import copy
import math
from abc import ABC
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, List, Optional, Union
import torch
from torch import Tensor
from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData
from torch_geometric.typing import EdgeType, EdgeTypeStr, NodeType, OptTensor
from torch_geometric.utils.mixin import CastMixin
class DataType(Enum):
r"""The data type a sampler is operating on."""
homogeneous = 'homogeneous'
heterogeneous = 'heterogeneous'
remote = 'remote'
@classmethod
def from_data(cls, data: Any):
if isinstance(data, Data):
return cls.homogeneous
elif isinstance(data, HeteroData):
return cls.heterogeneous
elif (isinstance(data, (list, tuple)) and len(data) == 2
and isinstance(data[0], FeatureStore)
and isinstance(data[1], GraphStore)):
return cls.remote
raise ValueError(f"Expected a 'Data', 'HeteroData', or a tuple of "
f"'FeatureStore' and 'GraphStore' "
f"(got '{type(data)}')")
[docs]@dataclass
class SamplerOutput(CastMixin):
r"""The sampling output of a :class:`~torch_geometric.sampler.BaseSampler`
on homogeneous graphs.
Args:
node (torch.Tensor): The sampled nodes in the original graph.
row (torch.Tensor): The source node indices of the sampled subgraph.
Indices must be re-indexed to :obj:`{ 0, ..., num_nodes - 1 }`
corresponding to the nodes in the :obj:`node` tensor.
col (torch.Tensor): The destination node indices of the sampled
subgraph.
Indices must be re-indexed to :obj:`{ 0, ..., num_nodes - 1 }`
corresponding to the nodes in the :obj:`node` tensor.
edge (torch.Tensor, optional): The sampled edges in the original graph.
This tensor is used to obtain edge features from the original
graph. If no edge attributes are present, it may be omitted.
batch (torch.Tensor, optional): The vector to identify the seed node
for each sampled node. Can be present in case of disjoint subgraph
sampling per seed node. (default: :obj:`None`)
num_sampled_nodes (List[int], optional): The number of sampled nodes
per hop. (default: :obj:`None`)
num_sampled_edges (List[int], optional): The number of sampled edges
per hop. (default: :obj:`None`)
metadata: (Any, optional): Additional metadata information.
(default: :obj:`None`)
"""
node: Tensor
row: Tensor
col: Tensor
edge: OptTensor
batch: OptTensor = None
num_sampled_nodes: Optional[List[int]] = None
num_sampled_edges: Optional[List[int]] = None
# TODO(manan): refine this further; it does not currently define a proper
# API for the expected output of a sampler.
metadata: Optional[Any] = None
[docs]@dataclass
class HeteroSamplerOutput(CastMixin):
r"""The sampling output of a :class:`~torch_geometric.sampler.BaseSampler`
on heterogeneous graphs.
Args:
node (Dict[str, torch.Tensor]): The sampled nodes in the original graph
for each node type.
row (Dict[Tuple[str, str, str], torch.Tensor]): The source node indices
of the sampled subgraph for each edge type.
Indices must be re-indexed to :obj:`{ 0, ..., num_nodes - 1 }`
corresponding to the nodes in the :obj:`node` tensor of the source
node type.
col (Dict[Tuple[str, str, str], torch.Tensor]): The destination node
indices of the sampled subgraph for each edge type.
Indices must be re-indexed to :obj:`{ 0, ..., num_nodes - 1 }`
corresponding to the nodes in the :obj:`node` tensor of the
destination node type.
edge (Dict[Tuple[str, str, str], torch.Tensor], optional): The sampled
edges in the original graph for each edge type.
This tensor is used to obtain edge features from the original
graph. If no edge attributes are present, it may be omitted.
batch (Dict[str, torch.Tensor], optional): The vector to identify the
seed node for each sampled node for each node type. Can be present
in case of disjoint subgraph sampling per seed node.
(default: :obj:`None`)
num_sampled_nodes (Dict[str, List[int]], optional): The number of
sampled nodes for each node type and each layer.
(default: :obj:`None`)
num_sampled_edges (Dict[EdgeType, List[int]], optional): The number of
sampled edges for each edge type and each layer.
(default: :obj:`None`)
metadata: (Any, optional): Additional metadata information.
