.. 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()






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 .. 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', 'GradientBoostingRegressor', 'LinearRegressor', 'MLPRegressor', 'MOERegressor', 'OTGaussianProcessRegressor', 'PCERegressor', 'PolynomialRegressor', 'RBFRegressor', 'RandomForestRegressor', 'RegressorChain', 'SVMRegressor', 'TPSRegressor']



.. 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 length scales.  either a unique lower-upper pair common  |            None           |
    |                           | to all the inputs or lower-upper pairs for some of them. when ``bounds`` is empty or when  |                           |
    |                           |  an input has no pair, the lower bound is 0.01 and the upper bound is 100.  this argument  |                           |
    |                           |                          is ignored when ``kernel`` is ``none``.                           |                           |
    |        input_names        |                              The names of the input variables                              |           array           |
    |           kernel          |        The kernel specifying the covariance model.  if ``none``, use a matérn(2.5).        |            None           |
    |    n_restarts_optimizer   |                          The number of restarts of the optimizer.                          |          integer          |
    |         optimizer         |              The optimization algorithm to find the parameter length scales.               |            None           |
    |        output_names       |                             The names of the output variables                              |           array           |
    |         parameters        |                                     Other parameters.                                      |           object          |
    |        random_state       |    The random state parameter.  if ``none``, use the global random state instance from     |            None           |
    |                           |   ``numpy.random``. creating the model multiple times will produce different results. if   |                           |
    |                           |  ``int``, use a new random number generator seeded by this integer. this will produce the  |                           |
    |                           |                                       same 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 - 08:37:42: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 08:37:42: |            Name           |                                        Description                                         |            Type           |
        INFO - 08:37:42: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 08:37:42: |           alpha           |                         The nugget effect to regularize the model.                         |           number          |
        INFO - 08:37:42: |           bounds          | The lower and upper bounds of the length scales.  either a unique lower-upper pair common  |            None           |
        INFO - 08:37:42: |                           | to all the inputs or lower-upper pairs for some of them. when ``bounds`` is empty or when  |                           |
        INFO - 08:37:42: |                           |  an input has no pair, the lower bound is 0.01 and the upper bound is 100.  this argument  |                           |
        INFO - 08:37:42: |                           |                          is ignored when ``kernel`` is ``none``.                           |                           |
        INFO - 08:37:42: |        input_names        |                              The names of the input variables                              |           array           |
        INFO - 08:37:42: |           kernel          |        The kernel specifying the covariance model.  if ``none``, use a matérn(2.5).        |            None           |
        INFO - 08:37:42: |    n_restarts_optimizer   |                          The number of restarts of the optimizer.                          |          integer          |
        INFO - 08:37:42: |         optimizer         |              The optimization algorithm to find the parameter length scales.               |            None           |
        INFO - 08:37:42: |        output_names       |                             The names of the output variables                              |           array           |
        INFO - 08:37:42: |         parameters        |                                     Other parameters.                                      |           object          |
        INFO - 08:37:42: |        random_state       |    The random state parameter.  if ``none``, use the global random state instance from     |            None           |
        INFO - 08:37:42: |                           |   ``numpy.random``. creating the model multiple times will produce different results. if   |                           |
        INFO - 08:37:42: |                           |  ``int``, use a new random number generator seeded by this integer. this will produce the  |                           |
        INFO - 08:37:42: |                           |                                       same results.                                        |                           |
        INFO - 08:37:42: |        transformer        |          The strategies to transform the variables.  the values are instances of           |           object          |
        INFO - 08:37:42: |                           |   :class:`.basetransformer` while the keys are the names of either the variables or the    |                           |
        INFO - 08:37:42: |                           |   groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression    |                           |
        INFO - 08:37:42: |                           | algorithms. if a group is specified, the :class:`.basetransformer` will be applied to all  |                           |
        INFO - 08:37:42: |                           |     the variables of this group. if :attr:`.identity`, do not transform the variables.     |                           |
        INFO - 08:37:42: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+

    {'additionalProperties': False, 'description': 'The settings of the Gaussian process regressor from scikit-learn.', 'properties': {'transformer': {'description': 'The strategies to transform the variables.\n\nThe values are instances of :class:`.BaseTransformer`\nwhile the keys are the names of\neither the variables\nor the groups of variables,\ne.g. ``"inputs"`` or ``"outputs"``\nin the case of the regression algorithms.\nIf a group is specified,\nthe :class:`.BaseTransformer` will be applied\nto all the variables of this group.\nIf :attr:`.IDENTITY`, do not transform the variables.', 'title': 'Transformer', 'type': 'object'}, 'parameters': {'description': 'Other parameters.', 'title': 'Parameters', 'type': 'object'}, 'input_names': {'default': [], 'description': 'The names of the input variables', 'items': {'type': 'string'}, 'title': 'Input Names', 'type': 'array'}, 'output_names': {'default': [], 'description': 'The names of the output variables', 'items': {'type': 'string'}, 'title': 'Output Names', 'type': 'array'}, 'kernel': {'anyOf': [{}, {'type': 'null'}], 'default': None, 'description': 'The kernel specifying the covariance model.\n\nIf ``None``, use a Matérn(2.5).', 'title': 'Kernel'}, 'bounds': {'anyOf': [{'items': {}, 'type': 'array'}, {'maxItems': 2, 'minItems': 2, 'prefixItems': [{'type': 'number'}, {'type': 'number'}], 'type': 'array'}, {'additionalProperties': {'maxItems': 2, 'minItems': 2, 'prefixItems': [{'type': 'number'}, {'type': 'number'}], 'type': 'array'}, 'type': 'object'}], 'default': [], 'description': 'The lower and upper bounds of the length scales.\n\nEither a unique lower-upper pair common to all the inputs\nor lower-upper pairs for some of them.\nWhen ``bounds`` is empty or when an input has no pair,\nthe lower bound is 0.01 and the upper bound is 100.\n\nThis argument is ignored when ``kernel`` is ``None``.', 'title': 'Bounds'}, 'alpha': {'default': 1e-10, 'description': 'The nugget effect to regularize the model.', 'title': 'Alpha', 'type': 'number'}, 'optimizer': {'anyOf': [{'type': 'string'}, {}], 'default': 'fmin_l_bfgs_b', 'description': 'The optimization algorithm to find the parameter length scales.', 'title': 'Optimizer'}, 'n_restarts_optimizer': {'default': 10, 'description': 'The number of restarts of the optimizer.', 'minimum': 0, 'title': 'N Restarts Optimizer', 'type': 'integer'}, 'random_state': {'anyOf': [{'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'default': 0, 'description': 'The random state parameter.\n\nIf ``None``, use the global random state instance from ``numpy.random``.\nCreating the model multiple times will produce different results.\nIf ``int``, use a new random number generator seeded by this integer.\nThis will produce the same results.', 'title': 'Random State'}}, 'title': 'GaussianProcessRegressor_Settings', 'type': 'object'}



