updates -- better usage of default arguments, removing in BODriverEX where we pass default arguments, we can just omit arguments here

Author: thartland

src/Drivers/hiopbbpy/BODriverCI.py

@@ -103,7 +103,7 @@
          max_obj[prob_type][acq_type] = max(max_obj[prob_type][acq_type], y_opt[prob_type][acq_type][n_repeat])
          min_obj[prob_type][acq_type] = min(min_obj[prob_type][acq_type], y_opt[prob_type][acq_type][n_repeat])
 
-#if(num_repeat >= 1000 ):
+#if(num_repeat == 1000 ):
 #   np.save("yopt_20iter_1000run.npy", y_opt)
 
 # Define percentiles

src/Drivers/hiopbbpy/BODriverEX.py

@@ -57,9 +57,9 @@ def con_jac_ineq(x):
 for prob_type in prob_type_l:
    print()
    if prob_type == "LpNorm":
-      problem = LpNormProblem(nx, xlimits, constraints=None)
+      problem = LpNormProblem(nx, xlimits)
    else:
-      problem = BraninProblem(constraints=None)
+      problem = BraninProblem()
    problem.set_constraints(user_constraint)
 
    for acq_type in acq_type_l:
@@ -78,6 +78,7 @@ def con_jac_ineq(x):
         'acquisition_type': acq_type,
         'bo_maxiter': 10,
         'opt_solver': 'IPOPT', #"SLSQP" "IPOPT"
+        'batch_size': 1,
         'solver_options': {
            'max_iter': 200,
            'print_level': 1

src/Drivers/hiopbbpy/LpNormProblem.py

@@ -8,7 +8,7 @@
 from hiopbbpy.problems.problem import Problem
 
 class LpNormProblem(Problem):
-    def __init__(self, ndim, xlimits, p=2.0, constraints=None):
+    def __init__(self, ndim, xlimits, p=2.0, constraints=[]):
         name = "LpNormProblem"
         super().__init__(ndim, xlimits, name=name, constraints=constraints)
         self.p = p

src/hiopbbpy/opt/boalgorithm.py

@@ -199,10 +199,7 @@ def optimize(self):
              # Update training set with the virtual point
              x_train         = np.vstack([x_train,         x_new    ])
              y_train_virtual = np.vstack([y_train_virtual, y_virtual])
-
-          # TODO: include a parallel evaluator
           y_new = self.evaluator.run(self.prob.evaluate, x_train[-self.batch_size:])
-          #y_new = self.prob.evaluate(np.atleast_2d(x_train[-self.batch_size:]))
 
 
           y_train = np.vstack([y_train, y_new])

src/hiopbbpy/problems/BraninProblem.py

@@ -19,7 +19,7 @@
 
 # define the Branin problem class
 class BraninProblem(Problem):
-    def __init__(self, constraints=None):
+    def __init__(self, constraints=[]):
         ndim = 2
         xlimits = np.array([[-5.0, 10], [0.0, 15]]) 
         name = 'Branin'

src/hiopbbpy/problems/problem.py

@@ -5,19 +5,19 @@
             Nai-Yuan Chiang <chiang7@llnl.gov>
 """
 import numpy as np
+import collections.abc
 from numpy.random import uniform
 from scipy.stats import qmc
 
 # define the general optimization problem class
 class Problem:
-    def __init__(self, ndim, xlimits, name=None, constraints=None):
+    def __init__(self, ndim, xlimits, name=" ", constraints=[]):
         self.ndim = ndim
         self.xlimits = xlimits
         assert self.xlimits.shape[0] == ndim            
-        if name is None:
-            self.name = " "
-        else:
-            self.name = name
+        assert isinstance(name, str)
+        assert isinstance(constraints, collections.abc.Sequence)
+        self.name = name
         self.sampler = qmc.LatinHypercube(ndim)
         self.constraints = constraints