API

Here are some examples of the machine learning API applied to regression models.

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()

Out:

<RootLogger root (INFO)>

Get available regression models

print(get_regression_models())

Out:

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

Get regression model options

print(get_regression_options("GaussianProcessRegressor"))

Out:

+---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
|            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 - 07:18:47: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    INFO - 07:18:47: |            Name           |                                        Description                                         |            Type           |
    INFO - 07:18:47: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    INFO - 07:18:47: |           alpha           |                         The nugget effect to regularize the model.                         |           number          |
    INFO - 07:18:47: |           bounds          |   The lower and upper bounds of the parameter length scales when ``kernel`` is ``none``.   |            null           |
    INFO - 07:18:47: |                           | either a unique lower-upper pair common to all the inputs or lower-upper pairs for some of |                           |
    INFO - 07:18:47: |                           |  them. when ``bounds`` is ``none`` or when an input has no pair, the lower bound is 0.01   |                           |
    INFO - 07:18:47: |                           |                                and the upper bound is 100.                                 |                           |
    INFO - 07:18:47: |        input_names        |   The names of the input variables. if ``none``, consider all the input variables of the   |            null           |
    INFO - 07:18:47: |                           |                                     learning dataset.                                      |                           |
    INFO - 07:18:47: |           kernel          |        The kernel specifying the covariance model. if ``none``, use a matérn(2.5).         |            null           |
    INFO - 07:18:47: |    n_restarts_optimizer   |                          The number of restarts of the optimizer.                          |          integer          |
    INFO - 07:18:47: |         optimizer         |              The optimization algorithm to find the parameter length scales.               |           string          |
    INFO - 07:18:47: |        output_names       |  The names of the output variables. if ``none``, consider all the output variables of the  |            null           |
    INFO - 07:18:47: |                           |                                     learning dataset.                                      |                           |
    INFO - 07:18:47: |        random_state       |    The seed used to initialize the centers. if none, the random number generator is the    |            null           |
    INFO - 07:18:47: |                           |                        randomstate instance used by `numpy.random`.                        |                           |
    INFO - 07:18:47: |        transformer        |           The strategies to transform the variables. the values are instances of           |            null           |
    INFO - 07:18:47: |                           |  :class:`.transformer` while the keys are the names of either the variables or the groups  |                           |
    INFO - 07:18:47: |                           |  of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. if a   |                           |
    INFO - 07:18:47: |                           | group is specified, the :class:`.transformer` will be applied to all the variables of this |                           |
    INFO - 07:18:47: |                           |                      group. if none, do not transform the variables.                       |                           |
    INFO - 07:18:47: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
{'$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']}

Create regression model

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)

Out:

    INFO - 07:18:47:
    INFO - 07:18:47: *** Start DOEScenario execution ***
    INFO - 07:18:47: DOEScenario
    INFO - 07:18:47:    Disciplines: func
    INFO - 07:18:47:    MDO formulation: DisciplinaryOpt
    INFO - 07:18:47: Optimization problem:
    INFO - 07:18:47:    minimize y_1(x_1, x_2)
    INFO - 07:18:47:    with respect to x_1, x_2
    INFO - 07:18:47:    over the design space:
    INFO - 07:18:47:    +------+-------------+-------+-------------+-------+
    INFO - 07:18:47:    | name | lower_bound | value | upper_bound | type  |
    INFO - 07:18:47:    +------+-------------+-------+-------------+-------+
    INFO - 07:18:47:    | x_1  |      0      |  None |      1      | float |
    INFO - 07:18:47:    | x_2  |      0      |  None |      1      | float |
    INFO - 07:18:47:    +------+-------------+-------+-------------+-------+
    INFO - 07:18:47: Solving optimization problem with algorithm fullfact:
    INFO - 07:18:47: Full factorial design required. Number of samples along each direction for a design vector of size 2 with 9 samples: 3
    INFO - 07:18:47: Final number of samples for DOE = 9 vs 9 requested
    INFO - 07:18:47: ...   0%|          | 0/9 [00:00<?, ?it]
    INFO - 07:18:48: ... 100%|██████████| 9/9 [00:00<00:00, 1386.19 it/sec, obj=6]
    INFO - 07:18:48: Optimization result:
    INFO - 07:18:48:    Optimizer info:
    INFO - 07:18:48:       Status: None
    INFO - 07:18:48:       Message: None
    INFO - 07:18:48:       Number of calls to the objective function by the optimizer: 9
    INFO - 07:18:48:    Solution:
    INFO - 07:18:48:       Objective: 1.0
    INFO - 07:18:48:       Design space:
    INFO - 07:18:48:       +------+-------------+-------+-------------+-------+
    INFO - 07:18:48:       | name | lower_bound | value | upper_bound | type  |
    INFO - 07:18:48:       +------+-------------+-------+-------------+-------+
    INFO - 07:18:48:       | x_1  |      0      |   0   |      1      | float |
    INFO - 07:18:48:       | x_2  |      0      |   0   |      1      | float |
    INFO - 07:18:48:       +------+-------------+-------+-------------+-------+
    INFO - 07:18:48: *** End DOEScenario execution (time: 0:00:00.015365) ***
LinearRegressor(fit_intercept=True, l2_penalty_ratio=1.0, penalty_level=0.0)
   based on the scikit-learn library
   built from 9 learning samples

Total running time of the script: ( 0 minutes 0.045 seconds)

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