:py:mod:`bluecast.ml_modelling.base_classes`
============================================

.. py:module:: bluecast.ml_modelling.base_classes

.. autoapi-nested-parse::

   Base classes for all ML models.



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

Classes
~~~~~~~

.. autoapisummary::

   bluecast.ml_modelling.base_classes.BaseClassMlModel
   bluecast.ml_modelling.base_classes.BaseClassMlRegressionModel




Attributes
~~~~~~~~~~

.. autoapisummary::

   bluecast.ml_modelling.base_classes.PredictedProbas
   bluecast.ml_modelling.base_classes.PredictedClasses


.. py:data:: PredictedProbas

   

.. py:data:: PredictedClasses

   

.. py:class:: BaseClassMlModel


   Bases: :py:obj:`abc.ABC`

   Base class for all ML models.

   Enforces the implementation of the fit and predict methods.
   If hyperparameter tuning is required, then the fit method should implement the tuning.

   .. py:method:: fit(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) -> Optional[Any]
      :abstractmethod:


   .. py:method:: predict(df: pandas.DataFrame) -> Tuple[PredictedProbas, PredictedClasses]
      :abstractmethod:

      Predict on unseen data.

      :return tuple of predicted probabilities and predicted classes



.. py:class:: BaseClassMlRegressionModel


   Bases: :py:obj:`abc.ABC`

   Base class for all ML models.

   Enforces the implementation of the fit and predict methods.
   If hyperparameter tuning is required, then the fit method should implement the tuning.

   .. py:method:: fit(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) -> Optional[Any]
      :abstractmethod:


   .. py:method:: predict(df: pandas.DataFrame) -> numpy.ndarray
      :abstractmethod:

      Predict on unseen data.

      :return numpy array of predictions