选择Scikit功能后保留功能名称 [英] Retain feature names after Scikit Feature Selection

问题描述

从Scikit-Learn对一组数据运行方差阈值后，它将删除几个功能.我觉得我在做一些简单而又愚蠢的事情，但是我想保留其余功能的名称.以下代码:

After running a Variance Threshold from Scikit-Learn on a set of data, it removes a couple of features. I feel I'm doing something simple yet stupid, but I'd like to retain the names of the remaining features. The following code:

def VarianceThreshold_selector(data):
    selector = VarianceThreshold(.5) 
    selector.fit(data)
    selector = (pd.DataFrame(selector.transform(data)))
    return selector
x = VarianceThreshold_selector(data)
print(x)

更改以下数据(这只是行的一小部分):

changes the following data (this is just a small subset of the rows):

Survived    Pclass  Sex Age SibSp   Parch   Nonsense
0             3      1  22   1        0        0
1             1      2  38   1        0        0
1             3      2  26   0        0        0

进入(再次只是一小部分行)

into this (again just a small subset of the rows)

     0         1      2     3
0    3      22.0      1     0
1    1      38.0      1     0
2    3      26.0      0     0

使用get_support方法，我知道它们是Pclass，Age，Sibsp和Parch，所以我宁愿这样返回的内容更像是:

Using the get_support method, I know that these are Pclass, Age, Sibsp, and Parch, so I'd rather this return something more like :

     Pclass         Age      Sibsp     Parch
0        3          22.0         1         0
1        1          38.0         1         0
2        3          26.0         0         0

有没有简单的方法可以做到这一点?我对Scikit Learn非常陌生，所以我可能只是在做一些愚蠢的事情.

Is there an easy way to do this? I'm very new with Scikit Learn, so I'm probably just doing something silly.

推荐答案

这样的帮助吗?如果将其传递给pandas数据框，它将获取列并使用get_support，如您提到的那样按列索引对其列进行迭代，以仅拉出满足方差阈值的列标题.

Would something like this help? If you pass it a pandas dataframe, it will get the columns and use get_support like you mentioned to iterate over the columns list by their indices to pull out only the column headers that met the variance threshold.

>>> df
   Survived  Pclass  Sex  Age  SibSp  Parch  Nonsense
0         0       3    1   22      1      0         0
1         1       1    2   38      1      0         0
2         1       3    2   26      0      0         0

>>> from sklearn.feature_selection import VarianceThreshold
>>> def variance_threshold_selector(data, threshold=0.5):
    selector = VarianceThreshold(threshold)
    selector.fit(data)
    return data[data.columns[selector.get_support(indices=True)]]

>>> variance_threshold_selector(df, 0.5)
   Pclass  Age
0       3   22
1       1   38
2       3   26
>>> variance_threshold_selector(df, 0.9)
   Age
0   22
1   38
2   26
>>> variance_threshold_selector(df, 0.1)
   Survived  Pclass  Sex  Age  SibSp
0         0       3    1   22      1
1         1       1    2   38      1
2         1       3    2   26      0

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