chore: remove manual warning catch in tests

pyproject.toml

@@ -128,6 +128,8 @@ filterwarnings = [
     "ignore:The --rsyncdir command line argument and rsyncdirs config variable are deprecated.:DeprecationWarning",
     "ignore:Converting a tensor to a NumPy array might cause the trace to be incorrect.",
     "ignore:torch.from_numpy results are registered as constants in the trace.",
+    "ignore:ONNX Preprocess - Removing mutation from node aten*:UserWarning",
+    "ignore:Liblinear failed to converge, increase the number of iterations.*:sklearn.exceptions.ConvergenceWarning",
 ]
 
 [tool.semantic_release]

src/concrete/ml/sklearn/linear_model.py

@@ -237,12 +237,6 @@ def __init__(
         if self.fit_encrypted:
             self.classes_: Optional[numpy.ndarray] = None
 
-            warnings.warn(
-                "FHE training is an experimental feature. Please be aware that the API might "
-                "change in future versions.",
-                stacklevel=2,
-            )
-
             # Check the presence of mandatory attributes
             if self.loss != "log_loss":
                 raise ValueError(
@@ -687,13 +681,7 @@ def fit(  # type: ignore[override]
 
         # If the model should be trained using FHE training
         if self.fit_encrypted:
-            if fhe is None:
-                fhe = "disable"
-                warnings.warn(
-                    "Parameter 'fhe' isn't set while FHE training is enabled.\n"
-                    f"Defaulting to '{fhe=}'",
-                    stacklevel=2,
-                )
+            fhe = "disable" if fhe is None else fhe
 
             # Make sure the `fhe` parameter is correct
             assert FheMode.is_valid(fhe), (

tests/common/test_pbs_error_probability_settings.py

@@ -1,11 +1,9 @@
 """Tests for the sklearn linear models."""
 
-import warnings
 from inspect import signature
 
 import numpy
 import pytest
-from sklearn.exceptions import ConvergenceWarning
 from torch import nn
 
 from concrete.ml.pytest.torch_models import FCSmall
@@ -34,10 +32,8 @@ def test_config_sklearn(model_class, parameters, kwargs, load_data):
 
     model = model_class()
 
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        # Fit the model
-        model.fit(x, y)
+    # Fit the model
+    model.fit(x, y)
 
     if kwargs.get("p_error", None) is not None and kwargs.get("global_p_error", None) is not None:
         with pytest.raises(ValueError) as excinfo:

tests/common/test_serialization.py

@@ -6,7 +6,6 @@
 
 import inspect
 import io
-import warnings
 from functools import partial
 
 import numpy
@@ -18,7 +17,6 @@
 from concrete.fhe.compilation import Circuit
 from numpy.random import RandomState
 from sklearn.datasets import make_regression
-from sklearn.exceptions import ConvergenceWarning
 from skops.io.exceptions import UntrustedTypesFoundException
 from skorch.dataset import ValidSplit
 from torch import nn
@@ -123,9 +121,7 @@ def test_serialize_sklearn_model(concrete_model_class, load_data):
     # Instantiate and fit a Concrete model to recover its underlying Scikit Learn model
     concrete_model = concrete_model_class()
 
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        _, sklearn_model = concrete_model.fit_benchmark(x, y)
+    _, sklearn_model = concrete_model.fit_benchmark(x, y)
 
     # Both JSON string are not compared as scikit-learn models are serialized using Skops or pickle,
     # which does not make string comparison possible

tests/deployment/test_client_server.py

@@ -3,15 +3,13 @@
 import json
 import os
 import tempfile
-import warnings
 import zipfile
 from functools import partial
 from pathlib import Path
 from shutil import copyfile
 
 import numpy
 import pytest
-from sklearn.exceptions import ConvergenceWarning
 from torch import nn
 
 from concrete.ml.deployment.fhe_client_server import (
@@ -115,15 +113,10 @@ def test_client_server_sklearn(
     x_test = x[-1:]
 
     # Instantiate the model
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=UserWarning)
-        model = instantiate_model_generic(model_class, n_bits=n_bits)
+    model = instantiate_model_generic(model_class, n_bits=n_bits)
 
