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

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

.. _sphx_glr_examples_mlearning_regression_model_plot_random_forest_regression.py:


Random forest regression
========================

We want to approximate a discipline with two inputs and two outputs:

- :math:`y_1=1+2x_1+3x_2`
- :math:`y_2=-1-2x_1-3x_2`

over the unit hypercube :math:`[0,1]\times[0,1]`.

.. GENERATED FROM PYTHON SOURCE LINES 35-37

Import
------

.. GENERATED FROM PYTHON SOURCE LINES 37-54

.. code-block:: default

    from __future__ import absolute_import, division, print_function, unicode_literals

    from future import standard_library
    from numpy import array

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

    configure_logger()

    standard_library.install_aliases()








.. GENERATED FROM PYTHON SOURCE LINES 55-59

Create the discipline to learn
------------------------------
We can implement this analytic discipline by means of the
:class:`~gemseo.core.analytic_discipline.AnalyticDiscipline` class.

.. GENERATED FROM PYTHON SOURCE LINES 59-64

.. 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
    )








.. GENERATED FROM PYTHON SOURCE LINES 65-68

Create the input sampling space
-------------------------------
We create the input sampling space by adding the variables one by one.

.. GENERATED FROM PYTHON SOURCE LINES 68-72

.. code-block:: default

    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)








.. GENERATED FROM PYTHON SOURCE LINES 73-78

Create the learning set
-----------------------
We can build a learning set by means of a
:class:`~gemseo.core.doe_scenario.DOEScenario` with a full factorial design of
experiments. The number of samples can be equal to 9 for example.

.. GENERATED FROM PYTHON SOURCE LINES 78-84

.. code-block:: default

    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})





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

 Out:

 .. code-block:: none


    {'eval_jac': False, 'algo': 'fullfact', 'n_samples': 9}



.. GENERATED FROM PYTHON SOURCE LINES 85-89

Create the regression model
---------------------------
Then, we build the linear regression model from the discipline cache and
displays this model.

.. GENERATED FROM PYTHON SOURCE LINES 89-94

.. code-block:: default

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





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

 Out:

 .. code-block:: none

    RandomForestRegressor(n_estimators=100)
    | based on the scikit-learn library
    | built from 9 learning samples




.. GENERATED FROM PYTHON SOURCE LINES 95-98

Predict output
--------------
Once it is built, we can use it for prediction.

.. GENERATED FROM PYTHON SOURCE LINES 98-101

.. code-block:: default

    input_value = {"x_1": array([1.0]), "x_2": array([2.0])}
    output_value = model.predict(input_value)
    print(output_value)




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

 Out:

 .. code-block:: none

    {'y_1': array([5.63]), 'y_2': array([-5.63])}





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

   **Total running time of the script:** ( 0 minutes  0.196 seconds)


.. _sphx_glr_download_examples_mlearning_regression_model_plot_random_forest_regression.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_random_forest_regression.py <plot_random_forest_regression.py>`



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

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


.. only:: html

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

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