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from typing import Callable, Union
import torch
from ignite.metrics.epoch_metric import EpochMetric
def average_precision_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float:
from sklearn.metrics import average_precision_score
y_true = y_targets.cpu().numpy()
y_pred = y_preds.cpu().numpy()
return average_precision_score(y_true, y_pred)
class AveragePrecision(EpochMetric):
"""Computes Average Precision accumulating predictions and the ground-truth during an epoch
and applying `sklearn.metrics.average_precision_score <https://scikit-learn.org/stable/modules/generated/
sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score>`_ .
Args:
output_transform: a callable that is used to transform the
:class:`~ignite.engine.engine.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: Default False. If True, `average_precision_score
<https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html
#sklearn.metrics.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: optional device specification for internal storage.
skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be
true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)``
Alternatively, ``output_transform`` can be used to handle this.
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:
.. code-block:: python
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:
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
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'])
.. testoutput::
0.9166...
.. versionchanged:: 0.5.1
``skip_unrolling`` argument is added.
"""
def __init__(
self,
output_transform: Callable = lambda x: x,
check_compute_fn: bool = False,
device: Union[str, torch.device] = torch.device("cpu"),
skip_unrolling: bool = False,
):
try:
from sklearn.metrics import average_precision_score # noqa: F401
except ImportError:
raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.")
super(AveragePrecision, self).__init__(
average_precision_compute_fn,
output_transform=output_transform,
check_compute_fn=check_compute_fn,
device=device,
skip_unrolling=skip_unrolling,
)
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