From 83c76cf02a181e7770f78a787bc466bf62faa968 Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 23 Apr 2024 09:09:44 -0600 Subject: [PATCH 01/12] higher level changes from autoARMA branch --- dependencies.xml | 1 + ravenframework/Models/ROM.py | 3 + .../SupervisedLearning/ROMCollection.py | 17 +++++- .../SupervisedLearning/SupervisedLearning.py | 38 +++++++++++- .../SupervisedLearning/SyntheticHistory.py | 61 ++++++++++++++++--- ravenframework/TSA/TSAUser.py | 36 ++++++++++- ravenframework/TSA/TimeSeriesAnalyzer.py | 47 +++++++++++++- ravenframework/unSupervisedLearning.py | 4 +- 8 files changed, 189 insertions(+), 18 deletions(-) diff --git a/dependencies.xml b/dependencies.xml index 6324050ae5..d12e2e978a 100644 --- a/dependencies.xml +++ b/dependencies.xml @@ -73,6 +73,7 @@ Note all install methods after "main" take + 1.2 0.9 diff --git a/ravenframework/Models/ROM.py b/ravenframework/Models/ROM.py index 18d9ee7672..0aea029425 100644 --- a/ravenframework/Models/ROM.py +++ b/ravenframework/Models/ROM.py @@ -237,8 +237,11 @@ def _readMoreXML(self,xmlNode): segment = xmlNode.find('Segment') romXml = copy.deepcopy(xmlNode) romXml.remove(segment) + # depending on segType, this ROM *will* have clusters and we will need this fact later + self._interfaceROM.setClusterability(segType in ['cluster', 'interpolate']) else: romXml = xmlNode + self._interfaceROM.setClusterability(False) # just making sure it's False otherwise self._interfaceROM._readMoreXML(romXml) if self.segment: diff --git a/ravenframework/SupervisedLearning/ROMCollection.py b/ravenframework/SupervisedLearning/ROMCollection.py index 802c3a97f0..4080652de4 100644 --- a/ravenframework/SupervisedLearning/ROMCollection.py +++ b/ravenframework/SupervisedLearning/ROMCollection.py @@ -636,7 +636,7 @@ def _writeSegmentsRealization(self, writeTo): """ Writes pointwise data about segmentation to a realization. @ In, writeTo, DataObject, data structure into which data should be written - @ Out, None + @ Out, rlz, dict, realization data structure where each entry is an np.ndarray """ # realization to add eventually @@ -956,12 +956,23 @@ def writePointwiseData(self, writeTo): featureNames = sorted(list(self._clusterInfo['features']['unscaled'].keys())) for scaling in ['unscaled', 'scaled']: for name in featureNames: - varName = 'ClusterFeature|{}|{}'.format(name, scaling) + varName = f'ClusterFeature|{name}|{scaling}' writeTo.addVariable(varName, np.array([]), classify='meta', indices=['segment_number']) rlz[varName] = np.asarray(self._clusterInfo['features'][scaling][name]) varName = 'ClusterLabels' writeTo.addVariable(varName, np.array([]), classify='meta', indices=['segment_number']) rlz[varName] = np.asarray(labels) + # below, we loop through all segment ROMs to find feature data to write to data object + segments = self.getSegmentRoms(full=True) + for i,rom in enumerate(segments): + romRlz = rom.getSegmentPointwiseData() + for feature, featureVal in romRlz.items(): + varName = f'Feature|{feature}' + if i==0: + writeTo.addVariable(varName, np.array([]), classify='meta', indices=['segment_number']) + rlz[varName] = featureVal + else: + rlz[varName] = np.r_[rlz[varName],featureVal] writeTo.addRealization(rlz) @@ -981,7 +992,7 @@ def writeXML(self, writeTo, targets=None, skip=None): labels = self._clusterInfo['labels'] for i, repRom in enumerate(self._roms): # find associated node - modify = xmlUtils.findPath(main, 'SegmentROM[@segment={}]'.format(i)) + modify = xmlUtils.findPath(main, f'SegmentROM[@segment={i}]') # make changes to reflect being a cluster modify.tag = 'ClusterROM' modify.attrib['cluster'] = modify.attrib.pop('segment') diff --git a/ravenframework/SupervisedLearning/SupervisedLearning.py b/ravenframework/SupervisedLearning/SupervisedLearning.py index f6de0e260b..912b99d2be 100644 --- a/ravenframework/SupervisedLearning/SupervisedLearning.py +++ b/ravenframework/SupervisedLearning/SupervisedLearning.py @@ -204,6 +204,8 @@ def __init__(self): # After the computation, the importances are set as attribute of the self.model # variable and called 'feature_importances_' and accessable as self.model.feature_importances_ self.computeImportances = False + # distinction between existing param `isClusterable` and whether it does, in fact, have clusters + self._hasClusters = False # can only be true if `isClusterable`==True def __getstate__(self): """ @@ -658,6 +660,17 @@ def writePointwiseData(self, *args): # by default, nothing to write! self.raiseAMessage('Writing ROM "{}", but no pointwise data found. Moving on ...') + def getSegmentPointwiseData(self): + """ + Allows the SVE to accumulate data arrays to later add to a DataObject + Overload in subclasses. + @ In, None + @ Out, segmentData, dict + """ + # by default, nothing to write! + self.raiseAMessage('Writing ROM, but no pointwise data found. Moving on ...') + return {} + def writeXML(self, writeTo, targets=None, skip=None): """ Allows the SVE to put whatever it wants into an XML to print to file. @@ -701,13 +714,36 @@ def setAdditionalParams(self, params): ### ROM Clustering (see ROMCollection.py) ### def isClusterable(self): """ - Allows ROM to declare whether it has methods for clustring. Default is no. + Allows ROM to declare whether it has methods for clustering. Default is no. @ In, None @ Out, isClusterable, bool, if True then has clustering mechanics. """ # only true if overridden. return False + def setClusterability(self, willHaveClusters: bool): + """ + Sets protected class member which tells ROM whether there will be clustering + @ In, willHaveClusters. bool, will the ROM have clustering in this run? + @ Out, None + """ + assert isinstance(willHaveClusters, bool) + if not self.isClusterable(): + # if ROM can't cluster in the first place... default to False + if willHaveClusters: + self.raiseAWarning("Clustering not allowed in this ROM, defaulting `hasClusters` to False") + self._hasClusters = False + else: + self._hasClusters = willHaveClusters + + def hasClusters(self): + """ + Allows ROM to declare if is *has* clusters, not just if it is capable. Default is no. + @ In, None + @ Out, hasClusters, bool, if True then contains clusters + """ + return self._hasClusters + def checkRequestedClusterFeatures(self, request): """ Takes the user-requested features (sometimes "all") and interprets them for this ROM. diff --git a/ravenframework/SupervisedLearning/SyntheticHistory.py b/ravenframework/SupervisedLearning/SyntheticHistory.py index 7dae3f3eb0..083fa3ffe8 100644 --- a/ravenframework/SupervisedLearning/SyntheticHistory.py +++ b/ravenframework/SupervisedLearning/SyntheticHistory.py @@ -78,7 +78,7 @@ def _handleInput(self, paramInput): @ Out, None """ SupervisedLearning._handleInput(self, paramInput) - self.readTSAInput(paramInput) + self.readTSAInput(paramInput, self.hasClusters()) if len(self._tsaAlgorithms)==0: self.raiseAWarning("No Segmenting algorithms were requested.") @@ -157,6 +157,21 @@ def writePointwiseData(self, writeTo): """ pass # TODO + def getSegmentPointwiseData(self): + """ + Allows the SVE to accumulate data arrays to later add to a DataObject + Overload in subclasses. + @ In, None + @ Out, segmentData, dict + """ + segmentNonFeatures = self.getTSApointwiseData() + formattedNonFeatures = {} + for algo,algoInfo in segmentNonFeatures.items(): + for target,targetInfo in algoInfo.items(): + for k,val in targetInfo.items(): + formattedNonFeatures[f'{target}|{algo}|{k}'] = val + return formattedNonFeatures + def writeXML(self, writeTo, targets=None, skip=None): """ Allows the SVE to put whatever it wants into an XML to print to file. @@ -212,16 +227,18 @@ def checkRequestedClusterFeatures(self, request): '\n '.join(errMsg)) return request - def _getClusterableFeatures(self): + def _getClusterableFeatures(self, trainGlobal=False): """ Provides a list of clusterable features. For this ROM, these are as "TSA_algorith|feature" such as "fourier|amplitude" @ In, None + @ In, trainGlobal, bool, if True then this method uses the globally trained algorithms @ Out, features, dict(list(str)), clusterable features by algorithm """ features = {} # check: is it possible tsaAlgorithms isn't populated by now? - for algo in self._tsaAlgorithms: + algorithms = self._tsaGlobalAlgorithms if trainGlobal else self._tsaAlgorithms + for algo in algorithms: if algo.canCharacterize(): features[algo.name] = algo._features else: @@ -320,8 +337,17 @@ def parametrizeGlobalRomFeatures(self, featureDict): @ In, featureDict, dict, dictionary of features to parametrize @ Out, params, dict, dictionary of collected parametrized features """ - # NOTE: only used during interpolation for global features! returning empty dict... + # NOTE: this should match the clustered features template. + featureTemplate = '{target}|{metric}|{id}' # TODO this kind of has to be the format currently params = {} + requests = self._getClusterableFeatures(trainGlobal=True) + + for algo in self._tsaGlobalAlgorithms: + if algo.name not in requests or not algo.canCharacterize(): + continue + algoReq = requests[algo.name] if requests is not None else None + algoFeatures = algo.getClusteringValues(featureTemplate, algoReq, self._tsaTrainedParams[algo]) + params.update(algoFeatures) return params def setGlobalRomFeatures(self, params, pivotValues): @@ -332,9 +358,30 @@ def setGlobalRomFeatures(self, params, pivotValues): @ In, pivotValues, np.array, values of time parameter @ Out, results, dict, global ROM feature set """ - # NOTE: only used during interpolation for global features! returning empty dict... - results = {} - return results + byAlgo = collections.defaultdict(list) + for feature, values in params.items(): + target, algoName, ident = feature.split('|', maxsplit=2) + byAlgo[algoName].append((target, ident, values)) + for algo in self._tsaAlgorithms: + settings = byAlgo.get(algo.name, None) + if settings: + # there might be multiple instances of same algo w/ different targets, need to filter by targets + # filtered_settings = [feat for feat in settings if feat[0] in self._tsaTrainedParams[algo]] + params = algo.setClusteringValues(settings, self._tsaTrainedParams[algo]) + self._tsaTrainedParams[algo] = params + return self._tsaTrainedParams + + def finalizeLocalRomSegmentEvaluation(self, settings, evaluation, globalPicker, localPicker=None): + """ + Allows global settings in "settings" to affect a LOCAL evaluation of a LOCAL ROM + Note this is called on the LOCAL subsegment ROM and not the GLOBAL templateROM. + @ In, settings, dict, as from getGlobalRomSegmentSettings + @ In, evaluation, dict, preliminary evaluation from the local segment ROM as {target: [values]} + @ In, globalPicker, slice, indexer for data range of this segment FROM GLOBAL SIGNAL + @ In, localPicker, slice, optional, indexer for part of signal that should be adjusted IN LOCAL SIGNAL + @ Out, evaluation, dict, {target: np.ndarray} adjusted global evaluation + """ + return evaluation ### ESSENTIALLY UNUSED ### def _localNormalizeData(self,values,names,feat): diff --git a/ravenframework/TSA/TSAUser.py b/ravenframework/TSA/TSAUser.py index f109b18825..f9867a915d 100644 --- a/ravenframework/TSA/TSAUser.py +++ b/ravenframework/TSA/TSAUser.py @@ -18,6 +18,7 @@ Contains a utility base class for accessing commonly-used TSA functions. """ import numpy as np +import copy from inspect import isabstract from ..utils import xmlUtils, InputData, InputTypes @@ -72,10 +73,11 @@ def __init__(self): self._tsaTargets = None # cached list of targets self.target = None - def readTSAInput(self, spec): + def readTSAInput(self, spec, hasClusters=None): """ Read in TSA algorithms @ In, spec, InputData.parameterInput, input specs filled with user entries + @ In, hasClusters, bool, is this a ROM with clustered segments? @ Out, None """ if self.pivotParameterID is None: # might be handled by parent @@ -83,7 +85,7 @@ def readTSAInput(self, spec): for sub in spec.subparts: if sub.name in factory.knownTypes(): algo = factory.returnInstance(sub.name) - self._tsaAlgoSettings[algo] = algo.handleInput(sub) + self._tsaAlgoSettings[algo] = algo.handleInput(sub,hasClusters) if self._tsaAlgoSettings[algo]['global']: self._tsaGlobalAlgorithms.append(algo) else: @@ -209,7 +211,8 @@ def trainTSASequential(self, targetVals, trainGlobal=False): if np.isnan(signal).any() and not algo.canAcceptMissingValues(): raise ValueError(f'Missing values (NaN) found in input to {algo.name}, ' f'but {algo.name} cannot accept missing values!') - params = algo.fit(signal, pivots, targets, settings) + trainedParams = copy.deepcopy(self._tsaTrainedParams) if algo.needsPriorAlgoFeatures() else None + params = algo.fit(signal, pivots, targets, settings, trainedParams=trainedParams) # store characteristics self._tsaTrainedParams[algo] = params # obtain residual; the part of the signal not characterized by this algo @@ -294,3 +297,30 @@ def writeTSAtoXML(self, xml): algoNode = xmlUtils.newNode(algo.name) algo.writeXML(algoNode, self._tsaTrainedParams[algo]) root.append(algoNode) + + def getTSApointwiseData(self): + """ + Allows the SVE to accumulate data arrays to later add to a DataObject + Overload in subclasses. + @ In, None, + @ Out, segmentData, dict + """ + # + segmentNonFeatures = {} + for algo in self._tsaAlgorithms: + if algo not in self._tsaTrainedParams: + continue + segmentNonFeatures[algo.name] = algo.getNonClusterFeatures(self._tsaTrainedParams[algo]) + return segmentNonFeatures + + def writeTSAPointwiseData(self, writeTo): + """ + Writes pointwise data about segmentation to a realization. + @ In, writeTo, DataObject, data structure into which data should be written + @ Out, rlz, dict, realization data structure where each entry is an np.ndarray + """ + for algo in self._tsaAlgorithms: + if algo not in self._tsaTrainedParams: + continue + algo_rlz = {} + return algo_rlz diff --git a/ravenframework/TSA/TimeSeriesAnalyzer.py b/ravenframework/TSA/TimeSeriesAnalyzer.py index 794e8fa697..ab6b44a3f8 100644 --- a/ravenframework/TSA/TimeSeriesAnalyzer.py +++ b/ravenframework/TSA/TimeSeriesAnalyzer.py @@ -30,6 +30,8 @@ class TimeSeriesAnalyzer(utils.metaclass_insert(abc.ABCMeta, object)): # class attribute ## defines if missing values are accepted by the characterization algorithm _acceptsMissingValues = False + ## defines if usage of algorithm requires output from a prior algorithm + _needsPriorAlgoFeatures = False @classmethod def getInputSpecification(cls): @@ -82,6 +84,15 @@ def canTransform(cls): """ return issubclass(cls, TimeSeriesTransformer) + @classmethod + def needsPriorAlgoFeatures(cls): + """ + Determines if the algorithm requires trained params from prior algorithms. + @ In, None + @ Out, needsPriorAlgoFeatures, bool, True if this algorithm requires prior trained params + """ + return cls._needsPriorAlgoFeatures + ### INHERITED METHODS ### def __init__(self, *args, **kwargs): """ @@ -93,7 +104,7 @@ def __init__(self, *args, **kwargs): self.name = self.