def main():
data_name = args.data
model_select = args.model_select
rank_out = args.rank
data_batch_size = 1024
dropout = args.dropout
recall_at = [20, 50, 100]
eval_batch_size = 5000 # the batch size when test
eval_every = args.eval_every
num_epoch = 100
neg = args.neg
_lr = args.lr
_decay_lr_every = 2
_lr_decay = 0.9
dat = load_data(data_name)
u_pref = dat['u_pref']
v_pref = dat['v_pref']
test_eval = dat['test_eval']
vali_eval = dat['vali_eval']
user_content = dat['user_content']
user_list = dat['user_list']
item_list = dat['item_list']
item_warm = np.unique(item_list)
timer = utils.timer(name='main').tic()
# prep eval
eval_batch_size = eval_batch_size
timer.tic()
test_eval.init_tf(u_pref,
v_pref,
user_content,
None,
eval_batch_size,
cold_user=True) # init data for evaluation
vali_eval.init_tf(u_pref,
v_pref,
user_content,
None,
eval_batch_size,
cold_user=True) # init data for evaluation
timer.toc('initialized eval data').tic()
heater = model.Heater(latent_rank_in=u_pref.shape[1],
user_content_rank=user_content.shape[1],
item_content_rank=0,
model_select=model_select,
rank_out=rank_out,
reg=args.reg,
alpha=args.alpha,
dim=args.dim)
heater.build_model()
heater.build_predictor(recall_at)
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
timer.toc('initialized tf')
best_epoch = 0
best_recall = 0 # val
best_test_recall = 0 # test
for epoch in range(num_epoch):
user_array, item_array, target_array = utils.negative_sampling(
user_list, item_list, neg, item_warm)
random_idx = np.random.permutation(user_array.shape[0])
n_targets = len(random_idx)
data_batch = [(n, min(n + data_batch_size, n_targets))
for n in range(0, n_targets, data_batch_size)]
loss_epoch = 0.
reg_loss_epoch = 0.
diff_loss_epoch = 0.
rec_loss_epoch = 0.
for (start, stop) in data_batch:
batch_idx = random_idx[start:stop]
batch_users = user_array[batch_idx]
batch_items = item_array[batch_idx]
batch_targets = target_array[batch_idx]
# content
user_content_batch = user_content[batch_users, :].todense()
# dropout
if dropout != 0:
n_to_drop = int(np.floor(
dropout *
len(batch_idx))) # number of u-i pairs to be dropped
zero_index = np.random.choice(np.arange(len(batch_idx)),
n_to_drop,
replace=False)
else:
zero_index = np.array([])
dropout_indicator = np.zeros_like(batch_targets).reshape(
(-1, 1))
if len(zero_index) > 0:
dropout_indicator[zero_index] = 1
_, _, loss_out, rec_loss_out, reg_loss_out, diff_loss_out = sess.run(
[
heater.preds, heater.optimizer, heater.loss,
heater.rec_loss, heater.reg_loss, heater.diff_loss
],
feed_dict={
heater.Uin: u_pref[batch_users, :],
heater.Vin: v_pref[batch_items, :],
heater.Ucontent: user_content_batch,
heater.dropout_user_indicator: dropout_indicator,
heater.target: batch_targets,
heater.lr_placeholder: _lr,
heater.is_training: True
})
loss_epoch += loss_out
rec_loss_out += rec_loss_out
reg_loss_epoch += reg_loss_out
diff_loss_epoch += diff_loss_out
if np.isnan(loss_epoch):
raise Exception('f is nan')
if (epoch + 1) % _decay_lr_every == 0:
_lr = _lr_decay * _lr
print('decayed lr:' + str(_lr))
if epoch % eval_every == 0:
recall, precision, ndcg = utils.batch_eval_recall(
sess,
heater.eval_preds_cold,
eval_feed_dict=heater.get_eval_dict,
recall_k=recall_at,
eval_data=vali_eval)
# checkpoint
if np.sum(recall) > np.sum(best_recall):
best_recall = recall
test_recall, test_precision, test_ndcg = utils.batch_eval_recall(
