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from typing import Any, Callable, cast, Tuple, Union
import torch
from ignite import distributed as idist
from ignite.exceptions import NotComputableError
from ignite.metrics.epoch_metric import EpochMetric
def roc_auc_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float:
from sklearn.metrics import roc_auc_score
y_true = y_targets.cpu().numpy()
y_pred = y_preds.cpu().numpy()
return roc_auc_score(y_true, y_pred)
def roc_auc_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> Tuple[Any, Any, Any]:
from sklearn.metrics import roc_curve
y_true = y_targets.cpu().numpy()
y_pred = y_preds.cpu().numpy()
return roc_curve(y_true, y_pred)
class ROC_AUC(EpochMetric):
"""Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC)
accumulating predictions and the ground-truth during an epoch and applying
`sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/
sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_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, `roc_curve
<https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#
sklearn.metrics.roc_auc_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:
ROC_AUC 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 = ROC_AUC(sigmoid_output_transform)
Examples:
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
roc_auc = ROC_AUC()
#The ``output_transform`` arg of the metric can be used to perform a sigmoid on the ``y_pred``.
roc_auc.attach(default_evaluator, 'roc_auc')
y_pred = torch.tensor([[0.0474], [0.5987], [0.7109], [0.9997]])
y_true = torch.tensor([[0], [0], [1], [0]])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['roc_auc'])
.. testoutput::
0.6666...
.. 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 roc_auc_score # noqa: F401
except ImportError:
raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.")
super(ROC_AUC, self).__init__(
roc_auc_compute_fn,
output_transform=output_transform,
check_compute_fn=check_compute_fn,
device=device,
skip_unrolling=skip_unrolling,
)
class RocCurve(EpochMetric):
"""Compute Receiver operating characteristic (ROC) for binary classification task
by accumulating predictions and the ground-truth during an epoch and applying
`sklearn.metrics.roc_curve <https://scikit-learn.org/stable/modules/generated/
sklearn.metrics.roc_curve.html#sklearn.metrics.roc_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, `sklearn.metrics.roc_curve
<https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#
sklearn.metrics.roc_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.
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:
RocCurve 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 = RocCurve(sigmoid_output_transform)
Examples:
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
roc_auc = RocCurve()
#The ``output_transform`` arg of the metric can be used to perform a sigmoid on the ``y_pred``.
roc_auc.attach(default_evaluator, 'roc_auc')
y_pred = torch.tensor([0.0474, 0.5987, 0.7109, 0.9997])
y_true = torch.tensor([0, 0, 1, 0])
state = default_evaluator.run([[y_pred, y_true]])
print("FPR", [round(i, 3) for i in state.metrics['roc_auc'][0].tolist()])
print("TPR", [round(i, 3) for i in state.metrics['roc_auc'][1].tolist()])
print("Thresholds", [round(i, 3) for i in state.metrics['roc_auc'][2].tolist()])
.. testoutput::
FPR [0.0, 0.333, 0.333, 1.0]
TPR [0.0, 0.0, 1.0, 1.0]
Thresholds [inf, 1.0, 0.711, 0.047]
.. versionchanged:: 0.4.11
added `device` argument
.. 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,
) -> None:
try:
from sklearn.metrics import roc_curve # noqa: F401
except ImportError:
raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.")
super(RocCurve, self).__init__(
roc_auc_curve_compute_fn, # type: ignore[arg-type]
output_transform=output_transform,
check_compute_fn=check_compute_fn,
device=device,
skip_unrolling=skip_unrolling,
)
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("RocCurve must have at least one example before it can be computed.")
_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
fpr, tpr, thresholds = cast(Tuple, self.compute_fn(_prediction_tensor, _target_tensor))
fpr = torch.tensor(fpr, device=_prediction_tensor.device)
tpr = torch.tensor(tpr, device=_prediction_tensor.device)
thresholds = torch.tensor(thresholds, device=_prediction_tensor.device)
else:
fpr, tpr, thresholds = None, None, None
if ws > 1:
# broadcast result to all processes
fpr = idist.broadcast(fpr, src=0, safe_mode=True)
tpr = idist.broadcast(tpr, src=0, safe_mode=True)
thresholds = idist.broadcast(thresholds, src=0, safe_mode=True)
return fpr, tpr, thresholds
|
