def train2():
import docclass as docclass
cl1 = docclass.naivebayes(docclass.getwords)
cl1.setdb('test2.db')
for a in range(2000):
docclass.sampletrain(cl1)
cl1.con.commit()
def train1():
import docclass as docclass
cl = docclass.classifier(docclass.getwords)
cl.setdb('test1.db')
for a in range(2000):
docclass.sampletrain(cl)
cl.con.commit()
def train3():
import docclass as docclass
cl2 = docclass.fisherclassifier(docclass.getwords)
cl2.setdb('test3.db')
for a in range(2000):
docclass.sampletrain(cl2)
cl2.con.commit()
def test_fisher_weightedprob():
sys.stderr.write("testing computation of fisher weightedprob...\n")
reload(docclass)
cl=docclass.fisherclassifier(docclass.getwords)
docclass.sampletrain(cl)
wp = cl.weightedprob('money', 'bad', cl.cprob)
sys.stdout.write("%f\n" %(wp)) # 0.75
def testProb(self):
cl = docclass.fisherclassifier(docclass.getwords)
cl.setdb('test.db')
docclass.sampletrain(cl)
self.assertAlmostEquals(0.57142857, cl.cprob('quick', 'good'))
self.assertAlmostEquals(0.78013987, cl.fisherprob('quick rabbit', 'good'))
self.assertAlmostEquals(0.35633596, cl.fisherprob('quick rabbit', 'bad'))
def test_fprob():
sys.stderr.write("testing sampletrain and computation of feature probability...\n")
reload(docclass)
cl=docclass.classifier(docclass.getwords)
docclass.sampletrain(cl)
fprob = cl.fprob('quick', 'good')
sys.stdout.write("%f\n" %(fprob)) # 0.6666...
def test_nb_prob():
sys.stderr.write("testing computation of naive bayes probability...\n")
reload(docclass)
cl=docclass.naivebayes(docclass.getwords)
docclass.sampletrain(cl)
p1 = cl.prob('quick rabbit', 'good')
sys.stdout.write("%f\n" %(p1)) # 0.15624999..
p2 = cl.prob('quick rabbit', 'bad')
sys.stdout.write("%f\n" %(p2)) # 0.05000000...
def test_fisher_cprob():
sys.stderr.write("testing computation of fisher cprob...\n")
reload(docclass)
cl=docclass.fisherclassifier(docclass.getwords)
docclass.sampletrain(cl)
cp1 = cl.cprob('quick', 'good')
sys.stdout.write("%f\n" %(cp1)) # 0.57142857...
cp2 = cl.cprob('money', 'bad')
sys.stdout.write("%f\n" %(cp2)) # 1.0
def test_weightedprob():
sys.stderr.write("testing computation of weightedprob (probability for unseen words)...\n")
reload(docclass)
cl=docclass.classifier(docclass.getwords)
docclass.sampletrain(cl)
wp1 = cl.weightedprob('money', 'good', cl.fprob)
sys.stdout.write("%f\n" %(wp1)) # 0.25
docclass.sampletrain(cl)
wp2 = cl.weightedprob('money', 'good', cl.fprob)
sys.stdout.write("%f\n" %(wp2)) # 0.1666...
def upload_csv_button(self):
self.file_path = tkFileDialog.askopenfilename(
initialdir='/',
title='Select file',
filetypes=(('csv files', '*.csv'), ('all files', '*.*')))
with open(self.file_path) as f:
s = f.read()
my_d = repr(s)
docclass.getwords(my_d)
c1 = docclass.classifier(docclass.getwords)
docclass.sampletrain(c1)
def testClassify(self):
cl = docclass.naivebayes(docclass.getwords)
cl.setdb('test.db')
docclass.sampletrain(cl)
self.assertEquals('good', cl.classify('quick rabbit', default='unknown'))
self.assertEquals('bad', cl.classify('quick money', default='unknown'))
cl.setthreshold('bad', 3.0)
self.assertEquals('unknown', cl.classify('quick money', default='unknown'))
for i in range(10): docclass.sampletrain(cl)
self.assertEquals('bad', cl.classify('quick money', default='unknown'))
def test_fisher_fisherprob():
sys.stderr.write("testing computation of fisher fisherprob...\n")
reload(docclass)
cl=docclass.fisherclassifier(docclass.getwords)
docclass.sampletrain(cl)
# cprob
cp = cl.cprob('quick', 'good')
sys.stdout.write("%f\n" %(cp)) # 0.57142857...
