.. 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

        Click :ref:`here <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 30-50

.. code-block:: default

    from __future__ import absolute_import, division, print_function, unicode_literals

    from future import standard_library

    from gemseo.api import (
        configure_logger,
        create_design_space,
        create_discipline,
        create_scenario,
    )
    from gemseo.mlearning.api import (
        create_regression_model,
        get_regression_models,
        get_regression_options,
    )

    configure_logger()

    standard_library.install_aliases()








.. GENERATED FROM PYTHON SOURCE LINES 51-53

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

.. GENERATED FROM PYTHON SOURCE LINES 53-55

.. code-block:: default

    print(get_regression_models())





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

 Out:

 .. code-block:: none

    ['GaussianProcessRegression', 'LinearRegression', 'MixtureOfExperts', 'PCERegression', 'PolynomialRegression', 'RBFRegression', 'RandomForestRegressor']




.. GENERATED FROM PYTHON SOURCE LINES 56-58

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

.. GENERATED FROM PYTHON SOURCE LINES 58-60

.. code-block:: default

    print(get_regression_options("GaussianProcessRegression"))





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

 Out:

 .. code-block:: none

    {'type': 'object', 'properties': {'transformer': {'description': 'transformation strategy for data groups.\nIf None, do not transform data. Default: None.\n:type transformer: dict(str)\n'}, 'input_names': {'description': 'names of the input variables.\n:type input_names: list(str)\n'}, 'output_names': {'description': 'names of the output variables.\n:type output_names: list(str)\n'}, 'kernel': {'description': 'kernel function. If None, use a Matern(2.5).\nDefault: None.\n:type kernel: openturns.Kernel\n'}, 'alpha': {'type': 'number', 'description': 'nugget effect. Default: 1e-10.\n:type alpha: float or array\n'}, 'optimizer': {'type': 'string', 'description': "optimization algorithm. Default: 'fmin_l_bfgs_b'.\n:type optimizer: str or callable\n"}, 'n_restarts_optimizer': {'type': 'integer', 'description': 'number of restarts of the optimizer.\nDefault: 10.\n:type n_restarts_optimizer: int\n'}, 'random_state': {'description': 'the seed used to initialize the centers.\nIf None, the random number generator is the RandomState instance\nused by `np.random`\nDefault: None.\n:type random_state: int'}}, 'required': ['alpha', 'n_restarts_optimizer', 'optimizer']}




.. GENERATED FROM PYTHON SOURCE LINES 61-63

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

.. GENERATED FROM PYTHON SOURCE LINES 63-83

.. code-block:: default

    expressions_dict = {"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_dict=expressions_dict
    )

    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)

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

    dataset = discipline.cache.export_to_dataset()
    model = create_regression_model("LinearRegression", data=dataset)
    model.learn()

    print(model)




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

 Out:

 .. code-block:: none

    LinearRegression(fit_intercept=True, penalty_level=0.0, l2_penalty_ratio=1.0)
    | based on the scikit-learn library
    | built from 9 learning samples





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

   **Total running time of the script:** ( 0 minutes  0.102 seconds)


.. _sphx_glr_download_examples_mlearning_regression_model_plot_regression_api.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_regression_api.py <plot_regression_api.py>`



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

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


.. only:: html

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

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