def fit(self, X, y, **fit_params):
input_dimension = X.shape[1]
if self.distribution is None:
self.distribution = BuildDistribution(X)
if self.enumerate == 'linear':
enumerateFunction = ot.LinearEnumerateFunction(input_dimension)
elif self.enumerate == 'hyperbolic':
enumerateFunction = ot.HyperbolicAnisotropicEnumerateFunction(input_dimension, self.q)
else:
raise ValueError('enumerate should be "linear" or "hyperbolic"')
polynomials = [ot.StandardDistributionPolynomialFactory(self.distribution.getMarginal(i)) for i in range(input_dimension)]
productBasis = ot.OrthogonalProductPolynomialFactory(polynomials, enumerateFunction)
adaptiveStrategy = ot.FixedStrategy(productBasis, enumerateFunction.getStrataCumulatedCardinal(self.degree))
if self.sparse:
projectionStrategy = ot.LeastSquaresStrategy(ot.LeastSquaresMetaModelSelectionFactory(ot.LARS(), ot.CorrectedLeaveOneOut()))
else:
projectionStrategy = ot.LeastSquaresStrategy(X, y.reshape(-1, 1))
algo = ot.FunctionalChaosAlgorithm(X, y.reshape(-1, 1), self.distribution, adaptiveStrategy, projectionStrategy)
algo.run()
self._result = algo.getResult()
output_dimension = self._result.getMetaModel().getOutputDimension()
# sensitivity
si = ot.FunctionalChaosSobolIndices(self._result)
if output_dimension == 1:
self.feature_importances_ = [si.getSobolIndex(i) for i in range(input_dimension)]
else:
self.feature_importances_ = [[0.0] * input_dimension] * output_dimension
for k in range(output_dimension):
for i in range(input_dimension):
self.feature_importances_[k][i] = si.getSobolIndex(i, k)
self.feature_importances_ = np.array(self.feature_importances_)
return self
def fit(self, sample, data):
"""Create the predictor.
The result of the Polynomial Chaos is stored as :attr:`pc_result` and
the surrogate is stored as :attr:`pc`. It exposes :attr:`self.weights`,
:attr:`self.coefficients` and Sobol' indices :attr:`self.s_first` and
:attr:`self.s_total`.
:param array_like sample: The sample used to generate the data
(n_samples, n_features).
:param array_like data: The observed data (n_samples, [n_features]).
"""
trunc_strategy = ot.FixedStrategy(self.basis, self.n_basis)
if self.strategy == 'LS': # least-squares method
proj_strategy = ot.LeastSquaresStrategy(sample, data)
_, self.weights = proj_strategy.getExperiment().generateWithWeights()
elif self.strategy == 'SparseLS':
app = ot.LeastSquaresMetaModelSelectionFactory(ot.LARS(), ot.CorrectedLeaveOneOut())
proj_strategy = ot.LeastSquaresStrategy(sample, data, app)
_, self.weights = proj_strategy.getExperiment().generateWithWeights()
max_considered_terms = self.sparse_param.get('max_considered_terms', 120)
most_significant = self.sparse_param.get('most_significant', 30)
significance_factor = self.sparse_param.get('significance_factor', 1e-3)
trunc_strategy = ot.CleaningStrategy(ot.OrthogonalBasis(self.basis),
max_considered_terms,
most_significant,
significance_factor, True)
else:
proj_strategy = self.proj_strategy
sample_ = np.zeros_like(self.sample)
sample_[:len(sample)] = sample
sample_arg = np.all(np.isin(sample_, self.sample), axis=1)
self.weights = np.array(self.weights)[sample_arg]
# PC fitting
pc_algo = ot.FunctionalChaosAlgorithm(sample, self.weights, data,
self.dist, trunc_strategy)
pc_algo.setProjectionStrategy(proj_strategy)
ot.Log.Show(ot.Log.ERROR)
pc_algo.run()
# Accessors
self.pc_result = pc_algo.getResult()
self.pc = self.pc_result.getMetaModel()
self.coefficients = self.pc_result.getCoefficients()
# sensitivity indices
sobol = ot.FunctionalChaosSobolIndices(self.pc_result)
self.s_first, self.s_total = [], []
for i, j in product(range(self.in_dim), range(np.array(data).shape[1])):
self.s_first.append(sobol.getSobolIndex(i, j))
self.s_total.append(sobol.getSobolTotalIndex(i, j))
self.s_first = np.array(self.s_first).reshape(self.in_dim, -1).T
self.s_total = np.array(self.s_total).reshape(self.in_dim, -1).T
def _buildChaosAlgo(self, inputSample, outputSample):
"""
Build the functional chaos algorithm without running it.
