Changeset 473

Timestamp:

07/07/11 08:44:38 (5 years ago)

Author:

mmckerns

Message:

converted 'constraints' keyword to SetConstraints? method;
enabled use of constraints in DifferentialEvolutionSolver?;
added new constraints example ('test_rosenbrock3b')

Location:

mystic

Files:

• examples/Make.mm (modified) (1 diff)
• examples/test_rosenbrock3.py (modified) (1 diff)
• examples/test_rosenbrock3b.py (added)
• mystic/abstract_solver.py (modified) (2 diffs)
• mystic/differential_evolution.py (modified) (8 diffs)
• mystic/nested.py (modified) (4 diffs)
• mystic/scipy_optimize.py (modified) (9 diffs)
• tests/solver_test_suite.py (modified) (1 diff)

mystic/examples/Make.mm

@@ -79 +79 @@
     test_rosenbrock2.py \
     test_rosenbrock3.py \
+    test_rosenbrock3b.py \
 ##  test_twistedgaussian.py \
 ##  test_twistedgaussian2.py \

mystic/examples/test_rosenbrock3.py

@@ -41 +41 @@
    #solver.SetEvaluationLimits(maxiter=13)
     solver.SetGenerationMonitor(stepmon)
+    solver.SetConstraints(constrain)
     solver.enable_signal_handler()
-    solver.Solve(rosen, NCOG(tolerance=1e-4), disp=1, constraints=constrain)
+    solver.Solve(rosen, NCOG(tolerance=1e-4), disp=1)
     print solver.bestSolution
    #print "Current function value: %s" % solver.bestEnergy

mystic/mystic/abstract_solver.py

@@ -134 +134 @@
         self._evalmon         = Null
 
+        self._constraints     = lambda x: x
+       #self._penalty         = lambda x: x
+
         import mystic.termination
         self._EARLYEXIT       = mystic.termination.EARLYEXIT
@@ -145 +148 @@
         """get the number of function calls"""
         return self._fcalls[0]
+
+    def SetConstraints(self, constraints):
+        """apply a constraints function to the optimization
+
+input::
+    - a constraints function of the form: xk' = constraints(xk),
+      where xk is the current parameter vector. Ideally, this function
+      is constructed so the parameter vector it passes to the cost function
+      will satisfy the desired (i.e. encoded) constraints."""
+        if not constraints:
+            self._constraints = lambda x: x
+        elif not callable(constraints):
+            raise TypeError, "'%s' is not a callable function" % constraints
+        else: #XXX: check for format: x' = constraints(x) ?
+            self._constraints = constraints
+        return
 
     def SetGenerationMonitor(self, monitor):

mystic/mystic/differential_evolution.py

@@ -241 +241 @@
         if kwds.has_key('StepMonitor'): \
            self._stepmon = kwds['StepMonitor']
+        if kwds.has_key('constraints'): \
+           self._constraints = kwds['constraints']
+        if not self._constraints: self._constraints = lambda x: x
         #-------------------------------------------------------------
 
@@ -266 +269 @@
             # generate trialSolution (within valid range)
             self.trialSolution[:] = self.population[candidate][:]
+            # -apply constraints-
+            self.trialSolution[:] = self._constraints(self.trialSolution[:])
+            # -end-
             trialEnergy = costfunction(self.trialSolution)
 
@@ -296 +302 @@
                 # generate trialSolution (within valid range)
                 strategy(self, candidate)
+                # -apply constraints-
+                self.trialSolution[:] = self._constraints(self.trialSolution[:])
+                # -end-
                 trialEnergy = costfunction(self.trialSolution)
 
@@ -406 +415 @@
         iteration.  It is called as callback(xk), where xk is
         the current parameter vector.  [default = None]
-    constraints -- an optional user-supplied function to call just
-        prior to each function evaluation.  It is called as
-        xk' = constraints(xk), where xk is the current parameter vector.
-        Ideally, this function is used to ensure each function evaulation
-        encounters a parameter set that satisfies user-supplied constraints.
     disp -- non-zero to print convergence messages.
         """
@@ -426 +430 @@
         if kwds.has_key('callback'): callback = kwds['callback']
         if kwds.has_key('disp'): disp = kwds['disp']
-        #XXX:[HACK] accept constraints
-        constraints = lambda x: x
-        if kwds.has_key('constraints'): constraints = kwds['constraints']
-        if not constraints: constraints = lambda x: x
-        #XXX:[HACK] -end-
         # backward compatibility
         if kwds.has_key('EvaluationMonitor'): \
@@ -436 +435 @@
         if kwds.has_key('StepMonitor'): \
            self._stepmon = kwds['StepMonitor']
+        if kwds.has_key('constraints'): \
+           self._constraints = kwds['constraints']
+        if not self._constraints: self._constraints = lambda x: x
         #-------------------------------------------------------------
 
@@ -467 +469 @@
             # generate trialSolution (within valid range)
             self.trialSolution[:] = self.population[candidate][:]
-            #XXX:[HACK] apply constraints
-            self.trialSolution[:] = constraints(self.trialSolution[:])
-            #XXX:[HACK] -end-
+            # -apply constraints-
+            self.trialSolution[:] = self._constraints(self.trialSolution[:])
+            # -end-
             trialPop[candidate][:] = self.trialSolution[:]
 
