test_stf.py

import openturns as ot

inputDimension = 2

""" 
Learning data
Box in [0,10]x[0,10]
"""
levels = [6, 3]
box = ot.Box(levels)
inputSample = box.generate()
inputSample *= 10.0

model = ot.SymbolicFunction(['x', 'y'], ['cos(0.5*x) + sin(y)'])
outputSample = model(inputSample)

# Validation
sampleSize = 10
inputValidSample = ot.ComposedDistribution(
    2 * [ot.Uniform(0, 10.0)]).getSample(sampleSize)
outputValidSample = model(inputValidSample)

# Basis definition
basis = ot.LinearBasisFactory(inputDimension).build()
rho = ot.SymbolicFunction(['x', 'y'], ['exp(-0.5* (x * x + y * y))'])
covarianceModel = ot.StationaryFunctionalCovarianceModel([5, 2], [1], rho)

# Kriging algorithm
algo = ot.KrigingAlgorithm(inputSample, outputSample, covarianceModel, basis)

algo.setOptimizationBounds(ot.Interval([1e-6, 1e-6], [20, 20]))
startingPoints = ot.LHSExperiment(ot.ComposedDistribution(2 * [ot.Uniform(0, 20.0)]), 10).generate()
#algo.setOptimizationAlgorithm(ot.MultiStart(ot.NLopt("GN_DIRECT"), startingPoints))
algo.setOptimizationAlgorithm(ot.MultiStart(ot.Cobyla(), startingPoints))
algo.run()
result = algo.getResult()
# Get meta model
metaModel = result.getMetaModel()
# Until we fix 1759, we disable TBB
ot.TBB.Disable()
val = ot.MetaModelValidation(inputValidSample, outputValidSample, metaModel)
print(val.computePredictivityFactor())