Add plotting 15/n

CHANGELOG.md

@@ -32,6 +32,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
     [#1621](https://github.com/Lightning-AI/metrics/pull/1621),
     [#1624](https://github.com/Lightning-AI/metrics/pull/1624),
     [#1623](https://github.com/Lightning-AI/metrics/pull/1623),
+    [#1638](https://github.com/Lightning-AI/metrics/pull/1638),
     [#1631](https://github.com/Lightning-AI/metrics/pull/1631),
 )
 

src/torchmetrics/classification/dice.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, Callable, Optional, Tuple, no_type_check
+from typing import Any, Callable, Optional, Sequence, Tuple, Union, no_type_check
 
 import torch
 from torch import Tensor
@@ -21,6 +21,11 @@
 from torchmetrics.functional.classification.stat_scores import _stat_scores_update
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.enums import AverageMethod, MDMCAverageMethod
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["Dice.plot"]
 
 
 class Dice(Metric):
@@ -235,3 +240,44 @@ def compute(self) -> Tensor:
         """Compute metric."""
         tp, fp, _, fn = self._get_final_stats()
         return _dice_compute(tp, fp, fn, self.average, self.mdmc_reduce, self.zero_division)
+
+    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 object and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> from torch import randint
+            >>> from torchmetrics.classification import Dice
+            >>> metric = Dice()
+            >>> metric.update(randint(2,(10,)), randint(2,(10,)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torch import randint
+            >>> from torchmetrics.classification import Dice
+            >>> metric = Dice()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(randint(2,(10,)), randint(2,(10,))))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/classification/exact_match.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, Optional
+from typing import Any, Optional, Sequence, Union
 
 import torch
 from torch import Tensor
@@ -33,6 +33,11 @@
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
 from torchmetrics.utilities.enums import ClassificationTaskNoBinary
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["MulticlassExactMatch.plot", "MultilabelExactMatch.plot"]
 
 
 class MulticlassExactMatch(Metric):
@@ -140,6 +145,47 @@ def compute(self) -> Tensor:
         correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
         return _exact_match_reduce(correct, self.total)
 
+    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 object and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value per class
+            >>> from torch import randint
+            >>> from torchmetrics.classification import MulticlassExactMatch
+            >>> metric = MulticlassExactMatch(num_classes=3)
+            >>> metric.update(randint(3, (20,5)), randint(3, (20,5)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> from torch import randint
+            >>> # Example plotting a multiple values per class
+            >>> from torchmetrics.classification import MulticlassExactMatch
+            >>> metric = MulticlassExactMatch(num_classes=3)
+            >>> values = []
+            >>> for _ in range(20):
+            ...     values.append(metric(randint(3, (20,5)), randint(3, (20,5))))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
+
 
 class MultilabelExactMatch(Metric):
     r"""Compute Exact match (also known as subset accuracy) for multilabel tasks.
@@ -261,6 +307,47 @@ def compute(self) -> Tensor:
         correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
         return _exact_match_reduce(correct, self.total)
 
+    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 value
+            >>> from torch import rand, randint
+            >>> from torchmetrics.classification import MultilabelExactMatch
+            >>> metric = MultilabelExactMatch(num_labels=3)
+            >>> metric.update(randint(2, (20, 3, 5)), randint(2, (20, 3, 5)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torch import rand, randint
+            >>> from torchmetrics.classification import MultilabelExactMatch
+            >>> metric = MultilabelExactMatch(num_labels=3)
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(randint(2, (20, 3, 5)), randint(2, (20, 3, 5))))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
+
 
 class ExactMatch:
     r"""Compute Exact match (also known as subset accuracy).

src/torchmetrics/classification/hamming.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, Optional
+from typing import Any, Optional, Sequence, Union
 
 from torch import Tensor
 from typing_extensions import Literal
@@ -20,6 +20,15 @@
 from torchmetrics.functional.classification.hamming import _hamming_distance_reduce
 from torchmetrics.metric import Metric
 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__ = [
+        "BinaryHammingDistance.plot",
+        "MulticlassHammingDistance.plot",
+        "MultilabelHammingDistance.plot",
+    ]
 
 
 class BinaryHammingDistance(BinaryStatScores):
@@ -98,6 +107,47 @@ def compute(self) -> Tensor:
         tp, fp, tn, fn = self._final_state()
         return _hamming_distance_reduce(tp, fp, tn, fn, average="binary", multidim_average=self.multidim_average)
 
+    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 object and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> from torch import rand, randint
+            >>> from torchmetrics.classification import BinaryHammingDistance
+            >>> metric = BinaryHammingDistance()
+            >>> metric.update(rand(10), randint(2,(10,)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torch import rand, randint
+            >>> from torchmetrics.classification import BinaryHammingDistance
+            >>> metric = BinaryHammingDistance()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(rand(10), randint(2,(10,))))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
+
 
 class MulticlassHammingDistance(MulticlassStatScores):
     r"""Compute the average `Hamming distance`_ (also known as Hamming loss) for multiclass tasks.
@@ -204,6 +254,47 @@ def compute(self) -> Tensor:
         tp, fp, tn, fn = self._final_state()
         return _hamming_distance_reduce(tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average)
 
+    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 object and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value per class
+            >>> from torch import randint
+            >>> from torchmetrics.classification import MulticlassHammingDistance
+            >>> metric = MulticlassHammingDistance(num_classes=3, average=None)
+            >>> metric.update(randint(3, (20,)), randint(3, (20,)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a multiple values per class
+            >>> from torch import randint
+            >>> from torchmetrics.classification import MulticlassHammingDistance
+            >>> metric = MulticlassHammingDistance(num_classes=3, average=None)
+            >>> values = []
+            >>> for _ in range(20):
+            ...     values.append(metric(randint(3, (20,)), randint(3, (20,))))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
+
 
 class MultilabelHammingDistance(MultilabelStatScores):
     r"""Compute the average `Hamming distance`_ (also known as Hamming loss) for multilabel tasks.
@@ -310,6 +401,47 @@ 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:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> from torch import rand, randint
+            >>> from torchmetrics.classification import MultilabelHammingDistance
+            >>> metric = MultilabelHammingDistance(num_labels=3)
+            >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3)))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torch import rand, randint
+            >>> from torchmetrics.classification import MultilabelHammingDistance
+            >>> metric = MultilabelHammingDistance(num_labels=3)
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(randint(2, (20, 3)), randint(2, (20, 3))))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
+
 
 class HammingDistance:
     r"""Compute the average `Hamming distance`_ (also known as Hamming loss).