timesead.data.dataset
=====================

.. py:module:: timesead.data.dataset


Classes
-------

.. autoapisummary::

   timesead.data.dataset.BaseTSDataset


Functions
---------

.. autoapisummary::

   timesead.data.dataset.collate_fn


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

.. py:class:: BaseTSDataset

   Bases: :py:obj:`abc.ABC`, :py:obj:`torch.utils.data.Dataset`


   Base class for all time-series datasets in TimeSeAD. Implementing the members in this abstract class provides the
   data pipeline system with the necessary information to process the data correctly.


   .. py:method:: __len__() -> int
      :abstractmethod:


      This should return the number of independent time series in the dataset



   .. py:property:: seq_len
      :type: Union[int, List[int]]

      :abstractmethod:


      This should return the length of each time series. If the time series have different lengths, the return
      value should be a list that contains the length of each sequence. If all sequences are of equal length,
      this should return an int.


   .. py:property:: num_features
      :type: Union[int, Tuple[int, Ellipsis]]

      :abstractmethod:


      Number of features of each datapoint. This can also be a tuple if the data has more than one feature dimension.


   .. py:method:: get_default_pipeline() -> Dict[str, Dict[str, Any]]
      :staticmethod:

      :abstractmethod:


      Return the default pipeline for this dataset that is used if the user does not specify a different pipeline.
      This must be a dict of the form::

          {
              '<name>': {'class': '<name-of-transform-class>', 'args': {'<args-for-constructor>', ...}},
              ...
          }



   .. py:method:: get_feature_names() -> List[str]
      :staticmethod:

      :abstractmethod:


      Return names for the features in the order they are present in the data tensors.

      :return: A list of strings with names for each feature.



   .. py:method:: __getitem__(index: int) -> Tuple[Tuple[torch.Tensor, Ellipsis], Tuple[torch.Tensor, Ellipsis]]
      :abstractmethod:


      Access the timeseries at position `index` and its corresponding label sequence. A call to this function should
      return a single time series that was sampled independently of the other time series in this dataset.

      :param index: The zero-based index of the time series to retrieve.
      :return: A tuple `(inputs, targets)`, where inputs is again a tuple of :class:`~torch.Tensor`\s with shape
          `(T, D*)`, where `D*` can very between the tensors. `targets` contains labels for the time series as tensors
          of shape `(T,)`.



.. py:function:: collate_fn(batch_dim: int) -> Callable

   Puts each data field into a tensor with outer dimension batch size.

   This was largely copied from PyTorch's :func:`~torch.utils.data._utils.default_collate` function except that it
   allows concatenating :class:`~torch.Tensor`\s along an arbitrary dimension instead of always stacking along the
   first dimension.

   :param batch_dim: The index of the dimension along which to stack the elements in the batch.
   :return: Batched tensors where elements have been stacked along the `batch_dim` dimension.