def test_2hop_using_gsl_with_undirected_edge(self):
""" Using gsl api and sample neighbor on undirected edges
whose source node and dst node has defferent type.
"""
gl.set_eager_mode(True)
expand_factor = [3, 2]
ids = self._seed_node1_ids
nbrs = self.g.V(self._node1_type, feed=ids) \
.outE(self._edge1_type).sample(expand_factor[0]).by("random") \
.inV() \
.inE(self._edge1_type).sample(expand_factor[1]).by("random") \
.inV().emit()
edges1 = nbrs[1]
nodes1 = nbrs[2]
edges2 = nbrs[3]
nodes2 = nbrs[4]
utils.check_fixed_edge_dst_ids(edges1,
dst_range=self._node2_range,
expected_src_ids=ids)
utils.check_edge_type(edges1, self._node1_type, self._node2_type,
self._edge1_type)
utils.check_edge_type(edges2, self._node2_type, self._node1_type,
self._edge1_type + "_reverse")
utils.check_node_type(nodes1, self._node2_type)
utils.check_node_type(nodes2, self._node1_type)
utils.check_node_ids(nodes2, self._node1_ids)
def test_1hop_using_gsl(self):
""" Using gsl api.
"""
gl.set_eager_mode(True)
expand_factor = 6
ids = self._seed_node1_ids
nbrs = self.g.V(self._node1_type, feed=ids) \
.outE(self._edge1_type).sample(expand_factor).by("in_degree") \
.inV().emit()
edges = nbrs[1]
nodes = nbrs[2]
utils.check_fixed_edge_dst_ids(edges,
dst_range=self._node2_range,
expected_src_ids=ids)
utils.check_edge_type(edges,
src_type=self._node1_type,
dst_type=self._node2_type,
edge_type=self._edge1_type)
utils.check_edge_shape(edges, ids.size * expand_factor)
utils.check_edge_attrs(edges)
utils.check_edge_labels(edges)
utils.check_equal(nodes.ids, edges.dst_ids)
utils.check_node_ids(nodes, self._node2_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
utils.check_node_weights(nodes)
utils.check_node_labels(nodes)
def test_1hop_with_neighbor_missing(self):
""" Test case for sample 1 hop neighbor with strategy of edge_weight.
Some of src_ids have no neighbor.
"""
expand_factor = 6
ids = self._seed_node1_ids_with_nbr_missing
nbr_s = self.g.neighbor_sampler(self._edge1_type,
expand_factor=expand_factor,
strategy="edge_weight")
nbrs = nbr_s.get(ids)
edges = nbrs.layer_edges(1)
nodes = nbrs.layer_nodes(1)
utils.check_fixed_edge_dst_ids(edges, dst_range=self._node2_range,
expected_src_ids=ids,
default_dst_id=self._default_dst_id)
utils.check_edge_type(edges,
src_type=self._node1_type,
dst_type=self._node2_type,
edge_type=self._edge1_type)
utils.check_edge_shape(edges, ids.size * expand_factor)
utils.check_not_exist_edge_attrs(
edges, default_int_attr=self._default_int_attr,
default_float_attr=self._default_float_attr,
default_string_attr=self._default_string_attr,)
utils.check_not_exist_edge_labels(edges)
utils.check_equal(nodes.ids, edges.dst_ids)
utils.check_node_ids(nodes, [self._default_dst_id])
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
utils.check_not_exist_node_weights(nodes)
utils.check_not_exist_node_labels(nodes)
def test_1hop(self):
""" Sample one hop of neighbors.
"""
expand_factor = 6
ids = self._seed_node1_ids
nbr_s = self.g.neighbor_sampler(self._edge1_type,
expand_factor=expand_factor,
strategy="random")
nbrs = nbr_s.get(ids)
edges = nbrs.layer_edges(1)
nodes = nbrs.layer_nodes(1)
print(edges.src_nodes.get_out_degrees(self._edge1_type))
utils.check_fixed_edge_dst_ids(edges, dst_range=self._node2_range,
expected_src_ids=ids)
utils.check_edge_type(edges,
src_type=self._node1_type,
dst_type=self._node2_type,
edge_type=self._edge1_type)
utils.check_edge_shape(edges, ids.size * expand_factor)
utils.check_edge_attrs(edges)
utils.check_edge_labels(edges)
utils.check_equal(nodes.ids, edges.dst_ids)
utils.check_node_ids(nodes, self._node2_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
utils.check_node_weights(nodes)
utils.check_node_labels(nodes)
def test_1hop_using_gsl(self):
""" Test case for sample 1 hop neighbor.
hetegerous graph with edge attrs, without edge weight.
