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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) | |

) | |

} | |