def vectActMovTag():
actors = di.getAllActors()
tags = di.getAllTags()
movies = di.getAllMovies()
years = di.getAllYears()
movYearsArray = di.getAllMovieYrs()
movYears = {}
for arr in movYearsArray:
movYears[arr[0]] = arr[1]
#print("movYears", movYears)
actMoviesDb = {}
for act in actors:
actMovies = di.getActorMovieIds(act[0])
actMov = []
for mov in actMovies:
actMov.append(mov[0])
actMoviesDb[act[0]] = actMov
vect = defaultdict(lambda: defaultdict(dict))
for mov in movies:
movTags = di.getMovieTagIds(mov[0])[0][0].split(",")
#print(len(movTags))
for act in actors:
actMovies = actMoviesDb[act[0]]
#print("actMovies:",actMovies)
for tag in tags:
#print("tag",tag[0])
vect[mov[0]][act[0]][tag[0]] = 0
#print("i am here")
# Set the value to 1 if the given cond. is satisfied
if ((mov[0] in actMovies) and
(tag[0] in movTags)): #and (movYears[mov[0]] == yr[0])):
vect[act[0]][mov[0]][tag[0]] = movYears[mov[0]]
#print(vect['1'])
return (vect, actors, movies, years)
def loadDataset(filename, trainingSet=[] , testSet=[]): test=[] with open(filename, 'r') as csvfile: lines = csv.reader(csvfile) dataset = list(lines) movies = di.getAllMovies() tagIds = di.getAllTags() allTagLen = len(tagIds) dataset_copy = [['' for i in range(allTagLen+1)] for j in range(len(movies))] idfMovArr = idf.idfMovieTag() for i in range(len(dataset)): idfVect = idf.tfIdfMovieTag(dataset[i][0], idfMovArr) for j in range(len(idfVect)): dataset_copy[i][j] = idfVect[j] dataset_copy[i][allTagLen]=dataset[i][1] trainingSet.append(dataset_copy[i]) train = [0 for i in range(len(dataset))] for i in range(len(dataset)): train[i] = int(dataset[i][0]) k=0 labels = ['0', '1'] testset_copy = [['' for i in range(allTagLen+1)] for j in range(len(movies)-len(train))] for i in range(len(movies)): if(int(movies[i][0]) in train): pass else: test.append(movies[i][0]) idfVect1 = idf.tfIdfMovieTag(movies[i][0], idfMovArr) for j in range(len(idfVect1)): testset_copy[k][j] = idfVect1[j] #testset_copy[k][allTagLen]=db.getMovieGenre(movies[i][0])[0] testset_copy[k][allTagLen]=random.choice(labels) testSet.append(testset_copy[k]) k=k+1 return test,trainingSet,testSet
def loadDataset(filename, trainingSet=[], testSet=[]):
with open(filename, 'r') as csvfile:
lines = csv.reader(csvfile)
dataset = list(lines)
labels = ['' for i in range(len(dataset))]
movies = di.getAllMovies()
tagIds = di.getAllTags()
allTagLen = len(tagIds)
dataset_copy = [['' for i in range(allTagLen)]
for j in range(len(dataset))]
#dataset_copy = numpy.zeros((len(movies),allTagLen+1))
#dataset_copy = [[0 for i in range(allTagLen+1)] for j in range(len(movies))]
idfMovArr = idf.idfMovieTag()
#print(idfMovArr)
for i in range(len(dataset)):
idfVect = idf.tfIdfMovieTag(dataset[i][0], idfMovArr)
for j in range(len(idfVect)):
dataset_copy[i][j] = idfVect[j]
#dataset_copy[i][allTagLen]=dataset[i][1]
labels[i] = dataset[i][1]
trainingSet.append(dataset_copy[i])
train = [0 for i in range(len(dataset))]
target = ['' for i in range(len(movies))]
for i in range(len(dataset)):
train[i] = int(dataset[i][0])
k = 0
test = []
label = ['0', '1']
testset_copy = [['' for i in range(allTagLen)] for j in range(len(movies))]
for i in range(len(movies)):
if (int(movies[i][0]) in train):
pass
else:
test.append(movies[i][0])
idfVect1 = idf.tfIdfMovieTag(movies[i][0], idfMovArr)
for j in range(len(idfVect1)):
testset_copy[k][j] = idfVect1[j]
#testset_copy[k][allTagLen]=di.getMovieGenre(movies[i][0])[0]
#testset_copy[k][allTagLen]=random.choice(labels)
target[k] = random.choice(label)
testSet.append(testset_copy[k])
k = k + 1
#print("train data =",trainingSet)
#print("\n\n test data =",testSet)
return trainingSet, testSet, labels, target, test
def idfMovieTag():
allTags = di.getAllTags()
allMovies = di.getAllMovies()
movieCount = len(allMovies)
idfMovTagArr = np.zeros(len(allTags))
movTags = []
for mov in allMovies:
movTags.append(di.getMovieTagIds(mov[0])[0][0].split(","))
for i in range(len(allTags)):
tagCount = 0
for j in range(len(allMovies)):
if (allTags[i][0] in movTags[j]):
tagCount = tagCount + 1
res = 0
if (tagCount != 0):
res = math.log(movieCount / tagCount)
idfMovTagArr[i] = res
#print(idfMovTagArr)
return idfMovTagArr
def vectTagMovRat():
tags = di.getAllTags()
movies = di.getAllMovies()
ratings = di.getAllRatings()
avgRatingsArray = di.getAllMovieRtngs()
avgRatings = {}
for arr in avgRatingsArray:
avgRatings[arr[0]] = arr[1]
#print("avgRatings",avgRatings)
vect = defaultdict(lambda: defaultdict(dict))
for mov in movies:
movTags = di.getMovieTagIds(mov[0])[0][0].split(",")
for tag in tags:
for rtng in ratings:
vect[tag[0]][mov[0]][rtng[0]] = 0
# Set the value to 1 if the given cond. is satisfied
if ((tag[0] in movTags) and (rtng[0] <= avgRatings[mov[0]])):
vect[tag[0]][mov[0]][rtng[0]] = 1
#print(vect['1'])
return (vect, tags, movies, ratings)
def vectActMovYr():
actors = di.getAllActors()
movies = di.getAllMovies()
years = di.getAllYears()
movYearsArray = di.getAllMovieYrs()
movYears = {}
for arr in movYearsArray:
movYears[arr[0]] = arr[1]
#print("movYears", movYears)
actMoviesDb = {}
moviesArr = []
for mov in movies:
moviesArr.append(mov[0])
for act in actors:
actMovies = di.getActorMovieIds(act[0])
actMov = []
chk = 0
for mov in actMovies:
actMov.append(mov[0])
if (mov[0] in moviesArr):
chk = 1
if (chk == 1):
actMoviesDb[act[0]] = actMov
print("\ngot the actor movies\n")
vect = defaultdict(lambda: defaultdict(dict))
for act in actMoviesDb:
#print("movie set", act)
for mov in moviesArr:
#print("mov",mov)
for yr in years:
#print("yr",yr[0])
vect[act][mov][yr[0]] = 0
# Set the value to 1 if the given cond. is satisfied
if ((mov in actMoviesDb[act]) and (movYears[mov] == yr[0])):
vect[act][mov][yr[0]] = 1
#print(vect['1'])
return (vect, actors, movies, years)
import dbInfo as di
import probFeedback as pf
import movieRecomm as mr
import numpy as np
userId = input("\nGive User Id: ")
numMovies = 5
movies = di.getAllMovies()
movieNames = di.getAllMovieNames()
moviesArr = []
for mov in movies:
moviesArr.append(mov[0])
mat = mr.formPPRMatrix()
matrix = np.matmul(mat,np.transpose(mat))
usrSeenMovies = di.getusrMovTime(userId)
if(len(usrSeenMovies) <= 0):
print("user has not watched any movies to give suggestions")
sys.exit()
usrMovies = []
for mov in usrSeenMovies:
usrMovies.append(mov[0])
print("\nseeds =", usrMovies)
rankedRes = mr.pprRes(matrix, usrMovies)
#print("ranked res",rankedRes)
print("\nRank\tMovie Id\tMovie Name\n")
for i in range(numMovies):
movIdx = moviesArr.index(rankedRes[i])
print(i+1,":\t",rankedRes[i],"\t\t",movieNames[movIdx][0])
import dbInfo as db
import numpy as np
import persPageRank as ppr
userId = input("\nGive User Id: ")
movies = db.getAllMovies()
movieNames = db.getAllMovieNames()
tfmovies = {}
for movieId in movies:
Taglist = db.getMovieTags(movieId[0])
UnqTags = db.getMovieTagIds(movieId[0])[0][0].split(",")
#print(UnqTags,movieId,Taglist)
tfvect = {}
for tag in UnqTags:
tffact = 0
for t in Taglist:
if (t[0] == tag):
tffact += 1
tfvect[tag[0]] = tffact / len(Taglist)
tfmovies[movieId[0]] = tfvect
tagids = db.getAllTags()
#print(tagids)
movietf = np.zeros((len(tfmovies), len(tagids)))
for i in range(len(tfmovies)):
for j in range(len(tagids)):
if (tagids[j][0] in tfmovies[movies[i][0]].keys()):
movietf[i][j] = tfmovies[movies[i][0]][tagids[j][0]]
matrix = np.matmul(movietf, np.transpose(movietf))
seedList = db.getUserMoviesRates(userId)
seeds = []
