From 061058eb1a99a518ac3c8a71b23110f1209df938 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Sun, 19 Feb 2023 12:48:53 -0700 Subject: [PATCH 01/48] NSGA-II implementation with properly printing optimal solutions at the iteration is completed. --- ravenframework/DataObjects/DataSet.py | 11 +- ravenframework/Optimizers/GeneticAlgorithm.py | 531 ++++++++++++++---- ravenframework/Optimizers/Optimizer.py | 11 +- ravenframework/Optimizers/RavenSampled.py | 212 +++++-- ravenframework/Optimizers/fitness/fitness.py | 34 ++ .../Optimizers/mutators/mutators.py | 51 +- .../parentSelectors/parentSelectors.py | 53 +- .../survivorSelectors/survivorSelectors.py | 98 +++- ravenframework/utils/frontUtils.py | 36 +- .../discrete/constrained/myConstraints.py | 108 ++++ .../discrete/constrained/myLocalSum_multi.py | 43 ++ 11 files changed, 943 insertions(+), 245 deletions(-) create mode 100644 tests/framework/Optimizers/NSGAII/discrete/constrained/myConstraints.py create mode 100644 tests/framework/Optimizers/NSGAII/discrete/constrained/myLocalSum_multi.py diff --git a/ravenframework/DataObjects/DataSet.py b/ravenframework/DataObjects/DataSet.py index 6dad7c88ff..a1c21f6f1d 100644 --- a/ravenframework/DataObjects/DataSet.py +++ b/ravenframework/DataObjects/DataSet.py @@ -213,7 +213,8 @@ def addRealization(self, rlz): indexMap = dict((key, val) for key, val in indexMap[0].items() if key in self.getVars()) # [0] because everything is nested in a list by now, it seems # clean out entries that aren't desired try: - rlz = dict((var, rlz[var]) for var in self.getVars() + self.indexes) + getVariables = self.getVars() + rlz = dict((var, rlz[var]) for var in getVariables + self.indexes) except KeyError as e: self.raiseAWarning('Variables provided:',rlz.keys()) self.raiseAnError(KeyError, f'Provided realization does not have all requisite values for object "{self.name}": "{e.args[0]}"') @@ -242,7 +243,8 @@ def addRealization(self, rlz): # This is because the cNDarray collector expects a LIST of realization, not a single realization. # Maybe the "append" method should be renamed to "extend" or changed to append one at a time. # set realizations as a list of realizations (which are ordered lists) - newData = np.array(list(rlz[var] for var in self._orderedVars)+[0.0], dtype=object) + orderedVariables = self._orderedVars + newData = np.array(list(rlz[var] for var in orderedVariables)+[0.0], dtype=object) newData = newData[:-1] # if data storage isn't set up, set it up if self._collector is None: @@ -1963,9 +1965,10 @@ def _setDataTypes(self, rlz): @ In, rlz, dict, standardized and formatted realization @ Out, None """ + getVariables = self.getVars() if self.types is None: - self.types = [None]*len(self.getVars()) - for v, name in enumerate(self.getVars()): + self.types = [None]*len(getVariables) + for v, name in enumerate(getVariables): val = rlz[name] self.types[v] = self._getCompatibleType(val) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index 8f90200ec9..9654c09dc5 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -32,10 +32,11 @@ # External Modules End------------------------------------------------------------------------------ # Internal Modules---------------------------------------------------------------------------------- -from ..utils import mathUtils, InputData, InputTypes +from ..utils import mathUtils, InputData, InputTypes, frontUtils from ..utils.gaUtils import dataArrayToDict, datasetToDataArray from .RavenSampled import RavenSampled from .parentSelectors.parentSelectors import returnInstance as parentSelectionReturnInstance +from .parentSelectors.parentSelectors import countConstViolation from .crossOverOperators.crossovers import returnInstance as crossoversReturnInstance from .mutators.mutators import returnInstance as mutatorsReturnInstance from .survivorSelectors.survivorSelectors import returnInstance as survivorSelectionReturnInstance @@ -66,16 +67,26 @@ def __init__(self): self._acceptRerun = {} # by traj, if True then override accept for point rerun self._convergenceInfo = {} # by traj, the persistence and convergence information for most recent opt self._requiredPersistence = 0 # consecutive persistence required to mark convergence - self.needDenormalized() # the default in all optimizers is to normalize the data which is not the case here + self.needDenormalized() # the default in all optimizers is to normalize the data which is not the case here self.batchId = 0 - self.population = None # panda Dataset container containing the population at the beginning of each generation iteration - self.popAge = None # population age - self.fitness = None # population fitness - self.ahdp = np.NaN # p-Average Hausdorff Distance between populations - self.ahd = np.NaN # Hausdorff Distance between populations + self.population = None # panda Dataset container containing the population at the beginning of each generation iteration + self.popAge = None # population age + self.fitness = None # population fitness + self.rank = None # population rank (for Multi-objective optimization only) + self.constraints = None + self.constraintsV = None + self.crowdingDistance = None # population crowding distance (for Multi-objective optimization only) + self.ahdp = np.NaN # p-Average Hausdorff Distance between populations + self.ahd = np.NaN # Hausdorff Distance between populations self.bestPoint = None self.bestFitness = None self.bestObjective = None + self.multiBestPoint = None + self.multiBestFitness = None + self.multiBestObjective = None + self.multiBestConstraint = None + self.multiBestRank = None + self.multiBestCD = None self.objectiveVal = None self._populationSize = None self._parentSelectionType = None @@ -238,18 +249,11 @@ def getInputSpecification(cls): contentType=InputTypes.StringType, printPriority=108, descr=r"""a subnode containing the implemented fitness functions. - This includes: \begin{itemize} - \item invLinear: - \[fitness = -a \times obj - b \times \sum\\_{j=1}^{nConstraint} max(0,-penalty\\_j) \]. - - \item logistic: - \[fitness = \frac{1}{1+e^{a\times(obj-b)}}\]. - - \item - feasibleFirst: \[fitness = - -obj \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{for} \ \ g\\_j(x)\geq 0 \; \forall j\] and - \[fitness = -obj\\_{worst} - \Sigma\\_{j=1}^{J} \ \ \ \ \ \ \ \ otherwise \] - \end{itemize}.""") + This includes: a. invLinear: $fitness = -a \times obj - b \times \sum_{j=1}^{nConstraint} max(0,-penalty\_j) $. + + b. logistic: $fitness = \frac{1}{1+e^{a\times(obj-b)}}$. + + c. feasibleFirst: $fitness = \left\{\begin{matrix} -obj & g_j(x)\geq 0 \; \forall j \\ -obj_{worst}- \Sigma_{j=1}^{J} & otherwise \\ \end{matrix}\right.$""") fitness.addParam("type", InputTypes.StringType, True, descr=r"""[invLin, logistic, feasibleFirst]""") objCoeff = InputData.parameterInputFactory('a', strictMode=True, @@ -296,6 +300,8 @@ def getSolutionExportVariableNames(cls): new = {} # new = {'': 'the size of step taken in the normalized input space to arrive at each optimal point'} new['conv_{CONV}'] = 'status of each given convergence criteria' + new['rank'] = 'rank' + new['CD'] = 'crowding distance' new['fitness'] = 'fitness of the current chromosome' new['age'] = 'age of current chromosome' new['batchId'] = 'Id of the batch to whom the chromosome belongs' @@ -355,7 +361,7 @@ def handleInput(self, paramInput): # Check if the fitness requested is among the constrained optimization fitnesses # Currently, only InvLin and feasibleFirst Fitnesses deal with constrained optimization # TODO: @mandd, please explore the possibility to convert the logistic fitness into a constrained optimization fitness. - if 'Constraint' in self.assemblerObjects and self._fitnessType not in ['invLinear','feasibleFirst']: + if 'Constraint' in self.assemblerObjects and self._fitnessType not in ['invLinear','feasibleFirst','rank_crowding']: self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support invLinear and feasibleFirst fitnesses, whereas provided fitness is {self._fitnessType}') self._objCoeff = fitnessNode.findFirst('a').value if fitnessNode.findFirst('a') is not None else None self._penaltyCoeff = fitnessNode.findFirst('b').value if fitnessNode.findFirst('b') is not None else None @@ -418,9 +424,9 @@ def needDenormalized(self): # overload as needed in inheritors return True - ############### - # Run Methods # - ############### + ########################################################################################################## + # Run Methods # + ########################################################################################################## def _useRealization(self, info, rlz): """ @@ -438,82 +444,202 @@ def _useRealization(self, info, rlz): info['step'] = self.counter # Developer note: each algorithm step is indicated by a number followed by the generation number - # e.g., '5 @ n-1' refers to step 5 for generation n-1 (i.e., previous generation) + # e.g., '0 @ n-1' refers to step 0 for generation n-1 (i.e., previous generation) # for more details refer to GRP-Raven-development/Disceret_opt channel on MS Teams - # 5 @ n-1: Survivor Selection from previous iteration (children+parents merging from previous generation) - - # 5.1 @ n-1: fitnessCalculation(rlz) + # 0 @ n-1: Survivor Selection from previous iteration (children+parents merging from previous generation) + # 0.1 @ n-1: fitnessCalculation(rlz) # perform fitness calculation for newly obtained children (rlz) - offSprings = datasetToDataArray(rlz, list(self.toBeSampled)) - objectiveVal = list(np.atleast_1d(rlz[self._objectiveVar].data)) - - # collect parameters that the constraints functions need (neglecting the default params such as inputs and objective functions) - constraintData = {} - if self._constraintFunctions or self._impConstraintFunctions: - params = [] - for y in (self._constraintFunctions + self._impConstraintFunctions): - params += y.parameterNames() - for p in list(set(params) -set([self._objectiveVar]) -set(list(self.toBeSampled.keys()))): - constraintData[p] = list(np.atleast_1d(rlz[p].data)) - # Compute constraint function g_j(x) for all constraints (j = 1 .. J) - # and all x's (individuals) in the population - g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) - - g = xr.DataArray(g0, - dims=['chromosome','Constraint'], - coords={'chromosome':np.arange(np.shape(offSprings)[0]), - 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) - # FIXME The constraint handling is following the structure of the RavenSampled.py, - # there are many utility functions that can be simplified and/or merged together - # _check, _handle, and _apply, for explicit and implicit constraints. - # This can be simplified in the near future in GradientDescent, SimulatedAnnealing, and here in GA - for index,individual in enumerate(offSprings): - newOpt = individual - opt = {self._objectiveVar:objectiveVal[index]} - for p, v in constraintData.items(): - opt[p] = v[index] - - for constIndex, constraint in enumerate(self._constraintFunctions + self._impConstraintFunctions): - if constraint in self._constraintFunctions: - g.data[index, constIndex] = self._handleExplicitConstraints(newOpt, constraint) - else: - g.data[index, constIndex] = self._handleImplicitConstraints(newOpt, opt, constraint) - offSpringFitness = self._fitnessInstance(rlz, - objVar=self._objectiveVar, - a=self._objCoeff, - b=self._penaltyCoeff, - penalty=None, - constraintFunction=g, - type=self._minMax) - - self._collectOptPoint(rlz, offSpringFitness, objectiveVal,g) - self._resolveNewGeneration(traj, rlz, objectiveVal, offSpringFitness, g, info) + if len(self._objectiveVar) == 1: # This is a single-objective Optimization case + offSprings = datasetToDataArray(rlz, list(self.toBeSampled)) + objectiveVal = list(np.atleast_1d(rlz[self._objectiveVar[0]].data)) + + # collect parameters that the constraints functions need (neglecting the default params such as inputs and objective functions) + constraintData = {} + if self._constraintFunctions or self._impConstraintFunctions: + params = [] + for y in (self._constraintFunctions + self._impConstraintFunctions): + params += y.parameterNames() + for p in list(set(params) -set([self._objectiveVar[0]]) -set(list(self.toBeSampled.keys()))): + # for p in list(set(params) -set([self._objectiveVar]) -set(list(self.toBeSampled.keys()))): + constraintData[p] = list(np.atleast_1d(rlz[p].data)) + # Compute constraint function g_j(x) for all constraints (j = 1 .. J) + # and all x's (individuals) in the population + g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) + + g = xr.DataArray(g0, + dims=['chromosome','Constraint'], + coords={'chromosome':np.arange(np.shape(offSprings)[0]), + 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) + # FIXME The constraint handling is following the structure of the RavenSampled.py, + # there are many utility functions that can be simplified and/or merged together + # _check, _handle, and _apply, for explicit and implicit constraints. + # This can be simplified in the near future in GradientDescent, SimulatedAnnealing, and here in GA + for index,individual in enumerate(offSprings): + newOpt = individual + opt = {self._objectiveVar[0]:objectiveVal[index]} + for p, v in constraintData.items(): + opt[p] = v[index] + + for constIndex, constraint in enumerate(self._constraintFunctions + self._impConstraintFunctions): + if constraint in self._constraintFunctions: + g.data[index, constIndex] = self._handleExplicitConstraints(newOpt, constraint) + else: + g.data[index, constIndex] = self._handleImplicitConstraints(newOpt, opt, constraint) + + offSpringFitness = self._fitnessInstance(rlz, + objVar=self._objectiveVar[0], + a=self._objCoeff, + b=self._penaltyCoeff, + penalty=None, + constraintFunction=g, + type=self._minMax) + + self._collectOptPoint(rlz, offSpringFitness, objectiveVal, g) + self._resolveNewGeneration(traj, rlz, objectiveVal, offSpringFitness, g, info) + + else: # This is a multi-objective Optimization case + objectiveVal = [] + offSprings = datasetToDataArray(rlz, list(self.toBeSampled)) + for i in range(len(self._objectiveVar)): + objectiveVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) + + # collect parameters that the constraints functions need (neglecting the default params such as inputs and objective functions) + constraintData = {} + if self._constraintFunctions or self._impConstraintFunctions: + params = [] + for y in (self._constraintFunctions + self._impConstraintFunctions): + params += y.parameterNames() + for p in list(set(params) -set(self._objectiveVar) -set(list(self.toBeSampled.keys()))): + constraintData[p] = list(np.atleast_1d(rlz[p].data)) + # Compute constraint function g_j(x) for all constraints (j = 1 .. J) + # and all x's (individuals) in the population + g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) + + g = xr.DataArray(g0, + dims=['chromosome','Constraint'], + coords={'chromosome':np.arange(np.shape(offSprings)[0]), + 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) + + for index,individual in enumerate(offSprings): + newOpt = individual + opt = dict(zip(self._objectiveVar, [item[index] for item in objectiveVal])) + for p, v in constraintData.items(): + opt[p] = v[index] + + for constIndex, constraint in enumerate(self._constraintFunctions + self._impConstraintFunctions): + if constraint in self._constraintFunctions: + g.data[index, constIndex] = self._handleExplicitConstraints(newOpt, constraint) + else: + g.data[index, constIndex] = self._handleImplicitConstraints(newOpt, opt, constraint) + + Fitness = np.zeros((len(offSprings), 1)) + for i in range(len(Fitness)): + Fitness[i] = countConstViolation(g.data[i]) + Fitness = [item for sublist in Fitness.tolist() for item in sublist] + + Fitness = xr.DataArray(Fitness, + dims=['NumOfConstraintViolated'], + coords={'NumOfConstraintViolated':np.arange(np.shape(Fitness)[0])}) + + # 0.2@ n-1: Survivor selection(rlz) + # update population container given obtained children if self._activeTraj: - # 5.2@ n-1: Survivor selection(rlz) - # update population container given obtained children - if self.counter > 1: - self.population,self.fitness,age,self.objectiveVal = self._survivorSelectionInstance(age=self.popAge, - variables=list(self.toBeSampled), - population=self.population, - fitness=self.fitness, - newRlz=rlz, - offSpringsFitness=offSpringFitness, - popObjectiveVal=self.objectiveVal) - self.popAge = age - else: - self.population = offSprings - self.fitness = offSpringFitness - self.objectiveVal = rlz[self._objectiveVar].data + if len(self._objectiveVar) == 1: # If the number of objectives is just 1: + if self.counter > 1: + self.population, self.fitness,\ + age,self.objectiveVal = self._survivorSelectionInstance(age=self.popAge, + variables=list(self.toBeSampled), + population=self.population, + fitness=self.fitness, + newRlz=rlz, + offSpringsFitness=offSpringFitness, + popObjectiveVal=self.objectiveVal) + self.popAge = age + else: + self.population = offSprings + self.fitness = offSpringFitness + self.objectiveVal = rlz[self._objectiveVar[0]].data + + else: # If the number of objectives is more than 1: + if self.counter > 1: + self.population,self.rank, \ + self.popAge,self.crowdingDistance, \ + self.objectiveVal,self.constraints, \ + self.constraintsV = self._survivorSelectionInstance(age=self.popAge, + variables=list(self.toBeSampled), + population=self.population, + offsprings=rlz, + popObjectiveVal=self.objectiveVal, + offObjectiveVal=objectiveVal, + popConst = self.constraints, + offConst = Fitness, + popConstV = self.constraintsV, + offConstV = g + ) + + + + self._collectOptPointMulti(self.population, + self.rank, + self.crowdingDistance, + self.objectiveVal, + self.constraints, + self.constraintsV) + self._resolveNewGenerationMulti(traj, rlz, info) + + ############################################################################## + objs_vals = [list(ele) for ele in list(zip(*self.objectiveVal))] + import matplotlib.pyplot as plt + # JY: Visualization: all points - This code block needs to be either deleted or revisited. + plt.plot(np.array(objs_vals)[:,0], np.array(objs_vals)[:,1],'*') + # plt.xlim(70,100) + # plt.ylim(5,20) + + # JY: Visualization: optimal points only - This code block needs to be either deleted or revisited. + plt.xlim(75,100) + plt.ylim(5,20) + plt.plot(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,0], + np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,1],'*') + # plt.xlim(75,100) + # plt.ylim(5,20) + for i in range(len(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,0])): + plt.text(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[i,0], + np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[i,1], str(self.batchId-1)) + # plt.pause() + ############################################################################## + + else: + self.population = offSprings + self.constraints = Fitness + self.constraintsV = g + self.rank, self.crowdingDistance = self._fitnessInstance(rlz, + objVals = self._objectiveVar + ) + self.objectiveVal = [] + for i in range(len(self._objectiveVar)): + self.objectiveVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) # 1 @ n: Parent selection from population # pair parents together by indexes - parents = self._parentSelectionInstance(self.population, - variables=list(self.toBeSampled), - fitness=self.fitness, - nParents=self._nParents) + + if len(self._objectiveVar) == 1: # If the number of objectives is just 1: + parents = self._parentSelectionInstance(self.population, + variables=list(self.toBeSampled), + fitness=self.fitness, + nParents=self._nParents) + + else: # This is for a multi-objective Optimization case + + parents = self._parentSelectionInstance(self.population, + variables=list(self.toBeSampled), + nParents=self._nParents, + rank = self.rank, + crowdDistance = self.crowdingDistance, + constraint = self.constraints + ) # 2 @ n: Crossover from set of parents # create childrenCoordinates (x1,...,xM) @@ -550,9 +676,9 @@ def _useRealization(self, info, rlz): children = children[:self._populationSize, :] daChildren = xr.DataArray(children, - dims=['chromosome','Gene'], - coords={'chromosome': np.arange(np.shape(children)[0]), - 'Gene':list(self.toBeSampled)}) + dims=['chromosome','Gene'], + coords={'chromosome': np.arange(np.shape(children)[0]), + 'Gene':list(self.toBeSampled)}) # 5 @ n: Submit children batch # submit children coordinates (x1,...,xm), i.e., self.childrenCoordinates @@ -593,12 +719,20 @@ def flush(self): self.population = None self.popAge = None self.fitness = None + self.rank = None + self.crowdingDistance = None self.ahdp = np.NaN self.ahd = np.NaN self.bestPoint = None self.bestFitness = None self.bestObjective = None self.objectiveVal = None + self.multiBestPoint = None + self.multiBestFitness = None + self.multiBestObjective = None + self.multiBestConstraint = None + self.multiBestRank = None + self.multiBestCD = None # END queuing Runs # * * * * * * * * * * * * * * * * @@ -627,7 +761,7 @@ def _resolveNewGeneration(self, traj, rlz, objectiveVal, fitness, g, info): for i in range(rlz.sizes['RAVEN_sample_ID']): varList = self._solutionExport.getVars('input') + self._solutionExport.getVars('output') + list(self.toBeSampled.keys()) rlzDict = dict((var,np.atleast_1d(rlz[var].data)[i]) for var in set(varList) if var in rlz.data_vars) - rlzDict[self._objectiveVar] = np.atleast_1d(rlz[self._objectiveVar].data)[i] + rlzDict[self._objectiveVar[0]] = np.atleast_1d(rlz[self._objectiveVar[0]].data)[i] rlzDict['fitness'] = np.atleast_1d(fitness.data)[i] for ind, consName in enumerate(g['Constraint'].values): rlzDict['ConstraintEvaluation_'+consName] = g[i,ind] @@ -636,7 +770,7 @@ def _resolveNewGeneration(self, traj, rlz, objectiveVal, fitness, g, info): if acceptable in ['accepted', 'first']: # record history bestRlz = {} - bestRlz[self._objectiveVar] = self.bestObjective + bestRlz[self._objectiveVar[0]] = self.bestObjective bestRlz['fitness'] = self.bestFitness bestRlz.update(self.bestPoint) self._optPointHistory[traj].append((bestRlz, info)) @@ -645,6 +779,65 @@ def _resolveNewGeneration(self, traj, rlz, objectiveVal, fitness, g, info): else: # e.g. rerun pass # nothing to do, just keep moving + def _resolveNewGenerationMulti(self, traj, rlz, info): + """ + Store a new Generation after checking convergence + @ In, traj, int, trajectory for this new point + @ In, rlz, dict, realized realization + @ In, objectiveVal, list, objective values at each chromosome of the realization + @ In, fitness, xr.DataArray, fitness values at each chromosome of the realization + @ In, g, xr.DataArray, the constraint evaluation function + @ In, info, dict, identifying information about the realization + """ + self.raiseADebug('*'*80) + self.raiseADebug(f'Trajectory {traj} iteration {info["step"]} resolving new state ...') + # note the collection of the opt point + self._stepTracker[traj]['opt'] = (rlz, info) + acceptable = 'accepted' if self.counter > 1 else 'first' + old = self.population + converged = self._updateConvergence(traj, rlz, old, acceptable) + if converged: + self._closeTrajectory(traj, 'converge', 'converged', self.bestObjective) + # NOTE: the solution export needs to be updated BEFORE we run rejectOptPoint or extend the opt + # point history. + objVal = [[] for x in range(len(self.objectiveVal[0]))] + for i in range(len(self.objectiveVal[0])): + objVal[i] = [item[i] for item in self.objectiveVal] + + objVal = xr.DataArray(objVal, + dims=['chromosome','obj'], + coords={'chromosome':np.arange(np.shape(objVal)[0]), + 'obj': self._objectiveVar}) + if self._writeSteps == 'every': + for i in range(rlz.sizes['RAVEN_sample_ID']): + rlzDict = dict((var,self.population.data[i][j]) for j, var in enumerate(self.population.Gene.data)) + for j in range(len(self._objectiveVar)): + rlzDict[self._objectiveVar[j]] = objVal.data[i][j] + rlzDict['rank'] = np.atleast_1d(self.rank.data)[i] + rlzDict['CD'] = np.atleast_1d(self.crowdingDistance.data)[i] + rlzDict['fitness'] = np.atleast_1d(self.constraints.data)[i] + for ind, consName in enumerate([y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]): + rlzDict['ConstraintEvaluation_'+consName] = self.constraintsV.data[i,ind] + self._updateSolutionExport(traj, rlzDict, acceptable, None) + + # decide what to do next + if acceptable in ['accepted', 'first']: + # record history + bestRlz = {} + for i in range(len(self._objectiveVar)): + bestRlz[self._objectiveVar[i]] = [item[i] for item in self.multiBestObjective] + bestRlz['fitness'] = self.multiBestFitness + bestRlz['rank'] = self.multiBestRank + bestRlz['CD'] = self.multiBestCD + for ind, consName in enumerate(self.multiBestConstraint.Constraint): + bestRlz['ConstraintEvaluation_'+consName.values.tolist()] = self.multiBestConstraint[ind].values + bestRlz.update(self.multiBestPoint) + self._optPointHistory[traj].append((bestRlz, info)) + elif acceptable == 'rejected': + self._rejectOptPoint(traj, info, old) + else: # e.g. rerun + pass # nothing to do, just keep moving + def _collectOptPoint(self, rlz, fitness, objectiveVal, g): """ Collects the point (dict) from a realization @@ -669,6 +862,63 @@ def _collectOptPoint(self, rlz, fitness, objectiveVal, g): return point + def _collectOptPointMulti(self, population, rank, CD, objectiveVal, constraints, constraintsV): + """ + Collects the point (dict) from a realization + @ In, population, Dataset, container containing the population + @ In, objectiveVal, list, objective values at each chromosome of the realization + @ In, rank, xr.DataArray, rank values at each chromosome of the realization + @ In, crowdingDistance, xr.DataArray, crowdingDistance values at each chromosome of the realization + @ Out, point, dict, point used in this realization + """ + objVal = [[] for x in range(len(objectiveVal[0]))] + for i in range(len(objectiveVal[0])): + objVal[i] = [item[i] for item in objectiveVal] + + optPointsConsIDX = [i for i, nFit in enumerate(constraints) if nFit == min(constraints)] # Find index of chromosome which has smallest numeber of violations among population + optPointsRankNConsIDX = [i for i, rankValue in enumerate(rank[optPointsConsIDX]) if rankValue == min(rank[optPointsConsIDX])] # Find index of chromosome which has smallest numeber of violations among population & smallest rank + + optPoints,optObjVal,optConstraints,optConstraintsV,optRank,optCD = population[optPointsRankNConsIDX], np.array(objVal)[optPointsRankNConsIDX], constraints.data[optPointsRankNConsIDX], constraintsV.data[optPointsRankNConsIDX], rank.data[optPointsRankNConsIDX], CD.data[optPointsRankNConsIDX] + + # Previous ################################################## + # points,multiFit,rankSorted,cdSorted,objSorted,constSorted = \ + # zip(*[[a,b,c,d,e,f] for a, b, c, d, e, f in sorted(zip(np.atleast_2d(population.data),np.atleast_1d(constraintsV.data),np.atleast_1d(rank.data),np.atleast_1d(CD.data), objVal, constraints), + # reverse=True,key=lambda x: (-x[1], -x[2], x[3]))]) + # optPoints = [points[i] for i, rank in enumerate(rankSorted) if rank == 1 ] + # optMultiFit = [multiFit[i] for i, rank in enumerate(rankSorted) if rank == 1 ] + # optObj = [objSorted[i] for i, rank in enumerate(rankSorted) if rank == 1 ] + # optConst = [constSorted[i] for i, rank in enumerate(rankSorted) if rank == 1 ] + # optRank = [rankSorted[i] for i, rank in enumerate(rankSorted) if rank == 1 ] + # optCD = [cdSorted[i] for i, rank in enumerate(rankSorted) if rank == 1 ] + # if (len(optMultiFit) != len([x for x in optMultiFit if x != 0]) ) : + # optPoints = [optPoints[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] + # optMultiFit = [x for x in optMultiFit if x == 0] + # optObj = [optObj[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] + # optConst = [optConst[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] + # optRank = [optRank[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] + # optCD = [optCD[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] + # Previous ################################################## + + optPointsDic = dict((var,np.array(optPoints)[:,i]) for i, var in enumerate(population.Gene.data)) + optConstNew = [] + for i in range(len(optConstraintsV)): + optConstNew.append(optConstraintsV[i]) + optConstNew = list(map(list, zip(*optConstNew))) + optConstNew = xr.DataArray(optConstNew, + dims=['Constraint','Evaluation'], + coords={'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)], + 'Evaluation':np.arange(np.shape(optConstNew)[1])}) + + self.multiBestPoint = optPointsDic + self.multiBestFitness = optConstraints + self.multiBestObjective = optObjVal + self.multiBestConstraint = optConstNew + self.multiBestRank = optRank + self.multiBestCD = optCD + + return optPointsDic + + def _checkAcceptability(self, traj): """ This is an abstract method for all RavenSampled Optimizer, whereas for GA all children are accepted @@ -685,16 +935,26 @@ def checkConvergence(self, traj, new, old): @ Out, any(convs.values()), bool, True of any of the convergence criteria was reached @ Out, convs, dict, on the form convs[conv] = bool, where conv is in self._convergenceCriteria """ - convs = {} - for conv in self._convergenceCriteria: - fName = conv[:1].upper() + conv[1:] - # get function from lookup - f = getattr(self, f'_checkConv{fName}') - # check convergence function - okay = f(traj, new=new, old=old) - # store and update - convs[conv] = okay - + if len(self._objectiveVar) == 1: + convs = {} + for conv in self._convergenceCriteria: + fName = conv[:1].upper() + conv[1:] + # get function from lookup + f = getattr(self, f'_checkConv{fName}') + # check convergence function + okay = f(traj, new=new, old=old) + # store and update + convs[conv] = okay + else: + convs = {} + for conv in self._convergenceCriteria: + fName = conv[:1].upper() + conv[1:] + # get function from lookup + f = getattr(self, f'_checkConv{fName}') + # check convergence function + okay = f(traj, new=new, old=old) + # store and update + convs[conv] = okay return any(convs.values()), convs def _checkConvObjective(self, traj, **kwargs): @@ -704,15 +964,28 @@ def _checkConvObjective(self, traj, **kwargs): @ In, kwargs, dict, dictionary of parameters for convergence criteria @ Out, converged, bool, convergence state """ - if len(self._optPointHistory[traj]) < 2: - return False - o1, _ = self._optPointHistory[traj][-1] - obj = o1[self._objectiveVar] - converged = (obj == self._convergenceCriteria['objective']) - self.raiseADebug(self.convFormat.format(name='objective', - conv=str(converged), - got=obj, - req=self._convergenceCriteria['objective'])) + if len(self._objectiveVar) == 1: # single objective optimization + if len(self._optPointHistory[traj]) < 2: + return False + o1, _ = self._optPointHistory[traj][-1] + obj = o1[self._objectiveVar[0]] + converged = (obj == self._convergenceCriteria['objective']) + self.raiseADebug(self.convFormat.format(name='objective', + conv=str(converged), + got=obj, + req=self._convergenceCriteria['objective'])) + else: # multi objective optimization + if len(self._optPointHistory[traj]) < 2: + return False + o1, _ = self._optPointHistory[traj][-1] + obj1 = o1[self._objectiveVar[0]] + obj2 = o1[self._objectiveVar[1]] + converged = (obj1 == self._convergenceCriteria['objective'] and obj2 == self._convergenceCriteria['objective']) + # JY: I stopped here. Codeline below needs to be revisited! 01/16/23 + # self.raiseADebug(self.convFormat.format(name='objective', + # conv=str(converged), + # got=obj1, + # req=self._convergenceCriteria['objective'])) return converged @@ -834,14 +1107,24 @@ def _updateConvergence(self, traj, new, old, acceptable): @ Out, converged, bool, True if converged on ANY criteria """ # NOTE we have multiple "if acceptable" trees here, as we need to update soln export regardless - if acceptable == 'accepted': - self.raiseADebug(f'Convergence Check for Trajectory {traj}:') - # check convergence - converged, convDict = self.checkConvergence(traj, new, old) - else: - converged = False - convDict = dict((var, False) for var in self._convergenceInfo[traj]) - self._convergenceInfo[traj].update(convDict) + if len(self._objectiveVar) == 1: # single-objective optimization + if acceptable == 'accepted': + self.raiseADebug(f'Convergence Check for Trajectory {traj}:') + # check convergence + converged, convDict = self.checkConvergence(traj, new, old) + else: + converged = False + convDict = dict((var, False) for var in self._convergenceInfo[traj]) + self._convergenceInfo[traj].update(convDict) + else: # multi-objective optimization + if acceptable == 'accepted': + self.raiseADebug(f'Convergence Check for Trajectory {traj}:') + # check convergence + converged, convDict = self.checkConvergence(traj, new, old) + else: + converged = False + convDict = dict((var, False) for var in self._convergenceInfo[traj]) + self._convergenceInfo[traj].update(convDict) return converged @@ -957,7 +1240,9 @@ def _addToSolutionExport(self, traj, rlz, acceptable): 'batchId': self.batchId, 'fitness': rlz['fitness'], 'AHDp': self.ahdp, - 'AHD': self.ahd} + 'AHD': self.ahd, + 'rank': 0 if len(self._objectiveVar) == 1 else rlz['rank'], + 'CD': 0 if len(self._objectiveVar) == 1 else rlz['CD']} for var, val in self.constants.items(): toAdd[var] = val diff --git a/ravenframework/Optimizers/Optimizer.py b/ravenframework/Optimizers/Optimizer.py index 517414d1dc..a3f99ed389 100644 --- a/ravenframework/Optimizers/Optimizer.py +++ b/ravenframework/Optimizers/Optimizer.py @@ -166,7 +166,7 @@ def __init__(self): self._cancelledTraj = {} # tracks cancelled trajectories, and reasons self._convergedTraj = {} # tracks converged trajectories, and values obtained self._numRepeatSamples = 1 # number of times to repeat sampling (e.g. denoising) - self._objectiveVar = None # objective variable for optimization + self._objectiveVar = [] # objective variable for optimization self._initialValuesFromInput = None # initial variable values from inputs, list of dicts (used to reset optimizer when re-running workflow) self._initialValues = None # initial variable values (trajectory starting locations), list of dicts self._variableBounds = None # dictionary of upper/lower bounds for each variable (may be inf?) @@ -247,8 +247,13 @@ def handleInput(self, paramInput): @ Out, None """ # the reading of variables (dist or func) and constants already happened in _readMoreXMLbase in Sampler - # objective var - self._objectiveVar = paramInput.findFirst('objective').value + + if bool(paramInput.findAll('GAparams')): + rawObjectiveVar = paramInput.findFirst('objective').value + self._objectiveVar = [rawObjectiveVar.split(",")[i] for i in range(0,len(rawObjectiveVar.split(","))) ] + + else: + self._objectiveVar = paramInput.findFirst('objective').value # sampler init # self.readSamplerInit() can't be used because it requires the xml node diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index d85181657c..39654feb70 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -301,7 +301,8 @@ def localFinalizeActualSampling(self, jobObject, model, myInput): # the sign of the objective function is flipped in case we do maximization # so get the correct-signed value into the realization if self._minMax == 'max': - rlz[self._objectiveVar] *= -1 + for i in range(len(self._objectiveVar)): + rlz[self._objectiveVar[i]] *= -1 # TODO FIXME let normalizeData work on an xr.DataSet (batch) not just a dictionary! rlz = self.normalizeData(rlz) self._useRealization(info, rlz) @@ -312,57 +313,145 @@ def finalizeSampler(self, failedRuns): @ In, failedRuns, list, runs that failed as part of this sampling @ Out, None """ - # get and print the best trajectory obtained - bestValue = None - bestTraj = None - bestPoint = None - s = -1 if self._minMax == 'max' else 1 - # check converged trajectories - self.raiseAMessage('*' * 80) - self.raiseAMessage('Optimizer Final Results:') - self.raiseADebug('') - self.raiseADebug(' - Trajectory Results:') - self.raiseADebug(' TRAJ STATUS VALUE') - statusTemplate = ' {traj:2d} {status:^11s} {val: 1.3e}' - # print cancelled traj - for traj, info in self._cancelledTraj.items(): - val = info['value'] - status = info['reason'] - self.raiseADebug(statusTemplate.format(status=status, traj=traj, val=s * val)) - # check converged traj - for traj, info in self._convergedTraj.items(): + if len(self._objectiveVar) == 1: + # get and print the best trajectory obtained + bestValue = None + bestTraj = None + bestPoint = None + s = -1 if self._minMax == 'max' else 1 + # check converged trajectories + self.raiseAMessage('*' * 80) + self.raiseAMessage('Optimizer Final Results:') + self.raiseADebug('') + self.raiseADebug(' - Trajectory Results:') + self.raiseADebug(' TRAJ STATUS VALUE') + statusTemplate = ' {traj:2d} {status:^11s} {val: 1.3e}' + # print cancelled traj + for traj, info in self._cancelledTraj.items(): + val = info['value'] + status = info['reason'] + self.raiseADebug(statusTemplate.format(status=status, traj=traj, val=s * val)) + # check converged traj + for traj, info in self._convergedTraj.items(): + opt = self._optPointHistory[traj][-1][0] + val = info['value'] + self.raiseADebug(statusTemplate.format(status='converged', traj=traj, val=s * val)) + if bestValue is None or val < bestValue: + bestTraj = traj + bestValue = val + # further check active unfinished trajectories + # FIXME why should there be any active, unfinished