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util.py
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util.py
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import community
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
# ---- NetworkX compatibility
def node_iter(G):
if float(nx.__version__)<2.0:
return G.nodes()
else:
return G.nodes
def node_dict(G):
if float(nx.__version__)>2.1:
node_dict = G.nodes
else:
node_dict = G.node
return node_dict
# ---------------------------
def imsave(fname, arr, vmin=None, vmax=None, cmap=None, format=None, origin=None):
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
fig = Figure(figsize=arr.shape[::-1], dpi=1, frameon=False)
canvas = FigureCanvas(fig)
fig.figimage(arr, cmap=cmap, vmin=vmin, vmax=vmax, origin=origin)
fig.savefig(fname, dpi=1, format=format)
def plot_graph(plt, G):
plt.title('num of nodes: '+str(G.number_of_nodes()), fontsize = 4)
parts = community.best_partition(G)
values = [parts.get(node) for node in G.nodes()]
colors = []
for i in range(len(values)):
if values[i] == 0:
colors.append('red')
if values[i] == 1:
colors.append('green')
if values[i] == 2:
colors.append('blue')
if values[i] == 3:
colors.append('yellow')
if values[i] == 4:
colors.append('orange')
if values[i] == 5:
colors.append('pink')
if values[i] == 6:
colors.append('black')
plt.axis("off")
pos = nx.spring_layout(G)
# pos = nx.spectral_layout(G)
nx.draw_networkx(G, with_labels=True, node_size=4, width=0.3, font_size = 3, node_color=colors,pos=pos)
def draw_graph_list(G_list, row, col, fname = 'figs/test'):
# draw graph view
plt.switch_backend('agg')
for i, G in enumerate(G_list):
plt.subplot(row,col,i+1)
plot_graph(plt, G)
plt.tight_layout()
plt.savefig(fname+'_view.png', dpi=600)
plt.close()
# draw degree distribution
plt.switch_backend('agg')
for i, G in enumerate(G_list):
plt.subplot(row, col, i + 1)
G_deg = np.array(list(G.degree(G.nodes()).values()))
bins = np.arange(20)
plt.hist(np.array(G_deg), bins=bins, align='left')
plt.xlabel('degree', fontsize = 3)
plt.ylabel('count', fontsize = 3)
G_deg_mean = 2*G.number_of_edges()/float(G.number_of_nodes())
plt.title('average degree: {:.2f}'.format(G_deg_mean), fontsize=4)
plt.tick_params(axis='both', which='major', labelsize=3)
plt.tick_params(axis='both', which='minor', labelsize=3)
plt.tight_layout()
plt.savefig(fname+'_degree.png', dpi=600)
plt.close()
# degree_sequence = sorted(nx.degree(G).values(), reverse=True) # degree sequence
# plt.loglog(degree_sequence, 'b-', marker='o')
# plt.title("Degree rank plot")
# plt.ylabel("degree")
# plt.xlabel("rank")
# plt.savefig('figures/degree_view_' + prefix + '.png', dpi=200)
# plt.close()
# draw clustering distribution
#plt.switch_backend('agg')
#for i, G in enumerate(G_list):
# plt.subplot(row, col, i + 1)
# G_cluster = list(nx.clustering(G).values())
# bins = np.linspace(0,1,20)
# plt.hist(np.array(G_cluster), bins=bins, align='left')
# plt.xlabel('clustering coefficient', fontsize=3)
# plt.ylabel('count', fontsize=3)
# G_cluster_mean = sum(G_cluster) / len(G_cluster)
# # if i % 2 == 0:
# # plt.title('real average clustering: {:.4f}'.format(G_cluster_mean), fontsize=4)
# # else:
# # plt.title('pred average clustering: {:.4f}'.format(G_cluster_mean), fontsize=4)
# plt.title('average clustering: {:.4f}'.format(G_cluster_mean), fontsize=4)
# plt.tick_params(axis='both', which='major', labelsize=3)
# plt.tick_params(axis='both', which='minor', labelsize=3)
#plt.tight_layout()
#plt.savefig(fname+'_clustering.png', dpi=600)
#plt.close()
## draw circle distribution
#plt.switch_backend('agg')
#for i, G in enumerate(G_list):
# plt.subplot(row, col, i + 1)
# cycle_len = []
# cycle_all = nx.cycle_basis(G)
# for item in cycle_all:
# cycle_len.append(len(item))
# bins = np.arange(20)
# plt.hist(np.array(cycle_len), bins=bins, align='left')
# plt.xlabel('cycle length', fontsize=3)
# plt.ylabel('count', fontsize=3)
# G_cycle_mean = 0
# if len(cycle_len)>0:
# G_cycle_mean = sum(cycle_len) / len(cycle_len)
# # if i % 2 == 0:
# # plt.title('real average cycle: {:.4f}'.format(G_cycle_mean), fontsize=4)
# # else:
# # plt.title('pred average cycle: {:.4f}'.format(G_cycle_mean), fontsize=4)
# plt.title('average cycle: {:.4f}'.format(G_cycle_mean), fontsize=4)
# plt.tick_params(axis='both', which='major', labelsize=3)
# plt.tick_params(axis='both', which='minor', labelsize=3)
#plt.tight_layout()
#plt.savefig(fname+'_cycle.png', dpi=600)
#plt.close()
## draw community distribution
#plt.switch_backend('agg')
#for i, G in enumerate(G_list):
# plt.subplot(row, col, i + 1)
# parts = community.best_partition(G)
# values = np.array([parts.get(node) for node in G.nodes()])
# counts = np.sort(np.bincount(values)[::-1])
# pos = np.arange(len(counts))
# plt.bar(pos,counts,align = 'edge')
# plt.xlabel('community ID', fontsize=3)
# plt.ylabel('count', fontsize=3)
# G_community_count = len(counts)
# # if i % 2 == 0:
# # plt.title('real average clustering: {}'.format(G_community_count), fontsize=4)
# # else:
# # plt.title('pred average clustering: {}'.format(G_community_count), fontsize=4)
# plt.title('average clustering: {}'.format(G_community_count), fontsize=4)
# plt.tick_params(axis='both', which='major', labelsize=3)
# plt.tick_params(axis='both', which='minor', labelsize=3)
#plt.tight_layout()
#plt.savefig(fname+'_community.png', dpi=600)
#plt.close()
def exp_moving_avg(x, decay=0.9):
shadow = x[0]
a = [shadow]
for v in x[1:]:
shadow -= (1-decay) * (shadow-v)
a.append(shadow)
return a