Add plotting 8/n (#1605)

CHANGELOG.md

@@ -26,6 +26,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
     [#1585](https://github.com/Lightning-AI/metrics/pull/1585),
     [#1593](https://github.com/Lightning-AI/metrics/pull/1593),
     [#1600](https://github.com/Lightning-AI/metrics/pull/1600),
+    [#1605](https://github.com/Lightning-AI/metrics/pull/1605),
 )
 
 

src/torchmetrics/regression/concordance.py

@@ -11,10 +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 Optional, Sequence, Union
+
 from torch import Tensor
 
 from torchmetrics.functional.regression.concordance import _concordance_corrcoef_compute
 from torchmetrics.regression.pearson import PearsonCorrCoef, _final_aggregation
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["ConcordanceCorrCoef.plot"]
 
 
 class ConcordanceCorrCoef(PearsonCorrCoef):
@@ -74,3 +81,44 @@ def compute(self) -> Tensor:
             corr_xy = self.corr_xy
             n_total = self.n_total
         return _concordance_corrcoef_compute(mean_x, mean_y, 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 ConcordanceCorrCoef
+            >>> metric = ConcordanceCorrCoef()
+            >>> 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 ConcordanceCorrCoef
+            >>> metric = ConcordanceCorrCoef()
+            >>> 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/cosine_similarity.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 typing_extensions import Literal
 
 from torchmetrics.functional.regression.cosine_similarity import _cosine_similarity_compute, _cosine_similarity_update
 from torchmetrics.metric import Metric
 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__ = ["CosineSimilarity.plot"]
 
 
 class CosineSimilarity(Metric):
@@ -84,3 +89,44 @@ def compute(self) -> Tensor:
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return _cosine_similarity_compute(preds, target, self.reduction)
+
+    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 CosineSimilarity
+            >>> metric = CosineSimilarity()
+            >>> 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 CosineSimilarity
+            >>> metric = CosineSimilarity()
+            >>> 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/explained_variance.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, Sequence, Union
+from typing import Any, Optional, Sequence, Union
 
 from torch import Tensor, tensor
 from typing_extensions import Literal
@@ -22,6 +22,11 @@
     _explained_variance_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__ = ["ExplainedVariance.plot"]
 
 
 class ExplainedVariance(Metric):
@@ -121,3 +126,44 @@ def compute(self) -> Union[Tensor, Sequence[Tensor]]:
             self.sum_squared_target,
             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 ExplainedVariance
+            >>> metric = ExplainedVariance()
+            >>> 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 ExplainedVariance
+            >>> metric = ExplainedVariance()
+            >>> 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/kendall.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, List, Optional, Tuple, Union
+from typing import Any, List, Optional, Sequence, Tuple, Union
 
 from torch import Tensor
 from typing_extensions import Literal
@@ -25,6 +25,11 @@
 )
 from torchmetrics.metric import Metric
 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__ = ["KendallRankCorrCoef.plot"]
 
 
 class KendallRankCorrCoef(Metric):
@@ -155,3 +160,44 @@ def compute(self) -> Union[Tensor, Tuple[Tensor, Tensor]]:
         if p_value is not None:
             return tau, p_value
         return tau
+
+    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 KendallRankCorrCoef
+            >>> metric = KendallRankCorrCoef()
+            >>> 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 KendallRankCorrCoef
+            >>> metric = KendallRankCorrCoef()
+            >>> 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/kl_divergence.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
@@ -20,6 +20,11 @@
 from torchmetrics.functional.regression.kl_divergence import _kld_compute, _kld_update
 from torchmetrics.metric import Metric
 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__ = ["KLDivergence.plot"]
 
 
 class KLDivergence(Metric):
@@ -117,3 +122,44 @@ def compute(self) -> Tensor:
             else self.measures  # type: ignore[assignment]
         )
         return _kld_compute(measures, self.total, self.reduction)
+
+    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 KLDivergence
+            >>> metric = KLDivergence()
+            >>> metric.update(randn(10,3).softmax(dim=-1), randn(10,3).softmax(dim=-1))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> from torch import randn
+            >>> # Example plotting multiple values
+            >>> from torchmetrics.regression import KLDivergence
+            >>> metric = KLDivergence()
+            >>> values = []
+            >>> for _ in range(10):
+            ...     values.append(metric(randn(10,3).softmax(dim=-1), randn(10,3).softmax(dim=-1)))
+            >>> fig, ax = metric.plot(values)
+        """
+        return self._plot(val, ax)

src/torchmetrics/regression/log_cosh.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
 
 from torchmetrics.functional.regression.log_cosh import _log_cosh_error_compute, _log_cosh_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__ = ["LogCoshError.plot"]
 
 
 class LogCoshError(Metric):
@@ -88,3 +93,44 @@ def update(self, preds: Tensor, target: Tensor) -> None:
     def compute(self) -> Tensor:
         """Compute LogCosh error over state."""
         return _log_cosh_error_compute(self.sum_log_cosh_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 LogCoshError
+            >>> metric = LogCoshError()
+            >>> 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 LogCoshError
+            >>> metric = LogCoshError()
+            >>> values = []
+            >>> for _ in range(10):
+            ...     values.append(metric(randn(10,), randn(10,)))
+            >>> fig, ax = metric.plot(values)
+        """
+        return self._plot(val, ax)