# Copyright 2020-2021 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# ============================================================================
"""Accuracy."""
import numpy as np
from .metric import EvaluationBase, rearrange_inputs
[docs]class Accuracy(EvaluationBase):
r"""
Calculates the accuracy for classification and multilabel data.
The accuracy class creates two local variables, the correct number and the total number that are used to compute the
frequency with which `y_pred` matches `y`. This frequency is ultimately returned as the accuracy: an
idempotent operation that simply divides the correct number by the total number.
.. math::
\text{accuracy} =\frac{\text{true_positive} + \text{true_negative}}
{\text{true_positive} + \text{true_negative} + \text{false_positive} + \text{false_negative}}
Args:
eval_type (str): The metric to calculate the accuracy over a dataset, for
classification (single-label), and multilabel (multilabel classification).
Default: 'classification'.
Examples:
>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
>>> y = Tensor(np.array([1, 0, 1]), mindspore.float32)
>>> metric = nn.Accuracy('classification')
>>> metric.clear()
>>> metric.update(x, y)
>>> accuracy = metric.eval()
>>> print(accuracy)
0.6666666666666666
"""
def __init__(self, eval_type='classification'):
super(Accuracy, self).__init__(eval_type)
self.clear()
[docs] def clear(self):
"""Clears the internal evaluation result."""
self._correct_num = 0
self._total_num = 0
self._class_num = 0
[docs] @rearrange_inputs
def update(self, *inputs):
"""
Updates the internal evaluation result :math:`y_{pred}` and :math:`y`.
Args:
inputs: Input `y_pred` and `y`. `y_pred` and `y` are a `Tensor`, a list or an array.
For the 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list
of floating numbers in range :math:`[0, 1]`
and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot
encoding is used or the shape is :math:`(N,)` with integer values if index of category is used.
For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with
values 0 or 1. Indices with 1 indicate the positive category. The shape of `y_pred` and `y`
are both :math:`(N, C)`.
Raises:
ValueError: If the number of the inputs is not 2.
"""
if len(inputs) != 2:
raise ValueError('The accuracy needs 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
y_pred = self._convert_data(inputs[0])
y = self._convert_data(inputs[1])
if self._type == 'classification' and y_pred.ndim == y.ndim and self._check_onehot_data(y):
y = y.argmax(axis=1)
self._check_shape(y_pred, y)
self._check_value(y_pred, y)
if self._class_num == 0:
self._class_num = y_pred.shape[1]
elif y_pred.shape[1] != self._class_num:
raise ValueError('The y_pred shape does not match the class number, the last input data contains '
'{} classes, but the current data contains {} classes'
.format(self._class_num, y_pred.shape[1]))
if self._type == 'classification':
indices = y_pred.argmax(axis=1)
result = (np.equal(indices, y) * 1).reshape(-1)
elif self._type == 'multilabel':
dimension_index = y_pred.ndim - 1
y_pred = y_pred.swapaxes(1, dimension_index).reshape(-1, self._class_num)
y = y.swapaxes(1, dimension_index).reshape(-1, self._class_num)
result = np.equal(y_pred, y).all(axis=1) * 1
self._correct_num += result.sum()
self._total_num += result.shape[0]
[docs] def eval(self):
"""
Computes the accuracy.
Returns:
Float, the computed result.
Raises:
RuntimeError: If the sample size is 0.
"""
if self._total_num == 0:
raise RuntimeError('The accuracy can not be calculated, because the number of samples is 0.')
return self._correct_num / self._total_num