mindspore.dataset.GraphData
- class mindspore.dataset.GraphData(dataset_file, num_parallel_workers=None, working_mode="local", hostname="127.0.0.1", port=50051, num_client=1, auto_shutdown=True)[source]
Reads the graph dataset used for GNN training from the shared file and database.
- Parameters
dataset_file (str) – One of file names in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the dataset in parallel (default=None).
working_mode (str, optional) –
Set working mode, now supports ‘local’/’client’/’server’ (default=’local’).
’local’, used in non-distributed training scenarios.
’client’, used in distributed training scenarios. The client does not load data, but obtains data from the server.
’server’, used in distributed training scenarios. The server loads the data and is available to the client.
hostname (str, optional) – Hostname of the graph data server. This parameter is only valid when working_mode is set to ‘client’ or ‘server’ (default=’127.0.0.1’).
port (int, optional) – Port of the graph data server. The range is 1024-65535. This parameter is only valid when working_mode is set to ‘client’ or ‘server’ (default=50051).
num_client (int, optional) – Maximum number of clients expected to connect to the server. The server will allocate resources according to this parameter. This parameter is only valid when working_mode is set to ‘server’ (default=1).
auto_shutdown (bool, optional) – Valid when working_mode is set to ‘server’, when the number of connected clients reaches num_client and no client is being connected, the server automatically exits (default=True).
Examples
>>> graph_dataset_dir = "/path/to/graph_dataset_file" >>> graph_dataset = ds.GraphData(dataset_file=graph_dataset_dir, num_parallel_workers=2) >>> nodes = graph_dataset.get_all_nodes(node_type=1) >>> features = graph_dataset.get_node_feature(node_list=nodes, feature_types=[1])
- get_all_edges(edge_type)[source]
Get all edges in the graph.
- Parameters
edge_type (int) – Specify the type of edge.
- Returns
numpy.ndarray, array of edges.
Examples
>>> edges = graph_dataset.get_all_edges(edge_type=0)
- Raises
TypeError – If edge_type is not integer.
- get_all_neighbors(node_list, neighbor_type, output_format=OutputFormat.NORMAL)[source]
Get neighbor_type neighbors of the nodes in node_list. We try to use the following example to illustrate the definition of these formats. 1 represents connected between two nodes, and 0 represents not connected.
Adjacent Matrix 0
1
2
3
0
0
1
0
0
1
0
0
1
0
2
1
0
0
1
3
1
0
0
0
Normal Format src
0
1
2
3
dst_0
1
2
0
1
dst_1
-1
-1
3
-1
COO Format src
0
1
2
2
3
dst
1
2
0
3
1
CSR Format offsetTable
0
1
2
4
dstTable
1
2
0
3
1
- Parameters
node_list (Union[list, numpy.ndarray]) – The given list of nodes.
neighbor_type (int) – Specify the type of neighbor.
output_format (OutputFormat, optional) – Output storage format (default=OutputFormat.NORMAL) It can be any of [OutputFormat.NORMAL, OutputFormat.COO, OutputFormat.CSR].
- Returns
For NORMAL format or COO format numpy.ndarray which represents the array of neighbors will return. As if CSR format is specified, two numpy.ndarrays will return. The first one is offset table, the second one is neighbors
Examples
>>> from mindspore.dataset.engine import OutputFormat >>> nodes = graph_dataset.get_all_nodes(node_type=1) >>> neighbors = graph_dataset.get_all_neighbors(node_list=nodes, neighbor_type=2) >>> neighbors_coo = graph_dataset.get_all_neighbors(node_list=nodes, neighbor_type=2, ... output_format=OutputFormat.COO) >>> offset_table, neighbors_csr = graph_dataset.get_all_neighbors(node_list=nodes, neighbor_type=2, ... output_format=OutputFormat.CSR)
- get_all_nodes(node_type)[source]
Get all nodes in the graph.
- Parameters
node_type (int) – Specify the type of node.
- Returns
numpy.ndarray, array of nodes.
Examples
>>> nodes = graph_dataset.get_all_nodes(node_type=1)
- Raises
TypeError – If node_type is not integer.