"""
if self._distribution is None:
# create default distribution : Uniform between min and max of the
# input sample
inputSample = ot.NumericalSample(inputSample)
inputMin = inputSample.getMin()
inputMin[0] = np.min(self._defectSizes)
inputMax = inputSample.getMax()
inputMax[0] = np.max(self._defectSizes)
marginals = [
ot.Uniform(inputMin[i], inputMax[i]) for i in range(self._dim)
]
self._distribution = ot.ComposedDistribution(marginals)
# put description of the inputSample into decription of the distribution
self._distribution.setDescription(inputSample.getDescription())
if self._adaptiveStrategy is None:
# Create the adaptive strategy : default is fixed strategy of degree 5
# with linear enumerate function
polyCol = [0.] * self._dim
for i in range(self._dim):
polyCol[i] = ot.StandardDistributionPolynomialFactory(
self._distribution.getMarginal(i))
enumerateFunction = ot.EnumerateFunction(self._dim)
multivariateBasis = ot.OrthogonalProductPolynomialFactory(
polyCol, enumerateFunction)
# default degree is 3 (in __init__)
indexMax = enumerateFunction.getStrataCumulatedCardinal(
self._degree)
self._adaptiveStrategy = ot.FixedStrategy(multivariateBasis,
indexMax)
if self._projectionStrategy is None:
# sparse polynomial chaos
basis_sequence_factory = ot.LAR()
fitting_algorithm = ot.KFold()
approximation_algorithm = ot.LeastSquaresMetaModelSelectionFactory(
basis_sequence_factory, fitting_algorithm)
self._projectionStrategy = ot.LeastSquaresStrategy(
inputSample, outputSample, approximation_algorithm)
return ot.FunctionalChaosAlgorithm(inputSample, outputSample, \
self._distribution, self._adaptiveStrategy, self._projectionStrategy)
def compute_cleaning_PCE(maximumConsideredTerms,
mostSignificant,
significanceFactor,
verbose=False):
"""
Compute a PCE using CleaningStrategy.
Parameters
----------
maximumConsideredTerms : int
The maximum number of coefficients considered by the algorithm during
intermediate steps.
mostSignificant : int
The maximum number of coefficients selected by the algorithm in
the final PCE.
significanceFactor : float
The relative part of any coefficient with respect to the maximum
coefficient.
verbose : bool
If set to True, print intermediate messages.
Returns
-------
Q2 : float
The Q2 score
"""
adaptiveStrategy = ot.CleaningStrategy(
multivariateBasis,
maximumConsideredTerms,
mostSignificant,
significanceFactor,
True,
)
chaosalgo = ot.FunctionalChaosAlgorithm(im.model, im.distributionX,
adaptiveStrategy,
projectionStrategy)
chaosalgo.run()
result = chaosalgo.getResult()
score_Q2 = compute_polynomial_chaos_Q2(result, im.model, im.distributionX)
if verbose:
print("Q2 = %.2f%%" % (100.0 * score_Q2))
printCoefficientsTable(result)
return score_Q2
def ComputeSparseLeastSquaresChaos(inputTrain, outputTrain, multivariateBasis,
totalDegree, myDistribution):
"""
Create a sparse polynomial chaos based on least squares.
* Uses the enumerate rule in multivariateBasis.
* Uses the LeastSquaresStrategy to compute the coefficients based on
least squares.
* Uses LeastSquaresMetaModelSelectionFactory to use the LARS selection method.
