Source code for torch_geometric.loader.link_neighbor_loader

from typing import Callable, Dict, List, Optional, Tuple, Union

from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData
from torch_geometric.loader.link_loader import LinkLoader
from torch_geometric.sampler import NegativeSampling, NeighborSampler
from torch_geometric.sampler.base import SubgraphType
from torch_geometric.typing import EdgeType, InputEdges, OptTensor


[docs]class LinkNeighborLoader(LinkLoader): r"""A link-based data loader derived as an extension of the node-based :class:`torch_geometric.loader.NeighborLoader`. This loader allows for mini-batch training of GNNs on large-scale graphs where full-batch training is not feasible. More specifically, this loader first selects a sample of edges from the set of input edges :obj:`edge_label_index` (which may or not be edges in the original graph) and then constructs a subgraph from all the nodes present in this list by sampling :obj:`num_neighbors` neighbors in each iteration. .. code-block:: python from torch_geometric.datasets import Planetoid from torch_geometric.loader import LinkNeighborLoader data = Planetoid(path, name='Cora')[0] loader = LinkNeighborLoader( data, # Sample 30 neighbors for each node for 2 iterations num_neighbors=[30] * 2, # Use a batch size of 128 for sampling training nodes batch_size=128, edge_label_index=data.edge_index, ) sampled_data = next(iter(loader)) print(sampled_data) >>> Data(x=[1368, 1433], edge_index=[2, 3103], y=[1368], train_mask=[1368], val_mask=[1368], test_mask=[1368], edge_label_index=[2, 128]) It is additionally possible to provide edge labels for sampled edges, which are then added to the batch: .. code-block:: python loader = LinkNeighborLoader( data, num_neighbors=[30] * 2, batch_size=128, edge_label_index=data.edge_index, edge_label=torch.ones(data.edge_index.size(1)) ) sampled_data = next(iter(loader)) print(sampled_data) >>> Data(x=[1368, 1433], edge_index=[2, 3103], y=[1368], train_mask=[1368], val_mask=[1368], test_mask=[1368], edge_label_index=[2, 128], edge_label=[128]) The rest of the functionality mirrors that of :class:`~torch_geometric.loader.NeighborLoader`, including support for heterogeneous graphs. In particular, the data loader will add the following attributes to the returned mini-batch: * :obj:`n_id` The global node index for every sampled node * :obj:`e_id` The global edge index for every sampled edge * :obj:`input_id`: The global index of the :obj:`edge_label_index` * :obj:`num_sampled_nodes`: The number of sampled nodes in each hop * :obj:`num_sampled_edges`: The number of sampled edges in each hop .. note:: Negative sampling is currently implemented in an approximate way, *i.e.* negative edges may contain false negatives. .. warning:: Note that the sampling scheme is independent from the edge we are making a prediction for. That is, by default supervision edges in :obj:`edge_label_index` **will not** get masked out during sampling. In case there exists an overlap between message passing edges in :obj:`data.edge_index` and supervision edges in :obj:`edge_label_index`, you might end up sampling an edge you are making a prediction for. You can generally avoid this behavior (if desired) by making :obj:`data.edge_index` and :obj:`edge_label_index` two disjoint sets of edges, *e.g.*, via the :class:`~torch_geometric.transforms.RandomLinkSplit` transformation and its :obj:`disjoint_train_ratio` argument. Args: data (Any): A :class:`~torch_geometric.data.Data`, :class:`~torch_geometric.data.HeteroData`, or (:class:`~torch_geometric.data.FeatureStore`, :class:`~torch_geometric.data.GraphStore`) data object. num_neighbors (List[int] or Dict[Tuple[str, str, str], List[int]]): The number of neighbors to sample for each node in each iteration. 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 each individual edge type. edge_label_index (Tensor or EdgeType or Tuple[EdgeType, Tensor]): The edge indices for which neighbors are sampled to create mini-batches. If set to :obj:`None`, all edges will be considered. In heterogeneous graphs, needs to be passed as a tuple that holds the edge type and corresponding edge indices. (default: :obj:`None`) edge_label (Tensor, optional): The labels of edge indices for which neighbors are sampled. Must be the same length as the :obj:`edge_label_index`. If set to :obj:`None` its set to `torch.zeros(...)` internally. (default: :obj:`None`) edge_label_time (Tensor, optional): The timestamps for edge indices for which neighbors are sampled. Must be the same length as :obj:`edge_label_index`. If set, temporal sampling will be used such that neighbors are guaranteed to fulfill temporal constraints, *i.e.*, neighbors have an earlier timestamp than the ouput edge. The :obj:`time_attr` needs to be set for this to work. (default: :obj:`None`) replace (bool, optional): If set to :obj:`True`, will sample with replacement. (default: :obj:`False`) subgraph_type (SubgraphType or str, optional): The type of the returned subgraph. If set to :obj:`"directional"`, the returned subgraph only holds the sampled (directed) edges which are necessary to compute representations for the sampled seed nodes. If set to :obj:`"bidirectional"`, sampled edges are converted to bidirectional edges. If set to :obj:`"induced"`, the returned subgraph contains the induced subgraph of all sampled nodes. (default: :obj:`"directional"`) disjoint (bool, optional): If set to :obj: `True`, each seed node will create its own disjoint subgraph. If set to :obj:`True`, mini-batch outputs will have a :obj:`batch` vector holding the mapping of nodes to their respective subgraph. Will get automatically set to :obj:`True` in case of temporal sampling. (default: :obj:`False`) temporal_strategy (str, optional): The sampling strategy when using temporal sampling (:obj:`"uniform"`, :obj:`"last"`). If