File size: 3,290 Bytes
d275ac7 f5f2008 d275ac7 c6db808 d275ac7 f5f2008 d275ac7 ace4a3e d275ac7 649b3ae d275ac7 649b3ae d275ac7 |
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 |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""HTER metric."""
import datasets
import evaluate
_DESCRIPTION = """
HTER (Half Total Error Rate) is a metric that combines the False Accept Rate (FAR) and False Reject Rate (FRR) to provide a comprehensive evaluation of a system's performance. It can be computed with:
HTER = (FAR + FRR) / 2
Where:
FAR (False Accept Rate) = FP / (FP + TN)
FRR (False Reject Rate) = FN / (FN + TP)
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""
_KWARGS_DESCRIPTION = """
Args:
predictions (`list` of `int`): Predicted labels.
references (`list` of `int`): Ground truth labels.
Returns:
HTER (`float` or `int`): HTER score. Minimum possible value is 0. Maximum possible value is 1.0.
Examples:
Example 1-A simple example
>>> hter_metric = evaluate.load("murinj/hter")
>>> results = hter_metric.compute(references=[0, 0], predictions=[0, 1])
>>> print(results)
{'HTER': 0.25}
"""
_CITATION = """
}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class Hter(evaluate.Metric):
def _info(self):
return evaluate.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Value("int32"),
"references": datasets.Value("int32"),
}
),
)
def _compute(self, predictions, references):
TP = 0 # 预测为欺骗（1），真实为欺骗（1）→ 正确拒绝攻击
TN = 0 # 预测为真实（0），真实为真实（0）→ 正确接受真实
FP = 0 # 预测为欺骗（1），真实为真实（0）→ 错误拒绝真实（导致FRR）
FN = 0 # 预测为真实（0），真实为欺骗（1）→ 错误接受欺骗（导致FAR）
for pred, ref in zip(predictions, references):
if pred == 1 and ref == 1:
TP += 1
elif pred == 0 and ref == 0:
TN += 1
elif pred == 1 and ref == 0:
FP += 1
elif pred == 0 and ref == 1:
FN += 1
# FAR = 欺骗样本中被错误接受的比例 = FN / (TP + FN)
FAR = FN / (TP + FN) if (TP + FN) > 0 else 0
# FRR = 真实样本中被错误拒绝的比例 = FP / (TN + FP)
FRR = FP / (TN + FP) if (TN + FP) > 0 else 0
HTER = (FAR + FRR) / 2.0
# 计算 HTER
HTER = (FAR + FRR) / 2.0
return {
"HTER": HTER
}
|