     # Fit the model
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        warnings.simplefilter("ignore", category=UserWarning)
-        model.fit(x_train, y_train)
+    model.fit(x_train, y_train)
 
     key_dir = default_configuration.insecure_key_cache_location
 
@@ -393,19 +386,14 @@ def test_save_mode_handling(n_bits, fit_encrypted, mode, error_message):
     y_train = y[:-1]
 
     # Instantiate the model
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=UserWarning)
-        parameters_range = [-1, 1] if fit_encrypted else None
-        model = instantiate_model_generic(
-            partial(SGDClassifier, fit_encrypted=fit_encrypted, parameters_range=parameters_range),
-            n_bits=n_bits,
-        )
+    parameters_range = [-1, 1] if fit_encrypted else None
+    model = instantiate_model_generic(
+        partial(SGDClassifier, fit_encrypted=fit_encrypted, parameters_range=parameters_range),
+        n_bits=n_bits,
+    )
 
     # Fit the model
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        warnings.simplefilter("ignore", category=UserWarning)
-        model.fit(x_train, y_train)
+    model.fit(x_train, y_train)
 
     # Compile
     model.compile(X=x_train)

tests/parameter_search/test_p_error_binary_search.py

@@ -1,14 +1,12 @@
 """Test binary search class."""
 
 import os
-import warnings
 from pathlib import Path
 
 import numpy
 import pytest
 import torch
 from sklearn.datasets import make_classification
-from sklearn.exceptions import ConvergenceWarning
 from sklearn.metrics import r2_score, top_k_accuracy_score
 from tensorflow import keras
 
@@ -126,9 +124,8 @@ def test_update_valid_attr_method(attr, value, model_name, quant_type, metric, l
         predict="predict",
         n_simulation=1,
     )
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        search.run(x=x_calib, ground_truth=y, strategy=all, **{attr: value})
+
+    search.run(x=x_calib, ground_truth=y, strategy=all, **{attr: value})
 
     assert getattr(search, attr) == value
 
@@ -167,10 +164,7 @@ def test_non_convergence_for_built_in_models(model_class, parameters, load_data,
         max_metric_loss=-10,
         is_qat=False,
     )
-
-    warnings.simplefilter("always")
-    with pytest.warns(UserWarning, match="ConvergenceWarning: .*"):
-        search.run(x=x_calib, ground_truth=y, strategy=all)
+    search.run(x=x_calib, ground_truth=y, strategy=all)
 
 
 @pytest.mark.parametrize("model_name, quant_type", [("CustomModel", "qat")])
@@ -205,9 +199,7 @@ def test_non_convergence_for_custom_models(model_name, quant_type):
         labels=numpy.arange(MODELS_ARGS[model_name]["dataset"]["n_classes"]),
     )
 
-    warnings.simplefilter("always")
-    with pytest.warns(UserWarning, match="ConvergenceWarning: .*"):
-        search.run(x=x_calib, ground_truth=y, strategy=all)
+    search.run(x=x_calib, ground_truth=y, strategy=all)
 
 
 @pytest.mark.parametrize(
@@ -279,9 +271,8 @@ def test_binary_search_for_custom_models(model_name, quant_type, threshold):
         k=1,
         labels=numpy.arange(MODELS_ARGS[model_name]["dataset"]["n_classes"]),
     )
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        largest_perror = search.run(x=x_calib, ground_truth=y, strategy=all)
+
+    largest_perror = search.run(x=x_calib, ground_truth=y, strategy=all)
 
     assert 1.0 > largest_perror > 0.0
     assert (
@@ -337,9 +328,7 @@ def test_binary_search_for_built_in_models(model_class, parameters, threshold, p
         # The model does not have `predict`
         return
 
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        largest_perror = search.run(x=x_calib, ground_truth=y, strategy=all)
+    largest_perror = search.run(x=x_calib, ground_truth=y, strategy=all)
 
     assert 1.0 > largest_perror > 0.0
     assert (
@@ -475,9 +464,7 @@ def test_success_save_option(model_name, quant_type, metric, directory, log_file
     # When instantiating the class, if the file exists, it is deleted, to avoid overwriting it
     assert not path.exists()
 
