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from typing import Dict, Any
import datasets
import evaluate
import numpy as np
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. Each inner list should contain the top-5 predicted class indices.
references (`list` of `int`): Ground truth labels.
Returns:
top5_error_rate (`float`): Top-5 Error Rate score. Minimum possible value is 0. Maximum possible value is 1.0.
Examples:
>>> metric = evaluate.load("top5_error_rate")
>>> results = metric.compute(
... references=[0, 1, 2],
... predictions=[[0, 1, 2, 3, 4], [1, 0, 2, 3, 4], [2, 0, 1, 3, 4]]
... )
>>> print(results)
{'top5_error_rate': 0.0}
"""
_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(datasets.Sequence(datasets.Value("float32"))),
"references": datasets.Sequence(datasets.Value("int32")),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Sequence(datasets.Value("float32")),
"references": datasets.Value("int32"),
}
),
reference_urls=[],
)
def _compute(
self,
*,
predictions: list[list[float]] = None,
references: list[int] = None,
**kwargs,
) -> Dict[str, Any]:
# to numpy array
outputs = np.array(predictions, dtype=np.float32)
labels = np.array(references)
# Top-1 ACC
pred = outputs.argmax(axis=1)
acc = (pred == labels).mean()
# Top-5 Error Rate
top5_indices = outputs.argsort(axis=1)[:, -5:]
correct = (labels.reshape(-1, 1) == top5_indices).any(axis=1)
top5_error_rate = 1 - correct.mean()
return {
"accuracy": float(acc),
"top5_error_rate": float(top5_error_rate)
}
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