def load_graph(args):
dataset_folder = args.dataset_folder
node_type = 'item'
edge_type = 'relation'
# shoud be split when distributed training.
node_path = dataset_folder + "node_table"
edge_path = dataset_folder + "edge_table"
train_path = dataset_folder + "train_table"
val_path = dataset_folder + "val_table"
test_path = dataset_folder + "test_table"
g = gl.Graph() \
.node(node_path, node_type=node_type,
decoder=gl.Decoder(labeled=True,
attr_types=["float"] * args.features_num,
attr_delimiter=":")) \
.edge(edge_path,
edge_type=(node_type, node_type, edge_type),
decoder=gl.Decoder(weighted=True), directed=False) \
.node(train_path, node_type=node_type,
decoder=gl.Decoder(weighted=True), mask=gl.Mask.TRAIN) \
.node(val_path, node_type=node_type,
decoder=gl.Decoder(weighted=True), mask=gl.Mask.VAL) \
.node(test_path, node_type=node_type,
decoder=gl.Decoder(weighted=True), mask=gl.Mask.TEST)
return g
def main(argv):
cur_path = sys.path[0]
cluster = ""
job_name = ""
task_index = 0
mode = 0
opts, args = getopt.getopt(
argv, 'c:j:t:', ['cluster=', 'job_name=', 'task_index=', 'mode='])
for opt, arg in opts:
if opt in ('-c', '--cluster'):
cluster = arg
elif opt in ('-j', '--job_name'):
job_name = arg
elif opt in ('-t', '--task_index'):
task_index = int(arg)
elif opt in ('-m', '--mode'):
mode = int(arg)
else:
pass
gl.set_tracker_mode(mode)
g = gl.Graph()
g.node(os.path.join(cur_path, "data/user"),
node_type="user", decoder=gl.Decoder(weighted=True)) \
.node(os.path.join(cur_path, "data/item"),
node_type="item", decoder=gl.Decoder(attr_types=['string', 'int', 'float', 'float', 'string'])) \
.edge(os.path.join(cur_path, "data/u-i"),
edge_type=("user", "item", "buy"), decoder=gl.Decoder(weighted=True))
g.init(cluster=cluster, job_name=job_name, task_index=task_index)
if job_name == "server":
print("Server {} started.".format(task_index))
g.wait_for_close()
if job_name == "client":
print("Client {} started.".format(task_index))
q = g.V("user").batch(10).values()
for i in range(3):
while True:
try:
print(g.run(q).ids)
except gl.OutOfRangeError:
print("Out of range......")
break
q = g.E("buy").batch(10).values()
for i in range(3):
while True:
try:
print(g.run(q).dst_ids)
except gl.OutOfRangeError:
print("Out of range......")
break
g.close()
def load_graph(config):
node_type = config['node_type']
edge_type = config['edge_type']
g = gl.Graph().edge("../../data/blogcatelog/edge_table",
edge_type=(node_type, node_type, edge_type),
decoder=gl.Decoder(weighted=True), directed=False)\
.node("../../data/blogcatelog/node_table", node_type=node_type,
decoder=gl.Decoder(weighted=True))
return g
def load_graph(config):
data_dir = config['dataset_folder']
g = gl.Graph() \
.node(data_dir+'ogbl_collab_node', node_type='i',
decoder=gl.Decoder(attr_types=['float'] * config['features_num'],
attr_dims=[0]*config['features_num'])) \
.edge(data_dir+'ogbl_collab_train_edge', edge_type=('i', 'i', 'train'),
decoder=gl.Decoder(weighted=True), directed=False)
return g
def load_graph(config):
g = gl.Graph()\
.node("../../data/u2i/u2i_node_attrs", node_type="i",
decoder=gl.Decoder(attr_types=config['i_attr_types'], attr_dims=config['i_attr_dims']))\
