move constraints into Problem class

Author: nychiang

src/Drivers/hiopbbpy/BODriverEX.py

@@ -57,9 +57,10 @@ def con_jac_ineq(x):
 for prob_type in prob_type_l:
    print()
    if prob_type == "LpNorm":
-      problem = LpNormProblem(nx, xlimits)
+      problem = LpNormProblem(nx, xlimits, constraints=None)
    else:
-      problem = BraninProblem()
+      problem = BraninProblem(constraints=None)
+   problem.set_constraints(user_constraint)
 
    for acq_type in acq_type_l:
       print("Problem name: ", problem.name)
@@ -84,8 +85,8 @@ def con_jac_ineq(x):
       }
 
       # Instantiate and run Bayesian Optimization
-      bo = BOAlgorithm(gp_model, x_train, y_train, options = options, user_constraints = user_constraint) #EI or LCB
-      bo.optimize(problem)
+      bo = BOAlgorithm(problem, gp_model, x_train, y_train, options = options) #EI or LCB
+      bo.optimize()
 
 sys.exit(retval)
 

src/Drivers/hiopbbpy/LpNormProblem.py

@@ -8,9 +8,9 @@
 from hiopbbpy.problems.problem import Problem
 
 class LpNormProblem(Problem):
-    def __init__(self, ndim, xlimits, p=2.0):
+    def __init__(self, ndim, xlimits, p=2.0, constraints=None):
         name = "LpNormProblem"
-        super().__init__(ndim, xlimits, name=name)
+        super().__init__(ndim, xlimits, name=name, constraints=constraints)
         self.p = p
 
     def _evaluate(self, x):

src/hiopbbpy/opt/boalgorithm.py

@@ -64,19 +64,18 @@ def getOptimalObjective(self):
 
 # A subclass of BOAlgorithmBase implementing a full Bayesian Optimization workflow
 class BOAlgorithm(BOAlgorithmBase):
-    def __init__(self, gpsurrogate, xtrain, ytrain,
+    def __init__(self, prob:Problem, gpsurrogate:GaussianProcess, xtrain, ytrain,
                  user_grad = None,
-                 user_constraints = None,
                  options = None):
         super().__init__()
 
         assert isinstance(gpsurrogate, GaussianProcess)
 
         self.setTrainingData(xtrain, ytrain)
+        self.prob = prob
         self.gpsurrogate = gpsurrogate
         self.bounds = self.gpsurrogate.get_bounds()
         self.fun_grad = None
-        self.constraints = None
 
         if options and 'bo_maxiter' in options:
             self.bo_maxiter = options['bo_maxiter']
@@ -101,16 +100,13 @@ def __init__(self, gpsurrogate, xtrain, ytrain,
             opt_solver = "SLSQP"
         self.set_method(opt_solver)
 
-        if user_constraints:
-            self.constraints = user_constraints
-
         if user_grad:
             self.fun_grad = user_grad
 
 
     # Method to set up a callback function to minimize the acquisition function
     def _setup_acqf_minimizer_callback(self):
-        self.acqf_minimizer_callback = lambda fun, x0: minimizer(fun, x0, self.opt_solver, self.bounds, self.constraints, self.solver_options)
+        self.acqf_minimizer_callback = lambda fun, x0: minimizer(fun, x0, self.opt_solver, self.bounds, self.prob.constraints, self.solver_options)
 
     # Method to train the GP model
     def _train_surrogate(self, x_train, y_train):
@@ -164,8 +160,7 @@ def set_options(self, solver_options):
         self.solver_options = solver_options
 
     # Method to perform Bayesian optimization
-    def optimize(self, prob:Problem):
-      self.prob = prob
+    def optimize(self):
       x_train = self.xtrain
       y_train = self.ytrain
 
@@ -184,7 +179,7 @@ def optimize(self, prob:Problem):
           x_new = self._find_best_point(x_train, y_train)
 
           # Evaluate the new sample point
-          y_new = prob.evaluate(np.atleast_2d(x_new))
+          y_new = self.prob.evaluate(np.atleast_2d(x_new))
 
           # Update training set
           x_train = np.vstack([x_train, x_new])
@@ -229,8 +224,8 @@ def minimizer(fun, x0, method, bounds, constraints, solver_options):
         ipopt_prob = IpoptProb(fun['obj'], fun['grad'], constraints, bounds, solver_options)
         sol, info = ipopt_prob.solve(x0)
 
-        status = info.get('status', -1)
-        msg = info.get('status_msg', -1)
+        status = info.get('status', -999)
+        msg = info.get('status_msg', b'unknown error')
         if status == 0:
             # ipopt returns 0 as success
             success = True

src/hiopbbpy/opt/optproblem.py

@@ -46,7 +46,7 @@ def __init__(self, objective, gradient, constraints_list, xbounds, solver_option
             lb=self.xl,
             ub=self.xu,
             cl=self.cl,
-            cu=self.xu
+            cu=self.cu
         )
 
     def objective(self, x):
@@ -76,4 +76,4 @@ def solve(self, x0, solver_options=None):
                 self.nlp.add_option(key, value)
 
         # Solve the optimization problem
-        return self.nlp.solve(x0)
+        return self.nlp.solve(x0)

src/hiopbbpy/problems/BraninProblem.py

@@ -19,11 +19,11 @@
 
 # define the Branin problem class
 class BraninProblem(Problem):
-    def __init__(self):
+    def __init__(self, constraints=None):
         ndim = 2
         xlimits = np.array([[-5.0, 10], [0.0, 15]]) 
         name = 'Branin'
-        super().__init__(ndim, xlimits, name=name)
+        super().__init__(ndim, xlimits, name=name, constraints=constraints)
 
     def _evaluate(self, x: np.ndarray) -> np.ndarray:
 
@@ -40,17 +40,10 @@ def _evaluate(self, x: np.ndarray) -> np.ndarray:
         arg1 = (x[:,1] - b * x[:,0]**2 + c * x[:,0] - r)
         y[:,0] = arg1**2 + s * (1 - t) * np.cos(x[:,0]) + s
 
-        '''
-        # compute derivatives
-        dy_dx0 = 2*arg1*(-2*b*x[:,0]+c) - s*(1-t)*np.sin(x[:,0])
-        dy_dx1 = 2*arg1
-        
-        dy_dx = np.array([dy_dx0, dy_dx1])
-        '''
-
         return y
 
 
 
 
 
+

src/hiopbbpy/problems/problem.py

@@ -10,7 +10,7 @@
 
 # define the general optimization problem class
 class Problem:
-    def __init__(self, ndim, xlimits, name=None):
+    def __init__(self, ndim, xlimits, name=None, constraints=None):
         self.ndim = ndim
         self.xlimits = xlimits
         assert self.xlimits.shape[0] == ndim            
@@ -19,6 +19,7 @@ def __init__(self, ndim, xlimits, name=None):
         else:
             self.name = name
         self.sampler = qmc.LatinHypercube(ndim)
+        self.constraints = constraints
 
     def _evaluate(self, x: np.ndarray) -> np.ndarray:
         """
@@ -80,6 +81,9 @@ def sample(self, nsample: int) -> np.ndarray:
 
         return xsample
 
+    def set_constraints(self, constraints: dict):
+        self.constraints = constraints
+