From de88e2de5183248e025bc6c04f0d41ae018a4b96 Mon Sep 17 00:00:00 2001
From: Xavier Sumba <c.uent@hotmail.com>
Date: Mon, 10 Jan 2022 16:35:28 -0500
Subject: [PATCH 1/6] r/fbeta/fbeta_score

---
 docs/source/references/functional.rst         |  4 +-
 docs/source/references/modules.rst            |  6 +--
 tests/classification/test_f_beta.py           | 20 ++++----
 torchmetrics/__init__.py                      |  2 +
 torchmetrics/classification/__init__.py       |  2 +-
 torchmetrics/classification/f_beta.py         | 48 +++++++++++++++++--
 torchmetrics/functional/__init__.py           |  3 +-
 .../functional/classification/__init__.py     |  2 +-
 .../functional/classification/f_beta.py       | 30 ++++++++++--
 9 files changed, 91 insertions(+), 26 deletions(-)

diff --git a/docs/source/references/functional.rst b/docs/source/references/functional.rst
index b7a4817f682..1ee860004a4 100644
--- a/docs/source/references/functional.rst
+++ b/docs/source/references/functional.rst
@@ -126,10 +126,10 @@ f1 [func]
     :noindex:
 
 
-fbeta [func]
+fbeta_score [func]
 ~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: torchmetrics.functional.fbeta
+.. autofunction:: torchmetrics.functional.fbeta_score
     :noindex:
 
 hamming_distance [func]
diff --git a/docs/source/references/modules.rst b/docs/source/references/modules.rst
index 5e7028a33c0..e2e06c056e6 100644
--- a/docs/source/references/modules.rst
+++ b/docs/source/references/modules.rst
@@ -286,10 +286,10 @@ F1Score
 .. autoclass:: torchmetrics.F1Score
     :noindex:
 
-FBeta
-~~~~~
+FBetaScore
+~~~~~~~~~~
 
-.. autoclass:: torchmetrics.FBeta
+.. autoclass:: torchmetrics.FBetaScore
     :noindex:
 
 HammingDistance
diff --git a/tests/classification/test_f_beta.py b/tests/classification/test_f_beta.py
index 582ce6847e9..7348b9bed1e 100644
--- a/tests/classification/test_f_beta.py
+++ b/tests/classification/test_f_beta.py
@@ -32,9 +32,9 @@
 from tests.classification.inputs import _input_multilabel_prob as _input_mlb_prob
 from tests.helpers import seed_all
 from tests.helpers.testers import NUM_BATCHES, NUM_CLASSES, THRESHOLD, MetricTester
-from torchmetrics import F1Score, FBeta, Metric
+from torchmetrics import F1Score, FBetaScore, Metric
 from torchmetrics.functional import f1_score as f1_score_pl
-from torchmetrics.functional import fbeta
+from torchmetrics.functional import fbeta_score as fbeta_score_pl
 from torchmetrics.utilities.checks import _input_format_classification
 from torchmetrics.utilities.enums import AverageMethod
 