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        search.run(x=x_calib, ground_truth=y)
+    search.run(x=x_calib, ground_truth=y)
 
     # Check that the file has been properly created
     assert path.exists()

tests/seeding/test_seeding.py

@@ -2,14 +2,11 @@
 
 import inspect
 import random
-import warnings
 
 import numpy
 import pytest
-from sklearn.exceptions import ConvergenceWarning
 from sklearn.tree import plot_tree
 
-from concrete.ml.common.utils import get_model_name
 from concrete.ml.pytest.utils import MODELS_AND_DATASETS
 
 
@@ -93,16 +90,10 @@ def test_seed_sklearn(model_class, parameters, load_data, default_configuration)
         model_params["random_state"] = numpy.random.randint(0, 2**15)
 
     # First case: user gives his own random_state
-    # Warning skip due to SGDClassifier with fit_encrypted = True
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=UserWarning)
-        model = model_class(**model_params)
-
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        warnings.simplefilter("ignore", category=UserWarning)
-        # Fit the model
-        model, sklearn_model = model.fit_benchmark(x, y)
+    model = model_class(**model_params)
+
+    # Fit the model
+    model, sklearn_model = model.fit_benchmark(x, y)
 
     lpvoid_ptr_plot_tree = getattr(model, "plot_tree", None)
     if callable(lpvoid_ptr_plot_tree):

tests/sklearn/test_common.py

@@ -1,11 +1,9 @@
 """Tests common to all sklearn models."""
 
 import inspect
-import warnings
 
 import numpy
 import pytest
-from sklearn.exceptions import ConvergenceWarning
 
 from concrete.ml.common.utils import get_model_class
 from concrete.ml.pytest.utils import MODELS_AND_DATASETS
@@ -44,10 +42,8 @@ def test_seed_sklearn(model_class, parameters, load_data):
     # First case: user gives his own random_state
     model = model_class(random_state=random_state_constructor)
 
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        # Fit the model
-        model, sklearn_model = model.fit_benchmark(x, y, random_state=random_state_user)
+    # Fit the model
+    model, sklearn_model = model.fit_benchmark(x, y, random_state=random_state_user)
 
     assert (
         model.random_state == random_state_user and sklearn_model.random_state == random_state_user
@@ -56,10 +52,8 @@ def test_seed_sklearn(model_class, parameters, load_data):
     # Second case: user does not give random_state but seeds the constructor
     model = model_class(random_state=random_state_constructor)
 
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        # Fit the model
-        model, sklearn_model = model.fit_benchmark(x, y)
+    # Fit the model
+    model, sklearn_model = model.fit_benchmark(x, y)
 
     assert (model.random_state == random_state_constructor) and (
         sklearn_model.random_state == random_state_constructor
@@ -69,10 +63,8 @@ def test_seed_sklearn(model_class, parameters, load_data):
     model = model_class(random_state=None)
     assert model.random_state is None
 
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-        # Fit the model
-        model, sklearn_model = model.fit_benchmark(x, y)
+    # Fit the model
+    model, sklearn_model = model.fit_benchmark(x, y)
 
     # model.random_state and sklearn_model.random_state should now be seeded with the same value
     assert model.random_state is not None and sklearn_model.random_state is not None

tests/sklearn/test_dump_onnx.py

@@ -6,7 +6,6 @@
 import numpy
 import onnx
 import pytest
-from sklearn.exceptions import ConvergenceWarning
 
 from concrete.ml.common.utils import is_model_class_in_a_list
 from concrete.ml.pytest.utils import UNIQUE_MODELS_AND_DATASETS, get_model_name
@@ -419,11 +418,6 @@ def check_onnx_file_dump(
         # KNN can only be compiled with small quantization bit numbers for now
         # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3979
         model.n_bits = 2
-
-    with warnings.catch_warnings():
-        # Sometimes, we miss convergence, which is not a problem for our test
-        warnings.simplefilter("ignore", category=ConvergenceWarning)
-
         model.fit(x, y)
 
     with warnings.catch_warnings():