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優れた本ProgrammingCollectiveIntelligenceによって提供されている単純ベイズ分類器のコードを変更してGAEデータストアに適合させようとしています（提供されているコードはpysqlite2を使用しています）。しかし、それをやろうとすると、私はこの行で遭遇しています：

       update.put()

このブロックから：

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()
   #     if update:
        update.count = count + 1
        update.put()
  #      else:
#      self.con.execute(
 #       "update fc set count=%d where feature='%s' and category='%s'"
  #      % (count+1,f,cat))

このエラー：

File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 151, in train
    self.incf(f,cat)
  File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 88, in incf
    update.put()
  File "C:\Program Files (x86)\Google\google_appengine\google\appengine\ext\db\__init__.py", line 1074, in put
    return datastore.Put(self._entity, **kwargs)
  File "C:\Program Files (x86)\Google\google_appengine\google\appengine\api\datastore.py", line 579, in Put
    return PutAsync(entities, **kwargs).get_result()
  File "C:\Program Files (x86)\Google\google_appengine\google\appengine\api\datastore.py", line 529, in PutAsync
    'Cannot put a partial entity: %s' % entity)
BadRequestError: Cannot put a partial entity: {u'count': 2L, 'category': None, 'feature': None}

私がやろうとしているのは、このSQLと同等です。

self.con.execute(
    "update fc set count=%d where feature='%s' and category='%s'"
    % (count+1,f,cat))

これどうやってするの？

ここにコード全体があります：

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()

fc_class = fc()

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

cc_class = cc()

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()
   #     if update:
        update.count = count + 1
        update.put()
  #      else:
#      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()
#    res=self.con.execute(
#      'select count from fc where feature="%s" and category="%s"'
#      %(f,cat)).fetchone()
    if res is None:
        return 0
    else:
        return res.count
#        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

1 に答える 1

2

そのGQL構造（ "SELECT count FROM fc ..."）を使用して、プロジェクションクエリを実行しています。射影クエリによって返されるエンティティは部分的にしか入力されていないため、データストアに保存することはできません。代わりに、完全なエンティティをフェッチすることができます（たとえば、GQL、 "SELECT * FROM fc ..."を使用）。これにより、データストアへのput（）が可能になります。

于 2012-08-15T05:05:29.963 に答える