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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_examples_mlearning_regression_model_plot_save.py>`
        to download the full example code

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

.. _sphx_glr_examples_mlearning_regression_model_plot_save.py:


Save and Load
=============

We want to build a regression model and save it on the disk.

This regression model is an approximation of 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]`.

.. GENERATED FROM PYTHON SOURCE LINES 35-46

.. code-block:: default

    from gemseo.api import configure_logger
    from gemseo.api import create_design_space
    from gemseo.api import create_discipline
    from gemseo.api import create_scenario
    from gemseo.mlearning.api import create_regression_model
    from gemseo.mlearning.api import import_regression_model
    from numpy import array

    configure_logger()






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

 Out:

 .. code-block:: none


    <RootLogger root (INFO)>



.. 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:: default

    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:: 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 65-70

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 70-75

.. code-block:: default

    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

        INFO - 10:06:54:  
        INFO - 10:06:54: *** Start DOEScenario execution ***
        INFO - 10:06:54: DOEScenario
        INFO - 10:06:54:    Disciplines: func
        INFO - 10:06:54:    MDO formulation: DisciplinaryOpt
        INFO - 10:06:54: Optimization problem:
        INFO - 10:06:54:    minimize y_1(x_1, x_2)
        INFO - 10:06:54:    with respect to x_1, x_2
        INFO - 10:06:54:    over the design space:
        INFO - 10:06:54:    +------+-------------+-------+-------------+-------+
        INFO - 10:06:54:    | name | lower_bound | value | upper_bound | type  |
        INFO - 10:06:54:    +------+-------------+-------+-------------+-------+
        INFO - 10:06:54:    | x_1  |      0      |  None |      1      | float |
        INFO - 10:06:54:    | x_2  |      0      |  None |      1      | float |
        INFO - 10:06:54:    +------+-------------+-------+-------------+-------+
        INFO - 10:06:54: Solving optimization problem with algorithm fullfact:
        INFO - 10:06:54: Full factorial design required. Number of samples along each direction for a design vector of size 2 with 9 samples: 3
        INFO - 10:06:54: Final number of samples for DOE = 9 vs 9 requested
        INFO - 10:06:54: ...   0%|          | 0/9 [00:00<?, ?it]
        INFO - 10:06:54: ... 100%|██████████| 9/9 [00:00<00:00, 1412.65 it/sec, obj=6]
        INFO - 10:06:54: Optimization result:
        INFO - 10:06:54:    Optimizer info:
        INFO - 10:06:54:       Status: None
        INFO - 10:06:54:       Message: None
        INFO - 10:06:54:       Number of calls to the objective function by the optimizer: 9
        INFO - 10:06:54:    Solution:
        INFO - 10:06:54:       Objective: 1.0
        INFO - 10:06:54:       Design space:
        INFO - 10:06:54:       +------+-------------+-------+-------------+-------+
        INFO - 10:06:54:       | name | lower_bound | value | upper_bound | type  |
        INFO - 10:06:54:       +------+-------------+-------+-------------+-------+
        INFO - 10:06:54:       | x_1  |      0      |   0   |      1      | float |
        INFO - 10:06:54:       | x_2  |      0      |   0   |      1      | float |
        INFO - 10:06:54:       +------+-------------+-------+-------------+-------+
        INFO - 10:06:54: *** End DOEScenario execution (time: 0:00:00.014816) ***

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



.. GENERATED FROM PYTHON SOURCE LINES 76-80

Create the regression model
---------------------------
Then, we build the linear regression model from the database and
displays this model.

.. GENERATED FROM PYTHON SOURCE LINES 80-84

.. code-block:: default

    dataset = scenario.export_to_dataset(opt_naming=False)
    model = create_regression_model("RBFRegressor", data=dataset)
    model.learn()








.. GENERATED FROM PYTHON SOURCE LINES 85-87

Use it for prediction
---------------------

.. GENERATED FROM PYTHON SOURCE LINES 87-90

.. code-block:: default

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





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

 Out:

 .. code-block:: none

    {'y_1': array([6.45029404])}




.. GENERATED FROM PYTHON SOURCE LINES 91-94

Save the regression model
-------------------------
Lastly, we save the model.

.. GENERATED FROM PYTHON SOURCE LINES 94-97

.. code-block:: default

    directory = model.save()
    print(directory)





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

 Out:

 .. code-block:: none

    r_b_f_regressor_DOEScenario




.. GENERATED FROM PYTHON SOURCE LINES 98-101

Load the regression model
-------------------------
In an other study, we could load this model.

.. GENERATED FROM PYTHON SOURCE LINES 101-104

.. code-block:: default

    loaded_model = import_regression_model(directory)
    print(loaded_model)





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

 Out:

 .. code-block:: none

    RBFRegressor(epsilon=None, function='multiquadric', norm='euclidean', smooth=0.0)
       based on the SciPy library
       built from 0 learning samples




.. GENERATED FROM PYTHON SOURCE LINES 105-108

Use the loaded regression model
-------------------------------
And use it!

.. GENERATED FROM PYTHON SOURCE LINES 108-109

.. code-block:: default

    print(loaded_model.predict(input_value))




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

 Out:

 .. code-block:: none

    {'y_1': array([6.45029404])}





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

   **Total running time of the script:** ( 0 minutes  0.038 seconds)


.. _sphx_glr_download_examples_mlearning_regression_model_plot_save.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_save.py <plot_save.py>`



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

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


.. only:: html

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

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