MultiLabelConfusionMatrix#

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

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.

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()[source]#

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()[source]#

Resets the metric to it’s initial state.

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

Return type: None

update(output)[source]#

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