基于中值绝对偏差 (MAD) 的异常值检测 [英] median-absolute-deviation (MAD) based outlier detection

问题描述

我想使用@Joe Kington 的答案应用基于中值绝对偏差 (MAD) 的异常值检测，如下所示:

I wanted to apply median-absolute-deviation (MAD) based outlier detection using the answer from @Joe Kington as given below:

一维观察中检测异常值的 Pythonic 方法数据

但是，我的代码出了什么问题，我不知道如何将异常值指定为 MY DATA 的 nan 值:

However, what's going wrong with my code, I could not figure out how to assign the outliers as nan values for MY DATA:

import numpy as np
data = np.array([55,32,4,5,6,7,8,9,11,0,2,1,3,4,5,6,7,8,25,25,25,25,10,11,12,25,26,27,28],dtype=float)
median = np.median(data, axis=0)
diff = np.sum((data - median)**2, axis=-1)
diff = np.sqrt(diff)
med_abs_deviation = np.median(diff)
modified_z_score = 0.6745 * diff / med_abs_deviation
data_without_outliers = data[modified_z_score < 3.5]
?????
print data_without_outliers

推荐答案

使用有什么问题:

data[modified_z_score > 3.5] = np.nan

请注意，这仅在 data 是浮点数组(如果您正在计算 MAD 时应该是)时才有效.

Note that this will only work if data is a floating point array (which it should be if you are calculating MAD).

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