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Source code for pytorch_lightning.metrics.functional.roc

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Sequence, Tuple, List, Union

import torch

from pytorch_lightning.metrics.functional.precision_recall_curve import (
    _precision_recall_curve_update,
    _binary_clf_curve
)


def _roc_update(
        preds: torch.Tensor,
        target: torch.Tensor,
        num_classes: Optional[int] = None,
        pos_label: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor, int, int]:
    return _precision_recall_curve_update(preds, target, num_classes, pos_label)


def _roc_compute(
        preds: torch.Tensor,
        target: torch.Tensor,
        num_classes: int,
        pos_label: int,
        sample_weights: Optional[Sequence] = None,
) -> Union[Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
           Tuple[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]]:

    if num_classes == 1:
        fps, tps, thresholds = _binary_clf_curve(
            preds=preds,
            target=target,
            sample_weights=sample_weights,
            pos_label=pos_label
        )
        # Add an extra threshold position
        # to make sure that the curve starts at (0, 0)
        tps = torch.cat([torch.zeros(1, dtype=tps.dtype, device=tps.device), tps])
        fps = torch.cat([torch.zeros(1, dtype=fps.dtype, device=fps.device), fps])
        thresholds = torch.cat([thresholds[0][None] + 1, thresholds])

        if fps[-1] <= 0:
            raise ValueError("No negative samples in targets, false positive value should be meaningless")
        fpr = fps / fps[-1]

        if tps[-1] <= 0:
            raise ValueError("No positive samples in targets, true positive value should be meaningless")
        tpr = tps / tps[-1]

        return fpr, tpr, thresholds

    # Recursively call per class
    fpr, tpr, thresholds = [], [], []
    for c in range(num_classes):
        preds_c = preds[:, c]
        res = roc(
            preds=preds_c,
            target=target,
            num_classes=1,
            pos_label=c,
            sample_weights=sample_weights,
        )
        fpr.append(res[0])
        tpr.append(res[1])
        thresholds.append(res[2])

    return fpr, tpr, thresholds


[docs]def roc( preds: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, pos_label: Optional[int] = None, sample_weights: Optional[Sequence] = None, ) -> Union[Tuple[torch.Tensor, torch.Tensor, torch.Tensor], Tuple[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]]: """ Computes the Receiver Operating Characteristic (ROC). Args: num_classes: integer with number of classes. Not nessesary to provide for binary problems. pos_label: integer determining the positive class. Default is ``None`` which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1] sample_weight: sample weights for each data point Returns: 3-element tuple containing fpr: tensor with false positive rates. If multiclass, this is a list of such tensors, one for each class. tpr: tensor with true positive rates. If multiclass, this is a list of such tensors, one for each class. thresholds: thresholds used for computing false- and true postive rates Example (binary case): >>> pred = torch.tensor([0, 1, 2, 3]) >>> target = torch.tensor([0, 1, 1, 1]) >>> fpr, tpr, thresholds = roc(pred, target, pos_label=1) >>> fpr tensor([0., 0., 0., 0., 1.]) >>> tpr tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000]) >>> thresholds tensor([4, 3, 2, 1, 0]) Example (multiclass case): >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05], ... [0.05, 0.05, 0.05, 0.75]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> fpr, tpr, thresholds = roc(pred, target, num_classes=4) >>> fpr [tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]), tensor([0.0000, 0.3333, 1.0000])] >>> tpr [tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.])] >>> thresholds # doctest: +NORMALIZE_WHITESPACE [tensor([1.7500, 0.7500, 0.0500]), tensor([1.7500, 0.7500, 0.0500]), tensor([1.7500, 0.7500, 0.0500]), tensor([1.7500, 0.7500, 0.0500])] """ preds, target, num_classes, pos_label = _roc_update(preds, target, num_classes, pos_label) return _roc_compute(preds, target, num_classes, pos_label, sample_weights)

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision c462b274.

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