# Copyright 2022 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
""""Class for Metric MatthewsCorrelation"""
import math
import numpy as np
from mindnlp.abc import Metric
from mindnlp.common.metrics import _convert_data_type, _check_onehot_data, _check_shape
[文档]class MatthewsCorrelation(Metric):
r"""
Calculates the Matthews correlation coefficient (MCC). MCC is in essence a correlation
coefficient between the observed and predicted binary classifications; it returns a value
between −1 and +1. A coefficient of +1 represents a perfect prediction, 0 no better than
random prediction and −1 indicates total disagreement between prediction and observation.
The function is shown as follows:
.. math::
MCC=\frac{TP \times TN-FP \times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}
where `TP` is the number of true posistive cases, `TN` is the number of true negative cases,
`FN` is the number of false negative cases, `FP` is the number of false positive cases.
Args:
name (str): Name of the metric.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindnlp.engine.metrics import MatthewsCorrelation
>>> preds = [[0.8, 0.2], [-0.5, 0.5], [0.1, 0.4], [0.6, 0.3], [0.6, 0.3]]
>>> labels = [0, 1, 0, 1, 0]
>>> metric = MatthewsCorrelation()
>>> metric.update(preds, labels)
>>> m_c_c = metric.eval()
>>> print(m_c_c)
0.16666666666666666
"""
def __init__(self, name='MatthewsCorrelation'):
super().__init__()
self._name = name
self.t_p = 0
self.f_p = 0
self.t_n = 0
self.f_n = 0
[文档] def clear(self):
"""Clears the internal evaluation results."""
self.t_p = 0
self.f_p = 0
self.t_n = 0
self.f_n = 0
[文档] def update(self, *inputs):
"""
Updates local variables.
Args:
inputs: Input `preds` and `labels`.
- preds (Union[Tensor, list, numpy.ndarray]): Predicted value. `preds` is a list of
floating numbers in range :math:`[0, 1]` and the shape of `preds` is
:math:`(N, C)` in most cases (not strictly), where :math:`N` is the number of
cases and :math:`C` is the number of categories.
- labels (Union[Tensor, list, numpy.ndarray]): Ground truth value. `labels` must be in
one-hot format that shape is :math:`(N, C)`, or can be transformed to one-hot
format that shape is :math:`(N,)`.
Raises:
ValueError: If the number of inputs is not 2.
"""
if len(inputs) != 2:
raise ValueError(f'For `MatthewsCorrelation.update`, it needs 2 inputs '
f'(`preds` and `labels`), but got {len(inputs)}.')
preds = inputs[0]
labels = inputs[1]
preds = _convert_data_type(preds)
labels = _convert_data_type(labels)
if preds.ndim == labels.ndim and _check_onehot_data(labels):
labels = labels.argmax(axis=1)
_check_shape(preds, labels)
preds = np.argmax(preds, axis=1)
labels = labels.reshape(-1, 1)
sample_num = labels.shape[0]
for i in range(sample_num):
pred = preds[i]
label = labels[i]
if pred == 1:
if pred == label:
self.t_p += 1
else:
self.f_p += 1
else:
if pred == label:
self.t_n += 1
else:
self.f_n += 1
[文档] def eval(self):
"""
Computes and returns the MCC.
Returns:
- **m_c_c** (float) - The computed result.
"""
if self.t_p == 0 or self.f_p == 0 or self.t_n == 0 or self.f_n == 0:
m_c_c = 0.0
else:
m_c_c = (self.t_p * self.t_n - self.f_p * self.f_n) / math.sqrt(
(self.t_p + self.f_p) * (self.t_p + self.f_n) *
(self.t_n + self.f_p) * (self.t_n + self.f_n))
return m_c_c
[文档] def get_metric_name(self):
"""
Returns the name of the metric.
"""
return self._name