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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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# ============================================================================
"""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 has two local variables, the correct number and the total number of samples, 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. Supports 'classification' and
'multilabel'. 'classification' means the dataset label is single. 'multilabel' means the dataset has multiple
labels. Default: 'classification'.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> 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 local variables. For 'classification', if the index of the maximum of the predict value
matches the label, the predict result is correct. For 'multilabel', the predict value match the label,
the predict result is correct.
Args:
inputs: Logits and labels. `y_pred` stands for logits, `y` stands for labels. `y_pred` and `y` must be a
`Tensor`, a list or an array.
For the 'classification' evaluation type, `y_pred` is a list of floating numbers in range :math:`[0, 1]`
and the shape 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. `y` 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,)`.
For 'multilabel' evaluation type, the value of `y_pred` and `y` can only be 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("For 'Accuracy.update', it needs 2 inputs (predicted value, true value), "
"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("For 'Accuracy.update', class number not match, last input predicted data contain {} "
"classes, but current predicted data contain {} classes, please check your predicted "
"value(inputs[0]).".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, "
"please check whether your inputs(predicted value, true value) are empty, "
"or has called update method before calling eval method.")
return self._correct_num / self._total_num