From a8f779f5628a4c1a932a8ad6badf0e93d49ee687 Mon Sep 17 00:00:00 2001 From: Daniel Garrett Date: Wed, 9 Feb 2022 08:15:14 -0700 Subject: [PATCH] EconomicRatio PostProcessor Enhancements (#1763) * inherited collectOutput method has more comprehensive error/warning checking no need to overload * adding steVals from BasicStatistics to EconomicRatio * update getInputSpecification * inherited inputToInternal is more comprehensive, use it * update initialize method to include _ste metadata and all BasicStatistics metrics * updating _handleInput to allow BasicStatistics metrics through to EconomicRatio and not throw warnings * updating handling of targets and run method * update __runLocal to handle vectorVals in BasicStatistics * __computePower copy/pasted from BasicStatistics change to _computePower and inherit * adding standard error calculation of percentiles * adding threshold name to variable names percentile, value at risk, sortinoRatio, gainLossRatio, and expectedShortfall * adding standard error calculation of value at risk similar to percentiles * update user manual to include requesting BasicStatistics metrics from EconomicRatio * removing empty list as optional argument, replaced with None * adding reference for standard error calculations * adding docstring to constructor * renaming tealVals to econVals since they don't come from TEAL * adding defensive check where all values are equal * update percentile/VaR standard error for data with pivotParameter * fixing implementation with DataSet and golding files from tests * remove misleading descriptions using teal * fixing implementation for DataSet output * fixing DataSet implementation * adding tests for EconomicRatio PostProcessor * adding additional samples to test --- .../PostProcessors/EconomicRatio.tex | 12 +- doc/user_manual/postprocessor.tex | 6 +- .../Models/PostProcessors/BasicStatistics.py | 143 +++-- .../Models/PostProcessors/EconomicRatio.py | 553 ++++++++++-------- .../datasetPrint.csv | 434 +++++++------- .../analyticalTest_basicStatPP_dump.csv | 4 +- .../analyticalTest_basicStatPP_dump.xml | 2 + .../economicRatioExternalModelTest.py | 53 ++ .../economicRatioTimeDependent/samples.csv | 6 + .../EconVaRResult_dump.csv | 2 + .../EconVaRResult_dump.xml | 11 + .../HistorySetPostProcTest_dump.csv | 2 + .../HistorySetPostProcTest_dump.xml | 168 ++++++ .../HistorySetPostProcTest_dump_0.csv | 4 + .../datasetPrint.csv | 217 +++++++ .../EconomicRatio/test_economic_ratio.xml | 156 +++++ .../PostProcessors/EconomicRatio/tests | 31 + .../PostProcessors/EconomicRatio/time_dep.xml | 124 ++++ .../EconomicRatio/time_dep_dataset.xml | 147 +++++ 19 files changed, 1584 insertions(+), 491 deletions(-) create mode 100644 tests/framework/PostProcessors/EconomicRatio/economicRatioTimeDependent/economicRatioExternalModelTest.py create mode 100644 tests/framework/PostProcessors/EconomicRatio/economicRatioTimeDependent/samples.csv create mode 100644 tests/framework/PostProcessors/EconomicRatio/gold/economicRatioGeneral/EconVaRResult_dump.csv create mode 100644 tests/framework/PostProcessors/EconomicRatio/gold/economicRatioGeneral/EconVaRResult_dump.xml create mode 100644 tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump.csv create mode 100644 tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump.xml create mode 100644 tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump_0.csv create mode 100644 tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/datasetPrint.csv create mode 100644 tests/framework/PostProcessors/EconomicRatio/test_economic_ratio.xml create mode 100644 tests/framework/PostProcessors/EconomicRatio/tests create mode 100644 tests/framework/PostProcessors/EconomicRatio/time_dep.xml create mode 100644 tests/framework/PostProcessors/EconomicRatio/time_dep_dataset.xml diff --git a/doc/user_manual/PostProcessors/EconomicRatio.tex b/doc/user_manual/PostProcessors/EconomicRatio.tex index 5538f0d9b4..b8fe30c001 100644 --- a/doc/user_manual/PostProcessors/EconomicRatio.tex +++ b/doc/user_manual/PostProcessors/EconomicRatio.tex @@ -2,6 +2,7 @@ \subsubsection{EconomicRatio} \label{EconomicRatio} The \xmlNode{EconomicRatio} post-processor provides the economic metrics from the percent change period return of the asset or strategy that is given as an input. These metrics measure the risk-adjusted returns. +\nb Any metric from \xmlNode{BasicStatistics} may be requested from \xmlNode{EconomicRatio}. % \ppType{EconomicRatio}{EconomicRatio} @@ -34,12 +35,17 @@ \subsubsection{EconomicRatio} \itemsep0em \item \xmlAttr{prefix}, \xmlDesc{required string attribute}, user-defined prefix for the given \textbf{metric}. For scalar quantifies, RAVEN will define a variable with name defined as: ``prefix'' + ``\_'' + ``parameter name''. - For example, if we define ``mean'' as the prefix for \textbf{expectedValue}, and parameter ``x'', then variable - ``mean\_x'' will be defined by RAVEN. + For example, if we define ``sharpe'' as the prefix for \textbf{sharpeRatio}, and parameter ``x'', then variable + ``sharpe\_x'' will be defined by RAVEN. For \textbf{metrics} that include a ``threshold'' parameter, + RAVEN will define a variable with name defined as: ``prefix'' + ``\_'' + ``threshold'' + ``\_'' + ``parameter name''. + For example, if we define ``VaR'' as the prefix for \textbf{valueAtRisk}, threshold ``0.05'', and parameter name ``x'', + then variable ``VaR\_0.05\_x'' will be defined by RAVEN. If we define ``glr'' as the prefix for \textbf{gainLossRatio}, + ``median'' as the threshold, and ``x'' as the parameter name, then the variable ``glr\_median\_x'' + will be defined by RAVEN. For matrix quantities, RAVEN will define a variable with name defined as: ``prefix'' + ``\_'' + ``target parameter name'' + ``\_'' + ``feature parameter name''. For example, if we define ``sen'' as the prefix for \textbf{sensitivity}, target ``y'' and feature ``x'', then variable ``sen\_y\_x'' will be defined by RAVEN. - \nb These variable will be used by RAVEN for the internal calculations. It is also accessible by the user through + \nb These variables will be used by RAVEN for the internal calculations. It is also accessible by the user through \textbf{DataObjects} and \textbf{OutStreams}. \end{itemize} diff --git a/doc/user_manual/postprocessor.tex b/doc/user_manual/postprocessor.tex index 240c17bdee..9978382d76 100644 --- a/doc/user_manual/postprocessor.tex +++ b/doc/user_manual/postprocessor.tex @@ -144,7 +144,10 @@ \subsubsection{BasicStatistics} \item \xmlAttr{prefix}, \xmlDesc{required string attribute}, user-defined prefix for the given \textbf{metric}. For scalar quantifies, RAVEN will define a variable with name defined as: ``prefix'' + ``\_'' + ``parameter name''. For example, if we define ``mean'' as the prefix for \textbf{expectedValue}, and parameter ``x'', then variable - ``mean\_x'' will be defined by RAVEN. + ``mean\_x'' will be defined by RAVEN. For \textbf{percentile}, RAVEN will define a variable with name defined as: + ``prefix'' + ``\_'' + ``percent'' + ``\_'' + ``parameter name''. For example, if we define ``perc'' as the prefix + for \textbf{percentile}, percent as ``10'', and parameter ``x'', then variable ``perc\_10\_x'' will + be defined by RAVEN. For matrix quantities, RAVEN will define a variable with name defined as: ``prefix'' + ``\_'' + ``target parameter name'' + ``\_'' + ``feature parameter name''. For example, if we define ``sen'' as the prefix for \textbf{sensitivity}, target ``y'' and feature ``x'', then variable ``sen\_y\_x'' will be defined by RAVEN. @@ -169,6 +172,7 @@ \subsubsection{BasicStatistics} \item \textbf{sigma} \item \textbf{skewness} \item \textbf{kurtosis} + \item \textbf{percentile} \end{itemize} RAVEN will define a variable with name defined as: ``prefix for given \textbf{metric}'' + ``\_ste\_'' + ``parameter name'' to store standard error of given \textbf{metric} with respect to given parameter. This information will be stored in the DataObjects, diff --git a/framework/Models/PostProcessors/BasicStatistics.py b/framework/Models/PostProcessors/BasicStatistics.py index 6a5bca9229..65e018ee02 100644 --- a/framework/Models/PostProcessors/BasicStatistics.py +++ b/framework/Models/PostProcessors/BasicStatistics.py @@ -14,7 +14,7 @@ """ Created on July 10, 2013 -@author: alfoa, wangc +@author: alfoa, wangc, dgarrett622 """ #External Modules--------------------------------------------------------------- import numpy as np @@ -23,7 +23,7 @@ from collections import OrderedDict, defaultdict import six import xarray as xr - +import scipy.stats as stats #External Modules End----------------------------------------------------------- #Internal Modules--------------------------------------------------------------- @@ -53,7 +53,7 @@ class BasicStatistics(PostProcessorInterface): 'higherPartialVariance', # Statistic metric not available yet 'higherPartialSigma', # Statistic metric not available yet 