(default: :obj:`None`)
"""
node: Dict[NodeType, Tensor]
row: Dict[EdgeType, Tensor]
col: Dict[EdgeType, Tensor]
edge: Optional[Dict[EdgeType, Tensor]]
batch: Optional[Dict[NodeType, Tensor]] = None
num_sampled_nodes: Optional[Dict[NodeType, List[int]]] = None
num_sampled_edges: Optional[Dict[EdgeType, List[int]]] = None
# TODO(manan): refine this further; it does not currently define a proper
# API for the expected output of a sampler.
metadata: Optional[Any] = None
[docs]@dataclass(frozen=True)
class NumNeighbors:
r"""The number of neighbors to sample in a homogeneous or heterogeneous
graph. In heterogeneous graphs, may also take in a dictionary denoting
the amount of neighbors to sample for individual edge types.
Args:
values (List[int] or Dict[Tuple[str, str, str], List[int]]): The
number of neighbors to sample.
If an entry is set to :obj:`-1`, all neighbors will be included.
In heterogeneous graphs, may also take in a dictionary denoting
the amount of neighbors to sample for individual edge types.
default (List[int], optional): The default number of neighbors for edge
types not specified in :obj:`values`. (default: :obj:`None`)
"""
values: Union[List[int], Dict[EdgeTypeStr, List[int]]]
default: Optional[List[int]] = None
def __init__(
self,
values: Union[List[int], Dict[EdgeType, List[int]]],
default: Optional[List[int]] = None,
):
if isinstance(values, (tuple, list)) and default is not None:
raise ValueError(f"'default' must be set to 'None' in case a "
f"single list is given as the number of "
f"neighbors (got '{type(default)})'")
if isinstance(values, dict):
values = {EdgeTypeStr(key): value for key, value in values.items()}
# Write to `__dict__` since dataclass is annotated with `frozen=True`:
self.__dict__['values'] = values
self.__dict__['default'] = default
def _get_values(
self,
edge_types: Optional[List[EdgeType]] = None,
mapped: bool = False,
) -> Union[List[int], Dict[Union[EdgeType, EdgeTypeStr], List[int]]]:
if edge_types is not None:
if isinstance(self.values, (tuple, list)):
default = self.values
elif isinstance(self.values, dict):
default = self.default
else:
assert False
out = {}
for edge_type in edge_types:
edge_type_str = EdgeTypeStr(edge_type)
if edge_type_str in self.values:
out[edge_type_str if mapped else edge_type] = (
self.values[edge_type_str])
else:
if default is None:
raise ValueError(f"Missing number of neighbors for "
f"edge type '{edge_type}'")
out[edge_type_str if mapped else edge_type] = default
elif isinstance(self.values, dict) and not mapped:
out = {key.to_tuple(): value for key, value in self.values.items()}
else:
out = copy.copy(self.values)
if isinstance(out, dict):
num_hops = set(len(v) for v in out.values())
if len(num_hops) > 1:
raise ValueError(f"Number of hops must be the same across all "
f"edge types (got {len(num_hops)} different "
f"number of hops)")
return out
[docs] def get_values(
self,
edge_types: Optional[List[EdgeType]] = None,
) -> Union[List[int], Dict[EdgeType, List[int]]]:
r"""Returns the number of neighbors.
Args:
edge_types (List[Tuple[str, str, str]], optional): The edge types
to generate the number of neighbors for. (default: :obj:`None`)
"""
if '_values' in self.__dict__:
return self.__dict__['_values']
values = self._get_values(edge_types, mapped=False)
self.__dict__['_values'] = values
return values
[docs] def get_mapped_values(
self,
edge_types: Optional[List[EdgeType]] = None,
) -> Union[List[int], Dict[str, List[int]]]:
r"""Returns the number of neighbors.
For heterogeneous graphs, a dictionary is returned in which edge type
tuples are converted to strings.
Args:
edge_types (List[Tuple[str, str, str]], optional): The edge types
to generate the number of neighbors for. (default: :obj:`None`)
"""
if '_mapped_values' in self.__dict__:
return self.__dict__['_mapped_values']
values = self._get_values(edge_types, mapped=True)
self.__dict__['_mapped_values'] = values
return values
@property
def num_hops(self) -> int:
r"""Returns the number of hops."""
if '_num_hops' in self.__dict__:
return self.__dict__['_num_hops']
if isinstance(self.values, (tuple, list)):
num_hops = max(len(self.values), len(self.default or []))
else: # isinstance(self.values, dict):
num_hops = max([0] + [len(v) for v in self.values.values()])
num_hops = max(num_hops, len(self.default or []))
self.__dict__['_num_hops'] = num_hops
return num_hops
def __len__(self) -> int:
r"""Returns the number of hops."""
return self.num_hops
class NegativeSamplingMode(Enum):
# 'binary': Randomly sample negative edges in the graph.
binary = 'binary'
# 'triplet': Randomly sample negative destination nodes for each positive
# source node.
triplet = 'triplet'
[docs]@dataclass
class NegativeSampling(CastMixin):
r"""The negative sampling configuration of a
:class:`~torch_geometric.sampler.BaseSampler` when calling
:meth:`~torch_geometric.sampler.BaseSampler.sample_from_edges`.
