运行sklearn分类器模型时出现数值错误

我对sklearn非常陌生，我试图使用scikit构建一个简单的文本分类器，但运行到ValueError中。它显示在fit()错误，但其他教程正在使用它，它运行良好。运行sklearn分类器模型时出现数值错误

这里是我的代码：

from sklearn.datasets import fetch_20newsgroups 
from sklearn.cross_validation import train_test_split 
from sklearn.feature_extraction.text import TfidfVectorizer 
from sklearn.pipeline import Pipeline 
from sklearn.naive_bayes import MultinomialNB 

news = fetch_20newsgroups(subset='all') 
print len(news.data) 



def train(classifier , X , y): 
     X_train , y_train , X_test , y_test = train_test_split(X,y,test_size =   0.20, random_state = 33) 
     classifier.fit(X_train ,y_train) 
     print "Accuracy %s" % classifier.score(X_test , y_test) 
     return classifier 

model1 = Pipeline([('vectorizer' , TfidfVectorizer()),('classifier' , MultinomialNB()),]) 

train(model1 , news.data , news.target)

当运行它，我得到一个值误差

Traceback (most recent call last): 
    File "/home/padam/Documents/git/ticketClassifier/news.py", line 30, in <module> 
    train(model1 , news.data , news.target) 
    File "/home/padam/Documents/git/ticketClassifier/news.py", line 24, in train 
    classifier.fit(X_train ,y_train) 
    File "/usr/lib/python2.7/dist-packages/sklearn/pipeline.py", line 165, in fit 
    self.steps[-1][-1].fit(Xt, y, **fit_params) 
    File "/usr/lib/python2.7/dist-packages/sklearn/naive_bayes.py", line 527, in fit 
    X, y = check_X_y(X, y, 'csr') 
    File "/usr/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 520, in check_X_y 
    check_consistent_length(X, y) 
    File "/usr/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 176, in check_consistent_length 
    "%s" % str(uniques)) 
ValueError: Found arrays with inconsistent numbers of samples: [ 3770 15076]

什么用的样本数量不一致的意思。其他stackoverflow解决方案建议重新排列矩阵numpy矩阵。但我没有使用numpy。谢谢！

来源

2017-06-20 Padam Sethia

错误在于您如何使用train_test_split。

您正在使用它作为

X_train , y_train , X_test , y_test = train_test_split(X, y, 
               test_size = 0.20, 
               random_state = 33)

但输出顺序是不同的实际as given in documentation。它是：

X_train , X_test , y_train , y_test = train_test_split(X, y, 
               test_size = 0.20, 
               random_state = 33)

此外，还有一个建议是，如果你正在使用scikit版本> = 0.18，那么该方案改变从cross_validation到model_selection，因为它的弃用，并将在新的版本中删除。

因此，而不是： -

from sklearn.cross_validation import train_test_split

使用以下：

from sklearn.model_selection import train_test_split

来源

2017-06-20 07:20:24

运行sklearn分类器模型时出现数值错误

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