def main(lamb, R):
#read in file
tmp = []
with open("../session.txt", "r") as f:
for line in f:
line = line.strip().rstrip(',').split(',')
line = list(map(float, line))
tmp.append(line)
f.close()
data = np.asarray(tmp)
tmp = []
with open("../user_feature.txt", "r") as f:
for line in f:
line = line.strip().rstrip(',').split(',')
line = list(map(float, line))
tmp.append(line)
f.close()
user = np.asarray(tmp)
tmp = []
with open("../app_feature.txt", "r") as f:
for line in f:
line = line.strip().rstrip(',').split(',')
line = list(map(float, line))
tmp.append(line)
f.close()
app = np.asarray(tmp)
#initialization
pool_size = app.shape[0]
user_row_n = user.shape[1]
app_row_n = app.shape[1]
d = int((user_row_n - 1) * (app_row_n - 1))
session_n = int(max(data[:, 0]))
train_ratio = 0.7
gamma = 1
# lamb = 0.1
theta = np.zeros(d)
beta = 1
delta = 0.9
V = lamb * np.eye(d)
X = np.zeros((1, d), dtype=np.float)
Y = np.zeros(1)
x_feature = np.zeros((pool_size, d), dtype=np.float)
UCB = np.zeros(pool_size, dtype=np.float)
session = np.zeros(session_n, dtype=np.float)
K = 5
click_n = 0
idex = np.random.permutation(session_n)
tr_idx = idex[:int(round(session_n * train_ratio))]
ts_idx = idex[int(round(session_n * train_ratio)):]
# open file for storing log info
cur_time = strftime("%Y%m%d_", localtime())
logFileName = '../LogFile/main_' + cur_time + '.txt'
logFile = open(logFileName, 'a+')
print >> logFile, '\n\n'
print >> logFile, '=' * 50
print >> logFile, 'experiment parameters: lambda=%f, R=%f' % (lamb, R)
print >> logFile, '\n\n'
#train
app = app[:, 1:]
expl = np.zeros(pool_size)
for i in range(tr_idx.shape[0]):
record = np.zeros(1)
u = np.zeros(1)
try:
record = data[np.where(data[:, 0] == tr_idx[i])]
u = user[np.where(user[:, 0] == record[0, 0])][0, 1:]
except IndexError:
continue
else:
for a in range(pool_size):
x_feature[a] = np.outer(u, app[a, :]).reshape(1, d)
x_feature[a] = np.divide(x_feature[a],
np.linalg.norm(x_feature[a]))
UCB[a] = utils.getUCB(theta, x_feature[a], beta, V)
# print "this is app "+str(a)+" UCB is " + str(UCB[a])
action = UCB.argsort()[-K:][::-1]
for ii in range(K):
if expl[action[ii]] == 0:
expl[action[ii]] = 1
#print expl
reward = match_app.match(record, action)
idx = []
val = []
if reward is not None:
for j in reward:
if reward[j] == 1 or reward[j] == 0:
idx.append(j)
val.append(reward[j])
idx = np.asarray(idx)
val = np.asarray(val)
x_t = x_feature[idx, :]
w = np.array(val.reshape(x_t.shape[0], 1))
# print w
[V, X, Y, theta,
beta] = utils.update_stat(V, x_t, X, Y, w, lamb, delta, R)
else:
continue
#test
cnt = 0
result = [0]
for i in range(ts_idx.shape[0]):
record = np.zeros(1)
u = np.zeros(1)
try:
record = data[np.where(data[:, 0] == ts_idx[i])]
u = user[np.where(user[:, 0] == record[0, 0])][0, 1:]
except IndexError:
continue
else:
for a in range(pool_size):
x_feature[a] = np.outer(u, app[a, :]).reshape(1, d)
x_feature[a] = np.divide(x_feature[a],
np.linalg.norm(x_feature[a]))
UCB[a] = utils.getUCB(theta, x_feature[a], beta, V)
# print "this is app "+str(a)+" UCB is " + str(UCB[a])
action = UCB.argsort()[-K:][::-1]
for ii in range(K):
if expl[action[ii]] == 0:
expl[action[ii]] = 1
# print expl
reward = match_app.match(record, action)
idx = []
val = []
if reward is not None:
for j in reward:
if reward[j] == 1 or reward[j] == 0:
cnt = cnt + 1
idx.append(j)
val.append(reward[j])
idx = np.asarray(idx)
val = np.asarray(val)
x_t = x_feature[idx, :]
w = np.array(val.reshape(x_t.shape[0], 1))
# print w
[V, X, Y, theta,
beta] = utils.update_stat(V, x_t, X, Y, w, lamb, delta, R)
click_n = click_n + utils.get_reward(w)
result.append((float(click_n) / cnt))
else:
continue
print >> logFile, "the reward is: %s" % result
print >> logFile, "cnt is: %s" % cnt
expl_n = sum(expl)
expl_n_rate = float(expl_n) / pool_size
print >> logFile, "expl_n is %s" % str(expl_n)
print >> logFile, "expl_rate is %s" % str(expl_n_rate)
logFile.close()
def main(lamb, R):
tmp = []
with open("../user_feature.txt", "r") as f:
for line in f:
line = line.strip().rstrip(',').split(',')
line = list(map(float, line))
tmp.append(line)
f.close()
user = np.asarray(tmp)
tmp = []
with open("../app_feature.txt", "r") as f:
for line in f:
line = line.strip().rstrip(',').split(',')
line = list(map(float, line))
tmp.append(line)
f.close()
app = np.asarray(tmp)
#initialization
B = 3
reward_acc = 0
cnt_acc = 0
cur_time = strftime("%Y%m%d_", localtime())
logFileName = '../LogFile/main_BT_nouser_' + cur_time + '.txt'
logFile = open(logFileName, 'a+')
print >> logFile, '\n\n'
print >> logFile, '=' * 3
print >> logFile, 'experiment parameters: lambda=%f, R=%f' % (lamb, R)
print >> logFile, '\n\n'
for t in range(0, B):
print "Round " + str(t + 1) + " starts!"
data = readSess(t)
sids = readDict(t)
start = time.clock()
pool_size = app.shape[0]
user_row_n = user.shape[1]
app_row_n = app.shape[1]
d = app_row_n - 1
session_n = sids.shape[0]
train_ratio = 0.7
gamma = 1
#lamb = 0.5
theta = np.zeros(d)
beta = 1
delta = 0.9
V = lamb * np.eye(d)
X = np.zeros((1, d), dtype=np.float)
Y = np.zeros(1)
x_feature = np.zeros((pool_size, d), dtype=np.float)
UCB = np.zeros(pool_size, dtype=np.float)
K = 5
click_n = 0
sids = np.random.permutation(sids)
tr_idx = sids[:int(round(session_n * train_ratio))]
ts_idx = sids[int(round(session_n * train_ratio)):]
#train
app = app[:, 1:]
expl = np.zeros(pool_size)
for i in range(tr_idx.shape[0]):
record = np.zeros(1)
#u = np.zeros(1)
try:
record = data[np.where(data[:, 0] == float(tr_idx[i]))]
#u = user[np.where(user[:, 0] == record[0, 0])][0,1:]
except IndexError:
continue
else:
for a in range(pool_size):
x_feature[a] = app[a, :]
x_feature[a] = np.divide(x_feature[a],
np.linalg.norm(x_feature[a]))
UCB[a] = utils.getUCB(theta, x_feature[a], beta, V)
#print "this is app "+str(a)+" UCB is " + str(UCB[a])
action = UCB.argsort()[-K:][::-1]
for ii in range(K):
if expl[action[ii]] == 0:
expl[action[ii]] = 1
# print expl
reward = match_app.match(record, action)
idx = []
val = []
if reward is not None:
for j in reward:
if reward[j] == 1 or reward[j] == 0:
idx.append(j)
val.append(reward[j])
idx = np.asarray(idx)
val = np.asarray(val)
x_t = x_feature[idx, :]
w = np.array(val.reshape(x_t.shape[0], 1))
print w
[V, X, Y, theta,
beta] = utils.update_stat(V, x_t, X, Y, w, lamb, delta, R)
else:
continue
#test
cnt = 0
result = [0]
for i in range(ts_idx.shape[0]):
record = np.zeros(1)
#u = np.zeros(1)
try:
record = data[np.where(data[:, 0] == float(ts_idx[i]))]
#u = user[np.where(user[:, 0] == record[0, 0])][0,1:]
except IndexError:
continue
else:
for a in range(pool_size):
