AveragePrecision#

class ignite.contrib.metrics.AveragePrecision(output_transform=<function AveragePrecision.<lambda>>, check_compute_fn=False, device=device(type='cpu'))[source]#

Computes Average Precision accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.average_precision_score .

Parameters

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.
check_compute_fn (bool) – Default False. If True, average_precision_score is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function.
device (Union[str, device]) – optional device specification for internal storage.

Note

AveragePrecision expects y to be comprised of 0’s and 1’s. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below:

def activated_output_transform(output):
    y_pred, y = output
    y_pred = torch.softmax(y_pred, dim=1)
    return y_pred, y
avg_precision = AveragePrecision(activated_output_transform)

Examples

y_pred = torch.Tensor([[0.79, 0.21], [0.30, 0.70], [0.46, 0.54], [0.16, 0.84]])
y_true = torch.tensor([[1, 1], [1, 1], [0, 1], [0, 1]])

avg_precision = AveragePrecision()
avg_precision.attach(default_evaluator, 'average_precision')
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['average_precision'])

0.9166...

Methods