我正在研究如何使用KNN来预测电影的收视率。我用视频和书来教自己如何做

我试图运行在书中找到的代码，但它给了我错误消息。我用错误信息搜索了一下，以便理解它并解决我的问题，但是我不知道如何适应该问题。

import numpy as np

import pandas as pd

r_cols = ['user_id','movie_id','rating']

ratings = pd.read_csv('C:/Users/dell/Downloads/DataScience/DataScience-Python3/ml-100k/u.data',sep='\t',engine='python',names=r_cols,usecols=range(3))  # please enter your file path here. The file is u.data

print(ratings.head())   

movieProperties = ratings.groupby('movie_id').agg({'rating': [np.size,np.mean]})

print(movieProperties.head())

movieNumRatings = pd.DataFrame(movieProperties['rating']['size'])

movieNormalizedNumRatings = movieNumRatings.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))

print(movieNormalizedNumRatings.head())

movieDict = {}

with open('C:/Users/dell/Downloads/DataScience/DataScience-Python3/ml-100k/u.item') as f:     # The file is u.item

    temp = ''

    for line in f:

        fields = line.rstrip('\n').split('|')

        movieID = int(fields[0])

        name = fields[1]

        genres = fields[5:25]

        genres = map(int,genres)

        movieDict[movieID] = (name,genres,movieNormalizedNumRatings.loc[movieID].get('size'),movieProperties.loc[movieID].rating.get('mean'))

print(movieDict[1])

from scipy import spatial

def ComputeDistance(a,b):

    genresA = np.array(list(a[1]))

    genresB = np.array(list(b[1]))

    genreDistance = spatial.distance.cosine(genresA,genresB)

    popularityA = np.array(a[2])

    popularityB = np.array(b[2])

    popularityDistance = abs(popularityA - popularityB)

    return genreDistance + popularityDistance 

print(ComputeDistance(movieDict[2],movieDict[4])) 

import operator

def getNeighbors(movieID,K):

    distances = []

    for movie in movieDict:

        if (movie != movieID):

            dist = ComputeDistance(movieDict[movieID],movieDict[movie])

            distances.append((movie,dist))

    distances.sort(key=operator.itemgetter(1))

    neighbors = []

    for x in range(K):

        neighbors.append(distance[x][0])

    return neighbors

K = 10

avgRating = 0

neighbors = getNeighbors(1,K)

我从PowerShell收到此错误消息：

回溯（最近通话最近一次）：

neighbors = getNeighbors(1,K)

dist = ComputeDistance(movieDict[movieID],movieDict[movie])

genreDistance = spatial.distance.cosine(genresA,genresB)

return correlation(u,v,w=w,centered=False)

uv = np.average(u*v,weights=w)

ValueError: operands could not be broadcast together with shape (19,)(0,)

当我尝试从ipython终端调试问题时收到以下错误消息：

c:\programdata\anaconda3\lib\site-packages\scipy\spatial\distance.py(695)correlation()

     693        u = u - umu

     694        v = v - vmu

---> 695        uv = np.average(u*v,weights=w)

     696        uu = np.average(np.square(u),weights=w)

     697        vv = np.average(np.square(v),weights=w)


**Note**: The code ran fine and produced results up until *print(Cprint(ComputeDistance(movieDict[2],movieDict[4]))*

My guess is the problem is with this part of the code: 


import operator
def getNeighbors(movieID,K):
    distances = []
    for movie in movieDict:
        if (movie != movieID):
            dist = ComputeDistance(movieDict[movieID],movieDict[movie])
            distances.append((movie,dist))
    distances.sort(key=operator.itemgetter(1))
    neighbors = []
    for x in range(K):
        neighbors.append(distance[x][0])
    return neighbors

K = 10
avgRating = 0
neighbors = getNeighbors(1,K) 


The code can be found in this link: https://hendra-herviawan.github.io/Movie-Recommendation-based-on-KNN-K-Nearest-Neighbors.html