Feedback code for items

#CODING=GBK" "Select User feedback items will score >3 defined as like" "ImportPandas as PDImportNumPy as NPImportCopy#get the degree of differentiationdefGetdiff (Userrates,movie): like=dict () Dislike=dict () Unknown=dict () Likearray=[] Dislikearray=[] Unknownarray=[]     forUser,ratingsinchUserrates.items ():ifMovie not inchRatings:unknown[user]=Ratings forMv,ratinginchRatings.items (): Unknownarray.append (rating)elifRatings[movie]>3: Like[user]=Ratings forMv,ratinginchRatings.items ():ifmv!=Movie:likeArray.append (rating)Else: Dislike[user]=Ratings forMv,ratinginchRatings.items ():ifmv!=Movie:dislikeArray.append (rating) diff=0ifLen (likearray)! =0:diff+=Np.var (Likearray)ifLen (dislikearray)! =0:diff+=Np.var (Dislikearray)ifLen (unknownarray)! =0:diff+=Np.var (Unknownarray)return(Diff,like,dislike,unknown)defSelect (MVS,USERRATES,NODE,EXCEPTMVS,LV): like=dict () Dislike=dict () Unknown=dict () MaxDiff=-100BESTMV=-1000 forMvinchMVS:ifMvinchExceptmvs:ContinueDIFF,TMPA,TMPB,TMPC=Getdiff (USERRATES,MV)ifDiff>MAXDIFF:BESTMV=MV MaxDiff=diff like=Tmpa dislike=TMPB Unknown=tmpc exceptmvs.append (BESTMV) node['movie']=BESTMVPrintLVPrintnode['Tag']    if(lv+1) <=3: node[' like']={'Tag':' like'} node['dislike']={'Tag':'dislike'} node['Unknown']={'Tag':'Unknown'} select (mvs,like,node[' like'],copy.deepcopy (EXCEPTMVS), lv+1) Select (mvs,dislike,node['dislike'],copy.deepcopy (EXCEPTMVS), lv+1) Select (mvs,unknown,node['Unknown'],copy.deepcopy (EXCEPTMVS), lv+1) Data=pd.read_csv ('Data/ratings.dat', sep='::', nrows=80000,header=None) Data=data.ix[:,0:2]groups=Data.groupby ([0])#organize data with rates[User [item]= scoring formrates=dict () forUser,groupinchGroups:rates[user]={a:b forA, binchGroup[[1,2]].itertuples (index=False)}#get a list of itemsMovies = Set ([j forI,j,kinchData.itertuples (index=False)]) Root={'Tag':'Root'}select (movies,rates,root,[],1)PrintRoot

Feedback code for items