* Uses FixedStrategy in order to keep all the coefficients that the
LARS method selected.
Parameters
----------
inputTrain : ot.Sample
The input design of experiments.
outputTrain : ot.Sample
The output design of experiments.
multivariateBasis : ot.Basis
The multivariate chaos basis.
totalDegree : int
The total degree of the chaos polynomial.
myDistribution : ot.Distribution.
The distribution of the input variable.
Returns
-------
result : ot.PolynomialChaosResult
The estimated polynomial chaos.
"""
selectionAlgorithm = ot.LeastSquaresMetaModelSelectionFactory()
projectionStrategy = ot.LeastSquaresStrategy(inputTrain, outputTrain,
selectionAlgorithm)
enumfunc = multivariateBasis.getEnumerateFunction()
P = enumfunc.getStrataCumulatedCardinal(totalDegree)
adaptiveStrategy = ot.FixedStrategy(multivariateBasis, P)
chaosalgo = ot.FunctionalChaosAlgorithm(inputTrain, outputTrain,
myDistribution, adaptiveStrategy,
projectionStrategy)
chaosalgo.run()
result = chaosalgo.getResult()
return result
orthogonal_basis = ot.OrthogonalProductPolynomialFactory(
dimension * [ot.LegendreFactory()], enumerate_function)
basis_size = 100
# Initial chaos algorithm
adaptive_strategy = ot.FixedStrategy(orthogonal_basis, basis_size)
# ProjectionStrategy ==> Sparse
fitting_algorithm = ot.KFold()
approximation_algorithm = ot.LeastSquaresMetaModelSelectionFactory(
ot.LARS(), fitting_algorithm)
projection_strategy = ot.LeastSquaresStrategy(input_database, output_database,
approximation_algorithm)
print('Surrogate model...')
distribution_ishigami = ot.ComposedDistribution(dimension *
[ot.Uniform(-pi, pi)])
algo_pc = ot.FunctionalChaosAlgorithm(input_database, output_database,
distribution_ishigami, adaptive_strategy,
projection_strategy)
algo_pc.run()
chaos_result = algo_pc.getResult()
print('Surrogate model computed')
# Validation
lhs_validation = ot.LHSExperiment(distribution_ishigami, 100)
input_validation = lhs_validation.generate()
output_validation = ishigami_model(input_validation)
# Chaos model evaluation
output_metamodel_sample = chaos_result.getMetaModel()(input_validation)
# Cloud validation ==> change from matplotlib to pure OT
#fig = plt.figure()
#plt.plot(output_validation, output_validation, 'b-', label='Model')
print("Compute PCE between coefficients")
degree = 1
dimension_xi_X = sample_xi_X.getDimension()
dimension_xi_Y = sample_xi_Y.getDimension()
enumerateFunction = ot.LinearEnumerateFunction(dimension_xi_X)
basis = ot.OrthogonalProductPolynomialFactory(
[ot.HermiteFactory()] * dimension_xi_X, enumerateFunction)
basisSize = enumerateFunction.getStrataCumulatedCardinal(degree)
adaptive = ot.FixedStrategy(basis, basisSize)
projection = ot.LeastSquaresStrategy(
ot.LeastSquaresMetaModelSelectionFactory(ot.LARS(),
ot.CorrectedLeaveOneOut()))
ot.ResourceMap.SetAsScalar("LeastSquaresMetaModelSelection-ErrorThreshold",
1.0e-7)
algo_chaos = ot.FunctionalChaosAlgorithm(sample_xi_X, sample_xi_Y,
basis.getMeasure(), adaptive,
projection)
algo_chaos.run()
result_chaos = algo_chaos.getResult()
meta_model = result_chaos.getMetaModel()
print("myConvolution=", myConvolution.getInputDimension(), "->",
myConvolution.getOutputDimension())
preprocessing = ot.KarhunenLoeveProjection(result_X)
print("preprocessing=", preprocessing.getInputDimension(), "->",
preprocessing.getOutputDimension())
print("meta_model=", meta_model.getInputDimension(), "->",
meta_model.getOutputDimension())
postprocessing = ot.KarhunenLoeveLifting(result_Y)
print("postprocessing=", postprocessing.getInputDimension(), "->",
postprocessing.getOutputDimension())
meta_model_field = ot.FieldToFieldConnection(
