Note
Click here to download the full example code
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 - 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']}
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 - 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
Total running time of the script: ( 0 minutes 0.046 seconds)