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Merge pull request #2345 from GabrielSoto-INL/multiResolutionTSA
Multi-Resolution Time Series Analysis
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ravenframework/SupervisedLearning/MultiResolutionTSA.py
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| # 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. | ||
| """ | ||
| Created on July 29, 2024 | ||
| @author: sotogj | ||
| Class to specifically handle multi-resolution time series analysis training | ||
| """ | ||
| # internal libraries | ||
| from .SupervisedLearning import SupervisedLearning | ||
| from .SyntheticHistory import SyntheticHistory | ||
| # | ||
| # | ||
| # | ||
| # | ||
| class MultiResolutionTSA(SupervisedLearning): | ||
| """ Class to specifically handle multi-resolution time series analysis training and evaluation.""" | ||
|
|
||
| @classmethod | ||
| def getInputSpecification(cls): | ||
| """ | ||
| Method to get a reference to a class that specifies the input data for | ||
| class cls. | ||
| @ In, cls, the class for which we are retrieving the specification | ||
| @ Out, inputSpecification, InputData.ParameterInput, class to use for | ||
| specifying input of cls. | ||
| """ | ||
| spec = super().getInputSpecification() | ||
| spec.description = r"""A ROM for characterizing and generating synthetic histories using multi-resolution time | ||
| series analysis. This ROM, like the SyntheticHistory ROM, makes use of the algorithms within the TimeSeriesAnalysis (TSA) | ||
| module here in RAVEN. The available algorithms are discussed in more detail below. They can be categorized as | ||
| characterizing, transforming, or generating algorithms. Given three algorithms in order $A_1$, $A_2$, and $A_3$, the | ||
| algorithms are applied to given signals as $A_1 \rightarrow A_2 \rightarrow A_3$ during the training step if the | ||
| algorithms are capable of characterizing or transforming the signal. In the generating step, so long as an inverse | ||
| operator for each algorithm exists, the algorithms are applied in reverse: $A_3^{-1} \rightarrow A_2^{-1} \rightarrow A_1^{-1}$. | ||
| This is the same process that the SyntheticHistory ROM performs. Where this ROM differs is that it can handle | ||
| multi-resolution decompositions of a signal. That is, some algorithms are capable of characterizing and splitting | ||
| a signal at different timescales to better learn the signal dynamics at those timescales. See the $\texttt{filterbankdwt}$ | ||
| below for an example of such an algorithm. The MultiResolutionTSA particularly handles the combination of learned | ||
| characteristics and signal generation from the different decomposition levels. This ROM also requires a SegmentROM | ||
| node of the decomposition type (an example is given below for the XML input structure). | ||
| // | ||
| In order to use this Reduced Order Model, the \xmlNode{ROM} attribute \xmlAttr{subType} needs to be | ||
| \xmlString{MultiResolutionTSA}. It must also have a SegmentROM node of \xmlAttr{subType} \xmlString{decomposition}""" | ||
| spec = SyntheticHistory.addTSASpecs(spec) | ||
| return spec | ||
|
|
||
| def __init__(self): | ||
| """ | ||
| Constructor. | ||
| @ In, None | ||
| @ Out, None | ||
| """ | ||
| super().__init__() | ||
| self.printTag = 'Multi-Resolution Synthetic History' | ||
| # _globalROM is an instance of the SyntheticHistoryROM: | ||
| # It handles all TSA algorithms up to and including one that splits signal (or creates decompositions). | ||
| # In other words, it handles all TSA training and evaluation on the "global" signal before decomposition. | ||
| # A decomposition TSA algorithms is specified with _multiResolution=True within its class in the TSA module. | ||
| self._globalROM = SyntheticHistory() | ||
| self.decompositionAlgorithm = None | ||
| self._dynamicHandling = True # This ROM is able to manage the time-series on its own. | ||
|
|
||
| def getGlobalROM(self): | ||
| """ | ||
| Getter for the globalROM (i.e., SyntheticHistory ROM) | ||
| @ In, None | ||
| @ Out, _globalROM, SyntheticHistory ROM, instance of a SyntheticHistory ROM | ||
| """ | ||
| return self._globalROM | ||
|
|
||
| def setGlobalROM(self, globalROM): | ||
| """ | ||
| Setter for the globalROM (i.e., SyntheticHistory ROM) | ||
| @ In, _globalROM, SyntheticHistory ROM, instance of a SyntheticHistory ROM | ||
| @ Out, None | ||
| """ | ||
| self._globalROM = globalROM | ||
|
|
||
| def _handleInput(self, paramInput): | ||
| """ | ||
| Function to handle the common parts of the model parameter input. | ||
| @ In, paramInput, InputData.ParameterInput, the already parsed input. | ||
| @ Out, None | ||
| """ | ||
| super()._handleInput(paramInput) | ||
| globalROM = self.getGlobalROM() | ||
| globalROM._handleInput(paramInput) | ||
|
|
||
