add back compatibility for deprecated metrics 2/n

pytorch_lightning/metrics/classification/__init__.py

@@ -14,7 +14,7 @@
 from pytorch_lightning.metrics.classification.accuracy import Accuracy
 from pytorch_lightning.metrics.classification.average_precision import AveragePrecision
 from pytorch_lightning.metrics.classification.confusion_matrix import ConfusionMatrix
-from pytorch_lightning.metrics.classification.f_beta import FBeta, F1
+from pytorch_lightning.metrics.classification.f_beta import FBeta, Fbeta, F1
 from pytorch_lightning.metrics.classification.precision_recall import Precision, Recall
 from pytorch_lightning.metrics.classification.precision_recall_curve import PrecisionRecallCurve
 from pytorch_lightning.metrics.classification.roc import ROC

pytorch_lightning/metrics/classification/f_beta.py

@@ -20,6 +20,7 @@
     _fbeta_compute
 )
 from pytorch_lightning.metrics.metric import Metric
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 class FBeta(Metric):
@@ -131,6 +132,34 @@ def compute(self) -> torch.Tensor:
                               self.actual_positives, self.beta, self.average)
 
 
+# todo: remove in v1.2
+class Fbeta(FBeta):
+    r"""
+    Computes `F-score <https://en.wikipedia.org/wiki/F-score>`_
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.classification.f_beta.FBeta`
+    """
+    def __init__(
+        self,
+        num_classes: int,
+        beta: float = 1.0,
+        threshold: float = 0.5,
+        average: str = "micro",
+        multilabel: bool = False,
+        compute_on_step: bool = True,
+        dist_sync_on_step: bool = False,
+        process_group: Optional[Any] = None,
+    ):
+        rank_zero_warn(
+            "This `Fbeta` was deprecated in v1.0.x in favor of"
+            " `from pytorch_lightning.metrics.classification.f_beta import FBeta`."
+            " It will be removed in v1.2.0", DeprecationWarning
+        )
+        super().__init__(
+            num_classes, beta, threshold, average, multilabel, compute_on_step, dist_sync_on_step, process_group
+        )
+
+
 class F1(FBeta):
     """
     Computes F1 metric. F1 metrics correspond to a harmonic mean of the

pytorch_lightning/metrics/functional/__init__.py

@@ -17,6 +17,8 @@
     auc,
     auroc,
     dice_score,
+    f1_score,
+    fbeta_score,
     get_num_classes,
     iou,
     multiclass_auroc,

pytorch_lightning/metrics/functional/classification.py

@@ -17,6 +17,7 @@
 import torch
 
 from pytorch_lightning.metrics.functional.average_precision import average_precision as __ap
+from pytorch_lightning.metrics.functional.f_beta import fbeta as __fb, f1 as __f1
 from pytorch_lightning.metrics.functional.precision_recall_curve import _binary_clf_curve, precision_recall_curve as __prc
 from pytorch_lightning.metrics.functional.roc import roc as __roc
 from pytorch_lightning.metrics.utils import (
@@ -871,3 +872,48 @@ def average_precision(
         " It will be removed in v1.3.0", DeprecationWarning
     )
     return __ap(preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label)
+
+
+# todo: remove in 1.2
+def fbeta_score(
+        pred: torch.Tensor,
+        target: torch.Tensor,
+        beta: float,
+        num_classes: Optional[int] = None,
+        class_reduction: str = 'micro',
+) -> torch.Tensor:
+    """
+    Computes the F-beta score which is a weighted harmonic mean of precision and recall.
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.f_beta.fbeta`
+    """
+    rank_zero_warn(
+        "This `average_precision` was deprecated in v1.0.x in favor of"
+        " `from pytorch_lightning.metrics.functional.f_beta import fbeta`."
+        " It will be removed in v1.2.0", DeprecationWarning
+    )
+    if num_classes is None:
+        num_classes = get_num_classes(pred, target)
+    return __fb(preds=pred, target=target, beta=beta, num_classes=num_classes, average=class_reduction)
+
+
+# todo: remove in 1.2
+def f1_score(
+        pred: torch.Tensor,
+        target: torch.Tensor,
+        num_classes: Optional[int] = None,
+        class_reduction: str = 'micro',
+) -> torch.Tensor:
+    """
+    Computes the F1-score (a.k.a F-measure), which is the harmonic mean of the precision and recall.
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.f_beta.f1`
+    """
+    rank_zero_warn(
+        "This `average_precision` was deprecated in v1.0.x in favor of"
+        " `from pytorch_lightning.metrics.functional.f_beta import f1`."
+        " It will be removed in v1.2.0", DeprecationWarning
+    )
+    if num_classes is None:
+        num_classes = get_num_classes(pred, target)
+    return __f1(preds=pred, target=target, num_classes=num_classes, average=class_reduction)

tests/test_deprecated.py

@@ -119,6 +119,20 @@ def test_tbd_remove_in_v1_2_0():
         checkpoint_cb = ModelCheckpoint(filepath='.', dirpath='.')
 
 
+def test_tbd_remove_in_v1_2_0_metrics():
+    from pytorch_lightning.metrics.classification import Fbeta
+    from pytorch_lightning.metrics.functional.classification import f1_score, fbeta_score
+
+    with pytest.deprecated_call(match='will be removed in v1.2'):
+        Fbeta(2)
+
+    with pytest.deprecated_call(match='will be removed in v1.2'):
+        fbeta_score(torch.tensor([0, 1, 2, 3]), torch.tensor([0, 1, 2, 1]), 0.2)
+
+    with pytest.deprecated_call(match='will be removed in v1.2'):
+        f1_score(torch.tensor([0, 1, 0, 1]), torch.tensor([0, 1, 0, 0]))
+
+
 # TODO: remove bool from Trainer.profiler param in v1.3.0, update profiler_connector.py
 @pytest.mark.parametrize(['profiler', 'expected'], [
     (True, SimpleProfiler),