Note
Go to the end to download the full example code.
Create a surrogate discipline#
We want to build an Discipline
based on a regression model approximating the following 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 \([0,1]\times[0,1]\).
For that,
we use a SurrogateDiscipline
relying on an BaseRegressor
.
from __future__ import annotations
from numpy import array
from gemseo import configure_logger
from gemseo import create_design_space
from gemseo import create_discipline
from gemseo import create_surrogate
from gemseo import sample_disciplines
Import#
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()
design_space.add_variable("x_1", lower_bound=0.0, upper_bound=1.0)
design_space.add_variable("x_2", lower_bound=0.0, upper_bound=1.0)
Create the training dataset#
We can build a training dataset
by sampling the discipline using the sample_disciplines()
with a full factorial design of experiments.
dataset = sample_disciplines(
[discipline], design_space, ["y_1", "y_2"], algo_name="PYDOE_FULLFACT", n_samples=9
)
Traceback (most recent call last):
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
Create the surrogate discipline#
Then, we build the Gaussian process regression model from the dataset and displays this model.
model = create_surrogate("GaussianProcessRegressor", data=dataset)
Predict output#
Once it is built, we can use it for prediction, either with default inputs
model.execute()
or with user-defined ones.
model.execute({"x_1": array([1.0]), "x_2": array([2.0])})
Total running time of the script: (0 minutes 0.013 seconds)