def recs_bpr(request, user_id, num=6):
sorted_items = BPRRecs().recommend_items(user_id, num)
data = {
'user_id': user_id,
'data': sorted_items
}
return JsonResponse(data, safe=False)
def evaluate_bpr_recommender():
number_of_factors = 30
number_of_iterations = 5
N = 10
save_path = './models/bpr/'
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = '{}-bpr-k.csv'.format(timestr)
with open(file_name, 'a', 1) as logfile:
logfile.write(
"ar, map, mae, N, error, factors, number_of_iterations\n")
builder = BayesianPersonalizationRanking(save_path)
for number_of_factors in np.arange(5, 50, 5):
recommender = BPRRecs(save_path + 'model/')
er = EvaluationRunner(0,
builder,
recommender,
N,
params={
'k': number_of_factors,
'num_iterations': number_of_iterations,
'save_path': save_path + 'model/'
})
result = er.calculate(10, 5)
map = result['map']
mae = result['mae']
ar = result['ar']
error = builder.error if builder else 0
logfile.write("{}, {}, {}, {}, {}, {}, {}\n".format(
ar, map, mae, N, error, number_of_factors,
number_of_iterations))
logfile.flush()
def evaluate_bpr_recommender(coverage=False):
save_path = './models/bpr/'
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = '{}-bpr-k.csv'.format(timestr)
with open(file_name, 'a', 1) as logfile:
logfile.write("rak,pak,mae,k,user_coverage,movie_coverage\n")
builder = BayesianPersonalizationRanking(save_path)
for k in np.arange(0, 20, 2):
recommender = BPRRecs()
er = EvaluationRunner(0,
builder,
recommender,
k,
params={
'k': 10,
'num_iterations': 20,
'save_path': save_path + 'model/'
})
result = er.calculate(1, 5)
builder = None
user_coverage, movie_coverage = 0, 0
if coverage:
user_coverage, movie_coverage = RecommenderCoverage(
recommender).calculate_coverage(k)
map = result['map']
mae = result['mae']
ar = result['ar']
logfile.write("{}, {}, {}, {}, {}, {}\n".format(
ar, map, mae, k, user_coverage, movie_coverage))
logfile.flush()
args = parser.parse_args()
print(args.fwls)
k = 10
cov = None
if args.fwls:
logger.debug("evaluating coverage of fwls")
cov = RecommenderCoverage(FeatureWeightedLinearStacking)
cov.calculate_coverage(K=k, recName='fwls{}'.format(k))
if args.cf:
logger.debug("evaluating coverage of cf")
cov = RecommenderCoverage(NeighborhoodBasedRecs())
cov.calculate_coverage(K=k, recName='cf{}'.format(k))
if args.cb:
logger.debug("evaluating coverage of cb")
cov = RecommenderCoverage(ContentBasedRecs())
cov.calculate_coverage(K=k, recName='cb{}'.format(k))
if args.ltr:
logger.debug("evaluating coverage of ltr")
cov = RecommenderCoverage(BPRRecs())
cov.calculate_coverage(K=k, recName='bpr{}'.format(k))
if args.funk:
logger.debug("evaluating coverage of funk")
cov = RecommenderCoverage(FunkSVDRecs())
cov.calculate_coverage(K=k, recName='funk{}'.format(k))