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

.. only:: html

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

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

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

.. _sphx_glr_examples_dataset_creation_plot_dataset_from_optimization_problem.py:


Convert a database to a dataset
===============================

In this example,
we will see how to convert a :class:`.Database` to a :class:`.Dataset`.

.. GENERATED FROM PYTHON SOURCE LINES 28-34

.. code-block:: Python


    from __future__ import annotations

    from gemseo import execute_algo
    from gemseo.problems.optimization.rosenbrock import Rosenbrock








.. GENERATED FROM PYTHON SOURCE LINES 35-37

Let us solve the :class:`.Rosenbrock` optimization problem
with the SLSQP algorithm and 10 iterations:

.. GENERATED FROM PYTHON SOURCE LINES 37-40

.. code-block:: Python

    optimization_problem = Rosenbrock()
    execute_algo(optimization_problem, "SLSQP", max_iter=10)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style='background-color: #fafae2; padding: 10px;'>Optimization result:<br/><ul><li>Design variables: [0.57066585 0.31608705]</li><li>Objective function: 0.19349100516217954</li><li>Feasible solution: True</li></ul></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 41-45

Then,
the :class:`.Database` attached to this :class:`.OptimizationProblem`
can be converted to an :class:`.OptimizationDataset`
using its method :meth:`~.OptimizationDataset.to_dataset`:

.. GENERATED FROM PYTHON SOURCE LINES 45-48

.. code-block:: Python

    dataset = optimization_problem.to_dataset()
    dataset






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th colspan="2" halign="left">designs</th>
          <th>functions</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th colspan="2" halign="left">x</th>
          <th>rosen</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>1</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>1</th>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>1.000000</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2.000000</td>
          <td>0.000000</td>
          <td>1601.000000</td>
        </tr>
        <tr>
          <th>3</th>
          <td>0.200000</td>
          <td>0.000000</td>
          <td>0.800000</td>
        </tr>
        <tr>
          <th>4</th>
          <td>2.000000</td>
          <td>0.899368</td>
          <td>962.392108</td>
        </tr>
        <tr>
          <th>5</th>
          <td>0.380000</td>
          <td>0.089937</td>
          <td>0.681024</td>
        </tr>
        <tr>
          <th>6</th>
          <td>1.309049</td>
          <td>2.000000</td>
          <td>8.297426</td>
        </tr>
        <tr>
          <th>7</th>
          <td>0.682846</td>
          <td>0.712569</td>
          <td>6.166440</td>
        </tr>
        <tr>
          <th>8</th>
          <td>0.449476</td>
          <td>0.232774</td>
          <td>0.397606</td>
        </tr>
        <tr>
          <th>9</th>
          <td>0.551433</td>
          <td>0.290027</td>
          <td>0.220957</td>
        </tr>
        <tr>
          <th>10</th>
          <td>0.570666</td>
          <td>0.316087</td>
          <td>0.193491</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 49-51

The design variables and output variables are in separate groups.
You can also use an :class:`.IODataset` instead of an :class:`.OptimizationDataset`:

.. GENERATED FROM PYTHON SOURCE LINES 51-54

.. code-block:: Python

    dataset = optimization_problem.to_dataset(opt_naming=False)
    dataset






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th colspan="2" halign="left">inputs</th>
          <th>outputs</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th colspan="2" halign="left">x</th>
          <th>rosen</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>1</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>1.000000</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2.000000</td>
          <td>0.000000</td>
          <td>1601.000000</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0.200000</td>
          <td>0.000000</td>
          <td>0.800000</td>
        </tr>
        <tr>
          <th>3</th>
          <td>2.000000</td>
          <td>0.899368</td>
          <td>962.392108</td>
        </tr>
        <tr>
          <th>4</th>
          <td>0.380000</td>
          <td>0.089937</td>
          <td>0.681024</td>
        </tr>
        <tr>
          <th>5</th>
          <td>1.309049</td>
          <td>2.000000</td>
          <td>8.297426</td>
        </tr>
        <tr>
          <th>6</th>
          <td>0.682846</td>
          <td>0.712569</td>
          <td>6.166440</td>
        </tr>
        <tr>
          <th>7</th>
          <td>0.449476</td>
          <td>0.232774</td>
          <td>0.397606</td>
        </tr>
        <tr>
          <th>8</th>
          <td>0.551433</td>
          <td>0.290027</td>
          <td>0.220957</td>
        </tr>
        <tr>
          <th>9</th>
          <td>0.570666</td>
          <td>0.316087</td>
          <td>0.193491</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 55-56

or simply do not separate the variables

.. GENERATED FROM PYTHON SOURCE LINES 56-58

.. code-block:: Python

    dataset = optimization_problem.to_dataset(categorize=False)
    dataset





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th colspan="3" halign="left">parameters</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th colspan="2" halign="left">x</th>
          <th>rosen</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>1</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>1.000000</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2.000000</td>
          <td>0.000000</td>
          <td>1601.000000</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0.200000</td>
          <td>0.000000</td>
          <td>0.800000</td>
        </tr>
        <tr>
          <th>3</th>
          <td>2.000000</td>
          <td>0.899368</td>
          <td>962.392108</td>
        </tr>
        <tr>
          <th>4</th>
          <td>0.380000</td>
          <td>0.089937</td>
          <td>0.681024</td>
        </tr>
        <tr>
          <th>5</th>
          <td>1.309049</td>
          <td>2.000000</td>
          <td>8.297426</td>
        </tr>
        <tr>
          <th>6</th>
          <td>0.682846</td>
          <td>0.712569</td>
          <td>6.166440</td>
        </tr>
        <tr>
          <th>7</th>
          <td>0.449476</td>
          <td>0.232774</td>
          <td>0.397606</td>
        </tr>
        <tr>
          <th>8</th>
          <td>0.551433</td>
          <td>0.290027</td>
          <td>0.220957</td>
        </tr>
        <tr>
          <th>9</th>
          <td>0.570666</td>
          <td>0.316087</td>
          <td>0.193491</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 59-64

.. 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
    :func:`~.OptimizationProblem.add_observable` method.


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

   **Total running time of the script:** (0 minutes 0.035 seconds)


.. _sphx_glr_download_examples_dataset_creation_plot_dataset_from_optimization_problem.py:

.. only:: html

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

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


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