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

.. only:: html

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

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

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

.. _sphx_glr_examples_scalable_plot_diagonal_doe.py:


Diagonal design of experiments
==============================

Here is an illustration of the diagonal design of experiments (DOE)
implemented by the :class:`.DiagonalDOE` class
and used by the :class:`.ScalableDiagonalModel`.
The idea is to sample the discipline by varying its inputs proportionally
on one of the diagonals of its input space.

.. GENERATED FROM PYTHON SOURCE LINES 31-43

.. code-block:: Python


    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.post.dataset.scatter_plot_matrix import ScatterMatrix

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 44-49

Create the discipline
---------------------
First, we create an :class:`.AnalyticDiscipline`
implementing the function: :math:`f(x)=2x-3\sin(2\pi y)`
and set its cache policy to ``"MemoryFullCache"``.

.. GENERATED FROM PYTHON SOURCE LINES 49-54

.. code-block:: Python


    discipline = create_discipline(
        "AnalyticDiscipline", expressions={"z": "2*x-3*sin(2*pi*y)"}
    )








.. GENERATED FROM PYTHON SOURCE LINES 55-59

Create the design space
-----------------------
Then, we create a :class:`.DesignSpace`
where :math:`x` and :math:`y` vary between 0 and 1.

.. GENERATED FROM PYTHON SOURCE LINES 59-63

.. code-block:: Python

    design_space = create_design_space()
    design_space.add_variable("x", l_b=0.0, u_b=1.0)
    design_space.add_variable("y", l_b=0.0, u_b=1.0)








.. GENERATED FROM PYTHON SOURCE LINES 64-72

Sample with the default mode
----------------------------
Lastly, we create a :class:`.DOEScenario`
and execute it with the :class:`.DiagonalDOE` algorithm
to get 10 evaluations of :math:`f`.
Note that we use the default configuration:
all the disciplinary inputs vary proportionally
from their lower bounds to their upper bounds.

.. GENERATED FROM PYTHON SOURCE LINES 72-79

.. code-block:: Python

    scenario = create_scenario(
        discipline, "DisciplinaryOpt", "z", design_space, scenario_type="DOE"
    )
    scenario.execute({"algo": "DiagonalDOE", "n_samples": 10})
    dataset = scenario.to_dataset(opt_naming=False)
    ScatterMatrix(dataset).execute(save=False, show=True)




.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_doe_001.png
   :alt: plot diagonal doe
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_doe_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

        INFO - 10:53:51:  
        INFO - 10:53:51: *** Start DOEScenario execution ***
        INFO - 10:53:51: DOEScenario
        INFO - 10:53:51:    Disciplines: AnalyticDiscipline
        INFO - 10:53:51:    MDO formulation: DisciplinaryOpt
        INFO - 10:53:51: Optimization problem:
        INFO - 10:53:51:    minimize z(x, y)
        INFO - 10:53:51:    with respect to x, y
        INFO - 10:53:51:    over the design space:
        INFO - 10:53:51:       +------+-------------+-------+-------------+-------+
        INFO - 10:53:51:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 10:53:51:       +------+-------------+-------+-------------+-------+
        INFO - 10:53:51:       | x    |      0      |  None |      1      | float |
        INFO - 10:53:51:       | y    |      0      |  None |      1      | float |
        INFO - 10:53:51:       +------+-------------+-------+-------------+-------+
        INFO - 10:53:51: Solving optimization problem with algorithm DiagonalDOE:
        INFO - 10:53:51:     10%|█         | 1/10 [00:00<00:00, 343.15 it/sec, obj=0]
        INFO - 10:53:51:     20%|██        | 2/10 [00:00<00:00, 543.48 it/sec, obj=-1.71]
        INFO - 10:53:51:     30%|███       | 3/10 [00:00<00:00, 691.03 it/sec, obj=-2.51]
        INFO - 10:53:51:     40%|████      | 4/10 [00:00<00:00, 806.21 it/sec, obj=-1.93]
        INFO - 10:53:51:     50%|█████     | 5/10 [00:00<00:00, 895.87 it/sec, obj=-.137]
        INFO - 10:53:51:     60%|██████    | 6/10 [00:00<00:00, 967.47 it/sec, obj=2.14]
        INFO - 10:53:51:     70%|███████   | 7/10 [00:00<00:00, 1023.43 it/sec, obj=3.93]
        INFO - 10:53:51:     80%|████████  | 8/10 [00:00<00:00, 1072.03 it/sec, obj=4.51]
        INFO - 10:53:51:     90%|█████████ | 9/10 [00:00<00:00, 1108.59 it/sec, obj=3.71]
        INFO - 10:53:51:    100%|██████████| 10/10 [00:00<00:00, 1145.01 it/sec, obj=2]
        INFO - 10:53:51: Optimization result:
        INFO - 10:53:51:    Optimizer info:
        INFO - 10:53:51:       Status: None
        INFO - 10:53:51:       Message: None
        INFO - 10:53:51:       Number of calls to the objective function by the optimizer: 10
        INFO - 10:53:51:    Solution:
        INFO - 10:53:51:       Objective: -2.5099788145921798
        INFO - 10:53:51:       Design space:
        INFO - 10:53:51:          +------+-------------+--------------------+-------------+-------+
        INFO - 10:53:51:          | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 10:53:51:          +------+-------------+--------------------+-------------+-------+
        INFO - 10:53:51:          | x    |      0      | 0.2222222222222222 |      1      | float |
        INFO - 10:53:51:          | y    |      0      | 0.2222222222222222 |      1      | float |
        INFO - 10:53:51:          +------+-------------+--------------------+-------------+-------+
        INFO - 10:53:51: *** End DOEScenario execution (time: 0:00:00.020183) ***

