Fix: sklearn clone incompatibility in AIRS class

aisp/base/core/_base.py

@@ -1,7 +1,7 @@
 """Base class for parameter introspection compatible with the scikit-learn API."""
-
 from __future__ import annotations
 
+from inspect import signature
 
 class Base:
     """
@@ -43,7 +43,7 @@ def get_params(self, deep: bool = True) -> dict:  # pylint: disable=W0613
             Dictionary containing the object's attributes that do not start with "_".
         """
         return {
-            key: value
-            for key, value in self.__dict__.items()
-            if not key.startswith("_")
-        }
+            key: getattr(self, key)
+            for key, _ in signature(self.__init__).parameters.items()
+            if key != "self" and not key.startswith("_") and hasattr(self, key)
+            }

aisp/base/core/tests/test_base.py

@@ -9,9 +9,13 @@ class TestBase:
 
     def setup_method(self):
         """Set up a Base instance with example attributes before each test."""
-        self.obj = Base()
-        self.obj.alpha = 1
-        self.obj.beta = 2
+        class BaseFixture(Base):
+            """Fixture class for Base tests."""
+            def __init__(self, alpha=1, beta=2):
+                self.alpha = alpha
+                self.beta = beta
+
+        self.obj = BaseFixture()
 
     def test_set_and_get_params_basic(self):
         """Test setting parameters using set_params and retrieving them with get_params."""
@@ -28,8 +32,7 @@ def test_get_params_excludes_private(self):
 
         params = self.obj.get_params()
         assert "_private" not in params
-        assert "public" in params
-        assert params["public"] == "ok"
+        assert "public" not in params
 
     def test_set_params_updates_existing(self):
         """Test that set_params updates existing attributes correctly."""

aisp/csa/_ai_recognition_sys.py

@@ -126,7 +126,7 @@ def __init__(
         resource_amplified: float = 1.0,
         metric: MetricType = "euclidean",
         seed: Optional[int] = None,
-        **kwargs,
+        p: float = 2.0,
     ) -> None:
         self.n_resources: float = sanitize_param(n_resources, 10, lambda x: x >= 1)
         self.rate_mc_init: float = sanitize_param(
@@ -153,7 +153,7 @@ def __init__(
 
         self.metric = sanitize_choice(metric, ["manhattan", "minkowski"], "euclidean")
 
-        self.p: np.float64 = np.float64(kwargs.get("p", 2.0))
+        self.p = p
 
         self._cells_memory: Optional[Dict[str | int, list[BCell]]] = None
         self._all_class_cell_vectors: Optional[List[Tuple[Any, np.ndarray]]] = None

aisp/csa/tests/test_ai_recognition_sys.py

@@ -34,7 +34,7 @@ class TestAIRSBinary:
     def test_fit_and_predict(self, b_airs_data):
         """Should the fit and predict methods of the AIRS class."""
         X, y, seed = b_airs_data
-        model = AIRS(algorithm="binary-features", seed=seed)
+        model = AIRS(seed=seed)
         model.fit(X, y, verbose=False)
         predictions = model.predict(X)
         assert predictions is not None
@@ -44,7 +44,7 @@ def test_fit_and_predict(self, b_airs_data):
     def test_predict_raises_feature_dimension_mismatch(self, b_airs_data):
         """Should raise FeatureDimensionMismatch when prediction input has wrong dimensions."""
         X, y, seed = b_airs_data
-        model = AIRS(algorithm="binary-features", seed=seed)
+        model = AIRS(seed=seed)
         model.fit(X, y, verbose=False)
         x_invalid = np.random.choice([True, False], size=(5, 5))
 
@@ -54,7 +54,7 @@ def test_predict_raises_feature_dimension_mismatch(self, b_airs_data):
     def test_score_range(self, b_airs_data):
         """Score should return a value between 0 and 1."""
         X, y, seed = b_airs_data
-        model = AIRS(algorithm="binary-features", seed=seed)
+        model = AIRS(seed=seed)
         model.fit(X, y, verbose=False)
         score = model.score(X, y)
         assert isinstance(score, float)