def JMF_SU(train_user,
train_item,
valid_user,
test_user,
R,
max_iter=50,
lambda_u=1,
lambda_v=100,
dimension=50,
lambda_p=0,
lambda_q=50,
momentum_flag=1):
# explicit setting
a = 1
b = 0
eta = -0.0005
beta = 0.02
epsilon = 1e-20
num_user = R.shape[0]
num_item = R.shape[1]
print "=================================== Models==================================="
print "\tnum_user is:{}".format(num_user)
print "\tnum_item is:{}".format(num_item)
print "==================================================================================="
PREV_LOSS = 1e-50
Train_R_I = train_user[
1] #this is rating; train_user_[0] is the item_index
Train_R_J = train_item[1]
Test_R = test_user[1]
Valid_R = valid_user[1]
# print train_user[1][0:5]
'''
compute the average of R
'''
train_sum = 0
test_sum = 0
valid_sum = 0
train_size = 0
test_size = 0
valid_size = 0
total_sum = 0
user_bais_sum = []
item_bais_sum = []
user_bais_size = []
item_bais_size = []
for item in train_user[1]:
train_sum = train_sum + np.sum(item)
train_size = train_size + np.size(item)
user_bais_sum.append(np.sum(item))
user_bais_size.append(len(item))
for item in test_user[1]:
test_sum = test_sum + np.sum(item)
test_size = test_size + np.size(item)
for item in valid_user[1]:
valid_sum = valid_sum + np.sum(item)
valid_size = valid_size + np.size(item)
for item in train_item[1]:
item_bais_sum.append(np.sum(item))
item_bais_size.append(len(item))
total_size = train_size + test_size + valid_size
total_sum = train_sum + test_sum + valid_sum
global_average = total_sum * 1.0 / total_size
user_bais = [
user_bais_sum[i] / user_bais_size[i] for i in range(len(user_bais_sum))
]
item_bais = [
item_bais_sum[i] / item_bais_size[i] for i in range(len(item_bais_sum))
]
print "######################################"
print "sum: ", train_sum, test_sum, valid_sum
print "size: ", train_size, test_size, valid_size
print "average: ", train_sum * 1.0 / train_size, test_sum * 1.0 / test_size, valid_sum * 1.0 / valid_size
print "global average: ", global_average
print "user_bais:", user_bais[0:10]
print "######################################"
'''
user's prefrence matrix
'''
S_Train_R_I = [] # train_user[1]
S_Train_R_J = [] #train_item[1]
S_Test_R = [] #test_user[1]
S_Valid_R = [] #valid_user[1]
uindx = 0
for item in train_user[1]:
new_item = item.copy()
for i in range(len(item)):
if item[i] > user_bais[uindx]:
new_item[i] = 1.0 / (
1.0 + (item[i] - user_bais[uindx])**(-2)
) #1.0/(1.0+math.exp(-item[i]+user_bais[iidex]))
else:
new_item[i] = 0
S_Train_R_I.append(new_item)
uindx = uindx + 1
S_Train_R_I = np.array(S_Train_R_I)
uindx = 0
for item in train_item[1]:
new_item = item.copy()
for i in range(len(item)):
temp_bais = train_item[0][uindx][i]
if item[i] > user_bais[temp_bais]:
new_item[i] = 1.0 / (
1.0 + (item[i] - user_bais[temp_bais])**(-2)
) #1.0/(1.0+math.exp(-item[i]+user_bais[temp_bais]))
else:
new_item[i] = 0
S_Train_R_J.append(new_item)
uindx = uindx + 1
S_Train_R_J = np.array(S_Train_R_J)
pre_val_eval = 1e10
V = np.random.uniform(0.1, 1, size=(num_item, dimension))
U = np.random.uniform(0.1, 1, size=(num_user, dimension))
Q = np.random.uniform(0.1, 1, size=(num_item, dimension))
P = U #np.random.uniform(0,0.5,size=(num_user,dimension))
'''
sqrs_User=np.zeros([num_user,dimension])
momentum_V_User=np.zeros([num_user,dimension])
sqrs_Item=np.zeros([num_item,dimension])
momentum_V_Item=np.zeros([num_item,dimension])
sqrs_Q = np.zeros([num_item,dimension])
momentum_V_Q=np.zeros([num_item,dimension])
momentum_eta=-0.005
iteration_flag=lambda_p
'''
sgd_eta = -0.0005
endure_count = 100
count = 0
better_rmse = 100
better_mae = 100.0
for iteration in xrange(max_iter):
loss = 0
tic = time.time()
print "%d iteration\t(patience: %d)" % (iteration, count)
# VV = b * (V.T.dot(V)) + lambda_u * np.eye(dimension)#diagonal matrix
sub_loss = np.zeros(num_user)
print "=================================================================="
print "the shape of U, U[i] {} {}".format(U.shape, U[0].shape)
print "the shape of V, V[i] {} {}".format(V.shape, V[0].shape)
print "=================================================================="
# if momentum_flag==1 and iteration <iteration_flag:
# print "momentum update",momentum_eta
# if iteration >=9:
# momentum_eta=-0.0001
# elif momentum_flag !=0:
# print "sgd update", sgd_eta
# else:
# print "grant=0"
for i in xrange(num_user):
idx_item = train_user[0][i]
#train_user[0]=[[item1,item2,item3...],[item1,itme3],[item3,item2]...]
#train_user[1]=[[rating1,rating2,rating3...],[rating1,rating3],[rating2,rating5]...]
V_i = V[idx_item]
R_i = Train_R_I[i] #[rating1,rating2,rating3...]
'''
preference matrix
'''
Q_i = Q[idx_item]
S_R_i = S_Train_R_I[i]
S_approx_R_i = P[i].dot(Q_i.T)
approx_R_i = U[i].dot(V_i.T)
upp = (Q_i * (np.tile(-S_R_i + S_approx_R_i,
(dimension, 1)).T)).sum(0)
g = ((V_i *
(np.tile(-R_i + approx_R_i,
(dimension, 1)).T)).sum(0)) + lambda_u * U[i] + upp
A = (a - b) * (
V_i.T.dot(V_i)) + lambda_u * np.eye(dimension) + Q_i.T.dot(Q_i)
B = (a * V_i * (np.tile(R_i, (dimension, 1)).T)).sum(0)
B = B + (Q_i * (np.tile(S_R_i, (dimension, 1)).T)).sum(0)
'''
# sgd method
# P[i]=P[i]+eta*(((Q_i * (np.tile(-S_R_i+S_approx_R_i, (dimension, 1)).T)).sum(0) )+lambda_p*P[i])
if momentum_flag ==1:
if iteration<iteration_flag:
try:
momentum_V_User[i],sqrs_User[i],div=adam(g,momentum_V_User[i],sqrs_User[i],-momentum_eta,iteration)
U[i]=U[i]-div
except Exception as err:
print err
return
else:
U[i]=U[i]+sgd_eta*g
else:
'''
# U[i]=U[i]+eta*g
U[i] = (np.linalg.solve(A.T, B.T)).T #AX=B,X=A^(-1)B
sub_loss[i] = sub_loss[i] - 0.5 * lambda_u * np.dot(U[i], U[i])
'''update S
'''
Xg = (S_R_i - S_approx_R_i)
S_R_i = S_R_i + eta * Xg
X = (1.0 / S_R_i - 1)**2
for i in range(len(X)):
if X[i] <= 0:
S_R_i[i] = 0
P = U
loss = loss + np.sum(sub_loss)
print "=================================================================="
print "the shape of V, V[i] {} {}".format(V.shape, V[0].shape)
print "=================================================================="
sub_loss = np.zeros(num_item)
# UU = b * (U.T.dot(U))
# SUU = b *(P.T.dot(P))
for j in xrange(num_item):
idx_user = train_item[0][j]
U_j = U[idx_user]
R_j = Train_R_J[j]
A = (a - b) * (U_j.T.dot(U_j))
B = (a * U_j * (np.tile(R_j, (dimension, 1)).T)).sum(0)
approx_R_j = U_j.dot(V[j].T)
g = (U_j * (np.tile(-R_j + approx_R_j,
(dimension, 1)).T)).sum(0) + lambda_v * V[j]
'''
# V[j]=V[j]-div
if momentum_flag==1:
if iteration <iteration_flag:
try:
momentum_V_Item[j],sqrs_Item[j],div=adam(g,momentum_V_Item[j],sqrs_Item[j],-momentum_eta,iteration)
V[j]=V[j]-div
except Exception as err:
print err
return
else:
V[j]=V[j]+ sgd_eta*g
else:
'''
# V[j]=V[j]+eta*g
V[j] = (np.linalg.solve((A + lambda_v * np.eye(dimension)).T,
B.T)).T #A*X=B X =A^-1*B
sub_loss[j] = -0.5 * lambda_v * np.dot(V[j], V[j])
sub_loss[j] = sub_loss[j] - 0.5 * np.square(R_j * a).sum()
sub_loss[j] = sub_loss[j] + a * np.sum((U_j.dot(V[j])) * R_j)
sub_loss[j] = sub_loss[j] - 0.5 * np.dot(V[j].dot(A), V[j])
'''
preference Matrix
'''
S_R_j = S_Train_R_J[j]
P_j = P[idx_user]
SA = (a - b) * (P_j.T.dot(P_j))
SB = (a * P_j * (np.tile(S_R_j, (dimension, 1)).T)).sum(0)
S_approx_R_j = P_j.dot(Q[j].T)
gq = (P_j * (np.tile(-S_R_j + S_approx_R_j,
(dimension, 1)).T)).sum(0) + lambda_q * Q[j]
'''
if momentum_flag==1:
if iteration <=iteration_flag:
try:
momentum_V_Q[j],sqrs_Q[j],div=adam(gq,momentum_V_Q[j],sqrs_Q[j],-momentum_eta,iteration)
Q[j]=Q[j]-div
except Exception as err:
print err
return
else:
Q[j]=Q[j]+sgd_eta*(gq)
else:
'''
# Q[j]=Q[j]+eta*(gq)
Q[j] = (np.linalg.solve((SA + lambda_q * np.eye(dimension)).T,
SB.T)).T #A*X=B X =A^-1*B
sub_loss[j] = sub_loss[j] - 0.5 * lambda_q * np.dot(Q[j], Q[j])
sub_loss[j] = sub_loss[j] - 0.5 * np.square(S_R_j * a).sum()
sub_loss[j] = sub_loss[j] + a * np.sum((P_j.dot(Q[j])) * S_R_j)
sub_loss[j] = sub_loss[j] - 0.5 * np.dot(Q[j].dot(SA), Q[j])
loss = (loss + np.sum(sub_loss))
seed = np.random.randint(100000)
topk = [3, 5, 10, 15, 20, 25, 30, 40, 50, 100]
tr_eval, tr_recall, tr_mae = eval_RMSE_bais_list(
train_user[1], U, V, train_user[0], topk, user_bais)
val_eval, va_recall, va_mae = eval_RMSE_bais_list(
valid_user[1], U, V, valid_user[0], topk, user_bais)
te_eval, te_recall, te_mae = eval_RMSE_bais_list(
test_user[1], U, V, test_user[0], topk, user_bais)
for i in range(len(topk)):
print "recall top-{}: Train:{} Validation:{} Test:{}".format(
topk[i], tr_recall[i], va_recall[i], te_recall[i])
toc = time.time()
elapsed = toc - tic
converge = abs((loss - PREV_LOSS) / PREV_LOSS)
if (val_eval < pre_val_eval):
print "Best Test result!!!!!"
