timesead.data.minismd_dataset
=============================

.. py:module:: timesead.data.minismd_dataset


Attributes
----------

.. autoapisummary::

   timesead.data.minismd_dataset.FILENAMES
   timesead.data.minismd_dataset.TRAIN_LENS
   timesead.data.minismd_dataset.TEST_LENS


Classes
-------

.. autoapisummary::

   timesead.data.minismd_dataset.MiniSMDDataset


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

.. py:data:: FILENAMES
   :value: ['machine-1-3.txt', 'machine-1-7.txt']


.. py:data:: TRAIN_LENS
   :value: [500, 1000]


.. py:data:: TEST_LENS
   :value: [500, 1000]


.. py:class:: MiniSMDDataset(server_id: int = 0, path: str = os.path.join(DATA_DIRECTORY, 'mini_smd'), training: bool = True, standardize: Union[bool, Callable] = True, preprocess: bool = True)

   Bases: :py:obj:`timesead.data.dataset.BaseTSDataset`


   This is a condensed version of the :class:`~timesead.data.smd_dataset.SMDDataset` containing only shortened time
   series for two different machines. Mostly used for testing purposes.

   :param server_id: ID of the server to load. Must be 0 or 1.
   :param path: Path to the data
   :param training: Whether to load the training or the test set.
   :param standardize: Can be either a bool that decides whether to apply the dataset-dependent default
       standardization or a function with signature (dataframe, stats) -> dataframe, where stats is a dictionary of
       common statistics on the training dataset (i.e., mean, std, median, etc. for each feature)


   .. py:attribute:: server_id
      :value: 0



   .. py:attribute:: path


   .. py:attribute:: training
      :value: True



   .. py:attribute:: standardize
      :value: True



   .. py:attribute:: inputs
      :value: None



   .. py:attribute:: targets
      :value: None



   .. py:attribute:: processed_dir


   .. py:method:: load_data() -> Tuple[numpy.ndarray, numpy.ndarray]


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


   .. py:method:: __len__() -> Optional[int]


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



   .. py:property:: num_features
      :type: int



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



   .. py:method:: get_feature_names()
      :staticmethod: