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#!/usr/bin/env python
import openturns as ot
import openturns.testing
import persalys
# ot.Log.Show(ot.Log.DBG)
ot.TBB.Disable()
ot.RandomGenerator.SetSeed(0)
myStudy = persalys.Study("myStudy")
# Model
x1 = persalys.Input("x1", ot.Uniform(0.0, 10.0))
x2 = persalys.Input("x2", ot.Uniform(0.0, 10.0))
x3 = persalys.Input("x3", 0.5)
y00 = persalys.Output("fake_y0")
y00.setIsSelected(False)
y0 = persalys.Output("y0")
formula = ["0.5*x1 + x2+ 28*x3^2"] * 2
model = persalys.SymbolicPhysicalModel("model", [x1, x2, x3], [y00, y0], formula)
myStudy.add(model)
# Design of Experiment
aDesign = persalys.FixedDesignOfExperiment("design", model)
validationInputSample = ot.LHSExperiment(model.getDistribution(), 10).generate()
inputSample = ot.Sample(validationInputSample)
inputSample.stack(ot.Sample(10, [0.5]))
aDesign.setOriginalInputSample(inputSample)
myStudy.add(aDesign)
aDesign.run()
# LM
analysis = persalys.PolynomialRegressionAnalysis("lm_0", aDesign)
analysis.setDegree(2)
analysis.setInteraction(True)
myStudy.add(analysis)
print(analysis)
analysis.run()
print(analysis.getResult())
metaModel = analysis.getResult().getResultForVariable("y0").getMetaModel()
openturns.testing.assert_almost_equal(
aDesign.getResult().getDesignOfExperiment().getOutputSample(),
metaModel(validationInputSample),
3.0e-5,
3.0e-5,
)
# boston price model
datamodel = persalys.DataModel(
"datamodel", "Housing-prices-Boston.csv", range(13), [13]
)
myStudy.add(datamodel)
analysis2 = persalys.PolynomialRegressionAnalysis("lm_1", datamodel)
analysis2.setDegree(2)
analysis2.setInteraction(True)
myStudy.add(analysis2)
analysis2.run()
print(analysis2.getResult())
metaModel2 = analysis2.getResult().getResultForVariable("MEDV").getMetaModel()
f = ot.SymbolicFunction(
datamodel.getInputSample().getDescription(), analysis2.getResult().getFormulas()
)
x = [1.5] * 13
openturns.testing.assert_almost_equal(metaModel2(x), f(x))
# script
script = myStudy.getPythonScript()
print(script)
exec(script)
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