modify object F1Score

docs/source/references/modules.rst

@@ -278,10 +278,10 @@ ConfusionMatrix
 .. autoclass:: torchmetrics.ConfusionMatrix
     :noindex:
 
-F1
+F1Score
 ~~
 
-.. autoclass:: torchmetrics.F1
+.. autoclass:: torchmetrics.F1Score
     :noindex:
 
 FBeta

tests/classification/test_f_beta.py

@@ -32,7 +32,7 @@
 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 F1, FBeta, Metric
+from torchmetrics import F1Score, FBeta, Metric
 from torchmetrics.functional import f1_score as f1_score_pl, fbeta
 from torchmetrics.utilities.checks import _input_format_classification
 from torchmetrics.utilities.enums import AverageMethod
@@ -93,7 +93,7 @@ def _sk_fbeta_f1_multidim_multiclass(
     "metric_class, metric_fn",
     [
         (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)),
-        (F1, f1_score_pl),
+        (F1Score, f1_score_pl),
     ],
 )
 @pytest.mark.parametrize(
@@ -129,7 +129,7 @@ def test_wrong_params(metric_class, metric_fn, average, mdmc_average, num_classe
     "metric_class, metric_fn",
     [
         (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)),
-        (F1, f1_score_pl),
+        (F1Score, f1_score_pl),
     ],
 )
 def test_zero_division(metric_class, metric_fn):
@@ -151,7 +151,7 @@ def test_zero_division(metric_class, metric_fn):
     "metric_class, metric_fn",
     [
         (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)),
-        (F1, f1_score_pl),
+        (F1Score, f1_score_pl),
     ],
 )
 def test_no_support(metric_class, metric_fn):
@@ -178,7 +178,7 @@ def test_no_support(metric_class, metric_fn):
     assert result_cl == result_fn == 0
 
 
-@pytest.mark.parametrize("metric_class, metric_fn", [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)), (F1, f1_score_pl)])
+@pytest.mark.parametrize("metric_class, metric_fn", [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)), (F1Score, f1_score_pl)])
 @pytest.mark.parametrize(
     "ignore_index, expected", [(None, torch.tensor([1.0, np.nan])), (0, torch.tensor([np.nan, np.nan]))]
 )
@@ -202,7 +202,7 @@ 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)), (F1, f1_score_pl, f1_score)],
+    [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)), (F1Score, f1_score_pl, f1_score)],
 )
 @pytest.mark.parametrize("average", ["micro", "macro", None, "weighted", "samples"])
 @pytest.mark.parametrize("ignore_index", [None, 0])
@@ -388,7 +388,7 @@ def test_fbeta_f1_differentiability(
     "metric_class, metric_fn",
     [
         (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)),
-        (F1, fbeta),
+        (F1Score, fbeta),
     ],
 )
 @pytest.mark.parametrize(
@@ -430,7 +430,7 @@ def test_top_k(
 @pytest.mark.parametrize("average", ["micro", "macro", "weighted"])
 @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)), (F1, f1_score_pl, f1_score)],
+    [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)), (F1Score, f1_score_pl, f1_score)],
 )
 def test_same_input(metric_class, metric_functional, sk_fn, average, ignore_index):
     preds = _input_miss_class.preds

torchmetrics/__init__.py

@@ -17,7 +17,7 @@
 from torchmetrics.classification import (  # noqa: E402, F401
     AUC,
     AUROC,
-    F1,
+    F1Score,
     ROC,
     Accuracy,
     AveragePrecision,
@@ -103,7 +103,7 @@
     "CosineSimilarity",
     "TweedieDevianceScore",
     "ExplainedVariance",
-    "F1",
+    "F1Score",
     "FBeta",
     "HammingDistance",
     "Hinge",

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, FBeta  # noqa: F401
+from torchmetrics.classification.f_beta import F1Score, FBeta  # noqa: F401
 from torchmetrics.classification.hamming_distance import HammingDistance  # noqa: F401
 from torchmetrics.classification.hinge import Hinge  # noqa: F401
 from torchmetrics.classification.iou import IoU  # noqa: F401

torchmetrics/classification/f_beta.py

@@ -174,7 +174,7 @@ def compute(self) -> Tensor:
         return _fbeta_compute(tp, fp, tn, fn, self.beta, self.ignore_index, self.average, self.mdmc_reduce)
 
 
-class F1(FBeta):
+class F1Score(FBeta):
     """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
@@ -261,10 +261,10 @@ class F1(FBeta):
 
 
     Example:
-        >>> from torchmetrics import F1
+        >>> from torchmetrics import F1Score
         >>> target = torch.tensor([0, 1, 2, 0, 1, 2])
         >>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
-        >>> f1 = F1(num_classes=3)
+        >>> f1 = F1Score(num_classes=3)
         >>> f1(preds, target)
         tensor(0.3333)
     """