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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. | |
"""Accuracy metric.""" | |
import datasets | |
from sklearn.metrics import accuracy_score | |
import evaluate | |
_DESCRIPTION = """ | |
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with: | |
Accuracy = (TP + TN) / (TP + TN + FP + FN) | |
Where: | |
TP: True positive | |
TN: True negative | |
FP: False positive | |
FN: False negative | |
""" | |
_KWARGS_DESCRIPTION = """ | |
Args: | |
predictions (`list` of `int`): Predicted labels. | |
references (`list` of `int`): Ground truth labels. | |
normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True. | |
sample_weight (`list` of `float`): Sample weights Defaults to None. | |
Returns: | |
accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy. | |
Examples: | |
Example 1-A simple example | |
>>> 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} | |
Example 2-The same as Example 1, except with `normalize` set to `False`. | |
>>> accuracy_metric = evaluate.load("accuracy") | |
>>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False) | |
>>> print(results) | |
{'accuracy': 3.0} | |
Example 3-The same as Example 1, except with `sample_weight` set. | |
>>> accuracy_metric = evaluate.load("accuracy") | |
>>> results = 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) | |
{'accuracy': 0.8778625954198473} | |
""" | |
_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 Accuracy(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.accuracy_score.html"], | |
) | |
def _compute(self, predictions, references, normalize=True, sample_weight=None): | |
return { | |
"accuracy": float( | |
accuracy_score(references, predictions, normalize=normalize, sample_weight=sample_weight) | |
) | |
} | |