Fix num_classes arg in F1 metric (#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>

CHANGELOG.md

@@ -188,6 +188,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Fixed `num_classes` argument in F1 metric ([#5663](https://github.com/PyTorchLightning/pytorch-lightning/pull/5663))
 
 - Fixed support custom DataLoader with DDP if they can be re-instantiated ([#5745](https://github.com/PyTorchLightning/pytorch-lightning/pull/5745))
 

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 @@ def __init__(
  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 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 @@ def test_fbeta(
  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,