import gensim

import networkx as nx

from scipy.spatial.distance import cosine

from datetime import timedelta

from interactions import InteractionsUtil as IU

from util import json_load

interactions = json_load('data/enron/interactions.json')

lda_model = gensim.models.ldamodel.LdaModel.load(

'data/enron/model-50-50.lda'

)

dictionary = gensim.corpora.dictionary.Dictionary.load(

'data/enron/dict.pkl'

)

different_weights = [

]

for weights in different_weights:

meta_graph_kws = {

'distance_weights': weights,

}

g = IU.get_topic_meta_graph(

interactions,

lda_model=lda_model,

dictionary=dictionary,

undirected=False,

given_topics=False,

decompose_interactions=False,

dist_func=cosine,

preprune_secs=timedelta(weeks=4).total_seconds(),

apply_pagerank=False,

**meta_graph_kws

)

print('weights: {}\n'.format(weights))

out_degrees = g.out_degree(g.nodes())

sorted_nodes = sorted(out_degrees,

key=lambda k: out_degrees[k],

reverse=True)

print('\n'.join(map(lambda n: g.node[n]['subject'], sorted_nodes)[:10]))

node = sorted_nodes[5]

def print_message(node):

)

print('Node')

print_message(node)

top_k_nodes = sorted(g.neighbors(node), key=lambda nb: g[node][nb]['c'])[:5]

for i, n in enumerate(top_k_nodes):

print('{}:'.format(i))

print_message(n)

print('*' * 100)