D. Jabs:

- Updated type hints and check functions for continuous.py

PyHyperparameterSpace/hp/continuous.py

@@ -113,13 +113,13 @@ def _is_legal_bounds(self, bounds: Union[tuple[float, float], tuple[int, int]]):
         pass
 
     @abstractmethod
-    def _check_distribution(self, distribution: Distribution) -> Distribution:
+    def _check_distribution(self, distribution: Union[Distribution, None]) -> Distribution:
         """
         Checks if the distribution is legal. A distribution is called legal, if the class of the distribution can be
         used for the given hyperparameter class.
 
         Args:
-            distribution (Distribution):
+            distribution (Union[Distribution, None]):
                 Distribution to check
 
         Returns:
@@ -198,13 +198,10 @@ def __init__(
             self,
             name: str,
             bounds: Union[tuple[float, float], tuple[int, int]],
-            default: Union[int, float, list, np.ndarray] = None,
+            default: Any = None,
             shape: Union[tuple[int, ...], None] = None,
-            distribution: Distribution = None,
+            distribution: Union[Distribution, None] = None,
     ):
-        if isinstance(default, list):
-            default = np.array(default, dtype=float)
-
         super().__init__(name=name, shape=shape, bounds=bounds, default=default, distribution=distribution)
 
     def _check_bounds(self, bounds: Union[tuple[float, float], tuple[int, int]]) \
@@ -216,31 +213,24 @@ def _check_bounds(self, bounds: Union[tuple[float, float], tuple[int, int]]) \
                 f"Illegal bounds {bounds}. The argument should have the format (lower, upper), where lower < upper!")
 
     def _is_legal_bounds(self, bounds: Union[tuple[float, float], tuple[int, int]]):
-        if isinstance(bounds, tuple) and len(bounds) == 2 and \
-                all(isinstance(b, (float, int, np.int_, np.float_)) for b in bounds) and bounds[0] < bounds[1]:
-            return True
-        else:
-            return False
+        if isinstance(bounds, tuple) and len(bounds) == 2:
+            return all(isinstance(b, (float, int, np.int_, np.float_)) for b in bounds) and bounds[0] < bounds[1]
+        return False
 
-    def _check_default(self, default: Union[int, float, np.ndarray]) -> Union[int, float, np.ndarray]:
+    def _check_default(self, default: Any) -> Any:
         if default is None and self._shape == (1,):
             # Case: default is not given and shape refers to single dimensional values
             return (self.lb + self.ub) / 2
         elif default is None and self._shape is not None and all(isinstance(s, (int, np.int_)) for s in self._shape):
             # Case: default is not given and shape refers to multi dimensional values
             return np.full(shape=self._shape, fill_value=((self.lb + self.ub) / 2))
-        elif self._is_legal_default(default):
+        elif default is not None and self._is_legal_default(default):
             return default
         else:
             raise Exception(f"Illegal default {default}. The argument should be in between the bounds (lower, upper)!")
 
     def _is_legal_default(self, default: Any) -> bool:
-        if not isinstance(default, (float, int, np.int_, np.float_)) and \
-                not (isinstance(default, np.ndarray) and np.issubdtype(default.dtype, np.floating)) and \
-                not (isinstance(default, np.ndarray) and np.issubdtype(default.dtype, np.integer)):
-            # Case: default is not in the right format
-            return False
-        if isinstance(default, (float, int, np.int_, np.float_)):
+        if isinstance(default, (float, int, np.float_, np.int_)):
             # Case: default is single dimensional
             return self.lb <= default < self.ub
         elif isinstance(default, np.ndarray):
@@ -249,30 +239,29 @@ def _is_legal_default(self, default: Any) -> bool:
         return False
 
     def _check_shape(self, shape: Union[tuple[int, ...], None]) -> tuple[int, ...]:
-        if shape is None and isinstance(self._default, (float, int, np.int_, np.float_)):
+        if shape is None and isinstance(self._default, (float, int, np.float_, np.int_)):
             # Case: shape is not given and default is single dimensional
             return 1,
         elif shape is None and isinstance(self._default, np.ndarray):
             # Case: shape is not given and default is multidimensional
             return self._default.shape
-        elif self._is_legal_shape(shape):
+        elif shape is not None and self._is_legal_shape(shape):
             # Case: shape is given and legal
             return shape
         else:
             # Case: shape is illegal
             raise Exception(f"Illegal shape {shape}. The argument should have the right format (dim1, ...)!")
 
