Graph Learning

class graphscope.learning.graph.Graph(*args: Any, **kwargs: Any)

E(edge_type, feed=None, reverse=False)

Entry of Gremlin-like query. Start from edge.

Parameters

• edge_type (string) – The type of edge which is the entry of query.

• feed (None| (np.ndarray, np.ndarray) | types.GeneratorType | Edges) – None: Default. Sample edges with the following .shuffle and .batch API. (np.ndarray, np.ndarray): src_ids, dst_ids. Get edges of the given (src_ids, dst_ids) and given edge_type. src_ids and dst_ids must be the same shape, dtype is int. types.Generator: A generator of (numpy.ndarray, numpy.ndarray). Get edges of generated (src_ids, dst_ids) and given edge_type. Edges: An Edges object.

Returns

A ‘Query’ object.

Example:

>>> import numpy as np
>>> g.E("buy").shuffle().batch(64)
>>> g.E("buy", feed=(np.array([1, 2, 3]), np.array([4, 5, 6]))
>>> def gen():
>>>   while True:
>>>     yield  (np.array([1, 2, 3]), np.array([4, 5, 6]))
>>> gen = gen()
>>> g.E("buy", feed=gen)

V(t, feed=None)

Entry of Gremlin-like query. Start from node.

Args: t (string): The type of node which is the entry of query or the type

of edge when node is from edge source or dst.

feed (None| numpy.ndarray | types.GeneratorType | Nodes): When feed

is not None, the type should be a node type, which means query the attributes of the specified node ids. None: Default. Sample nodes with the following .shuffle and .batch API. numpy.ndarray: Any shape of ids. Get nodes of the given ids and node_type. types.Generator: A generator of numpy.ndarray. Get nodes of generated ids and given node_type. Nodes: A Nodes object.

Return: A ‘Query’ object.

Example:

>>> import numpy as np
>>> g.V("user").shuffle().batch(64)
>>> g.V("user", feed=np.array([1, 2, 3]))
>>> def gen():
>>>   while True:
>>>     yield  np.array([1, 2, 3])
>>> gen = gen()
>>> g.V("user", feed=gen)

__init__(graph_node, handle, config=None, object_id=None)

Initialize a graph for the learning engine using a handle.

get_handle(worker_count=1)

Return a base64-encoded handle for distributed training.