# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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 Accuracy"""
import numpy as np
from mindnlp.abc import Metric
from .utils import _check_onehot_data, _check_shape, _convert_data_type
[docs]def accuracy_fn(preds, labels):
r"""
Calculates the accuracy. The function is shown as follows:
.. math::
\text{ACC} =\frac{\text{TP} + \text{TN}}
{\text{TP} + \text{TN} + \text{FP} + \text{FN}}
where `ACC` is accuracy, `TP` is the number of true posistive cases, `TN` is the number
of true negative cases, `FP` is the number of false posistive cases, `FN` is the number
of false negative cases.
Args:
preds (Union[Tensor, list, np.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, np.ndarray]): Ground truth. `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,)`.
Returns:
- **acc** (float) - The computed result.
Raises:
RuntimeError: If the number of samples is 0.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindnlp.common.metrics import accuracy
>>> preds = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
>>> labels = Tensor(np.array([1, 0, 1]), mindspore.int32)
>>> acc = accuracy(preds, labels)
>>> print(acc)
0.6666666666666666
"""
correct_num = 0
total_num = 0
y_pred = _convert_data_type(preds)
y_true = _convert_data_type(labels)
if y_pred.ndim == y_true.ndim and _check_onehot_data(y_true):
y_true = y_true.argmax(axis=1)
_check_shape(y_pred, y_true)
indices = y_pred.argmax(axis=1)
result = (np.equal(indices, y_true) * 1).reshape(-1)
correct_num += result.sum()
total_num += result.shape[0]
if total_num == 0:
raise RuntimeError(f'Accuracy can not be calculated, because the number of samples is '
f'{0}. Please check whether your inputs(predicted value, true value) '
f'are empty.')
acc = correct_num / total_num
return acc
[docs]class Accuracy(Metric):
r"""
Calculates accuracy. The function is shown as follows:
.. math::
\text{ACC} =\frac{\text{TP} + \text{TN}}
{\text{TP} + \text{TN} + \text{FP} + \text{FN}}
where `ACC` is accuracy, `TP` is the number of true posistive cases, `TN` is the number
of true negative cases, `FP` is the number of false posistive cases, `FN` is the number
of false negative cases.
Args:
name (str): Name of the metric.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import nn, Tensor
>>> from mindnlp.common.metrics import Accuracy
>>> preds = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
>>> labels = Tensor(np.array([1, 0, 1]), mindspore.int32)
>>> metric = Accuracy()
>>> metric.update(preds, labels)
>>> acc = metric.eval()
>>> print(acc)
0.6666666666666666
"""
def __init__(self, name='Accuracy'):
super().__init__()
self._name = name
self._correct_num = 0
self._total_num = 0
self._class_num = 0
[docs] def clear(self):
"""Clears the internal evaluation results."""
self._correct_num = 0
self._total_num = 0
self._class_num = 0
[docs] 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.
ValueError: class numbers of last input predicted data and current predicted data
not match.
"""
if len(inputs) != 2:
raise ValueError(f'For `Accuracy.update`, it needs 2 inputs (`preds` and `labels`), '
f'but got {len(inputs)}.')
preds = inputs[0]
labels = inputs[1]
y_pred = _convert_data_type(preds)
y_true = _convert_data_type(labels)
if self._class_num == 0:
self._class_num = y_pred.shape[1]
elif y_pred.shape[1] != self._class_num:
raise ValueError(f'For `Accuracy.update`, class numbers do not match. Last input '
f'predicted data contain {self._class_num} classes, but current '
f'predicted data contain {y_pred.shape[1]} classes. Please check '
f'your predicted value (`preds`).')
if self._class_num != 1 and y_pred.ndim == y_true.ndim and \
(_check_onehot_data(y_true) or y_true[0].shape == (1,)):
y_true = y_true.argmax(axis=1)
_check_shape(y_pred, y_true, self._class_num)
if self._class_num == 1:
indices = np.around(y_pred)
else:
indices = y_pred.argmax(axis=1)
res = (np.equal(indices, y_true) * 1).reshape(-1)
self._correct_num += res.sum()
self._total_num += res.shape[0]
[docs] def eval(self):
"""
Computes and returns the accuracy.
Returns:
- **acc** (float) - The computed result.
Raises:
RuntimeError: If the number of samples is 0.
"""
if self._total_num == 0:
raise RuntimeError(f'Accuracy can not be calculated, because the number of samples is'
f' {0}, please check whether your inputs(`preds`, `labels`) are '
f'empty, or you have called update method before calling eval '
f'method.')
acc = self._correct_num / self._total_num
return acc
[docs] def get_metric_name(self):
"""
Returns the name of the metric.
"""
return self._name
__all__ = ['accuracy_fn', 'Accuracy']