wehub-resource-sync
301 lines
9.0 KiB
Python
301 lines
9.0 KiB
Python
import os.path as osp
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from copy import deepcopy
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import dgl
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import torch
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from dgl import add_self_loop, DGLGraph, NID
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from dgl.dataloading.negative_sampler import Uniform
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from torch.utils.data import DataLoader, Dataset
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from tqdm import tqdm
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from utils import drnl_node_labeling
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class GraphDataSet(Dataset):
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"""
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GraphDataset for torch DataLoader
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"""
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def __init__(self, graph_list, tensor):
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self.graph_list = graph_list
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self.tensor = tensor
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def __len__(self):
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return len(self.graph_list)
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def __getitem__(self, index):
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return (self.graph_list[index], self.tensor[index])
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class PosNegEdgesGenerator(object):
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"""
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Generate positive and negative samples
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Attributes:
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g(dgl.DGLGraph): graph
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split_edge(dict): split edge
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neg_samples(int): num of negative samples per positive sample
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subsample_ratio(float): ratio of subsample
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shuffle(bool): if shuffle generated graph list
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"""
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def __init__(
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self, g, split_edge, neg_samples=1, subsample_ratio=0.1, shuffle=True
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):
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self.neg_sampler = Uniform(neg_samples)
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self.subsample_ratio = subsample_ratio
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self.split_edge = split_edge
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self.g = g
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self.shuffle = shuffle
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def __call__(self, split_type):
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if split_type == "train":
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subsample_ratio = self.subsample_ratio
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else:
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subsample_ratio = 1
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pos_edges = self.split_edge[split_type]["edge"]
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if split_type == "train":
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# Adding self loop in train avoids sampling the source node itself.
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g = add_self_loop(self.g)
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eids = g.edge_ids(pos_edges[:, 0], pos_edges[:, 1])
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neg_edges = torch.stack(self.neg_sampler(g, eids), dim=1)
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else:
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neg_edges = self.split_edge[split_type]["edge_neg"]
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pos_edges = self.subsample(pos_edges, subsample_ratio).long()
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neg_edges = self.subsample(neg_edges, subsample_ratio).long()
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edges = torch.cat([pos_edges, neg_edges])
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labels = torch.cat(
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[
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torch.ones(pos_edges.size(0), 1),
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torch.zeros(neg_edges.size(0), 1),
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]
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)
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if self.shuffle:
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perm = torch.randperm(edges.size(0))
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edges = edges[perm]
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labels = labels[perm]
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return edges, labels
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def subsample(self, edges, subsample_ratio):
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"""
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Subsample generated edges.
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Args:
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edges(Tensor): edges to subsample
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subsample_ratio(float): ratio of subsample
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Returns:
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edges(Tensor): edges
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"""
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num_edges = edges.size(0)
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perm = torch.randperm(num_edges)
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perm = perm[: int(subsample_ratio * num_edges)]
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edges = edges[perm]
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return edges
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class EdgeDataSet(Dataset):
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"""
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Assistant Dataset for speeding up the SEALSampler
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"""
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def __init__(self, edges, labels, transform):
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self.edges = edges
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self.transform = transform
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self.labels = labels
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def __len__(self):
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return len(self.edges)
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def __getitem__(self, index):
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subgraph = self.transform(self.edges[index])
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return (subgraph, self.labels[index])
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class SEALSampler(object):
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"""
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Sampler for SEAL in paper(no-block version)
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The strategy is to sample all the k-hop neighbors around the two target nodes.
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Attributes:
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graph(DGLGraph): The graph
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hop(int): num of hop
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num_workers(int): num of workers
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"""
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def __init__(self, graph, hop=1, num_workers=32, print_fn=print):
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self.graph = graph
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self.hop = hop
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self.print_fn = print_fn
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self.num_workers = num_workers
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def sample_subgraph(self, target_nodes):
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"""
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Args:
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target_nodes(Tensor): Tensor of two target nodes
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Returns:
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subgraph(DGLGraph): subgraph
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"""
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sample_nodes = [target_nodes]
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frontiers = target_nodes
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for i in range(self.hop):
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frontiers = self.graph.out_edges(frontiers)[1]
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frontiers = torch.unique(frontiers)
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sample_nodes.append(frontiers)
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sample_nodes = torch.cat(sample_nodes)
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sample_nodes = torch.unique(sample_nodes)
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subgraph = dgl.node_subgraph(self.graph, sample_nodes)
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# Each node should have unique node id in the new subgraph
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u_id = int(
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torch.nonzero(
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subgraph.ndata[NID] == int(target_nodes[0]), as_tuple=False
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)
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)
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v_id = int(
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torch.nonzero(
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subgraph.ndata[NID] == int(target_nodes[1]), as_tuple=False
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)
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)
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# remove link between target nodes in positive subgraphs.
