MultiLabelConfusionMatrix

class ignite.metrics.MultiLabelConfusionMatrix(num_classes, output_transform=<function MultiLabelConfusionMatrix.<lambda>>, device=device(type='cpu'), normalized=False)

Calculates a confusion matrix for multi-labelled, multi-class data.

• update must receive output of the form (y_pred, y).

• y_pred must contain 0s and 1s and has the following shape (batch_size, num_classes, …). For example, y_pred[i, j] = 1 denotes that the j’th class is one of the labels of the i’th sample as predicted.

• y should have the following shape (batch_size, num_classes, …) with 0s and 1s. For example, y[i, j] = 1 denotes that the j’th class is one of the labels of the i’th sample according to the ground truth.

• both y and y_pred must be torch Tensors having any of the following types: {torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64}. They must have the same dimensions.

• The confusion matrix ‘M’ is of dimension (num_classes, 2, 2).

  • M[i, 0, 0] corresponds to count/rate of true negatives of class i

  • M[i, 0, 1] corresponds to count/rate of false positives of class i

  • M[i, 1, 0] corresponds to count/rate of false negatives of class i

  • M[i, 1, 1] corresponds to count/rate of true positives of class i

• The classes present in M are indexed as 0, … , num_classes-1 as can be inferred from above.

Parameters

• num_classes (int) – Number of classes, should be > 1.

• output_transform (Callable) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.

• device (Union[str, device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU.

• normalized (bool) – whether to normalize confusion matrix by its sum or not.

Example

metric = MultiLabelConfusionMatrix(num_classes=3)
metric.attach(default_evaluator, "mlcm")
y_true = torch.Tensor([
    [0, 0, 1],
    [0, 0, 0],
    [0, 0, 0],
    [1, 0, 0],
    [0, 1, 1],
]).long()
y_pred = torch.Tensor([
    [1, 1, 0],
    [1, 0, 1],
    [1, 0, 0],
    [1, 0, 1],
    [1, 1, 0],
]).long()
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["mlcm"])

tensor([[[0, 4],
         [0, 1]],

        [[3, 1],
         [0, 1]],

        [[1, 2],
         [2, 0]]])

New in version 0.4.5.

Methods

compute	Computes the metric based on it's accumulated state.
reset	Resets the metric to it's initial state.
update	Updates the metric's state using the passed batch output.

compute()

Computes the metric based on it’s accumulated state.

By default, this is called at the end of each epoch.

Returns

the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type

None

update(output)

Updates the metric’s state using the passed batch output.

By default, this is called once for each batch.

Parameters

output (Sequence[Tensor]) – the is the output from the engine’s process function.

Return type

None