trajectories when we're cleaning up sampler? + traj = 0 # FIXME why only 0?? what if it's other trajectories that are active and unfinished? + # sanity check: if there's no history (we never got any answers) then report rather than crash + if len(self._optPointHistory[traj]) == 0: + self.raiseAnError(RuntimeError, f'There is no optimization history for traj {traj}! ' + + 'Perhaps the Model failed?') opt = self._optPointHistory[traj][-1][0] - val = info['value'] - self.raiseADebug(statusTemplate.format(status='converged', traj=traj, val=s * val)) + val = opt[self._objectiveVar[0]] + self.raiseADebug(statusTemplate.format(status='active', traj=traj, val=s * val)) if bestValue is None or val < bestValue: - bestTraj = traj bestValue = val - # further check active unfinished trajectories - # FIXME why should there be any active, unfinished trajectories when we're cleaning up sampler? - traj = 0 # FIXME why only 0?? what if it's other trajectories that are active and unfinished? - # sanity check: if there's no history (we never got any answers) then report than rather than crash - if len(self._optPointHistory[traj]) == 0: - self.raiseAnError(RuntimeError, f'There is no optimization history for traj {traj}! ' + - 'Perhaps the Model failed?') - opt = self._optPointHistory[traj][-1][0] - val = opt[self._objectiveVar] - self.raiseADebug(statusTemplate.format(status='active', traj=traj, val=s * val)) - if bestValue is None or val < bestValue: - bestValue = val - bestTraj = traj - bestOpt = self.denormalizeData(self._optPointHistory[bestTraj][-1][0]) - bestPoint = dict((var, bestOpt[var]) for var in self.toBeSampled) - self.raiseADebug('') - self.raiseAMessage(' - Final Optimal Point:') - finalTemplate = ' {name:^20s} {value: 1.3e}' - finalTemplateInt = ' {name:^20s} {value: 3d}' - self.raiseAMessage(finalTemplate.format(name=self._objectiveVar, value=s * bestValue)) - self.raiseAMessage(finalTemplateInt.format(name='trajID', value=bestTraj)) - for var, val in bestPoint.items(): - self.raiseAMessage(finalTemplate.format(name=var, value=val)) - self.raiseAMessage('*' * 80) - # write final best solution to soln export - self._updateSolutionExport(bestTraj, self.normalizeData(bestOpt), 'final', 'None') + bestTraj = traj + bestOpt = self.denormalizeData(self._optPointHistory[bestTraj][-1][0]) + bestPoint = dict((var, bestOpt[var]) for var in self.toBeSampled) + self.raiseADebug('') + self.raiseAMessage(' - Final Optimal Point:') + finalTemplate = ' {name:^20s} {value: 1.3e}' + finalTemplateInt = ' {name:^20s} {value: 3d}' + self.raiseAMessage(finalTemplate.format(name=self._objectiveVar[0], value=s * bestValue)) + self.raiseAMessage(finalTemplateInt.format(name='trajID', value=bestTraj)) + for var, val in bestPoint.items(): + self.raiseAMessage(finalTemplate.format(name=var, value=val)) + self.raiseAMessage('*' * 80) + # write final best solution to soln export + self._updateSolutionExport(bestTraj, self.normalizeData(bestOpt), 'final', 'None') + else: + # get and print the best trajectory obtained + bestValue = None + bestTraj = None + bestPoint = None + s = -1 if self._minMax == 'max' else 1 + # check converged trajectories + self.raiseAMessage('*' * 80) + self.raiseAMessage('Optimizer Final Results:') + self.raiseADebug('') + self.raiseADebug(' - Trajectory Results:') + self.raiseADebug(' TRAJ STATUS VALUE') + statusTemplate = ' {traj:2d} {status:^11s} {val: 1.3e}' + statusTemplate_multi = ' {traj:2d} {status:^11s} {val1: ^11s} {val2: ^11s}' + + # print cancelled traj + for traj, info in self._cancelledTraj.items(): + val = info['value'] + status = info['reason'] + self.raiseADebug(statusTemplate.format(status=status, traj=traj, val=s * val)) + # check converged traj + for traj, info in self._convergedTraj.items(): + opt = self._optPointHistory[traj][-1][0] + val = info['value'] + self.raiseADebug(statusTemplate.format(status='converged', traj=traj, val=s * val)) + if bestValue is None or val < bestValue: + bestTraj = traj + bestValue = val + # further check active unfinished trajectories + # FIXME why should there be any active, unfinished trajectories when we're cleaning up sampler? + traj = 0 # FIXME why only 0?? what if it's other trajectories that are active and unfinished? + # sanity check: if there's no history (we never got any answers) then report rather than crash + if len(self._optPointHistory[traj]) == 0: + self.raiseAnError(RuntimeError, f'There is no optimization history for traj {traj}! ' + + 'Perhaps the Model failed?') + + for i in range(len(self._optPointHistory[traj][-1][0]['obj1'])): + opt = self._optPointHistory[traj][-1][0] + key = list(opt.keys()) + val = [item[i] for item in opt.values()] + optElm = {key[a]: val[a] for a in range(len(key))} + optVal = [s*optElm[self._objectiveVar[b]] for b in range(len(self._objectiveVar))] + # self.raiseADebug(statusTemplate_multi.format(status='active', traj=traj, val1=val1, val2=val2)) + # bestValue_1 = val1 + # bestValue_2 = val2 + bestTraj = traj + bestOpt = self.denormalizeData(optElm) + bestPoint = dict((var, bestOpt[var]) for var in self.toBeSampled) + # self.raiseADebug('') + # self.raiseAMessage(' - Final Optimal Point:') + # finalTemplate = ' {name_1:^20s} {name_2:^20s} {value_1:^20s} {value_2:^20s}' + # finalTemplateInt = ' {name:^20s} {value: 3d}' + # self.raiseAMessage(finalTemplate.format(name_1=self._objectiveVar[0], name_2=self._objectiveVar[1], value_1=bestValue_1, value_2=bestValue_2)) + # self.raiseAMessage(finalTemplateInt.format(name='trajID', value=bestTraj)) + # JY: 23/01/20 two lines below are temperarily commented. If it is not needed, then will be deleted. + # for var, val in bestPoint.items(): + # self.raiseAMessage(finalTemplate.format(name=var, value=val)) + # self.raiseAMessage('*' * 80) + # write final best solution to soln export + self._updateSolutionExport(bestTraj, self.normalizeData(bestOpt), 'final', 'None') + + # ### Original start ### + # opt = self._optPointHistory[traj][-1][0] + # val = range(len(self._objectiveVar)) + # for i in range(len(val)): + # val[i] = [', '.join(map(str,(s*opt[self._objectiveVar[i]])))] + # # val1 = ', '.join(map(str,(s*opt[self._objectiveVar[0]]).tolist())) + # # val2 = ', '.join(map(str,(s*opt[self._objectiveVar[1]]).tolist())) + # # self.raiseADebug(statusTemplate_multi.format(status='active', traj=traj, val1=val1, val2=val2)) + # # bestValue_1 = val1 + # # bestValue_2 = val2 + # bestTraj = traj + # bestOpt = self.denormalizeData(self._optPointHistory[bestTraj][-1][0]) + # bestPoint = dict((var, bestOpt[var]) for var in self.toBeSampled) + # self.raiseADebug('') + # self.raiseAMessage(' - Final Optimal Point:') + # # finalTemplate = ' {name_1:^20s} {name_2:^20s} {value_1:^20s} {value_2:^20s}' + # # finalTemplateInt = ' {name:^20s} {value: 3d}' + # # self.raiseAMessage(finalTemplate.format(name_1=self._objectiveVar[0], name_2=self._objectiveVar[1], value_1=bestValue_1, value_2=bestValue_2)) + # # self.raiseAMessage(finalTemplateInt.format(name='trajID', value=bestTraj)) + # # JY: 23/01/20 two lines below are temperarily commented. If it is not needed, then will be deleted. + # # for var, val in bestPoint.items(): + # # self.raiseAMessage(finalTemplate.format(name=var, value=val)) + # self.raiseAMessage('*' * 80) + # # write final best solution to soln export + # self._updateSolutionExport(bestTraj, self.normalizeData(bestOpt), 'final', 'None') + # ### Original end ### def flush(self): """ @@ -498,10 +587,10 @@ def _handleImplicitConstraints(self, previous): @ Out, accept, bool, whether point was satisfied implicit constraints """ normed = copy.deepcopy(previous) - oldVal = normed[self._objectiveVar] - normed.pop(self._objectiveVar, oldVal) + oldVal = normed[self._objectiveVar[0]] + normed.pop(self._objectiveVar[0], oldVal) denormed = self.denormalizeData(normed) - denormed[self._objectiveVar] = oldVal + denormed[self._objectiveVar[0]] = oldVal accept = self._checkImpFunctionalConstraints(denormed) return accept @@ -569,9 +658,9 @@ def _resolveNewOptPoint(self, traj, rlz, optVal, info): # TODO could we ever use old rerun gradients to inform the gradient direction as well? self._rerunsSinceAccept[traj] += 1 N = self._rerunsSinceAccept[traj] + 1 - oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar] + oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar[0]] newAvg = ((N-1)*oldVal + optVal) / N - self._optPointHistory[traj][-1][0][self._objectiveVar] = newAvg + self._optPointHistory[traj][-1][0][self._objectiveVar[0]] = newAvg else: self.raiseAnError(f'Unrecognized acceptability: "{acceptable}"') @@ -635,10 +724,17 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): 'rejectReason': rejectReason }) # optimal point input and output spaces - objValue = rlz[self._objectiveVar] - if self._minMax == 'max': - objValue *= -1 - toExport[self._objectiveVar] = objValue + if len(self._objectiveVar) == 1: # Single Objective Optimization + objValue = rlz[self._objectiveVar[0]] + if self._minMax == 'max': + objValue *= -1 + toExport[self._objectiveVar[0]] = objValue + else: # Multi Objective Optimization + for i in range(len(self._objectiveVar)): + objValue = rlz[self._objectiveVar[i]] + if self._minMax == 'max': + objValue *= -1 + toExport[self._objectiveVar[i]] = objValue toExport.update(self.denormalizeData(dict((var, rlz[var]) for var in self.toBeSampled))) # constants and functions toExport.update(self.constants) diff --git a/ravenframework/Optimizers/fitness/fitness.py b/ravenframework/Optimizers/fitness/fitness.py index 53a27ff1c6..6158537cb1 100644 --- a/ravenframework/Optimizers/fitness/fitness.py +++ b/ravenframework/Optimizers/fitness/fitness.py @@ -18,6 +18,9 @@ Created June,16,2020 @authors: Mohammad Abdo, Diego Mandelli, Andrea Alfonsi """ +# Internal Modules---------------------------------------------------------------------------------- +from ...utils import frontUtils + # External Imports import numpy as np import xarray as xr @@ -75,6 +78,36 @@ def invLinear(rlz,**kwargs): coords={'chromosome': np.arange(len(data))}) return fitness +def rank_crowding(rlz,**kwargs): + r""" + Multiobjective optimization using NSGA-II requires the rank and crowding distance values to the objective function + + @ In, rlz, xr.Dataset, containing the evaluation of a certain + set of individuals (can be the initial population for the very first iteration, + or a population of offsprings) + @ In, kwargs, dict, dictionary of parameters for this rank_crowding method: + objVar, string, the names of the objective variables + @ Out, offSpringRank, xr.DataArray, the rank of the given objective corresponding to a specific chromosome. + offSpringCD, xr.DataArray, the crowding distance of the given objective corresponding to a specific chromosome. + """ + objectiveVal = [] + for i in range(len(kwargs['objVals'])): + objectiveVal.append(list(np.atleast_1d(rlz[kwargs['objVals'][i]].data))) + + offspringObjsVals = [list(ele) for ele in list(zip(*objectiveVal))] + + offSpringRank = frontUtils.rankNonDominatedFrontiers(np.array(offspringObjsVals)) + offSpringRank = xr.DataArray(offSpringRank, + dims=['rank'], + coords={'rank': np.arange(np.shape(offSpringRank)[0])}) + + offSpringCD = frontUtils.crowdingDistance(rank=offSpringRank, popSize=len(offSpringRank), objectives=np.array(offspringObjsVals)) + offSpringCD = xr.DataArray(offSpringCD, + dims=['CrowdingDistance'], + coords={'CrowdingDistance': np.arange(np.shape(offSpringCD)[0])}) + + return offSpringRank, offSpringCD + def feasibleFirst(rlz,**kwargs): r""" Efficient Parameter-less Feasible First Penalty Fitness method @@ -166,6 +199,7 @@ def logistic(rlz,**kwargs): __fitness['invLinear'] = invLinear __fitness['logistic'] = logistic __fitness['feasibleFirst'] = feasibleFirst +__fitness['rank_crowding'] = rank_crowding def returnInstance(cls, name): diff --git a/ravenframework/Optimizers/mutators/mutators.py b/ravenframework/Optimizers/mutators/mutators.py index 11d0aec836..d5e58e4661 100644 --- a/ravenframework/Optimizers/mutators/mutators.py +++ b/ravenframework/Optimizers/mutators/mutators.py @@ -33,14 +33,19 @@ def swapMutator(offSprings, distDict, **kwargs): E.g.: child=[a,b,c,d,e] --> b and d are selected --> child = [a,d,c,b,e] @ In, offSprings, xr.DataArray, children resulting from the crossover process - @ In, distDict, dict, dictionary containing distribution associated with each gene @ In, kwargs, dict, dictionary of parameters for this mutation method: locs, list, the 2 locations of the genes to be swapped mutationProb, float, probability that governs the mutation process, i.e., if prob < random number, then the mutation will occur variables, list, variables names. @ Out, children, xr.DataArray, the mutated chromosome, i.e., the child. """ - loc1,loc2 = locationsGenerator(offSprings, kwargs['locs']) + if kwargs['locs'] == None: + locs = list(set(randomUtils.randomChoice(list(np.arange(offSprings.data.shape[1])),size=2,replace=False))) + loc1 = np.minimum(locs[0], locs[1]) + loc2 = np.maximum(locs[0], locs[1]) + else: + loc1 = np.minimum(kwargs['locs'][0], kwargs['locs'][1]) + loc2 = np.maximum(kwargs['locs'][0], kwargs['locs'][1]) # initializing children children = xr.DataArray(np.zeros((np.shape(offSprings))), @@ -64,7 +69,6 @@ def scrambleMutator(offSprings, distDict, **kwargs): This method performs the scramble mutator. For each child, a subset of genes is chosen and their values are shuffled randomly. @ In, offSprings, xr.DataArray, offsprings after crossover - @ In, distDict, dict, dictionary containing distribution associated with each gene @ In, kwargs, dict, dictionary of parameters for this mutation method: chromosome, numpy.array, the chromosome that will mutate to the new child locs, list, the locations of the genes to be randomly scrambled @@ -72,7 +76,12 @@ def scrambleMutator(offSprings, distDict, **kwargs): variables, list, variables names. @ Out, child, np.array, the mutated chromosome, i.e., the child. """ - loc1,loc2 = locationsGenerator(offSprings, kwargs['locs']) + if kwargs['locs'] == None: + locs = list(set(randomUtils.randomChoice(list(np.arange(offSprings.data.shape[1])),size=2,replace=False))) + locs.sort() + else: + locs = [kwargs['locs'][0], kwargs['locs'][1]] + locs.sort() # initializing children children = xr.DataArray(np.zeros((np.shape(offSprings))), @@ -85,9 +94,9 @@ def scrambleMutator(offSprings, distDict, **kwargs): children[i,j] = distDict[offSprings[i].coords['Gene'].values[j]].cdf(float(offSprings[i,j].values)) for i in range(np.shape(offSprings)[0]): - for ind,element in enumerate([loc1,loc2]): + for ind,element in enumerate(locs): if randomUtils.random(dim=1,samples=1)< kwargs['mutationProb']: - children[i,loc1:loc2+1] = randomUtils.randomPermutation(list(children.data[i,loc1:loc2+1]),None) + children[i,locs[0]:locs[-1]+1] = randomUtils.randomPermutation(list(children.data[i,locs[0]:locs[-1]+1]),None) for i in range(np.shape(offSprings)[0]): for j in range(np.shape(offSprings)[1]): @@ -102,7 +111,6 @@ def bitFlipMutator(offSprings, distDict, **kwargs): The gene to be flipped is completely random. The new value of the flipped gene is is completely random. @ In, offSprings, xr.DataArray, children resulting from the crossover process - @ In, distDict, dict, dictionary containing distribution associated with each gene @ In, kwargs, dict, dictionary of parameters for this mutation method: mutationProb, float, probability that governs the mutation process, i.e., if prob < random number, then the mutation will occur @ Out, offSprings, xr.DataArray, children resulting from the crossover process @@ -128,7 +136,6 @@ def randomMutator(offSprings, distDict, **kwargs): """ This method is designed to randomly mutate a single gene in each chromosome with probability = mutationProb. @ In, offSprings, xr.DataArray, children resulting from the crossover process - @ In, distDict, dict, dictionary containing distribution associated with each gene @ In, kwargs, dict, dictionary of parameters for this mutation method: mutationProb, float, probability that governs the mutation process, i.e., if prob < random number, then the mutation will occur @ Out, offSprings, xr.DataArray, children resulting from the crossover process @@ -155,13 +162,18 @@ def inversionMutator(offSprings, distDict, **kwargs): E.g. given chromosome C = [0,1,2,3,4,5,6,7,8,9] and sampled locL=2 locU=6; New chromosome C' = [0,1,6,5,4,3,2,7,8,9] @ In, offSprings, xr.DataArray, children resulting from the crossover process - @ In, distDict, dict, dictionary containing distribution associated with each gene @ In, kwargs, dict, dictionary of parameters for this mutation method: mutationProb, float, probability that governs the mutation process, i.e., if prob < random number, then the mutation will occur @ Out, offSprings, xr.DataArray, children resulting from the crossover process """ # sample gene locations: i.e., determine locL and locU - locL,locU = locationsGenerator(offSprings, kwargs['locs']) + if kwargs['locs'] == None: + locs = list(set(randomUtils.randomChoice(list(np.arange(offSprings.data.shape[1])),size=2,replace=False))) + locL = np.minimum(locs[0], locs[1]) + locU = np.maximum(locs[0], locs[1]) + else: + locL = np.minimum(kwargs['locs'][0], kwargs['locs'][1]) + locU = np.maximum(kwargs['locs'][0], kwargs['locs'][1]) for child in offSprings: # the mutation is performed for each child independently @@ -170,7 +182,7 @@ def inversionMutator(offSprings, distDict, **kwargs): seq = np.arange(locL,locU+1) allElems = [] for i,elem in enumerate(seq): - allElems.append(distDict[child.coords['Gene'].values[i]].cdf(float(child[elem].values))) + allElems.append(distDict[child.coords['Gene'].values[i]].cdf(float(child[elem].values))) mirrSeq = allElems[::-1] mirrElems = [] @@ -181,23 +193,6 @@ def inversionMutator(offSprings, distDict, **kwargs): return offSprings -def locationsGenerator(offSprings,locs): - """ - Methods designed to process the locations for the mutators. These locations can be either user specified or - randomly generated. - @ In, offSprings, xr.DataArray, children resulting from the crossover process - @ In, locs, list, the two locations of the genes to be swapped - @ Out, loc1, loc2, int, the two ordered processed locations required by the mutators - """ - if locs == None: - locs = list(set(randomUtils.randomChoice(list(np.arange(offSprings.data.shape[1])),size=2,replace=False))) - loc1 = np.minimum(locs[0], locs[1]) - loc2 = np.maximum(locs[0], locs[1]) - else: - loc1 = np.minimum(locs[0], locs[1]) - loc2 = np.maximum(locs[0], locs[1]) - return loc1, loc2 - __mutators = {} __mutators['swapMutator'] = swapMutator __mutators['scrambleMutator'] = scrambleMutator diff --git a/ravenframework/Optimizers/parentSelectors/parentSelectors.py b/ravenframework/Optimizers/parentSelectors/parentSelectors.py index fc82522271..66bf37932f 100644 --- a/ravenframework/Optimizers/parentSelectors/parentSelectors.py +++ b/ravenframework/Optimizers/parentSelectors/parentSelectors.py @@ -78,6 +78,9 @@ def rouletteWheel(population,**kwargs): fitness = np.delete(fitness,counter,axis=0) return selectedParent +def countConstViolation(const): + return sum(1 for i in const if i < 0) + def tournamentSelection(population,**kwargs): """ Tournament Selection mechanism for parent selection @@ -88,21 +91,25 @@ def tournamentSelection(population,**kwargs): variables, list, variable names @ Out, newPopulation, xr.DataArray, selected parents, """ - fitness = kwargs['fitness'] + nParents= kwargs['nParents'] pop = population popSize = population.values.shape[0] - if 'rank' in kwargs: + if 'rank' in kwargs.keys(): # the key rank is used in multi-objective optimization where rank identifies which front the point belongs to rank = kwargs['rank'] + crowdDistance = kwargs['crowdDistance'] + constraintInfo = kwargs['constraint'] multiObjectiveRanking = True - matrixOperationRaw = np.zeros((popSize,3)) + matrixOperationRaw = np.zeros((popSize, 4)) matrixOperationRaw[:,0] = np.transpose(np.arange(popSize)) - matrixOperationRaw[:,1] = np.transpose(fitness.data) + matrixOperationRaw[:,1] = np.transpose(crowdDistance.data) matrixOperationRaw[:,2] = np.transpose(rank.data) - matrixOperation = np.zeros((popSize,3)) + matrixOperationRaw[:,3] = np.transpose(constraintInfo.data) + matrixOperation = np.zeros((popSize,len(matrixOperationRaw[0]))) else: + fitness = kwargs['fitness'] multiObjectiveRanking = False matrixOperationRaw = np.zeros((popSize,2)) matrixOperationRaw[:,0] = np.transpose(np.arange(popSize)) @@ -118,30 +125,32 @@ def tournamentSelection(population,**kwargs): for idx, val in enumerate(indexesShuffled): matrixOperation[idx,:] = matrixOperationRaw[val,:] - selectedParent = xr.DataArray( - np.zeros((nParents,np.shape(pop)[1])), - dims=['chromosome','Gene'], - coords={'chromosome':np.arange(nParents), - 'Gene': kwargs['variables']}) + selectedParent = xr.DataArray(np.zeros((nParents,np.shape(pop)[1])), + dims=['chromosome','Gene'], + coords={'chromosome':np.arange(nParents), + 'Gene': kwargs['variables']}) - if not multiObjectiveRanking: # single-objective implementation of tournamentSelection + if not multiObjectiveRanking: # single-objective implementation of tournamentSelection for i in range(nParents): if matrixOperation[2*i,1] > matrixOperation[2*i+1,1]: index = int(matrixOperation[2*i,0]) else: index = int(matrixOperation[2*i+1,0]) selectedParent[i,:] = pop.values[index,:] - else: # multi-objective implementation of tournamentSelection - for i in range(nParents-1): - if matrixOperation[2*i,2] > matrixOperation[2*i+1,2]: - index = int(matrixOperation[i,0]) - elif matrixOperation[2*i,2] < matrixOperation[2*i+1,2]: - index = int(matrixOperation[i+1,0]) - else: # same rank case - if matrixOperation[2*i,1] > matrixOperation[2*i+1,1]: - index = int(matrixOperation[i,0]) - else: - index = int(matrixOperation[i+1,0]) + else: # multi-objective implementation of tournamentSelection + for i in range(nParents): + if matrixOperation[2*i,3] > matrixOperation[2*i+1,3]: index = int(matrixOperation[2*i+1,0]) + elif matrixOperation[2*i,3] < matrixOperation[2*i+1,3]: index = int(matrixOperation[2*i,0]) + elif matrixOperation[2*i,3] == matrixOperation[2*i+1,3]: # if same number of constraints violations + if matrixOperation[2*i,2] > matrixOperation[2*i+1,2]: + index = int(matrixOperation[2*i+1,0]) + elif matrixOperation[2*i,2] < matrixOperation[2*i+1,2]: + index = int(matrixOperation[2*i,0]) + else: # same number of constraints and same rank case + if matrixOperation[2*i,1] > matrixOperation[2*i+1,1]: + index = int(matrixOperation[2*i,0]) + else: + index = int(matrixOperation[2*i+1,0]) selectedParent[i,:] = pop.values[index,:] return selectedParent diff --git a/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py b/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py index 1b754af494..6da9f67071 100644 --- a/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py +++ b/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py @@ -24,6 +24,7 @@ import numpy as np import xarray as xr +from ravenframework.utils import frontUtils # @profile def ageBased(newRlz,**kwargs): @@ -80,7 +81,7 @@ def fitnessBased(newRlz,**kwargs): It combines the parents and children/offsprings then keeps the fittest individuals to revert to the same population size. @ In, newRlz, xr.DataSet, containing either a single realization, or a batch of realizations. - @ In, kwargs, dict, dictionary of parameters for this mutation method: + @ In, kwargs, dict, dictionary of parameters for this survivor slection method: age, list, ages of each chromosome in the population of the previous generation offSpringsFitness, xr.DataArray, fitness of each new child, i.e., np.shape(offSpringsFitness) = nChildren x nGenes variables @@ -116,9 +117,9 @@ def fitnessBased(newRlz,**kwargs): newAge = sortedAgeT[:-len(offSprings)] newPopulationArray = xr.DataArray(newPopulationSorted, - dims=['chromosome','Gene'], - coords={'chromosome':np.arange(np.shape(newPopulationSorted)[0]), - 'Gene': kwargs['variables']}) + dims=['chromosome','Gene'], + coords={'chromosome':np.arange(np.shape(newPopulationSorted)[0]), + 'Gene': kwargs['variables']}) newFitness = xr.DataArray(newFitness, dims=['chromosome'], coords={'chromosome':np.arange(np.shape(newFitness)[0])}) @@ -126,9 +127,98 @@ def fitnessBased(newRlz,**kwargs): #return newPopulationArray,newFitness,newAge return newPopulationArray,newFitness,newAge,kwargs['popObjectiveVal'] +# @profile +def rankNcrowdingBased(offsprings, **kwargs): + """ + rankNcrowdingBased survivorSelection mechanism for new generation selection + It combines the parents and children/offsprings then calculates their rank and crowding distance. + After having ranks and crowding distance, it keeps the lowest ranks (and highest crowding distance if indivisuals have same rank. + @ In, newRlz, xr.DataSet, containing either a single realization, or a batch of realizations. + @ In, kwargs, dict, dictionary of parameters for this survivor slection method: + variables + population + @ Out, newPopulation, xr.DataArray, newPopulation for the new generation, i.e. np.shape(newPopulation) = populationSize x nGenes. + @ Out, newRank, xr.DataArray, rank of each chromosome in the new population + @ Out, newCD, xr.DataArray, crowding distance of each chromosome in the new population. + """ + popSize = np.shape(kwargs['population'])[0] + if ('age' not in kwargs.keys() or kwargs['age'] == None): + popAge = [0]*popSize + else: + popAge = kwargs['age'] + + population = np.atleast_2d(kwargs['population'].data) + offSprings = np.atleast_2d(offsprings[kwargs['variables']].to_array().transpose().data) + popObjectiveVal = kwargs['popObjectiveVal'] + offObjectiveVal = kwargs['offObjectiveVal'] + popConst = kwargs['popConst'].data + offConst = kwargs['offConst'].data + popConstV = kwargs['popConstV'].data + offConstV = kwargs['offConstV'].data + + newConstMerged = np.append(popConst, offConst) + newConstVMerged = np.array(popConstV.tolist() + offConstV.tolist()) + + newObjectivesMerged = np.array([i + j for i, j in zip(popObjectiveVal, offObjectiveVal)]) + newObjectivesMerged_pair = [list(ele) for ele in list(zip(*newObjectivesMerged))] + + newPopRank = frontUtils.rankNonDominatedFrontiers(np.array(newObjectivesMerged_pair)) + newPopRank = xr.DataArray(newPopRank, + dims=['rank'], + coords={'rank': np.arange(np.shape(newPopRank)[0])}) + + newPopCD = frontUtils.crowdingDistance(rank=newPopRank, popSize=len(newPopRank), objectives=np.array(newObjectivesMerged_pair)) + newPopCD = xr.DataArray(newPopCD, + dims=['CrowdingDistance'], + coords={'CrowdingDistance': np.arange(np.shape(newPopCD)[0])}) + + newAge = list(map(lambda x:x+1, popAge)) + newPopulationMerged = np.concatenate([population,offSprings]) + newAge.extend([0]*len(offSprings)) + + sortedConst,sortedRank,sortedCD,sortedAge,sortedPopulation,sortedObjectives,sortedConstV = \ + zip(*[(x,y,z,i,j,k,a) for x,y,z,i,j,k,a in \ + sorted(zip(newConstMerged,newPopRank.data,newPopCD.data,newAge,newPopulationMerged.tolist(),newObjectivesMerged_pair,newConstVMerged),reverse=False,key=lambda x: (x[0], x[1], -x[2]))]) + sortedConstT,sortedRankT,sortedCDT,sortedAgeT,sortedPopulationT,sortedObjectivesT,sortedConstVT = \ + np.atleast_1d(list(sortedConst)),np.atleast_1d(list(sortedRank)),list(sortedCD),list(sortedAge),np.atleast_1d(list(sortedPopulation)),np.atleast_1d(list(sortedObjectives)),np.atleast_1d(list(sortedConstV)) + + newPopulation = sortedPopulationT[:-len(offSprings)] + newObjectives = sortedObjectivesT[:-len(offSprings)] + + newRank = frontUtils.rankNonDominatedFrontiers(newObjectives) + newRank = xr.DataArray(newRank, + dims=['rank'], + coords={'rank': np.arange(np.shape(newRank)[0])}) + + newObjectivesP = [list(ele) for ele in list(zip(*newObjectives))] + newCD = frontUtils.crowdingDistance(rank=newRank, popSize=len(newRank), objectives=newObjectives) + newCD = xr.DataArray(newCD, + dims=['CrowdingDistance'], + coords={'CrowdingDistance': np.arange(np.shape(newCD)[0])}) + + newAge = sortedAgeT[:-len(offSprings)] + newConst = sortedConstT[:-len(offSprings)] + newConstV = sortedConstVT[:-len(offSprings)] + + newPopulationArray = xr.DataArray(newPopulation, + dims=['chromosome','Gene'], + coords={'chromosome':np.arange(np.shape(newPopulation)[0]), + 'Gene': kwargs['variables']}) + newConst = xr.DataArray(newConst, + dims=['NumOfConstViolated'], + coords={'NumOfConstViolated':np.arange(np.shape(newConst)[0])}) + + newConstV = xr.DataArray(newConstV, + dims=['chromosome','ConstEvaluation'], + coords={'chromosome':np.arange(np.shape(newPopulation)[0]), + 'ConstEvaluation':np.arange(np.shape(newConstV)[1])}) + + return newPopulationArray,newRank,newAge,newCD,newObjectivesP,newConst,newConstV + __survivorSelectors = {} __survivorSelectors['ageBased'] = ageBased __survivorSelectors['fitnessBased'] = fitnessBased +__survivorSelectors['rankNcrowdingBased'] = rankNcrowdingBased def returnInstance(cls, name): """ diff --git a/ravenframework/utils/frontUtils.py b/ravenframework/utils/frontUtils.py index c63eca57fc..8bb0e11c72 100644 --- a/ravenframework/utils/frontUtils.py +++ b/ravenframework/utils/frontUtils.py @@ -44,6 +44,7 @@ def nonDominatedFrontier(data, returnMask, minMask=None): Reference: the following code has been adapted from https://stackoverflow.com/questions/32791911/fast-calculation-of-pareto-front-in-python """ + if minMask is None: pass elif minMask is not None and minMask.shape[0] != data.shape[1]: @@ -56,8 +57,8 @@ def nonDominatedFrontier(data, returnMask, minMask=None): isEfficient = np.arange(data.shape[0]) nPoints = data.shape[0] nextPointIndex = 0 - while nextPointIndex= 0, + so if: + 1) f(x,y) >= 0 then g = f + 2) f(x,y) >= a then g = f - a + 3) f(x,y) <= b then g = b - f + 4) f(x,y) = c then g = 1e-6 - abs((f(x,y) - c)) (equality constraint) + """ + g = eval(Input.name)(Input) + return g + + +def expConstr1(Input):#You are free to pick this name but it has to be similar to the one in the xml# + """ + Let's assume that the constraint is: + $ x3+x4 < 8 $ + then g the constraint evaluation function (which has to be > 0) is taken to be: + g = 8 - (x3+x4) + in this case if g(\vec(x)) < 0 then this x violates the constraint and vice versa + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 1 evaluation function + """ + g = 8 - Input.x3 - Input.x4 + return g + +def expConstr2(Input): + """ + Explicit Equality Constraint: + let's consider the constraint x1**2 + x2**2 = 25 + The way to write g is to use a very small number for instance, epsilon = 1e-12 + and then g = epsilon - abs(constraint) + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 2 evaluation function + """ + g = 1e-12 - abs(Input.x1**2 + Input.x2**2 - 25) + return g + +def expConstr3(Input): + """ + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 3 evaluation function + """ + g = 10 - Input.x3 - Input.x4 + return g + +def impConstr1(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 1 evaluation function + """ + g = 10 - Input.x1**2 - Input.obj + return g + +def impConstr2(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 2 evaluation function + """ + g = Input.x1**2 + Input.obj - 10 + return g + +def impConstr3(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 3 evaluation function + """ + g = 100 - Input.obj1 + return g \ No newline at end of file diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/myLocalSum_multi.py b/tests/framework/Optimizers/NSGAII/discrete/constrained/myLocalSum_multi.py new file mode 100644 index 0000000000..86ef17bdeb --- /dev/null +++ b/tests/framework/Optimizers/NSGAII/discrete/constrained/myLocalSum_multi.py @@ -0,0 +1,43 @@ +# 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. + +# @author: Mohammad Abdo (@Jimmy-INL) + +def evaluate(Inputs): + Sum = 0 + LocalSum1 = 0 + LocalSum2 = 0 + # for ind,var in enumerate(Inputs.keys()): + # # write the objective function here + # Sum += (ind + 1) * Inputs[var] + # if (ind == 1): + # LocalSum1 = Sum + # return Sum[:], LocalSum1[:] + for ind,var in enumerate(Inputs.keys()): + # write the objective function here + Sum += (ind + 1) * Inputs[var] + if (ind == 0) or (ind == 1): + LocalSum1 += (ind + 1) * Inputs[var] + if (ind == 2) or (ind == 3): + LocalSum2 += (ind + 1) * Inputs[var] + return Sum[:], LocalSum1[:], LocalSum2[:] + +def run(self,Inputs): + """ + RAVEN API + @ In, self, object, RAVEN container + @ In, Inputs, dict, additional inputs + @ Out, None + """ + self.obj1,self.obj2,self.obj3 = evaluate(Inputs) # make sure the name of the objective is consistent with obj1, obj2, obj3. From ab4315c884d4358b41f82ee6cead05acb77b203c Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Sun, 19 Feb 2023 13:14:27 -0700 Subject: [PATCH 02/48] Unnecessary changes in DataSet.py have been removed. --- ravenframework/DataObjects/DataSet.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/ravenframework/DataObjects/DataSet.py b/ravenframework/DataObjects/DataSet.py index a1c21f6f1d..aff28737f9 100644 --- a/ravenframework/DataObjects/DataSet.py +++ b/ravenframework/DataObjects/DataSet.py @@ -213,8 +213,9 @@ def addRealization(self, rlz): indexMap = dict((key, val) for key, val in indexMap[0].items() if key in self.getVars()) # [0] because everything is nested in a list by now, it seems # clean out entries that aren't desired try: - getVariables = self.getVars() - rlz = dict((var, rlz[var]) for var in getVariables + self.indexes) + rlz = dict((var, rlz[var]) for var in self.getVars() + self.indexes) + # getVariables = self.getVars() + # rlz = dict((var, rlz[var]) for var in getVariables + self.indexes) except KeyError as e: self.raiseAWarning('Variables provided:',rlz.keys()) self.raiseAnError(KeyError, f'Provided realization does not have all requisite values for object "{self.name}": "{e.args[0]}"') @@ -243,8 +244,9 @@ def addRealization(self, rlz): # This is because the cNDarray collector expects a LIST of realization, not a single realization. # Maybe the "append" method should be renamed to "extend" or changed to append one at a time. # set realizations as a list of realizations (which are ordered lists) - orderedVariables = self._orderedVars - newData = np.array(list(rlz[var] for var in orderedVariables)+[0.0], dtype=object) + newData = np.array(list(rlz[var] for var in self._orderedVars)+[0.0], dtype=object) + # orderedVariables = self._orderedVars + # newData = np.array(list(rlz[var] for var in orderedVariables)+[0.0], dtype=object) newData = newData[:-1] # if data storage isn't set up, set it up if self._collector is None: @@ -1967,8 +1969,10 @@ def _setDataTypes(self, rlz): """ getVariables = self.getVars() if self.types is None: - self.types = [None]*len(getVariables) - for v, name in enumerate(getVariables): + self.types = [None]*len(self.getVars()) + for v, name in enumerate(self.getVars()): + # self.types = [None]*len(getVariables) + # for v, name in enumerate(getVariables): val = rlz[name] self.types[v] = self._getCompatibleType(val) From 8b7f5d3b99dac73def7dd7bdfd58b54a26236ee4 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Sun, 19 Feb 2023 13:18:26 -0700 Subject: [PATCH 03/48] Unnecessary changes in DataSet.py have been removed. --- ravenframework/DataObjects/DataSet.