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if subgraph.has_edges_between(u_id, v_id):
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link_id = subgraph.edge_ids(u_id, v_id, return_uv=True)[2]
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subgraph.remove_edges(link_id)
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if subgraph.has_edges_between(v_id, u_id):
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link_id = subgraph.edge_ids(v_id, u_id, return_uv=True)[2]
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subgraph.remove_edges(link_id)
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z = drnl_node_labeling(subgraph, u_id, v_id)
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subgraph.ndata["z"] = z
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return subgraph
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def _collate(self, batch):
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batch_graphs, batch_labels = map(list, zip(*batch))
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batch_graphs = dgl.batch(batch_graphs)
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batch_labels = torch.stack(batch_labels)
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return batch_graphs, batch_labels
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def __call__(self, edges, labels):
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subgraph_list = []
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labels_list = []
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edge_dataset = EdgeDataSet(
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edges, labels, transform=self.sample_subgraph
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)
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self.print_fn(
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"Using {} workers in sampling job.".format(self.num_workers)
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)
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sampler = DataLoader(
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edge_dataset,
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batch_size=32,
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num_workers=self.num_workers,
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shuffle=False,
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collate_fn=self._collate,
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)
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for subgraph, label in tqdm(sampler, ncols=100):
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label_copy = deepcopy(label)
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subgraph = dgl.unbatch(subgraph)
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del label
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subgraph_list += subgraph
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labels_list.append(label_copy)
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return subgraph_list, torch.cat(labels_list)
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class SEALData(object):
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"""
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1. Generate positive and negative samples
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2. Subgraph sampling
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Attributes:
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g(dgl.DGLGraph): graph
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split_edge(dict): split edge
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hop(int): num of hop
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neg_samples(int): num of negative samples per positive sample
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subsample_ratio(float): ratio of subsample
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use_coalesce(bool): True for coalesce graph. Graph with multi-edge need to coalesce
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"""
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def __init__(
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self,
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g,
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split_edge,
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hop=1,
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neg_samples=1,
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subsample_ratio=1,
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prefix=None,
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save_dir=None,
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num_workers=32,
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shuffle=True,
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use_coalesce=True,
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print_fn=print,
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):
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self.g = g
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self.hop = hop
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self.subsample_ratio = subsample_ratio
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self.prefix = prefix
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self.save_dir = save_dir
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self.print_fn = print_fn
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self.generator = PosNegEdgesGenerator(
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g=self.g,
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split_edge=split_edge,
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neg_samples=neg_samples,
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subsample_ratio=subsample_ratio,
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shuffle=shuffle,
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)
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if use_coalesce:
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for k, v in g.edata.items():
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g.edata[k] = v.float() # dgl.to_simple() requires data is float
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self.g = dgl.to_simple(
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g, copy_ndata=True, copy_edata=True, aggregator="sum"
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)
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self.ndata = {k: v for k, v in self.g.ndata.items()}
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self.edata = {k: v for k, v in self.g.edata.items()}
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self.g.ndata.clear()
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self.g.edata.clear()
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self.print_fn("Save ndata and edata in class.")
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self.print_fn("Clear ndata and edata in graph.")
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self.sampler = SEALSampler(
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graph=self.g, hop=hop, num_workers=num_workers, print_fn=print_fn
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)
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def __call__(self, split_type):
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if split_type == "train":
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subsample_ratio = self.subsample_ratio
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else:
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subsample_ratio = 1
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path = osp.join(
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self.save_dir or "",
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"{}_{}_{}-hop_{}-subsample.bin".format(
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self.prefix, split_type, self.hop, subsample_ratio
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),
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)
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if osp.exists(path):
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self.print_fn("Load existing processed {} files".format(split_type))
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graph_list, data = dgl.load_graphs(path)
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dataset = GraphDataSet(graph_list, data["labels"])
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else:
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self.print_fn("Processed {} files not exist.".format(split_type))
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edges, labels = self.generator(split_type)
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self.print_fn("Generate {} edges totally.".format(edges.size(0)))
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graph_list, labels = self.sampler(edges, labels)
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dataset = GraphDataSet(graph_list, labels)
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dgl.save_graphs(path, graph_list, {"labels": labels})
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self.print_fn("Save preprocessed subgraph to {}".format(path))
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return dataset
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