Args:
mode (str): The negative sampling mode
(:obj:`"binary"` or :obj:`"triplet"`).
If set to :obj:`"binary"`, will randomly sample negative links
from the graph.
If set to :obj:`"triplet"`, will randomly sample negative
destination nodes for each positive source node.
amount (int or float, optional): The ratio of sampled negative edges to
the number of positive edges. (default: :obj:`1`)
weight (torch.Tensor, optional): A node-level vector determining the
sampling of nodes. Does not necessariyl need to sum up to one.
If not given, negative nodes will be sampled uniformly.
(default: :obj:`None`)
"""
mode: NegativeSamplingMode
amount: Union[int, float] = 1
weight: Optional[Tensor] = None
def __init__(
self,
mode: Union[NegativeSamplingMode, str],
amount: Union[int, float] = 1,
weight: Optional[Tensor] = None,
):
self.mode = NegativeSamplingMode(mode)
self.amount = amount
self.weight = weight
if self.amount <= 0:
raise ValueError(f"The attribute 'amount' needs to be positive "
f"for '{self.__class__.__name__}' "
f"(got {self.amount})")
if self.is_triplet():
if self.amount != math.ceil(self.amount):
raise ValueError(f"The attribute 'amount' needs to be an "
f"integer for '{self.__class__.__name__}' "
f"with 'triplet' negative sampling "
f"(got {self.amount}).")
self.amount = math.ceil(self.amount)
def is_binary(self) -> bool:
return self.mode == NegativeSamplingMode.binary
def is_triplet(self) -> bool:
return self.mode == NegativeSamplingMode.triplet
[docs] def sample(self, num_samples: int,
num_nodes: Optional[int] = None) -> Tensor:
r"""Generates :obj:`num_samples` negative samples."""
if self.weight is None:
if num_nodes is None:
raise ValueError(
f"Cannot sample negatives in '{self.__class__.__name__}' "
f"without passing the 'num_nodes' argument")
return torch.randint(num_nodes, (num_samples, ))
if num_nodes is not None and self.weight.numel() != num_nodes:
raise ValueError(
f"The 'weight' attribute in '{self.__class__.__name__}' "
f"needs to match the number of nodes {num_nodes} "
f"(got {self.weight.numel()})")
return torch.multinomial(self.weight, num_samples, replacement=True)
[docs]class BaseSampler(ABC):
r"""An abstract base class that initializes a graph sampler and provides
:meth:`sample_from_nodes` and :meth:`sample_from_edges` routines.
.. note ::
Any data stored in the sampler will be *replicated* across data loading
workers that use the sampler since each data loading worker holds its
own instance of a sampler.
As such, it is recommended to limit the amount of information stored in
the sampler.
"""
[docs] def sample_from_nodes(
self,
index: NodeSamplerInput,
**kwargs,
) -> Union[HeteroSamplerOutput, SamplerOutput]:
r"""Performs sampling from the nodes specified in :obj:`index`,
returning a sampled subgraph in the specified output format.
The :obj:`index` is a tuple holding the following information:
1. The example indices of the seed nodes
2. The node indices to start sampling from
3. The timestamps of the given seed nodes (optional)
Args:
index (NodeSamplerInput): The node sampler input object.
"""
raise NotImplementedError
[docs] def sample_from_edges(
self,
index: EdgeSamplerInput,
neg_sampling: Optional[NegativeSampling] = None,
) -> Union[HeteroSamplerOutput, SamplerOutput]:
r"""Performs sampling from the edges specified in :obj:`index`,
returning a sampled subgraph in the specified output format.
The :obj:`index` is a tuple holding the following information:
1. The example indices of the seed links
2. The source node indices to start sampling from
3. The destination node indices to start sampling from
4. The labels of the seed links (optional)
5. The timestamps of the given seed nodes (optional)
Args:
index (EdgeSamplerInput): The edge sampler input object.
neg_sampling (NegativeSampling, optional): The negative sampling
configuration. (default: :obj:`None`)
"""
raise NotImplementedError
@property
def edge_permutation(self) -> Union[OptTensor, Dict[EdgeType, OptTensor]]:
r"""If the sampler performs any modification of edge ordering in the
original graph, this function is expected to return the permutation
tensor that defines the permutation from the edges in the original
graph and the edges used in the sampler. If no such permutation was
applied, :obj:`None` is returned. For heterogeneous graphs, the
expected return type is a permutation tensor for each edge type."""
return None