Spaces:
Running
Running
File size: 3,820 Bytes
758b953 835911f 758b953 835911f 758b953 835911f 758b953 835911f 758b953 835911f 758b953 835911f 758b953 835911f 758b953 835911f 758b953 835911f 758b953 835911f 758b953 835911f 758b953 835911f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
# 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: MCC is a correlation coefficient between the observed and predicted binary classifications, and takes into account true and false positives and negatives."""
import evaluate
import datasets
from sklearn.metrics import matthews_corrcoef
# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {MCC Metric},
authors={huggingface, Inc.},
year={2020}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
MCC (Matthews Correlation Coefficient) is a correlation coefficient between the observed and predicted binary classifications, and takes into account true and false positives and negatives. It can be computed with the equation:
MCC = (TP * TN - FP * FN) / sqrt((TP+FP) * (TP+FN) * (TN+FP) * (TN+FN))
Where TP is the true positives, TN is the true negatives, FP is the false positives, and FN is the false negatives.
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
Returns:
- **mcc** (`float`): The MCC score. Minimum possible value is -1. Maximum possible value is 1. A higher MCC means that the predicted and observed binary classifications agree better, while a negative MCC means that they agree worse than chance.
Examples:
Example 1-A simple example with some errors
>>> mcc_metric = evaluate.load('mcc')
>>> results = mcc_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
>>> print(results)
{'mcc': 0.16666666666666666}
Example 2-The same example as Example 1, but with some different labels
>>> mcc_metric = evaluate.load('mcc')
>>> results = mcc_metric.compute(references=[0, 1, 2, 2, 2], predictions=[0, 2, 2, 1, 2])
>>> print(results)
{'mcc': 0.2041241452319315}
"""
# TODO: Define external resources urls if needed
BAD_WORDS_URL = "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class MCC(evaluate.Metric):
"""Compute MCC Scores"""
def _info(self):
return evaluate.MetricInfo(
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features({
'predictions': datasets.Value('int64'),
'references': datasets.Value('int64'),
}),
# Homepage of the module for documentation
homepage="https://huggingface.co/evaluate-metric?message=Request%20sent",
# Additional links to the codebase or references
codebase_urls=[],
reference_urls=[]
)
def _compute(self, predictions, references):
"""Returns the mcc scores"""
# Computes the MCC score using matthews_corrcoef from sklearn
return {"mcc": matthews_corrcoef(references, predictions)} |