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main_hiv.py
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import argparse
import pickle
import torch.optim as optim
from time import time
from sklearn.externals import joblib
from collections import deque
from src.memory import *
from src.utils import *
from src.models import MDPnet
from src.config import hiv_config
from hiv_domain.fittedQiter import FittedQIteration
from src.train_pipeline import mdpmodel_train, mdpmodel_test
parser = argparse.ArgumentParser()
parser.add_argument("--file_num", type=int, default=1)
parser.add_argument("--N", type=int)
parser.add_argument("--train_num_traj", type=int)
parser.add_argument("--dev_num_traj", type=int, default=50)
args = parser.parse_args()
def generate_data(env, config, eval_env=None):
t0 = time()
memory = SampleSet(config)
dev_memory = SampleSet(config)
traj_set = TrajectorySet(config)
scores = deque()
if config.standardize_rewards:
with open("hiv_domain/standardization_data.pkl", "rb") as fobj:
standardization_data = pickle.load(fobj)
reward_mean = standardization_data[config.ins]["reward_mean"]
reward_std = standardization_data[config.ins]["reward_std"]
for i_episode in range(config.sample_num_traj):
if i_episode % 10 == 0:
print("{} trajectories generated".format(i_episode))
episode = env.run_episode(eps=config.behavior_eps, track=True)
done = False
n_steps = 0
factual = 1
traj_set.new_traj()
#acc_isweight = FloatTensor([1])
while not done:
action = int(np.where(episode[n_steps][1] == 1)[0][0])
if eval_env:
p_pie = float(eval_env.policy(episode[n_steps][0], eps=0) == action)
else:
p_pie = float(env.policy(episode[n_steps][0], eps=0) == action)
int_pib = float(env.policy(episode[n_steps][0], eps=0) == action)
if int_pib == 1:
p_pib = (1 - config.behavior_eps *
(config.action_size-1)/config.action_size)
else:
p_pib = config.behavior_eps / config.action_size
p_pie = FloatTensor([p_pie])
p_pib = FloatTensor([p_pib])
isweight = p_pie / p_pib
#acc_isweight = acc_isweight * (p_pie / p_pib)
last_factual = factual * (1 - p_pie)
factual = factual * p_pie
state = preprocess_state(episode[n_steps][0], config.state_dim)
next_state = preprocess_state(episode[n_steps][3], config.state_dim)
reward = episode[n_steps][2]
if config.standardize_rewards:
reward = (reward - reward_mean) / reward_std
reward = preprocess_reward(reward)
action = preprocess_action(action)
done = n_steps == len(episode) - 1
if i_episode < config.train_num_traj:
memory.push(state, action, next_state, reward, done,
isweight, None, n_steps, factual, last_factual, None, None, None, None, None)
else:
dev_memory.push(state, action, next_state, reward,
done, isweight, None, n_steps, factual, last_factual, None, None, None, None, None)
traj_set.push(state, action, next_state, reward, done, isweight, None,
n_steps, factual, last_factual, None, None, None, None, None)
n_steps += 1
memory.flatten() # prepare flatten data
dev_memory.flatten()
memory.update_u() # prepare u_{0:t}
dev_memory.update_u()
t1 = time()
print("Generating {} trajectories took {} minutes".format(
config.sample_num_traj, (t1-t0)/60))
return memory, dev_memory, traj_set, scores
def train_model(memory, dev_memory, config, loss_mode):
mdpnet = MDPnet(config)
best_train_loss = 100
lr = config.lr
train_loss_list = []
dev_loss_list = []
for i_episode in range(config.train_num_episodes):
train_loss = 0
dev_loss = 0
optimizer = optim.Adam(mdpnet.parameters(), lr=lr)
for i_batch in range(config.train_num_batches):
