Source code for ignite.contrib.metrics.precision_recall_curve
from ignite.metrics import EpochMetric
def precision_recall_curve_compute_fn(y_preds, y_targets):
try:
from sklearn.metrics import precision_recall_curve
except ImportError:
raise RuntimeError("This contrib module requires sklearn to be installed.")
y_true = y_targets.numpy()
y_pred = y_preds.numpy()
return precision_recall_curve(y_true, y_pred)
[docs]class PrecisionRecallCurve(EpochMetric):
"""Compute precision-recall pairs for different probability thresholds for binary classification task
by accumulating predictions and the ground-truth during an epoch and applying
`sklearn.metrics.precision_recall_curve <http://scikit-learn.org/stable/modules/generated/
sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve>`_ .
Args:
output_transform (callable, optional): 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 (bool): Optional default False. If True, `precision_recall_curve
<http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html
#sklearn.metrics.precision_recall_curve>`_ 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.
PrecisionRecallCurve 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.sigmoid(y_pred)
return y_pred, y
roc_auc = PrecisionRecallCurve(activated_output_transform)
"""
def __init__(self, output_transform=lambda x: x, check_compute_fn: bool = False):
super(PrecisionRecallCurve, self).__init__(
precision_recall_curve_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn
)