__class__.__name__ # the name the class shall be known by during its RAVEN life @abc.abstractmethod - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Fits the algorithm/model using the provided time series ("signal") using methods specific to the algorithm. @@ -101,14 +112,16 @@ def fit(self, signal, pivot, targets, settings): @ In, pivot, np.array, time-like parameter @ In, targets, list(str), names of targets @ In, settings, dict, additional settings specific to algorithm + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characterization of signal; structure as: params[target variable][characteristic] = value """ - def handleInput(self, spec): + def handleInput(self, spec, enforce_global=False): """ Reads user inputs into this object. @ In, spec, InputData.InputParams, input specifications + @ In, enforce_global, bool, @ Out, settings, dict, initialization settings for this algorithm """ settings = {} @@ -150,6 +163,14 @@ def writeXML(self, writeTo, params): @ Out, None """ + @abc.abstractmethod + def getNonClusterFeatures(self, params): + """ + Allows the engine to put whatever it wants into an XML to print to file. + @ In, params, dict, parameters from training this ROM + @ Out, None + """ + class TimeSeriesGenerator(TimeSeriesAnalyzer): """ @@ -192,6 +213,13 @@ def generate(self, params, pivot, settings): @ Out, synthetic, np.array(float), synthetic signal """ + def getNonClusterFeatures(self, params): + """ + Allows the engine to put whatever it wants into an XML to print to file. + @ In, params, dict, parameters from training this ROM + @ Out, None + """ + return {} class TimeSeriesCharacterizer(TimeSeriesAnalyzer): """ @@ -265,6 +293,13 @@ def setClusteringValues(self, fromCluster, params): params[target][identifier] = value return params + def getNonClusterFeatures(self, params): + """ + Allows the engine to put whatever it wants into an XML to print to file. + @ In, params, dict, parameters from training this ROM + @ Out, None + """ + return {} class TimeSeriesTransformer(TimeSeriesAnalyzer): """ @@ -306,3 +341,11 @@ def getComposite(self, initial, params, pivot, settings): @ In, settings, dict, additional settings specific to algorithm @ Out, composite, np.array, resulting composite signal """ + + def getNonClusterFeatures(self, params): + """ + Allows the engine to put whatever it wants into an XML to print to file. + @ In, params, dict, parameters from training this ROM + @ Out, None + """ + return {} diff --git a/ravenframework/unSupervisedLearning.py b/ravenframework/unSupervisedLearning.py index fdffb186e7..0b2ad75bcf 100644 --- a/ravenframework/unSupervisedLearning.py +++ b/ravenframework/unSupervisedLearning.py @@ -189,7 +189,7 @@ def train(self, tdict, metric=None): ## Check if a label feature is provided by the user and in the training data if self.labelFeature in tdict: - self.labelValues = tidct[self.labelFeature] + self.labelValues = tdict[self.labelFeature] resp = self.checkArrayConsistency(self.labelValues) if not resp[0]: self.raiseAnError(IOError, 'In training set for ground truth labels ' + self.labelFeature + ':' + resp[1]) @@ -857,7 +857,7 @@ def train(self, tdict): ## Check if a label feature is provided by the user and in the training data if self.labelFeature in names: - self.labelValues = tidct[self.labelFeature] + self.labelValues = tdict[self.labelFeature] resp = self.checkArrayConsistency(self.labelValues,[self.numberOfSample, self.numberOfHistoryStep]) if not resp[0]: self.raiseAnError(IOError, 'In training set for ground truth labels ' + self.labelFeature + ':' + resp[1]) From bfda092d90ad120d28e54b2f0ab70d00ff27e390 Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 23 Apr 2024 11:22:32 -0600 Subject: [PATCH 02/12] renaming a method for clarity --- ravenframework/Models/ROM.py | 4 ++-- ravenframework/SupervisedLearning/SupervisedLearning.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ravenframework/Models/ROM.py b/ravenframework/Models/ROM.py index 0aea029425..a10f422961 100644 --- a/ravenframework/Models/ROM.py +++ b/ravenframework/Models/ROM.py @@ -238,10 +238,10 @@ def _readMoreXML(self,xmlNode): romXml = copy.deepcopy(xmlNode) romXml.remove(segment) # depending on segType, this ROM *will* have clusters and we will need this fact later - self._interfaceROM.setClusterability(segType in ['cluster', 'interpolate']) + self._interfaceROM.overrideHasClusters(segType in ['cluster', 'interpolate']) else: romXml = xmlNode - self._interfaceROM.setClusterability(False) # just making sure it's False otherwise + self._interfaceROM.overrideHasClusters(False) # just making sure it's False otherwise self._interfaceROM._readMoreXML(romXml) if self.segment: diff --git a/ravenframework/SupervisedLearning/SupervisedLearning.py b/ravenframework/SupervisedLearning/SupervisedLearning.py index 912b99d2be..2f290e1775 100644 --- a/ravenframework/SupervisedLearning/SupervisedLearning.py +++ b/ravenframework/SupervisedLearning/SupervisedLearning.py @@ -721,7 +721,7 @@ def isClusterable(self): # only true if overridden. return False - def setClusterability(self, willHaveClusters: bool): + def overrideHasClusters(self, willHaveClusters: bool): """ Sets protected class member which tells ROM whether there will be clustering @ In, willHaveClusters. bool, will the ROM have clustering in this run? From e58f4c6b7cdcf62aa7f5367a3cb02f85695816b8 Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 23 Apr 2024 11:24:33 -0600 Subject: [PATCH 03/12] cleaner handling of strictly global algos (different from previous branch) --- ravenframework/TSA/TSAUser.py | 22 ++++++++++++++++++++-- ravenframework/TSA/TimeSeriesAnalyzer.py | 15 +++++++++++++-- 2 files changed, 33 insertions(+), 4 deletions(-) diff --git a/ravenframework/TSA/TSAUser.py b/ravenframework/TSA/TSAUser.py index f9867a915d..459ae7ab47 100644 --- a/ravenframework/TSA/TSAUser.py +++ b/ravenframework/TSA/TSAUser.py @@ -73,7 +73,7 @@ def __init__(self): self._tsaTargets = None # cached list of targets self.target = None - def readTSAInput(self, spec, hasClusters=None): + def readTSAInput(self, spec, hasClusters=False): """ Read in TSA algorithms @ In, spec, InputData.parameterInput, input specs filled with user entries @@ -85,7 +85,9 @@ def readTSAInput(self, spec, hasClusters=None): for sub in spec.subparts: if sub.name in factory.knownTypes(): algo = factory.returnInstance(sub.name) - self._tsaAlgoSettings[algo] = algo.handleInput(sub,hasClusters) + if not algo.canBeAppliedPerCluster() and hasClusters: + self.resolveGlobalvsCluster(sub) + self._tsaAlgoSettings[algo] = algo.handleInput(sub) if self._tsaAlgoSettings[algo]['global']: self._tsaGlobalAlgorithms.append(algo) else: @@ -102,6 +104,22 @@ def readTSAInput(self, spec, hasClusters=None): # NOTE this assumes that every TSAUser is also an InputUser! raise IOError('TSA: The pivotParameter must be included in the target space.') + def resolveGlobalvsCluster(self, spec): + """ + Method to resolve scenario when algorithm can not be applied per cluster and we're dealing + with a clustered ROM. If user does not specify it as a `global` algorithm, prints a warning. + If user specifically requests `global` as False, it raises an error. + @ In, spec, InputData.InputParams, input specifications + @ Out, None + """ + requestedGlobal = spec.parameterValues.get('global', None) + if requestedGlobal is None: + print(f"{spec.name} algorithm will be applied to the global signal.") + elif requestedGlobal is False: + msg = f"{spec.name} algorithm must be used as a global TSA algorithm when using a " + msg += "clustered TSA ROM. The `global` parameter must be set to True in the input." + raise IOError(msg) + def canCharacterize(self): """ Checks if any of the algorithms are characterizers diff --git a/ravenframework/TSA/TimeSeriesAnalyzer.py b/ravenframework/TSA/TimeSeriesAnalyzer.py index ab6b44a3f8..3a94630b1a 100644 --- a/ravenframework/TSA/TimeSeriesAnalyzer.py +++ b/ravenframework/TSA/TimeSeriesAnalyzer.py @@ -32,6 +32,8 @@ class TimeSeriesAnalyzer(utils.metaclass_insert(abc.ABCMeta, object)): _acceptsMissingValues = False ## defines if usage of algorithm requires output from a prior algorithm _needsPriorAlgoFeatures = False + ## defines if algorithm can be applied per cluster (otherwise it can only be used on global signal) + _canBeAppliedPerCluster = True @classmethod def getInputSpecification(cls): @@ -93,6 +95,16 @@ def needsPriorAlgoFeatures(cls): """ return cls._needsPriorAlgoFeatures + @classmethod + def canBeAppliedPerCluster(cls): + """ + Determines if algorithm can be applied per cluster. Otherwise it can only be used + on the global signal. + @ In, None + @ Out, canBeAppliedPerCluster, bool, True if this algorithm can be applied per cluster + """ + return cls._canBeAppliedPerCluster + ### INHERITED METHODS ### def __init__(self, *args, **kwargs): """ @@ -117,11 +129,10 @@ def fit(self, signal, pivot, targets, settings, trainedParams=None): params[target variable][characteristic] = value """ - def handleInput(self, spec, enforce_global=False): + def handleInput(self, spec): """ Reads user inputs into this object. @ In, spec, InputData.InputParams, input specifications - @ In, enforce_global, bool, @ Out, settings, dict, initialization settings for this algorithm """ settings = {} From 5646c2ec501b873df0887a088ba80711d6d3413c Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 23 Apr 2024 11:41:48 -0600 Subject: [PATCH 04/12] fixing non-ARMA algos --- ravenframework/TSA/Factory.py | 2 ++ ravenframework/TSA/Fourier.py | 2 +- ravenframework/TSA/MarkovAR.py | 3 ++- ravenframework/TSA/PolynomialRegression.py | 3 ++- ravenframework/TSA/README.md | 1 + ravenframework/TSA/RWD.py | 3 ++- ravenframework/TSA/STL.py | 3 ++- ravenframework/TSA/Transformers/Differencing.py | 3 ++- ravenframework/TSA/Transformers/Distributions.py | 3 ++- ravenframework/TSA/Transformers/Filters.py | 3 ++- ravenframework/TSA/Transformers/ScikitLearnBase.py | 6 ++++-- ravenframework/TSA/VARMA.py | 3 ++- ravenframework/TSA/Wavelet.py | 3 ++- ravenframework/TSA/__init__.py | 1 + 14 files changed, 27 insertions(+), 12 deletions(-) diff --git a/ravenframework/TSA/Factory.py b/ravenframework/TSA/Factory.py index 4ba39ccaa9..ca9d6704a8 100644 --- a/ravenframework/TSA/Factory.py +++ b/ravenframework/TSA/Factory.py @@ -22,6 +22,7 @@ from .Fourier import Fourier from .ARMA import ARMA from .VARMA import VARMA +from .AutoARMA import AutoARMA from .MarkovAR import MarkovAR from .Wavelet import Wavelet from .PolynomialRegression import PolynomialRegression @@ -36,6 +37,7 @@ aliases = {'Fourier': 'fourier', 'ARMA': 'arma', 'VARMA': 'varma', + 'AutoARMA': 'autoarma', 'RWD': 'rwd', 'Wavelet': 'wavelet', 'ZeroFilter': 'zerofilter', diff --git a/ravenframework/TSA/Fourier.py b/ravenframework/TSA/Fourier.py index 82dbb0ccdb..d9a38eecf8 100644 --- a/ravenframework/TSA/Fourier.py +++ b/ravenframework/TSA/Fourier.py @@ -83,7 +83,7 @@ def handleInput(self, spec): settings['periods'] = spec.findFirst('periods').value return settings - def fit(self, signal, pivot, targets, settings, simultFit=True): + def fit(self, signal, pivot, targets, settings, trainedParams=None, simultFit=True): """ Determines the charactistics of the signal based on this algorithm. @ In, signal, np.ndarray, time series with dims [time, target] diff --git a/ravenframework/TSA/MarkovAR.py b/ravenframework/TSA/MarkovAR.py index 13325e0c7a..0d2414ef0d 100644 --- a/ravenframework/TSA/MarkovAR.py +++ b/ravenframework/TSA/MarkovAR.py @@ -100,13 +100,14 @@ def setDefaults(self, settings): settings['engine'] = randomUtils.newRNG() return settings - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Determines the charactistics of the signal based on this algorithm. @ In, signal, np.ndarray, time series with dims [time, target] @ In, pivot, np.1darray, time-like parameter values @ In, targets, list(str), names of targets in same order as signal @ In, settings, dict, settings for this ROM + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characteristic parameters """ # lazy import statsmodels diff --git a/ravenframework/TSA/PolynomialRegression.py b/ravenframework/TSA/PolynomialRegression.py index 42f85094f2..65a184b7b8 100644 --- a/ravenframework/TSA/PolynomialRegression.py +++ b/ravenframework/TSA/PolynomialRegression.py @@ -66,13 +66,14 @@ def handleInput(self, spec): settings['degree'] = spec.findFirst('degree').value return settings - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Determines the charactistics of the signal based on this algorithm. @ In, signal, np.ndarray, time series with dims [time, target] @ In, pivot, np.1darray, time-like parameter values @ In, targets, list(str), names of targets in same order as signal @ In, settings, dict, additional settings specific to this algorithm + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characteristic parameters """ from sklearn.preprocessing import PolynomialFeatures diff --git a/ravenframework/TSA/README.md b/ravenframework/TSA/README.md index 50fa056763..5734fcdbfe 100644 --- a/ravenframework/TSA/README.md +++ b/ravenframework/TSA/README.md @@ -40,6 +40,7 @@ The following table shows which base classes each currently implemented TSA algo | Algorithm | Transformer | Generator | Characterizer | |------------------------|:-----------:|:---------:|:-------------:| +| `AutoARMA` | | | ✓ | | `ARMA` | ✓ | ✓ | ✓ | | `VARMA` | ✓ | ✓ | ✓ | | `MarkovAR` | ✓ | ✓ | | diff --git a/ravenframework/TSA/RWD.py b/ravenframework/TSA/RWD.py index fb456a765f..b268f0228e 100644 --- a/ravenframework/TSA/RWD.py +++ b/ravenframework/TSA/RWD.py @@ -96,13 +96,14 @@ def setDefaults(self, settings): settings['seed'] = 42 return settings #### - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Determines the charactistics of the signal based on this algorithm. @ In, signal, np.ndarray, time series with dims [time, target] @ In, pivot, np.1darray, time-like parameter values @ In, targets, list(str), names of targets in same order as signal @ In, settings, dict, settings for this ROM + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict of dict: 1st level contains targets/variables; 2nd contains: U vectors and features """ # settings: diff --git a/ravenframework/TSA/STL.py b/ravenframework/TSA/STL.py index b4f7d44012..99657db4b9 100644 --- a/ravenframework/TSA/STL.py +++ b/ravenframework/TSA/STL.py @@ -90,7 +90,7 @@ def setDefaults(self, settings): return settings - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Fits the algorithm/model using the provided time series ("signal") using methods specific to the algorithm. @@ -98,6 +98,7 @@ def fit(self, signal, pivot, targets, settings): @ In, pivot, np.array, time-like parameter @ In, targets, list(str), names of targets @ In, settings, dict, additional settings specific to algorithm + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characterization of signal; structure as: params[target variable][characteristic] = value """ diff --git a/ravenframework/TSA/Transformers/Differencing.py b/ravenframework/TSA/Transformers/Differencing.py index 58fc0337c1..c178c9a8ce 100644 --- a/ravenframework/TSA/Transformers/Differencing.py +++ b/ravenframework/TSA/Transformers/Differencing.py @@ -51,7 +51,7 @@ def handleInput(self, spec): settings['order'] = spec.findFirst('order').value return settings - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Fits the algorithm/model using the provided time series ("signal") using methods specific to the algorithm. @@ -59,6 +59,7 @@ def fit(self, signal, pivot, targets, settings): @ In, pivot, np.array, time-like parameter @ In, targets, list(str), names of targets @ In, settings, dict, additional settings specific to algorithm + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characterization of signal; structure as: params[target variable][characteristic] = value """ diff --git a/ravenframework/TSA/Transformers/Distributions.py b/ravenframework/TSA/Transformers/Distributions.py index ddfd57bebe..2945bae356 100644 --- a/ravenframework/TSA/Transformers/Distributions.py +++ b/ravenframework/TSA/Transformers/Distributions.py @@ -150,7 +150,7 @@ class cls. be used as the first transformer in the chain.""" return specs - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Fits the algorithm/model using the provided time series ("signal") using methods specific to the algorithm. @@ -158,6 +158,7 @@ def fit(self, signal, pivot, targets, settings): @ In, pivot, np.array, time-like parameter @ In, targets, list(str), names of targets @ In, settings, dict, additional settings specific to algorithm + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characterization of signal; structure as: params[target variable][characteristic] = value """ diff --git a/ravenframework/TSA/Transformers/Filters.py b/ravenframework/TSA/Transformers/Filters.py index ba02daf94c..868db9c89d 100644 --- a/ravenframework/TSA/Transformers/Filters.py +++ b/ravenframework/TSA/Transformers/Filters.py @@ -43,7 +43,7 @@ def criterion(self, signal): @ Out, mask, numpy.ndarray, numpy array of boolean values that masks values of X """ - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Fits the algorithm/model using the provided time series ("signal") using methods specific to the algorithm. @@ -51,6 +51,7 @@ def fit(self, signal, pivot, targets, settings): @ In, pivot, np.array, time-like parameter @ In, targets, list(str), names of targets @ In, settings, dict, additional settings specific to algorithm + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characterization of signal; structure as: params[target variable][characteristic] = value """ diff --git a/ravenframework/TSA/Transformers/ScikitLearnBase.py b/ravenframework/TSA/Transformers/ScikitLearnBase.py index 8e9d194cee..b197c99466 100644 --- a/ravenframework/TSA/Transformers/ScikitLearnBase.py +++ b/ravenframework/TSA/Transformers/ScikitLearnBase.py @@ -35,7 +35,7 @@ class SKLTransformer(TimeSeriesTransformer): def templateTransformer(self): """ Template transformer that must be implemented in child classes """ - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Fits the algorithm/model using the provided time series ("signal") using methods specific to the algorithm. @@ -43,6 +43,7 @@ def fit(self, signal, pivot, targets, settings): @ In, pivot, np.array, time-like parameter @ In, targets, list(str), names of targets @ In, settings, dict, additional settings specific to algorithm + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characterization of signal; structure as: params[target variable][characteristic] = value """ @@ -108,7 +109,7 @@ def writeXML(self, writeTo, params): class SKLCharacterizer(SKLTransformer, TimeSeriesCharacterizer): """ Wrapper for scikit-learn transformers that also provide a characterization of the data """ - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Fits the algorithm/model using the provided time series ("signal") using methods specific to the algorithm. @@ -116,6 +117,7 @@ def fit(self, signal, pivot, targets, settings): @ In, pivot, np.array, time-like parameter @ In, targets, list(str), names of targets @ In, settings, dict, additional settings specific to algorithm + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characterization of signal; structure as: params[target variable][characteristic] = value """ diff --git a/ravenframework/TSA/VARMA.py b/ravenframework/TSA/VARMA.py index 085feca7f1..c722ffc52d 100644 --- a/ravenframework/TSA/VARMA.py +++ b/ravenframework/TSA/VARMA.py @@ -108,13 +108,14 @@ def setDefaults(self, settings): settings['engine'] = randomUtils.newRNG() return settings - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Determines the charactistics of the signal based on this algorithm. @ In, signal, np.ndarray, time series with dims [time, target] @ In, pivot, np.1darray, time-like parameter values @ In, targets, list(str), names of targets in same order as signal @ In, settings, dict, settings for this ROM + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characteristic parameters """ # lazy statsmodels import diff --git a/ravenframework/TSA/Wavelet.py b/ravenframework/TSA/Wavelet.py index 20e01d225b..d871095558 100644 --- a/ravenframework/TSA/Wavelet.py +++ b/ravenframework/TSA/Wavelet.py @@ -94,7 +94,7 @@ def handleInput(self, spec): settings['family'] = spec.findFirst('family').value return settings - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ This function utilizes the Discrete Wavelet Transform to characterize a time-dependent series of data. @@ -103,6 +103,7 @@ def fit(self, signal, pivot, targets, settings): @ In, pivot, np.1darray, time-like parameter values @ In, targets, list(str), names of targets in same order as signal @ In, settings, dict, additional settings specific to this algorithm + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characteristic parameters """ # TODO extend to continuous wavelet transform diff --git a/ravenframework/TSA/__init__.py b/ravenframework/TSA/__init__.py index a90c42f269..c52006a6c7 100644 --- a/ravenframework/TSA/__init__.py +++ b/ravenframework/TSA/__init__.py @@ -23,6 +23,7 @@ from .Fourier import Fourier from .ARMA import ARMA from .VARMA import VARMA +from .AutoARMA import AutoARMA from .MarkovAR import MarkovAR from .RWD import RWD from .STL import STL From e9e4ee5afbe9728c48e34f2ee4ffee37dde706cf Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 23 Apr 2024 13:58:29 -0600 Subject: [PATCH 05/12] fixing ARMA and autoARMA; conversion script for P and Q --- ravenframework/TSA/ARMA.py | 122 +++++++++-- ravenframework/TSA/AutoARMA.py | 206 ++++++++++++++++++ .../SyntheticHistory_ARMA_lag.py | 51 +++++ 3 files changed, 363 insertions(+), 16 deletions(-) create mode 100644 ravenframework/TSA/AutoARMA.py create mode 100644 scripts/conversionScripts/SyntheticHistory_ARMA_lag.py diff --git a/ravenframework/TSA/ARMA.py b/ravenframework/TSA/ARMA.py index d1faec9a54..66c4074215 100644 --- a/ravenframework/TSA/ARMA.py +++ b/ravenframework/TSA/ARMA.py @@ -41,6 +41,7 @@ class ARMA(TimeSeriesGenerator, TimeSeriesCharacterizer, TimeSeriesTransformer): 'const'] _acceptsMissingValues = True _isStochastic = True + _needsPriorAlgoFeatures = False @classmethod def getInputSpecification(cls): @@ -83,14 +84,31 @@ class cls. \xmlNode{gaussianize} node preceding the \xmlNode{arma} node instead of this option. """, default=False) - specs.addSub(InputData.parameterInputFactory('SignalLag', contentType=InputTypes.IntegerType, - descr=r"""the number of terms in the AutoRegressive term to retain in the - regression; typically represented as $P$ in literature.""")) - specs.addSub(InputData.parameterInputFactory('NoiseLag', contentType=InputTypes.IntegerType, - descr=r"""the number of terms in the Moving Average term to retain in the - regression; typically represented as $Q$ in literature.""")) + specs.addParam('auto_select', param_type=InputTypes.BoolType, required=False, + descr=r""" """, default=False) + specs.addSub(InputData.parameterInputFactory('P', contentType=InputTypes.IntegerListType, + descr=r"""the number of terms in the AutoRegressive (AR) term to retain in the + regression; typically represented as $P$ or Signal Lag in literature. + Accepted as list or single value. If list, should be the same length as + number of target signals. Otherwise, the singular value is used for all + all signals.""")) + specs.addSub(InputData.parameterInputFactory('Q', contentType=InputTypes.IntegerListType, + descr=r"""the number of terms in the Moving Average (MA) term to retain in the + regression; typically represented as $Q$ or Noise Lag in literature. + Accepted as list or single value. If list, should be the same length as + number of target signals. Otherwise, the singular value is used for all + all signals.""")) return specs + @classmethod + def overrideNeedsPriorAlgoFeatures(cls, val): + """ + Method to overwrite the _needsPriorAlgoFeatures attribute + @ In, val, bool, True or False value for _needsPriorAlgoFeatures + @ Out, None + """ + cls._needsPriorAlgoFeatures = val + # # API Methods # @@ -112,8 +130,34 @@ def handleInput(self, spec): @ Out, settings, dict, initialization settings for this algorithm """ settings = super().handleInput(spec) - settings['P'] = spec.findFirst('SignalLag').value - settings['Q'] = spec.findFirst('NoiseLag').value + settings['auto_select'] = spec.parameterValues.get('auto_select', settings['auto_select']) + + targets = settings['target'] + if settings['auto_select']: + # if auto-selecting, replace P and Q with Nones to check for and replace later + settings['P'] = dict((target, None) for target in targets ) + settings['Q'] = dict((target, None) for target in targets ) + # overriding class attribute because we now need output from autoARMA to continue + self.overrideNeedsPriorAlgoFeatures(True) + else: + # getting P and Q values (number of Signal Lag and Noise Lag coefficients); checking validity + lagDict = {} + for lagType in ('P', 'Q'): #NOTE: not including 'd' here, as this is a Transformer + # user-defined Ps and Qs + lagVals = list(spec.findAll(lagType)[0].value) + # checking if number of P/Q values is acceptable + # >> if user provided only 1 value, we repeat it for all targets + # >> otherwise, the user has to provide a value for each target + if len(lagVals) == 1: + lagDict[lagType] = dict((target, lagVals[0]) for