refactor: use ctx manager; move code to .

python/ngen_cal/src/ngen/cal/_optimizers/grey_wolf.py

@@ -118,7 +118,12 @@ def __init_(
         self.name = __name__
 
     def optimize(
-        self, objective_func, iters, n_processes=None, verbose=True, **kwargs
+        self,
+        objective_func,
+        iters: int,
+        n_processes: Optional[int] = None,
+        verbose: bool = True,
+        **kwargs,
     ):
         """Optimize the swarm for a number of iterations
 
@@ -131,7 +136,7 @@ def optimize(
             objective function to be evaluated
         iters : int
             number of iterations
-        n_processes : int
+        n_processes : int, optional
             number of processes to use for parallel particle evaluation (default: None = no parallelization)
         verbose : bool
             enable or disable the logs and progress bar (default: True = enable logs)
@@ -143,7 +148,23 @@ def optimize(
         tuple
             the global best cost and the global best position.
         """
+        if n_processes is None:
+            return self._optimize(objective_func, iters, verbose, pool=None)
+        else:
+            with mp.Pool(n_processes) as pool:
+                return self._optimize(objective_func, iters, verbose, pool=pool)
 
+    def _optimize(
+        self,
+        objective_func,
+        iters: int,
+        verbose: bool = True,
+        pool: Optional[mp.Pool] = None,
+        **kwargs,
+    ):
+        """
+        `pool` lifecycle is managed by `optimize` method. DO NOT CLOSE IT HERE.
+        """
         # Apply verbosity
         if self.start_iter>0:
             verbose = False 
@@ -158,9 +179,7 @@ def optimize(
             lvl=log_level,
         )
 
-        # Setup Pool of processes for parallel evaluation
-        pool = None if n_processes is None else mp.Pool(n_processes)
-
+        # TODO: @hellkite500,  ftol_history is unused. should it be? or can we remove it?
         ftol_history = deque(maxlen=self.ftol_iter)
 
         # Compute cost of initial swarm  
@@ -204,6 +223,8 @@ def optimize(
             X2 = beta - A2 * Dbeta
             X3 = delta - A3 * Ddelta
             self.swarm.position = (X1 + X2 + X3) / 3
+            # TODO: @hellkite500, is this right?
+            assert self.bounds is not None
             self.swarm.position = np.clip(self.swarm.position, self.bounds[0], self.bounds[1])
             # Compute current cost and update local best
             self.swarm.current_cost = compute_objective_function(self.swarm, objective_func, pool=pool, **kwargs)
@@ -217,13 +238,12 @@ def optimize(
             self.update_history(i+2)
 
         # Obtain the final best_cost and the final best_position
+        # TODO: @hellkite500, `best_cost` should be `float` here... so no copy
+        #       method.
         final_best_cost = self.swarm.best_cost.copy()
         final_best_pos = self.swarm.best_pos.copy()
         # Write report in log and return final cost and position
         self.rep.log("Optimization finished | best cost: {}, best pos: {}".format(final_best_cost, final_best_pos), lvl=log_level) 
-        # Close Pool of Processes
-        if n_processes is not None:
-            pool.close()
         return (final_best_cost, final_best_pos)
 
     def _hist_to_csv(self, i: int, name: str, index: List, key: str, label: Optional[str]=None) -> None: