def main_calculate_credit_variance(args, credit_type):
"""
Calculate variance of credit by varing following items.
"""
def sampling(click_prob):
"""
click_prob: (n, n_class)
"""
n_class = click_prob.shape[1]
return np.int64([np.random.choice(n_class, 1, p=p) for p in click_prob]).reshape([-1])
assert args.gen_type == 'env'
ct_sim = get_ct_sim(args.sim_exp, args.use_cuda, args.train_mode, args.sim_cell_type, args.output_dim)
assert ct_sim.ckp_step > 0, (ct_sim.ckp_step)
ct_env = get_ct_env(args.env_exp, args.use_cuda, args.train_mode, args.env_output_type, args.output_dim)
assert ct_env.ckp_step > 0, (ct_env.ckp_step)
### dataset
sim_conf = ct_sim.alg.model.conf
dataset = NpzDataset(args.train_npz_list,
sim_conf.npz_config_path,
sim_conf.requested_names,
if_random_shuffle=True,
one_pass=True)
data_gen = dataset.get_data_generator(sim_conf.batch_size)
thread_data_gen = threaded_generator(data_gen, capacity=100)
n_vary = 64
base_batch_data = BatchData(sim_conf, thread_data_gen.next())
batch_size = base_batch_data.batch_size()
batch_credits = []
for pos in range(base_batch_data.seq_lens()[0]):
list_credits = []
for batch_id, tensor_dict in enumerate(thread_data_gen):
if len(list_credits) == n_vary:
break
ref_batch_data = BatchData(sim_conf, tensor_dict)
if ref_batch_data.batch_size() != batch_size:
continue
mix_batch_data = base_batch_data.replace_following_items(pos + 1, ref_batch_data)
sim_fetch_dict = ct_sim.inference(SimFeedConvertor.inference(mix_batch_data))
sim_response = sampling(np.array(sim_fetch_dict['click_prob'])).reshape([-1, 1]).astype('int64')
mix_batch_data.tensor_dict['click_id'] = FakeTensor(sim_response, mix_batch_data.seq_lens())
credit = generate_credit_one_batch(ct_env,
mix_batch_data,
credit_type=credit_type,
credit_gamma=args.credit_gamma,
globbase=None)
credit = credit.reshape([batch_size, -1])
list_credits.append(credit[:, pos].reshape(-1, 1))
list_credits = np.concatenate(list_credits, 1) # (b, n_vary)
batch_credits.append(list_credits)
batch_credits = np.concatenate(batch_credits, 0) # (seq_len*b, n_vary)
print(credit_type)
print(batch_credits.shape)
print('(s,a)-wise credit variance', np.mean(np.std(batch_credits, 1)))
def main_batch_rl(args):
"""
Include online inference and offline training.
"""
ct_sim = get_ct_sim(args.sim_exp, args.use_cuda, args.train_mode, args.sim_cell_type, args.output_dim)
assert ct_sim.ckp_step > 0, (ct_sim.ckp_step)
dict_gen_ct = {}
if args.gen_type in ['env', 'env_credit', 'env_rl']:
if args.gen_type == 'env_rl':
assert args.env_output_type == 'click', \
('env_rl only support click env, which will be used as a simulator', args.env_output_type)
ct_env = get_ct_env(args.env_exp, args.use_cuda, args.train_mode, args.env_output_type, args.output_dim)
dict_gen_ct['env'] = ct_env
if args.gen_type in ['env_credit', 'mc_credit']:
ct_credit = get_ct_credit(args.credit_exp, args.use_cuda, args.train_mode, args.credit_scale)
dict_gen_ct['credit'] = ct_credit
if args.gen_type in ['rl', 'env_rl']:
ct_rl = get_ct_rl(args.rl_exp, args.use_cuda, args.train_mode, args.rl_gamma, args.rl_Q_type)
dict_gen_ct['rl'] = ct_rl
if args.gen_type == 'ddpg':
ct_ddpg = get_ct_ddpg(args.ddpg_exp, args.use_cuda, args.train_mode, args.ddpg_gamma)
dict_gen_ct['ddpg'] = ct_ddpg
### dataset
sim_conf = ct_sim.alg.model.conf
dataset = NpzDataset(args.train_npz_list,
sim_conf.npz_config_path,
sim_conf.requested_names,
if_random_shuffle=True,
one_pass=True)
if args.gen_type == 'env_rl': # env_rl will need data from env_conf
env_conf = ct_env.alg.model.conf
env_dataset = NpzDataset(args.train_npz_list,
env_conf.npz_config_path,
env_conf.requested_names,
if_random_shuffle=True,
one_pass=False)
summary_writer = tf.summary.FileWriter(args.summary_dir)
max_test_steps = 1000
for epoch_id in range(50):
if args.gen_type == 'env_rl':
env_data_gen = env_dataset.get_data_generator(env_conf.batch_size)
thread_env_data_gen = threaded_generator(env_data_gen, capacity=10)
else:
thread_env_data_gen = None
data_gen = dataset.get_data_generator(sim_conf.batch_size)
thread_data_gen = threaded_generator(data_gen, capacity=100)
for batch_id, tensor_dict in enumerate(thread_data_gen):
if_save = True if batch_id == 0 else False
batch_data = BatchData(sim_conf, tensor_dict)
if batch_data.batch_size() == 1: # otherwise, rl will crash
continue
offline_training(args, epoch_id, [batch_data], dict_gen_ct, summary_writer, if_save=if_save, env_rl_data_gen=thread_env_data_gen)
if epoch_id % 1 == 0:
online_inference_for_test(args, epoch_id, max_test_steps, ct_sim, dict_gen_ct, summary_writer)
def online_inference(args, epoch_id, max_steps, data_gen, ct_sim, dict_gen_ct, summary_writer, if_print=True):
"""
Do inference for `max_steps` batches.
"""
sim_conf = ct_sim.alg.model.conf
replay_memory = []
list_sim_responses = []
### online inference
last_batch_data = BatchData(sim_conf, data_gen.next())
for batch_id in range(max_steps):
np.random.seed(epoch_id * max_steps + batch_id)
tensor_dict = data_gen.next()
batch_data = BatchData(sim_conf, tensor_dict)
batch_data.set_decode_len(batch_data.seq_lens())
batch_data.expand_candidates(last_batch_data, batch_data.seq_lens())
np.random.seed(None)
del batch_data.tensor_dict['click_id']
if batch_data.batch_size() == 1: # otherwise, rl will crash
continue
orders, sim_responses = inference_one_batch(args.gen_type, ct_sim, dict_gen_ct, batch_data, eps=args.infer_eps) # , (b, decode_len)
# save to replay memory
sim_batch_data = batch_data.get_reordered(orders, sim_responses)
replay_memory.append(sim_batch_data)
list_sim_responses.append(sim_responses)
last_batch_data = BatchData(sim_conf, tensor_dict)
if batch_id % 100 == 0 and if_print:
logging.info('inference epoch %d batch %d' % (epoch_id, batch_id))
if if_print:
list_sum_response = np.sum(np.concatenate(list_sim_responses, 0), 1) # (b,)
add_scalar_summary(summary_writer, epoch_id, 'inference/sim_responses', np.mean(list_sum_response))
return replay_memory