.node("../../data/u2i/u2i_node_attrs", node_type="u",
decoder=gl.Decoder(attr_types=config['u_attr_types'], attr_dims=config['u_attr_dims']))\
.edge("../../data/u2i/u2i_20200222_train", edge_type=("u", "i", "u-i"),
decoder=gl.Decoder(weighted=True), directed=False)
return g
def load_graph():
g = gl.Graph()\
.node("../../data/FB15k-237/entity_node_table", node_type="entity",
decoder=gl.Decoder(attr_types=["int"]))\
.node("../../data/FB15k-237/relation_node_table", node_type="relation",
decoder=gl.Decoder(attr_types=["int"]))\
.edge("../../data/FB15k-237/train_tuple_table",
edge_type=("entity", "entity", "hrt"),
decoder=gl.Decoder(attr_types=["int"], weighted=False))
return g
def load_graph(task_index):
node_table, edge_table = FLAGS.tables.split(',')[0:2]
attr_types = json.loads(FLAGS.attr_types)
attr_dims = json.loads(FLAGS.attr_dims)
g = gl.Graph() \
.node(node_table + str(task_index), node_type='i',
decoder=gl.Decoder(attr_types=attr_types,
attr_dims=attr_dims)) \
.edge(edge_table + str(task_index), edge_type=('i', 'i', 'train'),
decoder=gl.Decoder(weighted=True), directed=False)
return g
def load_graph(config):
node_type = config['node_type']
edge_type = config['edge_type']
g = gl.Graph()\
.node("../../data/arxiv/arxiv-links-train-node-attrs",
node_type=node_type,
decoder=gl.Decoder(attr_types=["int"])) \
.edge("../../data/arxiv/arxiv-links-train-edge",
edge_type=(node_type, node_type, edge_type),
decoder=gl.Decoder(weighted=True), directed=False)
return g
def load_graph(config):
dataset_folder = config['dataset_folder']
node_type = config['node_type']
edge_type = config['edge_type']
g = gl.Graph()\
.node(dataset_folder + "node_table", node_type=node_type,
decoder=gl.Decoder(attr_types=["float"]*50))\
.edge(dataset_folder + "edge_table",
edge_type=(node_type, node_type, edge_type),
decoder=gl.Decoder(weighted=True), directed=False)\
.node(dataset_folder + "node_table", node_type="train",
decoder=gl.Decoder(attr_types=["float"]*50))
return g
def setUp(self):
""" prepare the data and the decoder.
num_int_attrs = num_float_attrs = num_string_attrs = 2.
dst_ids = utils.fixed_dst_ids(src_ids, dst_range).
with fixed_dst_ids, the src_ids which src_id % 5 == 0 has no edge.
"""
self._node1_type = "node1"
self._node2_type = "node2"
self._edge1_type = "edge1"
self._edge2_type = "edge2"
self._edge3_type = "edge3"
# for subgraph sampler
self._node3_type = "entity"
self._edge4_type = "relation"
# for conditional negative sampler
self._cond_node_type = "cond_item"
self._cond_edge_type = "cond_sim"
self._node1_decoder = gl.Decoder(attr_types=utils.ATTR_TYPES)
self._node2_decoder = gl.Decoder(weighted=True, labeled=True)
self._edge1_decoder = gl.Decoder(attr_types=utils.ATTR_TYPES,
labeled=True)
self._edge2_decoder = gl.Decoder(attr_types=utils.ATTR_TYPES,
weighted=True)
self._edge3_decoder = gl.Decoder(weighted=True)
self._node3_decoder = gl.Decoder(attr_types=utils.ENTITY_ATTR_TYPES,
labeled=True)
self._edge4_decoder = gl.Decoder(weighted=True)
self._cond_node_deocder = gl.Decoder(attr_types=utils.COND_ATTR_TYPES,
weighted=True)
self._node1_range = (0, 100)
self._node2_range = (100, 200)
# test for mask.
self._train_node_range = (0, 50)
self._test_node_range = (50, 70)
self._val_node_range = (70, 100)
self._node1_ids = range(self._node1_range[0], self._node1_range[1])
self._node2_ids = utils.fixed_dst_ids(self._node1_ids,
self._node2_range)
self._seed_node1_ids = np.array([2, 7, 8])
self._seed_node2_ids = np.array([102, 107, 108])
self._seed_node1_ids_with_nbr_missing = np.array([5, 10, 110])
self._seed_node2_ids_with_nbr_missing = np.array([102, 105, 108])
# there has no edge whose src_id = 5 | 105
self._default_dst_id = -1
self._default_int_attr = 1000
self._default_float_attr = 999.9
self._default_string_attr = 'hehe'
if self.needs_initial:
self.initialize()
if not self.g:
time.sleep(1)
def init_graph(self):
user_attr_types = ['float'] * 4
item_attr_types = ['float', ('string', 100), ('string', 50)]
user_attr_dims = [None] * 4
item_attr_dims = [None, self.dim1, self.dim2]
g = gl.Graph() \
.node(self.user_path, 'u', decoder=gl.Decoder(
attr_types=user_attr_types, attr_dims=user_attr_dims)) \
.node(self.item_path, 'i', decoder=gl.Decoder(
attr_types=item_attr_types, attr_dims=item_attr_dims)) \
.edge(self.u2i_path, ('u', 'i', 'u-i'), decoder=gl.Decoder()) \
.edge(self.i2i_path, ('i', 'i', 'i-i'), decoder=gl.Decoder()) \
.init()
return g
def test_homo_sage_supervised(self):
item_path = self.gen_node_labeled('item')
i2i_path = utils.gen_edge_data('item',
'item', (0, 100), (0, 100),
schema=[])
g = gl.Graph() \
.node(item_path, 'i',
decoder=gl.Decoder(attr_types=['float'] * 4, attr_dims=[None] * 4,
labeled=True)) \
.edge(i2i_path, ('i', 'i', 'i-i'), decoder=gl.Decoder(), directed=False) \
.init()
query = g.V('i').batch(10).alias('i') \
.outV('i-i').sample(5).by('topk').alias('hop1') \
.outV('i-i').sample(5).by('random').alias('hop2') \
.values()
df = tfg.DataFlow(query)
dims = np.array([4, 16, 8])
model = tfg.HomoEgoGraphSAGE(dims,
bn_fn=None,
active_fn=tf.nn.relu,
droput=0.1)
eg = df.get_ego_graph('i')
embeddings = model.forward(eg)
nc = tfg.NodeClassifier(dims=[8, 4], class_num=2)
logits, loss = nc.forward(embeddings, eg.nodes.labels)
target_ids = eg.nodes.ids
out_degrees = eg.nodes.out_degrees
trainer = tfg.Trainer()
trainer.minimize(loss)
def trace(ret):
self.assertEqual(len(ret), 4)
self.assertEqual(list(ret[0].shape), [10, 2])
self.assertEqual(list(ret[2].shape), [10]) # ids
self.assertEqual(list(ret[3].shape), [10])
for deg in ret[3]:
assert deg in (0, 2, 4, 6, 8)
trainer.step_to_epochs(df, 10, [logits, loss, target_ids, out_degrees],
trace)
trainer.close()
g.close()
def test_node_iterate(self):
file_path = self.gen_test_data([utils.ATTRIBUTED])
decoder = gl.Decoder(attr_types=utils.ATTR_TYPES)
g = gl.Graph() \
.node(source=file_path, node_type=self.node_type_, decoder=decoder)
g.init(tracker=utils.TRACKER_PATH)
batch_size = 4
sampler = g.node_sampler('user',
batch_size=batch_size,
strategy="by_order")
res_ids = []
max_iter = 100
for i in range(max_iter):
try:
nodes = sampler.get()
utils.check_node_attrs(nodes)
res_ids.extend(list(nodes.ids))
except gl.OutOfRangeError:
break
ids = range(self.value_range_[0][0], self.value_range_[0][1])
utils.check_sorted_equal(res_ids, ids)
