Added rejection method for handling implicit constraints in the optim…

…izers (#1247)

* add rejection method for handling implicit constraints in the optimizers

Co-authored-by: ZHOUJ2 <[email protected]>

doc/user_manual/generated/optimizer.tex

@@ -22,6 +22,7 @@ \subsection{GradientDescent}
           \item \texttt{trajID}: integer identifier for different optimization starting locations and paths
              \item \texttt{iteration}: integer identifying which iteration (or step, or generation) a trajectory is on
              \item \texttt{accepted}: string acceptance status of the potential optimal point (algorithm dependent)
+             \item \texttt{rejectReason}: discription of reject reason, 'noImprovement' means rejected the new optimization point for no improvement from last point, 'implicitConstraintsViolation' means rejected by implicit constraints violation, return None if the point is accepted
              \item \texttt{\{VAR\}}: any variable from the \xmlNode{TargetEvaluation} input or output; gives the value of that variable at the optimal candidate for this iteration.
              \item \texttt{stepSize}: the size of step taken in the normalized input space to arrive at each optimal point
              \item \texttt{conv\_\{CONV\}}: status of each given convergence criteria
@@ -472,6 +473,7 @@ \subsection{SimulatedAnnealing}
           \item \texttt{trajID}: integer identifier for different optimization starting locations and paths
              \item \texttt{iteration}: integer identifying which iteration (or step, or generation) a trajectory is on
              \item \texttt{accepted}: string acceptance status of the potential optimal point (algorithm dependent)
+             \item \texttt{rejectReason}: discription of reject reason, 'noImprovement' means rejected the new optimization point for no improvement from last point, 'implicitConstraintsViolation' means rejected by implicit constraints violation, return None if the point is accepted
              \item \texttt{\{VAR\}}: any variable from the \xmlNode{TargetEvaluation} input or output; gives the value of that variable at the optimal candidate for this iteration.
              \item \texttt{conv\_\{CONV\}}: status of each given convergence criteria
              \item \texttt{amp\_\{VAR\}}: amplitued associated to each variable used to compute step size based on cooling method and the corresponding next neighbour

framework/Optimizers/GradientDescent.py

@@ -184,7 +184,6 @@ def getSolutionExportVariableNames(cls):
       new.update(gradReturnClass(grad, cls).getSolutionExportVariableNames())
     for step in stepKnownTypes():
       new.update(stepReturnClass(step, cls).getSolutionExportVariableNames())
-
     ok.update(new)
     return ok
 
@@ -565,6 +564,7 @@ def _checkAcceptability(self, traj, opt, optVal, info):
       @ In, info, dict, identifying information about the opt point
       @ Out, acceptable, str, acceptability condition for point
       @ Out, old, dict, old opt point
+      @ Out, rejectReason, str, reject reason of opt point, or return None if accepted
     """
     # Check acceptability
     if self._optPointHistory[traj]:
@@ -580,10 +580,11 @@ def _checkAcceptability(self, traj, opt, optVal, info):
           self.raiseADebug('Cancelling Trajectory {} because it is following Trajectory {}'.format(traj, following))
           self._trajectoryFollowers[following].append(traj) # "traj" is killed by "following"
           self._closeTrajectory(traj, 'cancel', 'following {}'.format(following), optVal)
-          return 'accepted', old
+          return 'accepted', old, 'None'
 
