.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/surrogate/plot_surrogate_discipline.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_surrogate_plot_surrogate_discipline.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_surrogate_plot_surrogate_discipline.py:


Create a surrogate discipline
=============================

We want to build an :class:`.Discipline`
based on a regression model approximating the following discipline
with two inputs and two outputs:

- :math:`y_1=1+2x_1+3x_2`
- :math:`y_2=-1-2x_1-3x_2`

over the unit hypercube :math:`[0,1]\times[0,1]`.
For that,
we use a :class:`.SurrogateDiscipline` relying on an :class:`.BaseRegressor`.

.. GENERATED FROM PYTHON SOURCE LINES 36-46

.. code-block:: Python


    from __future__ import annotations

    from numpy import array

    from gemseo import create_design_space
    from gemseo import create_discipline
    from gemseo import create_surrogate
    from gemseo import sample_disciplines








.. GENERATED FROM PYTHON SOURCE LINES 47-51

Create the discipline to learn
------------------------------
We can implement this analytic discipline by means of the
:class:`~gemseo.disciplines.analytic.AnalyticDiscipline` class.

.. GENERATED FROM PYTHON SOURCE LINES 51-56

.. code-block:: Python

    expressions = {"y_1": "1+2*x_1+3*x_2", "y_2": "-1-2*x_1-3*x_2"}
    discipline = create_discipline(
        "AnalyticDiscipline", name="func", expressions=expressions
    )








.. GENERATED FROM PYTHON SOURCE LINES 57-60

Create the input sampling space
-------------------------------
We create the input sampling space by adding the variables one by one.

.. GENERATED FROM PYTHON SOURCE LINES 60-64

.. code-block:: Python

    design_space = create_design_space()
    design_space.add_variable("x_1", lower_bound=0.0, upper_bound=1.0)
    design_space.add_variable("x_2", lower_bound=0.0, upper_bound=1.0)








.. GENERATED FROM PYTHON SOURCE LINES 65-70

Create the training dataset
---------------------------
We can build a training dataset
by sampling the discipline using the :func:`.sample_disciplines`
with a full factorial design of experiments.

.. GENERATED FROM PYTHON SOURCE LINES 70-74

.. code-block:: Python

    dataset = sample_disciplines(
        [discipline], design_space, ["y_1", "y_2"], algo_name="PYDOE_FULLFACT", n_samples=9
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:24:50: *** Start Sampling execution ***
        INFO - 16:24:50: Sampling
        INFO - 16:24:50:    Disciplines: func
        INFO - 16:24:50:    MDO formulation: MDF
        INFO - 16:24:50: Running the algorithm PYDOE_FULLFACT:
        INFO - 16:24:50:     11%|█         | 1/9 [00:00<00:00, 416.80 it/sec]
        INFO - 16:24:50:     22%|██▏       | 2/9 [00:00<00:00, 710.48 it/sec]
        INFO - 16:24:50:     33%|███▎      | 3/9 [00:00<00:00, 945.37 it/sec]
        INFO - 16:24:50:     44%|████▍     | 4/9 [00:00<00:00, 1144.50 it/sec]
        INFO - 16:24:50:     56%|█████▌    | 5/9 [00:00<00:00, 1314.58 it/sec]
        INFO - 16:24:50:     67%|██████▋   | 6/9 [00:00<00:00, 1454.08 it/sec]
        INFO - 16:24:50:     78%|███████▊  | 7/9 [00:00<00:00, 1582.16 it/sec]
        INFO - 16:24:50:     89%|████████▉ | 8/9 [00:00<00:00, 1694.41 it/sec]
        INFO - 16:24:50:    100%|██████████| 9/9 [00:00<00:00, 1760.59 it/sec]
        INFO - 16:24:50: *** End Sampling execution ***




.. GENERATED FROM PYTHON SOURCE LINES 75-79

Create the surrogate discipline
-------------------------------
Then, we build the Gaussian process regression model from the dataset and
displays this model.

.. GENERATED FROM PYTHON SOURCE LINES 79-81

.. code-block:: Python

    model = create_surrogate("GaussianProcessRegressor", data=dataset)








.. GENERATED FROM PYTHON SOURCE LINES 82-85

Predict output
--------------
Once it is built, we can use it for prediction, either with default inputs

.. GENERATED FROM PYTHON SOURCE LINES 85-86

.. code-block:: Python

    model.execute()




.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    {'x_1': array([0.5]), 'x_2': array([0.5]), 'y_1': array([3.5]), 'y_2': array([-3.5])}



.. GENERATED FROM PYTHON SOURCE LINES 87-88

or with user-defined ones.

.. GENERATED FROM PYTHON SOURCE LINES 88-89

.. code-block:: Python

    model.execute({"x_1": array([1.0]), "x_2": array([2.0])})




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

     WARNING - 16:24:50: The surrogate discipline GPR_Sampling is used at an input point outside its domain of validity: {'x_1': array([1.]), 'x_2': array([2.])}.

    {'x_1': array([1.]), 'x_2': array([2.]), 'y_1': array([6.03823007]), 'y_2': array([-6.03823007])}




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.068 seconds)


.. _sphx_glr_download_examples_surrogate_plot_surrogate_discipline.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_surrogate_discipline.ipynb <plot_surrogate_discipline.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_surrogate_discipline.py <plot_surrogate_discipline.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_surrogate_discipline.zip <plot_surrogate_discipline.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_