NSGA-II features Update on Optimizers/GeneticAlgorithm

.gitmodules

@@ -1,6 +1,3 @@
-[submodule "plugins/TEAL"]
-	path = plugins/TEAL
-	url = https://github.com/idaholab/TEAL.git
 [submodule "plugins/HERON"]
 	path = plugins/HERON
 	url = https://github.com/idaholab/HERON.git
@@ -16,3 +13,6 @@
 [submodule "plugins/FARM"]
 	path = plugins/FARM
 	url = https://github.com/Argonne-National-Laboratory/FARM.git
+[submodule "plugins/BayCal"]
+	path = plugins/BayCal
+	url = https://github.com/idaholab/BayCal.git

dependencies.xml

@@ -63,7 +63,6 @@ Note all install methods after "main" take
     <importlib_metadata/>
     <pyside2 optional='True'/>
     <nomkl os='linux' skip_check='True'/>
-    <numexpr os='linux'/>
     <cmake skip_check='True' optional='True'/>
     <dask source="pip" pip_extra="[complete]"/>
     <ray source="pip" pip_extra="[default]">2.6</ray>
@@ -97,7 +96,6 @@ Note all install methods after "main" take
     <pip>remove</pip>
     <python>remove</python>
     <nomkl>remove</nomkl>
-    <numexpr>remove</numexpr>
     <liblapack>remove</liblapack>
   </alternate>
   <alternate name="none">
@@ -106,6 +104,5 @@ Note all install methods after "main" take
     <pip>remove</pip>
     <python>remove</python>
     <nomkl>remove</nomkl>
-    <numexpr>remove</numexpr>
   </alternate>
-</dependencies>
+</dependencies>

doc/user_manual/generated/generateOptimizerDoc.py

@@ -152,7 +152,7 @@ def insertSolnExport(tex, obj):
       </samplerInit>
 
       <GAparams>
-        <populationSize>20</populationSize>
+        <populationSize>10</populationSize>
         <parentSelection>rouletteWheel</parentSelection>
         <reproduction>
           <crossover type="onePointCrossover">
@@ -177,32 +177,32 @@ def insertSolnExport(tex, obj):
 
       <variable name="x1">
         <distribution>uniform_dist_woRepl_1</distribution>
-        <initial>1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20</initial>
+        <initial>1,2,3,4,5,6,7,8,9,10</initial>
       </variable>
 
       <variable name="x2">
         <distribution>uniform_dist_woRepl_1</distribution>
-        <initial>2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,1</initial>
+        <initial>2,3,4,5,6,7,8,9,10,1</initial>
       </variable>
 
       <variable name="x3">
         <distribution>uniform_dist_woRepl_1</distribution>
-        <initial>3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,1,2</initial>
+        <initial>3,4,5,6,7,8,9,10,1,2</initial>
       </variable>
 
       <variable name="x4">
         <distribution>uniform_dist_woRepl_1</distribution>
-        <initial>4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,1,2,3</initial>
+        <initial>4,5,6,7,8,9,10,1,2,3</initial>
       </variable>
 
       <variable name="x5">
         <distribution>uniform_dist_woRepl_1</distribution>
-        <initial>5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,1,2,3,4</initial>
+        <initial>5,6,7,8,9,10,1,2,3,4</initial>
       </variable>
 
       <variable name="x6">
         <distribution>uniform_dist_woRepl_1</distribution>
-        <initial>6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,1,2,3,4,5</initial>
+        <initial>6,7,8,9,10,1,2,3,4,5</initial>
       </variable>
 
       <objective>ans</objective>

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
@@ -338,7 +339,11 @@ def _useRealization(self, info, rlz):
       @ Out, None
     """
     traj = info['traj']
-    optVal = rlz[self._objectiveVar]
+    # 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[0]]
     info['optVal'] = optVal
     purpose = info['purpose']
     if purpose.startswith('opt'):
@@ -353,13 +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],
@@ -378,7 +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
@@ -598,7 +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,
@@ -815,7 +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),

ravenframework/Optimizers/Optimizer.py

@@ -78,10 +78,10 @@ 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 <variable>
-        descr=r"""Name of the response variable (or ``objective function'') that should be optimized
-        (minimized or maximized)."""))
+        descr=r"""Name of the objective variable(s) (or ``objective function'') that should be optimized
+        (minimized or maximized). It can be a single string or a list of strings if it is a multi-objective problem. """))
 
     # modify Sampler variable nodes
     variable = specs.getSub('variable')
@@ -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)
@@ -160,7 +161,7 @@ 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
@@ -248,7 +249,6 @@ 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
 
     # sampler init
@@ -263,6 +263,10 @@ 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, 'The length of <type> in <Optimizers>-<GeneticAlgorithm>-<SamplerInit> and <objective> in <Optimizers>-<GeneticAlgorithm> must be of the same length!')
+        if list(set(self._minMax)-set(['min','max'])) != []:
+          self.raiseAnError(IOError, "<type> under <Optimizers>-<GeneticAlgorhtm> must be a either 'min' and/or 'max'")
 
     # variables additional reading
     for varNode in paramInput.findAll('variable'):