change CategoricalParameter and IntParameter in parameters.py to use optuna.distributions CategoricalDistribution and IntDistribution instead of skopt

freqtrade/optimize/hyperopt/hyperopt_optimizer.py

@@ -39,6 +39,7 @@ with warnings.catch_warnings():
     from skopt.space import Categorical, Dimension, Integer, Real
 
     from freqtrade.optimize.space.decimalspace import SKDecimal
+    from freqtrade.strategy.parameters import ft_CategoricalDistribution, ft_IntDistribution
 
 logger = logging.getLogger(__name__)
 
@@ -420,6 +421,11 @@ class HyperOptimizer:
                 o_dimensions[original_dim.name] = optuna.distributions.CategoricalDistribution(
                     list(original_dim.bounds)
                 )
+            # for preparing to remove old skopt spaces
+            elif isinstance(
+                original_dim, ft_CategoricalDistribution
+            ) or isinstance(original_dim, ft_IntDistribution):
+                o_dimensions[original_dim.name] = original_dim
             else:
                 raise Exception(f"Unknown search space {original_dim} / {type(original_dim)}")
         # logger.info(f"convert_dimensions_to_optuna_space: {s_dimensions} - {o_dimensions}")
@@ -429,7 +435,9 @@ class HyperOptimizer:
         self,
         random_state: int,
     ):
-        o_sampler = self.custom_hyperopt.generate_estimator(dimensions=self.dimensions)
+        o_sampler = self.custom_hyperopt.generate_estimator(
+            dimensions=self.dimensions, random_state=random_state
+        )
         self.o_dimensions = self.convert_dimensions_to_optuna_space(self.dimensions)
 
         # for save/restore

freqtrade/strategy/parameters.py

@@ -14,7 +14,8 @@ from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer
 
 
 with suppress(ImportError):
-    from skopt.space import Categorical, Integer, Real
+    from optuna.distributions import CategoricalDistribution, IntDistribution
+    from skopt.space import Integer, Real  # Categorical
 
     from freqtrade.optimize.space import SKDecimal
 
@@ -24,6 +25,25 @@ from freqtrade.exceptions import OperationalException
 logger = logging.getLogger(__name__)
 
 
+class ft_CategoricalDistribution(CategoricalDistribution):
+    name: str
+    def __init__(
+        self,
+        categories: Sequence[Any],
+        **kwargs,
+    ):
+        return super().__init__(categories)
+
+class ft_IntDistribution(IntDistribution):
+    name: str
+    def __init__(
+        self,
+        low: int,
+        high: int,
+        **kwargs,
+    ):
+        return super().__init__(low, high, **kwargs)
+
 class BaseParameter(ABC):
     """
     Defines a parameter that can be optimized by hyperopt.
@@ -51,7 +71,8 @@ class BaseParameter(ABC):
          name is prefixed with 'buy_' or 'sell_'.
         :param optimize: Include parameter in hyperopt optimizations.
         :param load: Load parameter value from {space}_params.
-        :param kwargs: Extra parameters to skopt.space.(Integer|Real|Categorical).
+        :param kwargs: Extra parameters to optuna.distributions.
+                (IntDistribution|Real|CategoricalDistribution).
         """
         if "name" in kwargs:
             raise OperationalException(
@@ -67,7 +88,9 @@ class BaseParameter(ABC):
         return f"{self.__class__.__name__}({self.value})"
 
     @abstractmethod
-    def get_space(self, name: str) -> Union["Integer", "Real", "SKDecimal", "Categorical"]:
+    def get_space(self, name: str) -> Union[
+        "ft_IntDistribution", "Real", "SKDecimal", "ft_CategoricalDistribution"
+    ]:
         """
         Get-space - will be used by Hyperopt to get the hyperopt Space
         """
@@ -151,7 +174,7 @@ class IntParameter(NumericParameter):
                       parameter fieldname is prefixed with 'buy_' or 'sell_'.
         :param optimize: Include parameter in hyperopt optimizations.
         :param load: Load parameter value from {space}_params.
-        :param kwargs: Extra parameters to skopt.space.Integer.
+        :param kwargs: Extra parameters to optuna.distributions.IntDistribution.
         """
 
         super().__init__(
@@ -160,10 +183,15 @@ class IntParameter(NumericParameter):
 
     def get_space(self, name: str) -> "Integer":
         """
-        Create skopt optimization space.
+        Create optuna distribution space.
         :param name: A name of parameter field.
         """
-        return Integer(low=self.low, high=self.high, name=name, **self._space_params)
+        # return Integer(low=self.low, high=self.high, name=name, **self._space_params)
+        result = ft_IntDistribution(
+                    self.low, self.high, **self._space_params
+                )
+        result.name = name
+        return result
 
     @property
     def range(self):
@@ -174,7 +202,7 @@ class IntParameter(NumericParameter):
         calculating 100ds of indicators.
         """
         if self.can_optimize():
-            # Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
+            # optuna distributions ranges are "inclusive", while python's "range" is exclusive
             return range(self.low, self.high + 1)
         else:
             return range(self.value, self.value + 1)
@@ -305,7 +333,7 @@ class CategoricalParameter(BaseParameter):
          name is prefixed with 'buy_' or 'sell_'.
         :param optimize: Include parameter in hyperopt optimizations.
         :param load: Load parameter value from {space}_params.
-        :param kwargs: Extra parameters to skopt.space.Categorical.
+        :param kwargs: Extra parameters to optuna.distributions.CategoricalDistribution.
         """
         if len(categories) < 2:
             raise OperationalException(
@@ -314,12 +342,15 @@ class CategoricalParameter(BaseParameter):
         self.opt_range = categories
         super().__init__(default=default, space=space, optimize=optimize, load=load, **kwargs)
 
-    def get_space(self, name: str) -> "Categorical":
+    def get_space(self, name: str) -> "ft_CategoricalDistribution":
         """
-        Create skopt optimization space.
+        Create optuna distribution space.
         :param name: A name of parameter field.
         """
-        return Categorical(self.opt_range, name=name, **self._space_params)
+        # Categorical(self.opt_range, name=name, **self._space_params)
+        result = ft_CategoricalDistribution(self.opt_range)
+        result.name = name
+        return result
 
     @property
     def range(self):
@@ -355,7 +386,7 @@ class BooleanParameter(CategoricalParameter):
          name is prefixed with 'buy_' or 'sell_'.
         :param optimize: Include parameter in hyperopt optimizations.
         :param load: Load parameter value from {space}_params.
-        :param kwargs: Extra parameters to skopt.space.Categorical.
+        :param kwargs: Extra parameters to optuna.distributions.CategoricalDistribution.
         """
 
         categories = [True, False]