Prune metrics: AUC & ROC 5/n

CHANGELOG.md

@@ -77,6 +77,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
     [#6515](https://github.com/PyTorchLightning/pytorch-lightning/pull/6515),
 
+    [#6572](https://github.com/PyTorchLightning/pytorch-lightning/pull/6572),
+
 )
 
 

pytorch_lightning/metrics/classification/auc.py

@@ -13,36 +13,14 @@
 # limitations under the License.
 from typing import Any, Callable, Optional
 
-import torch
-from torchmetrics import Metric
+from torchmetrics import AUC as _AUC
 
-from pytorch_lightning.metrics.functional.auc import _auc_compute, _auc_update
-from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities.deprecation import deprecated
 
 
-class AUC(Metric):
-    r"""
-    Computes Area Under the Curve (AUC) using the trapezoidal rule
-
-    Forward accepts two input tensors that should be 1D and have the same number
-    of elements
-
-    Args:
-        reorder: AUC expects its first input to be sorted. If this is not the case,
-            setting this argument to ``True`` will use a stable sorting algorithm to
-            sort the input in decending order
-        compute_on_step:
-            Forward only calls ``update()`` and return None if this is set to False.
-        dist_sync_on_step:
-            Synchronize metric state across processes at each ``forward()``
-            before returning the value at the step.
-        process_group:
-            Specify the process group on which synchronization is called. default: None (which selects the entire world)
-        dist_sync_fn:
-            Callback that performs the allgather operation on the metric state. When ``None``, DDP
-            will be used to perform the allgather
-    """
+class AUC(_AUC):
 
+    @deprecated(target=_AUC, ver_deprecate="1.3.0", ver_remove="1.5.0")
     def __init__(
         self,
         reorder: bool = False,
@@ -51,40 +29,9 @@ def __init__(
         process_group: Optional[Any] = None,
         dist_sync_fn: Callable = None,
     ):
-        super().__init__(
-            compute_on_step=compute_on_step,
-            dist_sync_on_step=dist_sync_on_step,
-            process_group=process_group,
-            dist_sync_fn=dist_sync_fn,
-        )
-
-        self.reorder = reorder
-
-        self.add_state("x", default=[], dist_reduce_fx=None)
-        self.add_state("y", default=[], dist_reduce_fx=None)
-
-        rank_zero_warn(
-            'Metric `AUC` will save all targets and predictions in buffer.'
-            ' For large datasets this may lead to large memory footprint.'
-        )
-
-    def update(self, x: torch.Tensor, y: torch.Tensor):
-        """
-        Update state with predictions and targets.
-
-        Args:
-            x: Predictions from model (probabilities, or labels)
-            y: Ground truth labels
         """
-        x, y = _auc_update(x, y)
+        This implementation refers to :class:`~torchmetrics.AUC`.
 
-        self.x.append(x)
-        self.y.append(y)
-
-    def compute(self) -> torch.Tensor:
-        """
-        Computes AUC based on inputs passed in to ``update`` previously.
+        .. deprecated::
+            Use :class:`~torchmetrics.AUC`. Will be removed in v1.5.0.
         """
-        x = torch.cat(self.x, dim=0)
-        y = torch.cat(self.y, dim=0)
-        return _auc_compute(x, y, reorder=self.reorder)

pytorch_lightning/metrics/classification/auroc.py

@@ -11,95 +11,16 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from distutils.version import LooseVersion
 from typing import Any, Callable, Optional
 
-import torch
-from torchmetrics import Metric
+from torchmetrics import AUROC as _AUROC
 
-from pytorch_lightning.metrics.functional.auroc import _auroc_compute, _auroc_update
-from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities.deprecation import deprecated
 
 
-class AUROC(Metric):
-    r"""Compute `Area Under the Receiver Operating Characteristic Curve (ROC AUC)
-    <https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Further_interpretations>`_.
-    Works for both binary, multilabel and multiclass problems. In the case of
-    multiclass, the values will be calculated based on a one-vs-the-rest approach.
-
-    Forward accepts
-
-    - ``preds`` (float tensor): ``(N, ...)`` (binary) or ``(N, C, ...)`` (multiclass) tensor
-      with probabilities, where C is the number of classes.
-
-    - ``target`` (long tensor): ``(N, ...)`` or ``(N, C, ...)`` with integer labels
-
-    For non-binary input, if the ``preds`` and ``target`` tensor have the same
-    size the input will be interpretated as multilabel and if ``preds`` have one
-    dimension more than the ``target`` tensor the input will be interpretated as
-    multiclass.
-
-    Args:
-       num_classes: integer with number of classes. Not nessesary to provide
-           for binary problems.
-       pos_label: integer determining the positive class. Default is ``None``
-           which for binary problem is translate to 1. For multiclass problems
-           this argument should not be set as we iteratively change it in the
-           range [0,num_classes-1]
-       average:
-           - ``'macro'`` computes metric for each class and uniformly averages them
-           - ``'weighted'`` computes metric for each class and does a weighted-average,
-             where each class is weighted by their support (accounts for class imbalance)
-           - ``None`` computes and returns the metric per class
-       max_fpr:
-           If not ``None``, calculates standardized partial AUC over the
-           range [0, max_fpr]. Should be a float between 0 and 1.
-       compute_on_step:
-           Forward only calls ``update()`` and return None if this is set to False. default: True
-       dist_sync_on_step:
-           Synchronize metric state across processes at each ``forward()``
-           before returning the value at the step.
-       process_group:
-           Specify the process group on which synchronization is called. default: None (which selects the entire world)
-       dist_sync_fn:
-           Callback that performs the allgather operation on the metric state. When ``None``, DDP
-           will be used to perform the allgather
-
-    Raises:
-        ValueError:
-            If ``average`` is none of ``None``, ``"macro"`` or ``"weighted"``.
-        ValueError:
-            If ``max_fpr`` is not a ``float`` in the range ``(0, 1]``.
-        RuntimeError:
-            If ``PyTorch version`` is ``below 1.6`` since max_fpr requires ``torch.bucketize``
-            which is not available below 1.6.
-        ValueError:
-            If the mode of data (binary, multi-label, multi-class) changes between batches.
-
-    Example (binary case):
-
-        >>> from pytorch_lightning.metrics import AUROC
-        >>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
-        >>> target = torch.tensor([0, 0, 1, 1, 1])
-        >>> auroc = AUROC(pos_label=1)
-        >>> auroc(preds, target)
-        tensor(0.5000)
-
-    Example (multiclass case):
-
-        >>> from pytorch_lightning.metrics import AUROC
-        >>> preds = torch.tensor([[0.90, 0.05, 0.05],
-        ...                       [0.05, 0.90, 0.05],
-        ...                       [0.05, 0.05, 0.90],
-        ...                       [0.85, 0.05, 0.10],
-        ...                       [0.10, 0.10, 0.80]])
-        >>> target = torch.tensor([0, 1, 1, 2, 2])
-        >>> auroc = AUROC(num_classes=3)
-        >>> auroc(preds, target)
-        tensor(0.7778)
-
-    """
+class AUROC(_AUROC):
 
+    @deprecated(target=_AUROC, ver_deprecate="1.3.0", ver_remove="1.5.0")
     def __init__(
         self,
         num_classes: Optional[int] = None,
@@ -111,74 +32,9 @@ def __init__(
         process_group: Optional[Any] = None,
         dist_sync_fn: Callable = None,
     ):
-        super().__init__(
-            compute_on_step=compute_on_step,
-            dist_sync_on_step=dist_sync_on_step,
-            process_group=process_group,
-            dist_sync_fn=dist_sync_fn,
-        )
-
-        self.num_classes = num_classes
-        self.pos_label = pos_label
-        self.average = average
-        self.max_fpr = max_fpr
-
-        allowed_average = (None, 'macro', 'weighted')
-        if self.average not in allowed_average:
-            raise ValueError(
-                f'Argument `average` expected to be one of the following: {allowed_average} but got {average}'
-            )
-
-        if self.max_fpr is not None:
-            if (not isinstance(max_fpr, float) and 0 < max_fpr <= 1):
-                raise ValueError(f"`max_fpr` should be a float in range (0, 1], got: {max_fpr}")
-
-            if LooseVersion(torch.__version__) < LooseVersion('1.6.0'):
-                raise RuntimeError(
-                    '`max_fpr` argument requires `torch.bucketize` which is not available below PyTorch version 1.6'
-                )
-
-        self.mode = None
-        self.add_state("preds", default=[], dist_reduce_fx=None)
-        self.add_state("target", default=[], dist_reduce_fx=None)
-
-        rank_zero_warn(
-            'Metric `AUROC` will save all targets and predictions in buffer.'
-            ' For large datasets this may lead to large memory footprint.'
-        )
-
-    def update(self, preds: torch.Tensor, target: torch.Tensor):
         """
-        Update state with predictions and targets.
+        This implementation refers to :class:`~torchmetrics.AUROC`.
 
-        Args:
-            preds: Predictions from model (probabilities, or labels)
-            target: Ground truth labels
-        """
-        preds, target, mode = _auroc_update(preds, target)
-
-        self.preds.append(preds)
-        self.target.append(target)
-
-        if self.mode is not None and self.mode != mode:
-            raise ValueError(
-                'The mode of data (binary, multi-label, multi-class) should be constant, but changed'
-                f' between batches from {self.mode} to {mode}'
-            )
-        self.mode = mode
-
-    def compute(self) -> torch.Tensor:
-        """
-        Computes AUROC based on inputs passed in to ``update`` previously.
+        .. deprecated::
+            Use :class:`~torchmetrics.AUROC`. Will be removed in v1.5.0.
         """
-        preds = torch.cat(self.preds, dim=0)
-        target = torch.cat(self.target, dim=0)
-        return _auroc_compute(
-            preds,
-            target,
-            self.mode,
-            self.num_classes,
-            self.pos_label,
-            self.average,
-            self.max_fpr,
-        )