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# Copyright 2023 HyperML Authors and the current HyperML 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.
"""Balanced Accuracy metric."""
import evaluate
import datasets
from sklearn.metrics import balanced_accuracy_score
_DESCRIPTION = """
Balanced Accuracy is the average of recall obtained on each class. It can be computed with:
Balanced Accuracy = (TPR + TNR) / N
Where:
TPR: True positive rate
TNR: True negative rate
N: Number of classes
"""
_KWARGS_DESCRIPTION = """
Args:
predictions (`list` of `int`): Predicted labels.
references (`list` of `int`): Ground truth labels.
sample_weight (`list` of `float`): Sample weights Defaults to None.
adjusted (`boolean`): When true, the result is adjusted for chance, so that random performance would score 0, while keeping perfect performance at a score of 1. Defaults to False.
Returns:
balanced_accuracy (`float`): Balanced Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0. A higher score means higher balanced accuracy.
Examples:
Example 1-A simple example
>>> balanced_accuracy_metric = evaluate.load("balanced_accuracy")
>>> results = balanced_accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
>>> print(results)
{'balanced_accuracy': 0.5}
Example 2-The same as Example 1, except with `sample_weight` set.
>>> balanced_accuracy_metric = evaluate.load("balanced_accuracy")
>>> results = balanced_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)
{'balanced_accuracy': 0.8778625954198473} # TODO: check if this is correct
Example 3-The same as Example 1, except with `adjusted` set to `True`.
>>> balanced_accuracy_metric = evaluate.load("balanced_accuracy")
>>> results = balanced_accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], adjusted=True)
>>> print(results)
{'balanced_accuracy': 0.8} # TODO: check if this is correct
"""
_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}
}
"""
class BalancedAccuracy(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.balanced_accuracy_score.html"],
)
def _compute(self, predictions, references, sample_weight=None, adjusted=False):
return {
"balanced_accuracy": float(
balanced_accuracy_score(references, predictions, sample_weight=sample_weight, adjusted=adjusted)
)
} |