def sdne_run():
Graph = read_graph('wiki/Wiki_edgelist.txt')
sden_model = sdne(
Graph=Graph,
dimension_size=128,
per_vertex=100,
walk_length=10,
window_size=5,
work=1,
beta=5,
alpha=1e-6,
verbose=1,
epochs=1000,
batch_size=512,
log_dir='logs/0/',
hidden_size_list=[256, 128],
l1=1e-5,
l2=1e-4
)
sden_model.train()
embeddings = sden_model.get_embeddings()
from evaluate import evaluate_tools
eval_tool = evaluate_tools(embeddings,label_path='wiki/Wiki_labels.txt')
eval_tool.plot_embeddings()
def deep_walk_run():
Graph = read_graph('wiki/Wiki_edgelist.txt')
deepwalk = DeepWalk(Graph=Graph,
per_vertex=80,
walk_length=10,
window_size=5,
dimension_size=128,
work=4)
embeddings = deepwalk.transform()
eval = evaluate_tools(embeddings=embeddings,
label_path='wiki/Wiki_labels.txt')
eval.plot_embeddings()
def node2vec_run():
Graph = read_graph('wiki/Wiki_edgelist.txt')
node_vec = node2vec(Graph=Graph,
per_vertex=80,
walk_length=10,
window_size=5,
dimension_size=128,
work=1,
p=0.25,
q=4)
embeddings = node_vec.transform()
eval_tool = evaluate_tools(embeddings, label_path='wiki/Wiki_labels.txt')
eval_tool.plot_embeddings()
def line_run():
Graph = read_graph('wiki/Wiki_edgelist.txt')
line = Line(
Graph=Graph,
dimension_size=128,
per_vertex=100,
walk_length=10,
window_size=5,
work=1,
negative_ratio=1,
batch_size=128,
log_dir='logs/0/',
epoch=100,
)
embeddings = line.transform()
tool = evaluate_tools(embeddings, label_path='wiki/Wiki_labels.txt')
tool.plot_embeddings()
sentence_list = []
all_node = self.all_nodes
for step in range(self.walk_epoch):
random.shuffle(all_node)
for node in all_node:
sentence = self.bias_random_walk(node)
sentence_list.append(sentence)
return sentence_list
def train(self):
sentence_list = self.struct_walk()
embeddings = self.embdding_train(sentence_list)
evaluate_tools(embeddings).plot_embeddings()
plt.show()
if __name__ == '__main__':
path = '../wiki/Wiki_edgelist.txt'
from util_tool import read_graph
G = read_graph(edgelist_path=path)
model = struct2vec(Graph=G,
walk_length=10,
k=8,
window_size=5,
dimension_size=128,
per_vertex=10,
work=4)
model.train()
return self.model
def get_embeddings(self):
embeddings = {}
pred_embeddings = self.embedding_model.predict(
self.W, batch_size=self.batch_size)
rank = 0
for embedding in pred_embeddings:
embeddings[self.idx2node[rank]] = embedding
rank += 1
return embeddings
if __name__ == '__main__':
Graph = read_graph()
sden_model = sdne(Graph=Graph,
dimension_size=128,
per_vertex=100,
walk_length=10,
window_size=5,
work=1,
beta=5,
alpha=1e-6,
verbose=1,
epochs=1000,
batch_size=512,
log_dir='logs/0/',
hidden_size_list=[256, 128],
l1=1e-5,
l2=1e-4)
self.embedding_dict['second'].get_weights()[0]))
idx2node = self.idx2node
for i, embedding in enumerate(embeddings):
self.embeddings[idx2node[i]] = embedding
return self.embeddings
def transform(self):
self.train()
self.get_embedding()
return self.embeddings
if __name__ == '__main__':
from util_tool import read_graph
Graph = read_graph('model/embedding/wiki/Wiki_edgelist.txt')
line = Line(
Graph=Graph,
dimension_size=128,
per_vertex=100,
walk_length=10,
window_size=5,
work=1,
negative_ratio=1,
batch_size=128,
log_dir='model/embedding/setence_model/logs/0/',
epoch=100,
)
embeddings = line.transform()
from evaluate import evaluate_tools
tool = evaluate_tools(embeddings)
for num in range(self.walk_epoch):
random.shuffle(self.all_nodes)
for node in self.all_nodes:
sentence = self.random_walk(node, is_edge_sampling=True)
sentence_list.append(sentence)
return sentence_list
def transform(self):
sentence_list = self.Learn_Feature()
embeddings = self.embdding_train(sentence_list)
return embeddings
if __name__ == '__main__':
Graph = read_graph('../wiki/Wiki_edgelist.txt')
node_vec = node2vec(Graph=Graph,
per_vertex=80,
walk_length=10,
window_size=5,
dimension_size=128,
work=1,
p=0.25,
q=4)
embeddings = node_vec.transform()
eval_tool = evaluate_tools(embeddings)
eval_tool.plot_embeddings()
