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Source code for ignite.contrib.metrics.roc_auc

from typing import Any, Callable, Tuple, Union

import torch

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.numpy()
    y_pred = y_preds.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 RuntimeError("This contrib module requires sklearn 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. 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] """ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: try: from sklearn.metrics import roc_curve # noqa: F401 except ImportError: raise RuntimeError("This contrib module requires sklearn to be installed.") super(RocCurve, self).__init__( roc_auc_curve_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn )

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