def __init__(self, c, args):
super(Shallow, self).__init__(c)
self.manifold = getattr(manifolds, args.manifold)()
self.use_feats = args.use_feats
weights = torch.Tensor(args.n_nodes, args.dim)
if not args.pretrained_embeddings:
weights = self.manifold.init_weights(weights, self.c)
trainable = True
else:
weights = torch.Tensor(np.load(args.pretrained_embeddings))
assert weights.shape[
0] == args.n_nodes, "The embeddings you passed seem to be for another dataset."
trainable = False
self.lt = manifolds.ManifoldParameter(weights, trainable,
self.manifold, self.c)
self.all_nodes = torch.LongTensor(list(range(args.n_nodes)))
layers = []
if args.pretrained_embeddings is not None and args.num_layers > 0:
# MLP layers after pre-trained embeddings
dims, acts = get_dim_act(args)
if self.use_feats:
dims[0] = args.feat_dim + weights.shape[1]
else:
dims[0] = weights.shape[1]
for i in range(len(dims) - 1):
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
layers.append(
Linear(in_dim, out_dim, args.dropout, act, args.bias))
self.layers = nn.Sequential(*layers)
self.encode_graph = False
def reset_parameteres(self):
if not self.pretrained_embeddings:
weights = self.manifold.init_weights(self.weights, self.c)
trainable = True
else:
weights = torch.Tensor(np.load(self.pretrained_embeddings))
assert weights.shape[
0] == self.n_nodes, "The embeddings you passed seem to be for another dataset."
trainable = False
self.lt = manifolds.ManifoldParameter(weights, trainable,
self.manifold, self.c)
self.all_nodes = torch.LongTensor(list(range(self.n_nodes)))
for _layer in self.layers:
_layer.reset_parameters()
def __init__(self, users_items, args):
super(HGCFModel, self).__init__()
self.c = torch.tensor([args.c]).to(default_device())
self.manifold = getattr(manifolds, "Hyperboloid")()
self.nnodes = args.n_nodes
self.encoder = getattr(encoders, "HGCF")(self.c, args)
self.num_users, self.num_items = users_items
self.margin = args.margin
self.weight_decay = args.weight_decay
self.num_layers = args.num_layers
self.args = args
self.embedding = nn.Embedding(num_embeddings=self.num_users + self.num_items,
embedding_dim=args.embedding_dim).to(default_device())
self.embedding.state_dict()['weight'].uniform_(-args.scale, args.scale)
self.embedding.weight = nn.Parameter(self.manifold.expmap0(self.embedding.state_dict()['weight'], self.c))
self.embedding.weight = manifolds.ManifoldParameter(self.embedding.weight, True, self.manifold, self.c)