else:
count = count + 1
pre_val_eval = val_eval
print "=====================RMSE============================="
print "Loss: %.5f Elpased: %.4fs Converge: %.6f Train: %.5f Validation: %.5f Test: %.5f" % (
loss, elapsed, converge, tr_eval, val_eval, te_eval)
print "=====================MAE============================="
print " Train: %.5f Validation: %.5f Test: %.5f" % (tr_mae, va_mae,
te_mae)
if te_eval < better_rmse:
better_rmse = te_eval
if te_mae < better_mae:
better_mae = te_mae
print "\n JMF_S========better_rmse:{} better_mae:{}==========\n".format(
better_rmse, better_mae)
if (count == endure_count):
break
PREV_LOSS = loss
def ConvMF(res_dir,
train_user,
train_item,
valid_user,
test_user,
R,
CNN_X,
vocab_size,
init_W=None,
give_item_weight=True,
max_iter=50,
lambda_u=1,
lambda_v=100,
dimension=50,
dropout_rate=0.2,
emb_dim=200,
max_len=300,
num_kernel_per_ws=100):
# explicit setting
a = 1
b = 0
num_user = R.shape[0]
num_item = R.shape[1]
# CNN_X=np.zeros([num_item,max_len])
'''
compute the average of R
'''
train_sum = 0
test_sum = 0
valid_sum = 0
train_size = 0
test_size = 0
valid_size = 0
total_sum = 0
user_bias_sum = []
item_bais_sum = []
user_bias_size = []
item_bais_size = []
for item in train_user[1]:
train_sum = train_sum + np.sum(item)
train_size = train_size + np.size(item)
user_bias_sum.append(np.sum(item))
user_bias_size.append(len(item))
for item in test_user[1]:
test_sum = test_sum + np.sum(item)
test_size = test_size + np.size(item)
for item in valid_user[1]:
valid_sum = valid_sum + np.sum(item)
valid_size = valid_size + np.size(item)
for item in train_item[1]:
item_bais_sum.append(np.sum(item))
item_bais_size.append(len(item))
total_size = train_size + test_size + valid_size
total_sum = train_sum + test_sum + valid_sum
global_average = total_sum * 1.0 / total_size
user_bias = [
user_bias_sum[i] / user_bias_size[i] for i in range(len(user_bias_sum))
]
item_bais = [
item_bais_sum[i] / item_bais_size[i] for i in range(len(item_bais_sum))
]
PREV_LOSS = 1e-50
# if not os.path.exists(res_dir):
# os.makedirs(res_dir)
# f1 = open(res_dir + '/state.log', 'w')
Train_R_I = train_user[1]
Train_R_J = train_item[1]
Test_R = test_user[1]
Valid_R = valid_user[1]
if give_item_weight is True:
item_weight = np.array([math.sqrt(len(i)) for i in Train_R_J],
dtype=float)
item_weight = (float(num_item) / item_weight.sum()) * item_weight
else:
item_weight = np.ones(num_item, dtype=float)
pre_val_eval = 1e10
cnn_module = CNN_module(dimension, vocab_size, dropout_rate, emb_dim,
max_len, num_kernel_per_ws, init_W)
theta = cnn_module.get_projection_layer(CNN_X)
np.random.seed(133)
U = np.random.uniform(0, 1, size=(num_user, dimension))
V = theta
print "------", type(V),
endure_count = 5
count = 0
better_rmse = 100.0
better_mae = 100.0
for iteration in xrange(max_iter):
loss = 0
tic = time.time()
print "%d iteration\t(patience: %d)" % (iteration, count)
print "=================================================================="
print "the shape of U, U[i] {} {}".format(U.shape, U[0].shape)
print "=================================================================="
VV = b * (V.T.dot(V)) + lambda_u * np.eye(dimension)
sub_loss = np.zeros(num_user)
for i in xrange(num_user):
idx_item = train_user[0][i]
V_i = V[idx_item]
R_i = Train_R_I[i]
A = VV + (a - b) * (V_i.T.dot(V_i))
B = (a * V_i * (np.tile(R_i, (dimension, 1)).T)).sum(0)
U[i] = np.linalg.solve(A, B)
sub_loss[i] = -0.5 * lambda_u * np.dot(U[i], U[i])
loss = loss + np.sum(sub_loss)
sub_loss = np.zeros(num_item)
UU = b * (U.T.dot(U))
for j in xrange(num_item):
idx_user = train_item[0][j]
U_j = U[idx_user]
R_j = Train_R_J[j]
tmp_A = UU + (a - b) * (U_j.T.dot(U_j))
A = tmp_A + lambda_v * item_weight[j] * np.eye(dimension)
B = (a * U_j * (np.tile(R_j, (dimension, 1)).T)
).sum(0) + lambda_v * item_weight[j] * theta[j]
V[j] = np.linalg.solve(A, B)
sub_loss[j] = -0.5 * np.square(R_j * a).sum()
sub_loss[j] = sub_loss[j] + a * np.sum((U_j.dot(V[j])) * R_j)
sub_loss[j] = sub_loss[j] - 0.5 * np.dot(V[j].dot(tmp_A), V[j])
loss = loss + np.sum(sub_loss)
seed = np.random.randint(100000)
history = cnn_module.train(CNN_X, V, item_weight, seed)
theta = cnn_module.get_projection_layer(CNN_X)
cnn_loss = history.history['loss'][-1]
loss = loss - 0.5 * lambda_v * cnn_loss * num_item
topk = [3, 5, 10, 15, 20, 25, 30, 40, 50, 100]
tr_eval, tr_recall, tr_mae, tr_ndcg = eval_RMSE_bais_list(
train_user[1], U, V, train_user[0], topk, user_bias)
val_eval, va_recall, va_mae, val_ndcg = eval_RMSE_bais_list(
valid_user[1], U, V, valid_user[0], topk, user_bias)
te_eval, te_recall, te_mae, te_ndcg = eval_RMSE_bais_list(
test_user[1], U, V, test_user[0], topk, user_bias)
for i in range(len(topk)):
print "recall top-{}: Train:{} Validation:{} Test:{}".format(
topk[i], tr_recall[i], va_recall[i], te_recall[i])
print "ndcg train {}, val {}, test {}".format(tr_ndcg, val_ndcg,
te_ndcg)
toc = time.time()
elapsed = toc - tic
converge = abs((loss - PREV_LOSS) / PREV_LOSS)
if (val_eval < pre_val_eval):
# cnn_module.save_model(res_dir + '/CNN_weights.hdf5')
# np.savetxt(res_dir + '/U.dat', U)
# np.savetxt(res_dir + '/V.dat', V)
# np.savetxt(res_dir + '/theta.dat', theta)
pass
else:
count = count + 1
pre_val_eval = val_eval
print "=====================RMSE============================="
print "Loss: %.5f Elpased: %.4fs Converge: %.6f Train: %.5f Validation: %.5f Test: %.5f" % (
loss, elapsed, converge, tr_eval, val_eval, te_eval)
print "=====================MAE============================="
print " Train: %.5f Validation: %.5f Test: %.5f" % (tr_mae, va_mae,
te_mae)
if te_eval < better_rmse:
better_rmse = te_eval
if te_mae < better_mae:
better_mae = te_mae
print "\nConvMF=============better_rmse:{}=====better_mae:{}==============\n".format(
better_rmse, better_mae)
if (count == endure_count):
break
PREV_LOSS = loss
temp_loss=temp_loss+a * np.sum((P_j.dot(qqj)) * S_R_j)
temp_loss=temp_loss - 0.5 * np.dot(qqj.dot(SA), qqj)
sub_loss[j] = sub_loss[j] + (1-alpha)*temp_loss
YY=V+Q
loss = loss + np.sum(sub_loss)
seed = np.random.randint(100000)
history = cnn_module.train(CNN_X, V, item_weight, seed)
theta = cnn_module.get_projection_layer(CNN_X)
cnn_loss = history.history['loss'][-1]
loss = loss - 0.5 * lambda_v * cnn_loss * num_item
topk=[3,5,10,15,20,25,30,40,50,100]
tr_eval,tr_recall,tr_mae=eval_RMSE_bais_list(Train_R_I, U, V, train_user[0],topk,user_bais)
val_eval,va_recall,va_mae = eval_RMSE_bais_list(Valid_R, U, V, valid_user[0],topk,user_bais)
te_eval,te_recall,te_mae = eval_RMSE_bais_list(Test_R, U, V, test_user[0],topk,user_bais)
for i in range(len(topk)):
print "recall top-{}: Train:{} Validation:{} Test:{}".format(topk[i],tr_recall[i],va_recall[i],te_recall[i])
toc = time.time()
elapsed = toc - tic
converge = abs((loss - PREV_LOSS) / PREV_LOSS)
if (val_eval < pre_val_eval):
# cnn_module.save_model(res_dir + '/CNN_weights.hdf5')
# np.savetxt(res_dir + '/U.dat', U)
# np.savetxt(res_dir + '/V.dat', V)
def JCMF_S(res_dir,
train_user,
train_item,
valid_user,
test_user,
R,
CNN_X,
vocab_size,
init_W=None,
give_item_weight=True,
max_iter=50,
lambda_u=1,
lambda_v=100,
dimension=50,
dropout_rate=0.2,
emb_dim=200,
max_len=300,
num_kernel_per_ws=100,
lambda_p=0,
lambda_q=1):
# explicit setting
a = 1
b = 0
eta = -0.0005
alpha = 0.8
num_user = R.shape[0]
num_item = R.shape[1]
# CNN_X=np.zeros([num_item,max_len])
PREV_LOSS = 1e-50
'''
if not os.path.exists(res_dir):
os.makedirs(res_dir)
f1 = open(res_dir + '/state.log', 'w')
'''
Train_R_I = train_user[1]
Train_R_J = train_item[1]
Test_R = test_user[1]
Valid_R = valid_user[1]
if give_item_weight is True:
item_weight = np.array([math.sqrt(len(i)) for i in Train_R_J],
dtype=float)
item_weight = (float(num_item) / item_weight.sum()) * item_weight
else:
item_weight = np.ones(num_item, dtype=float)
pre_val_eval = 1e12
'''
compute the average of R
'''
train_sum = 0
test_sum = 0
valid_sum = 0
train_size = 0
test_size = 0
valid_size = 0
total_sum = 0
user_bais_sum = []
item_bais_sum = []
user_bais_size = []
item_bais_size = []
for item in train_user[1]:
train_sum = train_sum + np.sum(item)
train_size = train_size + np.size(item)
user_bais_sum.append(np.sum(item))
user_bais_size.append(len(item))
for item in test_user[1]:
test_sum = test_sum + np.sum(item)
test_size = test_size + np.size(item)
for item in valid_user[1]:
valid_sum = valid_sum + np.sum(item)
valid_size = valid_size + np.size(item)
for item in train_item[1]:
item_bais_sum.append(np.sum(item))
item_bais_size.append(len(item))
total_size = train_size + test_size + valid_size
total_sum = train_sum + test_sum + valid_sum
global_average = total_sum * 1.0 / total_size
user_bais = [
user_bais_sum[i] / user_bais_size[i] for i in range(len(user_bais_sum))
]
item_bais = [
item_bais_sum[i] / item_bais_size[i] for i in range(len(item_bais_sum))
]
print "######################################"
print "sum: ", train_sum, test_sum, valid_sum
print "size: ", train_size, test_size, valid_size
print "average: ", train_sum * 1.0 / train_size, test_sum * 1.0 / test_size, valid_sum * 1.0 / valid_size
print "global average: ", global_average #3.58254603506
print "user_bais:", user_bais[0:50]
print "item_bais:", item_bais[0:50]
print "######################################"
# return
'''
preference matrix
'''
