Add COM (commas)

Author: PierreQuinton

pyproject.toml

@@ -143,6 +143,7 @@ select = [
     "RET",  # flake8-return
     "PYI",  # flake8-pyi
     "PIE",  # flake8-pie
+    "COM",  # flake8-commas
     "PERF", # Perflint
     "FURB", # refurb
     "RUF",  # Ruff-specific rules

src/torchjd/_linalg/_gramian.py

@@ -69,7 +69,9 @@ def regularize(gramian: PSDMatrix, eps: float) -> PSDMatrix:
     """
 
     regularization_matrix = eps * torch.eye(
-        gramian.shape[0], dtype=gramian.dtype, device=gramian.device
+        gramian.shape[0],
+        dtype=gramian.dtype,
+        device=gramian.device,
     )
     output = gramian + regularization_matrix
     return cast(PSDMatrix, output)

src/torchjd/aggregation/_aggregator_bases.py

@@ -21,7 +21,7 @@ def _check_is_matrix(matrix: Tensor) -> None:
         if not is_matrix(matrix):
             raise ValueError(
                 "Parameter `matrix` should be a tensor of dimension 2. Found `matrix.shape = "
-                f"{matrix.shape}`."
+                f"{matrix.shape}`.",
             )
 
     @abstractmethod

src/torchjd/aggregation/_aligned_mtl.py

@@ -105,7 +105,8 @@ def forward(self, gramian: PSDMatrix, /) -> Tensor:
 
     @staticmethod
     def _compute_balance_transformation(
-        M: Tensor, scale_mode: SUPPORTED_SCALE_MODE = "min"
+        M: Tensor,
+        scale_mode: SUPPORTED_SCALE_MODE = "min",
     ) -> Tensor:
         lambda_, V = torch.linalg.eigh(M, UPLO="U")  # More modern equivalent to torch.symeig
         tol = torch.max(lambda_) * len(M) * torch.finfo().eps
@@ -127,7 +128,7 @@ def _compute_balance_transformation(
             scale = lambda_.mean()
         else:
             raise ValueError(
-                f"Invalid scale_mode={scale_mode!r}. Expected 'min', 'median', or 'rmse'."
+                f"Invalid scale_mode={scale_mode!r}. Expected 'min', 'median', or 'rmse'.",
             )
 
         return scale.sqrt() * V @ sigma_inv @ V.T

src/torchjd/aggregation/_constant.py

@@ -39,7 +39,7 @@ def __init__(self, weights: Tensor):
         if weights.dim() != 1:
             raise ValueError(
                 "Parameter `weights` should be a 1-dimensional tensor. Found `weights.shape = "
-                f"{weights.shape}`."
+                f"{weights.shape}`.",
             )
 
         super().__init__()
@@ -53,5 +53,5 @@ def _check_matrix_shape(self, matrix: Tensor) -> None:
         if matrix.shape[0] != len(self.weights):
             raise ValueError(
                 f"Parameter `matrix` should have {len(self.weights)} rows (the number of specified "
-                f"weights). Found `matrix` with {matrix.shape[0]} rows."
+                f"weights). Found `matrix` with {matrix.shape[0]} rows.",
             )

src/torchjd/aggregation/_dualproj.py

@@ -40,7 +40,7 @@ def __init__(
         self._solver: SUPPORTED_SOLVER = solver
 
         super().__init__(
-            DualProjWeighting(pref_vector, norm_eps=norm_eps, reg_eps=reg_eps, solver=solver)
+            DualProjWeighting(pref_vector, norm_eps=norm_eps, reg_eps=reg_eps, solver=solver),
         )
 