    [<Figure size 640x480 with 9 Axes>]



.. GENERATED FROM PYTHON SOURCE LINES 80-89

Sample with reverse mode for :math:`y`
--------------------------------------
We can also change the configuration
in order to select another diagonal of the input space,
e.g. increasing :math:`x` and decreasing :math:`y`.
This configuration is illustrated in the new :class:`.ScatterMatrix` plot
where the :math:`(x,y)` points follow the :math:`t\mapsto -t` line
while  the :math:`(x,y)` points follow the :math:`t\mapsto t` line
with the default configuration.

.. GENERATED FROM PYTHON SOURCE LINES 89-99

.. code-block:: Python

    scenario = create_scenario(
        discipline, "DisciplinaryOpt", "z", design_space, scenario_type="DOE"
    )
    scenario.execute({
        "algo": "DiagonalDOE",
        "n_samples": 10,
        "algo_options": {"reverse": ["y"]},
    })
    dataset = scenario.to_dataset(opt_naming=False)
    ScatterMatrix(dataset).execute(save=False, show=True)



.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_doe_002.png
   :alt: plot diagonal doe
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_doe_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

        INFO - 10:53:52:  
        INFO - 10:53:52: *** Start DOEScenario execution ***
        INFO - 10:53:52: DOEScenario
        INFO - 10:53:52:    Disciplines: AnalyticDiscipline
        INFO - 10:53:52:    MDO formulation: DisciplinaryOpt
        INFO - 10:53:52: Optimization problem:
        INFO - 10:53:52:    minimize z(x, y)
        INFO - 10:53:52:    with respect to x, y
        INFO - 10:53:52:    over the design space:
        INFO - 10:53:52:       +------+-------------+--------------------+-------------+-------+
        INFO - 10:53:52:       | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 10:53:52:       +------+-------------+--------------------+-------------+-------+
        INFO - 10:53:52:       | x    |      0      | 0.2222222222222222 |      1      | float |
        INFO - 10:53:52:       | y    |      0      | 0.2222222222222222 |      1      | float |
        INFO - 10:53:52:       +------+-------------+--------------------+-------------+-------+
        INFO - 10:53:52: Solving optimization problem with algorithm DiagonalDOE:
        INFO - 10:53:52:     10%|█         | 1/10 [00:00<00:00, 1390.22 it/sec, obj=7.35e-16]
        INFO - 10:53:52:     20%|██        | 2/10 [00:00<00:00, 1378.57 it/sec, obj=2.15]
        INFO - 10:53:52:     30%|███       | 3/10 [00:00<00:00, 1429.06 it/sec, obj=3.4]
        INFO - 10:53:52:     40%|████      | 4/10 [00:00<00:00, 1509.69 it/sec, obj=3.26]
        INFO - 10:53:52:     50%|█████     | 5/10 [00:00<00:00, 1526.76 it/sec, obj=1.91]
        INFO - 10:53:52:     60%|██████    | 6/10 [00:00<00:00, 1543.92 it/sec, obj=0.0851]
        INFO - 10:53:52:     70%|███████   | 7/10 [00:00<00:00, 1556.08 it/sec, obj=-1.26]
        INFO - 10:53:52:     80%|████████  | 8/10 [00:00<00:00, 1557.99 it/sec, obj=-1.4]
        INFO - 10:53:52:     90%|█████████ | 9/10 [00:00<00:00, 1549.24 it/sec, obj=-.151]
        INFO - 10:53:52:    100%|██████████| 10/10 [00:00<00:00, 1572.96 it/sec, obj=2]
        INFO - 10:53:52: Optimization result:
        INFO - 10:53:52:    Optimizer info:
        INFO - 10:53:52:       Status: None
        INFO - 10:53:52:       Message: None
        INFO - 10:53:52:       Number of calls to the objective function by the optimizer: 10
        INFO - 10:53:52:    Solution:
        INFO - 10:53:52:       Objective: -1.398867703481069
        INFO - 10:53:52:       Design space:
        INFO - 10:53:52:          +------+-------------+--------------------+-------------+-------+
        INFO - 10:53:52:          | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 10:53:52:          +------+-------------+--------------------+-------------+-------+
        INFO - 10:53:52:          | x    |      0      | 0.7777777777777777 |      1      | float |
        INFO - 10:53:52:          | y    |      0      | 0.2222222222222223 |      1      | float |
        INFO - 10:53:52:          +------+-------------+--------------------+-------------+-------+
        INFO - 10:53:52: *** End DOEScenario execution (time: 0:00:00.017072) ***

    [<Figure size 640x480 with 9 Axes>]




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

   **Total running time of the script:** (0 minutes 0.834 seconds)


.. _sphx_glr_download_examples_scalable_plot_diagonal_doe.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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