target in targets ) + elif len(lagVals) == len(targets): + lagDict[lagType] = dict((target, lagVals[i]) for i,target in enumerate(targets) ) + else: + raise IOError(f'Number of {lagType} values {len(lagVals)} should be 1 or' +\ + f'equal to number of targets {len(targets)}') + settings['P'] = lagDict['P'] + settings['Q'] = lagDict['Q'] + settings['reduce_memory'] = spec.parameterValues.get('reduce_memory', settings['reduce_memory']) settings['gaussianize'] = spec.parameterValues.get('gaussianize', settings['gaussianize']) @@ -132,15 +176,18 @@ def setDefaults(self, settings): settings['engine'] = randomUtils.newRNG() if 'reduce_memory' not in settings: settings['reduce_memory'] = False + if 'auto_select' not in settings: + settings['auto_select'] = False return settings - def fit(self, signal, pivot, targets, settings): + def fit(self, signal, pivot, targets, settings, trainedParams=None): """ Determines the charactistics of the signal based on this algorithm. @ In, signal, np.ndarray, time series with dims [time, target] @ In, pivot, np.1darray, time-like parameter values @ In, targets, list(str), names of targets in same order as signal @ In, settings, dict, settings for this ROM + @ In, trainedParams, dict, running dict of trained algorithm params @ Out, params, dict, characteristic parameters """ # lazy import statsmodels @@ -160,7 +207,7 @@ def fit(self, signal, pivot, targets, settings): history = signal[:, tg] mask = ~np.isnan(history) if settings.get('gaussianize', True): - # Transform data to obatain normal distrbuted series. See + # Transform data to obtain normal distrbuted series. See # J.M.Morales, R.Minguez, A.J.Conejo "A methodology to generate statistically dependent wind speed scenarios," # Applied Energy, 87(2010) 843-855 # -> then train independent ARMAs @@ -169,10 +216,14 @@ def fit(self, signal, pivot, targets, settings): normed[mask] = mathUtils.gaussianize(history[mask], params[target]['cdf']) else: normed = history - # TODO correlation (VARMA) as well as singular -> maybe should be independent TSA algo? - P = settings['P'] - Q = settings['Q'] - d = settings.get('d', 0) + # auto-select P and Q values if desired + if settings['auto_select']: + P,d,Q = self.autoSelectParams(target, trainedParams) + else: + P = settings['P'][target] + Q = settings['Q'][target] + d = settings.get('d', 0) + d = d[target] if isinstance(d,dict) else d # TODO just use SARIMAX? model = statsmodels.tsa.arima.model.ARIMA(normed, order=(P, d, Q), trend='c') res = model.fit(low_memory=settings['reduce_memory']) @@ -210,6 +261,28 @@ def fit(self, signal, pivot, targets, settings): params[target]['arma']['residual'] = res.resid return params + def autoSelectParams(self, target, trainedParams): + """ + Auto-selects ARMA hyperparameters P and Q for signal and noise lag. Uses the StatsForecast + AutoARIMA methodology for selection, including BIC as the optimization criteria, + @ In, target, str, name of target signal + @ In, trainedParams, dict, running dict of trained algorithm params + @ Out, P, int, optimal signal lag parameter + @ Out, d, int, optimal differencing parameter + @ Out, Q, int, optimal noise lag parameter + """ + # find last applied AutoARMA algorithm + prevAutoARMA = [algo for algo in trainedParams if algo.name == 'AutoARMA'] + if len(prevAutoARMA) == 0: + raise IOError("'auto-select' was requested for ARMA but no previous AutoARMA algorithm was applied.") + + params = trainedParams[prevAutoARMA[-1]] + + P = params[target]['P_opt'] + d = params[target]['D_opt'] + Q = params[target]['Q_opt'] + return P,d,Q + def getResidual(self, initial, params, pivot, settings): """ Removes trained signal from data and find residual @@ -259,9 +332,9 @@ def getParamNames(self, settings): base = f'{self.name}__{target}' names.append(f'{base}__constant') names.append(f'{base}__variance') - for p in range(settings['P']): + for p in range(settings['P'][target]): names.append(f'{base}__AR__{p}') - for q in range(settings['Q']): + for q in range(settings['Q'][target]): names.append(f'{base}__MA__{q}') return names @@ -329,6 +402,23 @@ def writeXML(self, writeTo, params): for q, ma in enumerate(info['arma']['ma']): base.append(xmlUtils.newNode(f'MA_{q}', text=f'{float(ma):1.9e}')) base.append(xmlUtils.newNode('variance', text=f'{float(info["arma"]["var"]):1.9e}')) + if 'lags' in info["arma"].keys(): + base.append(xmlUtils.newNode('order', text=','.join([str(int(l)) for l in info["arma"]["lags"]]))) + + def getNonClusterFeatures(self, params): + """ + Allows the engine to put whatever it wants into an XML to print to file. + @ In, params, dict, parameters from training this ROM + @ Out, None + """ + nonFeatures = {} + for target, info in params.items(): + nonFeatures[target] = {} + if 'lags' in info["arma"].keys(): + nonFeatures[target]['p'] = np.array([info["arma"]["lags"][0]]) + nonFeatures[target]['d'] = np.array([info["arma"]["lags"][1]]) + nonFeatures[target]['q'] = np.array([info["arma"]["lags"][2]]) + return nonFeatures # clustering def getClusteringValues(self, nameTemplate: str, requests: list, params: dict) -> dict: diff --git a/ravenframework/TSA/AutoARMA.py b/ravenframework/TSA/AutoARMA.py new file mode 100644 index 0000000000..c74f9323b4 --- /dev/null +++ b/ravenframework/TSA/AutoARMA.py @@ -0,0 +1,206 @@ +# 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. +""" + AutoRegressive Moving Average time series analysis +""" +import copy +import collections +import numpy as np +import scipy as sp + +from .. import Decorators + +from ..utils import InputData, InputTypes, randomUtils, xmlUtils, mathUtils, importerUtils +statsmodels = importerUtils.importModuleLazy('statsmodels', globals()) + +from .. import Distributions +from .TimeSeriesAnalyzer import TimeSeriesCharacterizer + + +# utility methods +class AutoARMA(TimeSeriesCharacterizer): + r""" + AutoARMA time series characterizer algorithm + """ + # class attribute + _features = [] + ## Currently, AutoARMA is only a global algorithm. Applying it per segment is fine, but clusters + ## cause issues. Nominally (w/o auto-selection, ARMA models are trained per segment which means that + ## each segment model has the same number of coefficients but with different values. *With* auto + ## selection, each segment can have different number of coefficients if the algorithm chooses + ## different values of p and q for the segments. This can be mitigated by padding the coefficient + ## arrays according to largets p or q value; this works for Segmented ROMs. However, if we cluster + ## based on the coefficients then this causes issues if the coefficient arrays are padded to be + ## the same size. The zero or NaN values affect the resultant clusters and lead to bad synthetic + ## histories. Until a general solution is found, AutoARMA will remain a global algorithm + ## (i.e., not allowed if clustering). + _canBeAppliedPerCluster = False + + @classmethod + def getInputSpecification(cls): + """ + Method to get a reference to a class that specifies the input data for + class cls. + @ In, None + @ Out, specs, InputData.ParameterInput, class to use for + specifying input of cls. + """ + specs = super(AutoARMA, cls).getInputSpecification() + specs.name = 'autoarma' + specs.description = r"""characterizes the signal \textit{before} using Auto-Regressive and + Moving Average coefficients to stochastically fit the training signal. AutoARMA + automatically determines the number of coefficients to use in the regression. + The ARMA representation has the following form: + \begin{equation*} + A_t = \sum_{i=1}^P \phi_i A_{t-i} + \epsilon_t + \sum_{j=1}^Q \theta_j \epsilon_{t-j}, + \end{equation*} + where $t$ indicates a discrete time step, $\phi$ are the signal lag (or auto-regressive) + coefficients, $P$ is the number of signal lag terms to consider, $\epsilon$ is a random noise + term, $\theta$ are the noise lag (or moving average) coefficients, and $Q$ is the number of + noise lag terms to consider. The AutoARMA algorithm determines the optimal value of $P$ + and $Q$ for the each signal. The AutoARMA algorithms are developed in RAVEN using the + \texttt{statsforecast} Python library.""" + specs.addSub(InputData.parameterInputFactory('P_upper', contentType=InputTypes.IntegerType, + descr=r"""upper bound for the number of terms in the AutoRegressive term to retain + in the regression; typically represented as $P$ or Signal Lag in + literature.""")) + specs.addSub(InputData.parameterInputFactory('Q_upper', contentType=InputTypes.IntegerType, + descr=r"""upper bound for the number of terms in the Moving Average term to retain + in the regression; typically represented as $Q$ in Noise Lag + literature.""")) + return specs + + # + # API Methods + # + def __init__(self, *args, **kwargs): + """ + A constructor that will appropriately intialize a supervised learning object + @ In, args, list, an arbitrary list of positional values + @ In, kwargs, dict, an arbitrary dictionary of keywords and values + @ Out, None + """ + # general infrastructure + super().__init__(*args, **kwargs) + # maximum value that P+Q can have as an upper bound + self._maxCombinedPQ = 5 + + def handleInput(self, spec): + """ + Reads user inputs into this object. + @ In, spec, InputData.InputParams, input specifications + @ Out, settings, dict, initialization settings for this algorithm + """ + settings = super().handleInput(spec) + settings['P_upper'] = spec.findFirst('P_upper').value + settings['Q_upper'] = spec.findFirst('Q_upper').value + # AutoARMA is currently only a global TSA algorithm; this is enforced in TSAUser + + return settings + + def setDefaults(self, settings): + """ + Fills default values for settings with default values. + @ In, settings, dict, existing settings + @ Out, settings, dict, modified settings + """ + settings = super().setDefaults(settings) + if 'global' not in settings: + settings['global'] = True + if 'engine' not in settings: + settings['engine'] = randomUtils.newRNG() + return settings + + def fit(self, signal, pivot, targets, settings, trainedParams=None): + """ + Determines the charactistics of the signal based on this algorithm. + @ In, signal, np.ndarray, time series with dims [time, target] + @ In, pivot, np.1darray, time-like parameter values + @ In, targets, list(str), names of targets in same order as signal + @ In, settings, dict, settings for this ROM + @ In, trainedParams, dict, running dict of trained algorithm params + @ Out, params, dict, characteristic parameters + """ + # set seed for training + seed = settings['seed'] + if seed is not None: + randomUtils.randomSeed(seed, engine=settings['engine']) + + try: + from statsforecast.models import AutoARIMA + from statsforecast.arima import arima_string + import re + except ModuleNotFoundError as exc: + print("This RAVEN TSA Module requires the statsforecast library to be installed in the current python environment") + raise ModuleNotFoundError from exc + + maxOrder = np.max(np.r_[settings['P_upper'], settings['Q_upper'], self._maxCombinedPQ]) + statsforecastParams = { + "seasonal": False, # set to True if you want a SARIMA model + "stationary": True, # NOTE: basically ignored 'd' because it should be applied as a TSA Transformer + "start_p": 0, + "start_q": 0, + "max_p": settings['P_upper'], + "max_q": settings['Q_upper'], + "max_order": maxOrder, + "ic": 'aicc', + } + + params = {} + for tg, target in enumerate(targets): + params[target] = {} + history = signal[:, tg] + mask = ~np.isnan(history) + + SFAA = AutoARIMA(**statsforecastParams) + fittedARIMA = SFAA.fit(y=history[mask]) + + arma_str = re.findall(r'\(([^\\)]+)\)', arima_string(fittedARIMA.model_))[0] + p_opt,d_opt,q_opt = [int(a) for a in arma_str.split(',')] + + params[target]['P_opt'] = p_opt + params[target]['D_opt'] = d_opt + params[target]['Q_opt'] = q_opt + del SFAA, fittedARIMA + + return params + + def getParamsAsVars(self, params): + """ + Map characterization parameters into flattened variable format + @ In, params, dict, trained parameters (as from characterize) + @ Out, rlz, dict, realization-style response + """ + rlz = {} + for target, info in params.items(): + base = f'{self.name}__{target}' + rlz[f'{base}__P_opt'] = info['P_opt'] + rlz[f'{base}__D_opt'] = info['D_opt'] + rlz[f'{base}__Q_opt'] = info['Q_opt'] + return rlz + + def writeXML(self, writeTo, params): + """ + Allows the engine to put whatever it wants into an XML to print to file. + @ In, writeTo, xmlUtils.StaticXmlElement, entity to write to + @ In, params, dict, trained parameters as from self.characterize + @ Out, None + """ + for target, info in params.items(): + base = xmlUtils.newNode(target) + writeTo.append(base) + base.append(xmlUtils.newNode('P_opt', text=f'{int(info["P_opt"]):d}')) + base.append(xmlUtils.newNode('D_opt', text=f'{int(info["D_opt"]):d}')) + base.append(xmlUtils.newNode('Q_opt', text=f'{int(info["Q_opt"]):d}')) + diff --git a/scripts/conversionScripts/SyntheticHistory_ARMA_lag.py b/scripts/conversionScripts/SyntheticHistory_ARMA_lag.py new file mode 100644 index 0000000000..9713411e75 --- /dev/null +++ b/scripts/conversionScripts/SyntheticHistory_ARMA_lag.py @@ -0,0 +1,51 @@ +# 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. +import xml.etree.ElementTree as ET + +def convert(tree,fileName=None): + """ + Converts input files to be compatible with merge request #785: + Where ARMA exists, removes , , , and , and adds

and + @ In, tree, xml.etree.ElementTree.ElementTree object, the contents of a RAVEN input file + @ In, fileName, the name for the raven input file + @Out, tree, xml.etree.ElementTree.ElementTree object, the modified RAVEN input file + """ + simulation = tree.getroot() + models = simulation.find('Models') + if models is not None: + for child in models: + if child.tag == 'ROM' and child.attrib['subType'] == 'SyntheticHistory': + arma = child.find('arma') + if arma is None: + return tree + # store outdated nodes if existing + signalLag = arma.find('SignalLag') + noiseLag = arma.find('NoiseLag') + # replace outdated nodes with new node + if signalLag is not None: + newP = ET.Element('P') + newP.text = signalLag.text + arma.remove(signalLag) + arma.append(newP) + if noiseLag is not None: + newQ = ET.Element('Q') + newQ.text = noiseLag.text + arma.remove(noiseLag) + arma.append(newQ) + return tree + +if __name__=='__main__': + import convert_utils + import sys + convert_utils.standardMain(sys.argv,convert) From 677ebc19c1c366ff25e1ab8c85f2170d74ff16fa Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 23 Apr 2024 14:43:33 -0600 Subject: [PATCH 06/12] updating synthetic history test xmls --- .../ROM/TimeSeries/SyntheticHistory/arma.xml | 6 +++--- .../SyntheticHistory/arma_preserveCDF.xml | 6 +++--- .../ROM/TimeSeries/SyntheticHistory/clustered.xml | 6 +++--- .../SyntheticHistory/differencing_arma.xml | 6 +++--- .../TimeSeries/SyntheticHistory/fourier_arma.xml | 6 +++--- .../SyntheticHistory/global_fourier_arma.xml | 8 ++++---- .../TimeSeries/SyntheticHistory/interpolated.xml | 6 +++--- .../ROM/TimeSeries/SyntheticHistory/log_arma.xml | 6 +++--- .../ROM/TimeSeries/SyntheticHistory/sample.xml | 6 +++--- .../ROM/TimeSeries/SyntheticHistory/stl_arma.xml | 8 ++++---- .../zero_filter_discontinuous.xml | 15 ++++----------- 11 files changed, 36 insertions(+), 43 deletions(-) diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/arma.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/arma.xml index 3ed52a701b..d31dcd62a7 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/arma.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/arma.xml @@ -67,9 +67,9 @@ scaling seconds - - 2 - 3 + +

