Source code for torch_geometric.nn.models.metapath2vec

import torch
from torch.nn import Embedding
from torch.utils.data import DataLoader
from torch_sparse import SparseTensor

EPS = 1e-15


[docs]class MetaPath2Vec(torch.nn.Module): r"""The MetaPath2Vec model from the `"metapath2vec: Scalable Representation Learning for Heterogeneous Networks" <https://ericdongyx.github.io/papers/ KDD17-dong-chawla-swami-metapath2vec.pdf>`_ paper where random walks based on a given :obj:`metapath` are sampled in a heterogeneous graph, and node embeddings are learned via negative sampling optimization. .. note:: For an example of using MetaPath2Vec, see `examples/metapath2vec.py <https://github.com/rusty1s/pytorch_geometric/blob/master/examples/ metapath2vec.py>`_. Args: edge_index_dict (dict): Dictionary holding edge indices for each :obj:`(source_node_type, relation_type, target_node_type)` present in the heterogeneous graph. embedding_dim (int): The size of each embedding vector. metapath (list): The metapath described as a list of :obj:`(source_node_type, relation_type, target_node_type)` tuples. walk_length (int): The walk length. context_size (int): The actual context size which is considered for positive samples. This parameter increases the effective sampling rate by reusing samples across different source nodes. walks_per_node (int, optional): The number of walks to sample for each node. (default: :obj:`1`) num_negative_samples (int, optional): The number of negative samples to use for each positive sample. (default: :obj:`1`) num_nodes_dict (dict, optional): Dictionary holding the number of nodes for each node type. (default: :obj:`None`) sparse (bool, optional): If set to :obj:`True`, gradients w.r.t. to the weight matrix will be sparse. (default: :obj:`False`) """ def __init__(self, edge_index_dict, embedding_dim, metapath, walk_length, context_size, walks_per_node=1, num_negative_samples=1, num_nodes_dict=None, sparse=False): super(MetaPath2Vec, self).__init__() if num_nodes_dict is None: num_nodes_dict = {} for keys, edge_index in edge_index_dict.items(): key = keys[0] N = int(edge_index[0].max() + 1) num_nodes_dict[key] = max(N, num_nodes_dict.get(key, N)) key = keys[-1] N = int(edge_index[1].max() + 1) num_nodes_dict[key] = max(N, num_nodes_dict.get(key, N)) adj_dict = {} for keys, edge_index in edge_index_dict.items(): sizes = (num_nodes_dict[keys[0]], num_nodes_dict[keys[-1]]) row, col = edge_index adj = SparseTensor(row=row, col=col, sparse_sizes=sizes) adj = adj.to('cpu') adj_dict[keys] = adj assert metapath[0][0] == metapath[-1][-1] assert walk_length >= context_size self.adj_dict = adj_dict self.embedding_dim = embedding_dim self.metapath = metapath self.walk_length = walk_length self.context_size = context_size self.walks_per_node = walks_per_node self.num_negative_samples = num_negative_samples self.num_nodes_dict = num_nodes_dict types = set([x[0] for x in metapath]) | set([x[-1] for x in metapath]) types = sorted(list(types)) count = 0 self.start, self.end = {}, {} for key in types: self.start[key] = count count += num_nodes_dict[key] self.end[key] = count offset = [self.start[metapath[0][0]]] offset += [self.start[keys[-1]] for keys in metapath ] * int((walk_length / len(metapath)) + 1) offset = offset[:walk_length + 1] assert len(offset) == walk_length + 1 self.offset = torch.tensor(offset) self.embedding = Embedding(count, embedding_dim, sparse=sparse) self.reset_parameters()
[docs] def reset_parameters(self): self.embedding.reset_parameters()
[docs] def forward(self, node_type, batch=None): """Returns the embeddings for the nodes in :obj:`subset` of type :obj:`node_type`.""" emb = self.embedding.weight[self.start[node_type]:self.end[node_type]] return emb if batch is None else emb[batch]
[docs] def loader(self, **kwargs): return DataLoader(range(self.num_nodes_dict[self.metapath[0][0]]), collate_fn=self.sample, **kwargs)
[docs] def pos_sample(self, batch): # device = self.embedding.weight.device batch = batch.repeat(self.walks_per_node) rws = [batch] for i in range(self.walk_length): keys = self.metapath[i % len(self.metapath)] adj = self.adj_dict[keys] batch = adj.sample(num_neighbors=1, subset=batch).squeeze() rws.append(batch) rw = torch.stack(rws, dim=-1) rw.add_(self.offset.view(1, -1)) walks = [] num_walks_per_rw = 1 + self.walk_length + 1 - self.context_size for j in range(num_walks_per_rw): walks.append(rw[:, j:j + self.context_size]) return torch.cat(walks, dim=0)
[docs] def neg_sample(self, batch): batch = batch.repeat(self.walks_per_node * self.num_negative_samples) rws = [batch] for i in range(self.walk_length): keys = self.metapath[i % len(self.metapath)] batch = torch.randint(0, self.num_nodes_dict[keys[-1]], (batch.size(0), ), dtype=torch.long) rws.append(batch) rw = torch.stack(rws, dim=-1) rw.add_(self.offset.view(1, -1)) walks = [] num_walks_per_rw = 1 + self.walk_length + 1 - self.context_size for j in range(num_walks_per_rw): walks.append(rw[:, j:j + self.context_size]) return torch.cat(walks, dim=0)
[docs] def sample(self, batch): if not isinstance(batch, torch.Tensor): batch = torch.tensor(batch) return self.pos_sample(batch), self.neg_sample(batch)
[docs] def loss(self, pos_rw, neg_rw): r"""Computes the loss given positive and negative random walks.""" # Positive loss. start, rest = pos_rw[:, 0], pos_rw[:, 1:].contiguous() h_start = self.embedding(start).view(pos_rw.size(0), 1, self.embedding_dim) h_rest = self.embedding(rest.view(-1)).view(pos_rw.size(0), -1, self.embedding_dim) out = (h_start * h_rest).sum(dim=-1).view(-1) pos_loss = -torch.log(torch.sigmoid(out) + EPS).mean() # Negative loss. start, rest = neg_rw[:, 0], neg_rw[:, 1:].contiguous() h_start = self.embedding(start).view(neg_rw.size(0), 1, self.embedding_dim) h_rest = self.embedding(rest.view(-1)).view(neg_rw.size(0), -1, self.embedding_dim) out = (h_start * h_rest).sum(dim=-1).view(-1) neg_loss = -torch.log(1 - torch.sigmoid(out) + EPS).mean() return pos_loss + neg_loss
[docs] def test(self, train_z, train_y, test_z, test_y, solver='lbfgs', multi_class='auto', *args, **kwargs): r"""Evaluates latent space quality via a logistic regression downstream task.""" from sklearn.linear_model import LogisticRegression clf = LogisticRegression(solver=solver, multi_class=multi_class, *args, **kwargs).fit(train_z.detach().cpu().numpy(), train_y.detach().cpu().numpy()) return clf.score(test_z.detach().cpu().numpy(), test_y.detach().cpu().numpy())
def __repr__(self): return '{}({}, {})'.format(self.__class__.__name__, self.embedding.weight.size(0), self.embedding.weight.size(1))