Question

我是python（PYTHON 2.7）的新手，我正在尝试运行一个计算Pearson相关性的程序。代码来自“集体智慧” 当我导入函数并运行Pearson的相关性

时

我收到此错误：

>>> sim_pearson(critics,
...  'Lisa Rose','Gene Seymour')
Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
  File "recommendations.py", line 49, in sim_pearson
    sum1=sum([prefs[p1][it]] for it in si)
TypeError: unsupported operand type(s) for +: 'int' and 'list'
>>>

代码在这里

#a dictionary of movie critics and their ratings of a small set of movies 
critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on the Plane':3.5, 'Just My Luck': 1.5,
                       'superman returns': 5.0, 'You, Me and Dupree': 3.5}, 'Gene Seymour':{
                       'Lady in the water':1.0,'Snakes on the Plane':3.5, 
                       'superman returns':5.0, 'You, Me and Dupree':3.5}, 'Michale Philllips':{
                       'Lady in the Water': 2.5, 'Snakes on the Plane':3.0, 'superman returns': 3.5,
                       'The Night Listenr': 4.0}, 'Cludia Puig':{'Snakes on the Plane':3.5, 'Just My Luck': 3.0,
                       'The Night Listenr': 4.5, 'superman returns': 4.0, 'You, Me and Dupree': 2.5},
                       'Mick LaSalle':{'Lady in the Water': 3.0, 'Snakes on the Plane':4.0, 'Just My Luck': 2.0,
                       'The Night Listenr': 3.0, 'superman returns': 3.0, 'You, Me and Dupree': 2.0},
                       'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on the Plane':4.0, 
                       'The Night Listenr': 3.0, 'superman returns': 5.0, 'You, Me and Dupree': 3.5},
                       'Toby':{'Snakes on a Plane': 4.5, 'You, Me Dupree':1.0,'superman returns':4.0}}
#Returns a distance-based similarity score for person1 and p
def sim_distance(prefs,person1,person2):
#get the list of shared_items
    si={}
    for item in prefs[person1]:
        if item in prefs[person2]:
            si[item]=1

    #if they have no rating in common returns zero
    if len(si)==0: 
        return 0
    #Add up the squares of all the differences

    sum_of_squares=sum(pow(prefs[person1][item]-prefs[person2][item],2)
    for item in prefs[person1] if item in prefs[person2])
    return 1/(1+sum_of_squares)

#returns the pearson correlation coefficient for p1 and p2
def sim_pearson(prefs,p1,p2):
    #get list of mutually rated items
    si={}
    for item in prefs[p1]:
        if item in prefs[p2]: si[item]=1
        #find the number of elements 
    n=len(si)
#if they are no ratings in common, return 0
    if n==0: return 0
#add up all the preferences 
    sum1=sum([prefs[p1][it]] for it in si)#reported line 49
             #^
    sum2=sum(prefs[p2][it] for it in si)
    #sum up the squares 
    sum1sq=sum([pow(prefs[p1][it] for it in si)])
    sum2sq=sum([pow(prefs[p2][it] for it in si)])
    #sum up the products
    pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])
    #calculate peason score
    num=pSum-(sum1*sum2/n)
    den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n))
    r=num/den
    return r

Answer 1

我想你想写

sum1=sum([prefs[p1][it] for it in si])

而不是

sum1=sum([prefs[p1][it]] for it in si)

（见括号）。该错误意味着您正在尝试使用列表sum整数。

在python中运行pearson相关性得分时出错

1 个答案: