dicee.models.pykeen_models
==========================

.. py:module:: dicee.models.pykeen_models


Classes
-------

.. autoapisummary::

   dicee.models.pykeen_models.PykeenKGE


Module Contents
---------------

.. py:class:: PykeenKGE(args: dict)

   Bases: :py:obj:`dicee.models.base_model.BaseKGE`


   A class for using knowledge graph embedding models implemented in Pykeen

   Notes:
   Pykeen_DistMult: C
   Pykeen_ComplEx:
   Pykeen_QuatE:
   Pykeen_MuRE:
   Pykeen_CP:
   Pykeen_HolE:
   Pykeen_HolE:
   Pykeen_HolE:
   Pykeen_TransD:
   Pykeen_TransE:
   Pykeen_TransF:
   Pykeen_TransH:
   Pykeen_TransR:


   .. py:attribute:: model_kwargs


   .. py:attribute:: name


   .. py:attribute:: model


   .. py:attribute:: loss_history
      :value: []



   .. py:attribute:: args


   .. py:attribute:: entity_embeddings
      :value: None



   .. py:attribute:: relation_embeddings
      :value: None



   .. py:method:: forward_k_vs_all(x: torch.LongTensor)

      # => Explicit version by this we can apply bn and dropout

      # (1) Retrieve embeddings of heads and relations +  apply Dropout & Normalization if given.
      h, r = self.get_head_relation_representation(x)
      # (2) Reshape (1).
      if self.last_dim > 0:
          h = h.reshape(len(x), self.embedding_dim, self.last_dim)
          r = r.reshape(len(x), self.embedding_dim, self.last_dim)
      # (3) Reshape all entities.
      if self.last_dim > 0:
          t = self.entity_embeddings.weight.reshape(self.num_entities, self.embedding_dim, self.last_dim)
      else:
          t = self.entity_embeddings.weight
      # (4) Call the score_t from interactions to generate triple scores.
      return self.interaction.score_t(h=h, r=r, all_entities=t, slice_size=1)



   .. py:method:: forward_triples(x: torch.LongTensor) -> torch.FloatTensor

      # => Explicit version by this we can apply bn and dropout

      # (1) Retrieve embeddings of heads, relations and tails and apply Dropout & Normalization if given.
      h, r, t = self.get_triple_representation(x)
      # (2) Reshape (1).
      if self.last_dim > 0:
          h = h.reshape(len(x), self.embedding_dim, self.last_dim)
          r = r.reshape(len(x), self.embedding_dim, self.last_dim)
          t = t.reshape(len(x), self.embedding_dim, self.last_dim)
      # (3) Compute the triple score
      return self.interaction.score(h=h, r=r, t=t, slice_size=None, slice_dim=0)



   .. py:method:: forward_k_vs_sample(x: torch.LongTensor, target_entity_idx)
      :abstractmethod: