关于回归的均方根误差的计算

假设我得到了下面的熊猫数据框架用于回归分析。

import pandas
import math
import numpy

df = pandas.DataFrame(numpy.random.randint(0,100,size=(100, 2)), columns=['labels','predictions'])

我现在想把RMSE计算为

math.sqrt(numpy.mean((df["predictions"] - df["lables"]) ** 2)) 

对于间隔为7的标签值

这是一个非常难看的代码,能完成这项工作……如果你能帮我把它弄脏,那就太好了……

# define step
step = 7
# initialize counter
idx = 0
# initialize empty dataframe
rmse = pandas.DataFrame(columns=['bout' , 'rmse'],index=range(0,len(range(int(df['labels'].min())+step,int(df['labels'].max()),step))))

# start loop to calculate rmse every 7 units
for i in range(int(df['labels'].min())+step,int(df['labels'].max()),step):

    # select values in interval
    df_bout = df[(df['labels']>=i-step) & (df['labels']<i)]

    # calculate rmse in interval
    rmse.loc[idx] = [str(i-step)+'-'+str(i),math.sqrt(numpy.mean((df_bout.predictions - df_bout.labels) ** 2))]

    # increment counter
    idx = idx + 1