sampler = g.node_sampler('user',
batch_size=batch_size,
strategy="random")
max_iter = 10
for i in range(max_iter):
nodes = sampler.get()
utils.check_node_attrs(nodes)
utils.check_subset(nodes.ids, ids)
g.close()
def test_node_iterate_using_gsl(self):
gl.set_eager_mode(True)
file_path = self.gen_test_data([utils.ATTRIBUTED])
decoder = gl.Decoder(attr_types=utils.ATTR_TYPES)
g = gl.Graph() \
.node(source=file_path, node_type=self.node_type_, decoder=decoder)
g.init(tracker=utils.TRACKER_PATH)
batch_size = 4
query = g.V('user').batch(batch_size).values()
res_ids = []
max_iter = 100
for i in range(max_iter):
try:
nodes = g.run(query)
utils.check_node_attrs(nodes)
res_ids.extend(list(nodes.ids))
except gl.OutOfRangeError:
break
ids = range(self.value_range_[0], self.value_range_[1])
utils.check_sorted_equal(res_ids, ids)
query = g.V('user').batch(batch_size).shuffle().values()
max_iter = 10
for i in range(max_iter):
nodes = g.run(query)
utils.check_node_attrs(nodes)
utils.check_subset(nodes.ids, ids)
g.close()
def test_edge_iterate_using_gremlin(self):
file_path = self.gen_test_data([utils.WEIGHTED], False)
decoder = gl.Decoder(weighted=True)
g = gl.Graph() \
.edge(source=file_path, edge_type=self.edge_tuple_, decoder=decoder)
g.init(server_id=0, server_count=1, tracker=utils.TRACKER_PATH)
batch_size = 4
query = g.E('first').batch(batch_size).values()
res_src = []
res_dst = []
max_iter = 100
for i in range(max_iter):
try:
edges = g.run(query)
utils.check_edge_weights(edges)
res_src.extend(list(edges.src_ids))
res_dst.extend(list(edges.dst_ids))
except gl.OutOfRangeError:
break
src_ids = range(self.src_range_[0], self.src_range_[1])
dst_ids = range(self.dst_range_[0], self.dst_range_[1])
utils.check_sorted_equal(res_src, src_ids)
utils.check_sorted_equal(res_dst, dst_ids)
query = g.E('first').batch(batch_size).shuffle().values()
max_iter = 10
src_ids = range(self.src_range_[0], self.src_range_[1])
dst_ids = range(self.dst_range_[0], self.dst_range_[1])
for i in range(max_iter):
edges = g.run(query)
utils.check_edge_weights(edges)
utils.check_subset(edges.src_ids, src_ids)
utils.check_subset(edges.dst_ids, dst_ids)
def test_edge_shuffle(self):
file_path = self.gen_test_data([utils.WEIGHTED], False)
decoder = gl.Decoder(weighted=True)
g = gl.Graph() \
.edge(source=file_path, edge_type=self.edge_tuple_, decoder=decoder)
g.init(tracker=utils.TRACKER_PATH)
batch_size = 4
sampler = g.E('first').batch(batch_size).shuffle(
traverse=True).values()
res_src = []
res_dst = []
max_iter = 100
for i in range(max_iter):
try:
edges = sampler.next()
utils.check_edge_weights(edges)
res_src.extend(list(edges.src_ids))
res_dst.extend(list(edges.dst_ids))
except gl.OutOfRangeError:
break
src_ids = range(self.src_range_[0], self.src_range_[1])
dst_ids = range(self.dst_range_[0], self.dst_range_[1])
utils.check_sorted_equal(res_src, src_ids)
utils.check_sorted_equal(res_dst, dst_ids)
g.close()
def load_graph(config):
dataset_folder = config['dataset_folder']
node_type = config['node_type']
edge_type = config['edge_type']
g = gl.Graph() \
.node(dataset_folder + "node_table", node_type=node_type,