'lowerPartialSigma', # Statistic metric not available yet - 'lowerPartialVariance' # Statistic metric not available yet + 'lowerPartialVariance' # Statistic metric not available yet ] vectorVals = ['sensitivity', 'covariance', @@ -67,7 +67,8 @@ class BasicStatistics(PostProcessorInterface): 'variance_ste', 'sigma_ste', 'skewness_ste', - 'kurtosis_ste'] + 'kurtosis_ste', + 'percentile_ste'] @classmethod def getInputSpecification(cls): @@ -265,37 +266,62 @@ def initialize(self, runInfo, inputs, initDict): inputMetaKeys = [] outputMetaKeys = [] for metric, infos in self.toDo.items(): - steMetric = metric + '_ste' - if steMetric in self.steVals: - for info in infos: - prefix = info['prefix'] - for target in info['targets']: - metaVar = prefix + '_ste_' + target if not self.outputDataset else metric + '_ste' - metaDim = inputObj.getDimensions(target) - if len(metaDim[target]) == 0: - inputMetaKeys.append(metaVar) - else: - outputMetaKeys.append(metaVar) + if metric in self.scalarVals + self.vectorVals: + steMetric = metric + '_ste' + if steMetric in self.steVals: + for info in infos: + prefix = info['prefix'] + for target in info['targets']: + if metric == 'percentile': + for strPercent in info['strPercent']: + metaVar = prefix + '_' + strPercent + '_ste_' + target if not self.outputDataset else metric + '_ste' + metaDim = inputObj.getDimensions(target) + if len(metaDim[target]) == 0: + inputMetaKeys.append(metaVar) + else: + outputMetaKeys.append(metaVar) + else: + metaVar = prefix + '_ste_' + target if not self.outputDataset else metric + '_ste' + metaDim = inputObj.getDimensions(target) + if len(metaDim[target]) == 0: + inputMetaKeys.append(metaVar) + else: + outputMetaKeys.append(metaVar) metaParams = {} if not self.outputDataset: if len(outputMetaKeys) > 0: metaParams = {key:[self.pivotParameter] for key in outputMetaKeys} else: if len(outputMetaKeys) > 0: - params = {key:[self.pivotParameter,self.steMetaIndex] for key in outputMetaKeys + inputMetaKeys} + params = {} + for key in outputMetaKeys + inputMetaKeys: + # percentile standard error has additional index + if key == 'percentile_ste': + params[key] = [self.pivotParameter, self.steMetaIndex, 'percent'] + else: + params[key] = [self.pivotParameter, self.steMetaIndex] metaParams.update(params) elif len(inputMetaKeys) > 0: - params = {key:[self.steMetaIndex] for key in inputMetaKeys} + params = {} + for key in inputMetaKeys: + # percentile standard error has additional index + if key == 'percentile_ste': + params[key] = [self.pivotParameter, self.steMetaIndex, 'percent'] + else: + params[key] = [self.pivotParameter, self.steMetaIndex] metaParams.update(params) metaKeys = inputMetaKeys + outputMetaKeys self.addMetaKeys(metaKeys,metaParams) - def _handleInput(self, paramInput): + def _handleInput(self, paramInput, childVals=None): """ Function to handle the parsed paramInput for this class. @ In, paramInput, ParameterInput, the already parsed input. + @ In, childVals, list, optional, quantities requested from child statistical object @ Out, None """ + if childVals is None: + childVals = [] self.toDo = {} for child in paramInput.subparts: tag = child.getName() @@ -355,11 +381,12 @@ def _handleInput(self, paramInput): elif tag == "multipleFeatures": self.multipleFeatures = child.value else: - self.raiseAWarning('Unrecognized node in BasicStatistics "',tag,'" has been ignored!') + if tag not in childVals: + self.raiseAWarning('Unrecognized node in BasicStatistics "',tag,'" has been ignored!') assert (len(self.toDo)>0), self.raiseAnError(IOError, 'BasicStatistics needs parameters to work on! Please check input for PP: ' + self.name) - def __computePower(self, p, dataset): + def _computePower(self, p, dataset): """ Compute the p-th power of weights @ In, p, int, the power @@ -383,7 +410,7 @@ def __computeVp(self,p,weights): @ In, weights, xarray.Dataset, probability weights of all input variables @ Out, vp, xarray.Dataset, the sum of p-th power of weights """ - vp = self.__computePower(p,weights) + vp = self._computePower(p,weights) vp = vp.sum() return vp @@ -449,7 +476,7 @@ def _computeKurtosis(self, arrayIn, expValue, variance, pbWeight=None, dim=None) """ if dim is None: dim = self.sampleTag - vr = self.__computePower(2.0, variance) + vr = self._computePower(2.0, variance) if pbWeight is not None: unbiasCorr = self.__computeUnbiasedCorrection(4,pbWeight) if not self.biased else 1.0 vp = 1.0/self.__computeVp(1,pbWeight) @@ -482,7 +509,7 @@ def _computeSkewness(self, arrayIn, expValue, variance, pbWeight=None, dim=None) """ if dim is None: dim = self.sampleTag - vr = self.__computePower(1.5, variance) + vr = self._computePower(1.5, variance) if pbWeight is not None: unbiasCorr = self.__computeUnbiasedCorrection(3,pbWeight) if not self.biased else 1.0 vp = 1.0/self.__computeVp(1,pbWeight) @@ -590,7 +617,6 @@ def _computeWeightedPercentile(self,arrayIn,pbWeight,percent=0.5): result = sortedWeightsAndPoints[indexL,1] return result - def __runLocal(self, inputData): """ This method executes the postprocessor action. In this case, it computes all the requested statistical FOMs @@ -730,7 +756,7 @@ def __runLocal(self, inputData): metric = 'sigma' if len(needed[metric]['targets'])>0: self.raiseADebug('Starting "'+metric+'"...') - sigmaDS = self.__computePower(0.5,calculations['variance'][list(needed[metric]['targets'])]) + sigmaDS = self._computePower(0.5,calculations['variance'][list(needed[metric]['targets'])]) self.calculations[metric] = sigmaDS calculations[metric] = sigmaDS # @@ -806,7 +832,7 @@ def __runLocal(self, inputData): metric = 'lowerPartialSigma' if len(needed[metric]['targets'])>0: self.raiseADebug('Starting "'+metric+'"...') - lpsDS = self.__computePower(0.5,calculations['lowerPartialVariance'][list(needed[metric]['targets'])]) + lpsDS = self._computePower(0.5,calculations['lowerPartialVariance'][list(needed[metric]['targets'])]) calculations[metric] = lpsDS # # higherPartialVariance @@ -826,17 +852,22 @@ def __runLocal(self, inputData): metric = 'higherPartialSigma' if len(needed[metric]['targets'])>0: self.raiseADebug('Starting "'+metric+'"...') - hpsDS = self.__computePower(0.5,calculations['higherPartialVariance'][list(needed[metric]['targets'])]) + hpsDS = self._computePower(0.5,calculations['higherPartialVariance'][list(needed[metric]['targets'])]) calculations[metric] = hpsDS ############################################################ - # compute standard error for expectedValue + # Begin Standard Error Calculations + # + # Reference for standard error calculations (including percentile): + # B. Harding, C. Tremblay and D. Cousineau, "Standard errors: A review and evaluation of + # standard error estimators using Monte Carlo simulations", The Quantitative Methods of + # Psychology, Vol. 10, No. 2 (2014) ############################################################ metric = 'expectedValue' if len(needed[metric]['targets'])>0: self.raiseADebug('Starting calculate standard error on"'+metric+'"...') if self.pbPresent: - factor = self.__computePower(0.5,calculations['equivalentSamples']) + factor = self._computePower(0.5,calculations['equivalentSamples']) else: factor = np.sqrt(self.sampleSize) calculations[metric+'_ste'] = calculations['sigma'][list(needed[metric]['targets'])]/factor @@ -848,7 +879,7 @@ def __runLocal(self, inputData): if self.pbPresent: en = calculations['equivalentSamples'][varList] factor = 2.0 /(en - 1.0) - factor = self.__computePower(0.5,factor) + factor = self._computePower(0.5,factor) else: factor = np.sqrt(2.0/(float(self.sampleSize) - 1.0)) calculations[metric+'_ste'] = calculations['sigma'][varList]**2 * factor @@ -860,7 +891,7 @@ def __runLocal(self, inputData): if self.pbPresent: en = calculations['equivalentSamples'][varList] factor = 2.0 * (en - 1.0) - factor = self.__computePower(0.5,factor) + factor = self._computePower(0.5,factor) else: factor = np.sqrt(2.0 * (float(self.sampleSize) - 1.0)) calculations[metric+'_ste'] = calculations['sigma'][varList] / factor @@ -878,7 +909,7 @@ def __runLocal(self, inputData): if self.pbPresent: en = calculations['equivalentSamples'][varList] factor = 6.*en*(en-1.)/((en-2.)*(en+1.)*(en+3.)) - factor = self.__computePower(0.5,factor) + factor = self._computePower(0.5,factor) calculations[metric+'_ste'] = xr.full_like(calculations[metric],1.0) * factor else: en = float(self.sampleSize) @@ -891,8 +922,8 @@ def __runLocal(self, inputData): varList = list(needed[metric]['targets']) if self.pbPresent: en = calculations['equivalentSamples'][varList] - factor1 = self.__computePower(0.5,6.*en*(en-1.)/((en-2.)*(en+1.)*(en+3.))) - factor2 = self.__computePower(0.5,(en**2-1.)/((en-3.0)*(en+5.0))) + factor1 = self._computePower(0.5,6.*en*(en-1.)/((en-2.)*(en+1.)*(en+3.))) + factor2 = self._computePower(0.5,(en**2-1.)