python中的MAPE计算

问题描述

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，但这给出了错误并显示无效mask = a <> 行中的语法0

 def mape_vectorized_v2(a, b):掩码 = a <>0返回 (np.fabs(a - b)/a)[mask].mean()def mape_vectorized_v2(a, b):文件<ipython-input-5-afa5c1162e83>"，第 1 行def mape_vectorized_v2(a, b):^SyntaxError:解析时出现意外的 EOF

我正在使用 spyder3.我的预测值是 np.array 类型，真实值是数据框

类型(预测)出[7]:numpy.ndarray类型(y_test)出[8]:pandas.core.frame.DataFrame

如何清除此错误并继续进行 MAPE 计算?

predicted.head()出[22]:Total_kWh0 7.1636271 6.5849602 6.6380573 7.7854874 6.994427y_test.head()出[23]:Total_kWh79 7.2148 6.7143 6.7189 7.217 6.4np.abs(y_test[['Total_kWh']] - 预测[['Total_kWh']]).head()出[24]:Total_kWh0 南1 纳米2 南3 南4 0.094427

解决方案

在python中进行不等比较需要!=，而不是.>

所以需要:

def mape_vectorized_v2(a, b):掩码 = a != 0返回 (np.fabs(a - b)/a)[mask].mean()

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)返回 np.mean(np.abs((y_true - y_pred)/y_true)) * 100

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

    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

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 ?

Edit :

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

解决方案

In python for compare by not equal need !=, not <>.

So need:

def mape_vectorized_v2(a, b):
    mask = a != 0
    return (np.fabs(a - b)/a)[mask].mean()

Another solution from stats.stackexchange:

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

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