     def _is_legal_shape(self, shape: tuple[int, ...]) -> bool:
-        if shape == (1,) and isinstance(self._default, (float, int, np.int_, np.float_)):
-            # Case: shape and default refers to single dimensional
-            return True
-        elif isinstance(shape, tuple) and all(isinstance(s, int) for s in shape) and \
-                isinstance(self._default, np.ndarray) and shape == self._default.shape:
-            # Case: shape and default refers to multidimensional
-            return True
+        if shape == (1,):
+            # Case: shape refers to single dimensional
+            return isinstance(self._default, (float, int, np.int_, np.float_))
+        elif isinstance(shape, tuple) and all(isinstance(s, int) for s in shape):
+            # Case: shape refers to multidimensional
+            return isinstance(self._default, np.ndarray) and shape == self._default.shape
         return False
 
-    def _check_distribution(self, distribution: Union[Distribution, None]) -> Union[Distribution, None]:
+    def _check_distribution(self, distribution: Union[Distribution, None]) -> Distribution:
         if distribution is None:
             return Uniform(lb=self.lb, ub=self.ub)
         elif self._is_legal_distribution(distribution):
@@ -350,33 +339,29 @@ def sample(self, random: np.random.RandomState, size: Union[int, None] = None) -
             raise Exception(f"Unknown Distribution {self._distribution}!")
 
     def valid_configuration(self, value: Any) -> bool:
-        if isinstance(value, (list, np.ndarray)):
+        if isinstance(value, np.ndarray):
             # Case: Value is multidimensional
-            value = np.array(value)
             return np.all((self.lb <= value) & (value < self.ub)) and self._shape == value.shape
-        elif isinstance(value, (int, float)):
+        elif isinstance(value, (float, int, np.float_, np.int_)):
             # Case: value is single dimensional
             return self.lb <= value < self.ub
         return False
 
     def adjust_configuration(self, value: Any) -> Any:
-        if isinstance(value, (list, np.ndarray)) and \
+        if isinstance(value, np.ndarray) and \
                 (np.issubdtype(value.dtype, np.float_) or np.issubdtype(value.dtype, np.int_)):
             # Case: value is multidimensional
-            value = np.array(value)
-
             # Do not exceed lower, upper bound
             value[value < self.lb] = self.lb
             value[value >= self.ub] = self.ub - 1e-10
-
             return value
-        elif isinstance(value, (int, float, np.int_, np.float_)):
+        elif isinstance(value, (float, int, np.float_, np.int_)):
             # Case: value is single dimensional
+            # Do not exceed lower, upper bound
             if value < self.lb:
                 value = self.lb
             elif value >= self.ub:
                 value = self.ub - 1e-10
-
             return value
         else:
             # Case: value is illegal
@@ -392,8 +377,13 @@ def __hash__(self) -> int:
         return hash(self.__repr__())
 
     def __repr__(self) -> str:
-        text = f"Float({self._name}, bounds={self._bounds}, default={self._default}, shape={self._shape}, distribution={self._distribution})"
-        return text
+        header = f"Float({self._name}, "
+        bounds = f"bounds={self._bounds}, "
+        default = f"default={self._default}, "
+        shape = f"shape={self._shape}, "
+        distribution = f"distribution={self._distribution}"
+        end = ")"
+        return "".join([header, bounds, default, shape, distribution, end])
 
 
 class Integer(Continuous):
@@ -410,7 +400,7 @@ class Integer(Continuous):
             default (Any):
                 Default value of the hyperparameter
 
-            shape (Union[int, tuple[int, ...], None]):
+            shape (Union[tuple[int, ...], None]):
                 Shape of the hyperparameter
 
             distribution (Union[Distribution, None]):
@@ -421,50 +411,45 @@ def __init__(
             self,
             name: str,
             bounds: Union[tuple[int, int]],
-            default: Union[int, list, np.ndarray, None] = None,
+            default: Any = None,
             shape: Union[tuple[int, ...], None] = None,
-            distribution: Distribution = None,
+            distribution: Union[Distribution, None] = None,
     ):
         super().__init__(name=name, shape=shape, bounds=bounds, default=default, distribution=distribution)
 
     def _check_bounds(self, bounds: tuple[int, int]) -> tuple[int, int]:
         if self._is_legal_bounds(bounds):
             return bounds
         else:
-            raise Exception(f"Illegal bounds {bounds}!")
+            raise Exception(f"Illegal bounds {bounds}. The argument should have the right format (lower, upper), where lower < upper!")
 
     def _is_legal_bounds(self, bounds: tuple[int, int]) -> bool:
-        if isinstance(bounds, tuple) and len(bounds) == 2 and \
-                all(isinstance(b, (int, np.int_)) for b in bounds) and bounds[0] < bounds[1]:
-            return True
-        else:
-            return False
+        if isinstance(bounds, tuple) and len(bounds) == 2:
+            return all(isinstance(b, (int, np.int_)) for b in bounds) and bounds[0] < bounds[1]
+        return False
 
