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

.. only:: html

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

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

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

.. _sphx_glr_examples_dataset_plot_dataset_from_optproblem.py:


Dataset from an optimization problem
====================================

In this example, we will see how to build a :class:`.Dataset` from objects
of an :class:`.OptimizationProblem`.
For that, we need to import this :class:`.Dataset` class:

.. GENERATED FROM PYTHON SOURCE LINES 29-37

.. code-block:: default

    from gemseo.api import configure_logger
    from gemseo.api import create_discipline
    from gemseo.api import create_scenario
    from gemseo.problems.sellar.sellar_design_space import SellarDesignSpace

    configure_logger()






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

 Out:

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 38-42

Synthetic data
--------------
We can sample the :class:`.Sellar1` discipline and use the
corresponding :class:`.OptimizationProblem`:

.. GENERATED FROM PYTHON SOURCE LINES 42-52

.. code-block:: default


    discipline = create_discipline("Sellar1")
    design_space = SellarDesignSpace().filter(discipline.get_input_data_names())

    scenario = create_scenario(
        [discipline], "DisciplinaryOpt", "y_1", design_space, scenario_type="DOE"
    )
    scenario.execute({"algo": "lhs", "n_samples": 5})
    opt_problem = scenario.formulation.opt_problem





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

 Out:

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/gemseo/algos/design_space.py:448: ComplexWarning: Casting complex values to real discards the imaginary part
      self.__current_value[name] = array_value.astype(
        INFO - 10:06:04:  
        INFO - 10:06:04: *** Start DOEScenario execution ***
        INFO - 10:06:04: DOEScenario
        INFO - 10:06:04:    Disciplines: Sellar1
        INFO - 10:06:04:    MDO formulation: DisciplinaryOpt
        INFO - 10:06:04: Optimization problem:
        INFO - 10:06:04:    minimize y_1(x_local, x_shared, y_2)
        INFO - 10:06:04:    with respect to x_local, x_shared, y_2
        INFO - 10:06:04:    over the design space:
        INFO - 10:06:04:    +----------+-------------+-------+-------------+-------+
        INFO - 10:06:04:    | name     | lower_bound | value | upper_bound | type  |
        INFO - 10:06:04:    +----------+-------------+-------+-------------+-------+
        INFO - 10:06:04:    | x_local  |      0      |   1   |      10     | float |
        INFO - 10:06:04:    | x_shared |     -10     |   4   |      10     | float |
        INFO - 10:06:04:    | x_shared |      0      |   3   |      10     | float |
        INFO - 10:06:04:    | y_2      |     -100    |   1   |     100     | float |
        INFO - 10:06:04:    +----------+-------------+-------+-------------+-------+
        INFO - 10:06:04: Solving optimization problem with algorithm lhs:
        INFO - 10:06:04: ...   0%|          | 0/5 [00:00<?, ?it]
        INFO - 10:06:04: ... 100%|██████████| 5/5 [00:00<00:00, 1013.80 it/sec, obj=5.2+j]
        INFO - 10:06:04: Optimization result:
        INFO - 10:06:04:    Optimizer info:
        INFO - 10:06:04:       Status: None
        INFO - 10:06:04:       Message: None
        INFO - 10:06:04:       Number of calls to the objective function by the optimizer: 5
        INFO - 10:06:04:    Solution:
        INFO - 10:06:04:       Objective: (4.286541717010372+0j)
        INFO - 10:06:04:       Design space:
        INFO - 10:06:04:       +----------+-------------+--------------------+-------------+-------+
        INFO - 10:06:04:       | name     | lower_bound |       value        | upper_bound | type  |
        INFO - 10:06:04:       +----------+-------------+--------------------+-------------+-------+
        INFO - 10:06:04:       | x_local  |      0      | 0.8340440094051481 |      10     | float |
        INFO - 10:06:04:       | x_shared |     -10     | 0.1552669360134296 |      10     | float |
        INFO - 10:06:04:       | x_shared |      0      | 8.280773877190468  |      10     | float |
        INFO - 10:06:04:       | y_2      |     -100    | -46.17757091827809 |     100     | float |
        INFO - 10:06:04:       +----------+-------------+--------------------+-------------+-------+
        INFO - 10:06:04: *** End DOEScenario execution (time: 0:00:00.014476) ***




