[分割数据后,我尝试进行功能排名,但是当我尝试访问X_train.columns时,获取此'numpy.ndarray'对象没有属性'columns'。
from sklearn.model_selection import train_test_split
y=df['DIED'].values
x=df.drop('DIED',axis=1).values
X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=42)
print('X_train',X_train.shape)
print('X_test',X_test.shape)
print('y_train',y_train.shape)
print('y_test',y_test.shape)
bestfeatures = SelectKBest(score_func=chi2, k="all")
fit = bestfeatures.fit(X_train,y_train)
dfscores = pd.DataFrame(fit.scores_)
dfcolumns = pd.DataFrame(X_train.columns)
我知道训练测试拆分会返回一个numpy数组,但是我应该如何处理呢?
可能是这段代码清楚了:
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
# here i imitate your example of data
df = pd.DataFrame(data = np.random.randint(100, size = (50,5)), columns = ['DIED']+[f'col_{i}' for i in range(4)])
df.head()
Out[1]:
DIED col_0 col_1 col_2 col_3
0 36 0 23 43 55
1 81 59 83 37 31
2 32 86 94 50 87
3 10 69 4 69 27
4 1 16 76 98 74
#df here is a DataFrame, with all attributes, like df.columns
y=df['DIED'].values
x=df.drop('DIED',axis=1).values # <- here you get values, so the type of structure is array of array now (not DataFrame), so it hasn't any columns name
x
Out[2]:
array([[ 0, 23, 43, 55],
[59, 83, 37, 31],
[86, 94, 50, 87],
[69, 4, 69, 27],
[16, 76, 98, 74],
[17, 50, 52, 31],
[95, 4, 56, 68],
[82, 35, 67, 76],
.....
# now you can access to columns by index, like this:
x[:,2] # <- gives you access to the 3rd column
Out[3]:
array([43, 37, 50, 69, 98, 52, 56, 67, 81, 64, 48, 68, 14, 41, 78, 65, 11,
86, 80, 1, 11, 32, 93, 82, 93, 81, 63, 64, 47, 81, 79, 85, 60, 45,
80, 21, 27, 37, 87, 31, 97, 16, 59, 91, 20, 66, 66, 3, 9, 88])
# or you able to convert array of array back to DataFrame
pd.DataFrame(data = x, columns = df.columns[1:])
Out[4]:
col_0 col_1 col_2 col_3
0 0 23 43 55
1 59 83 37 31
2 86 94 50 87
3 69 4 69 27
....
使用所有变量的相同方法:X_train,X_test,Y_train,Y_test