       self.raiseADebug(' ... change: {d: 1.3e} new: {n: 1.6e} old: {o: 1.6e}'
                       .format(d=optVal-oldVal, o=oldVal, n=optVal))
+      rejectReason = 'None'
       ## some stepManipulators may need to override the acceptance criteria, e.g. conjugate gradient
       if self._stepInstance.needsAccessToAcceptance:
         acceptable = self._stepInstance.modifyAcceptance(old, oldVal, opt, optVal)
@@ -598,35 +599,42 @@ def _checkAcceptability(self, traj, opt, optVal, info):
         acceptable = 'accepted'
       else:
         if self._impConstraintFunctions:
-          accept = self._handleImplicitConstraints(old)
+          accept = self._handleImplicitConstraints(opt)
           if accept:
-            acceptable = self._checkForImprovement(optVal, oldVal)
+            acceptable, rejectReason = self._checkForImprovement(optVal, oldVal)
           else:
             acceptable = 'rejected'
+            rejectReason = 'implicitConstraintsViolation'
         else:
-          acceptable = self._checkForImprovement(optVal, oldVal)
+          acceptable, rejectReason = self._checkForImprovement(optVal, oldVal)
     else: # no history
       # if first sample, simply assume it's better!
+      rejectReason = 'None'
       if self._impConstraintFunctions:
         accept = self._handleImplicitConstraints(opt)
         if not accept:
           self.raiseAWarning('First point violate Implicit constraint, please change another point to start!')
+          rejectReason = 'implicitConstraintsViolation'
       acceptable = 'first'
       old = None
     self._acceptHistory[traj].append(acceptable)
     self.raiseADebug(' ... {a}!'.format(a=acceptable))
-    return acceptable, old
+    return acceptable, old, rejectReason
 
   def _checkForImprovement(self, new, old):
     """
       Determine if the new value is sufficient improved over the old.
       @ In, new, float, new optimization value
       @ In, old, float, previous optimization value
       @ Out, improved, bool, True if "sufficiently" improved or False if not.
+      @ Out, rejectReason, str, reject reason of opt point, or return None if accepted
     """
     # TODO could this be a base RavenSampled class?
     improved = self._acceptInstance.checkImprovement(new, old)
-    return 'accepted' if improved else 'rejected'
+    if improved:
+      return 'accepted', 'None'
+    else:
+      return 'rejected', 'noImprovement'
 
   def _updateConvergence(self, traj, new, old, acceptable):
     """
@@ -815,7 +823,6 @@ def _formatSolutionExportVariableNames(self, acceptable):
     # remaking the list is easier than using the existing one
     acceptable = RavenSampled._formatSolutionExportVariableNames(self, acceptable)
     new = []
-    print('DEBUGG acceptable:', acceptable)
     while acceptable:
       template = acceptable.pop()
       if '{CONV}' in template:

framework/Optimizers/Optimizer.py

@@ -336,12 +336,13 @@ def checkConvergence(self, traj):
     """
 
   @abc.abstractmethod
-  def _updateSolutionExport(self, traj, rlz, acceptable):
+  def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason):
     """
       Stores information to the solution export.
       @ In, traj, int, trajectory which should be written
       @ In, rlz, dict, collected point
       @ In, acceptable, bool, acceptability of opt point
+      @ In, rejectReason, str, reject reason of opt point, or return None if accepted
       @ Out, None
     """
 

framework/Optimizers/RavenSampled.py

@@ -95,6 +95,7 @@ def getSolutionExportVariableNames(cls):
     ok.update({'trajID': 'integer identifier for different optimization starting locations and paths',
                'iteration': 'integer identifying which iteration (or step, or generation) a trajectory is on',
                'accepted': 'string acceptance status of the potential optimal point (algorithm dependent)',
+               'rejectReason':'discription of reject reason, \'noImprovement\' means rejected the new optimization point for no improvement from last point, \'implicitConstraintsViolation\' means rejected by implicit constraints violation, return None if the point is accepted',
                '{VAR}': r'any variable from the \xmlNode{TargetEvaluation} input or output; gives the value of that variable at the optimal candidate for this iteration.',
               })
     return ok
@@ -314,7 +315,7 @@ def finalizeSampler(self, failedRuns):
       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')
+    self._updateSolutionExport(bestTraj, self.normalizeData(bestOpt), 'final', 'None')
 
   ###################
   # Utility Methods #
@@ -460,7 +461,7 @@ def _resolveNewOptPoint(self, traj, rlz, optVal, info):
     # note the collection of the opt point
     self._stepTracker[traj]['opt'] = (rlz, info)
     # FIXME check implicit constraints? Function call, - Jia
-    acceptable, old = self._checkAcceptability(traj, rlz, optVal, info)
+    acceptable, old, rejectReason = self._checkAcceptability(traj, rlz, optVal, info)
     converged = self._updateConvergence(traj, rlz, old, acceptable)
     # we only want to update persistance if we've accepted a new point.
     # We don't want rejected points to count against our convergence.
@@ -469,7 +470,7 @@ def _resolveNewOptPoint(self, traj, rlz, optVal, info):
     # NOTE: the solution export needs to be updated BEFORE we run rejectOptPoint or extend the opt
     #       point history.
     if self._writeSteps == 'every':
-      self._updateSolutionExport(traj, rlz, acceptable)
+      self._updateSolutionExport(traj, rlz, acceptable, rejectReason)
     self.raiseADebug('*'*80)
     # decide what to do next
     if acceptable in ['accepted', 'first']:
@@ -492,6 +493,7 @@ def _checkAcceptability(self, traj, opt, optVal):
       @ In, optVal, float, new optimization value
       @ Out, acceptable, str, acceptability condition for point
       @ Out, old, dict, old opt point
+      @ Out, rejectReason, str, reject reason of opt point, or return None if accepted
     """
 
   @abc.abstractmethod
@@ -522,12 +524,13 @@ def _rejectOptPoint(self, traj, info, old):
       @ In, old, dict, previous optimal point (to resubmit)
     """
 
-  def _updateSolutionExport(self, traj, rlz, acceptable):
+  def _updateSolutionExport(self, traj, rlz, acceptable, rejectReason):
     """
       Stores information to the solution export.
       @ In, traj, int, trajectory which should be written
       @ In, rlz, dict, collected point
       @ In, acceptable, bool, acceptability of opt point
+      @ In, rejectReason, str, reject reason of opt point, or return None if accepted
       @ Out, None
     """
     # make a holder for the realization that will go to the solutionExport
@@ -536,6 +539,7 @@ def _updateSolutionExport(self, traj, rlz, acceptable):
     toExport.update({'iteration': self.getIteration(traj),
                      'trajID': traj,
                      'accepted': acceptable,
+                     'rejectReason': rejectReason
                     })
     # optimal point input and output spaces
     objValue = rlz[self._objectiveVar]

framework/Optimizers/SimulatedAnnealing.py

@@ -419,6 +419,7 @@ def _checkAcceptability(self, traj, opt, optVal, info):
       @ In, info, dict, meta information about the opt point
       @ Out, acceptable, str, acceptability condition for point
       @ Out, old, dict, old opt point
+      @ Out, rejectReason, str, reject reason of opt point, or return None if accepted
     """
     # Check acceptability
     # NOTE: if self._optPointHistory[traj]: -> faster to use "try" for all but the first time
@@ -446,7 +447,7 @@ def _checkAcceptability(self, traj, opt, optVal, info):
       old = None
     self._acceptHistory[traj].append(acceptable)
     self.raiseADebug(' ... {a}!'.format(a=acceptable))
-    return acceptable, old
+    return acceptable, old, 'None'
 
   def _acceptabilityCriterion(self,currentObjective,newObjective):
     """

tests/framework/Optimizers/GradientDescent/constrain_function.xml

@@ -11,6 +11,7 @@
     </description>
     <revisions>
       <revision author="zhouj" date="2020-05-15">Add implicit constriant node in optimizer and function node with objective as variable.</revision>
+      <revision author="zhouj" date="2020-06-04">Add reject opt point reason.</revision>
     </revisions>
     <requirements>R-RM-2</requirements>
   </TestInfo>
@@ -103,7 +104,7 @@
     </PointSet>
     <PointSet name="opt_export">
       <Input>trajID</Input>
-      <Output>x,y,ans,stepSize,iteration,accepted,conv_gradient</Output>
+      <Output>x,y,ans,stepSize,iteration,accepted,conv_gradient,rejectReason</Output>
     </PointSet>
   </DataObjects>