.. GENERATED FROM PYTHON SOURCE LINES 53-55

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

.. GENERATED FROM PYTHON SOURCE LINES 55-77

.. 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", lower_bound=0.0, upper_bound=1.0)
    design_space.add_variable("x_2", lower_bound=0.0, upper_bound=1.0)

    scenario = create_scenario(
        [discipline],
        "y_1",
        design_space,
        scenario_type="DOE",
        formulation_name="DisciplinaryOpt",
    )
    scenario.execute(algo_name="PYDOE_FULLFACT", n_samples=9)

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




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 .. code-block:: none

        INFO - 08:37:42:  
        INFO - 08:37:42: *** Start DOEScenario execution ***
        INFO - 08:37:42: DOEScenario
        INFO - 08:37:42:    Disciplines: func
        INFO - 08:37:42:    MDO formulation: DisciplinaryOpt
        INFO - 08:37:42: Optimization problem:
        INFO - 08:37:42:    minimize y_1(x_1, x_2)
        INFO - 08:37:42:    with respect to x_1, x_2
        INFO - 08:37:42:    over the design space:
        INFO - 08:37:42:       +------+-------------+-------+-------------+-------+
        INFO - 08:37:42:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 08:37:42:       +------+-------------+-------+-------------+-------+
        INFO - 08:37:42:       | x_1  |      0      |  None |      1      | float |
        INFO - 08:37:42:       | x_2  |      0      |  None |      1      | float |
        INFO - 08:37:42:       +------+-------------+-------+-------------+-------+
        INFO - 08:37:42: Solving optimization problem with algorithm PYDOE_FULLFACT:
        INFO - 08:37:42:     11%|█         | 1/9 [00:00<00:00, 391.41 it/sec, obj=1]
        INFO - 08:37:42:     22%|██▏       | 2/9 [00:00<00:00, 654.95 it/sec, obj=2]
        INFO - 08:37:42:     33%|███▎      | 3/9 [00:00<00:00, 849.34 it/sec, obj=3]
        INFO - 08:37:42:     44%|████▍     | 4/9 [00:00<00:00, 1008.79 it/sec, obj=2.5]
        INFO - 08:37:42:     56%|█████▌    | 5/9 [00:00<00:00, 1136.79 it/sec, obj=3.5]
        INFO - 08:37:42:     67%|██████▋   | 6/9 [00:00<00:00, 1241.53 it/sec, obj=4.5]
        INFO - 08:37:42:     78%|███████▊  | 7/9 [00:00<00:00, 1328.03 it/sec, obj=4]
        INFO - 08:37:42:     89%|████████▉ | 8/9 [00:00<00:00, 1406.36 it/sec, obj=5]
        INFO - 08:37:42:    100%|██████████| 9/9 [00:00<00:00, 1474.39 it/sec, obj=6]
        INFO - 08:37:42: Optimization result:
        INFO - 08:37:42:    Optimizer info:
        INFO - 08:37:42:       Status: None
        INFO - 08:37:42:       Message: None
        INFO - 08:37:42:       Number of calls to the objective function by the optimizer: 9
        INFO - 08:37:42:    Solution:
        INFO - 08:37:42:       Objective: 1.0
        INFO - 08:37:42:       Design space:
        INFO - 08:37:42:          +------+-------------+-------+-------------+-------+
        INFO - 08:37:42:          | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 08:37:42:          +------+-------------+-------+-------------+-------+
        INFO - 08:37:42:          | x_1  |      0      |   0   |      1      | float |
        INFO - 08:37:42:          | x_2  |      0      |   0   |      1      | float |
        INFO - 08:37:42:          +------+-------------+-------+-------------+-------+
        INFO - 08:37:42: *** End DOEScenario execution (time: 0:00:00.010274) ***


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style='background-color: #fafae2; padding: 10px;'>LinearRegressor(fit_intercept=True, input_names=(), l2_penalty_ratio=1.0, output_names=(), parameters={}, penalty_level=0.0, random_state=0, transformer={&#x27;inputs&#x27;: &lt;gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7f6dfa7ca280&gt;, &#x27;outputs&#x27;: &lt;gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7f6dfa7ca490&gt;})<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 0.049 seconds)


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