chore: remove custom classifier attributes

src/concrete/ml/sklearn/base.py

@@ -699,31 +699,38 @@ class BaseClassifier(BaseEstimator):
     the predicted values as well as handling a mapping of classes in case they are not ordered.
     """
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    @property
+    def target_classes_(self) -> Optional[numpy.ndarray]:  # pragma: no cover
+        warnings.warn(
+            "Attribute 'target_classes_' is now deprecated. Please use 'classes_' instead.",
+            category=UserWarning,
+            stacklevel=2,
+        )
 
-        #: The classifier's different target classes. Is None if the model is not fitted.
-        self.target_classes_: Optional[numpy.ndarray] = None
+        return self.classes_
 
-        #: The classifier's number of different target classes. Is None if the model is not fitted.
-        self.n_classes_: Optional[int] = None
+    @property
+    def n_classes_(self) -> Optional[numpy.ndarray]:  # pragma: no cover
+        warnings.warn(
+            "Attribute 'n_classes_' is now deprecated. Please use 'len(classes_)' instead.",
+            category=UserWarning,
+            stacklevel=2,
+        )
+
+        return len(self.classes_)
 
     def _set_post_processing_params(self):
         super()._set_post_processing_params()
-        self.post_processing_params.update({"n_classes_": self.n_classes_})
+        self.post_processing_params.update({"classes_": self.classes_})
 
     def fit(self, X: Data, y: Target, **fit_parameters):
         X, y = check_X_y_and_assert_multi_output(X, y)
 
         # Retrieve the different target classes
         classes = numpy.unique(y)
-        self.target_classes_ = classes
-
-        # Compute the number of target classes
-        self.n_classes_ = len(classes)
 
         # Make sure y contains at least two classes
-        assert_true(self.n_classes_ > 1, "You must provide at least 2 classes in y.")
+        assert_true(len(classes) > 1, "You must provide at least 2 classes in y.")
 
         # Change to composition in order to avoid diamond inheritance and indirect super() calls
         # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3249
@@ -753,19 +760,17 @@ def predict(self, X: Data, fhe: Union[FheMode, str] = FheMode.DISABLE) -> numpy.
         # Retrieve the class with the highest probability
         y_preds = numpy.argmax(y_proba, axis=1)
 
-        assert self.target_classes_ is not None, self._is_not_fitted_error_message()
-
-        return self.target_classes_[y_preds]
+        return self.classes_[y_preds]
 
     def post_processing(self, y_preds: numpy.ndarray) -> numpy.ndarray:
         y_preds = super().post_processing(y_preds)
 
         # Retrieve the number of target classes
-        n_classes_ = self.post_processing_params["n_classes_"]
+        classes = self.post_processing_params["classes_"]
 
         # If the predictions only has one dimension (i.e., binary classification problem), apply the
         # sigmoid operator
-        if n_classes_ == 2:
+        if len(classes) == 2:
             y_preds = numpy_sigmoid(y_preds)[0]
 
             # If the prediction array is 1D (which happens with some models such as XGBCLassifier

src/concrete/ml/sklearn/linear_model.py

@@ -526,8 +526,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["post_processing_params"] = self.post_processing_params
 
         # Classifier
-        metadata["target_classes_"] = self.target_classes_
-        metadata["n_classes_"] = self.n_classes_
+        metadata["classes_"] = self.classes_
 
         # Scikit-Learn
         metadata["penalty"] = self.penalty
@@ -567,8 +566,7 @@ def load_dict(cls, metadata: Dict):
         obj.post_processing_params = metadata["post_processing_params"]
 
         # Classifier
-        obj.target_classes_ = metadata["target_classes_"]
-        obj.n_classes_ = metadata["n_classes_"]
+        obj.classes_ = metadata["classes_"]
 
         # Scikit-Learn
         obj.penalty = metadata["penalty"]

src/concrete/ml/sklearn/qnn.py

@@ -548,8 +548,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["post_processing_params"] = self.post_processing_params
 
         # Classifier
-        metadata["target_classes_"] = self.target_classes_
-        metadata["n_classes_"] = self.n_classes_
+        metadata["classes_"] = self.classes_
 