/((en-3.0)*(en+5.0))) factor = 2.0 * factor1 * factor2 calculations[metric+'_ste'] = xr.full_like(calculations[metric],1.0) * factor else: @@ -957,6 +988,46 @@ def __runLocal(self, inputData): percentileSet = percentileSet.rename({'quantile':'percent'}) calculations[metric] = percentileSet + # because percentile is different, calculate standard error here + self.raiseADebug('Starting calculate standard error on "'+metric+'"...') + percentileSteSet = xr.Dataset() + calculatedPercentiles = calculations[metric] + relWeight = pbWeights[list(needed[metric]['targets'])] + for target in needed[metric]['targets']: + targWeight = relWeight[target].values + en = targWeight.sum()**2/np.sum(targWeight**2) + targDa = dataSet[target] + if self.pivotParameter in targDa.sizes.keys(): + percentileSte = [] + for pct in percent: + subPercentileSte = [] + factor = np.sqrt(pct*(1.0 - pct)/en) + for label, group in targDa.groupby(self.pivotParameter): + if group.values.min() == group.values.max(): + # all values are the same + subPercentileSte.append(0.0) + else: + # get KDE + kde = stats.gaussian_kde(group.values, weights=targWeight) + val = calculatedPercentiles[target].sel(**{'percent': pct, self.pivotParameter: label}).values + subPercentileSte.append(factor/kde(val)[0]) + percentileSte.append(subPercentileSte) + da = xr.DataArray(percentileSte, dims=('percent', self.pivotParameter), coords={'percent': percent, self.pivotParameter: self.pivotValue}) + percentileSteSet[target] = da + else: + calcPercentiles = calculatedPercentiles[target] + if targDa.values.min() == targDa.values.max(): + # distribution is a delta function, so no KDE construction + percentileSte = list(np.zeros(calcPercentiles.shape)) + else: + # get KDE + kde = stats.gaussian_kde(targDa.values, weights=targWeight) + factor = np.sqrt(np.array(percent)*(1.0 - np.array(percent))/en) + percentileSte = list(factor/kde(calcPercentiles.values)) + da = xr.DataArray(percentileSte, dims=('percent'), coords={'percent': percent}) + percentileSteSet[target] = da + calculations[metric+'_ste'] = percentileSteSet + def startVector(metric): """ Common method among all metrics for establishing parameters @@ -1198,6 +1269,10 @@ def getCovarianceSubset(desired): varName = '_'.join([prefix,percent,target]) percentVal = float(percent)/100. outputDict[varName] = np.atleast_1d(outputSet[metric].sel(**{'targets':target,'percent':percentVal})) + steMetric = metric + '_ste' + if steMetric in self.steVals: + metaVar = '_'.join([prefix,percent,'ste',target]) + outputDict[metaVar] = np.atleast_1d(outputSet[steMetric].sel(**{'targets':target,'percent':percentVal})) else: #check if it was skipped for some reason skip = self.skipped.get(metric, None) diff --git a/framework/Models/PostProcessors/EconomicRatio.py b/framework/Models/PostProcessors/EconomicRatio.py index 9f1840431b..a8938465f2 100644 --- a/framework/Models/PostProcessors/EconomicRatio.py +++ b/framework/Models/PostProcessors/EconomicRatio.py @@ -14,11 +14,12 @@ """ Created on Aug 4, 2020 -@author: ZHOUJ2 +@author: ZHOUJ2, dgarrett622 """ #External Modules--------------------------------------------------------------- import numpy as np import xarray as xr +import scipy.stats as stats #External Modules End----------------------------------------------------------- #Internal Modules--------------------------------------------------------------- @@ -32,10 +33,14 @@ class EconomicRatio(BasicStatistics): """ EconomicRatio filter class. It computes economic metrics """ + + # values from BasicStatistics scalarVals = BasicStatistics.scalarVals vectorVals = BasicStatistics.vectorVals + steVals = BasicStatistics.steVals + ['valueAtRisk_ste'] - tealVals = ['sharpeRatio', #financial metric + # economic/financial metrics + econVals = ['sharpeRatio', #financial metric 'sortinoRatio', #financial metric 'gainLossRatio', #financial metric 'valueAtRisk', # Value at risk (alpha) @@ -51,81 +56,31 @@ class cls. @ Out, inputSpecification, InputData.ParameterInput, class to use for specifying input of cls. """ - inputSpecification = super(EconomicRatio, cls).getInputSpecification() - - for scalar in cls.scalarVals: - scalarSpecification = InputData.parameterInputFactory(scalar, contentType=InputTypes.StringListType) - scalarSpecification.addParam("prefix", InputTypes.StringType) - inputSpecification.addSub(scalarSpecification) - - for teal in cls.tealVals: - tealSpecification = InputData.parameterInputFactory(teal, contentType=InputTypes.StringListType) - if teal in['sortinoRatio','gainLossRatio']: - tealSpecification.addParam("threshold", InputTypes.StringType) - elif teal in['expectedShortfall','valueAtRisk']: - tealSpecification.addParam("threshold", InputTypes.FloatType) - tealSpecification.addParam("prefix", InputTypes.StringType) - inputSpecification.addSub(tealSpecification) - pivotParameterInput = InputData.parameterInputFactory('pivotParameter', contentType=InputTypes.StringType) - inputSpecification.addSub(pivotParameterInput) + # get input specification from BasicStatistics (scalarVals and vectorVals) + inputSpecification = super(EconomicRatio, cls).getInputSpecification() + # add econVals + for econ in cls.econVals: + econSpecification = InputData.parameterInputFactory(econ, + contentType=InputTypes.StringListType) + if econ in ["sortinoRatio", "gainLossRatio"]: + econSpecification.addParam("threshold", InputTypes.StringType) + elif econ in ["expectedShortfall", "valueAtRisk"]: + econSpecification.addParam("threshold", InputTypes.FloatType) + econSpecification.addParam("prefix", InputTypes.StringType) + inputSpecification.addSub(econSpecification) return inputSpecification - def inputToInternal(self, currentInp): + def __init__(self): """ - Method to convert an input object into the internal format that is - understandable by this pp. - @ In, currentInp, object, an object that needs to be converted - @ Out, (inputDataset, pbWeights), tuple, the dataset of inputs and the corresponding variable probability weight + Constructor + @ In, None + @ Out, None """ - # The EconomicRatio postprocessor only accept DataObjects - currentInput = currentInp [-1] if type(currentInp) == list else currentInp - if len(currentInput) == 0: - self.raiseAnError(IOError, "In post-processor " +self.name+" the input "+currentInput.name+" is empty.") - pbWeights = None - - if currentInput.type not in ['PointSet','HistorySet']: - self.raiseAnError(IOError, self, 'EconomicRatio postprocessor accepts PointSet and HistorySet only! Got ' + currentInput.type) - # extract all required data from input DataObjects, an input dataset is constructed - dataSet = currentInput.asDataset() - inputDataset = dataSet[self.parameters['targets']] - self.sampleTag = currentInput.sampleTag - - if currentInput.type == 'HistorySet': - dims = inputDataset.sizes.keys() - if self.pivotParameter is None: - if len(dims) > 1: - self.raiseAnError(IOError, self, 'Time-dependent statistics is requested (HistorySet) but no pivotParameter \ - got inputted!') - elif self.pivotParameter not in dims: - self.raiseAnError(IOError, self, 'Pivot parameter', self.pivotParameter, 'is not the associated index for \ - requested variables', ','.join(self.parameters['targets'])) - else: - if not currentInput.checkIndexAlignment(indexesToCheck=self.pivotParameter): - self.raiseAnError(IOError, "The data provided by the data objects", currentInput.name, "is not synchronized!") - self.pivotValue = inputDataset[self.pivotParameter].values - if self.pivotValue.size != len(inputDataset.groupby(self.pivotParameter)): - msg = "Duplicated values were identified in pivot parameter, please use the 'HistorySetSync'" + \ - " PostProcessor to syncronize your data before running 'EconomicRatio' PostProcessor." - self.raiseAnError(IOError, msg) - # extract all required meta data - metaVars = currentInput.getVars('meta') - self.pbPresent = True if 'ProbabilityWeight' in metaVars else False - - if self.pbPresent: - pbWeights = xr.Dataset() - self.realizationWeight = dataSet[['ProbabilityWeight']]/dataSet[['ProbabilityWeight']].sum() - for target in self.parameters['targets']: - pbName = 'ProbabilityWeight-' + target - if pbName in metaVars: - pbWeights[target] = dataSet[pbName]/dataSet[pbName].sum() - elif self.pbPresent: - pbWeights[target] = self.realizationWeight['ProbabilityWeight'] - else: - self.raiseAWarning('EconomicRatio postprocessor did not detect ProbabilityWeights! Assuming unit weights instead...') - return inputDataset, pbWeights + super().