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/ravenframework/DataObjects/DataSet.py b/ravenframework/DataObjects/DataSet.py index aff28737f9..6dad7c88ff 100644 --- a/ravenframework/DataObjects/DataSet.py +++ b/ravenframework/DataObjects/DataSet.py @@ -214,8 +214,6 @@ def addRealization(self, rlz): # clean out entries that aren't desired try: rlz = dict((var, rlz[var]) for var in self.getVars() + self.indexes) - # getVariables = self.getVars() - # rlz = dict((var, rlz[var]) for var in getVariables + self.indexes) except KeyError as e: self.raiseAWarning('Variables provided:',rlz.keys()) self.raiseAnError(KeyError, f'Provided realization does not have all requisite values for object "{self.name}": "{e.args[0]}"') @@ -245,8 +243,6 @@ def addRealization(self, rlz): # Maybe the "append" method should be renamed to "extend" or changed to append one at a time. # set realizations as a list of realizations (which are ordered lists) newData = np.array(list(rlz[var] for var in self._orderedVars)+[0.0], dtype=object) - # orderedVariables = self._orderedVars - # newData = np.array(list(rlz[var] for var in orderedVariables)+[0.0], dtype=object) newData = newData[:-1] # if data storage isn't set up, set it up if self._collector is None: @@ -1967,12 +1963,9 @@ def _setDataTypes(self, rlz): @ In, rlz, dict, standardized and formatted realization @ Out, None """ - getVariables = self.getVars() if self.types is None: self.types = [None]*len(self.getVars()) for v, name in enumerate(self.getVars()): - # self.types = [None]*len(getVariables) - # for v, name in enumerate(getVariables): val = rlz[name] self.types[v] = self._getCompatibleType(val) From 3fcde82ecfe166b53618c93f2adee23da423d464 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Wed, 22 Feb 2023 12:14:10 -0700 Subject: [PATCH 04/48] ZDT test is added. --- ravenframework/Optimizers/GeneticAlgorithm.py | 30 +++++++------- .../NSGAII/discrete/constrained/ZDT_model.py | 41 +++++++++++++++++++ 2 files changed, 57 insertions(+), 14 deletions(-) create mode 100644 tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index 9654c09dc5..838c8c79c4 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -540,8 +540,8 @@ def _useRealization(self, info, rlz): Fitness = [item for sublist in Fitness.tolist() for item in sublist] Fitness = xr.DataArray(Fitness, - dims=['NumOfConstraintViolated'], - coords={'NumOfConstraintViolated':np.arange(np.shape(Fitness)[0])}) + dims=['NumOfConstraintViolated'], + coords={'NumOfConstraintViolated':np.arange(np.shape(Fitness)[0])}) # 0.2@ n-1: Survivor selection(rlz) # update population container given obtained children @@ -595,16 +595,16 @@ def _useRealization(self, info, rlz): import matplotlib.pyplot as plt # JY: Visualization: all points - This code block needs to be either deleted or revisited. plt.plot(np.array(objs_vals)[:,0], np.array(objs_vals)[:,1],'*') - # plt.xlim(70,100) - # plt.ylim(5,20) # JY: Visualization: optimal points only - This code block needs to be either deleted or revisited. - plt.xlim(75,100) - plt.ylim(5,20) - plt.plot(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,0], - np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,1],'*') # plt.xlim(75,100) # plt.ylim(5,20) + plt.xlim(0,1) + plt.ylim(0,6) + plt.title(str('Iteration ' + str(self.counter-1))) + + plt.plot(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,0], + np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,1],'*') for i in range(len(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,0])): plt.text(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[i,0], np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[i,1], str(self.batchId-1)) @@ -829,8 +829,9 @@ def _resolveNewGenerationMulti(self, traj, rlz, info): bestRlz['fitness'] = self.multiBestFitness bestRlz['rank'] = self.multiBestRank bestRlz['CD'] = self.multiBestCD - for ind, consName in enumerate(self.multiBestConstraint.Constraint): - bestRlz['ConstraintEvaluation_'+consName.values.tolist()] = self.multiBestConstraint[ind].values + if len(self.multiBestConstraint) != 0: # No constraints + for ind, consName in enumerate(self.multiBestConstraint.Constraint): + bestRlz['ConstraintEvaluation_'+consName.values.tolist()] = self.multiBestConstraint[ind].values bestRlz.update(self.multiBestPoint) self._optPointHistory[traj].append((bestRlz, info)) elif acceptable == 'rejected': @@ -904,10 +905,11 @@ def _collectOptPointMulti(self, population, rank, CD, objectiveVal, constraints, for i in range(len(optConstraintsV)): optConstNew.append(optConstraintsV[i]) optConstNew = list(map(list, zip(*optConstNew))) - optConstNew = xr.DataArray(optConstNew, - dims=['Constraint','Evaluation'], - coords={'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)], - 'Evaluation':np.arange(np.shape(optConstNew)[1])}) + if (len(optConstNew)) != 0: + optConstNew = xr.DataArray(optConstNew, + dims=['Constraint','Evaluation'], + coords={'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)], + 'Evaluation':np.arange(np.shape(optConstNew)[1])}) self.multiBestPoint = optPointsDic self.multiBestFitness = optConstraints diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py b/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py new file mode 100644 index 0000000000..06433ea466 --- /dev/null +++ b/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py @@ -0,0 +1,41 @@ +# 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. + +# @author: Junyung Kim (@JunyungKim-INL) and Mohammad Abdo (@Jimmy-INL) + +import math + +def evaluate(Inputs): + Sum = 0 + obj1 = 0 + + for ind,var in enumerate(Inputs.keys()): + # write the objective function here + if (ind == 0) : + obj1 += Inputs[var] + if (ind != 0): + Sum += Inputs[var] + g = 1 + (9/len(Inputs.keys())*Sum ) + h = 1 - math.sqrt(obj1/g) + obj2 = g*h + return obj1[:], obj2[:] + +def run(self,Inputs): + """ + RAVEN API + @ In, self, object, RAVEN container + @ In, Inputs, dict, additional inputs + @ Out, None + """ + self.obj1,self.obj2 = evaluate(Inputs) # make sure the name of the objective is consistent with obj1, obj2, obj3. From 15debe4e9480662d149e3539da2cf0cc32b0f9e9 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Fri, 24 Feb 2023 12:14:31 -0700 Subject: [PATCH 05/48] Optimizer.py and RavenSampled.py are updated after having regression tests. Most of conflicts are resolved. --- ravenframework/Optimizers/Optimizer.py | 3 +- ravenframework/Optimizers/RavenSampled.py | 95 +++++++++---------- .../NSGAII/discrete/constrained/ZDT_model.py | 2 +- 3 files changed, 46 insertions(+), 54 deletions(-) diff --git a/ravenframework/Optimizers/Optimizer.py b/ravenframework/Optimizers/Optimizer.py index a3f99ed389..7621901b26 100644 --- a/ravenframework/Optimizers/Optimizer.py +++ b/ravenframework/Optimizers/Optimizer.py @@ -166,7 +166,8 @@ def __init__(self): self._cancelledTraj = {} # tracks cancelled trajectories, and reasons self._convergedTraj = {} # tracks converged trajectories, and values obtained self._numRepeatSamples = 1 # number of times to repeat sampling (e.g. denoising) - self._objectiveVar = [] # objective variable for optimization + # self._objectiveVar = [] # objective variable for optimization + self._objectiveVar = None # objective variable for optimization self._initialValuesFromInput = None # initial variable values from inputs, list of dicts (used to reset optimizer when re-running workflow) self._initialValues = None # initial variable values (trajectory starting locations), list of dicts self._variableBounds = None # dictionary of upper/lower bounds for each variable (may be inf?) diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 39654feb70..39a3d9e15d 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -301,8 +301,11 @@ def localFinalizeActualSampling(self, jobObject, model, myInput): # the sign of the objective function is flipped in case we do maximization # so get the correct-signed value into the realization if self._minMax == 'max': - for i in range(len(self._objectiveVar)): - rlz[self._objectiveVar[i]] *= -1 + if type(self._objectiveVar) == str: + rlz[self._objectiveVar] *= -1 + else: + for i in range(len(self._objectiveVar)): + rlz[self._objectiveVar[i]] *= -1 # TODO FIXME let normalizeData work on an xr.DataSet (batch) not just a dictionary! rlz = self.normalizeData(rlz) self._useRealization(info, rlz) @@ -401,57 +404,39 @@ def finalizeSampler(self, failedRuns): self.raiseAnError(RuntimeError, f'There is no optimization history for traj {traj}! ' + 'Perhaps the Model failed?') - for i in range(len(self._optPointHistory[traj][-1][0]['obj1'])): + if type(self._objectiveVar) == str: opt = self._optPointHistory[traj][-1][0] - key = list(opt.keys()) - val = [item[i] for item in opt.values()] - optElm = {key[a]: val[a] for a in range(len(key))} - optVal = [s*optElm[self._objectiveVar[b]] for b in range(len(self._objectiveVar))] - # self.raiseADebug(statusTemplate_multi.format(status='active', traj=traj, val1=val1, val2=val2)) - # bestValue_1 = val1 - # bestValue_2 = val2 - bestTraj = traj - bestOpt = self.denormalizeData(optElm) + val = opt[self._objectiveVar] + self.raiseADebug(statusTemplate.format(status='active', traj=traj, val=s * val)) + if bestValue is None or val < bestValue: + bestValue = val + bestTraj = traj + bestOpt = self.denormalizeData(self._optPointHistory[bestTraj][-1][0]) bestPoint = dict((var, bestOpt[var]) for var in self.toBeSampled) - # self.raiseADebug('') - # self.raiseAMessage(' - Final Optimal Point:') - # finalTemplate = ' {name_1:^20s} {name_2:^20s} {value_1:^20s} {value_2:^20s}' - # finalTemplateInt = ' {name:^20s} {value: 3d}' - # self.raiseAMessage(finalTemplate.format(name_1=self._objectiveVar[0], name_2=self._objectiveVar[1], value_1=bestValue_1, value_2=bestValue_2)) - # self.raiseAMessage(finalTemplateInt.format(name='trajID', value=bestTraj)) - # JY: 23/01/20 two lines below are temperarily commented. If it is not needed, then will be deleted. - # for var, val in bestPoint.items(): - # self.raiseAMessage(finalTemplate.format(name=var, value=val)) - # self.raiseAMessage('*' * 80) + self.raiseADebug('') + self.raiseAMessage(' - Final Optimal Point:') + finalTemplate = ' {name:^20s} {value: 1.3e}' + finalTemplateInt = ' {name:^20s} {value: 3d}' + self.raiseAMessage(finalTemplate.format(name=self._objectiveVar, value=s * bestValue)) + self.raiseAMessage(finalTemplateInt.format(name='trajID', value=bestTraj)) + for var, val in bestPoint.items(): + self.raiseAMessage(finalTemplate.format(name=var, value=val)) + self.raiseAMessage('*' * 80) # write final best solution to soln export self._updateSolutionExport(bestTraj, self.normalizeData(bestOpt), 'final', 'None') + else: + for i in range(len(self._optPointHistory[traj][-1][0][self._objectiveVar[0]])): + opt = self._optPointHistory[traj][-1][0] + key = list(opt.keys()) + val = [item[i] for item in opt.values()] + optElm = {key[a]: val[a] for a in range(len(key))} + optVal = [s*optElm[self._objectiveVar[b]] for b in range(len(self._objectiveVar))] + + bestTraj = traj + bestOpt = self.denormalizeData(optElm) + bestPoint = dict((var, bestOpt[var]) for var in self.toBeSampled) - # ### Original start ### - # opt = self._optPointHistory[traj][-1][0] - # val = range(len(self._objectiveVar)) - # for i in range(len(val)): - # val[i] = [', '.join(map(str,(s*opt[self._objectiveVar[i]])))] - # # val1 = ', '.join(map(str,(s*opt[self._objectiveVar[0]]).tolist())) - # # val2 = ', '.join(map(str,(s*opt[self._objectiveVar[1]]).tolist())) - # # self.raiseADebug(statusTemplate_multi.format(status='active', traj=traj, val1=val1, val2=val2)) - # # bestValue_1 = val1 - # # bestValue_2 = val2 - # bestTraj = traj - # bestOpt = self.denormalizeData(self._optPointHistory[bestTraj][-1][0]) - # bestPoint = dict((var, bestOpt[var]) for var in self.toBeSampled) - # self.raiseADebug('') - # self.raiseAMessage(' - Final Optimal Point:') - # # finalTemplate = ' {name_1:^20s} {name_2:^20s} {value_1:^20s} {value_2:^20s}' - # # finalTemplateInt = ' {name:^20s} {value: 3d}' - # # self.raiseAMessage(finalTemplate.format(name_1=self._objectiveVar[0], name_2=self._objectiveVar[1], value_1=bestValue_1, value_2=bestValue_2)) - # # self.raiseAMessage(finalTemplateInt.format(name='trajID', value=bestTraj)) - # # JY: 23/01/20 two lines below are temperarily commented. If it is not needed, then will be deleted. - # # for var, val in bestPoint.items(): - # # self.raiseAMessage(finalTemplate.format(name=var, value=val)) - # self.raiseAMessage('*' * 80) - # # write final best solution to soln export - # self._updateSolutionExport(bestTraj, self.normalizeData(bestOpt), 'final', 'None') - # ### Original end ### + self._updateSolutionExport(bestTraj, self.normalizeData(bestOpt), 'final', 'None') def flush(self): """ @@ -587,7 +572,10 @@ def _handleImplicitConstraints(self, previous): @ Out, accept, bool, whether point was satisfied implicit constraints """ normed = copy.deepcopy(previous) - oldVal = normed[self._objectiveVar[0]] + if type(self._objectiveVar) == str: + oldVal = normed[self._objectiveVar] + else: + oldVal = normed[self._objectiveVar[0]] normed.pop(self._objectiveVar[0], oldVal) denormed = self.denormalizeData(normed) denormed[self._objectiveVar[0]] = oldVal @@ -658,7 +646,10 @@ def _resolveNewOptPoint(self, traj, rlz, optVal, info): # TODO could we ever use old rerun gradients to inform the gradient direction as well? self._rerunsSinceAccept[traj] += 1 N = self._rerunsSinceAccept[traj] + 1 - oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar[0]] + if type(self._objectiveVar) == str: + oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar] + else: + oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar[0]] newAvg = ((N-1)*oldVal + optVal) / N self._optPointHistory[traj][-1][0][self._objectiveVar[0]] = newAvg else: @@ -724,8 +715,8 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): 'rejectReason': rejectReason }) # optimal point input and output spaces - if len(self._objectiveVar) == 1: # Single Objective Optimization - objValue = rlz[self._objectiveVar[0]] + if type(self._objectiveVar) == str: # Single Objective Optimization + objValue = rlz[self._objectiveVar] if self._minMax == 'max': objValue *= -1 toExport[self._objectiveVar[0]] = objValue diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py b/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py index 06433ea466..ca53da447d 100644 --- a/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py +++ b/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py @@ -38,4 +38,4 @@ def run(self,Inputs): @ In, Inputs, dict, additional inputs @ Out, None """ - self.obj1,self.obj2 = evaluate(Inputs) # make sure the name of the objective is consistent with obj1, obj2, obj3. + self.obj1,self.obj2 = evaluate(Inputs) From 64510df566b655faf5e04b42c781e019bc4e034d Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Fri, 24 Feb 2023 12:25:13 -0700 Subject: [PATCH 06/48] minor update on Optimizer.py --- ravenframework/Optimizers/Optimizer.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ravenframework/Optimizers/Optimizer.py b/ravenframework/Optimizers/Optimizer.py index 7621901b26..1cf5e2ae5e 100644 --- a/ravenframework/Optimizers/Optimizer.py +++ b/ravenframework/Optimizers/Optimizer.py @@ -166,7 +166,6 @@ def __init__(self): self._cancelledTraj = {} # tracks cancelled trajectories, and reasons self._convergedTraj = {} # tracks converged trajectories, and values obtained self._numRepeatSamples = 1 # number of times to repeat sampling (e.g. denoising) - # self._objectiveVar = [] # objective variable for optimization self._objectiveVar = None # objective variable for optimization self._initialValuesFromInput = None # initial variable values from inputs, list of dicts (used to reset optimizer when re-running workflow) self._initialValues = None # initial variable values (trajectory starting locations), list of dicts From b1f0c3f77e044e6ab3c21da4f3de2fa378c2eeac Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Fri, 10 Mar 2023 20:13:27 -0700 Subject: [PATCH 07/48] temporary fix, not the way I want --- ravenframework/Optimizers/GeneticAlgorithm.py | 3 ++- ravenframework/Optimizers/GradientDescent.py | 5 +++++ ravenframework/Optimizers/Optimizer.py | 12 +++--------- ravenframework/Optimizers/RavenSampled.py | 13 ++++++++----- ravenframework/Optimizers/SimulatedAnnealing.py | 5 +++++ 5 files changed, 23 insertions(+), 15 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index 838c8c79c4..d358e1934c 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -108,6 +108,7 @@ def __init__(self): self._penaltyCoeff = None self._fitnessInstance = None self._repairInstance = None + self._canHandleMultiObjective = True ########################## # Initialization Methods # @@ -451,7 +452,7 @@ def _useRealization(self, info, rlz): # 0.1 @ n-1: fitnessCalculation(rlz) # perform fitness calculation for newly obtained children (rlz) - if len(self._objectiveVar) == 1: # This is a single-objective Optimization case + if not self._canHandleMultiObjective or len(self._objectiveVar) == 1: # This is a single-objective Optimization case offSprings = datasetToDataArray(rlz, list(self.toBeSampled)) objectiveVal = list(np.atleast_1d(rlz[self._objectiveVar[0]].data)) diff --git a/ravenframework/Optimizers/GradientDescent.py b/ravenframework/Optimizers/GradientDescent.py index 452f579f4e..cd64cddc04 100644 --- a/ravenframework/Optimizers/GradientDescent.py +++ b/ravenframework/Optimizers/GradientDescent.py @@ -212,6 +212,7 @@ def __init__(self): self._followerProximity = 1e-2 # distance at which annihilation can start occurring, in ?normalized? space self._trajectoryFollowers = defaultdict(list) # map of trajectories to the trajectories following them self._functionalConstraintExplorationLimit = 500 # number of input-space explorations allowable for functional constraints + self._canHandleMultiObjective = False # Currently Gradient Descent cannot handle multiobjective optimization # __private # additional methods # register adaptive sample identification criteria @@ -338,6 +339,10 @@ def _useRealization(self, info, rlz): @ Out, None """ traj = info['traj'] + if not self._canHandleMultiObjective and len(self._objectiveVar) == 1: + self._objectiveVar = self._objectiveVar[0] + elif type(self._objectiveVar) == list: + self.raiseAnError(IOError, 'Gradient Descent does not support multiObjective optimization yet! objective variable must be a single variable for now!') optVal = rlz[self._objectiveVar] info['optVal'] = optVal purpose = info['purpose'] diff --git a/ravenframework/Optimizers/Optimizer.py b/ravenframework/Optimizers/Optimizer.py index 1cf5e2ae5e..011f6fd9d3 100644 --- a/ravenframework/Optimizers/Optimizer.py +++ b/ravenframework/Optimizers/Optimizer.py @@ -78,9 +78,9 @@ def getInputSpecification(cls): specs.description = 'Optimizers' # objective variable - specs.addSub(InputData.parameterInputFactory('objective', contentType=InputTypes.StringType, strictMode=True, + specs.addSub(InputData.parameterInputFactory('objective', contentType=InputTypes.StringListType, strictMode=True, printPriority=90, # more important than - descr=r"""Name of the response variable (or ``objective function'') that should be optimized + descr=r"""Name of the objective variable (or ``objective function'') that should be optimized (minimized or maximized).""")) # modify Sampler variable nodes @@ -247,13 +247,7 @@ def handleInput(self, paramInput): @ Out, None """ # the reading of variables (dist or func) and constants already happened in _readMoreXMLbase in Sampler - - if bool(paramInput.findAll('GAparams')): - rawObjectiveVar = paramInput.findFirst('objective').value - self._objectiveVar = [rawObjectiveVar.split(",")[i] for i in range(0,len(rawObjectiveVar.split(","))) ] - - else: - self._objectiveVar = paramInput.findFirst('objective').value + self._objectiveVar = paramInput.findFirst('objective').value # sampler init # self.readSamplerInit() can't be used because it requires the xml node diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 39a3d9e15d..7d761561f7 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -301,11 +301,14 @@ def localFinalizeActualSampling(self, jobObject, model, myInput): # the sign of the objective function is flipped in case we do maximization # so get the correct-signed value into the realization if self._minMax == 'max': - if type(self._objectiveVar) == str: + if not self._canHandleMultiObjective and len(self._objectiveVar) == 1: + self._objectiveVar = self._objectiveVar[0] rlz[self._objectiveVar] *= -1 - else: + elif type(self._objectiveVar) == list: for i in range(len(self._objectiveVar)): rlz[self._objectiveVar[i]] *= -1 + else: + rlz[self._objectiveVar] *= -1 # TODO FIXME let normalizeData work on an xr.DataSet (batch) not just a dictionary! rlz = self.normalizeData(rlz) self._useRealization(info, rlz) @@ -316,7 +319,7 @@ def finalizeSampler(self, failedRuns): @ In, failedRuns, list, runs that failed as part of this sampling @ Out, None """ - if len(self._objectiveVar) == 1: + if not self._canHandleMultiObjective or len(self._objectiveVar) == 1: # get and print the best trajectory obtained bestValue = None bestTraj = None @@ -350,7 +353,7 @@ def finalizeSampler(self, failedRuns): self.raiseAnError(RuntimeError, f'There is no optimization history for traj {traj}! ' + 'Perhaps the Model failed?') opt = self._optPointHistory[traj][-1][0] - val = opt[self._objectiveVar[0]] + val = opt[self._objectiveVar] self.raiseADebug(statusTemplate.format(status='active', traj=traj, val=s * val)) if bestValue is None or val < bestValue: bestValue = val @@ -719,7 +722,7 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): objValue = rlz[self._objectiveVar] if self._minMax == 'max': objValue *= -1 - toExport[self._objectiveVar[0]] = objValue + toExport[self._objectiveVar] = objValue else: # Multi Objective Optimization for i in range(len(self._objectiveVar)): objValue = rlz[self._objectiveVar[i]] diff --git a/ravenframework/Optimizers/SimulatedAnnealing.py b/ravenframework/Optimizers/SimulatedAnnealing.py index 03f1ba1445..ab47591812 100644 --- a/ravenframework/Optimizers/SimulatedAnnealing.py +++ b/ravenframework/Optimizers/SimulatedAnnealing.py @@ -190,6 +190,7 @@ def __init__(self): self._coolingMethod = None # initializing cooling method self._coolingParameters = {} # initializing the cooling schedule parameters self.info = {} + self._canHandleMultiObjective = False # Currently Simulated Annealing can only handle single objective def handleInput(self, paramInput): """ @@ -300,6 +301,10 @@ def _useRealization(self, info, rlz): @ Out, None """ traj = info['traj'] + if len(self._objectiveVar) == 1: + self._objectiveVar = self._objectiveVar[0] + elif type(self._objectiveVar) == list: + self.raiseAnError(IOError, 'Simulated Annealing does not support multiObjective yet! objective variable must be a single variable for now!') info['optVal'] = rlz[self._objectiveVar] self.incrementIteration(traj) self._resolveNewOptPoint(traj, rlz, rlz[self._objectiveVar], info) From 52389c389fbeedc582a3c7c49b8722dc14f251f1 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Mon, 13 Mar 2023 10:30:09 -0600 Subject: [PATCH 08/48] NSGA-II testing fiels (multiSum wConstratint and ZDT1) are added. --- ravenframework/Optimizers/RavenSampled.py | 4 +- .../MultiSumwConst/MinwoRepMultiObjective.xml | 160 ++++++++++ .../{ => MultiSumwConst}/myConstraints.py | 0 .../{ => MultiSumwConst}/myLocalSum_multi.py | 0 .../NSGAII/discrete/constrained/ZDT1/ZDT1.xml | 295 ++++++++++++++++++ .../constrained/{ => ZDT1}/ZDT_model.py | 0 6 files changed, 457 insertions(+), 2 deletions(-) create mode 100644 tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/MinwoRepMultiObjective.xml rename tests/framework/Optimizers/NSGAII/discrete/constrained/{ => MultiSumwConst}/myConstraints.py (100%) rename tests/framework/Optimizers/NSGAII/discrete/constrained/{ => MultiSumwConst}/myLocalSum_multi.py (100%) create mode 100644 tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT1.xml rename tests/framework/Optimizers/NSGAII/discrete/constrained/{ => ZDT1}/ZDT_model.py (100%) diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 39a3d9e15d..06fa1d628c 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -316,7 +316,7 @@ def finalizeSampler(self, failedRuns): @ In, failedRuns, list, runs that failed as part of this sampling @ Out, None """ - if len(self._objectiveVar) == 1: + if type(self._objectiveVar) == str: # get and print the best trajectory obtained bestValue = None bestTraj = None @@ -350,7 +350,7 @@ def finalizeSampler(self, failedRuns): self.raiseAnError(RuntimeError, f'There is no optimization history for traj {traj}! ' + 'Perhaps the Model failed?') opt = self._optPointHistory[traj][-1][0] - val = opt[self._objectiveVar[0]] + val = opt[self._objectiveVar] self.raiseADebug(statusTemplate.format(status='active', traj=traj, val=s * val)) if bestValue is None or val < bestValue: bestValue = val diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/MinwoRepMultiObjective.xml new file mode 100644 index 0000000000..a32463eb13 --- /dev/null +++ b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/MinwoRepMultiObjective.xml @@ -0,0 +1,160 @@ + + + + \raven\tests\framework\Optimizers\NSGAII\discrete\constrained\ + Junyung Kim, Mohammad Abdo + 2022-12-21 + + NSGA-II min-min test + + + + Multi_MinwoReplacement_Figure_720 + optimize,print + 2 + + + + + placeholder + myLocalSum + GAopt + opt_export + optOut + opt_export + + + opt_export + optOut + opt_export + optOut + + + + + + x1,x2,x3,x4,x5,x6,obj1,obj2 + + + + + + x1,x2,x3,x4,x5,x6 + + + x1,x2,x3,x4,x5,x6,obj1 + + + + + + 2 + 7 + withoutReplacement + + + + + + + 2 + 42 + every + min + + + + 10 + tournamentSelection + + + 0.7 + + + 0.7 + + + + + rankNcrowdingBased + + + 0.0 + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + obj1, obj2 + optOut + MC_samp + expConstr3 + impConstr3 + + + + + + + 10 + 050877 + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + + + + + + x1,x2,x3,x4,x5,x6 + obj1,obj2 + + + trajID + x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,ConstraintEvaluation_expConstr3, ConstraintEvaluation_impConstr3,fitness,accepted + + + + + + csv + optOut + + + csv + opt_export + trajID + + + diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/myConstraints.py b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py similarity index 100% rename from tests/framework/Optimizers/NSGAII/discrete/constrained/myConstraints.py rename to tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/myLocalSum_multi.py b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myLocalSum_multi.py similarity index 100% rename from tests/framework/Optimizers/NSGAII/discrete/constrained/myLocalSum_multi.py rename to tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myLocalSum_multi.py diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT1.xml b/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT1.xml new file mode 100644 index 0000000000..80ad0c28a7 --- /dev/null +++ b/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT1.xml @@ -0,0 +1,295 @@ + + + + raven\tests\framework\Optimizers\NSGAII\discrete\constrained\ + Junyung Kim, Mohammad Abdo + 2023-02-21 + + ZDT1 test using NSGA-II + + + + ZDT1_result_300iter_150Popu + optimize,print + 1 + + + + + placeholder + ZDT + GAopt + opt_export + optOut + opt_export + + + opt_export + optOut + opt_export + optOut + + + + + + x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,x21,x22,x23,x24,x25,x26,x27,x28,x29,x30,obj1,obj2 + + + + + + 0 + 1 + + + + + + + 300 + 42 + every + min + + + + 150 + tournamentSelection + + + 1.0 + + + 1.0 + + + + + rankNcrowdingBased + + + + 0.0 + + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + obj1, obj2 + optOut + MC_samp + + + + + + + 150 + 050877 + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + unifDist + + + + + + + + x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,x21,x22,x23,x24,x25,x26,x27,x28,x29,x30 + obj1,obj2 + + + trajID + x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,x21,x22,x23,x24,x25,x26,x27,x28,x29,x30,obj1,obj2,age,batchId,rank,CD,fitness,accepted + + + + + + csv + optOut + + + csv + opt_export + trajID + + + diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py b/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT_model.py similarity index 100% rename from tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT_model.py rename to tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT_model.py From 391b9c3f168a8cbe046e586220b873079ab8d03b Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Tue, 14 Mar 2023 09:28:26 -0600 Subject: [PATCH 09/48] moving models, xmls, and trying to resolve GD after converting objective to a list --- ravenframework/Optimizers/GeneticAlgorithm.py | 4 +- ravenframework/Optimizers/GradientDescent.py | 18 +- ravenframework/Optimizers/RavenSampled.py | 11 +- ravenframework/Optimizers/fitness/fitness.py | 2 + .../optimizing/myConstraints.py | 108 ++++++++++++ .../optimizing/myLocalSum_multi.py | 43 +++++ .../continuous/unconstrained/ZDT1.xml | 132 +++++++++++++++ .../constrained/MinwoRepMultiObjective.xml | 160 ++++++++++++++++++ .../unconstrained/ZDT1/opt_export_0.csv | 150 ++++++++++++++++ .../Multi_MinwoReplacement/opt_export_0.csv | 13 ++ .../Optimizers/GeneticAlgorithms/tests | 20 +++ 11 files changed, 644 insertions(+), 17 deletions(-) create mode 100644 tests/framework/AnalyticModels/optimizing/myConstraints.py create mode 100644 tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py create mode 100644 tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml create mode 100644 tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml create mode 100644 tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv create mode 100644 tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index d358e1934c..e321cc0441 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -1244,8 +1244,8 @@ def _addToSolutionExport(self, traj, rlz, acceptable): 'fitness': rlz['fitness'], 'AHDp': self.ahdp, 'AHD': self.ahd, - 'rank': 0 if len(self._objectiveVar) == 1 else rlz['rank'], - 'CD': 0 if len(self._objectiveVar) == 1 else rlz['CD']} + 'rank': 0 if ((type(self._objectiveVar) == list and len(self._objectiveVar) == 1) or type(self._objectiveVar) == str) else rlz['rank'], + 'CD': 0 if ((type(self._objectiveVar) == list and len(self._objectiveVar) == 1) or type(self._objectiveVar) == str) else rlz['CD']} for var, val in self.constants.items(): toAdd[var] = val diff --git a/ravenframework/Optimizers/GradientDescent.py b/ravenframework/Optimizers/GradientDescent.py index cd64cddc04..7a37205c28 100644 --- a/ravenframework/Optimizers/GradientDescent.py +++ b/ravenframework/Optimizers/GradientDescent.py @@ -339,11 +339,11 @@ def _useRealization(self, info, rlz): @ Out, None """ traj = info['traj'] - if not self._canHandleMultiObjective and len(self._objectiveVar) == 1: - self._objectiveVar = self._objectiveVar[0] - elif type(self._objectiveVar) == list: + # if not self._canHandleMultiObjective and len(self._objectiveVar) == 1: + # self._objectiveVar = self._objectiveVar[0] + if len(self._objectiveVar) > 1 and type(self._objectiveVar)==list: self.raiseAnError(IOError, 'Gradient Descent does not support multiObjective optimization yet! objective variable must be a single variable for now!') - optVal = rlz[self._objectiveVar] + optVal = rlz[self._objectiveVar[0]] info['optVal'] = optVal purpose = info['purpose'] if purpose.startswith('opt'): @@ -358,13 +358,13 @@ def _useRealization(self, info, rlz): gradMag, gradVersor, _ = self._gradientInstance.evaluate(opt, grads, gradInfos, - self._objectiveVar) + self._objectiveVar[0]) self.raiseADebug(' ... gradient calculated ...') self._gradHistory[traj].append((gradMag, gradVersor)) # get new step information try: newOpt, stepSize, stepInfo = self._stepInstance.step(opt, - objVar=self._objectiveVar, + objVar=self._objectiveVar[0], optHist=self._optPointHistory[traj], gradientHist=self._gradHistory[traj], prevStepSize=self._stepHistory[traj], @@ -383,7 +383,7 @@ def _useRealization(self, info, rlz): except NoConstraintResolutionFound: # we've tried everything, but we just can't hack it self.raiseAMessage(f'Optimizer "{self.name}" trajectory {traj} was unable to continue due to functional or boundary constraints.') - self._closeTrajectory(traj, 'converge', 'no constraint resolution', opt[self._objectiveVar]) + self._closeTrajectory(traj, 'converge', 'no constraint resolution', opt[self._objectiveVar[0]]) return # update values if modified by constraint handling @@ -603,7 +603,7 @@ def _checkAcceptability(self, traj, opt, optVal, info): # Check acceptability if self._optPointHistory[traj]: old, _ = self._optPointHistory[traj][-1] - oldVal = old[self._objectiveVar] + oldVal = old[self._objectiveVar[0]] # check if following another trajectory if self._terminateFollowers: following = self._stepInstance.trajIsFollowing(traj, self.denormalizeData(opt), info, @@ -820,7 +820,7 @@ def _checkConvObjective(self, traj): return False o1, _ = self._optPointHistory[traj][-1] o2, _ = self._optPointHistory[traj][-2] - delta = mathUtils.relativeDiff(o2[self._objectiveVar], o1[self._objectiveVar]) + delta = mathUtils.relativeDiff(o2[self._objectiveVar[0]], o1[self._objectiveVar[0]]) converged = abs(delta) < self._convergenceCriteria['objective'] self.raiseADebug(self.convFormat.format(name='objective', conv=str(converged), diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 7d761561f7..061824a8ee 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -302,8 +302,7 @@ def localFinalizeActualSampling(self, jobObject, model, myInput): # so get the correct-signed value into the realization if self._minMax == 'max': if not self._canHandleMultiObjective and len(self._objectiveVar) == 1: - self._objectiveVar = self._objectiveVar[0] - rlz[self._objectiveVar] *= -1 + rlz[self._objectiveVar[0]] *= -1 elif type(self._objectiveVar) == list: for i in range(len(self._objectiveVar)): rlz[self._objectiveVar[i]] *= -1 @@ -353,7 +352,7 @@ def finalizeSampler(self, failedRuns): self.raiseAnError(RuntimeError, f'There is no optimization history for traj {traj}! ' + 'Perhaps the Model failed?') opt = self._optPointHistory[traj][-1][0] - val = opt[self._objectiveVar] + val = opt[self._objectiveVar[0]] self.raiseADebug(statusTemplate.format(status='active', traj=traj, val=s * val)) if bestValue is None or val < bestValue: bestValue = val @@ -718,11 +717,11 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): 'rejectReason': rejectReason }) # optimal point input and output spaces - if type(self._objectiveVar) == str: # Single Objective Optimization - objValue = rlz[self._objectiveVar] + if len(self._objectiveVar) == 1: # Single Objective Optimization + objValue = rlz[self._objectiveVar[0]] if self._minMax == 'max': objValue *= -1 - toExport[self._objectiveVar] = objValue + toExport[self._objectiveVar[0]] = objValue else: # Multi Objective Optimization for i in range(len(self._objectiveVar)): objValue = rlz[self._objectiveVar[i]] diff --git a/ravenframework/Optimizers/fitness/fitness.py b/ravenframework/Optimizers/fitness/fitness.py index 6158537cb1..d2481a34b6 100644 --- a/ravenframework/Optimizers/fitness/fitness.py +++ b/ravenframework/Optimizers/fitness/fitness.py @@ -24,6 +24,7 @@ # External Imports import numpy as np import xarray as xr +import sys # Internal Imports # [MANDD] Note: the fitness function are bounded by 2 parameters: a and b @@ -102,6 +103,7 @@ def rank_crowding(rlz,**kwargs): coords={'rank': np.arange(np.shape(offSpringRank)[0])}) offSpringCD = frontUtils.crowdingDistance(rank=offSpringRank, popSize=len(offSpringRank), objectives=np.array(offspringObjsVals)) + offSpringCD[offSpringCD==np.inf] = sys.float_info.max offSpringCD = xr.DataArray(offSpringCD, dims=['CrowdingDistance'], coords={'CrowdingDistance': np.arange(np.shape(offSpringCD)[0])}) diff --git a/tests/framework/AnalyticModels/optimizing/myConstraints.py b/tests/framework/AnalyticModels/optimizing/myConstraints.py new file mode 100644 index 0000000000..bc704cfa57 --- /dev/null +++ b/tests/framework/AnalyticModels/optimizing/myConstraints.py @@ -0,0 +1,108 @@ +# 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. +# @ author: Mohammad Abdo (@Jimmy-INL) + +import numpy as np + +def constrain(Input):#Complete this: give the function the correct name# + """ + This function calls the explicit constraint whose name is passed through Input.name + the evaluation function g is negative if the explicit constraint is violated and positive otherwise. + This suits the constraint handling in the Genetic Algorithms, + but not the Gradient Descent as the latter expects True if the solution passes the constraint and False if it violates it. + @ In, Input, object, RAVEN container + @ Out, g, float, explicit constraint evaluation (negative if violated and positive otherwise) + """ + g = eval(Input.name)(Input) + return g + +def implicitConstraint(Input): + """ + Evaluates the implicit constraint function at a given point/solution ($\vec(x)$) + @ In, Input, object, RAVEN container + @ Out, g(inputs x1,x2,..,output or dependent variable), float, implicit constraint evaluation function + the way the constraint is designed is that + the constraint function has to be >= 0, + so if: + 1) f(x,y) >= 0 then g = f + 2) f(x,y) >= a then g = f - a + 3) f(x,y) <= b then g = b - f + 4) f(x,y) = c then g = 1e-6 - abs((f(x,y) - c)) (equality constraint) + """ + g = eval(Input.name)(Input) + return g + + +def expConstr1(Input):#You are free to pick this name but it has to be similar to the one in the xml# + """ + Let's assume that the constraint is: + $ x3+x4 < 8 $ + then g the constraint evaluation function (which has to be > 0) is taken to be: + g = 8 - (x3+x4) + in this case if g(\vec(x)) < 0 then this x violates the constraint and vice versa + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 1 evaluation function + """ + g = 8 - Input.x3 - Input.x4 + return g + +def expConstr2(Input): + """ + Explicit Equality Constraint: + let's consider the constraint x1**2 + x2**2 = 25 + The way to write g is to use a very small number for instance, epsilon = 1e-12 + and then g = epsilon - abs(constraint) + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 2 evaluation function + """ + g = 1e-12 - abs(Input.x1**2 + Input.x2**2 - 25) + return g + +def expConstr3(Input): + """ + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 3 evaluation function + """ + g = 10 - Input.x3 - Input.x4 + return g + +def impConstr1(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 1 evaluation function + """ + g = 10 - Input.x1**2 - Input.obj + return g + +def impConstr2(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 2 evaluation function + """ + g = Input.x1**2 + Input.obj - 10 + return g + +def impConstr3(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 3 evaluation function + """ + g = 100 - Input.obj1 + return g \ No newline at end of file diff --git a/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py b/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py new file mode 100644 index 0000000000..509f852fd1 --- /dev/null +++ b/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py @@ -0,0 +1,43 @@ +# 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. + +# @author: Mohammad Abdo (@Jimmy-INL) + +def evaluate(Inputs): + Sum = 0 + LocalSum1 = 0 + LocalSum2 = 0 + # for ind,var in enumerate(Inputs.keys()): + # # write the objective function here + # Sum += (ind + 1) * Inputs[var] + # if (ind == 1): + # LocalSum1 = Sum + # return Sum[:], LocalSum1[:] + for ind,var in enumerate(Inputs.keys()): + # write the objective function here + Sum += (ind + 1) * Inputs[var] + if (ind == 0) or (ind == 1): + LocalSum1 += (ind + 1) * Inputs[var] + if (ind == 2) or (ind == 3): + LocalSum2 += (ind + 1) * Inputs[var] + return Sum[:], LocalSum1[:], LocalSum2[:] + +def run(self,Inputs): + """ + RAVEN API + @ In, self, object, RAVEN container + @ In, Inputs, dict, additional inputs + @ Out, None + """ + self.obj1,self.obj2,self.obj3 = evaluate(Inputs) # make sure the name of the objective is consistent with obj1, obj2, obj3. diff --git a/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml b/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml new file mode 100644 index 0000000000..94d0caf169 --- /dev/null +++ b/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml @@ -0,0 +1,132 @@ + + + + raven/tests/framework/Optimizers/GA.MultiObjZDT1 + Junyung Kim + 2023-02-21 + + ZDT1 test using NSGA-II + + + + ZDT1 + optimize,print + 1 + + + + + placeholder + ZDT + GAopt + opt_export + optOut + opt_export + + + opt_export + optOut + opt_export + optOut + + + + + + x1,x2,x3,obj1,obj2 + + + + + + 0 + 1 + + + + + + + 15 + 42 + every + min + + + + 10 + tournamentSelection + + + 1.0 + + + 1.0 + + + + + rankNcrowdingBased + + + + 0.0 + + + + unifDist + + + unifDist + + + unifDist + + obj1, obj2 + optOut + MC_samp + + + + + + + 10 + 050877 + + + unifDist + + + unifDist + + + unifDist + + + + + + + + x1,x2,x3 + obj1,obj2 + + + trajID + x1,x2,x3,obj1,obj2,age,batchId,rank,CD,fitness,accepted + + + + + + csv + optOut + + + csv + opt_export + trajID + + + diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml new file mode 100644 index 0000000000..f3f6ce1208 --- /dev/null +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml @@ -0,0 +1,160 @@ + + + + raven/tests/framework/Optimizers/GA. + Junyung Kim + 2022-12-21 + + NSGA-II min-min test + + + + Multi_MinwoReplacement + optimize,print + 2 + + + + + placeholder + myLocalSum + GAopt + opt_export + optOut + opt_export + + + opt_export + optOut + opt_export + optOut + + + + + + x1,x2,x3,x4,x5,x6,obj1,obj2 + + + + + + x1,x2,x3,x4,x5,x6 + + + x1,x2,x3,x4,x5,x6,obj1 + + + + + + 2 + 7 + withoutReplacement + + + + + + + 2 + 42 + every + min + + + + 10 + tournamentSelection + + + 0.7 + + + 0.7 + + + + + rankNcrowdingBased + + + 0.0 + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + obj1, obj2 + optOut + MC_samp + expConstr3 + impConstr3 + + + + + + + 10 + 050877 + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + woRep_dist + + + + + + + + x1,x2,x3,x4,x5,x6 + obj1,obj2 + + + trajID + x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,ConstraintEvaluation_expConstr3, ConstraintEvaluation_impConstr3,fitness,accepted + + + + + + csv + optOut + + + csv + opt_export + trajID + + + diff --git a/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv b/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv new file mode 100644 index 0000000000..a4e2cdcdce --- /dev/null +++ b/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv @@ -0,0 +1,150 @@ +x1,x2,x3,obj1,obj2,age,batchId,rank,CD,fitness,accepted 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+0.0480589254405,0.00975325447734,0.173364647239,0.0480589254405,1.27647960561,4.0,15.0,1.0,0.150132989794,0.0,final diff --git a/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv new file mode 100644 index 0000000000..aa89cef81f --- /dev/null +++ b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv @@ -0,0 +1,13 @@ +x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,ConstraintEvaluation_expConstr3,ConstraintEvaluation_impConstr3,fitness,accepted +7.0,4.0,5.0,3.0,6.0,2.0,84.0,15.0,1.0,2.0,1.0,inf,2.0,16.0,0.0,accepted +7.0,2.0,5.0,3.0,6.0,4.0,92.0,11.0,1.0,2.0,1.0,inf,2.0,8.0,0.0,accepted +4.0,5.0,3.0,6.0,7.0,2.0,94.0,14.0,1.0,2.0,2.0,2.0,1.0,6.0,0.0,accepted +7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,1.0,2.0,2.0,inf,2.0,14.0,0.0,accepted +6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,1.0,2.0,2.0,inf,3.0,3.0,0.0,accepted +7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,1.0,2.0,3.0,inf,3.0,11.0,0.0,accepted +4.0,5.0,3.0,6.0,2.0,7.0,99.0,14.0,1.0,2.0,3.0,inf,1.0,1.0,0.0,accepted +7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,1.0,2.0,5.0,inf,3.0,11.0,0.0,accepted +7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,1.0,2.0,4.0,inf,3.0,11.0,0.0,accepted +4.0,6.0,7.0,2.0,3.0,5.0,90.0,16.0,1.0,2.0,6.0,inf,1.0,10.0,0.0,accepted +7.0,4.0,5.0,3.0,6.0,2.0,84.0,15.0,1.0,2.0,1.0,inf,2.0,16.0,0.0,final +7.0,2.0,5.0,3.0,6.0,4.0,92.0,11.0,1.0,2.0,1.0,inf,2.0,8.0,0.0,final diff --git a/tests/framework/Optimizers/GeneticAlgorithms/tests b/tests/framework/Optimizers/GeneticAlgorithms/tests index 50dfdf499d..65fb37ace3 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/tests +++ b/tests/framework/Optimizers/GeneticAlgorithms/tests @@ -320,4 +320,24 @@ rel_err = 0.001 [../] [../] + + [./NSGA-II_MinwoRepMultiObjective] + type = 'RavenFramework' + input = 'discrete/constrained/MinwoRepMultiObjective.xml' + [./csv] + type = OrderedCSV + output = 'discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv' + rel_err = 0.001 + [../] + [../] + + [./NSGA-II_ZDT1] + type = 'RavenFramework' + input = 'continuous/unconstrained/ZDT1.xml' + [./csv] + type = OrderedCSV + output = 'continuous/unconstrained/ZDT1/opt_export_0.csv' + rel_err = 0.001 + [../] + [../] [] From da9e0dde401f88215714cda5c9e79fa4d21aca34 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Tue, 21 Mar 2023 14:38:43 -0600 Subject: [PATCH 10/48] fixing simulated annealing to accept a list of objectives --- .../Optimizers/SimulatedAnnealing.py | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/ravenframework/Optimizers/SimulatedAnnealing.py b/ravenframework/Optimizers/SimulatedAnnealing.py index ab47591812..18364ac655 100644 --- a/ravenframework/Optimizers/SimulatedAnnealing.py +++ b/ravenframework/Optimizers/SimulatedAnnealing.py @@ -301,13 +301,11 @@ def _useRealization(self, info, rlz): @ Out, None """ traj = info['traj'] - if len(self._objectiveVar) == 1: - self._objectiveVar = self._objectiveVar[0] - elif type(self._objectiveVar) == list: + if len(self._objectiveVar) > 1 and type(self._objectiveVar)==str: self.raiseAnError(IOError, 'Simulated Annealing does not support multiObjective yet! objective variable must be a single variable for now!') - info['optVal'] = rlz[self._objectiveVar] + info['optVal'] = rlz[self._objectiveVar[0]] self.incrementIteration(traj) - self._resolveNewOptPoint(traj, rlz, rlz[self._objectiveVar], info) + self._resolveNewOptPoint(traj, rlz, rlz[self._objectiveVar[0]], info) if self._stepTracker[traj]['opt'] is None: # revert to the last accepted point rlz = self._optPointHistory[traj][-1][0] @@ -326,7 +324,7 @@ def _useRealization(self, info, rlz): except NoConstraintResolutionFound: # we've tried everything, but we just can't hack it self.raiseAMessage(f'Optimizer "{self.name}" trajectory {traj} was unable to continue due to functional or boundary constraints.') - self._closeTrajectory(traj, 'converge', 'no constraint resolution', newPoint[self._objectiveVar]) + self._closeTrajectory(traj, 'converge', 'no constraint resolution', newPoint[self._objectiveVar[0]]) return self._submitRun(suggested, traj, self.getIteration(traj)) @@ -398,7 +396,7 @@ def _checkConvObjective(self, traj): return False o1, _ = self._optPointHistory[traj][-1] o2, _ = self._optPointHistory[traj][-2] - delta = o2[self._objectiveVar]-o1[self._objectiveVar] + delta = o2[self._objectiveVar[0]]-o1[self._objectiveVar[0]] converged = abs(delta) < self._convergenceCriteria['objective'] self.raiseADebug(self.convFormat.format(name='objective', conv=str(converged), @@ -447,9 +445,9 @@ def _checkAcceptability(self, traj, opt, optVal, info): # NOTE: if self._optPointHistory[traj]: -> faster to use "try" for all but the first time try: old, _ = self._optPointHistory[traj][-1] - oldVal = old[self._objectiveVar] + oldVal = old[self._objectiveVar[0]] # check if same point - self.raiseADebug(f' ... change: {opt[self._objectiveVar]-oldVal:1.3e} new objective: {opt[self._objectiveVar]:1.6e} old objective: {oldVal:1.6e}') + self.raiseADebug(f' ... change: {opt[self._objectiveVar[0]]-oldVal:1.3e} new objective: {opt[self._objectiveVar[0]]:1.6e} old objective: {oldVal:1.6e}') # if this is an opt point rerun, accept it without checking. if self._acceptRerun[traj]: acceptable = 'rerun' @@ -458,7 +456,7 @@ def _checkAcceptability(self, traj, opt, optVal, info): # this is the classic "same point" trap; we accept the same point, and check convergence later acceptable = 'accepted' else: - if self._acceptabilityCriterion(oldVal,opt[self._objectiveVar])>randomUtils.random(dim=1, samples=1): # TODO replace it back + if self._acceptabilityCriterion(oldVal,opt[self._objectiveVar[0]])>randomUtils.random(dim=1, samples=1): # TODO replace it back acceptable = 'accepted' else: acceptable = 'rejected' From 1fd2175ac0e2d49e454a9ada92087189cb3e089a Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Tue, 21 Mar 2023 22:52:18 -0600 Subject: [PATCH 11/48] fixing rook to compare infs --- rook/OrderedCSVDiffer.py | 5 ++++ .../constrained/MinwoRepMultiObjective.xml | 2 +- .../Multi_MinwoReplacement/opt_export_0.csv | 25 ++++++++++--------- 3 files changed, 19 insertions(+), 13 deletions(-) diff --git a/rook/OrderedCSVDiffer.py b/rook/OrderedCSVDiffer.py index 65d002d7ba..9561832f42 100644 --- a/rook/OrderedCSVDiffer.py +++ b/rook/OrderedCSVDiffer.py @@ -17,6 +17,7 @@ from __future__ import division, print_function, unicode_literals, absolute_import import sys import csv +import numpy as np from Tester import Differ @@ -92,6 +93,10 @@ def matches(self, a_obj, b_obj, is_number, tol): """ if not is_number: return a_obj == b_obj, 0 + if np.isnan(a_obj) and np.isnan(b_obj): + return True, 0 + if np.isinf(a_obj) and np.isinf(b_obj) and a_obj==b_obj: + return True, 0 if self.__ignore_sign: a_obj = abs(a_obj) b_obj = abs(b_obj) diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml index f3f6ce1208..b72dd0532a 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml @@ -1,7 +1,7 @@ - raven/tests/framework/Optimizers/GA. + raven/tests/framework/Optimizers/GeneticAlgorithms.NSGAII Junyung Kim 2022-12-21 diff --git a/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv index aa89cef81f..ecd64246ae 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv +++ b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv @@ -1,13 +1,14 @@ x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,ConstraintEvaluation_expConstr3,ConstraintEvaluation_impConstr3,fitness,accepted -7.0,4.0,5.0,3.0,6.0,2.0,84.0,15.0,1.0,2.0,1.0,inf,2.0,16.0,0.0,accepted -7.0,2.0,5.0,3.0,6.0,4.0,92.0,11.0,1.0,2.0,1.0,inf,2.0,8.0,0.0,accepted -4.0,5.0,3.0,6.0,7.0,2.0,94.0,14.0,1.0,2.0,2.0,2.0,1.0,6.0,0.0,accepted -7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,1.0,2.0,2.0,inf,2.0,14.0,0.0,accepted -6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,1.0,2.0,2.0,inf,3.0,3.0,0.0,accepted -7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,1.0,2.0,3.0,inf,3.0,11.0,0.0,accepted -4.0,5.0,3.0,6.0,2.0,7.0,99.0,14.0,1.0,2.0,3.0,inf,1.0,1.0,0.0,accepted -7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,1.0,2.0,5.0,inf,3.0,11.0,0.0,accepted -7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,1.0,2.0,4.0,inf,3.0,11.0,0.0,accepted -4.0,6.0,7.0,2.0,3.0,5.0,90.0,16.0,1.0,2.0,6.0,inf,1.0,10.0,0.0,accepted -7.0,4.0,5.0,3.0,6.0,2.0,84.0,15.0,1.0,2.0,1.0,inf,2.0,16.0,0.0,final -7.0,2.0,5.0,3.0,6.0,4.0,92.0,11.0,1.0,2.0,1.0,inf,2.0,8.0,0.0,final +4.0,5.0,3.0,6.0,7.0,2.0,94.0,14.0,0.0,2.0,1.0,2.0,1.0,6.0,0.0,accepted +7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,0.0,2.0,1.0,inf,2.0,14.0,0.0,accepted +4.0,5.0,7.0,2.0,3.0,6.0,94.0,14.0,0.0,2.0,2.0,inf,1.0,6.0,0.0,accepted +6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,0.0,2.0,1.0,inf,3.0,3.0,0.0,accepted +4.0,6.0,7.0,3.0,5.0,2.0,86.0,16.0,0.0,2.0,2.0,inf,0.0,14.0,0.0,accepted +7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,0.0,2.0,2.0,2.0,3.0,11.0,0.0,accepted +7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,0.0,2.0,3.0,inf,3.0,11.0,0.0,accepted +7.0,5.0,3.0,2.0,6.0,4.0,88.0,17.0,0.0,2.0,3.0,inf,5.0,12.0,0.0,accepted +4.0,6.0,7.0,2.0,3.0,5.0,90.0,16.0,0.0,2.0,4.0,inf,1.0,10.0,0.0,accepted +7.0,4.0,3.0,2.0,5.0,6.0,93.0,15.0,0.0,2.0,4.0,inf,5.0,7.0,0.0,accepted +4.0,5.0,3.0,6.0,7.0,2.0,94.0,14.0,0.0,2.0,1.0,2.0,1.0,6.0,0.0,final +7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,0.0,2.0,1.0,inf,2.0,14.0,0.0,final +6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,0.0,2.0,1.0,inf,3.0,3.0,0.0,final From 305c2aced9988ab30a229f92d2d6e4414ab136bb Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 1 Apr 2023 00:00:22 -0600 Subject: [PATCH 12/48] making one mod in RAVENSAmpled --- plugins/HERON | 1 - plugins/TEAL | 2 +- ravenframework/Optimizers/RavenSampled.py | 4 ++-- 3 files changed, 3 insertions(+), 4 deletions(-) delete mode 160000 plugins/HERON diff --git a/plugins/HERON b/plugins/HERON deleted file mode 160000 index a5853fa4c6..0000000000 --- a/plugins/HERON +++ /dev/null @@ -1 +0,0 @@ -Subproject commit a5853fa4c6fdfe6a5cde6023608a974122135e5e diff --git a/plugins/TEAL b/plugins/TEAL index cf79281b57..8572ef2e6f 160000 --- a/plugins/TEAL +++ b/plugins/TEAL @@ -1 +1 @@ -Subproject commit cf79281b57afbeaed4bce0aa26be2376799ac336 +Subproject commit 8572ef2e6fa6b89b6651db40c9973b47d8b76a45 diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 6755c27ec4..c79f3a7cd8 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -352,7 +352,7 @@ def finalizeSampler(self, failedRuns): self.raiseAnError(RuntimeError, f'There is no optimization history for traj {traj}! ' + 'Perhaps the Model failed?') opt = self._optPointHistory[traj][-1][0] - val = opt[self._objectiveVar] + val = opt[self._objectiveVar[0]] self.raiseADebug(statusTemplate.format(status='active', traj=traj, val=s * val)) if bestValue is None or val < bestValue: bestValue = val @@ -419,7 +419,7 @@ def finalizeSampler(self, failedRuns): self.raiseAMessage(' - Final Optimal Point:') finalTemplate = ' {name:^20s} {value: 1.3e}' finalTemplateInt = ' {name:^20s} {value: 3d}' - self.raiseAMessage(finalTemplate.format(name=self._objectiveVar, value=s * bestValue)) + # self.raiseAMessage(finalTemplate.format(name=self._objectiveVar, value=s * bestValue)) self.raiseAMessage(finalTemplateInt.format(name='trajID', value=bestTraj)) for var, val in bestPoint.items(): self.raiseAMessage(finalTemplate.format(name=var, value=val)) From c820eeab83f88dc3294d442bc2484d3110dd6a23 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Mon, 3 Apr 2023 10:53:46 -0600 Subject: [PATCH 13/48] making self._minMax a list --- ravenframework/Optimizers/Optimizer.py | 7 +++++-- ravenframework/Optimizers/RavenSampled.py | 14 ++++++++------ .../continuous/unconstrained/ZDT1.xml | 2 +- .../constrained/MinwoRepMultiObjective.xml | 2 +- 4 files changed, 15 insertions(+), 10 deletions(-) diff --git a/ravenframework/Optimizers/Optimizer.py b/ravenframework/Optimizers/Optimizer.py index 011f6fd9d3..5c3a2af2f1 100644 --- a/ravenframework/Optimizers/Optimizer.py +++ b/ravenframework/Optimizers/Optimizer.py @@ -103,7 +103,8 @@ def getInputSpecification(cls): descr=r"""seed for random number generation. Note that by default RAVEN uses an internal seed, so this seed must be changed to observe changed behavior. \default{RAVEN-determined}""") minMaxEnum = InputTypes.makeEnumType('MinMax', 'MinMaxType', ['min', 'max']) - minMax = InputData.parameterInputFactory('type', contentType=minMaxEnum, + minMaxList = InputTypes.StringListType() + minMax = InputData.parameterInputFactory('type', contentType=minMaxList, descr=r"""the type of optimization to perform. \xmlString{min} will search for the lowest \xmlNode{objective} value, while \xmlString{max} will search for the highest value.""") init.addSub(seed) @@ -161,7 +162,7 @@ def __init__(self): # public # _protected self._seed = None # random seed to apply - self._minMax = 'min' # maximization or minimization? + self._minMax = ['min'] # maximization or minimization? self._activeTraj = [] # tracks live trajectories self._cancelledTraj = {} # tracks cancelled trajectories, and reasons self._convergedTraj = {} # tracks converged trajectories, and values obtained @@ -261,6 +262,8 @@ def handleInput(self, paramInput): minMax = init.findFirst('type') if minMax is not None: self._minMax = minMax.value + if len(self._minMax) != len(self._objectiveVar): + self.raiseAnError(IOError, 'minMax and objectiveVar must be of the same length!') # variables additional reading for varNode in paramInput.findAll('variable'): diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index c79f3a7cd8..70b0518c3d 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -300,12 +300,14 @@ def localFinalizeActualSampling(self, jobObject, model, myInput): # # testing suggests no big deal on smaller problem # the sign of the objective function is flipped in case we do maximization # so get the correct-signed value into the realization - if self._minMax == 'max': + + if 'max' in self._minMax: if not self._canHandleMultiObjective and len(self._objectiveVar) == 1: rlz[self._objectiveVar[0]] *= -1 elif type(self._objectiveVar) == list: for i in range(len(self._objectiveVar)): - rlz[self._objectiveVar[i]] *= -1 + if self._minMax[i] == 'max': + rlz[self._objectiveVar[i]] *= -1 else: rlz[self._objectiveVar] *= -1 # TODO FIXME let normalizeData work on an xr.DataSet (batch) not just a dictionary! @@ -323,7 +325,7 @@ def finalizeSampler(self, failedRuns): bestValue = None bestTraj = None bestPoint = None - s = -1 if self._minMax == 'max' else 1 + s = -1 if 'max' in self._minMax else 1 # check converged trajectories self.raiseAMessage('*' * 80) self.raiseAMessage('Optimizer Final Results:') @@ -432,7 +434,7 @@ def finalizeSampler(self, failedRuns): key = list(opt.keys()) val = [item[i] for item in opt.values()] optElm = {key[a]: val[a] for a in range(len(key))} - optVal = [s*optElm[self._objectiveVar[b]] for b in range(len(self._objectiveVar))] + optVal = [(-1*(self._minMax[b]=='max')+(self._minMax[b]=='min'))*optElm[self._objectiveVar[b]] for b in range(len(self._objectiveVar))] bestTraj = traj bestOpt = self.denormalizeData(optElm) @@ -719,13 +721,13 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): # optimal point input and output spaces if len(self._objectiveVar) == 1: # Single Objective Optimization objValue = rlz[self._objectiveVar[0]] - if self._minMax == 'max': + if 'max' in self._minMax: objValue *= -1 toExport[self._objectiveVar[0]] = objValue else: # Multi Objective Optimization for i in range(len(self._objectiveVar)): objValue = rlz[self._objectiveVar[i]] - if self._minMax == 'max': + if self._minMax[i] == 'max': objValue *= -1 toExport[self._objectiveVar[i]] = objValue toExport.update(self.denormalizeData(dict((var, rlz[var]) for var in self.toBeSampled))) diff --git a/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml b/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml index 94d0caf169..04cd9bce62 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml @@ -50,7 +50,7 @@ 15 42 every - min + min,min diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml index b72dd0532a..d6695c1571 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml @@ -60,7 +60,7 @@ 2 42 every - min + min,min From 21bf42d9ba9f95c18752d76e8b6eab4f6e12f1ab Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Mon, 3 Apr 2023 12:32:52 -0600 Subject: [PATCH 14/48] erroring out if type is not in ['min', 'max'] --- ravenframework/Optimizers/Optimizer.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/ravenframework/Optimizers/Optimizer.py b/ravenframework/Optimizers/Optimizer.py index 5c3a2af2f1..511a094797 100644 --- a/ravenframework/Optimizers/Optimizer.py +++ b/ravenframework/Optimizers/Optimizer.py @@ -263,7 +263,9 @@ def handleInput(self, paramInput): if minMax is not None: self._minMax = minMax.value if len(self._minMax) != len(self._objectiveVar): - self.raiseAnError(IOError, 'minMax and objectiveVar must be of the same length!') + self.raiseAnError(IOError, 'type and objective must be of the same length!') + if list(set(self._minMax)-set(['min','max'])) != []: + self.raiseAnError(IOError, "type must be a list of 'min' and/or 'max'") # variables additional reading for varNode in paramInput.findAll('variable'): From e639803f3f24fc158b4c308cd97370d4993a1938 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Mon, 3 Apr 2023 16:59:36 -0600 Subject: [PATCH 15/48] updating HERON to b316024 --- plugins/HERON | 1 + 1 file changed, 1 insertion(+) create mode 160000 plugins/HERON diff --git a/plugins/HERON b/plugins/HERON new file mode 160000 index 0000000000..b31602460c --- /dev/null +++ b/plugins/HERON @@ -0,0 +1 @@ +Subproject commit b31602460c83b7ff9cddfd2a6d1fd206b2f9d7bc From be64a4db6826a7691016cb5384a52439fbdf4984 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Mon, 3 Apr 2023 21:03:41 -0600 Subject: [PATCH 16/48] updating dependencies --- dependencies.xml | 25 +++++++++---------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/dependencies.xml b/dependencies.xml index b29ebc7270..e7989d9cd3 100644 --- a/dependencies.xml +++ b/dependencies.xml @@ -36,25 +36,19 @@ Note all install methods after "main" take
- 1.21 + 1.22 1.7 1.0 1.3 - - 0.19 + + 2023 1.5 - 3.3 + 3.5 0.13 2.2 - 2.9 + 2.10 - 3.7 + 3.8 3 @@ -64,11 +58,11 @@ Note all install methods after "main" take - + - 2.1 + 2.2 1.13 @@ -84,7 +78,6 @@ Note all install methods after "main" take - 3
@@ -103,4 +96,4 @@ Note all install methods after "main" take remove remove - + \ No newline at end of file From 95682a16c3bbe248cddea39d7cdd8cfc0f3430dd Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Tue, 4 Apr 2023 13:51:03 -0600 Subject: [PATCH 17/48] removing a trailing space --- tests/framework/AnalyticModels/optimizing/myLocalSum.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/framework/AnalyticModels/optimizing/myLocalSum.py b/tests/framework/AnalyticModels/optimizing/myLocalSum.py index faec353eb6..d4b6fec246 100644 --- a/tests/framework/AnalyticModels/optimizing/myLocalSum.py +++ b/tests/framework/AnalyticModels/optimizing/myLocalSum.py @@ -81,5 +81,4 @@ def constrain(self): and negative if violated. """ explicitConstrain = constraint(self) - return explicitConstrain - + return explicitConstrain \ No newline at end of file From c3688e2e82cc87fdcc977b1e0dc120f4bd9af565 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Tue, 4 Apr 2023 17:01:41 -0600 Subject: [PATCH 18/48] removing windows line endings --- .../MultiObjective/myLocalSum_multi.py | 43 +++++++++++++++++++ .../optimizing/myConstraints.py | 2 +- .../optimizing/myLocalSum_multi.py | 8 +--- 3 files changed, 45 insertions(+), 8 deletions(-) create mode 100644 tests/framework/AnalyticModels/optimizing/MultiObjective/myLocalSum_multi.py diff --git a/tests/framework/AnalyticModels/optimizing/MultiObjective/myLocalSum_multi.py b/tests/framework/AnalyticModels/optimizing/MultiObjective/myLocalSum_multi.py new file mode 100644 index 0000000000..86ef17bdeb --- /dev/null +++ b/tests/framework/AnalyticModels/optimizing/MultiObjective/myLocalSum_multi.py @@ -0,0 +1,43 @@ +# 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. + +# @author: Mohammad Abdo (@Jimmy-INL) + +def evaluate(Inputs): + Sum = 0 + LocalSum1 = 0 + LocalSum2 = 0 + # for ind,var in enumerate(Inputs.keys()): + # # write the objective function here + # Sum += (ind + 1) * Inputs[var] + # if (ind == 1): + # LocalSum1 = Sum + # return Sum[:], LocalSum1[:] + for ind,var in enumerate(Inputs.keys()): + # write the objective function here + Sum += (ind + 1) * Inputs[var] + if (ind == 0) or (ind == 1): + LocalSum1 += (ind + 1) * Inputs[var] + if (ind == 2) or (ind == 3): + LocalSum2 += (ind + 1) * Inputs[var] + return Sum[:], LocalSum1[:], LocalSum2[:] + +def run(self,Inputs): + """ + RAVEN API + @ In, self, object, RAVEN container + @ In, Inputs, dict, additional inputs + @ Out, None + """ + self.obj1,self.obj2,self.obj3 = evaluate(Inputs) # make sure the name of the objective is consistent with obj1, obj2, obj3. diff --git a/tests/framework/AnalyticModels/optimizing/myConstraints.py b/tests/framework/AnalyticModels/optimizing/myConstraints.py index bc704cfa57..b57cda4ce6 100644 --- a/tests/framework/AnalyticModels/optimizing/myConstraints.py +++ b/tests/framework/AnalyticModels/optimizing/myConstraints.py @@ -102,7 +102,7 @@ def impConstr3(Input): The implicit constraint involves variables from the output space, for example the objective variable or a dependent variable that is not in the optimization search space @ In, Input, object, RAVEN container - @ out, g, float, implicit constraint 3 evaluation function + @ out, g, float, implicit constraint #3 evaluation function """ g = 100 - Input.obj1 return g \ No newline at end of file diff --git a/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py b/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py index 509f852fd1..e012b851e9 100644 --- a/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py +++ b/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py @@ -18,12 +18,6 @@ def evaluate(Inputs): Sum = 0 LocalSum1 = 0 LocalSum2 = 0 - # for ind,var in enumerate(Inputs.keys()): - # # write the objective function here - # Sum += (ind + 1) * Inputs[var] - # if (ind == 1): - # LocalSum1 = Sum - # return Sum[:], LocalSum1[:] for ind,var in enumerate(Inputs.keys()): # write the objective function here Sum += (ind + 1) * Inputs[var] @@ -40,4 +34,4 @@ def run(self,Inputs): @ In, Inputs, dict, additional inputs @ Out, None """ - self.obj1,self.obj2,self.obj3 = evaluate(Inputs) # make sure the name of the objective is consistent with obj1, obj2, obj3. + self.obj1,self.obj2,self.obj3 = evaluate(Inputs) From e25cc37eaf22505e5df947980c5735e0e5025073 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Wed, 5 Apr 2023 08:51:06 -0600 Subject: [PATCH 19/48] change to unix ending --- .../optimizing/myConstraints.py | 216 +++++++++--------- .../optimizing/myLocalSum_multi.py | 74 +++--- 2 files changed, 145 insertions(+), 145 deletions(-) diff --git a/tests/framework/AnalyticModels/optimizing/myConstraints.py b/tests/framework/AnalyticModels/optimizing/myConstraints.py index b57cda4ce6..d63407ef6f 100644 --- a/tests/framework/AnalyticModels/optimizing/myConstraints.py +++ b/tests/framework/AnalyticModels/optimizing/myConstraints.py @@ -1,108 +1,108 @@ -# 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. -# @ author: Mohammad Abdo (@Jimmy-INL) - -import numpy as np - -def constrain(Input):#Complete this: give the function the correct name# - """ - This function calls the explicit constraint whose name is passed through Input.name - the evaluation function g is negative if the explicit constraint is violated and positive otherwise. - This suits the constraint handling in the Genetic Algorithms, - but not the Gradient Descent as the latter expects True if the solution passes the constraint and False if it violates it. - @ In, Input, object, RAVEN container - @ Out, g, float, explicit constraint evaluation (negative if violated and positive otherwise) - """ - g = eval(Input.name)(Input) - return g - -def implicitConstraint(Input): - """ - Evaluates the implicit constraint function at a given point/solution ($\vec(x)$) - @ In, Input, object, RAVEN container - @ Out, g(inputs x1,x2,..,output or dependent variable), float, implicit constraint evaluation function - the way the constraint is designed is that - the constraint function has to be >= 0, - so if: - 1) f(x,y) >= 0 then g = f - 2) f(x,y) >= a then g = f - a - 3) f(x,y) <= b then g = b - f - 4) f(x,y) = c then g = 1e-6 - abs((f(x,y) - c)) (equality constraint) - """ - g = eval(Input.name)(Input) - return g - - -def expConstr1(Input):#You are free to pick this name but it has to be similar to the one in the xml# - """ - Let's assume that the constraint is: - $ x3+x4 < 8 $ - then g the constraint evaluation function (which has to be > 0) is taken to be: - g = 8 - (x3+x4) - in this case if g(\vec(x)) < 0 then this x violates the constraint and vice versa - @ In, Input, object, RAVEN container - @ out, g, float, explicit constraint 1 evaluation function - """ - g = 8 - Input.x3 - Input.x4 - return g - -def expConstr2(Input): - """ - Explicit Equality Constraint: - let's consider the constraint x1**2 + x2**2 = 25 - The way to write g is to use a very small number for instance, epsilon = 1e-12 - and then g = epsilon - abs(constraint) - @ In, Input, object, RAVEN container - @ out, g, float, explicit constraint 2 evaluation function - """ - g = 1e-12 - abs(Input.x1**2 + Input.x2**2 - 25) - return g - -def expConstr3(Input): - """ - @ In, Input, object, RAVEN container - @ out, g, float, explicit constraint 3 evaluation function - """ - g = 10 - Input.x3 - Input.x4 - return g - -def impConstr1(Input): - """ - The implicit constraint involves variables from the output space, for example the objective variable or - a dependent variable that is not in the optimization search space - @ In, Input, object, RAVEN container - @ out, g, float, implicit constraint 1 evaluation function - """ - g = 10 - Input.x1**2 - Input.obj - return g - -def impConstr2(Input): - """ - The implicit constraint involves variables from the output space, for example the objective variable or - a dependent variable that is not in the optimization search space - @ In, Input, object, RAVEN container - @ out, g, float, implicit constraint 2 evaluation function - """ - g = Input.x1**2 + Input.obj - 10 - return g - -def impConstr3(Input): - """ - The implicit constraint involves variables from the output space, for example the objective variable or - a dependent variable that is not in the optimization search space - @ In, Input, object, RAVEN container - @ out, g, float, implicit constraint #3 evaluation function - """ - g = 100 - Input.obj1 - return g \ No newline at end of file +# 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. +# @ author: Mohammad Abdo (@Jimmy-INL) + +import numpy as np + +def constrain(Input):#Complete this: give the function the correct name# + """ + This function calls the explicit constraint whose name is passed through Input.name + the evaluation function g is negative if the explicit constraint is violated and positive otherwise. + This suits the constraint handling in the Genetic Algorithms, + but not the Gradient Descent as the latter expects True if the solution passes the constraint and False if it violates it. + @ In, Input, object, RAVEN container + @ Out, g, float, explicit constraint evaluation (negative if violated and positive otherwise) + """ + g = eval(Input.name)(Input) + return g + +def implicitConstraint(Input): + """ + Evaluates the implicit constraint function at a given point/solution ($\vec(x)$) + @ In, Input, object, RAVEN container + @ Out, g(inputs x1,x2,..,output or dependent variable), float, implicit constraint evaluation function + the way the constraint is designed is that + the constraint function has to be >= 0, + so if: + 1) f(x,y) >= 0 then g = f + 2) f(x,y) >= a then g = f - a + 3) f(x,y) <= b then g = b - f + 4) f(x,y) = c then g = 1e-6 - abs((f(x,y) - c)) (equality constraint) + """ + g = eval(Input.name)(Input) + return g + + +def expConstr1(Input):#You are free to pick this name but it has to be similar to the one in the xml# + """ + Let's assume that the constraint is: + $ x3+x4 < 8 $ + then g the constraint evaluation function (which has to be > 0) is taken to be: + g = 8 - (x3+x4) + in this case if g(\vec(x)) < 0 then this x violates the constraint and vice versa + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 1 evaluation function + """ + g = 8 - Input.x3 - Input.x4 + return g + +def expConstr2(Input): + """ + Explicit Equality Constraint: + let's consider the constraint x1**2 + x2**2 = 25 + The way to write g is to use a very small number for instance, epsilon = 1e-12 + and then g = epsilon - abs(constraint) + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 2 evaluation function + """ + g = 1e-12 - abs(Input.x1**2 + Input.x2**2 - 25) + return g + +def expConstr3(Input): + """ + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 3 evaluation function + """ + g = 10 - Input.x3 - Input.x4 + return g + +def impConstr1(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 1 evaluation function + """ + g = 10 - Input.x1**2 - Input.obj + return g + +def impConstr2(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 2 evaluation function + """ + g = Input.x1**2 + Input.obj - 10 + return g + +def impConstr3(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint #3 evaluation function + """ + g = 100 - Input.obj1 + return g diff --git a/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py b/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py index e012b851e9..2bad9f6b44 100644 --- a/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py +++ b/tests/framework/AnalyticModels/optimizing/myLocalSum_multi.py @@ -1,37 +1,37 @@ -# 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. - -# @author: Mohammad Abdo (@Jimmy-INL) - -def evaluate(Inputs): - Sum = 0 - LocalSum1 = 0 - LocalSum2 = 0 - for ind,var in enumerate(Inputs.keys()): - # write the objective function here - Sum += (ind + 1) * Inputs[var] - if (ind == 0) or (ind == 1): - LocalSum1 += (ind + 1) * Inputs[var] - if (ind == 2) or (ind == 3): - LocalSum2 += (ind + 1) * Inputs[var] - return Sum[:], LocalSum1[:], LocalSum2[:] - -def run(self,Inputs): - """ - RAVEN API - @ In, self, object, RAVEN container - @ In, Inputs, dict, additional inputs - @ Out, None - """ - self.obj1,self.obj2,self.obj3 = evaluate(Inputs) +# 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. + +# @author: Mohammad Abdo (@Jimmy-INL) + +def evaluate(Inputs): + Sum = 0 + LocalSum1 = 0 + LocalSum2 = 0 + for ind,var in enumerate(Inputs.keys()): + # write the objective function here + Sum += (ind + 1) * Inputs[var] + if (ind == 0) or (ind == 1): + LocalSum1 += (ind + 1) * Inputs[var] + if (ind == 2) or (ind == 3): + LocalSum2 += (ind + 1) * Inputs[var] + return Sum[:], LocalSum1[:], LocalSum2[:] + +def run(self,Inputs): + """ + RAVEN API + @ In, self, object, RAVEN container + @ In, Inputs, dict, additional inputs + @ Out, None + """ + self.obj1,self.obj2,self.obj3 = evaluate(Inputs) From f0d141293d7b65111d1fcf7cc3edbdf4b53d4df7 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Wed, 5 Apr 2023 09:31:29 -0600 Subject: [PATCH 20/48] adding the zdt_model.py --- .../AnalyticModels/optimizing/ZDT_model.py | 41 +++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 tests/framework/AnalyticModels/optimizing/ZDT_model.py diff --git a/tests/framework/AnalyticModels/optimizing/ZDT_model.py b/tests/framework/AnalyticModels/optimizing/ZDT_model.py new file mode 100644 index 0000000000..851773cee6 --- /dev/null +++ b/tests/framework/AnalyticModels/optimizing/ZDT_model.py @@ -0,0 +1,41 @@ +# 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. + +# @author: Junyung Kim (@JunyungKim-INL) and Mohammad Abdo (@Jimmy-INL) + +import math + +def evaluate(Inputs): + Sum = 0 + obj1 = 0 + + for ind,var in enumerate(Inputs.keys()): + # write the objective function here + if (ind == 0) : + obj1 += Inputs[var] + if (ind != 0): + Sum += Inputs[var] + g = 1 + (9/len(Inputs.keys())*Sum ) + h = 1 - math.sqrt(obj1/g) + obj2 = g*h + return obj1[:], obj2[:] + +def run(self,Inputs): + """ + RAVEN API + @ In, self, object, RAVEN container + @ In, Inputs, dict, additional inputs + @ Out, None + """ + self.obj1,self.obj2 = evaluate(Inputs) From c2ca46e50906b13206efbba4d41dd3c9db6e44fd Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Wed, 5 Apr 2023 09:41:57 -0600 Subject: [PATCH 21/48] converting zdt to unix line endings --- .../AnalyticModels/optimizing/ZDT_model.py | 82 +++++++++---------- 1 file changed, 41 insertions(+), 41 deletions(-) diff --git a/tests/framework/AnalyticModels/optimizing/ZDT_model.py b/tests/framework/AnalyticModels/optimizing/ZDT_model.py index 851773cee6..ca53da447d 100644 --- a/tests/framework/AnalyticModels/optimizing/ZDT_model.py +++ b/tests/framework/AnalyticModels/optimizing/ZDT_model.py @@ -1,41 +1,41 @@ -# 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. - -# @author: Junyung Kim (@JunyungKim-INL) and Mohammad Abdo (@Jimmy-INL) - -import math - -def evaluate(Inputs): - Sum = 0 - obj1 = 0 - - for ind,var in enumerate(Inputs.keys()): - # write the objective function here - if (ind == 0) : - obj1 += Inputs[var] - if (ind != 0): - Sum += Inputs[var] - g = 1 + (9/len(Inputs.keys())*Sum ) - h = 1 - math.sqrt(obj1/g) - obj2 = g*h - return obj1[:], obj2[:] - -def run(self,Inputs): - """ - RAVEN API - @ In, self, object, RAVEN container - @ In, Inputs, dict, additional inputs - @ Out, None - """ - self.obj1,self.obj2 = evaluate(Inputs) +# 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. + +# @author: Junyung Kim (@JunyungKim-INL) and Mohammad Abdo (@Jimmy-INL) + +import math + +def evaluate(Inputs): + Sum = 0 + obj1 = 0 + + for ind,var in enumerate(Inputs.keys()): + # write the objective function here + if (ind == 