.. GENERATED FROM PYTHON SOURCE LINES 53-58

Create a dataset
----------------
We can easily build a dataset from this :class:`.OptimizationProblem`:
either by separating the design parameters from the function
(default option):

.. GENERATED FROM PYTHON SOURCE LINES 58-60

.. code-block:: default

    dataset = opt_problem.export_to_dataset("sellar1_doe")
    print(dataset)




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

 Out:

 .. code-block:: none

    sellar1_doe
       Number of samples: 5
       Number of variables: 4
       Variables names and sizes by group:
          design_parameters: x_local (1), x_shared (2), y_2 (1)
          functions: y_1 (1)
       Number of dimensions (total = 5) by group:
          design_parameters: 4
          functions: 1




.. GENERATED FROM PYTHON SOURCE LINES 61-62

or by considering all features as default parameters:

.. GENERATED FROM PYTHON SOURCE LINES 62-64

.. code-block:: default

    dataset = opt_problem.export_to_dataset("sellar1_doe", categorize=False)
    print(dataset)




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

 Out:

 .. code-block:: none

    sellar1_doe
       Number of samples: 5
       Number of variables: 4
       Variables names and sizes by group:
          parameters: x_local (1), x_shared (2), y_2 (1), y_1 (1)
       Number of dimensions (total = 5) by group:
          parameters: 5




.. GENERATED FROM PYTHON SOURCE LINES 65-66

or by using an input-output naming rather than an optimization naming:

.. GENERATED FROM PYTHON SOURCE LINES 66-68

.. code-block:: default

    dataset = opt_problem.export_to_dataset("sellar1_doe", opt_naming=False)
    print(dataset)




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

 Out:

 .. code-block:: none

    sellar1_doe
       Number of samples: 5
       Number of variables: 4
       Variables names and sizes by group:
          inputs: x_local (1), x_shared (2), y_2 (1)
          outputs: y_1 (1)
       Number of dimensions (total = 5) by group:
          inputs: 4
          outputs: 1




.. GENERATED FROM PYTHON SOURCE LINES 69-74

.. note::
    Only design variables and functions (objective function, constraints) are
    stored in the database. If you want to store state variables, you must add
    them as observables before the problem is executed. Use the
    :meth:`~gemseo.core.scenario.Scenario.add_observable` method.

.. GENERATED FROM PYTHON SOURCE LINES 76-78

Access properties
-----------------

.. GENERATED FROM PYTHON SOURCE LINES 78-79

.. code-block:: default

    dataset = opt_problem.export_to_dataset("sellar1_doe")







.. GENERATED FROM PYTHON SOURCE LINES 80-83

Variables names
~~~~~~~~~~~~~~~
We can access the variables names:

.. GENERATED FROM PYTHON SOURCE LINES 83-85

.. code-block:: default

    print(dataset.variables)





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

 Out:

 .. code-block:: none

    ['x_local', 'x_shared', 'y_1', 'y_2']




.. GENERATED FROM PYTHON SOURCE LINES 86-89

Variables sizes
~~~~~~~~~~~~~~~
We can access the variables sizes:

.. GENERATED FROM PYTHON SOURCE LINES 89-91

.. code-block:: default

    print(dataset.sizes)





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

 Out:

 .. code-block:: none

    {'x_local': 1, 'x_shared': 2, 'y_2': 1, 'y_1': 1}




.. GENERATED FROM PYTHON SOURCE LINES 92-95

Variables groups
~~~~~~~~~~~~~~~~
We can access the variables groups:

.. GENERATED FROM PYTHON SOURCE LINES 95-97

.. code-block:: default

    print(dataset.groups)





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

 Out:

 .. code-block:: none

    ['design_parameters', 'functions']




.. GENERATED FROM PYTHON SOURCE LINES 98-103

Access data
-----------
Access by group
~~~~~~~~~~~~~~~
We can get the data by group, either as an array (default option):

.. GENERATED FROM PYTHON SOURCE LINES 103-104

.. code-block:: default

    print(dataset.get_data_by_group("design_parameters"))




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

 Out:

 .. code-block:: none

    [[ 4.79353495e+00  5.51246975e+00  4.83838903e+00 -8.79066971e+01]
     [ 8.34044009e-01  1.55266936e-01  8.28077388e+00 -4.61775709e+01]
     [ 6.40890450e+00 -7.11870203e+00  6.05477519e+00  7.40878002e+00]
     [ 8.83460960e+00  8.23475931e+00  2.28749635e-04  6.79240596e+01]
     [ 2.29351178e+00 -5.63064562e+00  2.37252042e+00  4.68187004e+01]]




.. GENERATED FROM PYTHON SOURCE LINES 105-106

or as a dictionary indexed by the variables names:

.. GENERATED FROM PYTHON SOURCE LINES 106-108

.. code-block:: default

    print(dataset.get_data_by_group("design_parameters", True))





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

 Out:

 .. code-block:: none

    {'x_local': array([[4.79353495],
           [0.83404401],
           [6.4089045 ],
           [8.8346096 ],
           [2.29351178]]), 'x_shared': array([[ 5.51246975e+00,  4.83838903e+00],
           [ 1.55266936e-01,  8.28077388e+00],
           [-7.11870203e+00,  6.05477519e+00],
           [ 8.23475931e+00,  2.28749635e-04],
           [-5.63064562e+00,  2.37252042e+00]]), 'y_2': array([[-87.90669709],
           [-46.17757092],
           [  7.40878002],
           [ 67.92405956],
           [ 46.81870041]])}




.. GENERATED FROM PYTHON SOURCE LINES 109-113

Access by variable name
~~~~~~~~~~~~~~~~~~~~~~~
We can get the data by variables names,
either as a dictionary indexed by the variables names (default option):

.. GENERATED FROM PYTHON SOURCE LINES 113-114

.. code-block:: default

    print(dataset.get_data_by_names(["x_shared", "y_2"]))




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

 Out:

 .. code-block:: none

    {'x_shared': array([[ 5.51246975e+00,  4.83838903e+00],
           [ 1.55266936e-01,  8.28077388e+00],
           [-7.11870203e+00,  6.05477519e+00],
           [ 8.23475931e+00,  2.28749635e-04],
           [-5.63064562e+00,  2.37252042e+00]]), 'y_2': array([[-87.90669709],
           [-46.17757092],
           [  7.40878002],
           [ 67.92405956],
           [ 46.81870041]])}




.. GENERATED FROM PYTHON SOURCE LINES 115-116

or as an array:

.. GENERATED FROM PYTHON SOURCE LINES 116-118

.. code-block:: default

    print(dataset.get_data_by_names(["x_shared", "y_2"], False))





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

 Out:

 .. code-block:: none

    [[ 5.51246975e+00  4.83838903e+00 -8.79066971e+01]
     [ 1.55266936e-01  8.28077388e+00 -4.61775709e+01]
     [-7.11870203e+00  6.05477519e+00  7.40878002e+00]
     [ 8.23475931e+00  2.28749635e-04  6.79240596e+01]
     [-5.63064562e+00  2.37252042e+00  4.68187004e+01]]




.. GENERATED FROM PYTHON SOURCE LINES 119-122

Access all data
~~~~~~~~~~~~~~~
We can get all the data, either as a large array:

.. GENERATED FROM PYTHON SOURCE LINES 122-123

.. code-block:: default

    print(dataset.get_all_data())




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

 Out:

 .. code-block:: none

    ({'design_parameters': array([[ 4.79353495e+00,  5.51246975e+00,  4.83838903e+00,
            -8.79066971e+01],
           [ 8.34044009e-01,  1.55266936e-01,  8.28077388e+00,
            -4.61775709e+01],
           [ 6.40890450e+00, -7.11870203e+00,  6.05477519e+00,
             7.40878002e+00],
           [ 8.83460960e+00,  8.23475931e+00,  2.28749635e-04,
             6.79240596e+01],
           [ 2.29351178e+00, -5.63064562e+00,  2.37252042e+00,
             4.68187004e+01]]), 'functions': array([[7.589505  +0.j],
           [4.28654172+0.j],
           [7.85225077+0.j],
           [7.94111374+0.j],
           [5.19677421+0.j]])}, {'design_parameters': ['x_local', 'x_shared', 'y_2'], 'functions': ['y_1']}, {'x_local': 1, 'x_shared': 2, 'y_2': 1, 'y_1': 1})




.. GENERATED FROM PYTHON SOURCE LINES 124-125

or as a dictionary indexed by variables names:

.. GENERATED FROM PYTHON SOURCE LINES 125-126

.. code-block:: default

    print(dataset.get_all_data(as_dict=True))




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

 Out:

 .. code-block:: none

    {'design_parameters': {'x_local': array([[4.79353495],
           [0.83404401],
           [6.4089045 ],
           [8.8346096 ],
           [2.29351178]]), 'x_shared': array([[ 5.51246975e+00,  4.83838903e+00],
           [ 1.55266936e-01,  8.28077388e+00],
           [-7.11870203e+00,  6.05477519e+00],
           [ 8.23475931e+00,  2.28749635e-04],
           [-5.63064562e+00,  2.37252042e+00]]), 'y_2': array([[-87.90669709],
           [-46.17757092],
           [  7.40878002],
           [ 67.92405956],
           [ 46.81870041]])}, 'functions': {'y_1': array([[7.589505  +0.j],
           [4.28654172+0.j],
           [7.85225077+0.j],
           [7.94111374+0.j],
           [5.19677421+0.j]])}}




.. GENERATED FROM PYTHON SOURCE LINES 127-129

We can get these data sorted by category, either with a large array for each
category:

.. GENERATED FROM PYTHON SOURCE LINES 129-130

.. code-block:: default

    print(dataset.get_all_data(by_group=False))




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

 Out:

 .. code-block:: none

    (array([[ 4.79353495e+00+0.j,  5.51246975e+00+0.j,  4.83838903e+00+0.j,
            -8.79066971e+01+0.j,  7.58950500e+00+0.j],
           [ 8.34044009e-01+0.j,  1.55266936e-01+0.j,  8.28077388e+00+0.j,
            -4.61775709e+01+0.j,  4.28654172e+00+0.j],
           [ 6.40890450e+00+0.j, -7.11870203e+00+0.j,  6.05477519e+00+0.j,
             7.40878002e+00+0.j,  7.85225077e+00+0.j],
           [ 8.83460960e+00+0.j,  8.23475931e+00+0.j,  2.28749635e-04+0.j,
             6.79240596e+01+0.j,  7.94111374e+00+0.j],
           [ 2.29351178e+00+0.j, -5.63064562e+00+0.j,  2.37252042e+00+0.j,
             4.68187004e+01+0.j,  5.19677421e+00+0.j]]), ['x_local', 'x_shared', 'y_2', 'y_1'], {'x_local': 1, 'x_shared': 2, 'y_2': 1, 'y_1': 1})




.. GENERATED FROM PYTHON SOURCE LINES 131-132

or with a dictionary of variables names:

.. GENERATED FROM PYTHON SOURCE LINES 132-133

.. code-block:: default

    print(dataset.get_all_data(by_group=False, as_dict=True))




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

 Out:

 .. code-block:: none

    {'x_local': array([[4.79353495],
           [0.83404401],
           [6.4089045 ],
           [8.8346096 ],
           [2.29351178]]), 'x_shared': array([[ 5.51246975e+00,  4.83838903e+00],
           [ 1.55266936e-01,  8.28077388e+00],
           [-7.11870203e+00,  6.05477519e+00],
           [ 8.23475931e+00,  2.28749635e-04],
           [-5.63064562e+00,  2.37252042e+00]]), 'y_2': array([[-87.90669709],
           [-46.17757092],
           [  7.40878002],
           [ 67.92405956],
           [ 46.81870041]]), 'y_1': array([[7.589505  +0.j],
           [4.28654172+0.j],
           [7.85225077+0.j],
           [7.94111374+0.j],
           [5.19677421+0.j]])}





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

   **Total running time of the script:** ( 0 minutes  0.039 seconds)


.. _sphx_glr_download_examples_dataset_plot_dataset_from_optproblem.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_dataset_from_optproblem.py <plot_dataset_from_optproblem.py>`



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

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


.. only:: html

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

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