.. 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 31-39

.. code-block:: default

    from __future__ import division, unicode_literals

    from gemseo.api import configure_logger, create_discipline, 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 40-44

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

.. GENERATED FROM PYTHON SOURCE LINES 44-54

.. 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

        INFO - 14:43:44:  
        INFO - 14:43:44: *** Start DOE Scenario execution ***
        INFO - 14:43:44: DOEScenario
        INFO - 14:43:44:    Disciplines: Sellar1
        INFO - 14:43:44:    MDOFormulation: DisciplinaryOpt
        INFO - 14:43:44:    Algorithm: lhs
        INFO - 14:43:44: Optimization problem:
        INFO - 14:43:44:    Minimize: y_1(x_local, x_shared, y_2)
        INFO - 14:43:44:    With respect to: x_local, x_shared, y_2
        INFO - 14:43:44: DOE sampling:   0%|          | 0/5 [00:00<?, ?it]
        INFO - 14:43:44: DOE sampling: 100%|██████████| 5/5 [00:00<00:00, 1104.29 it/sec, obj=5.2]
        INFO - 14:43:44: Optimization result:
        INFO - 14:43:44: Objective value = 4.286541717010372
        INFO - 14:43:44: The result is feasible.
        INFO - 14:43:44: Status: None
        INFO - 14:43:44: Optimizer message: None
        INFO - 14:43:44: Number of calls to the objective function by the optimizer: 5
        INFO - 14:43:44: Design space:
        INFO - 14:43:44: +----------+-------------+--------------------------+-------------+-------+
        INFO - 14:43:44: | name     | lower_bound |          value           | upper_bound | type  |
        INFO - 14:43:44: +----------+-------------+--------------------------+-------------+-------+
        INFO - 14:43:44: | x_local  |      0      | (0.8340440094051481+0j)  |      10     | float |
        INFO - 14:43:44: | x_shared |     -10     | (0.15526693601342956+0j) |      10     | float |
        INFO - 14:43:44: | x_shared |      0      |  (8.280773877190468+0j)  |      10     | float |
        INFO - 14:43:44: | y_2      |     -100    | (-46.177570918278086+0j) |     100     | float |
        INFO - 14:43:44: +----------+-------------+--------------------------+-------------+-------+
        INFO - 14:43:44: *** DOE Scenario run terminated ***




.. GENERATED FROM PYTHON SOURCE LINES 55-60

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 60-62

.. 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 63-64

or by considering all features as default parameters:

.. GENERATED FROM PYTHON SOURCE LINES 64-66

.. 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 67-68

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

.. GENERATED FROM PYTHON SOURCE LINES 68-70

.. 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 71-76

.. 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 78-80

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

.. GENERATED FROM PYTHON SOURCE LINES 80-81

.. code-block:: default

    dataset = opt_problem.export_to_dataset("sellar1_doe")







.. GENERATED FROM PYTHON SOURCE LINES 82-85

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

.. GENERATED FROM PYTHON SOURCE LINES 85-87

.. 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 88-91

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

.. GENERATED FROM PYTHON SOURCE LINES 91-93

.. 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 94-97

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

.. GENERATED FROM PYTHON SOURCE LINES 97-99

.. code-block:: default

    print(dataset.groups)





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

 Out:

 .. code-block:: none

    ['design_parameters', 'functions']




.. GENERATED FROM PYTHON SOURCE LINES 100-105

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

.. GENERATED FROM PYTHON SOURCE LINES 105-106

.. code-block:: default

    print(dataset.get_data_by_group("design_parameters"))




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

 Out:

 .. code-block:: none

    [[ 4.79353495e+00+0.j  5.51246975e+00+0.j  4.83838903e+00+0.j
      -8.79066971e+01+0.j]
     [ 8.34044009e-01+0.j  1.55266936e-01+0.j  8.28077388e+00+0.j
      -4.61775709e+01+0.j]
     [ 6.40890450e+00+0.j -7.11870203e+00+0.j  6.05477519e+00+0.j
       7.40878002e+00+0.j]
     [ 8.83460960e+00+0.j  8.23475931e+00+0.j  2.28749635e-04+0.j
       6.79240596e+01+0.j]
     [ 2.29351178e+00+0.j -5.63064562e+00+0.j  2.37252042e+00+0.j
       4.68187004e+01+0.j]]




.. GENERATED FROM PYTHON SOURCE LINES 107-108

or as a dictionary indexed by the variables names:

.. GENERATED FROM PYTHON SOURCE LINES 108-110

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




.. GENERATED FROM PYTHON SOURCE LINES 111-115

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 115-116

.. 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+0.j,  4.83838903e+00+0.j],
           [ 1.55266936e-01+0.j,  8.28077388e+00+0.j],
           [-7.11870203e+00+0.j,  6.05477519e+00+0.j],
           [ 8.23475931e+00+0.j,  2.28749635e-04+0.j],
           [-5.63064562e+00+0.j,  2.37252042e+00+0.j]]), 'y_2': array([[-87.90669709+0.j],
           [-46.17757092+0.j],
           [  7.40878002+0.j],
           [ 67.92405956+0.j],
           [ 46.81870041+0.j]])}




.. GENERATED FROM PYTHON SOURCE LINES 117-118

or as an array:

.. GENERATED FROM PYTHON SOURCE LINES 118-120

.. 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+0.j  4.83838903e+00+0.j -8.79066971e+01+0.j]
     [ 1.55266936e-01+0.j  8.28077388e+00+0.j -4.61775709e+01+0.j]
     [-7.11870203e+00+0.j  6.05477519e+00+0.j  7.40878002e+00+0.j]
     [ 8.23475931e+00+0.j  2.28749635e-04+0.j  6.79240596e+01+0.j]
     [-5.63064562e+00+0.j  2.37252042e+00+0.j  4.68187004e+01+0.j]]




.. GENERATED FROM PYTHON SOURCE LINES 121-124

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

.. GENERATED FROM PYTHON SOURCE LINES 124-125

.. code-block:: default

    print(dataset.get_all_data())




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

 Out:

 .. code-block:: none

    ({'design_parameters': array([[ 4.79353495e+00+0.j,  5.51246975e+00+0.j,  4.83838903e+00+0.j,
            -8.79066971e+01+0.j],
           [ 8.34044009e-01+0.j,  1.55266936e-01+0.j,  8.28077388e+00+0.j,
            -4.61775709e+01+0.j],
           [ 6.40890450e+00+0.j, -7.11870203e+00+0.j,  6.05477519e+00+0.j,
             7.40878002e+00+0.j],
           [ 8.83460960e+00+0.j,  8.23475931e+00+0.j,  2.28749635e-04+0.j,
             6.79240596e+01+0.j],
           [ 2.29351178e+00+0.j, -5.63064562e+00+0.j,  2.37252042e+00+0.j,
             4.68187004e+01+0.j]]), 'functions': array([[7.589505  ],
           [4.28654172],
           [7.85225077],
           [7.94111374],
           [5.19677421]])}, {'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 126-127

or as a dictionary indexed by variables names:

.. GENERATED FROM PYTHON SOURCE LINES 127-128

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




.. GENERATED FROM PYTHON SOURCE LINES 129-131

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

.. GENERATED FROM PYTHON SOURCE LINES 131-132

.. 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 133-134

or with a dictionary of variables names:

.. GENERATED FROM PYTHON SOURCE LINES 134-135

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





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

   **Total running time of the script:** ( 0 minutes  0.031 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>`_