sess,
heater.eval_preds_cold,
eval_feed_dict=heater.get_eval_dict,
recall_k=recall_at,
eval_data=test_eval)
best_test_recall = test_recall
best_epoch = epoch
# print results at every epoch
timer.toc(
'%d loss=%.4f reg_loss=%.4f diff_loss=%.4f rec_loss=%.4f' %
(epoch, loss_epoch / len(data_batch), reg_loss_epoch /
len(data_batch), diff_loss_epoch / len(data_batch),
rec_loss_epoch / len(data_batch))).tic()
print('\t\t\t' + '\t '.join([
('@' + str(i)).ljust(6) for i in recall_at
])) # ljust: padding to fixed len
print('Current recall\t\t%s' %
(' '.join(['%.6f' % i for i in recall])))
print('Current precision\t%s' %
(' '.join(['%.6f' % i for i in precision])))
print('Current ndcg\t\t%s' % (' '.join(['%.6f' % i
for i in ndcg])))
print('Current test recall\t%s' %
(' '.join(['%.6f' % i for i in test_recall])))
print('Current test precision\t%s' %
(' '.join(['%.6f' % i for i in test_precision])))
print('Current test ndcg\t%s' %
(' '.join(['%.6f' % i for i in test_ndcg])))
print('best[%d] vali recall:\t%s' %
(best_epoch, ' '.join(['%.6f' % i for i in best_recall])))
print('best[%d] test recall:\t%s' %
(best_epoch, ' '.join(['%.6f' % i
for i in best_test_recall])))
def main():
data_name = args.data
model_select = args.model_select
rank_out = args.rank
data_batch_size = 1024
dropout = args.dropout
eval_batch_size = 5000 # the batch size when test
num_epoch = 100
neg = args.neg
_lr = args.lr
_decay_lr_every = 2
_lr_decay = 0.9
dat = load_data(data_name)
u_pref = dat['u_pref'] # all user pre embedding
v_pref = dat['v_pref'] # all item pre embedding
user_content = dat['user_content'] # all item context matrix
test_eval = dat['test_eval'] # EvalData
val_eval = dat['val_eval'] # EvalData
warm_test_eval = dat['warm_test'] # EvalData
user_list = dat['user_list'] # users of train interactions
item_list = dat['item_list'] # items of train interactions
item_warm = np.unique(item_list) # train item set
timer = utils.timer(name='main')
# prep eval
timer.tic()
test_eval.init_tf(u_pref, v_pref, user_content, None, eval_batch_size, cold_user=True) # init data for evaluation
val_eval.init_tf(u_pref, v_pref, user_content, None, eval_batch_size, cold_user=True)
warm_test_eval.init_tf(u_pref, v_pref, user_content, None, eval_batch_size, cold_user=True)
timer.toc('initialized eval data').tic()
heater = model.Heater(latent_rank_in=u_pref.shape[1],
user_content_rank=user_content.shape[1],
item_content_rank=0,
model_select=model_select,
rank_out=rank_out, reg=args.reg, alpha=args.alpha, dim=args.dim)
heater.build_model()
heater.build_predictor()
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
timer.toc('initialized tf')
# original result
org_warm_test = utils.batch_eval(sess, heater.eval_preds_warm,
eval_feed_dict=heater.get_eval_dict,
eval_data=warm_test_eval,
metric=dat['metric']['warm_test'],
warm=True)
best_epoch = 0
patience = 0
val_auc, best_val_auc = 0., 0.
best_warm_test = np.zeros(3)
best_cold_test = np.zeros(3)
for epoch in range(num_epoch):
user_array, item_array, target_array = utils.negative_sampling(user_list, item_list, neg, item_warm)
random_idx = np.random.permutation(user_array.shape[0])
n_targets = len(random_idx)
data_batch = [(n, min(n + data_batch_size, n_targets)) for n in
range(0, n_targets, data_batch_size)]
loss_epoch = 0.
reg_loss_epoch = 0.
diff_loss_epoch = 0.
rec_loss_epoch = 0.
