"""Sampling Graphs."""
from typing import Dict, Union
import torch
__all__ = ["SamplingGraph"]
[docs]
class SamplingGraph:
r"""Class for sampling graph."""
def __init__(self):
pass
def __repr__(self) -> str:
"""Return a string representation of the graph.
Returns
-------
str
String representation of the graph.
"""
raise NotImplementedError
@property
def num_nodes(self) -> Union[int, Dict[str, int]]:
"""The number of nodes in the graph.
- If the graph is homogenous, returns an integer.
- If the graph is heterogenous, returns a dictionary.
Returns
-------
Union[int, Dict[str, int]]
The number of nodes. Integer indicates the total nodes number of a
homogenous graph; dict indicates nodes number per node types of a
heterogenous graph.
"""
raise NotImplementedError
@property
def num_edges(self) -> Union[int, Dict[str, int]]:
"""The number of edges in the graph.
- If the graph is homogenous, returns an integer.
- If the graph is heterogenous, returns a dictionary.
Returns
-------
Union[int, Dict[str, int]]
The number of edges. Integer indicates the total edges number of a
homogenous graph; dict indicates edges number per edge types of a
heterogenous graph.
"""
raise NotImplementedError
[docs]
def copy_to_shared_memory(self, shared_memory_name: str) -> "SamplingGraph":
"""Copy the graph to shared memory.
Parameters
----------
shared_memory_name : str
Name of the shared memory.
Returns
-------
SamplingGraph
The copied SamplingGraph object on shared memory.
"""
raise NotImplementedError
# pylint: disable=invalid-name
[docs]
def to(self, device: torch.device) -> "SamplingGraph":
"""Copy graph to the specified device.
Parameters
----------
device : torch.device
The destination device.
Returns
-------
SamplingGraph
The graph on the specified device.
"""
raise NotImplementedError