def get(self, ids):
""" Get batched samples.
Args:
ids (numpy.array): A 1d numpy array of whose negative dst nodes
will be sampled.
Return:
A `Nodes` object, shape=[ids.shape, `expand_factor`].
"""
if not isinstance(ids, np.ndarray):
raise ValueError("ids must be a numpy array, got {}.".format(
type(ids)))
ids = ids.flatten()
req = self._make_req(ids)
res = pywrap.new_sampling_response()
status = self._client.sample_neighbor(req, res)
if status.ok():
nbrs = pywrap.get_sampling_node_ids(res)
neg_nbrs = self._graph.get_nodes(self._dst_type,
nbrs,
shape=(ids.shape[0],
self._expand_factor))
pywrap.del_op_response(res)
pywrap.del_op_request(req)
raise_exception_on_not_ok_status(status)
return neg_nbrs
def get(self, src_ids, dst_ids):
""" Get batched negative samples.
Args:
src_ids (numpy.array): A 1d numpy array of whose negative dst nodes
will be sampled.
dst_ids (numpy.array): A 1d numpy array of positive dst nodes.
Return:
A `Nodes` object, shape=[ids.shape, `expand_factor`].
"""
src_ids = np.array(src_ids).flatten()
dst_ids = np.array(dst_ids).flatten()
req = self._make_req(src_ids, dst_ids)
res = pywrap.new_sampling_response()
status = self._client.cond_neg_sample(req, res)
if status.ok():
nbrs = pywrap.get_sampling_node_ids(res)
neg_nbrs = self._graph.get_nodes(self._dst_type,
nbrs,
shape=(dst_ids.shape[0],
self._expand_factor))
pywrap.del_op_response(res)
pywrap.del_op_request(req)
errors.raise_exception_on_not_ok_status(status)
return neg_nbrs
def get(self, ids): # pylint: disable=unused-argument
""" Get batched samples.
Args:
ids: A 1d numpy array, the input ids whose neighbors will be returned,
type=np.int64.
Return:
A `Layers` object.
"""
if len(self._meta_path) != len(self._expand_factor):
raise ValueError("The meta_path must have the same number"
"of elements as num_at_each_hop")
src_ids = ids
current_batch_size = ids.size
layers = Layers()
for i in xrange(len(self._meta_path)):
req = self._make_req(i, src_ids)
res = pywrap.new_sampling_response()
status = self._client.sample_neighbor(req, res)
if status.ok():
nbr_ids = pywrap.get_sampling_node_ids(res)
edge_ids = pywrap.get_sampling_edge_ids(res)
pywrap.del_op_response(res)
pywrap.del_op_request(req)
raise_exception_on_not_ok_status(status)
dst_type = self._dst_types[i]
layer_nodes = self._graph.get_nodes(dst_type,
nbr_ids,
shape=(current_batch_size,
self._expand_factor[i]))
ids = src_ids.repeat(self._expand_factor[i]).flatten()
nbr_ids_flat = nbr_ids.flatten()
layer_edges = \
self._graph.get_edges(self._meta_path[i],
ids,
nbr_ids_flat,
shape=(current_batch_size,
self._expand_factor[i]))
layer_edges.edge_ids = edge_ids
layers.append_layer(Layer(layer_nodes, layer_edges))
current_batch_size = nbr_ids_flat.size
src_ids = nbr_ids
return layers
def get(self, ids): # pylint: disable=unused-argument
if len(self._meta_path) != len(self._expand_factor):
raise ValueError("The length of meta_path must be same with hop count.")
ids = np.array(ids).flatten()
src_ids = ids
current_batch_size = ids.size
layers = Layers()
for i in range(len(self._meta_path)):
# req, res & call method.
req = self._make_req(i, src_ids)
res = pywrap.new_sampling_response()
status = self._client.sample_neighbor(req, res)
if status.ok():
src_degrees = pywrap.get_sampling_node_degrees(res)
dense_shape = (current_batch_size, max(src_degrees))
nbr_ids = pywrap.get_sampling_node_ids(res)
edge_ids = pywrap.get_sampling_edge_ids(res)
pywrap.del_op_response(res)
pywrap.del_op_request(req)
errors.raise_exception_on_not_ok_status(status)
dst_type = self._dst_types[i]
layer_nodes = self._graph.get_nodes(
dst_type,
nbr_ids,
offsets=src_degrees,
shape=dense_shape)
ids = np.concatenate([src_ids[idx].repeat(d) for \
idx, d in enumerate(src_degrees)])
nbr_ids_flat = nbr_ids.flatten()
layer_edges = self._graph.get_edges(
self._meta_path[i],
ids,
nbr_ids_flat,
offsets=src_degrees,
shape=dense_shape)
layer_edges.edge_ids = edge_ids
layers.append_layer(Layer(layer_nodes, layer_edges))
current_batch_size = nbr_ids_flat.size
src_ids = nbr_ids
return layers
