false_positive_rate.py

import datasets
from sklearn.metrics import confusion_matrix
import evaluate

_DESCRIPTION = """
FPR is the proportion of negative cases incorrectly identified as positive cases in the data (i.e. the probability that false alerts will be raised). It is defined as:
FPR = FP / (FP + TN)
 Where:
TN: True negative
FP: False positive
"""


_KWARGS_DESCRIPTION = """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth (correct) target values.
    normalize (`boolean`): Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. If None, confusion matrix will not be normalized.
    sample_weight (`list` of `float`): Sample weights. Defaults to None.
Returns:
    false positive rate (`float` or `int`): FPR score. Minimum possible value is 0. Maximum possible value is 1.0.
"""

_CITATION = """
@misc{ enwiki:1178431122,
    author = "{Wikipedia contributors}",
    title = "False positives and false negatives --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2023",
    url = "https://en.wikipedia.org/w/index.php?title=False_positives_and_false_negatives&oldid=1178431122",
    note = "[Online; accessed 17-November-2023]"
}
"""

@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class FPR(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.confusion_matrix.html"],
        )

    def _compute(self, predictions, references, normalize=None, sample_weight=None):
        tn, fp, fn, tp = confusion_matrix(references, predictions, normalize=normalize, sample_weight=sample_weight).ravel()
        fpr = fp / (fp + tn)
        return {"false_positive_rate": fpr}