如何使用sklearn库对朴素贝叶斯进行文本分类？

Question

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.培训数据有超过20K的记录.我知道SVM会是一个更好的选择,但我想使用sklearn库与Naive Bayes一起使用.

{[{"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        "}]}

这是我到目前为止所尝试的,

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ValueError:无法将字符串转换为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？

Answer 1

sklearn中的所有分类器都需要将输入表示为某些固定维度的向量.对于文本有CountVectorizer,HashingVectorizer并且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对测试集进行矢量化

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

查看有关使用sklearn和文本数据的教程.