def test_run(self, startid, endid, starti, endi): n_iter = 0 max_iter = (endid-startid)*(endi - starti) util.update_progress(0) start_time = time.time() for runid in np.arange(startid, endid+1, 1): for i in np.arange(starti, endi+1, 1): context = self.contextGetter.call(i, runid) page, pageindex = self.give_page(context) response = responder.respond_with_page(i=i, runid=runid, page = page, teampw = self.teampw) success = response['effect']['Success'] self.revenue += success*page['price'] self.update_alpha_betas(success, pageindex, context) n_iter += 1 util.update_progress(n_iter/max_iter) if n_iter%10 == 0: util.save_profit(self.revenue) util.update_progress(1) time_elapsed = time.time()-start_time av_time = float(time_elapsed/max_iter) print "Total time: " + str(time_elapsed) + " av_time: " + str(av_time)
def train(iteration_rounds=10, iterations=25000, filename='training'):
print 'starting training'
alpha_list = []
beta_list = []
for q in range(1, iteration_rounds + 1):
alphas = np.ones(len(possible_pages))
betas = np.ones(len(possible_pages))
print 'Starting round ' + str(q)
for i in range(0, iterations):
randrunid = random.randint(0, 10000)
randi = random.randint(0, 10000)
page_index = beta_util.draw_from_beta_distributions(
alphas=alphas, betas=betas,
possible_pages=possible_pages) # welke arm wint
bla = 0
while bla < 50:
try:
response = responder.respond_with_page(
i=randi,
runid=randrunid,
page=possible_pages[page_index],
teampw=teampw)
except Exception:
bla = bla + 1
continue
break
success = response['effect']['Success'] # 1 of 0
alphas, betas = beta_util.update_alphas_betas(
index=page_index,
success=success,
price=possible_pages[page_index]['price'],
alphas=alphas,
betas=betas)
util.update_progress(i / iterations)
alpha_list.append(alphas)
beta_list.append(betas)
util.update_progress(1)
print 'Round ' + str(q) + ' of ' + str(iteration_rounds) + ' finished.'
alpha_list = np.array(alpha_list)
beta_list = np.array(beta_list)
final_alphas = alpha_list.mean(axis=0)
final_betas = beta_list.mean(axis=0)
beta_util.save_ab_to_filename(final_alphas, final_betas, name=filename)
print 'training complete'
def test_model(ab_path='../data/alpha_beta/training.npz'):
print 'starting training'
alphas, betas = beta_util.load_ab(filename=ab_path)
for q in range(10001, 10101):
print 'Starting runId ' + str(q)
runid = q
revenue = 0
for i in range(0, 10001):
page_index = beta_util.draw_from_beta_distributions(
alphas=alphas, betas=betas,
possible_pages=possible_pages) # welke arm wint
bla = 0
while bla < 50:
try:
response = responder.respond_with_page(
i=i,
runid=runid,
page=possible_pages[page_index],
teampw=teampw)
except Exception:
bla = bla + 1
continue
break
success = response['effect']['Success'] # 1 of 0
revenue = revenue + success * possible_pages[page_index]['price']
alphas, betas = beta_util.update_alphas_betas(
index=page_index,
success=success,
price=possible_pages[page_index]['price'],
alphas=alphas,
betas=betas)
util.update_progress(i / 10001)
util.save_profit(revenue)
beta_util.save_ab(alphas, betas)
util.update_progress(1)
print 'RunId ' + str(q) + ' of ' + str(10100) + ' finished.'
beta_util.save_ab_to_filename(alphas, betas, name='testing')
print 'training complete'
def train(iteration_rounds = 10, iterations = 25000, filename = 'training'):
print 'starting training'
alpha_list = []
beta_list = []
for q in range(1,iteration_rounds+1):
alphas = np.ones(len(possible_pages))
betas = np.ones(len(possible_pages))
print 'Starting round ' + str(q)
for i in range(0,iterations):
randrunid = random.randint(0,10000)
randi = random.randint(0,10000)
page_index = beta_util.draw_from_beta_distributions(alphas=alphas, betas=betas, possible_pages = possible_pages) # welke arm wint
bla = 0
while bla<50:
try:
response = responder.respond_with_page(i=randi, runid=randrunid, page = possible_pages[page_index], teampw = teampw)
except Exception:
bla = bla+1
continue
break
success = response['effect']['Success'] # 1 of 0
alphas, betas = beta_util.update_alphas_betas(index=page_index, success = success, price = possible_pages[page_index]['price'], alphas = alphas, betas = betas)
util.update_progress(i/iterations)
alpha_list.append(alphas)
beta_list.append(betas)
util.update_progress(1)
print 'Round ' + str(q) + ' of ' + str(iteration_rounds) + ' finished.'
alpha_list = np.array(alpha_list)
beta_list = np.array(beta_list)
final_alphas = alpha_list.mean(axis=0)
final_betas = beta_list.mean(axis=0)
beta_util.save_ab_to_filename(final_alphas, final_betas, name=filename)
print 'training complete'
def run(iterations = 350000, filename = '../data/alpha_beta/1.npz'):
if not os.path.isfile(filename):
alphas = np.ones(len(possible_pages))
betas = np.ones(len(possible_pages))
revenue = 0
else:
alphas, betas = beta_util.load_ab()
revenue = util.load_profit()
for i in range(0, iterations):
randi = random.randint(0,9000)
randrunid = random.randint(0,10000)
page_index = beta_util.draw_from_beta_distributions(alphas=alphas, betas=betas, possible_pages = possible_pages) # welke arm wint
bla = 0
while bla<50:
try:
response = responder.respond_with_page(i=randi, runid=randrunid, page = possible_pages[page_index], teampw = teampw)
except Exception:
bla = bla+1
continue
break
success = response['effect']['Success'] # 1 of 0
alphas, betas = beta_util.update_alphas_betas(index=page_index, success = success, price = possible_pages[page_index]['price'], alphas = alphas, betas = betas)
revenue = revenue + success*possible_pages[page_index]['price']
if i%100 == 0:
beta_util.save_ab(alphas, betas)
util.save_profit(revenue)
util.update_progress(i/iterations)
util.update_progress(1)
def run(self, iterations = 400000): for i in range(0, iterations): randi = random.randint(0,9900) randrunid = random.randint(0,10000) page, pageindex = self.give_page()# welke arm wint response = responder.respond_with_page(i=randi, runid=randrunid, page = page, teampw = self.teampw) success = response['effect']['Success'] # 1 of 0 self.update_alpha_betas(success, pageindex) self.revenue += success*page['price'] if i%10 == 0: self.save_ab() # util.save_profit(self.revenue) util.update_progress(i/iterations) util.update_progress(1)
def test_model(ab_path = '../data/alpha_beta/training.npz'):
print 'starting training'
alphas,betas = beta_util.load_ab(filename=ab_path)
for q in range(10001,10101):
print 'Starting runId ' + str(q)
runid = q
revenue = 0
for i in range(0,10001):
page_index = beta_util.draw_from_beta_distributions(alphas=alphas, betas=betas, possible_pages = possible_pages) # welke arm wint
bla = 0
while bla<50:
try:
response = responder.respond_with_page(i=i, runid=runid, page = possible_pages[page_index], teampw = teampw)
except Exception:
bla = bla+1
continue
break
success = response['effect']['Success'] # 1 of 0
revenue = revenue + success*possible_pages[page_index]['price']
alphas, betas = beta_util.update_alphas_betas(index=page_index, success = success, price = possible_pages[page_index]['price'], alphas = alphas, betas = betas)
util.update_progress(i/10001)
util.save_profit(revenue)
beta_util.save_ab(alphas, betas)
util.update_progress(1)
print 'RunId ' + str(q) + ' of ' + str(10100) + ' finished.'
beta_util.save_ab_to_filename(alphas, betas, name='testing')
print 'training complete'