.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/mlearning/regression_model/plot_regression_api.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_regression_api.py>`
        to download the full example code

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

.. _sphx_glr_examples_mlearning_regression_model_plot_regression_api.py:


API
===

Here are some examples of the machine learning API
applied to regression models.

.. GENERATED FROM PYTHON SOURCE LINES 28-42

.. 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.mlearning import create_regression_model
    from gemseo.mlearning import get_regression_models
    from gemseo.mlearning import get_regression_options

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 43-45

Get available regression models
-------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 45-47

.. code-block:: Python

    get_regression_models()





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

 .. code-block:: none


    ['GaussianProcessRegressor', 'LinearRegressor', 'MOERegressor', 'PCERegressor', 'PolynomialRegressor', 'RBFRegressor', 'RandomForestRegressor']



.. GENERATED FROM PYTHON SOURCE LINES 48-50

Get regression model options
----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 50-52

.. code-block:: Python

    get_regression_options("GaussianProcessRegressor")





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

 .. code-block:: none

    +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    |            Name           |                                        Description                                         |            Type           |
    +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    |           alpha           |                         The nugget effect to regularize the model.                         |           number          |
    |           bounds          |   The lower and upper bounds of the parameter length scales when ``kernel`` is ``none``.   |            null           |
    |                           | either a unique lower-upper pair common to all the inputs or lower-upper pairs for some of |                           |
    |                           |  them. when ``bounds`` is ``none`` or when an input has no pair, the lower bound is 0.01   |                           |
    |                           |                                and the upper bound is 100.                                 |                           |
    |        input_names        |   The names of the input variables. if ``none``, consider all the input variables of the   |            null           |
    |                           |                                     learning dataset.                                      |                           |
    |           kernel          |        The kernel specifying the covariance model. if ``none``, use a matérn(2.5).         |            null           |
    |    n_restarts_optimizer   |                          The number of restarts of the optimizer.                          |          integer          |
    |         optimizer         |              The optimization algorithm to find the parameter length scales.               |           string          |
    |        output_names       |  The names of the output variables. if ``none``, consider all the output variables of the  |            null           |
    |                           |                                     learning dataset.                                      |                           |
    |        random_state       |  The random state passed to the random number generator. use an integer for reproducible   |          integer          |
    |                           |                                          results.                                          |                           |
    |        transformer        |           The strategies to transform the variables. the values are instances of           |           object          |
    |                           |   :class:`.basetransformer` while the keys are the names of either the variables or the    |                           |
    |                           |   groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression    |                           |
    |                           | algorithms. if a group is specified, the :class:`.basetransformer` will be applied to all  |                           |
    |                           |     the variables of this group. if :attr:`.identity`, do not transform the variables.     |                           |
    +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 08:58:10: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 08:58:10: |            Name           |                                        Description                                         |            Type           |
        INFO - 08:58:10: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 08:58:10: |           alpha           |                         The nugget effect to regularize the model.                         |           number          |
        INFO - 08:58:10: |           bounds          |   The lower and upper bounds of the parameter length scales when ``kernel`` is ``none``.   |            null           |
        INFO - 08:58:10: |                           | either a unique lower-upper pair common to all the inputs or lower-upper pairs for some of |                           |
        INFO - 08:58:10: |                           |  them. when ``bounds`` is ``none`` or when an input has no pair, the lower bound is 0.01   |                           |
        INFO - 08:58:10: |                           |                                and the upper bound is 100.                                 |                           |
        INFO - 08:58:10: |        input_names        |   The names of the input variables. if ``none``, consider all the input variables of the   |            null           |
        INFO - 08:58:10: |                           |                                     learning dataset.                                      |                           |
        INFO - 08:58:10: |           kernel          |        The kernel specifying the covariance model. if ``none``, use a matérn(2.5).         |            null           |
        INFO - 08:58:10: |    n_restarts_optimizer   |                          The number of restarts of the optimizer.                          |          integer          |
        INFO - 08:58:10: |         optimizer         |              The optimization algorithm to find the parameter length scales.               |           string          |
        INFO - 08:58:10: |        output_names       |  The names of the output variables. if ``none``, consider all the output variables of the  |            null           |
        INFO - 08:58:10: |                           |                                     learning dataset.                                      |                           |
        INFO - 08:58:10: |        random_state       |  The random state passed to the random number generator. use an integer for reproducible   |          integer          |
        INFO - 08:58:10: |                           |                                          results.                                          |                           |
        INFO - 08:58:10: |        transformer        |           The strategies to transform the variables. the values are instances of           |           object          |
        INFO - 08:58:10: |                           |   :class:`.basetransformer` while the keys are the names of either the variables or the    |                           |
        INFO - 08:58:10: |                           |   groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression    |                           |
        INFO - 08:58:10: |                           | algorithms. if a group is specified, the :class:`.basetransformer` will be applied to all  |                           |
        INFO - 08:58:10: |                           |     the variables of this group. if :attr:`.identity`, do not transform the variables.     |                           |
        INFO - 08:58:10: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+

