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5ccd75a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | # Copyright 2020 MONAI Consortium
# 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.
import warnings
from typing import Callable, List, Optional, Union, cast
import numpy as np
import torch
from monai.networks import one_hot
from monai.utils import Average
def _calculate(y: torch.Tensor, y_pred: torch.Tensor) -> float:
assert y.ndimension() == y_pred.ndimension() == 1 and len(y) == len(
y_pred
), "y and y_pred must be 1 dimension data with same length."
assert y.unique().equal(
torch.tensor([0, 1], dtype=y.dtype, device=y.device)
), "y values must be 0 or 1, can not be all 0 or all 1."
n = len(y)
indices = y_pred.argsort()
y = y[indices].cpu().numpy()
y_pred = y_pred[indices].cpu().numpy()
nneg = auc = tmp_pos = tmp_neg = 0.0
for i in range(n):
y_i = cast(float, y[i])
if i + 1 < n and y_pred[i] == y_pred[i + 1]:
tmp_pos += y_i
tmp_neg += 1 - y_i
continue
if tmp_pos + tmp_neg > 0:
tmp_pos += y_i
tmp_neg += 1 - y_i
nneg += tmp_neg
auc += tmp_pos * (nneg - tmp_neg / 2)
tmp_pos = tmp_neg = 0
continue
if y_i == 1:
auc += nneg
else:
nneg += 1
return auc / (nneg * (n - nneg))
def compute_roc_auc(
y_pred: torch.Tensor,
y: torch.Tensor,
to_onehot_y: bool = False,
softmax: bool = False,
other_act: Optional[Callable] = None,
average: Union[Average, str] = Average.MACRO,
) -> Union[np.ndarray, List[float], float]:
"""Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to:
`sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/
sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_.
Args:
y_pred: input data to compute, typical classification model output.
it must be One-Hot format and first dim is batch, example shape: [16] or [16, 2].
y: ground truth to compute ROC AUC metric, the first dim is batch.
example shape: [16, 1] will be converted into [16, 2] (where `2` is inferred from `y_pred`).
to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.
softmax: whether to add softmax function to `y_pred` before computation. Defaults to False.
other_act: callable function to replace `softmax` as activation layer if needed, Defaults to ``None``.
for example: `other_act = lambda x: torch.log_softmax(x)`.
average: {``"macro"``, ``"weighted"``, ``"micro"``, ``"none"``}
Type of averaging performed if not binary classification.
Defaults to ``"macro"``.
- ``"macro"``: calculate metrics for each label, and find their unweighted mean.
This does not take label imbalance into account.
- ``"weighted"``: calculate metrics for each label, and find their average,
weighted by support (the number of true instances for each label).
- ``"micro"``: calculate metrics globally by considering each element of the label
indicator matrix as a label.
- ``"none"``: the scores for each class are returned.
Raises:
ValueError: When ``y_pred`` dimension is not one of [1, 2].
ValueError: When ``y`` dimension is not one of [1, 2].
ValueError: When ``softmax=True`` and ``other_act is not None``. Incompatible values.
TypeError: When ``other_act`` is not an ``Optional[Callable]``.
ValueError: When ``average`` is not one of ["macro", "weighted", "micro", "none"].
Note:
ROCAUC expects y to be comprised of 0's and 1's. `y_pred` must be either prob. estimates or confidence values.
"""
y_pred_ndim = y_pred.ndimension()
y_ndim = y.ndimension()
if y_pred_ndim not in (1, 2):
raise ValueError("Predictions should be of shape (batch_size, n_classes) or (batch_size, ).")
if y_ndim not in (1, 2):
raise ValueError("Targets should be of shape (batch_size, n_classes) or (batch_size, ).")
if y_pred_ndim == 2 and y_pred.shape[1] == 1:
y_pred = y_pred.squeeze(dim=-1)
y_pred_ndim = 1
if y_ndim == 2 and y.shape[1] == 1:
y = y.squeeze(dim=-1)
if y_pred_ndim == 1:
if to_onehot_y:
warnings.warn("y_pred has only one channel, to_onehot_y=True ignored.")
if softmax:
warnings.warn("y_pred has only one channel, softmax=True ignored.")
return _calculate(y, y_pred)
else:
n_classes = y_pred.shape[1]
if to_onehot_y:
y = one_hot(y, n_classes)
if softmax and other_act is not None:
raise ValueError("Incompatible values: softmax=True and other_act is not None.")
if softmax:
y_pred = y_pred.float().softmax(dim=1)
if other_act is not None:
if not callable(other_act):
raise TypeError(f"other_act must be None or callable but is {type(other_act).__name__}.")
y_pred = other_act(y_pred)
assert y.shape == y_pred.shape, "data shapes of y_pred and y do not match."
average = Average(average)
if average == Average.MICRO:
return _calculate(y.flatten(), y_pred.flatten())
else:
y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)
auc_values = [_calculate(y_, y_pred_) for y_, y_pred_ in zip(y, y_pred)]
if average == Average.NONE:
return auc_values
if average == Average.MACRO:
return np.mean(auc_values)
if average == Average.WEIGHTED:
weights = [sum(y_) for y_ in y]
return np.average(auc_values, weights=weights)
raise ValueError(
f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].'
)
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