def mass_annotate_mp(inputs, vectorizer, score_attribute='importance', estimator=None, multi_process=False, invert_score=False):
'''
graph annotation is slow. i dont want to do it twice in fit and predict :)
'''
# 1st check if already annotated
if inputs[0].graph.get('mass_annotate_mp_was_here', False):
return inputs
if multi_process == False:
inputs = filter(lambda v: v is not None, inputs)
res = list(vectorizer.annotate(inputs, estimator=estimator))
#if invert_score:
# def f(n,d): d['importance'] = -d['importance']
# res=utils.map_node_operation(res,f)
res[0].graph['mass_annotate_mp_was_here'] = True
return res
else:
pool = mp.Pool()
mpres = [eden.apply_async(pool, mass_annotate_mp, args=(graphs, vectorizer, score_attribute, estimator)) for
graphs in eden.grouper(inputs, 50)]
result = []
for res in mpres:
result += res.get()
pool.close()
pool.join()
return result
def _make_multi_process_batches(self, problem_iter):
'''
we do two things here:
-break tasks into batches to be multiprocessed.
-multiprocess sometimes does not terminate properly so we observe how many tasks go in and terminate
once that number of outs is reached.
Parameters
----------
problem_iter: problems to put into the multiprocess queue
Returns
-------
yields a batchsize sized problem chunks
'''
try:
s = dill.dumps(self, byref=False)
except Exception as exc:
print exc
print "dill dump failed in graphlearn.py (dill dies silently sometimes)"
self.multiprocess_jobcount = 0
self.multiprocess_all_prepared = False
for e in grouper(problem_iter, self.batch_size):
# cant just take batch size here because output of nons will be suppressed
problems = [1 for problem in e if problem != None]
self.multiprocess_jobcount += sum(problems)
batch = dill.dumps(e)
yield (s, batch)
self.multiprocess_all_prepared = True
def _multi_process_argbuilder(self, graphs, batch_size=10):
args = self._get_args()
function = self.get_cip_extractor()
self.multiprocess_jobcount = 0
self.mp_prepared = False
for batch in grouper(graphs, batch_size):
self.multiprocess_jobcount += batch_size
yield dill.dumps((function, args, batch))
self.mp_prepared = True
def mass_annotate_mp(inputs, vectorizer, score_attribute='importance', estimator=None, multi_process=False, annotate_dilude_scores=False):
'''
graph annotation is slow. i dont want to do it twice in fit and predict :)
'''
# 1st check if already annotated
#if inputs[0].graph.get('mass_annotate_mp_was_here', False):
# return inputs
if multi_process == False:
inputs = filter(lambda v: v is not None, inputs)
res = list(vectorizer.annotate(inputs, estimator=estimator))
def dilute_graph( graph):
for n, d in graph.nodes(data=True):
neighsum = [graph.node[other][score_attribute][0] for other in graph.neighbors(n)]
if neighsum != []:
allfacs = neighsum + [graph.node[n][score_attribute][0]] * len(neighsum)
score = sum(allfacs) / float(len(allfacs))
else:
score = d[score_attribute][0]
d['tmpscore'] = score
for n, d in graph.nodes(data=True):
d[score_attribute] = [d['tmpscore'], 0]
# self.attribute = lambda x: x['tmpscore']
if annotate_dilude_scores:
map(dilute_graph,res)
#if invert_score:
# def f(n,d): d['importance'] = -d['importance']
# res=utils.map_node_operation(res,f)
res[0].graph['mass_annotate_mp_was_here'] = True
return res
else:
pool = mp.Pool()
mpres = [eden.apply_async(pool, mass_annotate_mp, args=(graphs, vectorizer, score_attribute, estimator)) for
graphs in eden.grouper(inputs, 50)]
result = []
for res in mpres:
result += res.get()
pool.close()
pool.join()
return result
def _multi_process_argbuilder(self, graphs, batch_size=10):
args = self._get_args()
function = self.get_cip_extractor()
for batch in grouper(graphs, batch_size):
yield dill.dumps((function, args, batch))
def _argbuilder(self, problem_iter):
# for multiprocessing divide task into small multiprocessable bites
s = dill.dumps(self)
for e in grouper(problem_iter, self.batch_size):
batch = dill.dumps(e)
yield (s, batch)
def _multi_process_argbuilder(self, graphs, batch_size=10):
args = self._get_args()
function = self.get_cip_extractor()
for batch in grouper(graphs, batch_size):
yield dill.dumps((function, args, batch))