0) : + obj1 += Inputs[var] + if (ind != 0): + Sum += Inputs[var] + g = 1 + (9/len(Inputs.keys())*Sum ) + h = 1 - math.sqrt(obj1/g) + obj2 = g*h + return obj1[:], obj2[:] + +def run(self,Inputs): + """ + RAVEN API + @ In, self, object, RAVEN container + @ In, Inputs, dict, additional inputs + @ Out, None + """ + self.obj1,self.obj2 = evaluate(Inputs) From 1f1b969ab6287869ac81cf7c29e05d59d80f8840 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Thu, 6 Apr 2023 09:43:58 -0600 Subject: [PATCH 22/48] Juan's change to simulateData for the interface --- .../SIMULATE3/SimulateData.py | 40 ++++++++++++++++++- 1 file changed, 39 insertions(+), 1 deletion(-) diff --git a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py index 817a7d5381..7b9fc67d09 100644 --- a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py +++ b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py @@ -43,6 +43,8 @@ def __init__(self,filen): self.data["PinPowerPeaking"] = self.pinPeaking() self.data["exposure"] = self.burnupEOC() self.data["assembly_power"] = self.assemblyPeakingFactors() + self.data["fuel_type"] = self.fa_type() +# self.data["pin_peaking"] = self.pinPeaking() # this is a dummy variable for demonstration with MOF # check if something has been found if all(v is None for v in self.data.values()): @@ -211,7 +213,7 @@ def EOCEFPD(self): if not list_: return ValueError("No values returned. Check Simulate File executed correctly") else: - outputDict = {'info_ids':['MaxEFPD'], 'values': [list_[-1]] } + outputDict = {'info_ids':['MaxEFPD'], 'values': [float(1/list_[-1])]} return outputDict @@ -486,6 +488,42 @@ def burnupEOC(self): return outputDict + def fa_type(self): + ''' + Extracts the fuel type and calculates the fuel cost based on the amount and enrichment of each fuel type. + ''' + #fuel_type = [] + FAlist = [] + for line in self.lines: + if "'FUE.TYP'" in line: + p1 = line.index(",") + p2 = line.index("/") + search_space = line[p1:p2] + search_space = search_space.replace(",","") + tmp= search_space.split() + for ii in tmp: + FAlist.append(float(ii)) + FAtype = list(set(FAlist)) + FAlist_A = FAlist[0] + FAlist_B = FAlist[1:9] + FAlist[9:73:9] + FAlist_C = FAlist[10:18] + FAlist[19:27] + FAlist[28:36] + FAlist[37:45] + FAlist[46:54] + FAlist[55:63] + FAlist[64:72] + FAlist[73:81] + FAcount_A = [float(fa == FAlist_A) for fa in FAtype] + FAcount_B = [float(FAlist_B.count(fa)*2) for fa in FAtype] + FAcount_C = [float(FAlist_C.count(fa)*4) for fa in FAtype] + FAcount = [FAcount_A[j] + FAcount_B[j] + FAcount_C[j] for j in range(len(FAtype))] + print(FAcount) + #stop + #Considering that: FA type 0 is empty, type 1 reflector, type 2 2% enrichment, types 3 and 4 2.5% enrichment, and types 5 and 6 3.2% enrichment. The cost of burnable is not being considered + fuel_cost = (FAcount[0] + FAcount[1])*0 + FAcount[2]*4.94262343 + (FAcount[3] + FAcount[4])*5.67862599 + (FAcount[5] + FAcount[6])*6.7274349 + print(fuel_cost) + #fuel_type.append(float(search_space)) + #stop + if not fuel_cost: + return ValueError("No values returned. Check Simulate File executed correctly") + else: + outputDict = {'info_ids':['fuel_cost'], 'values': [fuel_cost]} + return outputDict + def writeCSV(self, fileout): """ Print Data into CSV format From c7aebf3e4b4220b4055a702b36d6ea23e3efe29f Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Thu, 6 Apr 2023 11:58:36 -0600 Subject: [PATCH 23/48] resolving diff based on different batch Size, thanks @wangcj05 --- ravenframework/Optimizers/GeneticAlgorithm.py | 14 +++++++---- .../constrained/MinwoRepMultiObjective.xml | 2 +- .../Multi_MinwoReplacement/opt_export_0.csv | 25 +++++++++---------- 3 files changed, 22 insertions(+), 19 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index e321cc0441..bcde391287 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -593,6 +593,8 @@ def _useRealization(self, info, rlz): ############################################################################## objs_vals = [list(ele) for ele in list(zip(*self.objectiveVal))] + ##TODO: remove all the plots and maybe design new plots in outstreams if our current cannot be used + ## These are currently for debugging purposes import matplotlib.pyplot as plt # JY: Visualization: all points - This code block needs to be either deleted or revisited. plt.plot(np.array(objs_vals)[:,0], np.array(objs_vals)[:,1],'*') @@ -604,11 +606,11 @@ def _useRealization(self, info, rlz): plt.ylim(0,6) plt.title(str('Iteration ' + str(self.counter-1))) - plt.plot(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,0], - np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,1],'*') - for i in range(len(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[:,0])): - plt.text(np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[i,0], - np.array(list(zip(self._optPointHistory[traj][-1][0]['obj1'], self._optPointHistory[traj][-1][0]['obj2'])))[i,1], str(self.batchId-1)) + plt.plot(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,0], + np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,1],'*') + for i in range(len(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,0])): + plt.text(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,0], + np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,1], str(self.batchId-1)) # plt.pause() ############################################################################## @@ -810,6 +812,8 @@ def _resolveNewGenerationMulti(self, traj, rlz, info): coords={'chromosome':np.arange(np.shape(objVal)[0]), 'obj': self._objectiveVar}) if self._writeSteps == 'every': + print("### rlz.sizes['RAVEN_sample_ID'] = {}".format(rlz.sizes['RAVEN_sample_ID'])) + print("### self.population.shape is {}".format(self.population.shape)) for i in range(rlz.sizes['RAVEN_sample_ID']): rlzDict = dict((var,self.population.data[i][j]) for j, var in enumerate(self.population.Gene.data)) for j in range(len(self._objectiveVar)): diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml index d6695c1571..a1d7cd7452 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml @@ -11,7 +11,7 @@ Multi_MinwoReplacement optimize,print - 2 + 1 diff --git a/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv index ecd64246ae..abca0e7d1a 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv +++ b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv @@ -1,14 +1,13 @@ x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,ConstraintEvaluation_expConstr3,ConstraintEvaluation_impConstr3,fitness,accepted -4.0,5.0,3.0,6.0,7.0,2.0,94.0,14.0,0.0,2.0,1.0,2.0,1.0,6.0,0.0,accepted -7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,0.0,2.0,1.0,inf,2.0,14.0,0.0,accepted -4.0,5.0,7.0,2.0,3.0,6.0,94.0,14.0,0.0,2.0,2.0,inf,1.0,6.0,0.0,accepted -6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,0.0,2.0,1.0,inf,3.0,3.0,0.0,accepted -4.0,6.0,7.0,3.0,5.0,2.0,86.0,16.0,0.0,2.0,2.0,inf,0.0,14.0,0.0,accepted -7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,0.0,2.0,2.0,2.0,3.0,11.0,0.0,accepted -7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,0.0,2.0,3.0,inf,3.0,11.0,0.0,accepted -7.0,5.0,3.0,2.0,6.0,4.0,88.0,17.0,0.0,2.0,3.0,inf,5.0,12.0,0.0,accepted -4.0,6.0,7.0,2.0,3.0,5.0,90.0,16.0,0.0,2.0,4.0,inf,1.0,10.0,0.0,accepted -7.0,4.0,3.0,2.0,5.0,6.0,93.0,15.0,0.0,2.0,4.0,inf,5.0,7.0,0.0,accepted -4.0,5.0,3.0,6.0,7.0,2.0,94.0,14.0,0.0,2.0,1.0,2.0,1.0,6.0,0.0,final -7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,0.0,2.0,1.0,inf,2.0,14.0,0.0,final -6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,0.0,2.0,1.0,inf,3.0,3.0,0.0,final +7.0,3.0,5.0,4.0,6.0,2.0,86.0,13.0,1.0,2.0,1.0,inf,1.0,14.0,0.0,accepted +7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,1.0,2.0,2.0,inf,2.0,14.0,0.0,accepted +6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,1.0,2.0,1.0,inf,3.0,3.0,0.0,accepted +6.0,4.0,7.0,2.0,3.0,5.0,88.0,14.0,1.0,2.0,2.0,inf,1.0,12.0,0.0,accepted +6.0,4.0,3.0,7.0,5.0,2.0,88.0,14.0,1.0,2.0,3.0,inf,0.0,12.0,0.0,accepted +7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,1.0,2.0,4.0,inf,3.0,11.0,0.0,accepted +4.0,5.0,3.0,6.0,7.0,2.0,94.0,14.0,1.0,2.0,4.0,inf,1.0,6.0,0.0,accepted +4.0,5.0,7.0,2.0,3.0,6.0,94.0,14.0,1.0,2.0,5.0,inf,1.0,6.0,0.0,accepted +7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,1.0,2.0,5.0,inf,3.0,11.0,0.0,accepted +4.0,6.0,7.0,2.0,3.0,5.0,90.0,16.0,1.0,2.0,6.0,inf,1.0,10.0,0.0,accepted +7.0,3.0,5.0,4.0,6.0,2.0,86.0,13.0,1.0,2.0,1.0,inf,1.0,14.0,0.0,final +6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,1.0,2.0,1.0,inf,3.0,3.0,0.0,final From 64e97a9d5a497e2f7e882372e940c1da4e1bca3c Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Fri, 7 Apr 2023 18:25:01 -0600 Subject: [PATCH 24/48] converting SimukateData.py to unix line endings --- ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py index 7b9fc67d09..fd3c3db140 100644 --- a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py +++ b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py @@ -543,4 +543,3 @@ def writeCSV(self, fileout): index=index+1 numpy.savetxt(fileObject, outputMatrix.T, delimiter=',', header=','.join(headers), comments='') fileObject.close() - From b29661b4efbc4dc1b8dd6cc91f73b50932ce2d6c Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Tue, 11 Apr 2023 16:44:45 -0600 Subject: [PATCH 25/48] regolding to print all batches in MOO --- ravenframework/Optimizers/GeneticAlgorithm.py | 23 ++++++++++++------- .../unconstrained/ZDT1/opt_export_0.csv | 10 ++++++++ .../Multi_MinwoReplacement/opt_export_0.csv | 10 ++++++++ 3 files changed, 35 insertions(+), 8 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index bcde391287..ff3e020576 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -535,14 +535,14 @@ def _useRealization(self, info, rlz): else: g.data[index, constIndex] = self._handleImplicitConstraints(newOpt, opt, constraint) - Fitness = np.zeros((len(offSprings), 1)) - for i in range(len(Fitness)): - Fitness[i] = countConstViolation(g.data[i]) - Fitness = [item for sublist in Fitness.tolist() for item in sublist] + fitness = np.zeros((len(offSprings), 1)) + for i in range(len(fitness)): + fitness[i] = countConstViolation(g.data[i]) + fitness = [item for sublist in fitness.tolist() for item in sublist] - Fitness = xr.DataArray(Fitness, + fitness = xr.DataArray(fitness, dims=['NumOfConstraintViolated'], - coords={'NumOfConstraintViolated':np.arange(np.shape(Fitness)[0])}) + coords={'NumOfConstraintViolated':np.arange(np.shape(fitness)[0])}) # 0.2@ n-1: Survivor selection(rlz) # update population container given obtained children @@ -576,7 +576,7 @@ def _useRealization(self, info, rlz): popObjectiveVal=self.objectiveVal, offObjectiveVal=objectiveVal, popConst = self.constraints, - offConst = Fitness, + offConst = fitness, popConstV = self.constraintsV, offConstV = g ) @@ -616,7 +616,7 @@ def _useRealization(self, info, rlz): else: self.population = offSprings - self.constraints = Fitness + self.constraints = fitness self.constraintsV = g self.rank, self.crowdingDistance = self._fitnessInstance(rlz, objVals = self._objectiveVar @@ -625,6 +625,13 @@ def _useRealization(self, info, rlz): for i in range(len(self._objectiveVar)): self.objectiveVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) + self._collectOptPointMulti(self.population, + self.rank, + self.crowdingDistance, + self.objectiveVal, + self.constraints, + self.constraintsV) + self._resolveNewGenerationMulti(traj, rlz, info) # 1 @ n: Parent selection from population # pair parents together by indexes diff --git a/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv b/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv index a4e2cdcdce..39e05c235f 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv +++ b/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv @@ -1,4 +1,14 @@ x1,x2,x3,obj1,obj2,age,batchId,rank,CD,fitness,accepted +0.902940987587,0.947612243227,0.840374259707,0.902940987587,3.96681957049,0.0,1.0,3.0,1.79769313486e+308,0.0,first +0.227236453264,0.847510234417,0.362760231915,0.227236453264,3.60499993579,0.0,1.0,2.0,1.79769313486e+308,0.0,first +0.766431005617,0.192211290866,0.39961784645,0.766431005617,1.3169883176,0.0,1.0,1.0,1.79769313486e+308,0.0,first +0.633202111729,0.793545654927,0.564774226762,0.633202111729,3.28234279694,0.0,1.0,2.0,2.0,0.0,first +0.306377726911,0.00975325447734,0.613563748918,0.306377726911,1.93224686343,0.0,1.0,1.0,1.32735741676,0.0,first +0.110044764846,0.604534715322,0.738899003886,0.110044764846,4.28628616584,0.0,1.0,1.0,1.79769313486e+308,0.0,first +0.331692186261,0.571854743308,0.965348788995,0.331692186261,4.24730587019,0.0,1.0,3.0,1.79769313486e+308,0.0,first +0.267873673297,0.166777967281,0.847808119107,0.267873673297,3.00298144409,0.0,1.0,1.0,0.749061564967,0.0,first +0.713407223745,0.641621648018,0.334449647072,0.713407223745,2.25417202135,0.0,1.0,2.0,1.79769313486e+308,0.0,first +0.13264102096,0.630711117673,0.405532905694,0.13264102096,3.37050011696,0.0,1.0,1.0,0.672642583243,0.0,first 0.766431005617,0.192211290866,0.39961784645,0.766431005617,1.3169883176,0.0,2.0,1.0,inf,0.0,accepted 0.110044764846,0.604534715322,0.738899003886,0.110044764846,4.28628616584,0.0,2.0,1.0,inf,0.0,accepted 0.306377726911,0.00975325447734,0.613563748918,0.306377726911,1.93224686343,0.0,2.0,1.0,1.17548106192,0.0,accepted diff --git a/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv index abca0e7d1a..2ec6bec9a9 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv +++ b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv @@ -1,4 +1,14 @@ x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,ConstraintEvaluation_expConstr3,ConstraintEvaluation_impConstr3,fitness,accepted +4.0,3.0,5.0,7.0,2.0,6.0,99.0,10.0,0.0,1.0,2.0,1.79769313486e+308,-2.0,1.0,1.0,first +7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,0.0,1.0,2.0,1.79769313486e+308,3.0,11.0,0.0,first +4.0,3.0,6.0,7.0,2.0,5.0,96.0,10.0,0.0,1.0,1.0,1.79769313486e+308,-3.0,4.0,1.0,first +7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,0.0,1.0,1.0,1.79769313486e+308,2.0,14.0,0.0,first +4.0,6.0,7.0,2.0,3.0,5.0,90.0,16.0,0.0,1.0,3.0,1.79769313486e+308,1.0,10.0,0.0,first +4.0,6.0,2.0,5.0,7.0,3.0,95.0,16.0,0.0,1.0,4.0,1.79769313486e+308,3.0,5.0,0.0,first +4.0,7.0,2.0,5.0,3.0,6.0,95.0,18.0,0.0,1.0,5.0,1.79769313486e+308,3.0,5.0,0.0,first +6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,0.0,1.0,2.0,2.0,3.0,3.0,0.0,first +4.0,5.0,3.0,6.0,7.0,2.0,94.0,14.0,0.0,1.0,1.0,2.0,1.0,6.0,0.0,first +2.0,4.0,6.0,5.0,3.0,7.0,105.0,10.0,0.0,1.0,3.0,1.79769313486e+308,-1.0,-5.0,2.0,first 7.0,3.0,5.0,4.0,6.0,2.0,86.0,13.0,1.0,2.0,1.0,inf,1.0,14.0,0.0,accepted 7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,1.0,2.0,2.0,inf,2.0,14.0,0.0,accepted 6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,1.0,2.0,1.0,inf,3.0,3.0,0.0,accepted From 96269569d7576db900291fc952384771d895ddfa Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Tue, 11 Apr 2023 22:28:24 -0600 Subject: [PATCH 26/48] slight mods --- .../CodeInterfaceClasses/SIMULATE3/SimulateData.py | 7 +++++-- ravenframework/Optimizers/GeneticAlgorithm.py | 9 ++++++--- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py index fd3c3db140..4c7134e652 100644 --- a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py +++ b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py @@ -514,10 +514,13 @@ def fa_type(self): print(FAcount) #stop #Considering that: FA type 0 is empty, type 1 reflector, type 2 2% enrichment, types 3 and 4 2.5% enrichment, and types 5 and 6 3.2% enrichment. The cost of burnable is not being considered - fuel_cost = (FAcount[0] + FAcount[1])*0 + FAcount[2]*4.94262343 + (FAcount[3] + FAcount[4])*5.67862599 + (FAcount[5] + FAcount[6])*6.7274349 + if len(FAcount) == 7: + fuel_cost = (FAcount[0] + FAcount[1])*0 + FAcount[2]*4.94262343 + (FAcount[3] + FAcount[4])*5.67862599 + (FAcount[5] + FAcount[6])*6.7274349 + else: + fuel_cost = (FAcount[0] + FAcount[1])*0 + FAcount[2]*4.94262343 + (FAcount[3] + FAcount[4])*5.67862599 + (FAcount[5])*6.7274349 print(fuel_cost) #fuel_type.append(float(search_space)) - #stop + #stop if not fuel_cost: return ValueError("No values returned. Check Simulate File executed correctly") else: diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index ff3e020576..b36b2a4607 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -602,8 +602,8 @@ def _useRealization(self, info, rlz): # JY: Visualization: optimal points only - This code block needs to be either deleted or revisited. # plt.xlim(75,100) # plt.ylim(5,20) - plt.xlim(0,1) - plt.ylim(0,6) + # plt.xlim(0,1) + # plt.ylim(0,6) plt.title(str('Iteration ' + str(self.counter-1))) plt.plot(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,0], @@ -611,7 +611,7 @@ def _useRealization(self, info, rlz): for i in range(len(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,0])): plt.text(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,0], np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,1], str(self.batchId-1)) - # plt.pause() + plt.savefig('PF.png') ############################################################################## else: @@ -830,6 +830,9 @@ def _resolveNewGenerationMulti(self, traj, rlz, info): rlzDict['fitness'] = np.atleast_1d(self.constraints.data)[i] for ind, consName in enumerate([y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]): rlzDict['ConstraintEvaluation_'+consName] = self.constraintsV.data[i,ind] + for var in set(rlz.keys())-set(rlzDict.keys()): + if var not in ['batchId','prefix'] and not var.__contains__('Prob'): + rlzDict[var] = rlz[var] self._updateSolutionExport(traj, rlzDict, acceptable, None) # decide what to do next From 34d5cb236542881375e3ebceee7e9f8b1156b814 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Wed, 12 Apr 2023 15:31:04 -0600 Subject: [PATCH 27/48] regolding and reverting inf in fitness --- ravenframework/Optimizers/fitness/fitness.py | 1 - .../unconstrained/ZDT1/opt_export_0.csv | 12 ++++++------ .../Multi_MinwoReplacement/opt_export_0.csv | 16 ++++++++-------- 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/ravenframework/Optimizers/fitness/fitness.py b/ravenframework/Optimizers/fitness/fitness.py index d2481a34b6..1770ef02cc 100644 --- a/ravenframework/Optimizers/fitness/fitness.py +++ b/ravenframework/Optimizers/fitness/fitness.py @@ -103,7 +103,6 @@ def rank_crowding(rlz,**kwargs): coords={'rank': np.arange(np.shape(offSpringRank)[0])}) offSpringCD = frontUtils.crowdingDistance(rank=offSpringRank, popSize=len(offSpringRank), objectives=np.array(offspringObjsVals)) - offSpringCD[offSpringCD==np.inf] = sys.float_info.max offSpringCD = xr.DataArray(offSpringCD, dims=['CrowdingDistance'], coords={'CrowdingDistance': np.arange(np.shape(offSpringCD)[0])}) diff --git a/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv b/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv index 39e05c235f..25ec63951b 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv +++ b/tests/framework/Optimizers/GeneticAlgorithms/gold/continuous/unconstrained/ZDT1/opt_export_0.csv @@ -1,13 +1,13 @@ x1,x2,x3,obj1,obj2,age,batchId,rank,CD,fitness,accepted -0.902940987587,0.947612243227,0.840374259707,0.902940987587,3.96681957049,0.0,1.0,3.0,1.79769313486e+308,0.0,first -0.227236453264,0.847510234417,0.362760231915,0.227236453264,3.60499993579,0.0,1.0,2.0,1.79769313486e+308,0.0,first -0.766431005617,0.192211290866,0.39961784645,0.766431005617,1.3169883176,0.0,1.0,1.0,1.79769313486e+308,0.0,first +0.902940987587,0.947612243227,0.840374259707,0.902940987587,3.96681957049,0.0,1.0,3.0,inf,0.0,first +0.227236453264,0.847510234417,0.362760231915,0.227236453264,3.60499993579,0.0,1.0,2.0,inf,0.0,first +0.766431005617,0.192211290866,0.39961784645,0.766431005617,1.3169883176,0.0,1.0,1.0,inf,0.0,first 0.633202111729,0.793545654927,0.564774226762,0.633202111729,3.28234279694,0.0,1.0,2.0,2.0,0.0,first 0.306377726911,0.00975325447734,0.613563748918,0.306377726911,1.93224686343,0.0,1.0,1.0,1.32735741676,0.0,first -0.110044764846,0.604534715322,0.738899003886,0.110044764846,4.28628616584,0.0,1.0,1.0,1.79769313486e+308,0.0,first -0.331692186261,0.571854743308,0.965348788995,0.331692186261,4.24730587019,0.0,1.0,3.0,1.79769313486e+308,0.0,first +0.110044764846,0.604534715322,0.738899003886,0.110044764846,4.28628616584,0.0,1.0,1.0,inf,0.0,first +0.331692186261,0.571854743308,0.965348788995,0.331692186261,4.24730587019,0.0,1.0,3.0,inf,0.0,first 0.267873673297,0.166777967281,0.847808119107,0.267873673297,3.00298144409,0.0,1.0,1.0,0.749061564967,0.0,first -0.713407223745,0.641621648018,0.334449647072,0.713407223745,2.25417202135,0.0,1.0,2.0,1.79769313486e+308,0.0,first +0.713407223745,0.641621648018,0.334449647072,0.713407223745,2.25417202135,0.0,1.0,2.0,inf,0.0,first 0.13264102096,0.630711117673,0.405532905694,0.13264102096,3.37050011696,0.0,1.0,1.0,0.672642583243,0.0,first 0.766431005617,0.192211290866,0.39961784645,0.766431005617,1.3169883176,0.0,2.0,1.0,inf,0.0,accepted 0.110044764846,0.604534715322,0.738899003886,0.110044764846,4.28628616584,0.0,2.0,1.0,inf,0.0,accepted diff --git a/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv index 2ec6bec9a9..dc39c524eb 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv +++ b/tests/framework/Optimizers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv @@ -1,14 +1,14 @@ x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,ConstraintEvaluation_expConstr3,ConstraintEvaluation_impConstr3,fitness,accepted -4.0,3.0,5.0,7.0,2.0,6.0,99.0,10.0,0.0,1.0,2.0,1.79769313486e+308,-2.0,1.0,1.0,first -7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,0.0,1.0,2.0,1.79769313486e+308,3.0,11.0,0.0,first -4.0,3.0,6.0,7.0,2.0,5.0,96.0,10.0,0.0,1.0,1.0,1.79769313486e+308,-3.0,4.0,1.0,first -7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,0.0,1.0,1.0,1.79769313486e+308,2.0,14.0,0.0,first -4.0,6.0,7.0,2.0,3.0,5.0,90.0,16.0,0.0,1.0,3.0,1.79769313486e+308,1.0,10.0,0.0,first -4.0,6.0,2.0,5.0,7.0,3.0,95.0,16.0,0.0,1.0,4.0,1.79769313486e+308,3.0,5.0,0.0,first -4.0,7.0,2.0,5.0,3.0,6.0,95.0,18.0,0.0,1.0,5.0,1.79769313486e+308,3.0,5.0,0.0,first +4.0,3.0,5.0,7.0,2.0,6.0,99.0,10.0,0.0,1.0,2.0,inf,-2.0,1.0,1.0,first +7.0,4.0,5.0,2.0,3.0,6.0,89.0,15.0,0.0,1.0,2.0,inf,3.0,11.0,0.0,first +4.0,3.0,6.0,7.0,2.0,5.0,96.0,10.0,0.0,1.0,1.0,inf,-3.0,4.0,1.0,first +7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,0.0,1.0,1.0,inf,2.0,14.0,0.0,first +4.0,6.0,7.0,2.0,3.0,5.0,90.0,16.0,0.0,1.0,3.0,inf,1.0,10.0,0.0,first +4.0,6.0,2.0,5.0,7.0,3.0,95.0,16.0,0.0,1.0,4.0,inf,3.0,5.0,0.0,first +4.0,7.0,2.0,5.0,3.0,6.0,95.0,18.0,0.0,1.0,5.0,inf,3.0,5.0,0.0,first 6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,0.0,1.0,2.0,2.0,3.0,3.0,0.0,first 4.0,5.0,3.0,6.0,7.0,2.0,94.0,14.0,0.0,1.0,1.0,2.0,1.0,6.0,0.0,first -2.0,4.0,6.0,5.0,3.0,7.0,105.0,10.0,0.0,1.0,3.0,1.79769313486e+308,-1.0,-5.0,2.0,first +2.0,4.0,6.0,5.0,3.0,7.0,105.0,10.0,0.0,1.0,3.0,inf,-1.0,-5.0,2.0,first 7.0,3.0,5.0,4.0,6.0,2.0,86.0,13.0,1.0,2.0,1.0,inf,1.0,14.0,0.0,accepted 7.0,4.0,3.0,5.0,6.0,2.0,86.0,15.0,1.0,2.0,2.0,inf,2.0,14.0,0.0,accepted 6.0,3.0,5.0,2.0,4.0,7.0,97.0,12.0,1.0,2.0,1.0,inf,3.0,3.0,0.0,accepted From e0df314aadfee6d6280560e359bad987ba1e40cd Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Wed, 12 Apr 2023 15:35:02 -0600 Subject: [PATCH 28/48] trying to add all outputs to the rlz --- ravenframework/Optimizers/GeneticAlgorithm.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index b36b2a4607..a2b2c9b134 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -822,7 +822,10 @@ def _resolveNewGenerationMulti(self, traj, rlz, info): print("### rlz.sizes['RAVEN_sample_ID'] = {}".format(rlz.sizes['RAVEN_sample_ID'])) print("### self.population.shape is {}".format(self.population.shape)) for i in range(rlz.sizes['RAVEN_sample_ID']): - rlzDict = dict((var,self.population.data[i][j]) for j, var in enumerate(self.population.Gene.data)) + varList = self._solutionExport.getVars('input') + self._solutionExport.getVars('output') + list(self.toBeSampled.keys()) + rlzDict = dict((var,np.atleast_1d(rlz[var].data)[i]) for var in set(varList) if var in rlz.data_vars) + + # rlzDict = dict((var,self.population.data[i][j]) for j, var in enumerate(self.population.Gene.data)) for j in range(len(self._objectiveVar)): rlzDict[self._objectiveVar[j]] = objVal.data[i][j] rlzDict['rank'] = np.atleast_1d(self.rank.data)[i] From c0476f703b832ea1c582b806a13953df60e44648 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Wed, 12 Apr 2023 19:25:53 -0600 Subject: [PATCH 29/48] adding everything to bestPoint --- ravenframework/Optimizers/GeneticAlgorithm.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index a2b2c9b134..031550a20f 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -611,7 +611,7 @@ def _useRealization(self, info, rlz): for i in range(len(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,0])): plt.text(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,0], np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,1], str(self.batchId-1)) - plt.savefig('PF.png') + plt.savefig('PF'+str(i)+'.png') ############################################################################## else: @@ -833,15 +833,14 @@ def _resolveNewGenerationMulti(self, traj, rlz, info): rlzDict['fitness'] = np.atleast_1d(self.constraints.data)[i] for ind, consName in enumerate([y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]): rlzDict['ConstraintEvaluation_'+consName] = self.constraintsV.data[i,ind] - for var in set(rlz.keys())-set(rlzDict.keys()): - if var not in ['batchId','prefix'] and not var.__contains__('Prob'): - rlzDict[var] = rlz[var] self._updateSolutionExport(traj, rlzDict, acceptable, None) # decide what to do next if acceptable in ['accepted', 'first']: # record history bestRlz = {} + varList = self._solutionExport.getVars('input') + self._solutionExport.getVars('output') + list(self.toBeSampled.keys()) + bestRlz = dict((var,np.atleast_1d(rlz[var].data)) for var in set(varList) if var in rlz.data_vars) for i in range(len(self._objectiveVar)): bestRlz[self._objectiveVar[i]] = [item[i] for item in self.multiBestObjective] bestRlz['fitness'] = self.multiBestFitness From 81dc58024df90ccb4a1ff999f53816425bd20e90 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Thu, 13 Apr 2023 14:39:01 -0600 Subject: [PATCH 30/48] chenging type==str to len(self._objectVar) == 1 --- ravenframework/Optimizers/GeneticAlgorithm.py | 2 +- ravenframework/Optimizers/RavenSampled.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index 031550a20f..a77557e0fc 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -611,7 +611,7 @@ def _useRealization(self, info, rlz): for i in range(len(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,0])): plt.text(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,0], np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,1], str(self.batchId-1)) - plt.savefig('PF'+str(i)+'.png') + plt.savefig('PF'+str(i)+'_'+str(self.counter-1)+'.png') ############################################################################## else: diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 70b0518c3d..3727f0d41c 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -408,7 +408,7 @@ def finalizeSampler(self, failedRuns): self.raiseAnError(RuntimeError, f'There is no optimization history for traj {traj}! ' + 'Perhaps the Model failed?') - if type(self._objectiveVar) == str: + if len(self._objectiveVar) == 1: opt = self._optPointHistory[traj][-1][0] val = opt[self._objectiveVar] self.raiseADebug(statusTemplate.format(status='active', traj=traj, val=s * val)) @@ -576,7 +576,7 @@ def _handleImplicitConstraints(self, previous): @ Out, accept, bool, whether point was satisfied implicit constraints """ normed = copy.deepcopy(previous) - if type(self._objectiveVar) == str: + if len(self._objectiveVar) == 1: oldVal = normed[self._objectiveVar] else: oldVal = normed[self._objectiveVar[0]] @@ -650,7 +650,7 @@ def _resolveNewOptPoint(self, traj, rlz, optVal, info): # TODO could we ever use old rerun gradients to inform the gradient direction as well? self._rerunsSinceAccept[traj] += 1 N = self._rerunsSinceAccept[traj] + 1 - if type(self._objectiveVar) == str: + if len(self._objectiveVar) == 1: oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar] else: oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar[0]] From 3f27965b456f28dc644d439bf35c63b9970dec3b Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Tue, 18 Apr 2023 13:11:26 -0600 Subject: [PATCH 31/48] removing unnecessary if statement, this needs revisiting --- ravenframework/Optimizers/RavenSampled.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 3727f0d41c..82505cf3e2 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -576,10 +576,7 @@ def _handleImplicitConstraints(self, previous): @ Out, accept, bool, whether point was satisfied implicit constraints """ normed = copy.deepcopy(previous) - if len(self._objectiveVar) == 1: - oldVal = normed[self._objectiveVar] - else: - oldVal = normed[self._objectiveVar[0]] + oldVal = normed[self._objectiveVar[0]] normed.pop(self._objectiveVar[0], oldVal) denormed = self.denormalizeData(normed) denormed[self._objectiveVar[0]] = oldVal @@ -651,7 +648,7 @@ def _resolveNewOptPoint(self, traj, rlz, optVal, info): self._rerunsSinceAccept[traj] += 1 N = self._rerunsSinceAccept[traj] + 1 if len(self._objectiveVar) == 1: - oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar] + oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar[0]] else: oldVal = self._optPointHistory[traj][-1][0][self._objectiveVar[0]] newAvg = ((N-1)*oldVal + optVal) / N From facf74e9c5e87b3e3e7a37c05308129b35e5b9cd Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Thu, 20 Apr 2023 13:39:38 -0600 Subject: [PATCH 32/48] modifying reverting cycle length to its value not the inverse --- ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py index 4c7134e652..f66f479581 100644 --- a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py +++ b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py @@ -213,7 +213,7 @@ def EOCEFPD(self): if not list_: return ValueError("No values returned. Check Simulate File executed correctly") else: - outputDict = {'info_ids':['MaxEFPD'], 'values': [float(1/list_[-1])]} + outputDict = {'info_ids':['MaxEFPD'], 'values': [float(list_[-1])]} return outputDict From a92049c0b723a500f31f64b46a9a24b082762f00 Mon Sep 17 00:00:00 2001 From: Kim Date: Mon, 12 Jun 2023 10:48:20 -0600 Subject: [PATCH 33/48] simulateData updating cost model. --- .../CodeInterfaceClasses/SIMULATE3/SimulateData.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py index f66f479581..18044a736b 100644 --- a/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py +++ b/ravenframework/CodeInterfaceClasses/SIMULATE3/SimulateData.py @@ -213,7 +213,7 @@ def EOCEFPD(self): if not list_: return ValueError("No values returned. Check Simulate File executed correctly") else: - outputDict = {'info_ids':['MaxEFPD'], 'values': [float(list_[-1])]} + outputDict = {'info_ids':['MaxEFPD'], 'values': [list_[-1]]} return outputDict @@ -515,9 +515,9 @@ def fa_type(self): #stop #Considering that: FA type 0 is empty, type 1 reflector, type 2 2% enrichment, types 3 and 4 2.5% enrichment, and types 5 and 6 3.2% enrichment. The cost of burnable is not being considered if len(FAcount) == 7: - fuel_cost = (FAcount[0] + FAcount[1])*0 + FAcount[2]*4.94262343 + (FAcount[3] + FAcount[4])*5.67862599 + (FAcount[5] + FAcount[6])*6.7274349 + fuel_cost = (FAcount[0] + FAcount[1])*0 + FAcount[2]*2.69520839 + (FAcount[3] + FAcount[4])*3.24678409 + (FAcount[5] + FAcount[6])*4.03739539 else: - fuel_cost = (FAcount[0] + FAcount[1])*0 + FAcount[2]*4.94262343 + (FAcount[3] + FAcount[4])*5.67862599 + (FAcount[5])*6.7274349 + fuel_cost = (FAcount[0] + FAcount[1])*0 + FAcount[2]*2.69520839 + (FAcount[3] + FAcount[4])*3.24678409 + (FAcount[5])*4.03739539 print(fuel_cost) #fuel_type.append(float(search_space)) #stop From 0faeb9c9157eb90f40fa95585fbc5eb05ab8c916 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Sat, 15 Jul 2023 11:13:57 -0600 Subject: [PATCH 34/48] minor change is made in ZDT1 test. --- .../Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT_model.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT_model.py b/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT_model.py index ca53da447d..829307f73e 100644 --- a/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT_model.py +++ b/tests/framework/Optimizers/NSGAII/discrete/constrained/ZDT1/ZDT_model.py @@ -23,7 +23,7 @@ def evaluate(Inputs): for ind,var in enumerate(Inputs.keys()): # write the objective function here if (ind == 0) : - obj1 += Inputs[var] + obj1 = Inputs[var] if (ind != 0): Sum += Inputs[var] g = 1 + (9/len(Inputs.keys())*Sum ) @@ -39,3 +39,5 @@ def run(self,Inputs): @ Out, None """ self.obj1,self.obj2 = evaluate(Inputs) + + From dbad22c1a00890fce2ab59cbf83b49e41a2d4768 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Thu, 27 Jul 2023 08:52:36 -0600 Subject: [PATCH 35/48] myConstraints for MultiSum is updated. --- .../MultiSumwConst/myConstraints.py | 34 ++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py index f71254ec67..ec32efb940 100644 --- a/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py +++ b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py @@ -105,4 +105,36 @@ def impConstr3(Input): @ out, g, float, implicit constraint 3 evaluation function """ g = 100 - Input.obj1 - return g \ No newline at end of file + return g + +def impConstr4(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 3 evaluation function + """ + g = Input.obj2 - 20 + return g + + """ + Evaluates the implicit constraint function at a given point/solution ($\vec(x)$) + @ In, Input, object, RAVEN container + @ Out, g(inputs x1,x2,..,output or dependent variable), float, implicit constraint evaluation function + the way the constraint is designed is that + the constraint function has to be >= 0, + so if: + 1) f(x,y) >= 0 then g = f + 2) f(x,y) >= a then g = f - a + 3) f(x,y) <= b then g = b - f + 4) f(x,y) = c then g = 1e-6 - abs((f(x,y) - c)) (equality constraint) + """ + """ + Let's assume that the constraint is: + $ x3+x4 < 8 $ + then g the constraint evaluation function (which has to be > 0) is taken to be: + g = 8 - (x3+x4) + in this case if g(\vec(x)) < 0 then this x violates the constraint and vice versa + @ In, Input, object, RAVEN container + @ out, g, float, explicit constraint 1 evaluation function + """ \ No newline at end of file From 699b3deede03040f78012d92f4bc50a1e1a40838 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Mon, 7 Aug 2023 18:45:20 -0600 Subject: [PATCH 36/48] Two issues are resolved: population and objective value mismatch, min/max gives effects to constraints. --- ravenframework/Optimizers/GeneticAlgorithm.py | 11 +++++++---- ravenframework/Optimizers/RavenSampled.py | 3 ++- .../MultiSumwConst/MinwoRepMultiObjective.xml | 8 ++++---- .../constrained/MultiSumwConst/myConstraints.py | 12 +++++++++++- 4 files changed, 24 insertions(+), 10 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index a77557e0fc..566bea0ba1 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -363,7 +363,7 @@ def handleInput(self, paramInput): # Currently, only InvLin and feasibleFirst Fitnesses deal with constrained optimization # TODO: @mandd, please explore the possibility to convert the logistic fitness into a constrained optimization fitness. if 'Constraint' in self.assemblerObjects and self._fitnessType not in ['invLinear','feasibleFirst','rank_crowding']: - self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support invLinear and feasibleFirst fitnesses, whereas provided fitness is {self._fitnessType}') + self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support invLinear, feasibleFirst and rank_crowding as a fitness, whereas provided fitness is {self._fitnessType}') self._objCoeff = fitnessNode.findFirst('a').value if fitnessNode.findFirst('a') is not None else None self._penaltyCoeff = fitnessNode.findFirst('b').value if fitnessNode.findFirst('b') is not None else None self._fitnessInstance = fitnessReturnInstance(self,name = self._fitnessType) @@ -525,7 +525,10 @@ def _useRealization(self, info, rlz): for index,individual in enumerate(offSprings): newOpt = individual + objOpt = dict(zip(self._objectiveVar, + list(map(lambda x:-1 if x=="max" else 1 , self._minMax)))) opt = dict(zip(self._objectiveVar, [item[index] for item in objectiveVal])) + opt = {k: objOpt[k]*opt[k] for k in opt} for p, v in constraintData.items(): opt[p] = v[index] @@ -823,9 +826,9 @@ def _resolveNewGenerationMulti(self, traj, rlz, info): print("### self.population.shape is {}".format(self.population.shape)) for i in range(rlz.sizes['RAVEN_sample_ID']): varList = self._solutionExport.getVars('input') + self._solutionExport.getVars('output') + list(self.toBeSampled.keys()) - rlzDict = dict((var,np.atleast_1d(rlz[var].data)[i]) for var in set(varList) if var in rlz.data_vars) - - # rlzDict = dict((var,self.population.data[i][j]) for j, var in enumerate(self.population.Gene.data)) + # rlzDict = dict((var,np.atleast_1d(rlz[var].data)[i]) for var in set(varList) if var in rlz.data_vars) + rlzDict = dict((var,self.population.data[i][j]) for j, var in enumerate(self.population.Gene.data)) + rlzDict['batchId'] = rlz['batchId'][i] for j in range(len(self._objectiveVar)): rlzDict[self._objectiveVar[j]] = objVal.data[i][j] rlzDict['rank'] = np.atleast_1d(self.rank.data)[i] diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 82505cf3e2..1f12715d9d 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -721,6 +721,7 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): if 'max' in self._minMax: objValue *= -1 toExport[self._objectiveVar[0]] = objValue + else: # Multi Objective Optimization for i in range(len(self._objectiveVar)): objValue = rlz[self._objectiveVar[i]] @@ -739,7 +740,7 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): toExport[var] = rlz[var] # formatting toExport = dict((var, np.atleast_1d(val)) for var, val in toExport.items()) - self._solutionExport.addRealization(toExport) + self._solutionExport.addRealization(toExport) # It will stop when it is final def _addToSolutionExport(self, traj, rlz, acceptable): """ diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/MinwoRepMultiObjective.xml index a32463eb13..700d248103 100644 --- a/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/MinwoRepMultiObjective.xml +++ b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/MinwoRepMultiObjective.xml @@ -57,14 +57,14 @@ - 2 + 3 42 every - min + min, max - 10 + 15 tournamentSelection @@ -110,7 +110,7 @@ - 10 + 15 050877 diff --git a/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py index ec32efb940..4d3b5f51c9 100644 --- a/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py +++ b/tests/framework/Optimizers/NSGAII/discrete/constrained/MultiSumwConst/myConstraints.py @@ -114,7 +114,17 @@ def impConstr4(Input): @ In, Input, object, RAVEN container @ out, g, float, implicit constraint 3 evaluation function """ - g = Input.obj2 - 20 + g = Input.obj2 - 16 + return g + +def impConstr5(Input): + """ + The implicit constraint involves variables from the output space, for example the objective variable or + a dependent variable that is not in the optimization search space + @ In, Input, object, RAVEN container + @ out, g, float, implicit constraint 3 evaluation function + """ + g = 200 - Input.obj1 return g """ From 8cffedbeaff82f0f04efa79a1043adc7aaa27426 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Tue, 8 Aug 2023 15:32:46 -0600 Subject: [PATCH 37/48] minor things are corrected. Nothing important. --- ravenframework/Optimizers/RavenSampled.py | 3 +-- ravenframework/utils/frontUtils.py | 10 ++-------- 2 files changed, 3 insertions(+), 10 deletions(-) diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 1f12715d9d..82505cf3e2 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -721,7 +721,6 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): if 'max' in self._minMax: objValue *= -1 toExport[self._objectiveVar[0]] = objValue - else: # Multi Objective Optimization for i in range(len(self._objectiveVar)): objValue = rlz[self._objectiveVar[i]] @@ -740,7 +739,7 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): toExport[var] = rlz[var] # formatting toExport = dict((var, np.atleast_1d(val)) for var, val in toExport.items()) - self._solutionExport.addRealization(toExport) # It will stop when it is final + self._solutionExport.addRealization(toExport) def _addToSolutionExport(self, traj, rlz, acceptable): """ diff --git a/ravenframework/utils/frontUtils.py b/ravenframework/utils/frontUtils.py index 8bb0e11c72..94b4b3efd6 100644 --- a/ravenframework/utils/frontUtils.py +++ b/ravenframework/utils/frontUtils.py @@ -80,7 +80,7 @@ def rankNonDominatedFrontiers(data): @ out, nonDominatedRank, list, a list of length nPoints that has the ranking of the front passing through each point """ - ## tentative code block start + # tentative code block start # import matplotlib.pyplot as plt # from mpl_toolkits.mplot3d import Axes3D # xdata = [item[0] for item in data] @@ -109,6 +109,7 @@ def rankNonDominatedFrontiers(data): # ax3d.text(x, y, z, label) # plt.title("Data") # plt.show() + return nonDominatedRank def crowdingDistance(rank, popSize, objectives): @@ -119,13 +120,6 @@ def crowdingDistance(rank, popSize, objectives): @ In, objectives, np.array, matrix contains objective values for each element of the population @ Out, crowdDist, np.array, array of crowding distances """ - # # tentative code block start - # import matplotlib.pyplot as plt - # from mpl_toolkits.mplot3d import Axes3D - # xdata = [item[0] for item in objectives] - # ydata = [item[1] for item in objectives] - # zdata = [item[2] for item in objectives] - crowdDist = np.zeros(popSize) fronts = np.unique(rank) fronts = fronts[fronts!