def commit_allocations(request):
if request.is_ajax():
if request.method == 'POST':
user = request.user
question_ids = request.POST.getlist('question_ids[]', False)
allocs = request.POST.getlist('allocs[]', False)
batch_id = request.POST.get('batch_id', False)
price = request.POST.get('price', False)
domain_id = None
response = dict()
if not all([allocs, question_ids, batch_id, price]):
response['status'] = 'failure'
response['msg'] = 'Missing inputs.'
return dict_to_json_response(response)
if len(question_ids) != len(allocs):
response['status'] = 'failure'
response['msg'] = 'Numbers of questions and allocations do not match.'
return dict_to_json_response(response)
batch = Batch.objects.get(pk=int(batch_id))
try:
user.pay_out(batch, float(price))
except InsufficientFundsException as e:
response['status'] = 'failure'
response['msg'] = str(e) # TODO: change this.
return dict_to_json_response(response)
for x in range(len(question_ids)):
q = BaseQuestion.objects.get(pk=int(question_ids[x]))
if domain_id is None:
domain_id = q.domain_id
for uid in allocs[x].split(','):
assn = Assignment()
assn.answerer = User.objects.get(pk=int(uid))
assn.question = q
assn.agreed_price = get_overview_record(assn.answerer, q.domain)['price']
assn.complete = False
assn.save()
# remove after initial demo
profile = assn.answerer.get_profile()
profile.has_been_assigned = True
profile.save()
batch.is_allocated = True
batch.save()
# TODO: get rid of this.
assert domain_id is not None
if settings.LOG_MARKET_STATS:
log_market_stats(domain_id)
if settings.DYNPRICING:
# update prices in level records
dp.update_prices()
# update prices in allocation stems
allc.update_prices(domain_id)
response['status'] = 'success'
response['msg'] = 'Committed successfully. Redirecting...'
return dict_to_json_response(response)
def do_batch(reprice=False):
batch_mu = CONFIG['batch_size_mean']
batch_sigma = CONFIG['batch_size_stddev']
min_price_prob = CONFIG['min_price_prob']
conf_mu = CONFIG['confidence_req_mean']
conf_sigma = CONFIG['confidence_req_stddev']
price_mu = CONFIG['max_price_mean']
price_sigma = CONFIG['max_price_stddev']
# if random.random() < min_price_prob:
# method = 'min_price'
# crit = sigmoid(random.gauss(conf_mu, conf_sigma))
# else:
# method = 'max_conf'
# crit = random.gauss(price_mu, price_sigma)
batch_size = int(max([1, math.ceil(random.gauss(batch_mu, batch_sigma))]))
# get answerers
assgns = db.create_assignments(DOMAIN.id, .9, batch_size, 'min_price')
if not assgns:
print "failing over to max_conf"
assgns = db.create_assignments(DOMAIN.id, 10000, batch_size, 'max_conf')
if not assgns:
return
# create questions
questions = {}
for x in range(batch_size):
q = NRTrainingQuestion(asker=ASKER,
domain=DOMAIN,
question_type=TYPE)
q.save()
true = NRTrainingChoice(question=q)
true.save()
false = NRTrainingChoice(question=q)
false.save()
assgn = assgns[x]
ALLOCATIONS.append(assgn.get_dict())
for u in assgn.members:
keymatch = lambda z: z.id == u
user_rec = filter(keymatch, USERS.keys())[0]
user_acc = USERS[user_rec]
a = Assignment(answerer=user_rec, question=q, completed=True)
a.save()
chosen = false
if random.random() <= user_acc:
chosen = true
answer = NRTrainingAnswer(answerer=user_rec,
question=q,
confidence=1.,
authority=1.)
answer.save()
review = NRTrainingReview(reviewer=ASKER,
answer=answer,
is_correct=(chosen == true),
confidence=1.,
authority=1.)
review.save()
if reprice:
dp.update_prices()
allocs.update_prices(DOMAIN.id)
db.log_market_stats(DOMAIN.id)
take_system_snapshot()
return True