torch_geometric_signed_directed.nn.directed.DiGCL
=================================================

.. py:module:: torch_geometric_signed_directed.nn.directed.DiGCL


Classes
-------

.. autoapisummary::

   torch_geometric_signed_directed.nn.directed.DiGCL.DiGCL_Encoder
   torch_geometric_signed_directed.nn.directed.DiGCL.DiGCL


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

.. py:class:: DiGCL_Encoder(in_channels: int, out_channels: int, activation: str, num_layers: int = 2)

   Bases: :py:obj:`torch.nn.Module`


   An implementation of the DiGCL encoder model from the
   `Directed Graph Contrastive Learning
   <https://proceedings.neurips.cc/paper/2021/file/a3048e47310d6efaa4b1eaf55227bc92-Paper.pdf>`_ paper.

   :Parameters: * **in_channels** (*int*) -- Dimension of input features.
                * **out_channels** (*int*) -- Dimension of output representations.
                * **activation** (*str*) -- Activation funciton to use.
                * **num_layers** (*int, Optional*) -- Number of layers for encoder. (Default: 2)


   .. py:attribute:: conv


   .. py:attribute:: activation


   .. py:method:: reset_parameters()


   .. py:method:: forward(x: torch.Tensor, edge_index: torch.Tensor, edge_weight: torch.Tensor = None)

      Making a forward pass of the DiGCL encoder model.

      Arg types:
          * x (PyTorch FloatTensor) - Node features.
          * edge_index (PyTorch LongTensor) - Edge indices.
          * edge_weight (PyTorch FloatTensor, optional) - Edge weights corresponding to edge indices.
      Return types:
          * x (PyTorch FloatTensor) - Embeddings for all nodes, with shape (num_nodes, out_channels).



.. py:class:: DiGCL(in_channels: int, activation: str, num_hidden: int, num_proj_hidden: int, tau: float, num_layers: int)

   Bases: :py:obj:`torch.nn.Module`


   An implementation of the DiGCL model from the
   `Directed Graph Contrastive Learning
   <https://proceedings.neurips.cc/paper/2021/file/a3048e47310d6efaa4b1eaf55227bc92-Paper.pdf>`_ paper.

   :Parameters: * **in_channels** (*int*) -- Dimension of input features.
                * **activation** (*str*) -- Activation funciton to use.
                * **num_hidden** (*int*) -- Hidden dimension.
                * **num_proj_hidden** (*int*) -- Hidden dimension for projection.
                * **tau** (*float*) -- Tau value in the loss.
                * **num_layers** (*int*) -- Number of layers for encoder.


   .. py:attribute:: encoder


   .. py:attribute:: tau
      :type:  float


   .. py:attribute:: fc1


   .. py:attribute:: fc2


   .. py:method:: reset_parameters()


   .. py:method:: forward(x: torch.Tensor, edge_index: torch.Tensor, edge_weight: torch.Tensor = None) -> torch.Tensor

      Making a forward pass of the DiGCL model.

      Arg types:
          * x (PyTorch FloatTensor) - Node features.
          * edge_index (PyTorch LongTensor) - Edge indices.
          * edge_weight (PyTorch FloatTensor, optional) - Edge weights corresponding to edge indices.

      Return types:
          * x (PyTorch FloatTensor) - Embeddings for all nodes, with shape (num_nodes, out_channels).



   .. py:method:: projection(z: torch.Tensor) -> torch.Tensor

      Nonlinear transformation of the input hidden feature.

      Args types::
          * z (PyTorch FloatTensor) - Node features.

      Return types:
          * z (PyTorch FloatTensor) - Projected node features.



   .. py:method:: sim(z1: torch.Tensor, z2: torch.Tensor)

      Normalized similarity calculation.

      Args types::
          * z1 (PyTorch FloatTensor) - Node features.
          * z2 (PyTorch FloatTensor) - Node features.

      Return types:
          * z (PyTorch FloatTensor) - Node-wise similarity.



   .. py:method:: semi_loss(z1: torch.Tensor, z2: torch.Tensor)

      Semi-supervised loss function.

      Arg types:
          * z1 (PyTorch FloatTensor) - Node features.
          * z2 (PyTorch FloatTensor) - Node features.

      Return types:
          * loss (PyTorch FloatTensor) - Loss.



   .. py:method:: batched_semi_loss(z1: torch.Tensor, z2: torch.Tensor, batch_size: int)

      Semi-supervised loss function. Space complexity: O(BN) (semi_loss: O(N^2))

      Args types::
          * z1 (PyTorch FloatTensor) - Node features.
          * z2 (PyTorch FloatTensor) - Node features.

      Return types:
          * loss (PyTorch FloatTensor) - Loss.



   .. py:method:: loss(z1: torch.Tensor, z2: torch.Tensor, mean: bool = True, batch_size: int = 0)

      The DiGCL contrastive loss.

      Arg types:
          * z1, z2 (PyTorch FloatTensor) - Node hidden representations.
          * mean (bool, optional) - Whether to return the mean of loss values, default True, otherwise return sum.
          * batch_size (int, optional) - Batch size, if 0 this means full-batch. Default 0.
      Return types:
          * ret (PyTorch FloatTensor) - Loss.