set to :obj:`"uniform"`, will sample uniformly across neighbors that fulfill temporal constraints. If set to :obj:`"last"`, will sample the last `num_neighbors` that fulfill temporal constraints. (default: :obj:`"uniform"`) neg_sampling (NegativeSampling, optional): The negative sampling configuration. For negative sampling mode :obj:`"binary"`, samples can be accessed via the attributes :obj:`edge_label_index` and :obj:`edge_label` in the respective edge type of the returned mini-batch. In case :obj:`edge_label` does not exist, it will be automatically created and represents a binary classification task (:obj:`0` = negative edge, :obj:`1` = positive edge). In case :obj:`edge_label` does exist, it has to be a categorical label from :obj:`0` to :obj:`num_classes - 1`. After negative sampling, label :obj:`0` represents negative edges, and labels :obj:`1` to :obj:`num_classes` represent the labels of positive edges. Note that returned labels are of type :obj:`torch.float` for binary classification (to facilitate the ease-of-use of :meth:`F.binary_cross_entropy`) and of type :obj:`torch.long` for multi-class classification (to facilitate the ease-of-use of :meth:`F.cross_entropy`). For negative sampling mode :obj:`"triplet"`, samples can be accessed via the attributes :obj:`src_index`, :obj:`dst_pos_index` and :obj:`dst_neg_index` in the respective node types of the returned mini-batch. :obj:`edge_label` needs to be :obj:`None` for :obj:`"triplet"` negative sampling mode. If set to :obj:`None`, no negative sampling strategy is applied. (default: :obj:`None`) neg_sampling_ratio (int or float, optional): The ratio of sampled negative edges to the number of positive edges. Deprecated in favor of the :obj:`neg_sampling` argument. (default: :obj:`None`) time_attr (str, optional): The name of the attribute that denotes timestamps for either the nodes or edges in the graph. If set, temporal sampling will be used such that neighbors are guaranteed to fulfill temporal constraints, *i.e.* neighbors have an earlier or equal timestamp than the center node. Only used if :obj:`edge_label_time` is set. (default: :obj:`None`) weight_attr (str, optional): The name of the attribute that denotes edge weights in the graph. If set, weighted/biased sampling will be used such that neighbors are more likely to get sampled the higher their edge weights are. Edge weights do not need to sum to one, but must be non-negative, finite and have a non-zero sum within local neighborhoods. (default: :obj:`None`) transform (callable, optional): A function/transform that takes in a sampled mini-batch and returns a transformed version. (default: :obj:`None`) transform_sampler_output (callable, optional): A function/transform that takes in a :class:`torch_geometric.sampler.SamplerOutput` and returns a transformed version. (default: :obj:`None`) is_sorted (bool, optional): If set to :obj:`True`, assumes that :obj:`edge_index` is sorted by column. If :obj:`time_attr` is set, additionally requires that rows are sorted according to time within individual neighborhoods. This avoids internal re-sorting of the data and can improve runtime and memory efficiency. (default: :obj:`False`) filter_per_worker (bool, optional): If set to :obj:`True`, will filter the returned data in each worker's subprocess. If set to :obj:`False`, will filter the returned data in the main process. If set to :obj:`None`, will automatically infer the decision based on whether data partially lives on the GPU (:obj:`filter_per_worker=True`) or entirely on the CPU (:obj:`filter_per_worker=False`). There exists different trade-offs for setting this option. Specifically, setting this option to :obj:`True` for in-memory datasets will move all features to shared memory, which may result in too many open file handles. (default: :obj:`None`) **kwargs (optional): Additional arguments of :class:`torch.utils.data.DataLoader`, such as :obj:`batch_size`, :obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`. """ def __init__( self, data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], num_neighbors: Union[List[int], Dict[EdgeType, List[int]]], edge_label_index: InputEdges = None, edge_label: OptTensor = None, edge_label_time: OptTensor = None, replace: bool = False, subgraph_type: Union[SubgraphType, str] = 'directional', disjoint: bool = False, temporal_strategy: str = 'uniform', neg_sampling: Optional[NegativeSampling] = None, neg_sampling_ratio: Optional[Union[int, float]] = None, time_attr: Optional[str] = None, weight_attr: Optional[str] = None, transform: Optional[Callable] = None, transform_sampler_output: Optional[Callable] = None, is_sorted: bool = False, filter_per_worker: Optional[bool] = None, neighbor_sampler: Optional[NeighborSampler] = None, directed: bool = True, # Deprecated. **kwargs, ): if (edge_label_time is not None) != (time_attr is not None): raise ValueError( f"Received conflicting 'edge_label_time' and 'time_attr' " f"arguments: 'edge_label_time' is " f"{'set' if edge_label_time is not None else 'not set'} " f"while 'time_attr' is " f"{'set' if time_attr is not None else 'not set'}. " f"Both arguments must be provided for temporal sampling.") if neighbor_sampler is None: neighbor_sampler = NeighborSampler( data, num_neighbors=num_neighbors, replace=replace, subgraph_type=subgraph_type, disjoint=disjoint, temporal_strategy=temporal_strategy, time_attr=time_attr, weight_attr=weight_attr, is_sorted=is_sorted, share_memory=kwargs.get('num_workers', 0) > 0, directed=directed, ) super().__init__( data=data, link_sampler=neighbor_sampler, edge_label_index=edge_label_index, edge_label=edge_label, edge_label_time=edge_label_time, neg_sampling=neg_sampling, neg_sampling_ratio=neg_sampling_ratio, transform=transform, transform_sampler_output=transform_sampler_output, filter_per_worker=filter_per_worker, **kwargs, )