3697053ea6eabb77909d9b43044d784098b48bb9,torch_geometric/nn/models/metapath2vec.py,MetaPath2Vec,__positive_sampling__,#MetaPath2Vec#Any#,72

Before Change

        return emb if subset is None else emb[subset]

    def __positive_sampling__(self, subset):
        raise NotImplementedError

    def __negative_sampling__(self, subset):
        subsets = []
        for keys in self.metapath:

After Change

    def __positive_sampling__(self, subset):
        device = self.embedding.weight.device

        subsets = []
        for keys in self.metapath:
            adj = self.adj_dict[keys]
            subset = adj.sample(num_neighbors=1, subset=subset).squeeze()
            subsets.append(subset)
        out = torch.stack(subsets, dim=-1).to(device)
        out.add_(self.offset[1:].view(1, -1))
        return out

    def __negative_sampling__(self, subset):
        device = self.embedding.weight.device

Instances