decoder=gl.Decoder(labeled=True, attr_types=["float"] * (config['features_num']), attr_delimiter=":")) \
.edge(dataset_folder + "edge_table", edge_type=(node_type, node_type, edge_type),
decoder=gl.Decoder(weighted=True), directed=False) \
.node(dataset_folder + "train_table", node_type="train",
decoder=gl.Decoder(weighted=True)) \
.node(dataset_folder + "val_table", node_type="val",
decoder=gl.Decoder(weighted=True)) \
.node(dataset_folder + "test_table", node_type="test",
decoder=gl.Decoder(weighted=True))
return g
def test_node_iterate_from_graph(self):
file_path = self.gen_test_data([utils.ATTRIBUTED], False)
decoder = gl.Decoder(attr_types=utils.ATTR_TYPES)
g = gl.Graph() \
.edge(source=file_path, edge_type=self.edge_tuple_, decoder=decoder)
g.init(tracker=utils.TRACKER_PATH)
batch_size = 4
sampler = g.node_sampler('first',
batch_size=batch_size,
strategy="by_order",
node_from=gl.EDGE_SRC)
res_ids = []
max_iter = 100
for i in range(max_iter):
try:
nodes = sampler.get()
utils.check_node_type(nodes, "user")
res_ids.extend(list(nodes.ids))
except gl.OutOfRangeError:
break
ids = range(self.src_range_[0], self.src_range_[1])
utils.check_sorted_equal(res_ids, ids)
sampler = g.node_sampler('first',
batch_size=batch_size,
strategy="random",
node_from=gl.EDGE_SRC)
max_iter = 10
for i in range(max_iter):
nodes = sampler.get()
utils.check_subset(nodes.ids, ids)
sampler = g.node_sampler('first',
batch_size=batch_size,
strategy="by_order",
node_from=gl.EDGE_DST)
res_ids = []
max_iter = 100
for i in range(max_iter):
try:
nodes = sampler.get()
utils.check_node_type(nodes, "item")
res_ids.extend(list(nodes.ids))
except gl.OutOfRangeError:
break
ids = range(self.dst_range_[0], self.dst_range_[1])
utils.check_sorted_equal(res_ids, ids)
sampler = g.node_sampler('first',
batch_size=batch_size,
strategy="random",
node_from=gl.EDGE_DST)
max_iter = 10
for i in range(max_iter):
nodes = sampler.get()
utils.check_subset(nodes.ids, ids)
g.close()
def main(argv):
cur_path = sys.path[0]
# Step 1: Construct graph with data source.
# Edges:
# user<--(buy)-->item
# entity<--(relation)-->entity
# cond_node--(cond_edge)-->cond_node
g = gl.Graph()
g.node(os.path.join(cur_path, "data/user"),
node_type="user", decoder=gl.Decoder(weighted=True)) \
.node(os.path.join(cur_path, "data/item"),
node_type="item", decoder=gl.Decoder(attr_types=['string', 'int', 'float', 'float', 'string'])) \
.edge(os.path.join(cur_path, "data/u-i"),
edge_type=("user", "item", "buy"), decoder=gl.Decoder(weighted=True), directed=False) \
.node(os.path.join(cur_path, "data/entity"),
node_type="entity", decoder=gl.Decoder(attr_types=['float', 'float', 'float', 'float'], labeled=True)) \
.edge(os.path.join(cur_path, "data/relation"),
edge_type=("entity", "entity", "relation"), decoder=gl.Decoder(weighted=True), directed=False) \
.edge(os.path.join(cur_path, "data/relation"),
edge_type=("cond_node", "cond_node", "cond_edge"), decoder=gl.Decoder(weighted=True), directed=True) \
.node(os.path.join(cur_path, "data/cond_node"),
node_type="cond_node", decoder=gl.Decoder(attr_types=['int','int','float','string'], weighted=True))
g.init()
