我正在尝试修改优秀书籍提供的朴素贝叶斯分类器的代码集体智慧编程 https://rads.stackoverflow.com/amzn/click/com/0596529325，使其适应 GAE 数据存储（提供的代码使用 pysqlite2）。但尝试这样做时，我遇到了难以调试的错误。错误是这样的：
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 216, in post
sampletrain(nb)
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 201, in sampletrain
cl.train('Nobody owns the water.','good')
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 139, in train
self.incf(f,cat)
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 71, in incf
count=self.fcount(f,cat)
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 92, in fcount
return float(res)
TypeError: float() argument must be a string or a number
错误就在这个块中：
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:
res = fc.count
return float(res)
# return float(res[0])
如果我把set_trace()在第 91 行，像这样：
def fcount(self,f,cat):
res = db.GqlQuery("SELECT * FROM fc WHERE feature =:feature AND category =:category", feature = f, category = cat).get()
set_trace()
# 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
set_trace()
return float(res)
我得到这个错误轨道：
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 224, in post
sampletrain(nb)
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 209, in sampletrain
cl.train('Nobody owns the water.','good')
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 147, in train
self.incf(f,cat)
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 77, in incf
count=self.fcount(f,cat)
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 95, in fcount
if res is None: return 0
File "C:\Users\CG\Desktop\Google Drive\Sci&Tech\projects\naivebayes\main.py", line 95, in fcount
if res is None: return 0
File "C:\Python27\lib\bdb.py", line 48, in trace_dispatch
return self.dispatch_line(frame)
File "C:\Python27\lib\bdb.py", line 67, in dispatch_line
if self.quitting: raise BdbQuit
BdbQuit
它与 GqlQuery 有关。我想在Python IDE中测试代码，一步步打印变量和查询，试图找出问题出在哪里。但是当我尝试在 python IDE 中执行此操作时，我收到错误消息（例如"ImportError: No module named webapp2"）。而且我不太熟悉成功更改它的程序流程。实际上，我尝试这样做，但迷失了：我是一名新手程序员，最近才开始学习 OOP）。在这种情况下找到错误的最佳方法是什么？
预期的答案应包括此错误标识。
预先感谢您的任何帮助！
这里是整个代码：
#!/usr/bin/env python
#
# Copyright 2007 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
import os
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')))
import random
from google.appengine.ext import db
import re
import math
def set_trace():
import pdb, sys
debugger = pdb.Pdb(stdin=sys.__stdin__,
stdout=sys.__stdout__)
debugger.set_trace(sys._getframe().f_back)
class fc(db.Model):
feature = db.StringProperty(required = True)
category = db.StringProperty(required = True)
count = db.IntegerProperty(required = True)
class cc(db.Model):
category = db.StringProperty(required = True)
count = db.IntegerProperty(required = True)
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()
# 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 float(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)