    {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'transformer': {'description': 'The strategies to transform the variables. The values are instances of :class:`.BaseTransformer` while the keys are the names of either the variables or the groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. If a group is specified, the :class:`.BaseTransformer` will be applied to all the variables of this group. If :attr:`.IDENTITY`, do not transform the variables.', 'type': 'object'}, 'input_names': {'description': 'The names of the input variables. If ``None``, consider all the input variables of the learning dataset.', 'type': 'null'}, 'output_names': {'description': 'The names of the output variables. If ``None``, consider all the output variables of the learning dataset.', 'type': 'null'}, 'kernel': {'description': 'The kernel specifying the covariance model. If ``None``, use a Matérn(2.5).', 'type': 'null'}, 'bounds': {'description': 'The lower and upper bounds of the parameter length scales when ``kernel`` is ``None``. Either a unique lower-upper pair common to all the inputs or lower-upper pairs for some of them. When ``bounds`` is ``None`` or when an input has no pair, the lower bound is 0.01 and the upper bound is 100.', 'type': 'null'}, 'alpha': {'description': 'The nugget effect to regularize the model.', 'type': 'number'}, 'optimizer': {'description': 'The optimization algorithm to find the parameter length scales.', 'type': 'string'}, 'n_restarts_optimizer': {'description': 'The number of restarts of the optimizer.', 'type': 'integer'}, 'random_state': {'description': 'The random state passed to the random number generator. Use an integer for reproducible results.', 'type': 'integer'}}}



.. GENERATED FROM PYTHON SOURCE LINES 53-55

Create regression model
-----------------------

.. GENERATED FROM PYTHON SOURCE LINES 55-73

.. code-block:: Python

    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
    )

    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)

    scenario = create_scenario(
        [discipline], "DisciplinaryOpt", "y_1", design_space, scenario_type="DOE"
    )
    scenario.execute({"algo": "fullfact", "n_samples": 9})

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




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

 .. code-block:: none

        INFO - 08:58:10:  
        INFO - 08:58:10: *** Start DOEScenario execution ***
        INFO - 08:58:10: DOEScenario
        INFO - 08:58:10:    Disciplines: func
        INFO - 08:58:10:    MDO formulation: DisciplinaryOpt
        INFO - 08:58:10: Optimization problem:
        INFO - 08:58:10:    minimize y_1(x_1, x_2)
        INFO - 08:58:10:    with respect to x_1, x_2
        INFO - 08:58:10:    over the design space:
        INFO - 08:58:10:       +------+-------------+-------+-------------+-------+
        INFO - 08:58:10:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 08:58:10:       +------+-------------+-------+-------------+-------+
        INFO - 08:58:10:       | x_1  |      0      |  None |      1      | float |
        INFO - 08:58:10:       | x_2  |      0      |  None |      1      | float |
        INFO - 08:58:10:       +------+-------------+-------+-------------+-------+
        INFO - 08:58:10: Solving optimization problem with algorithm fullfact:
        INFO - 08:58:10:     11%|█         | 1/9 [00:00<00:00, 368.67 it/sec, obj=1]
        INFO - 08:58:10:     22%|██▏       | 2/9 [00:00<00:00, 602.15 it/sec, obj=2]
        INFO - 08:58:10:     33%|███▎      | 3/9 [00:00<00:00, 770.21 it/sec, obj=3]
        INFO - 08:58:10:     44%|████▍     | 4/9 [00:00<00:00, 895.79 it/sec, obj=2.5]
        INFO - 08:58:10:     56%|█████▌    | 5/9 [00:00<00:00, 998.69 it/sec, obj=3.5]
        INFO - 08:58:10:     67%|██████▋   | 6/9 [00:00<00:00, 1081.98 it/sec, obj=4.5]
        INFO - 08:58:10:     78%|███████▊  | 7/9 [00:00<00:00, 1145.72 it/sec, obj=4]
        INFO - 08:58:10:     89%|████████▉ | 8/9 [00:00<00:00, 1202.45 it/sec, obj=5]
        INFO - 08:58:10:    100%|██████████| 9/9 [00:00<00:00, 1241.57 it/sec, obj=6]
        INFO - 08:58:10: Optimization result:
        INFO - 08:58:10:    Optimizer info:
        INFO - 08:58:10:       Status: None
        INFO - 08:58:10:       Message: None
        INFO - 08:58:10:       Number of calls to the objective function by the optimizer: 9
        INFO - 08:58:10:    Solution:
        INFO - 08:58:10:       Objective: 1.0
        INFO - 08:58:10:       Design space:
        INFO - 08:58:10:          +------+-------------+-------+-------------+-------+
        INFO - 08:58:10:          | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 08:58:10:          +------+-------------+-------+-------------+-------+
        INFO - 08:58:10:          | x_1  |      0      |   0   |      1      | float |
        INFO - 08:58:10:          | x_2  |      0      |   0   |      1      | float |
        INFO - 08:58:10:          +------+-------------+-------+-------------+-------+
        INFO - 08:58:10: *** End DOEScenario execution (time: 0:00:00.018071) ***


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style='background-color: #fafae2; padding: 10px;'>LinearRegressor(fit_intercept=True, l2_penalty_ratio=1.0, penalty_level=0.0, random_state=0)<br/><ul><li>based on the scikit-learn library</li><li>built from 9 learning samples</li></ul></div>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 0.055 seconds)


.. _sphx_glr_download_examples_mlearning_regression_model_plot_regression_api.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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