D. Jabs:

- Changed comments
- Improved readability

PyHyperparameterSpace/dist/categorical.py

@@ -8,18 +8,22 @@
 class Choice(Distribution):
     """
     Class for representing a Categorical choice dist.
+
+        Args:
+            weights (Union[list[float], np.ndarray]):
+                Probability distribution to select the item
     """
 
-    def __init__(self, weights: Union[list[float], list[int], np.ndarray]):
+    def __init__(self, weights: Union[list[float], np.ndarray]):
         self.weights = None
         self.change_distribution(weights)
 
-    def change_distribution(self, weights: Union[list[float], list[int], np.ndarray]):
+    def change_distribution(self, weights: Union[list[float], np.ndarray]):
         weights = np.array(weights)
 
         assert weights.ndim == 1, f"Illegal weights {weights}. Argument should be a matrix of size (n,)!"
         assert np.all(0.0 <= w for w in weights), \
-            f"Illegal weights {weights}. Each p inside weights should >= 0.0!"
+            f"Illegal weights {weights}. Each element inside weights should be higher or equal to 0.0!"
 
         # Normalize the weights
         self.weights = self._normalize(weights)

PyHyperparameterSpace/dist/continuous.py

@@ -7,6 +7,16 @@
 class MatrixNormal(Distribution):
     """
     Class for representing a Matrix Normal (Gaussian) Distribution ~M N_n,p(M, U, V)
+
+    Args:
+        M (Union[list[list[float]], np.ndarray]):
+            The mean matrix
+
+        U (Union[list[list[float]], np.ndarray]):
+            Covariance matrix for the rows
+
+        V (Union[list[list[float]], np.ndarray]):
+            Covariance matrix for the columns
     """
 
     def __init__(
@@ -30,11 +40,11 @@ def change_distribution(
         U = np.array(U)
         V = np.array(V)
 
-        assert M.ndim == 2, f"Illegal M {M}. Argument should be a matrix of size (n, p)!"
+        assert M.ndim == 2, f"Illegal M {M}. The argument should be a matrix of size (n, p)!"
         assert U.ndim == 2 and U.shape == (M.shape[0], M.shape[0]), \
-            f"Illegal U {U}. Argument should be a matrix of size (n, n)!"
+            f"Illegal U {U}. The argument should be a matrix of size (n, n)!"
         assert V.ndim == 2 and V.shape == (M.shape[1], M.shape[1]), \
-            f"Illegal V {V}. Argument should be a matrix of size (p, p)!"
+            f"Illegal V {V}. The argument should be a matrix of size (p, p)!"
 
         self.M = M
         self.U = U
@@ -50,6 +60,13 @@ def __repr__(self):
 class MultivariateNormal(Distribution):
     """
     Class for representing a Multivariate Normal (Gaussian) Distribution ~N(mean, Covariance)
+
+        Args:
+            mean (Union[list[float], np.ndarray]):
+                The mean vector
+
+            cov (Union[list[list[float]], np.ndarray]):
+                The covariance matrix
     """
 
     def __init__(self, mean: Union[list[float], np.ndarray], cov: Union[list[list[float]], np.ndarray]):
@@ -80,6 +97,13 @@ def __repr__(self):
 class Normal(Distribution):
     """
     Class for representing a Normal (Gaussian) Distribution ~N(mean, std).
+
+        Args:
+            mean (float):
+                The mean value
+
+            std (float):
+                The standard deviation
     """
 
     def __init__(self, mean: float, std: float):

PyHyperparameterSpace/hp/abstract_hp.py

@@ -11,9 +11,14 @@ class Hyperparameter(ABC):
     Abstract class to represent a hyperparameter.
 
         Attributes:
-            name (str): name of the hyperparameter
-            default (Any): default value of the hyperparameter
-            shape (Union[int, tuple[int], None]): shape of the hyperparameter
+            name (str):
+                Name of the hyperparameter
+
+            default (Any):
+                Default value of the hyperparameter
+
+            shape (Union[int, tuple[int, ...], None]):
+                Shape of the hyperparameter
     """
 
     def __init__(
@@ -37,21 +42,24 @@ def __init__(
     def get_name(self) -> str:
         """
         Returns:
-            str: name of the hyperparameter
+            str:
+                Name of the hyperparameter
         """
         return self._name
 
     def get_default(self) -> Any:
         """
         Returns:
-            Any: default value of the hyperparameter
+            Any:
+                Default value of the hyperparameter
         """
         return self._default
 
     def get_shape(self) -> Union[int, tuple[int, ...], None]:
         """
         Returns:
-            Union[int, tuple[int, ...], None]: shape of the hyperparameter
+            Union[int, tuple[int, ...], None]:
+                Shape of the hyperparameter
         """
         return self._shape
 
@@ -68,10 +76,12 @@ def _check_default(self, default: Any) -> Any:
             - if weights are not given: first option of choices
 
         Args:
-            default (Any): default value to check
+            default (Any):
+                Default value to check
 
         Returns:
-            Any: default value according to the description
+            Any:
+                Default value according to the description
         """
         pass
 
@@ -84,10 +94,12 @@ def _is_legal_default(self, default: Any) -> bool:
             - bounds and choices are not given (constant hyperparameter)
 
         Args:
-            default (Any): default value to check
+            default (Any):
+                Default value to check
 
         Returns:
-            bool: True if default value is legal, otherwise False
+            bool:
+                True if default value is legal, otherwise False
         """
         pass
 
@@ -102,10 +114,12 @@ def _check_shape(self, shape: Union[int, tuple[int, ...], None]) -> Union[int, t
         and has the same dimension as the given default value.
 
         Args:
-            shape (Union[int, tuple[int, ...], None]): shape to check
+            shape (Union[int, tuple[int, ...], None]):
+                Shape to check
 
         Returns:
-            Union[int, tuple[int, ...], None]: legal shape
+            Union[int, tuple[int, ...], None]:
+                Legal shape
         """
         pass
 
@@ -120,10 +134,12 @@ def _is_legal_shape(self, shape: Union[int, tuple[int, ...], None]) -> bool:
         and has the same dimension as the given default value.
 
         Args:
-            shape (Union[int, tuple[int, ...], None]): shape to check
+            shape (Union[int, tuple[int, ...], None]):
+                Shape to check
 
         Returns:
-            bool: True if given shape is legal
+            bool:
+                True if given shape is legal
         """
         pass
 
@@ -133,11 +149,15 @@ def sample(self, random: np.random.RandomState, size: Union[int, None] = None) -
         Returns a sample of values from the given hyperparameter, according to the given distribution.
 
         Args:
-            random (np.random.RandomState): random generator for the sampling procedure
-            size (Union[int, None]): number of samples
+            random (np.random.RandomState):
+                Random generator for the sampling procedure
+
+            size (Union[int, None]):
+                Number of samples
 
         Returns:
-            Any: sample of values from the given hyperparameter
+            Any:
+                Sample of values from the given hyperparameter
         """
         pass
 
@@ -148,7 +168,7 @@ def valid_configuration(self, value: Any) -> bool:
 
         Args:
             value (Any):
-                The configuration you want to check.
+                The configuration you want to check
 
         Returns:
             bool:
@@ -202,11 +222,11 @@ def _get_sample_size(
             Union[int, tuple[int, ...]]:
                 Shape of the samples
         """
-        assert size is None or size > 0, "#ERROR_HYPERPARAMETER: size should be None or higher than 0!"
+        assert size is None or size > 0, f"Illegal size {size}. The argument should be None or higher than 0!"
         if isinstance(shape, int):
-            assert shape > 0, "#ERROR_HYPERPARAMETER: shape should be higher than 0!"
+            assert shape > 0, f"Illegal shape {shape}. The argument should be higher than 0!"
         elif isinstance(shape, tuple):
-            assert all(s > 0 for s in shape), "#ERROR_HYPERPARAMETER: shape should be higher than 0!"
+            assert all(s > 0 for s in shape), f"Illegal shape {shape}. The argument should be higher than 0!"
 
         if shape == 1 or shape == (1,):
             # Case: Shape of the hyperparameter is just a single value