def run():
_user_ratings = int(request.args.get('user-ratings'))
_item_ratings = int(request.args.get('item-ratings'))
_splitter = request.args.get('splitter')
_ratio = float(request.args.get('ratio')) / 100
_k = int(request.args.get('length'))
loader = sequeval.UIRTLoader(user_ratings=_user_ratings,
item_ratings=_item_ratings)
ratings = loader.load('datasets/yes_reduced.csv')
builder = sequeval.Builder('1000 s')
sequences, items = builder.build(ratings)
profiler = sequeval.Profiler(sequences)
response = {
'profiler': {
'users': profiler.users(),
'items': profiler.items(),
'ratings': profiler.ratings(),
'sequences': profiler.sequences(),
'sparsity': parse(profiler.sparsity()),
'length': parse(profiler.sequence_length())
}
}
if _splitter == 'random':
splitter = sequeval.RandomSplitter(_ratio)
else:
splitter = sequeval.TimestampSplitter(_ratio)
training_set, test_set = splitter.split(sequences)
response['splitter'] = {
'training': len(training_set),
'test': len(test_set)
}
evaluator = sequeval.Evaluator(training_set, test_set, items, _k)
cosine = sequeval.CosineSimilarity(training_set, items)
response['evaluator'] = []
most_popular = baseline.MostPopularRecommender(training_set, items)
response['evaluator'].append(evaluation(evaluator, most_popular, cosine))
random = baseline.RandomRecommender(training_set, items)
response['evaluator'].append(evaluation(evaluator, random, cosine))
unigram = baseline.UnigramRecommender(training_set, items)
response['evaluator'].append(evaluation(evaluator, unigram, cosine))
bigram = baseline.BigramRecommender(training_set, items)
response['evaluator'].append(evaluation(evaluator, bigram, cosine))
return json.dumps(response)
def test_users(self):
profiler = sequeval.Profiler(sequences)
self.assertEqual(2, profiler.users())
def test_sequence_length(self):
profiler = sequeval.Profiler(sequences)
self.assertAlmostEqual(8 / 3, profiler.sequence_length())
def test_sparsity(self):
profiler = sequeval.Profiler(sequences)
self.assertAlmostEqual(8 / 9, profiler.sparsity())
def test_sequences(self):
profiler = sequeval.Profiler(sequences)
self.assertEqual(3, profiler.sequences())
def test_ratings(self):
profiler = sequeval.Profiler(sequences)
self.assertEqual(8, profiler.ratings())
print("Ratio:", args.ratio)
print("Length:", args.length)
# Set the random seed
if args.seed is not None:
random.seed(args.seed)
np.random.seed(args.seed)
loader = sequeval.UIRTLoader(user_ratings=args.user, item_ratings=args.item)
ratings = loader.load(args.file)
builder = sequeval.Builder(args.delta)
sequences, items = builder.build(ratings)
print("\n# Profiler")
profiler = sequeval.Profiler(sequences)
print("Users:", profiler.users())
print("Items:", profiler.items())
print("Ratings:", profiler.ratings())
print("Sequences:", profiler.sequences())
print("Sparsity:", profiler.sparsity())
print("Length:", profiler.sequence_length())
if args.splitter == 'random':
print("\n# Random splitter")
splitter = sequeval.RandomSplitter(args.ratio)
elif args.splitter == 'timestamp':
print("\n# Timestamp splitter")
splitter = sequeval.TimestampSplitter(args.ratio)
else:
raise RuntimeError('Unknown splitter ' + args.splitter)