Fix `num_classes` arg in F1 metric (PL^5663)

* fix f1 metric

* Apply suggestions from code review

* chlog

Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Jirka Borovec <jirka.borovec@seznam.cz>
(cherry picked from commit ef8efc611c12209ebf158aab7f444756ba35927e)

pytorch_lightning/metrics/classification/f_beta.py

@@ -185,7 +185,7 @@ class F1(FBeta):
 
     def __init__(
         self,
-        num_classes: int = 1,
+        num_classes: int,
         threshold: float = 0.5,
         average: str = "micro",
         multilabel: bool = False,
@@ -201,6 +201,7 @@ class F1(FBeta):
             beta=1.0,
             threshold=threshold,
             average=average,
+            multilabel=multilabel,
             compute_on_step=compute_on_step,
             dist_sync_on_step=dist_sync_on_step,
             process_group=process_group,

tests/metrics/classification/test_f_beta.py

@@ -5,7 +5,7 @@ import pytest
 import torch
 from sklearn.metrics import fbeta_score
 
-from pytorch_lightning.metrics import FBeta
+from pytorch_lightning.metrics import F1, FBeta
 from pytorch_lightning.metrics.functional import f1, fbeta
 from tests.metrics.classification.inputs import (
     _binary_inputs,
@@ -97,22 +97,23 @@ def _sk_fbeta_multidim_multiclass(preds, target, average='micro', beta=1.0):
     ],
 )
 @pytest.mark.parametrize("average", ['micro', 'macro', 'weighted', None])
-@pytest.mark.parametrize("beta", [0.5, 1.0])
+@pytest.mark.parametrize("beta", [0.5, 1.0, 2.0])
 class TestFBeta(MetricTester):
     @pytest.mark.parametrize("ddp", [True, False])
     @pytest.mark.parametrize("dist_sync_on_step", [True, False])
     def test_fbeta(
         self, preds, target, sk_metric, num_classes, multilabel, average, beta, ddp, dist_sync_on_step
     ):
+        metric_class = F1 if beta == 1.0 else partial(FBeta, beta=beta)
+
         self.run_class_metric_test(
             ddp=ddp,
             preds=preds,
             target=target,
-            metric_class=FBeta,
+            metric_class=metric_class,
             sk_metric=partial(sk_metric, average=average, beta=beta),
             dist_sync_on_step=dist_sync_on_step,
             metric_args={
-                "beta": beta,
                 "num_classes": num_classes,
                 "average": average,
                 "multilabel": multilabel,
@@ -125,12 +126,13 @@ class TestFBeta(MetricTester):
     def test_fbeta_functional(
         self, preds, target, sk_metric, num_classes, multilabel, average, beta
     ):
+        metric_functional = f1 if beta == 1.0 else partial(fbeta, beta=beta)
+
         self.run_functional_metric_test(preds=preds,
                                         target=target,
-                                        metric_functional=fbeta,
+                                        metric_functional=metric_functional,
                                         sk_metric=partial(sk_metric, average=average, beta=beta),
                                         metric_args={
-                                            "beta": beta,
                                             "num_classes": num_classes,
                                             "average": average,
                                             "multilabel": multilabel,