for (start, stop) in data_batch:
batch_idx = random_idx[start:stop]
batch_users = user_array[batch_idx]
batch_items = item_array[batch_idx]
batch_targets = target_array[batch_idx]
# dropout
if dropout != 0:
n_to_drop = int(np.floor(dropout * len(batch_idx))) # number of u-i pairs to be dropped
zero_index = np.random.choice(np.arange(len(batch_idx)), n_to_drop, replace=False)
else:
zero_index = np.array([])
user_content_batch = user_content[batch_users, :].todense()
dropout_indicator = np.zeros_like(batch_targets).reshape((-1, 1))
if len(zero_index) > 0:
dropout_indicator[zero_index] = 1
_, _, loss_out, rec_loss_out, reg_loss_out, diff_loss_out = sess.run(
[heater.preds, heater.optimizer, heater.loss,
heater.rec_loss, heater.reg_loss, heater.diff_loss],
feed_dict={
heater.Uin: u_pref[batch_users, :],
heater.Vin: v_pref[batch_items, :],
heater.Ucontent: user_content_batch,
heater.dropout_user_indicator: dropout_indicator,
heater.target: batch_targets,
heater.lr_placeholder: _lr,
heater.is_training: True
}
)
loss_epoch += loss_out
rec_loss_epoch += rec_loss_out
reg_loss_epoch += reg_loss_out
diff_loss_epoch += diff_loss_out
if np.isnan(loss_epoch):
raise Exception('f is nan')
if (epoch + 1) % _decay_lr_every == 0:
_lr = _lr_decay * _lr
print('decayed lr:' + str(_lr))
val_auc = utils.batch_eval_auc(sess, heater.eval_preds_cold,
eval_feed_dict=heater.get_eval_dict,
eval_data=val_eval)
# checkpoint
if val_auc > best_val_auc:
patience = 0
best_val_auc = val_auc
best_warm_test = utils.batch_eval(sess, heater.eval_preds_cold,
eval_feed_dict=heater.get_eval_dict,
eval_data=warm_test_eval,
metric=dat['metric']['warm_test'],
warm=True)
best_cold_test = utils.batch_eval(sess, heater.eval_preds_cold,
eval_feed_dict=heater.get_eval_dict,
eval_data=test_eval,
metric=dat['metric']['cold_test'])
best_epoch = epoch
# print results at every epoch
timer.toc('%d loss=%.4f reg_loss=%.4f diff_loss=%.4f rec_loss=%.4f' % (
epoch, loss_epoch / len(data_batch), reg_loss_epoch / len(data_batch),
diff_loss_epoch / len(data_batch), rec_loss_epoch / len(data_batch)
)).tic()
print('Current val auc:%.4f\tbest:%.4f' % (val_auc, best_val_auc))
print('\t\t\t\t\t' + '\t '.join([str(i).ljust(6) for i in ['auc', 'hr', 'ndcg']])) # padding to fixed len
print('origin warm test:\t%s' % (' '.join(['%.6f' % i for i in org_warm_test])))
print('best[%d] warm test:\t%s' % (best_epoch, ' '.join(['%.6f' % i for i in best_warm_test])))
print('best[%d] cold test:\t%s' % (best_epoch, ' '.join(['%.6f' % i for i in best_cold_test])))
# early stop
patience += 1
if patience > 10:
print(f"Early stop at epoch {epoch}")
break
dat = load_data(data_name)
u_pref = dat['u_pref'] # all user pre embedding
v_pref = dat['v_pref'] # all item pre embedding
item_content = dat['item_content'].todense() # all item context matrix
test_eval = dat['cold_eval'] # EvalData
val_eval = dat['val_eval'] # EvalData
warm_test_eval = dat['warm_eval'] # EvalData
user_list = dat['user_list'] # users of train interactions
item_list = dat['item_list'] # items of train interactions
item_warm = np.unique(item_list) # train item set
timer = utils.timer(name='main').tic()
# build model
heater = model.Heater(latent_rank_in=u_pref.shape[1],
user_content_rank=0,
item_content_rank=item_content.shape[1],
model_select=model_select, rank_out=rank_out,
reg=args.reg, alpha=args.alpha, dim=args.dim)
heater.build_model()
heater.build_predictor()
saver = tf.train.Saver()
save_path = './model_save/'
if not os.path.exists(save_path):
os.makedirs(save_path)
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
timer.toc('initialized tf').tic()
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
data_name = args.data
data_batch_size = 1024 # train batch size
dropout = args.dropout
num_epoch = 1000
neg = args.neg # negative sampling rate
_lr = args.lr
_decay_lr_every = 10
_lr_decay = 0.8
dat = load_data(data_name)
u_pref = dat['u_pref'] # all user pre embedding
v_pref = dat['v_pref'] # all item pre embedding
item_content = dat['item_content'] # all item context matrix
item_fake_pref = dat['item_fake_pref']
test_eval = dat['cold_eval'] # EvalData
val_eval = dat['val_eval'] # EvalData
warm_test_eval = dat['warm_eval'] # EvalData
user_list = dat['user_list'] # users of train interactions
item_list = dat['item_list'] # items of train interactions
item_warm = np.unique(item_list) # train item set
timer = utils.timer(name='main').tic()
# build model
heater = model.Heater(latent_rank_in=u_pref.shape[-1],
user_content_rank=0,
item_content_rank=item_content.shape[1],
args=args)
heater.build_model()
heater.build_predictor()
saver = tf.train.Saver()
save_path = './model_save/'
if not os.path.exists(save_path):
os.makedirs(save_path)
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
timer.toc('initialized tf').tic()
best_epoch = 0
patience = 0
val_auc, best_val_auc = 0., 0.
