chore: update v3

src/concrete/ml/sklearn/tree_to_numpy.py

@@ -136,25 +136,25 @@ def assert_add_node_and_constant_in_xgboost_regressor_graph(onnx_model: onnx.Mod
     )
 
 
-def add_transpose_after_last_node(onnx_model: onnx.ModelProto, use_fhe_sum: bool):
+def add_transpose_after_last_node(onnx_model: onnx.ModelProto, fhe_ensembling: bool):
     """Add transpose after last node.
 
     Args:
         onnx_model (onnx.ModelProto): The ONNX model.
-        use_fhe_sum (bool): Determines whether the sum of the trees' outputs is computed in FHE.
+        fhe_ensembling (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]
 
-    # The state of the 'use_fhe_sum' variable affects the structure of the model's ONNX graph.
+    # The state of the 'fhe_ensembling' variable affects the structure of the model's ONNX graph.
     # When the option is enabled, the graph is cut after the ReduceSum node.
     # When it is disabled, the graph is cut at the ReduceSum node, which alters the output shape.
     # Therefore, it is necessary to adjust this shape with the correct permutation.
 
     # When using FHE sum for tree ensembles, create the node with perm attribute equal to (1, 0)
     # Otherwise, create the node with perm attribute equal to (2, 1, 0)
-    perm = [1, 0] if use_fhe_sum else [2, 1, 0]
+    perm = [1, 0] if fhe_ensembling else [2, 1, 0]
 
     transpose_node = onnx.helper.make_node(
         "Transpose",
@@ -221,7 +221,7 @@ def tree_onnx_graph_preprocessing(
     onnx_model: onnx.ModelProto,
     framework: str,
     expected_number_of_outputs: int,
-    use_fhe_sum: bool = False,
+    fhe_ensembling: bool = False,
 ):
     """Apply pre-processing onto the ONNX graph.
 
@@ -230,7 +230,7 @@ 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): Determines whether the sum of the trees' outputs is computed in FHE.
+        fhe_ensembling (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
@@ -258,7 +258,7 @@ def tree_onnx_graph_preprocessing(
 
     # Cut the graph after the ReduceSum node to remove
     # argmax, sigmoid, softmax from the graph.
-    if use_fhe_sum:
+    if fhe_ensembling:
         clean_graph_after_node_op_type(onnx_model, "ReduceSum")
     else:
         clean_graph_at_node_op_type(onnx_model, "ReduceSum")
@@ -274,7 +274,7 @@ def tree_onnx_graph_preprocessing(
         # sklearn models apply the reduce sum before the transpose.
         # To have equivalent output between xgboost in sklearn,
         # apply the transpose before returning the output.
-        add_transpose_after_last_node(onnx_model, use_fhe_sum)
+        add_transpose_after_last_node(onnx_model, fhe_ensembling)
 
     # Cast nodes are not necessary so remove them.
     remove_node_types(onnx_model, op_types_to_remove=["Cast"])

tests/sklearn/test_sklearn_models.py

@@ -148,8 +148,8 @@ def get_n_bits_non_correctness(model_class):
     # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3979
     if get_model_name(model_class) == "KNeighborsClassifier":
         n_bits = 2
-
-    n_bits = min(N_BITS_REGULAR_BUILDS)
+    else:
+        n_bits = min(N_BITS_REGULAR_BUILDS)
 
     return n_bits