balanced_accuracy.py

# Copyright 2023 HyperML Authors and the current HyperML contributor.
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# Licensed under the Apache License, Version 2.0 (the "License");
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"""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)
            )
        }