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AUROC

Module Interface

class torchmetrics.AUROC(num_classes=None, pos_label=None, average='macro', max_fpr=None, task=None, thresholds=None, num_labels=None, ignore_index=None, validate_args=True, **kwargs)[source]

Note

From v0.10 an ‘binary_*’, ‘multiclass_*’, `’multilabel_*’ version now exist of each classification metric. Moving forward we recommend using these versions. This base metric will still work as it did prior to v0.10 until v0.11. From v0.11 the task argument introduced in this metric will be required and the general order of arguments may change, such that this metric will just function as an single entrypoint to calling the three specialized versions.

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC). Works for both binary, multilabel and multiclass problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.

Forward accepts

  • preds (float tensor): (N, ...) (binary) or (N, C, ...) (multiclass) tensor with probabilities, where C is the number of classes.

  • target (long tensor): (N, ...) or (N, C, ...) with integer labels

For non-binary input, if the preds and target tensor have the same size the input will be interpretated as multilabel and if preds have one dimension more than the target tensor the input will be interpretated as multiclass.

Note

If either the positive class or negative class is completly missing in the target tensor, the auroc score is meaningless in this case and a score of 0 will be returned together with an warning.

Parameters
  • num_classes (Optional[int]) –

    integer with number of classes for multi-label and multiclass problems.

    Should be set to None for binary problems

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translated to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0, num_classes-1]

  • average (Optional[Literal[‘micro’, ‘macro’, ‘weighted’, ‘none’]]) –

    • 'micro' computes metric globally. Only works for multilabel problems

    • 'macro' computes metric for each class and uniformly averages them

    • 'weighted' computes metric for each class and does a weighted-average, where each class is weighted by their support (accounts for class imbalance)

    • None computes and returns the metric per class

  • max_fpr (Optional[float]) – If not None, calculates standardized partial AUC over the range [0, max_fpr]. Should be a float between 0 and 1.

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises
  • ValueError – If average is none of None, "macro" or "weighted".

  • ValueError – If max_fpr is not a float in the range (0, 1].

  • RuntimeError – If PyTorch version is below 1.6 since max_fpr requires torch.bucketize which is not available below 1.6.

  • ValueError – If the mode of data (binary, multi-label, multi-class) changes between batches.

Example (binary case):
>>> from torchmetrics import AUROC
>>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
>>> target = torch.tensor([0, 0, 1, 1, 1])
>>> auroc = AUROC(pos_label=1)
>>> auroc(preds, target)
tensor(0.5000)
Example (multiclass case):
>>> preds = torch.tensor([[0.90, 0.05, 0.05],
...                       [0.05, 0.90, 0.05],
...                       [0.05, 0.05, 0.90],
...                       [0.85, 0.05, 0.10],
...                       [0.10, 0.10, 0.80]])
>>> target = torch.tensor([0, 1, 1, 2, 2])
>>> auroc = AUROC(num_classes=3)
>>> auroc(preds, target)
tensor(0.7778)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Computes AUROC based on inputs passed in to update previously.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model (probabilities, or labels)

  • target (Tensor) – Ground truth labels

Return type

None

BinaryAUROC

class torchmetrics.classification.BinaryAUROC(max_fpr=None, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) for binary tasks. The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing.

Accepts the following input tensors:

  • preds (float tensor): (N, ...). 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 (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size \mathcal{O}(n_{samples}) whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size \mathcal{O}(n_{thresholds}) (constant memory).

Parameters
  • max_fpr (Optional[float]) – If not None, calculates standardized partial AUC over the range [0, max_fpr].

  • thresholds (Union[int, List[float], Tensor, None]) –

    Can be one of:

    • If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.

    • If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.

    • If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation

    • If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.

  • validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns

A single scalar with the auroc score

Example

>>> from torchmetrics.classification import BinaryAUROC
>>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
>>> target = torch.tensor([0, 1, 1, 0])
>>> metric = BinaryAUROC(thresholds=None)
>>> metric(preds, target)
tensor(0.5000)
>>> metric = BinaryAUROC(thresholds=5)
>>> metric(preds, target)
tensor(0.5000)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type

Tensor

MulticlassAUROC

class torchmetrics.classification.MulticlassAUROC(num_classes, average='macro', thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) for multiclass tasks. The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing.

