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# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 collections.abc import Sequence
from typing import Any, Optional, Union
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.classification.base import _ClassificationTaskWrapper
from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
from torchmetrics.functional.classification.precision_recall import (
_precision_recall_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__ = [
"BinaryPrecision.plot",
"MulticlassPrecision.plot",
"MultilabelPrecision.plot",
"BinaryRecall.plot",
"MulticlassRecall.plot",
"MultilabelRecall.plot",
]
class BinaryPrecision(BinaryStatScores):
r"""Compute `Precision`_ for binary tasks.
.. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}
Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives
respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is
encountered a score of `zero_division` (0 or 1, default is 0) is returned.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): A int or float tensor of shape ``(N, ...)``. If preds is a floating point
tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per
element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``bp`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, the metric returns a scalar
value. If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a
scalar value per sample.
If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
which the reduction will then be applied over instead of the sample dimension ``N``.
Args:
threshold: Threshold for transforming probability to binary {0,1} predictions
multidim_average:
Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
- ``global``: Additional dimensions are flatted along the batch dimension
- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
The statistics in this case are calculated over the additional dimensions.
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
zero_division: Should be `0` or `1`. The value returned when :math:`\text{TP} + \text{FP} = 0`.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import BinaryPrecision
>>> target = tensor([0, 1, 0, 1, 0, 1])
>>> preds = tensor([0, 0, 1, 1, 0, 1])
>>> metric = BinaryPrecision()
>>> metric(preds, target)
tensor(0.6667)
Example (preds is float tensor):
>>> from torchmetrics.classification import BinaryPrecision
>>> target = tensor([0, 1, 0, 1, 0, 1])
>>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92])
>>> metric = BinaryPrecision()
>>> metric(preds, target)
tensor(0.6667)
Example (multidim tensors):
>>> from torchmetrics.classification import BinaryPrecision
>>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> metric = BinaryPrecision(multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.4000, 0.0000])
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
def compute(self) -> Tensor:
"""Compute metric."""
tp, fp, tn, fn = self._final_state()
return _precision_recall_reduce(
"precision",
tp,
fp,
tn,
fn,
average="binary",
multidim_average=self.multidim_average,
zero_division=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
>>> from torch import rand, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import BinaryPrecision
>>> metric = BinaryPrecision()
>>> 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 BinaryPrecision
>>> metric = BinaryPrecision()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(rand(10), randint(2,(10,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class MulticlassPrecision(MulticlassStatScores):
r"""Compute `Precision`_ for multiclass tasks.
.. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}
Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives
respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is
encountered for any class, the metric for that class will be set to `zero_division` (0 or 1, default is 0) and
the overall metric may therefore be affected in turn.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)``.
If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert
probabilities/logits into an int tensor.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``mcp`` (:class:`~torch.Tensor`): The returned shape depends on the ``average`` and ``multidim_average``
arguments:
- If ``multidim_average`` is set to ``global``:
- If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
- If ``average=None/'none'``, the shape will be ``(C,)``
- If ``multidim_average`` is set to ``samplewise``:
- If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
- If ``average=None/'none'``, the shape will be ``(N, C)``
If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
which the reduction will then be applied over instead of the sample dimension ``N``.
Args:
num_classes: Integer specifying the number of classes
average:
Defines the reduction that is applied over labels. Should be one of the following:
- ``micro``: Sum statistics over all labels
- ``macro``: Calculate statistics for each label and average them
- ``weighted``: calculates statistics for each label and computes weighted average using their support
- ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
top_k:
Number of highest probability or logit score predictions considered to find the correct label.
Only works when ``preds`` contain probabilities/logits.
multidim_average:
Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
- ``global``: Additional dimensions are flatted along the batch dimension
- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
The statistics in this case are calculated over the additional dimensions.
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
zero_division: Should be `0` or `1`. The value returned when :math:`\text{TP} + \text{FP} = 0`.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import MulticlassPrecision
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([2, 1, 0, 1])
>>> metric = MulticlassPrecision(num_classes=3)
>>> metric(preds, target)
tensor(0.8333)
>>> mcp = MulticlassPrecision(num_classes=3, average=None)
>>> mcp(preds, target)
tensor([1.0000, 0.5000, 1.0000])
Example (preds is float tensor):
>>> from torchmetrics.classification import MulticlassPrecision
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([[0.16, 0.26, 0.58],
... [0.22, 0.61, 0.17],
... [0.71, 0.09, 0.20],
... [0.05, 0.82, 0.13]])
>>> metric = MulticlassPrecision(num_classes=3)
>>> metric(preds, target)
tensor(0.8333)
>>> mcp = MulticlassPrecision(num_classes=3, average=None)
>>> mcp(preds, target)
tensor([1.0000, 0.5000, 1.0000])
Example (multidim tensors):
>>> from torchmetrics.classification import MulticlassPrecision
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassPrecision(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.3889, 0.2778])
>>> mcp = MulticlassPrecision(num_classes=3, multidim_average='samplewise', average=None)
>>> mcp(preds, target)
tensor([[0.6667, 0.0000, 0.5000],
[0.0000, 0.5000, 0.3333]])
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
plot_legend_name: str = "Class"
def compute(self) -> Tensor:
"""Compute metric."""
tp, fp, tn, fn = self._final_state()
return _precision_recall_reduce(
"precision",
tp,
fp,
tn,
fn,
average=self.average,
multidim_average=self.multidim_average,
top_k=self.top_k,
zero_division=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
>>> from torch import randint
>>> # Example plotting a single value per class
>>> from torchmetrics.classification import MulticlassPrecision
>>> metric = MulticlassPrecision(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 MulticlassPrecision
>>> metric = MulticlassPrecision(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 MultilabelPrecision(MultilabelStatScores):
r"""Compute `Precision`_ for multilabel tasks.
.. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}
Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives
respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is
encountered for any label, the metric for that label will be set to `zero_division` (0 or 1, default is 0) and
the overall metric may therefore be affected in turn.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): An int tensor or float tensor of shape ``(N, C, ...)``.
If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and
will auto apply sigmoid per element. Additionally, we convert to int tensor with thresholding using the value
in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``.
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``mlp`` (:class:`~torch.Tensor`): The returned shape depends on the ``average`` and ``multidim_average``
arguments:
- If ``multidim_average`` is set to ``global``:
- If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
- If ``average=None/'none'``, the shape will be ``(C,)``
- If ``multidim_average`` is set to ``samplewise``:
- If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
- If ``average=None/'none'``, the shape will be ``(N, C)``
If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
which the reduction will then be applied over instead of the sample dimension ``N``.
Args:
num_labels: Integer specifying the number of labels
threshold: Threshold for transforming probability to binary (0,1) predictions
average:
Defines the reduction that is applied over labels. Should be one of the following:
- ``micro``: Sum statistics over all labels
- ``macro``: Calculate statistics for each label and average them
- ``weighted``: calculates statistics for each label and computes weighted average using their support
- ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
multidim_average:
Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
- ``global``: Additional dimensions are flatted along the batch dimension
- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
The statistics in this case are calculated over the additional dimensions.
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
zero_division: Should be `0` or `1`. The value returned when :math:`\text{TP} + \text{FP} = 0`.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import MultilabelPrecision
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0, 0, 1], [1, 0, 1]])
>>> metric = MultilabelPrecision(num_labels=3)
>>> metric(preds, target)
tensor(0.5000)
>>> mlp = MultilabelPrecision(num_labels=3, average=None)
>>> mlp(preds, target)
tensor([1.0000, 0.0000, 0.5000])
Example (preds is float tensor):
>>> from torchmetrics.classification import MultilabelPrecision
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> metric = MultilabelPrecision(num_labels=3)
>>> metric(preds, target)
tensor(0.5000)
>>> mlp = MultilabelPrecision(num_labels=3, average=None)
>>> mlp(preds, target)
tensor([1.0000, 0.0000, 0.5000])
Example (multidim tensors):
>>> from torchmetrics.classification import MultilabelPrecision
>>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> metric = MultilabelPrecision(num_labels=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.3333, 0.0000])
>>> mlp = MultilabelPrecision(num_labels=3, multidim_average='samplewise', average=None)
>>> mlp(preds, target)
tensor([[0.5000, 0.5000, 0.0000],
[0.0000, 0.0000, 0.0000]])
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
plot_legend_name: str = "Label"
def compute(self) -> Tensor:
"""Compute metric."""
tp, fp, tn, fn = self._final_state()
return _precision_recall_reduce(
"precision",
tp,
fp,
tn,
fn,
average=self.average,
multidim_average=self.multidim_average,
multilabel=True,
zero_division=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
>>> from torch import rand, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import MultilabelPrecision
>>> metric = MultilabelPrecision(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 MultilabelPrecision
>>> metric = MultilabelPrecision(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 BinaryRecall(BinaryStatScores):
r"""Compute `Recall`_ for binary tasks.
.. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}
Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives
respectively. The metric is only proper defined when :math:`\text{TP} + \text{FN} \neq 0`. If this case is
encountered a score of `zero_division` (0 or 1, default is 0) is returned.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): An int tensor or float tensor of shape ``(N, ...)``. If preds is a
floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply
sigmoid per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``br`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, the metric returns a scalar
value. If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of
a scalar value per sample.
If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
which the reduction will then be applied over instead of the sample dimension ``N``.
Args:
threshold: Threshold for transforming probability to binary {0,1} predictions
multidim_average:
Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
- ``global``: Additional dimensions are flatted along the batch dimension
- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
The statistics in this case are calculated over the additional dimensions.
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
zero_division: Should be `0` or `1`. The value returned when :math:`\text{TP} + \text{FN} = 0`.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import BinaryRecall
>>> target = tensor([0, 1, 0, 1, 0, 1])
>>> preds = tensor([0, 0, 1, 1, 0, 1])
>>> metric = BinaryRecall()
>>> metric(preds, target)
tensor(0.6667)
Example (preds is float tensor):
>>> from torchmetrics.classification import BinaryRecall
>>> target = tensor([0, 1, 0, 1, 0, 1])
>>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92])
>>> metric = BinaryRecall()
>>> metric(preds, target)
tensor(0.6667)
Example (multidim tensors):
>>> from torchmetrics.classification import BinaryRecall
>>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> metric = BinaryRecall(multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.6667, 0.0000])
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
def compute(self) -> Tensor:
"""Compute metric."""
tp, fp, tn, fn = self._final_state()
return _precision_recall_reduce(
"recall",
tp,
fp,
tn,
fn,
average="binary",
multidim_average=self.multidim_average,
zero_division=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
>>> from torch import rand, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import BinaryRecall
>>> metric = BinaryRecall()
>>> 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 BinaryRecall
>>> metric = BinaryRecall()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(rand(10), randint(2,(10,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class MulticlassRecall(MulticlassStatScores):
r"""Compute `Recall`_ for multiclass tasks.
.. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}
Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives
respectively. The metric is only proper defined when :math:`\text{TP} + \text{FN} \neq 0`. If this case is
encountered for any class, the metric for that class will be set to `zero_division` (0 or 1, default is 0) and
the overall metric may therefore be affected in turn.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)``
If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert
probabilities/logits into an int tensor.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``mcr`` (:class:`~torch.Tensor`): The returned shape depends on the ``average`` and ``multidim_average``
arguments:
- If ``multidim_average`` is set to ``global``:
- If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
- If ``average=None/'none'``, the shape will be ``(C,)``
- If ``multidim_average`` is set to ``samplewise``:
- If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
- If ``average=None/'none'``, the shape will be ``(N, C)``
If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
which the reduction will then be applied over instead of the sample dimension ``N``.
Args:
num_classes: Integer specifying the number of classes
average:
Defines the reduction that is applied over labels. Should be one of the following:
- ``micro``: Sum statistics over all labels
- ``macro``: Calculate statistics for each label and average them
- ``weighted``: calculates statistics for each label and computes weighted average using their support
- ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
top_k:
Number of highest probability or logit score predictions considered to find the correct label.
Only works when ``preds`` contain probabilities/logits.
multidim_average:
Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
- ``global``: Additional dimensions are flatted along the batch dimension
- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
The statistics in this case are calculated over the additional dimensions.
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
zero_division: Should be `0` or `1`. The value returned when :math:`\text{TP} + \text{FN} = 0`.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import MulticlassRecall
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([2, 1, 0, 1])
>>> metric = MulticlassRecall(num_classes=3)
>>> metric(preds, target)
tensor(0.8333)
>>> mcr = MulticlassRecall(num_classes=3, average=None)
>>> mcr(preds, target)
tensor([0.5000, 1.0000, 1.0000])
Example (preds is float tensor):
>>> from torchmetrics.classification import MulticlassRecall
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([[0.16, 0.26, 0.58],
... [0.22, 0.61, 0.17],
... [0.71, 0.09, 0.20],
... [0.05, 0.82, 0.13]])
>>> metric = MulticlassRecall(num_classes=3)
>>> metric(preds, target)
tensor(0.8333)
>>> mcr = MulticlassRecall(num_classes=3, average=None)
>>> mcr(preds, target)
tensor([0.5000, 1.0000, 1.0000])
Example (multidim tensors):
>>> from torchmetrics.classification import MulticlassRecall
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassRecall(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.5000, 0.2778])
>>> mcr = MulticlassRecall(num_classes=3, multidim_average='samplewise', average=None)
>>> mcr(preds, target)
tensor([[1.0000, 0.0000, 0.5000],
[0.0000, 0.3333, 0.5000]])
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
plot_legend_name: str = "Class"
def compute(self) -> Tensor:
"""Compute metric."""
