# PhysicalSpaceCrossEntropyImportanceSampling¶

class PhysicalSpaceCrossEntropyImportanceSampling(*args)

Physical Space Cross-Entropy Importance Sampling.

Warning

This class is experimental and likely to be modified in future releases. To use it, import the openturns.experimental submodule.

Parameters:
eventThresholdEvent

Event we are computing the probability of.

List of active parameters indices for the auxiliary distribution.

Initial value of active parameters of the auxiliary distribution.

boundsInterval

Bounds on the active parameters of the auxiliary distribution.

auxiliaryDistributionDistribution

Auxiliary distribution for the Cross Entropy Importance Sampling algorithm.

quantileLevelfloat

Intermediate quantile level. The default number can be tweaked with the CrossEntropyImportanceSampling-DefaultQuantileLevel key from ResourceMap.

Notes

The following explanations are given for a failure event defined as with a random vector following a joint PDF , a threshold and a limit state function, without loss of generality.

The Importance Sampling (IS) probability estimate is given by:

with the auxiliary PDF of Importance Sampling, the number of independent samples generated with and the indicator function of the failure domain.

The optimal density minimizing the variance of the estimator is defined as:

with the failure probability which is inaccessible in practice since this probability is the quantity of interest and unknown.

The Physical Space Cross-Entropy Importance Sampling algorithm [rubinstein2017] uses a parametric auxiliary distribution in order to optimize its parameters to compute the probability of interest with accuracy.

It involves an auxiliary optimization problem to find the auxiliary distribution parameters minimizing the Kullback-Leibler divergence with respect to . The following algorithm is used:

1. , set the quantile level and

2. Generate the population according to the PDF , apply the function in order to have

3. Compute the empirical -quantile using the floor of .

4. If : go to Step 7

5. Estimate the auxiliary distribution parameters:

6. , go to Step 2

7. Estimate the probability .

Default solver for the optimization of auxiliary distribution is LD_LBFGS from NLopt.

Examples

>>> import openturns as ot
>>> import openturns.experimental as otexp
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution_R = ot.LogNormalMuSigma(300.0, 30.0, 0.0).getDistribution()
>>> distribution_F = ot.Normal(75e3, 5e3)
>>> marginals = [distribution_R, distribution_F]
>>> distribution = ot.ComposedDistribution(marginals)
>>> # create the model
>>> model = ot.SymbolicFunction(['R', 'F'], ['R-F/(pi_*100.0)'])
>>> # create the event
>>> vect = ot.RandomVector(distribution)
>>> G = ot.CompositeRandomVector(model, vect)
>>> event = ot.ThresholdEvent(G, ot.Less(), 0.0)
>>> distribution_marginR = ot.LogNormalMuSigma().getDistribution()
>>> distribution_marginF = ot.Normal()
>>> aux_marginals = [distribution_marginR, distribution_marginF]
>>> aux_distribution = ot.ComposedDistribution(aux_marginals)
>>> # definition of auxiliary distribution parameters to be optimized
>>> active_parameters = [0,1,2,3,4]
>>> ### WARNING : native parameters of distribution have to be considered
>>> bounds = ot.Interval([3,0.09,0.,50e3,2e3], [7,0.5,0.5,100e3,10e3])# bounds on the active parameters
>>> initial_theta= [5.70,0.1,0.,75e3,5e3] # initial value of the active parameters
>>> my_IS_physical = otexp.PhysicalSpaceCrossEntropyImportanceSampling(event,aux_distribution,active_parameters,initial_theta,bounds,0.3)
>>> my_IS_physical.run()
>>> myResult = my_IS_physical.getResult()


Methods

 Draw the probability convergence at a given level. Accessor to the block size. Accessor to the object's name. Accessor to the convergence strategy. Accessor to the event. Accessor to the object's id. Accessor to the maximum coefficient of variation. Accessor to the maximum sample size. Accessor to the maximum standard deviation. Accessor to the object's name. Accessor to the optimization algorithm used to update auxiliary distribution parameters. Accessor to the intermediate quantile level. Accessor to the intermediate quantile level. Accessor to the object's shadowed id. Accessor to verbosity. Accessor to the object's visibility state. Test if the object is named. Test if the object has a distinguishable name. Launch simulation. setBlockSize(blockSize) Accessor to the block size. setConvergenceStrategy(convergenceStrategy) Accessor to the convergence strategy. Accessor to the maximum coefficient of variation. setMaximumOuterSampling(maximumOuterSampling) Accessor to the maximum sample size. Accessor to the maximum standard deviation. setName(name) Accessor to the object's name. Accessor to the optimization algorithm used to update auxiliary distribution parameters. Set up a progress callback. setQuantileLevel(quantileLevel) Accessor to the intermediate quantile level. Accessor to the object's shadowed id. setStopCallback(*args) Set up a stop callback. setVerbose(verbose) Accessor to verbosity. setVisibility(visible) Accessor to the object's visibility state.
__init__(*args)
drawProbabilityConvergence(*args)

Draw the probability convergence at a given level.

Parameters:
levelfloat, optional

The probability convergence is drawn at this given confidence length level. By default level is 0.95.

Returns:
graph

probability convergence graph

getBlockSize()

Accessor to the block size.

Returns:
blockSizeint

Number of terms in the probability simulation estimator grouped together. It is set by default to 1.

getClassName()

Accessor to the object’s name.

Returns:
class_namestr

The object class name (object.__class__.__name__).

getConvergenceStrategy()

Accessor to the convergence strategy.

Returns:
storage_strategyHistoryStrategy

Storage strategy used to store the values of the probability estimator and its variance during the simulation algorithm.

getEvent()

Accessor to the event.

