def train(config, graph):
def model_fn():
return BipartiteGraphSage(graph,
config['batch_size'],
config['hidden_dim'],
config['output_dim'],
config['hops_num'],
config['u_neighs_num'],
config['i_neighs_num'],
u_features_num=config['u_features_num'],
u_categorical_attrs_desc=config['u_categorical_attrs_desc'],
i_features_num=config['i_features_num'],
i_categorical_attrs_desc=config['i_categorical_attrs_desc'],
neg_num=config['neg_num'],
use_input_bn=config['use_input_bn'],
act=config['act'],
agg_type=config['agg_type'],
need_dense=config['need_dense'],
in_drop_rate=config['drop_out'],
ps_hosts=config['ps_hosts'])
trainer = gl.LocalTFTrainer(model_fn,
epoch=config['epoch'],
optimizer=gl.get_tf_optimizer(
config['learning_algo'],
config['learning_rate'],
config['weight_decay']))
trainer.train()
u_embs = trainer.get_node_embedding("u")
np.save('u_emb', u_embs)
i_embs = trainer.get_node_embedding("i")
np.save('i_emb', i_embs)
def train(config, graph):
def model_fn():
return LINE(graph, config['node_count'], config['hidden_dim'],
config['neg_num'], config['batch_size'], config['s2h'],
config['ps_hosts'], config['proximity'],
config['node_type'], config['edge_type'])
trainer = gl.LocalTFTrainer(model_fn,
epoch=config['epoch'],
optimizer=gl.get_tf_optimizer(
config['learning_algo'],
config['learning_rate']))
trainer.train()
embs = trainer.get_node_embedding()
np.save(config['emb_save_dir'], embs)
def train(config, graph):
def model_fn():
return DeepWalk(graph,
config['walk_len'],
config['window_size'],
config['node_count'],
config['hidden_dim'],
config['neg_num'],
config['batch_size'],
s2h=config['s2h'],
ps_hosts=config['ps_hosts'],
temperature=config['temperature'])
trainer = gl.LocalTFTrainer(model_fn,
epoch=config['epoch'],
optimizer=gl.get_tf_optimizer(
config['learning_algo'],
config['learning_rate']))
trainer.train()
embs = trainer.get_node_embedding()
np.save(config['emb_save_dir'], embs)
def train(config, graph):
def model_fn():
return TransE(graph,
config['neg_num'],
config['batch_size'],
config['margin'],
config['entity_num'],
config['relation_num'],
config['hidden_dim'],
s2h=config['s2h'],
ps_hosts=config['ps_hosts'])
trainer = gl.LocalTFTrainer(model_fn,
epoch=config['epoch'],
optimizer=gl.get_tf_optimizer(
config['learning_algo'],
config['learning_rate']))
trainer.train()
entity_embs = trainer.get_node_embedding('entity')
relation_embs = trainer.get_node_embedding('relation')
return entity_embs, relation_embs
def train(config, graph):
def model_fn():
return GCN(graph,
config['class_num'],
config['features_num'],
config['batch_size'],
val_batch_size=config['val_batch_size'],
test_batch_size=config['test_batch_size'],
categorical_attrs_desc=config['categorical_attrs_desc'],
hidden_dim=config['hidden_dim'],
in_drop_rate=config['in_drop_rate'],
hops_num=config['hops_num'],
neighs_num=config['neighs_num'],
full_graph_mode = config['full_graph_mode'])
trainer = gl.LocalTFTrainer(model_fn,
epoch=config['epoch'],
optimizer=gl.get_tf_optimizer(
config['learning_algo'],
config['learning_rate'],
config['weight_decay']))
trainer.train_and_evaluate()
def train(config, graph):
def model_fn():
return GraphSage(graph,
config['class_num'],
config['features_num'],
config['batch_size'],
categorical_attrs_desc=config['categorical_attrs_desc'],
hidden_dim=config['hidden_dim'],
in_drop_rate=config['in_drop_rate'],
neighs_num=config['neighs_num'],
full_graph_mode=config['full_graph_mode'],
unsupervised=config['unsupervised'],
neg_num=config['neg_num'],
agg_type=config['agg_type'])
trainer = gl.LocalTFTrainer(model_fn,
epoch=config['epoch'],
optimizer=gl.get_tf_optimizer(
config['learning_algo'],
config['learning_rate'],
config['weight_decay']))
trainer.train()
embs = trainer.get_node_embedding()
np.save(config['emb_save_dir'], embs)
