fixing SA after fix in costfunc

Author: benvanwerkhoven

kernel_tuner/strategies/simulated_annealing.py

@@ -54,7 +54,7 @@ def tune(searchspace: Searchspace, runner, tuning_options):
                     print(e)
                 return cost_func.results
 
-            ap = acceptance_prob(old_cost, new_cost, T)
+            ap = acceptance_prob(old_cost, new_cost, T, tuning_options)
             r = random.random()
 
             if ap > r:
@@ -85,20 +85,26 @@ def tune(searchspace: Searchspace, runner, tuning_options):
 
 tune.__doc__ = common.get_strategy_docstring("Simulated Annealing", _options)
 
-def acceptance_prob(old_cost, new_cost, T):
+def acceptance_prob(old_cost, new_cost, T, tuning_options):
     """Annealing equation, with modifications to work towards a lower value."""
     error_val = sys.float_info.max
+    res = 0.0
     # if start pos is not valid, always move
     if old_cost == error_val:
-        return 1.0
+        res = 1.0
     # if we have found a valid ps before, never move to nonvalid pos
-    if new_cost == error_val:
-        return 0.0
+    elif new_cost == error_val:
+        res = 0.0
     # always move if new cost is better
-    if new_cost < old_cost:
-        return 1.0
+    elif new_cost < old_cost:
+        res = 1.0
     # maybe move if old cost is better than new cost depending on T and random value
-    return np.exp(((old_cost-new_cost)/old_cost)/T)
+    else:
+        if tuning_options.objective_higher_is_better:
+            res = np.exp(((new_cost-old_cost)/new_cost)/T)
+        else:
+            res = np.exp(((old_cost-new_cost)/old_cost)/T)
+    return res
 
 
 def neighbor(pos, searchspace: Searchspace):