Removed not yet fully implemented bayesian optimization references, made tests work

Author: fjwillemsen

kernel_tuner/interface.py

@@ -50,11 +50,6 @@
 from kernel_tuner.strategies import (
     basinhopping,
     bayes_opt,
-    bayes_opt_alt_BOTorch,
-    bayes_opt_BOTorch,
-    bayes_opt_GPyTorch,
-    bayes_opt_GPyTorch_lean,
-    bayes_opt_old,
     brute_force,
     diff_evo,
     dual_annealing,
@@ -85,12 +80,7 @@
     "pso": pso,
     "simulated_annealing": simulated_annealing,
     "firefly_algorithm": firefly_algorithm,
-    "bayes_opt": bayes_opt,
-    "bayes_opt_old": bayes_opt_old,
-    "bayes_opt_GPyTorch": bayes_opt_GPyTorch,
-    "bayes_opt_GPyTorch_lean": bayes_opt_GPyTorch_lean,
-    "bayes_opt_BOTorch": bayes_opt_BOTorch,
-    "bayes_opt_BOTorch_alt": bayes_opt_alt_BOTorch,
+    "bayes_opt": bayes_opt
 }
 
 

kernel_tuner/strategies/bayes_opt.py

@@ -238,7 +238,7 @@ def get_hyperparam(name: str, default, supported_values=list()):
         self.invalid_value = 1e20
         self.opt_direction = opt_direction
         if opt_direction == "min":
-            self.worst_value = np.PINF
+            self.worst_value = np.inf
             self.argopt = np.argmin
         elif opt_direction == "max":
             self.worst_value = np.NINF
@@ -265,7 +265,7 @@ def get_hyperparam(name: str, default, supported_values=list()):
         self.__visited_num = 0
         self.__visited_valid_num = 0
         self.__visited_searchspace_indices = [False] * self.searchspace_size
-        self.__observations = [np.NaN] * self.searchspace_size
+        self.__observations = [np.nan] * self.searchspace_size
         self.__valid_observation_indices = [False] * self.searchspace_size
         self.__valid_params = list()
         self.__valid_observations = list()
@@ -314,7 +314,7 @@ def is_not_visited(self, index: int) -> bool:
 
     def is_valid(self, observation: float) -> bool:
         """Returns whether an observation is valid."""
-        return not (observation is None or observation == self.invalid_value or observation == np.NaN)
+        return not (observation is None or observation == self.invalid_value or observation == np.nan)
 
     def get_af_by_name(self, name: str):
         """Get the basic acquisition functions by their name."""