如何使用 sklearn 库使用朴素贝叶斯执行文本分类？

Posted 2023-03-12

【中文标题】如何使用 sklearn 库使用朴素贝叶斯执行文本分类？

【英文标题】：How to perform text classification with naive bayes using sklearn library?

【发布时间】：2015-07-15 03:04:51

【问题描述】：

我正在尝试使用朴素贝叶斯文本分类器进行文本分类。 我的数据采用以下格式，根据问题和摘录，我必须决定问题的主题。训练数据有超过 20K 条记录。我知道 SVM 在这里会是一个更好的选择，但我想选择 Naive Bayes using sklearn library。

["topic":"electronics","question":"What is the effective differencial effective of this circuit","excerpt":"I'm trying to work out, in general terms, the effective capacitance of this circuit (see diagram: http://i.stack.imgur.com/BS85b.png).  \n\nWhat is the effective capacitance of this circuit and will the ...\r\n        ",
"topic":"electronics","question":"Outlet Installation--more wires than my new outlet can use [on hold]","excerpt":"I am replacing a wall outlet with a Cooper Wiring USB outlet (TR7745).  The new outlet has 3 wires coming out of it--a black, a white, and a green.  Each one needs to be attached with a wire nut to ...\r\n        "]

这是我迄今为止尝试过的，

import numpy as np
import json
from sklearn.naive_bayes import *

topic = []
question = []
excerpt = []

with open('training.json') as f:
    for line in f:
        data = json.loads(line)
        topic.append(data["topic"])
        question.append(data["question"])
        excerpt.append(data["excerpt"])

unique_topics = list(set(topic))
new_topic = [x.encode('UTF8') for x in topic]
numeric_topics = [name.replace('gis', '1').replace('security', '2').replace('photo', '3').replace('mathematica', '4').replace('unix', '5').replace('wordpress', '6').replace('scifi', '7').replace('electronics', '8').replace('android', '9').replace('apple', '10') for name in new_topic]
numeric_topics = [float(i) for i in numeric_topics]

x1 = np.array(question)
x2 = np.array(excerpt)
X = zip(*[x1,x2])
Y = np.array(numeric_topics)
print X[0]
clf = BernoulliNB()
clf.fit(X, Y)
print "Prediction:", clf.predict( ['hello'] )

但正如预期的那样，我得到了 ValueError: could not convert string to float。我的问题是如何创建一个简单的分类器来将问题和摘录分类到相关主题中？

【参考方案1】：

sklearn 中的所有分类器都要求将输入表示为某些固定维度的向量。对于文本，有CountVectorizer、HashingVectorizer 和TfidfVectorizer 可以将您的字符串转换为浮点数的向量。

vect = TfidfVectorizer()
X = vect.fit_transform(X)

显然，您需要以相同的方式向量化您的测试集

clf.predict( vect.transform(['hello']) )

查看tutorial on using sklearn with textual data。

【讨论】：

我收到错误 AttributeError: 'tuple' object has no attribute 'lower' while using X = vect.fit_transform(X), X is a iterable list..