dicee.models.complex
====================

.. py:module:: dicee.models.complex


Classes
-------

.. autoapisummary::

   dicee.models.complex.ConEx
   dicee.models.complex.AConEx
   dicee.models.complex.ComplEx


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

.. py:class:: ConEx(args)

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


   Convolutional ComplEx Knowledge Graph Embeddings


   .. py:attribute:: name
      :value: 'ConEx'



   .. py:attribute:: conv2d


   .. py:attribute:: fc_num_input


   .. py:attribute:: fc1


   .. py:attribute:: norm_fc1


   .. py:attribute:: bn_conv2d


   .. py:attribute:: feature_map_dropout


   .. py:method:: residual_convolution(C_1: Tuple[torch.Tensor, torch.Tensor], C_2: Tuple[torch.Tensor, torch.Tensor]) -> torch.FloatTensor

      Compute residual score of two complex-valued embeddings.
      :param C_1: a tuple of two pytorch tensors that corresponds complex-valued embeddings
      :param C_2: a tuple of two pytorch tensors that corresponds complex-valued embeddings
      :return:



   .. py:method:: forward_k_vs_all(x: torch.Tensor) -> torch.FloatTensor


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

      :param x:



   .. py:method:: forward_k_vs_sample(x: torch.Tensor, target_entity_idx: torch.Tensor)


.. py:class:: AConEx(args)

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


   Additive Convolutional ComplEx Knowledge Graph Embeddings


   .. py:attribute:: name
      :value: 'AConEx'



   .. py:attribute:: conv2d


   .. py:attribute:: fc_num_input


   .. py:attribute:: fc1


   .. py:attribute:: norm_fc1


   .. py:attribute:: bn_conv2d


   .. py:attribute:: feature_map_dropout


   .. py:method:: residual_convolution(C_1: Tuple[torch.Tensor, torch.Tensor], C_2: Tuple[torch.Tensor, torch.Tensor]) -> torch.FloatTensor

      Compute residual score of two complex-valued embeddings.
      :param C_1: a tuple of two pytorch tensors that corresponds complex-valued embeddings
      :param C_2: a tuple of two pytorch tensors that corresponds complex-valued embeddings
      :return:



   .. py:method:: forward_k_vs_all(x: torch.Tensor) -> torch.FloatTensor


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

      :param x:



   .. py:method:: forward_k_vs_sample(x: torch.Tensor, target_entity_idx: torch.Tensor)


.. py:class:: ComplEx(args)

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


   Base class for all neural network modules.

   Your models should also subclass this class.

   Modules can also contain other Modules, allowing them to be nested in
   a tree structure. You can assign the submodules as regular attributes::

       import torch.nn as nn
       import torch.nn.functional as F

       class Model(nn.Module):
           def __init__(self) -> None:
               super().__init__()
               self.conv1 = nn.Conv2d(1, 20, 5)
               self.conv2 = nn.Conv2d(20, 20, 5)

           def forward(self, x):
               x = F.relu(self.conv1(x))
               return F.relu(self.conv2(x))

   Submodules assigned in this way will be registered, and will also have their
   parameters converted when you call :meth:`to`, etc.

   .. note::
       As per the example above, an ``__init__()`` call to the parent class
       must be made before assignment on the child.

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool


   .. py:attribute:: name
      :value: 'ComplEx'



   .. py:method:: score(head_ent_emb: torch.FloatTensor, rel_ent_emb: torch.FloatTensor, tail_ent_emb: torch.FloatTensor)
      :staticmethod:



   .. py:method:: k_vs_all_score(emb_h: torch.FloatTensor, emb_r: torch.FloatTensor, emb_E: torch.FloatTensor)
      :staticmethod:


      :param emb_h:
      :param emb_r:
      :param emb_E:



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


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