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我正在尝试修改优秀书籍 Programming Collective Intelligence 提供的朴素贝叶斯分类器的代码，使其适应 GAE 数据存储（提供的代码使用 pysqlite2）。但试图做到这一点，我在这一行遇到：

update.count = count + 1

从这个块：

def incf(self,f,cat):
    count=self.fcount(f,cat)
    if count==0:
      fc_value = fc(feature = f, category = cat, count = 1)
      fc_value.put()
    else:
        update = db.GqlQuery("SELECT count FROM fc where feature =:feature AND category =:category", feature = f, category = cat).get()
        update.count = count + 1
        update.put()
#      self.con.execute(
 #       "update fc set count=%d where feature='%s' and category='%s'"
  #      % (count+1,f,cat))

这个错误：

  File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 221, in post
    sampletrain(nb)
  File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 206, in sampletrain
    cl.train('Nobody owns the water.','good')
  File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 144, in train
    self.incf(f,cat)
  File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 82, in incf
    update.count = count + 1
TypeError: unsupported operand type(s) for +: 'IntegerProperty' and 'int'

而且我不明白：为什么我不能count用 1 递增？

为什么update = db.GqlQuery("SELECT count FROM cc where category =:category", category = cat).get() update.count = count + 1是“IntegerProperty”而不是整数？

我该如何解决？update.count = count + 1执行此操作的正确语法是什么？

这里是整个代码：

import os

import random

import re

import math

from google.appengine.ext import db

import webapp2

import jinja2

from jinja2 import Environment, FileSystemLoader

jinja_environment = jinja2.Environment(autoescape=True,
    loader=jinja2.FileSystemLoader(os.path.join(os.path.dirname(__file__), 'templates')))

class fc(db.Model):
    feature = db.StringProperty()
    category = db.StringProperty()
    count = db.IntegerProperty()

class cc(db.Model):
    category = db.StringProperty()
    count = db.IntegerProperty()

def getfeatures(doc):
  splitter=re.compile('\\W*')
  # Split the words by non-alpha characters
  words=[s.lower() for s in splitter.split(doc)
          if len(s)>2 and len(s)<20]
  return dict([(w,1) for w in words])

class classifier:
  def __init__(self,getfeatures, filename=None):
    # Counts of feature/category combinations
    self.fc={}
    # Counts of documents in each category
    self.cc={}
    self.getfeatures=getfeatures

#  def setdb(self,dbfile):
#    self.con=sqlite.connect('db_file')
#    self.con=sqlite3.connect(":memory:")
#    self.con.execute('create table if not exists fc(feature,category,count)')
#    self.con.execute('create table if not exists cc(category,count)')

  def incf(self,f,cat):
    count=self.fcount(f,cat)
    if count==0:
      fc_value = fc(feature = f, category = cat, count = 1)
      fc_value.put()
    else:
        update = db.GqlQuery("SELECT count FROM fc where feature =:feature AND category =:category", feature = f, category = cat).get()
        update.count = count + 1
        update.put()
#      self.con.execute(
 #       "update fc set count=%d where feature='%s' and category='%s'"
  #      % (count+1,f,cat))

  def fcount(self,f,cat):
    res = db.GqlQuery("SELECT * FROM fc WHERE feature =:feature AND category =:category", feature = f, category = cat).get()
    logging.debug('This is a log message.')
#    res=self.con.execute(
#      'select count from fc where feature="%s" and category="%s"'
#      %(f,cat)).fetchone()
    if res is None: return 0
    else:
        res = fc.count
        return res
#        return float(res[0])

  def incc(self,cat):
    count=self.catcount(cat)
    if count==0:
      #  self.con.execute("insert into cc values ('%s',1)" % (cat))
      cc_value = cc(category = cat, count = 1)
      cc_value.put()
    else:
        update = db.GqlQuery("SELECT count FROM cc where category =:category", category = cat).get()
        update.count = count + 1
        update.put()
#      self.con.execute("update cc set count=%d where category='%s'"
#                       % (count+1,cat))

  def catcount(self,cat):
#    res=self.con.execute('select count from cc where category="%s"'
 #                        %(cat)).fetchone()
    res = db.GqlQuery("SELECT count FROM cc WHERE category =:category", category = cat).get()
    if res is None: return 0
#    else: return float(res[0])
    else: return float(res)

  def categories(self):
#    cur = self.con.execute('select category from cc');
    cur = db.GqlQuery("SELECT category FROM cc").fetch(999)
    return [d[0] for d in cur]

  def totalcount(self):
   # res=self.con.execute('select sum(count) from cc').fetchone();
    all_cc = db.GqlQuery("SELECT * FROM cc").fetch(999)
    res = 0
    for cc in all_cc:
        count = cc.count
        res+=count
#    res = db.GqlQuery("SELECT sum(count) FROM cc").get()
#    if res==None: return 0
    if res == 0: return 0
#    return res[0]
    return res

  def train(self,item,cat):
    features=self.getfeatures(item)
    # Increment the count for every feature with this category
    for f in features.keys():
##    for f in features:
      self.incf(f,cat)
    # Increment the count for this category
    self.incc(cat)
#    self.con.commit()

  def fprob(self,f,cat):
    if self.catcount(cat)==0: return 0
    # The total number of times this feature appeared in this
    # category divided by the total number of items in this category
    return self.fcount(f,cat)/self.catcount(cat)

  def weightedprob(self,f,cat,prf,weight=1.0,ap=0.5):
    # Calculate current probability
    basicprob=prf(f,cat)
    # Count the number of times this feature has appeared in
    # all categories
    totals=sum([self.fcount(f,c) for c in self.categories()])

    # Calculate the weighted average
    bp=((weight*ap)+(totals*basicprob))/(weight+totals)
    return bp

class naivebayes(classifier):
  def __init__(self,getfeatures):
    classifier.__init__(self, getfeatures)
    self.thresholds={}

  def docprob(self,item,cat):
    features=self.getfeatures(item)
    # Multiply the probabilities of all the features together
    p=1
    for f in features: p*=self.weightedprob(f,cat,self.fprob)
    return p

  def prob(self,item,cat):
    catprob=self.catcount(cat)/self.totalcount()
    docprob=self.docprob(item,cat)
    return docprob*catprob

  def setthreshold(self,cat,t):
    self.thresholds[cat]=t

  def getthreshold(self,cat):
    if cat not in self.thresholds: return 1.0
    return self.thresholds[cat]

  def classify(self,item,default=None):
    probs={}
    # Find the category with the highest probability
    max=0.0
    for cat in self.categories():
      probs[cat]=self.prob(item,cat)
      if probs[cat]>max:
        max=probs[cat]
        best=cat
    # Make sure the probability exceeds threshold*next best
    for cat in probs:
      if cat==best: continue
      if probs[cat]*self.getthreshold(best)>probs[best]: return default
    return best

def sampletrain(cl):
  cl.train('Nobody owns the water.','good')
  cl.train('the quick rabbit jumps fences','good')
  cl.train('buy pharmaceuticals now','bad')
  cl.train('make quick money at the online casino','bad')
  cl.train('the quick brown fox jumps','good')


class MainHandler(webapp2.RequestHandler):
    def get(self):
        template_values = {"given_sentence":'put a name here'}
        template = jinja_environment.get_template('index.html')
        self.response.out.write(template.render(template_values))

    def post(self):
        nb = naivebayes(getfeatures)
        sampletrain(nb)
        given_sentence = self.request.get("given_sentence")
        spam_result = nb.classify(given_sentence)
        submit_button = self.request.get("submit_button")
        if submit_button:
            self.redirect('/test_result?spam_result=%s&given_sentence=%s' % (spam_result, given_sentence))

class test_resultHandler(webapp2.RequestHandler):
    def get(self):
        spam_result = self.request.get("spam_result")
        given_sentence = self.request.get("given_sentence")
        test_result_values = {"spam_result": spam_result,
                             "given_sentence": given_sentence}
        template = jinja_environment.get_template('test_result.html')
        self.response.out.write(template.render(test_result_values))

app = webapp2.WSGIApplication([('/', MainHandler), ('/test_result', test_resultHandler)],
                              debug=True)
4

2 回答 2

3

“属性”通常意味着在对象实例上访问，而不是在类上访问。尝试改变：

class fc(db.Model):
    feature = db.StringProperty()
    category = db.StringProperty()
    count = db.IntegerProperty()

类似于：

class fc_class(db.Model):
    feature = db.StringProperty()
    category = db.StringProperty()
    count = db.IntegerProperty()
fc = fc_class()

（并不是说这是一个很棒的设计，但我认为它会解决您遇到的问题 - 即属性对象仅在通过类实例而不是通过类本身访问时才会获得特殊行为。）

于 2012-08-14T02:51:21.733 回答
2

查看您的代码，似乎问题可能出在res作业中：

def fcount(self,f,cat):
  res = db.GqlQuery("SELECT * FROM fc WHERE feature =:feature AND category =:category", feature = f, category = cat).get()
  logging.debug('This is a log message.')
  if res is None: return 0
  else:
    res = fc.count
    return res

据我所知，您正在分配一个 to 的未绑定方法class fc，res正如@Hamish 指出的那样，它确实是一个IntegerProperty(). 相反（同样，这里不是专业人士），您可能希望返回如下值：

def fcount(self,f,cat):
  res = db.GqlQuery("SELECT * FROM fc WHERE feature =:feature AND category =:category", feature = f, category = cat).get()
  logging.debug('This is a log message.')
  if res is None: return 0
  else:
    return res.count

我相信这res已经是一个 Query 实例（这是您想要的），然后您可以拉取count属性以获取您想要的值。

于 2012-08-14T03:06:19.627 回答