mmgp.backends.scikit_learn
==========================

.. py:module:: mmgp.backends.scikit_learn


Classes
-------

.. autoapisummary::

   mmgp.backends.scikit_learn.Regressor


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

.. py:class:: Regressor(options: dict)

   Bases: :py:obj:`mmgp.regressor.RegressorBase`


   scikit-learn regressor

   :param algo: The regression algorithm to use.
   :type algo: str
   :param options: A dictionary of options specific to the chosen algorithm. Allowed fields are "kernel", "optim", "num_restarts", "max_iters" and "anisotropic".
   :type options: dict


   .. py:attribute:: algo
      :value: 'scikit-learn'



   .. py:attribute:: options


   .. py:method:: fit(X: numpy.ndarray, y: numpy.ndarray) -> Self

      Train the regression model on the provided data.

      :param X: The input features for training.
      :type X: np.ndarray
      :param y: The target values for training.
      :type y: np.ndarray



   .. py:method:: predict(X: numpy.ndarray, return_var: bool) -> numpy.ndarray

      Make predictions using the trained regression model.

      :param X: The input features for making predictions. Array of size 1 x self.input_dim
      :type X: np.ndarray
      :param return_var: True if prediction variance is computed
      :type return_var: bool

      :returns: Predicted target values (and variances).  One array (two arrays) of size 1 x self.output_dim
      :rtype: np.ndarray



   .. py:method:: predict_Monte_Carlo_draw(X: numpy.ndarray) -> numpy.ndarray

      Generate Monte Carlo draws from the trained regression model.

      :param X: The input features for generating draws. Array of size 1 x self.input_dim
      :type X: np.ndarray

      :returns: Monte Carlo draw from the posterior of the regression model. Array of size 1 x self.output_dim
      :rtype: np.ndarray