1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
|
#!/usr/bin/env python
import openturns as ot
import openturns.testing
import persalys
import os
import tempfile
import shutil
# 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)
analysis.setDirection(ot.LinearModelStepwiseAlgorithm.FORWARD)
analysis.setPenalty(persalys.PolynomialRegressionAnalysis.AIC)
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,
)
analysis.setStepwise(False)
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,
)
# test export
temp_path = tempfile.mkdtemp()
path_py = os.path.join(temp_path, "metamodel0.py")
analysis.getResult().getMetaModel().exportStandalonePythonScript(path_py)
assert os.path.exists(path_py)
shutil.rmtree(temp_path)
# 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)
|