Accepts the following input tensors:

  • preds (float tensor): (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 softmax per sample.

  • target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size \mathcal{O}(n_{samples}) whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size \mathcal{O}(n_{thresholds} \times n_{classes}) (constant memory).

Parameters
  • num_classes (int) – Integer specifing the number of classes

  • average (Optional[Literal[‘macro’, ‘weighted’, ‘none’]]) –

    Defines the reduction that is applied over classes. Should be one of the following:

    • macro: Calculate score for each class and average them

    • weighted: Calculates score for each class and computes weighted average using their support

    • "none" or None: Calculates score for each class and applies no reduction

  • thresholds (Union[int, List[float], Tensor, None]) –

    Can be one of:

    • If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.

    • If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.

    • If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation

    • If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.

  • validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns

If average=None|”none” then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class. If average=”macro”|”weighted” then a single scalar is returned.

Example

>>> from torchmetrics.classification import MulticlassAUROC
>>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                       [0.05, 0.75, 0.05, 0.05, 0.05],
...                       [0.05, 0.05, 0.75, 0.05, 0.05],
...                       [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> metric = MulticlassAUROC(num_classes=5, average="macro", thresholds=None)
>>> metric(preds, target)
tensor(0.5333)
>>> metric = MulticlassAUROC(num_classes=5, average=None, thresholds=None)
>>> metric(preds, target)
tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000])
>>> metric = MulticlassAUROC(num_classes=5, average="macro", thresholds=5)
>>> metric(preds, target)
tensor(0.5333)
>>> metric = MulticlassAUROC(num_classes=5, average=None, thresholds=5)
>>> metric(preds, target)
tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type

Tensor

MultilabelAUROC

class torchmetrics.classification.MultilabelAUROC(num_labels, average='macro', thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) for multilabel tasks. The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing.

Accepts the following input tensors:

  • preds (float tensor): (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 (int tensor): (N, C, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size \mathcal{O}(n_{samples}) whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size \mathcal{O}(n_{thresholds} \times n_{labels}) (constant memory).

Parameters
  • num_labels (int) – Integer specifing the number of labels

  • average (Optional[Literal[‘micro’, ‘macro’, ‘weighted’, ‘none’]]) –

    Defines the reduction that is applied over labels. Should be one of the following:

    • micro: Sum score over all labels

    • macro: Calculate score for each label and average them

    • weighted: Calculates score for each label and computes weighted average using their support

    • "none" or None: Calculates score for each label and applies no reduction

  • thresholds (Union[int, List[float], Tensor, None]) –

    Can be one of:

    • If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.

    • If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.

    • If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation

    • If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.

  • validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns

If average=None|”none” then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class. If average=”micro|macro”|”weighted” then a single scalar is returned.

Example

>>> from torchmetrics.classification import MultilabelAUROC
>>> preds = torch.tensor([[0.75, 0.05, 0.35],
...                       [0.45, 0.75, 0.05],
...                       [0.05, 0.55, 0.75],
...                       [0.05, 0.65, 0.05]])
>>> target = torch.tensor([[1, 0, 1],
...                        [0, 0, 0],
...                        [0, 1, 1],
...                        [1, 1, 1]])
>>> metric = MultilabelAUROC(num_labels=3, average="macro", thresholds=None)
>>> metric(preds, target)
tensor(0.6528)
>>> metric = MultilabelAUROC(num_labels=3, average=None, thresholds=None)
>>> metric(preds, target)
tensor([0.6250, 0.5000, 0.8333])
>>> metric = MultilabelAUROC(num_labels=3, average="macro", thresholds=5)
>>> metric(preds, target)
tensor(0.6528)
>>> metric = MultilabelAUROC(num_labels=3, average=None, thresholds=5)
>>> metric(preds, target)
tensor([0.6250, 0.5000, 0.8333])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type

Tensor

Functional Interface

torchmetrics.functional.auroc(preds, target, num_classes=None, pos_label=None, average='macro', max_fpr=None, sample_weights=None, task=None, thresholds=None, num_labels=None, ignore_index=None, validate_args=True)[source]

Note

From v0.10 an ‘binary_*’, ‘multiclass_*’, `’multilabel_*’ version now exist of each classification metric. Moving forward we recommend using these versions. This base metric will still work as it did prior to v0.10 until v0.11. From v0.11 the task argument introduced in this metric will be required and the general order of arguments may change, such that this metric will just function as an single entrypoint to calling the three specialized versions.

