def get_edge_feature(self, edge_list, feature_types):
"""
Get `feature_types` feature of the edges in `edge_list`.
Args:
edge_list (list or numpy.ndarray): The given list of edges.
feature_types (list or numpy.ndarray): The given list of feature types.
Returns:
numpy.ndarray: array of features.
Examples:
>>> import mindspore.dataset as ds
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> edges = data_graph.get_all_edges(0)
>>> features = data_graph.get_edge_feature(edges, [1])
Raises:
TypeError: If `edge_list` is not list or ndarray.
TypeError: If `feature_types` is not list or ndarray.
"""
if isinstance(edge_list, list):
edge_list = np.array(edge_list, dtype=np.int32)
return [
t.as_array() for t in self._graph.get_edge_feature(
Tensor(edge_list), feature_types)
]
def get_edge_feature(self, edge_list, feature_types):
"""
Get `feature_types` feature of the edges in `edge_list`.
Args:
edge_list (Union[list, numpy.ndarray]): The given list of edges.
feature_types (Union[list, numpy.ndarray]): The given list of feature types.
Returns:
numpy.ndarray, array of features.
Examples:
>>> edges = graph_dataset.get_all_edges(edge_type=0)
>>> features = graph_dataset.get_edge_feature(edge_list=edges, feature_types=[1])
Raises:
TypeError: If `edge_list` is not list or ndarray.
TypeError: If `feature_types` is not list or ndarray.
"""
if self._working_mode == 'server':
raise Exception(
"This method is not supported when working mode is server.")
if isinstance(edge_list, list):
edge_list = np.array(edge_list, dtype=np.int32)
return [
t.as_array() for t in self._graph_data.get_edge_feature(
Tensor(edge_list), feature_types)
]
def get_node_feature(self, node_list, feature_types):
"""
Get `feature_types` feature of the nodes in `node_list`.
Args:
node_list (Union[list, numpy.ndarray]): The given list of nodes.
feature_types (Union[list, numpy.ndarray]): The given list of feature types.
Returns:
numpy.ndarray, array of features.
Examples:
>>> import mindspore.dataset as ds
>>>
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> nodes = data_graph.get_all_nodes(0)
>>> features = data_graph.get_node_feature(nodes, [1])
Raises:
TypeError: If `node_list` is not list or ndarray.
TypeError: If `feature_types` is not list or ndarray.
"""
if self._working_mode == 'server':
raise Exception(
"This method is not supported when working mode is server.")
if isinstance(node_list, list):
node_list = np.array(node_list, dtype=np.int32)
return [
t.as_array() for t in self._graph_data.get_node_feature(
Tensor(node_list), feature_types)
]