Returns:
eventRandomVector

Event we want to evaluate the probability.

getId()

Accessor to the object’s id.

Returns:
idint

Internal unique identifier.

getMaximumCoefficientOfVariation()

Accessor to the maximum coefficient of variation.

Returns:
coefficientfloat

Maximum coefficient of variation of the simulated sample.

getMaximumOuterSampling()

Accessor to the maximum sample size.

Returns:
outerSamplingint

Maximum number of groups of terms in the probability simulation estimator.

getMaximumStandardDeviation()

Accessor to the maximum standard deviation.

Returns:
sigmafloat,

Maximum standard deviation of the estimator.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getOptimizationAlgorithm()

Accessor to the optimization algorithm used to update auxiliary distribution parameters.

Returns:
algorithmOptimizationAlgorithm
getQuantileLevel()

Accessor to the intermediate quantile level.

Returns:
quantileLevel: float

Result of Cross Entropy algorithm.

getResult()

Accessor to the intermediate quantile level.

Returns:
CrossEntropyResult: class:~openturns.CrossEntropyResult

Contains all the results of the Cross Entropy algorithm simulation.

Accessor to the object’s shadowed id.

Returns:
idint

Internal unique identifier.

getVerbose()

Accessor to verbosity.

Returns:
verbosity_enabledbool

If True, the computation is verbose. By default it is verbose.

getVisibility()

Accessor to the object’s visibility state.

Returns:
visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns:
hasVisibleNamebool

True if the name is not empty and not the default one.

run()

Launch simulation.

setBlockSize, setMaximumOuterSampling, ResourceMap, SimulationResult

Notes

It launches the simulation and creates a SimulationResult, structure containing all the results obtained after simulation. It computes the probability of occurrence of the given event by computing the empirical mean of a sample of size at most outerSampling * blockSize, this sample being built by blocks of size blockSize. It allows one to use efficiently the distribution of the computation as well as it allows one to deal with a sample size by a combination of blockSize and outerSampling.

setBlockSize(blockSize)

Accessor to the block size.

Parameters:
blockSizeint,

Number of terms in the probability simulation estimator grouped together. It is set by default to 1.

Notes

For Monte Carlo, LHS and Importance Sampling methods, this allows one to save space while allowing multithreading, when available we recommend to use the number of available CPUs; for the Directional Sampling, we recommend to set it to 1.

setConvergenceStrategy(convergenceStrategy)

Accessor to the convergence strategy.

Parameters:
storage_strategyHistoryStrategy

Storage strategy used to store the values of the probability estimator and its variance during the simulation algorithm.

setMaximumCoefficientOfVariation(maximumCoefficientOfVariation)

Accessor to the maximum coefficient of variation.

Parameters:
coefficientfloat

Maximum coefficient of variation of the simulated sample.

setMaximumOuterSampling(maximumOuterSampling)

Accessor to the maximum sample size.

Parameters:
outerSamplingint

Maximum number of groups of terms in the probability simulation estimator.

setMaximumStandardDeviation(maximumStandardDeviation)

Accessor to the maximum standard deviation.

Parameters:
sigmafloat,

Maximum standard deviation of the estimator.

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

setOptimizationAlgorithm(solver)

Accessor to the optimization algorithm used to update auxiliary distribution parameters.

Parameters:
algorithmOptimizationAlgorithm
setProgressCallback(*args)

Set up a progress callback.

Can be used to programmatically report the progress of a simulation.

Parameters:
callbackcallable

Takes a float as argument as percentage of progress.

Examples

>>> import sys
>>> import openturns as ot
>>> experiment = ot.MonteCarloExperiment()
>>> X = ot.RandomVector(ot.Normal())
>>> Y = ot.CompositeRandomVector(ot.SymbolicFunction(['X'], ['1.1*X']), X)
>>> event = ot.ThresholdEvent(Y, ot.Less(), -2.0)
>>> algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
>>> algo.setMaximumOuterSampling(100)
>>> algo.setMaximumCoefficientOfVariation(-1.0)
>>> def report_progress(progress):
...     sys.stderr.write('-- progress=' + str(progress) + '%\n')
>>> algo.setProgressCallback(report_progress)
>>> algo.run()

setQuantileLevel(quantileLevel)

Accessor to the intermediate quantile level.

Parameters:
quantileLevel: float

Intermediate quantile level, must belong to the interval (0,1).

Accessor to the object’s shadowed id.

Parameters:
idint

Internal unique identifier.

setStopCallback(*args)

Set up a stop callback.

Can be used to programmatically stop a simulation.

Parameters:
callbackcallable

Returns an int deciding whether to stop or continue.

Examples

Stop a Monte Carlo simulation algorithm using a time limit

>>> import openturns as ot
>>> experiment = ot.MonteCarloExperiment()
>>> X = ot.RandomVector(ot.Normal())
>>> Y = ot.CompositeRandomVector(ot.SymbolicFunction(['X'], ['1.1*X']), X)
>>> event = ot.ThresholdEvent(Y, ot.Less(), -2.0)
>>> algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
>>> algo.setMaximumOuterSampling(10000000)
>>> algo.setMaximumCoefficientOfVariation(-1.0)
>>> timer = ot.TimerCallback(0.1)
>>> algo.setStopCallback(timer)
>>> algo.run()

setVerbose(verbose)

Accessor to verbosity.

Parameters:
verbosity_enabledbool

If True, make the computation verbose. By default it is verbose.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:
visiblebool

Visibility flag.

## Examples using the class¶

Cross Entropy Importance Sampling

Cross Entropy Importance Sampling