diff --git a/developer_tools/XSDSchemas/Samplers.xsd b/developer_tools/XSDSchemas/Samplers.xsd index e495471746..78350c4ed8 100644 --- a/developer_tools/XSDSchemas/Samplers.xsd +++ b/developer_tools/XSDSchemas/Samplers.xsd @@ -21,6 +21,7 @@ + @@ -526,4 +527,52 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/doc/user_manual/sampler.tex b/doc/user_manual/sampler.tex index 310bd9338e..d6a8e420ef 100644 --- a/doc/user_manual/sampler.tex +++ b/doc/user_manual/sampler.tex @@ -3137,3 +3137,130 @@ \subsubsection{Adaptive Sobol Decomposition} \end{lstlisting} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Markov Chain Monte Carlo %%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\subsection{Markov Chain Monte Carlo} +\label{subsec:MCMC} +The Markov chain Monte Carlo (MCMC) is a Sampler entity in the RAVEN framework. +It provides enormous scope for realistic statistical modeling. MCMC is essentially +Monte Carlo integration using Markov chain. Bayesians, and sometimes also frequentists, +need to integrate over possibly high-dimensional probability distributions to make inference +about model parameters or to make predictions. Bayesians need to integrate over the posterior +distributions of model parameters given the data, and frequentists may need to integrate +over the distribution of observables given parameter values. Monte Carlo integration draws +samples from the required distribution, and then forms samples averages to approximate expectations. +MCMC draws these samples by running a cleverly constructed Markov chain for a long time. +There are a large number of MCMC algorithms, and popular families include Gibbs sampling, +Metropolis-Hastings, slice sampling, Hamiltonian Monte Carlo, and many others. Regardless +of the algorithm, the goal in Bayesian inference is to maximize the unnormalized joint +posterior distribution and collect samples of the target distributions, which are marginal +posterior distributions, later to be used for inference. + +\subsubsection{Metropolis (Metropolis-Hastings Sampler)} +\label{subsubsubsec:metropolis} +The Metropolis-Hastings (MH) algorithm is a MCMC method for obtaining a sequence of random samples from a probability +distribution from which direct sampling is difficult. This sequence can be used to approximate the distribution or +to compute an integral. It simulates from a probability distribution by making +use of the full joint density function and (independent) proposal distributions for each of +the variables of interest. + +\specBlock{an}{Metropolis} +% +\attrIntro + +\begin{itemize} + \itemsep0em + \item \nameDescription +\end{itemize} + +\variableIntro{Metropolis} + +\begin{itemize} + \item \variableDescription + \variableChildrenIntro + \begin{itemize} + \item \distributionDescription + \item \functionDescription + \item \xmlNode{initial}, \xmlDesc{float, optional field}, specified the initial value for given variable. + \item \xmlNode{proposal}, \xmlDesc{Assembler Object}, specifies the proposal distribution for this variable. + This node must contain the following two attributes: + \begin{itemize} + \item \xmlAttr{class}, \xmlDesc{required string attribute}, the main + ``class'' of the listed object. Only ``Distributions'' is allowed. + \item \xmlAttr{type}, \xmlDesc{required string attribute}, the object + identifier or sub-type. + \end{itemize} + \end{itemize} + \nb For MCMC sampler, we only allow "continuous" distributions as input to \xmlNode{variable}. + \item \constantVariablesDescription +\end{itemize} + + +In the \textbf{Metropolis} input block, the user needs to specify the variables need to be sampled. +As already mentioned, these variables are inputted within consecutive xml blocks called \xmlNode{variable}. +In addition, the settings for this sampler need to be specified in the \xmlNode{samplerInit} XML block: +\begin{itemize} + \item \xmlNode{samplerInit}, \xmlDesc{required field}. In this xml-node, the following xml sub-nodes need to be specified: + \begin{itemize} + \item \xmlNode{limit}, \xmlDesc{integer, required field}, number of Metropolis samples needs to be generated; + \item \xmlNode{initialSeed}, \xmlDesc{integer, optional field}, initial seeding of random number generator; + \item \xmlNode{burnIn}, \xmlDesc{integer, optional field}, specifies the number of initial samples that would be discarded. + \end{itemize} +\end{itemize} + +In addition to the \xmlNode{variable} nodes, the main XML node +\xmlNode{Metropolis} needs to contain the following supplementary sub-nodes: + +\begin{itemize} + \item \xmlNode{likelihood}, \xmlDesc{string, required node}, the output from the user provided likelihood function + This node accept one attribute: + \begin{itemize} + \item \xmlAttr{log}, \xmlDesc{bool, optional field}, indicates whether the the log likelihood value is + provided or not. When True, the code expects to receive the log likehood value. + \default{`False'} + \end{itemize} + \item \assemblerDescription{Metropolis} + \constantSourceDescription{Metropolis} + \begin{itemize} + \item \xmlNode{TargetEvaluation}, \xmlDesc{string, required field}, + represents the container where the system evaluations are stored. + % + From a practical point of view, this XML node must contain the name of + a data object defined in the \xmlNode{DataObjects} block (see + Section~\ref{sec:DataObjects}). The object here specified must be + input as \xmlNode{Output} in the Steps that employ this sampling strategy. + % + The Metropolis sampling accepts ``DataObjects'' of type + ``PointSet'' only. + \end{itemize} + \restartDescription{Metropolis} +\end{itemize} + +Example: +\begin{lstlisting}[style=XML] + + ... + + + 1000 + 070419 + 10 + + zout + + normal + 0 + normal + + + normal + 0 + normal + + + outSet + + ... + +\end{lstlisting} diff --git a/framework/Distributions.py b/framework/Distributions.py index fcd920fccb..b43f5012e9 100644 --- a/framework/Distributions.py +++ b/framework/Distributions.py @@ -125,7 +125,7 @@ def __init__(self): self.lowerBound = None # Left bound self.__adjustmentType = '' # this describe how the re-normalization to preserve the probability should be done for truncated distributions self.dimensionality = None # Dimensionality of the distribution (1D or ND) - self.disttype = None # Distribution type (continuous or discrete) + self.distType = None # Distribution type (continuous or discrete) self.memory = False # This variable flags if the distribution has history dependence in the sampling process (True) or not (False) self.printTag = 'DISTRIBUTIONS' self.preferredPolynomials = None # best polynomial for probability-weighted norm of error @@ -358,13 +358,13 @@ def getDimensionality(self): """ return self.dimensionality - def getDisttype(self): + def getDistType(self): """ Function return distribution type @ In, None - @ Out, disttype, string, ('Continuous' or 'Discrete') + @ Out, distType, string, ('Continuous' or 'Discrete') """ - return self.disttype + return self.distType def getMemory(self): """ @@ -403,7 +403,7 @@ def __init__(self): """ Distribution.__init__(self) self.dimensionality = 1 - self.disttype = 'Continuous' + self.distType = 'Continuous' def cdf(self,x): """ @@ -439,6 +439,15 @@ def pdf(self,x): returnPdf = self._distribution.pdf(x) return returnPdf + def logPdf(self,x): + """ + Function to get the log pdf at a provided coordinate + @ In, x, float, value to get the pdf at + @ Out, logPdf, float, requested log pdf + """ + logPdf = np.log(self.pdf(x)) + return logPdf + def untruncatedCdfComplement(self, x): """ Function to get the untruncated cdf complement at a provided coordinate @@ -516,7 +525,7 @@ def __init__(self, lowerBound = None, upperBound = None): BoostDistribution.__init__(self) self.range = 0.0 self.type = 'Uniform' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.compatibleQuadrature.append('Legendre') self.compatibleQuadrature.append('ClenshawCurtis') self.compatibleQuadrature.append('CDF') @@ -525,15 +534,12 @@ def __init__(self, lowerBound = None, upperBound = None): if upperBound is not None: self.upperBound = upperBound self.upperBoundUsed = True - print("upperBound", self.upperBound) if lowerBound is not None: self.lowerBound = lowerBound self.lowerBoundUsed = True - print("lowerBound", self.lowerBound) if self.lowerBoundUsed and self.upperBoundUsed: self.range = self.upperBound - self.lowerBound - def _localSetState(self,pdict): """ Set the pickling state (local) @@ -648,7 +654,7 @@ def __init__(self, mean=0.0, sigma=1.0): self.sigma = sigma self.hasInfiniteBound = True self.type = 'Normal' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.compatibleQuadrature.append('Hermite') self.compatibleQuadrature.append('CDF') #THESE get set in initializeDistribution, since it depends on truncation @@ -804,7 +810,7 @@ def __init__(self, low=0.0, alpha=0.0, beta=1.0): self.alpha = alpha self.beta = beta self.type = 'Gamma' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.hasInfiniteBound = True self.compatibleQuadrature.append('Laguerre') self.compatibleQuadrature.append('CDF') @@ -961,7 +967,7 @@ def __init__(self): self.alpha = 0.0 self.beta = 0.0 self.type = 'Beta' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.hasInfiniteBound = True self.compatibleQuadrature.append('Jacobi') self.compatibleQuadrature.append('CDF') @@ -1135,7 +1141,7 @@ def __init__(self): self.min = None # domain lower boundary self.max = None # domain upper boundary self.type = 'Triangular' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.compatibleQuadrature.append('CDF') self.preferredQuadrature = 'CDF' self.preferredPolynomials = 'CDF' @@ -1250,7 +1256,7 @@ def __init__(self): self.mu = 0.0 self.type = 'Poisson' self.hasInfiniteBound = True - self.disttype = 'Discrete' + self.distType = 'Discrete' self.compatibleQuadrature.append('CDF') self.preferredQuadrature = 'CDF' self.preferredPolynomials = 'CDF' @@ -1346,7 +1352,7 @@ def __init__(self): self.p = 0.0 self.type = 'Binomial' self.hasInfiniteBound = True - self.disttype = 'Discrete' + self.distType = 'Discrete' self.compatibleQuadrature.append('CDF') self.preferredQuadrature = 'CDF' self.preferredPolynomials = 'CDF' @@ -1445,7 +1451,7 @@ def __init__(self): BoostDistribution.__init__(self) self.p = 0.0 self.type = 'Bernoulli' - self.disttype = 'Discrete' + self.distType = 'Discrete' self.lowerBound = 0.0 self.upperBound = 1.0 self.compatibleQuadrature.append('CDF') @@ -1538,7 +1544,7 @@ def __init__(self): BoostDistribution.__init__(self) self.p = 0.0 self.type = 'Geometric' - self.disttype = 'Discrete' + self.distType = 'Discrete' self.lowerBound = 0.0 self.upperBound = 1.0 self.compatibleQuadrature.append('CDF') @@ -1640,7 +1646,7 @@ def __init__(self): self.values = set() self.type = 'Categorical' self.dimensionality = 1 - self.disttype = 'Discrete' + self.distType = 'Discrete' def _handleInput(self, paramInput): """ @@ -1814,7 +1820,7 @@ def __init__(self): Distribution.__init__(self) self.type = 'UniformDiscrete' self.dimensionality = 1 - self.disttype = 'Discrete' + self.distType = 'Discrete' self.memory = True def _handleInput(self, paramInput): @@ -1968,7 +1974,7 @@ def __init__(self): """ Categorical.__init__(self) self.dimensionality = 1 - self.disttype = 'Discrete' + self.distType = 'Discrete' self.type = 'MarkovCategorical' self.steadyStatePb = None # variable containing the steady state probabilities of the Markov Model self.transition = None # transition matrix of a continuous time Markov Model @@ -2087,7 +2093,7 @@ def __init__(self): self.location = 0.0 self.scale = 1.0 self.type = 'Logistic' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.hasInfiniteBound = True self.compatibleQuadrature.append('CDF') self.preferredQuadrature = 'CDF' @@ -2197,7 +2203,7 @@ def __init__(self): self.location = 0.0 self.scale = 1.0 self.type = 'Laplace' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.hasInfiniteBound = True self.compatibleQuadrature.append('CDF') self.preferredQuadrature = 'CDF' @@ -2300,7 +2306,7 @@ def __init__(self): self.lambdaVar = 1.0 self.low = 0.0 self.type = 'Exponential' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.hasInfiniteBound = True self.compatibleQuadrature.append('CDF') self.preferredQuadrature = 'CDF' @@ -2440,7 +2446,7 @@ def __init__(self): self.sigma = 1.0 self.low = 0.0 self.type = 'LogNormal' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.hasInfiniteBound = True self.compatibleQuadrature.append('CDF') self.preferredQuadrature = 'CDF' @@ -2556,7 +2562,7 @@ def __init__(self): self.lambdaVar = 1.0 self.k = 1.0 self.type = 'Weibull' - self.disttype = 'Continuous' + self.distType = 'Continuous' self.low = 0.0 self.hasInfiniteBound = True self.compatibleQuadrature.append('CDF') @@ -2678,7 +2684,7 @@ def __init__(self): self.functionID = None self.variableID = None self.dimensionality = 1 - self.disttype = 'Continuous' + self.distType = 'Continuous' # Scipy.interpolate.UnivariateSpline is used self.k = 4 # Degree of the smoothing spline, Must be <=5 self.s = 0 # Positive smoothing factor used to choose the number of knots diff --git a/framework/JobHandler.py b/framework/JobHandler.py index b82e62b245..111811b890 100644 --- a/framework/JobHandler.py +++ b/framework/JobHandler.py @@ -377,14 +377,22 @@ def addClientJob(self, args, functionToRun, identifier, metadata=None, uniqueHan forceUseThreads = True, uniqueHandler = uniqueHandler, clientQueue = True) - def addFinishedJob(self, data): + def addFinishedJob(self, data, metadata=None, uniqueHandler="any", profile=False): """ Takes an already-finished job (for example, a restart realization) and adds it to the finished queue. @ In, data, dict, completed realization + @ In, data, dict, fully-evaluated realization + @ In, metadata, dict, optional, dictionary of metadata associated with + this run + @ In, uniqueHandler, string, optional, it is a special keyword attached to + this runner. For example, if present, to retrieve this runner using the + method jobHandler.getFinished, the uniqueHandler needs to be provided. + If uniqueHandler == 'any', every "client" can get this runner + @ In, profile, bool, optional, if True then at deconstruction timing statements will be printed @ Out, None """ # create a placeholder runner - run = Runners.PassthroughRunner(self.messageHandler, data) + run = Runners.PassthroughRunner(self.messageHandler, data, metadata=metadata, uniqueHandler=uniqueHandler, profile=profile) # place it on the finished queue with self.__queueLock: self.__finished.append(run) @@ -762,4 +770,3 @@ def terminateJobs(self, ids): self.raiseADebug('Terminated job "{}" by request.'.format(job.identifier)) if len(ids): self.raiseADebug('Tried to remove some jobs but not found in any queues:',', '.join(ids)) - diff --git a/framework/Models/Model.py b/framework/Models/Model.py index 2707e18766..955d52bdc5 100644 --- a/framework/Models/Model.py +++ b/framework/Models/Model.py @@ -126,7 +126,8 @@ class cls. 