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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script 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.
from typing import Dict
import evaluate
import datasets
from torch import from_numpy, amax
from torchmetrics.functional.classification.calibration_error import (
binary_calibration_error,
multiclass_calibration_error,
)
from numpy import ndarray
_CITATION = """\
@InProceedings{huggingface:ece,
title = {Expected calibration error (ECE)},
authors={Nathan Fradet},
year={2023}
}
"""
_DESCRIPTION = """\
This metrics computes the expected calibration error (ECE).
It directly calls the torchmetrics package:
https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html
"""
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of predictions to score. They must have a shape (N,C,...) if multiclass, or (N,...) if binary.
references: list of reference for each prediction, with a shape (N,...).
Returns:
ece: expected calibration error
Examples:
>>> ece = evaluate.load("Natooz/ece")
>>> results = ece.compute(
... references=np.array([[0.25, 0.20, 0.55],
... [0.55, 0.05, 0.40],
... [0.10, 0.30, 0.60],
... [0.90, 0.05, 0.05]]),
... predictions=np.array(),
... num_classes=3,
... n_bins=3,
... norm="l1",
... )
>>> print(results)
{'ece': 0.2000}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class ECE(evaluate.Metric):
"""
Proxy to the BinaryCalibrationError (ECE) metric of the torchmetrics package:
https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html
"""
def _info(self):
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("float32")),
"references": datasets.Value("int64"),
}
),
# Homepage of the module for documentation
homepage="https://huggingface.co/spaces/Natooz/ece",
# Additional links to the codebase or references
codebase_urls=[
"https://github.com/Lightning-AI/torchmetrics/blob/v0.11.4/src/torchmetrics/classification/calibration_error.py"
],
reference_urls=[
"https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html"
],
)
def _compute(self, predictions=None, references=None, **kwargs) -> Dict[str, float]:
"""Returns the ece.
See the torchmetrics documentation for more information on the arguments to pass.
https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html
predictions: (N,C,...) if multiclass or (N,...) if binary
references: (N,...)
If "num_classes" is not provided in a multiclasses setting, the number maximum label index will
be used as "num_classes".
"""
# Convert the input
if isinstance(predictions, ndarray):
predictions = from_numpy(predictions)
if isinstance(references, ndarray):
references = from_numpy(references)
max_label = amax(references, list(range(references.dim())))
if max_label > 1 and "num_classes" not in kwargs:
kwargs["num_classes"] = max_label
# Compute the calibration
if max_label > 1:
ece = multiclass_calibration_error(predictions, references, **kwargs)
else:
ece = binary_calibration_error(predictions, references, **kwargs)
return {
"ece": float(ece),
}
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