# 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 import torch from torch import Tensor from torchmetrics.functional.classification.ranking import ( _multilabel_confusion_matrix_arg_validation, _multilabel_confusion_matrix_format, _multilabel_coverage_error_update, _multilabel_ranking_average_precision_update, _multilabel_ranking_loss_update, _multilabel_ranking_tensor_validation, _ranking_reduce, ) from torchmetrics.metric import Metric from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = [ "MultilabelCoverageError.plot", "MultilabelRankingAveragePrecision.plot", "MultilabelRankingLoss.plot", ] class MultilabelCoverageError(Metric): """Compute `Multilabel coverage error`_. The score measure how far we need to go through the ranked scores to cover all true labels. The best value is equal to the average number of labels in the target tensor per sample. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mlce`` (:class:`~torch.Tensor`): A tensor containing the multilabel coverage error. Args: num_labels: Integer specifying the number of labels 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. Example: >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelCoverageError >>> preds = rand(10, 5) >>> target = randint(2, (10, 5)) >>> mlce = MultilabelCoverageError(num_labels=5) >>> mlce(preds, target) tensor(3.9000) """ higher_is_better: bool = False is_differentiable: bool = False 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, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ) -> None: super().__init__(**kwargs) if validate_args: _multilabel_confusion_matrix_arg_validation(num_labels, threshold=0.0, ignore_index=ignore_index) self.validate_args = validate_args self.num_labels = num_labels self.ignore_index = ignore_index self.add_state("measure", torch.tensor(0.0), dist_reduce_fx="sum") self.add_state("total", torch.tensor(0.0), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: """Update metric states.""" if self.validate_args: _multilabel_ranking_tensor_validation(preds, target, self.num_labels, self.ignore_index) preds, target = _multilabel_confusion_matrix_format( preds, target, self.num_labels, threshold=0.0, ignore_index=self.ignore_index, should_threshold=False ) measure, num_elements = _multilabel_coverage_error_update(preds, target) if not isinstance(self.measure, Tensor): raise TypeError(f"Expected 'self.measure' to be of type Tensor, but got {type(self.measure)}.") if not isinstance(self.total, Tensor): raise TypeError(f"Expected 'self.total' to be of type Tensor, but got {type(self.total)}.") self.measure += measure self.total += num_elements def compute(self) -> Tensor: """Compute metric.""" if not isinstance(self.measure, Tensor): raise TypeError(f"Expected 'self.measure' to be of type Tensor, but got {type(self.measure)}.") if not isinstance(self.total, Tensor): raise TypeError(f"Expected 'self.total' to be of type Tensor, but got {type(self.total)}.") return _ranking_reduce(self.measure, int(self.total.item())) 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 MultilabelCoverageError >>> metric = MultilabelCoverageError(num_labels=3) >>> metric.update(rand(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 MultilabelCoverageError >>> metric = MultilabelCoverageError(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(20, 3), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax) class MultilabelRankingAveragePrecision(Metric): """Compute label ranking average precision score for multilabel data [1]. The score is the average over each ground truth label assigned to each sample of the ratio of true vs. total labels with lower score. Best score is 1. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mlrap`` (:class:`~torch.Tensor`): A tensor containing the multilabel ranking average precision. Args: num_labels: Integer specifying the number of labels 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. Example: >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelRankingAveragePrecision >>> preds = rand(10, 5) >>> target = randint(2, (10, 5)) >>> mlrap = MultilabelRankingAveragePrecision(num_labels=5) >>> mlrap(preds, target) tensor(0.7744) """ higher_is_better: bool = True is_differentiable: bool = False 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, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ) -> None: super().