# 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.f_beta import ( _binary_fbeta_score_arg_validation, _fbeta_reduce, _multiclass_fbeta_score_arg_validation, _multilabel_fbeta_score_arg_validation, ) 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): r"""Compute `F-score`_ metric for binary tasks. .. math:: F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. 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: - ``bfbs`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument: - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)`` 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: beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight 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 \wedge \text{TP} + \text{FN} = 0`. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import BinaryFBetaScore >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0, 0, 1, 1, 0, 1]) >>> metric = BinaryFBetaScore(beta=2.0) >>> metric(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryFBetaScore >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> metric = BinaryFBetaScore(beta=2.0) >>> metric(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.classification import BinaryFBetaScore >>> 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 = BinaryFBetaScore(beta=2.0, multidim_average='samplewise') >>> metric(preds, target) tensor([0.5882, 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 __init__( self, beta: float, threshold: float = 0.5, multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any, ) -> None: super().__init__( threshold=threshold, multidim_average=multidim_average, ignore_index=ignore_index, validate_args=False, **kwargs, ) if validate_args: _binary_fbeta_score_arg_validation(beta, threshold, multidim_average, ignore_index, zero_division) self.validate_args = validate_args self.zero_division = zero_division self.beta = beta def compute(self) -> Tensor: """Compute metric.""" tp, fp, tn, fn = self._final_state() return _fbeta_reduce( tp, fp, tn, fn, self.beta, 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 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. .. math:: F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. 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: - ``mcfbs`` (:class:`~torch.Tensor`): A tensor whose 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: beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight 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 \wedge \text{TP} + \text{FN} = 0`. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import MulticlassFBetaScore >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> metric = MulticlassFBetaScore(beta=2.0, num_classes=3) >>> metric(preds, target) tensor(0.7963) >>> mcfbs = MulticlassFBetaScore(beta=2.0, num_classes=3, average=None) >>> mcfbs(preds, target) tensor([0.5556, 0.8333, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.classification import MulticlassFBetaScore >>> 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 = MulticlassFBetaScore(beta=2.0, num_classes=3) >>> metric(preds, target) tensor(0.7963) >>> mcfbs = MulticlassFBetaScore(beta=2.0, num_classes=3, average=None) >>> mcfbs(preds, target) tensor([0.5556, 0.8333, 1.0000]) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassFBetaScore >>> 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 = MulticlassFBetaScore(beta=2.0, num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.4697, 0.2706]) >>> mcfbs = MulticlassFBetaScore(beta=2.0, num_classes=3, multidim_average='samplewise', average=None) >>> mcfbs(preds, target) tensor([[0.9091, 0.0000, 0.5000], [0.0000, 0.3571, 0.4545]]) """ 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 __init__( self, beta: float, num_classes: int, top_k: int = 1, average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro", multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any, ) -> None: super().__init__( num_classes=num_classes, top_k=top_k, average=average, multidim_average=multidim_average, ignore_index=ignore_index, validate_args=False, **kwargs, ) if validate_args: _multiclass_fbeta_score_arg_validation( beta, num_classes, top_k, average, multidim_average, ignore_index, zero_division ) self.validate_args = validate_args self.zero_division = zero_division self.beta = beta def compute(self) -> Tensor: """Compute metric.""" tp, fp, tn, fn = self._final_state() return _fbeta_reduce( tp, fp, tn, fn, self.beta, average=self.average, 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 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. .. math:: F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. 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: - ``mlfbs`` (:class:`~torch.Tensor`): A tensor whose 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: beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight 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 \wedge \text{TP} + \text{FN} = 0`. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import MultilabelFBetaScore >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelFBetaScore(beta=2.0, num_labels=3) >>> metric(preds, target) tensor(0.6111) >>> mlfbs = MultilabelFBetaScore(beta=2.0, num_labels=3, average=None) >>> mlfbs(preds, target) tensor([1.0000, 0.0000, 0.8333]) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelFBetaScore >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> metric = MultilabelFBetaScore(beta=2.0, num_labels=3) >>> metric(preds, target) tensor(0.6111) >>> mlfbs = MultilabelFBetaScore(beta=2.0, num_labels=3, average=None) >>> mlfbs(preds, target) tensor([1.0000, 0.0000, 0.8333]) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelFBetaScore >>> 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 = MultilabelFBetaScore(num_labels=3, beta=2.0, multidim_average='samplewise') >>> metric(preds, target) tensor([0.5556, 0.0000]) >>> mlfbs = MultilabelFBetaScore(num_labels=3, beta=2.0, multidim_average='samplewise', average=None) >>> mlfbs(preds, target) tensor([[0.8333, 0.8333, 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 __init__( self, beta: float, num_labels: int, threshold: float = 0.5, average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro", multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any, ) -> None: super().__init__( num_labels=num_labels, threshold=threshold, average=average, multidim_average=multidim_average, ignore_index=ignore_index, validate_args=False, **kwargs, ) if validate_args: _multilabel_fbeta_score_arg_validation( beta, num_labels, threshold, average, multidim_average, ignore_index, zero_division ) self.validate_args = validate_args self.zero_division = zero_division self.beta = beta def compute(self) -> Tensor: """Compute metric.""" tp, fp, tn, fn = self._final_state() return _fbeta_reduce( tp, fp, tn, fn, self.beta, 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 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. .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. 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 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: - ``bf1s`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument: - 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 \wedge \text{TP} + \text{FN} = 0`. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import BinaryF1Score >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0, 0, 1, 1, 0, 1]) >>> metric = BinaryF1Score() >>> metric(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryF1Score >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> metric = BinaryF1Score() >>> metric(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.classification import BinaryF1Score >>> 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 = BinaryF1Score(multidim_average='samplewise') >>> metric(preds, target) tensor([0.5000, 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 __init__( self, threshold: float = 0.5, multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any, ) -> None: super().__init__( beta=1.0, threshold=threshold, multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args, zero_division=zero_division, **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. .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. 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: - ``mcf1s`` (:class:`~torch.Tensor`): A tensor whose 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: preds: Tensor with predictions target: Tensor with true labels 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 \wedge \text{TP} + \text{FN} = 0`. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import MulticlassF1Score >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> metric = MulticlassF1Score(num_classes=3) >>> metric(preds, target) tensor(0.7778) >>> mcf1s = MulticlassF1Score(num_classes=3, average=None) >>> mcf1s(preds, target) tensor([0.6667, 0.6667, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.classification import MulticlassF1Score >>> 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 = MulticlassF1Score(num_classes=3) >>> metric(preds, target) tensor(0.7778) >>> mcf1s = MulticlassF1Score(num_classes=3, average=None) >>> mcf1s(preds, target) tensor([0.6667, 0.6667, 1.0000]) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassF1Score >>> 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 = MulticlassF1Score(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.4333, 0.2667]) >>> mcf1s = MulticlassF1Score(num_classes=3, multidim_average='samplewise', average=None) >>> mcf1s(preds, target) tensor([[0.8000, 0.0000, 0.5000], [0.0000, 0.4000, 0.4000]]) """ 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 __init__( self, num_classes: int, top_k: int = 1, average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro", multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any, ) -> None: super().__init__( beta=1.0, num_classes=num_classes, top_k=top_k, average=average, multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args, zero_division=zero_division, **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. .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. 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: - ``mlf1s`` (:class:`~torch.Tensor`): A tensor whose 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 \wedge \text{TP} + \text{FN} = 0`. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import MultilabelF1Score >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelF1Score(num_labels=3) >>> metric(preds, target) tensor(0.5556) >>> mlf1s = MultilabelF1Score(num_labels=3, average=None) >>> mlf1s(preds, target) tensor([1.0000, 0.0000, 0.6667]) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelF1Score >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> metric = MultilabelF1Score(num_labels=3) >>> metric(preds, target) tensor(0.5556) >>> mlf1s = MultilabelF1Score(num_labels=3, average=None) >>> mlf1s(preds, target) tensor([1.0000, 0.0000, 0.6667]) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelF1Score >>> 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 = MultilabelF1Score(num_labels=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.4444, 0.0000]) >>> mlf1s = MultilabelF1Score(num_labels=3, multidim_average='samplewise', average=None) >>> mlf1s(preds, target) tensor([[0.6667, 0.6667, 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 __init__( self, num_labels: int, threshold: float = 0.5, average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro", multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any, ) -> None: super().__init__( beta=1.0, num_labels=num_labels, threshold=threshold, average=average, multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args, zero_division=zero_division, **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(_ClassificationTaskWrapper): r"""Compute `F-score`_ metric. .. math:: F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. If this case is encountered for any class/label, the metric for that class/label will be set to `zero_division` (0 or 1, default is 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.BinaryFBetaScore`, :class:`~torchmetrics.classification.MulticlassFBetaScore` and :class:`~torchmetrics.classification.MultilabelFBetaScore` for the specific details of each argument influence and examples. Legcy Example: >>> from torch import tensor >>> target = tensor([0, 1, 2, 0, 1, 2]) >>> preds = tensor([0, 2, 1, 0, 0, 1]) >>> f_beta = FBetaScore(task="multiclass", num_classes=3, beta=0.5) >>> f_beta(preds, target) tensor(0.3333) """ def __new__( # type: ignore[misc] cls: type["FBetaScore"], task: Literal["binary", "multiclass", "multilabel"], beta: float = 1.0, 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, zero_division: float = 0, **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, "zero_division": zero_division, }) if task == ClassificationTask.BINARY: return BinaryFBetaScore(beta, 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 MulticlassFBetaScore(beta, 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 MultilabelFBetaScore(beta, num_labels, threshold, average, **kwargs) raise ValueError(f"Task {task} not supported!") class F1Score(_ClassificationTaskWrapper): r"""Compute F-1 score. .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. If this case is encountered for any class/label, the metric for that class/label will be set to `zero_division` (0 or 1, default is 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.BinaryF1Score`, :class:`~torchmetrics.classification.MulticlassF1Score` and :class:`~torchmetrics.classification.MultilabelF1Score` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([0, 1, 2, 0, 1, 2]) >>> preds = tensor([0, 2, 1, 0, 0, 1]) >>> f1 = F1Score(task="multiclass", num_classes=3) >>> f1(preds, target) tensor(0.3333) """ def __new__( # type: ignore[misc] cls: type["F1Score"], 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, zero_division: float = 0, **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, "zero_division": zero_division, }) if task == ClassificationTask.BINARY: return BinaryF1Score(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 MulticlassF1Score(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 MultilabelF1Score(num_labels, threshold, average, **kwargs) raise ValueError(f"Task {task} not supported!")