AdaptiveHitMissPOD¶

class
AdaptiveHitMissPOD
(*args)¶ Adaptive algorithm for hit miss data type.
Available constructor:
AdaptiveHitMissPOD(inputDOE, outputDOE, physicalModel, nMorePoints, detection, noiseThres, saturationThres)
 Parameters
 inputDOE2d sequence of float
Vector of the input values. The first column must correspond with the defect sizes.
 outputDOE2d sequence of float
Vector of the signals, of dimension 1.
 physicalModel
openturns.Function
True model used to compute the real hit miss value of the signal value to be added to the DOE.
 nMorePointspositive int
The number of points to add to the DOE, computed by the physicalModel.
 detectionfloat
Detection value of the signal if the physical model does not return a hit miss value.
 noiseThresfloat
Value for low censored data. Default is None.
 saturationThresfloat
Value for high censored data. Default is None
Warning
The first column of the input sample must corresponds with the defect sizes.
Notes
This class aims at building the POD based on a classifier model where the design of experiments is iteratively enriched. The initial design of experiments is given as input parameters. The enrichment criterion is based on the misclassification empirical risk. The criterion is computed on several candidate points. The sample of candidate points is created using a low discrepancy sequence (Sobol’) if the input distribution has an independant copula, otherwise a Monte Carlo experiment is used. The stopping criterion is only based on the number of points that must be added to the design of experiments.
The classifier algorithms availables are the SVC and the random forests. The choice of the algorithm can be defined using setClassifierType. The default algorithm is the random forests.
The physical model can return either the hit miss value (0 or 1) or the signal value. In this case, the detection value must be given and the physical model is transformed in order to provide a hit miss value.
The POD are computed by a Monte Carlo simulation for several defect values. The accuracy of the Monte Carlo simulation is taken into account using the TCL. The return POD model corresponds with an interpolate function built with the POD values computed for the given defect sizes. The default values are 20 defect sizes between the minimum and maximum value of the defect sample. The defect sizes can be changed using the method setDefectSizes.
A progress bar is shown if the verbosity is enabled. It can be disabled using the method setVerbose.
Methods
computeDetectionSize
(self, probabilityLevel)Compute the detection size for a given probability level.
drawBoxCoxLikelihood
(self[, name])Draw the loglikelihood versus the Box Cox parameter.
drawPOD
(self[, probabilityLevel, …])Draw the POD curve.
getBoxCoxParameter
(self)Accessor to the Box Cox parameter.
getCandidateSize
(self)Accessor to the number of candidate points.
getClassifier
(self)Accessor to the classifier model.
getClassifierParameters
(self)Accessor to the classifier parameters.
getClassifierType
(self)Accessor to the classifier type.
getConfusionMatrix
(self)Accessor to the confusion matrix.
getDefectSizes
(self)Accessor to the defect size where POD is computed.
getDistribution
(self)Accessor to the parameters distribution.
getGraphActive
(self)Accessor to the graph verbosity.
getInputDOE
(self)Accessor to the final input values of the DOE.
getOutputDOE
(self)Accessor to the final output values of the DOE.
getPMax
(self)Accessor to the upper probability bound for the point selections.
getPMin
(self)Accessor to the lower probability bound for the point selections.
getPODCLModel
(self[, confidenceLevel])Accessor to the POD model at a given confidence level.
getPODModel
(self)Accessor to the POD model.
getSamplingSize
(self)Accessor to the Monte Carlo sampling size.
getSimulationSize
(self)Accessor to the simulation size.
getVerbose
(self)Accessor to the verbosity.
run
(self)Launch the algorithm and build the POD models.
setCandidateSize
(self, size)Accessor to the number of candidate points.
setClassifierParameters
(self, parameters)Accessor to the classifier parameters.
setClassifierType
(self, classifier)Accessor to the classifier type.
setDefectSizes
(self, size)Accessor to the defect size where POD is computed.
setDistribution
(self, distribution)Accessor to the parameters distribution.
setGraphActive
(self, graphVerbose[, …])Accessor to the graph verbosity.
setPMax
(self, pmax)Accessor to the upper probability bound for the point selections.
setPMin
(self, pmin)Accessor to the lower probability bound for the point selections.
setSamplingSize
(self, size)Accessor to the Monte Carlo sampling size.
setSimulationSize
(self, size)Accessor to the simulation size.
setVerbose
(self, verbose)Accessor to the verbosity.

computeDetectionSize
(self, probabilityLevel, confidenceLevel=None)¶ Compute the detection size for a given probability level.
 Parameters
 probabilityLevelfloat
The probability level for which the defect size is computed.
 confidenceLevelfloat
The confidence level associated to the given probability level the defect size is computed. Default is None.
 Returns
 resultcollection of
openturns.PointWithDescription
A PointWithDescription containing the detection size computed at the given probability level and confidence level if provided.
 resultcollection of

drawBoxCoxLikelihood
(self, name=None)¶ Draw the loglikelihood versus the Box Cox parameter.
 Parameters
 namestring
name of the figure to be saved with transparent option sets to True and bbox_inches=’tight’. It can be only the file name or the full path name. Default is None.
 Returns
 figmatplotlib.figure
Matplotlib figure object.
 axmatplotlib.axes
Matplotlib axes object.
Notes
This method is available only when the parameter boxCox is set to True.

