Create a surrogate discipline

We want to build an MDODiscipline 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 a MLRegressionAlgo

from __future__ import division, unicode_literals

Import

from numpy import array

from gemseo.api import (
    configure_logger,
    create_design_space,
    create_discipline,
    create_scenario,
    create_surrogate,
)

configure_logger()

Out:

<RootLogger root (INFO)>

Create the discipline to learn

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

expressions_dict = {"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_dict=expressions_dict
)

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", l_b=0.0, u_b=1.0)
design_space.add_variable("x_2", l_b=0.0, u_b=1.0)

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.

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

Out:

    INFO - 14:41:13:
    INFO - 14:41:13: *** Start DOE Scenario execution ***
    INFO - 14:41:13: DOEScenario
    INFO - 14:41:13:    Disciplines: func
    INFO - 14:41:13:    MDOFormulation: DisciplinaryOpt
    INFO - 14:41:13:    Algorithm: fullfact
    INFO - 14:41:13: Optimization problem:
    INFO - 14:41:13:    Minimize: y_1(x_1, x_2)
    INFO - 14:41:13:    With respect to: x_1, x_2
    INFO - 14:41:13: Full factorial design required. Number of samples along each direction for a design vector of size 2 with 9 samples: 3
    INFO - 14:41:13: Final number of samples for DOE = 9 vs 9 requested
    INFO - 14:41:13: DOE sampling:   0%|          | 0/9 [00:00<?, ?it]
    INFO - 14:41:13: DOE sampling: 100%|██████████| 9/9 [00:00<00:00, 577.72 it/sec, obj=6]
    INFO - 14:41:13: Optimization result:
    INFO - 14:41:13: Objective value = 1.0
    INFO - 14:41:13: The result is feasible.
    INFO - 14:41:13: Status: None
    INFO - 14:41:13: Optimizer message: None
    INFO - 14:41:13: Number of calls to the objective function by the optimizer: 9
    INFO - 14:41:13: Design space:
    INFO - 14:41:13: +------+-------------+-------+-------------+-------+
    INFO - 14:41:13: | name | lower_bound | value | upper_bound | type  |
    INFO - 14:41:13: +------+-------------+-------+-------------+-------+
    INFO - 14:41:13: | x_1  |      0      |   0   |      1      | float |
    INFO - 14:41:13: | x_2  |      0      |   0   |      1      | float |
    INFO - 14:41:13: +------+-------------+-------+-------------+-------+
    INFO - 14:41:13: *** DOE Scenario run terminated ***

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

Create the surrogate discipline

Then, we build the Gaussian process regression model from the discipline cache and displays this model.

dataset = discipline.cache.export_to_dataset()
model = create_surrogate("GaussianProcessRegression", data=dataset)

Out:

INFO - 14:41:13: Build the surrogate discipline: GPR_func
INFO - 14:41:13:    Dataset name: func
INFO - 14:41:13:    Dataset size: 9
INFO - 14:41:13:    Surrogate model: GaussianProcessRegression
INFO - 14:41:13: Use the surrogate discipline: GPR_func
INFO - 14:41:13:    Inputs: x_1, x_2
INFO - 14:41:13:    Outputs: y_1, y_2
INFO - 14:41:13:    Jacobian: use finite differences

Predict output

Once it is built, we can use it for prediction, either with default inputs or with user-defined ones.

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

Out:

{'x_1': array([0.5]), 'x_2': array([0.5]), 'y_1': array([3.49999999]), 'y_2': array([-3.50000001])}
{'x_1': array([1.]), 'x_2': array([2.]), 'y_1': array([8.50166027]), 'y_2': array([-8.56035161])}

Total running time of the script: ( 0 minutes 0.153 seconds)

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