def draw_example_meta_graph(output_path):
_, _, ints = load_meta_graph_necessities()
g = IU.get_meta_graph(ints, decompose_interactions=True)
plt.clf()
pos = nx.spring_layout(g)
nx.draw_networkx_nodes(g, pos=pos)
nx.draw_networkx_edges(g, pos=pos)
nx.draw_networkx_labels(g,
pos=pos,
labels=dict(zip(g.nodes(), g.nodes())),
font_size=8)
plt.savefig(output_path)
def draw_example_meta_graph(output_path):
_, _, ints = load_meta_graph_necessities()
g = IU.get_meta_graph(
ints,
decompose_interactions=True
)
plt.clf()
pos = nx.spring_layout(g)
nx.draw_networkx_nodes(g, pos=pos)
nx.draw_networkx_edges(g, pos=pos)
nx.draw_networkx_labels(g, pos=pos,
labels=dict(zip(g.nodes(), g.nodes())),
font_size=8)
plt.savefig(output_path)
def extract_event_context(interactions, event_tree, undirected=False):
span = MetaGraphStat(event_tree).time_span()
start = span['start_time']
end = span['end_time']
filtered_interactions = []
for i in interactions:
assert 'datetime' in i
dt = i['datetime']
assert isinstance(dt, datetime)
if dt >= start and dt <= end:
filtered_interactions.append(i)
context_dag = IU.get_meta_graph(filtered_interactions,
decompose_interactions=False,
remove_singleton=True,
undirected=undirected)
return context_dag
def test_gen_event_with_known_tree_structure():
event_size = 100
participants_n = 10
event = gen_event_with_known_tree_structure(
event_size=event_size,
participants=range(participants_n),
start_time=10, end_time=110,
event_topic_param=random_topic(10, topic_noise=0.0001)[0],
topic_noise=1,
alpha=1.0, tau=0.8,
forward_proba=0.3,
reply_proba=0.5,
create_new_proba=0.2
)
for n in event.nodes_iter():
sid, rid = event.node[n]['sender_id'], event.node[n]['recipient_ids'][0]
assert_true(sid != rid)
for s, t in event.edges_iter():
sid1, rid1 = event.node[s]['sender_id'], event.node[s]['recipient_ids'][0]
sid2, rid2 = event.node[t]['sender_id'], event.node[t]['recipient_ids'][0]
c_type = event[s][t]['c_type']
if c_type == 'r':
assert_equal(sid1, rid2)
assert_equal(sid2, rid1)
elif c_type == 'f':
assert_equal(rid1, sid2)
assert_true(rid2 != sid1)
else:
assert_equal(sid1, sid2)
interactions = [event.node[n] for n in event.nodes_iter()]
g = IU.get_meta_graph(
interactions,
decompose_interactions=False,
remove_singleton=True,
given_topics=True,
convert_time=False
)
assert_equal(1, len(get_roots(g)))
assert_equal(event_size, len(interactions))
assert_true(nx.is_arborescence(event))
def test_make_artificial_data(self):
events, all_interactions, params = make_artificial_data(**self.params)
assert_equal(self.params['n_events'],
len(params))
assert_equal(
self.params['n_events'],
len(events)
)
assert_equal(
self.params['event_size_mu'] * self.params['n_events'] +
self.params['n_noisy_interactions'],
len(all_interactions)
)
for i in all_interactions:
assert_true('message_id' in i)
# make sure it's jsonable
assert_true(isinstance(i['topics'], list))
# all ids are unique
all_ids = list(itertools.chain(*[e.nodes() for e in events]))
assert_equal(len(all_ids), len(set(all_ids)))
for e in events:
# make sure nodes are relabeled
for n in e.nodes_iter():
assert_equal(n, e.node[n]['message_id'])
interactions = [e.node[n] for n in e.nodes_iter()]
assert_equal(len(interactions),
IU.get_meta_graph(
interactions,
decompose_interactions=False,
remove_singleton=True,
given_topics=True).number_of_nodes())
for i in interactions:
assert_true(isinstance(i['topics'], list))
for i in all_interactions:
assert_true(i['sender_id'].startswith('u-'))
def random_events(n_events, event_size_mu, event_size_sigma,
n_total_participants, participant_mu, participant_sigma,
min_time, max_time, event_duration_mu, event_duration_sigma,
n_topics, topic_scaling_factor, topic_noise,
alpha, tau,
forward_proba,
reply_proba,
create_new_proba,
taboo_topics=set(),
accumulate_taboo=False):
# add main events
events = []
