Source code for ignite.contrib.metrics.precision_recall_curve
from typing import Any, Callable, cast, Tuple, Union
import torch
import ignite.distributed as idist
from ignite.exceptions import NotComputableError
from ignite.metrics import EpochMetric
def precision_recall_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> Tuple[Any, Any, Any]:
try:
from sklearn.metrics import precision_recall_curve
except ImportError:
raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.")
y_true = y_targets.cpu().numpy()
y_pred = y_preds.cpu().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 <https://scikit-learn.org/stable/modules/generated/
sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve>`_ .
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, `precision_recall_curve
<https://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.
Note:
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 sigmoid_output_transform(output):
y_pred, y = output
y_pred = torch.sigmoid(y_pred)
return y_pred, y
avg_precision = PrecisionRecallCurve(sigmoid_output_transform)
Examples:
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
y_pred = torch.tensor([0.0474, 0.5987, 0.7109, 0.9997])
y_true = torch.tensor([0, 0, 1, 1])
prec_recall_curve = PrecisionRecallCurve()
prec_recall_curve.attach(default_evaluator, 'prec_recall_curve')
state = default_evaluator.run([[y_pred, y_true]])
print("Precision", [round(i, 4) for i in state.metrics['prec_recall_curve'][0].tolist()])
print("Recall", [round(i, 4) for i in state.metrics['prec_recall_curve'][1].tolist()])
print("Thresholds", [round(i, 4) for i in state.metrics['prec_recall_curve'][2].tolist()])
.. testoutput::
Precision [0.5, 0.6667, 1.0, 1.0, 1.0]
Recall [1.0, 1.0, 1.0, 0.5, 0.0]
Thresholds [0.0474, 0.5987, 0.7109, 0.9997]
"""
def __init__(
self,
output_transform: Callable = lambda x: x,
check_compute_fn: bool = False,
device: Union[str, torch.device] = torch.device("cpu"),
) -> None:
super(PrecisionRecallCurve, self).__init__(
precision_recall_curve_compute_fn, # type: ignore[arg-type]
output_transform=output_transform,
check_compute_fn=check_compute_fn,
device=device,
)
[docs] def compute(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # type: ignore[override]
if len(self._predictions) < 1 or len(self._targets) < 1:
raise NotComputableError("PrecisionRecallCurve must have at least one example before it can be computed.")
if self._result is None:
_prediction_tensor = torch.cat(self._predictions, dim=0)
_target_tensor = torch.cat(self._targets, dim=0)
ws = idist.get_world_size()
if ws > 1:
# All gather across all processes
_prediction_tensor = cast(torch.Tensor, idist.all_gather(_prediction_tensor))
_target_tensor = cast(torch.Tensor, idist.all_gather(_target_tensor))
if idist.get_rank() == 0:
# Run compute_fn on zero rank only
precision, recall, thresholds = cast(Tuple, self.compute_fn(_prediction_tensor, _target_tensor))
precision = torch.tensor(precision, device=_prediction_tensor.device)
recall = torch.tensor(recall, device=_prediction_tensor.device)
# thresholds can have negative strides, not compatible with torch tensors
# https://discuss.pytorch.org/t/negative-strides-in-tensor-error/134287/2
thresholds = torch.tensor(thresholds.copy(), device=_prediction_tensor.device)
else:
precision, recall, thresholds = None, None, None
if ws > 1:
# broadcast result to all processes
precision = idist.broadcast(precision, src=0, safe_mode=True)
recall = idist.broadcast(recall, src=0, safe_mode=True)
thresholds = idist.broadcast(thresholds, src=0, safe_mode=True)
self._result = (precision, recall, thresholds) # type: ignore[assignment]
return cast(Tuple[torch.Tensor, torch.Tensor, torch.Tensor], self._result)