drop deprecated reorder from AUC (#5004)

* drop deprecated reorder from AUC

* chlog

* fix

* fix

* simple

* fix

* fix

* fix

Co-authored-by: Roger Shieh <sh.rog@protonmail.ch>

CHANGELOG.md

@@ -126,6 +126,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
+- Removed `reorder` parameter of the `auc` metric ([#5004](https://github.com/PyTorchLightning/pytorch-lightning/pull/5004))
+
 
 
 ### Fixed

pytorch_lightning/metrics/functional/classification.py

@@ -482,17 +482,13 @@ def __multiclass_roc(
 def auc(
         x: torch.Tensor,
         y: torch.Tensor,
-        reorder: bool = True
 ) -> torch.Tensor:
     """
     Computes Area Under the Curve (AUC) using the trapezoidal rule
 
     Args:
         x: x-coordinates
         y: y-coordinates
-        reorder: reorder coordinates, so they are increasing. The unstable algorithm of torch.argsort is
-            used internally to sort `x` which may in some cases cause inaccuracies in the result.
-            WARNING: Deprecated and will be removed in v1.1.
 
     Return:
         Tensor containing AUC score (float)
@@ -504,51 +500,38 @@ def auc(
         >>> auc(x, y)
         tensor(4.)
     """
-    direction = 1.
-
-    if reorder:
-        rank_zero_warn("The `reorder` parameter to `auc` has been deprecated and will be removed in v1.1"
-                       " Note that when `reorder` is True, the unstable algorithm of torch.argsort is"
-                       " used internally to sort 'x' which may in some cases cause inaccuracies"
-                       " in the result.",
-                       DeprecationWarning)
-        # can't use lexsort here since it is not implemented for torch
-        order = torch.argsort(x)
-        x, y = x[order], y[order]
+    dx = x[1:] - x[:-1]
+    if (dx < 0).any():
+        if (dx <= 0).all():
+            direction = -1.
+        else:
+            raise ValueError(f"The 'x' array is neither increasing or decreasing: {x}. Reorder is not supported.")
     else:
-        dx = x[1:] - x[:-1]
-        if (dx < 0).any():
-            if (dx, 0).all():
-                direction = -1.
-            else:
-                # TODO: Update message on removing reorder
-                raise ValueError("Reorder is not turned on, and the 'x' array is"
-                                 f" neither increasing or decreasing: {x}")
-
+        direction = 1.
     return direction * torch.trapz(y, x)
 
 
-def auc_decorator(reorder: bool = True) -> Callable:
+def auc_decorator() -> Callable:
     def wrapper(func_to_decorate: Callable) -> Callable:
         @wraps(func_to_decorate)
         def new_func(*args, **kwargs) -> torch.Tensor:
             x, y = func_to_decorate(*args, **kwargs)[:2]
 
-            return auc(x, y, reorder=reorder)
+            return auc(x, y)
 
         return new_func
 
     return wrapper
 
 
-def multiclass_auc_decorator(reorder: bool = True) -> Callable:
+def multiclass_auc_decorator() -> Callable:
     def wrapper(func_to_decorate: Callable) -> Callable:
         @wraps(func_to_decorate)
         def new_func(*args, **kwargs) -> torch.Tensor:
             results = []
             for class_result in func_to_decorate(*args, **kwargs):
                 x, y = class_result[:2]
-                results.append(auc(x, y, reorder=reorder))
+                results.append(auc(x, y))
 
             return torch.stack(results)
 
@@ -587,7 +570,7 @@ def auroc(
                          ' target tensor contains value different from 0 and 1.'
                          ' Use `multiclass_auroc` for multi class classification.')
 
-    @auc_decorator(reorder=True)
+    @auc_decorator()
     def _auroc(pred, target, sample_weight, pos_label):
         return __roc(pred, target, sample_weight, pos_label)
 
@@ -640,7 +623,7 @@ def multiclass_auroc(
             f"Number of classes deduced from 'pred' ({pred.size(1)}) does not equal"
             f" the number of classes passed in 'num_classes' ({num_classes}).")
 
-    @multiclass_auc_decorator(reorder=False)
+    @multiclass_auc_decorator()
     def _multiclass_auroc(pred, target, sample_weight, num_classes):
         return __multiclass_roc(pred, target, sample_weight, num_classes)
 

tests/test_deprecated.py

@@ -135,8 +135,3 @@ def test_dataloader(self):
 
     def test_end(self, outputs):
         return {'test_loss': torch.tensor(0.7)}
-
-
-def test_reorder_remove_in_v1_1():
-    with pytest.deprecated_call(match='The `reorder` parameter to `auc` has been deprecated'):
-        _ = auc(torch.tensor([0, 1, 2, 3]), torch.tensor([0, 1, 2, 2]), reorder=True)