| # check that there is a multiresolution algorithm | ||
| allAlgorithms = globalROM.getTsaAlgorithms() # get non-global algorithms | ||
| allAlgorithms.extend(globalROM.getGlobalTsaAlgorithms()) # add all global algorithms | ||
| foundMRAalgorithm = False | ||
| for algo in allAlgorithms: | ||
| if algo.canTransform(): | ||
| if algo.isMultiResolutionAlgorithm(): | ||
| foundMRAalgorithm = True | ||
| self.decompositionAlgorithm = algo.name | ||
| break | ||
| if not foundMRAalgorithm: | ||
| msg = 'The MultiResolutionTSA ROM class requires a TSA algorithm capable of ' | ||
| msg += ' multiresolution time series analysis. None were found. Example: FilterBankDWT.' | ||
| self.raiseAnError(IOError, msg) | ||
|
|
||
| def _train(self, featureVals, targetVals): | ||
| """ | ||
| Perform training on input database stored in featureVals. | ||
| @ In, featureVals, array, shape=[n_timeStep, n_dimensions], an array of input data # Not use for ARMA training | ||
| @ In, targetVals, array, shape = [n_timeStep, n_dimensions], an array of time series data | ||
| @ Out, None | ||
| """ | ||
| self._globalROM.trainTSASequential(targetVals) | ||
|
|
||
| def _getMRTrainedParams(self): | ||
| """ | ||
| Get trained params for multi-resolution time series analysis. Returns number of decomposition levels | ||
| and a sorted (or re-indexed) version of the trained params dictionary. | ||
| @ In, None | ||
| @ Out, numLevels, int, number of decomposition levels | ||
| @ Out, sortedTrainedParams, dict, dictionary of trained parameters | ||
| """ | ||
| globalROM = self.getGlobalROM() | ||
|
|
||
| # get all trained parameters from final algorithm so far (should be multiresolution transformer) | ||
| trainedParams = list(globalROM.getTsaTrainedParams().items()) | ||
| mrAlgo, mrTrainedParams = trainedParams[-1] | ||
| if mrAlgo.name != self.decompositionAlgorithm: | ||
| msg = f'TSA Algorithm {self.decompositionAlgorithm} for multiresolution time series analysis must be ' | ||
| msg += 'the *last* algorithm applied before the <Segment> node.' | ||
| self.raiseAnError(IOError, msg) | ||
|
|
||
| # eh, maybe this should live in TSAUser in the future... | ||
| # extract settings used for that last algorithm (should have some sort of "levels") | ||
| numLevels = mrAlgo.getDecompositionLevels() | ||
|
|
||
| # reformat the trained params | ||
| sortedTrainedParams = mrAlgo.sortTrainedParamsByLevels(mrTrainedParams) | ||
| return numLevels, sortedTrainedParams | ||
|
|
||
| def _updateMRTrainedParams(self, params): | ||
| """ | ||
| Method to update trained parameter dictionary from this class using imported `params` dictionary | ||
| containing multiple-decomposition-level trainings | ||
| @ In, params, dict, dictionary of trained parameters from previous decomposition levels | ||
| @ Out, None | ||
| """ | ||
| # get all trained parameters from final algorithm (should be multiresolution transformer) | ||
| trainedParams = list(self._globalROM.getTsaTrainedParams().items()) | ||
| mrAlgo, mrTrainedParams = trainedParams[-1] | ||
|
|
||
| mrAlgo.combineTrainedParamsByLevels(mrTrainedParams, params) | ||
|
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||
|
|
||
| def writeXML(self, writeTo, targets=None, skip=None): | ||
| """ | ||
| Allows the SVE to put whatever it wants into an XML to print to file. | ||
| Overload in subclasses. | ||
| @ In, writeTo, xmlUtils.StaticXmlElement, entity to write to | ||
| @ In, targets, list, optional, unused (kept for compatability) | ||
| @ In, skip, list, optional, unused (kept for compatability) | ||
| @ Out, None | ||
| """ | ||
| self._globalROM.writeTSAtoXML(writeTo) | ||
|
|
||
| def __evaluateLocal__(self, featureVals): | ||
| """ | ||
| Evaluate algorithms for ROM generation | ||
| @ In, featureVals, float, a scalar feature value is passed as scaling factor | ||
| @ Out, rlz, dict, realization dictionary of values for each target | ||
| """ | ||
| rlz = self._globalROM.evaluateTSASequential() | ||
| return rlz | ||
|
|
||
|
|
||
| ### ESSENTIALLY UNUSED ### | ||
| def __confidenceLocal__(self,featureVals): | ||
| """ | ||
| This method is currently not needed for ARMA | ||
| """ | ||
| pass | ||
|
|
||
| def __resetLocal__(self,featureVals): | ||
| """ | ||
| After this method the ROM should be described only by the initial parameter settings | ||
| Currently not implemented for ARMA | ||
| """ | ||
| pass | ||
|
|
||
| def __returnInitialParametersLocal__(self): | ||
| """ | ||
| there are no possible default parameters to report | ||
| """ | ||
| localInitParam = {} | ||
| return localInitParam | ||
|
|
||
| def __returnCurrentSettingLocal__(self): | ||
| """ | ||
| override this method to pass the set of parameters of the ROM that can change during simulation | ||
| Currently not implemented for ARMA | ||
| """ | ||
| pass |
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