# fisher prob
fp1 = cl.fisherprob('quick rabbit', 'good')
sys.stdout.write("%f\n" %(fp1)) # 0.780139
fp2 = cl.fisherprob('quick rabbit', 'bad')
sys.stdout.write("%f\n" %(fp2)) # 0.356335
def testClassify(self):
cl = docclass.fisherclassifier(docclass.getwords)
cl.setdb('test.db')
docclass.sampletrain(cl)
self.assertEquals('good', cl.classify('quick rabbit', default='unknown'))
self.assertEquals('bad', cl.classify('quick money', default='unknown'))
cl.setminimum('bad', 0.8)
self.assertEquals('good', cl.classify('quick money', default='unknown'))
cl.setminimum('bad', 0.4)
self.assertEquals('bad', cl.classify('quick money', default='unknown'))
def run_spamche_button(self):
c1 = docclass.naivebayes(docclass.getwords)
docclass.sampletrain(c1)
c1.setthreshold('bad', 3.0)
user_input = self.lbox.get()
if user_input == '':
self.var.set('Please load the spam training test!')
else:
ans = c1.classify(user_input, default='unkown')
if ans == 'bad':
self.var.set('spam')
else:
self.var.set('ham')
def main():
cl = docclass.classifier(docclass.getwords)
cl.setdb('test1.db')
docclass.sampletrain(cl)
print cl.fprob('quick', 'good')
print cl.weighted_prob('money', 'good', cl.fprob)
docclass.sampletrain(cl)
print cl.weighted_prob('money', 'good', cl.fprob)
clnb = docclass.naivebayes(docclass.getwords)
clnb.setdb('test1.db')
docclass.sampletrain(clnb)
print clnb.prob('quick rabbit', 'good')
print clnb.prob('quick rabbit', 'bad')
print clnb.classify('quick rabbit', default='unknown')
print clnb.classify('quick money', default='unknown')
clnb.setthreshold('bad', 3.0)
print clnb.classify('quick money', default='unknown')
clfs = docclass.fisherclassifier(docclass.getwords)
clfs.setdb('test1.db')
docclass.sampletrain(clfs)
print clfs.cprob('quick', 'good')
print clfs.cprob('money', 'bad')
print clfs.weighted_prob('money', 'bad', clfs.cprob)
print clfs.fisherprob('quick rabbit', 'good')
print clfs.fisherprob('quick rabbit', 'bad')
print clfs.classify('quick rabbit')
print clfs.classify('quick money')
clfs2 = docclass.fisherclassifier(docclass.getwords)
clfs2.setdb('test1.db')
feedclassifier('feed_sample2.rss', clfs2)
print clfs2.cprob('Pandas', 'python')
print clfs2.cprob('python', 'python')
def test_nb_classify():
sys.stderr.write("testing naive bayes classification...\n")
reload(docclass)
cl=docclass.naivebayes(docclass.getwords)
docclass.sampletrain(cl)
c1 = cl.classify('quick rabbit', default='unknown')
sys.stdout.write("%s\n" %(c1)) # 'good'
c2 = cl.classify('quick money', default='unknown')
sys.stdout.write("%s\n" %(c2)) # 'bad'
# test threshold
cl.setthreshold('bad', 3.0)
c3 = cl.classify('quick money', default='unknown')
sys.stdout.write("%s\n" %(c3)) # 'unknown'
for i in range(10): docclass.sampletrain(cl)
c4 = cl.classify('quick money', default='unknown')
sys.stdout.write("%s\n" %(c4)) # 'bad'
def test_fisher_classify():
sys.stderr.write("testing fisher classification...\n")
reload(docclass)
cl=docclass.fisherclassifier(docclass.getwords)
docclass.sampletrain(cl)
# classify
c1 = cl.classify('quick rabbit')
sys.stdout.write("%s\n" %(c1)) # 'good'
c2 = cl.classify('quick money')
sys.stdout.write("%s\n" %(c2)) # 'bad'
# set minimum for 'bad'
cl.setminimum('bad', 0.8)
c3 = cl.classify('quick money')
sys.stdout.write("%s\n" %(c3)) # 'good'
# set minimum for 'good'
cl.setminimum('good', 0.4)
c4 = cl.classify('quick money')
sys.stdout.write("%s\n" %(c4)) # 'good'
def crawl(self, pages, depth=2, maxpages=1000):
import docclass
classifier = docclass.naivebayes(docclass.getwords)
docclass.sampletrain(classifier)
iter = 0
for i in range(depth):
newpages = set()
for page in pages:
print iter
iter = iter + 1
if (iter > maxpages):
return
try:
c = urllib2.urlopen(page)
except:
print "Could not open %s" % page
continue
content = c.read()
charset = chardet.detect(content[:400])['encoding']
content = content.decode(charset, "ignore").encode('UTF-8')
soup = BeautifulSoup(content, 'html.parser')
self.addtoindex(page, soup, classifier)
links = soup('a')
