def main1():
C, P, T1, T2 = ('CEO', 'PM', 'T1', 'T2')
p = 'progress'
s = 'suggetion'
f = 'football'
correct_edge_to_color = {
('a', 'b'): 'red',
('b', 'c'): 'red',
('c', 'd'): 'red',
('e', 'f'): 'green'
}
interactions = [('a', C, [P], p, 1), ('b', P, [T1, T2], p, 2),
('c', T1, [P], p, 3), ('d', P, [C], p, 4),
('e', T2, [P], s, 3), ('f', P, [C], p, 5),
('g', T2, [T1], f, 4)]
new_interactions = []
for msg_id, sender, recs, topic, time in interactions:
new_interactions.append(
{
'sender_id': sender,
'recipient_ids': recs,
'datetime': time,
'message_id': msg_id
}, )
node_names, sources, targets, time_stamps = InteractionsUtil.unzip_interactions(
new_interactions)
graph = convert_to_meta_graph(node_names, sources, targets, time_stamps)
# nx.write_dot(graph, 'tmp/illustration.dot')
print """digraph {
node [fontsize=20];
"""
for u, v in graph.edges():
print "{} -> {}[color={}];".format(
u,
v,
# correct_edge_to_color.get((u, v), 'gray')
'black')
print "}"
df = pd.DataFrame(new_interactions,
columns=['sender_id', 'recipient_ids', 'datetime'],
index=[i[0] for i in interactions])
df = df.rename(columns={
'sender_id': 'sender',
'recipient_ids': 'recipients',
'datetime': 'time'
})
mapping = {1: 'Mon', 2: 'Tue', 3: 'Wed', 4: 'Thu', 5: 'Fri'}
df['time'] = df['time'].map(lambda t: mapping[t])
df.to_latex('tmp/example.tex')
def main():
K, M, C, H = 'K', 'M', 'C', 'H'
interactions = [
{'sender_id': K, 'recipient_ids': (M, C), 'datetime': 1, 'message_id': 'K->(M, C): code(1)'},
{'sender_id': M, 'recipient_ids': [K], 'datetime': 3, 'message_id': 'M->K: read(3)'},
{'sender_id': K, 'recipient_ids': [M], 'datetime': 4, 'message_id': 'K->M: read(3)'},
{'sender_id': C, 'recipient_ids': [H], 'datetime': 2, 'message_id': 'C->H: eat(2)'},
{'sender_id': H, 'recipient_ids': [C], 'datetime': 3, 'message_id': 'H->C: eat(2)'},
]
InteractionsUtil.decompose_interactions(interactions)
node_names, sources, targets, time_stamps = InteractionsUtil.unzip_interactions(
InteractionsUtil.decompose_interactions(interactions)
)
graph = convert_to_meta_graph(node_names, sources, targets, time_stamps)
print graph.edges()
def main():
K, M, C, H = 'K', 'M', 'C', 'H'
interactions = [
{
'sender_id': K,
'recipient_ids': (M, C),
'datetime': 1,
'message_id': 'K->(M, C): code(1)'
},
{
'sender_id': M,
'recipient_ids': [K],
'datetime': 3,
'message_id': 'M->K: read(3)'
},
{
'sender_id': K,
'recipient_ids': [M],
'datetime': 4,
'message_id': 'K->M: read(3)'
},
{
'sender_id': C,
'recipient_ids': [H],
'datetime': 2,
'message_id': 'C->H: eat(2)'
},
{
'sender_id': H,
'recipient_ids': [C],
'datetime': 3,
'message_id': 'H->C: eat(2)'
},
]
InteractionsUtil.decompose_interactions(interactions)
node_names, sources, targets, time_stamps = InteractionsUtil.unzip_interactions(
InteractionsUtil.decompose_interactions(interactions))
graph = convert_to_meta_graph(node_names, sources, targets, time_stamps)
print graph.edges()
def get_meta_graph(cls, interactions,
undirected=False,
preprune_secs=None,
decompose_interactions=True,
remove_singleton=True,
given_topics=False,
apply_pagerank=False,
convert_time=True):
"""
Return the meta graph together with temporally sorted interactions
Decompose interactions if requested
"""
if decompose_interactions:
if undirected:
raise ValueError('Non-sense to deompose for undirected graph')
logger.info("decomposing and cleaning interactions...")