@@ -508 +510 @@
                 # generate trialSolution (within valid range)
                 strategy(self, candidate)
-                #XXX:[HACK] apply constraints
-                self.trialSolution[:] = constraints(self.trialSolution[:])
-                #XXX:[HACK] -end-
+                # -apply constraints-
+                self.trialSolution[:] = self._constraints(self.trialSolution[:])
+                # -end-
                 trialPop[candidate][:] = self.trialSolution[:]
 

mystic/mystic/nested.py

@@ -89 +89 @@
         if kwds.has_key('StepMonitor'): \
            self._stepmon = kwds['StepMonitor']
+       #if kwds.has_key('constraints'): \
+       #   self._constraints = kwds['constraints']
+       #if not self._constraints: self._constraints = lambda x: x
         #-------------------------------------------------------------
 
@@ -152 +155 @@
     solver.SetStrictRanges(min=%s, max=%s)
 """ % (str(lower), str(upper))
+#        local_opt += """\n
+#    solver.SetConstraints(%s)
+#""" % (self._solver._constraints)  #FIXME: needs to take a string
         local_opt += """\n
     solver.SetEvaluationLimits(%s, %s)
@@ -273 +279 @@
         if kwds.has_key('StepMonitor'): \
            self._stepmon = kwds['StepMonitor']
+       #if kwds.has_key('constraints'): \
+       #   self._constraints = kwds['constraints']
+       #if not self._constraints: self._constraints = lambda x: x
         #-------------------------------------------------------------
 
@@ -329 +338 @@
     solver.SetStrictRanges(min=%s, max=%s)
 """ % (str(lower), str(upper))
+#        local_opt += """\n
+#    solver.SetConstraints(%s)
+#""" % (self._solver._constraints)  #FIXME: needs to take a string
         local_opt += """\n
     solver.SetEvaluationLimits(%s, %s)

mystic/mystic/scipy_optimize.py

@@ -162 +162 @@
         callback=None  #user-supplied function, called after each step
         radius=0.05    #percentage change for initial simplex values
-        constraints = lambda x: x # Constraints function to be imposed
         if kwds.has_key('callback'): callback = kwds['callback']
         if kwds.has_key('disp'): disp = kwds['disp']
         if kwds.has_key('radius'): radius = kwds['radius']
-        if kwds.has_key('constraints'): constraints = kwds['constraints']
-        if not constraints: constraints = lambda x: x
         # backward compatibility
         if kwds.has_key('EvaluationMonitor'): \
@@ -173 +170 @@
         if kwds.has_key('StepMonitor'): \
            self._stepmon = kwds['StepMonitor']
+        if kwds.has_key('constraints'): \
+           self._constraints = kwds['constraints']
+        if not self._constraints: self._constraints = lambda x: x
         #-------------------------------------------------------------
 
@@ -189 +189 @@
 
         # wrap constraints function
-        func = wrap_nested(func, constraints)
+        func = wrap_nested(func, self._constraints)
 
         id = self.id
         x0 = asfarray(x0).flatten()
-        x0 = asfarray(constraints(x0))
+        x0 = asfarray(self._constraints(x0))
         N = len(x0) #XXX: this should be equal to self.nDim
         rank = len(x0.shape)
@@ -245 +245 @@
 
             # apply constraints  #XXX: is this the only appropriate place???
-            sim[0] = asfarray(constraints(sim[0]))
+            sim[0] = asfarray(self._constraints(sim[0]))
 
             xbar = numpy.add.reduce(sim[:-1],0) / N
@@ -501 +501 @@
         callback=None  #user-supplied function, called after each step
         xtol=1e-4      #line-search error tolerance
-        constraints = lambda x: x # Constraints function to be imposed
         if kwds.has_key('callback'): callback = kwds['callback']
         if kwds.has_key('direc'): direc = kwds['direc']  #XXX: best interface?
         if kwds.has_key('xtol'): xtol = kwds['xtol']
         if kwds.has_key('disp'): disp = kwds['disp']
-        if kwds.has_key('constraints'): constraints = kwds['constraints']
-        if not constraints: constraints = lambda x: x
         # backward compatibility
         if kwds.has_key('EvaluationMonitor'): \
@@ -513 +510 @@
         if kwds.has_key('StepMonitor'): \
            self._stepmon = kwds['StepMonitor']
+        if kwds.has_key('constraints'): \
+           self._constraints = kwds['constraints']
+        if not self._constraints: self._constraints = lambda x: x
         #-------------------------------------------------------------
 
@@ -529 +529 @@
 
         # wrap constraints function
-        func = wrap_nested(func, constraints)
+        func = wrap_nested(func, self._constraints)
 
         id = self.id
         x = asfarray(x0).flatten()
-        x = asfarray(constraints(x))
+        x = asfarray(self._constraints(x))
         if retall:
             allvecs = [x]
@@ -577 +577 @@
 
                 # apply constraints
-                x = asfarray(constraints(x))
+                x = asfarray(self._constraints(x))
 
             iter += 1
@@ -607 +607 @@
                         direc[-1] = direc1
  
-               #        x = asfarray(constraints(x))
+               #        x = asfarray(self._constraints(x))
 
                 self.energy_history[-1] = fval #...update to 'best' energy

mystic/tests/solver_test_suite.py

@@ -1 +1 @@
 #!/usr/bin/python
-"""A test suite for Mystic solvers and constraints.
+"""A test suite for Mystic solvers.
 Note: VTR termination with default tolerance shouldn't work for functions 
 whose value at the minimum is negative!