@@ -93,7 +93,7 @@ def _sk_fbeta_f1_multidim_multiclass(
 @pytest.mark.parametrize(
     "metric_class, metric_fn",
     [
-        (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)),
+        (partial(FBetaScore, beta=2.0), partial(fbeta_score_pl, beta=2.0)),
         (F1Score, f1_score_pl),
     ],
 )
@@ -129,7 +129,7 @@ def test_wrong_params(metric_class, metric_fn, average, mdmc_average, num_classe
 @pytest.mark.parametrize(
     "metric_class, metric_fn",
     [
-        (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)),
+        (partial(FBetaScore, beta=2.0), partial(fbeta_score_pl, beta=2.0)),
         (F1Score, f1_score_pl),
     ],
 )
@@ -151,7 +151,7 @@ def test_zero_division(metric_class, metric_fn):
 @pytest.mark.parametrize(
     "metric_class, metric_fn",
     [
-        (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)),
+        (partial(FBetaScore, beta=2.0), partial(fbeta_score_pl, beta=2.0)),
         (F1Score, f1_score_pl),
     ],
 )
@@ -182,7 +182,7 @@ def test_no_support(metric_class, metric_fn):
 @pytest.mark.parametrize(
     "metric_class, metric_fn",
     [
-        (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)),
+        (partial(FBetaScore, beta=2.0), partial(fbeta_score_pl, beta=2.0)),
         (F1Score, f1_score_pl),
     ],
 )
@@ -210,7 +210,7 @@ def test_class_not_present(metric_class, metric_fn, ignore_index, expected):
 @pytest.mark.parametrize(
     "metric_class, metric_fn, sk_fn",
     [
-        (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)),
+        (partial(FBetaScore, beta=2.0), partial(fbeta_score_pl, beta=2.0), partial(fbeta_score, beta=2.0)),
         (F1Score, f1_score_pl, f1_score),
     ],
 )
@@ -397,8 +397,8 @@ def test_fbeta_f1_differentiability(
 @pytest.mark.parametrize(
     "metric_class, metric_fn",
     [
-        (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)),
-        (F1Score, fbeta),
+        (partial(FBetaScore, beta=2.0), partial(fbeta_score_pl, beta=2.0)),
+        (F1Score, fbeta_score_pl),
     ],
 )
 @pytest.mark.parametrize(
@@ -441,7 +441,7 @@ def test_top_k(
 @pytest.mark.parametrize(
     "metric_class, metric_functional, sk_fn",
     [
-        (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)),
+        (partial(FBetaScore, beta=2.0), partial(fbeta_score_pl, beta=2.0), partial(fbeta_score, beta=2.0)),
         (F1Score, f1_score_pl, f1_score),
     ],
 )
diff --git a/torchmetrics/__init__.py b/torchmetrics/__init__.py
index 56293457904..899f845e661 100644
--- a/torchmetrics/__init__.py
+++ b/torchmetrics/__init__.py
@@ -39,6 +39,7 @@
     ConfusionMatrix,
     F1Score,
     FBeta,
+    FBetaScore,
     HammingDistance,
     Hinge,
     HingeLoss,
@@ -118,6 +119,7 @@
     "F1",
     "F1Score",
     "FBeta",
+    "FBetaScore",
     "HammingDistance",
     "Hinge",
     "HingeLoss",
diff --git a/torchmetrics/classification/__init__.py b/torchmetrics/classification/__init__.py
index 327d9a7aa51..afde28c9062 100644
--- a/torchmetrics/classification/__init__.py
+++ b/torchmetrics/classification/__init__.py
@@ -21,7 +21,7 @@
 from torchmetrics.classification.calibration_error import CalibrationError  # noqa: F401
 from torchmetrics.classification.cohen_kappa import CohenKappa  # noqa: F401
 from torchmetrics.classification.confusion_matrix import ConfusionMatrix  # noqa: F401
-from torchmetrics.classification.f_beta import F1, F1Score, FBeta  # noqa: F401
+from torchmetrics.classification.f_beta import F1, F1Score, FBeta, FBetaScore  # noqa: F401
 from torchmetrics.classification.hamming_distance import HammingDistance  # noqa: F401
 from torchmetrics.classification.hinge import Hinge, HingeLoss  # noqa: F401
 from torchmetrics.classification.iou import IoU  # noqa: F401
diff --git a/torchmetrics/classification/f_beta.py b/torchmetrics/classification/f_beta.py
index e36c0b71434..f696b96d37d 100644
--- a/torchmetrics/classification/f_beta.py
+++ b/torchmetrics/classification/f_beta.py
@@ -22,7 +22,7 @@
 from torchmetrics.utilities.enums import AverageMethod
 
 
-class FBeta(StatScores):
+class FBetaScore(StatScores):
     r"""
     Computes `F-score`_, specifically:
 
@@ -124,10 +124,10 @@ class FBeta(StatScores):
             If ``average`` is none of ``"micro"``, ``"macro"``, ``"weighted"``, ``"none"``, ``None``.
 
     Example:
-        >>> from torchmetrics import FBeta
+        >>> from torchmetrics import FBetaScore
         >>> target = torch.tensor([0, 1, 2, 0, 1, 2])
         >>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
-        >>> f_beta = FBeta(num_classes=3, beta=0.5)
+        >>> f_beta = FBetaScore(num_classes=3, beta=0.5)
         >>> f_beta(preds, target)
         tensor(0.3333)
 
@@ -174,8 +174,48 @@ def compute(self) -> Tensor:
         tp, fp, tn, fn = self._get_final_stats()
         return _fbeta_compute(tp, fp, tn, fn, self.beta, self.ignore_index, self.average, self.mdmc_reduce)
 