2

+ 3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/arma_preserveCDF.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/arma_preserveCDF.xml index d7433ef449..6ae4df912a 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/arma_preserveCDF.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/arma_preserveCDF.xml @@ -60,9 +60,9 @@ seconds - - 2 - 3 + +

2

+ 3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/clustered.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/clustered.xml index e6157d10f8..59d9461987 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/clustered.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/clustered.xml @@ -65,9 +65,9 @@ scaling pivot - - 1 - 0 + +

1

+ 0 classifier diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/differencing_arma.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/differencing_arma.xml index fee02055b9..c5041e6c18 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/differencing_arma.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/differencing_arma.xml @@ -68,9 +68,9 @@ 1 - - 2 - 3 + +

2

+ 3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/fourier_arma.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/fourier_arma.xml index 3b705544bc..fe55fabdc2 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/fourier_arma.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/fourier_arma.xml @@ -68,9 +68,9 @@ 2, 5, 10 - - 2 - 3 + +

2

+ 3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/global_fourier_arma.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/global_fourier_arma.xml index c00d9171c7..2e6b506317 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/global_fourier_arma.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/global_fourier_arma.xml @@ -67,10 +67,10 @@ 2, 5, 10, 20, 50 - - - 1 - 0 + + +

1

+ 0 classifier diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/interpolated.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/interpolated.xml index 80aef9d12b..b16cabe921 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/interpolated.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/interpolated.xml @@ -64,9 +64,9 @@ signal0, seconds scaling seconds - - 2 - 3 + +

2

+ 3 macro diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/log_arma.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/log_arma.xml index 6a1365f1a5..1b0123b073 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/log_arma.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/log_arma.xml @@ -65,9 +65,9 @@ seconds - - 2 - 3 + +

2

+ 3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/sample.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/sample.xml index 63a45e0d09..db95359970 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/sample.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/sample.xml @@ -47,9 +47,9 @@ signal0, signal1, seconds scaling seconds - - 2 - 3 + +

2

+ 3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/stl_arma.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/stl_arma.xml index 0dda14d73a..3b9ada2956 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/stl_arma.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/stl_arma.xml @@ -4,7 +4,7 @@ framework/ROM/TimeSeries/SyntheticHistory.FourierARMA j-bryan 2023-08-18 - SupervisedLearning.SyntheticHistory,TSA.ST>,TSA.ARMA + SupervisedLearning.SyntheticHistory,TSA.ST>,TSA.ARMA Tests the SyntheticHistory ROM using STL decomposition then ARMA TimeSeriesAnalyzer algorithms. @@ -68,9 +68,9 @@ 10 - - 2 - 3 + +

2

+ 3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/zero_filter_discontinuous.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/zero_filter_discontinuous.xml index ac5a020c92..0591695d68 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/zero_filter_discontinuous.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/zero_filter_discontinuous.xml @@ -32,23 +32,19 @@ input input - input arma - arma meta romMeta - arma pk - placeholder arma @@ -56,7 +52,6 @@ synth synth - @@ -64,7 +59,6 @@ scaling OutputPlaceHolder - scaling,YEAR TOTALLOAD,WIND,SOLAR,HOUR @@ -72,7 +66,6 @@ HOUR - scaling TOTALLOAD,WIND,SOLAR @@ -95,9 +88,9 @@ 8760, 4380, 2920, 2190, 438, 168, 24, 12, 6, 3 - - 1 - 0 + +

