Exact Match

Module Interface

MultilabelExactMatch

class torchmetrics.classification.MultilabelExactMatch(num_labels, threshold=0.5, multidim_average='global', ignore_index=None, validate_args=True, **kwargs)

Computes Exact match (also known as subset accuracy) for multilabel tasks. Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified.

Accepts the following input tensors:

• preds (int or float tensor): (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. Addtionally, we convert to int tensor with thresholding using the value in threshold.

• target (int tensor): (N, C, ...)

The influence of the additional dimension ... (if present) will be determined by the multidim_average argument.

Parameters

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

• threshold (float) – Threshold for transforming probability to binary (0,1) predictions

• multidim_average (Literal[‘global’, ‘samplewise’]) –

  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 (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

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

Returns

• 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)

Return type

The returned shape depends on the average and multidim_average arguments

Example (preds is int tensor):

>>> from torchmetrics.classification import MultilabelExactMatch
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> metric = MultilabelExactMatch(num_labels=3)
>>> metric(preds, target)
tensor(0.5000)

Example (preds is float tensor):

>>> from torchmetrics.classification import MultilabelExactMatch
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> metric = MultilabelExactMatch(num_labels=3)
>>> metric(preds, target)
tensor(0.5000)

Example (multidim tensors):

>>> from torchmetrics.classification import MultilabelExactMatch
>>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = torch.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 = MultilabelExactMatch(num_labels=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0., 0.])

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

compute()

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

Return type

Tensor

update(preds, target)

Update state with predictions and targets.

Parameters

• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

Return type

None

Functional Interface

multilabel_exact_match

torchmetrics.functional.classification.multilabel_exact_match(preds, target, num_labels, threshold=0.5, multidim_average='global', ignore_index=None, validate_args=True)

Computes Exact match (also known as subset accuracy) for multilabel tasks. Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified.

Accepts the following input tensors:

• preds (int or float tensor): (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. Addtionally, we convert to int tensor with thresholding using the value in threshold.

• target (int tensor): (N, C, ...)

The influence of the additional dimension ... (if present) will be determined by the multidim_average argument.

Parameters

• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

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

• threshold (float) – Threshold for transforming probability to binary (0,1) predictions

• multidim_average (Literal[‘global’, ‘samplewise’]) –

  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 (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

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

Returns

• 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)

Return type

The returned shape depends on the average and multidim_average arguments

Example (preds is int tensor):

>>> from torchmetrics.functional.classification import multilabel_exact_match
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> multilabel_exact_match(preds, target, num_labels=3)
tensor(0.5000)

Example (preds is float tensor):

>>> from torchmetrics.functional.classification import multilabel_exact_match
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> multilabel_exact_match(preds, target, num_labels=3)
tensor(0.5000)

Example (multidim tensors):

>>> from torchmetrics.functional.classification import multilabel_exact_match
>>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = torch.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]],
...     ]
... )
>>> multilabel_exact_match(preds, target, num_labels=3, multidim_average='samplewise')
tensor([0., 0.])