vertices)
vertex_order = tree.elm_order()
# return elimination order of tbn nodes
stats = f'elm order: cls max {tree.width}'
return self.vertices2nodes(vertex_order), tree.width, stats