v0.5.3

aisp/base/core/_classifier.py

@@ -18,6 +18,13 @@ class BaseClassifier(ABC, Base):
     This class defines the core interface for classification models. It enforces the
     implementation of the ``fit`` and ``predict`` methods in all derived classes,
     and provides a default implementation of ``score`` and utility functions.
+
+    Attributes
+    ----------
+    classes : Optional[npt.NDArray]
+        Class labels identified during training.
+    _n_features : int
+        The number of features of the data.
     """
 
     classes: Optional[npt.NDArray] = None
@@ -64,7 +71,7 @@ def predict(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
 
         Returns
         -------
-        Predictions : Optional[npt.NDArray]
+        predictions : npt.NDArray
             Predicted values for each input sample.
         """
 
@@ -113,7 +120,7 @@ def _slice_index_list_by_class(self, y: npt.NDArray) -> dict:
 
         Returns
         -------
-        dict: dict
+        indices_by_class : dict
             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

@@ -3,7 +3,8 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Union
+from typing import Optional, Union
+from warnings import warn
 
 import numpy.typing as npt
 
@@ -16,8 +17,29 @@ class BaseClusterer(ABC, Base):
     This class defines the core interface for clustering models. It enforces
     the implementation of the `fit` and `predict` methods in all derived classes,
     and provides a default implementation for `fit_predict` and `get_params`.
+
+    Attributes
+    ----------
+    labels : Optional[npt.NDArray]
+        Labels for the clusters generated during model fitting.
     """
 
+    labels: Optional[npt.NDArray] = None
+
+    @property
+    def classes(self) -> Optional[npt.NDArray]:
+        """Deprecated alias kept for backward compatibility.
+
+        Use `labels` instead of `classes`.
+        """
+        warn(
+            "The `classes` attribute is deprecated and will be removed in future "
+            "versions. Use labels instead.",
+            FutureWarning,
+            2
+        )
+        return self.labels
+
     @abstractmethod
     def fit(self, X: Union[npt.NDArray, list], verbose: bool = True) -> BaseClusterer:
         """

aisp/base/core/_optimizer.py

@@ -15,6 +15,19 @@ class BaseOptimizer(ABC, Base):
     This class defines the core interface for optimization strategies. It keeps track of cost
     history, evaluated solutions, and the best solution found during the optimization process.
     Subclasses must implement ``optimize`` and ``affinity_function``.
+
+    Attributes
+    ----------
+    cost_history : List[float]
+        History of best costs found at each iteration.
+    solution_history : List
+        History of the best solution found at each iteration.
+    best_solution : Any
+        The best solution found.
+    best_cost : Optional[float]
+        Cost of the best solution found.
+    mode : {"min", "max"}, default="min"
+        Defines whether the algorithm minimizes or maximizes the cost function.
     """
 
     def __init__(self) -> None:
@@ -57,6 +70,8 @@ def _record_best(self, cost: float, best_solution: Any) -> None:
         ----------
         cost : float
             Cost value to be added to the history.
+        best_solution : Any
+            The best solution associated with the given cost.
         """
         self._solution_history.append(best_solution)
         self._cost_history.append(cost)
@@ -128,9 +143,9 @@ def optimize(
 
         Parameters
         ----------
-        max_iters : int
+        max_iters : int, default=50
             Maximum number of iterations
-        n_iter_no_change: int, default=10
+        n_iter_no_change : int, default=10
             the maximum number of iterations without updating the best
         verbose : bool, default=True
             Flag to enable or disable detailed output during optimization.

aisp/base/immune/cell.py

@@ -78,7 +78,7 @@ def hyper_clonal_mutate(
         ----------
         n : int
             Number of clones to be generated from mutations of the original cell.
-        feature_type : Literal["binary-features", "continuous-features", "ranged-features"]
+        feature_type : { "binary-features", "continuous-features", "ranged-features" }
             Specifies the type of feature_type to use based on the nature of the input features
         bounds : npt.NDArray[np.float64], optional
             Array (n_features, 2) with min and max per dimension.