import torch
from kge import Config, Dataset
from kge.model.kge_model import RelationalScorer, KgeModel
from torch.nn import functional as F
class TransEScorer(RelationalScorer):
r"""Implementation of the TransE KGE scorer."""
def __init__(self, config: Config, dataset: Dataset, configuration_key=None):
super().__init__(config, dataset, configuration_key)
self._norm = self.get_option("l_norm")
def score_emb(self, s_emb, p_emb, o_emb, combine: str):
n = p_emb.size(0)
if combine == "spo":
out = -F.pairwise_distance(s_emb + p_emb, o_emb, p=self._norm)
elif combine == "sp_":
out = -torch.cdist(s_emb + p_emb, o_emb, p=self._norm)
elif combine == "_po":
out = -torch.cdist(o_emb - p_emb, s_emb, p=self._norm)
else:
out = super().score_emb(s_emb, p_emb, o_emb, combine)
return out.view(n, -1)
class TransE(KgeModel):
r"""Implementation of the TransE KGE model."""
def __init__(
self,
config: Config,
dataset: Dataset,
configuration_key=None,
init_for_load_only=False,
):
super().__init__(
config=config,
dataset=dataset,
scorer=TransEScorer,
configuration_key=configuration_key,
init_for_load_only=init_for_load_only,
)
