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import openturns as ot
import openturns.testing as ott
import persalys
from math import sin, exp
myStudy = persalys.Study("myStudy")
# +++++++++++++++++++++++++++++++++ Model 1 +++++++++++++++++++++++++++++++++ #
# simple example with a data model
model1 = persalys.DataModel(
"model1", "Housing-prices-Boston.csv", list(range(13)), [13]
)
myStudy.add(model1)
print(model1)
print("inputNames=", model1.getInputNames())
print("outputNames=", model1.getOutputNames())
# Sensitivity analysis
analysis1 = persalys.DataSensitivityAnalysis("analysis1", model1)
myStudy.add(analysis1)
analysis1.run()
result1 = analysis1.getResult()
ott.assert_almost_equal(
result1.getFirstOrderSobolIndices()[0],
[
0.269789,
0.712661,
0.709397,
0.687175,
0.698848,
0.626429,
0.166526,
0.240896,
0.69844,
0.71514,
0.707562,
0.267871,
0.649162,
],
)
ott.assert_almost_equal(
result1.getFirstOrderSobolIndicesInterval()[0].getLowerBound(),
[
0.190449,
0.0968657,
0.325595,
-0.104384,
0.497543,
0.541836,
0.0273068,
0.123202,
0.0962543,
0.243149,
0.268534,
0.0559757,
0.600002,
],
)
ott.assert_almost_equal(
result1.getFirstOrderSobolIndicesInterval()[0].getUpperBound(),
[
0.363531,
0.307685,
0.547926,
0.142314,
0.647108,
0.688973,
0.218533,
0.33305,
0.314734,
0.460371,
0.482617,
0.225703,
0.699414,
],
)
ott.assert_almost_equal(
result1.getSignedSRCIndices(),
[
[
-0.100175,
0.117651,
0.0155602,
0.0742493,
-0.224215,
0.290666,
0.00229872,
-0.33788,
0.289376,
-0.225936,
-0.22444,
0.0932346,
-0.407997,
]
],
)
ott.assert_almost_equal(
result1.getSRCIndices(), [[x**2 for x in result1.getSignedSRCIndices()[0]]]
)
ott.assert_almost_equal(
result1.getSignedSRCIndicesInterval()[0].getLowerBound()[:3],
[-0.151228, 0.0624121, -0.0406055],
)
ott.assert_almost_equal(
result1.getSignedSRCIndicesInterval()[0].getUpperBound()[:3],
[-0.0307025, 0.171362, 0.0924589],
)
ott.assert_almost_equal(result1.getR2()[0], 0.638872)
# +++++++++++++++++++++++++++++++++ Model 2 +++++++++++++++++++++++++++++++++ #
# Model with several outputs and dependences between inputs
# ------------------------------model definition----------------------------- #
marginals = [
ot.Normal(), # X0
ot.Uniform(0, 1), # X1
ot.Normal(), # X2
ot.Normal(), # X3
ot.Normal(), # X4
]
R = ot.CorrelationMatrix(5)
R[0, 2] = 0.3
R[3, 4] = -0.2
copula = ot.NormalCopula(R)
distribution = ot.JointDistribution(marginals, copula)
distribution.setDescription(["X0", "X1", "X2", "X3", "X4"])
def model2_def(X):
x0, x1, x2, x3, x4 = X
y0 = x0 + x1**2 + sin(x2)
y1 = x3 * x4 + exp(-x0)
y2 = x1 + x2 + x3 + x4
return [y0, y1, y2]
ot_model2 = ot.PythonFunction(5, 3, model2_def)
sample_size = 300
ot.RandomGenerator.SetSeed(0)
sample = distribution.getSample(sample_size)
output_sample = ot_model2(sample)
output_sample.setDescription(["Y0", "Y1", "Y2"])
sample.stack(output_sample)
# saving the sample to a CSV file
filename = "DataSensitivityAnalysis_std.csv"
sample.exportToCSVFile(filename)
# ------------------------test of the persalys model------------------------- #
model2 = persalys.DataModel("model2", filename, list(range(5)), list(range(5, 8)))
myStudy.add(model2)
print()
print(model2)
analysis2 = persalys.DataSensitivityAnalysis("analysis2", model2)
myStudy.add(analysis2)
analysis2.run()
result2 = analysis2.getResult()
for i in range(3):
print(f"Output Y{i} first order indices: ", result2.getFirstOrderSobolIndices()[i])
print(
f"Output Y{i} first order indices intervals: ",
result2.getFirstOrderSobolIndicesInterval()[i],
)
print("Is independent ?", result2.isIndependent())
print("Warning message: ", result2.getIndependenceWarningMessage())
# -------------------------test without output sample------------------------ #
model2_no_output = persalys.DataModel("model2_no_output", filename, list(range(5)), [])
myStudy.add(model2_no_output)
analysis2_no_output = persalys.DataSensitivityAnalysis(
"analysis2_no_output", model2_no_output
)
myStudy.add(analysis2_no_output)
with ott.assert_raises(RuntimeError):
analysis2_no_output.run()
|
