mypy issues

Signed-off-by: thibaultdvx <[email protected]>

Author: thibaultdvx

monai/metrics/r2_score.py

@@ -68,7 +68,7 @@ def __init__(self, multi_output: MultiOutput | str = MultiOutput.UNIFORM, p: int
         self.multi_output = multi_output
         self.p = p
 
-    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:  # type: ignore[override]
         _check_dim(y_pred, y)
         return y_pred, y
 
@@ -100,7 +100,7 @@ def _check_dim(y_pred: torch.Tensor, y: torch.Tensor) -> None:
         )
 
 
-def _check_r2_params(multi_output, p) -> tuple[MultiOutput, int]:
+def _check_r2_params(multi_output: MultiOutput | str, p: int) -> tuple[MultiOutput | str, int]:
     multi_output = look_up_option(multi_output, MultiOutput)
     if not isinstance(p, int) or p < 0:
         raise ValueError(f"`p` must be an integer larger or equal to 0, got {p}.")
@@ -115,7 +115,7 @@ def _calculate(y_pred: np.ndarray, y: np.ndarray, p: int) -> float:
     r2 = 1 - (rss / tss)
     r2_adjusted = 1 - (1 - r2) * (num_obs - 1) / (num_obs - p - 1)
 
-    return r2_adjusted
+    return r2_adjusted  # type: ignore[no-any-return]
 
 
 def compute_r2_score(
@@ -154,28 +154,29 @@ def compute_r2_score(
     _check_dim(y_pred, y)
     dim = y.ndimension()
     n = y.shape[0]
-    y = y.cpu().numpy()
-    y_pred = y_pred.cpu().numpy()
+    y = y.cpu().numpy()  # type: ignore[assignment]
+    y_pred = y_pred.cpu().numpy()  # type: ignore[assignment]
 
     if n < 2:
         raise ValueError("There is no enough data for computing. Needs at least two samples to calculate r2 score.")
     if p >= n - 1:
         raise ValueError("`p` must be smaller than n_samples - 1, " f"got p={p}, n_samples={n}.")
 
     if dim == 2 and y_pred.shape[1] == 1:
-        y_pred = np.squeeze(y_pred, axis=-1)
-        y = np.squeeze(y, axis=-1)
+        y_pred = np.squeeze(y_pred, axis=-1)  # type: ignore[assignment]
+        y = np.squeeze(y, axis=-1)  # type: ignore[assignment]
         dim = 1
 
     if dim == 1:
-        return _calculate(y_pred, y, p)
+        return _calculate(y_pred, y, p)  # type: ignore[arg-type]
 
-    y, y_pred = np.transpose(y, axes=(1, 0)), np.transpose(y_pred, axes=(1, 0))
+    y, y_pred = np.transpose(y, axes=(1, 0)), np.transpose(y_pred, axes=(1, 0))  # type: ignore[assignment]
     r2_values = [_calculate(y_pred_, y_, p) for y_pred_, y_ in zip(y_pred, y)]
     if multi_output == MultiOutput.RAW:
         return r2_values
     if multi_output == MultiOutput.UNIFORM:
         return np.mean(r2_values)
-    if multi_output == multi_output.VARIANCE:
+    if multi_output == MultiOutput.VARIANCE:
         weights = np.var(y, axis=1)
-        return np.average(r2_values, weights=weights)
+        return np.average(r2_values, weights=weights)  # type: ignore[no-any-return]
+    raise ValueError(f'Unsupported multi_output: {multi_output}, available options are ["raw_values", "uniform_average", "variance_weighted"].')