def sample_predecessor(self,
nodes,
max_degree,
return_eids=False,
shuffle=False):
"""Sample predecessor of given nodes.
Args:
nodes: Given nodes whose predecessor will be sampled.
max_degree: The max sampled predecessor for each nodes.
return_eids: Whether to return the corresponding eids.
Return:
Return a list of numpy.ndarray and each numpy.ndarray represent a list
of sampled predecessor ids for given nodes. If :code:`return_eids=True`, there will
be an additional list of numpy.ndarray and each numpy.ndarray represent
a list of eids that connected nodes to their predecessors.
"""
if self.is_tensor():
raise ValueError(
"You must call Graph.numpy() first. Tensor object don't supprt sample_predecessor now."
)
else:
node_pred = self.predecessor(nodes, return_eids=return_eids)
if return_eids:
node_pred, node_pred_eid = node_pred
if nodes is None:
nodes = self.nodes
node_pred = node_pred.tolist()
if return_eids:
node_pred_eid = node_pred_eid.tolist()
if return_eids:
return graph_kernel.sample_subset_with_eid(
node_pred, node_pred_eid, max_degree, shuffle)
else:
return graph_kernel.sample_subset(node_pred, max_degree,
shuffle)
def sample_successor(self,
nodes,
max_degree,
return_eids=False,
shuffle=False):
"""Sample successors of given nodes.
Args:
nodes: Given nodes whose successors will be sampled.
max_degree: The max sampled successors for each nodes.
return_eids: Whether to return the corresponding eids.
Return:
Return a list of numpy.ndarray and each numpy.ndarray represent a list
of sampled successor ids for given nodes. If :code:`return_eids=True`, there will
be an additional list of numpy.ndarray and each numpy.ndarray represent
a list of eids that connected nodes to their successors.
"""
node_succ = self.successor(nodes, return_eids=return_eids)
if return_eids:
node_succ, node_succ_eid = node_succ
if nodes is None:
nodes = self.nodes
node_succ = node_succ.tolist()
if return_eids:
node_succ_eid = node_succ_eid.tolist()
if return_eids:
return graph_kernel.sample_subset_with_eid(node_succ,
node_succ_eid,
max_degree, shuffle)
else:
return graph_kernel.sample_subset(node_succ, max_degree, shuffle)