+class FBeta(FBetaScore):
+    r"""
+    Computes `F-score`_, specifically:
+
+    .. deprecated:: v0.7
+        Use :class:`torchmetrics.FBetaScore`. Will be removed in v0.8.
+    """
+
+    def __init__(
+        self,
+        num_classes: Optional[int] = None,
+        beta: float = 1.0,
+        threshold: float = 0.5,
+        average: str = "micro",
+        mdmc_average: Optional[str] = None,
+        ignore_index: Optional[int] = None,
+        top_k: Optional[int] = None,
+        multiclass: Optional[bool] = None,
+        compute_on_step: bool = True,
+        dist_sync_on_step: bool = False,
+        process_group: Optional[Any] = None,
+        dist_sync_fn: Callable = None,
+    ) -> None:
+        warn("`FBeta` was renamed to `FBetaScore` in v0.7 and it will be removed in v0.8", DeprecationWarning)
+        super().__init__(
+            num_classes=num_classes,
+            beta=beta,
+            threshold=threshold,
+            average=average,
+            mdmc_average=mdmc_average,
+            ignore_index=ignore_index,
+            top_k=top_k,
+            multiclass=multiclass,
+            compute_on_step=compute_on_step,
+            dist_sync_on_step=dist_sync_on_step,
+            process_group=process_group,
+            dist_sync_fn=dist_sync_fn,
+        )
+
+
 
-class F1Score(FBeta):
+class F1Score(FBetaScore):
     """Computes F1 metric. F1 metrics correspond to a harmonic mean of the precision and recall scores.
 
     Works with binary, multiclass, and multilabel data. Accepts logits or probabilities from a model
diff --git a/torchmetrics/functional/__init__.py b/torchmetrics/functional/__init__.py
index 7d6f9942bf2..2b13101d2da 100644
--- a/torchmetrics/functional/__init__.py
+++ b/torchmetrics/functional/__init__.py
@@ -24,7 +24,7 @@
 from torchmetrics.functional.classification.cohen_kappa import cohen_kappa
 from torchmetrics.functional.classification.confusion_matrix import confusion_matrix
 from torchmetrics.functional.classification.dice import dice_score
-from torchmetrics.functional.classification.f_beta import f1, f1_score, fbeta
+from torchmetrics.functional.classification.f_beta import f1, f1_score, fbeta, fbeta_score
 from torchmetrics.functional.classification.hamming_distance import hamming_distance
 from torchmetrics.functional.classification.hinge import hinge, hinge_loss
 from torchmetrics.functional.classification.iou import iou  # noqa: F401
@@ -96,6 +96,7 @@
     "f1",
     "f1_score",
     "fbeta",
+    "fbeta_score",
     "hamming_distance",
     "hinge",
     "hinge_loss",
diff --git a/torchmetrics/functional/classification/__init__.py b/torchmetrics/functional/classification/__init__.py
index 5c7995e3215..14395425c16 100644
--- a/torchmetrics/functional/classification/__init__.py
+++ b/torchmetrics/functional/classification/__init__.py
@@ -19,7 +19,7 @@
 from torchmetrics.functional.classification.cohen_kappa import cohen_kappa  # noqa: F401
 from torchmetrics.functional.classification.confusion_matrix import confusion_matrix  # noqa: F401
 from torchmetrics.functional.classification.dice import dice_score  # noqa: F401
-from torchmetrics.functional.classification.f_beta import f1, f1_score, fbeta  # noqa: F401
+from torchmetrics.functional.classification.f_beta import f1, f1_score, fbeta, fbeta_score  # noqa: F401
 from torchmetrics.functional.classification.hamming_distance import hamming_distance  # noqa: F401
 from torchmetrics.functional.classification.hinge import hinge, hinge_loss  # noqa: F401
 from torchmetrics.functional.classification.jaccard import jaccard_index  # noqa: F401
diff --git a/torchmetrics/functional/classification/f_beta.py b/torchmetrics/functional/classification/f_beta.py
index e8c8410edc1..4194f421fb9 100644
--- a/torchmetrics/functional/classification/f_beta.py
+++ b/torchmetrics/functional/classification/f_beta.py
@@ -109,7 +109,7 @@ def _fbeta_compute(
     )
 
 
-def fbeta(
+def fbeta_score(
     preds: Tensor,
     target: Tensor,
     beta: float = 1.0,
@@ -208,10 +208,10 @@ def fbeta(
           of classes
 
     Example:
-        >>> from torchmetrics.functional import fbeta
+        >>> from torchmetrics.functional import fbeta_score
         >>> target = torch.tensor([0, 1, 2, 0, 1, 2])
         >>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
-        >>> fbeta(preds, target, num_classes=3, beta=0.5)
+        >>> fbeta_score(preds, target, num_classes=3, beta=0.5)
         tensor(0.3333)
 
     """
@@ -244,6 +244,28 @@ def fbeta(
     return _fbeta_compute(tp, fp, tn, fn, beta, ignore_index, average, mdmc_average)
 