S_Train_R_I = [] # train_user[1]
S_Train_R_J = [] #train_item[1]
S_Test_R = [] #test_user[1]
S_Valid_R = [] #valid_user[1]
'''
user preference matrix
'''
iidex = 0
for item in train_user[1]:
new_item = item.copy()
for i in range(len(item)):
if item[i] > user_bais[iidex]:
new_item[i] = 1.0 / (
1.0 + (item[i] - user_bais[iidex])**(-2)
) #1.0/(1.0+math.exp(-item[i]+user_bais[iidex]))
else:
new_item[i] = 0
S_Train_R_I.append(new_item)
iidex = iidex + 1
S_Train_R_I = np.array(S_Train_R_I)
iidex = 0
for item in train_item[1]:
new_item = item.copy()
for i in range(len(item)):
temp_bais = train_item[0][iidex][i]
if item[i] > user_bais[temp_bais]:
new_item[i] = 1.0 / (
1.0 + (item[i] - user_bais[temp_bais])**(-2)
) #1.0/(1.0+math.exp(-item[i]+user_bais[temp_bais]))
else:
new_item[i] = 0
S_Train_R_J.append(new_item)
iidex = iidex + 1
S_Train_R_J = np.array(S_Train_R_J)
Q = np.random.uniform(0.001, 1, size=(num_item, dimension))
P = np.random.uniform(0.001, 1, size=(num_user, dimension))
cnn_module = CNN_module(dimension, vocab_size, dropout_rate, emb_dim,
max_len, num_kernel_per_ws, init_W)
theta = cnn_module.get_projection_layer(CNN_X)
np.random.seed(133)
U = np.random.uniform(size=(num_user, dimension))
V = theta
endure_count = 5
count = 0
better_rmse = 100.0
better_mae = 100.0
for iteration in xrange(max_iter):
loss = 0
tic = time.time()
print "%d iteration\t(patience: %d)" % (iteration, count)
print "=================================================================="
print "the shape of U, U[i] {} {}".format(U.shape, U[0].shape)
print "the shape of V, V[i] {} {}".format(V.shape, V[0].shape)
print "=================================================================="
VV = b * (V.T.dot(V)) + lambda_u * np.eye(dimension)
SVV = b * (Q.T.dot(Q)) + lambda_q * np.eye(dimension)
sub_loss = np.zeros(num_user)
for i in xrange(num_user):
idx_item = train_user[0][i]
V_i = V[idx_item]
R_i = Train_R_I[i]
A = (a - b) * (V_i.T.dot(V_i)) + lambda_u * np.eye(dimension)
B = (a * V_i * (np.tile(R_i, (dimension, 1)).T)).sum(0)
Q_i = Q[idx_item]
S_R_i = S_Train_R_I[i]
A = A + Q_i.T.dot(Q_i)
B = B + (Q_i * (np.tile(S_R_i, (dimension, 1)).T)).sum(0)
U[i] = (np.linalg.solve(A.T, B.T)).T #AX=B,X=A^(-1)B
sub_loss[i] = sub_loss[i] - 0.5 * lambda_u * np.dot(U[i], U[i])
P = U
loss = loss + np.sum(sub_loss)
sub_loss = np.zeros(num_item)
UU = b * (U.T.dot(U))
SUU = b * (P.T.dot(P))
for j in xrange(num_item):
idx_user = train_item[0][j]
U_j = U[idx_user]
R_j = Train_R_J[j]
tmp_A = UU + (a - b) * (U_j.T.dot(U_j))
A = tmp_A + lambda_v * item_weight[j] * np.eye(dimension)
B = (a * U_j * (np.tile(R_j, (dimension, 1)).T)
).sum(0) + lambda_v * item_weight[j] * theta[j]
V[j] = (np.linalg.solve(A.T, B.T)).T
sub_loss[j] = -0.5 * lambda_v * np.dot(V[j], V[j])
temp_loss = -0.5 * np.square(R_j * a).sum()
temp_loss = temp_loss + a * np.sum((U_j.dot(V[j])) * R_j)
temp_loss = temp_loss - 0.5 * np.dot(V[j].dot(A), V[j])
sub_loss[j] = sub_loss[j] + alpha * temp_loss
'''
perference Matrix
'''
S_R_j = S_Train_R_J[j]
P_j = P[idx_user]
SA = (a - b) * (P_j.T.dot(P_j)) + lambda_q * np.eye(dimension)
SB = (a * P_j * (np.tile(S_R_j, (dimension, 1)).T)).sum(0)
Q[j] = (np.linalg.solve(SA.T, SB.T)).T
#SGD
# S_approx_R_j = P_j.dot(Q[j].T)
# Q[j]=Q[j]+eta*((P_j * (np.tile(-S_R_j+S_approx_R_j, (dimension, 1)).T)).sum(0)+lambda_q*Q[j])
sub_loss[j] = sub_loss[j] - 0.5 * lambda_q * np.dot(Q[j], Q[j])
temp_loss = -0.5 * np.square(S_R_j * a).sum()
temp_loss = temp_loss + a * np.sum((P_j.dot(Q[j])) * S_R_j)
temp_loss = temp_loss - 0.5 * np.dot(Q[j].dot(SA), Q[j])
sub_loss[j] = sub_loss[j] + temp_loss #(1-alpha)*
loss = loss + np.sum(sub_loss)
seed = np.random.randint(100000)
history = cnn_module.train(CNN_X, V, item_weight, seed)
theta = cnn_module.get_projection_layer(CNN_X)
cnn_loss = history.history['loss'][-1]
loss = loss - 0.5 * lambda_v * cnn_loss * num_item
topk = [3, 5, 10, 15, 20, 25, 30, 40, 50, 100]
tr_eval, tr_recall, tr_mae = eval_RMSE_bais_list(
Train_R_I, U, V, train_user[0], topk, user_bais)
val_eval, va_recall, va_mae = eval_RMSE_bais_list(
Valid_R, U, V, valid_user[0], topk, user_bais)
te_eval, te_recall, te_mae = eval_RMSE_bais_list(
Test_R, U, V, test_user[0], topk, user_bais)
for i in range(len(topk)):
print "recall top-{}: Train:{} Validation:{} Test:{}".format(
topk[i], tr_recall[i], va_recall[i], te_recall[i])
toc = time.time()
elapsed = toc - tic
converge = abs((loss - PREV_LOSS) / PREV_LOSS)
if (val_eval < pre_val_eval):
# cnn_module.save_model(res_dir + '/CNN_weights.hdf5')
# np.savetxt(res_dir + '/U.dat', U)
# np.savetxt(res_dir + '/V.dat', V)
# np.savetxt(res_dir + '/theta.dat', theta)
pass
else:
count = count + 1
pre_val_eval = val_eval
print "=====================RMSE============================="
print "Loss: %.5f Elpased: %.4fs Converge: %.6f Train: %.5f Validation: %.5f Test: %.5f" % (
loss, elapsed, converge, tr_eval, val_eval, te_eval)
print "=====================MAE============================="
print " Train: %.5f Validation: %.5f Test: %.5f" % (tr_mae, va_mae,
te_mae)
if te_eval < better_rmse:
better_rmse = te_eval
if te_mae < better_mae:
better_mae = te_mae
print "\ JCMF_S=============better_rmse:{}=====better_mae:{}==============\n".format(
better_rmse, better_mae)
if (count == endure_count):
break
PREV_LOSS = loss
def JMF_Double(train_user, train_item, valid_user, test_user,
R,max_iter=500, lambda_u=1, lambda_v=100,lambda_q=10,lambda_p=10):
# explicit setting
a = 1
b = 0
eta=-0.0001
alpha=0.8
lamda_m=1000
lamda_n= 1000
num_user = R.shape[0]
num_item = R.shape[1]
print "===================================ConvMF Models==================================="
print "\tnum_user is:{}".format(num_user)
print "\tnum_item is:{}".format(num_item)
print "==================================================================================="
PREV_LOSS = 1e-50
Train_R_I = train_user[1] #this is rating; train_user_[0] is the item_index
Train_R_J = train_item[1]
Test_R = test_user[1]
Valid_R = valid_user[1]
# print train_user[1][0:5]
'''
compute the average of R
'''
train_sum=0
test_sum=0
valid_sum=0
train_size=0
test_size=0
valid_size=0
total_sum=0
user_bais_sum=[]
item_bais_sum=[]
user_bais_size=[]
item_bais_size=[]
for item in train_user[1]:
train_sum=train_sum+ np.sum(item)
train_size=train_size+np.size(item)
user_bais_sum.append(np.sum(item))
user_bais_size.append(len(item))
for item in test_user[1]:
test_sum=test_sum+ np.sum(item)
test_size=test_size+np.size(item)
for item in valid_user[1]:
valid_sum=valid_sum+ np.sum(item)
valid_size=valid_size+np.size(item)
for item in train_item[1]:
item_bais_sum.append(np.sum(item))
item_bais_size.append(len(item))
total_size=train_size+test_size+valid_size
total_sum=train_sum+test_sum+valid_sum
global_average=total_sum*1.0/total_size
user_bais=[user_bais_sum[i]/user_bais_size[i] for i in range(len(user_bais_sum))]
item_bais=[item_bais_sum[i]/item_bais_size[i] for i in range(len(item_bais_sum))]
print "######################################"
print "sum: ",train_sum,test_sum,valid_sum
print "size: ",train_size,test_size,valid_size
print "average: ",train_sum*1.0/train_size, test_sum*1.0/test_size, valid_sum*1.0/valid_size
print "global average: ",global_average
print "user_bais:", user_bais[0:50]
print "item_bais:", item_bais[0:50]
print "######################################"
'''
preference matrix
'''
S_Train_R_I =[]# train_user[1]
S_Train_R_J = []#train_item[1]
S_Test_R = []#test_user[1]
S_Valid_R = []#valid_user[1]
iidex=0
for item in train_user[1]:
new_item=item.copy()
for i in range(len(item)):
if item[i] >= user_bais[iidex]: #global_average:
new_item[i]=1#1.0/(1.0+math.exp(-item[i]+user_bais[iidex]))
else:
new_item[i]=0
S_Train_R_I.append(new_item)
iidex=iidex+1
S_Train_R_I=np.array(S_Train_R_I)
iidex=0
for item in train_item[1]:
new_item=item.copy()
for i in range(len(item)):
if item[i] >= item_bais[iidex]: #global_average:
new_item[i]=1#1.0/(1.0+math.exp(-item[i]+item_bais[iidex]))
else:
new_item[i]=0
S_Train_R_J.append(new_item)
iidex=iidex+1
S_Train_R_J=np.array(S_Train_R_J)
pre_val_eval = 1e10
V = np.random.uniform(0,0.5,size=(num_item,dimension))
U = np.random.uniform(0,0.5,size=(num_user, dimension))
Q = np.random.uniform(0,0.5,size=(num_item,dimension))
P = np.random.uniform(0,0.5,size=(num_user,dimension))
M = np.random.uniform(0,0.5,size=(dimension,dimension))
N = np.random.uniform(0,0.5,size=(dimension,dimension))
endure_count = 100
count = 0
XX=U+P.dot(M)
YY=V+Q.dot(N)
better_rmse=100
for iteration in xrange(max_iter):
loss = 0
tic = time.time()
print "%d iteration\t(patience: %d)" % (iteration, count)
# VV = b * (V.T.dot(V)) + lambda_u * np.eye(dimension)#diagonal matrix
sub_loss = np.zeros(num_user)
print "=================================================================="
print "the shape of U, U[i] {} {}".format(U.shape,U[0].shape)
print "=================================================================="
tm1=np.zeros([dimension,dimension])
tn1=np.zeros([dimension,dimension])
for i in xrange(num_user):
idx_item = train_user[0][i]
#train_user[0]=[[item1,item2,item3...],[item1,itme3],[item3,item2]...]
#train_user[1]=[[rating1,rating2,rating3...],[rating1,rating3],[rating2,rating5]...]
# V_i = V[idx_item]
YY_i=YY[idx_item]
R_i = Train_R_I[i]#[rating1,rating2,rating3...]
'''
perference matrix
'''
Q_i = Q[idx_item]
S_R_i = S_Train_R_I[i]
S_approx_R_i=P[i].dot(Q_i.T)
approx_R_i =(XX[i]).dot(YY_i.T)
t1=(YY_i * (np.tile(-R_i+approx_R_i, (dimension, 1)).T)).sum(0)
U[i]=U[i]+eta*(t1+lambda_u*U[i])
t2=((Q_i * (np.tile(-S_R_i+S_approx_R_i, (dimension, 1)).T)).sum(0) )+lambda_p*P[i]
P[i]=P[i]+eta*(t1.dot(M)+t2)
sub_loss[i] =sub_loss[i] -0.5 * lambda_u * np.dot(U[i], U[i])
sub_loss[i] =sub_loss[i]-0.5 * lambda_p * np.dot(P[i], P[i])
tm1=t1.T.dot(P[i])+tm1
M=M+eta*(tm1+lamda_m * M)
XX=U+P.dot(M)
loss = loss + np.sum(sub_loss)
print "=================================================================="
print "the shape of V, V[i] {} {}".format(V.shape,V[0].shape)
print "=================================================================="
sub_loss = np.zeros(num_item)
# UU = b * (U.T.dot(U))
# SUU = b *(P.T.dot(P))
for j in xrange(num_item):
idx_user = train_item[0][j]
XX_j = XX[idx_user]
R_j = Train_R_J[j]
A = (a - b) * (XX_j.T.dot(XX_j))
# B = (a * XX_j * (np.tile(R_j, (dimension, 1)).T)).sum(0)
approx_R_j = XX_j.dot(YY[j].T)
t1=(XX_j * (np.tile(-R_j+approx_R_j, (dimension, 1)).T)).sum(0)
V[j]=V[j]+eta*(t1+lambda_v*V[j])
sub_loss[j] = -0.5 * lambda_v * np.dot(V[j], V[j])
temp_loss=-0.5 * np.square(R_j * a).sum()
temp_loss=temp_loss+a * np.sum((XX_j.dot(YY[j])) * R_j)
temp_loss=temp_loss - 0.5 * np.dot(YY[j].dot(A), YY[j])
sub_loss[j]=sub_loss[j]+alpha*temp_loss
'''
Sentiment Matrix
'''
S_R_j = S_Train_R_J[j]
P_j = P[idx_user]
SA = (a - b) * (P_j.T.dot(P_j))
# SB = (a * P_j * (np.tile(S_R_j, (dimension, 1)).T)).sum(0)
S_approx_R_j = P_j.dot(Q[j].T)
t2=(P_j * (np.tile(-S_R_j+S_approx_R_j, (dimension, 1)).T)).sum(0)+lambda_q*Q[j]
qqj=Q[j].copy()
Q[j]=Q[j]+eta*(t1.dot(N)+t2)
tn1=tn1+t1.T.dot(Q[j])
sub_loss[j] =sub_loss[j] -0.5 * lambda_q * np.dot(Q[j], Q[j])
temp_loss=-0.5 * np.square(S_R_j * a).sum()
temp_loss=temp_loss+a * np.sum((P_j.dot(qqj)) * S_R_j)
temp_loss=temp_loss - 0.5 * np.dot(qqj.dot(SA), qqj)
sub_loss[j] = sub_loss[j] + (1-alpha)*temp_loss
N=N+eta*(tn1+lamda_n*N)
YY=V+Q.dot(N)
loss = loss + np.sum(sub_loss)+lamda_m*np.sum(np.square(M))+lamda_n *np.sum(np.square(N))
seed = np.random.randint(100000)
topk=[3,5,10,15,20,25,30,40,50,100]
tr_eval,tr_recall,tr_mae=eval_RMSE_bais_list(train_user[1], U, V, train_user[0],topk,user_bais)
val_eval,va_recall,va_mae = eval_RMSE_bais_list(valid_user[1], U, V, valid_user[0],topk,user_bais)
te_eval,te_recall,te_mae = eval_RMSE_bais_list(test_user[1], U, V, test_user[0],topk,user_bais)
for i in range(len(topk)):
print "recall top-{}: Train:{} Validation:{} Test:{}".format(topk[i],tr_recall[i],va_recall[i],te_recall[i])
toc = time.time()
elapsed = toc - tic
converge = abs((loss - PREV_LOSS) / PREV_LOSS)
if (val_eval < pre_val_eval):
print "Best Test result!!!!!"
else:
count = count + 1
pre_val_eval = val_eval
print "=====================RMSE============================="
print "Loss: %.5f Elpased: %.4fs Converge: %.6f Train: %.5f Validation: %.5f Test: %.5f" % (
loss, elapsed, converge, tr_eval, val_eval, te_eval)
print "=====================MAE============================="
print " Train: %.5f Validation: %.5f Test: %.5f" % ( tr_mae, va_mae, te_mae)
if te_eval <better_rmse:
better_rmse=te_eval
print "==============better_rmse:{}===================".format(better_rmse)
if (count == endure_count):
break
PREV_LOSS = loss
def Sentiment(res_dir,
train_user,
train_item,
valid_user,
test_user,
R,
CNN_X,
vocab_size,
init_W=None,
give_item_weight=True,
max_iter=50,
lambda_u=1,
lambda_v=100,
dimension=50,
dropout_rate=0.2,
emb_dim=200,
max_len=300,
num_kernel_per_ws=100,
lambda_p=1,
lambda_q=1):
# explicit setting
a = 1
b = 0
num_user = R.shape[0]
num_item = R.shape[1]
print "===================================ConvMF Models==================================="
print "\tnum_user is:{}".format(num_user)
print "\tnum_item is:{}".format(num_item)
print "==================================================================================="
PREV_LOSS = 1e-50
if not os.path.exists(res_dir):
os.makedirs(res_dir)
f1 = open(res_dir + '/state_CNN_bais.log', 'w')
Train_R_I = train_user[
1] #this is rating; train_user_[0] is the item_index
Train_R_J = train_item[1]
Test_R = test_user[1]
Valid_R = valid_user[1]
if give_item_weight is True:
item_weight = np.array([math.sqrt(len(i)) for i in Train_R_J],
dtype=float)
item_weight = (float(num_item) / item_weight.sum()) * item_weight
else:
item_weight = np.ones(num_item, dtype=float)
pre_val_eval = 1e10
train_sum = 0
test_sum = 0
valid_sum = 0
train_size = 0
test_size = 0
valid_size = 0
total_sum = 0
user_bais_sum = []
item_bais_sum = []
user_bais_size = []
item_bais_size = []
for item in train_user[1]:
train_sum = train_sum + np.sum(item)
train_size = train_size + np.size(item)
user_bais_sum.append(np.sum(item))
user_bais_size.append(len(item))
for item in test_user[1]:
test_sum = test_sum + np.sum(item)
test_size = test_size + np.size(item)
for item in valid_user[1]:
valid_sum = valid_sum + np.sum(item)
valid_size = valid_size + np.size(item)
for item in train_item[1]:
item_bais_sum.append(np.sum(item))
item_bais_size.append(len(item))
total_size = train_size + test_size + valid_size
total_sum = train_sum + test_sum + valid_sum
global_average = total_sum * 1.0 / total_size
user_bais = [
user_bais_sum[i] / user_bais_size[i] for i in range(len(user_bais_sum))
]
item_bais = [
item_bais_sum[i] / item_bais_size[i] for i in range(len(item_bais_sum))
]
# cnn_module = CNN_module(dimension, vocab_size, dropout_rate,
# emb_dim, max_len, num_kernel_per_ws, init_W )
cnn_module = CNN_module(dimension, vocab_size, dropout_rate, emb_dim,
max_len, num_kernel_per_ws, init_W, num_item,
lambda_v, lambda_p, lambda_q)
'''
add index of items
'''
theta = np.random.uniform(size=(num_item, dimension))
item_index = np.arange(num_item).reshape(-1, 1)
m_VV = np.concatenate((theta, item_index), axis=1)
theta = cnn_module.get_projection_layer(CNN_X, m_VV)
np.random.seed(133)
U = np.random.uniform(size=(num_user, dimension))
V = theta
endure_count = 5
count = 0
better_mae = 100
better_rmse = 100
max_iter = 100
for iteration in xrange(max_iter):
loss = 0
tic = time.time()
print "%d iteration\t(patience: %d)" % (iteration, count)
VV = b * (V.T.dot(V)) + lambda_u * np.eye(dimension) #diagonal matrix
sub_loss = np.zeros(num_user)
print "=================================================================="
print "the shape of U, U[i] {} {}".format(U.shape, U[0].shape)
print "=================================================================="
for i in xrange(num_user):
idx_item = train_user[0][i]
#train_user[0]=[[item1,item2,item3...],[item1,itme3],[item3,item2]...]
#train_user[1]=[[rating1,rating2,rating3...],[rating1,rating3],[rating2,rating5]...]
V_i = V[idx_item]
R_i = Train_R_I[i] #[rating1,rating2,rating3...]
A = VV + (a - b) * (V_i.T.dot(V_i))
B = (a * V_i * (np.tile(R_i, (dimension, 1)).T)).sum(
0
) #np.tile() array copy; sum(0) is the sum of each column,sum(1) is the sum of each row;
U[i] = np.linalg.solve(A, B) #AX=B,X=A^(-1)B
sub_loss[i] = -0.5 * lambda_u * np.dot(U[i], U[i])
loss = loss + np.sum(sub_loss)
print "=================================================================="
print "the shape of V, V[i] {} {}".format(V.shape, V[0].shape)
print "=================================================================="
sub_loss = np.zeros(num_item)
UU = b * (U.T.dot(U))
old_V = V
for j in xrange(num_item):
idx_user = train_item[0][j]
U_j = U[idx_user]
R_j = Train_R_J[j]
tmp_A = UU + (a - b) * (U_j.T.dot(U_j))
A = tmp_A + lambda_v * item_weight[j] * np.eye(dimension)
B = (a * U_j * (np.tile(R_j, (dimension, 1)).T)
).sum(0) + lambda_v * item_weight[j] * theta[j]
V[j] = np.linalg.solve(A, B) #A*X=B X =A^-1*B
sub_loss[j] = -0.5 * np.square(R_j * a).sum()
sub_loss[j] = sub_loss[j] + a * np.sum((U_j.dot(V[j])) * R_j)
sub_loss[j] = sub_loss[j] - 0.5 * np.dot(V[j].dot(tmp_A), V[j])
loss = loss + np.sum(sub_loss)
seed = np.random.randint(100000)
# history = cnn_module.train(CNN_X, V, item_weight, seed)
# theta = cnn_module.get_projection_layer(CNN_X)
# print history,history.history.keys(),
# print history.history['loss']
# cnn_loss = history.history['loss'][-1]
m_VV = np.concatenate((V, item_index), axis=1)
cnn_module.train(CNN_X, m_VV, item_weight, seed)
cnn_loss = cnn_module.train_loss
l1_p_loss = cnn_module.l1_p_loss
l2_p_loss = cnn_module.l2_p_loss
logging.info("----------------------------------")
logging.info("CNN loss {},l2_P_loss {},l1_p_loss {}".format(
cnn_loss, l2_p_loss, l1_p_loss))
logging.info("----------------------------------")
theta = cnn_module.get_projection_layer(CNN_X, m_VV)
# loss = loss - 0.5 * lambda_v * cnn_loss * num_item
# loss = loss - 0.5 * lambda_v * cnn_loss * num_item-l2_p_loss*lambda_p -l1_p_loss*lambda_q
loss = loss - 0.5 * cnn_loss
topk = [3, 5, 10, 15, 20, 25, 30, 40, 50, 100]
tr_eval, tr_recall, tr_mae = eval_RMSE_bais_list(
Train_R_I, U, V, train_user[0], topk, user_bais)
val_eval, va_recall, va_mae = eval_RMSE_bais_list(
Valid_R, U, V, valid_user[0], topk, user_bais)
te_eval, te_recall, te_mae = eval_RMSE_bais_list(
Test_R, U, V, test_user[0], topk, user_bais)
for i in range(len(topk)):
print "recall top-{}: Train:{} Validation:{} Test:{}".format(
topk[i], tr_recall[i], va_recall[i], te_recall[i])
toc = time.time()
elapsed = toc - tic
converge = abs((loss - PREV_LOSS) / PREV_LOSS)
if (val_eval < pre_val_eval):
# cnn_module.save_model(res_dir + '/CNN_weights.hdf5')
# np.savetxt(res_dir + '/U.dat', U)
# np.savetxt(res_dir + '/V.dat', V)
# np.savetxt(res_dir + '/theta.dat', theta)
pass
else:
count = count + 1
pre_val_eval = val_eval
print "=====================RMSE============================="
print "Loss: %.5f Elpased: %.4fs Converge: %.6f Train: %.5f Validation: %.5f Test: %.5f" % (
loss, elapsed, converge, tr_eval, val_eval, te_eval)
print "=====================MAE============================="
print " Train: %.5f Validation: %.5f Test: %.5f" % (tr_mae, va_mae,
te_mae)
if te_eval < better_rmse:
better_rmse = te_eval
if te_mae < better_mae:
better_mae = te_mae
print "\Sentiment=============better_rmse:{}=====better_mae:{}==============\n".format(
better_rmse, better_mae)
if (count == endure_count):
break
PREV_LOSS = loss