add up to 5 retries for ask in case of duplicate params

freqtrade/optimize/hyperopt/hyperopt.py

@@ -92,6 +92,7 @@ class Hyperopt:
         self.print_json = self.config.get("print_json", False)
 
         self.hyperopter = HyperOptimizer(self.config, self.data_pickle_file)
+        self.count_skipped_epochs = 0
 
     @staticmethod
     def get_lock_filename(config: Config) -> str:
@@ -170,35 +171,46 @@ class Hyperopt:
             asked.append(self.opt.ask(dimensions))
         return asked
 
-    def check_optuna_asked_points(self, trial: Trial) -> bool:
+    def duplicate_optuna_asked_points(self, trial: Trial) -> bool:
         trials_to_consider = trial.study.get_trials(deepcopy=False, states=[TrialState.COMPLETE])
         # Check whether we already evaluated the sampled `params`.
         for t in reversed(trials_to_consider):
             if trial.params == t.params:
-                logger.warning(
+                # logger.warning(
-                    f"duplicate trial: Trial {trial.number} has same params as {t.number}"
+                #     f"duplicate trial: Trial {trial.number} has same params as {t.number}"
-                )
+                # )
                 return True
         return False
 
     def get_asked_points(self, n_points: int, dimensions: dict) -> tuple[list[Any], list[bool]]:
         """
-        TBD: need to change
         Enforce points returned from `self.opt.ask` have not been already evaluated
 
         Steps:
         1. Try to get points using `self.opt.ask` first
         2. Discard the points that have already been evaluated
-        3. Retry using `self.opt.ask` up to 3 times
+        3. Retry using `self.opt.ask` up to 5 times
-        4. If still some points are missing in respect to `n_points`, random sample some points
-        5. Repeat until at least `n_points` points in the `asked_non_tried` list
-        6. Return a list with length truncated at `n_points`
         """
         asked_non_tried: list[list[Any]] = []
+        asked_duplicates: list[Trial] = []
         optuna_asked_trials = self.get_optuna_asked_points(n_points=n_points, dimensions=dimensions)
-        asked_non_tried += [x for x in optuna_asked_trials if not self.check_optuna_asked_points(x)]
+        asked_non_tried += [
+            x for x in optuna_asked_trials if not self.duplicate_optuna_asked_points(x)
+        ]
+        i = 0
+        while i < 5 and len(asked_non_tried) < n_points:
+            asked_new = self.get_optuna_asked_points(n_points=1, dimensions=dimensions)[0]
+            if not self.duplicate_optuna_asked_points(asked_new):
+                asked_non_tried.append(asked_new)
+            else:
+                asked_duplicates.append(asked_new)
+            i += 1
+        if len(asked_duplicates) > 0 and len(asked_non_tried) < n_points:
+            for asked_duplicate in asked_duplicates:
+                logger.warning(f"duplicate params for Epoch {asked_duplicate.number}")
+            self.count_skipped_epochs += len(asked_duplicates)
 
-        return asked_non_tried, [False for _ in range(n_points)]
+        return asked_non_tried, [False for _ in range(len(asked_non_tried))]
 
     def evaluate_result(self, val: dict[str, Any], current: int, is_random: bool):
         """
@@ -284,6 +296,7 @@ class Hyperopt:
                             parallel,
                             [asked1.params for asked1 in asked],
                         )
+
                         f_val_loss = [v["loss"] for v in f_val]
                         for o_ask, v in zip(asked, f_val_loss, strict=False):
                             self.opt.tell(o_ask, v)
@@ -307,6 +320,12 @@ class Hyperopt:
         except KeyboardInterrupt:
             print("User interrupted..")
 
+        if self.count_skipped_epochs > 0:
+            logger.info(
+                f"{self.count_skipped_epochs} {plural(self.count_skipped_epochs, 'epoch')} "
+                f"skipped due to duplicate parameters."
+            )
+
         logger.info(
             f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
             f"saved to '{self.results_file}'."