解决：ImportError: cannot import name ‘mean_absolute_percentage_error‘ from ‘sklearn.metrics‘

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我是靠谱客的博主矮小未来，最近开发中收集的这篇文章主要介绍解决：ImportError: cannot import name ‘mean_absolute_percentage_error‘ from ‘sklearn.metrics‘，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

Python问题：

ImportError: cannot import name ‘mean_absolute_percentage_error‘ from ‘sklearn.metrics‘

问题原因：

就是在源脚本中，metric文件夹中mean_absolute_percentage_error所归属的regression.py文件中，没有mean_absolute_percentage_error()函数

解决方法：

第一步：找到电脑中Libsite-packagessklearnmetrics位置。
（我的是在D:InstallPositionpython×××Libsite-packagessklearnmetrics）
第二步：从sklearn的GitHub源码（链接：https://github.com/scikit-learn/scikit-learn/blob/2beed5584/sklearn/metrics/_regression.py#L197）中，把mean_absolute_percentage_error这部分的脚本拷贝到自己电脑安装sklearn包的regression.py文件中。
为了方便，大家也可以不用到这个网站里面找，直接复制粘贴下面的代码放到regression.py文件（有的为_regression.py）文件中就可以了（后面还有第三步，记得看，要不然修不好bug）

def mean_absolute_percentage_error(y_true, y_pred,
sample_weight=None,
multioutput='uniform_average'):
"""Mean absolute percentage error regression loss.
Note here that we do not represent the output as a percentage in range
[0, 100]. Instead, we represent it in range [0, 1/eps]. Read more in the
:ref:`User Guide <mean_absolute_percentage_error>`.
.. versionadded:: 0.24
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average'} or array-like
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
If input is list then the shape must be (n_outputs,).
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats in the range [0, 1/eps]
If multioutput is 'raw_values', then mean absolute percentage error
is returned for each output separately.
If multioutput is 'uniform_average' or an ndarray of weights, then the
weighted average of all output errors is returned.
MAPE output is non-negative floating point. The best value is 0.0.
But note the fact that bad predictions can lead to arbitarily large
MAPE values, especially if some y_true values are very close to zero.
Note that we return a large value instead of `inf` when y_true is zero.
Examples
--------
>>> from sklearn.metrics import mean_absolute_percentage_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_absolute_percentage_error(y_true, y_pred)
0.3273...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_percentage_error(y_true, y_pred)
0.5515...
>>> mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.6198...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
check_consistent_length(y_true, y_pred, sample_weight)
epsilon = np.finfo(np.float64).eps
mape = np.abs(y_pred - y_true) / np.maximum(np.abs(y_true), epsilon)
output_errors = np.average(mape,
weights=sample_weight, axis=0)
if isinstance(multioutput, str):
if multioutput == 'raw_values':
return output_errors
elif multioutput == 'uniform_average':
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)

第三步：在Libsite-packagessklearnmetrics文件夹中，找到__init__.py文件，增加一行下面的代码：

from ._regression import mean_absolute_percentage_error

这句代码的意思就是记得去_regression.py文件中找mean_absolute_percentage_error。

第四步：在__init__.py文件和_regression.py中还存在__all__()函数，可以把‘mean_absolute_percentage_error’或者“mean_absolute_percentage_error”按照函数里的几个格式照葫芦画瓢地写上去。
为了确保能早点解决错误，笔者在这里是在__all__()函数里面也按照格式写上了mean_absolute_percentage_error，但是自身认为可能不写也可以，所以认为这点可有可无。
如果大家看到博文这里有兴趣进行尝试，欢迎将该种方案是否可行评论区或者私信告诉笔者哦~~(**^▽^**)，欢迎交流

参考文献：

https://blog.csdn.net/qq_40206371/article/details/120549324
https://github.com/scikit-learn/scikit-learn/blob/2beed5584/sklearn/metrics/_regression.py#L197
https://blog.csdn.net/u010614781/article/details/118080544