python之如何修复 sklearn/python 中的 "ValueError: Expected 2D array, got 1D array instead"_编程设计

我在那里。我刚开始用一个简单的例子来学习机器学习。因此，我想使用分类器根据文件类型对磁盘中的文件进行分类。我写的代码是，

import sklearn 
import numpy as np 
 
 
#Importing a local data set from the desktop 
import pandas as pd 
mydata = pd.read_csv('file_format.csv',skipinitialspace=True) 
print mydata 
 
 
x_train = mydata.script 
y_train = mydata.label 
 
#print x_train 
#print y_train 
x_test = mydata.script 
 
from sklearn import tree 
classi = tree.DecisionTreeClassifier() 
 
classi.fit(x_train, y_train) 
 
predictions = classi.predict(x_test) 
print predictions

我得到的错误是，

  script  class  div   label 
0       5      6    7    html 
1       0      0    0  python 
2       1      1    1     csv 
Traceback (most recent call last): 
  File "newtest.py", line 21, in <module> 
  classi.fit(x_train, y_train) 
  File "/home/initiouser2/.local/lib/python2.7/site- 
packages/sklearn/tree/tree.py", line 790, in fit 
    X_idx_sorted=X_idx_sorted) 
  File "/home/initiouser2/.local/lib/python2.7/site- 
packages/sklearn/tree/tree.py", line 116, in fit 
    X = check_array(X, dtype=DTYPE, accept_sparse="csc") 
  File "/home/initiouser2/.local/lib/python2.7/site- 
packages/sklearn/utils/validation.py", line 410, in check_array 
    "if it contains a single sample.".format(array)) 
ValueError: Expected 2D array, got 1D array instead: 
array=[ 5.  0.  1.]. 
Reshape your data either using array.reshape(-1, 1) if your data has a  
single feature or array.reshape(1, -1) if it contains a single sample.

如果有人可以帮助我编写代码，那将对我很有帮助!!

请您参考如下方法:

将输入传递给分类器时，传递二维数组(形状为(M, N)，其中 N >= 1)，而不是一维数组(形状为 (N,))。错误信息很清楚，

Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

from sklearn.model_selection import train_test_split 
 
# X.shape should be (N, M) where M >= 1 
X = mydata[['script']]   
# y.shape should be (N, 1) 
y = mydata['label']  
# perform label encoding if "label" contains strings 
# y = pd.factorize(mydata['label'])[0].reshape(-1, 1)  
X_train, X_test, y_train, y_test = train_test_split( 
                      X, y, test_size=0.33, random_state=42) 
... 
 
clf.fit(X_train, y_train)  
print(clf.score(X_test, y_test))

一些其他有用的提示 -

将您的数据分成有效的训练和测试部分。不要使用您的训练数据进行测试 - 这会导致对分类器强度的估计不准确
我建议您对标签进行因式分解，这样您就可以处理整数。只是更容易。