.. 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 30-47

.. code-block:: default

    from __future__ import division, unicode_literals

    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()






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

 Out:

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 48-50

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

.. GENERATED FROM PYTHON SOURCE LINES 50-52

.. 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 53-55

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

.. GENERATED FROM PYTHON SOURCE LINES 55-57

.. code-block:: default

    print(get_regression_options("GaussianProcessRegression"))





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

 Out:

 .. code-block:: none

    {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'transformer': {'type': 'null'}, 'input_names': {'type': 'null'}, 'output_names': {'type': 'null'}, 'kernel': {'description': 'The kernel function. If None, use a ``Matern(2.5)``.', 'type': 'null'}, 'alpha': {'description': 'The nugget effect to regularize the model.', 'type': 'number'}, 'optimizer': {'description': 'The optimization algorithm to find the hyperparameters.', 'type': 'string'}, 'n_restarts_optimizer': {'description': 'The number of restarts of the optimizer.', 'type': 'integer'}, 'random_state': {'description': 'The seed used to initialize the centers. If None, the random number generator is the RandomState instance used by `numpy.random`.', 'type': 'null'}}, 'required': ['alpha', 'n_restarts_optimizer', 'optimizer']}




.. GENERATED FROM PYTHON SOURCE LINES 58-60

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

.. GENERATED FROM PYTHON SOURCE LINES 60-80

.. 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

        INFO - 09:23:13:  
        INFO - 09:23:13: *** Start DOE Scenario execution ***
        INFO - 09:23:13: DOEScenario
        INFO - 09:23:13:    Disciplines: func
        INFO - 09:23:13:    MDOFormulation: DisciplinaryOpt
        INFO - 09:23:13:    Algorithm: fullfact
        INFO - 09:23:13: Optimization problem:
        INFO - 09:23:13:    Minimize: y_1(x_1, x_2)
        INFO - 09:23:13:    With respect to: x_1, x_2
        INFO - 09:23:13: Full factorial design required. Number of samples along each direction for a design vector of size 2 with 9 samples: 3
        INFO - 09:23:13: Final number of samples for DOE = 9 vs 9 requested
        INFO - 09:23:13: DOE sampling:   0%|          | 0/9 [00:00<?, ?it]
        INFO - 09:23:13: DOE sampling: 100%|██████████| 9/9 [00:00<00:00, 417.87 it/sec, obj=6]
        INFO - 09:23:13: Optimization result:
        INFO - 09:23:13: Objective value = 1.0
        INFO - 09:23:13: The result is feasible.
        INFO - 09:23:13: Status: None
        INFO - 09:23:13: Optimizer message: None
        INFO - 09:23:13: Number of calls to the objective function by the optimizer: 9
        INFO - 09:23:13: Design Space:
        INFO - 09:23:13: +------+-------------+-------+-------------+-------+
        INFO - 09:23:13: | name | lower_bound | value | upper_bound | type  |
        INFO - 09:23:13: +------+-------------+-------+-------------+-------+
        INFO - 09:23:13: | x_1  |      0      |   0   |      1      | float |
        INFO - 09:23:13: | x_2  |      0      |   0   |      1      | float |
        INFO - 09:23:13: +------+-------------+-------+-------------+-------+
        INFO - 09:23:13: *** DOE Scenario run terminated ***
    LinearRegression(fit_intercept=True, l2_penalty_ratio=1.0, penalty_level=0.0)
       based on the scikit-learn library
       built from 9 learning samples





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

   **Total running time of the script:** ( 0 minutes  0.110 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>`_