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)

For non-binary input, if the preds and target tensor have the same size the input will be interpretated as multilabel and if preds have one dimension more than the target tensor the input will be interpretated as multiclass.

Note

If either the positive class or negative class is completly missing in the target tensor, the auroc score is meaningless in this case and a score of 0 will be returned together with a warning.

Parameters
  • preds (Tensor) – predictions from model (logits or probabilities)

  • target (Tensor) – Ground truth labels

  • num_classes (Optional[int]) – integer with number of classes for multi-label and multiclass problems. Should be set to None for binary problems

  • pos_label (Optional[int]) – integer determining the positive class. Default is None which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]

  • average (Optional[Literal[‘macro’, ‘weighted’, ‘none’]]) –

    • 'macro' computes metric for each class and uniformly averages them

    • 'weighted' computes metric for each class and does a weighted-average, where each class is weighted by their support (accounts for class imbalance)

    • None computes and returns the metric per class

  • max_fpr (Optional[float]) – If not None, calculates standardized partial AUC over the range [0, max_fpr]. Should be a float between 0 and 1.

  • sample_weights (Optional[Sequence]) – sample weights for each data point

Raises
  • ValueError – If max_fpr is not a float in the range (0, 1].

  • RuntimeError – If PyTorch version is below 1.6 since max_fpr requires torch.bucketize which is not available below 1.6.

  • ValueError – If max_fpr is not set to None and the mode is not binary since partial AUC computation is not available in multilabel/multiclass.

  • ValueError – If average is none of None, "macro" or "weighted".

Example (binary case):
>>> from torchmetrics.functional import auroc
>>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
>>> target = torch.tensor([0, 0, 1, 1, 1])
>>> auroc(preds, target, pos_label=1)
tensor(0.5000)
Example (multiclass case):
>>> preds = torch.tensor([[0.90, 0.05, 0.05],
...                       [0.05, 0.90, 0.05],
...                       [0.05, 0.05, 0.90],
...                       [0.85, 0.05, 0.10],
...                       [0.10, 0.10, 0.80]])
>>> target = torch.tensor([0, 1, 1, 2, 2])
>>> auroc(preds, target, num_classes=3)
tensor(0.7778)
Return type

Union[Tensor, Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]

binary_auroc

torchmetrics.functional.classification.binary_auroc(preds, target, max_fpr=None, thresholds=None, ignore_index=None, validate_args=True)[source]

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) for binary tasks. The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing.

Accepts the following input tensors:

  • preds (float tensor): (N, ...). 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 (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size \mathcal{O}(n_{samples}) whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size \mathcal{O}(n_{thresholds}) (constant memory).

Parameters
  • preds (Tensor) – Tensor with predictions

  • target (Tensor) – Tensor with true labels

  • max_fpr (Optional[float]) – If not None, calculates standardized partial AUC over the range [0, max_fpr].

  • thresholds (Union[int, List[float], Tensor, None]) –

    Can be one of:

    • If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.

    • If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.

    • If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation

    • If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.

  • validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Return type

Tuple[Tensor, Tensor, Tensor]

Returns

A single scalar with the auroc score

Example

>>> from torchmetrics.functional.classification import binary_auroc
>>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
>>> target = torch.tensor([0, 1, 1, 0])
>>> binary_auroc(preds, target, thresholds=None)
tensor(0.5000)
>>> binary_auroc(preds, target, thresholds=5)
tensor(0.5000)

multiclass_auroc

torchmetrics.functional.classification.multiclass_auroc(preds, target, num_classes, average='macro', thresholds=None, ignore_index=None, validate_args=True)[source]

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) for multiclass tasks. The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing.

