API

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

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()
<RootLogger root (INFO)>

Get available regression models

print(get_regression_models())
['GaussianProcessRegressor', 'LinearRegressor', 'MOERegressor', 'PCERegressor', 'PolynomialRegressor', 'RBFRegressor', 'RandomForestRegressor']

Get regression model options

print(get_regression_options("GaussianProcessRegressor"))
+---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
|            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 - 11:24:46: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    INFO - 11:24:46: |            Name           |                                        Description                                         |            Type           |
    INFO - 11:24:46: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    INFO - 11:24:46: |           alpha           |                         The nugget effect to regularize the model.                         |           number          |
    INFO - 11:24:46: |           bounds          |   The lower and upper bounds of the parameter length scales when ``kernel`` is ``none``.   |            null           |
    INFO - 11:24:46: |                           | either a unique lower-upper pair common to all the inputs or lower-upper pairs for some of |                           |
    INFO - 11:24:46: |                           |  them. when ``bounds`` is ``none`` or when an input has no pair, the lower bound is 0.01   |                           |
    INFO - 11:24:46: |                           |                                and the upper bound is 100.                                 |                           |
    INFO - 11:24:46: |        input_names        |   The names of the input variables. if ``none``, consider all the input variables of the   |            null           |
    INFO - 11:24:46: |                           |                                     learning dataset.                                      |                           |
    INFO - 11:24:46: |           kernel          |        The kernel specifying the covariance model. if ``none``, use a matérn(2.5).         |            null           |
    INFO - 11:24:46: |    n_restarts_optimizer   |                          The number of restarts of the optimizer.                          |          integer          |
    INFO - 11:24:46: |         optimizer         |              The optimization algorithm to find the parameter length scales.               |           string          |
    INFO - 11:24:46: |        output_names       |  The names of the output variables. if ``none``, consider all the output variables of the  |            null           |
    INFO - 11:24:46: |                           |                                     learning dataset.                                      |                           |
    INFO - 11:24:46: |        random_state       |    The seed used to initialize the centers. if none, the random number generator is the    |            null           |
    INFO - 11:24:46: |                           |                        randomstate instance used by `numpy.random`.                        |                           |
    INFO - 11:24:46: |        transformer        |           The strategies to transform the variables. the values are instances of           |           object          |
    INFO - 11:24:46: |                           |  :class:`.transformer` while the keys are the names of either the variables or the groups  |                           |
    INFO - 11:24:46: |                           | of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. |                           |
    INFO - 11:24:46: |                           | if a group is specified, the :class:`.transformer` will be applied to all the variables of |                           |
    INFO - 11:24:46: |                           |             this group. if :attr:`.identity`, do not transform the variables.              |                           |
    INFO - 11:24:46: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
{'$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']}

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)
    INFO - 11:24:46:
    INFO - 11:24:46: *** Start DOEScenario execution ***
    INFO - 11:24:46: DOEScenario
    INFO - 11:24:46:    Disciplines: func
    INFO - 11:24:46:    MDO formulation: DisciplinaryOpt
    INFO - 11:24:46: Optimization problem:
    INFO - 11:24:46:    minimize y_1(x_1, x_2)
    INFO - 11:24:46:    with respect to x_1, x_2
    INFO - 11:24:46:    over the design space:
    INFO - 11:24:46:    +------+-------------+-------+-------------+-------+
    INFO - 11:24:46:    | name | lower_bound | value | upper_bound | type  |
    INFO - 11:24:46:    +------+-------------+-------+-------------+-------+
    INFO - 11:24:46:    | x_1  |      0      |  None |      1      | float |
    INFO - 11:24:46:    | x_2  |      0      |  None |      1      | float |
    INFO - 11:24:46:    +------+-------------+-------+-------------+-------+
    INFO - 11:24:46: Solving optimization problem with algorithm fullfact:
    INFO - 11:24:46: ...   0%|          | 0/9 [00:00<?, ?it]
    INFO - 11:24:46: ... 100%|██████████| 9/9 [00:00<00:00, 1473.24 it/sec, obj=6]
    INFO - 11:24:46: Optimization result:
    INFO - 11:24:46:    Optimizer info:
    INFO - 11:24:46:       Status: None
    INFO - 11:24:46:       Message: None
    INFO - 11:24:46:       Number of calls to the objective function by the optimizer: 9
    INFO - 11:24:46:    Solution:
    INFO - 11:24:46:       Objective: 1.0
    INFO - 11:24:46:       Design space:
    INFO - 11:24:46:       +------+-------------+-------+-------------+-------+
    INFO - 11:24:46:       | name | lower_bound | value | upper_bound | type  |
    INFO - 11:24:46:       +------+-------------+-------+-------------+-------+
    INFO - 11:24:46:       | x_1  |      0      |   0   |      1      | float |
    INFO - 11:24:46:       | x_2  |      0      |   0   |      1      | float |
    INFO - 11:24:46:       +------+-------------+-------+-------------+-------+
    INFO - 11:24:46: *** End DOEScenario execution (time: 0:00:00.015175) ***
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/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

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

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