def scores_from_votes(votes):
eliminated_players = votes.columns
# Turn vote matrix into graph object
v = process_votes.compare_votes(votes)
g = make_graphs.make_graph(v)
# Calculate scores
scores = network.centrality_scores(votes, g)
# Add votes for and against
vca = votes_correct_against(votes)
scores = scores.join(vca, on='name')
# Binary classification of winners (1) and losers (0)
scores['place'] = np.where(scores['place'] == 1, 1, 0)
# Rearrange columns
scores = scores[['name','deg','close','btw','eig','page','votes_correct','votes_against','place']]
# Filter out eliminated players
fltr = [i.strip() not in eliminated_players for i in scores['name']]
return scores.loc[fltr, :]
def scores_from_votes(votes):
eliminated_players = votes.columns
# Turn vote matrix into graph object
v = process_votes.compare_votes(votes)
g = make_graphs.make_graph(v)
# Calculate scores
scores = network.centrality_scores(votes, g)
# Add votes for and against
vca = votes_correct_against(votes)
scores = scores.join(vca, on='name')
# Binary classification of winners (1) and losers (0)
scores['place'] = np.where(scores['place'] == 1, 1, 0)
# Rearrange columns
scores = scores[[
'name', 'deg', 'close', 'btw', 'eig', 'page', 'votes_correct',
'votes_against', 'place'
]]
# Filter out eliminated players
fltr = [i.strip() not in eliminated_players for i in scores['name']]
return scores.loc[fltr, :]
import parser, make_graphs
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.legend_handler import HandlerLine2D
if __name__ == '__main__':
filenames = ["0301/{}.txt".format(i) for i in range(0, 3)]
data = parser.Data(filenames)
graph = make_graphs.make_graph(data)
print graph.GetNodes()
#centr_files = ['GetBetweennessCentr-0-2.txt', 'GetClosenessCentr-0-2.txt', 'GetDegreeCentr-0-2.txt']
centr_files = ['GetDegreeCentr-0-2.txt']
for centr in centr_files:
with open(centr) as f:
contents = f.readlines()
split = [l.strip().split(' ') for l in contents]
parsed = [(int(nid), float(centr)) for nid, centr in split]
results = [(data.lookup[data.videoid[nid]].age, centr)
for nid, centr in parsed]
x, y = zip(*results)
print y[:20]
print x[:20]
#plt.loglog(x, y, 'o')
plt.title('Degree Centrality vs Age')
plt.xlabel('age')
plt.ylabel('centrality score')
plt.scatter(x, y)
plt.show()
# example save and load
#save_graph_data(data, graph, "test")
#data, graph = load_graph_data("test")
# example reading and processing
for n in graph.Nodes():
nid = n.GetId()
vid = data.videoid[nid]
if vid in data.lookup:
print data.lookup[vid]
'''
filenames = [ "0301/{}.txt".format(i) for i in range(0, 3) ]
data = parser.Data(filenames)
graph = make_graphs.make_graph(data)
Graph = snap.ConvertGraph(snap.PUNGraph, graph)
GraphClustCoeff = snap.GetClustCf (Graph, -1)
print "Average clustering coefficient of the graph is ", GraphClustCoeff
for category in data.categories:
graph1 = make_graphs.make_graph(data,[category])
Graph1 = snap.ConvertGraph(snap.PUNGraph, graph1)
print category, Graph1.GetNodes(), Graph1.GetEdges()
GraphClustCoeff1 = snap.GetClustCf (Graph1, -1)
print "Average clustering coefficient of the " +category + " graph is ", GraphClustCoeff1