train_loss_batch = mdpmodel_train(memory, mdpnet, optimizer,
loss_mode, config)
train_loss = ((train_loss * i_batch + train_loss_batch)
/ (i_batch + 1))
if config.dev_num_traj > 0:
dev_loss_batch = mdpmodel_test(dev_memory, mdpnet, 0,
config)
dev_loss = (dev_loss * i_batch + dev_loss_batch) / (i_batch + 1)
if (i_episode+1) % config.print_per_epi == 0:
print('Episode {}: train loss {:.3e}, dev loss {:.3e}'.format(
i_episode+1, train_loss, dev_loss))
if train_loss < best_train_loss:
best_train_loss = train_loss
else:
lr *= config.lr_decay
train_loss_list += [train_loss]
dev_loss_list += [dev_loss]
return train_loss_list, dev_loss_list, mdpnet
def rollout_batch(env, config, init_states, mdpnet, num_rollout,
init_done=None, init_actions=None):
ori_batch_size = init_states.size()[0]
batch_size = init_states.size()[0] * num_rollout
init_states = init_states.repeat(num_rollout, 1)
if init_actions is not None:
init_actions = init_actions.repeat(num_rollout, 1)
if init_done is not None:
init_done = init_done.repeat(num_rollout)
states = init_states
if init_done is None:
done = ByteTensor(batch_size)
done.fill_(0)
else:
done = init_done
if init_actions is None:
actions = []
for i_actions in range(batch_size):
actions.append(int(env.policy(states[i_actions],
eps=0)))
else:
actions = init_actions
n_steps = 0
t_reward = torch.zeros(batch_size)
done = False
while not done:
if n_steps > 0:
actions = []
for i_actions in range(batch_size):
actions.append(int(env.policy(states[i_actions], eps=0)))
states = Variable(Tensor(states))
states_diff, reward, _ = mdpnet.forward(states)
states_diff = states_diff.data
actions = LongTensor(actions)
reward = reward.data.gather(1, actions.view(-1, 1)).squeeze()
expanded_actions = actions.view(-1, 1).unsqueeze(2)
expanded_actions = expanded_actions.expand(-1, -1, config.state_dim)
states_diff = states_diff.gather(1, expanded_actions).squeeze()
# state_diff = state_diff.view(-1, config.state_dim)
next_states = states_diff + states.data
states = next_states
t_reward = t_reward + config.gamma**n_steps * reward
# done_this_step = is_done.forward(Variable(states)).data[:,0] > 0
done = n_steps == config.max_length - 1
n_steps += 1
value = t_reward.numpy()
value = np.reshape(value, [num_rollout, ori_batch_size])
return np.mean(value, 0)
def compute_values(env, traj_set, model, config, model_type='MDP'):
num_samples = len(traj_set)
traj_len = np.zeros(num_samples, 'int')
state_tensor = FloatTensor(num_samples, config.max_length, config.state_dim).zero_()
action_tensor = LongTensor(num_samples, config.max_length, 1).zero_()
done_tensor = ByteTensor(num_samples, config.max_length).fill_(1)
V_value = np.zeros((num_samples, config.max_length))
Q_value = np.zeros((num_samples, config.max_length))
for i_traj in range(num_samples):
traj_len[i_traj] = len(traj_set.trajectories[i_traj])
state_tensor[i_traj,0:traj_len[i_traj],:] = torch.cat([ t.state for t in traj_set.trajectories[i_traj] ])
action_tensor[i_traj, 0:traj_len[i_traj], :] = torch.cat([t.action for t in traj_set.trajectories[i_traj]])
done_tensor[i_traj, 0:traj_len[i_traj] ].fill_(0)
if traj_len[i_traj] < config.max_length:
done_tensor[i_traj, traj_len[i_traj] :].fill_(1)
# Cut off unnecessary computation, if at a time step t all IS weights are zero
nonzero_is = np.zeros(config.max_length,'int')
for i_traj in range(num_samples):
w = 1
for t in traj_set.trajectories[i_traj]:
if w > 0:
nonzero_is[t.time] += 1
w *= t.isweight[0]
for i_step in range(config.max_length):
# if nonzero_is[i_step] == 0:
# break
if model_type == 'MDP':
V_value[:, i_step] = rollout_batch(env=env, init_states=state_tensor[:, i_step, :], mdpnet=model,
num_rollout=config.eval_num_rollout, config=config,
init_done=done_tensor[:, i_step])
Q_value[:, i_step] = rollout_batch(env=env, init_states=state_tensor[:, i_step, :], mdpnet=model,
num_rollout=config.eval_num_rollout, config=config,
init_done=done_tensor[:, i_step],
init_actions=action_tensor[:, i_step, :])
elif model_type == 'IS':
pass
return V_value, Q_value
def doubly_robust(traj_set, V_value, Q_value, config, wis=False):
num_samples = len(traj_set)
weights = np.zeros((num_samples,config.max_length))
weights_sum = np.zeros(config.max_length)
for i_traj in range(num_samples):
for n in range(config.max_length):
if n >= len(traj_set.trajectories[i_traj]):
weights[i_traj:,n] = weights[i_traj,n-1]
break
if n == 0:
weights[i_traj, n] = 1.0
else:
weights[i_traj,n] = weights[i_traj,n-1]*traj_set.trajectories[i_traj][n].isweight[0].item()
if wis:
for n in range(config.max_length):
weights_sum[n] = np.sum(weights[:,n])
if weights_sum[n] != 0:
weights[:,n] = (weights[:,n]*num_samples)/weights_sum[n]
value = np.zeros(num_samples)
for i_traj in range(num_samples):
w = 1
for t in traj_set.trajectories[i_traj]:
#print(t.reward[0].item(), t.time, weights[i_traj,t.time], Q_value[i_traj,t.time], V_value[i_traj,t.time])
value[i_traj] += weights[i_traj,t.time]*(t.reward[0].item() - Q_value[i_traj,t.time]) + w*V_value[i_traj,t.time]
w = weights[i_traj,t.time]
if w == 0:
break
return value
def importance_sampling(traj_set, wis=False):
num_samples = len(traj_set)
value = np.zeros(num_samples)
weights = np.zeros(num_samples)
for i_traj in range(num_samples):
l = len(traj_set.trajectories[i_traj])
tmp = 1
for n in range(l):
tmp *= traj_set.trajectories[i_traj][n].isweight[0].item()
weights[i_traj] = tmp
if wis:
weights = (weights*num_samples)/np.sum(weights)
for i_traj in range(num_samples):
l = len(traj_set.trajectories[i_traj])
value[i_traj] = l*weights[i_traj]
return value
if __name__ == "__main__":
estm_list = []
estm_bsl_list = []
wdr_list = []
wdr_bsl_list = []
ips_list = []
pdis_list = []
wpdis_list = []
empirical_scores_list = []
config = hiv_config
if args.train_num_traj:
config.train_num_traj = args.train_num_traj
config.dev_num_traj = args.dev_num_traj
config.sample_num_traj = args.train_num_traj + args.dev_num_traj
else:
config.sample_num_traj = config.train_num_traj + config.dev_num_traj
if config.train_batch_size > config.train_num_traj:
config.train_batch_size = config.train_num_traj
""" Load hiv environment - the environment comes with a policy which can be
made eps greedy. """
with open('hiv_domain/hiv_simulator/hiv_preset_hidden_params', 'rb') as f:
preset_hidden_params = pickle.load(f, encoding='latin1')
env = FittedQIteration(perturb_rate=0.05,
preset_params=preset_hidden_params[config.ins],
gamma=config.gamma,
ins=config.ins,
episode_length=config.max_length)
env.tree = joblib.load('hiv_domain/extra_tree_gamma_ins20.pkl')
eval_env = FittedQIteration(perturb_rate=0.05,
preset_params=preset_hidden_params[config.ins],
gamma=config.gamma,
ins=config.ins,
episode_length=config.max_length)
eval_env.tree= joblib.load('hiv_domain/extra_tree_gamma_ins20.pkl')
if config.fix_data:
memory, dev_memory, traj_set, scores = generate_data(env, config, eval_env)
if args.N:
config.N = args.N
for i in range(config.N):
print("exp {}".format(i+1))
if not config.fix_data:
memory, dev_memory, traj_set, scores = generate_data(env, config, eval_env)
print('Learn our mdp model')
train_loss_list, dev_loss_list, mdpnet = (
train_model(memory, dev_memory, config, 1))
print('Learn the baseline mdp model')
train_loss_list, dev_loss_list, mdpnet_unweight = (
train_model(memory, dev_memory, config, 0))
print('Evaluate models using evaluation policy on the same initial states')
mdpnet.eval()
mdpnet_unweight.eval()
init_states = []
for i_episode in range(config.eval_pib_num_rollout):
env.task.reset(perturb_params=True,
**preset_hidden_params[config.ins])
init_states.append(preprocess_state(env.task.observe(),
config.state_dim))
init_states = torch.cat(init_states)
estm = rollout_batch(eval_env, config, init_states, mdpnet,
config.eval_num_rollout)
estm_bsl = rollout_batch(eval_env, config, init_states, mdpnet_unweight,
config.eval_num_rollout)
# V,Q = compute_values(eval_env, traj_set, mdpnet, config, model_type='MDP')
# wdr = doubly_robust(traj_set, V, Q, config, wis=True)
#
# V, Q = compute_values(eval_env, traj_set, mdpnet_unweight, config, model_type='MDP')
# wdr_bsl = doubly_robust(traj_set, V, Q, config, wis=True)
V, Q = compute_values(eval_env, traj_set, None, config, model_type='IS')
ips = importance_sampling(traj_set)
pdis = doubly_robust(traj_set, V, Q, config, wis=False)
wpdis = doubly_robust(traj_set, V, Q, config, wis=True)
with open("hiv_domain/standardization_data.pkl", "rb") as fobj:
standardization_data = pickle.load(fobj)
reward_mean = standardization_data[config.ins]["reward_mean"]
reward_std = standardization_data[config.ins]["reward_std"]
estm_rescaled = np.array(estm)
estm_rescaled = estm_rescaled * reward_std + reward_mean * (1-config.gamma**config.max_length)/(1-config.gamma)
estm_bsl_rescaled = np.array(estm_bsl)
estm_bsl_rescaled = estm_bsl_rescaled * reward_std + reward_mean * (1-config.gamma**config.max_length)/(1-config.gamma)
# wdr_rescaled = np.array(wdr)
# wdr_rescaled = wdr_rescaled * reward_std + reward_mean * (1 - config.gamma ** config.max_length) / (
# 1 - config.gamma)
# wdr_bsl_rescaled = np.array(wdr_bsl)
# wdr_bsl_rescaled = wdr_bsl_rescaled * reward_std + reward_mean * (1 - config.gamma ** config.max_length) / (
# 1 - config.gamma)
ips_rescaled = np.array(ips)
ips_rescaled = ips_rescaled * reward_std + reward_mean * (1 - config.gamma ** config.max_length) / (
1 - config.gamma)
pdis_rescaled = np.array(pdis)
pdis_rescaled = pdis_rescaled * reward_std + reward_mean * (1 - config.gamma ** config.max_length) / (
1 - config.gamma)
wpdis_rescaled = np.array(wpdis)
wpdis_rescaled = wpdis_rescaled * reward_std + reward_mean * (1 - config.gamma ** config.max_length) / (
1 - config.gamma)
print("RepBM MDP model", np.mean(estm_rescaled))
print("MDP model", np.mean(estm_bsl_rescaled))
# print(np.mean(wdr_rescaled))
# print(np.mean(wdr_bsl_rescaled))
print("IS", np.mean(ips_rescaled))
print("PSIS", np.mean(pdis_rescaled))
print("WPSIS", np.mean(wpdis_rescaled))
estm_list += [np.mean(estm_rescaled)]
estm_bsl_list += [np.mean(estm_bsl_rescaled)]
# wdr_list += [np.mean(wdr_rescaled)]
# wdr_bsl_list += [np.mean(wdr_bsl_rescaled)]
ips_list += [np.mean(ips_rescaled)]
pdis_list += [np.mean(pdis_rescaled)]
wpdis_list += [np.mean(wpdis_rescaled)]
if args.train_num_traj:
fname = "results/temp_data_hiv_N" + str(args.train_num_traj) + "_" + str(args.file_num) + ".npz"
else:
fname = "results/data_hiv.npz"
np.savez(fname,
estm_list=estm_list,
estm_bsl_list=estm_bsl_list,
ips_list=ips_list,
pdis_list=pdis_list,
wpdis_list=wpdis_list
)