'AdaptiveSobol', 'EnsembleForward', 'CustomSampler', - 'AdaptiveMonteCarlo'] + 'AdaptiveMonteCarlo', + 'Metropolis'] validateDict['Optimizer'].append(testDict.copy()) validateDict['Optimizer'][0]['class' ] ='Optimizers' validateDict['Optimizer'][0]['required' ] = False diff --git a/framework/Samplers/Factory.py b/framework/Samplers/Factory.py index 7ffd8243c9..2feb45d5c9 100644 --- a/framework/Samplers/Factory.py +++ b/framework/Samplers/Factory.py @@ -44,6 +44,10 @@ # Dynamic Event Tree-based Samplers from Samplers.DynamicEventTree import DynamicEventTree from Samplers.AdaptiveDynamicEventTree import AdaptiveDynamicEventTree + +# MCMC Samplers +from .MCMC import Metropolis + ## [ Add new class here ] ################################################################################ ## Alternatively, to fully automate this file: @@ -72,6 +76,7 @@ __interFaceDict['DynamicEventTree' ] = DynamicEventTree __interFaceDict['AdaptiveDynamicEventTree'] = AdaptiveDynamicEventTree __interFaceDict['AdaptiveMonteCarlo' ] = AdaptiveMonteCarlo +__interFaceDict['Metropolis' ] = Metropolis def knownTypes(): """ diff --git a/framework/Samplers/Grid.py b/framework/Samplers/Grid.py index 59b0729e51..60ea16b98b 100644 --- a/framework/Samplers/Grid.py +++ b/framework/Samplers/Grid.py @@ -223,7 +223,7 @@ def localGenerateInput(self,model,myInput): self.inputInfo['SampledVarsPb'][varName] = self.distDict[varName].pdf(ndCoordinate) # Compute the ProbabilityWeight if ("" in varName) or (self.variables2distributionsMapping[varName]['totDim']==1): - if self.distDict[varName].getDisttype() == 'Discrete': + if self.distDict[varName].getDistType() == 'Discrete': gridWeight = self.distDict[varName].pdf(coordinates[varName]) else: if self.gridInfo[varName]=='CDF': diff --git a/framework/Samplers/MCMC/MCMC.py b/framework/Samplers/MCMC/MCMC.py new file mode 100644 index 0000000000..dddce8c733 --- /dev/null +++ b/framework/Samplers/MCMC/MCMC.py @@ -0,0 +1,309 @@ +# 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. +""" + Markov Chain Monte Carlo + This base class defines the principle methods required for MCMC + + Created on June 26, 2020 + @author: wangc +""" + +#External Modules------------------------------------------------------------------------------------ +import numpy as np +import copy +import abc +#External Modules End-------------------------------------------------------------------------------- + +#Internal Modules------------------------------------------------------------------------------------ +import Distributions +from Samplers import AdaptiveSampler +from utils import utils,randomUtils,InputData, InputTypes +#Internal Modules End-------------------------------------------------------------------------------- + +class MCMC(AdaptiveSampler): + """ + Markov Chain Monte Carlo Sampler. + """ + + @classmethod + def userManualDescription(cls): + """ + Provides a user manual description for this actor. Should only be needed for base classes. + @ In, None + @ Out, descr, string, description + """ + descr = r""" + \subsection{Markov Chain Monte Carlo} + \label{subsec:MCMC} + The Markov chain Monte Carlo (MCMC) is a Sampler entity in the RAVEN framework. + It provides enormous scope for realistic statistical modeling. MCMC is essentially + Monte Carlo integration using Markov chain. Bayesians, and sometimes also frequentists, + need to integrate over possibly high-dimensional probability distributions to make inference + about model parameters or to make predictions. Bayesians need to integrate over the posterior + distributions of model parameters given the data, and frequentists may need to integrate + over the distribution of observables given parameter values. Monte Carlo integration draws + samples from the required distribution, and then forms samples averages to approximate expectations. + MCMC draws these samples by running a cleverly constructed Markov chain for a long time. + There are a large number of MCMC algorithms, and popular families include Gibbs sampling, + Metropolis-Hastings, slice sampling, Hamiltonian Monte Carlo, and many others. Regardless + of the algorithm, the goal in Bayesian inference is to maximize the unnormalized joint + posterior distribution and collect samples of the target distributions, which are marginal + posterior distributions, later to be used for inference. + """ + return descr + + @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. + """ + inputSpecification = super(MCMC, cls).getInputSpecification() + + samplerInitInput = InputData.parameterInputFactory("samplerInit", strictMode=True, + printPriority=10, + descr=r"""collection of nodes that describe the initialization of the MCMC algorithm.""") + limitInput = InputData.parameterInputFactory("limit", contentType=InputTypes.IntegerType, + descr=r"""the limit for the total samples""") + samplerInitInput.addSub(limitInput) + initialSeedInput = InputData.parameterInputFactory("initialSeed", contentType=InputTypes.IntegerType, + descr='') + samplerInitInput.addSub(initialSeedInput) + burnInInput = InputData.parameterInputFactory("burnIn", contentType=InputTypes.IntegerType, + descr='') + samplerInitInput.addSub(burnInInput) + inputSpecification.addSub(samplerInitInput) + likelihoodInp = InputData.parameterInputFactory("likelihood",contentType=InputTypes.StringType, + printPriority=5, + descr=r"""Output of likelihood function""") + likelihoodInp.addParam('log', InputTypes.BoolType, required=False, + descr=r"""True if the user provided is the log likelihood, otherwise, treat it as + the standard likelihood""") + inputSpecification.addSub(likelihoodInp) + # modify Sampler variable nodes + variable = inputSpecification.getSub('variable') + variable.addSub(InputData.parameterInputFactory('initial', contentType=InputTypes.FloatType, + descr=r"""inital value for given variable""")) + variable.addSub(InputData.assemblyInputFactory('proposal', contentType=InputTypes.StringType, strictMode=True, + printPriority=30, + descr=r"""name of the Distribution that is used as proposal distribution""")) + + inputSpecification.addSub(variable) + return inputSpecification + + @classmethod + def getSolutionExportVariableNames(cls): + """ + Compiles a list of acceptable SolutionExport variable options. + @ In, None + @ Out, vars, dict, {varName: manual description} for each solution export option + """ + vars = super(AdaptiveSampler, cls).getSolutionExportVariableNames() + # TODO: multiple chains for MCMC + new = {'traceID': 'integer identifying which iteration a Markov chain is on', + '{VAR}': r'any variable from the \xmlNode{TargetEvaluation} input or output at current iteration' + } + vars.update(new) + return vars + + def __init__(self): + """ + Default Constructor that will initialize member variables with reasonable + defaults or empty lists/dictionaries where applicable. + @ In, None + @ Out, None + """ + AdaptiveSampler.__init__(self) + self._initialValues = {} # dict stores the user provided initial values, i.e. {var: val} + self._updateValues = {} # dict stores input variables values for the current MCMC iteration, i.e. {var:val} + self._proposal = {} # dict stores the proposal distributions for input variables, i.e. {var:dist} + self._burnIn = 0 # integers indicate how many samples will be discarded + self._likelihood = None # stores the output from the likelihood + self._logLikelihood = False # True if the user provided likelihood is in log format + self._availProposal = {'normal': Distributions.Normal(0.0, 1.0), + 'uniform': Distributions.Uniform(-1.0, 1.0)} # available proposal distributions + self._acceptDist = Distributions.Uniform(0.0, 1.0) # uniform distribution for accept/rejection purpose + # assembler objects + self.addAssemblerObject('proposal', '-n', True) + + def localInputAndChecks(self, xmlNode, paramInput): + """ + Method that serves as a pass-through for input reading. + comes from inheriting from Sampler and _readMoreXML chain. + @ In, xmlNode, xml.etree.ElementTree.Element, xml element node (don't use!) + @ In, paramInput, InputData.ParameterInput, parameter specs interpreted + @ Out, None + """ + # this is just a passthrough until sampler gets reworked or renamed + self.handleInput(paramInput) + + def handleInput(self, paramInput): + """ + Read input specs + @ In, paramInput, InputData.ParameterInput, parameter specs interpreted + @ Out, None + """ + likelihood = paramInput.findFirst('likelihood') + if likelihood is not None: + self._likelihood = likelihood.value + self._logLikelihood = likelihood.parameterValues.get('log', False) + else: + self.raiseAnError(IOError, "likelihood is required, but not provided!") + init = paramInput.findFirst('samplerInit') + if init is not None: + # limit + limit = init.findFirst('limit') + if limit is not None: + self.limit = limit.value + else: + self.raiseAnError(IOError, 'MCMC', self.name, 'needs the limit block (number of samples) in the samplerInit block') + # initialSeed + seed = init.findFirst('initialSeed') + if seed is not None: + self.initSeed = seed.value + else: + self.initSeed = randomUtils.randomIntegers(0,2**31,self) + burnIn = init.findFirst('burnIn') + if burnIn is not None: + self._burnIn = burnIn.value + else: + self.raiseAnError(IOError, 'MCMC', self.name, 'needs the samplerInit block') + if self._burnIn >= self.limit: + self.raiseAnError(IOError, 'Provided "burnIn" value must be less than "limit" value!') + # variables additional reading + for varNode in paramInput.findAll('variable'): + var = varNode.parameterValues['name'] + initNode = varNode.findFirst('initial') + if initNode: + self._initialValues[var] = initNode.value + else: + self._initialValues[var] = None + proposal = varNode.findFirst('proposal') + if proposal: + self._proposal[var] = proposal.value + else: + self._proposal[var] = None + # TargetEvaluation Node (Required) + targetEval = paramInput.findFirst('TargetEvaluation') + self._targetEvaluation = targetEval.value + self._updateValues = copy.copy(self._initialValues) + + def initialize(self, externalSeeding=None, solutionExport=None): + """ + This function should be called every time a clean MCMC is needed. Called before takeAstep in + @ In, externalSeeding, int, optional, external seed + @ In, solutionExport, DataObject, optional, a PointSet to hold the solution + @ Out, None + """ + # TODO: currently, we only consider uncorrelated case + # initialize distributions + for _, dist in self._availProposal.items(): + dist.initializeDistribution() + self._acceptDist.initializeDistribution() + for var, dist in self._proposal.items(): + if dist: + self._proposal[var] = self.retrieveObjectFromAssemblerDict('proposal', dist) + distType = self._proposal[var].getDistType() + if distType != 'Continuous': + self.raiseAnError(IOError, 'variable "{}" requires continuous proposal distribution, but "{}" is provided!'.format(var, distType)) + else: + self._proposal[var] = self._availProposal['normal'] + AdaptiveSampler.initialize(self, externalSeeding=externalSeeding, solutionExport=solutionExport) + ## TODO: currently AdaptiveSampler is still using self.assemblerDict to retrieve the target evaluation. + # We should change it to using the following method. + # retrieve target evaluation + # self._targetEvaluation = self.retrieveObjectFromAssemblerDict('TargetEvaluation', self._targetEvaluation) + + # initialize the input variable values + for key, value in self._updateValues.items(): + totDim = self.variables2distributionsMapping[key]['totDim'] + dist = self.distDict[key] + distType = dist.getDistType() + if distType != 'Continuous': + self.raiseAnError(IOError, 'variable "{}" requires continuous distribution, but "{}" is provided!'.format(key, distType)) + if totDim != 1: + self.raiseAnError(IOError,"Total dimension for given distribution {} should be 1".format(dist.type)) + if value is None: + value = dist.rvs() + self._updateValues[key] = value + + def localGenerateInput(self, model, myInput): + """ + Provides the next sample to take. + After this method is called, the self.inputInfo should be ready to be sent + to the model + @ In, model, model instance, an instance of a model + @ In, myInput, list, a list of the original needed inputs for the model (e.g. list of files, etc.) + @ Out, None + """ + for key, value in self._updateValues.items(): + self.values[key] = value + self.inputInfo['SampledVarsPb'][key] = self.distDict[key].pdf(value) + self.inputInfo['ProbabilityWeight-' + key] = 1. + self.inputInfo['PointProbability'] = 1.0 + self.inputInfo['ProbabilityWeight' ] = 1.0 + + def localFinalizeActualSampling(self, jobObject, model, myInput): + """ + General function (available to all samplers) that finalizes the sampling + calculation just ended. In this case, The function is aimed to check if + all the batch calculations have been performed + @ In, jobObject, instance, an instance of a JobHandler + @ In, model, model instance, it is the instance of a RAVEN model + @ In, myInput, list, the generating input + @ Out, None + """ + AdaptiveSampler.localFinalizeActualSampling(self, jobObject, model, myInput) + + def localStillReady(self, ready): + """ + Determines if sampler is prepared to provide another input. If not, and + if jobHandler is finished, this will end sampling. + @ In, ready, bool, a boolean representing whether the caller is prepared for another input. + @ Out, ready, bool, a boolean representing whether the caller is prepared for another input. + """ + ready = AdaptiveSampler.localStillReady(self, ready) + return ready + + def _localHandleFailedRuns(self, failedRuns): + """ + Specialized method for samplers to handle failed runs. Defaults to failing runs. + @ In, failedRuns, list, list of JobHandler.ExternalRunner objects + @ Out, None + """ + if len(failedRuns)>0: + self.raiseADebug(' Continuing with reduced-size MCMC sampling.') + + def _formatSolutionExportVariableNames(self, acceptable): + """ + Does magic formatting for variables, based on this class's needs. + Extend in inheritors as needed. + @ In, acceptable, set, set of acceptable entries for solution export for this entity + @ Out, acceptable, set, modified set of acceptable variables with all formatting complete + """ + acceptable = AdaptiveSampler._formatSolutionExportVariableNames(self, acceptable) + return acceptable + + def _addToSolutionExport(self, rlz): + """ + add realizations to solution export + @ In, rlz, dict, sampled realization + @ Out, None + """ + if self._burnIn < self.counter: + rlz = dict((var, np.atleast_1d(val)) for var, val in rlz.items()) + self._solutionExport.addRealization(rlz) diff --git a/framework/Samplers/MCMC/Metropolis.py b/framework/Samplers/MCMC/Metropolis.py new file mode 100644 index 0000000000..9e3126d3d5 --- /dev/null +++ b/framework/Samplers/MCMC/Metropolis.py @@ -0,0 +1,162 @@ +# 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. +""" + Markov Chain Monte Carlo + This base class defines the principle methods required for MCMC + + Created on June 26, 2020 + @author: wangc +""" + +#External Modules------------------------------------------------------------------------------------ +import numpy as np +import copy +import abc +#External Modules End-------------------------------------------------------------------------------- + +#Internal Modules------------------------------------------------------------------------------------ + +from .MCMC import MCMC +from utils import utils,randomUtils,InputData, InputTypes +#Internal Modules End-------------------------------------------------------------------------------- + +class Metropolis(MCMC): + """ + Metropolis Sampler + """ + @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. + """ + inputSpecification = super(Metropolis, cls).getInputSpecification() + return inputSpecification + + def __init__(self): + """ + Default Constructor that will initialize member variables with reasonable + defaults or empty lists/dictionaries where applicable. + @ In, None + @ Out, None + """ + MCMC.