Update the documentation to API format, describing only the public methods.

aisp/__init__.py

@@ -31,5 +31,5 @@
 from . import nsa
 
 __author__ = "AISP Development Team"
-__version__ = "0.5.1"
+__version__ = "0.5.4"
 __all__ = ["csa", "nsa", "ina"]

aisp/base/core/_classifier.py

@@ -67,7 +67,7 @@ def predict(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
         Parameters
         ----------
         X : Union[npt.NDArray, list]
-            Input data for which predictions will be generated.
+            Data for which predictions will be generated.
 
         Returns
         -------
@@ -83,8 +83,8 @@ def score(
         """
         Score function calculates forecast accuracy.
 
-        Details
-        -------
+        Notes
+        -----
         This function performs the prediction of X and checks how many elements are equal
         between vector y and y_predicted. This function was added for compatibility with some
         scikit-learn functions.
@@ -116,11 +116,11 @@ def _slice_index_list_by_class(self, y: npt.NDArray) -> dict:
         Parameters
         ----------
         y : npt.NDArray
-            Receives a y ``n_samples`` array with the output classes of the ``X`` sample array.
+            Receives a y `n_samples` array with the output classes of the `X` sample array.
 
         Returns
         -------
         indices_by_class : dict
-            A dictionary with the list of array positions(``y``), with the classes as key.
+            A dictionary with the list of array positions(`y`), with the classes as key.
         """
         return slice_index_list_by_class(self.classes, y)

aisp/base/core/_clusterer.py

@@ -70,7 +70,7 @@ def predict(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
         Parameters
         ----------
         X : Union[npt.NDArray, list]
-            Input data for which predictions will be generated.
+            Data for which predictions will be generated.
 
         Returns
         -------
@@ -87,7 +87,7 @@ def fit_predict(self, X: Union[npt.NDArray, list], verbose: bool = True) -> npt.
         Parameters
         ----------
         X : Union[npt.NDArray, list]
-            Input data for which predictions will be generated.
+            Data for which predictions will be generated.
         verbose : bool, default=True
             Flag to enable or disable detailed output during training.
 

aisp/base/core/_optimizer.py

@@ -160,7 +160,9 @@ def optimize(
     def affinity_function(self, solution: Any) -> float:
         """Evaluate the affinity of a candidate solution.
 
-        This abstract method must be implemented by the subclass to define the problem-specific.
+        This method must be implemented according to the specific optimization problem, defining
+        how the solution will be evaluated. The returned value should represent the quality of
+        the evaluated solution.
 
         Parameters
         ----------
@@ -186,10 +188,10 @@ def register(self, alias: str, function: Callable[..., Any]) -> None:
         Raises
         ------
         TypeError
-            If `function` is not callable.
+            If the provided `function` is not callable.
         AttributeError
-            If `alias` is protected and cannot be modified. Or if `alias` does not exist in the
-            optimizer class.
+            If `alias` is protected and cannot be modified, or does not exist in the
+            class.
         """
         if not callable(function):
             raise TypeError(f"Expected a function for '{alias}', got {type(function).__name__}")
@@ -202,7 +204,10 @@ def register(self, alias: str, function: Callable[..., Any]) -> None:
         setattr(self, alias, function)
 
     def reset(self):
-        """Reset the object's internal state, clearing history and resetting values."""
+        """Reset the object's internal state.
+
+        Clears the optimization history and resetting values.
+        """
         self._cost_history = []
         self._solution_history = []
         self._best_solution = None

aisp/base/immune/cell.py

@@ -72,14 +72,14 @@ def hyper_clonal_mutate(
         bounds: Optional[npt.NDArray[np.float64]] = None
     ) -> npt.NDArray:
         """
-        Clones N features from a cell's features, generating a set of mutated vectors.
+        Generate N clones of the current cell and apply hypermutation to the clones.
 
         Parameters
         ----------
         n : int
-            Number of clones to be generated from mutations of the original cell.
+            Number of clones to generate from the original b-cell.
         feature_type : { "binary-features", "continuous-features", "ranged-features" }
-            Specifies the type of feature_type to use based on the nature of the input features
+            Specifies the type of features of the cell.
         bounds : npt.NDArray[np.float64], optional
             Array (n_features, 2) with min and max per dimension.
 

aisp/base/immune/mutation.py

@@ -19,9 +19,8 @@ def clone_and_mutate_continuous(
     """
     Generate a set of mutated clones from a cell represented by a continuous vector.
 
-    This function creates `n` clones of the input vector and applies random mutations to each of
-    them, simulating the process of clonal expansion in artificial immune systems. Each clone
-    will have a random number of mutations applied in distinct positions of the original vector.
+    This function creates `n` clones of the input vector and applies mutations to each of
+    them, simulating the process of clonal expansion in artificial immune systems.
 
