[英]How to fix this Python TypeError: iteration over non-sequence?
When I run the entire code below, this line: 当我在下面运行整个代码时,此行:
for f in features:
From this function (where getfeatures
returns a dictionary): 通过此函数(其中
getfeatures
返回字典):
def train(self,item,cat):
features=self.getfeatures(item)
# Increment the count for every feature with this category
for f in features:
self.incf(f,cat)
# Increment the count for this category
self.incc(cat)
self.con.commit()
Produces this error: 产生此错误:
TypeError: iteration over non-sequence
I tried replace this line: for f in features:
for this: for f in features.keys():
but didn't worked ( "AttributeError: classifier instance has no attribute 'keys'"
). 我试过替换以下行:
for f in features:
为此: for f in features.keys():
但是没有用( "AttributeError: classifier instance has no attribute 'keys'"
)。 When I try this: 当我尝试这个:
print getfeatures('Nobody owns the water.')
It give me what was expected: 它给了我预期的结果:
{'water': 1, 'the': 1, 'nobody': 1, 'owns': 1}
How to fix this error and iterate properly in f
dictionary? 如何修复此错误并在
f
字典中正确迭代?
This code is from the (excellent) book " Programming Collective Intelligence ". 该代码来自(优秀)《 编程集体智能 》一书。 I just copied it from here (I also bought the book) and cut part of the code (the fisherclassifier, because I'm using only the naivebayes classifier).
我只是从这里复制了它(我也买了这本书)并剪切了部分代码(fisherclassifier,因为我只使用naivebayes分类器)。 I find it hard to believe that this error has not been realized.
我发现很难相信这个错误尚未实现。 I might be doing something wrong.
我可能做错了。
Here the entire code:
import sqlite3
#from pysqlite2 import dbapi2 as sqlite
import re
import math
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 the unique set of words only
# return dict([(w,1) for w in words]).iteritems()
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(dbfile)
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:
self.con.execute("insert into fc values ('%s','%s',1)"
% (f,cat))
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=self.con.execute(
'select count from fc where feature="%s" and category="%s"'
%(f,cat)).fetchone()
if res==None: return 0
else: 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))
else:
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()
if res==None: return 0
else: return float(res[0])
def categories(self):
cur=self.con.execute('select category from cc');
return [d[0] for d in cur]
def totalcount(self):
res=self.con.execute('select sum(count) from cc').fetchone();
if res==None: return 0
return res[0]
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')
nb = naivebayes(classifier)
sampletrain(nb)
#print ('\nbuy is classified as %s'%nb.classify('buy'))
#print ('\nquick is classified as %s'%nb.classify('quick'))
##print getfeatures('Nobody owns the water.')
It looks like you're initializing an instance of naivebayes
using classifier
: 看来您正在使用
classifier
初始化naivebayes
实例:
nb = naivebayes(classifier)
You probably meant to do this instead: 您可能打算这样做:
nb = naivebayes(getfeatures)
Inside the for
loop in the train
method, instead of getting a dict from getfeatures
, you were repeatedly instantiating a new instance of classifier
. 在
train
方法的for
循环内部,而不是从getfeatures
获取命令,您是在重复实例化classifier
的新实例。
Your initialization never actually passes in the getfeatures function as you expect. 初始化实际上不会像您期望的那样实际传递getfeatures函数。
The giveaway is this: 赠品是这样的:
tried replace this line: for f in features: for this: for f in features.keys(): but didn't worked ("AttributeError: classifier instance has no attribute 'keys'").
尝试替换此行:对于功能中的f:对于此功能:对于features.keys()中的f:但没有起作用(“ AttributeError:分类器实例没有属性'keys'”)。
Note that it's saying that features is a classifier instance, not a dictionary. 请注意,这是指功能是分类器实例,而不是字典。
So, looking at your code, you create: 因此,查看您的代码,您将创建:
nb = naivebayes(classifier)
The init for naivebayes is: naivebayes的初始化为:
def __init__(self, getfeatures):
classifier.__init__(self,getfeatures)
self.thresholds={}
So, in this case, you're passing in classifier, which is going to be passed as the variable getfeatures
to the init for classifier . 因此,在这种情况下,您要传入分类器,它将作为变量
getfeatures
传递给分类器的init。 . 。 .
。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.