.. 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

        Click :ref:`here <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 a :class:`.MDODiscipline`
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 a :class:`.MLRegressionAlgo`

.. GENERATED FROM PYTHON SOURCE LINES 37-41

.. code-block:: default

    from __future__ import absolute_import, division, print_function, unicode_literals

    from future import standard_library








.. GENERATED FROM PYTHON SOURCE LINES 42-44

Import
------

.. GENERATED FROM PYTHON SOURCE LINES 44-59

.. code-block:: default

    from numpy import array

    from gemseo.api import (
        configure_logger,
        create_design_space,
        create_discipline,
        create_scenario,
        create_surrogate,
    )

    configure_logger()


    standard_library.install_aliases()








.. GENERATED FROM PYTHON SOURCE LINES 60-64

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

.. GENERATED FROM PYTHON SOURCE LINES 64-69

.. code-block:: default

    expressions_dict = {"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_dict=expressions_dict
    )








.. GENERATED FROM PYTHON SOURCE LINES 70-73

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

.. GENERATED FROM PYTHON SOURCE LINES 73-77

.. code-block:: default

    design_space = create_design_space()
    design_space.add_variable("x_1", l_b=0.0, u_b=1.0)
    design_space.add_variable("x_2", l_b=0.0, u_b=1.0)








.. GENERATED FROM PYTHON SOURCE LINES 78-83

Create the learning set
-----------------------
We can build a learning set by means of a
:class:`~gemseo.core.doe_scenario.DOEScenario` with a full factorial design of
experiments. The number of samples can be equal to 9 for example.

.. GENERATED FROM PYTHON SOURCE LINES 83-89

.. code-block:: default

    discipline.set_cache_policy(discipline.MEMORY_FULL_CACHE)
    scenario = create_scenario(
        [discipline], "DisciplinaryOpt", "y_1", design_space, scenario_type="DOE"
    )
    scenario.execute({"algo": "fullfact", "n_samples": 9})





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

 Out:

 .. code-block:: none


    {'eval_jac': False, 'algo': 'fullfact', 'n_samples': 9}



.. GENERATED FROM PYTHON SOURCE LINES 90-94

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

.. GENERATED FROM PYTHON SOURCE LINES 94-97

.. code-block:: default

    dataset = discipline.cache.export_to_dataset()
    model = create_surrogate("GaussianProcessRegression", data=dataset)








.. GENERATED FROM PYTHON SOURCE LINES 98-102

Predict output
--------------
Once it is built, we can use it for prediction, either with default inputs
or with user-defined ones.

.. GENERATED FROM PYTHON SOURCE LINES 102-106

.. code-block:: default

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




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

 Out:

 .. code-block:: none

    {'x_1': array([0.]), 'x_2': array([0.]), 'y_1': array([1.]), 'y_2': array([-1.])}
    {'x_1': array([1.]), 'x_2': array([2.]), 'y_1': array([3.91371997]), 'y_2': array([-3.91371997])}





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

   **Total running time of the script:** ( 0 minutes  0.182 seconds)


.. _sphx_glr_download_examples_surrogate_plot_surrogate_discipline.py:


.. only :: html

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



  .. 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-jupyter

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


.. only:: html

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

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