tp, fp, tn, fn = self._final_state()
return _precision_recall_reduce(
"recall",
tp,
fp,
tn,
fn,
average=self.average,
multidim_average=self.multidim_average,
top_k=self.top_k,
zero_division=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
>>> from torch import randint
>>> # Example plotting a single value per class
>>> from torchmetrics.classification import MulticlassRecall
>>> metric = MulticlassRecall(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 MulticlassRecall
>>> metric = MulticlassRecall(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 MultilabelRecall(MultilabelStatScores):
r"""Compute `Recall`_ for multilabel tasks.
.. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}
Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives
respectively. The metric is only proper defined when :math:`\text{TP} + \text{FN} \neq 0`. If this case is
encountered for any label, the metric for that label will be set to `zero_division` (0 or 1, default is 0) and
the overall metric may therefore be affected in turn.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(N, C, ...)``. If preds is a floating
point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid
per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``mlr`` (:class:`~torch.Tensor`): The returned shape depends on the ``average`` and ``multidim_average``
arguments:
- If ``multidim_average`` is set to ``global``:
- If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
- If ``average=None/'none'``, the shape will be ``(C,)``
- If ``multidim_average`` is set to ``samplewise``:
- If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
- If ``average=None/'none'``, the shape will be ``(N, C)``
If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
which the reduction will then be applied over instead of the sample dimension ``N``.
Args:
num_labels: Integer specifying the number of labels
threshold: Threshold for transforming probability to binary (0,1) predictions
average:
Defines the reduction that is applied over labels. Should be one of the following:
- ``micro``: Sum statistics over all labels
- ``macro``: Calculate statistics for each label and average them
- ``weighted``: calculates statistics for each label and computes weighted average using their support
- ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
multidim_average:
Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
- ``global``: Additional dimensions are flatted along the batch dimension
- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
The statistics in this case are calculated over the additional dimensions.
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
zero_division: Should be `0` or `1`. The value returned when :math:`\text{TP} + \text{FN} = 0`.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import MultilabelRecall
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0, 0, 1], [1, 0, 1]])
>>> metric = MultilabelRecall(num_labels=3)
>>> metric(preds, target)
tensor(0.6667)
>>> mlr = MultilabelRecall(num_labels=3, average=None)
>>> mlr(preds, target)
tensor([1., 0., 1.])
Example (preds is float tensor):
>>> from torchmetrics.classification import MultilabelRecall
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> metric = MultilabelRecall(num_labels=3)
>>> metric(preds, target)
tensor(0.6667)
>>> mlr = MultilabelRecall(num_labels=3, average=None)
>>> mlr(preds, target)
tensor([1., 0., 1.])
Example (multidim tensors):
>>> from torchmetrics.classification import MultilabelRecall
>>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> metric = MultilabelRecall(num_labels=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.6667, 0.0000])
>>> mlr = MultilabelRecall(num_labels=3, multidim_average='samplewise', average=None)
>>> mlr(preds, target)
tensor([[1., 1., 0.],
[0., 0., 0.]])
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
plot_legend_name: str = "Label"
def compute(self) -> Tensor:
"""Compute metric."""
tp, fp, tn, fn = self._final_state()
return _precision_recall_reduce(
"recall",
tp,
fp,
tn,
fn,
average=self.average,
multidim_average=self.multidim_average,
multilabel=True,
zero_division=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
>>> from torch import rand, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import MultilabelRecall
>>> metric = MultilabelRecall(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 MultilabelRecall
>>> metric = MultilabelRecall(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 Precision(_ClassificationTaskWrapper):
r"""Compute `Precision`_.
.. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}
Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives
respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is
encountered for any class/label, the metric for that class/label will be set to 0 and the overall metric may
therefore be affected in turn.