Accepts the following input tensors:

  • preds (float tensor): (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 softmax per sample.

  • target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size \mathcal{O}(n_{samples}) whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size \mathcal{O}(n_{thresholds} \times n_{classes}) (constant memory).

Parameters
  • preds (Tensor) – Tensor with predictions

  • target (Tensor) – Tensor with true labels

  • num_classes (int) – Integer specifing the number of classes

  • average (Optional[Literal[‘macro’, ‘weighted’, ‘none’]]) –

    Defines the reduction that is applied over classes. Should be one of the following:

    • macro: Calculate score for each class and average them

    • weighted: Calculates score for each class and computes weighted average using their support

    • "none" or None: Calculates score for each class and applies no reduction

  • thresholds (Union[int, List[float], Tensor, None]) –

    Can be one of:

    • If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.

    • If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.

    • If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation

    • If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.

  • validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Return type

Tensor

Returns

If average=None|”none” then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class. If average=”macro”|”weighted” then a single scalar is returned.

Example

>>> from torchmetrics.functional.classification import multiclass_auroc
>>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                       [0.05, 0.75, 0.05, 0.05, 0.05],
...                       [0.05, 0.05, 0.75, 0.05, 0.05],
...                       [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> multiclass_auroc(preds, target, num_classes=5, average="macro", thresholds=None)
tensor(0.5333)
>>> multiclass_auroc(preds, target, num_classes=5, average=None, thresholds=None)
tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000])
>>> multiclass_auroc(preds, target, num_classes=5, average="macro", thresholds=5)
tensor(0.5333)
>>> multiclass_auroc(preds, target, num_classes=5, average=None, thresholds=5)
tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000])

multilabel_auroc

torchmetrics.functional.classification.multilabel_auroc(preds, target, num_labels, average='macro', thresholds=None, ignore_index=None, validate_args=True)[source]

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) for multilabel tasks. The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing.

Accepts the following input tensors:

  • preds (float tensor): (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 (int tensor): (N, C, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size \mathcal{O}(n_{samples}) whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size \mathcal{O}(n_{thresholds} \times n_{labels}) (constant memory).

Parameters
  • preds (Tensor) – Tensor with predictions

  • target (Tensor) – Tensor with true labels

  • num_labels (int) – Integer specifing the number of labels

  • average (Optional[Literal[‘micro’, ‘macro’, ‘weighted’, ‘none’]]) –

    Defines the reduction that is applied over labels. Should be one of the following:

    • micro: Sum score over all labels

    • macro: Calculate score for each label and average them

    • weighted: Calculates score for each label and computes weighted average using their support

    • "none" or None: Calculates score for each label and applies no reduction

  • thresholds (Union[int, List[float], Tensor, None]) –

    Can be one of:

    • If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.

    • If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.

    • If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation

    • If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.

  • validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Return type

Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]

Returns

If average=None|”none” then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class. If average=”micro|macro”|”weighted” then a single scalar is returned.

Example

>>> from torchmetrics.functional.classification import multilabel_auroc
>>> preds = torch.tensor([[0.75, 0.05, 0.35],
...                       [0.45, 0.75, 0.05],
...                       [0.05, 0.55, 0.75],
...                       [0.05, 0.65, 0.05]])
>>> target = torch.tensor([[1, 0, 1],
...                        [0, 0, 0],
...                        [0, 1, 1],
...                        [1, 1, 1]])
>>> multilabel_auroc(preds, target, num_labels=3, average="macro", thresholds=None)
tensor(0.6528)
>>> multilabel_auroc(preds, target, num_labels=3, average=None, thresholds=None)
tensor([0.6250, 0.5000, 0.8333])
>>> multilabel_auroc(preds, target, num_labels=3, average="macro", thresholds=5)
tensor(0.6528)
>>> multilabel_auroc(preds, target, num_labels=3, average=None, thresholds=5)
tensor([0.6250, 0.5000, 0.8333])
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