def __init__(self, args, env, env_params):
self.args = args
self.env = env
self.env_params = env_params
# _build up the actor/critic evaluated network
self.actor_net = Actor(env_params, hidden_units=256)
self.critic_net = Critic(env_params, hidden_units=256)
# sync the networks across the cpus for parallel training (when running at workstation)
sync_networks(self.actor_net)
sync_networks(self.critic_net)
# _build up the actor/critic target network
self.actor_target_net = Actor(env_params, hidden_units=256)
self.critic_target_net = Critic(env_params, hidden_units=256)
# if gpu is used
if self.args.cuda:
self.actor_net.cuda()
self.critic_net.cuda()
self.actor_target_net.cuda()
self.critic_target_net.cuda()
# the optimizer of the networks
self.actor_optimizer = torch.optim.Adam(
self.actor_net.parameters(), lr=self.args.learning_rate_actor)
self.critic_optimizer = torch.optim.Adam(
self.critic_net.parameters(), lr=self.args.learning_rate_critic)
# HER sample function
self.her_sample = HER(self.args.replay_strategy,
self.args.replay_ratio, self.env.compute_reward)
# experience buffer
self.exp_buffer = ReplayBuffer(self.env_params, self.args.buffer_size,
self.her_sample.her_sample_transitions)
# the normalization of the observation and goal
self.obs_norm = Normalizer(size=env_params['obs'],
clip_range=self.args.clip_range)
self.goal_norm = Normalizer(size=env_params['d_goal'],
clip_range=self.args.clip_range)
# create the dictionary to save the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# get the model path
self.model_path = os.path.join(self.args.save_dir,
self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
def naive_summarizer(text, P=5):
ranking = defaultdict(int)
sentances = Normalizer(text).sent_tokenize()
words = [" ".join(Normalizer(sent).clean_up()) for sent in sentances]
freq = Counter(words)
for i, sent in enumerate(sentances):
for w in re.findall(_WORD_PAT, sent):
ranking[i] += freq[w]
description_indecies = nlargest(P, ranking, key=ranking.get)
return [sentances[i] for i in sorted(description_indecies)]
def summarize(text, P=5):
sents = Normalizer(text).sent_tokenize()
words = [Normalizer(sent).clean_up() for sent in sents]
sim_mat = get_bm25_weights(words, n_jobs=-1)
sim_mat_np = np.array(sim_mat)
graph = nx.from_numpy_array(sim_mat_np)
scores = nx.pagerank(graph)
weighted = sorted(list(set(((scores[i], s) for i, s in enumerate(sents)))),
reverse=True)[:P]
return _to_text([tup[1] for tup in weighted]), len(words)
def main():
nameOfNSP = ['1', '2', '3']
nameOfCLASS = ['A', 'B', 'C', 'D', 'SH', 'AD', 'DE', 'LD', 'FS', 'SUSP']
ourData = rd.ReadData()
ourData = rd.CleanData(ourData)
normalizer = Normalizer()
#Te linijki to zgrupowanie według klas i zliczenie ile elementow ma kazda klasa, dwa rodzaje
NSPCardiotography = ourData['NSP'].sort_values().value_counts(sort=False)
ClassCardiotography = ourData['CLASS'].sort_values().value_counts(
sort=False)
#Histogramy dwa
#hoc.CreateHistNSP(ourData['NSP'])
#hoc.CreateHistClass(ourData['CLASS'])
#Wyciagniecie dwoch kolumn po ktorych klasyfikujemy
classification_columns = ourData[['NSP', 'CLASS']]
#ourData = ourData.drop(labels=['NSP', 'CLASS'], axis=1)
#Tu odbywa sie normalizacja/standaryzacja itd + wykresy/ narazie zakomentowane
# Plot a graph comparing scaled data
#normalizer.compareStandarizationMethods(ourData)
# normalize data
ourDataNormalized = normalizer.normalize(ourData)
# transform data with PCA and plot results
dataTransformedPCA = transformPCA(normalizer.standarizeStandard(ourData))
Plot.scatterPlotNSP(dataTransformedPCA, ourData['NSP'])
Plot.scatterPlotCLASS(dataTransformedPCA, ourData['CLASS'])
# transform data with LDA and plot results
dataTransformedLDA = transformLDA(ourData, 'NSP')
Plot.scatterPlotNSP(dataTransformedLDA, ourData['NSP'])
dataTransformedLDA = transformLDA(ourData, 'CLASS')
Plot.scatterPlotCLASS(dataTransformedLDA, ourData['CLASS'])
# plot heatmap - correlation between data and
Plot.heatmap(ourDataNormalized)
ourDataNormalized = ourDataNormalized.drop(labels=['CLASS', 'NSP'], axis=1)
#crossValidationDataNSP = CrossValidation.crossValidationNSP(ourDataNormalized)
#crossValidationDataCLASS = CrossValidation.crossValidationCLASS(ourDataNormalized)
#hoc.CreateHistAfterValidationNSP(crossValidationDataNSP, ourData['NSP'])
#hoc.CreateHistAfterValidationCLASS(crossValidationDataCLASS, ourData['CLASS'])
#Data without LDA and normalization
allClassification(ourData, classification_columns, nameOfNSP, nameOfCLASS)
def __init__(self, texts):
self.texts = Normalizer.clean_up(texts)
def __init__(self, text, window=2):
self.text = text
self.window = window
self.words = Normalizer(text).clean_up()
class DDPGAgent:
def __init__(self, args, env, env_params):
self.args = args
self.env = env
self.env_params = env_params
# _build up the actor/critic evaluated network
self.actor_net = Actor(env_params, hidden_units=256)
self.critic_net = Critic(env_params, hidden_units=256)
# sync the networks across the cpus for parallel training (when running at workstation)
sync_networks(self.actor_net)
sync_networks(self.critic_net)
# _build up the actor/critic target network
self.actor_target_net = Actor(env_params, hidden_units=256)
self.critic_target_net = Critic(env_params, hidden_units=256)
# if gpu is used
if self.args.cuda:
self.actor_net.cuda()
self.critic_net.cuda()
self.actor_target_net.cuda()
self.critic_target_net.cuda()
# the optimizer of the networks
self.actor_optimizer = torch.optim.Adam(
self.actor_net.parameters(), lr=self.args.learning_rate_actor)
self.critic_optimizer = torch.optim.Adam(
self.critic_net.parameters(), lr=self.args.learning_rate_critic)
# HER sample function
self.her_sample = HER(self.args.replay_strategy,
self.args.replay_ratio, self.env.compute_reward)
# experience buffer
self.exp_buffer = ReplayBuffer(self.env_params, self.args.buffer_size,
self.her_sample.her_sample_transitions)
# the normalization of the observation and goal
self.obs_norm = Normalizer(size=env_params['obs'],
clip_range=self.args.clip_range)
self.goal_norm = Normalizer(size=env_params['d_goal'],
clip_range=self.args.clip_range)
# create the dictionary to save the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# get the model path
self.model_path = os.path.join(self.args.save_dir,
self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
###############################
# Name: learning
# Function: Training the model
# Comment:
###############################
def learning(self):
success_rate_history = []
for epoch in range(self.args.n_epochs):
for _ in range(self.args.n_cycles):
exp_obs_buff, exp_a_goal_buff, exp_d_goal_buff, exp_actions_buff = [], [], [], []
for _ in range(self.args.num_exp_per_mpi):
# reset the environment and experience
exp_obs, exp_a_goal, exp_d_goal, exp_actions = [], [], [], []
observations = self.env.reset()
obs = observations['observation']
a_goal = observations['achieved_goal']
d_goal = observations['desired_goal']
# interact with the environment
for t in range(self.env_params['max_timesteps']):
with torch.no_grad():
input_tensor = self._pre_process_inputs(
obs, d_goal)
policy_predictions = self.actor_net(input_tensor)
action = self._choose_action(policy_predictions)
# get the observations from the action
observations_next, _, _, info = self.env.step(action)
obs_next = observations_next['observation']
a_goal_next = observations_next['achieved_goal']
exp_obs.append(obs.copy())
exp_a_goal.append(a_goal.copy())
exp_d_goal.append(d_goal.copy())
exp_actions.append(action.copy())
# update the state
obs = obs_next
a_goal = a_goal_next
exp_obs.append(obs.copy())
