Merge branch 'main' into dependabot/github_actions/astral-sh/setup-uv-7.3.0

Author: emersodb

.github/workflows/static_code_checks.yaml

@@ -55,6 +55,9 @@ jobs:
           # keras>=3.12.0 which needs tensorflow>=2.16, but we're pinning to tensorflow 2.15 due to tensorflow-io
           # compatibility constraints.
           # CVE-2026-0994 is a protobuff vulnerability without a fix yet.
+          # CVE-2026-26007 is a cryptography vulnerability that requires cryptography>=46.0.5, but flwr (flower)
+          # requires cryptography<45.0.0, blocking the upgrade.
+          # CVE-2026-25990 is a pillow vulnerability that requires pillow>=12.1.1, pending dependency resolution.
           ignore-vulns: |
             GHSA-3749-ghw9-m3mg
             GHSA-887c-mr87-cxwp
@@ -65,6 +68,8 @@ jobs:
             CVE-2025-12058
             CVE-2025-12060
             CVE-2026-0994
+            CVE-2026-26007
+            CVE-2026-25990
 
       # Deleting some temporary files and useless folders to free up space
       # Deleting /usr/share/dotnet should clear ~4GB of space.

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/uv-pre-commit
-    rev: 0.9.28
+    rev: 0.10.0
     hooks:
       - id: uv-lock
 
@@ -26,7 +26,7 @@ repos:
       - id: check-toml
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 'v0.14.14'
+    rev: 'v0.15.0'
     hooks:
     - id: ruff-check
       args: [--fix, --exit-non-zero-on-fix]

examples/ae_examples/cvae_examples/mlp_cvae_example/client.py

@@ -49,7 +49,7 @@ def get_data_loaders(self, config: Config) -> tuple[DataLoader, DataLoader]:
         sampler = DirichletLabelBasedSampler(list(range(10)), sample_percentage=0.75, beta=100)
         # ToTensor transform is used to make sure pixels stay in the range [0.0, 1.0].
         # Flattening the image data to match the input shape of the model.
-        flatten_transform = transforms.Lambda(lambda x: torch.flatten(x))
+        flatten_transform = transforms.Lambda(torch.flatten)
         transform = transforms.Compose([ToNumpy(), transforms.ToTensor(), flatten_transform])
         train_loader, val_loader, _ = load_mnist_data(
             data_dir=self.data_path,

fl4health/feature_alignment/string_columns_transformer.py

@@ -29,7 +29,7 @@ def fit(self, x: pd.DataFrame, y: pd.DataFrame | None = None) -> TextMulticolumn
         Returns:
             (TextMulticolumnTransformer): The fit transformer
         """
-        joined_x = x.apply(lambda x: " ".join(x), axis=1)
+        joined_x = x.apply(" ".join, axis=1)
         self.transformer.fit(joined_x)
         return self
 
@@ -43,7 +43,7 @@ def transform(self, x: pd.DataFrame) -> pd.DataFrame:
         Returns:
             (pd.DataFrame): Transformed dataframe.
         """
-        joined_x = x.apply(lambda x: " ".join(x), axis=1)
+        joined_x = x.apply(" ".join, axis=1)
         return self.transformer.transform(joined_x)
 
 

fl4health/utils/functions.py

@@ -105,4 +105,4 @@ def decode_and_pseudo_sort_results(
         (client_proxy, parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
         for client_proxy, fit_res in results
     ]
-    return sorted(ndarrays_results, key=lambda x: pseudo_sort_scoring_function(x))
+    return sorted(ndarrays_results, key=pseudo_sort_scoring_function)

tests/metrics/compound_metrics/transforms_metric_test.py

@@ -90,7 +90,7 @@ def test_multiple_transforms_metric_computation() -> None:
     accuracy_metric = AccuracyForTest("accuracy")
     transform_accuracy_metric = TransformsMetric(
         accuracy_metric,
-        [lambda x: x + 1.0, lambda x: binarize(x)],
+        [lambda x: x + 1.0, binarize],
         [lambda x: x + 1.0, lambda x: x - 1.0],
     )