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
’sprocess_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, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
update
arguments ensures theupdate
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
Computes the metric based on it's accumulated state.
Resets the metric to it's initial state.
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 whencompleted()
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
- 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[torch.Tensor]) – the is the output from the engine’s process function.
- Return type