     Parameters
     ----------
@@ -30,9 +29,8 @@ def clone_and_mutate_continuous(
     n : int
         The number of mutated clones to be generated.
     mutation_rate : float, default=1
-        If 0 <= mutation_rate < 1: probability of mutating each component.
-        If mutation_rate >= 1 or mutation_rate <= 0: the mutation randomizes
-        a number of components between 1 and len(vector).
+        If 0 ≤ mutation_rate < 1, mutation probability per feature.
+        Otherwise, a random number of features is mutated.
 
     Returns
     -------
@@ -67,9 +65,8 @@ def clone_and_mutate_binary(
     """
     Generate a set of mutated clones from a cell represented by a binary vector.
 
-    This function creates `n` clones of the input vector and applies random mutations to each of
-    them, changing some bits randomly. The process simulates clonal expansion in artificial
-    immune systems with discrete representations.
+    This function creates `n` clones of the input binary vector and applies mutations to the
+    bits, simulating clonal expansion in artificial immune systems with discrete representations.
 
     Parameters
     ----------
@@ -78,9 +75,8 @@ def clone_and_mutate_binary(
     n : int
         The number of mutated clones to be generated.
     mutation_rate : float, default=1
-        If 0 <= mutation_rate < 1: probability of mutating each component.
-        If mutation_rate >= 1 or mutation_rate <= 0: the mutation randomizes
-        a number of components between 1 and len(vector).
+        If 0 ≤ mutation_rate < 1, mutation probability per feature.
+        Otherwise, a random number of features is mutated.
 
     Returns
     -------
@@ -116,9 +112,8 @@ def clone_and_mutate_ranged(
     """
     Generate a set of mutated clones from a cell represented by custom ranges per dimension.
 
-    This function creates `n` clones of the input vector and applies random mutations to each of
-    them, simulating the process of clonal expansion in artificial immune systems. Each clone
-    will have a random number of mutations applied in distinct positions of the original vector.
+    This function creates `n` clones of the input vector and applies mutations to each of
+    them, simulating the process of clonal expansion in artificial immune systems.
 
     Parameters
     ----------
@@ -129,9 +124,8 @@ def clone_and_mutate_ranged(
     bounds : np.ndarray
         Array (n_features, 2) with min and max per dimension.
     mutation_rate : float, default=1
-        If 0 <= mutation_rate < 1: probability of mutating each component.
-        If mutation_rate >= 1 or mutation_rate <= 0: the mutation randomizes
-        a number of components between 1 and len(vector).
+        If 0 ≤ mutation_rate < 1, mutation probability per feature.
+        Otherwise, a random number of features is mutated.
 
     Returns
     -------
@@ -166,7 +160,7 @@ def clone_and_mutate_permutation(
     n: int,
     mutation_rate: float
 ) -> npt.NDArray[np.int64]:
-    """Generate a set of mutated clones by random permutation.
+    """Generate a set of mutated clones by permutation.
 
     Parameters
     ----------
@@ -175,7 +169,7 @@ def clone_and_mutate_permutation(
     n : int
         The number of mutated clones to be generated.
     mutation_rate : float
-        Probability of mutating each component 0 <= mutation_rate < 1.
+        Probability of mutating each feature 0 ≤ mutation_rate < 1.
 
     Returns
     -------

aisp/base/immune/populations.py

@@ -23,21 +23,21 @@ def generate_random_antibodies(
         Number of antibodies (samples) to generate.
     n_features : int
         Number of features (dimensions) for each antibody.
-    feature_type : FeatureType, default="continuous-features"
+    feature_type : FeatureTypeAll, default="continuous-features"
         Specifies the type of features: "continuous-features", "binary-features",
         "ranged-features", or "permutation-features".
     bounds : npt.NDArray[np.float64], optional
         Array (n_features, 2) with min and max per dimension.
 
-    Raises
-    ------
-    ValueError
-        If number of features must be greater than zero.
-
     Returns
     -------
     npt.NDArray
         Array of shape (n_samples, n_features) containing the generated antibodies.
+
+    Raises
+    ------
+    ValueError
+        If the number of features is less than or equal to zero.
     """
     if n_features <= 0:
         raise ValueError("Number of features must be greater than zero.")

aisp/csa/_ai_recognition_sys.py

@@ -53,18 +53,17 @@ class AIRS(BaseClassifier):
     k : int, default=3
         The number of K nearest neighbors that will be used to choose a label in the prediction.
     max_iters : int, default=100
-        Maximum number of interactions in the refinement process of the ARB set exposed to aᵢ.
+        Maximum number of iterations in the refinement process of the ARB set exposed to aᵢ.
     resource_amplified : float, default=1.0
         Resource consumption amplifier is multiplied with the incentive to subtract resources.
-        Defaults to 1.0 without amplification.
     metric : {"euclidean", "minkowski", "manhattan"}, default="euclidean"
         Distance metric used to compute affinity between cells and samples.
     seed : int
-        Seed for the random generation of detector values. Defaults to None.
+        Seed for random generation.
 
