def test_size_of_logs_in_cache(self):
"""
[recommendation.cache.SimpleLogger] Test size of cache is 10 for all users in system
"""
for user in USERS:
user = User.get_user_by_external_id(user["external_id"])
assert len(LogEntry.get_logs_for(user.pk)) == 10, \
"logs size are bigger than predicted (%s != 10)" % len(LogEntry.get_logs_for(user.pk))
def test_size_of_logs_in_cache(self):
"""
[recommendation.cache.SimpleLogger] Test size of cache is 10 for all users in system
"""
for user in USERS:
user = User.get_user_by_external_id(user["external_id"])
assert len(LogEntry.get_logs_for(user.pk)) == 10, \
"logs size are bigger than predicted (%s != 10)" % len(LogEntry.get_logs_for(user.pk))
def bulk_load(self, user, recommendation):
try:
new_logs = [
LogEntry(user=user, item_id=iid, type=self.log_type, value=i)
for i, iid in enumerate(recommendation, start=1)
]
LogEntry.objects.bulk_create(new_logs)
LogEntry.add_logs(user, new_logs)
except Exception:
logging.error(logging.error(traceback.format_exc()))
else:
logging.debug("Recommendation logged for user %s" %
user.external_id)
def __call__(self, user, recommendation, size=4, **kwargs):
"""
Calculate the new rank based on logs
"""
logs = LogEntry.get_logs_for(user.pk)
#m = recommendation.mean()
#print m
#rec = {v: k for k, v in enumerate(recommendation, start=1)}
for log in logs:
try:
recommendation[log.item_id-1] += (self.evaluate(log, size) * 0.01)
except IndexError:
pass
return recommendation
def __call__(self, user, recommendation, size=4, **kwargs):
"""
Calculate the new rank based on logs
"""
logs = LogEntry.get_logs_for(user.pk)
#m = recommendation.mean()
#print m
#rec = {v: k for k, v in enumerate(recommendation, start=1)}
for log in logs:
try:
recommendation[log.item_id - 1] += (self.evaluate(log, size) *
0.01)
except IndexError:
pass
return recommendation