# Use a design of experiments from a file¶

from __future__ import division, unicode_literals

from gemseo.api import create_design_space, create_discipline, create_scenario


Let us consider a discipline implementing the function $$y=a+b$$

discipline = create_discipline("AnalyticDiscipline", expressions_dict={"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", "r")
print(f.read())


Out:

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"}})


Out:

{'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())


Out:

  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 0.032 seconds)

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