def LoadMovieLensData():
ml = MovieLens()
print("Loading movie ratings...")
data = ml.loadMovieLensLatestSmall()
print("\nComputing movie popularity ranks so we can measure novelty later...")
rankings = ml.getPopularityRanks()
return ml, data, rankings
"""
Created on Wed May 9 10:10:04 2018
@author: Frank
"""
from CollaborativeFiltering.MovieLens import MovieLens
from surprise import KNNBasic
import heapq
from collections import defaultdict
from operator import itemgetter
testSubject = '85'
k = 10
ml = MovieLens()
data = ml.loadMovieLensLatestSmall()
trainSet = data.build_full_trainset()
sim_options = {'name': 'cosine', 'user_based': False}
model = KNNBasic(sim_options=sim_options)
model.fit(trainSet)
simsMatrix = model.compute_similarities()
testUserInnerID = trainSet.to_inner_uid(testSubject)
# Get the top K items we rated
testUserRatings = trainSet.ur[testUserInnerID] # (item, rating)
kNeighbors = heapq.nlargest(k, testUserRatings, key=lambda t: t[1])