__init__(**kwargs) if validate_args: _multilabel_confusion_matrix_arg_validation(num_labels, threshold=0.0, ignore_index=ignore_index) self.validate_args = validate_args self.num_labels = num_labels self.ignore_index = ignore_index self.add_state("measure", torch.tensor(0.0), dist_reduce_fx="sum") self.add_state("total", torch.tensor(0.0), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: """Update metric states.""" if self.validate_args: _multilabel_ranking_tensor_validation(preds, target, self.num_labels, self.ignore_index) preds, target = _multilabel_confusion_matrix_format( preds, target, self.num_labels, threshold=0.0, ignore_index=self.ignore_index, should_threshold=False ) if not isinstance(self.measure, Tensor): raise TypeError(f"Expected 'self.measure' to be of type Tensor, but got {type(self.measure)}.") if not isinstance(self.total, Tensor): raise TypeError(f"Expected 'self.total' to be of type Tensor, but got {type(self.total)}.") measure, num_elements = _multilabel_ranking_average_precision_update(preds, target) self.measure += measure self.total += num_elements def compute(self) -> Tensor: """Compute metric.""" if not isinstance(self.measure, Tensor): raise TypeError(f"Expected 'self.measure' to be of type Tensor, but got {type(self.measure)}.") if not isinstance(self.total, Tensor): raise TypeError(f"Expected 'self.total' to be of type Tensor, but got {type(self.total)}.") return _ranking_reduce(self.measure, int(self.total.item())) 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 MultilabelRankingAveragePrecision >>> metric = MultilabelRankingAveragePrecision(num_labels=3) >>> metric.update(rand(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 MultilabelRankingAveragePrecision >>> metric = MultilabelRankingAveragePrecision(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(20, 3), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax) class MultilabelRankingLoss(Metric): """Compute the label ranking loss for multilabel data [1]. The score is corresponds to the average number of label pairs that are incorrectly ordered given some predictions weighted by the size of the label set and the number of labels not in the label set. The best score is 0. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mlrl`` (:class:`~torch.Tensor`): A tensor containing the multilabel ranking loss. Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifying the number of labels 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. Example: >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelRankingLoss >>> preds = rand(10, 5) >>> target = randint(2, (10, 5)) >>> mlrl = MultilabelRankingLoss(num_labels=5) >>> mlrl(preds, target) tensor(0.4167) """ higher_is_better: bool = False is_differentiable: bool = False 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, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ) -> None: super().__init__(**kwargs) if validate_args: _multilabel_confusion_matrix_arg_validation(num_labels, threshold=0.0, ignore_index=ignore_index) self.validate_args = validate_args self.num_labels = num_labels self.ignore_index = ignore_index self.add_state("measure", torch.tensor(0.0), dist_reduce_fx="sum") self.add_state("total", torch.tensor(0.0), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: """Update metric states.""" if self.validate_args: _multilabel_ranking_tensor_validation(preds, target, self.num_labels, self.ignore_index) preds, target = _multilabel_confusion_matrix_format( preds, target, self.num_labels, threshold=0.0, ignore_index=self.ignore_index, should_threshold=False ) if not isinstance(self.measure, Tensor): raise TypeError(f"Expected 'self.measure' to be of type Tensor, but got {type(self.measure)}.") if not isinstance(self.total, Tensor): raise TypeError(f"Expected 'self.total' to be of type Tensor, but got {type(self.total)}.") measure, num_elements = _multilabel_ranking_loss_update(preds, target) self.measure += measure self.total += num_elements def compute(self) -> Tensor: """Compute metric.""" if not isinstance(self.measure, Tensor): raise TypeError(f"Expected 'self.measure' to be of type Tensor, but got {type(self.measure)}.") if not isinstance(self.total, Tensor): raise TypeError(f"Expected 'self.total' to be of type Tensor, but got {type(self.total)}.") return _ranking_reduce(self.measure, int(self.total.item())) 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 MultilabelRankingLoss >>> metric = MultilabelRankingLoss(num_labels=3) >>> metric.update(rand(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 MultilabelRankingLoss >>> metric = MultilabelRankingLoss(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(20, 3), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax)