chore: update comments

src/concrete/ml/quantization/post_training.py

@@ -50,7 +50,7 @@ def _inspect_tree_n_bits(n_bits):
     """
 
     detailed_message = (
-        "Invalid 'n_bits', either pass a non-null positive integer or a dictionary containing "
+        "Invalid 'n_bits', either pass a strictly positive integer or a dictionary containing "
         "integer values for the following keys:\n"
         "- 'op_inputs' (mandatory): number of bits to quantize the input values\n"
         "- 'op_leaves' (optional): number of bits to quantize the leaves, must be less than or "
@@ -63,7 +63,7 @@ def _inspect_tree_n_bits(n_bits):
 
     if isinstance(n_bits, int):
         if n_bits <= 0:
-            error_message = "n_bits must be a non-null, positive integer"
+            error_message = "n_bits must be a strictly positive integer"
     elif isinstance(n_bits, dict):
         if "op_inputs" not in n_bits.keys():
             error_message = "Invalid keys in `n_bits` dictionary. The key 'op_inputs' is mandatory"
@@ -73,7 +73,7 @@ def _inspect_tree_n_bits(n_bits):
                 "(optional) are allowed"
             )
         elif not all(isinstance(value, int) and value > 0 for value in n_bits.values()):
-            error_message = "All values in 'n_bits' dictionary must be non-null, positive integers"
+            error_message = "All values in 'n_bits' dictionary must be strictly positive integers"
 
         elif n_bits.get("op_leaves", 0) > n_bits.get("op_inputs", 0):
             error_message = "'op_leaves' must be less than or equal to 'op_inputs'"

src/concrete/ml/sklearn/base.py

@@ -1335,7 +1335,6 @@ def fhe_ensembling(self, value: bool) -> None:
         assert isinstance(value, bool), "Value must be a boolean type"
 
         if value is True:
-            print("LAA")
             warnings.simplefilter("always")
             warnings.warn(
                 "Enabling `fhe_ensembling` computes the sum of the ouputs of tree ensembles in "

tests/sklearn/test_sklearn_models.py

@@ -1955,8 +1955,8 @@ def test_fhe_sum_for_tree_based_models(
 @pytest.mark.parametrize(
     "n_bits, error_message",
     [
-        (0, "n_bits must be a non-null, positive integer"),
-        (-1, "n_bits must be a non-null, positive integer"),
+        (0, "n_bits must be a strictly positive integer"),
+        (-1, "n_bits must be a strictly positive integer"),
         ({"op_leaves": 2}, "The key 'op_inputs' is mandatory"),
         (
             {"op_inputs": 4, "op_leaves": 2, "op_weights": 2},
@@ -1965,15 +1965,15 @@ def test_fhe_sum_for_tree_based_models(
         ),
         (
             {"op_inputs": -2, "op_leaves": -5},
-            "All values in 'n_bits' dictionary must be non-null, positive integers",
+            "All values in 'n_bits' dictionary must be strictly positive integers",
         ),
         ({"op_inputs": 2, "op_leaves": 5}, "'op_leaves' must be less than or equal to 'op_inputs'"),
         (0.5, "n_bits must be either an integer or a dictionary"),
     ],
 )
 @pytest.mark.parametrize("model_class", _get_sklearn_tree_models())
 def test_invalid_n_bits_setting(model_class, n_bits, error_message):
-    """Check if the model instantiation raises an exception with invalid 'n_bits' settings."""
+    """Check if the model instantiation raises an exception with invalid `n_bits` settings."""
 
     with pytest.raises(ValueError, match=f"{error_message}. Got '{type(n_bits)}' and '{n_bits}'.*"):
         instantiate_model_generic(model_class, n_bits=n_bits)
@@ -1991,7 +1991,7 @@ def test_valid_n_bits_setting(
     is_weekly_option,
     verbose=True,
 ):
-    """Check valid `n_bits' settings."""
+    """Check valid `n_bits` settings."""
 
     if verbose:
         print("Run test_valid_n_bits_setting")