Merge branch 'master' into classification/enum

CHANGELOG.md

@@ -14,7 +14,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added support for `SpecificityAtSensitivity` Metric ([#1432](https://github.com/Lightning-AI/metrics/pull/1432))
 
 
-- Added support for plotting of metrics through `.plot()` method ([#1328](https://github.com/Lightning-AI/metrics/pull/1328))
+- Added support for plotting of metrics through `.plot()` method (
+    [#1328](https://github.com/Lightning-AI/metrics/pull/1328),
+    [#1481](https://github.com/Lightning-AI/metrics/pull/1481)
+)
 
 
 - Added `classes` to output from `MAP` metric ([#1419](https://github.com/Lightning-AI/metrics/pull/1419))

src/torchmetrics/classification/accuracy.py

@@ -23,7 +23,7 @@
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
 
 if not _MATPLOTLIB_AVAILABLE:
-    __doctest_skip__ = ["BinaryAccuracy.plot", "MulticlassAccuracy.plot"]
+    __doctest_skip__ = ["BinaryAccuracy.plot", "MulticlassAccuracy.plot", "MultilabelAccuracy.plot"]
 
 from torchmetrics.classification.stat_scores import (  # isort:skip
     BinaryStatScores,
@@ -403,6 +403,52 @@ def compute(self) -> Tensor:
             tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average, multilabel=True
         )
 
+    def plot(
+        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+    ) -> _PLOT_OUT_TYPE:
+        """Plot a single or multiple values from the metric.
+
+        Args:
+            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+                If no value is provided, will automatically call `metric.compute` and plot that result.
+            ax: An matplotlib axis object. If provided will add plot to that axis
+
+        Returns:
+            fig: Figure object
+            ax: Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> from torch import rand, randint
+            >>> # Example plotting a single value
+            >>> from torchmetrics.classification import MultilabelAccuracy
+            >>> metric = MultilabelAccuracy(num_labels=3)
+            >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> from torch import rand, randint
+            >>> # Example plotting multiple values
+            >>> from torchmetrics.classification import MultilabelAccuracy
+            >>> metric = MultilabelAccuracy(num_labels=3)
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(randint(2, (20, 3)), randint(2, (20, 3))))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        val = val or self.compute()
+        fig, ax = plot_single_or_multi_val(
+            val, ax=ax, higher_is_better=self.higher_is_better, **self.plot_options, name=self.__class__.__name__
+        )
+        return fig, ax
+
 
 class Accuracy:
     r"""Compute `Accuracy`_.