fix(docs): fix sphinx generation error

Author: xImoZA

rework_pysatl_mpest/core/mixture.py

@@ -72,6 +72,7 @@ class MixtureModel(Generic[DType]):
         :toctree: generated/
 
         add_component
+        astype
         remove_component
         pdf
         lpdf
@@ -89,7 +90,7 @@ def __init__(
     ):
         n_components = len(components)
         if n_components == 0:
-            raise ValueError("List of components cannot be an empty")
+            raise ValueError("List of components cannot be empty")
 
         self._dtype = dtype
 
@@ -130,7 +131,7 @@ def _validate_weights(self, n_components: int, weights: NDArray[DType]):
 
         atol = np.sqrt(np.finfo(self.dtype).eps)
         if not np.isclose(np.sum(weights), self.dtype(1.0), atol=atol):
-            raise ValueError(f"Sum of the weights must be equal 1, but it equal {np.sum(weights)}.")
+            raise ValueError(f"Sum of the weights must be equal 1, but it equals {np.sum(weights)}.")
 
     @property
     def dtype(self) -> type[DType]:

rework_pysatl_mpest/core/parameter.py

@@ -50,6 +50,17 @@ class Parameter:
         The name of the attribute used to store the value within an instance
         of the owner class.
 
+    Notes
+    -----
+    **Initialization Order**
+
+    This descriptor casts the assigned value to the owner instance's ``dtype``.
+    Therefore, the instance must have its ``dtype`` attribute initialized
+    **before** any value is assigned to this parameter.
+
+    In subclasses, ensure ``super().__init__(dtype=...)`` is called before
+    assigning parameter values to avoid defaulting to ``np.float64``.
+
     Examples
     --------
 
@@ -61,6 +72,11 @@ class NormalDistribution(ContinuousDistribution):
             # Scale parameter must be a positive number
             scale = Parameter(invariant=lambda s: s > 0, error_message="Standard deviation (scale) must be positive.")
 
+            def __init__(self, loc, scale, dtype=np.float64):
+                # Correct order: initialize dtype first
+                super().__init__(dtype=dtype)
+                self.loc = loc
+                self.scale = scale
     """
 
     def __init__(

rework_pysatl_mpest/distributions/beta.py

@@ -15,31 +15,33 @@
 
 
 class Beta(ContinuousDistribution):
-    """Class for the four-parameteric beta distribution.
-       Parameters
-       ----------
-       alpha : float
-           The first shape parameter. Must be positive or zero.
-       beta : float
-           The second shape parameter. Must be positive or zero.
-       left_border : float
-           Left border of section [a, b]. Can be any real number.
-       right_border : float
-           Right border of section [a, b]. Can be any real number.
-
-       Attributes
-       ----------
-       loc : float
-           Location parameter.
-       scale : float
-           Scale parameter.
-       scale : float
-           Scale parameter (gamma). Must be positive.
-       scale : float
-           Scale parameter (gamma). Must be positive.
-
-       Methods
-       -------
+    """Class for the four-parametric beta distribution.
+
+    Parameters
+    ----------
+    alpha : float
+        The first shape parameter. Must be positive or zero.
+    beta : float
+        The second shape parameter. Must be positive or zero.
+    left_border : float
+        Left border of section [a, b]. Can be any real number.
+    right_border : float
+        Right border of section [a, b]. Can be any real number.
+
+    Attributes
+    ----------
+    alpha : float
+        The first shape parameter.
+    beta : float
+        The second shape parameter.
+    left_border : float
+        Left border of the distribution support [a, b].
+    right_border : float
+        Right border of the distribution support [a, b].
+
+
+    Methods
+    -------
 
     .. autosummary::
         :toctree: generated/
@@ -99,7 +101,7 @@ def pdf(self, X):
         where :math:`a` is the left_border parameter, :math:`b` is the
         right_border parameter, :math:`\\alpha` is the first shape parameter and
         :math:`\\beta` is the second shape parameter,:math:`B(\\alpha, \\beta) =
-        \frac{\\Gamma(\\alpha)\\Gamma(\\beta)}{\\Gamma(\\alpha + \\beta)}`
+        \\frac{\\Gamma(\\alpha)\\Gamma(\\beta)}{\\Gamma(\\alpha + \\beta)}`
         is the Beta function.
 
