def run_test(test_data, net, rev_emb_dict, end_token, device="cuda"):
argmax_reward_sum = 0.0
argmax_reward_count = 0.0
# p1 is one sentence, p2 is sentence list.
for p1, p2 in test_data:
# Transform sentence to padded embeddings.
input_seq = net.pack_input(p1, net.emb, device)
# Get hidden states from encoder.
# enc = net.encode(input_seq)
context, enc = net.encode_context(input_seq)
# Decode sequence by feeding predicted token to the net again. Act greedily.
# Return N*outputvocab, N output token indices.
_, tokens = net.decode_chain_argmax(enc,
net.emb(beg_token),
seq_len=data.MAX_TOKENS,
context=context[0],
stop_at_token=end_token)
# Show what the output action sequence is.
action_tokens = []
for temp_idx in tokens:
if temp_idx in rev_emb_dict and rev_emb_dict.get(
temp_idx) != '#END':
action_tokens.append(str(rev_emb_dict.get(temp_idx)).upper())
# Using 0-1 reward to compute accuracy.
reward = utils.calc_True_Reward_webqsp_novar(action_tokens, p2, False)
# reward = random.random()
argmax_reward_sum += float(reward)
argmax_reward_count += 1
if argmax_reward_count == 0:
return 0.0
else:
return float(argmax_reward_sum) / float(argmax_reward_count)
def run_test_true_reward(test_data, net, rev_emb_dict, end_token, device="cuda"):
argmax_reward_sum = 0.0
argmax_reward_count = 0.0
# BEGIN token
beg_token = torch.LongTensor([emb_dict[data.BEGIN_TOKEN]]).to(device)
beg_token = beg_token.cuda()
# p1 is one sentence, p2 is sentence list.
for p1, p2 in test_data:
p_list = [(p1, p2)]
input_ids, attention_masks = tokenizer_encode(tokenizer, p_list, rev_emb_dict, device, max_tokens)
output, output_hidden_states = net.bert_encode(input_ids, attention_masks)
context, enc = output_hidden_states, (output.unsqueeze(0), output.unsqueeze(0))
input_seq = net.pack_input(p1, net.emb, device)
# Return logits (N*outputvocab), res_tokens (1*N)
# Always use the first token in input sequence, which is '#BEG' as the initial input of decoder.
# The maximum length of the output is defined in class libbots.data.
_, tokens = net.decode_chain_argmax(enc, input_seq.data[0:1],
seq_len=data.MAX_TOKENS,
context=context[0],
stop_at_token=end_token)
action_tokens = []
for temp_idx in tokens:
if temp_idx in rev_emb_dict and rev_emb_dict.get(temp_idx) != '#END':
action_tokens.append(str(rev_emb_dict.get(temp_idx)).upper())
# Using 0-1 reward to compute accuracy.
if args.dataset == "csqa":
argmax_reward_sum += float(utils.calc_True_Reward(action_tokens, p2, False))
else:
argmax_reward_sum += float(utils.calc_True_Reward_webqsp_novar(action_tokens, p2, False))
argmax_reward_count += 1
if argmax_reward_count == 0:
return 0.0
else:
return float(argmax_reward_sum) / float(argmax_reward_count)
item_enc,
beg_embedding,
data.MAX_TOKENS,
context[idx],
stop_at_token=end_token)
# Show what the output action sequence is.
action_tokens = []
for temp_idx in actions:
if temp_idx in rev_emb_dict and rev_emb_dict.get(
temp_idx) != '#END':
action_tokens.append(
str(rev_emb_dict.get(temp_idx)).upper())
# Get the highest BLEU score as baseline used in self-critic.
# If the last parameter is false, it means that the 0-1 reward is used to calculate the accuracy.
# Otherwise the adaptive reward is used.
argmax_reward = utils.calc_True_Reward_webqsp_novar(
action_tokens, qa_info, args.adaptive)
# argmax_reward = random.random()
true_reward_argmax.append(argmax_reward)
if args.NSM and 'pseudo_gold_program_reward' not in qa_info:
pseudo_program_tokens = str(
qa_info['pseudo_gold_program']).strip().split()
pseudo_program_reward = utils.calc_True_Reward_webqsp_novar(
pseudo_program_tokens, qa_info, args.adaptive)
qa_info[
'pseudo_gold_program_reward'] = pseudo_program_reward
# # In this case, the BLEU score is so high that it is not needed to train such case with RL.
# if not args.disable_skip and argmax_reward > 0.99:
# skipped_samples += 1
# continue
bleu = utils.calc_bleu_many(tokens, references)
# print(tokens)
# print(references)
# Show what the output action sequence is.
action_tokens = []
for temp_idx in tokens:
if temp_idx in rev_emb_dict and rev_emb_dict.get(
temp_idx) != '#END':
action_tokens.append(str(rev_emb_dict.get(temp_idx)).upper())
# Get the highest BLEU score as baseline used in self-critic.
# If the last parameter is false, it means that the 0-1 reward is used to calculate the accuracy.
# Otherwise the adaptive reward is used.
true_reward_F1score = utils.calc_True_Reward_webqsp_novar(
action_tokens, targets, False)
print("true_reward_F1score", true_reward_F1score)
sum_turereward_f1 += true_reward_F1score
intersec = set(tokens).intersection(set(references[0]))
if len(references) == 0:
prec = 0.0
else:
prec = float(len(intersec)) / float(len(references[0]))
rec = float(len(intersec)) / float(len(references[0]))
if prec == 0 and rec == 0:
f1 = 0
else:
sum_f1 += (2.0 * prec * rec) / (prec + rec)