fixup

onedal/common/_backend.py

@@ -99,22 +99,22 @@ def decorate_functions(
             backend_method, backend_type=backend_type, name=formatted_name
         )
 
+        # assign it to the class under the correct method name
+        destination = name_overrides.get(method, method)
+
         # Log a message about what's happening
-        existing = getattr(cls, method, None)
-        is_abstract = getattr(existing, "__isabstractmethod__", False)
+        existing = getattr(cls, destination, None)
+        is_abstract = getattr(destination, "__isabstractmethod__", False)
         if existing and not is_abstract:
             method_type = getattr(existing, "backend_type", None)
             logger.debug(
-                f"Replaced existing method '{method}'. Old backend {method_type}, new backend {backend_type}"
+                f"Replaced existing method '{destination}'. Old backend {method_type}, new backend {backend_type}"
             )
         else:
             logger.debug(
                 f"Assigned method '{cls.__name__}.{method}' from {backend_type} backend"
             )
 
-        # assign it to the class under the correct method name
-        destination = name_overrides.get(method, method)
-
         setattr(cls, destination, wrapped_method)
 
         if is_abstract:

onedal/neighbors/neighbors.py

@@ -81,10 +81,6 @@ def train(self, *args): ...
     @abstractmethod
     def infer(self, *args): ...
 
-    # direct access to the backend model constructor
-    @abstractmethod
-    def model(self): ...
-
     def _validate_data(
         self, X, y=None, reset=True, validate_separately=None, **check_params
     ):
@@ -187,7 +183,6 @@ def _get_daal_params(self, data, n_neighbors=None):
         return params
 
 
-@bind_default_backend("neighbors.classification", ["train", "infer", "model"])
 class NeighborsBase(NeighborsCommonBase, metaclass=ABCMeta):
     def __init__(
         self,
@@ -350,7 +345,7 @@ def _kneighbors(self, X=None, n_neighbors=None, return_distance=True, queue=None
             type(self._onedal_model) is kdtree_knn_classification_model
             or type(self._onedal_model) is bf_knn_classification_model
         ):
-            params = self._get_daal_params(X, n_neighbors=n_neighbors)
+            params = super()._get_daal_params(X, n_neighbors=n_neighbors)
             prediction_results = self._onedal_predict(
                 self._onedal_model, X, params, queue=queue
             )
@@ -412,6 +407,7 @@ def _kneighbors(self, X=None, n_neighbors=None, return_distance=True, queue=None
         return neigh_ind
 
 
+@bind_default_backend("neighbors.classification", ["train", "infer", "model"])
 class KNeighborsClassifier(NeighborsBase, ClassifierMixin):
     def __init__(
         self,
@@ -434,8 +430,12 @@ def __init__(
         )
         self.weights = weights
 
+    # direct access to the backend model constructor
+    @abstractmethod
+    def model(self): ...
+
     def _get_daal_params(self, data):
-        params = self._get_daal_params(data)
+        params = super()._get_daal_params(data)
         params["resultsToEvaluate"] = "computeClassLabels"
         params["resultsToCompute"] = ""
         return params
@@ -590,7 +590,7 @@ def _get_onedal_params(self, X, y=None):
         return params
 
     def _get_daal_params(self, data):
-        params = self._get_daal_params(data)
+        params = super()._get_daal_params(data)
         params["resultsToCompute"] = "computeIndicesOfNeighbors|computeDistances"
         params["resultsToEvaluate"] = "none"
         return params