1

+ 0 @@ -107,7 +100,6 @@ csv meta - csv synth @@ -123,4 +115,5 @@ 1.0 + From b2e03ba5924d3b05f873dd6a6a8c0717b98f1ab1 Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 23 Apr 2024 14:45:20 -0600 Subject: [PATCH 07/12] regolding romMeta xmls to include P and Q from ARMA --- .../SyntheticHistory/gold/ARMA/romMeta.xml | 30 +++++++------ .../gold/ARMAPreserveCDF/romMeta.xml | 30 +++++++------ .../gold/Clustered/romMeta.xml | 24 +++++----- .../gold/DiffARMA/romMeta.xml | 30 +++++++------ .../gold/FourierARMA/romMeta.xml | 30 +++++++------ .../gold/GlobalFourierARMA/romMeta.xml | 10 +++-- .../gold/Interpolated/romMeta.xml | 45 ++++++++++--------- .../SyntheticHistory/gold/LogARMA/romMeta.xml | 26 ++++++----- .../SyntheticHistory/gold/STLARMA/romMeta.xml | 30 +++++++------ .../gold/ZeroFilterDiscontinuous/romMeta.xml | 9 ++-- 10 files changed, 145 insertions(+), 119 deletions(-) diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ARMA/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ARMA/romMeta.xml index 55ed807e48..0d48ad1523 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ARMA/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ARMA/romMeta.xml @@ -11,22 +11,24 @@ - -2.342847668e-02 - 5.181329679e-01 - -6.530269669e-02 - -2.950415467e-02 - 3.140371412e-01 - 1.246305413e-01 - 8.738162828e-01 + -2.342614487e-02 + 5.181096172e-01 + -6.529552185e-02 + -2.948567247e-02 + 3.140363129e-01 + 1.246413733e-01 + 8.738153821e-01 + 2,0,3 - 2.448632326e-02 - 8.721221585e-01 - -7.094838121e-02 - -4.123799927e-01 - 1.474309685e-01 - -1.712627050e-01 - 9.269180521e-01 + 2.448632351e-02 + 8.721221530e-01 + -7.094837791e-02 + -4.123799873e-01 + 1.474309682e-01 + -1.712627042e-01 + 9.269180517e-01 + 2,0,3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ARMAPreserveCDF/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ARMAPreserveCDF/romMeta.xml index 7681bfcc19..2edf02119d 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ARMAPreserveCDF/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ARMAPreserveCDF/romMeta.xml @@ -15,22 +15,24 @@ - -2.342847668e-02 - 5.181329679e-01 - -6.530269669e-02 - -2.950415467e-02 - 3.140371412e-01 - 1.246305413e-01 - 8.738162828e-01 + -2.342614487e-02 + 5.181096172e-01 + -6.529552185e-02 + -2.948567247e-02 + 3.140363129e-01 + 1.246413733e-01 + 8.738153821e-01 + 2,0,3 - 2.448632326e-02 - 8.721221585e-01 - -7.094838121e-02 - -4.123799927e-01 - 1.474309685e-01 - -1.712627050e-01 - 9.269180521e-01 + 2.448632351e-02 + 8.721221530e-01 + -7.094837791e-02 + -4.123799873e-01 + 1.474309682e-01 + -1.712627042e-01 + 9.269180517e-01 + 2,0,3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/Clustered/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/Clustered/romMeta.xml index 2751f663ca..596ee1c65a 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/Clustered/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/Clustered/romMeta.xml @@ -9,11 +9,11 @@ romMeta - ClusterFeature|signal0|ARMA|ar_0|scaled,ClusterFeature|signal0|ARMA|ar_0|unscaled,ClusterFeature|signal0|ARMA|const|scaled,ClusterFeature|signal0|ARMA|const|unscaled,ClusterFeature|signal0|ARMA|var|scaled,ClusterFeature|signal0|ARMA|var|unscaled,ClusterFeature|signal1|ARMA|ar_0|scaled,ClusterFeature|signal1|ARMA|ar_0|unscaled,ClusterFeature|signal1|ARMA|const|scaled,ClusterFeature|signal1|ARMA|const|unscaled,ClusterFeature|signal1|ARMA|var|scaled,ClusterFeature|signal1|ARMA|var|unscaled,ClusterLabels,seg_index_end,seg_index_start,seg_pivot_end,seg_pivot_start + ClusterFeature|signal0|ARMA|ar_0|scaled,ClusterFeature|signal0|ARMA|ar_0|unscaled,ClusterFeature|signal0|ARMA|const|scaled,ClusterFeature|signal0|ARMA|const|unscaled,ClusterFeature|signal0|ARMA|var|scaled,ClusterFeature|signal0|ARMA|var|unscaled,ClusterFeature|signal1|ARMA|ar_0|scaled,ClusterFeature|signal1|ARMA|ar_0|unscaled,ClusterFeature|signal1|ARMA|const|scaled,ClusterFeature|signal1|ARMA|const|unscaled,ClusterFeature|signal1|ARMA|var|scaled,ClusterFeature|signal1|ARMA|var|unscaled,ClusterLabels,Feature|signal0|ARMA|d,Feature|signal0|ARMA|p,Feature|signal0|ARMA|q,Feature|signal1|ARMA|d,Feature|signal1|ARMA|p,Feature|signal1|ARMA|q,seg_index_end,seg_index_start,seg_pivot_end,seg_pivot_start RAVEN_sample_ID - + Clustered ROM @@ -28,14 +28,16 @@ - -8.003280283e-04 - 5.063587372e-01 + -8.003288827e-04 + 5.063587371e-01 1.066660163e+00 + 1,0,0 - 1.159304248e-03 + 1.159304232e-03 -4.529610029e-01 1.142738326e+00 + 1,0,0 0, 1, 2, 3, 4 @@ -48,19 +50,21 @@ - -2.222335898e-03 - -5.122369801e-01 + -2.222335945e-03 + -5.122369800e-01 1.073478650e+00 + 1,0,0 - 4.528710922e-04 + 4.528710748e-04 3.788900499e-01 - 1.233379552e+00 + 1.233379551e+00 + 1,0,0 5, 6, 7, 8, 9 100, 199 - + diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/DiffARMA/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/DiffARMA/romMeta.xml index 0395e92f78..21dd041483 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/DiffARMA/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/DiffARMA/romMeta.xml @@ -19,22 +19,24 @@ - 3.695660154e-03 - 1.357688600e+00 - -5.877792959e-01 - -1.724927763e+00 - 1.036930326e+00 - -3.110458392e-01 - 1.138354562e+00 + 3.693047633e-03 + 1.358329046e+00 + -5.883576468e-01 + -1.725276552e+00 + 1.036554063e+00 + -3.104232725e-01 + 1.136158905e+00 + 2,0,3 - 6.300578434e-03 - 1.612942238e-01 - 5.161770874e-01 - -5.313720979e-01 - -3.390657493e-01 - -1.293981956e-01 - 1.128554230e+00 + 6.302590917e-03 + 1.611749684e-01 + 5.162844774e-01 + -5.312384748e-01 + -3.392185984e-01 + -1.293191073e-01 + 1.128704598e+00 + 2,0,3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/FourierARMA/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/FourierARMA/romMeta.xml index 7ea22315b4..4ff940499d 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/FourierARMA/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/FourierARMA/romMeta.xml @@ -43,22 +43,24 @@ - 4.845532824e-03 - 4.711740544e-01 - 1.073644196e-01 - 1.495391809e-01 - 1.881403539e-01 - 1.434737494e-01 - 4.917653477e-01 + 4.845534460e-03 + 4.711740521e-01 + 1.073644214e-01 + 1.495391834e-01 + 1.881403533e-01 + 1.434737504e-01 + 4.917653474e-01 + 2,0,3 - 3.270470871e-03 - 6.989112456e-01 - 8.753311577e-02 - -1.698365143e-01 - 9.744189247e-02 - -1.413858343e-02 - 4.949581023e-01 + 3.270397313e-03 + 6.989113244e-01 + 8.753301419e-02 + -1.698366098e-01 + 9.744195391e-02 + -1.413859360e-02 + 4.949581056e-01 + 2,0,3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/GlobalFourierARMA/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/GlobalFourierARMA/romMeta.xml index 333ca22447..f641f19756 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/GlobalFourierARMA/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/GlobalFourierARMA/romMeta.xml @@ -9,11 +9,11 @@ romMeta - ClusterFeature|signal0|ARMA|ar_0|scaled,ClusterFeature|signal0|ARMA|ar_0|unscaled,ClusterFeature|signal0|ARMA|const|scaled,ClusterFeature|signal0|ARMA|const|unscaled,ClusterFeature|signal0|ARMA|var|scaled,ClusterFeature|signal0|ARMA|var|unscaled,ClusterFeature|signal1|ARMA|ar_0|scaled,ClusterFeature|signal1|ARMA|ar_0|unscaled,ClusterFeature|signal1|ARMA|const|scaled,ClusterFeature|signal1|ARMA|const|unscaled,ClusterFeature|signal1|ARMA|var|scaled,ClusterFeature|signal1|ARMA|var|unscaled,ClusterLabels,seg_index_end,seg_index_start,seg_pivot_end,seg_pivot_start + ClusterFeature|signal0|ARMA|ar_0|scaled,ClusterFeature|signal0|ARMA|ar_0|unscaled,ClusterFeature|signal0|ARMA|const|scaled,ClusterFeature|signal0|ARMA|const|unscaled,ClusterFeature|signal0|ARMA|var|scaled,ClusterFeature|signal0|ARMA|var|unscaled,ClusterFeature|signal1|ARMA|ar_0|scaled,ClusterFeature|signal1|ARMA|ar_0|unscaled,ClusterFeature|signal1|ARMA|const|scaled,ClusterFeature|signal1|ARMA|const|unscaled,ClusterFeature|signal1|ARMA|var|scaled,ClusterFeature|signal1|ARMA|var|unscaled,ClusterLabels,Feature|signal0|ARMA|d,Feature|signal0|ARMA|p,Feature|signal0|ARMA|q,Feature|signal1|ARMA|d,Feature|signal1|ARMA|p,Feature|signal1|ARMA|q,seg_index_end,seg_index_start,seg_pivot_end,seg_pivot_start RAVEN_sample_ID - + Clustered ROM @@ -31,11 +31,13 @@ 2.067324766e-02 5.789104500e-01 9.548311183e-01 + 1,0,0 -2.949861285e-04 5.181471892e-01 1.051423590e+00 + 1,0,0 0, 1, 2, 3, 5, 6, 7, 8, 9 @@ -51,16 +53,18 @@ 1.726144055e-02 7.454777809e-01 6.347865921e-01 + 1,0,0 8.705061198e-02 7.343611299e-01 8.113936096e-01 + 1,0,0 4 100, 199 - + diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/Interpolated/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/Interpolated/romMeta.xml index 1bc9526177..3e37666bf4 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/Interpolated/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/Interpolated/romMeta.xml @@ -16,13 +16,14 @@ - -3.405762107e-02 - -3.800997642e-01 - 4.633854069e-01 - 1.067776517e+00 - 3.878969804e-01 - 3.198774407e-01 - 9.566733107e-01 + -3.404606100e-02 + -3.800884139e-01 + 4.633974912e-01 + 1.067769629e+00 + 3.878824930e-01 + 3.198708707e-01 + 9.566706516e-01 + 2,0,3 0 @@ -34,13 +35,14 @@ - 2.331304717e-01 - 3.193866976e-01 - 2.986141579e-01 - 2.177388904e-01 - 1.773139327e-01 - -1.022136980e-01 - 8.389602626e-01 + 2.331304669e-01 + 3.193866942e-01 + 2.986141638e-01 + 2.177388985e-01 + 1.773139333e-01 + -1.022136939e-01 + 8.389602662e-01 + 2,0,3 0 @@ -52,13 +54,14 @@ - 9.953642532e-02 - -3.035653331e-02 - 3.809997824e-01 - 6.427577039e-01 - 2.826054566e-01 - 1.088318713e-01 - 8.978167866e-01 + 9.954220293e-02 + -3.035085983e-02 + 3.810058275e-01 + 6.427542636e-01 + 2.825982131e-01 + 1.088285884e-01 + 8.978154589e-01 + 2,0,3 0 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/LogARMA/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/LogARMA/romMeta.xml index 75b4353ddb..bb474b62c4 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/LogARMA/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/LogARMA/romMeta.xml @@ -14,22 +14,24 @@ - -2.017786565e-02 - -2.254403826e-01 - 5.928885641e-01 - 8.315085708e-01 - -3.393858013e-02 - 8.594632095e-02 - 8.219099887e-01 + -2.018299974e-02 + -2.254407616e-01 + 5.928879269e-01 + 8.315082140e-01 + -3.394205618e-02 + 8.594340747e-02 + 8.219115519e-01 + 2,0,3 - 3.324618133e-02 + 3.324618115e-02 1.284845806e+00 - -4.694213600e-01 - -8.193080567e-01 - 2.676701089e-01 - 2.033831083e-01 + -4.694213604e-01 + -8.193080573e-01 + 2.676701091e-01 + 2.033831080e-01 7.028920133e-01 + 2,0,3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/STLARMA/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/STLARMA/romMeta.xml index 8da3c3f861..18b6a6c37a 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/STLARMA/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/STLARMA/romMeta.xml @@ -15,22 +15,24 @@ - 6.670730262e-04 - 1.882699526e+00 - -9.798770716e-01 - -1.200831024e+00 - 1.458241630e-01 - 2.571487190e-01 - 1.548102632e-01 + 6.670671814e-04 + 1.882699525e+00 + -9.798770703e-01 + -1.200831015e+00 + 1.458241661e-01 + 2.571487099e-01 + 1.548102627e-01 + 2,0,3 - 4.662838791e-03 - 1.837474821e+00 - -9.001123859e-01 - -1.158935528e+00 - 3.824566825e-01 - -2.176215817e-01 - 1.888250309e-01 + 4.659278533e-03 + 1.837455793e+00 + -9.001458617e-01 + -1.159132433e+00 + 3.825866795e-01 + -2.175443267e-01 + 1.888159946e-01 + 2,0,3 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ZeroFilterDiscontinuous/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ZeroFilterDiscontinuous/romMeta.xml index aba4b4aa18..3c8bfd0923 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ZeroFilterDiscontinuous/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/ZeroFilterDiscontinuous/romMeta.xml @@ -119,19 +119,22 @@ - -4.158865848e-02 + -4.158865841e-02 8.702202491e-01 2.973272651e-01 + 1,0,0 - -2.220072382e-02 + -2.220072394e-02 9.040423880e-01 2.258342079e-01 + 1,0,0 - -1.304799638e-02 + -1.304799671e-02 7.022137015e-01 7.048554959e-01 + 1,0,0 From fe8e2bcfff6a98aff73bc56dd008899cb66741fd Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 23 Apr 2024 15:01:54 -0600 Subject: [PATCH 08/12] adding autoARMA test --- .../TimeSeries/SyntheticHistory/auto_arma.xml | 158 +++ .../gold/AutoARMA/romMeta.xml | 93 ++ .../gold/AutoARMA/samples_0.csv | 1001 +++++++++++++++++ .../gold/AutoARMA/samples_1.csv | 1001 +++++++++++++++++ .../ROM/TimeSeries/SyntheticHistory/tests | 17 + 5 files changed, 2270 insertions(+) create mode 100644 tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml create mode 100644 tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/romMeta.xml create mode 100644 tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/samples_0.csv create mode 100644 tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/samples_1.csv diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml new file mode 100644 index 0000000000..94a0f9b4a2 --- /dev/null +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml @@ -0,0 +1,158 @@ + + + + framework/ROM/TimeSeries/SyntheticHistory.AutoARMA + talbpaul + 2021-02-11 + SupervisedLearning.SyntheticHistory,TSA.Fourier,TSA.ARMA + + Tests clustering for the SyntheticHistory ROM with Fourier and ARMA steps. + + + + + AutoARMA + read, train, print, sample, plot + 1 + + + + + infile + indata + + + indata + synth + + + synth + romMeta + romMeta + + + placeholder + synth + mc + samples + samples + + + indata + samples + compare_0 + compare_1 + + + + + ../TrainingData/GlobalFourierARMA_A.csv + + + + + + 2 + 42 + + 1.0 + + + + + + signal0, signal1, pivot + scaling + pivot + + 2, 5, 10, 20, 50 + + + + 5 + 5 + + +

1

+ 0 + + + + + + + csv + samples + + + csv + romMeta + + + plot_samples_0 + + + line + indata|Output|pivot + indata|Output|signal0 + 1000 + + + line + samples|Output|pivot + samples|Output|signal0 + 1000 + + + + + <text>Signal 0 comparison</text> + + png + + + + plot_samples_1 + + + line + indata|Output|pivot + indata|Output|signal1 + 1000 + + + line + samples|Output|pivot + samples|Output|signal1 + 1000 + + + + + <text>Signal 1 comparison</text> + + png + + + + + + + + scaling + signal0, signal1 + + pivot + + + + scaling + signal0, signal1 + + pivot + + + + + + diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/romMeta.xml new file mode 100644 index 0000000000..f760d70da2 --- /dev/null +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/romMeta.xml @@ -0,0 +1,93 @@ + + + + SyntheticHistoryROM + scaling + signal0,signal1,pivot + + + + 4.190088012e+01 + + 8.158731520e+00 + -9.086070095e-03 + + + 9.876028874e+00 + 7.730227735e-01 + + + 1.195819352e+01 + 3.107425093e+00 + + + 1.510292050e+01 + 3.864956787e-01 + + + 4.984277709e+01 + 1.531541001e-03 + + + + 4.179230611e+01 + + 8.078937429e+00 + 8.082315775e-03 + + + 9.637360630e+00 + 7.955316550e-01 + + + 1.198551278e+01 + 3.130758005e+00 + + + 1.515150978e+01 + 3.893342125e-01 + + + 5.032208201e+01 + 8.256117239e-04 + + + + + + + + + + 5 + 0 + 0 + + + 2 + 0 + 0 + + + + + -1.753877510e-03 + 6.975955072e-01 + 2.145354665e-01 + -1.124237205e-01 + -1.274829227e-01 + 5.274823933e-02 + 4.303670076e-01 + 5,0,0 + + + 8.584247694e-04 + 5.127096640e-01 + 2.679723462e-01 + 4.902910626e-01 + 2,0,0 + + + + + diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/samples_0.csv b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/samples_0.csv new file mode 100644 index 0000000000..bd5b34fac7 --- /dev/null +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/samples_0.csv @@ 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+97.8,21.817146606,21.6079996484 +97.9,25.4935738335,22.860667985 +98.0,29.5911505187,26.6372170545 +98.1,32.6271048617,30.0976372536 +98.2,34.1584985081,31.7041351483 +98.3,38.3929041803,33.8498064146 +98.4,39.2901153393,36.3194969633 +98.5,41.2633676094,38.0335126733 +98.6,40.0181497966,38.2206041875 +98.7,39.6539037922,41.3068104438 +98.8,38.6798925312,40.6833236451 +98.9,37.4714731397,41.1613759836 +99.0,38.5735283964,40.0597245643 +99.1,37.0999305735,39.6690028518 +99.2,36.8544083015,38.5442761294 +99.3,37.3160243919,38.0464324711 +99.4,37.8243691385,39.2043965664 +99.5,38.4981674459,39.4764615141 +99.6,41.1048352768,39.7569958448 +99.7,44.4184339363,41.0722335235 +99.8,49.2447958197,45.2057937773 +99.9,51.9267480395,50.4699551271 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/tests b/tests/framework/ROM/TimeSeries/SyntheticHistory/tests index d6c08ca228..10a98f3fc9 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/tests +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/tests @@ -25,6 +25,23 @@ [../] [../] + [./AutoARMA] + type = 'RavenFramework' + input = 'auto_arma.xml' + [./csv] + type = OrderedCSV + output = 'AutoARMA/samples_0.csv AutoARMA/samples_1.csv' + rel_err = 4.7e-1 + zero_threshold = 1e-12 + [../] + [./xml] + type = XML + output = 'AutoARMA/romMeta.xml' + rel_err = 1e-2 + zero_threshold = 1e-3 + [../] + [../] + [./ARMASample] prereq = ARMA type = 'RavenFramework' From cfecfbd3738d8f1fbe35862b1e4d8eebac95e5da Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 23 Apr 2024 15:24:50 -0600 Subject: [PATCH 09/12] missed some file updates --- doc/user_manual/generated/generateRomDoc.py | 4 ++-- tests/framework/PostProcessors/TSACharacterizer/basic.xml | 4 ++-- tests/framework/unit_tests/TSA/testARMA.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/user_manual/generated/generateRomDoc.py b/doc/user_manual/generated/generateRomDoc.py index 1f1851f7d7..eb4957f5f9 100644 --- a/doc/user_manual/generated/generateRomDoc.py +++ b/doc/user_manual/generated/generateRomDoc.py @@ -221,8 +221,8 @@ 12, 24 - 2 - 3 +

2

+ 3 ... diff --git a/tests/framework/PostProcessors/TSACharacterizer/basic.xml b/tests/framework/PostProcessors/TSACharacterizer/basic.xml index 0a6bbaf9b3..6e2b2a31e5 100644 --- a/tests/framework/PostProcessors/TSACharacterizer/basic.xml +++ b/tests/framework/PostProcessors/TSACharacterizer/basic.xml @@ -39,8 +39,8 @@ 2, 5, 10 - 2 - 3 +

2

+ 3 diff --git a/tests/framework/unit_tests/TSA/testARMA.py b/tests/framework/unit_tests/TSA/testARMA.py index 77e1913fb1..a0db1d2f3a 100644 --- a/tests/framework/unit_tests/TSA/testARMA.py +++ b/tests/framework/unit_tests/TSA/testARMA.py @@ -187,8 +187,8 @@ def checkFails(comment, errstr, function, update=True, args=None, kwargs=None): ###################################### def createARMAXML(targets, P, Q): xml = xmlUtils.newNode('ARMA', attrib={'target':','.join(targets)}) - xml.append(xmlUtils.newNode('SignalLag', text=f'{P}')) - xml.append(xmlUtils.newNode('NoiseLag', text=f'{Q}')) + xml.append(xmlUtils.newNode('P', text=f'{P}')) + xml.append(xmlUtils.newNode('Q', text=f'{Q}')) return xml def createFromXML(xml): From 1eb419ec450ed037dc77ee769253a26f981c0872 Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Wed, 24 Apr 2024 09:34:34 -0600 Subject: [PATCH 10/12] user can change information criterion as input --- ravenframework/TSA/AutoARMA.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/ravenframework/TSA/AutoARMA.py b/ravenframework/TSA/AutoARMA.py index c74f9323b4..d1f9e2ca2d 100644 --- a/ravenframework/TSA/AutoARMA.py +++ b/ravenframework/TSA/AutoARMA.py @@ -79,6 +79,12 @@ class cls. descr=r"""upper bound for the number of terms in the Moving Average term to retain in the regression; typically represented as $Q$ in Noise Lag literature.""")) + opt_criterion = InputTypes.makeEnumType("criterion", "criterionType", ['aic', 'aicc','bic']) + specs.addSub(InputData.parameterInputFactory('criterion', contentType=opt_criterion, + descr=r"""information criterion used to determine optimal ARMA parameters. The + options are `aic` for Akaike Information Criterion, `aicc` for corrected AIC which + is used when number of observations is small, and `bic` for Bayesian Information + Criterion. Default is `aicc`.""")) return specs # @@ -95,6 +101,7 @@ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # maximum value that P+Q can have as an upper bound self._maxCombinedPQ = 5 + self._ic = 'aicc' def handleInput(self, spec): """ @@ -103,8 +110,9 @@ def handleInput(self, spec): @ Out, settings, dict, initialization settings for this algorithm """ settings = super().handleInput(spec) - settings['P_upper'] = spec.findFirst('P_upper').value - settings['Q_upper'] = spec.findFirst('Q_upper').value + + for sub in spec.subparts: + settings[sub.name] = sub.value # AutoARMA is currently only a global TSA algorithm; this is enforced in TSAUser return settings @@ -120,6 +128,12 @@ def setDefaults(self, settings): settings['global'] = True if 'engine' not in settings: settings['engine'] = randomUtils.newRNG() + if 'P_upper' not in settings: + settings['P_upper'] = self._maxCombinedPQ + if 'Q_upper' not in settings: + settings['Q_upper'] = self._maxCombinedPQ + if 'criterion' not in settings: + settings['criterion'] = self._ic return settings def fit(self, signal, pivot, targets, settings, trainedParams=None): From 2ff74b0990a5be4b561780ce24ddfa596a4204aa Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Fri, 26 Apr 2024 13:18:12 -0600 Subject: [PATCH 11/12] not using approximation heuristic by default anymore, regolding fixes diff issue between Linux and Windows --- ravenframework/TSA/AutoARMA.py | 13 +- .../TimeSeries/SyntheticHistory/auto_arma.xml | 1 + .../gold/AutoARMA/romMeta.xml | 18 +- .../gold/AutoARMA/samples_0.csv | 2000 ++++++++--------- .../gold/AutoARMA/samples_1.csv | 2000 ++++++++--------- 5 files changed, 2021 insertions(+), 2011 deletions(-) diff --git a/ravenframework/TSA/AutoARMA.py b/ravenframework/TSA/AutoARMA.py index d1f9e2ca2d..98e2dfb17d 100644 --- a/ravenframework/TSA/AutoARMA.py +++ b/ravenframework/TSA/AutoARMA.py @@ -85,6 +85,12 @@ class cls. options are `aic` for Akaike Information Criterion, `aicc` for corrected AIC which is used when number of observations is small, and `bic` for Bayesian Information Criterion. Default is `aicc`.""")) + specs.addSub(InputData.parameterInputFactory('use_approximation', contentType=InputTypes.BoolType, + descr=r"""if True, this uses the default version of the AutoARIMA algorithm + within `statsforecast` which uses heuristics to find an approximate solution + in much faster time. This previously led to different answers between Linux and + Windows, but may be a good option if the alternative is taking too long. + Default is False.""")) return specs # @@ -101,6 +107,7 @@ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # maximum value that P+Q can have as an upper bound self._maxCombinedPQ = 5 + self._use_approximation = False self._ic = 'aicc' def handleInput(self, spec): @@ -134,6 +141,8 @@ def setDefaults(self, settings): settings['Q_upper'] = self._maxCombinedPQ if 'criterion' not in settings: settings['criterion'] = self._ic + if 'use_approximation' not in settings: + settings['use_approximation'] = self._use_approximation return settings def fit(self, signal, pivot, targets, settings, trainedParams=None): @@ -168,7 +177,9 @@ def fit(self, signal, pivot, targets, settings, trainedParams=None): "max_p": settings['P_upper'], "max_q": settings['Q_upper'], "max_order": maxOrder, - "ic": 'aicc', + "ic": settings['criterion'], + "stepwise": settings['use_approximation'], + "approximation": settings['use_approximation'], } params = {} diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml index 94a0f9b4a2..1002b2ab0a 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml @@ -71,6 +71,7 @@ 5 5 + bic

1

diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/romMeta.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/romMeta.xml index f760d70da2..72a6157e6f 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/romMeta.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/romMeta.xml @@ -59,9 +59,9 @@ - 5 + 1 0 - 0 + 2 2 @@ -71,14 +71,12 @@ - -1.753877510e-03 - 6.975955072e-01 - 2.145354665e-01 - -1.124237205e-01 - -1.274829227e-01 - 5.274823933e-02 - 4.303670076e-01 - 5,0,0 + -2.228884514e-03 + 6.875771216e-01 + -3.706671624e-03 + 2.338466293e-01 + 4.328239816e-01 + 1,0,2 8.584247694e-04 diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/samples_0.csv b/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/samples_0.csv index bd5b34fac7..7d210bf080 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/gold/AutoARMA/samples_0.csv +++ 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+99.3,36.3099256068,38.5652685842 +99.4,37.1206169536,38.8942879243 +99.5,38.8091857055,40.616045434 +99.6,40.8398496547,42.0772426978 +99.7,42.904474237,44.3786587588 +99.8,46.8291710834,48.262838134 +99.9,51.1241624829,50.903562328 From 6d2458ff8f97198c481f0a335df118d3e26218ed Mon Sep 17 00:00:00 2001 From: "Gabriel J. Soto" Date: Tue, 7 May 2024 12:15:17 -0600 Subject: [PATCH 12/12] fixing some docstrings, handling P and Q if auto-select is True --- ravenframework/TSA/ARMA.py | 8 +++++++- ravenframework/TSA/TSAUser.py | 2 +- .../ROM/TimeSeries/SyntheticHistory/auto_arma.xml | 5 +---- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/ravenframework/TSA/ARMA.py b/ravenframework/TSA/ARMA.py index 66c4074215..1ef9e668f3 100644 --- a/ravenframework/TSA/ARMA.py +++ b/ravenframework/TSA/ARMA.py @@ -85,7 +85,10 @@ class cls. option. """, default=False) specs.addParam('auto_select', param_type=InputTypes.BoolType, required=False, - descr=r""" """, default=False) + descr=r"""if set to True, the ARMA algorithm will use P and Q, the signal and + noise lag respectively, determined by the `autoarma` TSA algorithm. + The `autoarma` algorithm must be selected in the TSA input sequence before + the `ARMA` algorithm.""", default=False) specs.addSub(InputData.parameterInputFactory('P', contentType=InputTypes.IntegerListType, descr=r"""the number of terms in the AutoRegressive (AR) term to retain in the regression; typically represented as $P$ or Signal Lag in literature. @@ -134,6 +137,9 @@ def handleInput(self, spec): targets = settings['target'] if settings['auto_select']: + # checking to make sure P and Q are not selected + if spec.findAll('P') or spec.findAll('Q'): + raise IOError("P and Q values for ARMA are not accepted if `auto_select` is True.") # if auto-selecting, replace P and Q with Nones to check for and replace later settings['P'] = dict((target, None) for target in targets ) settings['Q'] = dict((target, None) for target in targets ) diff --git a/ravenframework/TSA/TSAUser.py b/ravenframework/TSA/TSAUser.py index 459ae7ab47..33c7dd839e 100644 --- a/ravenframework/TSA/TSAUser.py +++ b/ravenframework/TSA/TSAUser.py @@ -323,7 +323,7 @@ def getTSApointwiseData(self): @ In, None, @ Out, segmentData, dict """ - # + # gathering features that TSA does NOT use to cluster, but still useful to report back segmentNonFeatures = {} for algo in self._tsaAlgorithms: if algo not in self._tsaTrainedParams: diff --git a/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml b/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml index 1002b2ab0a..1d2f69ee58 100644 --- a/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml +++ b/tests/framework/ROM/TimeSeries/SyntheticHistory/auto_arma.xml @@ -73,10 +73,7 @@ 5 bic - -

1

- 0 - +