- get_edge_feature(edge_list, feature_types)[source]
Get feature_types feature of the edges in edge_list.
- Parameters
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])
- get_edges_from_nodes(node_list)[source]
Get edges from the nodes.
- Parameters
node_list (Union[list[tuple], numpy.ndarray]) – The given list of pair nodes ID.
- Returns
numpy.ndarray, array of edges ID.
Examples
>>> edges = graph_dataset.get_edges_from_nodes(node_list=[(101, 201), (103, 207)])
- Raises
TypeError – If edge_list is not list or ndarray.
- get_neg_sampled_neighbors(node_list, neg_neighbor_num, neg_neighbor_type)[source]
Get neg_neighbor_type negative sampled neighbors of the nodes in node_list.
- Parameters
node_list (Union[list, numpy.ndarray]) – The given list of nodes.
neg_neighbor_num (int) – Number of neighbors sampled.
neg_neighbor_type (int) – Specify the type of negative neighbor.
- Returns
numpy.ndarray, array of neighbors.
Examples
>>> nodes = graph_dataset.get_all_nodes(node_type=1) >>> neg_neighbors = graph_dataset.get_neg_sampled_neighbors(node_list=nodes, neg_neighbor_num=5, ... neg_neighbor_type=2)
- get_node_feature(node_list, feature_types)[source]
Get feature_types feature of the nodes in node_list.
- Parameters
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
>>> nodes = graph_dataset.get_all_nodes(node_type=1) >>> features = graph_dataset.get_node_feature(node_list=nodes, feature_types=[2, 3])
- get_nodes_from_edges(edge_list)[source]
Get nodes from the edges.
- Parameters
edge_list (Union[list, numpy.ndarray]) – The given list of edges.
- Returns
numpy.ndarray, array of nodes.
- Raises
TypeError – If edge_list is not list or ndarray.
- get_sampled_neighbors(node_list, neighbor_nums, neighbor_types, strategy=SamplingStrategy.RANDOM)[source]
Get sampled neighbor information.
The api supports multi-hop neighbor sampling. That is, the previous sampling result is used as the input of next-hop sampling. A maximum of 6-hop are allowed.
The sampling result is tiled into a list in the format of [input node, 1-hop sampling result, 2-hop sampling result …]
- Parameters
node_list (Union[list, numpy.ndarray]) – The given list of nodes.
neighbor_nums (Union[list, numpy.ndarray]) – Number of neighbors sampled per hop.
neighbor_types (Union[list, numpy.ndarray]) – Neighbor type sampled per hop.
strategy (SamplingStrategy, optional) –
Sampling strategy (default=SamplingStrategy.RANDOM). It can be any of [SamplingStrategy.RANDOM, SamplingStrategy.EDGE_WEIGHT].
SamplingStrategy.RANDOM, random sampling with replacement.
SamplingStrategy.EDGE_WEIGHT, sampling with edge weight as probability.
- Returns
numpy.ndarray, array of neighbors.
Examples
>>> nodes = graph_dataset.get_all_nodes(node_type=1) >>> neighbors = graph_dataset.get_sampled_neighbors(node_list=nodes, neighbor_nums=[2, 2], ... neighbor_types=[2, 1])
- graph_info()[source]
Get the meta information of the graph, including the number of nodes, the type of nodes, the feature information of nodes, the number of edges, the type of edges, and the feature information of edges.
- Returns
dict, meta information of the graph. The key is node_type, edge_type, node_num, edge_num, node_feature_type and edge_feature_type.
- random_walk(target_nodes, meta_path, step_home_param=1.0, step_away_param=1.0, default_node=-1)[source]
Random walk in nodes.
- Parameters
step_home_param (float, optional) – return hyper parameter in node2vec algorithm (Default = 1.0).
step_away_param (float, optional) – in out hyper parameter in node2vec algorithm (Default = 1.0).
default_node (int, optional) – default node if no more neighbors found (Default = -1). A default value of -1 indicates that no node is given.
- Returns
numpy.ndarray, array of nodes.
Examples
>>> nodes = graph_dataset.get_all_nodes(node_type=1) >>> walks = graph_dataset.random_walk(target_nodes=nodes, meta_path=[2, 1, 2])