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Source code for ignite.contrib.metrics.roc_auc

from typing import Any, Callable, cast, Tuple, Union

import torch

from ignite import distributed as idist
from ignite.exceptions import NotComputableError
from ignite.metrics 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)


[docs]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. 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... """ def __init__( self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ): 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 )
[docs]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. 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 [2.0, 1.0, 0.711, 0.047] .. versionchanged:: 0.4.11 added `device` argument """ def __init__( self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ) -> 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, )
[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("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

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