 
+def fbeta(
+    preds: Tensor,
+    target: Tensor,
+    beta: float = 1.0,
+    average: str = "micro",
+    mdmc_average: Optional[str] = None,
+    ignore_index: Optional[int] = None,
+    num_classes: Optional[int] = None,
+    threshold: float = 0.5,
+    top_k: Optional[int] = None,
+    multiclass: Optional[bool] = None,
+) -> Tensor:
+    r"""
+    Computes f_beta metric.
+
+    .. deprecated:: v0.7
+        Use :class:`torchmetrics.functional.f1_score`. Will be removed in v0.8.
+    """
+    warn("`f1` was renamed to `f1_score` in v0.7 and it will be removed in v0.8", DeprecationWarning)
+    return fbeta_score(preds, target, beta, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass)
+
+
 def f1_score(
     preds: Tensor,
     target: Tensor,
@@ -349,7 +371,7 @@ def f1_score(
         >>> f1_score(preds, target, num_classes=3)
         tensor(0.3333)
     """
-    return fbeta(preds, target, 1.0, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass)
+    return fbeta_score(preds, target, 1.0, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass)
 
 
 def f1(

From f840411506ccdaf53291ad39810145565d2554c0 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 10 Jan 2022 21:37:31 +0000
Subject: [PATCH 2/6] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 torchmetrics/classification/f_beta.py            | 2 +-
 torchmetrics/functional/classification/f_beta.py | 8 ++++++--
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/torchmetrics/classification/f_beta.py b/torchmetrics/classification/f_beta.py
index f696b96d37d..dedc722b4f8 100644
--- a/torchmetrics/classification/f_beta.py
+++ b/torchmetrics/classification/f_beta.py
@@ -174,6 +174,7 @@ def compute(self) -> Tensor:
         tp, fp, tn, fn = self._get_final_stats()
         return _fbeta_compute(tp, fp, tn, fn, self.beta, self.ignore_index, self.average, self.mdmc_reduce)
 
+
 class FBeta(FBetaScore):
     r"""
     Computes `F-score`_, specifically:
@@ -214,7 +215,6 @@ def __init__(
         )
 
 
-
 class F1Score(FBetaScore):
     """Computes F1 metric. F1 metrics correspond to a harmonic mean of the precision and recall scores.
 
diff --git a/torchmetrics/functional/classification/f_beta.py b/torchmetrics/functional/classification/f_beta.py
index 4194f421fb9..ae9cbe71358 100644
--- a/torchmetrics/functional/classification/f_beta.py
+++ b/torchmetrics/functional/classification/f_beta.py
@@ -263,7 +263,9 @@ def fbeta(
         Use :class:`torchmetrics.functional.f1_score`. Will be removed in v0.8.
     """
     warn("`f1` was renamed to `f1_score` in v0.7 and it will be removed in v0.8", DeprecationWarning)
-    return fbeta_score(preds, target, beta, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass)
+    return fbeta_score(
+        preds, target, beta, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass
+    )
 
 
 def f1_score(
@@ -371,7 +373,9 @@ def f1_score(
         >>> f1_score(preds, target, num_classes=3)
         tensor(0.3333)
     """
-    return fbeta_score(preds, target, 1.0, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass)
+    return fbeta_score(
+        preds, target, 1.0, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass
+    )
 
 
 def f1(

From 6aaeef2d675427c2be48d32dd99b44edcafd9ec9 Mon Sep 17 00:00:00 2001
From: Xavier Sumba <c.uent@hotmail.com>
Date: Mon, 10 Jan 2022 16:38:55 -0500
Subject: [PATCH 3/6] update changelog

---
 CHANGELOG.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index db69e788c12..f1fee0318cf 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -91,6 +91,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   * `torchmetrics.functional.hinge` -> `torchmetrics.functional.hinge_loss`
   * `torchmetrics.Hinge` -> `torchmetrics.HingeLoss`
 
+- Renamed F-Beta metrics: ([#729](https://github.com/PyTorchLightning/metrics/pull/729))
+  * `torchmetrics.functional.fbeta` -> `torchmetrics.functional.fbeta_score`
+  * `torchmetrics.FBeta` -> `torchmetrics.FBetaScore`
+
 
 ### Removed
 

From c89ce111c21cc78f2f837760520b7c34773262bd Mon Sep 17 00:00:00 2001
From: Xavier Sumba <c.uent@hotmail.com>
Date: Mon, 10 Jan 2022 16:41:57 -0500
Subject: [PATCH 4/6] update PR number in changelog

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f1fee0318cf..ea049b0224f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -91,7 +91,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   * `torchmetrics.functional.hinge` -> `torchmetrics.functional.hinge_loss`
   * `torchmetrics.Hinge` -> `torchmetrics.HingeLoss`
 
