def test_make_artificial_data_with_minor_events(self):
self.params['n_minor_events'] = 10
events, all_interactions, _ = make_artificial_data(**self.params)
assert_equal(
self.params['event_size_mu'] * self.params['n_events'] +
self.params['minor_event_size_mu'] * self.params['n_minor_events'] +
self.params['n_noisy_interactions'],
len(all_interactions)
)
event_msg_ids = set([e.node[n]['message_id']
for e in events
for n in e.nodes_iter()])
event_topics = set([np.asarray(e.node[n]['topics']).argmax()
for e in events
for n in e.nodes_iter()])
other_topics = set()
for i in all_interactions:
id_ = i['message_id']
if id_ not in event_msg_ids: # either noise or minor
topic = np.asarray(i['topics']).argmax()
other_topics.add(topic)
assert(topic not in event_topics)
# should complement each other and sum to all topics
assert_equal(set(range(10)),
other_topics | event_topics)
def test_make_artificial_data_with_large_noise_percentage(self):
fraction = 1.1
self.params['n_noisy_interactions'] = None
self.params['n_noisy_interactions_fraction'] = fraction
events, all_interactions, _ = make_artificial_data(**self.params)
n_event_interactions = sum([1 for e in events for _ in e])
total = len(all_interactions)
assert_equal(
int(n_event_interactions * fraction),
total - n_event_interactions
)
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-'))
