def user_trigram_pp(users, V, prior, alpha_bi=1e-2, alpha_tri=0.01):
pp_in, pp_out = [], []
pred_in, pred_out = [], []
count = 0
print 'Estimating trigram models for each user...'
for user in users:
if (user.getActiveDays() < 60):
continue
X_train, X_test = build_corpus(user.tripList, 30)
trigram = lm.trigramModel(X_train, V, prior=prior,
alpha_bi=alpha_bi, alpha_tri=alpha_tri)
ppIn, ppOut = trigram.perplexity_OD(X_test)
predIn, predOut = trigram.prediction(X_test)
pp_in.append(ppIn)
pp_out.append(ppOut)
pred_in.append(predIn)
pred_out.append(predOut)
count += 1
if count % 100 == 0:
print count
print 'Median In Perplexity = {}'.format(np.median(pp_in))
print 'Median Out Perplexity = {}'.format(np.median(pp_out))
print 'Median In Prediction Accuracy = {}'.format(np.median(pred_in))
print 'Median Out Prediction Accuracy = {}'.format(np.median(pred_out))
return pp_in, pp_out, pred_in, pred_out
def user_trigram(users, V, prior, alpha_bi=1e-2, alpha_tri=0.1):
pp = []
count = 0
print 'Estimating trigram models for each user...'
for user in users:
if (user.getActiveDays() < 60):
continue
X_train, X_test = build_corpus(user.tripList, 30)
trigram = lm.trigramModel(X_train, V, prior=prior,
alpha_bi=alpha_bi, alpha_tri=alpha_tri)
perplexity = trigram.perplexity(X_test)
pp.append(perplexity)
count += 1
if count % 100 == 0:
print count
print 'Median Perplexity = {}'.format(np.median(pp))
return pp
