python之SelectKBest 回归给出 "unknown label type"-error

我试图让 SelectKBest 示例的一个稍微修改的版本工作，但不断收到 ValueError("Unknown label type: %s"% repr(ys))

这是我的代码:

# Importing dependencies 
import numpy as np 
from sklearn.feature_selection import SelectKBest 
from sklearn.feature_selection import chi2 
from sklearn.datasets import load_iris 
 
#The Example from: 
#http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection 
iris = load_iris() 
X, Y = iris.data, iris.target 
print(X.shape, type(X), type(X[0,0])) 
print(Y.shape, type(Y), type(Y[0])) 
X_new = SelectKBest(chi2, k=2).fit_transform(X, Y) 
 
#My toyproblem: 
X = np.random.uniform(0,1, size=(5000, 10)) 
Y = np.random.uniform(0,1, size=(5000,)) 
 
#Type cast which might solve my problem by thi suggestion: 
# https://stackoverflow.com/questions/45346550/valueerror-unknown-label-type-unknown 
X=X.astype('float') 
Y=Y.astype('float') 
 
print(X.shape, type(X), type(X[0,0])) 
print(Y.shape, type(Y), type(Y[0])) 
 
X_new = SelectKBest(chi2, k=2).fit_transform(X, Y) 

输入数据的形状完全相同，输入数据类型也几乎相同:

(150, 4) <class 'numpy.ndarray'> <class 'numpy.float64'> 
(150,) <class 'numpy.ndarray'> <class 'numpy.int32'> 
(5000, 10) <class 'numpy.ndarray'> <class 'numpy.float64'> 
(5000,) <class 'numpy.ndarray'> <class 'numpy.float64'> 

但是我的代码崩溃并抛出上述的 ValueError。阅读 SelectKBest Doc，从分类到回归的转换应该不是问题。谁能帮我找出问题所在？

Traceback (most recent call last): 
  File "featureSelection_toyproblem.py", line 26, in <module> 
    X_new = SelectKBest(chi2, k=2).fit_transform(X, Y) 
  File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib 
\site-packages\sklearn\base.py", line 520, in fit_transform 
    return self.fit(X, y, **fit_params).transform(X) 
  File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib 
\site-packages\sklearn\feature_selection\univariate_selection.py", line 349, in 
fit 
    score_func_ret = self.score_func(X, y) 
  File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib 
\site-packages\sklearn\feature_selection\univariate_selection.py", line 217, in 
chi2 
    Y = LabelBinarizer().fit_transform(y) 
  File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib 
\site-packages\sklearn\preprocessing\label.py", line 307, in fit_transform 
    return self.fit(y).transform(y) 
  File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib 
\site-packages\sklearn\preprocessing\label.py", line 284, in fit 
    self.classes_ = unique_labels(y) 
  File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib 
\site-packages\sklearn\utils\multiclass.py", line 97, in unique_labels 
    raise ValueError("Unknown label type: %s" % repr(ys)) 
ValueError: Unknown label type: (array([ 0.42595241,  0.79859991,  0.22947246, . 
..,  0.86011766, 
        0.52335991,  0.27046173]),) 

请您参考如下方法:

查看chi2 文档 here ，它只能用于分类。您的玩具问题目标 Y 是真实值而不是标签。