# GP regression¶

We want to approximate a discipline with two inputs and two outputs:

• $$y_1=1+2x_1+3x_2$$

• $$y_2=-1-2x_1-3x_2$$

over the unit hypercube $$[0,1]\times[0,1]$$.

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 numpy import array

configure_logger()

<RootLogger root (INFO)>


## Create the discipline to learn¶

We can implement this analytic discipline by means of the AnalyticDiscipline class.

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
)


## Create the input sampling space¶

We create the input sampling space by adding the variables one by one.

design_space = create_design_space()


## Create the learning set¶

We can build a learning set by means of a DOEScenario with a full factorial design of experiments. The number of samples can be equal to 9 for example.

scenario = create_scenario(
[discipline], "DisciplinaryOpt", "y_1", design_space, scenario_type="DOE"
)
scenario.execute({"algo": "fullfact", "n_samples": 9})

    INFO - 14:47:52:
INFO - 14:47:52: *** Start DOEScenario execution ***
INFO - 14:47:52: DOEScenario
INFO - 14:47:52:    Disciplines: func
INFO - 14:47:52:    MDO formulation: DisciplinaryOpt
INFO - 14:47:52: Optimization problem:
INFO - 14:47:52:    minimize y_1(x_1, x_2)
INFO - 14:47:52:    with respect to x_1, x_2
INFO - 14:47:52:    over the design space:
INFO - 14:47:52:    +------+-------------+-------+-------------+-------+
INFO - 14:47:52:    | name | lower_bound | value | upper_bound | type  |
INFO - 14:47:52:    +------+-------------+-------+-------------+-------+
INFO - 14:47:52:    | x_1  |      0      |  None |      1      | float |
INFO - 14:47:52:    | x_2  |      0      |  None |      1      | float |
INFO - 14:47:52:    +------+-------------+-------+-------------+-------+
INFO - 14:47:52: Solving optimization problem with algorithm fullfact:
INFO - 14:47:52: ...   0%|          | 0/9 [00:00<?, ?it]
INFO - 14:47:52: ... 100%|██████████| 9/9 [00:00<00:00, 1497.61 it/sec, obj=6]
INFO - 14:47:52: Optimization result:
INFO - 14:47:52:    Optimizer info:
INFO - 14:47:52:       Status: None
INFO - 14:47:52:       Message: None
INFO - 14:47:52:       Number of calls to the objective function by the optimizer: 9
INFO - 14:47:52:    Solution:
INFO - 14:47:52:       Objective: 1.0
INFO - 14:47:52:       Design space:
INFO - 14:47:52:       +------+-------------+-------+-------------+-------+
INFO - 14:47:52:       | name | lower_bound | value | upper_bound | type  |
INFO - 14:47:52:       +------+-------------+-------+-------------+-------+
INFO - 14:47:52:       | x_1  |      0      |   0   |      1      | float |
INFO - 14:47:52:       | x_2  |      0      |   0   |      1      | float |
INFO - 14:47:52:       +------+-------------+-------+-------------+-------+
INFO - 14:47:52: *** End DOEScenario execution (time: 0:00:00.015906) ***

{'eval_jac': False, 'algo': 'fullfact', 'n_samples': 9}


## Create the regression model¶

Then, we build the linear regression model from the database and displays this model.

dataset = scenario.export_to_dataset(opt_naming=False)
model = create_regression_model("GaussianProcessRegressor", data=dataset)
model.learn()
print(model)

GaussianProcessRegressor(alpha=1e-10, kernel=Matern, n_restarts_optimizer=10, optimizer=fmin_l_bfgs_b, random_state=None)
based on the scikit-learn library
built from 9 learning samples


## Predict output¶

Once it is built, we can use it for prediction.

input_value = {"x_1": array([1.0]), "x_2": array([2.0])}
output_value = model.predict(input_value)
print(output_value)

{'y_1': array([8.50166028])}


Total running time of the script: ( 0 minutes 0.138 seconds)

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