chore: cleanup some code

freqtrade/optimize/hyperopt/hyperopt.py

@@ -306,8 +306,6 @@ class Hyperopt:
                         asked, is_random = self.get_asked_points(
                             n_points=current_jobs, dimensions=self.hyperopter.o_dimensions
                         )
-                        # asked_params = [asked1.params for asked1 in asked]
-                        # logger.info(f"asked iteration {i}: {asked} {asked_params}")
 
                         f_val = self.run_optimizer_parallel(
                             parallel,
@@ -317,7 +315,6 @@ class Hyperopt:
                         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)
-                        # logger.info(f"result iteration {i}: {asked} {f_val_loss}")
 
                         for j, val in enumerate(f_val):
                             # Use human-friendly indexes here (starting from 1)

freqtrade/optimize/hyperopt/hyperopt_optimizer.py

@@ -147,11 +147,8 @@ class HyperOptimizer:
                 self.hyperopt_pickle_magic(modules.__bases__)
 
     def _get_params_dict(
-        self,
+        self, dimensions: list[DimensionProtocol], raw_params: dict[str, Any]
-        dimensions: list[DimensionProtocol],
-        raw_params: dict[str, Any],
     ) -> dict[str, Any]:
-        # logger.info(f"_get_params_dict: {raw_params}")
         # Ensure the number of dimensions match
         # the number of parameters in the list.
         if len(raw_params) != len(dimensions):
@@ -159,9 +156,6 @@ class HyperOptimizer:
 
         # Return a dict where the keys are the names of the dimensions
         # and the values are taken from the list of parameters.
-        # result = {d.name: v for d, v in zip(dimensions, raw_params, strict=False)}
-        # logger.info(f"d_get_params_dict: {result}")
-        # return {d.name: v for d, v in zip(dimensions, raw_params.params, strict=False)}
         return raw_params
 
     def _get_params_details(self, params: dict) -> dict:
@@ -273,12 +267,9 @@ class HyperOptimizer:
                 # noinspection PyProtectedMember
                 attr.value = params_dict[attr_name]
 
-    # @profile
-    # fp=open('memory_profiler.log','w+')
-    # @profile(stream=fp)
     def generate_optimizer(
         self, backtesting: Backtesting, raw_params: dict[str, Any]
-    ) -> dict[str, Any]:  # list[Any]
+    ) -> dict[str, Any]:
         """
         Used Optimize function.
         Called once per epoch to optimize whatever is configured.
@@ -330,7 +321,7 @@ class HyperOptimizer:
 
             backtesting.strategy.max_open_trades = updated_max_open_trades
 
-        with Path(self.data_pickle_file).open("rb") as f:
+        with self.data_pickle_file.open("rb") as f:
             processed = load(f, mmap_mode="r")
         if self.analyze_per_epoch:
             # Data is not yet analyzed, rerun populate_indicators.
@@ -419,7 +410,6 @@ class HyperOptimizer:
                     f"Unknown search space {original_dim.name} - {original_dim} / \
                         {type(original_dim)}"
                 )
-        # logger.info(f"convert_dimensions_to_optuna_space: {s_dimensions} - {o_dimensions}")
         return o_dimensions
 
     def get_optimizer(
@@ -431,11 +421,6 @@ class HyperOptimizer:
         )
         self.o_dimensions = self.convert_dimensions_to_optuna_space(self.dimensions)
 
-        # for save/restore
-        # with open("sampler.pkl", "wb") as fout:
-        #     pickle.dump(study.sampler, fout)
-        # restored_sampler = pickle.load(open("sampler.pkl", "rb"))
-
         if isinstance(o_sampler, str):
             if o_sampler not in optuna_samplers_dict.keys():
                 raise OperationalException(f"Optuna Sampler {o_sampler} not supported.")