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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.
"""TODO: Add a description here."""
import datasets
from sklearn.metrics import accuracy_score
import evaluate
_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}
}
"""
_DESCRIPTION = """\
Accuracy classification score.
"""
_KWARGS_DESCRIPTION = """
Note: To be consistent with the `evaluate` input conventions the scikit-learn inputs are renamed:
- `y_true`: `references`
- `y_pred`: `predictions`
Scikit-learn docstring:
Accuracy classification score.
In multilabel classification, this function computes subset accuracy:
the set of labels predicted for a sample must *exactly* match the
corresponding set of labels in y_true.
Read more in the :ref:`User Guide <accuracy_score>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) labels.
y_pred : 1d array-like, or label indicator array / sparse matrix
Predicted labels, as returned by a classifier.
normalize : bool, default=True
If ``False``, return the number of correctly classified samples.
Otherwise, return the fraction of correctly classified samples.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
score : float
If ``normalize == True``, return the fraction of correctly
classified samples (float), else returns the number of correctly
classified samples (int).
The best performance is 1 with ``normalize == True`` and the number
of samples with ``normalize == False``.
See Also
--------
balanced_accuracy_score : Compute the balanced accuracy to deal with
imbalanced datasets.
jaccard_score : Compute the Jaccard similarity coefficient score.
hamming_loss : Compute the average Hamming loss or Hamming distance between
two sets of samples.
zero_one_loss : Compute the Zero-one classification loss. By default, the
function will return the percentage of imperfectly predicted subsets.
Notes
-----
In binary classification, this function is equal to the `jaccard_score`
function.
Examples
--------
>>> from sklearn.metrics import accuracy_score
>>> y_pred = [0, 2, 1, 3]
>>> y_true = [0, 1, 2, 3]
>>> accuracy_score(y_true, y_pred)
0.5
>>> accuracy_score(y_true, y_pred, normalize=False)
2
In the multilabel case with binary label indicators:
>>> import numpy as np
>>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
0.5
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class AccuracyScore(evaluate.Metric):
"""Accuracy classification score."""
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("int32")),
"references": datasets.Sequence(datasets.Value("int32")),
}
),
datasets.Features(
{"predictions": datasets.Sequence(datasets.Value("int32")), "references": datasets.Value("int32")}
),
datasets.Features(
{"predictions": datasets.Value("int32"), "references": datasets.Sequence(datasets.Value("int32"))}
),
datasets.Features({"predictions": datasets.Value("int32"), "references": datasets.Value("int32")}),
],
# Homepage of the module for documentation
homepage="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html",
# Additional links to the codebase or references
codebase_urls=["https://github.com/scikit-learn/scikit-learn"],
reference_urls=["https://scikit-learn.org/stable/index.html"],
)
def _compute(self, predictions, references, normalize=True, sample_weight=None):
"""Returns the scores"""
score = accuracy_score(y_true=references, y_pred=predictions, normalize=normalize, sample_weight=sample_weight)
return {"accuracy_score": score}
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