SelectKBest 将分数作为 nan 值

Question

我有一个数据集，我正在尝试使用 SelectKBest 和 Chi2 获取特征重要性，但 SelectKBest 给出的特征分数为 nan。

数据文件和代码文件存在于this链接

# Path to the data file
file_path = r"D:\Data_Sets\Mobile_Prices\data.csv"

# Reading the data from the Southern Second Order file, and also passing the column names to south_data data frame
south_data = pd.read_csv(file_path)


# Printing the number of data points and the number of columns of south_data data frame
print("The number of data points in the data  :", south_data.shape[0])
print("The features of the data :", south_data.shape[1])

# Printing the head of south_data data frame
print(south_data.head())

# Check for the nulls
print(south_data.isnull().sum())

# Separate the x and y
x = south_data.drop("tss", axis = 1)
y = south_data["tss"]

# Find the scores of features
bestfit = SelectKBest(score_func=chi2, k=5)
features = bestfit.fit(x,y)
x_new = features.transform(x)

print(features.scores_)

# The output of features.scores_ is displayed as
# array([nan, nan, nan, nan, nan, nan, nan, nan, nan])

Answer 1

目标变量中的所有值都是1。这就是nan 值在您的scores_ 中的原因。因此，请验证您的目标变量。

仅作说明：

>>> from sklearn.datasets import load_digits
import numpy as np
>>> from sklearn.feature_selection import SelectKBest, chi2
>>> X, y = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> feature_selector = SelectKBest(chi2, k=20)
>>> X_new = feature_selector.fit_transform(X, np.ones(len(X)) )
>>> feature_selector.scores_
array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) 

Answer 2

'bestfit' 是一个对象，调用 fit 方法时不需要为其分配变量。试试：

# Find the scores of features
bestfit = SelectKBest(score_func=chi2, k=5)
bestfit.fit(x,y)
x_new = bestfit.transform(x)
print(bestfit.scores_)

或者，您可以同时调用 fit 和 transform：

# Find the scores of features
bestfit = SelectKBest(score_func=chi2, k=5)
x_new = bestfit.fit_transform(x)
print(bestfit.scores_)

这能解决你的问题吗？