=np.inf] From 9f4eecdc018fd24aaa8b21a92ecf350642a9a742 Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Tue, 8 Aug 2023 15:36:44 -0600 Subject: [PATCH 38/48] Additional minor changes are made. Nothing important. --- ravenframework/utils/frontUtils.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/ravenframework/utils/frontUtils.py b/ravenframework/utils/frontUtils.py index 94b4b3efd6..8302ed247a 100644 --- a/ravenframework/utils/frontUtils.py +++ b/ravenframework/utils/frontUtils.py @@ -80,7 +80,7 @@ def rankNonDominatedFrontiers(data): @ out, nonDominatedRank, list, a list of length nPoints that has the ranking of the front passing through each point """ - # tentative code block start + ## tentative code block start # import matplotlib.pyplot as plt # from mpl_toolkits.mplot3d import Axes3D # xdata = [item[0] for item in data] @@ -109,7 +109,6 @@ def rankNonDominatedFrontiers(data): # ax3d.text(x, y, z, label) # plt.title("Data") # plt.show() - return nonDominatedRank def crowdingDistance(rank, popSize, objectives): @@ -120,6 +119,13 @@ def crowdingDistance(rank, popSize, objectives): @ In, objectives, np.array, matrix contains objective values for each element of the population @ Out, crowdDist, np.array, array of crowding distances """ + # # tentative code block start + # import matplotlib.pyplot as plt + # from mpl_toolkits.mplot3d import Axes3D + # xdata = [item[0] for item in data] + # ydata = [item[1] for item in data] + # zdata = [item[2] for item in data] + crowdDist = np.zeros(popSize) fronts = np.unique(rank) fronts = fronts[fronts!=np.inf] From 2487621433fd08755a5cb371118ea33141d4c5cb Mon Sep 17 00:00:00 2001 From: JunyungKim Date: Tue, 8 Aug 2023 15:38:46 -0600 Subject: [PATCH 39/48] Additional minor change is made. Nothing important. --- ravenframework/utils/frontUtils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ravenframework/utils/frontUtils.py b/ravenframework/utils/frontUtils.py index 8302ed247a..8bb0e11c72 100644 --- a/ravenframework/utils/frontUtils.py +++ b/ravenframework/utils/frontUtils.py @@ -122,9 +122,9 @@ def crowdingDistance(rank, popSize, objectives): # # tentative code block start # import matplotlib.pyplot as plt # from mpl_toolkits.mplot3d import Axes3D - # xdata = [item[0] for item in data] - # ydata = [item[1] for item in data] - # zdata = [item[2] for item in data] + # xdata = [item[0] for item in objectives] + # ydata = [item[1] for item in objectives] + # zdata = [item[2] for item in objectives] crowdDist = np.zeros(popSize) fronts = np.unique(rank) From 3657634f61ad1d93a5c95b615bd40ca55f3e19d0 Mon Sep 17 00:00:00 2001 From: Junyung Kim Date: Wed, 30 Aug 2023 10:08:19 -0600 Subject: [PATCH 40/48] fitness data structure is changed from data xarray to dataSet. It works well with sinlge objective optimization. --- ravenframework/Optimizers/GeneticAlgorithm.py | 83 +++++++++++------ ravenframework/Optimizers/fitness/fitness.py | 93 +++++++++++++++---- .../parentSelectors/parentSelectors.py | 19 ++-- .../survivorSelectors/survivorSelectors.py | 15 ++- .../constrained/MinwoRepMultiObjective.xml | 14 +-- .../constrained/testGAMinwRepConstrained.xml | 10 +- 6 files changed, 164 insertions(+), 70 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index 566bea0ba1..e6276f9020 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -142,7 +142,7 @@ def getInputSpecification(cls): \item tournamentSelection. \item rankSelection. \end{itemize} - \item Reproduction: + \item Reproduction: \begin{itemize} \item crossover: \begin{itemize} @@ -158,11 +158,16 @@ def getInputSpecification(cls): \item bitFlipMutator. \end{itemize} \end{itemize} - \item survivorSelectors: + \item survivorSelectors: \begin{itemize} \item ageBased. \item fitnessBased. \end{itemize} + \item constraintHandling: + \begin{itemize} + \item hard. + \item soft. + \end{itemize} \end{itemize}""") # Population Size populationSize = InputData.parameterInputFactory('populationSize', strictMode=True, @@ -170,6 +175,14 @@ def getInputSpecification(cls): printPriority=108, descr=r"""The number of chromosomes in each population.""") GAparams.addSub(populationSize) + + # Constraint Handling + constraintHandling = InputData.parameterInputFactory('constraintHandling', strictMode=True, + contentType=InputTypes.StringType, + printPriority=108, + descr=r"""a node indicating whether GA will handle constraints hardly or softly.""") + GAparams.addSub(constraintHandling) + # Parent Selection parentSelection = InputData.parameterInputFactory('parentSelection', strictMode=True, contentType=InputTypes.StringType, @@ -358,11 +371,9 @@ def handleInput(self, paramInput): # Fitness fitnessNode = gaParamsNode.findFirst('fitness') self._fitnessType = fitnessNode.parameterValues['type'] - # Check if the fitness requested is among the constrained optimization fitnesses - # Currently, only InvLin and feasibleFirst Fitnesses deal with constrained optimization # TODO: @mandd, please explore the possibility to convert the logistic fitness into a constrained optimization fitness. - if 'Constraint' in self.assemblerObjects and self._fitnessType not in ['invLinear','feasibleFirst','rank_crowding']: + if 'Constraint' in self.assemblerObjects and self._fitnessType not in ['invLinear','feasibleFirst','hardConstraint']: self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support invLinear, feasibleFirst and rank_crowding as a fitness, whereas provided fitness is {self._fitnessType}') self._objCoeff = fitnessNode.findFirst('a').value if fitnessNode.findFirst('a') is not None else None self._penaltyCoeff = fitnessNode.findFirst('b').value if fitnessNode.findFirst('b') is not None else None @@ -463,16 +474,15 @@ def _useRealization(self, info, rlz): for y in (self._constraintFunctions + self._impConstraintFunctions): params += y.parameterNames() for p in list(set(params) -set([self._objectiveVar[0]]) -set(list(self.toBeSampled.keys()))): - # for p in list(set(params) -set([self._objectiveVar]) -set(list(self.toBeSampled.keys()))): constraintData[p] = list(np.atleast_1d(rlz[p].data)) # Compute constraint function g_j(x) for all constraints (j = 1 .. J) # and all x's (individuals) in the population g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) g = xr.DataArray(g0, - dims=['chromosome','Constraint'], - coords={'chromosome':np.arange(np.shape(offSprings)[0]), - 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) + dims=['chromosome','Constraint'], + coords={'chromosome':np.arange(np.shape(offSprings)[0]), + 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) # FIXME The constraint handling is following the structure of the RavenSampled.py, # there are many utility functions that can be simplified and/or merged together # _check, _handle, and _apply, for explicit and implicit constraints. @@ -519,9 +529,9 @@ def _useRealization(self, info, rlz): g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) g = xr.DataArray(g0, - dims=['chromosome','Constraint'], - coords={'chromosome':np.arange(np.shape(offSprings)[0]), - 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) + dims=['chromosome','Constraint'], + coords={'chromosome':np.arange(np.shape(offSprings)[0]), + 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) for index,individual in enumerate(offSprings): newOpt = individual @@ -538,14 +548,13 @@ def _useRealization(self, info, rlz): else: g.data[index, constIndex] = self._handleImplicitConstraints(newOpt, opt, constraint) - fitness = np.zeros((len(offSprings), 1)) - for i in range(len(fitness)): - fitness[i] = countConstViolation(g.data[i]) - fitness = [item for sublist in fitness.tolist() for item in sublist] - - fitness = xr.DataArray(fitness, - dims=['NumOfConstraintViolated'], - coords={'NumOfConstraintViolated':np.arange(np.shape(fitness)[0])}) + offSpringFitness = self._fitnessInstance(rlz, + objVar=self._objectiveVar, + a=self._objCoeff, + b=self._penaltyCoeff, + penalty=None, + constraintFunction=g, + type=self._minMax) # 0.2@ n-1: Survivor selection(rlz) # update population container given obtained children @@ -579,7 +588,7 @@ def _useRealization(self, info, rlz): popObjectiveVal=self.objectiveVal, offObjectiveVal=objectiveVal, popConst = self.constraints, - offConst = fitness, + offConst = offSpringFitness, popConstV = self.constraintsV, offConstV = g ) @@ -619,11 +628,27 @@ def _useRealization(self, info, rlz): else: self.population = offSprings - self.constraints = fitness + self.constraints = offSpringFitness self.constraintsV = g - self.rank, self.crowdingDistance = self._fitnessInstance(rlz, - objVals = self._objectiveVar - ) + + offObjVal = [] + for i in range(len(self._objectiveVar)): + offObjVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) + + offspringObjsVals = [list(ele) for ele in list(zip(*offObjVal))] + + offSpringRank = frontUtils.rankNonDominatedFrontiers(np.array(offspringObjsVals)) + self.rank = xr.DataArray(offSpringRank, + dims=['rank'], + coords={'rank': np.arange(np.shape(offSpringRank)[0])}) + + offSpringCD = frontUtils.crowdingDistance(rank=offSpringRank, + popSize=len(offSpringRank), + objectives=np.array(offspringObjsVals)) + self.crowdingDistance = xr.DataArray(offSpringCD, + dims=['CrowdingDistance'], + coords={'CrowdingDistance': np.arange(np.shape(offSpringCD)[0])}) + self.objectiveVal = [] for i in range(len(self._objectiveVar)): self.objectiveVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) @@ -775,7 +800,7 @@ def _resolveNewGeneration(self, traj, rlz, objectiveVal, fitness, g, info): varList = self._solutionExport.getVars('input') + self._solutionExport.getVars('output') + list(self.toBeSampled.keys()) rlzDict = dict((var,np.atleast_1d(rlz[var].data)[i]) for var in set(varList) if var in rlz.data_vars) rlzDict[self._objectiveVar[0]] = np.atleast_1d(rlz[self._objectiveVar[0]].data)[i] - rlzDict['fitness'] = np.atleast_1d(fitness.data)[i] + rlzDict['fitness'] = np.atleast_1d(fitness.to_array()[:,i]) for ind, consName in enumerate(g['Constraint'].values): rlzDict['ConstraintEvaluation_'+consName] = g[i,ind] self._updateSolutionExport(traj, rlzDict, acceptable, None) @@ -867,12 +892,14 @@ def _collectOptPoint(self, rlz, fitness, objectiveVal, g): @ In, fitness, xr.DataArray, fitness values at each chromosome of the realization @ Out, point, dict, point used in this realization """ - varList = list(self.toBeSampled.keys()) + self._solutionExport.getVars('input') + self._solutionExport.getVars('output') varList = set(varList) selVars = [var for var in varList if var in rlz.data_vars] population = datasetToDataArray(rlz, selVars) - optPoints,fit,obj,gOfBest = zip(*[[x,y,z,w] for x, y, z,w in sorted(zip(np.atleast_2d(population.data),np.atleast_1d(fitness.data),objectiveVal,np.atleast_2d(g.data)),reverse=True,key=lambda x: (x[1]))]) + if self._fitnessType == 'hardConstraint': + optPoints,fit,obj,gOfBest = zip(*[[x,y,z,w] for x, y, z,w in sorted(zip(np.atleast_2d(population.data),datasetToDataArray(fitness, self._objectiveVar).data,objectiveVal,np.atleast_2d(g.data)),reverse=True,key=lambda x: (x[1],-x[2]))]) + else: + optPoints,fit,obj,gOfBest = zip(*[[x,y,z,w] for x, y, z,w in sorted(zip(np.atleast_2d(population.data),datasetToDataArray(fitness, self._objectiveVar).data,objectiveVal,np.atleast_2d(g.data)),reverse=True,key=lambda x: (x[1]))]) point = dict((var,float(optPoints[0][i])) for i, var in enumerate(selVars) if var in rlz.data_vars) gOfBest = dict(('ConstraintEvaluation_'+name,float(gOfBest[0][i])) for i, name in enumerate(g.coords['Constraint'].values)) if (self.counter > 1 and obj[0] <= self.bestObjective and fit[0] >= self.bestFitness) or self.counter == 1: diff --git a/ravenframework/Optimizers/fitness/fitness.py b/ravenframework/Optimizers/fitness/fitness.py index 1770ef02cc..6dc500e641 100644 --- a/ravenframework/Optimizers/fitness/fitness.py +++ b/ravenframework/Optimizers/fitness/fitness.py @@ -20,6 +20,7 @@ """ # Internal Modules---------------------------------------------------------------------------------- from ...utils import frontUtils +from ..parentSelectors.parentSelectors import countConstViolation # External Imports import numpy as np @@ -75,8 +76,8 @@ def invLinear(rlz,**kwargs): fitness = -a * (rlz[objVar].data).reshape(-1,1) - b * np.sum(np.maximum(0,-penalty),axis=-1).reshape(-1,1) fitness = xr.DataArray(np.squeeze(fitness), - dims=['chromosome'], - coords={'chromosome': np.arange(len(data))}) + dims=['chromosome'], + coords={'chromosome': np.arange(len(data))}) return fitness def rank_crowding(rlz,**kwargs): @@ -109,6 +110,34 @@ def rank_crowding(rlz,**kwargs): return offSpringRank, offSpringCD + +def hardConstraint(rlz,**kwargs): + r""" + Multiobjective optimization using NSGA-II requires the rank and crowding distance values to the objective function + + @ In, rlz, xr.Dataset, containing the evaluation of a certain + set of individuals (can be the initial population for the very first iteration, + or a population of offsprings) + @ In, kwargs, dict, dictionary of parameters for this rank_crowding method: + objVar, string, the names of the objective variables + @ Out, offSpringRank, xr.DataArray, the rank of the given objective corresponding to a specific chromosome. + offSpringCD, xr.DataArray, the crowding distance of the given objective corresponding to a specific chromosome. + """ + objVar = kwargs['objVar'] + g = kwargs['constraintFunction'] + data = np.atleast_1d(rlz[objVar].data) + fitness = np.zeros((len(data), 1)) + for i in range(len(fitness)): + fitness[i] = countConstViolation(g.data[i]) + fitness = [-item for sublist in fitness.tolist() for item in sublist] + + fitness = xr.DataArray(fitness, + dims=['NumOfConstraintViolated'], + coords={'NumOfConstraintViolated':np.arange(np.shape(fitness)[0])}) + + return fitness + + def feasibleFirst(rlz,**kwargs): r""" Efficient Parameter-less Feasible First Penalty Fitness method @@ -117,11 +146,13 @@ def feasibleFirst(rlz,**kwargs): 1. As the objective function decreases (comes closer to the min value), the fitness value increases 2. As the objective function increases (away from the min value), the fitness value decreases 3. As the solution violates the constraints the fitness should decrease and hence the solution is less favored by the algorithm. - 4. For the violating solutions, the fitness is starts from the worst solution in the population + 4. For the violating solutions, the fitness starts from the worst solution in the population (i.e., max objective in minimization problems and min objective in maximization problems) For maximization problems the objective value is multiplied by -1 and hence the previous trends are inverted. A great quality of this fitness is that if the objective value is equal for multiple solutions it selects the furthest from constraint violation. + + Reference: Deb, Kalyanmoy. "An efficient constraint handling method for genetic algorithms." Computer methods in applied mechanics and engineering 186.2-4 (2000): 311-338. .. math:: @@ -139,23 +170,46 @@ def feasibleFirst(rlz,**kwargs): 'constraintFunction', xr.Dataarray, containing all constraint functions (explicit and implicit) evaluations for the whole population @ Out, fitness, xr.DataArray, the fitness function of the given objective corresponding to a specific chromosome. """ - objVar = kwargs['objVar'] + if isinstance(kwargs['objVar'], str) == True: + objVar = [kwargs['objVar']] + else: + objVar = kwargs['objVar'] g = kwargs['constraintFunction'] - data = np.atleast_1d(rlz[objVar].data) - worstObj = max(data) - fitness = [] - for ind in range(data.size): - if np.all(g.data[ind, :]>=0): - fit=(data[ind]) + for i in range(len(objVar)): + data = np.atleast_1d(rlz[objVar][objVar[i]].data) + worstObj = max(data) + fitness = [] + for ind in range(data.size): + if np.all(g.data[ind, :]>=0): + fit=(data[ind]) + else: + fit = worstObj + for constInd,_ in enumerate(g['Constraint'].data): + fit+=(max(0,-1 * g.data[ind, constInd])) + fitness.append(-1 * fit) + fitness = xr.DataArray(np.array(fitness), + dims=['chromosome'], + coords={'chromosome': np.arange(len(data))}) + if i == 0: + fitnessSet = fitness.to_dataset(name = objVar[i]) else: - fit = worstObj - for constInd,_ in enumerate(g['Constraint'].data): - fit+=(max(0,-1 * g.data[ind, constInd])) - fitness.append(-1 * fit) - fitness = xr.DataArray(np.array(fitness), - dims=['chromosome'], - coords={'chromosome': np.arange(len(data))}) - return fitness + fitnessSet[objVar[i]] = fitness + ### This code block is for sinlge objective ### + # data = np.atleast_1d(rlz[objVar].data) + # worstObj = max(data) + # fitness = [] + # for ind in range(data.size): + # if np.all(g.data[ind, :]>=0): + # fit=(data[ind]) + # else: + # fit = worstObj + # for constInd,_ in enumerate(g['Constraint'].data): + # fit+=(max(0,-1 * g.data[ind, constInd])) + # fitness.append(-1 * fit) + # fitness = xr.DataArray(np.array(fitness), + # dims=['chromosome'], + # coords={'chromosome': np.arange(len(data))}) + return fitnessSet def logistic(rlz,**kwargs): """ @@ -201,6 +255,7 @@ def logistic(rlz,**kwargs): __fitness['logistic'] = logistic __fitness['feasibleFirst'] = feasibleFirst __fitness['rank_crowding'] = rank_crowding +__fitness['hardConstraint'] = hardConstraint def returnInstance(cls, name): @@ -211,5 +266,5 @@ def returnInstance(cls, name): @ Out, __crossovers[name], instance of class """ if name not in __fitness: - cls.raiseAnError (IOError, "{} FITNESS FUNCTION NOT IMPLEMENTED!!!!!".format(name)) + cls.raiseAnError (IOError, "{} is not a supported fitness function. ".format(name)) return __fitness[name] diff --git a/ravenframework/Optimizers/parentSelectors/parentSelectors.py b/ravenframework/Optimizers/parentSelectors/parentSelectors.py index 66bf37932f..cf7d5b33f4 100644 --- a/ravenframework/Optimizers/parentSelectors/parentSelectors.py +++ b/ravenframework/Optimizers/parentSelectors/parentSelectors.py @@ -21,10 +21,15 @@ Created June,16,2020 @authors: Mohammad Abdo, Diego Mandelli, Andrea Alfonsi """ - +# External Modules---------------------------------------------------------------------------------- import numpy as np import xarray as xr from ...utils import randomUtils +# External Modules---------------------------------------------------------------------------------- + +# Internal Modules---------------------------------------------------------------------------------- +from ...utils.gaUtils import dataArrayToDict, datasetToDataArray +# Internal Modules End------------------------------------------------------------------------------ # For mandd: to be updated with RAVEN official tools from itertools import combinations @@ -42,7 +47,7 @@ def rouletteWheel(population,**kwargs): """ # Arguments pop = population - fitness = kwargs['fitness'] + fitness = np.array([item for sublist in datasetToDataArray(kwargs['fitness'], list(kwargs['fitness'].keys())).data for item in sublist]) nParents= kwargs['nParents'] # if nparents = population size then do nothing (whole population are parents) if nParents == pop.shape[0]: @@ -62,11 +67,11 @@ def rouletteWheel(population,**kwargs): roulettePointer = randomUtils.random(dim=1, samples=1) # initialize Probability counter = 0 - if np.all(fitness.data>=0) or np.all(fitness.data<=0): - selectionProb = fitness.data/np.sum(fitness.data) # Share of the pie (rouletteWheel) + if np.all(fitness>=0) or np.all(fitness<=0): + selectionProb = fitness/np.sum(fitness) # Share of the pie (rouletteWheel) else: # shift the fitness to be all positive - shiftedFitness = fitness.data + abs(min(fitness.data)) + shiftedFitness = fitness + abs(min(fitness)) selectionProb = shiftedFitness/np.sum(shiftedFitness) # Share of the pie (rouletteWheel) sumProb = selectionProb[counter] @@ -109,11 +114,11 @@ def tournamentSelection(population,**kwargs): matrixOperationRaw[:,3] = np.transpose(constraintInfo.data) matrixOperation = np.zeros((popSize,len(matrixOperationRaw[0]))) else: - fitness = kwargs['fitness'] + fitness = np.array([item for sublist in datasetToDataArray(kwargs['fitness'], list(kwargs['fitness'].keys())).data for item in sublist]) multiObjectiveRanking = False matrixOperationRaw = np.zeros((popSize,2)) matrixOperationRaw[:,0] = np.transpose(np.arange(popSize)) - matrixOperationRaw[:,1] = np.transpose(fitness.data) + matrixOperationRaw[:,1] = np.transpose(fitness) matrixOperation = np.zeros((popSize,2)) indexes = list(np.arange(popSize)) diff --git a/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py b/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py index 6da9f67071..6848c22275 100644 --- a/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py +++ b/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py @@ -21,10 +21,15 @@ Created June,16,2020 @authors: Mohammad Abdo, Diego Mandelli, Andrea Alfonsi """ - +# External Modules---------------------------------------------------------------------------------- import numpy as np import xarray as xr from ravenframework.utils import frontUtils +# External Modules End------------------------------------------------------------------------------ + +# Internal Modules---------------------------------------------------------------------------------- +from ...utils.gaUtils import dataArrayToDict, datasetToDataArray +# Internal Modules End------------------------------------------------------------------------------ # @profile def ageBased(newRlz,**kwargs): @@ -97,11 +102,12 @@ def fitnessBased(newRlz,**kwargs): else: popAge = kwargs['age'] - offSpringsFitness = np.atleast_1d(kwargs['offSpringsFitness']) + offSpringsFitness = datasetToDataArray(kwargs['offSpringsFitness'], list(kwargs['offSpringsFitness'].keys())).data + offSpringsFitness = np.array([item for sublist in offSpringsFitness for item in sublist]) offSprings = np.atleast_2d(newRlz[kwargs['variables']].to_array().transpose().data) population = np.atleast_2d(kwargs['population'].data) - popFitness = np.atleast_1d(kwargs['fitness'].data) - + popFitness = datasetToDataArray(kwargs['fitness'], list(kwargs['fitness'].keys())).data + popFitness = np.array([item for sublist in popFitness for item in sublist]) newPopulation = population newFitness = popFitness newAge = list(map(lambda x:x+1, popAge)) @@ -123,6 +129,7 @@ def fitnessBased(newRlz,**kwargs): newFitness = xr.DataArray(newFitness, dims=['chromosome'], coords={'chromosome':np.arange(np.shape(newFitness)[0])}) + newFitness = newFitness.to_dataset(name = list(kwargs['fitness'].keys())[0]) #return newPopulationArray,newFitness,newAge return newPopulationArray,newFitness,newAge,kwargs['popObjectiveVal'] diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml index a1d7cd7452..05460107a7 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml @@ -9,7 +9,7 @@ - Multi_MinwoReplacement + Multi_MinwoReplacement_wo_constraints_20_5 optimize,print 1 @@ -57,14 +57,13 @@ - 2 + 5 42 every min,min - - 10 + 20 tournamentSelection @@ -74,7 +73,7 @@ 0.7 - + rankNcrowdingBased @@ -110,7 +109,7 @@ - 10 + 20 050877 @@ -142,7 +141,8 @@ trajID - x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,ConstraintEvaluation_expConstr3, ConstraintEvaluation_impConstr3,fitness,accepted + + x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,fitness,accepted diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/testGAMinwRepConstrained.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/testGAMinwRepConstrained.xml index 4da8636350..e546e6d971 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/testGAMinwRepConstrained.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/testGAMinwRepConstrained.xml @@ -55,14 +55,14 @@ - 20 + 5 42 every - 20 - rouletteWheel + 10 + tournamentSelection 0.8 @@ -71,7 +71,7 @@ 0.9 - + fitnessBased @@ -101,7 +101,7 @@ - 20 + 10 20021986 From 285575f98770c008698f0d40369164b924c22a7c Mon Sep 17 00:00:00 2001 From: Junyung Kim Date: Thu, 31 Aug 2023 10:24:19 -0600 Subject: [PATCH 41/48] single objective optimization works well with three different types of fitness. Now changes for multi-objective optimization are in need. Specially fitness values need to be replaced with objective values for rank and CD. --- ravenframework/Optimizers/fitness/fitness.py | 44 ++++++++------------ 1 file changed, 18 insertions(+), 26 deletions(-) diff --git a/ravenframework/Optimizers/fitness/fitness.py b/ravenframework/Optimizers/fitness/fitness.py index 6dc500e641..5ec7942d22 100644 --- a/ravenframework/Optimizers/fitness/fitness.py +++ b/ravenframework/Optimizers/fitness/fitness.py @@ -113,7 +113,7 @@ def rank_crowding(rlz,**kwargs): def hardConstraint(rlz,**kwargs): r""" - Multiobjective optimization using NSGA-II requires the rank and crowding distance values to the objective function + Fitness method counting the number of constraints violated @ In, rlz, xr.Dataset, containing the evaluation of a certain set of individuals (can be the initial population for the very first iteration, @@ -123,19 +123,26 @@ def hardConstraint(rlz,**kwargs): @ Out, offSpringRank, xr.DataArray, the rank of the given objective corresponding to a specific chromosome. offSpringCD, xr.DataArray, the crowding distance of the given objective corresponding to a specific chromosome. """ - objVar = kwargs['objVar'] + if isinstance(kwargs['objVar'], str) == True: + objVar = [kwargs['objVar']] + else: + objVar = kwargs['objVar'] g = kwargs['constraintFunction'] - data = np.atleast_1d(rlz[objVar].data) - fitness = np.zeros((len(data), 1)) - for i in range(len(fitness)): - fitness[i] = countConstViolation(g.data[i]) - fitness = [-item for sublist in fitness.tolist() for item in sublist] - fitness = xr.DataArray(fitness, - dims=['NumOfConstraintViolated'], - coords={'NumOfConstraintViolated':np.arange(np.shape(fitness)[0])}) + for j in range(len(objVar)): + fitness = np.zeros((len(g.data), 1)) + for i in range(len(fitness)): + fitness[i] = countConstViolation(g.data[i]) + fitness = [-item for sublist in fitness.tolist() for item in sublist] + fitness = xr.DataArray(fitness, + dims=['NumOfConstraintViolated'], + coords={'NumOfConstraintViolated':np.arange(np.shape(fitness)[0])}) + if j == 0: + fitnessSet = fitness.to_dataset(name = objVar[j]) + else: + fitnessSet[objVar[j]] = fitness - return fitness + return fitnessSet def feasibleFirst(rlz,**kwargs): @@ -194,21 +201,6 @@ def feasibleFirst(rlz,**kwargs): fitnessSet = fitness.to_dataset(name = objVar[i]) else: fitnessSet[objVar[i]] = fitness - ### This code block is for sinlge objective ### - # data = np.atleast_1d(rlz[objVar].data) - # worstObj = max(data) - # fitness = [] - # for ind in range(data.size): - # if np.all(g.data[ind, :]>=0): - # fit=(data[ind]) - # else: - # fit = worstObj - # for constInd,_ in enumerate(g['Constraint'].data): - # fit+=(max(0,-1 * g.data[ind, constInd])) - # fitness.append(-1 * fit) - # fitness = xr.DataArray(np.array(fitness), - # dims=['chromosome'], - # coords={'chromosome': np.arange(len(data))}) return fitnessSet def logistic(rlz,**kwargs): From 7707f67a29d904d62c7c9116f4e5296097b6dc2a Mon Sep 17 00:00:00 2001 From: Junyung Kim Date: Mon, 4 Sep 2023 15:13:17 -0600 Subject: [PATCH 42/48] NSGA-II improvement is in progress. --- ravenframework/Optimizers/GeneticAlgorithm.py | 105 +++++++++--------- ravenframework/Optimizers/RavenSampled.py | 2 +- ravenframework/Optimizers/fitness/fitness.py | 41 +------ .../parentSelectors/parentSelectors.py | 25 ++--- .../survivorSelectors/survivorSelectors.py | 42 ++++--- .../constrained/MinwoRepMultiObjective.xml | 13 ++- .../constrained/testGAMinwRepConstrained.xml | 4 +- 7 files changed, 103 insertions(+), 129 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index e6276f9020..edcc00e9e1 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -322,6 +322,7 @@ def getSolutionExportVariableNames(cls): new['AHDp'] = 'p-Average Hausdorff Distance between populations' new['AHD'] = 'Hausdorff Distance between populations' new['ConstraintEvaluation_{CONSTRAINT}'] = 'Constraint function evaluation (negative if violating and positive otherwise)' + new['FitnessEvaluation_{OBJ}'] = 'Fitness evaluation of each objective' ok.update(new) return ok @@ -374,7 +375,7 @@ def handleInput(self, paramInput): # Check if the fitness requested is among the constrained optimization fitnesses # TODO: @mandd, please explore the possibility to convert the logistic fitness into a constrained optimization fitness. if 'Constraint' in self.assemblerObjects and self._fitnessType not in ['invLinear','feasibleFirst','hardConstraint']: - self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support invLinear, feasibleFirst and rank_crowding as a fitness, whereas provided fitness is {self._fitnessType}') + self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support invLinear, feasibleFirst and hardConstraint as a fitness, whereas provided fitness is {self._fitnessType}') self._objCoeff = fitnessNode.findFirst('a').value if fitnessNode.findFirst('a') is not None else None self._penaltyCoeff = fitnessNode.findFirst('b').value if fitnessNode.findFirst('b') is not None else None self._fitnessInstance = fitnessReturnInstance(self,name = self._fitnessType) @@ -552,9 +553,9 @@ def _useRealization(self, info, rlz): objVar=self._objectiveVar, a=self._objCoeff, b=self._penaltyCoeff, - penalty=None, + penalty =None, constraintFunction=g, - type=self._minMax) + type =self._minMax) # 0.2@ n-1: Survivor selection(rlz) # update population container given obtained children @@ -580,15 +581,15 @@ def _useRealization(self, info, rlz): if self.counter > 1: self.population,self.rank, \ self.popAge,self.crowdingDistance, \ - self.objectiveVal,self.constraints, \ + self.objectiveVal,self.fitness, \ self.constraintsV = self._survivorSelectionInstance(age=self.popAge, variables=list(self.toBeSampled), population=self.population, offsprings=rlz, popObjectiveVal=self.objectiveVal, offObjectiveVal=objectiveVal, - popConst = self.constraints, - offConst = offSpringFitness, + popFit = self.fitness, + offFit = offSpringFitness, popConstV = self.constraintsV, offConstV = g ) @@ -599,7 +600,7 @@ def _useRealization(self, info, rlz): self.rank, self.crowdingDistance, self.objectiveVal, - self.constraints, + self.fitness, self.constraintsV) self._resolveNewGenerationMulti(traj, rlz, info) @@ -628,26 +629,30 @@ def _useRealization(self, info, rlz): else: self.population = offSprings - self.constraints = offSpringFitness + self.fitness = offSpringFitness self.constraintsV = g + # offspringObjsVals for Rank and CD calculation offObjVal = [] for i in range(len(self._objectiveVar)): offObjVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) offspringObjsVals = [list(ele) for ele in list(zip(*offObjVal))] - offSpringRank = frontUtils.rankNonDominatedFrontiers(np.array(offspringObjsVals)) + # offspringFitVals for Rank and CD calculation + fitVal = datasetToDataArray(self.fitness, self._objectiveVar).data + offspringFitVals = fitVal.tolist() + offSpringRank = frontUtils.rankNonDominatedFrontiers(np.array(offspringFitVals)) self.rank = xr.DataArray(offSpringRank, - dims=['rank'], - coords={'rank': np.arange(np.shape(offSpringRank)[0])}) - + dims=['rank'], + coords={'rank': np.arange(np.shape(offSpringRank)[0])}) offSpringCD = frontUtils.crowdingDistance(rank=offSpringRank, popSize=len(offSpringRank), - objectives=np.array(offspringObjsVals)) + objectives=np.array(offspringFitVals)) + self.crowdingDistance = xr.DataArray(offSpringCD, - dims=['CrowdingDistance'], - coords={'CrowdingDistance': np.arange(np.shape(offSpringCD)[0])}) + dims=['CrowdingDistance'], + coords={'CrowdingDistance': np.arange(np.shape(offSpringCD)[0])}) self.objectiveVal = [] for i in range(len(self._objectiveVar)): @@ -657,7 +662,7 @@ def _useRealization(self, info, rlz): self.rank, self.crowdingDistance, self.objectiveVal, - self.constraints, + self.fitness, self.constraintsV) self._resolveNewGenerationMulti(traj, rlz, info) # 1 @ n: Parent selection from population @@ -676,7 +681,7 @@ def _useRealization(self, info, rlz): nParents=self._nParents, rank = self.rank, crowdDistance = self.crowdingDistance, - constraint = self.constraints + fitness = self.fitness ) # 2 @ n: Crossover from set of parents @@ -853,12 +858,13 @@ def _resolveNewGenerationMulti(self, traj, rlz, info): varList = self._solutionExport.getVars('input') + self._solutionExport.getVars('output') + list(self.toBeSampled.keys()) # rlzDict = dict((var,np.atleast_1d(rlz[var].data)[i]) for var in set(varList) if var in rlz.data_vars) rlzDict = dict((var,self.population.data[i][j]) for j, var in enumerate(self.population.Gene.data)) - rlzDict['batchId'] = rlz['batchId'][i] + rlzDict['batchId'] = rlz['batchId'].data[i] for j in range(len(self._objectiveVar)): rlzDict[self._objectiveVar[j]] = objVal.data[i][j] rlzDict['rank'] = np.atleast_1d(self.rank.data)[i] rlzDict['CD'] = np.atleast_1d(self.crowdingDistance.data)[i] - rlzDict['fitness'] = np.atleast_1d(self.constraints.data)[i] + for ind, fitName