如何修复sklearn/python中的“ValueError:Expected 2D array，got 1D array”呢？

Question

我在那儿。我刚刚从机器学习开始，举了一个简单的例子来尝试学习。所以，我想使用一个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.

如果有人能帮我写代码，那对我太有帮助了！！

Answer 1

将输入传递给分类器时，传递2D数组（属于形状(M, N)，其中N>；=1），而不是1D数组（具有形状(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))

其他一些有用的提示-

1. 将数据分成有效的训练和测试部分。不要使用你的训练数据来测试-这会导致对分类器强度的不准确估计
2. 我建议你把你的标签分解，所以你要处理整数。只是比较容易。