Merge branch 'master' into ci/gpu-pt2.0

CHANGELOG.md

@@ -35,6 +35,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
  [#1638](https://github.com/Lightning-AI/metrics/pull/1638),
  [#1631](https://github.com/Lightning-AI/metrics/pull/1631),
  [#1639](https://github.com/Lightning-AI/metrics/pull/1639),
+ [#1660](https://github.com/Lightning-AI/metrics/pull/1660)
 )
 
 

examples/plotting.py

@@ -393,9 +393,37 @@ def mean_average_precision() -> tuple:
  return fig, ax
 
 
+def roc_example() -> tuple:
+ """Plot roc metric."""
+ from torchmetrics.classification import BinaryROC, MulticlassROC, MultilabelROC
+
+ p = lambda: torch.rand(20)
+ t = lambda: torch.randint(2, (20,))
+
+ metric = BinaryROC()
+ metric.update(p(), t())
+ fig, ax = metric.plot()
+
+ p = lambda: torch.randn(200, 5)
+ t = lambda: torch.randint(5, (200,))
+
+ metric = MulticlassROC(5)
+ metric.update(p(), t())
+ fig, ax = metric.plot()
+
+ p = lambda: torch.rand(20, 2)
+ t = lambda: torch.randint(2, (20, 2))
+
+ metric = MultilabelROC(2)
+ metric.update(p(), t())
+
+ return fig, ax
+
+
 if __name__ == "__main__":
  metrics_func = {
  "accuracy": accuracy_example,
+ "roc": roc_example,
  "pesq": pesq_example,
  "pit": pit_example,
  "sdr": sdr_example,

src/torchmetrics/classification/f_beta.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
@@ -25,6 +25,18 @@
 )
 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__ = [
+ "BinaryFBetaScore.plot",
+ "MulticlassFBetaScore.plot",
+ "MultilabelFBetaScore.plot",
+ "BinaryF1Score.plot",
+ "MulticlassF1Score.plot",
+ "MultilabelF1Score.plot",
+ ]
 
 
 class BinaryFBetaScore(BinaryStatScores):
@@ -124,6 +136,47 @@ def compute(self) -> Tensor:
  tp, fp, tn, fn = self._final_state()
  return _fbeta_reduce(tp, fp, tn, fn, self.beta, 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
+
+ >>> from torch import rand, randint
+ >>> # Example plotting a single value
+ >>> from torchmetrics.classification import BinaryFBetaScore
+ >>> metric = BinaryFBetaScore(beta=2.0)
+ >>> metric.update(rand(10), randint(2,(10,)))
+ >>> fig_, ax_ = metric.plot()
+
+ .. plot::
+ :scale: 75
+
+ >>> from torch import rand, randint
+ >>> # Example plotting multiple values
+ >>> from torchmetrics.classification import BinaryFBetaScore
+ >>> metric = BinaryFBetaScore(beta=2.0)
+ >>> values = [ ]
+ >>> for _ in range(10):
+ ... values.append(metric(rand(10), randint(2,(10,))))
+ >>> fig_, ax_ = metric.plot(values)
+ """
+ return self._plot(val, ax)
+
 