# Create the adaptive strategy
degree = 8
basisSize = enumerateFunction.getStrataCumulatedCardinal(degree)
adaptiveStrategy = ot.FixedStrategy(productBasis, basisSize)
# Select the fitting algorithm
fittingAlgorithm = ot.KFold()
leastSquaresFactory = ot.LeastSquaresMetaModelSelectionFactory(
ot.LARS(), fittingAlgorithm)
# Projection strategy
projectionStrategy = ot.LeastSquaresStrategy(
inputSample, outputSample, leastSquaresFactory)
algo = ot.FunctionalChaosAlgorithm(
inputSample, outputSample, distribution, adaptiveStrategy, projectionStrategy)
# Reinitialize the RandomGenerator to see the effect of the sampling
# method only
ot.RandomGenerator.SetSeed(0)
algo.run()
# Get the results
result = algo.getResult()
# MetaModelValidation - SPC
metaModelValidationSPC = ot.MetaModelValidation(
inputValidation, outputValidation, result.getMetaModel())
print("")
print("Sparse chaos scoring")
print(
"Q2 = ", round(metaModelValidationSPC.computePredictivityFactor(), 5))
polyColl[i] = ot.HermiteFactory()
enumerateFunction = ot.LinearEnumerateFunction(dim)
multivariateBasis = ot.OrthogonalProductPolynomialFactory(polyColl, enumerateFunction)
basisSequenceFactory = ot.LARS()
fittingAlgorithm = ot.CorrectedLeaveOneOut()
approximationAlgorithm = ot.LeastSquaresMetaModelSelectionFactory(basisSequenceFactory, fittingAlgorithm)
evalStrategy = ot.LeastSquaresStrategy(Data, Result, approximationAlgorithm)
order = 3
P = enumerateFunction.getStrataCumulatedCardinal(order)
truncatureBasisStrategy = ot.FixedStrategy(multivariateBasis, P)
polynomialChaosAlgorithm = ot.FunctionalChaosAlgorithm(Data, Result, ot.Distribution(myDistribution), truncatureBasisStrategy, evalStrategy)
polynomialChaosAlgorithm.run()
The_Result = polynomialChaosAlgorithm.getResult()
Error = The_Result.getRelativeErrors()
ChaosRV = ot.FunctionalChaosRandomVector(The_Result)
Mean = ChaosRV.getMean()[0]
StD = np.sqrt(ChaosRV.getCovariance()[0,0])
print("")
print("Response mean : ", Mean)
print("")
print("Response standard deviation : ", StD)
print("")
Y = myModel(X)
dimension = X.getDimension()
# %%
# build the orthogonal basis
coll = [
ot.StandardDistributionPolynomialFactory(distribution.getMarginal(i))
for i in range(dimension)
]
enumerateFunction = ot.LinearEnumerateFunction(dimension)
productBasis = ot.OrthogonalProductPolynomialFactory(coll, enumerateFunction)
# %%
# create the algorithm
degree = 6
adaptiveStrategy = ot.FixedStrategy(
productBasis, enumerateFunction.getStrataCumulatedCardinal(degree))
projectionStrategy = ot.LeastSquaresStrategy()
algo = ot.FunctionalChaosAlgorithm(X, Y, distribution, adaptiveStrategy,
projectionStrategy)
algo.run()
# %%
# get the metamodel function
result = algo.getResult()
metamodel = result.getMetaModel()
# %%
# Print residuals
result.getResiduals()
# %%
distribution = ot.Normal(dimension)
samplesize = 80
inputSample = distribution.getSample(samplesize)
outputSample = model(inputSample)
# %%
# Create a functional chaos model.
# First, we need to fit a distribution on the input sample. We can do this automatically with the Lilliefors test.
# %%
ot.ResourceMap.SetAsUnsignedInteger("FittingTest-LillieforsMaximumSamplingSize", 100)
# %%
algo = ot.FunctionalChaosAlgorithm(inputSample, outputSample)
algo.run()
result = algo.getResult()
metamodel = result.getMetaModel()
# %%
# Plot the second output of our model depending on :math:`x_2` with :math:`x_1=0.5`. In order to do this, we create a `ParametricFunction` and set the value of :math:`x_1`. Then we use the `getMarginal` method to extract the second output (which index is equal to 1).
