Python：使用scikit-learn预测，给出空白预测

Question

我在客户支持方面工作，并且使用scikit-learn预测我们的票的标签，给定一组训练券（约40,000张训练券组）。

我使用基于this one的分类模型。它只是预测“（）”作为我的许多测试集的标签，即使训练集中没有任何标签没有标签。

我对标签的训练数据是一个列表的列表，如：

tags_train = [['international_solved'], ['from_build_guidelines my_new_idea eligibility'], ['dropbox other submitted_faq submitted_help'], ['my_new_idea_solved'], ['decline macro_backer_paypal macro_prob_errored_pledge_check_credit_card_us loading_problems'], ['dropbox macro__turnaround_time other plq__turnaround_time submitted_help'], ['dropbox macro_creator__logo_style_guide outreach press submitted_help']] 

虽然我的票说明训练数据只是一个字符串列表，如：

descs_train = ['description of ticket one', 'description of ticket two', etc] 

下面是有关我的代码部分构建模型：

import numpy as np 
import scipy 
from sklearn.pipeline import Pipeline 
from sklearn.feature_extraction.text import CountVectorizer 
from sklearn.feature_extraction.text import TfidfTransformer 
from sklearn.multiclass import OneVsRestClassifier 
from sklearn.svm import LinearSVC 

# We have lists called tags_train, descs_train, tags_test, descs_test with the test and train data 

X_train = np.array(descs_train) 
y_train = tags_train 
X_test = np.array(descs_test) 

classifier = Pipeline([ 
    ('vectorizer', CountVectorizer()), 
    ('tfidf', TfidfTransformer()), 
    ('clf', OneVsRestClassifier(LinearSVC(class_weight='auto')))]) 

classifier.fit(X_train, y_train) 
predicted = classifier.predict(X_test) 

然而，“预言”给出，看起来像一个列表：

predicted = [(), ('account_solved',),(), ('images_videos_solved',), ('my_new_idea_solved',),(),(),(),(),(), ('images_videos_solved', 'account_solved', 'macro_launched__edit_update other tips'), ('from_guidelines my_new_idea', 'from_guidelines my_new_idea macro__eligibility'),()] 

我不明白为什么在训练集中没有任何东西时预测为空（）。它不应该预测最接近的标签吗？任何人都可以推荐我使用的模型的任何改进？

非常感谢您的帮助！

Answer 1

问题出在您的tags_train变量。根据OneVsRestClassifier文档，目标需要是“一系列标签序列”，并且您的目标是一个元素列表。

下面是你的代码的编辑，自包含和工作版本。请注意0​​中的更改，尤其是tags_train是一个元素的元组。

import numpy as np 
import scipy 
from sklearn.pipeline import Pipeline 
from sklearn.feature_extraction.text import CountVectorizer 
from sklearn.feature_extraction.text import TfidfTransformer 
from sklearn.multiclass import OneVsRestClassifier 
from sklearn.svm import LinearSVC 


# We have lists called tags_train, descs_train, tags_test, descs_test with the test and train data 
tags_train = [('label',), ('international' ,'solved'), ('international','open')] 
descs_train = ['description of ticket one', 'some other ticket two', 'label'] 

X_train = np.array(descs_train) 
y_train = tags_train 
X_test = np.array(descs_train) 

classifier = Pipeline([ 
    ('vectorizer', CountVectorizer()), 
    ('tfidf', TfidfTransformer()), 
    ('clf', OneVsRestClassifier(LinearSVC(class_weight='auto')))]) 

classifier = classifier.fit(X_train, y_train) 
predicted = classifier.predict(X_test) 

print predicted 

输出是

[('international',), ('international',), ('international', 'open')]