         Parameters
@@ -112,7 +114,6 @@ def pdf(self, X):
         DType | NDArray[DType]
             The PDF values corresponding to each point in :attr:`X`.
             Return a scalar when given a scalar, and to return an array when given an array.
-
         """
 
         X = np.asarray(X, dtype=self.dtype)
@@ -177,7 +178,7 @@ def lpdf(self, X):
         where :math:`a` is the left_border parameter, :math:`b` is the
         right_border parameter, :math:`\\alpha` is the first shape parameter and
         :math:`\\beta` is the second shape parameter, :math:`B(\\alpha, \\beta) =
-        \frac{\\Gamma(\\alpha)\\Gamma(\\beta)}{\\Gamma(\\alpha + \\beta)}`
+        \\frac{\\Gamma(\\alpha)\\Gamma(\\beta)}{\\Gamma(\\alpha + \\beta)}`
 
         Parameters
         ----------
@@ -211,7 +212,7 @@ def _dlog_alpha(self, X):
 
         .. math::
 
-            \frac{\\partial \\ln f(x | \\alpha, \\beta, a, b)}{\\partial \\alpha} =
+            \\frac{\\partial \\ln f(x | \\alpha, \\beta, a, b)}{\\partial \\alpha} =
             \\ln(x - a) - \\ln(b - a)
             - \\psi(\\alpha) + \\psi(\\alpha + \\beta)
 

rework_pysatl_mpest/distributions/continuous_dist.py

@@ -32,7 +32,7 @@ class ContinuousDistribution(ABC, Generic[DType]):
     name: str
         The name of the distribution (e.g., 'Normal', 'Gamma').
     params: set[str]
-        The names of all parameters of the distribution
+        The names of all parameters of the distribution.
     params_to_optimize: set[str]
         Parameters names that are not fixed and can be optimized.
     fixed_params : set[str]

rework_pysatl_mpest/distributions/uniform.py

@@ -85,7 +85,7 @@ def pdf(self, X):
 
         .. math::
 
-            f(x | \\alpha, \\beta) = frac{1}{\\beta - \\alpha}
+            f(x | \\alpha, \\beta) = \\frac{1}{\\beta - \\alpha}
 
         where :math:`\\alpha` is the left_border parameter and :math:`\\beta` is the
         right_border parameter. The function is zero for :math:`x < \\alpha` or :math:`x > \\beta`.

rework_pysatl_mpest/estimators/iterative/pipeline_step.py

@@ -90,6 +90,7 @@ def clear_after_prune(self, removed_components_indices: list[int]) -> None:
         components.
 
         Common use cases include:
+
         - Deleting cached values or buffers associated with pruned components
         - Re-indexing remaining component-specific data to maintain contiguous
           indexing

rework_pysatl_mpest/estimators/iterative/steps/maximization_step.py

@@ -159,8 +159,6 @@ def clear_after_prune(self, removed_components_indices: list[int]) -> None:
 
         Parameters
         ----------
-        state : PipelineState
-            The current pipeline state containing removed_components_indices
         removed_components_indices : list[int]
             Tracks which component indices were removed during pruning.
         """

rework_tests/unit/core/test_mixture.py

@@ -92,7 +92,7 @@ def test_init_with_invalid_weights_raises_value_error(
     @pytest.mark.parametrize(
         "invalid_weights, error_msg",
         [
-            ([0.4, 0.4], "Sum of the weights must be equal 1, but it equal "),
+            ([0.4, 0.4], "Sum of the weights must be equal 1, but it equals "),
         ],
     )
     def test_init_with_invalid_sum_of_weights_raises_value_error(
@@ -108,7 +108,7 @@ def test_init_with_invalid_sum_of_weights_raises_value_error(
     def test_init_with_empty_components_raises_value_error(self, dtype):
         """Tests that initialization with an empty component list raises a ValueError."""
 
-        with pytest.raises(ValueError, match="List of components cannot be an empty"):
+        with pytest.raises(ValueError, match="List of components cannot be empty"):
             MixtureModel(components=[], dtype=dtype)
 
     def test_init_casts_component_dtypes(self, dtype):