:py:mod:`ForeTiS.model._additionalmodels.lstmbayes_intel`
=========================================================

.. py:module:: ForeTiS.model._additionalmodels.lstmbayes_intel


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

Classes
~~~~~~~

.. autoapisummary::

   ForeTiS.model._additionalmodels.lstmbayes_intel.LSTM




.. py:class:: LSTM(optuna_trial, datasets, featureset_name, optimize_featureset, pca_transform = None, current_model_name = None, batch_size = None, n_epochs = None, target_column = None)

   Bases: :py:obj:`ForeTiS.model._torch_model.TorchModel`

   Implementation of a class for a bayesian Long Short-Term Memory (LSTM) network.

   See :obj:`~ForeTiS.model._base_model.BaseModel` and :obj:`~ForeTiS.model._torch_model.TorchModel` for more information on the attributes.

   .. py:method:: define_model()

      Definition of a bayesian LSTM network.

      Architecture:
          - Bayesian LSTM, Dropout, Linear
          - Bayesian Linear output layer

      Number of output channels of the first layer, dropout rate, frequency of a doubling of the output channels and
      number of units in the first linear layer. may be fixed or optimized.


   .. py:method:: define_hyperparams_to_tune()

      See :obj:`~ForeTiS.model._base_model.BaseModel` for more information on the format.

      See :obj:`~ForeTiS.model._torch_model.TorchModel` for more information on hyperparameters common for all torch models.


   .. py:method:: train_val_loader(train, val)

      Get the Dataloader with training and validation data

      :poram train: training data
      :param val: validation data

      :return: train_loader, val_loader, val


   .. py:method:: predict(X_in)

      Implementation of a prediction based on input features for the bayes lstm model.
      See :obj:`~ForeTiS.model._base_model.BaseModel` for more information


   .. py:method:: get_dataloader(X, y = None, only_transform = None, predict = False, shuffle = False)

      Get a Pytorch DataLoader using the specified data and batch size

      :param X: feature matrix to use
      :param y: optional target vector to use
      :param only_transform: whether to only transform or not
      :param predict: weather to use the data for predictions or not
      :param shuffle: shuffle parameter for DataLoader

      :return: Pytorch DataLoader


   .. py:method:: create_sequences(X, y)

      Create sequenced data according to self.seq_length

      :return: sequenced data and labels