def test(graph, model, args):
tfg.conf.training = False
query_test = query(graph, gl.Mask.TEST, args)
dataset = tfg.Dataset(query_test, window=5)
eg_test = dataset.get_egograph('test')
test_embeddings = model.forward(eg_test)
test_acc = accuracy(test_embeddings, eg_test.src.labels)
return dataset.iterator, test_acc
def train(graph, model, args):
tfg.conf.training = True
query_train = query(graph, gl.Mask.TRAIN, args)
dataset = tfg.Dataset(query_train, window=5)
eg_train = dataset.get_egograph('train')
train_embeddings = model.forward(eg_train)
loss = supervised_loss(train_embeddings, eg_train.src.labels)
return dataset.iterator, loss
def save_node_embedding(graph, model):
tfg.conf.training = False
seed = graph.V('i').batch(FLAGS.batch_size).alias('i')
nbrs_num = json.loads(FLAGS.nbrs_num)
query_save = meta_path_sample(seed, 'i', nbrs_num, FLAGS.sampler).values()
dataset = tfg.Dataset(query_save, window=1)
ego_graph = dataset.get_egograph('i')
emb = model.forward(ego_graph)
return dataset.iterator, ego_graph.src.ids, emb
def train(graph, model):
tfg.conf.training = True
query_train = query(graph, gl.Mask.TRAIN)
dataset = tfg.Dataset(query_train, window=5)
data_dict = dataset.get_data_dict()
feature_handler = tfg.FeatureHandler('feature_handler',
query_train.get_node("train").decoder.feature_spec)
x_list = reformat_node_feature(data_dict, query_train.list_alias(), feature_handler)
train_embeddings = model.forward(x_list, nbrs_num)
loss = supervised_loss(train_embeddings, data_dict['train'].labels)
return dataset.iterator, loss
def test(graph, model):
tfg.conf.training = False
query_test = query(graph, gl.Mask.TEST)
dataset = tfg.Dataset(query_test, window=5)
data_dict = dataset.get_data_dict()
feature_handler = tfg.FeatureHandler('feature_handler',
query_test.get_node("test").decoder.feature_spec)
x_list = reformat_node_feature(data_dict, query_test.list_alias(), feature_handler)
test_embeddings = model.forward(x_list, nbrs_num)
test_acc = accuracy(test_embeddings, data_dict['test'].labels)
return dataset.iterator, test_acc, data_dict['test'].ids,\
data_dict['test'].labels, tf.nn.softmax(test_embeddings)
def train(graph, model, config):
tfg.conf.training = True
query_train = query(graph, config)
dataset = tfg.Dataset(query_train, window=5)
src_ego = dataset.get_egograph('src')
dst_ego = dataset.get_egograph('dst')
neg_dst_ego = dataset.get_egograph('neg_dst')
src_emb = model.forward(src_ego)
dst_emb = model.forward(dst_ego)
neg_dst_emb = model.forward(neg_dst_ego)
# use sampled softmax loss with temperature.
loss = tfg.unsupervised_softmax_cross_entropy_loss(
src_emb, dst_emb, neg_dst_emb, temperature=config['temperature'])
return dataset.iterator, loss
def test(g, model, predictor, config, edge_type='test'):
tfg.conf.training = False
seed = g.E(edge_type).batch(config['batch_size'])
src = seed.outV().alias(tfg.SubKeys.POS_SRC)
src.outV('train').sample(config['nbrs_num']).by('full').alias('src_hop1')
dst = seed.inV().alias(tfg.SubKeys.POS_DST)
dst.outV('train').sample(config['nbrs_num']).by('full').alias('dst_hop1')
query = seed.values()
dataset = tfg.Dataset(query,
induce_func=induce_graph_cn_with_edge,
induce_additional_spec=config['strut_label_spec'])
pos_graph = dataset.get_batchgraph(tfg.SubKeys.POS_SRC)
pos_src, pos_dst = model.forward(batchgraph=pos_graph)
logits = predictor(pos_src * pos_dst)
return dataset.iterator, logits
def train(g, model, config):
seed = g.E('train').batch(config['batch_size']).shuffle(traverse=True)
src = seed.outV().alias(tfg.SubKeys.POS_SRC)
src.outV('train').sample(config['nbrs_num']).by('full').alias('src_hop1')
dst = seed.inV().alias(tfg.SubKeys.POS_DST)
dst.outV('train').sample(config['nbrs_num']).by('full').alias('dst_hop1')
src.outNeg('train').sample(1).by('random').alias(tfg.SubKeys.NEG_DST).\
outV('train').sample(config['nbrs_num']).by('full').alias('neg_hop1')
query = seed.values()
dataset = tfg.Dataset(query, induce_func=induce_graph_with_edge)
pos_graph = dataset.get_batchgraph(tfg.SubKeys.POS_SRC)
neg_graph = dataset.get_batchgraph(tfg.SubKeys.NEG_DST)
pos_src, pos_dst = model.forward(batchgraph=pos_graph)
neg_src, neg_dst = model.forward(batchgraph=neg_graph)
pos_h = tf.reduce_sum(pos_src * pos_dst, axis=-1)
neg_h = tf.reduce_sum(neg_src * neg_dst, axis=-1)
loss = tfg.sigmoid_cross_entropy_loss(pos_h, neg_h)
return dataset.iterator, loss
def train(g, model, predictor, config):
tfg.conf.training = True
seed = g.E('train').batch(config['batch_size']).shuffle(traverse=True)
src = seed.outV().alias(tfg.SubKeys.POS_SRC)
src.outV('train').sample(config['nbrs_num']).by('full').alias('src_hop1')
dst = seed.inV().alias(tfg.SubKeys.POS_DST)
dst.outV('train').sample(config['nbrs_num']).by('full').alias('dst_hop1')
src.outNeg('train').sample(1).by('random').alias(tfg.SubKeys.NEG_DST).\
outV('train').sample(config['nbrs_num']).by('full').alias('neg_hop1')
query = seed.values()
dataset = tfg.Dataset(query,
induce_func=induce_graph_cn_with_edge,
induce_additional_spec=config['strut_label_spec'])
pos_graph = dataset.get_batchgraph(tfg.SubKeys.POS_SRC)
neg_graph = dataset.get_batchgraph(tfg.SubKeys.NEG_DST)
pos_src, pos_dst = model.forward(batchgraph=pos_graph)
neg_src, neg_dst = model.forward(batchgraph=neg_graph)
pos_h = predictor(pos_src * pos_dst)
neg_h = predictor(neg_src * neg_dst)
# train loss
loss = tfg.sigmoid_cross_entropy_loss(pos_h, neg_h)
return dataset.iterator, loss
