在 Scikit 特征选择后保留特征名称

Question

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)

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

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

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

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

Answer 1

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

Answer 2

I came here looking for a way to get transform() or fit_transform() to return a data frame, but I suspect it's not supported.

However, you can subset the data a bit more cleanly like this:

data_transformed = data.loc[:, selector.get_support()]

Answer 3

There's probably better ways to do this, but for those interested here's how I did:

def VarianceThreshold_selector(data):

    #Select Model
    selector = VarianceThreshold(0) #Defaults to 0.0, e.g. only remove features with the same value in all samples

    #Fit the Model
    selector.fit(data)
    features = selector.get_support(indices = True) #returns an array of integers corresponding to nonremoved features
    features = [column for column in data[features]] #Array of all nonremoved features' names

    #Format and Return
    selector = pd.DataFrame(selector.transform(data))
    selector.columns = features
    return selector

Answer 4

As I had some problems with the function by Jarad, I have mixed it up with the solution by pteehan, which I found is more reliable. I also added NA replacement as a standard as VarianceThreshold does not like NA values.

def variance_threshold_select(df, thresh=0.0, na_replacement=-999):
    df1 = df.copy(deep=True) # Make a deep copy of the dataframe
    selector = VarianceThreshold(thresh)
    selector.fit(df1.fillna(na_replacement)) # Fill NA values as VarianceThreshold cannot deal with those
    df2 = df.loc[:,selector.get_support(indices=False)] # Get new dataframe with columns deleted that have NA values

    return df2

Answer 5

how about this as a code?

columns = [col for col in df.columns]

low_var_cols = []

for col in train_file.columns:
if statistics.variance(df[col]) <= 0.1:
    low_var_cols.append(col)

then drop the columns from the dataframe?

Answer 6

您也可以使用 Pandas 进行阈值处理

data_new = data.loc[:, data.std(axis=0) > 0.75]