def __init__(self, args, user_triplet_set, rela_2_index, act_dim, gamma=0.99, hidden_sizes=[512, 256]):
super(ActorCritic_lstm_mf_base_test, self).__init__()
self.args = args
self.act_dim = act_dim
self.device = args.device
self.sub_batch_size = args.sub_batch_size
self.gamma = gamma
self.p_hop = args.p_hop
self.hidden_sizes = hidden_sizes
self.n_memory = args.n_memory
self.cast_st_save = args.cast_st_save
self.embed_size = args.embed_size
self.user_o = args.user_o
self.h0_embbed = args.h0_embbed
self.user_triplet_set = user_triplet_set
self.rela_2_index = rela_2_index
if self.args.envir == 'p1':
self._get_next_node_type = self._get_next_node_type_meta
self.kg_emb = KnowledgeEmbedding_memory(args)
elif self.args.envir == 'p2':
self._get_next_node_type = self._get_next_node_type_graph
self.kg_emb = KnowledgeEmbedding_memory_graph(args)
dataset = load_dataset_core(args, args.dataset)
self.et_idx2ty = dataset.et_idx2ty
self.entity_list = dataset.entity_list
self.rela_list = dataset.rela_list
self.bulid_model_rl()
class ActorCritic_lstm_mf_base_test(nn.Module):
def __init__(self,
args,
user_triplet_set,
rela_2_index,
act_dim,
gamma=0.99,
hidden_sizes=[512, 256]):
super(ActorCritic_lstm_mf_base_test, self).__init__()
self.args = args
self.act_dim = act_dim
self.device = args.device
self.sub_batch_size = args.sub_batch_size
self.gamma = gamma
self.p_hop = args.p_hop
self.hidden_sizes = hidden_sizes
self.n_memory = args.n_memory
self.cast_st_save = args.cast_st_save
self.embed_size = args.embed_size
self.user_o = args.user_o
self.h0_embbed = args.h0_embbed
self.user_triplet_set = user_triplet_set
self.rela_2_index = rela_2_index
if self.args.envir == 'p1':
self._get_next_node_type = self._get_next_node_type_meta
self.kg_emb = KnowledgeEmbedding_memory(args)
elif self.args.envir == 'p2':
self._get_next_node_type = self._get_next_node_type_graph
self.kg_emb = KnowledgeEmbedding_memory_graph(args)
dataset = load_dataset_core(args, args.dataset)
self.et_idx2ty = dataset.et_idx2ty
self.entity_list = dataset.entity_list
self.rela_list = dataset.rela_list
self.bulid_model_rl()
def _get_next_node_type_meta(self, curr_node_type, next_relation,
next_entity_id):
# print('curr_node_type = ', curr_node_type)
# print('next_relation = ', next_relation)
return KG_RELATION[curr_node_type][next_relation]
def _get_next_node_type_graph(self, curr_node_type, next_relation,
next_entity_id):
return self.et_idx2ty[next_entity_id]
def bulid_model_rl(self):
self.state_lstm = KGState_LSTM(self.args, history_len=1)
self.state_prop_l1 = nn.Linear(4 * self.embed_size,
2 * self.embed_size)
self.state_prop_l2 = nn.Linear(2 * self.embed_size, 1)
self.transfor_state = nn.Linear(2 * self.embed_size,
2 * self.embed_size)
self.state_tr_query = nn.Linear(self.embed_size * 3, self.embed_size)
self.l1 = nn.Linear(2 * self.embed_size, self.hidden_sizes[1])
self.l2 = nn.Linear(self.hidden_sizes[0], self.hidden_sizes[1])
self.actor = nn.Linear(self.hidden_sizes[1], self.act_dim)
self.critic = nn.Linear(self.hidden_sizes[1], 1)
self.saved_actions = []
self.rewards = []
self.entropy = []
# def forward(self, inputs):
# state, batch_next_action_emb, act_mask = inputs # state: [bs, state_dim], act_mask: [bs, act_dim]
# print('state = ', state.shape)
# # state = state.squeeze()
# print('state = ', state.shape)
# # state_tr = state.unsqueeze(1).repeat(1, batch_next_action_emb.shape[1], 1)
# print('batch_next_action_emb = ', batch_next_action_emb.shape)
# # # input()
# # state_output = th.cat([state_tr, batch_next_action_emb], -1)
# state = state.squeeze()
# state_tr = state.unsqueeze(1).repeat(1, batch_next_action_emb.shape[1], 1)
# probs = state_tr * batch_next_action_emb
# probs = probs.sum(-1)
# probs = probs.masked_fill(~act_mask, value=torch.tensor(-1e10))
# act_probs = F.softmax(probs, dim=-1)
# x = self.l1(state)
# x = F.dropout(F.elu(x), p=0.4)
# state_values = self.critic(x) # Tensor of [bs, 1]
# return act_probs, state_values
def forward(self, inputs):
state, batch_next_action_emb, act_mask = inputs # state: [bs, state_dim], act_mask: [bs, act_dim]
state = state.squeeze().unsqueeze(0)
# print('state = ', state.shape)
# # state = state.squeeze()
# print('state = ', state.shape)
# # state_tr = state.unsqueeze(1).repeat(1, batch_next_action_emb.shape[1], 1)
# print('batch_next_action_emb = ', batch_next_action_emb.shape)
state_tr = state.unsqueeze(1).repeat(1, batch_next_action_emb.shape[1],
1)
probs = state_tr * batch_next_action_emb
probs = probs.sum(-1)
probs = probs.masked_fill(~act_mask, value=torch.tensor(-1e10))
act_probs = F.softmax(probs, dim=-1)
x = self.l1(state)
x = F.dropout(F.elu(x), p=0.4)
state_values = self.critic(x) # Tensor of [bs, 1]
return act_probs, state_values
# def forward(self, inputs):
# state, batch_next_action_emb, act_mask = inputs # state: [bs, state_dim], act_mask: [bs, act_dim]
# state = state.squeeze().unsqueeze(0)
# state_tr = state.unsqueeze(1).repeat(1, batch_next_action_emb.shape[1], 1)
# state_output = th.cat([state_tr, batch_next_action_emb], -1)
# # [batch_size, n_memory]
# state_output_ = self.state_prop_l1(state_output).squeeze().unsqueeze(0)
# probs = self.state_prop_l2(state_output_).squeeze().unsqueeze(0)
# probs = probs.masked_fill(~act_mask, value=torch.tensor(-1e10))
# act_probs = F.softmax(probs, dim=-1)
# x = self.l1(state)
# x = F.dropout(F.elu(x), p=0.4)
# state_values = self.critic(x) # Tensor of [bs, 1]
# return act_probs, state_values
# # [batch_size, n_memory]
# state_output_ = self.state_prop_l1(state_output).squeeze().unsqueeze(0)
# probs = self.state_prop_l2(state_output_).squeeze().unsqueeze(0)
# probs = probs.masked_fill(~act_mask, value=torch.tensor(-1e10))
# act_probs = F.softmax(probs, dim=-1)
# x = self.l1(state)
# x = F.dropout(F.elu(x), p=0.4)
# state_values = self.critic(x) # Tensor of [bs, 1]
# return act_probs, state_values
def select_action(self, batch_state, batch_next_action_emb, batch_act_mask,
device):
act_mask = torch.BoolTensor(batch_act_mask).to(
device) # Tensor of [bs, act_dim]
probs, value = self(
(batch_state, batch_next_action_emb,
act_mask)) # act_probs: [bs, act_dim], state_value: [bs, 1]
m = Categorical(probs)
acts = m.sample() # Tensor of [bs, ], requires_grad=False
# [CAVEAT] If sampled action is out of action_space, choose the first action in action_space.
valid_idx = act_mask.gather(1, acts.view(-1, 1)).view(-1)
acts[valid_idx == 0] = 0
self.saved_actions.append(SavedAction(m.log_prob(acts), value))
self.entropy.append(m.entropy())
return acts.cpu().numpy().tolist()
def reset(self, uids=None):
self.uids = [uid for uid in uids for _ in range(self.sub_batch_size)]
self.dummy_rela = torch.ones(
max(self.user_triplet_set) * 10 + 1, 1, self.embed_size)
# print('len(self.user_triplet_set) = ', len(self.user_triplet_set))
# print('max(self.user_triplet_set) = ', max(self.user_triplet_set))
# self.dummy_rela = self.dummy_rela.to(self.device)
self.dummy_rela = nn.Parameter(self.dummy_rela,
requires_grad=True).to(self.device)
self.prev_state_h, self.prev_state_c = self.state_lstm.set_up_hidden_state(
len(self.uids))
def update_path_info(self, up_date_hop):
new_uids = []
for row in up_date_hop:
new_uids.append(self.uids[row])
self.uids = new_uids
new_prev_state_h = []
new_prev_state_c = []
for row in up_date_hop:
new_prev_state_h.append(self.prev_state_h[:, row, :].unsqueeze(1))
new_prev_state_c.append(self.prev_state_c[:, row, :].unsqueeze(1))
self.prev_state_h = th.cat(new_prev_state_h, 1).to(self.device)
self.prev_state_c = th.cat(new_prev_state_c, 1).to(self.device)
def generate_st_emb(self, batch_path, up_date_hop=None):
if up_date_hop != None:
self.update_path_info(up_date_hop)
self.current_step = {}
all_state = th.cat([
self._get_state_update(index, path).unsqueeze(0)
for index, path in enumerate(batch_path)
], 0)
state_output, self.prev_state_h, self.prev_state_c = self.state_lstm(
all_state, self.prev_state_h, self.prev_state_c)
return state_output
def action_encoder(self, relation_emb, entitiy_emb):
action_embedding = th.cat([relation_emb, entitiy_emb], -1)
return action_embedding
def _get_state_update(self, index, path):
"""Return state of numpy vector: [user_embed, curr_node_embed, last_node_embed, last_relation]."""
if len(path) == 1:
if self.user_o == True:
user_embed = self.global_user[index, :].unsqueeze(0)
else:
user_embed = self.kg_emb.lookup_emb(
USER,
type_index=torch.LongTensor([path[0][-1]]).to(
self.device))[0].unsqueeze(0)
curr_node_embed = user_embed
dummy_rela = self.dummy_rela[path[0][-1], :, :]
st_emb = self.action_encoder(dummy_rela, user_embed)
else:
last_relation, curr_node_type, curr_node_id = path[-1]
curr_node_embed = self.kg_emb.lookup_emb(
curr_node_type,
type_index=torch.LongTensor([curr_node_id]).to(
self.device))[0].unsqueeze(0)
last_relation_embed = self.kg_emb.lookup_rela_emb(
last_relation)[0].unsqueeze(0)
st_emb = self.action_encoder(last_relation_embed, curr_node_embed)
return st_emb
def generate_act_emb(self, batch_path, batch_curr_actions):
self.current_step['user'] = str(self.uids[0])
batch_path_tmp = batch_path.copy()
batch_curr_actions_tmp = batch_curr_actions.copy()
batch_path_tmp[0] = batch_path[0].copy()
batch_curr_actions_tmp[0] = batch_curr_actions[0].copy()
# print('batch_path = ', batch_path)
batch_path_tmp[0][0] = list(batch_path_tmp[0][0]).copy()
batch_path_tmp[0][0][0] = self.args.rela_2_name[
batch_path_tmp[0][0][0]] if batch_path_tmp[0][0][
0] in self.args.rela_2_name else batch_path_tmp[0][0][0]
batch_path_tmp[0][0][2] = str(batch_path_tmp[0][0][2])
batch_path_tmp[0][0][2] = self.args.index_2_entity[
batch_path_tmp[0][0][2]] if batch_path_tmp[0][0][
2] in self.args.index_2_entity else batch_path_tmp[0][0][2]
b_path = [
','.join([str(uni) for uni in bpa]) for bpa in batch_path_tmp[0]
]
for index_1 in range(len(batch_curr_actions_tmp[0])):
batch_curr_actions_tmp[0][index_1] = list(
batch_curr_actions_tmp[0][index_1]).copy()
batch_curr_actions_tmp[0][index_1][0] = self.args.rela_2_name[
batch_curr_actions_tmp[0][index_1]
[0]] if batch_curr_actions_tmp[0][index_1][
0] in self.args.rela_2_name else batch_curr_actions_tmp[0][
index_1][0]
batch_curr_actions_tmp[0][index_1][1] = str(
batch_curr_actions_tmp[0][index_1][1])
batch_curr_actions_tmp[0][index_1][1] = self.args.index_2_entity[batch_curr_actions_tmp[0][index_1][1]] \
if batch_curr_actions_tmp[0][index_1][1] in self.args.index_2_entity else batch_curr_actions_tmp[0][index_1][1]
b_action = [
', '.join([str(uni) for uni in bpa])
for bpa in batch_curr_actions_tmp[0]
]
self.current_step["path"] = 'path = ' + ', next = '.join(b_path)
self.current_step["actions"] = b_action
all_action_set = [
self._get_actions(index, actions_sets[0], actions_sets[1])
for index, actions_sets in enumerate(
zip(batch_path, batch_curr_actions))
]
enti_emb = th.cat(
[action_set[0].unsqueeze(0) for action_set in all_action_set], 0)
next_action_state = th.cat(
[action_set[1].unsqueeze(0) for action_set in all_action_set], 0)
print('next_action_state = ', next_action_state.shape)
# inpu
return next_action_state
def _get_actions(self, index, curr_path, curr_actions):
last_relation, curr_node_type, curr_node_id = curr_path[-1]
entities_embs = []
relation_embs = []
for action_set in curr_actions:
if action_set[0] == SELF_LOOP: next_node_type = curr_node_type
else:
next_node_type = self._get_next_node_type(
curr_node_type, action_set[0], action_set[1])
enti_emb = self.kg_emb.lookup_emb(
next_node_type,
type_index=torch.LongTensor([action_set[1]]).to(self.device))
entities_embs.append(enti_emb)
rela_emb = self.kg_emb.lookup_rela_emb(action_set[0])
relation_embs.append(rela_emb)
pad_emb = self.kg_emb.lookup_rela_emb(PADDING)
for _ in range(self.act_dim - len(entities_embs)):
entities_embs.append(pad_emb)
relation_embs.append(pad_emb)
enti_emb = th.cat(entities_embs, 0)
rela_emb = th.cat(relation_embs, 0)
next_action_state = th.cat([enti_emb, rela_emb], -1)
return [enti_emb, next_action_state]
def _get_next_node_type(self, curr_node_type, next_relation,
next_entity_id):
pass
def update(self, optimizer, env_model, device, ent_weight, step):
if len(self.rewards) <= 0:
del self.rewards[:]
del self.saved_actions[:]
del self.entropy[:]
return 0.0, 0.0, 0.0
batch_rewards = np.vstack(
self.rewards).T # numpy array of [bs, #steps]
batch_rewards = torch.FloatTensor(batch_rewards).to(device)
num_steps = batch_rewards.shape[1]
for i in range(1, num_steps):
batch_rewards[:, num_steps - i -
1] += self.gamma * batch_rewards[:, num_steps - i]
actor_loss = 0
critic_loss = 0
entropy_loss = 0
for i in range(0, num_steps):
log_prob, value = self.saved_actions[
i] # log_prob: Tensor of [bs, ], value: Tensor of [bs, 1]
advantage = batch_rewards[:, i] - value.squeeze(
1) # Tensor of [bs, ]
actor_loss += -log_prob * advantage.detach() # Tensor of [bs, ]
critic_loss += advantage.pow(2) # Tensor of [bs, ]
entropy_loss += -self.entropy[i] # Tensor of [bs, ]
actor_loss = actor_loss.mean()
critic_loss = critic_loss.mean()
entropy_loss = entropy_loss.mean()
loss = actor_loss + critic_loss + ent_weight * entropy_loss
optimizer.zero_grad()
loss.backward()
# if step % 100 == 0:
# # print('step = ', step)
# plot_grad_flow_v2(self.named_parameters(), self.gradient_plot_save, step)
optimizer.step()
del self.rewards[:]
del self.saved_actions[:]
del self.entropy[:]
return loss.item(), actor_loss.item(), critic_loss.item(
), entropy_loss.item()
def _record_case_study(self):
# print('self.cast_st_save = ', self.cast_st_save)
eva_file = open(self.cast_st_save, "a")
eva_file.write("*" * 50)
eva_file.write('\n')
eva_file.write('user = '******'user'])
eva_file.write('\n')
# print('path = ', self.current_step['path'])
# input()
eva_file.write(self.current_step['path'])
eva_file.write('\n')
eva_file.write("*" * 50)
eva_file.write('\n')
for ac_pro in self.current_step['actions_pro']:
# print(ac_pro)
eva_file.write(ac_pro)
eva_file.write('\n')
eva_file.write("next_action = " + self.current_step['next_acts'])
eva_file.write('\n')
eva_file.write("*" * 50)
eva_file.close()
def _record_case_study_az(self):
# print('self.cast_st_save = ', self.cast_st_save)
eva_file = open(self.cast_st_save, "a")
eva_file.write("*" * 50)
eva_file.write('\n')
eva_file.write('user = '******'user'])
eva_file.write('\n')
eva_file.write(self.current_step['path'])
eva_file.write('\n')
eva_file.write("*" * 50)
eva_file.write('\n')
eva_file.write("querying result")
eva_file.write('\n')
for hop in range(self.p_hop):
eva_file.write("*" * 50)
eva_file.write('\n')
eva_file.write("hop = " + str(hop))
eva_file.write('\n')
tmp_list = []
for rn_step in range(self.reasoning_step):
tmp_list.append(self.current_step[rn_step][hop])
for state_s in zip(*tmp_list):
# print('state_s = ', list(state_s))
eva_file.write(' n_hop = '.join(list(state_s)))
eva_file.write('\n')
eva_file.write("*" * 50)
eva_file.write('\n')
for ac_pro in self.current_step['actions_pro']:
# print(ac_pro)
eva_file.write(ac_pro)
eva_file.write('\n')
eva_file.write("next_action = " + self.current_step['next_acts'])
eva_file.write('\n')
eva_file.write("*" * 50)
eva_file.close()