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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 28-41

.. code-block:: default

    from __future__ import annotations

    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)>



.. GENERATED FROM PYTHON SOURCE LINES 42-44

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

.. GENERATED FROM PYTHON SOURCE LINES 44-46

.. code-block:: default

    print(get_regression_models())





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

 .. code-block:: none

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




.. GENERATED FROM PYTHON SOURCE LINES 47-49

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

.. GENERATED FROM PYTHON SOURCE LINES 49-51

.. code-block:: default

    print(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 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           |           object          |
    |                           |  :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 :attr:`.identity`, do not transform the variables.              |                           |
    +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 16:59:30: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 16:59:30: |            Name           |                                        Description                                         |            Type           |
        INFO - 16:59:30: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 16:59:30: |           alpha           |                         The nugget effect to regularize the model.                         |           number          |
        INFO - 16:59:30: |           bounds          |   The lower and upper bounds of the parameter length scales when ``kernel`` is ``none``.   |            null           |
        INFO - 16:59:30: |                           | either a unique lower-upper pair common to all the inputs or lower-upper pairs for some of |                           |
        INFO - 16:59:30: |                           |  them. when ``bounds`` is ``none`` or when an input has no pair, the lower bound is 0.01   |                           |
        INFO - 16:59:30: |                           |                                and the upper bound is 100.                                 |                           |
        INFO - 16:59:30: |        input_names        |   The names of the input variables. if ``none``, consider all the input variables of the   |            null           |
        INFO - 16:59:30: |                           |                                     learning dataset.                                      |                           |
        INFO - 16:59:30: |           kernel          |        The kernel specifying the covariance model. if ``none``, use a matérn(2.5).         |            null           |
        INFO - 16:59:30: |    n_restarts_optimizer   |                          The number of restarts of the optimizer.                          |          integer          |
        INFO - 16:59:30: |         optimizer         |              The optimization algorithm to find the parameter length scales.               |           string          |
        INFO - 16:59:30: |        output_names       |  The names of the output variables. if ``none``, consider all the output variables of the  |            null           |
        INFO - 16:59:30: |                           |                                     learning dataset.                                      |                           |
        INFO - 16:59:30: |        random_state       |    The seed used to initialize the centers. if none, the random number generator is the    |            null           |
        INFO - 16:59:30: |                           |                        randomstate instance used by `numpy.random`.                        |                           |
        INFO - 16:59:30: |        transformer        |           The strategies to transform the variables. the values are instances of           |           object          |
        INFO - 16:59:30: |                           |  :class:`.transformer` while the keys are the names of either the variables or the groups  |                           |
        INFO - 16:59:30: |                           | of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. |                           |
        INFO - 16:59:30: |                           | if a group is specified, the :class:`.transformer` will be applied to all the variables of |                           |
        INFO - 16:59:30: |                           |             this group. if :attr:`.identity`, do not transform the variables.              |                           |
        INFO - 16:59:30: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    {'$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 :attr:`.IDENTITY`, do not transform the variables.', 'type': 'object'}, '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', 'transformer']}




.. GENERATED FROM PYTHON SOURCE LINES 52-54

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

.. GENERATED FROM PYTHON SOURCE LINES 54-73

.. 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

 .. code-block:: none

        INFO - 16:59:30:  
        INFO - 16:59:30: *** Start DOEScenario execution ***
        INFO - 16:59:30: DOEScenario
        INFO - 16:59:30:    Disciplines: func
        INFO - 16:59:30:    MDO formulation: DisciplinaryOpt
        INFO - 16:59:30: Optimization problem:
        INFO - 16:59:30:    minimize y_1(x_1, x_2)
        INFO - 16:59:30:    with respect to x_1, x_2
        INFO - 16:59:30:    over the design space:
        INFO - 16:59:30:    +------+-------------+-------+-------------+-------+
        INFO - 16:59:30:    | name | lower_bound | value | upper_bound | type  |
        INFO - 16:59:30:    +------+-------------+-------+-------------+-------+
        INFO - 16:59:30:    | x_1  |      0      |  None |      1      | float |
        INFO - 16:59:30:    | x_2  |      0      |  None |      1      | float |
        INFO - 16:59:30:    +------+-------------+-------+-------------+-------+
        INFO - 16:59:30: Solving optimization problem with algorithm fullfact:
        INFO - 16:59:30: ...   0%|          | 0/9 [00:00<?, ?it]
        INFO - 16:59:30: ...  11%|█         | 1/9 [00:00<00:00, 345.10 it/sec, obj=1]
        INFO - 16:59:30: ...  22%|██▏       | 2/9 [00:00<00:00, 574.17 it/sec, obj=2]
        INFO - 16:59:30: ...  33%|███▎      | 3/9 [00:00<00:00, 749.83 it/sec, obj=3]
        INFO - 16:59:30: ...  44%|████▍     | 4/9 [00:00<00:00, 886.79 it/sec, obj=2.5]
        INFO - 16:59:30: ...  56%|█████▌    | 5/9 [00:00<00:00, 994.43 it/sec, obj=3.5]
        INFO - 16:59:30: ...  67%|██████▋   | 6/9 [00:00<00:00, 1064.68 it/sec, obj=4.5]
        INFO - 16:59:30: ...  78%|███████▊  | 7/9 [00:00<00:00, 1123.10 it/sec, obj=4]
        INFO - 16:59:30: ...  89%|████████▉ | 8/9 [00:00<00:00, 1181.37 it/sec, obj=5]
        INFO - 16:59:30: ... 100%|██████████| 9/9 [00:00<00:00, 1236.77 it/sec, obj=6]
        INFO - 16:59:30: Optimization result:
        INFO - 16:59:30:    Optimizer info:
        INFO - 16:59:30:       Status: None
        INFO - 16:59:30:       Message: None
        INFO - 16:59:30:       Number of calls to the objective function by the optimizer: 9
        INFO - 16:59:30:    Solution:
        INFO - 16:59:30:       Objective: 1.0
        INFO - 16:59:30:       Design space: 
        INFO - 16:59:30:       +------+-------------+-------+-------------+-------+
        INFO - 16:59:30:       | name | lower_bound | value | upper_bound | type  |
        INFO - 16:59:30:       +------+-------------+-------+-------------+-------+
        INFO - 16:59:30:       | x_1  |      0      |   0   |      1      | float |
        INFO - 16:59:30:       | x_2  |      0      |   0   |      1      | float |
        INFO - 16:59:30:       +------+-------------+-------+-------------+-------+
        INFO - 16:59:30: *** End DOEScenario execution (time: 0:00:00.016089) ***
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.3.0.post0/lib/python3.9/site-packages/gemseo/mlearning/transform/scaler/min_max_scaler.py:73: RuntimeWarning: divide by zero encountered in divide
      self.coefficient = where(is_constant, nan_to_num(1 / l_b), 1 / delta)
    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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