-- Renamed F-Beta metrics: ([#729](https://github.com/PyTorchLightning/metrics/pull/729))
+- Renamed F-Beta metrics: ([#740](https://github.com/PyTorchLightning/metrics/pull/740))
   * `torchmetrics.functional.fbeta` -> `torchmetrics.functional.fbeta_score`
   * `torchmetrics.FBeta` -> `torchmetrics.FBetaScore`
 

From 5837224a384f625233a9e44f513682369bcdd3a6 Mon Sep 17 00:00:00 2001
From: Jirka Borovec <Borda@users.noreply.github.com>
Date: Mon, 10 Jan 2022 22:58:33 +0100
Subject: [PATCH 5/6] Apply suggestions from code review

---
 torchmetrics/classification/f_beta.py            | 4 ++++
 torchmetrics/functional/classification/f_beta.py | 3 +++
 2 files changed, 7 insertions(+)

diff --git a/torchmetrics/classification/f_beta.py b/torchmetrics/classification/f_beta.py
index dedc722b4f8..7cb58268a1a 100644
--- a/torchmetrics/classification/f_beta.py
+++ b/torchmetrics/classification/f_beta.py
@@ -181,6 +181,10 @@ class FBeta(FBetaScore):
 
     .. deprecated:: v0.7
         Use :class:`torchmetrics.FBetaScore`. Will be removed in v0.8.
+    Example:
+        >>> f_beta = FBetaScore(num_classes=3, beta=0.5)
+        >>> f_beta(torch.tensor([0, 2, 1, 0, 0, 1]), torch.tensor([0, 1, 2, 0, 1, 2]))
+        tensor(0.3333)
     """
 
     def __init__(
diff --git a/torchmetrics/functional/classification/f_beta.py b/torchmetrics/functional/classification/f_beta.py
index ae9cbe71358..9ff877803d3 100644
--- a/torchmetrics/functional/classification/f_beta.py
+++ b/torchmetrics/functional/classification/f_beta.py
@@ -261,6 +261,9 @@ def fbeta(
 
     .. deprecated:: v0.7
         Use :class:`torchmetrics.functional.f1_score`. Will be removed in v0.8.
+    Example:
+        >>> fbeta(torch.tensor([0, 2, 1, 0, 0, 1]), torch.tensor([0, 1, 2, 0, 1, 2]), num_classes=3, beta=0.5)
+        tensor(0.3333)
     """
     warn("`f1` was renamed to `f1_score` in v0.7 and it will be removed in v0.8", DeprecationWarning)
     return fbeta_score(

From faffa8603e003d5d7b9fffa35aeae24e15a6ef74 Mon Sep 17 00:00:00 2001
From: Jirka Borovec <Borda@users.noreply.github.com>
Date: Tue, 11 Jan 2022 13:19:28 +0100
Subject: [PATCH 6/6] Apply suggestions from code review

Co-authored-by: Ethan Harris <ewah1g13@soton.ac.uk>
---
 torchmetrics/classification/f_beta.py            | 3 ++-
 torchmetrics/functional/classification/f_beta.py | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/torchmetrics/classification/f_beta.py b/torchmetrics/classification/f_beta.py
index 7cb58268a1a..04bda4ff462 100644
--- a/torchmetrics/classification/f_beta.py
+++ b/torchmetrics/classification/f_beta.py
@@ -181,7 +181,8 @@ class FBeta(FBetaScore):
 
     .. deprecated:: v0.7
         Use :class:`torchmetrics.FBetaScore`. Will be removed in v0.8.
-    Example:
+
+    Example::
         >>> f_beta = FBetaScore(num_classes=3, beta=0.5)
         >>> f_beta(torch.tensor([0, 2, 1, 0, 0, 1]), torch.tensor([0, 1, 2, 0, 1, 2]))
         tensor(0.3333)
diff --git a/torchmetrics/functional/classification/f_beta.py b/torchmetrics/functional/classification/f_beta.py
index 9ff877803d3..b6b709d5888 100644
--- a/torchmetrics/functional/classification/f_beta.py
+++ b/torchmetrics/functional/classification/f_beta.py
@@ -261,7 +261,8 @@ def fbeta(
 
     .. deprecated:: v0.7
         Use :class:`torchmetrics.functional.f1_score`. Will be removed in v0.8.
-    Example:
+
+    Example::
         >>> fbeta(torch.tensor([0, 2, 1, 0, 0, 1]), torch.tensor([0, 1, 2, 0, 1, 2]), num_classes=3, beta=0.5)
         tensor(0.3333)
     """