# %%
x1index = 0
x1value = 0.5
x2min = -3.
x2max = 3.
outputIndex = 1
metamodelParametric = ot.ParametricFunction(metamodel, [x1index], [x1value])
graph = metamodelParametric.getMarginal(outputIndex).draw(x2min, x2max)
# %%
# Build a metamodel over each segment
degree = 5
samplingSize = 100
enumerateFunction = ot.LinearEnumerateFunction(dimension)
productBasis = ot.OrthogonalProductPolynomialFactory(
[ot.LegendreFactory()] * dimension, enumerateFunction)
adaptiveStrategy = ot.FixedStrategy(
productBasis, enumerateFunction.getStrataCumulatedCardinal(degree))
projectionStrategy = ot.LeastSquaresStrategy(
ot.MonteCarloExperiment(samplingSize))
# %%
# Segment 1: (-1.0; 0.0)
d1 = ot.Uniform(-1.0, 0.0)
fc1 = ot.FunctionalChaosAlgorithm(f, d1, adaptiveStrategy, projectionStrategy)
fc1.run()
mm1 = fc1.getResult().getMetaModel()
graph = mm1.draw(-1.0, -1e-6)
view = viewer.View(graph)
# %%
# Segment 2: (0.0, 1.0)
d2 = ot.Uniform(0.0, 1.0)
fc2 = ot.FunctionalChaosAlgorithm(f, d2, adaptiveStrategy, projectionStrategy)
fc2.run()
mm2 = fc2.getResult().getMetaModel()
graph = mm2.draw(1e-6, 1.0)
view = viewer.View(graph)
# %%
dim = model.getInputDimension()
print("dim=", dim)
size = dim + 1
distribution = ot.ComposedDistribution([ot.Normal()] * dim)
weightedExperiment = ot.MonteCarloExperiment(distribution, size)
inSample, weights = weightedExperiment.generateWithWeights()
print("Sample model")
t0 = time()
outSample = model(inSample)
t1 = time()
print("t=", t1 - t0, "s, speed=", inSample.getSize() / (t1 - t0), "evals/s")
basis = ot.OrthogonalProductPolynomialFactory([ot.HermiteFactory()] * dim)
adaptive = ot.FixedStrategy(basis, dim + 1)
projection = ot.LeastSquaresStrategy(weightedExperiment)
algo = ot.FunctionalChaosAlgorithm(inSample, outSample, distribution, adaptive,
projection)
algo.run()
vector = ot.FunctionalChaosRandomVector(algo.getResult())
# Field of Sobol indices
#for i in range(dim):
for i in range(15, 16):
print("i=", i)
sobol = [
vector.getSobolIndex(i, j) for j in range(mesh.getVerticesNumber())
]
field = ot.Field(mesh, [[x] for x in sobol])
graph = field.draw()
graph.setTitle("Sobol index field - component " + str(i))
#graph.add(field.drawMarginal(0))
view = View(graph, (800, 600))
# %%
fittingAlgorithm = ot.CorrectedLeaveOneOut()
# Finally the metamodel selection algorithm embbeded in LeastSquaresStrategy
approximationAlgorithm = ot.LeastSquaresMetaModelSelectionFactory(
basisSequenceFactory, fittingAlgorithm)
evaluationCoeffStrategy_2 = ot.LeastSquaresStrategy(
ot.MonteCarloExperiment(sampleSize), approximationAlgorithm)
