python中的MAPE计算
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/47648133/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
MAPE calculation in python
提问by Magg_rs
I want to calculate Mean Absolute percentage error (MAPE) of predicted and true values. I found a solution from here, but this gives error and shows invalid syntax in the line mask = a <> 0
我想计算预测值和真实值的平均绝对百分比误差 (MAPE)。我从这里找到了一个解决方案，但这给出了错误并在行中显示了无效的语法mask = a <> 0
def mape_vectorized_v2(a, b):
mask = a <> 0
return (np.fabs(a - b)/a)[mask].mean()
def mape_vectorized_v2(a, b):
File "<ipython-input-5-afa5c1162e83>", line 1
def mape_vectorized_v2(a, b):
^
SyntaxError: unexpected EOF while parsing
I am using spyder3. My predicted value is a type np.array and true value is dataframe
我正在使用 spyder3。我的预测值是 np.array 类型，真实值是数据框
type(predicted)
Out[7]: numpy.ndarray
type(y_test)
Out[8]: pandas.core.frame.DataFrame
How do i clear this error and proceed with MAPE Calculation ?
如何清除此错误并继续进行 MAPE 计算？
Edit :
编辑 ：
predicted.head()
Out[22]:
Total_kWh
0 7.163627
1 6.584960
2 6.638057
3 7.785487
4 6.994427
y_test.head()
Out[23]:
Total_kWh
79 7.2
148 6.7
143 6.7
189 7.2
17 6.4
np.abs(y_test[['Total_kWh']] - predicted[['Total_kWh']]).head()
Out[24]:
Total_kWh
0 NaN
1 NaN
2 NaN
3 NaN
4 0.094427
回答by jezrael
In python for compare by not equal need !=, not <>.
在 python 中比较需要不相等!=，而不是<>.
So need:
所以需要：
def mape_vectorized_v2(a, b):
mask = a != 0
return (np.fabs(a - b)/a)[mask].mean()
Another solution from stats.stackexchange:
来自stats.stackexchange 的另一个解决方案：
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
回答by Pablo Andrés Casta?eda
Both solutions are not working with zero values. This is working form me:
两种解决方案都不适用于零值。这是我的工作：
def percentage_error(actual, predicted):
res = np.empty(actual.shape)
for j in range(actual.shape[0]):
if actual[j] != 0:
res[j] = (actual[j] - predicted[j]) / actual[j]
else:
res[j] = predicted[j] / np.mean(actual)
return res
def mean_absolute_percentage_error(y_true, y_pred):
return np.mean(np.abs(percentage_error(np.asarray(y_true), np.asarray(y_pred)))) * 100
I hope it helps.
我希望它有帮助。
回答by Satyam Anand
Since the actual values can also be zeroes I am taking the average of the actual values in the denominator, instead of the actual values:
由于实际值也可以为零，我取分母中实际值的平均值，而不是实际值：
Error = np.sum(np.abs(np.subtract(data_4['y'],data_4['pred'])))
Average = np.sum(data_4['y'])
MAPE = Error/Average