__init__() + self.printTag = "PostProcessor ECONOMIC RATIO" def initialize(self, runInfo, inputs, initDict): """ @@ -136,25 +91,76 @@ def initialize(self, runInfo, inputs, initDict): @ In, initDict, dict, dictionary with initialization options @ Out, None """ - #construct a list of all the parameters that have requested values into self.allUsedParams - self.allUsedParams = set() - for metricName in self.scalarVals + self.tealVals: + + # construct a list of all the parameters that have requested values from scalarVals + # and vectorVals into self.allUsedParams + super().initialize(runInfo, inputs, initDict) + + # add a list of all the parameters that have requested values from econVals into + # self.allUsedParams + for metricName in self.econVals: if metricName in self.toDo.keys(): for entry in self.toDo[metricName]: - self.allUsedParams.update(entry['targets']) - - #for backward compatibility, compile the full list of parameters used in Economic Ratio calculations - self.parameters['targets'] = list(self.allUsedParams) - PostProcessorInterface.initialize(self, runInfo, inputs, initDict) - inputObj = inputs[-1] if type(inputs) == list else inputs + self.allUsedParams.update(entry["targets"]) + try: + self.allUsedParams.update(entry["features"]) + except KeyError: + pass + + # for backward compatibility, compile the full list of parameters used in EconomicRatio calculations + self.parameters["targets"] = list(self.allUsedParams) + inputObj = inputs[-1] if type(inputs) == list else inputs + if inputObj.type == "HistorySet": + self.dynamic = True inputMetaKeys = [] outputMetaKeys = [] + for metric, infos in self.toDo.items(): + if metric in self.econVals: + steMetric = metric + "_ste" + if steMetric in self.steVals: + for info in infos: + prefix = info["prefix"] + for target in info["targets"]: + if metric == 'valueAtRisk': + for strThreshold in info['strThreshold']: + metaVar = prefix + '_' + strThreshold + '_ste_' + target if not self.outputDataset else metric + '_ste' + metaDim = inputObj.getDimensions(target) + if len(metaDim[target]) == 0: + inputMetaKeys.append(metaVar) + else: + outputMetaKeys.append(metaVar) + else: + metaVar = prefix + '_ste_' + target if not self.outputDataset else metric + '_ste' + metaDim = inputObj.getDimensions(target) + if len(metaDim[target]) == 0: + inputMetaKeys.append(metaVar) + else: + outputMetaKeys.append(metaVar) metaParams = {} - if len(outputMetaKeys) > 0: - metaParams = {key:[self.pivotParameter] for key in outputMetaKeys} - + if not self.outputDataset: + if len(outputMetaKeys) > 0: + metaParams = {key:[self.pivotParameter] for key in outputMetaKeys} + else: + if len(outputMetaKeys) > 0: + params = {} + for key in outputMetaKeys + inputMetaKeys: + # valueAtRisk standard error has additional index + if key == "valueAtRisk_ste": + params[key] = [self.pivotParameter, self.steMetaIndex, "threshold"] + else: + params[key] = [self.pivotParameter, self.steMetaIndex] + metaParams.update(params) + elif len(inputMetaKeys) > 0: + params = {} + for key in inputMetaKeys: + # valueAtRisk standard error has additional index + if key == "valueAtRisk_ste": + params[key] = [self.pivotParameter, self.steMetaIndex, "threshold"] + else: + params[key] = [self.pivotParameter, self.steMetaIndex] + metaParams.update(params) metaKeys = inputMetaKeys + outputMetaKeys - self.addMetaKeys(metaKeys,metaParams) + self.addMetaKeys(metaKeys, metaParams) def _localReadMoreXML(self, xmlNode): """ @@ -173,98 +179,118 @@ def _handleInput(self, paramInput): @ In, paramInput, ParameterInput, the already parsed input. @ Out, None """ - self.toDo = {} + + # handle scalarVals and vectorVals from BasicStatistics + super()._handleInput(paramInput, childVals=self.econVals) + + # now handle econVals for child in paramInput.subparts: tag = child.getName() - if tag in self.scalarVals + self.tealVals: - if 'prefix' not in child.parameterValues: + if tag in self.econVals: + if "prefix" not in child.parameterValues: self.raiseAnError(IOError, "No prefix is provided for node: ", tag) - #get the prefix - prefix = child.parameterValues['prefix'] - - if tag in self.tealVals: - if tag in ['sortinoRatio', 'gainLossRatio']: - #get targets + # get the prefix + prefix = child.parameterValues["prefix"] + if tag in ["sortinoRatio", "gainLossRatio"]: + # get targets targets = set(child.value) + # if targets are not specified by user + if len(targets) < 1: + self.raiseAWarning("No targets were specified in text of <"+tag+">! Skipping metric...") + continue if tag not in self.toDo.keys(): - self.toDo[tag] = [] # list of {'targets':(), 'prefix':str, 'threshold':str} - if 'threshold' not in child.parameterValues: - threshold = 'zero' + self.toDo[tag] = [] # list of {"targets": (), "prefix": str, "threshold": str} + if "threshold" not in child.parameterValues: + threshold = "zero" else: - threshold = child.parameterValues['threshold'].lower() - if threshold not in ['zero','median']: - self.raiseAWarning('Unrecognized threshold in {}, prefix \'{}\' use zero instead!'.format(tag, prefix)) - threshold = 'zero' - - if 'expectedValue' not in self.toDo.keys(): - self.toDo['expectedValue'] = [] - if 'median' not in self.toDo.keys(): - self.toDo['median'] = [] - self.toDo['expectedValue'].append({'targets':set(child.value), - 'prefix':'BSMean'}) - self.toDo['median'].append({'targets':set(child.value), - 'prefix':'BSMED'}) - self.toDo[tag].append({'targets':set(targets), - 'prefix':prefix, - 'threshold':threshold}) - - elif tag in ['expectedShortfall', 'valueAtRisk']: - #get targets + threshold = child.parameterValues["threshold"].lower() + if threshold not in ["zero", "median"]: + self.raiseAWarning("Unrecognized threshold in {}, prefix '{}' use zero instead!".format(tag, prefix)) + threshold = "zero" + + if "expectedValue" not in self.toDo.keys(): + self.toDo["expectedValue"] = [] + if "median" not in self.toDo.keys(): + self.toDo["median"] = [] + # add any expectedValue targets that are missing + expectedValueToAdd = self._additionalTargetsToAdd("expectedValue", child.value) + if len(expectedValueToAdd) > 0: + self.toDo["expectedValue"].append({"targets": expectedValueToAdd, "prefix": "BSMean"}) + # add any median targets that are missing + medianValueToAdd = self._additionalTargetsToAdd("median", child.value) + if len(medianValueToAdd) > 0: + self.toDo["median"].append({"targets": medianValueToAdd, "prefix": "BSMED"}) + self.toDo[tag].append({"targets": set(targets), "prefix": prefix, "threshold": threshold}) + elif tag in ["expectedShortfall", "valueAtRisk"]: + # get targets targets = set(child.value) if tag not in self.toDo.keys(): - self.toDo[tag] = [] # list of {'targets':(), 'prefix':str, 'threshold':str} - if 'threshold' not in child.parameterValues: - threshold = 0.05 - else: - threshold = child.parameterValues['threshold'] - if threshold >1 or threshold <0: - self.raiseAnError('Threshold in {}, prefix \'{}\' out of range, please use a float in range (0, 1)!'.format(tag, prefix)) - - self.toDo[tag].append({'targets':set(targets), - 'prefix':prefix, - 'threshold':threshold}) + self.toDo[tag] = [] # list of {"targets": (), "prefix": str, "threshold": str} + if "threshold" not in child.parameterValues: + threshold = 0.05 + else: + threshold = child.parameterValues["threshold"] + if threshold > 1 or threshold < 0: + self.raiseAnError("Threshold in {}, prefix '{}' out of range, please use a float in range (0, 1)!".format(tag, prefix)) + # cast threshold to set + try: + thresholdSet = set(threshold) + except TypeError: + # not iterable, must be float or int + thresholdSet = set([threshold]) + strThreshold = set() + for val in thresholdSet: + strThreshold.add(str(val)) + self.toDo[tag].append({"targets": set(targets), + "prefix": prefix, + "threshold": thresholdSet, + "strThreshold": strThreshold}) else: if tag not in self.toDo.keys(): - self.toDo[tag] = [] # list of {'targets':(), 'prefix':str} - if 'expectedValue' not in self.toDo.keys(): - self.toDo['expectedValue'] = [] - if 'sigma' not in self.toDo.keys(): - self.toDo['sigma'] = [] - self.toDo['expectedValue'].append({'targets':set(child.value), - 'prefix':'BSMean'}) - self.toDo['sigma'].append({'targets':set(child.value), - 'prefix':'BSSigma'}) - self.toDo[tag].append({'targets':set(child.value), - 'prefix':prefix}) - elif tag in self.scalarVals: - if tag not in self.toDo.keys(): - self.toDo[tag] = [] # list of {'targets':(), 'prefix':str} - self.toDo[tag].append({'targets':set(child.value), - 'prefix':prefix}) - elif tag == "pivotParameter": - self.pivotParameter = child.value - + self.toDo[tag] = [] # list of {"targets": (), "prefix": str} + if "expectedValue" not in self.toDo.keys(): + self.toDo["expectedValue"] = [] + if "sigma" not in self.toDo.keys(): + self.toDo["sigma"] = [] + # add any expectedValue targets that are missing + expectedValueToAdd = self._additionalTargetsToAdd("expectedValue", child.value) + if len(expectedValueToAdd) > 0: + self.toDo["expectedValue"].append({"targets": expectedValueToAdd, "prefix": "BSMean"}) + # add any sigma targets that are missing + sigmaToAdd = self._additionalTargetsToAdd("sigma", child.value) + if len(sigmaToAdd) > 0: + self.toDo["sigma"].append({"targets": sigmaToAdd, "prefix": "BSSigma"}) + self.toDo[tag].append({"targets": set(child.value), "prefix": prefix}) else: - self.raiseAWarning('Unrecognized node in EconomicRatio "',tag,'" has been ignored!') + if tag not in self.scalarVals + self.vectorVals: + self.raiseAWarning("Unrecognized node in EconomicRatio '" + tag + "' has been ignored!") - assert (len(self.toDo)>0), self.raiseAnError(IOError, 'EconomicRatio needs parameters to work on! Please check input for PP: ' + self.name) + assert(len(self.toDo) > 0), self.raiseAnError(IOError, "EconomicRatio needs parameters to work on! please check input for PP: " + self.name) - def __computePower(self, p, dataset): + def _additionalTargetsToAdd(self, metric, childValue): """ - Compute the p-th power of weights - @ In, p, int, the power - @ In, dataset, xarray.Dataset, probability weights of all input variables - @ Out, pw, xarray.Dataset, the p-th power of weights + Some EconomicRatio metrics require additional metrics from BasicStatistics, find necessary additions + @ In, metric, str, name of metric + @ In, childValue, set, set of targets + @ Out, valsToAdd, set, set of targets to add to toDo """ - pw = {} - coords = dataset.coords - for target, targValue in dataset.variables.items(): - ##remove index variable - if target in coords: - continue - pw[target] = np.power(targValue,p) - pw = xr.Dataset(data_vars=pw,coords=coords) - return pw + + if len(self.toDo[metric]) > 0: + # check if metric should be added + vals = set() # currently collected + for tmp_dict in self.toDo[metric]: + if len(vals) == 0: + vals = set(tmp_dict["targets"]) + else: + vals = vals.union(tmp_dict["targets"]) + valsToAdd = set() + for val in childValue: + if val not in vals: + valsToAdd.add(val) + else: + valsToAdd = set(childValue) + + return valsToAdd def _computeSortedWeightsAndPoints(self,arrayIn,pbWeight,percent): """ @@ -293,10 +319,13 @@ def __runLocal(self, inputData): #storage dictionary for skipped metrics self.skipped = {} #construct a dict of required computations - needed = dict((metric,{'targets':set()}) for metric in self.scalarVals + self.tealVals) + needed = dict((metric,{'targets':set()}) for metric in self.scalarVals + self.econVals) needed.update(dict((metric,{'targets':set(),'threshold':{}}) for metric in ['sortinoRatio','gainLossRatio'])) - needed.update(dict((metric,{'targets':set(),'threshold':[]}) for metric in ['valueAtRisk', 'expectedShortfall'])) + needed.update(dict((metric,{'targets':set(),'threshold':set()}) for metric in ['valueAtRisk', 'expectedShortfall'])) for metric, params in self.toDo.items(): + if metric in self.vectorVals: + # vectorVals handled in BasicStatistics, not here + continue for entry in params: needed[metric]['targets'].update(entry['targets']) if 'threshold' in entry.keys() : @@ -311,8 +340,8 @@ def __runLocal(self, inputData): else: thd = entry['threshold'] if thd not in needed[metric]['threshold']: - needed[metric]['threshold'].append(thd) - pass + needed[metric]['threshold'].update(thd) + # variable | needs | needed for # -------------------------------------------------------------------- # median needs | | sortinoRatio, gainLossRatio @@ -323,9 +352,6 @@ def __runLocal(self, inputData): # sortinoRatio needs | expectedValue,median | # gainLossRatio needs | expectedValue,median | - - - # update needed dictionary when standard errors are requested needed['expectedValue']['targets'].update(needed['sigma']['targets']) needed['expectedValue']['targets'].update(needed['variance']['targets']) @@ -338,7 +364,6 @@ def __runLocal(self, inputData): needed['median']['targets'].update(needed['sortinoRatio']['targets']) needed['median']['targets'].update(needed['gainLossRatio']['targets']) - for metric, params in needed.items(): needed[metric]['targets'] = list(params['targets']) @@ -349,7 +374,7 @@ def __runLocal(self, inputData): calculations = self.calculations ################# - # TEAL VALUES # + # ECON VALUES # ################# # # SharpeRatio @@ -368,7 +393,7 @@ def __runLocal(self, inputData): if len(needed[metric]['targets'])>0: self.raiseADebug('Starting "'+metric+'"...') dataSet = inputDataset[list(needed[metric]['targets'])] - threshold = needed[metric]['threshold'] + threshold = list(needed[metric]['threshold']) VaRSet = xr.Dataset() relWeight = pbWeights[list(needed[metric]['targets'])] targWarn = "" # targets that return negative VaR for warning @@ -378,11 +403,11 @@ def __runLocal(self, inputData): VaRList = [] for thd in threshold: if self.pivotParameter in targDa.sizes.keys(): - VaR = [self._computeWeightedPercentile(group.values,targWeight,percent=thd) for label,group in targDa.groupby(self.pivotParameter)] + VaR = [-self._computeWeightedPercentile(group.values,targWeight,percent=thd) for label,group in targDa.groupby(self.pivotParameter)] else: - VaR = self._computeWeightedPercentile(targDa.values,targWeight,percent=thd) - VaRList.append(-VaR) - if any(np.array(VaRList) < 0): + VaR = -self._computeWeightedPercentile(targDa.values,targWeight,percent=thd) + VaRList.append(VaR) + if np.any(np.array(VaRList) < 0): targWarn += target + ", " if self.pivotParameter in targDa.sizes.keys(): da = xr.DataArray(VaRList,dims=('threshold',self.pivotParameter),coords={'threshold':threshold,self.pivotParameter:self.pivotValue}) @@ -393,6 +418,51 @@ def __runLocal(self, inputData): if len(targWarn) > 0: self.raiseAWarning("At least one negative VaR value calculated for target(s) {}. Negative VaR implies high probability of profit.".format(targWarn[:-2])) calculations[metric] = VaRSet + + # calculate value at risk standard error here + # Reference for percentile standard error calculation: + # B. Harding, C. Tremblay and D. Cousineau, "Standard errors: A review and evaluation of + # standard error estimators using Monte Carlo simulations", The Quantitative Methods of + # Psychology, Vol. 10, No. 2 (2014) + self.raiseADebug('Starting "'+metric+'" standard error...') + VaRSteSet = xr.Dataset() + calculatedVaR = calculations[metric] + relWeight = pbWeights[list(needed[metric]['targets'])] + for target in needed[metric]['targets']: + targWeight = relWeight[target].values + en = targWeight.sum()**2/np.sum(targWeight**2) + targDa = dataSet[target] + if self.pivotParameter in targDa.sizes.keys(): + VaRSte = [] + for thd in threshold: + subVaRSte = [] + factor = np.sqrt(thd*(1.0 - thd)/en) + for label, group in targDa.groupby(self.pivotParameter): + if group.values.min() == group.values.max(): + # all values are the same + subVaRSte.append(0.0) + else: + # get KDE + kde = stats.gaussian_kde(group.values, weights=targWeight) + val = calculatedVaR[target].sel(**{'threshold': thd, self.pivotParameter: label}).values + subVaRSte.append(factor/kde(val)[0]) + VaRSte.append(subVaRSte) + da = xr.DataArray(VaRSte, dims=('threshold', self.pivotParameter), + coords={'threshold': threshold, self.pivotParameter: self.pivotValue}) + VaRSteSet[target] = da + else: + calcVaR = calculatedVaR[target] + if targDa.values.min() == targDa.values.max(): + # distribution is a delta function, so no KDE construction + VaRSte = list(np.zeros(calcVaR.shape)) + else: + # get KDE + kde = stats.gaussian_kde(targDa.values, weights=targWeight) + factor = np.sqrt(np.array(threshold)*(1.0 - np.array(threshold))/en) + VaRSte = list(factor/kde(calcVaR.values)) + da = xr.DataArray(VaRSte, dims=('threshold'), coords={'threshold': threshold}) + VaRSteSet[target] = da + calculations[metric+'_ste'] = VaRSteSet # # ExpectedShortfall # @@ -400,7 +470,7 @@ def __runLocal(self, inputData): if len(needed[metric]['targets'])>0: self.raiseADebug('Starting "'+metric+'"...') dataSet = inputDataset[list(needed[metric]['targets'])] - threshold = needed[metric]['threshold'] + threshold = list(needed[metric]['threshold']) CVaRSet = xr.Dataset() relWeight = pbWeights[list(needed[metric]['targets'])] for target in needed[metric]['targets']: @@ -409,16 +479,22 @@ def __runLocal(self, inputData): CVaRList = [] for thd in threshold: if self.pivotParameter in targDa.sizes.keys(): - sortedWeightsAndPoints, indexL = [self._computeSortedWeightsAndPoints(group.values,targWeight,thd) for label,group in targDa.groupby(self.pivotParameter)] - quantile = [self._computeWeightedPercentile(group.values,targWeight,percent=thd) for label,group in targDa.groupby(self.pivotParameter)] + subCVaR = [] + for label, group in targDa.groupby(self.pivotParameter): + sortedWeightsAndPoints, indexL = self._computeSortedWeightsAndPoints(group.values, targWeight,thd) + quantile = self._computeWeightedPercentile(group.values, targWeight, percent=thd) + lowerPartialE = np.sum(sortedWeightsAndPoints[:indexL, 0]*sortedWeightsAndPoints[:indexL,1]) + lowerPartialP = np.sum(sortedWeightsAndPoints[:indexL,0]) + Es = lowerPartialE + quantile*(thd - lowerPartialP) + subCVaR.append(-Es/(thd)) + CVaRList.append(subCVaR) else: sortedWeightsAndPoints, indexL = self._computeSortedWeightsAndPoints(targDa.values,targWeight,thd) - quantile = self._computeWeightedPercentile(targDa.values,targWeight,percent=thd) - lowerPartialE = np.sum(sortedWeightsAndPoints[:indexL,0]*sortedWeightsAndPoints[:indexL,1]) - lowerPartialP = np.sum(sortedWeightsAndPoints[:indexL,0]) - Es = lowerPartialE + quantile*(thd -lowerPartialP) - CVaRList.append(-Es/(thd)) - + quantile = self._computeWeightedPercentile(targDa.values,targWeight,percent=thd) + lowerPartialE = np.sum(sortedWeightsAndPoints[:indexL,0]*sortedWeightsAndPoints[:indexL,1]) + lowerPartialP = np.sum(sortedWeightsAndPoints[:indexL,0]) + Es = lowerPartialE + quantile*(thd -lowerPartialP) + CVaRList.append(-Es/(thd)) if self.pivotParameter in targDa.sizes.keys(): da = xr.DataArray(CVaRList,dims=('threshold',self.pivotParameter),coords={'threshold':threshold,self.pivotParameter:self.pivotValue}) else: @@ -445,7 +521,7 @@ def __runLocal(self, inputData): zeroSet = orgZeroSet[list(zeroTarget)] dataSet = inputDataset[list(zeroTarget)] lowerPartialVarianceDS = self._computeLowerPartialVariance(dataSet,zeroSet,pbWeight=relWeight,dim=self.sampleTag) - lpsDS = self.