def __init__(
self,
initial_entity_emb,
initial_relation_emb,
entity_out_dim,
relation_out_dim,
drop_GAT,
drop_conv,
alpha,
alpha_conv,
nheads_GAT,
conv_out_channels,
):
"""Sparse version of KBGAT
entity_in_dim -> Entity Input Embedding dimensions
entity_out_dim -> Entity Output Embedding dimensions, passed as a list
num_relation -> number of unique relations
relation_dim -> Relation Embedding dimensions
num_nodes -> number of nodes in the Graph
nheads_GAT -> Used for Multihead attention, passed as a list """
super().__init__()
self.num_nodes = initial_entity_emb.shape[0]
self.entity_in_dim = initial_entity_emb.shape[1]
self.entity_out_dim_1 = entity_out_dim[0]
self.nheads_GAT_1 = nheads_GAT[0]
self.entity_out_dim_2 = entity_out_dim[1]
self.nheads_GAT_2 = nheads_GAT[1]
# Properties of Relations
self.num_relation = initial_relation_emb.shape[0]
self.relation_dim = initial_relation_emb.shape[1]
self.relation_out_dim_1 = relation_out_dim[0]
self.drop_GAT = drop_GAT
self.drop_conv = drop_conv
self.alpha = alpha # For leaky relu
self.alpha_conv = alpha_conv
self.conv_out_channels = conv_out_channels
self.final_entity_embeddings = nn.Parameter(
torch.randn(self.num_nodes,
self.entity_out_dim_1 * self.nheads_GAT_1))
self.final_relation_embeddings = nn.Parameter(
torch.randn(self.num_relation,
self.entity_out_dim_1 * self.nheads_GAT_1))
self.convKB = ConvKB(
self.entity_out_dim_1 * self.nheads_GAT_1,
3,
1,
self.conv_out_channels,
self.drop_conv,
self.alpha_conv,
)
def __init__(self, final_entity_emb, final_relation_emb, entity_out_dim,
relation_out_dim, drop_conv, alpha_conv, nheads_GAT,
conv_out_channels, variational, temperature,
sigma_p): # NOTE removed alpha as it doesn't seem to get used
'''
Sparse version of KBGAT
entity_in_dim -> Entity Input Embedding dimensions
entity_out_dim -> Entity Output Embedding dimensions, passed as a list
num_relation -> number of unique relations
relation_dim -> Relation Embedding dimensions
num_nodes -> number of nodes in the Graph
nheads_GAT -> Used for Multihead attention, passed as a list
'''
super().__init__()
self.num_nodes = final_entity_emb.shape[0]
emb_dim = entity_out_dim[0] * nheads_GAT[0]
# Properties of Relations
self.num_relation = final_relation_emb.shape[0]
self.relation_dim = final_relation_emb.shape[1]
self.relation_out_dim_1 = relation_out_dim[0]
self.drop_conv = drop_conv
# self.alpha = alpha # For leaky relu
self.alpha_conv = alpha_conv
self.conv_out_channels = conv_out_channels
self.variational = variational
self.temperature = temperature
self.sigma_p = sigma_p
assert final_entity_emb.shape == (
self.num_nodes,
emb_dim,
)
assert final_relation_emb.shape == (
self.num_relation,
emb_dim,
)
self.entity_embeddings_from_gat = final_entity_emb.clone(
) # requires we always load GAT before initialising this
self.relation_embeddings_from_gat = final_relation_emb.clone()
self.final_entity_embeddings_mean = nn.Parameter(
final_entity_emb.clone())
self.final_relation_embeddings_mean = nn.Parameter(
final_relation_emb.clone()
) # this is learnable more. is this desired?
if self.variational:
self.entity_logstddev = nn.Parameter(final_entity_emb * 0 - 2)
self.relation_logstddev = nn.Parameter(final_relation_emb * 0 - 2)
self.convKB = ConvKB(emb_dim, 3, 1, self.conv_out_channels,
self.drop_conv, self.alpha_conv)