Hamming Distance¶

Module Interface¶

class torchmetrics.HammingDistance(threshold=0.5, compute_on_step=None, **kwargs)[source]

Computes the average Hamming distance (also known as Hamming loss) between targets and predictions:

$\text{Hamming distance} = \frac{1}{N \cdot L}\sum_i^N \sum_l^L 1(y_{il} \neq \hat{y_{il}})$

Where $y$ is a tensor of target values, $\hat{y}$ is a tensor of predictions, and $\bullet_{il}$ refers to the $l$ -th label of the $i$ -th sample of that tensor.

This is the same as 1-accuracy for binary data, while for all other types of inputs it treats each possible label separately - meaning that, for example, multi-class data is treated as if it were multi-label.

Accepts all input types listed in Input types.

Parameters

threshold¶ (float) – Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.
compute_on_step¶ (Optional[bool]) –
Forward only calls update() and returns None if this is set to False.

Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Raises

ValueError – If threshold is not between 0 and 1.

Example

>>> from torchmetrics import HammingDistance
>>> target = torch.tensor([[0, 1], [1, 1]])
>>> preds = torch.tensor([[0, 1], [0, 1]])
>>> hamming_distance = HammingDistance()
>>> hamming_distance(preds, target)
tensor(0.2500)

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

compute()[source]

Computes hamming distance based on inputs passed in to update previously.

Return type: Tensor

update(preds, target)[source]

Update state with predictions and targets.

See Input types for more information on input types.

Parameters

preds¶ (Tensor) – Predictions from model (probabilities, logits or labels)
target¶ (Tensor) – Ground truth labels

Return type

None

Functional Interface¶

torchmetrics.functional.hamming_distance(preds, target, threshold=0.5)[source]

Computes the average Hamming distance (also known as Hamming loss) between targets and predictions:

$\text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})$

Where $y$ is a tensor of target values, $\hat{y}$ is a tensor of predictions, and $\bullet_{il}$ refers to the $l$ -th label of the $i$ -th sample of that tensor.

This is the same as 1-accuracy for binary data, while for all other types of inputs it treats each possible label separately - meaning that, for example, multi-class data is treated as if it were multi-label.

Accepts all input types listed in Input types.

Parameters

preds¶ (Tensor) – Predictions from model (probabilities, logits or labels)
target¶ (Tensor) – Ground truth
threshold¶ (float) – Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.

Example

>>> from torchmetrics.functional import hamming_distance
>>> target = torch.tensor([[0, 1], [1, 1]])
>>> preds = torch.tensor([[0, 1], [0, 1]])
>>> hamming_distance(preds, target)
tensor(0.2500)

Return type: Tensor