__init__(self) + self._localReady = True # True if the submitted job finished + self._currentRlz = None # dict stores the current realizations, i.e. {var: val} + + def handleInput(self, paramInput): + """ + Read input specs + @ In, paramInput, InputData.ParameterInput, parameter specs interpreted + @ Out, None + """ + MCMC.handleInput(self, paramInput) + + def initialize(self, externalSeeding=None, solutionExport=None): + """ + This function should be called every time a clean MCMC is needed. Called before takeAstep in + @ In, externalSeeding, int, optional, external seed + @ In, solutionExport, DataObject, optional, a PointSet to hold the solution + @ Out, None + """ + MCMC.initialize(self, externalSeeding=externalSeeding, solutionExport=solutionExport) + + def localGenerateInput(self, model, myInput): + """ + Provides the next sample to take. + After this method is called, the self.inputInfo should be ready to be sent + to the model + @ In, model, model instance, an instance of a model + @ In, myInput, list, a list of the original needed inputs for the model (e.g. list of files, etc.) + @ Out, None + """ + if self.counter < 2: + MCMC.localGenerateInput(self, model, myInput) + else: + self._localReady = False + for key, value in self._updateValues.items(): + # update value based on proposal distribution + newVal = value + self._proposal[key].rvs() + self.values[key] = newVal + self.inputInfo['SampledVarsPb'][key] = self.distDict[key].pdf(newVal) + self.inputInfo['ProbabilityWeight-' + key] = 1. + self.inputInfo['PointProbability'] = 1.0 + self.inputInfo['ProbabilityWeight' ] = 1.0 + self.inputInfo['SamplerType'] = 'Metroplis' + + def localFinalizeActualSampling(self, jobObject, model, myInput): + """ + General function (available to all samplers) that finalize the sampling + calculation just ended. In this case, The function is aimed to check if + all the batch calculations have been performed + @ In, jobObject, instance, an instance of a JobHandler + @ In, model, model instance, it is the instance of a RAVEN model + @ In, myInput, list, the generating input + @ Out, None + """ + self._localReady = True + MCMC.localFinalizeActualSampling(self, jobObject, model, myInput) + prefix = jobObject.getMetadata()['prefix'] + _, full = self._targetEvaluation.realization(matchDict={'prefix': prefix}) + rlz = dict((var, full[var]) for var in (list(self.toBeSampled.keys()) + [self._likelihood] + list(self.dependentSample.keys()))) + rlz['traceID'] = self.counter + if self.counter == 1: + self._addToSolutionExport(rlz) + self._currentRlz = rlz + if self.counter > 1: + acceptable = self._useRealization(rlz, self._currentRlz) + if acceptable: + self._currentRlz = rlz + self._addToSolutionExport(rlz) + self._updateValues = dict((var, rlz[var]) for var in self._updateValues) + else: + self._addToSolutionExport(self._currentRlz) + self._updateValues = dict((var, self._currentRlz[var]) for var in self._updateValues) + + def _useRealization(self, newRlz, currentRlz): + """ + Used to feedback the collected runs within the sampler + @ In, newRlz, dict, new generated realization + @ In, currentRlz, dict, the current existing realization + @ Out, acceptable, bool, True if we accept the new sampled point + """ + netLogPosterior = 0 + # compute net log prior + for var in self._updateValues: + newVal = newRlz[var] + currVal = currentRlz[var] + dist = self.distDict[var] + netLogPrior = dist.logPdf(newVal) - dist.logPdf(currVal) + netLogPosterior += netLogPrior + if not self._logLikelihood: + netLogLikelihood = np.log(newRlz[self._likelihood]) - np.log(currentRlz[self._likelihood]) + else: + netLogLikelihood = newRlz[self._likelihood] - currentRlz[self._likelihood] + netLogPosterior += netLogLikelihood + acceptValue = np.log(self._acceptDist.rvs()) + acceptable = netLogPosterior > acceptValue + return acceptable + + def localStillReady(self, ready): + """ + Determines if sampler is prepared to provide another input. If not, and + if jobHandler is finished, this will end sampling. + @ In, ready, bool, a boolean representing whether the caller is prepared for another input. + @ Out, ready, bool, a boolean representing whether the caller is prepared for another input. + """ + ready = self._localReady and MCMC.localStillReady(self, ready) + return ready diff --git a/framework/Samplers/MCMC/__init__.py b/framework/Samplers/MCMC/__init__.py new file mode 100644 index 0000000000..81d53805f1 --- /dev/null +++ b/framework/Samplers/MCMC/__init__.py @@ -0,0 +1,24 @@ +# 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. + +""" + Markov Chain Samplers + + created on June 26, 2020 + @author: wangc +""" +from .MCMC import MCMC +from .Metropolis import Metropolis + +__all__ = ['Metroplis'] diff --git a/framework/Samplers/__init__.py b/framework/Samplers/__init__.py index 55090fba9f..5a1919fa41 100644 --- a/framework/Samplers/__init__.py +++ b/framework/Samplers/__init__.py @@ -45,6 +45,10 @@ # Dynamic Event Tree-based Samplers from .DynamicEventTree import DynamicEventTree from .AdaptiveDynamicEventTree import AdaptiveDynamicEventTree + +# MCMC Samplers +from .MCMC import Metropolis + # Factory methods from .Factory import knownTypes from .Factory import returnInstance @@ -53,4 +57,5 @@ # We should not really need this as we do not use wildcard imports __all__ = ['Sampler','AdaptiveSampler','ForwardSampler','MonteCarlo','Grid','CustomSampler','Stratified', 'FactorialDesign','ResponseSurfaceDesign','Sobol','EnsembleForward','SparseGridCollocation', - 'DynamicEventTree','LimitSurfaceSearch','AdaptiveDynamicEventTree','AdaptiveSparseGrid','AdaptiveSobol','AdaptiveMonteCarlo'] + 'DynamicEventTree','LimitSurfaceSearch','AdaptiveDynamicEventTree','AdaptiveSparseGrid','AdaptiveSobol', + 'AdaptiveMonteCarlo','Metropolis'] diff --git a/framework/Steps.py b/framework/Steps.py index 05bc3263ae..076971aeda 100644 --- a/framework/Steps.py +++ b/framework/Steps.py @@ -751,8 +751,9 @@ def _findANewInputToRun(self, sampler, model, inputs, outputs, jobHandler): # case 1: found the input in restart, and newInp is a realization dicitonary of data to use found, newInp = sampler.generateInput(model,inputs) if found == 1: + kwargs = copy.deepcopy(sampler.inputInfo) # "submit" the finished run - jobHandler.addFinishedJob(newInp) + jobHandler.addFinishedJob(newInp, metadata=kwargs) return None # NOTE: we return None here only because the Sampler's "counter" is not correctly passed # through if we add several samples at once through the restart. 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0000000000..95eaf04e0f --- /dev/null +++ b/tests/framework/MCMC/metropolis/likelihood.py @@ -0,0 +1,30 @@ +# 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 scipy.stats as st +import numpy as np + +def run(self, inputDict): + """ + Method required by RAVEN to run this as an external model. + log likelihood function + @ In, self, object, object to store members on + @ In, inputDict, dict, dictionary containing inputs from RAVEN + @ Out, None + """ + self.xin = inputDict['xin'] + self.yin = inputDict['yin'] + mus = np.array([5, 5]) + sigmas = np.array([[1, .9], [.9, 1]]) + self.zout = st.multivariate_normal.pdf(np.concatenate((self.xin, self.yin)), mean=mus, cov=sigmas) diff --git a/tests/framework/MCMC/test_metropolis.xml b/tests/framework/MCMC/test_metropolis.xml new file mode 100644 index 0000000000..9ecc886a53 --- /dev/null +++ b/tests/framework/MCMC/test_metropolis.xml @@ -0,0 +1,160 @@ + + + + framework/MCMC.Metropolis + wangc + 2020-06-26 + MCMC.Metropolis + + Test the Markov Chain Monte Carlo algorithm: Metropolis Sampling + A 2-D multivariate normal distribution is used as the likelihood function. + mean: [5, 5], cov=[[1, 0.9], [0.9, 1]] + Both input parameters have the standard normal distribution as their prior distribution. + The proposal distributions for the input variables are also standard normal distribution. + ``TargetEvaluation'' is used to collect the inputs and outputs from the likelihood model. + ``SolutionExport'' is used to store the posterior values for the input variables, i.e. ``xin'' and ``yin'' + + + + + metropolis + mh, makeRestart, print + 1 + False + + + + + 0 + 1 + + + + + + xin, yin, zout + + + + + + + 1000 + 070419 + 10 + + zout + + normal + 0 + normal + + + normal + 0 + normal + + + outSet + + + + 1010 + 070419 + 10 + + zout + + normal + 0 + normal + + + normal + 0 + normal + + + solnsRestart + outSet + + + + + + inputHolder + likelihood + Metropolis + out_export + outSet + + + outSet + likelihood + RestartMetropolis + exportRestart + solnsRestart + + + out_export + outSet + exportRestart + solnsRestart + dumpExport + dumpOut + dumpRestartExport + dumpRestartOut + + + + + + csv + outSet + input, output + + + csv + out_export + input, output + + + csv + solnsRestart + input, output + + + csv + exportRestart + input, output + + + + + + xin, yin + OutputPlaceHolder + + + + xin, yin + zout + + + + traceID + xin, yin + + + + xin, yin + zout + + + + traceID + xin, yin + + + + diff --git a/tests/framework/MCMC/tests b/tests/framework/MCMC/tests new file mode 100644 index 0000000000..407d7a4dd3 --- /dev/null +++ b/tests/framework/MCMC/tests @@ -0,0 +1,13 @@ +[Tests] + [./Metropolis] + type = 'RavenFramework' + input = 'test_metropolis.xml' + max_time = 500 + [./csv] + type = OrderedCSV + output = 'metropolis/dumpExport.csv metropolis/dumpOut.csv metropolis/dumpRestartExport.csv metropolis/dumpRestartOut.csv' + rel_err = 0.001 + [../] + [../] + +[]