# %%
# Try integration.
# %%
marginalDegrees = [2] * inputDimension
evaluationCoeffStrategy_3 = ot.IntegrationStrategy(
ot.GaussProductExperiment(distributionMu, marginalDegrees))
# %%
# STEP 4: Creation of the Functional Chaos Algorithm
# --------------------------------------------------
#
# The `FunctionalChaosAlgorithm` class combines
#
# * the model : `model`
# * the distribution of the input random vector : `distribution`
# * the truncature strategy of the multivariate basis
# * and the evaluation strategy of the coefficients
# %%
polynomialChaosAlgorithm = ot.FunctionalChaosAlgorithm(
model, distribution, truncatureBasisStrategy, evaluationCoeffStrategy)
def fit(self, X, y, **fit_params):
"""Fit PC regression model.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Training data.
y : array-like, shape = (n_samples, [n_output_dims])
Target values.
Returns
-------
self : returns an instance of self.
"""
if len(X) == 0:
raise ValueError(
"Can not perform chaos expansion with empty sample")
# check data type is accurate
if (len(np.shape(X)) != 2):
raise ValueError("X has incorrect shape.")
input_dimension = len(X[1])
if (len(np.shape(y)) != 2):
raise ValueError("y has incorrect shape.")
if self.distribution is None:
self.distribution = ot.MetaModelAlgorithm.BuildDistribution(X)
if self.enumeratef == 'linear':
enumerateFunction = ot.LinearEnumerateFunction(input_dimension)
elif self.enumeratef == 'hyperbolic':
enumerateFunction = ot.HyperbolicAnisotropicEnumerateFunction(
input_dimension, self.q)
else:
raise ValueError('enumeratef should be "linear" or "hyperbolic"')
polynomials = [
ot.StandardDistributionPolynomialFactory(
self.distribution.getMarginal(i))
for i in range(input_dimension)
]
productBasis = ot.OrthogonalProductPolynomialFactory(
polynomials, enumerateFunction)
adaptiveStrategy = ot.FixedStrategy(
productBasis,
enumerateFunction.getStrataCumulatedCardinal(self.degree))
if self.sparse:
# Filter according to the sparse_fitting_algorithm key
if self.sparse_fitting_algorithm == "cloo":
fitting_algorithm = ot.CorrectedLeaveOneOut()
else:
fitting_algorithm = ot.KFold()
# Define the correspondinding projection strategy
projectionStrategy = ot.LeastSquaresStrategy(
ot.LeastSquaresMetaModelSelectionFactory(
ot.LARS(), fitting_algorithm))
else:
projectionStrategy = ot.LeastSquaresStrategy(X, y)
algo = ot.FunctionalChaosAlgorithm(X, y, self.distribution,
adaptiveStrategy,
projectionStrategy)
algo.run()
self.result_ = algo.getResult()
output_dimension = self.result_.getMetaModel().getOutputDimension()
# sensitivity
si = ot.FunctionalChaosSobolIndices(self.result_)
if output_dimension == 1:
self.feature_importances_ = [
si.getSobolIndex(i) for i in range(input_dimension)
]
else:
self.feature_importances_ = [[0.0] * input_dimension
] * output_dimension
for k in range(output_dimension):
for i in range(input_dimension):
self.feature_importances_[k][i] = si.getSobolIndex(i, k)
self.feature_importances_ = np.array(self.feature_importances_)
return self
sample = myCorrelatedInputDistribution.getSample(2000)
# Orthogonal basis
enumerateFunction = ot.LinearEnumerateFunction(inputDimension)
productBasis = ot.OrthogonalProductPolynomialFactory(
[ot.HermiteFactory()] * inputDimension, enumerateFunction)
# Adaptive strategy
adaptiveStrategy = ot.FixedStrategy(
productBasis, enumerateFunction.getStrataCumulatedCardinal(4))
# Projection strategy
samplingSize = 250
projectionStrategy = ot.LeastSquaresStrategy(
ot.MonteCarloExperiment(samplingSize))
# Polynomial chaos algorithm
algo = ot.FunctionalChaosAlgorithm(
model, distribution, adaptiveStrategy, projectionStrategy)
algo.run()
# Post-process the results
result = ot.FunctionalChaosResult(algo.getResult())
ancova = ot.ANCOVA(result, sample)
indices = ancova.getIndices()
uncorrelatedIndices = ancova.getUncorrelatedIndices()
for i in range(inputDimension):
value = indices[i]
print("ANCOVA index", i, "= %.6g" %
value, "absolute error=%.6g" % fabs(value - Si[i][0]))
value = uncorrelatedIndices[i]
print("ANCOVA uncorrelated index", i, "= %.8f" %
value, "absolute error=%.6g" % fabs(value - Si[i][1]))
distribution = distribution.getMarginal(complement)
model = ot.ParametricFunction(model, selection,
distribution.getMarginal(selection).getMean())
input_names_copy = list(input_names)
input_names = itemgetter(*complement)(input_names)
dimension = len(complement)
# %%
# design of experiment
size = 1000
X = distribution.getSample(size)
Y = model(X)
# %%
# create a functional chaos model
algo = ot.FunctionalChaosAlgorithm(X, Y)
algo.run()
result = algo.getResult()
print(result.getResiduals())
print(result.getRelativeErrors())
# %%
# Quick summary of sensitivity analysis
sensitivityAnalysis = ot.FunctionalChaosSobolIndices(result)
print(sensitivityAnalysis.summary())
# %%
# draw Sobol' indices
first_order = [sensitivityAnalysis.getSobolIndex(i) for i in range(dimension)]
total_order = [
sensitivityAnalysis.getSobolTotalIndex(i) for i in range(dimension)
# %%
# Create the chaos.
# %%
multivariateBasis = ot.OrthogonalProductPolynomialFactory(
[im.X1, im.X2, im.X3])
selectionAlgorithm = ot.LeastSquaresMetaModelSelectionFactory()
projectionStrategy = ot.LeastSquaresStrategy(inputTrain, outputTrain,
selectionAlgorithm)
totalDegree = 8
enumfunc = multivariateBasis.getEnumerateFunction()
P = enumfunc.getStrataCumulatedCardinal(totalDegree)
adaptiveStrategy = ot.FixedStrategy(multivariateBasis, P)
chaosalgo = ot.FunctionalChaosAlgorithm(inputTrain, outputTrain,
im.distributionX, adaptiveStrategy,
projectionStrategy)
# %%
chaosalgo.run()
result = chaosalgo.getResult()
metamodel = result.getMetaModel()
# %%
# Print Sobol' indices
# %%
chaosSI = ot.FunctionalChaosSobolIndices(result)
print(chaosSI.summary())
# %%
]
sob_T2 = [
sob_2[0] + sob_2[1] + sob_3[0], sob_2[0] + sob_2[2] + sob_3[0],
sob_2[1] + sob_2[2] + sob_3[0]
]
sob_T3 = [sob_3[0]]
model = ot.SymbolicFunction(['xi1', 'xi2', 'xi3'], [
'sin(xi1) + (' + str(a) + ') * (sin(xi2)) ^ 2 + (' + str(b) +
') * xi3^4 * sin(xi1)'
])
# Create the input distribution
distribution = ot.ComposedDistribution([ot.Uniform(-pi, pi)] * dimension)
size = 1000
x = distribution.getSample(size)
y = model(x)
# To reduce the time needed by the test
ot.ResourceMap.SetAsUnsignedInteger(
"FittingTest-LillieforsMinimumSamplingSize", 4)
ot.ResourceMap.SetAsUnsignedInteger(
"FittingTest-LillieforsMaximumSamplingSize", 4)
algo = ot.FunctionalChaosAlgorithm(x, y)
algo.run()
result = algo.getResult()
sensitivity = ot.FunctionalChaosSobolIndices(result)
print(sensitivity.summary())
dim_input = im.model.getInputDimension()
marginalDegrees = [marginal_number_of_nodes] * dim_input
experiment = ot.GaussProductExperiment(standard_distribution, marginalDegrees)
print("Sample size = ", experiment.generate().getSize())
