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.. "examples/mlearning/regression_model/plot_regression_api.py"
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        Click :ref:`here <sphx_glr_download_examples_mlearning_regression_model_plot_regression_api.py>`
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.. _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 28-39

.. code-block:: default

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

    configure_logger()






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 .. code-block:: none


    <RootLogger root (INFO)>



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

.. GENERATED FROM PYTHON SOURCE LINES 42-44

.. code-block:: default

    print(get_regression_models())





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

 Out:

 .. code-block:: none

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




.. GENERATED FROM PYTHON SOURCE LINES 45-47

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

.. GENERATED FROM PYTHON SOURCE LINES 47-49

.. code-block:: default

    print(get_regression_options("GaussianProcessRegressor"))





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

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 .. 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 seed used to initialize the centers. if none, the random number generator is the    |            null           |
    |                           |                        randomstate instance used by `numpy.random`.                        |                           |
    |        transformer        |           The strategies to transform the variables. the values are instances of           |            null           |
    |                           |  :class:`.transformer` 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:`.transformer` will be applied to all the variables of this |                           |
    |                           |                      group. if none, do not transform the variables.                       |                           |
    +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 10:06:44: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 10:06:44: |            Name           |                                        Description                                         |            Type           |
        INFO - 10:06:44: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 10:06:44: |           alpha           |                         The nugget effect to regularize the model.                         |           number          |
        INFO - 10:06:44: |           bounds          |   The lower and upper bounds of the parameter length scales when ``kernel`` is ``none``.   |            null           |
        INFO - 10:06:44: |                           | either a unique lower-upper pair common to all the inputs or lower-upper pairs for some of |                           |
        INFO - 10:06:44: |                           |  them. when ``bounds`` is ``none`` or when an input has no pair, the lower bound is 0.01   |                           |
        INFO - 10:06:44: |                           |                                and the upper bound is 100.                                 |                           |
        INFO - 10:06:44: |        input_names        |   The names of the input variables. if ``none``, consider all the input variables of the   |            null           |
        INFO - 10:06:44: |                           |                                     learning dataset.                                      |                           |
        INFO - 10:06:44: |           kernel          |        The kernel specifying the covariance model. if ``none``, use a matérn(2.5).         |            null           |
        INFO - 10:06:44: |    n_restarts_optimizer   |                          The number of restarts of the optimizer.                          |          integer          |
        INFO - 10:06:44: |         optimizer         |              The optimization algorithm to find the parameter length scales.               |           string          |
        INFO - 10:06:44: |        output_names       |  The names of the output variables. if ``none``, consider all the output variables of the  |            null           |
        INFO - 10:06:44: |                           |                                     learning dataset.                                      |                           |
        INFO - 10:06:44: |        random_state       |    The seed used to initialize the centers. if none, the random number generator is the    |            null           |
        INFO - 10:06:44: |                           |                        randomstate instance used by `numpy.random`.                        |                           |
        INFO - 10:06:44: |        transformer        |           The strategies to transform the variables. the values are instances of           |            null           |
        INFO - 10:06:44: |                           |  :class:`.transformer` while the keys are the names of either the variables or the groups  |                           |
        INFO - 10:06:44: |                           |  of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. if a   |                           |
        INFO - 10:06:44: |                           | group is specified, the :class:`.transformer` will be applied to all the variables of this |                           |
        INFO - 10:06:44: |                           |                      group. if none, do not transform the variables.                       |                           |
        INFO - 10:06:44: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'transformer': {'description': 'The strategies to transform the variables. The values are instances of :class:`.Transformer` 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:`.Transformer` will be applied to all the variables of this group. If None, do not transform the variables.', 'type': 'null'}, '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 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']}




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

.. GENERATED FROM PYTHON SOURCE LINES 52-71

.. code-block:: default

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

    print(model)




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

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 .. code-block:: none

        INFO - 10:06:44:  
        INFO - 10:06:44: *** Start DOEScenario execution ***
        INFO - 10:06:44: DOEScenario
        INFO - 10:06:44:    Disciplines: func
        INFO - 10:06:44:    MDO formulation: DisciplinaryOpt
        INFO - 10:06:44: Optimization problem:
        INFO - 10:06:44:    minimize y_1(x_1, x_2)
        INFO - 10:06:44:    with respect to x_1, x_2
        INFO - 10:06:44:    over the design space:
        INFO - 10:06:44:    +------+-------------+-------+-------------+-------+
        INFO - 10:06:44:    | name | lower_bound | value | upper_bound | type  |
        INFO - 10:06:44:    +------+-------------+-------+-------------+-------+
        INFO - 10:06:44:    | x_1  |      0      |  None |      1      | float |
        INFO - 10:06:44:    | x_2  |      0      |  None |      1      | float |
        INFO - 10:06:44:    +------+-------------+-------+-------------+-------+
        INFO - 10:06:44: Solving optimization problem with algorithm fullfact:
        INFO - 10:06:44: Full factorial design required. Number of samples along each direction for a design vector of size 2 with 9 samples: 3
        INFO - 10:06:44: Final number of samples for DOE = 9 vs 9 requested
        INFO - 10:06:44: ...   0%|          | 0/9 [00:00<?, ?it]
        INFO - 10:06:44: ... 100%|██████████| 9/9 [00:00<00:00, 1393.05 it/sec, obj=6]
        INFO - 10:06:44: Optimization result:
        INFO - 10:06:44:    Optimizer info:
        INFO - 10:06:44:       Status: None
        INFO - 10:06:44:       Message: None
        INFO - 10:06:44:       Number of calls to the objective function by the optimizer: 9
        INFO - 10:06:44:    Solution:
        INFO - 10:06:44:       Objective: 1.0
        INFO - 10:06:44:       Design space:
        INFO - 10:06:44:       +------+-------------+-------+-------------+-------+
        INFO - 10:06:44:       | name | lower_bound | value | upper_bound | type  |
        INFO - 10:06:44:       +------+-------------+-------+-------------+-------+
        INFO - 10:06:44:       | x_1  |      0      |   0   |      1      | float |
        INFO - 10:06:44:       | x_2  |      0      |   0   |      1      | float |
        INFO - 10:06:44:       +------+-------------+-------+-------------+-------+
        INFO - 10:06:44: *** End DOEScenario execution (time: 0:00:00.015188) ***
    LinearRegressor(fit_intercept=True, l2_penalty_ratio=1.0, penalty_level=0.0)
       based on the scikit-learn library
       built from 9 learning samples





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