This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
:class:`~torchmetrics.classification.BinaryPrecision`, :class:`~torchmetrics.classification.MulticlassPrecision` and
:class:`~torchmetrics.classification.MultilabelPrecision` for the specific details of each argument influence and
examples.
Legacy Example:
>>> from torch import tensor
>>> preds = tensor([2, 0, 2, 1])
>>> target = tensor([1, 1, 2, 0])
>>> precision = Precision(task="multiclass", average='macro', num_classes=3)
>>> precision(preds, target)
tensor(0.1667)
>>> precision = Precision(task="multiclass", average='micro', num_classes=3)
>>> precision(preds, target)
tensor(0.2500)
"""
def __new__( # type: ignore[misc]
cls: type["Precision"],
task: Literal["binary", "multiclass", "multilabel"],
threshold: float = 0.5,
num_classes: Optional[int] = None,
num_labels: Optional[int] = None,
average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
multidim_average: Optional[Literal["global", "samplewise"]] = "global",
top_k: Optional[int] = 1,
ignore_index: Optional[int] = None,
validate_args: bool = True,
**kwargs: Any,
) -> Metric:
"""Initialize task metric."""
assert multidim_average is not None # noqa: S101 # needed for mypy
kwargs.update({
"multidim_average": multidim_average,
"ignore_index": ignore_index,
"validate_args": validate_args,
})
task = ClassificationTask.from_str(task)
if task == ClassificationTask.BINARY:
return BinaryPrecision(threshold, **kwargs)
if task == ClassificationTask.MULTICLASS:
if not isinstance(num_classes, int):
raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
if not isinstance(top_k, int):
raise ValueError(f"`top_k` is expected to be `int` but `{type(top_k)} was passed.`")
return MulticlassPrecision(num_classes, top_k, average, **kwargs)
if task == ClassificationTask.MULTILABEL:
if not isinstance(num_labels, int):
raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`")
return MultilabelPrecision(num_labels, threshold, average, **kwargs)
raise ValueError(f"Task {task} not supported!")
class Recall(_ClassificationTaskWrapper):
r"""Compute `Recall`_.
.. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}
Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and
false negatives respectively. The metric is only proper defined when :math:`\text{TP} + \text{FN} \neq 0`. If this
case is encountered for any class/label, the metric for that class/label will be set to 0 and the overall metric may
therefore be affected in turn.
This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
:class:`~torchmetrics.classification.BinaryRecall`,
:class:`~torchmetrics.classification.MulticlassRecall` and :class:`~torchmetrics.classification.MultilabelRecall`
for the specific details of each argument influence and examples.
Legacy Example:
>>> from torch import tensor
>>> preds = tensor([2, 0, 2, 1])
>>> target = tensor([1, 1, 2, 0])
>>> recall = Recall(task="multiclass", average='macro', num_classes=3)
>>> recall(preds, target)
tensor(0.3333)
>>> recall = Recall(task="multiclass", average='micro', num_classes=3)
>>> recall(preds, target)
tensor(0.2500)
"""
def __new__( # type: ignore[misc]
cls: type["Recall"],
task: Literal["binary", "multiclass", "multilabel"],
threshold: float = 0.5,
num_classes: Optional[int] = None,
num_labels: Optional[int] = None,
average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
multidim_average: Optional[Literal["global", "samplewise"]] = "global",
top_k: Optional[int] = 1,
ignore_index: Optional[int] = None,
validate_args: bool = True,
**kwargs: Any,
) -> Metric:
"""Initialize task metric."""
task = ClassificationTask.from_str(task)
assert multidim_average is not None # noqa: S101 # needed for mypy
kwargs.update({
"multidim_average": multidim_average,
"ignore_index": ignore_index,
"validate_args": validate_args,
})
if task == ClassificationTask.BINARY:
return BinaryRecall(threshold, **kwargs)
if task == ClassificationTask.MULTICLASS:
if not isinstance(num_classes, int):
raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
if not isinstance(top_k, int):
raise ValueError(f"`top_k` is expected to be `int` but `{type(top_k)} was passed.`")
return MulticlassRecall(num_classes, top_k, average, **kwargs)
if task == ClassificationTask.MULTILABEL:
if not isinstance(num_labels, int):
raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`")
return MultilabelRecall(num_labels, threshold, average, **kwargs)
return None # type: ignore[return-value]
|