Add plotting 7/n

CHANGELOG.md

@@ -23,6 +23,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
     [#1480](https://github.com/Lightning-AI/metrics/pull/1480),
     [#1490](https://github.com/Lightning-AI/metrics/pull/1490),
     [#1581](https://github.com/Lightning-AI/metrics/pull/1581),
+    [#1593](https://github.com/Lightning-AI/metrics/pull/1593),
 )
 
 

src/torchmetrics/classification/auroc.py

@@ -140,15 +140,24 @@ def plot(
         .. plot::
             :scale: 75
 
-            >>> from torch import randn, randint
-            >>> import torch.nn.functional as F
-            >>> # Example plotting a combined value across all classes
+            >>> # Example plotting a single
+            >>> import torch
             >>> from torchmetrics.classification import BinaryAUROC
-            >>> preds = F.softmax(randn(20, 2), dim=1)
-            >>> target = randint(2, (20,))
             >>> metric = BinaryAUROC()
-            >>> metric.update(preds[:, 1], target)
+            >>> metric.update(torch.rand(20,), torch.randint(2, (20,)))
             >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics.classification import BinaryAUROC
+            >>> metric = BinaryAUROC()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(torch.rand(20,), torch.randint(2, (20,))))
+            >>> fig_, ax_ = metric.plot(values)
         """
         return self._plot(val, ax)
 
@@ -276,12 +285,24 @@ def plot(
         .. plot::
             :scale: 75
 
-            >>> from torch import randn, randint
-            >>> # Example plotting a combined value across all classes
+            >>> # Example plotting a single
+            >>> import torch
             >>> from torchmetrics.classification import MulticlassAUROC
-            >>> metric = MulticlassAUROC(num_classes=3, average="macro")
-            >>> metric.update(randn(20, 3), randint(3, (20,)))
+            >>> metric = MulticlassAUROC(num_classes=3)
+            >>> metric.update(torch.randn(20, 3), torch.randint(3,(20,)))
             >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics.classification import MulticlassAUROC
+            >>> metric = MulticlassAUROC(num_classes=3)
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(torch.randn(20, 3), torch.randint(3, (20,))))
+            >>> fig_, ax_ = metric.plot(values)
         """
         return self._plot(val, ax)
 
@@ -367,7 +388,7 @@ class MultilabelAUROC(MultilabelPrecisionRecallCurve):
     full_state_update: bool = False
     plot_lower_bound = 0.0
     plot_upper_bound = 1.0
-    plot_legend_name = "Class"
+    plot_legend_name = "Label"
 
     def __init__(
         self,
@@ -411,19 +432,24 @@ def plot(
         .. plot::
             :scale: 75
 
-            >>> from torch import tensor
+            >>> # Example plotting a single
+            >>> import torch
             >>> from torchmetrics.classification import MultilabelAUROC
-            >>> preds = tensor([[0.75, 0.05, 0.35],
-            ...                [0.45, 0.75, 0.05],
-            ...                [0.05, 0.55, 0.75],
-            ...                [0.05, 0.65, 0.05]])
-            >>> target = tensor([[1, 0, 1],
-            ...                [0, 0, 0],
-            ...                [0, 1, 1],
-            ...                [1, 1, 1]])
-            >>> metric = MultilabelAUROC(num_labels=3, average="macro", thresholds=None)
-            >>> metric.update(preds, target)
+            >>> metric = MultilabelAUROC(num_labels=3)
+            >>> metric.update(torch.rand(20,3), torch.randint(2, (20,3)))
             >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics.classification import MultilabelAUROC
+            >>> metric = MultilabelAUROC(num_labels=3)
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(torch.rand(20,3), torch.randint(2, (20,3))))
+            >>> fig_, ax_ = metric.plot(values)
         """
         return self._plot(val, ax)
 

src/torchmetrics/classification/average_precision.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, List, Optional, Union
+from typing import Any, List, Optional, Sequence, Union
 
 from torch import Tensor
 from typing_extensions import Literal
@@ -31,6 +31,15 @@
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
 from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = [
+        "BinaryAveragePrecision.plot",
+        "MulticlassAveragePrecision.plot",
+        "MultilabelAveragePrecision.plot",
+    ]
 
 
 class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
@@ -97,12 +106,56 @@ class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
     is_differentiable: bool = False
     higher_is_better: Optional[bool] = None
     full_state_update: bool = False
+    plot_lower_bound = 0.0
+    plot_upper_bound = 1.0
 
     def compute(self) -> Tensor:
         """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _binary_average_precision_compute(state, self.thresholds)
 
+    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:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single
+            >>> import torch
+            >>> from torchmetrics.classification import BinaryAveragePrecision
+            >>> metric = BinaryAveragePrecision()
+            >>> metric.update(torch.rand(20,), torch.randint(2, (20,)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics.classification import BinaryAveragePrecision
+            >>> metric = BinaryAveragePrecision()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(torch.rand(20,), torch.randint(2, (20,))))
+            >>> fig_, ax_ = metric.plot(values)
+
+        """
+        return self._plot(val, ax)
+
 
 class MulticlassAveragePrecision(MulticlassPrecisionRecallCurve):
     r"""Compute the average precision (AP) score for binary tasks.
@@ -185,6 +238,9 @@ class MulticlassAveragePrecision(MulticlassPrecisionRecallCurve):
     is_differentiable: bool = False
     higher_is_better: Optional[bool] = None
     full_state_update: bool = False
+    plot_lower_bound = 0.0
+    plot_upper_bound = 1.0
+    plot_legend_name = "Class"
 
     def __init__(
         self,
@@ -208,6 +264,47 @@ def compute(self) -> Tensor:
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multiclass_average_precision_compute(state, self.num_classes, self.average, self.thresholds)
 
+    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:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single
+            >>> import torch
+            >>> from torchmetrics.classification import MulticlassAveragePrecision
+            >>> metric = MulticlassAveragePrecision(num_classes=3)
+            >>> metric.update(torch.randn(20, 3), torch.randint(3,(20,)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics.classification import MulticlassAveragePrecision
+            >>> metric = MulticlassAveragePrecision(num_classes=3)
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(torch.randn(20, 3), torch.randint(3, (20,))))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
+
 
 class MultilabelAveragePrecision(MultilabelPrecisionRecallCurve):
     r"""Compute the average precision (AP) score for binary tasks.
@@ -293,6 +390,9 @@ class MultilabelAveragePrecision(MultilabelPrecisionRecallCurve):
     is_differentiable: bool = False
     higher_is_better: Optional[bool] = None
     full_state_update: bool = False
+    plot_lower_bound = 0.0
+    plot_upper_bound = 1.0
+    plot_legend_name = "Label"
 
     def __init__(
         self,
@@ -318,6 +418,47 @@ def compute(self) -> Tensor:
             state, self.num_labels, self.average, self.thresholds, self.ignore_index
         )
 
+    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:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single
+            >>> import torch
+            >>> from torchmetrics.classification import MultilabelAveragePrecision
+            >>> metric = MultilabelAveragePrecision(num_labels=3)
+            >>> metric.update(torch.rand(20,3), torch.randint(2, (20,3)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics.classification import MultilabelAveragePrecision
+            >>> metric = MultilabelAveragePrecision(num_labels=3)
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(torch.rand(20,3), torch.randint(2, (20,3))))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
+
 
 class AveragePrecision:
     r"""Compute the average precision (AP) score.