KG-Evaluation__Bachelor-Thesis__/models/translation/trans_h.py

from __future__ import annotations

from typing import TYPE_CHECKING

import torch
from pykeen.losses import MarginRankingLoss
from pykeen.models import TransH as PyKEENTransH
from pykeen.regularizers import LpRegularizer
from torch import nn

from models.base_model import ERModel

if TYPE_CHECKING:
    from pykeen.typing import MappedTriples


class TransH(ERModel):
    """
    TransH model for knowledge graph embedding.
    """

    def __init__(
        self,
        num_entities: int,
        num_relations: int,
        triples_factory: MappedTriples,
        dim: int = 200,
        p_norm: bool = True,
        margin: float | None = None,
        epsilon: float | None = None,
        device: torch.device = torch.device("cpu"),
        **kwargs,
    ) -> None:
        super().__init__(num_entities, num_relations, dim)
        self.dim = dim
        self.p_norm = p_norm
        self.margin = margin
        self.epsilon = epsilon

        self.inner_model = PyKEENTransH(
            triples_factory=triples_factory,
            embedding_dim=dim,
            scoring_fct_norm=1,
            loss=self.loss,
            power_norm=p_norm,
            entity_initializer="xavier_uniform_",  # good default
            relation_initializer="xavier_uniform_",
            random_seed=42,
        ).to(device)

    @property
    def loss(self) -> torch.Tensor:
        return MarginRankingLoss(margin=self.margin, reduction="mean")

    def reset_parameters(self, margin: float | None = None, epsilon: float | None = None) -> None:
        # Parameter initialization
        if margin is None or epsilon is None:
            # If no margin/epsilon are provided, use Xavier uniform initialization
            nn.init.xavier_uniform_(self.ent_embeddings.weight.data)
            nn.init.xavier_uniform_(self.rel_embeddings.weight.data)
        else:
            # Otherwise, initialize uniformly in [ -(margin+epsilon)/dim , +(margin+epsilon)/dim ]
            self.embedding_range = nn.Parameter(
                torch.Tensor([(margin + epsilon) / self.dim]),
                requires_grad=False,
            )
            nn.init.uniform_(
                tensor=self.ent_embeddings.weight.data,
                a=-self.embedding_range.item(),
                b=self.embedding_range.item(),
            )
            nn.init.uniform_(
                tensor=self.rel_embeddings.weight.data,
                a=-self.embedding_range.item(),
                b=self.embedding_range.item(),
            )