drawPOD
(self, probabilityLevel=None, confidenceLevel=None, defectMin=None, defectMax=None, nbPt=100, name=None)¶ Draw the POD curve.
 Parameters
 probabilityLevelfloat
The probability level for which the defect size is computed. Default is None.
 confidenceLevelfloat
The confidence level associated to the given probability level the defect size is computed. Default is None.
 defectMin, defectMaxfloat
Define the interval where the curve is plotted. Default : min and max values of the input sample.
 nbPtint
The number of points to draw the curves. Default is 100.
 namestring
name of the figure to be saved with transparent option sets to True and bbox_inches=’tight’. It can be only the file name or the full path name. Default is None.
 Returns
 figmatplotlib.figure
Matplotlib figure object.
 axmatplotlib.axes
Matplotlib axes object.

getBoxCoxParameter
(self)¶ Accessor to the Box Cox parameter.
 Returns
 lambdaBoxCoxfloat
The Box Cox parameter used to transform the data. If the transformation is not enabled None is returned.

getCandidateSize
(self)¶ Accessor to the number of candidate points.
 Returns
 sizeint
The number of candidate points on which the criterion is computed.

getClassifier
(self)¶ Accessor to the classifier model.
 Returns
 resultclassifier
The classifier model, either random forest or svm.

getClassifierParameters
(self)¶ Accessor to the classifier parameters.

getClassifierType
(self)¶ Accessor to the classifier type.

getConfusionMatrix
(self)¶ Accessor to the confusion matrix.

getDefectSizes
(self)¶ Accessor to the defect size where POD is computed.
 Returns
 defectSizesequence of float
The defect sizes where the Monte Carlo simulation is performed to compute the POD.

getDistribution
(self)¶ Accessor to the parameters distribution.
 Returns
 distribution
openturns.ComposedDistribution
The input parameters distribution, default is a Uniform distribution for all parameters.
 distribution

getGraphActive
(self)¶ Accessor to the graph verbosity.
 Returns
 graphVerbosebool
Enable or disable the display of the POD graph at each iteration. Default is False.

getInputDOE
(self)¶ Accessor to the final input values of the DOE.

getOutputDOE
(self)¶ Accessor to the final output values of the DOE.

getPMax
(self)¶ Accessor to the upper probability bound for the point selections.

getPMin
(self)¶ Accessor to the lower probability bound for the point selections.

getPODCLModel
(self, confidenceLevel=0.95)¶ Accessor to the POD model at a given confidence level.
 Parameters
 confidenceLevelfloat
The confidence level the POD must be computed. Default is 0.95
 Returns
 PODModelCl
openturns.Function
The function which computes the probability of detection for a given defect value at the confidence level given as parameter.
 PODModelCl

getPODModel
(self)¶ Accessor to the POD model.
 Returns
 PODModel
openturns.Function
The function which computes the probability of detection for a given defect value.
 PODModel

getSamplingSize
(self)¶ Accessor to the Monte Carlo sampling size.
 Returns
 sizeint
The size of the Monte Carlo simulation used to compute the POD for each defect size.

getSimulationSize
(self)¶ Accessor to the simulation size.
 Returns
 sizeint
The size of the simulation used to compute the confidence interval.

getVerbose
(self)¶ Accessor to the verbosity.
 Returns
 verbosebool
Enable or disable the verbosity. Default is True.

run
(self)¶ Launch the algorithm and build the POD models.
Notes
This method launches the iterative algorithm. Once the algorithm stops, it builds the POD models : Monte Carlo simulation are performed for each defect sizes with the final classifier model. Eventually, the sample is used to compute the mean POD and the POD at the confidence level.

setCandidateSize
(self, size)¶ Accessor to the number of candidate points.
 Parameters
 sizeint
The number of candidate points on which the criterion is computed

setClassifierParameters
(self, parameters)¶ Accessor to the classifier parameters.

setClassifierType
(self, classifier)¶ Accessor to the classifier type.

setDefectSizes
(self, size)¶ Accessor to the defect size where POD is computed.
 Parameters
 defectSizesequence of float
The defect sizes where the Monte Carlo simulation is performed to compute the POD.

setDistribution
(self, distribution)¶ Accessor to the parameters distribution.
 Parameters
 distribution
openturns.ComposedDistribution
The input parameters distribution.
 distribution

setGraphActive
(self, graphVerbose, probabilityLevel=None, confidenceLevel=None, directory=None)¶ Accessor to the graph verbosity.
 Parameters
 graphVerbosebool
Enable or disable the display of the POD graph at each iteration.
 probabilityLevelfloat
The probability level for which the defect size is computed. Default is None.
 confidenceLevelfloat
The confidence level associated to the given probability level the defect size is computed. Default is None.
 directorystring
Directory where to save the graphs as png files.

setPMax
(self, pmax)¶ Accessor to the upper probability bound for the point selections.

setPMin
(self, pmin)¶ Accessor to the lower probability bound for the point selections.

setSamplingSize
(self, size)¶ Accessor to the Monte Carlo sampling size.
 Parameters
 sizeint
The size of the Monte Carlo simulation used to compute the POD for each defect size.

setSimulationSize
(self, size)¶ Accessor to the simulation size.
 Parameters
 sizeint
The size of the simulation used to compute the confidence interval.

setVerbose
(self, verbose)¶ Accessor to the verbosity.
 Parameters
 verbosebool
Enable or disable the verbosity.