# %%
#
# We see that 216 nodes are involved in this quadrature rule, which is the result
# of :math:`6^3 = 216`.
#
# In the next cell, we compute the coefficients of the polynomial chaos expansion
# using integration.
# %%
projectionStrategy = ot.IntegrationStrategy(experiment)
chaosalgo = ot.FunctionalChaosAlgorithm(im.model, im.distributionX,
adaptiveStrategy, projectionStrategy)
chaosalgo.run()
result = chaosalgo.getResult()
# %%
#
# We now validate the metamodel by drawing the validation graph. We see that
# many points are close to the red test line, which indicates that the
# predictions of the polynomial chaos expansion are close to the output
# observations from the model.
# %%
view = draw_polynomial_chaos_validation(result, im.model, im.distributionX)
# %%
#
# We can see the size of the associated design of experiments.
# %%
experiment.generate().getSize()
# %%
# The choice of the `GaussProductExperiment` rule leads to 256 evaluations of the model.
# %%
projectionStrategy = ot.IntegrationStrategy(experiment)
# %%
# We can now create the functional chaos.
# %%
chaosalgo = ot.FunctionalChaosAlgorithm(g, myDistribution, adaptiveStrategy,
projectionStrategy)
chaosalgo.run()
# %%
# Get the result
#
# %%
result = chaosalgo.getResult()
# %%
# The `getMetaModel` method returns the metamodel function.
# %%
metamodel = result.getMetaModel()
def train(self):
self.input_dim = self.training_points[None][0][0].shape[1]
x_train = ot.Sample(self.training_points[None][0][0])
y_train = ot.Sample(self.training_points[None][0][1])
# Distribution choice of the inputs to Create the input distribution
distributions = []
dist_specs = self.options["uncertainty_specs"]
if dist_specs:
if len(dist_specs) != self.input_dim:
raise SurrogateOpenturnsException(
"Number of distributions should be equal to input \
dimensions. Should be {}, got {}".format(
self.input_dim, len(dist_specs)
)
)
for ds in dist_specs:
dist_klass = getattr(sys.modules["openturns"], ds["name"])
args = [ds["kwargs"][name] for name in DISTRIBUTION_SIGNATURES[ds["name"]]]
distributions.append(dist_klass(*args))
else:
for i in range(self.input_dim):
mean = np.mean(x_train[:, i])
lower, upper = 0.95 * mean, 1.05 * mean
if mean < 0:
lower, upper = upper, lower
distributions.append(ot.Uniform(lower, upper))
distribution = ot.ComposedDistribution(distributions)
# Polynomial basis
# step 1 - Construction of the multivariate orthonormal basis:
# Build orthonormal or orthogonal univariate polynomial families
# (associated to associated input distribution)
polynoms = [0.0] * self.input_dim
for i in range(distribution.getDimension()):
polynoms[i] = ot.StandardDistributionPolynomialFactory(
distribution.getMarginal(i)
)
enumerateFunction = ot.LinearEnumerateFunction(self.input_dim)
productBasis = ot.OrthogonalProductPolynomialFactory(
polynoms, enumerateFunction
)
# step 2 - Truncation strategy of the multivariate orthonormal basis:
# a strategy must be chosen for the selection of the different terms
# of the multivariate basis.
# Truncature strategy of the multivariate orthonormal basis
# We choose all the polynomials of degree <= degree
degree = self.options["pce_degree"]
index_max = enumerateFunction.getStrataCumulatedCardinal(degree)
adaptive_strategy = ot.FixedStrategy(productBasis, index_max)
basis_sequenceFactory = ot.LARS()
fitting_algorithm = ot.CorrectedLeaveOneOut()
approximation_algorithm = ot.LeastSquaresMetaModelSelectionFactory(
basis_sequenceFactory, fitting_algorithm
)
projection_strategy = ot.LeastSquaresStrategy(
x_train, y_train, approximation_algorithm
)
algo = ot.FunctionalChaosAlgorithm(
x_train, y_train, distribution, adaptive_strategy, projection_strategy
)
# algo = ot.FunctionalChaosAlgorithm(X_train_NS, Y_train_NS)
algo.run()
self._pce_result = algo.getResult()