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plot_at_author_vecs.py
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plot_at_author_vecs.py
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import matplotlib.pyplot as plt
import networkx as nx
def topics_per_author_read(file_name):
"""
Make a dictionary from txt file
"""
#sample line: ["'Baer'"]: [(4, 0.5755257259125403), (5, 0.3571996042432249), (3, 0.03692681114427046), (9, 0.030326159524861684)]
author_topic_prob_list = []
def parse(line):
author, topics = line.split(':')
author = author.strip('["\']')
print('author:', author)
topic_prob_tuples = topics.strip(' [()]').split('), (')
for topic_prob in topic_prob_tuples:
topic, prob = topic_prob.split(',')
print('topic_prob:', topic_prob)
print('topic:', topic)
print('prob:', prob)
if author != '':
author_topic_prob = [str(author), str(topic), float(prob)]
author_topic_prob_list.append(author_topic_prob)
file = open(file_name, 'rt')
lines = file.read().split('\n')
for l in lines[0:]:
if l != '':
parse(l)
print('author_topic_prob_list:', author_topic_prob_list)
file.close()
return author_topic_prob_list
def plot_weighted_graph(author_topic_prob_list):
G = nx.Graph()
#try:
for author_topic_prob in author_topic_prob_list:
G.add_edge(author_topic_prob[0], author_topic_prob[1], weight=author_topic_prob[2])
#except:
#continue
e1 = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] <= 0.1]
e2 = [(u, v) for (u, v, d) in G.edges(data=True) if 0.1 < d["weight"] <= 0.2]
e3 = [(u, v) for (u, v, d) in G.edges(data=True) if 0.2 < d["weight"] <= 0.3]
e4 = [(u, v) for (u, v, d) in G.edges(data=True) if 0.3 < d["weight"] <= 0.4]
e5 = [(u, v) for (u, v, d) in G.edges(data=True) if 0.4 < d["weight"] <= 0.5]
e6 = [(u, v) for (u, v, d) in G.edges(data=True) if 0.5 < d["weight"] <= 0.6]
e7 = [(u, v) for (u, v, d) in G.edges(data=True) if 0.6 < d["weight"] <= 0.7]
e8 = [(u, v) for (u, v, d) in G.edges(data=True) if 0.7 < d["weight"] <= 0.8]
e9 = [(u, v) for (u, v, d) in G.edges(data=True) if 0.8 < d["weight"] <= 0.9]
e10 = [(u, v) for (u, v, d) in G.edges(data=True) if 0.9 < d["weight"]]
pos = nx.spring_layout(G, seed=7)
nx.draw_networkx_nodes(G, pos, node_size=500)
unit_width = 0.2
nx.draw_networkx_edges(G, pos, edgelist=e1, width=1*unit_width)
nx.draw_networkx_edges(G, pos, edgelist=e2, width=2*unit_width)
nx.draw_networkx_edges(G, pos, edgelist=e3, width=3*unit_width)
nx.draw_networkx_edges(G, pos, edgelist=e4, width=4*unit_width)
nx.draw_networkx_edges(G, pos, edgelist=e5, width=5*unit_width)
nx.draw_networkx_edges(G, pos, edgelist=e6, width=6*unit_width)
nx.draw_networkx_edges(G, pos, edgelist=e7, width=7*unit_width)
nx.draw_networkx_edges(G, pos, edgelist=e8, width=8*unit_width)
nx.draw_networkx_edges(G, pos, edgelist=e9, width=9*unit_width)
nx.draw_networkx_edges(G, pos, edgelist=e10, width=10*unit_width)
# node labels
nx.draw_networkx_labels(G, pos, font_size=5, font_family="sans-serif")
# edge weight labels
#edge_labels = nx.get_edge_attributes(G, "weight")
#nx.draw_networkx_edge_labels(G, pos, edge_labels)
ax = plt.gca()
#ax.margins(0.08)
plt.axis("off")
plt.tight_layout()
plt.savefig('topics_per_author_num_topic=10.png')
plt.show()
if __name__ == "__main__":
author_topic_prob_list = topics_per_author_read(file_name='at_model_author_vecs_num_topics=10.txt')
plot_weighted_graph(author_topic_prob_list)