Sklearn 错误： [Int64索引（[2， 3]， dtype='int64'）] 中没有一个在 [列] 中

Question

有人可以解释为什么这段代码：

from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from sklearn.svm import SVC
import numpy as np

#df = pd.read_csv('missing_data.csv',sep=',')

df = pd.DataFrame(np.array([[1, 2, 3,4,5,6,7,8,9,1],
                            [4, 5, 6,3,4,5,7,5,4,1],
                            [7, 8, 9,6,2,3,6,5,4,1],
                            [7, 8, 9,6,1,3,2,2,4,0],
                            [7, 8, 9,6,5,6,6,5,4,0]]),
                            columns=['a', 'b', 'c','d','e','f','g','h','i','j'])

X_train = df.iloc[:,:-1]
y_train = df.iloc[:,-1]


clf=SVC(kernel='linear')
kfold = StratifiedKFold(n_splits=2,random_state=42,shuffle=True)
for train_index,test_index in kfold.split(X_train,y_train):
    x_train_fold,x_test_fold = X_train[train_index],X_train[test_index]
    y_train_fold,y_test_fold = y_train[train_index],y_train[test_index]
    clf.fit(x_train_fold,y_train_fold)

引发此错误:

Traceback (most recent call last):
  File "test_traintest.py", line 23, in <module>
    x_train_fold,x_test_fold = X_train[train_index],X_train[test_index]
  File "/Users/slowat/anaconda/envs/nlp_course/lib/python3.7/site-packages/pandas/core/frame.py", line 3030, in __getitem__
    indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
  File "/Users/slowat/anaconda/envs/nlp_course/lib/python3.7/site-packages/pandas/core/indexing.py", line 1266, in _get_listlike_indexer
    self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
  File "/Users/slowat/anaconda/envs/nlp_course/lib/python3.7/site-packages/pandas/core/indexing.py", line 1308, in _validate_read_indexer
    raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Int64Index([2, 3], dtype='int64')] are in the [columns]"

我看到了这个答案，但我的专栏长度是相等的。

Answer 1

KFold.split（）返回训练和测试索引，这些索引应该与如下的DataFrame一起使用：

X_train.iloc[train_index]

根据您的语法，您正试图将它们用作列名。将代码更改为：

from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from sklearn.svm import SVC
import numpy as np

#df = pd.read_csv('missing_data.csv',sep=',')

df = pd.DataFrame(np.array([[1, 2, 3,4,5,6,7,8,9,1],
                            [4, 5, 6,3,4,5,7,5,4,1],
                            [7, 8, 9,6,2,3,6,5,4,1],
                            [7, 8, 9,6,1,3,2,2,4,0],
                            [7, 8, 9,6,5,6,6,5,4,0]]),
                            columns=['a', 'b', 'c','d','e','f','g','h','i','j'])

X_train = df.iloc[:,:-1]
y_train = df.iloc[:,-1]


clf=SVC(kernel='linear')
kfold = StratifiedKFold(n_splits=2,random_state=42,shuffle=True)
for train_index,test_index in kfold.split(X_train,y_train):
    x_train_fold,x_test_fold = X_train.iloc[train_index],X_train.iloc[test_index]
    y_train_fold,y_test_fold = y_train.iloc[train_index],y_train.iloc[test_index]
    clf.fit(x_train_fold,y_train_fold)

请注意，我们使用＜code＞。iloc而不是.loc。这是因为＜code＞。iloc使用整数索引，就像我们从split（）获得的索引一样，而则使用整数索引。loc适用于索引值。在您的情况下，这并不重要，因为熊猫索引与整数索引匹配，但在其他项目中，您可能会遇到这种情况，所以请使用.iloc。

或者，当您提取< code>X_train和< code>y_train时，您可以将它们转换为numpy数组:

X_train = df.iloc[:,:-1].to_numpy()
y_train = df.iloc[:,-1].to_numpy()

然后你的代码就可以正常工作，因为 numpy 数组可以很好地处理整数索引。