in enumerate(list(self.fitness.keys())): + rlzDict['FitnessEvaluation_'+fitName] = self.fitness[fitName].data[i] for ind, consName in enumerate([y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]): rlzDict['ConstraintEvaluation_'+consName] = self.constraintsV.data[i,ind] self._updateSolutionExport(traj, rlzDict, acceptable, None) @@ -871,12 +877,14 @@ def _resolveNewGenerationMulti(self, traj, rlz, info): bestRlz = dict((var,np.atleast_1d(rlz[var].data)) for var in set(varList) if var in rlz.data_vars) for i in range(len(self._objectiveVar)): bestRlz[self._objectiveVar[i]] = [item[i] for item in self.multiBestObjective] - bestRlz['fitness'] = self.multiBestFitness + bestRlz['rank'] = self.multiBestRank bestRlz['CD'] = self.multiBestCD if len(self.multiBestConstraint) != 0: # No constraints for ind, consName in enumerate(self.multiBestConstraint.Constraint): bestRlz['ConstraintEvaluation_'+consName.values.tolist()] = self.multiBestConstraint[ind].values + for ind, fitName in enumerate(list(self.multiBestFitness.keys())): + bestRlz['FitnessEvaluation_'+ fitName] = self.multiBestFitness[fitName].data bestRlz.update(self.multiBestPoint) self._optPointHistory[traj].append((bestRlz, info)) elif acceptable == 'rejected': @@ -910,7 +918,7 @@ def _collectOptPoint(self, rlz, fitness, objectiveVal, g): return point - def _collectOptPointMulti(self, population, rank, CD, objectiveVal, constraints, constraintsV): + def _collectOptPointMulti(self, population, rank, CD, objVal, fitness, constraintsV): """ Collects the point (dict) from a realization @ In, population, Dataset, container containing the population @@ -919,33 +927,20 @@ def _collectOptPointMulti(self, population, rank, CD, objectiveVal, constraints, @ In, crowdingDistance, xr.DataArray, crowdingDistance values at each chromosome of the realization @ Out, point, dict, point used in this realization """ - objVal = [[] for x in range(len(objectiveVal[0]))] - for i in range(len(objectiveVal[0])): - objVal[i] = [item[i] for item in objectiveVal] - - optPointsConsIDX = [i for i, nFit in enumerate(constraints) if nFit == min(constraints)] # Find index of chromosome which has smallest numeber of violations among population - optPointsRankNConsIDX = [i for i, rankValue in enumerate(rank[optPointsConsIDX]) if rankValue == min(rank[optPointsConsIDX])] # Find index of chromosome which has smallest numeber of violations among population & smallest rank - - optPoints,optObjVal,optConstraints,optConstraintsV,optRank,optCD = population[optPointsRankNConsIDX], np.array(objVal)[optPointsRankNConsIDX], constraints.data[optPointsRankNConsIDX], constraintsV.data[optPointsRankNConsIDX], rank.data[optPointsRankNConsIDX], CD.data[optPointsRankNConsIDX] - - # Previous ################################################## - # points,multiFit,rankSorted,cdSorted,objSorted,constSorted = \ - # zip(*[[a,b,c,d,e,f] for a, b, c, d, e, f in sorted(zip(np.atleast_2d(population.data),np.atleast_1d(constraintsV.data),np.atleast_1d(rank.data),np.atleast_1d(CD.data), objVal, constraints), - # reverse=True,key=lambda x: (-x[1], -x[2], x[3]))]) - # optPoints = [points[i] for i, rank in enumerate(rankSorted) if rank == 1 ] - # optMultiFit = [multiFit[i] for i, rank in enumerate(rankSorted) if rank == 1 ] - # optObj = [objSorted[i] for i, rank in enumerate(rankSorted) if rank == 1 ] - # optConst = [constSorted[i] for i, rank in enumerate(rankSorted) if rank == 1 ] - # optRank = [rankSorted[i] for i, rank in enumerate(rankSorted) if rank == 1 ] - # optCD = [cdSorted[i] for i, rank in enumerate(rankSorted) if rank == 1 ] - # if (len(optMultiFit) != len([x for x in optMultiFit if x != 0]) ) : - # optPoints = [optPoints[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] - # optMultiFit = [x for x in optMultiFit if x == 0] - # optObj = [optObj[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] - # optConst = [optConst[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] - # optRank = [optRank[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] - # optCD = [optCD[i] for i, nFit in enumerate(optMultiFit) if nFit == 0 ] - # Previous ################################################## + rankOneIDX = [i for i, rankValue in enumerate(rank.data) if rankValue == 1] + optPoints = population[rankOneIDX] + optObjVal = np.array([list(ele) for ele in list(zip(*objVal))])[rankOneIDX] + count = 0 + for i in list(fitness.keys()): + data = fitness[i][rankOneIDX] + if count == 0: + fitSet = data.to_dataset(name = i) + else: + fitSet[i] = data + count = count + 1 + optConstraintsV = constraintsV.data[rankOneIDX] + optRank = rank.data[rankOneIDX] + optCD = CD.data[rankOneIDX] optPointsDic = dict((var,np.array(optPoints)[:,i]) for i, var in enumerate(population.Gene.data)) optConstNew = [] @@ -954,15 +949,15 @@ def _collectOptPointMulti(self, population, rank, CD, objectiveVal, constraints, optConstNew = list(map(list, zip(*optConstNew))) if (len(optConstNew)) != 0: optConstNew = xr.DataArray(optConstNew, - dims=['Constraint','Evaluation'], - coords={'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)], - 'Evaluation':np.arange(np.shape(optConstNew)[1])}) + dims=['Constraint','Evaluation'], + coords={'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)], + 'Evaluation':np.arange(np.shape(optConstNew)[1])}) self.multiBestPoint = optPointsDic - self.multiBestFitness = optConstraints + self.multiBestFitness = fitSet self.multiBestObjective = optObjVal self.multiBestConstraint = optConstNew - self.multiBestRank = optRank + self.multiBestRank = optRank #TODO JY: MultiBestRank is not anymore in need. This should be removed later. self.multiBestCD = optCD return optPointsDic @@ -1287,7 +1282,7 @@ def _addToSolutionExport(self, traj, rlz, acceptable): # meta variables toAdd = {'age': 0 if self.popAge is None else self.popAge, 'batchId': self.batchId, - 'fitness': rlz['fitness'], + # 'fitness': rlz['fitness'], 'AHDp': self.ahdp, 'AHD': self.ahd, 'rank': 0 if ((type(self._objectiveVar) == list and len(self._objectiveVar) == 1) or type(self._objectiveVar) == str) else rlz['rank'], @@ -1318,6 +1313,8 @@ def _formatSolutionExportVariableNames(self, acceptable): new.extend([template.format(CONV=conv) for conv in self._convergenceCriteria]) elif '{VAR}' in template: new.extend([template.format(VAR=var) for var in self.toBeSampled]) + elif '{OBJ}' in template: + new.extend([template.format(OBJ=obj) for obj in self._objectiveVar]) elif '{CONSTRAINT}' in template: new.extend([template.format(CONSTRAINT=constraint.name) for constraint in self._constraintFunctions + self._impConstraintFunctions]) else: diff --git a/ravenframework/Optimizers/RavenSampled.py b/ravenframework/Optimizers/RavenSampled.py index 82505cf3e2..b3d13274bc 100644 --- a/ravenframework/Optimizers/RavenSampled.py +++ b/ravenframework/Optimizers/RavenSampled.py @@ -731,7 +731,7 @@ def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason): # constants and functions toExport.update(self.constants) toExport.update(dict((var, rlz[var]) for var in self.dependentSample if var in rlz)) - # additional from from inheritors + # additional from inheritors toExport.update(self._addToSolutionExport(traj, rlz, acceptable)) # check for anything else that solution export wants that rlz might provide for var in self._solutionExport.getVars(): diff --git a/ravenframework/Optimizers/fitness/fitness.py b/ravenframework/Optimizers/fitness/fitness.py index 5ec7942d22..725e28c10f 100644 --- a/ravenframework/Optimizers/fitness/fitness.py +++ b/ravenframework/Optimizers/fitness/fitness.py @@ -80,37 +80,6 @@ def invLinear(rlz,**kwargs): coords={'chromosome': np.arange(len(data))}) return fitness -def rank_crowding(rlz,**kwargs): - r""" - Multiobjective optimization using NSGA-II requires the rank and crowding distance values to the objective function - - @ In, rlz, xr.Dataset, containing the evaluation of a certain - set of individuals (can be the initial population for the very first iteration, - or a population of offsprings) - @ In, kwargs, dict, dictionary of parameters for this rank_crowding method: - objVar, string, the names of the objective variables - @ Out, offSpringRank, xr.DataArray, the rank of the given objective corresponding to a specific chromosome. - offSpringCD, xr.DataArray, the crowding distance of the given objective corresponding to a specific chromosome. - """ - objectiveVal = [] - for i in range(len(kwargs['objVals'])): - objectiveVal.append(list(np.atleast_1d(rlz[kwargs['objVals'][i]].data))) - - offspringObjsVals = [list(ele) for ele in list(zip(*objectiveVal))] - - offSpringRank = frontUtils.rankNonDominatedFrontiers(np.array(offspringObjsVals)) - offSpringRank = xr.DataArray(offSpringRank, - dims=['rank'], - coords={'rank': np.arange(np.shape(offSpringRank)[0])}) - - offSpringCD = frontUtils.crowdingDistance(rank=offSpringRank, popSize=len(offSpringRank), objectives=np.array(offspringObjsVals)) - offSpringCD = xr.DataArray(offSpringCD, - dims=['CrowdingDistance'], - coords={'CrowdingDistance': np.arange(np.shape(offSpringCD)[0])}) - - return offSpringRank, offSpringCD - - def hardConstraint(rlz,**kwargs): r""" Fitness method counting the number of constraints violated @@ -135,8 +104,8 @@ def hardConstraint(rlz,**kwargs): fitness[i] = countConstViolation(g.data[i]) fitness = [-item for sublist in fitness.tolist() for item in sublist] fitness = xr.DataArray(fitness, - dims=['NumOfConstraintViolated'], - coords={'NumOfConstraintViolated':np.arange(np.shape(fitness)[0])}) + dims=['NumOfConstraintViolated'], + coords={'NumOfConstraintViolated':np.arange(np.shape(fitness)[0])}) if j == 0: fitnessSet = fitness.to_dataset(name = objVar[j]) else: @@ -182,6 +151,7 @@ def feasibleFirst(rlz,**kwargs): else: objVar = kwargs['objVar'] g = kwargs['constraintFunction'] + penalty = kwargs['b'] for i in range(len(objVar)): data = np.atleast_1d(rlz[objVar][objVar[i]].data) worstObj = max(data) @@ -192,8 +162,8 @@ def feasibleFirst(rlz,**kwargs): else: fit = worstObj for constInd,_ in enumerate(g['Constraint'].data): - fit+=(max(0,-1 * g.data[ind, constInd])) - fitness.append(-1 * fit) + fit+= penalty*(max(0,-1*g.data[ind, constInd])) + fitness.append(fit) fitness = xr.DataArray(np.array(fitness), dims=['chromosome'], coords={'chromosome': np.arange(len(data))}) @@ -246,7 +216,6 @@ def logistic(rlz,**kwargs): __fitness['invLinear'] = invLinear __fitness['logistic'] = logistic __fitness['feasibleFirst'] = feasibleFirst -__fitness['rank_crowding'] = rank_crowding __fitness['hardConstraint'] = hardConstraint diff --git a/ravenframework/Optimizers/parentSelectors/parentSelectors.py b/ravenframework/Optimizers/parentSelectors/parentSelectors.py index cf7d5b33f4..5ddc0d87ae 100644 --- a/ravenframework/Optimizers/parentSelectors/parentSelectors.py +++ b/ravenframework/Optimizers/parentSelectors/parentSelectors.py @@ -105,13 +105,13 @@ def tournamentSelection(population,**kwargs): # the key rank is used in multi-objective optimization where rank identifies which front the point belongs to rank = kwargs['rank'] crowdDistance = kwargs['crowdDistance'] - constraintInfo = kwargs['constraint'] + # constraintInfo = kwargs['constraint'] multiObjectiveRanking = True - matrixOperationRaw = np.zeros((popSize, 4)) + matrixOperationRaw = np.zeros((popSize, 3)) #NOTE if constraint is needed to eliminate chromosome violating constraints, then poopSize should be 4. matrixOperationRaw[:,0] = np.transpose(np.arange(popSize)) matrixOperationRaw[:,1] = np.transpose(crowdDistance.data) matrixOperationRaw[:,2] = np.transpose(rank.data) - matrixOperationRaw[:,3] = np.transpose(constraintInfo.data) + # matrixOperationRaw[:,3] = np.transpose(constraintInfo.data) matrixOperation = np.zeros((popSize,len(matrixOperationRaw[0]))) else: fitness = np.array([item for sublist in datasetToDataArray(kwargs['fitness'], list(kwargs['fitness'].keys())).data for item in sublist]) @@ -144,18 +144,13 @@ def tournamentSelection(population,**kwargs): selectedParent[i,:] = pop.values[index,:] else: # multi-objective implementation of tournamentSelection for i in range(nParents): - if matrixOperation[2*i,3] > matrixOperation[2*i+1,3]: index = int(matrixOperation[2*i+1,0]) - elif matrixOperation[2*i,3] < matrixOperation[2*i+1,3]: index = int(matrixOperation[2*i,0]) - elif matrixOperation[2*i,3] == matrixOperation[2*i+1,3]: # if same number of constraints violations - if matrixOperation[2*i,2] > matrixOperation[2*i+1,2]: - index = int(matrixOperation[2*i+1,0]) - elif matrixOperation[2*i,2] < matrixOperation[2*i+1,2]: - index = int(matrixOperation[2*i,0]) - else: # same number of constraints and same rank case - if matrixOperation[2*i,1] > matrixOperation[2*i+1,1]: - index = int(matrixOperation[2*i,0]) - else: - index = int(matrixOperation[2*i+1,0]) + if matrixOperation[2*i,2] > matrixOperation[2*i+1,2]: index = int(matrixOperation[2*i+1,0]) + elif matrixOperation[2*i,2] < matrixOperation[2*i+1,2]: index = int(matrixOperation[2*i,0]) + elif matrixOperation[2*i,2] == matrixOperation[2*i+1,2]: # if same rank, then compare CD + if matrixOperation[2*i,1] > matrixOperation[2*i+1,1]: index = int(matrixOperation[2*i,0]) + elif matrixOperation[2*i,2] < matrixOperation[2*i+1,2]: index = int(matrixOperation[2*i+1,0]) + else: # same rank and same CD + index = int(matrixOperation[2*i+1,0]) #NOTE if rank and CD are same, then any chromosome can be selected. selectedParent[i,:] = pop.values[index,:] return selectedParent diff --git a/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py b/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py index 6848c22275..c3dce2f1a2 100644 --- a/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py +++ b/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py @@ -158,12 +158,19 @@ def rankNcrowdingBased(offsprings, **kwargs): offSprings = np.atleast_2d(offsprings[kwargs['variables']].to_array().transpose().data) popObjectiveVal = kwargs['popObjectiveVal'] offObjectiveVal = kwargs['offObjectiveVal'] - popConst = kwargs['popConst'].data - offConst = kwargs['offConst'].data + popFit = kwargs['popFit'] + popFitArray = [] + offFit = kwargs['offFit'] + offFitArray = [] + for i in list(popFit.keys()): #NOTE popFit.keys() and offFit.keys() must be same. + popFitArray.append(popFit[i].data.tolist()) + offFitArray.append(offFit[i].data.tolist()) + + newFitMerged = np.array([i + j for i, j in zip(popFitArray, offFitArray)]) + newFitMerged_pair = [list(ele) for ele in list(zip(*newFitMerged))] + popConstV = kwargs['popConstV'].data offConstV = kwargs['offConstV'].data - - newConstMerged = np.append(popConst, offConst) newConstVMerged = np.array(popConstV.tolist() + offConstV.tolist()) newObjectivesMerged = np.array([i + j for i, j in zip(popObjectiveVal, offObjectiveVal)]) @@ -182,15 +189,16 @@ def rankNcrowdingBased(offsprings, **kwargs): newAge = list(map(lambda x:x+1, popAge)) newPopulationMerged = np.concatenate([population,offSprings]) newAge.extend([0]*len(offSprings)) - - sortedConst,sortedRank,sortedCD,sortedAge,sortedPopulation,sortedObjectives,sortedConstV = \ + + sortedRank,sortedCD,sortedAge,sortedPopulation,sortedFit,sortedObjectives,sortedConstV = \ zip(*[(x,y,z,i,j,k,a) for x,y,z,i,j,k,a in \ - sorted(zip(newConstMerged,newPopRank.data,newPopCD.data,newAge,newPopulationMerged.tolist(),newObjectivesMerged_pair,newConstVMerged),reverse=False,key=lambda x: (x[0], x[1], -x[2]))]) - sortedConstT,sortedRankT,sortedCDT,sortedAgeT,sortedPopulationT,sortedObjectivesT,sortedConstVT = \ - np.atleast_1d(list(sortedConst)),np.atleast_1d(list(sortedRank)),list(sortedCD),list(sortedAge),np.atleast_1d(list(sortedPopulation)),np.atleast_1d(list(sortedObjectives)),np.atleast_1d(list(sortedConstV)) + sorted(zip(newPopRank.data, newPopCD.data, newAge, newPopulationMerged.tolist(), newFitMerged_pair, newObjectivesMerged_pair, newConstVMerged),reverse=False,key=lambda x: (x[0], -x[1]))]) + sortedRankT, sortedCDT, sortedAgeT, sortedPopulationT, sortedFitT, sortedObjectivesT, sortedConstVT = \ + np.atleast_1d(list(sortedRank)), list(sortedCD), list(sortedAge),np.atleast_1d(list(sortedPopulation)),np.atleast_1d(list(sortedFit)),np.atleast_1d(list(sortedObjectives)),np.atleast_1d(list(sortedConstV)) newPopulation = sortedPopulationT[:-len(offSprings)] newObjectives = sortedObjectivesT[:-len(offSprings)] + newFit = sortedFitT[:-len(offSprings)] newRank = frontUtils.rankNonDominatedFrontiers(newObjectives) newRank = xr.DataArray(newRank, @@ -204,23 +212,27 @@ def rankNcrowdingBased(offsprings, **kwargs): coords={'CrowdingDistance': np.arange(np.shape(newCD)[0])}) newAge = sortedAgeT[:-len(offSprings)] - newConst = sortedConstT[:-len(offSprings)] newConstV = sortedConstVT[:-len(offSprings)] + for i in range(len(list(popFit.keys()))): + fitness = xr.DataArray(newFit[:,i], + dims=['chromosome'], + coords={'chromosome': np.arange(len(newFit[:,i]))}) + if i == 0: + newFitnessSet = fitness.to_dataset(name = list(popFit.keys())[i]) + else: + newFitnessSet[list(popFit.keys())[i]] = fitness + newPopulationArray = xr.DataArray(newPopulation, dims=['chromosome','Gene'], coords={'chromosome':np.arange(np.shape(newPopulation)[0]), 'Gene': kwargs['variables']}) - newConst = xr.DataArray(newConst, - dims=['NumOfConstViolated'], - coords={'NumOfConstViolated':np.arange(np.shape(newConst)[0])}) - newConstV = xr.DataArray(newConstV, dims=['chromosome','ConstEvaluation'], coords={'chromosome':np.arange(np.shape(newPopulation)[0]), 'ConstEvaluation':np.arange(np.shape(newConstV)[1])}) - return newPopulationArray,newRank,newAge,newCD,newObjectivesP,newConst,newConstV + return newPopulationArray,newRank,newAge,newCD,newObjectivesP,newFitnessSet,newConstV __survivorSelectors = {} __survivorSelectors['ageBased'] = ageBased diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml index 05460107a7..9bf8993c9d 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml @@ -9,7 +9,7 @@ - Multi_MinwoReplacement_wo_constraints_20_5 + Multi_MinwoReplacement_wo_constraints_20_5_M optimize,print 1 @@ -67,13 +67,14 @@ tournamentSelection - 0.7 + 0.8 - - 0.7 + + 0.8 - + + 100 rankNcrowdingBased @@ -142,7 +143,7 @@ trajID - x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,fitness,accepted + x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,FitnessEvaluation_obj1,FitnessEvaluation_obj2,ConstraintEvaluation_expConstr3, ConstraintEvaluation_impConstr3,accepted diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/testGAMinwRepConstrained.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/testGAMinwRepConstrained.xml index e546e6d971..08267e5c89 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/testGAMinwRepConstrained.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/testGAMinwRepConstrained.xml @@ -61,7 +61,7 @@ - 10 + 20 tournamentSelection @@ -101,7 +101,7 @@ - 10 + 20 20021986 From a32a45c2d5f81d08f7c1f7bd831a37eced294367 Mon Sep 17 00:00:00 2001 From: Junyung Kim Date: Wed, 6 Sep 2023 17:18:12 -0600 Subject: [PATCH 43/48] fitness-based NSGA-II is in progress. min-min is working well with tournament selection and feasibleFirst. --- ravenframework/Optimizers/GeneticAlgorithm.py | 13 +++++++++---- ravenframework/Optimizers/Optimizer.py | 4 ++-- ravenframework/Optimizers/fitness/fitness.py | 5 ++++- .../survivorSelectors/survivorSelectors.py | 4 ++-- .../AnalyticModels/optimizing/myConstraints.py | 2 +- .../constrained/MinwoRepMultiObjective.xml | 18 +++++++++++------- 6 files changed, 29 insertions(+), 17 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index edcc00e9e1..69e3f4205b 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -271,12 +271,12 @@ def getInputSpecification(cls): fitness.addParam("type", InputTypes.StringType, True, descr=r"""[invLin, logistic, feasibleFirst]""") objCoeff = InputData.parameterInputFactory('a', strictMode=True, - contentType=InputTypes.FloatType, + contentType=InputTypes.FloatListType, printPriority=108, descr=r""" a: coefficient of objective function.""") fitness.addSub(objCoeff) penaltyCoeff = InputData.parameterInputFactory('b', strictMode=True, - contentType=InputTypes.FloatType, + contentType=InputTypes.FloatListType, printPriority=108, descr=r""" b: coefficient of constraint penalty.""") fitness.addSub(penaltyCoeff) @@ -369,15 +369,20 @@ def handleInput(self, paramInput): survivorSelectionNode = gaParamsNode.findFirst('survivorSelection') self._survivorSelectionType = survivorSelectionNode.value self._survivorSelectionInstance = survivorSelectionReturnInstance(self,name = self._survivorSelectionType) + if len(self._objectiveVar) == 1 and self._survivorSelectionType == 'rankNcrowdingBased': + self.raiseAnError(IOError, f'(rankNcrowdingBased) in only supports when the number of objective in is bigger than two. ') # Fitness fitnessNode = gaParamsNode.findFirst('fitness') self._fitnessType = fitnessNode.parameterValues['type'] - # Check if the fitness requested is among the constrained optimization fitnesses # TODO: @mandd, please explore the possibility to convert the logistic fitness into a constrained optimization fitness. if 'Constraint' in self.assemblerObjects and self._fitnessType not in ['invLinear','feasibleFirst','hardConstraint']: self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support invLinear, feasibleFirst and hardConstraint as a fitness, whereas provided fitness is {self._fitnessType}') self._objCoeff = fitnessNode.findFirst('a').value if fitnessNode.findFirst('a') is not None else None self._penaltyCoeff = fitnessNode.findFirst('b').value if fitnessNode.findFirst('b') is not None else None + expConstr = self.assemblerObjects['Constraint'][0] + impConstr = self.assemblerObjects['ImplicitConstraint'][0] + if len(self._penaltyCoeff) != len(self._objectiveVar) * (len([ele for ele in expConstr if ele != 'Functions' if ele !='External']) + len([ele for ele in impConstr if ele != 'Functions' if ele !='External']) ): + self.raiseAnError(IOError, f'The number of penaltyCoeff. in should be identical with the number of objective in and the number of constraints (i.e., and )') self._fitnessInstance = fitnessReturnInstance(self,name = self._fitnessType) self._repairInstance = repairReturnInstance(self,name='replacementRepair') # currently only replacement repair is implemented. @@ -637,7 +642,7 @@ def _useRealization(self, info, rlz): for i in range(len(self._objectiveVar)): offObjVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) - offspringObjsVals = [list(ele) for ele in list(zip(*offObjVal))] + # offspringObjsVals = [list(ele) for ele in list(zip(*offObjVal))] # offspringFitVals for Rank and CD calculation fitVal = datasetToDataArray(self.fitness, self._objectiveVar).data diff --git a/ravenframework/Optimizers/Optimizer.py b/ravenframework/Optimizers/Optimizer.py index ddad3eed36..d12e96d71b 100644 --- a/ravenframework/Optimizers/Optimizer.py +++ b/ravenframework/Optimizers/Optimizer.py @@ -265,9 +265,9 @@ def handleInput(self, paramInput): if minMax is not None: self._minMax = minMax.value if len(self._minMax) != len(self._objectiveVar): - self.raiseAnError(IOError, 'type and objective must be of the same length!') + self.raiseAnError(IOError, 'The number of in -- and in - must be of the same length!') if list(set(self._minMax)-set(['min','max'])) != []: - self.raiseAnError(IOError, "type must be a list of 'min' and/or 'max'") + self.raiseAnError(IOError, " must be a either 'min' and/or 'max'") # variables additional reading for varNode in paramInput.findAll('variable'): diff --git a/ravenframework/Optimizers/fitness/fitness.py b/ravenframework/Optimizers/fitness/fitness.py index 725e28c10f..3384cf98d0 100644 --- a/ravenframework/Optimizers/fitness/fitness.py +++ b/ravenframework/Optimizers/fitness/fitness.py @@ -152,6 +152,9 @@ def feasibleFirst(rlz,**kwargs): objVar = kwargs['objVar'] g = kwargs['constraintFunction'] penalty = kwargs['b'] + pen = [penalty[i:i+len(g['Constraint'].data)] for i in range(0, len(penalty), len(g['Constraint'].data))] + objPen = dict(map(lambda i,j : (i,j), objVar, pen)) + for i in range(len(objVar)): data = np.atleast_1d(rlz[objVar][objVar[i]].data) worstObj = max(data) @@ -162,7 +165,7 @@ def feasibleFirst(rlz,**kwargs): else: fit = worstObj for constInd,_ in enumerate(g['Constraint'].data): - fit+= penalty*(max(0,-1*g.data[ind, constInd])) + fit+= objPen[objVar[i]][constInd]*(max(0,-1*g.data[ind, constInd])) #NOTE: objPen[objVar[i]][constInd] is "objective & Constraint specific penalty." fitness.append(fit) fitness = xr.DataArray(np.array(fitness), dims=['chromosome'], diff --git a/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py b/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py index c3dce2f1a2..9702a91192 100644 --- a/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py +++ b/ravenframework/Optimizers/survivorSelectors/survivorSelectors.py @@ -176,12 +176,12 @@ def rankNcrowdingBased(offsprings, **kwargs): newObjectivesMerged = np.array([i + j for i, j in zip(popObjectiveVal, offObjectiveVal)]) newObjectivesMerged_pair = [list(ele) for ele in list(zip(*newObjectivesMerged))] - newPopRank = frontUtils.rankNonDominatedFrontiers(np.array(newObjectivesMerged_pair)) + newPopRank = frontUtils.rankNonDominatedFrontiers(np.array(newFitMerged_pair)) newPopRank = xr.DataArray(newPopRank, dims=['rank'], coords={'rank': np.arange(np.shape(newPopRank)[0])}) - newPopCD = frontUtils.crowdingDistance(rank=newPopRank, popSize=len(newPopRank), objectives=np.array(newObjectivesMerged_pair)) + newPopCD = frontUtils.crowdingDistance(rank=newPopRank, popSize=len(newPopRank), objectives=np.array(newFitMerged_pair)) newPopCD = xr.DataArray(newPopCD, dims=['CrowdingDistance'], coords={'CrowdingDistance': np.arange(np.shape(newPopCD)[0])}) diff --git a/tests/framework/AnalyticModels/optimizing/myConstraints.py b/tests/framework/AnalyticModels/optimizing/myConstraints.py index d63407ef6f..ced89f7b48 100644 --- a/tests/framework/AnalyticModels/optimizing/myConstraints.py +++ b/tests/framework/AnalyticModels/optimizing/myConstraints.py @@ -94,7 +94,7 @@ def impConstr2(Input): @ In, Input, object, RAVEN container @ out, g, float, implicit constraint 2 evaluation function """ - g = Input.x1**2 + Input.obj - 10 + g = Input.x1**2 + Input.obj1 - 10 return g def impConstr3(Input): diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml index 9bf8993c9d..83e3c898d2 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml @@ -9,9 +9,9 @@ - Multi_MinwoReplacement_wo_constraints_20_5_M + Multi_MinwoReplacement_wo_constraints_50_10 optimize,print - 1 + 4 @@ -41,6 +41,9 @@ x1,x2,x3,x4,x5,x6 + x1,x2,x3,x4,x5,x6,obj1 @@ -57,10 +60,10 @@ - 5 + 10 42 every - min,min + min 20 @@ -74,7 +77,7 @@ - 100 + 50, 50, 100, 100 rankNcrowdingBased @@ -99,10 +102,11 @@ woRep_dist - obj1, obj2 + obj1 optOut MC_samp expConstr3 + impConstr3 @@ -138,7 +142,7 @@ x1,x2,x3,x4,x5,x6 - obj1,obj2 + obj1 trajID From a9577f4566563373ea5433b32c744b68a2fcced8 Mon Sep 17 00:00:00 2001 From: Junyung Kim Date: Thu, 7 Sep 2023 14:16:01 -0600 Subject: [PATCH 44/48] NSGA-II fitness-based rank and CD calcuration is completed. Temporary plotting of pareto frontier is also updated reflectig optimization objectives (min or mix). --- ravenframework/Optimizers/GeneticAlgorithm.py | 40 ++++++++++--------- ravenframework/Optimizers/Optimizer.py | 4 +- .../constrained/MinwoRepMultiObjective.xml | 21 +++++----- 3 files changed, 33 insertions(+), 32 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index 69e3f4205b..6804195d11 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -343,6 +343,8 @@ def handleInput(self, paramInput): parentSelectionNode = gaParamsNode.findFirst('parentSelection') self._parentSelectionType = parentSelectionNode.value self._parentSelectionInstance = parentSelectionReturnInstance(self, name=parentSelectionNode.value) + if len(self._objectiveVar) >=2 and self._parentSelectionType != 'tournamentSelection': + self.raiseAnError(IOError, f'tournamentSelection in is a sole mechanism supportive in multi-objective optimization.') # reproduction node reproductionNode = gaParamsNode.findFirst('reproduction') self._nParents = int(np.ceil(1/2 + np.sqrt(1+4*self._populationSize)/2)) @@ -350,6 +352,8 @@ def handleInput(self, paramInput): # crossover node crossoverNode = reproductionNode.findFirst('crossover') self._crossoverType = crossoverNode.parameterValues['type'] + if self._crossoverType not in ['onePointCrossover','twoPointsCrossover','uniformCrossover']: + self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support onePointCrossover, twoPointsCrossover and uniformCrossover as a crossover, whereas provided crossover is {self._crossoverType}') if crossoverNode.findFirst('points') is None: self._crossoverPoints = None else: @@ -359,6 +363,8 @@ def handleInput(self, paramInput): # mutation node mutationNode = reproductionNode.findFirst('mutation') self._mutationType = mutationNode.parameterValues['type'] + if self._mutationType not in ['swapMutator','scrambleMutator','inversionMutator','bitFlipMutator']: + self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support swapMutator, scrambleMutator, inversionMutator, and bitFlipMutator as a mutator, whereas provided mutator is {self._mutationType}') if mutationNode.findFirst('locs') is None: self._mutationLocs = None else: @@ -369,6 +375,8 @@ def handleInput(self, paramInput): survivorSelectionNode = gaParamsNode.findFirst('survivorSelection') self._survivorSelectionType = survivorSelectionNode.value self._survivorSelectionInstance = survivorSelectionReturnInstance(self,name = self._survivorSelectionType) + if self._survivorSelectionType not in ['ageBased','fitnessBased','rankNcrowdingBased']: + self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support ageBased, fitnessBased, and rankNcrowdingBased as a survivorSelector, whereas provided survivorSelector is {self._survivorSelectionType}') if len(self._objectiveVar) == 1 and self._survivorSelectionType == 'rankNcrowdingBased': self.raiseAnError(IOError, f'(rankNcrowdingBased) in only supports when the number of objective in is bigger than two. ') # Fitness @@ -610,26 +618,22 @@ def _useRealization(self, info, rlz): self._resolveNewGenerationMulti(traj, rlz, info) ############################################################################## - objs_vals = [list(ele) for ele in list(zip(*self.objectiveVal))] ##TODO: remove all the plots and maybe design new plots in outstreams if our current cannot be used ## These are currently for debugging purposes import matplotlib.pyplot as plt - # JY: Visualization: all points - This code block needs to be either deleted or revisited. - plt.plot(np.array(objs_vals)[:,0], np.array(objs_vals)[:,1],'*') - - # JY: Visualization: optimal points only - This code block needs to be either deleted or revisited. - # plt.xlim(75,100) - # plt.ylim(5,20) - # plt.xlim(0,1) - # plt.ylim(0,6) - plt.title(str('Iteration ' + str(self.counter-1))) - - plt.plot(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,0], - np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,1],'*') - for i in range(len(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[:,0])): - plt.text(np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,0], - np.array(list(zip(self._optPointHistory[traj][-1][0][self._objectiveVar[0]], self._optPointHistory[traj][-1][0][self._objectiveVar[1]])))[i,1], str(self.batchId-1)) - plt.savefig('PF'+str(i)+'_'+str(self.counter-1)+'.png') + + signChange = list(map(lambda x:-1 if x=="max" else 1 , self._minMax)) + for i in range(0, len(self.multiBestObjective)): + newMultiBestObjective = self.multiBestObjective * signChange + + plt.title(str('BatchID = ' + str(self.batchId))) + plt.plot(newMultiBestObjective[:,0], + newMultiBestObjective[:,1],'*') + + for i in range(len(self.multiBestObjective[:,0])): + plt.text(newMultiBestObjective[i,0], + newMultiBestObjective[i,1], str(self.batchId)) + plt.savefig('PF'+str(i)+'_'+str(self.batchId)+'.png') ############################################################################## else: @@ -642,8 +646,6 @@ def _useRealization(self, info, rlz): for i in range(len(self._objectiveVar)): offObjVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) - # offspringObjsVals = [list(ele) for ele in list(zip(*offObjVal))] - # offspringFitVals for Rank and CD calculation fitVal = datasetToDataArray(self.fitness, self._objectiveVar).data offspringFitVals = fitVal.tolist() diff --git a/ravenframework/Optimizers/Optimizer.py b/ravenframework/Optimizers/Optimizer.py index d12e96d71b..a9b5600615 100644 --- a/ravenframework/Optimizers/Optimizer.py +++ b/ravenframework/Optimizers/Optimizer.py @@ -162,7 +162,7 @@ def __init__(self): # public # _protected self._seed = None # random seed to apply - self._minMax = ['min'] # maximization or minimization? + self._minMax = ['min'] # maximization or minimization? self._activeTraj = [] # tracks live trajectories self._cancelledTraj = {} # tracks cancelled trajectories, and reasons self._convergedTraj = {} # tracks converged trajectories, and values obtained @@ -267,7 +267,7 @@ def handleInput(self, paramInput): if len(self._minMax) != len(self._objectiveVar): self.raiseAnError(IOError, 'The number of in -- and in - must be of the same length!') if list(set(self._minMax)-set(['min','max'])) != []: - self.raiseAnError(IOError, " must be a either 'min' and/or 'max'") + self.raiseAnError(IOError, " under - must be a either 'min' and/or 'max'") # variables additional reading for varNode in paramInput.findAll('variable'): diff --git a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml index 83e3c898d2..1483ffa8f5 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/discrete/constrained/MinwoRepMultiObjective.xml @@ -9,7 +9,7 @@ - Multi_MinwoReplacement_wo_constraints_50_10 + Multi_MinwoReplacement_wo_constraints_min_max_min_50_20 optimize,print 4 @@ -33,7 +33,7 @@ - x1,x2,x3,x4,x5,x6,obj1,obj2 + x1,x2,x3,x4,x5,x6,obj1,obj2,obj3 @@ -60,13 +60,13 @@ - 10 + 5 42 every - min + min, max, min - 20 + 50 tournamentSelection @@ -77,7 +77,7 @@ - 50, 50, 100, 100 + 50, 50, 100, 100, 100, 150 rankNcrowdingBased @@ -102,7 +102,7 @@ woRep_dist - obj1 + obj1, obj2, obj3 optOut MC_samp expConstr3 @@ -114,7 +114,7 @@ - 20 + 50 050877 @@ -142,12 +142,11 @@ x1,x2,x3,x4,x5,x6 - obj1 + obj1, obj2, obj3 trajID - - x1,x2,x3,x4,x5,x6,obj1,obj2,age,batchId,rank,CD,FitnessEvaluation_obj1,FitnessEvaluation_obj2,ConstraintEvaluation_expConstr3, ConstraintEvaluation_impConstr3,accepted + x1,x2,x3,x4,x5,x6,obj1,obj2,obj3,age,batchId,rank,CD,FitnessEvaluation_obj1,FitnessEvaluation_obj2,ConstraintEvaluation_expConstr3, ConstraintEvaluation_impConstr3,accepted From 9b42d7d66d51c3299cd30ab65cd3c812c4742b47 Mon Sep 17 00:00:00 2001 From: Junyung Kim Date: Sun, 10 Sep 2023 17:31:50 -0600 Subject: [PATCH 45/48] minor bugs are fixed. --- ravenframework/Optimizers/GeneticAlgorithm.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index 6804195d11..26fbbdbbbe 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -389,8 +389,11 @@ def handleInput(self, paramInput): self._penaltyCoeff = fitnessNode.findFirst('b').value if fitnessNode.findFirst('b') is not None else None expConstr = self.assemblerObjects['Constraint'][0] impConstr = self.assemblerObjects['ImplicitConstraint'][0] - if len(self._penaltyCoeff) != len(self._objectiveVar) * (len([ele for ele in expConstr if ele != 'Functions' if ele !='External']) + len([ele for ele in impConstr if ele != 'Functions' if ele !='External']) ): - self.raiseAnError(IOError, f'The number of penaltyCoeff. in should be identical with the number of objective in and the number of constraints (i.e., and )') + if self._penaltyCoeff != None: + if len(self._penaltyCoeff) != len(self._objectiveVar) * (len([ele for ele in expConstr if ele != 'Functions' if ele !='External']) + len([ele for ele in impConstr if ele != 'Functions' if ele !='External']) ): + self.raiseAnError(IOError, f'The number of penaltyCoeff. in should be identical with the number of objective in and the number of constraints (i.e., and )') + else: + pass self._fitnessInstance = fitnessReturnInstance(self,name = self._fitnessType) self._repairInstance = repairReturnInstance(self,name='replacementRepair') # currently only replacement repair is implemented. From f6ecb5f194763ad73aa112f84eb6007eec268a97 Mon Sep 17 00:00:00 2001 From: Junyung Kim Date: Sun, 17 Sep 2023 16:38:00 -0600 Subject: [PATCH 46/48] Every type of fitness is now working with newly updated GA interface (fitnes: Xarray array=> Xarray dataset) which is for handling multi-objective optimization. Regression tests passed except newly included multi-objective optimization tests (i.e., ZDT1, and multisum) due to its survival selection method change. --- ravenframework/Optimizers/GeneticAlgorithm.py | 176 +++++++++++------- ravenframework/Optimizers/fitness/fitness.py | 70 ++++--- .../continuous/unconstrained/ZDT1.xml | 2 +- 3 files changed, 153 insertions(+), 95 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index 26fbbdbbbe..cff7e23c89 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -17,12 +17,14 @@ Genetic Algorithm-based optimization. Multiple strategies for mutations, cross-overs, etc. are available. Created June,3,2020 - @authors: Mohammad Abdo, Diego Mandelli, Andrea Alfonsi + Updated Sepember,17,2023 + @authors: Mohammad Abdo, Diego Mandelli, Andrea Alfonsi, Junyung Kim References ---------- .. [1] Holland, John H. "Genetic algorithms." Scientific American 267.1 (1992): 66-73. - [2] Z. Michalewicz, "Genetic Algorithms. + Data Structures. = Evolution Programs," Third, Revised - and Extended Edition, Springer (1996). + [2] Z. Michalewicz, "Genetic Algorithms. + Data Structures. = Evolution Programs," Third, Revised and Extended Edition, Springer (1996). + [3] Deb, Kalyanmoy, et al. "A fast and elitist multiobjective genetic algorithm: NSGA-II." IEEE transactions on evolutionary computation 6.2 (2002): 182-197. + [4] Deb, Kalyanmoy. "An efficient constraint handling method for genetic algorithms." Computer methods in applied mechanics and engineering 186.2-4 (2000): 311-338. """ # External Modules---------------------------------------------------------------------------------- from collections import deque, defaultdict @@ -267,9 +269,13 @@ def getInputSpecification(cls): b. logistic: $fitness = \frac{1}{1+e^{a\times(obj-b)}}$. - c. feasibleFirst: $fitness = \left\{\begin{matrix} -obj & g_j(x)\geq 0 \; \forall j \\ -obj_{worst}- \Sigma_{j=1}^{J} & otherwise \\ \end{matrix}\right.$""") + c. feasibleFirst: $fitness = \left\{\begin{matrix} -obj & g_j(x)\geq 0 \; \forall j \\ -obj_{worst}- \Sigma_{j=1}^{J} & otherwise \\ \end{matrix}\right.$ + + d. hardConstraint: $fitness = the number of constraints violated.$ + + """) fitness.addParam("type", InputTypes.StringType, True, - descr=r"""[invLin, logistic, feasibleFirst]""") + descr=r"""[invLin, logistic, feasibleFirst, hardConstraint]""") objCoeff = InputData.parameterInputFactory('a', strictMode=True, contentType=InputTypes.FloatListType, printPriority=108, @@ -334,22 +340,36 @@ def handleInput(self, paramInput): @ Out, None """ RavenSampled.handleInput(self, paramInput) - # GAparams + #################################################################################### + # GAparams # + #################################################################################### gaParamsNode = paramInput.findFirst('GAparams') - # populationSize + + #################################################################################### + # populationSize # + #################################################################################### populationSizeNode = gaParamsNode.findFirst('populationSize') self._populationSize = populationSizeNode.value - # parent selection + + #################################################################################### + # parent selection node # + #################################################################################### parentSelectionNode = gaParamsNode.findFirst('parentSelection') self._parentSelectionType = parentSelectionNode.value self._parentSelectionInstance = parentSelectionReturnInstance(self, name=parentSelectionNode.value) if len(self._objectiveVar) >=2 and self._parentSelectionType != 'tournamentSelection': self.raiseAnError(IOError, f'tournamentSelection in is a sole mechanism supportive in multi-objective optimization.') - # reproduction node + + #################################################################################### + # reproduction node # + #################################################################################### reproductionNode = gaParamsNode.findFirst('reproduction') self._nParents = int(np.ceil(1/2 + np.sqrt(1+4*self._populationSize)/2)) self._nChildren = int(2*comb(self._nParents,2)) - # crossover node + + #################################################################################### + # crossover node # + #################################################################################### crossoverNode = reproductionNode.findFirst('crossover') self._crossoverType = crossoverNode.parameterValues['type'] if self._crossoverType not in ['onePointCrossover','twoPointsCrossover','uniformCrossover']: @@ -360,18 +380,24 @@ def handleInput(self, paramInput): self._crossoverPoints = crossoverNode.findFirst('points').value self._crossoverProb = crossoverNode.findFirst('crossoverProb').value self._crossoverInstance = crossoversReturnInstance(self,name = self._crossoverType) - # mutation node + + #################################################################################### + # mutation node # + #################################################################################### mutationNode = reproductionNode.findFirst('mutation') self._mutationType = mutationNode.parameterValues['type'] - if self._mutationType not in ['swapMutator','scrambleMutator','inversionMutator','bitFlipMutator']: - self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support swapMutator, scrambleMutator, inversionMutator, and bitFlipMutator as a mutator, whereas provided mutator is {self._mutationType}') + if self._mutationType not in ['swapMutator','scrambleMutator','inversionMutator','bitFlipMutator','randomMutator']: + self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support swapMutator, scrambleMutator, inversionMutator, bitFlipMutator, and randomMutator as a mutator, whereas provided mutator is {self._mutationType}') if mutationNode.findFirst('locs') is None: self._mutationLocs = None else: self._mutationLocs = mutationNode.findFirst('locs').value self._mutationProb = mutationNode.findFirst('mutationProb').value self._mutationInstance = mutatorsReturnInstance(self,name = self._mutationType) - # Survivor selection + + #################################################################################### + # survivor selection node # + #################################################################################### survivorSelectionNode = gaParamsNode.findFirst('survivorSelection') self._survivorSelectionType = survivorSelectionNode.value self._survivorSelectionInstance = survivorSelectionReturnInstance(self,name = self._survivorSelectionType) @@ -379,25 +405,53 @@ def handleInput(self, paramInput): self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support ageBased, fitnessBased, and rankNcrowdingBased as a survivorSelector, whereas provided survivorSelector is {self._survivorSelectionType}') if len(self._objectiveVar) == 1 and self._survivorSelectionType == 'rankNcrowdingBased': self.raiseAnError(IOError, f'(rankNcrowdingBased) in only supports when the number of objective in is bigger than two. ') - # Fitness + + #################################################################################### + # fitness node # + #################################################################################### fitnessNode = gaParamsNode.findFirst('fitness') self._fitnessType = fitnessNode.parameterValues['type'] + + #################################################################################### + # constraint node # + #################################################################################### # TODO: @mandd, please explore the possibility to convert the logistic fitness into a constrained optimization fitness. - if 'Constraint' in self.assemblerObjects and self._fitnessType not in ['invLinear','feasibleFirst','hardConstraint']: - self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support invLinear, feasibleFirst and hardConstraint as a fitness, whereas provided fitness is {self._fitnessType}') - self._objCoeff = fitnessNode.findFirst('a').value if fitnessNode.findFirst('a') is not None else None - self._penaltyCoeff = fitnessNode.findFirst('b').value if fitnessNode.findFirst('b') is not None else None - expConstr = self.assemblerObjects['Constraint'][0] - impConstr = self.assemblerObjects['ImplicitConstraint'][0] - if self._penaltyCoeff != None: - if len(self._penaltyCoeff) != len(self._objectiveVar) * (len([ele for ele in expConstr if ele != 'Functions' if ele !='External']) + len([ele for ele in impConstr if ele != 'Functions' if ele !='External']) ): + if 'Constraint' in self.assemblerObjects and self._fitnessType not in ['invLinear','logistic', 'feasibleFirst','hardConstraint']: + self.raiseAnError(IOError, f'Currently constrained Genetic Algorithms only support invLinear, logistic, feasibleFirst and hardConstraint as a fitness, whereas provided fitness is {self._fitnessType}') + self._expConstr = self.assemblerObjects['Constraint'][0] if 'Constraint' in self.assemblerObjects else None + self._impConstr = self.assemblerObjects['ImplicitConstraint'][0] if 'ImplicitConstraint' in self.assemblerObjects else None + if self._expConstr != None and self._impConstr != None: + self._numOfConst = len([ele for ele in self._expConstr if ele != 'Functions' if ele !='External']) + len([ele for ele in self._impConstr if ele != 'Functions' if ele !='External']) + elif self._expConstr == None and self._impConstr != None: + self._numOfConst = len([ele for ele in self._impConstr if ele != 'Functions' if ele !='External']) + elif self._expConstr != None and self._impConstr == None: + self._numOfConst = len([ele for ele in self._expConstr if ele != 'Functions' if ele !='External']) + else: + self._numOfConst = 0 + if (self._expConstr != None) and (self._impConstr != None) and (self._penaltyCoeff != None): + if len(self._penaltyCoeff) != len(self._objectiveVar) * self._numOfConst: self.raiseAnError(IOError, f'The number of penaltyCoeff. in should be identical with the number of objective in and the number of constraints (i.e., and )') else: pass + self._objCoeff = fitnessNode.findFirst('a').value if fitnessNode.findFirst('a') is not None else None + #NOTE the code lines below are for 'feasibleFirst' temperarily. It will be generalized for invLinear as well. + if self._fitnessType == 'feasibleFirst': + if self._numOfConst != 0 and fitnessNode.findFirst('b') is not None: + self._penaltyCoeff = fitnessNode.findFirst('b').value + elif self._numOfConst == 0 and fitnessNode.findFirst('b') is not None: + self.raiseAnError(IOError, f'The number of constraints used are 0 but there are penalty coefficieints') + elif self._numOfConst != 0 and fitnessNode.findFirst('b') is None: + self._penaltyCoeff = list(np.repeat(1, self._numOfConst * len(self._objectiveVar))) #NOTE if penaltyCoeff is not provided, then assume they are all 1. + else: + self._penaltyCoeff = list(np.repeat(0, len(self._objectiveVar))) + else: + self._penaltyCoeff = fitnessNode.findFirst('b').value if fitnessNode.findFirst('b') is not None else None self._fitnessInstance = fitnessReturnInstance(self,name = self._fitnessType) self._repairInstance = repairReturnInstance(self,name='replacementRepair') # currently only replacement repair is implemented. - # Convergence Criterion + #################################################################################### + # convergence criterion node # + #################################################################################### convNode = paramInput.findFirst('convergence') if convNode is not None: for sub in convNode.subparts: @@ -453,9 +507,9 @@ def needDenormalized(self): # overload as needed in inheritors return True - ########################################################################################################## - # Run Methods # - ########################################################################################################## + #################################################################################### + # Run Methods # + #################################################################################### def _useRealization(self, info, rlz): """ @@ -480,11 +534,11 @@ def _useRealization(self, info, rlz): # 0.1 @ n-1: fitnessCalculation(rlz) # perform fitness calculation for newly obtained children (rlz) - if not self._canHandleMultiObjective or len(self._objectiveVar) == 1: # This is a single-objective Optimization case + if not self._canHandleMultiObjective or len(self._objectiveVar) == 1: # This is for a single-objective Optimization case. offSprings = datasetToDataArray(rlz, list(self.toBeSampled)) objectiveVal = list(np.atleast_1d(rlz[self._objectiveVar[0]].data)) - # collect parameters that the constraints functions need (neglecting the default params such as inputs and objective functions) + # Collect parameters that the constraints functions need (neglecting the default params such as inputs and objective functions) constraintData = {} if self._constraintFunctions or self._impConstraintFunctions: params = [] @@ -492,8 +546,7 @@ def _useRealization(self, info, rlz): params += y.parameterNames() for p in list(set(params) -set([self._objectiveVar[0]]) -set(list(self.toBeSampled.keys()))): constraintData[p] = list(np.atleast_1d(rlz[p].data)) - # Compute constraint function g_j(x) for all constraints (j = 1 .. J) - # and all x's (individuals) in the population + # Compute constraint function g_j(x) for all constraints (j = 1 .. J) and all x's (individuals) in the population g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) g = xr.DataArray(g0, @@ -522,18 +575,19 @@ def _useRealization(self, info, rlz): b=self._penaltyCoeff, penalty=None, constraintFunction=g, + constraintNum = self._numOfConst, type=self._minMax) self._collectOptPoint(rlz, offSpringFitness, objectiveVal, g) self._resolveNewGeneration(traj, rlz, objectiveVal, offSpringFitness, g, info) - else: # This is a multi-objective Optimization case + else: # This is for a multi-objective Optimization case. objectiveVal = [] offSprings = datasetToDataArray(rlz, list(self.toBeSampled)) for i in range(len(self._objectiveVar)): objectiveVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) - # collect parameters that the constraints functions need (neglecting the default params such as inputs and objective functions) + # Collect parameters that the constraints functions need (neglecting the default params such as inputs and objective functions) constraintData = {} if self._constraintFunctions or self._impConstraintFunctions: params = [] @@ -541,8 +595,7 @@ def _useRealization(self, info, rlz): params += y.parameterNames() for p in list(set(params) -set(self._objectiveVar) -set(list(self.toBeSampled.keys()))): constraintData[p] = list(np.atleast_1d(rlz[p].data)) - # Compute constraint function g_j(x) for all constraints (j = 1 .. J) - # and all x's (individuals) in the population + # Compute constraint function g_j(x) for all constraints (j = 1 .. J) and all x's (individuals) in the population g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) g = xr.DataArray(g0, @@ -569,15 +622,14 @@ def _useRealization(self, info, rlz): objVar=self._objectiveVar, a=self._objCoeff, b=self._penaltyCoeff, - penalty =None, constraintFunction=g, + constraintNum = self._numOfConst, type =self._minMax) - # 0.2@ n-1: Survivor selection(rlz) - # update population container given obtained children - + # 0.2@ n-1: Survivor selection(rlz) + # Update population container given obtained children if self._activeTraj: - if len(self._objectiveVar) == 1: # If the number of objectives is just 1: + if len(self._objectiveVar) == 1: # This is for a single-objective Optimization case. if self.counter > 1: self.population, self.fitness,\ age,self.objectiveVal = self._survivorSelectionInstance(age=self.popAge, @@ -593,7 +645,7 @@ def _useRealization(self, info, rlz): self.fitness = offSpringFitness self.objectiveVal = rlz[self._objectiveVar[0]].data - else: # If the number of objectives is more than 1: + else: # This is for a multi-objective Optimization case. if self.counter > 1: self.population,self.rank, \ self.popAge,self.crowdingDistance, \ @@ -675,16 +727,16 @@ def _useRealization(self, info, rlz): self.fitness, self.constraintsV) self._resolveNewGenerationMulti(traj, rlz, info) + # 1 @ n: Parent selection from population # pair parents together by indexes - - if len(self._objectiveVar) == 1: # If the number of objectives is just 1: + if len(self._objectiveVar) == 1: # This is for a single-objective Optimization case. parents = self._parentSelectionInstance(self.population, variables=list(self.toBeSampled), fitness=self.fitness, nParents=self._nParents) - else: # This is for a multi-objective Optimization case + else: # This is for a multi-objective Optimization case. parents = self._parentSelectionInstance(self.population, variables=list(self.toBeSampled), @@ -695,14 +747,14 @@ def _useRealization(self, info, rlz): ) # 2 @ n: Crossover from set of parents - # create childrenCoordinates (x1,...,xM) + # Create childrenCoordinates (x1,...,xM) childrenXover = self._crossoverInstance(parents=parents, variables=list(self.toBeSampled), crossoverProb=self._crossoverProb, points=self._crossoverPoints) # 3 @ n: Mutation - # perform random directly on childrenCoordinates + # Perform random directly on childrenCoordinates childrenMutated = self._mutationInstance(offSprings=childrenXover, distDict=self.distDict, locs=self._mutationLocs, @@ -710,7 +762,7 @@ def _useRealization(self, info, rlz): variables=list(self.toBeSampled)) # 4 @ n: repair/replacement - # repair should only happen if multiple genes in a single chromosome have the same values (), + # Repair should only happen if multiple genes in a single chromosome have the same values (), # and at the same time the sampling of these genes should be with Out replacement. needsRepair = False for chrom in range(self._nChildren): @@ -734,7 +786,7 @@ def _useRealization(self, info, rlz): 'Gene':list(self.toBeSampled)}) # 5 @ n: Submit children batch - # submit children coordinates (x1,...,xm), i.e., self.childrenCoordinates + # Submit children coordinates (x1,...,xm), i.e., self.childrenCoordinates for i in range(self.batch): newRlz = {} for _, var in enumerate(self.toBeSampled.keys()): @@ -758,7 +810,6 @@ def _submitRun(self, point, traj, step, moreInfo=None): }) # NOTE: Currently, GA treats explicit and implicit constraints similarly # while box constraints (Boundary constraints) are automatically handled via limits of the distribution - # self.raiseADebug(f'Adding run to queue: {self.denormalizeData(point)} | {info}') self._submissionQueue.append((point, info)) @@ -967,7 +1018,7 @@ def _collectOptPointMulti(self, population, rank, CD, objVal, fitness, constrain self.multiBestFitness = fitSet self.multiBestObjective = optObjVal self.multiBestConstraint = optConstNew - self.multiBestRank = optRank #TODO JY: MultiBestRank is not anymore in need. This should be removed later. + self.multiBestRank = optRank self.multiBestCD = optCD return optPointsDic @@ -1018,7 +1069,7 @@ def _checkConvObjective(self, traj, **kwargs): @ In, kwargs, dict, dictionary of parameters for convergence criteria @ Out, converged, bool, convergence state """ - if len(self._objectiveVar) == 1: # single objective optimization + if len(self._objectiveVar) == 1: # This is for a single-objective Optimization case. if len(self._optPointHistory[traj]) < 2: return False o1, _ = self._optPointHistory[traj][-1] @@ -1028,19 +1079,13 @@ def _checkConvObjective(self, traj, **kwargs): conv=str(converged), got=obj, req=self._convergenceCriteria['objective'])) - else: # multi objective optimization + else: # This is for a multi-objective Optimization case. if len(self._optPointHistory[traj]) < 2: return False o1, _ = self._optPointHistory[traj][-1] obj1 = o1[self._objectiveVar[0]] obj2 = o1[self._objectiveVar[1]] converged = (obj1 == self._convergenceCriteria['objective'] and obj2 == self._convergenceCriteria['objective']) - # JY: I stopped here. Codeline below needs to be revisited! 01/16/23 - # self.raiseADebug(self.convFormat.format(name='objective', - # conv=str(converged), - # got=obj1, - # req=self._convergenceCriteria['objective'])) - return converged def _checkConvAHDp(self, traj, **kwargs): @@ -1161,7 +1206,7 @@ def _updateConvergence(self, traj, new, old, acceptable): @ Out, converged, bool, True if converged on ANY criteria """ # NOTE we have multiple "if acceptable" trees here, as we need to update soln export regardless - if len(self._objectiveVar) == 1: # single-objective optimization + if len(self._objectiveVar) == 1: # This is for a single-objective Optimization case. if acceptable == 'accepted': self.raiseADebug(f'Convergence Check for Trajectory {traj}:') # check convergence @@ -1170,7 +1215,7 @@ def _updateConvergence(self, traj, new, old, acceptable): converged = False convDict = dict((var, False) for var in self._convergenceInfo[traj]) self._convergenceInfo[traj].update(convDict) - else: # multi-objective optimization + else: # This is for a multi-objective Optimization case. if acceptable == 'accepted': self.raiseADebug(f'Convergence Check for Trajectory {traj}:') # check convergence @@ -1214,8 +1259,9 @@ def _rejectOptPoint(self, traj, info, old): """ return - # * * * * * * * * * * * * - # Constraint Handling + ############################### + # Constraint Handling # + ############################### def _handleExplicitConstraints(self, point, constraint): """ Computes explicit (i.e. input-based) constraints @@ -1278,9 +1324,9 @@ def _checkImpFunctionalConstraints(self, point, opt, impConstraint): g = impConstraint.evaluate('implicitConstraint', inputs) return g - - # END constraint handling - # * * * * * * * * * * * * + ############################### + # END constraint handling # + ############################### def _addToSolutionExport(self, traj, rlz, acceptable): """ Contributes additional entries to the solution export. diff --git a/ravenframework/Optimizers/fitness/fitness.py b/ravenframework/Optimizers/fitness/fitness.py index 3384cf98d0..68d8dfe07a 100644 --- a/ravenframework/Optimizers/fitness/fitness.py +++ b/ravenframework/Optimizers/fitness/fitness.py @@ -16,7 +16,8 @@ currently the implemented fitness function is a linear combination of the objective function and penalty function for constraint violation: Created June,16,2020 - @authors: Mohammad Abdo, Diego Mandelli, Andrea Alfonsi + Updated September,17,2023 + @authors: Mohammad Abdo, Diego Mandelli, Andrea Alfonsi, Junyung Kim """ # Internal Modules---------------------------------------------------------------------------------- from ...utils import frontUtils @@ -59,26 +60,29 @@ def invLinear(rlz,**kwargs): @ Out, fitness, xr.DataArray, the fitness function of the given objective corresponding to a specific chromosome. """ if kwargs['a'] == None: - a = 1.0 + a = [1.0] else: a = kwargs['a'] if kwargs['b'] == None: - b = 10.0 + b = [10.0] else: b = kwargs['b'] if kwargs['constraintFunction'].all() == None: penalty = 0.0 else: penalty = kwargs['constraintFunction'].data - - objVar = kwargs['objVar'] - data = np.atleast_1d(rlz[objVar].data) - - fitness = -a * (rlz[objVar].data).reshape(-1,1) - b * np.sum(np.maximum(0,-penalty),axis=-1).reshape(-1,1) - fitness = xr.DataArray(np.squeeze(fitness), - dims=['chromosome'], - coords={'chromosome': np.arange(len(data))}) - return fitness + objVar = [kwargs['objVar']] + for j in range(len(objVar)): + data = np.atleast_1d(rlz[objVar][objVar[j]].data) + fitness = -a[0] * (rlz[objVar][objVar[j]].data).reshape(-1,1) - b[0] * np.sum(np.maximum(0,-penalty),axis=-1).reshape(-1,1) + fitness = xr.DataArray(np.squeeze(fitness), + dims=['chromosome'], + coords={'chromosome': np.arange(len(data))}) + if j == 0: + fitnessSet = fitness.to_dataset(name = objVar[j]) + else: + fitnessSet[objVar[j]] = fitness + return fitnessSet def hardConstraint(rlz,**kwargs): r""" @@ -150,23 +154,26 @@ def feasibleFirst(rlz,**kwargs): objVar = [kwargs['objVar']] else: objVar = kwargs['objVar'] - g = kwargs['constraintFunction'] - penalty = kwargs['b'] - pen = [penalty[i:i+len(g['Constraint'].data)] for i in range(0, len(penalty), len(g['Constraint'].data))] + if kwargs['constraintNum'] == 0: + pen = kwargs['b'] + else: + g = kwargs['constraintFunction'] + penalty = kwargs['b'] + pen = [penalty[i:i+len(g['Constraint'].data)] for i in range(0, len(penalty), len(g['Constraint'].data))] + objPen = dict(map(lambda i,j : (i,j), objVar, pen)) - for i in range(len(objVar)): data = np.atleast_1d(rlz[objVar][objVar[i]].data) worstObj = max(data) fitness = [] for ind in range(data.size): - if np.all(g.data[ind, :]>=0): + if kwargs['constraintNum'] == 0 or np.all(g.data[ind, :]>=0): fit=(data[ind]) else: fit = worstObj for constInd,_ in enumerate(g['Constraint'].data): fit+= objPen[objVar[i]][constInd]*(max(0,-1*g.data[ind, constInd])) #NOTE: objPen[objVar[i]][constInd] is "objective & Constraint specific penalty." - fitness.append(fit) + fitness.append(-1*fit) fitness = xr.DataArray(np.array(fitness), dims=['chromosome'], coords={'chromosome': np.arange(len(data))}) @@ -194,25 +201,30 @@ def logistic(rlz,**kwargs): @ Out, fitness, xr.DataArray, the fitness function of the given objective corresponding to a specific chromosome. """ if kwargs['a'] == None: - a = 1.0 + a = [1.0] else: a = kwargs['a'] if kwargs['b'] == None: - b = 0.0 + b = [0.0] else: b = kwargs['b'] - objVar = kwargs['objVar'] - val = rlz[objVar] - data = np.atleast_1d(rlz[objVar].data) - denom = 1.0 + np.exp(-a * (val - b)) - fitness = 1.0 / denom - fitness = xr.DataArray(np.array(fitness), - dims=['chromosome'], - coords={'chromosome': np.arange(len(data))}) + objVar = [kwargs['objVar']] + for i in range(len(objVar)): + val = rlz[objVar][objVar[i]].data + data = np.atleast_1d(rlz[objVar][objVar[i]].data) + denom = 1.0 + np.exp(-a[0] * (val - b[0])) + fitness = 1.0 / denom + fitness = xr.DataArray(fitness.data, + dims=['chromosome'], + coords={'chromosome': np.arange(len(data))}) + if i == 0: + fitnessSet = fitness.to_dataset(name = objVar[i]) + else: + fitnessSet[objVar[i]] = fitness - return fitness + return fitnessSet __fitness = {} diff --git a/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml b/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml index 04cd9bce62..d100a928b0 100644 --- a/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml +++ b/tests/framework/Optimizers/GeneticAlgorithms/continuous/unconstrained/ZDT1.xml @@ -64,7 +64,7 @@ 1.0 - + rankNcrowdingBased From 8a262855fc4ad0e597481ae27501f73bfd30559e Mon Sep 17 00:00:00 2001 From: Junyung Kim Date: Thu, 21 Sep 2023 14:16:05 -0600 Subject: [PATCH 47/48] multi-objective optimization using invLinear and logistics now works. --- ravenframework/Optimizers/fitness/fitness.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/ravenframework/Optimizers/fitness/fitness.py b/ravenframework/Optimizers/fitness/fitness.py index 68d8dfe07a..3dc0625e75 100644 --- a/ravenframework/Optimizers/fitness/fitness.py +++ b/ravenframework/Optimizers/fitness/fitness.py @@ -71,7 +71,10 @@ def invLinear(rlz,**kwargs): penalty = 0.0 else: penalty = kwargs['constraintFunction'].data - objVar = [kwargs['objVar']] + if isinstance(kwargs['objVar'], str) == True: + objVar = [kwargs['objVar']] + else: + objVar = kwargs['objVar'] for j in range(len(objVar)): data = np.atleast_1d(rlz[objVar][objVar[j]].data) fitness = -a[0] * (rlz[objVar][objVar[j]].data).reshape(-1,1) - b[0] * np.sum(np.maximum(0,-penalty),axis=-1).reshape(-1,1) @@ -204,13 +207,14 @@ def logistic(rlz,**kwargs): a = [1.0] else: a = kwargs['a'] - if kwargs['b'] == None: b = [0.0] else: b = kwargs['b'] - - objVar = [kwargs['objVar']] + if isinstance(kwargs['objVar'], str) == True: + objVar = [kwargs['objVar']] + else: + objVar = kwargs['objVar'] for i in range(len(objVar)): val = rlz[objVar][objVar[i]].data data = np.atleast_1d(rlz[objVar][objVar[i]].data) From 51eb867199c1a5a3e569bec12aca719d9856b11f Mon Sep 17 00:00:00 2001 From: Junyung Kim Date: Thu, 21 Sep 2023 14:56:32 -0600 Subject: [PATCH 48/48] constraint handling for single and multi objective optimization in _useRealization method is now separated and new methods called singleConstraint and multiConstraint are created to avoid if-else statement for single/multi objective optimization. --- ravenframework/Optimizers/GeneticAlgorithm.py | 170 ++++++++++-------- 1 file changed, 92 insertions(+), 78 deletions(-) diff --git a/ravenframework/Optimizers/GeneticAlgorithm.py b/ravenframework/Optimizers/GeneticAlgorithm.py index cff7e23c89..fced405aed 100644 --- a/ravenframework/Optimizers/GeneticAlgorithm.py +++ b/ravenframework/Optimizers/GeneticAlgorithm.py @@ -507,32 +507,11 @@ def needDenormalized(self): # overload as needed in inheritors return True - #################################################################################### - # Run Methods # - #################################################################################### - - def _useRealization(self, info, rlz): - """ - Used to feedback the collected runs into actionable items within the sampler. - This is called by localFinalizeActualSampling, and hence should contain the main skeleton. - @ In, info, dict, identifying information about the realization - @ In, rlz, xr.Dataset, new batched realizations - @ Out, None - """ - # The whole skeleton should be here, this should be calling all classes and _private methods. + def singleConstraint(self, info, rlz): traj = info['traj'] for t in self._activeTraj[1:]: self._closeTrajectory(t, 'cancel', 'Currently GA is single trajectory', 0) self.incrementIteration(traj) - info['step'] = self.counter - - # Developer note: each algorithm step is indicated by a number followed by the generation number - # e.g., '0 @ n-1' refers to step 0 for generation n-1 (i.e., previous generation) - # for more details refer to GRP-Raven-development/Disceret_opt channel on MS Teams - - # 0 @ n-1: Survivor Selection from previous iteration (children+parents merging from previous generation) - # 0.1 @ n-1: fitnessCalculation(rlz) - # perform fitness calculation for newly obtained children (rlz) if not self._canHandleMultiObjective or len(self._objectiveVar) == 1: # This is for a single-objective Optimization case. offSprings = datasetToDataArray(rlz, list(self.toBeSampled)) @@ -550,13 +529,9 @@ def _useRealization(self, info, rlz): g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) g = xr.DataArray(g0, - dims=['chromosome','Constraint'], - coords={'chromosome':np.arange(np.shape(offSprings)[0]), - 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) - # FIXME The constraint handling is following the structure of the RavenSampled.py, - # there are many utility functions that can be simplified and/or merged together - # _check, _handle, and _apply, for explicit and implicit constraints. - # This can be simplified in the near future in GradientDescent, SimulatedAnnealing, and here in GA + dims=['chromosome','Constraint'], + coords={'chromosome':np.arange(np.shape(offSprings)[0]), + 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) for index,individual in enumerate(offSprings): newOpt = individual opt = {self._objectiveVar[0]:objectiveVal[index]} @@ -570,64 +545,103 @@ def _useRealization(self, info, rlz): g.data[index, constIndex] = self._handleImplicitConstraints(newOpt, opt, constraint) offSpringFitness = self._fitnessInstance(rlz, - objVar=self._objectiveVar[0], - a=self._objCoeff, - b=self._penaltyCoeff, - penalty=None, - constraintFunction=g, - constraintNum = self._numOfConst, - type=self._minMax) + objVar=self._objectiveVar[0], + a=self._objCoeff, + b=self._penaltyCoeff, + penalty=None, + constraintFunction=g, + constraintNum = self._numOfConst, + type=self._minMax) self._collectOptPoint(rlz, offSpringFitness, objectiveVal, g) self._resolveNewGeneration(traj, rlz, objectiveVal, offSpringFitness, g, info) + return traj, g, objectiveVal, offSprings, offSpringFitness + + def multiConstraint(self, info, rlz): + traj = info['traj'] + for t in self._activeTraj[1:]: + self._closeTrajectory(t, 'cancel', 'Currently GA is single trajectory', 0) + self.incrementIteration(traj) - else: # This is for a multi-objective Optimization case. - objectiveVal = [] - offSprings = datasetToDataArray(rlz, list(self.toBeSampled)) - for i in range(len(self._objectiveVar)): - objectiveVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) + objectiveVal = [] + offSprings = datasetToDataArray(rlz, list(self.toBeSampled)) + for i in range(len(self._objectiveVar)): + objectiveVal.append(list(np.atleast_1d(rlz[self._objectiveVar[i]].data))) + + # Collect parameters that the constraints functions need (neglecting the default params such as inputs and objective functions) + constraintData = {} + if self._constraintFunctions or self._impConstraintFunctions: + params = [] + for y in (self._constraintFunctions + self._impConstraintFunctions): + params += y.parameterNames() + for p in list(set(params) -set(self._objectiveVar) -set(list(self.toBeSampled.keys()))): + constraintData[p] = list(np.atleast_1d(rlz[p].data)) + # Compute constraint function g_j(x) for all constraints (j = 1 .. J) and all x's (individuals) in the population + g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) + + g = xr.DataArray(g0, + dims=['chromosome','Constraint'], + coords={'chromosome':np.arange(np.shape(offSprings)[0]), + 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) + + for index,individual in enumerate(offSprings): + newOpt = individual + objOpt = dict(zip(self._objectiveVar, + list(map(lambda x:-1 if x=="max" else 1 , self._minMax)))) + opt = dict(zip(self._objectiveVar, [item[index] for item in objectiveVal])) + opt = {k: objOpt[k]*opt[k] for k in opt} + for p, v in constraintData.items(): + opt[p] = v[index] + + for constIndex, constraint in enumerate(self._constraintFunctions + self._impConstraintFunctions): + if constraint in self._constraintFunctions: + g.data[index, constIndex] = self._handleExplicitConstraints(newOpt, constraint) + else: + g.data[index, constIndex] = self._handleImplicitConstraints(newOpt, opt, constraint) - # Collect parameters that the constraints functions need (neglecting the default params such as inputs and objective functions) - constraintData = {} - if self._constraintFunctions or self._impConstraintFunctions: - params = [] - for y in (self._constraintFunctions + self._impConstraintFunctions): - params += y.parameterNames() - for p in list(set(params) -set(self._objectiveVar) -set(list(self.toBeSampled.keys()))): - constraintData[p] = list(np.atleast_1d(rlz[p].data)) - # Compute constraint function g_j(x) for all constraints (j = 1 .. J) and all x's (individuals) in the population - g0 = np.zeros((np.shape(offSprings)[0],len(self._constraintFunctions)+len(self._impConstraintFunctions))) + offSpringFitness = self._fitnessInstance(rlz, + objVar=self._objectiveVar, + a=self._objCoeff, + b=self._penaltyCoeff, + constraintFunction=g, + constraintNum = self._numOfConst, + type =self._minMax) + return traj, g, objectiveVal, offSprings, offSpringFitness - g = xr.DataArray(g0, - dims=['chromosome','Constraint'], - coords={'chromosome':np.arange(np.shape(offSprings)[0]), - 'Constraint':[y.name for y in (self._constraintFunctions + self._impConstraintFunctions)]}) - for index,individual in enumerate(offSprings): - newOpt = individual - objOpt = dict(zip(self._objectiveVar, - list(map(lambda x:-1 if x=="max" else 1 , self._minMax)))) - opt = dict(zip(self._objectiveVar, [item[index] for item in objectiveVal])) - opt = {k: objOpt[k]*opt[k] for k in opt} - for p, v in constraintData.items(): - opt[p] = v[index] - for constIndex, constraint in enumerate(self._constraintFunctions + self._impConstraintFunctions): - if constraint in self._constraintFunctions: - g.data[index, constIndex] = self._handleExplicitConstraints(newOpt, constraint) - else: - g.data[index, constIndex] = self._handleImplicitConstraints(newOpt, opt, constraint) + ######################################################################################################### + # Run Methods # + ######################################################################################################### - offSpringFitness = self._fitnessInstance(rlz, - objVar=self._objectiveVar, - a=self._objCoeff, - b=self._penaltyCoeff, - constraintFunction=g, - constraintNum = self._numOfConst, - type =self._minMax) - - # 0.2@ n-1: Survivor selection(rlz) - # Update population container given obtained children + ######################################################################################################### + # Developer note: + # Each algorithm step is indicated by a number followed by the generation number + # e.g., '0 @ n-1' refers to step 0 for generation n-1 (i.e., previous generation) + # for more details refer to GRP-Raven-development/Disceret_opt channel on MS Teams. + ######################################################################################################### + + def _useRealization(self, info, rlz): + """ + Used to feedback the collected runs into actionable items within the sampler. + This is called by localFinalizeActualSampling, and hence should contain the main skeleton. + @ In, info, dict, identifying information about the realization + @ In, rlz, xr.Dataset, new batched realizations + @ Out, None + """ + + info['step'] = self.counter + + # 0 @ n-1: Survivor Selection from previous iteration (children+parents merging from previous generation) + # 0.1 @ n-1: fitnessCalculation(rlz): Perform fitness calculation for newly obtained children (rlz) + + objInd = 1 if len(self._objectiveVar) == 1 else 2 + constraintFuncs: dict = {1: GeneticAlgorithm.singleConstraint, 2: GeneticAlgorithm.multiConstraint} + const = constraintFuncs.get(objInd, GeneticAlgorithm.singleConstraint) + traj, g, objectiveVal, offSprings, offSpringFitness = const(self, info, rlz) + + + # 0.2@ n-1: Survivor selection(rlz): Update population container given obtained children if self._activeTraj: if len(self._objectiveVar) == 1: # This is for a single-objective Optimization case. if self.counter > 1: