:py:mod:`ontolearn.data_struct`
===============================

.. py:module:: ontolearn.data_struct

.. autoapi-nested-parse::

   Data structures.



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

Classes
~~~~~~~

.. autoapisummary::

   ontolearn.data_struct.PrepareBatchOfPrediction
   ontolearn.data_struct.PrepareBatchOfTraining
   ontolearn.data_struct.Experience
   ontolearn.data_struct.NCESBaseDataLoader
   ontolearn.data_struct.NCESDataLoader
   ontolearn.data_struct.NCESDataLoaderInference
   ontolearn.data_struct.CLIPDataLoader
   ontolearn.data_struct.CLIPDataLoaderInference




.. py:class:: PrepareBatchOfPrediction(current_state: torch.FloatTensor, next_state_batch: torch.FloatTensor, p: torch.FloatTensor, n: torch.FloatTensor)


   Bases: :py:obj:`torch.utils.data.Dataset`

   An abstract class representing a :class:`Dataset`.

   All datasets that represent a map from keys to data samples should subclass
   it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
   data sample for a given key. Subclasses could also optionally overwrite
   :meth:`__len__`, which is expected to return the size of the dataset by many
   :class:`~torch.utils.data.Sampler` implementations and the default options
   of :class:`~torch.utils.data.DataLoader`.

   .. note::
     :class:`~torch.utils.data.DataLoader` by default constructs a index
     sampler that yields integral indices.  To make it work with a map-style
     dataset with non-integral indices/keys, a custom sampler must be provided.

   .. py:method:: __len__()


   .. py:method:: __getitem__(idx)


   .. py:method:: get_all()



.. py:class:: PrepareBatchOfTraining(current_state_batch: torch.Tensor, next_state_batch: torch.Tensor, p: torch.Tensor, n: torch.Tensor, q: torch.Tensor)


   Bases: :py:obj:`torch.utils.data.Dataset`

   An abstract class representing a :class:`Dataset`.

   All datasets that represent a map from keys to data samples should subclass
   it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
   data sample for a given key. Subclasses could also optionally overwrite
   :meth:`__len__`, which is expected to return the size of the dataset by many
   :class:`~torch.utils.data.Sampler` implementations and the default options
   of :class:`~torch.utils.data.DataLoader`.

   .. note::
     :class:`~torch.utils.data.DataLoader` by default constructs a index
     sampler that yields integral indices.  To make it work with a map-style
     dataset with non-integral indices/keys, a custom sampler must be provided.

   .. py:method:: __len__()


   .. py:method:: __getitem__(idx)



.. py:class:: Experience(maxlen: int)


   A class to model experiences for Replay Memory.

   .. py:method:: __len__()


   .. py:method:: append(e)

      Append.
      :param e: A tuple of s_i, s_j and reward, where s_i and s_j represent refining s_i and reaching s_j.


   .. py:method:: retrieve()


   .. py:method:: clear()



.. py:class:: NCESBaseDataLoader(vocab, inv_vocab)


   .. py:method:: decompose(concept_name: str) -> list
      :staticmethod:


   .. py:method:: get_labels(target)



.. py:class:: NCESDataLoader(data: list, embeddings, vocab, inv_vocab, shuffle_examples, max_length, example_sizes=None, sorted_examples=True)


   Bases: :py:obj:`NCESBaseDataLoader`, :py:obj:`torch.utils.data.Dataset`

   An abstract class representing a :class:`Dataset`.

   All datasets that represent a map from keys to data samples should subclass
   it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
   data sample for a given key. Subclasses could also optionally overwrite
   :meth:`__len__`, which is expected to return the size of the dataset by many
   :class:`~torch.utils.data.Sampler` implementations and the default options
   of :class:`~torch.utils.data.DataLoader`.

   .. note::
     :class:`~torch.utils.data.DataLoader` by default constructs a index
     sampler that yields integral indices.  To make it work with a map-style
     dataset with non-integral indices/keys, a custom sampler must be provided.

   .. py:method:: __len__()


   .. py:method:: __getitem__(idx)



.. py:class:: NCESDataLoaderInference(data: list, embeddings, vocab, inv_vocab, shuffle_examples, sorted_examples=True)


   Bases: :py:obj:`NCESBaseDataLoader`, :py:obj:`torch.utils.data.Dataset`

   An abstract class representing a :class:`Dataset`.

   All datasets that represent a map from keys to data samples should subclass
   it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
   data sample for a given key. Subclasses could also optionally overwrite
   :meth:`__len__`, which is expected to return the size of the dataset by many
   :class:`~torch.utils.data.Sampler` implementations and the default options
   of :class:`~torch.utils.data.DataLoader`.

   .. note::
     :class:`~torch.utils.data.DataLoader` by default constructs a index
     sampler that yields integral indices.  To make it work with a map-style
     dataset with non-integral indices/keys, a custom sampler must be provided.

   .. py:method:: __len__()


   .. py:method:: __getitem__(idx)



.. py:class:: CLIPDataLoader(data: list, embeddings, shuffle_examples, example_sizes: list = None, k=5, sorted_examples=True)


   Bases: :py:obj:`torch.utils.data.Dataset`

   An abstract class representing a :class:`Dataset`.

   All datasets that represent a map from keys to data samples should subclass
   it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
   data sample for a given key. Subclasses could also optionally overwrite
   :meth:`__len__`, which is expected to return the size of the dataset by many
   :class:`~torch.utils.data.Sampler` implementations and the default options
   of :class:`~torch.utils.data.DataLoader`.

   .. note::
     :class:`~torch.utils.data.DataLoader` by default constructs a index
     sampler that yields integral indices.  To make it work with a map-style
     dataset with non-integral indices/keys, a custom sampler must be provided.

   .. py:method:: __len__()


   .. py:method:: __getitem__(idx)



.. py:class:: CLIPDataLoaderInference(data: list, embeddings, shuffle_examples, sorted_examples=True)


   Bases: :py:obj:`torch.utils.data.Dataset`

   An abstract class representing a :class:`Dataset`.

   All datasets that represent a map from keys to data samples should subclass
   it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
   data sample for a given key. Subclasses could also optionally overwrite
   :meth:`__len__`, which is expected to return the size of the dataset by many
   :class:`~torch.utils.data.Sampler` implementations and the default options
   of :class:`~torch.utils.data.DataLoader`.

   .. note::
     :class:`~torch.utils.data.DataLoader` by default constructs a index
     sampler that yields integral indices.  To make it work with a map-style
     dataset with non-integral indices/keys, a custom sampler must be provided.

   .. py:method:: __len__()


   .. py:method:: __getitem__(idx)