         # skorch attributes that cannot be serialized
         # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3550
@@ -638,8 +637,7 @@ def load_dict(cls, metadata: Dict):
         obj.post_processing_params = metadata["post_processing_params"]
 
         # Classifier
-        obj.target_classes_ = metadata["target_classes_"]
-        obj.n_classes_ = metadata["n_classes_"]
+        obj.classes_ = metadata["classes_"]
 
         # skorch
         obj.lr = metadata["lr"]

src/concrete/ml/sklearn/rf.py

@@ -86,8 +86,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["post_processing_params"] = self.post_processing_params
 
         # Classifier
-        metadata["target_classes_"] = self.target_classes_
-        metadata["n_classes_"] = self.n_classes_
+        metadata["classes_"] = self.classes_
 
         # Scikit-Learn
         metadata["n_estimators"] = self.n_estimators
@@ -131,8 +130,7 @@ def load_dict(cls, metadata: Dict):
         obj.post_processing_params = metadata["post_processing_params"]
 
         # Classifier
-        obj.target_classes_ = metadata["target_classes_"]
-        obj.n_classes_ = metadata["n_classes_"]
+        obj.classes_ = metadata["classes_"]
 
         # Scikit-Learn
         obj.n_estimators = metadata["n_estimators"]

src/concrete/ml/sklearn/svm.py

@@ -193,8 +193,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["post_processing_params"] = self.post_processing_params
 
         # Classifier
-        metadata["target_classes_"] = self.target_classes_
-        metadata["n_classes_"] = self.n_classes_
+        metadata["classes_"] = self.classes_
 
         # Scikit-Learn
         metadata["penalty"] = self.penalty
@@ -232,8 +231,7 @@ def load_dict(cls, metadata: Dict):
         obj.post_processing_params = metadata["post_processing_params"]
 
         # Classifier
-        obj.target_classes_ = metadata["target_classes_"]
-        obj.n_classes_ = metadata["n_classes_"]
+        obj.classes_ = metadata["classes_"]
 
         # Scikit-Learn
         obj.penalty = metadata["penalty"]

src/concrete/ml/sklearn/tree.py

@@ -86,8 +86,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["post_processing_params"] = self.post_processing_params
 
         # Classifier
-        metadata["target_classes_"] = self.target_classes_
-        metadata["n_classes_"] = self.n_classes_
+        metadata["classes_"] = self.classes_
 
         # Scikit-Learn
         metadata["criterion"] = self.criterion
@@ -126,8 +125,7 @@ def load_dict(cls, metadata: Dict):
         obj.post_processing_params = metadata["post_processing_params"]
 
         # Classifier
-        obj.target_classes_ = metadata["target_classes_"]
-        obj.n_classes_ = metadata["n_classes_"]
+        obj.classes_ = metadata["classes_"]
 
         # Scikit-Learn
         obj.criterion = metadata["criterion"]

src/concrete/ml/sklearn/xgb.py

@@ -127,8 +127,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["post_processing_params"] = self.post_processing_params
 
         # Classifier
-        metadata["target_classes_"] = self.target_classes_
-        metadata["n_classes_"] = self.n_classes_
+        metadata["classes_"] = self.classes_
 
         # XGBoost
         metadata["max_depth"] = self.max_depth
@@ -185,8 +184,7 @@ def load_dict(cls, metadata: Dict):
         obj.post_processing_params = metadata["post_processing_params"]
 
         # Classifier
-        obj.target_classes_ = metadata["target_classes_"]
-        obj.n_classes_ = metadata["n_classes_"]
+        obj.classes_ = metadata["classes_"]
 
         # XGBoost
         obj.max_depth = metadata["max_depth"]

tests/sklearn/test_qnn.py

@@ -219,7 +219,7 @@ def test_compile_and_calib(
     if is_classifier_or_partial_classifier(model_class):
         y_pred_clear = model.predict(x_train, fhe="disable")
         # Check that the predicted classes are all contained in the model class list
-        assert set(numpy.unique(y_pred_clear)).issubset(set(model.target_classes_))
+        assert set(numpy.unique(y_pred_clear)).issubset(set(model.classes_))
 
     # Compile the model
     model.compile(