Use a design of experiments from a file

from __future__ import annotations

from gemseo.api import create_design_space
from gemseo.api import create_discipline
from gemseo.api import create_scenario

Let us consider a discipline implementing the function \(y=a*b\)

discipline = create_discipline("AnalyticDiscipline", expressions={"y": "a*b"})

where \(a,b\in\{1,2,\ldots,10\}\):

design_space = create_design_space()
design_space.add_variable("a", 1, design_space.INTEGER, 1, 10)
design_space.add_variable("b", 1, design_space.INTEGER, 1, 10)

We want to evaluate this discipline over this design space by using the input samples defined in the file “doe.txt”:

f = open("doe.txt")
print(f.read())
1,2
3,4
5,6

In this file, rows are points and columns are variables whose order must be consistent with that of the design space. In this example, we can see that the first input value is defined by \(a=1\) and \(b=2\).

For that, we can create a scenario and execute it with a CustomDOE, with the option “doe_file”. We could also change the delimiter (default: ‘,’) or skip the first rows in the file.

scenario = create_scenario(
    [discipline], "DisciplinaryOpt", "y", design_space, scenario_type="DOE"
)
scenario.execute({"algo": "CustomDOE", "algo_options": {"doe_file": "doe.txt"}})
{'eval_jac': False, 'algo': 'CustomDOE', 'algo_options': {'doe_file': 'doe.txt'}}

We can display the content of the database as a dataframe and check the values of the output, which should be the product of \(a\) and \(b\).

opt_problem = scenario.formulation.opt_problem
dataset = opt_problem.export_to_dataset(name="custom_sampling", opt_naming=False)
print(dataset.export_to_dataframe())
  inputs      outputs
       a    b       y
       0    0       0
0    1.0  2.0     2.0
1    3.0  4.0    12.0
2    5.0  6.0    30.0

Total running time of the script: ( 0 minutes 0.041 seconds)

Gallery generated by Sphinx-Gallery