     **kwargs
         p : float
-            This parameter stores the value of ``p`` used in the Minkowski distance. The default
+            This parameter stores the value of `p` used in the Minkowski distance. The default
             is ``2``, which represents normalized Euclidean distance.\
             Different values of p lead to different variants of the Minkowski Distance.
 
@@ -107,7 +106,7 @@ class AIRS(BaseClassifier):
     >>> airs = airs.fit(x_train, y_train, verbose=False)
     >>> x_test = [
     ...     [0.15, 0.45],  # Expected: Class 0
-    ...     [0.85, 0.65],  # Esperado: Classe 1
+    ...     [0.85, 0.65],  # Esperado: Class 1
     ... ]
     >>> y_pred = airs.predict(x_test)
     >>> print(y_pred)
@@ -175,7 +174,7 @@ def fit(
         """
         Fit the model to the training data using the AIRS.
 
-        The function ``fit(...)``, performs the training according to ``X`` and ``y``, using the
+        The function ``fit(...)``, performs the training according to `X` and `y`, using the
         method AIRS.
 
         Parameters
@@ -184,14 +183,19 @@ def fit(
             Training array, containing the samples and their characteristics,
             Shape: (n_samples, n_features).
         y : Union[npt.NDArray, list]
-            Array of target classes of ``X`` with (``n_samples``).
+            Array of target classes of `X` with (`n_samples`).
         verbose : bool
             Feedback on which sample aᵢ the memory cells are being generated.
 
         Returns
         -------
         AIRS
             Returns the instance itself.
+
+        Raises
+        ------
+        TypeError
+            If X or y are not ndarrays or have incompatible shapes.
         """
         X = self._prepare_features(X)
         y = check_array_type(y, "y")
@@ -258,7 +262,13 @@ def predict(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
         Parameters
         ----------
         X : Union[npt.NDArray, list]
-            Array with input samples with  Shape: (``n_samples, n_features``)
+            Array with input samples with  Shape: (`n_samples, n_features`)
+
+        Returns
+        -------
+        C : npt.NDArray
+            An ndarray of the form `C` (`n_samples`), containing the predicted classes for
+            `X`.
 
         Raises
         ------
@@ -269,12 +279,6 @@ def predict(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
         ModelNotFittedError
             If the mode has not yet been adjusted and does not have defined memory cells, it is
             not able to predictions
-
-        Returns
-        -------
-        C : npt.NDArray
-            An ndarray of the form ``C`` (``n_samples``), containing the predicted classes for
-            ``X``.
         """
         if self._all_class_cell_vectors is None:
             raise ModelNotFittedError("AIRS")
@@ -524,6 +528,13 @@ def _prepare_features(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
         -------
         X : npt.NDArray
             The processed input data.
+
+        Raises
+        ------
+        TypeError:
+            If X is not a ndarray or a list.
+        UnsupportedTypeError
+            If the data type of the vector is not supported.
         """
         X = check_array_type(X)
         self._feature_type = detect_vector_data_type(X)

aisp/csa/_clonalg.py

@@ -38,7 +38,7 @@ class Clonalg(BaseOptimizer):
         Dimension of the problem to be minimized.
     N : int, default=50
         Number of memory cells (antibodies) in the population.
-    rate_clonal : float, default=10
+    rate_clonal : int, default=10
         Maximum number of possible clones of a cell. This value is multiplied by
         cell_affinity to determine the number of clones.
     rate_hypermutation : float, default=1.0
@@ -182,19 +182,19 @@ def optimize(
         max_iters : int, default=50
             Maximum number of iterations when searching for the best solution using clonalg.
         n_iter_no_change: int, default=10
-            The maximum number of iterations without updating the best cell
+            The maximum number of iterations without updating the best cell.
         verbose : bool, default=True
             Feedback on iterations, indicating the best antibody.
 
-        Raises
-        ------
-        NotImplementedError
-            If no affinity function has been provided to model.
-
         Returns
         -------
         population : List[Antibody]
             Antibody population after clonal expansion.
+
+        Raises
+        ------
+        NotImplementedError
+            If no affinity function has been provided to evaluate candidate solutions.
         """
         self.reset()