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338 lines (292 loc) · 14 KB
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import pickle
import tensorflow as tf
import Model
import numpy as np
PARA_DATA = "pro_data/music/music.para"
TRAIN_DATA = "pro_data/music/music.train"
VALID_DATA = "pro_data/music/music.test"
EMBEDDING_DIM = 50
FILTER_SIZE = "5"
NUM_FILTERS = 16 # out_channels
n_pos_aspect = 32
n_neg_aspect = 32
word2vec = True
batch_size = 500
factor_dim = 32
def train_step_1(u_pos_batch, u_neg_batch, i_pos_batch, i_neg_batch, uid, iid, y_batch):
feed_dict = {
model.input_u_pos: u_pos_batch,
model.input_u_neg: u_neg_batch,
model.input_i_pos: i_pos_batch,
model.input_i_neg: i_neg_batch,
model.input_uid: uid,
model.input_iid: iid,
model.input_y: y_batch
}
_, loss = sess.run([train_op_1, model.loss], feed_dict)
accuracy, mae = sess.run([model.accuracy, model.mae], feed_dict)
return accuracy, mae
def train_step_2(u_pos_batch, u_neg_batch, i_pos_batch, i_neg_batch, uid, iid, y_batch):
feed_dict = {
model.input_u_pos: u_pos_batch,
model.input_u_neg: u_neg_batch,
model.input_i_pos: i_pos_batch,
model.input_i_neg: i_neg_batch,
model.input_uid: uid,
model.input_iid: iid,
model.input_y: y_batch
}
_, loss = sess.run([train_op_2, model.loss], feed_dict)
accuracy, mae = sess.run([model.accuracy, model.mae], feed_dict)
return accuracy, mae
def train_step_3(u_pos_batch, u_neg_batch, i_pos_batch, i_neg_batch, uid, iid, y_batch):
feed_dict = {
model.input_u_pos: u_pos_batch,
model.input_u_neg: u_neg_batch,
model.input_i_pos: i_pos_batch,
model.input_i_neg: i_neg_batch,
model.input_uid: uid,
model.input_iid: iid,
model.input_y: y_batch
}
_, loss = sess.run([train_op_3, model.loss], feed_dict)
accuracy, mae = sess.run([model.accuracy, model.mae], feed_dict)
return accuracy, mae
def dev_step(u_pos_valid, u_neg_valid, i_pos_valid, i_neg_valid, userid_valid, itemid_valid, y_valid):
"""
Evaluates model
"""
feed_dict = {
model.input_u_pos: u_pos_valid,
model.input_u_neg: u_neg_valid,
model.input_i_pos: i_pos_valid,
model.input_i_neg: i_neg_valid,
model.input_uid: userid_valid,
model.input_iid: itemid_valid,
model.input_y: y_valid
}
def clip_labels(x):
if x > 5:
return 5
elif x < 1:
return 1
else:
return x
preds, loss, accuracy, mae = sess.run([model.predictions, model.loss, model.accuracy, model.mae], feed_dict)
acc_preds = [clip_labels(x) for x in preds]
labels = [x[0] for x in y_valid]
mse = calculateMSE(acc_preds, labels)**0.5
return loss, mse, mae
def calculateMSE(X, Y):
return sum([(x - y)**2 for x, y in zip(X, Y)])/len(X)
if __name__ == '__main__':
print('loading data...')
pkl_file = open(PARA_DATA, 'rb')
para = pickle.load(pkl_file)
user_num = para['user_num']
item_num = para['item_num']
user_pos_length = para['user_pos_length']
user_neg_length = para['user_neg_length']
item_pos_length = para['item_pos_length']
item_neg_length = para['item_neg_length']
vocabulary_user_pos = para['user_pos_vocab']
vocabulary_user_neg = para['user_neg_vocab']
vocabulary_item_pos = para['item_pos_vocab']
vocabulary_item_neg = para['item_neg_vocab']
u_pos_text = para['u_pos_text']
u_neg_text = para['u_neg_text']
i_pos_text = para['i_pos_text']
i_neg_text = para['i_neg_text']
train_length = para['train_length']
test_length = para['test_length']
with tf.Graph().as_default():
session_conf = tf.ConfigProto()
session_conf.gpu_options.allow_growth = True
sess = tf.Session(config=session_conf)
with sess.as_default():
model = Model.Model(
user_num=user_num,
item_num=item_num,
f=factor_dim,
user_pos_length=user_pos_length,
user_neg_length=user_neg_length,
item_pos_length=item_pos_length,
item_neg_length=item_neg_length,
user_pos_vocab_size=len(vocabulary_user_pos),
user_neg_vocab_size=len(vocabulary_user_neg),
item_pos_vocab_size=len(vocabulary_item_pos),
item_neg_vocab_size=len(vocabulary_item_neg),
embedding_size=EMBEDDING_DIM,
filter_sizes=list(map(int, FILTER_SIZE.split(','))),
num_filters=NUM_FILTERS,
n_pos_aspect=n_pos_aspect,
n_neg_aspect=n_neg_aspect
)
optimizer = tf.train.AdamOptimizer(3e-4, beta1=0.9, beta2=0.999, epsilon=1e-8)
train_op_1 = optimizer.minimize(loss=model.loss, var_list=[model.user_Matrix])
train_op_2 = optimizer.minimize(loss=model.loss, var_list=[model.item_Matrix])
train_op_3 = optimizer.minimize(loss=model.loss, var_list=model.variables + [model.pos_W, model.neg_W])
sess.run(tf.initialize_all_variables())
if word2vec:
sess.run(model.Wu_pos.assign(np.load('data/yelp/pre_Wu_pos.npy')))
sess.run(model.Wu_neg.assign(np.load('data/yelp/pre_Wu_neg.npy')))
sess.run(model.Wi_pos.assign(np.load('data/yelp/pre_Wi_pos.npy')))
sess.run(model.Wi_neg.assign(np.load('data/yelp/pre_Wi_neg.npy')))
l = (train_length / batch_size) + 1
print(l)
ll = 0
epoch = 1
best_mae = 5
best_rmse = 5
best_mse = 25
train_mae = 0
train_rmse = 0
pkl_file = open(TRAIN_DATA, 'rb')
train_data = pickle.load(pkl_file)
train_data = np.array(train_data)
pkl_file.close()
pkl_file = open(VALID_DATA, 'rb')
valid_data = pickle.load(pkl_file)
valid_data = np.array(valid_data)
pkl_file.close()
data_size_train = len(train_data)
data_size_valid = len(valid_data)
ll = int(len(train_data) / batch_size) + 1
for epoch in range(2):
# Shuffle the data at each epoch
shuffle_indices = np.random.permutation(np.arange(data_size_train))
shuffled_data = train_data[shuffle_indices]
# training for user_Matrix
for iter in range(5):
for batch_num in range(ll):
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size, data_size_train)
data_train = shuffled_data[start_index: end_index]
uid, iid, y_batch = zip(*data_train)
u_pos_batch = []
u_neg_batch = []
i_pos_batch = []
i_neg_batch = []
for i in range(len(uid)):
u_pos_batch.append(u_pos_text[uid[i][0]])
u_neg_batch.append(u_neg_text[uid[i][0]])
i_pos_batch.append(i_pos_text[iid[i][0]])
i_neg_batch.append(i_neg_text[iid[i][0]])
u_pos_batch = np.array(u_pos_batch)
u_neg_batch = np.array(u_neg_batch)
i_pos_batch = np.array(i_pos_batch)
i_neg_batch = np.array(i_neg_batch)
t_rmse, t_mae = train_step_1(u_pos_batch, u_neg_batch, i_pos_batch, i_neg_batch, uid, iid, y_batch)
# training for item_Matrix
for iter in range(5):
for batch_num in range(ll):
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size, data_size_train)
data_train = shuffled_data[start_index: end_index]
uid, iid, y_batch = zip(*data_train)
u_pos_batch = []
u_neg_batch = []
i_pos_batch = []
i_neg_batch = []
for i in range(len(uid)):
u_pos_batch.append(u_pos_text[uid[i][0]])
u_neg_batch.append(u_neg_text[uid[i][0]])
i_pos_batch.append(i_pos_text[iid[i][0]])
i_neg_batch.append(i_neg_text[iid[i][0]])
u_pos_batch = np.array(u_pos_batch)
u_neg_batch = np.array(u_neg_batch)
i_pos_batch = np.array(i_pos_batch)
i_neg_batch = np.array(i_neg_batch)
t_rmse, t_mae = train_step_2(u_pos_batch, u_neg_batch, i_pos_batch, i_neg_batch, uid, iid, y_batch)
# training for other variables
for iter in range(5):
for batch_num in range(ll):
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size, data_size_train)
data_train = shuffled_data[start_index: end_index]
uid, iid, y_batch = zip(*data_train)
u_pos_batch = []
u_neg_batch = []
i_pos_batch = []
i_neg_batch = []
for i in range(len(uid)):
u_pos_batch.append(u_pos_text[uid[i][0]])
u_neg_batch.append(u_neg_text[uid[i][0]])
i_pos_batch.append(i_pos_text[iid[i][0]])
i_neg_batch.append(i_neg_text[iid[i][0]])
u_pos_batch = np.array(u_pos_batch)
u_neg_batch = np.array(u_neg_batch)
i_pos_batch = np.array(i_pos_batch)
i_neg_batch = np.array(i_neg_batch)
t_rmse, t_mae = train_step_3(u_pos_batch, u_neg_batch, i_pos_batch, i_neg_batch, uid, iid, y_batch)
# train loss
for batch_num in range(ll):
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size, data_size_train)
data_train = shuffled_data[start_index: end_index]
uid, iid, y_batch = zip(*data_train)
u_pos_batch = []
u_neg_batch = []
i_pos_batch = []
i_neg_batch = []
for i in range(len(uid)):
u_pos_batch.append(u_pos_text[uid[i][0]])
u_neg_batch.append(u_neg_text[uid[i][0]])
i_pos_batch.append(i_pos_text[iid[i][0]])
i_neg_batch.append(i_neg_text[iid[i][0]])
u_pos_batch = np.array(u_pos_batch)
u_neg_batch = np.array(u_neg_batch)
i_pos_batch = np.array(i_pos_batch)
i_neg_batch = np.array(i_neg_batch)
loss, t_rmse, t_mae = dev_step(u_pos_batch, u_neg_batch, i_pos_batch, i_neg_batch,
uid, iid, y_batch)
train_rmse = train_rmse + len(uid) * np.square(t_rmse)
train_mae = train_mae + len(uid) * t_mae
print('Epoch' + str(epoch))
print("train_rmse, mae:", train_rmse / data_size_train, train_mae / data_size_train)
train_rmse = 0
train_mae = 0
loss_s = 0
accuracy_s = 0
mae_s = 0
ll_test = int(len(valid_data) / batch_size) + 1
for batch_num2 in range(ll_test):
start_index = batch_num2 * batch_size
end_index = min((batch_num2 + 1) * batch_size, data_size_valid)
data_valid = valid_data[start_index: end_index]
userid_valid, itemid_valid, y_valid = zip(*data_valid)
u_pos_valid = []
u_neg_valid = []
i_pos_valid = []
i_neg_valid = []
for i in range(len(userid_valid)):
u_pos_valid.append(u_pos_text[userid_valid[i][0]])
u_neg_valid.append(u_neg_text[userid_valid[i][0]])
i_pos_valid.append(i_pos_text[itemid_valid[i][0]])
i_neg_valid.append(i_neg_text[itemid_valid[i][0]])
u_pos_valid = np.array(u_pos_valid)
u_neg_valid = np.array(u_neg_valid)
i_pos_valid = np.array(i_pos_valid)
i_neg_valid = np.array(i_neg_valid)
loss, accuracy, mae = dev_step(u_pos_valid, u_neg_valid, i_pos_valid, i_neg_valid,
userid_valid, itemid_valid, y_valid)
loss_s = loss_s + loss
accuracy_s = accuracy_s + len(userid_valid) * np.square(accuracy)
mae_s = mae_s + len(userid_valid) * mae
print("loss_valid {:g}, mse_valid {:g}, rmse_valid {:g}, mae_valid {:g}".format(loss_s / test_length, accuracy_s / test_length,
np.sqrt(accuracy_s / test_length),
mae_s / test_length))
mse = accuracy_s / test_length
rmse = np.sqrt(accuracy_s / test_length)
mae = mae_s / test_length
if best_mse > mse:
best_mse = mse
if best_rmse > rmse:
best_rmse = rmse
if best_mae > mae:
best_mae = mae
print("")
print('best mse:', best_mse)
print('best rmse:', best_rmse)
print('best mae:', best_mae)
print('end')