.. 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

        :ref:`Go to the end <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 35-48

.. code-block:: default

    from __future__ import annotations

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

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 49-53

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

.. GENERATED FROM PYTHON SOURCE LINES 53-58

.. 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 59-62

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

.. GENERATED FROM PYTHON SOURCE LINES 62-66

.. 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 67-72

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

.. GENERATED FROM PYTHON SOURCE LINES 72-77

.. 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

 .. code-block:: none

        INFO - 13:54:00:  
        INFO - 13:54:00: *** Start DOEScenario execution ***
        INFO - 13:54:00: DOEScenario
        INFO - 13:54:00:    Disciplines: func
        INFO - 13:54:00:    MDO formulation: DisciplinaryOpt
        INFO - 13:54:00: Optimization problem:
        INFO - 13:54:00:    minimize y_1(x_1, x_2)
        INFO - 13:54:00:    with respect to x_1, x_2
        INFO - 13:54:00:    over the design space:
        INFO - 13:54:00:    +------+-------------+-------+-------------+-------+
        INFO - 13:54:00:    | name | lower_bound | value | upper_bound | type  |
        INFO - 13:54:00:    +------+-------------+-------+-------------+-------+
        INFO - 13:54:00:    | x_1  |      0      |  None |      1      | float |
        INFO - 13:54:00:    | x_2  |      0      |  None |      1      | float |
        INFO - 13:54:00:    +------+-------------+-------+-------------+-------+
        INFO - 13:54:00: Solving optimization problem with algorithm fullfact:
        INFO - 13:54:00: ...   0%|          | 0/9 [00:00<?, ?it]
        INFO - 13:54:00: ...  11%|█         | 1/9 [00:00<00:00, 274.59 it/sec, obj=1]
        INFO - 13:54:00: ...  22%|██▏       | 2/9 [00:00<00:00, 429.55 it/sec, obj=2]
        INFO - 13:54:00: ...  33%|███▎      | 3/9 [00:00<00:00, 549.11 it/sec, obj=3]
        INFO - 13:54:00: ...  44%|████▍     | 4/9 [00:00<00:00, 636.25 it/sec, obj=2.5]
        INFO - 13:54:00: ...  56%|█████▌    | 5/9 [00:00<00:00, 710.92 it/sec, obj=3.5]
        INFO - 13:54:00: ...  67%|██████▋   | 6/9 [00:00<00:00, 772.22 it/sec, obj=4.5]
        INFO - 13:54:00: ...  78%|███████▊  | 7/9 [00:00<00:00, 821.24 it/sec, obj=4]
        INFO - 13:54:00: ...  89%|████████▉ | 8/9 [00:00<00:00, 864.72 it/sec, obj=5]
        INFO - 13:54:00: ... 100%|██████████| 9/9 [00:00<00:00, 897.52 it/sec, obj=6]
        INFO - 13:54:00: Optimization result:
        INFO - 13:54:00:    Optimizer info:
        INFO - 13:54:00:       Status: None
        INFO - 13:54:00:       Message: None
        INFO - 13:54:00:       Number of calls to the objective function by the optimizer: 9
        INFO - 13:54:00:    Solution:
        INFO - 13:54:00:       Objective: 1.0
        INFO - 13:54:00:       Design space:
        INFO - 13:54:00:       +------+-------------+-------+-------------+-------+
        INFO - 13:54:00:       | name | lower_bound | value | upper_bound | type  |
        INFO - 13:54:00:       +------+-------------+-------+-------------+-------+
        INFO - 13:54:00:       | x_1  |      0      |   0   |      1      | float |
        INFO - 13:54:00:       | x_2  |      0      |   0   |      1      | float |
        INFO - 13:54:00:       +------+-------------+-------+-------------+-------+
        INFO - 13:54:00: *** End DOEScenario execution (time: 0:00:00.022399) ***

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



.. GENERATED FROM PYTHON SOURCE LINES 78-82

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

.. GENERATED FROM PYTHON SOURCE LINES 82-86

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 87-89

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

.. GENERATED FROM PYTHON SOURCE LINES 89-92

.. code-block:: default

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





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

 .. code-block:: none


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



.. GENERATED FROM PYTHON SOURCE LINES 93-96

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

.. GENERATED FROM PYTHON SOURCE LINES 96-99

.. code-block:: default

    directory = model.to_pickle()
    directory





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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.0/lib/python3.9/site-packages/gemseo/mlearning/core/ml_algo.py:334: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
      self.learning_set.data = {}

    'r_b_f_regressor_DOEScenario'



.. GENERATED FROM PYTHON SOURCE LINES 100-103

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

.. GENERATED FROM PYTHON SOURCE LINES 103-106

.. code-block:: default

    loaded_model = import_regression_model(directory)
    loaded_model






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style='background-color: #fafae2; padding: 10px;'>RBFRegressor(epsilon=None, function=multiquadric, norm=euclidean, smooth=0.0)<br/><ul><li>based on the SciPy library</li><li>built from 9 learning samples</li></ul></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 107-110

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

.. GENERATED FROM PYTHON SOURCE LINES 110-111

.. code-block:: default

    loaded_model.predict(input_value)




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

 .. code-block:: none


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




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

   **Total running time of the script:** (0 minutes 0.066 seconds)


.. _sphx_glr_download_examples_mlearning_regression_model_plot_save.py:

.. only:: html

  .. container:: sphx-glr-footer 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>`_