"""
gl.set_eager_mode(True)
expand_factor = 6
ids = self._seed_node1_ids
nbrs = self.g.V(self._node1_type, feed=ids) \
.outE(self._edge1_type).sample(expand_factor).by("edge_weight") \
.inV().emit()
edges = nbrs[1]
nodes = nbrs[2]
utils.check_fixed_edge_dst_ids(edges, dst_range=self._node2_range,
expected_src_ids=ids)
utils.check_edge_type(edges,
src_type=self._node1_type,
dst_type=self._node2_type,
edge_type=self._edge1_type)
utils.check_edge_shape(edges, ids.size * expand_factor)
utils.check_edge_attrs(edges)
utils.check_edge_labels(edges)
utils.check_equal(nodes.ids, edges.dst_ids)
utils.check_node_ids(nodes, self._node2_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
utils.check_node_weights(nodes)
utils.check_node_labels(nodes)
def test_1hop(self):
""" Test case for sample 1 hop neighbor with strategy of edge_weight.
All the src_ids have neighbors.
"""
expand_factor = 6
ids = self._seed_node1_ids
nbr_s = self.g.neighbor_sampler(self._edge1_type,
expand_factor=expand_factor,
strategy="edge_weight")
nbrs = nbr_s.get(ids)
edges = nbrs.layer_edges(1)
nodes = nbrs.layer_nodes(1)
utils.check_fixed_edge_dst_ids(edges, dst_range=self._node2_range,
expected_src_ids=ids)
utils.check_edge_type(edges,
src_type=self._node1_type,
dst_type=self._node2_type,
edge_type=self._edge1_type)
utils.check_edge_shape(edges, ids.size * expand_factor)
utils.check_edge_attrs(edges)
utils.check_edge_labels(edges)
utils.check_equal(nodes.ids, edges.dst_ids)
utils.check_node_ids(nodes, self._node2_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
utils.check_node_weights(nodes)
utils.check_node_labels(nodes)
def test_1hop_with_neighbor_missing(self):
""" Sample neighbors for nodes which have no out neighbors,
and get the default neighbor id.
"""
expand_factor = 6
ids = self._seed_node1_ids_with_nbr_missing
nbr_s = self.g.neighbor_sampler(self._edge1_type,
expand_factor=expand_factor,
strategy="in_degree")
nbrs = nbr_s.get(ids)
edges = nbrs.layer_edges(1)
nodes = nbrs.layer_nodes(1)
utils.check_fixed_edge_dst_ids(edges, dst_range=self._node2_range,
expected_src_ids=ids,
default_dst_id=self._default_dst_id)
utils.check_edge_type(edges,
src_type=self._node1_type,
dst_type=self._node2_type,
edge_type=self._edge1_type)
utils.check_edge_shape(edges, ids.size * expand_factor)
utils.check_not_exist_edge_attrs(
edges, default_int_attr=self._default_int_attr,
default_float_attr=self._default_float_attr,
default_string_attr=self._default_string_attr,)
utils.check_not_exist_edge_labels(edges)
utils.check_equal(nodes.ids, edges.dst_ids)
utils.check_node_ids(nodes, [self._default_dst_id])
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
utils.check_not_exist_node_weights(nodes)
utils.check_not_exist_node_labels(nodes)
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 test_neg(self):
""" Sample negative neighbors with node_weight of the target nodes.
"""
expand_factor = 6
ids = self._seed_node2_ids
nbr_s = self.g.negative_sampler(self._node2_type,
expand_factor=expand_factor,
strategy="node_weight")
nodes = nbr_s.get(ids)
utils.check_ids(nodes.ids, [i for i in range(100, 200) if i not in ids])
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
def test_1hop_with_expand_factor(self):
""" Test case for sample 1 hop neighbor with strategy of edge_weight.
All the src_ids have neighbors.
"""
expand_factor = 6
ids = self._seed_node1_ids
nbr_s = self.g.neighbor_sampler(self._edge1_type,
expand_factor,
strategy="full")
nbrs = nbr_s.get(ids)
edges = nbrs.layer_edges(1)
nodes = nbrs.layer_nodes(1)
utils.check_node_ids(nodes, self._node2_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
def test_2hop(self):
""" Sample 2 hops of neighbors.
"""
gl.set_eager_mode(True)
expand_factor = [3, 2]
ids = self._seed_node1_ids
nbr_s = self.g.neighbor_sampler([self._edge1_type, self._edge2_type],
expand_factor=expand_factor,
strategy="in_degree")
nbrs = nbr_s.get(ids)
edges = nbrs.layer_edges(1)
nodes = nbrs.layer_nodes(1)
utils.check_fixed_edge_dst_ids(edges,
dst_range=self._node2_range,
expected_src_ids=ids)
utils.check_edge_type(edges,
src_type=self._node1_type,
dst_type=self._node2_type,
edge_type=self._edge1_type)
utils.check_edge_shape(edges, ids.size * expand_factor[0])
utils.check_edge_attrs(edges)
utils.check_edge_labels(edges)
utils.check_equal(nodes.ids, edges.dst_ids)
utils.check_node_ids(nodes, self._node2_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor[0])
utils.check_node_weights(nodes)
utils.check_node_labels(nodes)
ids = nodes.ids.reshape(-1)
edges = nbrs.layer_edges(2)
nodes = nbrs.layer_nodes(2)
utils.check_fixed_edge_dst_ids(edges,
dst_range=self._node1_range,
expected_src_ids=ids)
utils.check_edge_type(edges,
src_type=self._node2_type,
dst_type=self._node1_type,
edge_type=self._edge2_type)
utils.check_edge_shape(edges, ids.size * expand_factor[1])
utils.check_edge_attrs(edges)
utils.check_edge_weights(edges)
utils.check_equal(nodes.ids, edges.dst_ids)
utils.check_node_ids(nodes, self._node1_ids)
utils.check_node_type(nodes, node_type=self._node1_type)
utils.check_node_shape(nodes, ids.size * expand_factor[1])
def test_neg_using_gremlin(self):
""" Using gremlin-like api.
"""
expand_factor = 6
ids = self._seed_node1_ids
nbrs = self.g.V(self._node2_type, feed=ids) \
.Neg(self._node2_type).sample(expand_factor).by("node_weight") \
.emit()
nodes = nbrs[1]
utils.check_ids(nodes.ids,
[i for i in range(100, 200) if i not in ids])
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
def test_neg_using_gsl(self):
""" Using gsl api.
"""
import graphlearn as gl
gl.set_eager_mode(True)
expand_factor = 6
ids = self._seed_node1_ids
nbrs = self.g.V(self._node2_type, feed=ids) \
.Neg(self._node2_type).sample(expand_factor).by("node_weight") \
.emit()
nodes = nbrs[1]
utils.check_ids(nodes.ids, [i for i in range(100, 200) if i not in ids])
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
def test_neg(self):
""" Sample negative neighbors with in-degree of the target nodes.
"""
expand_factor = 6
ids = self._seed_node1_ids
nbr_s = self.g.negative_sampler(self._edge1_type,
expand_factor=expand_factor,
strategy="in_degree")
nodes = nbr_s.get(ids)
for i, e in enumerate(ids):
expected_ids = [iid for iid in self._node2_ids if \
iid not in utils.fixed_dst_ids(e, self._node2_range)]
utils.check_ids(nodes.ids[i], expected_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
def test_neg(self):
""" Using primative api.
"""
expand_factor = 6
ids = self._seed_node1_ids
nbr_s = self.g.negative_sampler(self._edge1_type,
expand_factor=expand_factor,
strategy="random")
nodes = nbr_s.get(ids)
for i, e in enumerate(ids):
expected_ids = [iid for iid in self._node2_ids if \
iid not in utils.fixed_dst_ids(e, self._node2_range)]
utils.check_ids(nodes.ids[i], expected_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
def test_neg_using_gsl(self):
""" Using gsl api.
"""
gl.set_eager_mode(True)
expand_factor = 6
ids = self._seed_node1_ids
nbrs = self.g.V(self._node1_type, feed=ids) \
.outNeg(self._edge1_type).sample(expand_factor).by("in_degree") \
.emit()
nodes = nbrs[1]
for i, e in enumerate(ids):
expected_ids = [iid for iid in self._node2_ids if \
iid not in utils.fixed_dst_ids(e, self._node2_range)]
utils.check_ids(nodes.ids[i], expected_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
def test_neg_using_gremlin(self):
""" Using gremlin-like api.
"""
expand_factor = 6
ids = self._seed_node1_ids
nbrs = self.g.V(self._node1_type, feed=ids) \
.outNeg(self._edge1_type).sample(expand_factor).by("random") \
.emit()
nodes = nbrs[1]
for i, e in enumerate(ids):
expected_ids = [iid for iid in self._node2_ids \
if iid not in utils.fixed_dst_ids(e, self._node2_range)]
utils.check_ids(
nodes.ids[i * expand_factor:(i + 1) * expand_factor],
expected_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_shape(nodes, ids.size * expand_factor)
def test_1hop_using_gremlin(self):
""" Full neighbor sample with gremlin-like api.
"""
expand_factor = 2
ids = self._seed_node1_ids
nbrs = self.g.V(self._node1_type, feed=ids) \
.outE(self._edge1_type).sample().by("full") \
.inV().emit()
nodes = nbrs[2]
index = 0
for node in nodes:
utils.check_sorted_equal(
utils.fixed_dst_ids(ids[index], self._node2_range), node.ids)
index += 1
utils.check_node_ids(nodes, self._node2_ids)
utils.check_node_type(nodes, node_type=self._node2_type)
utils.check_node_weights(nodes)
utils.check_node_labels(nodes)