exp_a_goal.append(a_goal.copy())
exp_obs_buff.append(exp_obs)
exp_a_goal_buff.append(exp_a_goal)
exp_d_goal_buff.append(exp_d_goal)
exp_actions_buff.append(exp_actions)
exp_obs_buff = np.array(exp_obs_buff)
exp_a_goal_buff = np.array(exp_a_goal_buff)
exp_d_goal_buff = np.array(exp_d_goal_buff)
exp_actions_buff = np.array(exp_actions_buff)
# store the transitions
self.exp_buffer.store_transition([
exp_obs_buff, exp_a_goal_buff, exp_d_goal_buff,
exp_actions_buff
])
self._update_normalizer([
exp_obs_buff, exp_a_goal_buff, exp_d_goal_buff,
exp_actions_buff
])
for _ in range(self.args.n_batches):
self._update_network() # training the network
# soft update the network parameter
self._soft_update_target_network(self.actor_target_net,
self.actor_net)
self._soft_update_target_network(self.critic_target_net,
self.critic_net)
# start evaluation
success_rate = self._evaluate_agent()
if MPI.COMM_WORLD.Get_rank() == 0:
print('[{}] epoch is: {}, eval success rate is: {:.3f}'.format(
datetime.now(), epoch, success_rate))
torch.save([
self.obs_norm.mean, self.obs_norm.std, self.goal_norm.mean,
self.goal_norm.std,
self.actor_net.state_dict()
], self.model_path + '/model.pt')
success_rate_history.append(success_rate)
success_rate_history = np.array(success_rate_history)
np.savetxt('Plot_Data/Pen_HER.txt',
success_rate_history,
fmt='%f',
delimiter=',')
###############################
# Name: _pre_process_inputs
# Function: process the inputs for the actor network
# Comment:
###############################
def _pre_process_inputs(self, obs, goal):
obs_norm = self.obs_norm.normalize(obs)
goal_norm = self.goal_norm.normalize(goal)
# concatenate the stuffs
inputs = np.concatenate([obs_norm, goal_norm])
inputs = torch.tensor(inputs, dtype=torch.float32).unsqueeze(0)
if self.args.cuda:
inputs = inputs.cuda()
return inputs
def _choose_action(self, policy_predictions):
action = policy_predictions.cpu().numpy().squeeze()
# create the noise
action += self.args.noise_epsilon * self.env_params[
'action_max'] * np.random.randn(*action.shape)
action = np.clip(action, -self.env_params['action_max'],
self.env_params['action_max'])
random_action = np.random.uniform(low=-self.env_params['action_max'],
high=self.env_params['action_max'],
size=self.env_params['action'])
# decide random or not
action += np.random.binomial(1, self.args.random_epsilon,
1)[0] * (random_action - action)
return action
def _update_normalizer(self, experience_buff):
exp_obs, exp_a_goal, exp_d_goal, exp_actions = experience_buff
exp_obs_next = exp_obs[:, 1:, :]
exp_a_goal_next = exp_a_goal[:, 1:, :]
num_exps = exp_actions.shape[1]
buffer_temp = {
'obs': exp_obs,
'a_goal': exp_a_goal,
'd_goal': exp_d_goal,
'actions': exp_actions,
'obs_next': exp_obs_next,
'a_goal_next': exp_a_goal_next,
}
transitions = self.her_sample.her_sample_transitions(
buffer_temp, num_exps)
obs, d_goal = transitions['obs'], transitions['d_goal']
transitions['obs'], transitions['d_goal'] = self._pre_process_obs_goal(
obs, d_goal)
# update
self.obs_norm.update(transitions['obs'])
self.goal_norm.update(transitions['d_goal'])
# recompute the stats
self.obs_norm.recompute_stats()
self.goal_norm.recompute_stats()
###############################
# Name: _pre_process_obs_goal
# Function: process the observation and desired goal for the normalization
# Comment:
###############################
def _pre_process_obs_goal(self, obs, goal):
obs_proceed = np.clip(obs, -self.args.clip_obs, self.args.clip_obs)
goal_proceed = np.clip(goal, -self.args.clip_obs, self.args.clip_obs)
return obs_proceed, goal_proceed
###############################
# Name: _soft_update_target_network
# Function: soft update the parameters of the target network
# Comment:
###############################
def _soft_update_target_network(self, target_net, eval_net):
for target_param, param in zip(target_net.parameters(),
eval_net.parameters()):
target_param.data.copy_((1 - self.args.avg_coeff) * param.data +
self.args.avg_coeff * target_param.data)
###############################
# Name: _update_network
# Function: train the parameters of the actor network and critic network
# Comment:
###############################
def _update_network(self):
# sample the transitions
transitions = self.exp_buffer.sample(self.args.batch_size)
obs, obs_next, d_goal = transitions['obs'], transitions[
'obs_next'], transitions['d_goal']
transitions['obs'], transitions['d_goal'] = self._pre_process_obs_goal(
obs, d_goal)
transitions['obs_next'], transitions[
'd_goal_next'] = self._pre_process_obs_goal(obs_next, d_goal)
observation_norm = self.obs_norm.normalize(transitions['obs'])
d_goal_norm = self.goal_norm.normalize(transitions['d_goal'])
inputs_norm = np.concatenate([observation_norm, d_goal_norm], axis=1)
observation_next_norm = self.obs_norm.normalize(
transitions['obs_next'])
d_goal_next_norm = self.goal_norm.normalize(transitions['d_goal_next'])
inputs_next_norm = np.concatenate(
[observation_next_norm, d_goal_next_norm], axis=1)
inputs_norm_tensor = torch.tensor(inputs_norm, dtype=torch.float32)
inputs_next_norm_tensor = torch.tensor(inputs_next_norm,
dtype=torch.float32)
actions_tensor = torch.tensor(transitions['actions'],
dtype=torch.float32)
reward_tensor = torch.tensor(transitions['reward'],
dtype=torch.float32)
if self.args.cuda:
inputs_norm_tensor = inputs_norm_tensor.cuda()
inputs_next_norm_tensor = inputs_next_norm_tensor.cuda()
actions_tensor = actions_tensor.cuda()
reward_tensor = reward_tensor.cuda()
# calculate the target Q value function
with torch.no_grad():
actions_next = self.actor_target_net(inputs_next_norm_tensor)
q_next_value = self.critic_target_net(inputs_next_norm_tensor,
actions_next)
q_next_value = q_next_value.detach()
target_q_value = reward_tensor + self.args.gamma * q_next_value
target_q_value = target_q_value.detach()
clip_return = 1 / (1 - self.args.gamma) # ??????????????
target_q_value = torch.clamp(target_q_value, -clip_return, 0)
# calculate the loss
real_q_value = self.critic_net(inputs_norm_tensor, actions_tensor)
critic_loss = (target_q_value - real_q_value).pow(2).mean()
# the actor loss
actions_real = self.actor_net(inputs_norm_tensor)
actor_loss = -self.critic_net(inputs_norm_tensor, actions_real).mean()
actor_loss += self.args.action_l2 * (
actions_real / self.env_params['action_max']).pow(2).mean()
# start to train the network
self.actor_optimizer.zero_grad()
actor_loss.backward()
sync_grads(self.actor_net)
self.actor_optimizer.step()
self.critic_optimizer.zero_grad()
critic_loss.backward()
sync_grads(self.critic_net)
self.critic_optimizer.step()
###############################
# Name: _evaluate_agent
# Function: evaluate the agent
# Comment:
###############################
def _evaluate_agent(self):
all_success_rate = []
for _ in range(self.args.n_eval):
per_success_rate = []
observations = self.env.reset()
obs = observations['observation']
d_goal = observations['desired_goal']
for _ in range(self.env_params['max_timesteps']):
with torch.no_grad():
input_tensor = self._pre_process_inputs(obs, d_goal)
policy_predictions = self.actor_net(input_tensor)
action = policy_predictions.detach().cpu().numpy().squeeze(
)
observations_next, _, _, info = self.env.step(action)
obs = observations_next['observation']
d_goal = observations_next['desired_goal']
per_success_rate.append(info['is_success'])
all_success_rate.append(per_success_rate)
all_success_rate = np.array(all_success_rate)
local_success_rate = np.mean(all_success_rate[:, -1])
global_success_rate = MPI.COMM_WORLD.allreduce(local_success_rate,
op=MPI.SUM)
return global_success_rate / MPI.COMM_WORLD.Get_size()