top5_error_rate.py

from typing import Dict, Any

import datasets
import evaluate
from evaluate.utils.file_utils import add_start_docstrings

_DESCRIPTION = """
The "top-5 error" is the percentage of times that the target label does not appear among the 5 highest-probability predictions. It can be computed with:
Top-5 Error Rate = 1 - Top-5 Accuracy
or equivalently:
Top-5 Error Rate = (Number of incorrect top-5 predictions) / (Total number of cases processed)
 Where:
- Top-5 Accuracy: The proportion of cases where the true label is among the model's top 5 predicted classes.
- Incorrect top-5 prediction: The true label is not in the top 5 predicted classes (ranked by probability).
"""


_KWARGS_DESCRIPTION = """
Args:
    predictions (`list` of `list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input.
Examples:
    >>> 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}
"""


_CITATION = """
"""


@add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class Top5ErrorRate(evaluate.Metric):
    def _info(self):
        return evaluate.MetricInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            features=datasets.Features(
                {
                    "predictions": datasets.Sequence(list[datasets.Value("int32")]),
                    "references": datasets.Sequence(datasets.Value("int32")),
                }
                if self.config_name == "multilabel"
                else {
                    "predictions": datasets.Sequence(datasets.Value("int32")),
                    "references": datasets.Value("int32"),
                }
            ),
            reference_urls=[],
        )

    def _compute(
        self,
        *,
        predictions: list[list[int]] = None,
        references: list[int] = None,
        **kwargs,
    ) -> Dict[str, Any]:
        total = len(references)
        correct = sum(1 for pred, ref in zip(predictions, references) if ref in pred)

        error_rate = 1.0 - (correct / total)

        return {
            "top5_error_rate": float(error_rate)
        }