:py:mod:`ForeTiS.model.mlpbayes`
================================

.. py:module:: ForeTiS.model.mlpbayes


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

Classes
~~~~~~~

.. autoapisummary::

   ForeTiS.model.mlpbayes.Mlpbayes




.. py:class:: Mlpbayes(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 feedforward Multilayer Perceptron (MLP).

   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 an MLP network.

      Architecture:

          - N_LAYERS of (bayesian Linear (+ ActivationFunction) (+ BatchNorm) + Dropout)
          - Bayesian Linear output layer
          - Dropout layer

      Number of units in the first bayesian linear layer and percentage decrease after each 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:: predict(X_in)

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