taboo_topics = set(taboo_topics)
for i in xrange(n_events):
# randomly select a topic and add some noise to it
event = []
event_topic_param, topic_id = random_topic(
n_topics,
topic_noise,
taboo_topics
)
if accumulate_taboo:
taboo_topics.add(topic_id)
print('event_topic_param:', event_topic_param)
event_size = 0
while event_size <= 0:
event_size = int(round(
np.random.normal(event_size_mu, event_size_sigma)
))
assert event_size > 0
# randomly select participants
n_participants = 0
while n_participants <= 2:
n_participants = int(round(
np.random.normal(participant_mu, participant_sigma)
))
assert n_participants > 2
participants = np.random.permutation(
n_total_participants
)[:n_participants]
print('participants:', participants)
# event timespan
start_time = np.random.uniform(min_time, max_time - event_duration_mu)
end_time = start_time + np.random.normal(event_duration_mu,
event_duration_sigma)
if end_time > max_time:
end_time = max_time
event = gen_event_with_known_tree_structure(
event_size, participants, start_time, end_time,
event_topic_param,
topic_noise,
alpha, tau,
forward_proba,
reply_proba,
create_new_proba
)
# some checking
g = IU.get_meta_graph(
[event.node[n] for n in event.nodes_iter()],
decompose_interactions=False,
remove_singleton=True,
given_topics=True,
convert_time=False)
n_interactions_in_mg = g.number_of_nodes()
if n_interactions_in_mg == len(event):
roots = [n
for n, d in g.in_degree(g.nodes_iter()).items()
if d == 0]
if len(roots) > 1:
print(roots)
for r in roots:
print(event[r])
print("WARNING: roots number {}".format(len(roots)))
raise
else:
print(
'invalid meta graph. {} < {}'.format(
n_interactions_in_mg,
len(event)
))
raise
events.append(event)
return events, taboo_topics
def random_events(n_events,
event_size_mu,
event_size_sigma,
n_total_participants,
participant_mu,
participant_sigma,
min_time,
max_time,
event_duration_mu,
event_duration_sigma,
n_topics,
topic_scaling_factor,
topic_noise,
alpha,
tau,
forward_proba,
reply_proba,
create_new_proba,
taboo_topics=set(),
accumulate_taboo=False):
# add main events
events = []
taboo_topics = set(taboo_topics)
for i in xrange(n_events):
# randomly select a topic and add some noise to it
event = []
event_topic_param, topic_id = random_topic(n_topics, topic_noise,
taboo_topics)
if accumulate_taboo:
taboo_topics.add(topic_id)
print('event_topic_param:', event_topic_param)
event_size = 0
while event_size <= 0:
event_size = int(
round(np.random.normal(event_size_mu, event_size_sigma)))
assert event_size > 0
# randomly select participants
n_participants = 0
while n_participants <= 2:
n_participants = int(
round(np.random.normal(participant_mu, participant_sigma)))
assert n_participants > 2
participants = np.random.permutation(
n_total_participants)[:n_participants]
print('participants:', participants)
# event timespan
start_time = np.random.uniform(min_time, max_time - event_duration_mu)
end_time = start_time + np.random.normal(event_duration_mu,
event_duration_sigma)
if end_time > max_time:
end_time = max_time
event = gen_event_with_known_tree_structure(event_size, participants,
start_time, end_time,
event_topic_param,
topic_noise, alpha, tau,
forward_proba, reply_proba,
create_new_proba)
# some checking
g = IU.get_meta_graph([event.node[n] for n in event.nodes_iter()],
decompose_interactions=False,
remove_singleton=True,
given_topics=True,
convert_time=False)
n_interactions_in_mg = g.number_of_nodes()
if n_interactions_in_mg == len(event):
roots = [
n for n, d in g.in_degree(g.nodes_iter()).items() if d == 0
]
if len(roots) > 1:
print(roots)
for r in roots:
print(event[r])
print("WARNING: roots number {}".format(len(roots)))
raise
else:
print('invalid meta graph. {} < {}'.format(n_interactions_in_mg,
len(event)))
raise
events.append(event)
return events, taboo_topics