# Step 2: Describe the queries on graph.
test_node_iterate(g, local=True)
test_edge_iterate(g, local=True)
test_truncated_full_edge_sample(g)
test_conditional_negtaive_sample(g)
g.close()
def test_attributed(self):
file_path = self.gen_test_data([utils.ATTRIBUTED])
decoder = gl.Decoder(attr_types=utils.ATTR_TYPES)
g = gl.Graph() \
.node(source=file_path, node_type=self.node_type_, decoder=decoder)
g.init(server_id=0, server_count=1, tracker=utils.TRACKER_PATH)
nodes = g.get_nodes(node_type=self.node_type_, ids=self.ids_)
self.check_attrs(nodes)
def initialize(self):
self.__class__.needs_initial = False
file_path = self.gen_test_data([utils.ATTRIBUTED])
decoder = gl.Decoder(attr_types=utils.ATTR_TYPES)
self.__class__.g = gl.Graph() \
.node(source=file_path,
node_type=self.node_type_,
decoder=decoder)
self.__class__.g.init(tracker=utils.TRACKER_PATH)
def test_weighted(self):
file_path = self.gen_test_data([utils.WEIGHTED])
decoder = gl.Decoder(weighted=True)
g = gl.Graph() \
.node(source=file_path, node_type=self.node_type_, decoder=decoder)
g.init(server_id=0, server_count=1, tracker=utils.TRACKER_PATH)
nodes = g.get_nodes(node_type=self.node_type_, ids=self.ids_)
self.check_weights(nodes)
def setUp(self):
""" prepare the data and the decoder.
num_int_attrs = num_float_attrs = num_string_attrs = 2.
dst_ids = utils.fixed_dst_ids(src_ids, dst_range).
with fixed_dst_ids, the src_ids which src_id % 5 == 0 has no edge.
"""
self._node1_type = "node1"
self._node2_type = "node2"
self._edge1_type = "edge1"
self._edge2_type = "edge2"
self._edge3_type = "edge3"
self._node1_range = (0, 100)
self._node2_range = (100, 200)
self._node1_ids = range(self._node1_range[0], self._node1_range[1])
self._node2_ids = utils.fixed_dst_ids(self._node1_ids,
self._node2_range)
self._node1_decoder = gl.Decoder(attr_types=utils.ATTR_TYPES)
self._node2_decoder = gl.Decoder(weighted=True, labeled=True)
self._edge1_decoder = gl.Decoder(attr_types=utils.ATTR_TYPES,
labeled=True)
self._edge2_decoder = gl.Decoder(attr_types=utils.ATTR_TYPES,
weighted=True)
self._edge3_decoder = gl.Decoder(weighted=True)
self._seed_node1_ids = np.array([2, 7, 8])
self._seed_node2_ids = np.array([102, 107, 108])
self._seed_node1_ids_with_nbr_missing = np.array([5, 10, 110])
self._seed_node2_ids_with_nbr_missing = np.array([102, 105, 108])
# there has no edge whose src_id = 5 | 105
self._default_dst_id = -1
self._default_int_attr = 1000
self._default_float_attr = 999.9
self._default_string_attr = 'hehe'
if self.needs_initial:
self.initialize()
if not self.g:
time.sleep(1)
def load_graph(args):
dataset_folder = args.dataset_folder
node_type = args.node_type
edge_type = args.edge_type
g = gl.Graph() \
.node(dataset_folder + "node_table", node_type=node_type,
decoder=gl.Decoder(labeled=True,
attr_types=["float"] * args.features_num,
attr_delimiter=":")) \
.edge(dataset_folder + "edge_table",
edge_type=(node_type, node_type, edge_type),
decoder=gl.Decoder(weighted=True), directed=False) \
.node(dataset_folder + "train_table", node_type=node_type,
decoder=gl.Decoder(weighted=True), mask=gl.Mask.TRAIN) \
.node(dataset_folder + "val_table", node_type=node_type,
decoder=gl.Decoder(weighted=True), mask=gl.Mask.VAL) \
.node(dataset_folder + "test_table", node_type=node_type,
decoder=gl.Decoder(weighted=True), mask=gl.Mask.TEST)
return g
def test_attributed(self):
file_path = self.gen_test_data([utils.ATTRIBUTED], False)
decoder = gl.Decoder(attr_types=utils.ATTR_TYPES)
g = gl.Graph() \
.edge(source=file_path, edge_type=self.edge_tuple_, decoder=decoder)
g.init(server_id=0, server_count=1, tracker=utils.TRACKER_PATH)
edges = g.get_edges(edge_type="first",
src_ids=self.src_ids_,
dst_ids=self.dst_ids_)
def test_labeled(self):
file_path = self.gen_test_data([utils.LABELED])
decoder = gl.Decoder(labeled=True)
g = gl.Graph() \
.node(source=file_path, node_type=self.node_type_, decoder=decoder)
g.init(tracker=utils.TRACKER_PATH)
nodes = g.get_nodes(node_type=self.node_type_, ids=self.ids_)
self.check_labels(nodes)
g.close()
def test_weighted_attributed(self):
file_path = self.gen_test_data([utils.WEIGHTED, utils.ATTRIBUTED])
decoder = gl.Decoder(weighted=True, attr_types=utils.ATTR_TYPES)
g = gl.Graph() \
.node(source=file_path, node_type=self.node_type_, decoder=decoder)
g.init(tracker=utils.TRACKER_PATH)
nodes = g.get_nodes(node_type=self.node_type_, ids=self.ids_)
self.check_weights(nodes)
self.check_attrs(nodes)
g.close()
def test_basic(self):
file_path = self.gen_test_data([], False)
decoder = gl.Decoder()
g = gl.Graph() \
.edge(source=file_path, edge_type=self.edge_tuple_, decoder=decoder)
g.init(server_id=0, server_count=1, tracker=utils.TRACKER_PATH)
edges = g.E("first").batch(4).emit()
utils.check_ids(edges.src_ids,
range(self.src_range_[0], self.src_range_[1]))
utils.check_ids(edges.dst_ids,
range(self.dst_range_[0], self.dst_range_[1]))
def test_labeled(self):
file_path = self.gen_test_data([utils.LABELED], False)
decoder = gl.Decoder(labeled=True)
g = gl.Graph() \
.edge(source=file_path, edge_type=self.edge_tuple_, decoder=decoder)
g.init(server_id=0, server_count=1, tracker=utils.TRACKER_PATH)
edges = g.E("first").batch(self.batch_size_).emit()
utils.check_ids(edges.src_ids,
range(self.src_range_[0], self.src_range_[1]))
utils.check_ids(edges.dst_ids,
range(self.dst_range_[0], self.dst_range_[1]))
utils.check_edge_labels(edges)
def load_graph():
""" Load node and edge data to build graph.
Note that node_type must be "i", and edge_type must be "r_i",
the number of edge tables must be the same as FLAGS.num_relations.
"""
cur_path = sys.path[0]
dataset_folder = os.path.join(cur_path, '../../data/cora/')
g = gl.Graph()\
.node(dataset_folder + "node_table", node_type="i",
decoder=gl.Decoder(labeled=True,
attr_types=attr_types,
attr_delimiter=":")) \
.edge(dataset_folder + "edge_table",
edge_type=("i", "i", "r_0"),
decoder=gl.Decoder(weighted=True), directed=False) \
.edge(dataset_folder + "edge_table_with_self_loop",
edge_type=("i", "i", "r_1"),
decoder=gl.Decoder(weighted=True), directed=False) \
.node(dataset_folder + "train_table", node_type="i",
decoder=gl.Decoder(weighted=True), mask=gl.Mask.TRAIN) \
.node(dataset_folder + "test_table", node_type="i",
decoder=gl.Decoder(weighted=True), mask=gl.Mask.TEST)
return g