for link in links:
#print link
if ('href' in dict(link.attrs)):
url = urljoin(page, link['href'])
if url.find("'") != -1: continue
url = url.split('#')[0] #去掉位置部分
if url[0:4] == 'http' and not self.isindexed(url):
newpages.add(url)
linkText = self.gettextonly(link)
self.addlinkref(page, url, linkText)
self.dbcommit()
pages = newpages
#-------------------------------------------------------------------------------
# Name: module1
# Purpose:
#
# Author: Administrator
#
# Created: 30/10/2012
# Copyright: (c) Administrator 2012
# Licence: <your licence>
#-------------------------------------------------------------------------------
import docclass
"""
cl = docclass.classifier(docclass.get_words)
docclass.sampletrain(cl)
print cl.cat_count('good')
print cl.feat_cat_count('money','bad')
print cl.feat_cat_count('money','good')
print cl.fprob('money','good')
print cl.weightedprob('money','good',cl.fprob)
docclass.sampletrain(cl)
print cl.weightedprob('money','good',cl.fprob)
"""
cl = docclass.naivebayes(docclass.get_words)
import docclass as d
cl = d.fisherclassifier(d.getwords)
d.sampletrain(cl)
print cl.classify('quick rabbit')
print cl.classify('quick money')
cl.setminimum('bad', 0.8)
print cl.classify('quick money')
cl.setminimum('good', 0.4)
print cl.classify('quick money')
for i in range(10):
d.sampletrain(cl)
print cl.classify('quick money')
import docclass
###最开始调用getwords进行单词计数的过程中，后来是如何标记每个词分在每一类的呢。
'''###贝叶斯之前 146页为止
c1= docclass.classifier(docclass.getwords)
#c1.train('the quick brown fox jumps over the quick lazy dog','good')
#c1.train('make quick money in the online casino','bad')
#print(c1.fcount('quick','good'))
#print(c1.fcount('quick','bad'))
docclass.sampletrain(c1)
#print(c1.fprob('quick','good'))
print(c1.weightedprob('money','good',c1.fprob))
docclass.sampletrain(c1)
print(c1.weightedprob('money','good',c1.fprob))
##ddd 贝叶斯 到150页结束
c1=docclass.naivebayes(docclass.getwords)
#docclass.sampletrain(c1)
#print(c1.prob('quick rabbit','good'))
#print(c1.prob('quick rabbit','bad'))
#print(c1.classify('owns water',default='unknown'))
#print(c1.classify('quick money',default='unknown'))
c1.setthreshold('bad',5.0)#这个后面的数字是设定出现多少次会被认为是符合‘bad’条件，而不是后续的‘unknown’
c1.setthreshold('good',4.0)
def testProb(self):
cl = docclass.naivebayes(docclass.getwords)
cl.setdb('test.db')
docclass.sampletrain(cl)
self.assertAlmostEquals(0.15624999, cl.prob('good', 'quick rabbit'))
self.assertAlmostEquals(0.05, cl.prob('bad', 'quick rabbit'))
# print cl.prob('quick money', 'bad')
# for i in range(10):
# docclass.sampletrain(cl)
# print cl.classify('quick money', defalt='unknow')
# print cl.prob('quick money', 'good')
# print cl.prob('quick money', 'bad')
# cl=fisherClassifier.FisherClassifier(docclass.getwords)
# docclass.sampletrain(cl)
# print cl.cprob('quick', 'good')
# print cl.cprob('money', 'bad')
# cl=fisherClassifier.FisherClassifier(docclass.getwords)
# docclass.sampletrain(cl)
# print cl.cprob('quick', 'good')
# print cl.fisherprob('quick rabbit', 'good')
# print cl.fisherprob('quick rabbit', 'bad')
# cl=fisherClassifier.FisherClassifier(docclass.getwords)
# docclass.sampletrain(cl)
# print cl.classify('quick rabbit')
# print cl.classify("quick money")
# cl.setminimum('bad', 0.8)
# print cl.classify("quick money")
cl = fisherClassifier.FisherClassifier(docclass.getwords)
cl.setdb("test1.db")
docclass.sampletrain(cl)
cl2 = naivebayes.naivebayes(docclass.getwords)
cl2.setdb("test1.db")
print cl2.classify('quick money')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import docclass
cl = docclass.naivebayes(docclass.getwords)
docclass.sampletrain(cl)
print(cl.classify('quick rabbit', default='unknown'))
print(cl.classify('quick money', default='unknown'))
cl.setthreshold('bad', 3.0)
print(cl.classify('quick money', default='unknown'))
for i in range(10): docclass.sampletrain(cl)
print(cl.classify('quick money', default='unknown'))
def setUp(self):
docclass.sampletrain(self.client)
def setUp(self):
docclass.sampletrain(self.client)
