Matthews Corr. Coef.¶

Module Interface¶

class torchmetrics.MatthewsCorrCoef(num_classes, threshold=0.5, **kwargs)[source]

Calculates Matthews correlation coefficient that measures the general correlation or quality of a classification.

In the binary case it is defined as:

$MCC = \frac{TP*TN - FP*FN}{\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}}$

where TP, TN, FP and FN are respectively the true postitives, true negatives, false positives and false negatives. Also works in the case of multi-label or multi-class input.

Note

This metric produces a multi-dimensional output, so it can not be directly logged.

Forward accepts

preds (float or long tensor): (N, ...) or (N, C, ...) where C is the number of classes
target (long tensor): (N, ...)

If preds and target are the same shape and preds is a float tensor, we use the self.threshold argument to convert into integer labels. This is the case for binary and multi-label probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

Parameters

num_classes¶ (int) – Number of classes in the dataset.
threshold¶ (float) – Threshold value for binary or multi-label probabilites.
kwargs¶ (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics import MatthewsCorrCoef
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> matthews_corrcoef = MatthewsCorrCoef(num_classes=2)
>>> matthews_corrcoef(preds, target)
tensor(0.5774)

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

compute()[source]

Computes matthews correlation coefficient.

Return type: Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters

preds¶ (Tensor) – Predictions from model
target¶ (Tensor) – Ground truth values

Return type

None

Functional Interface¶

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

Calculates Matthews correlation coefficient that measures the general correlation or quality of a classification. In the binary case it is defined as:

$MCC = \frac{TP*TN - FP*FN}{\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}}$

where TP, TN, FP and FN are respectively the true postitives, true negatives, false positives and false negatives. Also works in the case of multi-label or multi-class input.

Parameters

preds¶ (Tensor) – (float or long tensor), Either a (N, ...) tensor with labels or (N, C, ...) where C is the number of classes, tensor with labels/probabilities
target¶ (Tensor) – target (long tensor), tensor with shape (N, ...) with ground true labels
num_classes¶ (int) – Number of classes in the dataset.
threshold¶ (float) – Threshold value for binary or multi-label probabilities.

Example

>>> from torchmetrics.functional import matthews_corrcoef
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> matthews_corrcoef(preds, target, num_classes=2)
tensor(0.5774)

Return type: Tensor