mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
# 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.
|
|
"""Accuracy metric."""
|
|
|
|
import datasets
|
|
from sklearn.metrics import accuracy_score
|
|
|
|
import evaluate
|
|
|
|
|
|
_DESCRIPTION = """
|
|
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
|
|
Accuracy = (TP + TN) / (TP + TN + FP + FN)
|
|
Where:
|
|
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.
|
|
normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
|
|
sample_weight (`list` of `float`): Sample weights Defaults to None.
|
|
|
|
Returns:
|
|
accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.
|
|
|
|
Examples:
|
|
|
|
Example 1-A simple example
|
|
>>> accuracy_metric = evaluate.load("accuracy")
|
|
>>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
|
|
>>> print(results)
|
|
{'accuracy': 0.5}
|
|
|
|
Example 2-The same as Example 1, except with `normalize` set to `False`.
|
|
>>> accuracy_metric = evaluate.load("accuracy")
|
|
>>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
|
|
>>> print(results)
|
|
{'accuracy': 3.0}
|
|
|
|
Example 3-The same as Example 1, except with `sample_weight` set.
|
|
>>> accuracy_metric = evaluate.load("accuracy")
|
|
>>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
|
|
>>> print(results)
|
|
{'accuracy': 0.8778625954198473}
|
|
"""
|
|
|
|
|
|
_CITATION = """
|
|
@article{scikit-learn,
|
|
title={Scikit-learn: Machine Learning in {P}ython},
|
|
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
|
|
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
|
|
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
|
|
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
|
|
journal={Journal of Machine Learning Research},
|
|
volume={12},
|
|
pages={2825--2830},
|
|
year={2011}
|
|
}
|
|
"""
|
|
|
|
|
|
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
|
|
class Accuracy(evaluate.Metric):
|
|
def _info(self):
|
|
return evaluate.MetricInfo(
|
|
description=_DESCRIPTION,
|
|
citation=_CITATION,
|
|
inputs_description=_KWARGS_DESCRIPTION,
|
|
features=datasets.Features(
|
|
{
|
|
"predictions": datasets.Sequence(datasets.Value("int32")),
|
|
"references": datasets.Sequence(datasets.Value("int32")),
|
|
}
|
|
if self.config_name == "multilabel"
|
|
else {
|
|
"predictions": datasets.Value("int32"),
|
|
"references": datasets.Value("int32"),
|
|
}
|
|
),
|
|
reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"],
|
|
)
|
|
|
|
def _compute(self, predictions, references, normalize=True, sample_weight=None):
|
|
return {
|
|
"accuracy": float(
|
|
accuracy_score(references, predictions, normalize=normalize, sample_weight=sample_weight)
|
|
)
|
|
}
|