for epoch in range(num_epoch):
user_array, item_array, target_array = utils.negative_sampling(
user_list, item_list, neg, item_warm)
random_idx = np.random.permutation(
user_array.shape[0]) # 生成一个打乱的 range 序列作为下标
data_batch = [(n, min(n + data_batch_size, len(random_idx)))
for n in range(0, len(random_idx), data_batch_size)]
loss_epoch = 0.
reg_loss_epoch = 0.
diff_loss_epoch = 0.
rec_loss_epoch = 0.
for (start, stop) in data_batch:
batch_idx = random_idx[start:stop]
batch_users = user_array[batch_idx]
batch_items = item_array[batch_idx]
batch_targets = target_array[batch_idx]
# content
item_content_batch = item_content[batch_items, :].todense()
# dropout: used in randomized training
# indicator's target is the CF pretrain rep
# set the dropped rows' position in indicator to be 1
if dropout != 0:
n_to_drop = int(np.floor(
dropout *
len(batch_idx))) # number of u-i pairs to be dropped
zero_index = np.random.choice(np.arange(len(batch_idx)),
n_to_drop,
replace=False)
else:
zero_index = np.array([])
dropout_indicator = np.zeros_like(batch_targets).reshape(
(-1, 1))
dropout_indicator[zero_index] = 1
_, _, loss_out, rec_loss_out, reg_loss_out, diff_loss_out = sess.run(
[
heater.preds, heater.optimizer, heater.loss,
heater.rec_loss, heater.reg_loss, heater.diff_loss
],
feed_dict={
heater.Uin: u_pref[batch_users, :],
heater.Vin: v_pref[batch_items, :],
heater.Vcontent: item_content_batch,
heater.fake_v_nei_1: item_fake_pref[batch_items, 1, :],
heater.fake_v_nei_2: item_fake_pref[batch_items, 2, :],
heater.dropout_item_indicator: dropout_indicator,
heater.target: batch_targets,
heater.lr_placeholder: _lr,
heater.is_training: True
})
loss_epoch += loss_out
rec_loss_epoch += rec_loss_out
reg_loss_epoch += reg_loss_out
diff_loss_epoch += diff_loss_out
if np.isnan(loss_epoch):
raise Exception('f is nan')
timer.toc(
'%d loss=%.4f reg_loss=%.4f diff_loss=%.4f rec_loss=%.4f' %
(epoch, loss_epoch / len(data_batch), reg_loss_epoch /
len(data_batch), diff_loss_epoch / len(data_batch),
rec_loss_epoch / len(data_batch))).tic()
if (epoch + 1) % _decay_lr_every == 0:
_lr = _lr_decay * _lr
print('decayed lr:' + str(_lr))
# eval on val
val_auc = utils.batch_eval(sess,
heater.eval_preds_cold,
eval_feed_dict=heater.get_eval_dict,
eval_data=val_eval,
U_pref=u_pref,
V_pref=v_pref,
excluded_dict=dat['pos_nb'],
V_content=item_content,
v_fake_pref=item_fake_pref,
val=True)
# if get a better eval result on val, update test result
# best_recall and best_test_recall are global variables while others are local ones
if val_auc > best_val_auc:
saver.save(sess, save_path + args.data + args.warm_model)
patience = 0
best_val_auc = val_auc
best_epoch = epoch
# print val results at every epoch
timer.toc('[%d/10] Current val auc:%.4f\tbest:%.4f' %
(patience, val_auc, best_val_auc)).tic()
# early stop
patience += 1
if patience > 10:
print(f"Early stop at epoch {epoch}")
break
saver.restore(sess, save_path + args.data + args.warm_model)
best_warm_test = utils.batch_eval(sess,
heater.eval_preds_cold,
eval_feed_dict=heater.get_eval_dict,
eval_data=warm_test_eval,
U_pref=u_pref,
V_pref=v_pref,
excluded_dict=dat['pos_nb'],
V_content=item_content,
v_fake_pref=item_fake_pref,
metric=dat['metric']['warm_test'],
warm=True)
best_cold_test = utils.batch_eval(sess,
heater.eval_preds_cold,
eval_feed_dict=heater.get_eval_dict,
eval_data=test_eval,
U_pref=u_pref,
V_pref=v_pref,
excluded_dict=dat['pos_nb'],
V_content=item_content,
v_fake_pref=item_fake_pref,
metric=dat['metric']['cold_test'])
timer.toc('Test').tic()
print('\t\t\t\t\t' + '\t '.join(
[str(i).ljust(6)
for i in ['auc', 'hr', 'ndcg']])) # padding to fixed len
print('best[%d] warm test:\t%s' %
(best_epoch, ' '.join(['%.6f' % i for i in best_warm_test])))
print('best[%d] cold test:\t%s' %
(best_epoch, ' '.join(['%.6f' % i for i in best_cold_test])))