 class MulticlassFBetaScore(MulticlassStatScores):
  r"""Compute `F-score`_ metric for multiclass tasks.
@@ -258,6 +311,47 @@ def compute(self) -> Tensor:
  tp, fp, tn, fn = self._final_state()
  return _fbeta_reduce(tp, fp, tn, fn, self.beta, 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
+
+ >>> from torch import randint
+ >>> # Example plotting a single value per class
+ >>> from torchmetrics.classification import MulticlassFBetaScore
+ >>> metric = MulticlassFBetaScore(num_classes=3, beta=2.0, average=None)
+ >>> metric.update(randint(3, (20,)), randint(3, (20,)))
+ >>> fig_, ax_ = metric.plot()
+
+ .. plot::
+ :scale: 75
+
+ >>> from torch import randint
+ >>> # Example plotting a multiple values per class
+ >>> from torchmetrics.classification import MulticlassFBetaScore
+ >>> metric = MulticlassFBetaScore(num_classes=3, beta=2.0, 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 MultilabelFBetaScore(MultilabelStatScores):
  r"""Compute `F-score`_ metric for multilabel tasks.
@@ -388,6 +482,47 @@ def compute(self) -> Tensor:
  tp, fp, tn, fn = self._final_state()
  return _fbeta_reduce(tp, fp, tn, fn, self.beta, 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 and Axes object
+
+ Raises:
+ ModuleNotFoundError:
+ If `matplotlib` is not installed
+
+ .. plot::
+ :scale: 75
+
+ >>> from torch import rand, randint
+ >>> # Example plotting a single value
+ >>> from torchmetrics.classification import MultilabelFBetaScore
+ >>> metric = MultilabelFBetaScore(num_labels=3, beta=2.0)
+ >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3)))
+ >>> fig_, ax_ = metric.plot()
+
+ .. plot::
+ :scale: 75
+
+ >>> from torch import rand, randint
+ >>> # Example plotting multiple values
+ >>> from torchmetrics.classification import MultilabelFBetaScore
+ >>> metric = MultilabelFBetaScore(num_labels=3, beta=2.0)
+ >>> 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 BinaryF1Score(BinaryFBetaScore):
  r"""Compute F-1 score for binary tasks.
@@ -475,6 +610,47 @@ def __init__(
  **kwargs,
  )
 
+ 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
+
+ >>> from torch import rand, randint
+ >>> # Example plotting a single value
+ >>> from torchmetrics.classification import BinaryF1Score
+ >>> metric = BinaryF1Score()
+ >>> metric.update(rand(10), randint(2,(10,)))
+ >>> fig_, ax_ = metric.plot()
+
+ .. plot::
+ :scale: 75
+
+ >>> from torch import rand, randint
+ >>> # Example plotting multiple values
+ >>> from torchmetrics.classification import BinaryF1Score
+ >>> metric = BinaryF1Score()
+ >>> values = [ ]
+ >>> for _ in range(10):
+ ... values.append(metric(rand(10), randint(2,(10,))))
+ >>> fig_, ax_ = metric.plot(values)
+ """
+ return self._plot(val, ax)
+
 
 class MulticlassF1Score(MulticlassFBetaScore):
  r"""Compute F-1 score for multiclass tasks.
@@ -598,6 +774,47 @@ def __init__(
  **kwargs,
  )
 
+ 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
+
+ >>> from torch import randint
+ >>> # Example plotting a single value per class
+ >>> from torchmetrics.classification import MulticlassF1Score
+ >>> metric = MulticlassF1Score(num_classes=3, average=None)
+ >>> metric.update(randint(3, (20,)), randint(3, (20,)))
+ >>> fig_, ax_ = metric.plot()
+
+ .. plot::
+ :scale: 75
+
+ >>> from torch import randint
+ >>> # Example plotting a multiple values per class
+ >>> from torchmetrics.classification import MulticlassF1Score
+ >>> metric = MulticlassF1Score(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 MultilabelF1Score(MultilabelFBetaScore):
  r"""Compute F-1 score for multilabel tasks.
@@ -717,6 +934,47 @@ def __init__(
  **kwargs,
  )
 
+ 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 rand, randint
+ >>> # Example plotting a single value
+ >>> from torchmetrics.classification import MultilabelF1Score
+ >>> metric = MultilabelF1Score(num_labels=3)
+ >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3)))
+ >>> fig_, ax_ = metric.plot()
+
+ .. plot::
+ :scale: 75
+
+ >>> from torch import rand, randint
+ >>> # Example plotting multiple values
+ >>> from torchmetrics.classification import MultilabelF1Score
+ >>> metric = MultilabelF1Score(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 FBetaScore:
  r"""Compute `F-score`_ metric.