def test_funksvd_ev_build(self):
k = 5
save_path = 'evaluation/precision/'
recommender = FunkSVDRecs()
er = EvaluationRunner(0,
None, # builder
recommender,
k,
params={'k': 20,
'save_path': save_path + 'model/'})
result = er.calculate(1, 5)
print(result)
recs1 = recommender.recommend_items('14620', num=5)
k2 = 7
recommender2 = FunkSVDRecs()
er = EvaluationRunner(0,
None, # builder,
recommender2,
k2,
params={'k': 20,
'save_path': save_path + 'model/'})
result = er.calculate(1, 5)
print(result)
recs2 = recommender2.recommend_items('14620', num=7)
self.compare_recs(recs1, recs2)
def test_rec(self):
recommender = FunkSVDRecs(self.save_path)
recs = recommender.recommend_items_by_ratings('1',
[{'movie_id': AVENGERS, 'rating': 7},
{'movie_id': ALIEN, 'rating': 10},
{'movie_id': CAPTAIN_AMERICA, 'rating': 6}], num=2)
self.assertIsNotNone(recs)
self.assertEqual(len(recs), 2)
def test_rec2(self):
recommender = FunkSVDRecs(self.save_path)
recs = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=5)
self.assertIsNotNone(recs)
self.assertEqual(len(recs), 5)
top = [r[0] for r in recs][:2]
self.assertIn(HARRY, top, '{} was missing from {}'.format(HARRY, top))
self.assertIn(JACQUES, top, '{} was missing from {}'.format(JACQUES, top))
def test_rec2(self):
recommender = FunkSVDRecs(self.save_path)
recs = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=5)
self.assertIsNotNone(recs)
self.assertEqual(len(recs), 5)
top = [r[0] for r in recs][:2]
self.assertIn(HARRY, top, '{} was missing from {}'.format(HARRY, top))
self.assertIn(JACQUES, top, '{} was missing from {}'.format(JACQUES, top))
def test_rec(self):
recommender = FunkSVDRecs(self.save_path)
recs = recommender.recommend_items_by_ratings('1',
[{'movie_id': AVENGERS, 'rating': 7},
{'movie_id': ALIEN, 'rating': 10},
{'movie_id': CAPTAIN_AMERICA, 'rating': 6}], num=2)
self.assertIsNotNone(recs)
self.assertEqual(len(recs), 2)
def test_rec_increasing2(self):
recommender = FunkSVDRecs(self.save_path)
recs4 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=4)
self.assertIsNotNone(recs4)
self.assertEqual(len(recs4), 4)
self.assertAlmostEqual(recs4[1][1]['prediction'], 7.812836963)
recs6 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=6)
self.assertIsNotNone(recs6)
self.assertEqual(len(recs6), 6)
self.compare_recs(recs4, recs6)
recommender = FunkSVDRecs(self.save_path)
recs42 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=4)
self.compare_recs(recs4, recs42)
recs1 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=7)
recs2 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=9)
self.compare_recs(recs1, recs2)
def test_rec_increasing(self):
recommender = FunkSVDRecs(self.save_path)
recs1 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=2)
self.assertIsNotNone(recs1)
self.assertEqual(len(recs1), 2)
recs2 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=3)
self.assertIsNotNone(recs2)
self.assertEqual(len(recs2), 3)
self.assertEqual(recs1[0],recs2[0] )
self.assertEqual(recs1[1],recs2[1] )
def test_rec_increasing(self):
recommender = FunkSVDRecs(self.save_path)
recs1 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=2)
self.assertIsNotNone(recs1)
self.assertEqual(len(recs1), 2)
recs2 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=3)
self.assertIsNotNone(recs2)
self.assertEqual(len(recs2), 3)
self.assertEqual(recs1[0],recs2[0] )
self.assertEqual(recs1[1],recs2[1] )
def evaluate_funksvd_recommender():
save_path = './models/funkSVD/'
K = 20
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = '{}-funksvd-k.csv'.format(timestr)
with open(file_name, 'a', 1) as logfile:
logfile.write("rak,pak,mae,k\n")
builder = MatrixFactorization(save_path)
for k in np.arange(0, 20, 2):
recommender = FunkSVDRecs(save_path + 'model/')
er = EvaluationRunner(0,
builder,
recommender,
k,
params={
'k': 20,
'save_path': save_path + 'model/'
})
result = er.calculate(20, 10)
builder = None
map = result['map']
mae = result['mae']
ar = result['ar']
logfile.write("{}, {}, {}, {}, {}, {}\n".format(ar, map, mae, k))
logfile.flush()
def recs_funksvd(request, user_id, num=6):
sorted_items = FunkSVDRecs().recommend_items(user_id, num)
data = {
'user_id': user_id,
'data': sorted_items
}
return JsonResponse(data, safe=False)
def test_rec_increasing2(self):
recommender = FunkSVDRecs(self.save_path)
recs4 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=4)
self.assertIsNotNone(recs4)
self.assertEqual(len(recs4), 4)
self.assertAlmostEqual(recs4[1][1]['prediction'], 7.812836963)
recs6 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=6)
self.assertIsNotNone(recs6)
self.assertEqual(len(recs6), 6)
self.compare_recs(recs4, recs6)
recommender = FunkSVDRecs(self.save_path)
recs42 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=4)
self.compare_recs(recs4, recs42)
recs1 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=7)
recs2 = recommender.recommend_items_by_ratings('5',
[{'movie_id': AVENGERS, 'rating': 1}], num=9)
self.compare_recs(recs1, recs2)
def test_funksvd_ev_build(self):
k = 5
save_path = 'evaluation/precision/'
recommender = FunkSVDRecs()
er = EvaluationRunner(0,
None, # builder
recommender,
k,
params={'k': 20,
'save_path': save_path + 'model/'})
result = er.calculate(1, 5)
print(result)
recs1 = recommender.recommend_items('14620', num=5)
k2 = 7
recommender2 = FunkSVDRecs()
er = EvaluationRunner(0,
None, # builder,
recommender2,
k2,
params={'k': 20,
'save_path': save_path + 'model/'})
result = er.calculate(1, 5)
print(result)
recs2 = recommender2.recommend_items('14620', num=7)
self.compare_recs(recs1, recs2)
def evaluate_funksvd_recommender():
save_path = '/Users/u0157492/Projects/moviegeek/evaluation/precision/'
K = 20
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = '{}-funksvd-k.csv'.format(timestr)
with open(file_name, 'a', 1) as logfile:
logfile.write("rak,pak,mae,k,user_coverage,movie_coverage\n")
#builder = MatrixFactorization(save_path)
for k in np.arange(5, 50, 2):
recommender = FunkSVDRecs()
er = EvaluationRunner(
0,
None, #builder,
recommender,
k,
params={
'k': 20,
'save_path': save_path + 'model/'
})
result = er.calculate(1, 5)
builder = None # Enough to train it once.
#user_coverage, movie_coverage = RecommenderCoverage(recommender).calculate_coverage()
user_coverage, movie_coverage = 0, 0
pak = result['pak']
mae = result['mae']
rak = result['rak']
logfile.write("{}, {}, {}, {}, {}, {}\n".format(
rak, pak, mae, k, user_coverage, movie_coverage))
#logfile.write("{}, {}, {}, {}\n".format(rak, pak, mae, K))
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))