Add plotting 11/n

CHANGELOG.md

@@ -29,6 +29,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
     [#1605](https://github.com/Lightning-AI/metrics/pull/1605),
     [#1610](https://github.com/Lightning-AI/metrics/pull/1610),
     [#1609](https://github.com/Lightning-AI/metrics/pull/1609),
+    [#1621](https://github.com/Lightning-AI/metrics/pull/1621),
 )
 
 

src/torchmetrics/regression/pearson.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, Tuple
+from typing import Any, List, Optional, Sequence, Tuple, Union
 
 import torch
 from torch import Tensor
 
 from torchmetrics.functional.regression.pearson import _pearson_corrcoef_compute, _pearson_corrcoef_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__ = ["PearsonCorrCoef.plot"]
 
 
 def _final_aggregation(
@@ -159,3 +164,44 @@ def compute(self) -> Tensor:
             corr_xy = self.corr_xy
             n_total = self.n_total
         return _pearson_corrcoef_compute(var_x, var_y, corr_xy, n_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
+
+            >>> from torch import randn
+            >>> # Example plotting a single value
+            >>> from torchmetrics.regression import PearsonCorrCoef
+            >>> metric = PearsonCorrCoef()
+            >>> metric.update(randn(10,), randn(10,))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> from torch import randn
+            >>> # Example plotting multiple values
+            >>> from torchmetrics.regression import PearsonCorrCoef
+            >>> metric = PearsonCorrCoef()
+            >>> values = []
+            >>> for _ in range(10):
+            ...     values.append(metric(randn(10,), randn(10,)))
+            >>> fig, ax = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/regression/r2.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
+from typing import Any, Optional, Sequence, Union
 
 import torch
 from torch import Tensor, tensor
 
 from torchmetrics.functional.regression.r2 import _r2_score_compute, _r2_score_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__ = ["R2Score.plot"]
 
 
 class R2Score(Metric):
@@ -129,3 +134,44 @@ def compute(self) -> Tensor:
         return _r2_score_compute(
             self.sum_squared_error, self.sum_error, self.residual, self.total, self.adjusted, self.multioutput
         )
+
+    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
+
+            >>> from torch import randn
+            >>> # Example plotting a single value
+            >>> from torchmetrics.regression import R2Score
+            >>> metric = R2Score()
+            >>> metric.update(randn(10,), randn(10,))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> from torch import randn
+            >>> # Example plotting multiple values
+            >>> from torchmetrics.regression import R2Score
+            >>> metric = R2Score()
+            >>> values = []
+            >>> for _ in range(10):
+            ...     values.append(metric(randn(10,), randn(10,)))
+            >>> fig, ax = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/regression/spearman.py

@@ -11,14 +11,19 @@
 # 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
+from typing import Any, List, Optional, Sequence, Union
 
 from torch import Tensor
 
 from torchmetrics.functional.regression.spearman import _spearman_corrcoef_compute, _spearman_corrcoef_update
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["SpearmanCorrCoef.plot"]
 
 
 class SpearmanCorrCoef(Metric):
@@ -95,3 +100,44 @@ def compute(self) -> Tensor:
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return _spearman_corrcoef_compute(preds, target)
+
+    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
+
+            >>> from torch import randn
+            >>> # Example plotting a single value
+            >>> from torchmetrics.regression import SpearmanCorrCoef
+            >>> metric = SpearmanCorrCoef()
+            >>> metric.update(randn(10,), randn(10,))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> from torch import randn
+            >>> # Example plotting multiple values
+            >>> from torchmetrics.regression import SpearmanCorrCoef
+            >>> metric = SpearmanCorrCoef()
+            >>> values = []
+            >>> for _ in range(10):
+            ...     values.append(metric(randn(10,), randn(10,)))
+            >>> fig, ax = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/regression/symmetric_mape.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
+from typing import Any, Optional, Sequence, Union
 
 from torch import Tensor, tensor
 
@@ -20,6 +20,11 @@
     _symmetric_mean_absolute_percentage_error_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__ = ["SymmetricMeanAbsolutePercentageError.plot"]
 
 
 class SymmetricMeanAbsolutePercentageError(Metric):
@@ -74,3 +79,44 @@ def update(self, preds: Tensor, target: Tensor) -> None:
     def compute(self) -> Tensor:
         """Compute mean absolute percentage error over state."""
         return _symmetric_mean_absolute_percentage_error_compute(self.sum_abs_per_error, 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
+
+            >>> from torch import randn
+            >>> # Example plotting a single value
+            >>> from torchmetrics.regression import SymmetricMeanAbsolutePercentageError
+            >>> metric = SymmetricMeanAbsolutePercentageError()
+            >>> metric.update(randn(10,), randn(10,))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> from torch import randn
+            >>> # Example plotting multiple values
+            >>> from torchmetrics.regression import SymmetricMeanAbsolutePercentageError
+            >>> metric = SymmetricMeanAbsolutePercentageError()
+            >>> values = []
+            >>> for _ in range(10):
+            ...     values.append(metric(randn(10,), randn(10,)))
+            >>> fig, ax = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/regression/tweedie_deviance.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
+from typing import Any, Optional, Sequence, Union
 
 import torch
 from torch import Tensor
@@ -21,6 +21,11 @@
     _tweedie_deviance_score_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__ = ["TweedieDevianceScore.plot"]
 
 
 class TweedieDevianceScore(Metric):
@@ -99,3 +104,44 @@ def update(self, preds: Tensor, targets: Tensor) -> None:
     def compute(self) -> Tensor:
         """Compute metric."""
         return _tweedie_deviance_score_compute(self.sum_deviance_score, self.num_observations)
+
+    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
+
+            >>> from torch import randn
+            >>> # Example plotting a single value
+            >>> from torchmetrics.regression import TweedieDevianceScore
+            >>> metric = TweedieDevianceScore()
+            >>> metric.update(randn(10,), randn(10,))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> from torch import randn
+            >>> # Example plotting multiple values
+            >>> from torchmetrics.regression import TweedieDevianceScore
+            >>> metric = TweedieDevianceScore()
+            >>> values = []
+            >>> for _ in range(10):
+            ...     values.append(metric(randn(10,), randn(10,)))
+            >>> fig, ax = metric.plot(values)
+        """
+        return self._plot(val, ax)