Spaces:
Running
Running
| # 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) | |
| ) | |
| } | |