Add plotting 14/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),
+    [#1631](https://github.com/Lightning-AI/metrics/pull/1631),
 )
 
 

src/torchmetrics/text/cer.py

@@ -11,14 +11,18 @@
 # 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, Union
+from typing import Any, List, Optional, Sequence, Union
 
 import torch
 from torch import Tensor, tensor
 
 from torchmetrics.functional.text.cer import _cer_compute, _cer_update
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["CharErrorRate.plot"]
 
 
 class CharErrorRate(Metric):
@@ -84,3 +88,46 @@ def update(self, preds: Union[str, List[str]], target: Union[str, List[str]]) ->
     def compute(self) -> Tensor:
         """Calculate the character error rate."""
         return _cer_compute(self.errors, 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 torchmetrics import CharErrorRate
+            >>> metric = CharErrorRate()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torchmetrics import CharErrorRate
+            >>> metric = CharErrorRate()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/text/eed.py

@@ -11,14 +11,18 @@
 # 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, Sequence, Tuple, Union
+from typing import Any, List, Optional, Sequence, Tuple, Union
 
 from torch import Tensor, stack
 from typing_extensions import Literal
 
 from torchmetrics.functional.text.eed import _eed_compute, _eed_update
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["ExtendedEditDistance.plot"]
 
 
 class ExtendedEditDistance(Metric):
@@ -112,3 +116,46 @@ def compute(self) -> Union[Tensor, Tuple[Tensor, Tensor]]:
         if self.return_sentence_level_score:
             return average, stack(self.sentence_eed)
         return 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 and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> from torchmetrics import ExtendedEditDistance
+            >>> metric = ExtendedEditDistance()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torchmetrics import ExtendedEditDistance
+            >>> metric = ExtendedEditDistance()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/text/mer.py

@@ -11,14 +11,18 @@
 # 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, Union
+from typing import Any, List, Optional, Sequence, Union
 
 import torch
 from torch import Tensor, tensor
 
 from torchmetrics.functional.text.mer import _mer_compute, _mer_update
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["MatchErrorRate.plot"]
 
 
 class MatchErrorRate(Metric):
@@ -85,3 +89,46 @@ def update(
     def compute(self) -> Tensor:
         """Calculate the Match error rate."""
         return _mer_compute(self.errors, 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 torchmetrics import MatchErrorRate
+            >>> metric = MatchErrorRate()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torchmetrics import MatchErrorRate
+            >>> metric = MatchErrorRate()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/text/wer.py

@@ -11,13 +11,18 @@
 # 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, Union
+from typing import Any, List, Optional, Sequence, Union
 
 import torch
 from torch import Tensor, tensor
 
 from torchmetrics.functional.text.wer import _wer_compute, _wer_update
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["WordErrorRate.plot"]
 
 
 class WordErrorRate(Metric):
@@ -82,3 +87,46 @@ def update(self, preds: Union[str, List[str]], target: Union[str, List[str]]) ->
     def compute(self) -> Tensor:
         """Calculate the word error rate."""
         return _wer_compute(self.errors, 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 torchmetrics import WordErrorRate
+            >>> metric = WordErrorRate()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torchmetrics import WordErrorRate
+            >>> metric = WordErrorRate()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/text/wil.py

@@ -11,13 +11,17 @@
 # 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, Union
+from typing import Any, List, Optional, Sequence, Union
 
 from torch import Tensor, tensor
 
 from torchmetrics.functional.text.wil import _wil_compute, _wil_update
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["WordInfoLost.plot"]
 
 
 class WordInfoLost(Metric):
@@ -83,3 +87,46 @@ def update(self, preds: Union[str, List[str]], target: Union[str, List[str]]) ->
     def compute(self) -> Tensor:
         """Calculate the Word Information Lost."""
         return _wil_compute(self.errors, self.target_total, self.preds_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 torchmetrics import WordInfoLost
+            >>> metric = WordInfoLost()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torchmetrics import WordInfoLost
+            >>> metric = WordInfoLost()
+            >>> preds = ["this is the prediction", "there is an other sample"]
+            >>> target = ["this is the reference", "there is another one"]
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)