__computePower(0.5,lowerPartialVarianceDS) + lpsDS = self._computePower(0.5,lowerPartialVarianceDS) incapableZeroTarget = [x for x in zeroTarget if not lpsDS[x].values != 0] for target in incapableZeroTarget: needed[metric]['threshold'][target].remove('zero') @@ -460,7 +536,7 @@ def __runLocal(self, inputData): medSet = orgMedSet[list(medTarget)] dataSet = inputDataset[list(medTarget)] lowerPartialVarianceDS = self._computeLowerPartialVariance(dataSet,medSet,pbWeight=relWeight,dim=self.sampleTag) - lpsDS = self.__computePower(0.5,lowerPartialVarianceDS) + lpsDS = self._computePower(0.5,lowerPartialVarianceDS) incapableMedTarget = [x for x in medTarget if not lpsDS[x].values != 0] medTarget = [x for x in medTarget if not lpsDS[x].values == 0] if incapableMedTarget: @@ -490,7 +566,6 @@ def __runLocal(self, inputData): zeroSet = orgZeroSet[list(zeroTarget)] dataSet = inputDataset[list(zeroTarget)] - higherSet = (dataSet-zeroSet).clip(min=0) lowerSet = (zeroSet-dataSet).clip(min=0) relWeight = pbWeights[list(needed[metric]['targets'])] if self.pbPresent else None @@ -512,14 +587,12 @@ def __runLocal(self, inputData): medSet = orgMedSet[list(medTarget)] dataSet = inputDataset[list(medTarget)] - higherSet = (dataSet-medSet).clip(min=0) lowerSet = (medSet-dataSet).clip(min=0) relWeight = pbWeights[list(needed[metric]['targets'])] if self.pbPresent else None higherMeanSet = (higherSet * relWeight).sum(dim = self.sampleTag) lowerMeanSet = (lowerSet * relWeight).sum(dim = self.sampleTag) - incapableMedTarget = [x for x in medTarget if not lowerMeanSet[x].values != 0] medTarget = [x for x in medTarget if not lowerMeanSet[x].values == 0] if incapableMedTarget: @@ -530,39 +603,49 @@ def __runLocal(self, inputData): daMed = daMed.expand_dims('threshold') calculations[metric] = xr.merge([daMed, daZero]) - for metric, ds in calculations.items(): - if metric in self.scalarVals + self.tealVals +['equivalentSamples'] and metric !='samples': + if metric in self.scalarVals + self.steVals + self.econVals + ['equivalentSamples'] and metric !='samples': calculations[metric] = ds.to_array().rename({'variable':'targets'}) outputSet = xr.Dataset(data_vars=calculations) - outputDict = {} - for metric, requestList in self.toDo.items(): - for targetDict in requestList: - prefix = targetDict['prefix'].strip() - for target in targetDict['targets']: - if metric in self.scalarVals: - varName = prefix + '_' + target - outputDict[varName] = np.atleast_1d(outputSet[metric].sel(**{'targets':target})) - steMetric = metric + '_ste' - elif metric in self.tealVals: - varName = prefix + '_' + target - if 'threshold' in targetDict.keys(): - try: + + if self.outputDataset: + # Add 'RAVEN_sample_ID' to output dataset for consistence + if 'RAVEN_sample_ID' not in outputSet.sizes.keys(): + outputSet = outputSet.expand_dims('RAVEN_sample_ID') + outputSet['RAVEN_sample_ID'] = [0] + return outputSet + else: + outputDict = {} + for metric, requestList in self.toDo.items(): + for targetDict in requestList: + prefix = targetDict['prefix'].strip() + for target in targetDict['targets']: + if metric in self.econVals: + if metric in ['sortinoRatio','gainLossRatio']: + varName = prefix + '_' + targetDict['threshold'] + '_' + target outputDict[varName] = np.atleast_1d(outputSet[metric].sel(**{'targets':target,'threshold':targetDict['threshold']})) - except KeyError: - outputDict[varName] = np.nan + elif metric in ['valueAtRisk','expectedShortfall']: + for thd in targetDict['strThreshold']: + varName = '_'.join([prefix,thd,target]) + thdVal = float(thd) + outputDict[varName] = np.atleast_1d(outputSet[metric].sel(**{'targets':target,'threshold':thdVal})) + steMetric = metric + '_ste' + if steMetric in self.steVals: + metaVar = '_'.join([prefix,thd,'ste',target]) + outputDict[metaVar] = np.atleast_1d(outputSet[steMetric].sel(**{'targets':target,'threshold':thdVal})) + else: + varName = prefix + '_' + target + outputDict[varName] = np.atleast_1d(outputSet[metric].sel(**{'targets':target})) else: - outputDict[varName] = np.atleast_1d(outputSet[metric].sel(**{'targets':target})) - steMetric = metric + '_ste' - else: - #check if it was skipped for some reason - skip = self.skipped.get(metric, None) - if skip is not None: - self.raiseADebug('Metric',metric,'was skipped for parameters',targetDict,'! See warnings for details. Ignoring...') - continue - if self.pivotParameter in outputSet.sizes.keys(): - outputDict[self.pivotParameter] = np.atleast_1d(self.pivotValue) - return outputDict + #check if it was skipped for some reason + skip = self.skipped.get(metric, None) + if skip is not None: + self.raiseADebug('Metric',metric,'was skipped for parameters',targetDict,'! See warnings for details. Ignoring...') + continue + if self.pivotParameter in outputSet.sizes.keys(): + outputDict[self.pivotParameter] = np.atleast_1d(self.pivotValue) + + return outputDict def run(self, inputIn): """ @@ -570,25 +653,27 @@ def run(self, inputIn): @ In, inputIn, object, object contained the data to process. (inputToInternal output) @ Out, outputSet, xarray.Dataset or dictionary, dataset or dictionary containing the results """ - BasicStatistics.run(self,inputIn) + # get metrics from BasicStatistics + outputSetBasicStatistics = BasicStatistics.run(self, inputIn) + + # get metrics from EconomicRatio inputData = self.inputToInternal(inputIn) outputSet = self.__runLocal(inputData) - return outputSet - def collectOutput(self, finishedJob, output): - """ - Function to place all of the computed data into the output object - @ In, finishedJob, JobHandler External or Internal instance, A JobHandler object that is in charge of running this post-processor - @ In, output, dataObjects, The object where we want to place our computed results - @ Out, None - """ - evaluation = finishedJob.getEvaluation() - outputRealization = evaluation[1] - if output.type in ['PointSet','HistorySet']: - self.raiseADebug('Dumping output in data object named ' + output.name) - output.addRealization(outputRealization) - elif output.type in ['DataSet']: - self.raiseADebug('Dumping output in DataSet named ' + output.name) - output.load(outputRealization,style='dataset') + # combine results + if isinstance(outputSet, dict): + if isinstance(outputSetBasicStatistics, dict): + # BasicStatistics and EconomicRatio returned dictionaries + outputSet.update(outputSetBasicStatistics) + else: + # BasicStatistics returned a xr.Dataset, EconomicRatio returned dict (empty) + outputSet = outputSetBasicStatistics else: - self.raiseAnError(IOError, 'Output type ' + str(output.type) + ' unknown.') + if isinstance(outputSetBasicStatistics, dict): + # EconomicRatio returned xarray.Dataset, but BasicStatistics was dict (empty) + pass + else: + # Both BasicStatistics and EconomicRatio returned xarray.Datasets + outputSet = xr.merge([outputSet, outputSetBasicStatistics]) + + return outputSet diff --git a/tests/framework/PostProcessors/BasicStatistics/gold/basicStatisticsTimeDependent/datasetPrint.csv b/tests/framework/PostProcessors/BasicStatistics/gold/basicStatisticsTimeDependent/datasetPrint.csv index f84b05cb1b..ab8cdd3f69 100644 --- a/tests/framework/PostProcessors/BasicStatistics/gold/basicStatisticsTimeDependent/datasetPrint.csv +++ b/tests/framework/PostProcessors/BasicStatistics/gold/basicStatisticsTimeDependent/datasetPrint.csv @@ -1,217 +1,217 @@ -RAVEN_sample_ID,features,percent,targets,time,variance,covariance,percentile,pearson,expectedValue,sigma,kurtosis,skewness,variationCoefficient,samples,median,variance_ste,skewness_ste,median_ste,sigma_ste,kurtosis_ste,expectedValue_ste -0,x0,0.05,x,-0.05,1000.0,1000.0,21.0,1.0,61.0,31.6227766017,-1.72,0.0,0.518406173798,5.0,61.0,707.106781187,0.912870929175,17.7245385091,11.1803398875,2.0,14.1421356237 -0,x0,0.05,x,0.0,990.025,995.0,20.9,1.0,60.7,31.4646627187,-1.72,-7.29912733146e-16,0.518363471477,5.0,60.7,700.053391044,0.912870929175,17.6359158165,11.1244381881,2.0,14.0714249456 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+0,x0,0.95,z,0.0,757.90625,862.5,77.25,0.9907209790776259,38.75,27.530097166555734,-1.5512656207304383,0.5000992630760116,0.7104541204272448,5.0,34.75,14.96866666349494,535.920648878667,0.9128709291752769,9.733359196598059,12.311833738318596,2.0000000000000004,15.43059528051264 +0,x0,0.95,z,0.05,3287.509788353749,1780.3349999999998,153.61124999999998,0.9819021308996262,70.55735,57.33681006433606,-1.3602918938174393,0.693228768976189,0.8126270340983053,5.0,58.955650000000006,33.33947800299236,2324.620464562088,0.9128709291752769,20.27162360404856,25.641800983369908,2.0000000000000004,32.137231678687996 +0,x0,0.95,z0,-0.05,62.5,250.0,25.0,1.0,15.0,7.905694150420948,-1.7200000000000004,0.0,0.5270462766947299,5.0,15.0,3.6150863783146705,44.19417382415922,0.9128709291752769,2.7950849718747373,3.535533905932738,2.0000000000000004,4.43113462726379 +0,x0,0.95,z0,0.0,62.5,250.0,25.0,1.0,15.0,7.905694150420948,-1.7200000000000004,0.0,0.5270462766947299,5.0,15.0,3.6150863783146705,44.19417382415922,0.9128709291752769,2.7950849718747373,3.535533905932738,2.0000000000000004,4.43113462726379 +0,x0,0.95,z0,0.05,62.5,250.0,25.0,1.0,15.0,7.905694150420948,-1.7200000000000004,0.0,0.5270462766947299,5.0,15.0,3.6150863783146705,44.19417382415922,0.9128709291752769,2.7950849718747373,3.535533905932738,2.0000000000000004,4.43113462726379 diff --git a/tests/framework/PostProcessors/BasicStatistics/gold/basicStatsMonteCarloPercentileFloat/analyticalTest_basicStatPP_dump.csv b/tests/framework/PostProcessors/BasicStatistics/gold/basicStatsMonteCarloPercentileFloat/analyticalTest_basicStatPP_dump.csv index 785844d2e7..de774aee2e 100644 --- a/tests/framework/PostProcessors/BasicStatistics/gold/basicStatsMonteCarloPercentileFloat/analyticalTest_basicStatPP_dump.csv +++ b/tests/framework/PostProcessors/BasicStatistics/gold/basicStatsMonteCarloPercentileFloat/analyticalTest_basicStatPP_dump.csv @@ -1,2 +1,2 @@ -percentile_0.1_x,percentile_0.1_y,percentile_0.5_x,percentile_0.5_y --41.8819801615,-50.0579791506,-28.382261166,-25.4006855506 +percentile_0.1_x,percentile_0.1_y,percentile_0.5_x,percentile_0.5_y,percentile_0.5_ste_x,percentile_0.5_ste_y,percentile_0.1_ste_y,percentile_0.1_ste_x +-41.8819801615,-50.0579791506,-28.382261166,-25.4006855506,5.04614933683,5.92738756757,8.90352567663,4.60333267966 diff --git a/tests/framework/PostProcessors/BasicStatistics/gold/basicStatsMonteCarloPercentileFloat/analyticalTest_basicStatPP_dump.xml b/tests/framework/PostProcessors/BasicStatistics/gold/basicStatsMonteCarloPercentileFloat/analyticalTest_basicStatPP_dump.xml index 463a5ac75c..612f5c46ed 100644 --- a/tests/framework/PostProcessors/BasicStatistics/gold/basicStatsMonteCarloPercentileFloat/analyticalTest_basicStatPP_dump.xml +++ b/tests/framework/PostProcessors/BasicStatistics/gold/basicStatsMonteCarloPercentileFloat/analyticalTest_basicStatPP_dump.xml @@ -1,7 +1,9 @@ + analyticalTest_basicStatPP percentile_0.1_x,percentile_0.1_y,percentile_0.5_x,percentile_0.5_y + percentile_0.1_ste_x,percentile_0.1_ste_y,percentile_0.5_ste_x,percentile_0.5_ste_y RAVEN_sample_ID diff --git a/tests/framework/PostProcessors/EconomicRatio/economicRatioTimeDependent/economicRatioExternalModelTest.py b/tests/framework/PostProcessors/EconomicRatio/economicRatioTimeDependent/economicRatioExternalModelTest.py new file mode 100644 index 0000000000..04e9361ed1 --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/economicRatioTimeDependent/economicRatioExternalModelTest.py @@ -0,0 +1,53 @@ +# Copyright 2017 Battelle Energy Alliance, LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +''' from wikipedia: dx/dt = sigma*(y-x) ; dy/dt = x*(rho-z)-y dz/dt = x*y-beta*z ; ''' + +import numpy as np +import copy + +def initialize(self,runInfoDict,inputFiles): + print('Life is beautiful my friends. Do not waste it!') + self.max_time = 0.03 + self.t_step = 0.01 + self.numberTimeSteps = int(self.max_time/self.t_step) + self.x = np.zeros(self.numberTimeSteps) + self.y = np.zeros(self.numberTimeSteps) + self.z = np.zeros(self.numberTimeSteps) + self.time = np.zeros(self.numberTimeSteps)-self.t_step + self.cnt = 0.0 + return + +def createNewInput(self,myInput,samplerType,**Kwargs): return Kwargs['SampledVars'] + +def run(self,Input): + self.cnt = 1.0 + self.x0 = 1.0 + self.y0 = 1.0 + self.z0 = 1.0 + self.x01 = copy.deepcopy(self.cnt+Input['x0']) + self.x02 = copy.deepcopy(self.cnt+Input['x0']) + self.z01 = copy.deepcopy(self.cnt+Input['x0']) + self.z02 = 101.0 - copy.deepcopy(self.cnt+Input['x0']) + self.y01 = copy.deepcopy(Input['x0']) + self.y02 = copy.deepcopy(Input['y0']) + self.time[0]= -self.t_step*5.0 + self.x[0] = copy.deepcopy(self.cnt+Input['x0']) + self.y[0] = copy.deepcopy(self.cnt+Input['y0']) + self.z[0] = copy.deepcopy(self.cnt+Input['z0']) + for t in range ( self.numberTimeSteps-1): + self.time[t+1] = self.time[t] + self.t_step*5.0 + self.x[t+1] = self.x[t] + (self.y[t]-self.x[t])*self.t_step + self.y[t+1] = self.y[t] + (self.x[t]*self.z[t]-self.y[t])*self.t_step + self.z[t+1] = self.z[t] + (self.x[t]*self.y[t]-self.z[t])*self.t_step diff --git a/tests/framework/PostProcessors/EconomicRatio/economicRatioTimeDependent/samples.csv b/tests/framework/PostProcessors/EconomicRatio/economicRatioTimeDependent/samples.csv new file mode 100644 index 0000000000..acd5730eeb --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/economicRatioTimeDependent/samples.csv @@ -0,0 +1,6 @@ +y0,x0,z0,OutputPlaceHolder +-30,-20,-15,1 +-10,0,-10,2 +0,20,-5,3 +10,40,0,4 +20,60,5,5 diff --git a/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioGeneral/EconVaRResult_dump.csv b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioGeneral/EconVaRResult_dump.csv new file mode 100644 index 0000000000..b57a6989d2 --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioGeneral/EconVaRResult_dump.csv @@ -0,0 +1,2 @@ +VaR_0.05_NPV,CVaR_0.05_NPV,sort_median_NPV,sharpe_NPV,glr_median_NPV,mean_NPV,med_NPV,sig_NPV,samp_NPV,min_NPV,max_NPV,var_NPV,perc_10_NPV,varcoeff_NPV,skew_NPV,kurt_NPV,sen_NPV_y,sen_NPV_x,cov_NPV_y,cov_NPV_x,pear_NPV_y,pear_NPV_x,spear_NPV_y,spear_NPV_x,normsen_NPV_y,normsen_NPV_x,varsen_NPV_y,varsen_NPV_x,mean_ste_NPV,sig_ste_NPV,skew_ste_NPV,kurt_ste_NPV,var_ste_NPV,perc_10_ste_NPV,med_ste_NPV,VaR_0.05_ste_NPV +3.19019335261,4.79790968612,1.44299824341,1.03145450012,0.978595869574,4.99180485314,5.0335298332,4.83957833578,1000.0,-8.6237856656,19.3728162055,23.4215184681,-1.68420932502,0.969504713858,-0.0127319037077,-0.185027438349,0.0674698813574,-0.000768272706587,0.26902026153,-0.00209976859942,0.0278389292623,-0.000444859391169,0.0369355924076,-0.0089557042957,0.134249848747,-0.000461111226891,0.0674698813574,-0.000768272706587,0.153040904559,0.108270410158,0.0773438156196,0.154534254685,1.04796626281,0.275499074615,0.191808329271,0.0924455447964 diff --git a/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioGeneral/EconVaRResult_dump.xml b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioGeneral/EconVaRResult_dump.xml new file mode 100644 index 0000000000..4148205e01 --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioGeneral/EconVaRResult_dump.xml @@ -0,0 +1,11 @@ + + + + EconVaRResult + VaR_0.05_NPV,CVaR_0.05_NPV,sort_median_NPV,sharpe_NPV,glr_median_NPV,mean_NPV,med_NPV,sig_NPV,samp_NPV,min_NPV,max_NPV,var_NPV,perc_10_NPV,varcoeff_NPV,skew_NPV,kurt_NPV,sen_NPV_y,sen_NPV_x,cov_NPV_y,cov_NPV_x,pear_NPV_y,pear_NPV_x,spear_NPV_y,spear_NPV_x,normsen_NPV_y,normsen_NPV_x,varsen_NPV_y,varsen_NPV_x + VaR_0.05_ste_NPV,kurt_ste_NPV,mean_ste_NPV,med_ste_NPV,perc_10_ste_NPV,sig_ste_NPV,skew_ste_NPV,var_ste_NPV + RAVEN_sample_ID + + + + diff --git a/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump.csv b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump.csv new file mode 100644 index 0000000000..8be62c3730 --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump.csv @@ -0,0 +1,2 @@ +filename +HistorySetPostProcTest_dump_0.csv diff --git a/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump.xml b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump.xml new file mode 100644 index 0000000000..a0bf791637 --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump.xml @@ -0,0 +1,168 @@ + + + + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + time + + + economicRatioHistory + VaR_0.05_x0,VaR_0.05_y0,VaR_0.05_z0,VaR_0.05_x,VaR_0.05_y,VaR_0.05_z,VaR_0.05_ste_x0,VaR_0.05_ste_y0,VaR_0.05_ste_z0,VaR_0.05_ste_x,VaR_0.05_ste_y,VaR_0.05_ste_z,es_0.05_x0,es_0.05_y0,es_0.05_z0,es_0.05_x,es_0.05_y,es_0.05_z,sharpe_x0,sharpe_y0,sharpe_z0,sharpe_x,sharpe_y,sharpe_z,var_x0,var_y0,var_z0,var_x,var_y,var_z,mean_x0,mean_y0,mean_z0,mean_x,mean_y,mean_z,sigma_x0,sigma_y0,sigma_z0,sigma_x,sigma_y,sigma_z,vc_x0,vc_y0,vc_z0,vc_x,vc_y,vc_z,skew_x0,skew_y0,skew_z0,skew_x,skew_y,skew_z,kurt_x0,kurt_y0,kurt_z0,kurt_x,kurt_y,kurt_z,median_x0,median_y0,median_z0,median_x,median_y,median_z,samp_x0,samp_y0,samp_z0,samp_x,samp_y,samp_z,percentile_5_x0,percentile_5_y0,percentile_5_z0,percentile_5_x,percentile_5_y,percentile_5_z,percentile_95_x0,percentile_95_y0,percentile_95_z0,percentile_95_x,percentile_95_y,percentile_95_z,cov_x0_x0,cov_y0_x0,cov_z0_x0,cov_x_x0,cov_y_x0,cov_z_x0,cov_x0_y0,cov_y0_y0,cov_z0_y0,cov_x_y0,cov_y_y0,cov_z_y0,cov_x0_z0,cov_y0_z0,cov_z0_z0,cov_x_z0,cov_y_z0,cov_z_z0,cov_x0_x,cov_y0_x,cov_z0_x,cov_x_x,cov_y_x,cov_z_x,cov_x0_y,cov_y0_y,cov_z0_y,cov_x_y,cov_y_y,cov_z_y,cov_x0_z,cov_y0_z,cov_z0_z,cov_x_z,cov_y_z,cov_z_z,pearson_x0_x0,pearson_y0_x0,pearson_z0_x0,pearson_x_x0,pearson_y_x0,pearson_z_x0,pearson_x0_y0,pearson_y0_y0,pearson_z0_y0,pearson_x_y0,pearson_y_y0,pearson_z_y0,pearson_x0_z0,pearson_y0_z0,pearson_z0_z0,pearson_x_z0,pearson_y_z0,pearson_z_z0,pearson_x0_x,pearson_y0_x,pearson_z0_x,pearson_x_x,pearson_y_x,pearson_z_x,pearson_x0_y,pearson_y0_y,pearson_z0_y,pearson_x_y,pearson_y_y,pearson_z_y,pearson_x0_z,pearson_y0_z,pearson_z0_z,pearson_x_z,pearson_y_z,pearson_z_z + RAVEN_sample_ID + + + + diff --git a/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump_0.csv b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump_0.csv new file mode 100644 index 0000000000..d29497210b --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/gold/economicRatioTimeDependent/HistorySetPostProcTest_dump_0.csv @@ -0,0 +1,4 @@ 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+0,z,0.95,z0,0.05,0.05,15.0,15.0,-0.632455532034,62.5,115.46925,5.0,0.8542063899725237,-5.0,7.905694150420948,-1.7200000000000004,0.0,-1.5811388300841895,5.0,-5.0,4.43113462726379,3.615086378314669,27.6761545521,2.0000000000000004,3.535533905932738,44.19417382415922,2.7950849718747373,0.9128709291752769 diff --git a/tests/framework/PostProcessors/EconomicRatio/test_economic_ratio.xml b/tests/framework/PostProcessors/EconomicRatio/test_economic_ratio.xml new file mode 100644 index 0000000000..d7bde35ca8 --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/test_economic_ratio.xml @@ -0,0 +1,156 @@ + + + + framework/PostProcessors/EconomicRatio/general + dgarrett622 + 2022-02-08 + PostProcessors.EconomicRatio + + This tests point set economic ratio quantities and passing basic statistics. + + + + + + economicRatioGeneral + 1 + MCSample,VaREcon + + + + 5 + 5 + + + 3 + 1 + + + 10 + 2 + + + + + + 1000 + + + NPV_dist + + + x_dist + + + y_dist + + + + + + + NPV + NPV + NPV + NPV + NPV + NPV + NPV + NPV + NPV + NPV + NPV + NPV + NPV + NPV + NPV + NPV + + NPV + x,y + + + NPV + x,y + + + NPV + x,y + + + NPV + x,y + + + NPV + x,y + + + NPV + x,y + + + + + + InputPlaceholder + MyDummy + normalMC + MCData + + + MCData + EconRatio + EconVaRResult + EconVaRResult_dump + + + + + NPV,x,y + OutputPlaceHolder + + + NPV,x,y + OutputPlaceHolder + + + + VaR_0.05_NPV, + CVaR_0.05_NPV, + sort_median_NPV, + sharpe_NPV, + glr_median_NPV, + mean_NPV, + med_NPV, + sig_NPV, + samp_NPV, + min_NPV, + max_NPV, + var_NPV, + perc_10_NPV, + varcoeff_NPV, + skew_NPV, + kurt_NPV, + sen_NPV_y, + sen_NPV_x, + cov_NPV_y, + cov_NPV_x, + pear_NPV_y, + pear_NPV_x, + spear_NPV_y, + spear_NPV_x, + normsen_NPV_y, + normsen_NPV_x, + varsen_NPV_y, + varsen_NPV_x + + + + + csv + EconVaRResult + + + + diff --git a/tests/framework/PostProcessors/EconomicRatio/tests b/tests/framework/PostProcessors/EconomicRatio/tests new file mode 100644 index 0000000000..2a464c712a --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/tests @@ -0,0 +1,31 @@ +[Tests] + [./general] + #REQUIREMENT_TEST R-RA-5 + type = 'RavenFramework' + input = 'test_economic_ratio.xml' + UnorderedCsv = 'economicRatioGeneral/EconVaRResult_dump.csv' + UnorderedXml = 'economicRatioGeneral/EconVaRResult_dump.xml' + max_time = 500 + rel_err = 0.00001 + zero_threshold = 1e-14 + [../] + + [./timeDependent] + type = 'RavenFramework' + input = 'time_dep.xml' + output = 'economicRatioTimeDependent/HistorySetPostProcTest_dump.csv' + UnorderedCsv = 'economicRatioTimeDependent/HistorySetPostProcTest_dump_0.csv' + UnorderedXml = 'economicRatioTimeDependent/HistorySetPostProcTest_dump.xml' + rel_err = 1e-5 + zero_threshold = 1e-14 + [../] + + [./timeDepDataset] + type = 'RavenFramework' + input = 'time_dep_dataset.xml' + UnorderedCsv = 'economicRatioTimeDependent/datasetPrint.csv' + rel_err = 1e-5 + zero_threshold = 1e-14 + [../] + +[] diff --git a/tests/framework/PostProcessors/EconomicRatio/time_dep.xml b/tests/framework/PostProcessors/EconomicRatio/time_dep.xml new file mode 100644 index 0000000000..a647ffca51 --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/time_dep.xml @@ -0,0 +1,124 @@ + + + + economicRatioTimeDependent + FirstMRun,timeDepEconomicRatioPP + 1 + + + framework/PostProcessors/EconomicRatio/timeDependent + dgarrett622 + 2022-02-08 + PostProcessors.EconomicRatio + + This tests time-dependent economic ratio quantities and passing through basic statistics. + + + + + + economicRatioExternalModelTest.py + samples.csv + + + + x,y,z,time,x0,x01,x02,y0,y02,y01,z0,z02,z01 + + + time + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + + + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + + + + + + samples.csv + + + + + + + + + economicRatioExternalModelTest.py + PythonModule + customSamplerFile + HistorySetPostProcTest + + + HistorySetPostProcTest + timeDepEconomicRatio + economicRatioHistory + HistorySetPostProcTest_dump + + + + + + csv + economicRatioHistory + input,output + + + + + + x0,y0,z0 + time,x,y,z + + + + VaR_0.05_x0,VaR_0.05_y0,VaR_0.05_z0,VaR_0.05_x,VaR_0.05_y,VaR_0.05_z, + VaR_0.05_ste_x0,VaR_0.05_ste_y0,VaR_0.05_ste_z0,VaR_0.05_ste_x,VaR_0.05_ste_y,VaR_0.05_ste_z, + es_0.05_x0,es_0.05_y0,es_0.05_z0,es_0.05_x,es_0.05_y,es_0.05_z, + sharpe_x0,sharpe_y0,sharpe_z0,sharpe_x,sharpe_y,sharpe_z, + var_x0,var_y0,var_z0,var_x,var_y,var_z, + mean_x0, mean_y0, mean_z0, mean_x, mean_y, mean_z, + sigma_x0, sigma_y0, sigma_z0, sigma_x, sigma_y, sigma_z, + vc_x0, vc_y0, vc_z0, vc_x, vc_y, vc_z, + skew_x0, skew_y0, skew_z0, skew_x, skew_y, skew_z, + kurt_x0, kurt_y0, kurt_z0, kurt_x, kurt_y, kurt_z, + median_x0, median_y0, median_z0, median_x, median_y, median_z, + samp_x0, samp_y0, samp_z0, samp_x, samp_y, samp_z, + percentile_5_x0, percentile_5_y0, percentile_5_z0, percentile_5_x, percentile_5_y, percentile_5_z, + percentile_95_x0, percentile_95_y0, percentile_95_z0, percentile_95_x, percentile_95_y, percentile_95_z, + cov_x0_x0, cov_y0_x0, cov_z0_x0, cov_x_x0, cov_y_x0, cov_z_x0, + cov_x0_y0, cov_y0_y0, cov_z0_y0, cov_x_y0, cov_y_y0, cov_z_y0, + cov_x0_z0, cov_y0_z0, cov_z0_z0, cov_x_z0, cov_y_z0, cov_z_z0, + cov_x0_x, cov_y0_x, cov_z0_x, cov_x_x, cov_y_x, cov_z_x, + cov_x0_y, cov_y0_y, cov_z0_y, cov_x_y, cov_y_y, cov_z_y, + cov_x0_z, cov_y0_z, cov_z0_z, cov_x_z, cov_y_z, cov_z_z, + pearson_x0_x0, pearson_y0_x0, pearson_z0_x0, pearson_x_x0, pearson_y_x0, pearson_z_x0, + pearson_x0_y0, pearson_y0_y0, pearson_z0_y0, pearson_x_y0, pearson_y_y0, pearson_z_y0, + pearson_x0_z0, pearson_y0_z0, pearson_z0_z0, pearson_x_z0, pearson_y_z0, pearson_z_z0, + pearson_x0_x, pearson_y0_x, pearson_z0_x, pearson_x_x, pearson_y_x, pearson_z_x, + pearson_x0_y, pearson_y0_y, pearson_z0_y, pearson_x_y, pearson_y_y, pearson_z_y, + pearson_x0_z, pearson_y0_z, pearson_z0_z, pearson_x_z, pearson_y_z, pearson_z_z + + + time + + + + + diff --git a/tests/framework/PostProcessors/EconomicRatio/time_dep_dataset.xml b/tests/framework/PostProcessors/EconomicRatio/time_dep_dataset.xml new file mode 100644 index 0000000000..db2d5e4f9a --- /dev/null +++ b/tests/framework/PostProcessors/EconomicRatio/time_dep_dataset.xml @@ -0,0 +1,147 @@ + + + + framework/PostProcessors/EconomicRatio/timeDepDataset + dgarrett622 + 2022-02-08 + PostProcessors.EconomicRatio + + This tests dumping time-dependent economic ratio quantities into dataset and passing basic statistics. + + + + + + economicRatioTimeDependent + FirstMRun,timeDepEconomicRatioPP + 1 + + + economicRatioExternalModelTest.py + samples.csv + + + + x,y,z,time,x0,x01,x02,y0,y02,y01,z0,z02,z01 + + + True + time + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + + + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + x0,y0,z0,x,y,z + + + + + + samples.csv + + + + + + + + + economicRatioExternalModelTest.py + PythonModule + customSamplerFile + HistorySetPostProcTest + + + HistorySetPostProcTest + timeDepEconomicRatio + economicRatioSet + datasetPrint + + + + + + csv + economicRatioSet + + + + + + x0,y0,z0 + time,x,y,z + + + + valueAtRisk, + expectedShortfall, + sharpeRatio, + variance, + covariance, + percentile, + pearson, + expectedValue, + sigma, + kurtosis, + skewness, + variationCoefficient, + samples, + median + + + valueAtRisk, + expectedShortfall, + sharpeRatio, + variance, + covariance, + percentile, + pearson, + expectedValue, + sigma, + kurtosis, + skewness, + variationCoefficient, + median, + samples + + + valueAtRisk, + expectedShortfall, + sharpeRatio, + variance, + covariance, + percentile, + pearson, + expectedValue, + sigma, + kurtosis, + skewness, + variationCoefficient, + samples, + median + + + covariance, + pearson + + percentile + valueAtRisk,expectedShortfall + + + +