interactions = cls.decompose_interactions(
cls.clean_interactions(
interactions,
undirected=undirected,
convert_time=convert_time
)
)
else:
logger.info("cleaning interactions...")
interactions = cls.clean_interactions(
interactions,
undirected=undirected,
convert_time=convert_time
)
if not undirected:
logger.info('processing **directed** interactions')
g = convert_to_meta_graph(*cls.unzip_interactions(interactions),
preprune_secs=preprune_secs)
else:
logger.info('processing **undirected** interactions')
g = convert_to_meta_graph_undirected(
*cls.unzip_interactions_undirected(interactions),
preprune_secs=preprune_secs
)
for i in interactions:
n = i['message_id']
if decompose_interactions:
g.node[n]['message_id'] = i['original_message_id']
else:
g.node[n]['message_id'] = i['message_id']
if not given_topics:
g.node[n]['body'] = i['body']
g.node[n]['subject'] = i['subject']
else:
g.node[n]['topics'] = i['topics']
g.node[n]['datetime'] = i['datetime']
if 'hashtags' in i:
g.node[n]['hashtags'] = i['hashtags']
g.node[n][cls.VERTEX_REWARD_KEY] = 1
if decompose_interactions:
g.node[n]['peers'] = i['peers']
if undirected:
g.node[n]['participant_ids'] = i['participant_ids']
else:
g.node[n]['sender_id'] = i['sender_id']
g.node[n]['recipient_ids'] = i['recipient_ids']
if remove_singleton:
for n in g.nodes():
if g.degree(n) == 0:
g.remove_node(n)
# override reward scores
if apply_pagerank:
logger.info('Appling pagerank to get node rewark')
g = cls.add_rewards_to_nodes_using_pagerank(g, interactions)
return g
def main1():
C, P, T1, T2 = ('CEO', 'PM', 'T1', 'T2')
p = 'progress'
s = 'suggetion'
f = 'football'
correct_edge_to_color = {
('a', 'b'): 'red',
('b', 'c'): 'red',
('c', 'd'): 'red',
('e', 'f'): 'green'
}
interactions = [
('a', C, [P], p, 1),
('b', P, [T1, T2], p, 2),
('c', T1, [P], p, 3),
('d', P, [C], p, 4),
('e', T2, [P], s, 3),
('f', P, [C], p, 5),
('g', T2, [T1], f, 4)
]
new_interactions = []
for msg_id, sender, recs, topic, time in interactions:
new_interactions.append(
{'sender_id': sender,
'recipient_ids': recs,
'datetime': time,
'message_id': msg_id},
)
node_names, sources, targets, time_stamps = InteractionsUtil.unzip_interactions(
new_interactions
)
graph = convert_to_meta_graph(node_names, sources, targets, time_stamps)
# nx.write_dot(graph, 'tmp/illustration.dot')
print """digraph {
node [fontsize=20];
"""
for u, v in graph.edges():
print "{} -> {}[color={}];".format(
u, v,
# correct_edge_to_color.get((u, v), 'gray')
'black'
)
print "}"
df = pd.DataFrame(new_interactions,
columns=['sender_id', 'recipient_ids', 'datetime'],
index=[i[0] for i in interactions])
df = df.rename(columns={'sender_id': 'sender',
'recipient_ids': 'recipients',
'datetime': 'time'})
mapping = {
1: 'Mon',
2: 'Tue',
3: 'Wed',
4: 'Thu',
5: 'Fri'
}
df['time'] = df['time'].map(lambda t: mapping[t])
df.to_latex('tmp/example.tex')