Introduce the DRSC algorithm for constraint handling

CHANGELOG.md

@@ -1,5 +1,17 @@
 # Release history
 
+## Version 1.1.3 [unpublished]
+
+### Changes
+
+- Implemented the DRSC algorithm for SumEquals constraints. A side-effect of this implementation is that you can now
+  ask for multiple points with opt.ask() while using this type of constraint (previously you could only ask for one).
+- Dropped support for Python 3.9
+
+### Bugfixes
+
+- Fixed TypeError when combining categorical and numerical dimensions in get_Brownie_Bee_Pareto
+
 ## Version 1.1.2 [unpublished]
 
 ### Changes

ProcessOptimizer/optimizer/optimizer.py

@@ -269,9 +269,11 @@ def __init__(
             )
 
         # check if regressor
-        if not is_regressor(base_estimator) and base_estimator is not None:
-            raise ValueError("%s has to be a regressor." % base_estimator)
-
+        try:
+            if base_estimator is not None and not is_regressor(base_estimator):
+                raise ValueError("%s has to be a regressor." % base_estimator)
+        except AttributeError as me:
+            raise ValueError("%s has to be a regressor." % base_estimator) from me
         # treat per second acqusition function specially
         is_multi_regressor = isinstance(base_estimator, MultiOutputRegressor)
         if "ps" in self.acq_func and not is_multi_regressor:
@@ -1309,12 +1311,12 @@ def __MinimalDistance(X, Y):
     # This function calls NSGAII to estimate the Pareto Front
     def NSGAII(self, MU=40):
         from ._NSGA2 import NSGAII
-        
+
         if MU % 4 != 0:
             raise ValueError(
                 "Number of simulated points must be divisible by 4 for the NSGAII algorithm"
             )
-            
+
         pop, logbook, front = NSGAII(
             self.n_objectives,
             self.__ObjectiveGP,