x_feature[a] = app[a, :]
x_feature[a] = np.divide(x_feature[a],
np.linalg.norm(x_feature[a]))
UCB[a] = utils.getUCB(theta, x_feature[a], beta, V)
#print "this is app "+str(a)+" UCB is " + str(UCB[a])
action = UCB.argsort()[-K:][::-1]
for ii in range(K):
if expl[action[ii]] == 0:
expl[action[ii]] = 1
print expl
reward = match_app.match(record, action)
idx = []
val = []
if reward is not None:
for j in reward:
if reward[j] == 1 or reward[j] == 0:
cnt = cnt + 1
idx.append(j)
val.append(reward[j])
idx = np.asarray(idx)
val = np.asarray(val)
x_t = x_feature[idx, :]
w = np.array(val.reshape(x_t.shape[0], 1))
print w
[V, X, Y, theta,
beta] = utils.update_stat(V, x_t, X, Y, w, lamb, delta, R)
click_n = click_n + utils.get_reward(w)
result.append((float(click_n) / cnt))
else:
continue
print >> logFile, "the reward is: %s" % result[-1]
print >> logFile, "cnt is: %s" % cnt
expl_n = sum(expl)
expl_n_rate = float(expl_n / pool_size)
print >> logFile, "expl_n is %s" % str(expl_n)
print >> logFile, "expl_rate is %s" % str(expl_n_rate)
reward_acc += result[-1]
cnt_acc += cnt
reward_avg = reward_acc / B
cnt_avg = cnt_acc / B
print >> logFile, "the average cnt is: %s" % cnt_avg
print >> logFile, "the average reward is: %s" % reward_avg
logFile.close()
def main(lamb, R):
#read in file
tmp = []
with open("../session.txt", "r") as f:
for line in f:
line = line.strip().rstrip(',').split(',')
line = list(map(float, line))
tmp.append(line)
f.close()
data = np.asarray(tmp)
tmp = []
with open("../user_feature.txt", "r") as f:
for line in f:
line = line.strip().rstrip(',').split(',')
line = list(map(float, line))
tmp.append(line)
f.close()
user = np.asarray(tmp)
tmp = []
with open("../app_feature.txt", "r") as f:
for line in f:
line = line.strip().rstrip(',').split(',')
line = list(map(float, line))
tmp.append(line)
f.close()
app = np.asarray(tmp)
#initialization
pool_size = app.shape[0]
#user_row_n = user.shape[1]
#feature length
app_row_n = app.shape[1]
d = app_row_n - 1
session_n = int(max(data[:, 0]))
train_ratio = 0.5
gamma = 1
# lamb = 0.1
theta = np.zeros(d)
beta = 1
delta = 0.9
V = lamb * np.eye(d)
X = np.zeros((1, d), dtype=np.float)
Y = np.zeros(1)
x_feature = np.zeros((pool_size, d), dtype=np.float)
UCB = np.zeros(pool_size, dtype=np.float)
session = np.zeros(session_n, dtype=np.float)
K = 5
click_n = 0
idex = np.random.permutation(session_n)
tr_idx = idex[:int(round(session_n * train_ratio))]
ts_idx = idex[int(round(session_n * train_ratio)):]
# open file for storing log info
cur_time = strftime("%Y%m%d_", localtime())
logFileName = '../LogFile/main_newtr_nouser' + cur_time + '.txt'
logFile = open(logFileName, 'a+')
print >> logFile, '\n\n'
print >> logFile, '=' * 50
print >> logFile, 'experiment parameters: lambda=%f, R=%f' % (lamb, R)
print >> logFile, '\n\n'
#app = app[:,1:]
expl = np.zeros(pool_size)
#train
for i in range(tr_idx.shape[0]):
record = np.zeros(1)
#user_feature = np.zeros(1)
try:
record = data[np.where(data[:, 0] == tr_idx[i])]
#user_feature = user[np.where(user[:, 0] == record[0, 0])][0,1:]
except IndexError:
continue
else:
# app_dict = {}
# val = []
# for r in range(record.shape[0]):
# if record[r,1] not in app_dict:
# app_dict[record[r,1]] = record[r,2]
# else if record[r,1] == 11 or record[r,1] == 10:
# app_dict[record[r,1]] = record[r,2]
# for a in app_dict:
# app_feature = app[np.where(app[:,0] == a)][0,1:]
# x_feature[record[r,1]] = np.outer(user_feature, app_feature).reshape(1, d)
# x_feature[a] = np.divide(x_feature[a], np.linalg.norm(x_feature[a]))
# if app_dict[a]==61:
# val.append(0)
# else:
# val.append(1)
# val.np.asarray(val)
# x_t = x_feature
# w =np.array(val.reshape(val.shape[1],1))
# [V, X, Y, theta, beta] = utils.update_stat(V, x_t, X, Y, w, lamb, delta, R)
val = []
idx = []
for a in range(pool_size):
#get all apps from the session data of one user
if app[a, 0] in record[:, 1]:
idx.append(a)
x_feature[a] = app[a, 1:]
x_feature[a] = np.divide(x_feature[a],
np.linalg.norm(x_feature[a]))
#get the user's feedback to one app
feedback = record[np.where(record[:, 1] == app[a, 0])][:,
2]
if 10 in feedback or 11 in feedback:
val.append(1)
else:
val.append(0)
val = np.asarray(val)
print val
idx = np.asarray(idx)
x_t = x_feature[idx, :]
w = np.array(val.reshape(val.shape[1], 1))
# print w
[V, X, Y, theta, beta] = utils.update_stat(V, x_t, X, Y, w, lamb,
delta, R)
#test
cnt = 0
result = [0]
while cnt < 4000:
i = random.randint(0, ts_idx.shape[0])
#for i in range(ts_idx.shape[0]):
record = np.zeros(1)
#u = np.zeros(1)
try:
record = data[np.where(data[:, 0] == ts_idx[i])]
#u = user[np.where(user[:, 0] == record[0, 0])][0,1:]
except IndexError:
continue
else:
for a in range(pool_size):
x_feature[a] = app[a, 1:]
x_feature[a] = np.divide(x_feature[a],
np.linalg.norm(x_feature[a]))
UCB[a] = utils.getUCB(theta, x_feature[a], beta, V)
# print "this is app "+str(a)+" UCB is " + str(UCB[a])
action = UCB.argsort()[-K:][::-1]
for ii in range(K):
if expl[action[ii]] == 0:
expl[action[ii]] = 1
# print expl
reward = match_app.match(record, action)
idx = []
val = []
if reward is not None:
for j in reward:
if reward[j] == 1 or reward[j] == 0:
cnt = cnt + 1
idx.append(j)
val.append(reward[j])
idx = np.asarray(idx)
val = np.asarray(val)
x_t = x_feature[idx, :]
w = np.array(val.reshape(x_t.shape[0], 1))
# print w
[V, X, Y, theta,
beta] = utils.update_stat(V, x_t, X, Y, w, lamb, delta, R)
click_n = click_n + utils.get_reward(w)
result.append((float(click_n) / cnt))
else:
continue
print >> logFile, "the reward is: %s" % result
print >> logFile, "cnt is: %s" % cnt
expl_n = sum(expl)
expl_n_rate = float(expl_n) / pool_size
print >> logFile, "expl_n is %s" % str(expl_n)
print >> logFile, "expl_rate is %s" % str(expl_n_rate)
logFile.close()
#train
app = app[:, 1:]
for i in range(tr_idx.shape[0]):
record = np.zeros(1)
u = np.zeros(1)
try:
record = data[np.where(data[:, 0] == tr_idx[i])]
u = user[np.where(user[:, 0] == record[0, 0])][0, 1:]
except IndexError:
continue
else:
for a in range(pool_size):
x_feature[a] = np.outer(u, app[a, :]).reshape(1, d)
x_feature[a] = np.divide(x_feature[a],
np.linalg.norm(x_feature[a]))
UCB[a] = utils.getUCB(theta, x_feature[a], beta, V)
print "this is app " + str(a) + " UCB is " + str(UCB[a])
action = UCB.argsort()[-K:][::-1]
reward = match_app.match(record, action)
idx = []
val = []
if reward is not None:
for j in reward:
if reward[j] == 1 or reward[j] == 0:
idx.append(j)
val.append(reward[j])
idx = np.asarray(idx)
val = np.asarray(val)
x_t = x_feature[idx, :]
w = np.array(val.reshape(x_t.shape[0], 1))
print "this is w %s" % str(w)