chore: update v1

src/concrete/ml/sklearn/base.py

@@ -104,9 +104,6 @@
 # However, for internal testing purposes, we retain the capability to disable this feature
 os.environ["TREES_USE_ROUNDING"] = os.environ.get("TREES_USE_ROUNDING", "1")
 
-# By default, the decision of the tree ensembles is made in clear
-TREES_USE_FHE_SUM = False
-
 # pylint: disable=too-many-public-methods
 
 
@@ -1313,6 +1310,7 @@ def __init__(self, n_bits: Union[int, Dict[str, int]]):
         self._tree_inference: Optional[Callable] = None
 
         #: Wether to perform the sum of the output's tree ensembles in FHE or not.
+        # By default, the decision of the tree ensembles is made in clear.
         self._use_fhe_sum = False
 
         BaseEstimator.__init__(self)
@@ -1327,11 +1325,11 @@ def use_fhe_sum(self) -> bool:
         return self._use_fhe_sum
 
     @use_fhe_sum.setter
-    def use_fhe_sum(self, value) -> None:
+    def use_fhe_sum(self, value: bool) -> None:
         """Property setter for `use_fhe_sum`.
 
         Args:
-            value (int): Whether to enable or disable the feature.
+            value (bool): Whether to enable or disable the feature.
         """
 
         assert isinstance(value, bool), "Value must be a boolean type"

src/concrete/ml/sklearn/tree_to_numpy.py

@@ -141,8 +141,8 @@ def add_transpose_after_last_node(onnx_model: onnx.ModelProto, use_fhe_sum: bool
 
     Args:
         onnx_model (onnx.ModelProto): The ONNX model.
-        use_fhe_sum (bool): This parameter is exclusively used to tree-based models.
-            It determines whether the sum of the trees' outputs is computed in FHE.
+        use_fhe_sum (bool): Determines whether the sum of the trees' outputs is computed in FHE.
+            Default to False.
     """
     # Get the output node
     output_node = onnx_model.graph.output[0]
@@ -230,8 +230,8 @@ def tree_onnx_graph_preprocessing(
         framework (str): The framework from which the ONNX model is generated.
             (options: 'xgboost', 'sklearn')
         expected_number_of_outputs (int): The expected number of outputs in the ONNX model.
-        use_fhe_sum (bool): This parameter is exclusively used to tree-based models.
-            It determines whether the sum of the trees' outputs is computed in FHE.
+        use_fhe_sum (bool): Determines whether the sum of the trees' outputs is computed in FHE.
+            Default to False.
     """
     # Make sure the ONNX version returned by Hummingbird is OPSET_VERSION_FOR_ONNX_EXPORT
     onnx_version = get_onnx_opset_version(onnx_model)
@@ -339,10 +339,10 @@ def tree_to_numpy(
     Args:
         model (Callable): The tree model to convert.
         x (numpy.ndarray): The input data.
-        use_rounding (bool): This parameter is exclusively used to tree-based models.
-            It determines whether the rounding feature is enabled or disabled.
-        use_fhe_sum (bool): This parameter is exclusively used to tree-based models.
-            It determines whether the sum of the trees' outputs is computed in FHE.
+        use_rounding (bool): Determines whether the rounding feature is enabled or disabled.
+            Default to True.
+        use_fhe_sum (bool): Determines whether the sum of the trees' outputs is computed in FHE.
+            Default to False.
         framework (str): The framework from which the ONNX model is generated.
             (options: 'xgboost', 'sklearn')
         output_n_bits (int): The number of bits of the output. Default to 8.