         # This prevents considering the computed weights as constant w.r.t. the matrix.

src/torchjd/aggregation/_graddrop.py

@@ -30,7 +30,7 @@ def __init__(self, f: Callable = _identity, leak: Tensor | None = None):
         if leak is not None and leak.dim() != 1:
             raise ValueError(
                 "Parameter `leak` should be a 1-dimensional tensor. Found `leak.shape = "
-                f"{leak.shape}`."
+                f"{leak.shape}`.",
             )
 
         super().__init__()
@@ -64,7 +64,7 @@ def _check_matrix_has_enough_rows(self, matrix: Tensor) -> None:
         if self.leak is not None and n_rows != len(self.leak):
             raise ValueError(
                 f"Parameter `matrix` should be a matrix of exactly {len(self.leak)} rows (i.e. the "
-                f"number of leak scalars). Found `matrix` of shape `{matrix.shape}`."
+                f"number of leak scalars). Found `matrix` of shape `{matrix.shape}`.",
             )
 
     def __repr__(self) -> str:

src/torchjd/aggregation/_krum.py

@@ -49,13 +49,13 @@ def __init__(self, n_byzantine: int, n_selected: int = 1):
         if n_byzantine < 0:
             raise ValueError(
                 "Parameter `n_byzantine` should be a non-negative integer. Found `n_byzantine = "
-                f"{n_byzantine}`."
+                f"{n_byzantine}`.",
             )
 
         if n_selected < 1:
             raise ValueError(
                 "Parameter `n_selected` should be a positive integer. Found `n_selected = "
-                f"{n_selected}`."
+                f"{n_selected}`.",
             )
 
         self.n_byzantine = n_byzantine
@@ -83,11 +83,11 @@ def _check_matrix_shape(self, gramian: PSDMatrix) -> None:
         if gramian.shape[0] < min_rows:
             raise ValueError(
                 f"Parameter `gramian` should have at least {min_rows} rows (n_byzantine + 3). Found"
-                f" `gramian` with {gramian.shape[0]} rows."
+                f" `gramian` with {gramian.shape[0]} rows.",
             )
 
         if gramian.shape[0] < self.n_selected:
             raise ValueError(
                 f"Parameter `gramian` should have at least {self.n_selected} rows (n_selected). "
-                f"Found `gramian` with {gramian.shape[0]} rows."
+                f"Found `gramian` with {gramian.shape[0]} rows.",
             )

src/torchjd/aggregation/_nash_mtl.py

@@ -84,7 +84,7 @@ def __init__(
                 max_norm=max_norm,
                 update_weights_every=update_weights_every,
                 optim_niter=optim_niter,
-            )
+            ),
         )
         self._n_tasks = n_tasks
         self._max_norm = max_norm
@@ -144,7 +144,7 @@ def _stop_criteria(self, gtg: np.ndarray, alpha_t: np.ndarray) -> bool:
         return bool(
             (self.alpha_param.value is None)
             or (np.linalg.norm(gtg @ alpha_t - 1 / (alpha_t + 1e-10)) < 1e-3)
-            or (np.linalg.norm(self.alpha_param.value - self.prvs_alpha_param.value) < 1e-6)
+            or (np.linalg.norm(self.alpha_param.value - self.prvs_alpha_param.value) < 1e-6),
         )
 
     def _solve_optimization(self, gtg: np.ndarray) -> np.ndarray:

src/torchjd/aggregation/_trimmed_mean.py

@@ -20,7 +20,7 @@ def __init__(self, trim_number: int):
         if trim_number < 0:
             raise ValueError(
                 "Parameter `trim_number` should be a non-negative integer. Found `trim_number` = "
-                f"{trim_number}`."
+                f"{trim_number}`.",
             )
         self.trim_number = trim_number
 
@@ -40,7 +40,7 @@ def _check_matrix_has_enough_rows(self, matrix: Tensor) -> None:
         if n_rows < min_rows:
             raise ValueError(
                 f"Parameter `matrix` should be a matrix of at least {min_rows} rows "
-                f"(i.e. `2 * trim_number + 1`). Found `matrix` of shape `{matrix.shape}`."
+                f"(i.e. `2 * trim_number + 1`). Found `matrix` of shape `{matrix.shape}`.",
             )
 
     def __repr__(self) -> str: