better fit logic (and multi-opt was never fitting -_-)

freqtrade/optimize/hyperopt.py

@@ -605,7 +605,7 @@ class Hyperopt:
                 i, backend.optimizers, jobs, backend.results_board)
             for i in range(first_try, first_try + tries))
         # each worker will return a list containing n_points, so compact into a single list
-        return functools.reduce(lambda x, y: [*x, *y], results)
+        return functools.reduce(lambda x, y: [*x, *y], results, [])
 
     def opt_ask_and_tell(self, jobs: int, tries: int):
         """
@@ -617,7 +617,6 @@ class Hyperopt:
         vals = []
         to_ask: deque = deque()
         evald: Set[Tuple] = set()
-        fit = False
         opt = self.opt
         for r in range(tries):
             while not backend.results.empty():
@@ -628,15 +627,11 @@ class Hyperopt:
                 if void_filtered:  # again if all are filtered
                     opt.tell([list(v['params_dict'].values()) for v in void_filtered],
                              [v['loss'] for v in void_filtered],
-                             fit=fit)
-                    if fit:
-                        fit = False
+                             fit=(len(to_ask) < 1)) # only fit when out of points
                     del vals[:], void_filtered[:]
 
             if not to_ask:
-                opt.update_next()
                 to_ask.extend(opt.ask(n_points=self.n_points))
-                fit = True
             a = tuple(to_ask.popleft())
             while a in evald:
                 logger.info("this point was evaluated before...")
@@ -675,8 +670,7 @@ class Hyperopt:
         # put back the updated results
         results_board.put(results)
         if len(past_Xi) > 0:
-            opt.tell(past_Xi, past_yi, fit=False)
-            opt.update_next()
+            opt.tell(past_Xi, past_yi, fit=True)
 
         # ask for points according to config
         asked = opt.ask(n_points=self.n_points, strategy=self.get_next_point_strategy())
@@ -688,7 +682,9 @@ class Hyperopt:
         if opt.void_loss != VOID_LOSS or len(void_filtered) > 0:
             Xi = [list(v['params_dict'].values()) for v in void_filtered]
             yi = [v['loss'] for v in void_filtered]
-            opt.tell(Xi, yi, fit=False)
+            # because we fit with points from other runs
+            # only fit if at the current dispatch there were no points
+            opt.tell(Xi, yi, fit=(len(past_Xi) < 1))
             # update the board with the new results
             results = results_board.get()
             results.append([void_filtered, jobs - 1])
@@ -956,6 +952,9 @@ class Hyperopt:
                         f_val = jobs_scheduler(parallel, batch_len, epochs_so_far, self.n_jobs)
                         saved = self.log_results(f_val, epochs_so_far, epochs_limit())
                         # stop if no epochs have been evaluated
+                        if len(f_val) < 1:
+                            logger.warning("All epochs evaluated were void, "
+                                        "check the loss function and the search space.")
                         if (not saved and len(f_val) > 1) or batch_len < 1:
                             break
                         # log_results add