:py:mod:`ForeTiS.model.evars-gpr`
=================================

.. py:module:: ForeTiS.model.evars-gpr


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

Classes
~~~~~~~

.. autoapisummary::

   ForeTiS.model.evars-gpr.Evars_gpr




.. py:class:: Evars_gpr(optuna_trial, datasets, featureset_name, pca_transform = None, target_column = None, optimize_featureset = None, scale_thr = None, scale_seasons = None, scale_window_factor = None, cf_r = None, cf_order = None, cf_smooth = None, cf_thr_perc = None, scale_window_minimum = None, max_samples_factor = None)

   Bases: :py:obj:`ForeTiS.model._tensorflow_model.TensorflowModel`

   Implementation of a class for Gpr.

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

   .. py:method:: get_augmented_data()

      get augmented data

      :return: augmented dataset


   .. py:method:: retrain(retrain)

      Implementation of the retraining for models with sklearn-like API.
      See :obj:`~ForeTiS.model._base_model.BaseModel` for more information


   .. py:method:: update(update, period)

      Implementation of the retraining for models with sklearn-like API.
      See :obj:`~ForeTiS.model._base_model.BaseModel` for more information


   .. py:method:: predict(X_in)

      Implementation of a prediction based on input features for models with sklearn-like API.
      See :obj:`~ForeTiS.model._base_model.BaseModel` for more information


   .. py:method:: pca_transform_train_test(train)

      Deliver PCA transformed train and test set

      :param train: data for the training

      :return: tuple of transformed train and test dataset


   .. py:method:: define_hyperparams_to_tune()

      See :obj:`~ForeTiSHortiCo-Hortico.model._base_model.BaseModel` for more information on the format.