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_get_gen_cand_tree_params():
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=0.1)[0],
topic_noise=1,
alpha=1.0, tau=0.8,
forward_proba=0.3,
reply_proba=0.5,
create_new_proba=0.2
)
event = IU.assign_edge_weights(event, cosine)
params = get_gen_cand_tree_params(event)
assert_true(params['U'] > 0)
assert_equal(99, params['preprune_secs'])
assert_equal([0], params['roots'])
def test_uniform_topic(self):
topic, _ = random_topic(10, 1)
self.seems_like_uniform_distribution(topic)
def test_random_topic_with_taboo(self):
topic, main_topic = random_topic(10, 0.00001, taboo_topics=range(9))
np.testing.assert_almost_equal(1, topic.sum())
self.seems_like_skewed_distribution(topic)
assert_equal(9, topic.argmax())
assert_equal(9, main_topic)
def test_random_topic(self):
topic, _ = random_topic(10, 0.00001)
np.testing.assert_almost_equal(1, topic.sum())
self.seems_like_skewed_distribution(topic)