-    def _check_default(self, default: Union[int, np.ndarray]) -> Union[int, np.ndarray]:
+    def _check_default(self, default: Any) -> Any:
         if default is None and self._shape == (1,):
             # Case: default is not given and shape refers to single dimensional values
             return int((self.lb + self.ub) / 2)
         elif default is None and self._shape is not None and all(isinstance(s, (int, np.int_)) for s in self._shape):
             # Case: default is not given and shape refers to multidimensional values
             return np.full(shape=self._shape, fill_value=int((self.lb + self.ub) / 2))
-        elif self._is_legal_default(default):
+        elif default is not None and self._is_legal_default(default):
             # Case: default value is legal
             return default
         else:
             # Case: default value is illegal
             raise Exception(f"Illegal default {default}. The argument should be in between the bounds (lower, upper)!")
 
-    def _is_legal_default(self, default: Union[int, np.ndarray]) -> bool:
-        if not isinstance(default, int) and \
-                not (isinstance(default, np.ndarray) and np.issubdtype(default.dtype, np.integer)):
-            # Case: default is not in the right format!
-            return False
+    def _is_legal_default(self, default: Any) -> bool:
         if isinstance(default, (int, np.int_)):
             # Case: default is single dimensional
             return self.lb <= default <= self.ub
         else:
             # Case: default is multidimensional
             return np.all((default >= self.lb) & (default <= self.ub))
+        return False
 
     def _check_shape(self, shape: Union[tuple[int, ...], None]) -> tuple[int, ...]:
         if shape is None and isinstance(self._default, (int, np.int_)):
@@ -473,20 +458,20 @@ def _check_shape(self, shape: Union[tuple[int, ...], None]) -> tuple[int, ...]:
         elif shape is None and isinstance(self._default, np.ndarray):
             # Case: shape is not given and default is multidimensional
             return self._default.shape
-        elif self._is_legal_shape(shape):
+        elif shape is not None and self._is_legal_shape(shape):
             # Case: shape is given
             return shape
         else:
             raise Exception(
-                f"Illegal shape {shape}. The argument should be in the format (lower, upper), where lower < upper!")
+                f"Illegal shape {shape}. The argument should be in the format (dim1, ...)!")
 
     def _is_legal_shape(self, shape: tuple[int, ...]) -> bool:
-        if shape == (1,) and isinstance(self._default, (int, np.int_)):
-            # Case: shape and default refers to single dimensional
-            return True
-        elif isinstance(shape, tuple) and all(isinstance(s, (int, np.int_)) for s in shape) and \
-                isinstance(self._default, np.ndarray) and shape == self._default.shape:
-            return True
+        if shape == (1,):
+            # Case: shape refers to single dimensional
+            return isinstance(self._default, (int, np.int_))
+        elif isinstance(shape, tuple) and all(isinstance(s, (int, np.int_)) for s in shape):
+            # Case: shape refers to multidimensional
+            return isinstance(self._default, np.ndarray) and shape == self._default.shape
         return False
 
     def _check_distribution(self, distribution: Union[Distribution, None]) -> Distribution:
@@ -526,9 +511,8 @@ def sample(self, random: np.random.RandomState, size: Union[int, None] = None) -
             raise Exception(f"Unknown Distribution {self._distribution}!")
 
     def valid_configuration(self, value: Any) -> bool:
-        if isinstance(value, (list, np.ndarray)):
+        if isinstance(value, np.ndarray):
             # Case: Value is multi-dimensional
-            value = np.array(value)
             return np.all((self.lb <= value) & (value < self.ub)) and self._shape == value.shape
         elif isinstance(value, (int, np.int_)):
             # Case: value is single-dimensional
@@ -537,17 +521,15 @@ def valid_configuration(self, value: Any) -> bool:
             return False
 
     def adjust_configuration(self, value: Any) -> Any:
-        if isinstance(value, (list, np.ndarray)) and np.issubdtype(value.dtype, np.int_):
+        if isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.int_):
             # Case: value is multidimensional
-            value = np.array(value)
-
             # Do not exceed lower, upper bound
             value[value < self.lb] = self.lb
             value[value >= self.ub] = self.ub - 1
-
             return value
         elif isinstance(value, (int, np.int_)):
             # Case: value is single dimensional
+            # Do not exceed lower, upper bound
             if value < self.lb:
                 value = self.lb
             elif value >= self.ub:
@@ -567,5 +549,9 @@ def __hash__(self) -> int:
         return hash(self.__repr__())
 
     def __repr__(self) -> str:
-        text = f"Integer({self._name}, bounds={self._bounds}, default={self._default}, shape={self._shape})"
-        return text
+        header = f"Integer({self._name}, "
+        bounds = f"bounds={self._bounds}, "
+        default = f"default={self._default}, "
+        shape = f"shape={self._shape}"
+        end = ")"
+        return "".join([header, bounds, default, shape, end])