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eval_yelp.py
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eval_yelp.py
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import torch
import time
from data_utils_yelp import DataUtil
from models import StyleTransformer
#from train import train, auto_eval
from cnn_classify import test, CNNClassify, BiLSTMClassify
from lm_lstm import LSTM_LM, lm_ppl
import os
from utils import tensor2text, list2text
from evaluator import Evaluator, EvaluatorYelp
import numpy as np
class Config():
train_src_file0 = 'data/yelp/cleaned_train_0.txt'
train_src_file1 = 'data/yelp/cleaned_train_1.txt'
train_trg_file = 'data/yelp/train.attr'
dev_src_file0 = 'data/yelp/cleaned_test_li0.txt'
dev_src_file1 = 'data/yelp/cleaned_test_li1.txt'
dev_trg_file = 'data/yelp/dev.attr'
dev_trg_file0 = 'data/yelp/test_0.attr'
dev_trg_file1 = 'data/yelp/test_1.attr'
dev_trg_ref = 'data/yelp/cleaned_dev_ref.txt'
trg_vocab = 'data/yelp/attr.vocab'
data_path = './data/yelp/'
log_dir = 'runs/exp'
save_path = './save'
pretrained_embed_path = './embedding/'
device = torch.device('cuda' if True and torch.cuda.is_available() else 'cpu')
discriminator_method = 'Multi' # 'Multi' or 'Cond'
load_pretrained_embed = False
min_freq = 3
max_length = 32
embed_size = 256
d_model = 256
h = 4
num_styles = 2
num_classes = num_styles + 1 if discriminator_method == 'Multi' else 2
num_layers = 4
batch_size = 64
lr_F = 0.0001
lr_D = 0.0001
L2 = 0
iter_D = 10
iter_F = 5
F_pretrain_iter = 500#57500
log_steps = 5
eval_steps = 200
learned_pos_embed = True
dropout = 0
drop_rate_config = [(0.3, 0), (0.4, 230), (0.5, 11500)]
temperature_config = [(1, 0), (1, 1150), (0.8, 4600), (0.6, 11500)]
slf_factor = 0.1
cyc_factor = 0.2
adv_factor = 1
inp_shuffle_len = 0
inp_unk_drop_fac = 0
inp_rand_drop_fac = 0
inp_drop_prob = 1
decode = False
#max_len = 10000
lambda_span = 10000
word_mass = 0.5
word_mask = 0.8
word_keep = 0.1
word_rand = 0.1
albert_kd = False
kd_alpha = 0.5
kd_temperature = 5
bert_dump0 = 'data/targets/teacher0_yelp'
bert_dump1 = 'data/targets/teacher1_yelp'
translate = False
ckpt = '3500_F.pth'
model_name = 'best_model_yelp'
beam_size = 1
valid_file_0 = 'save/best_yelp_b3/best_yelp_b3_0'#'baseline_outputs/shakespeare/strap/cleaned_0to1'
valid_file_1 = 'save/best_yelp_b3/best_yelp_b3_1'#'baseline_outputs/shakespeare/strap/cleaned_1to0'
def get_lengths(tokens, eos_idx):
lengths = torch.cumsum(tokens == eos_idx, 1)
lengths = (lengths == 0).long().sum(-1)
lengths = lengths + 1 # +1 for <eos> token
return lengths
def auto_eval(config, data, model_F, model_name, temperature=1):
model_F.eval()
vocab_size = len(data.tokenizer)
eos_idx = config.eos_id
config.save_folder = config.save_path + '/' + model_name
os.makedirs(config.save_folder)
print('Save Path:', config.save_folder)
def inference(data, raw_style):
gold_text = []
raw_output = []
rev_output = []
while True:
if raw_style == 0:
inp_tokens, _ , eop = data.next_dev0(dev_batch_size = 128, sort = False)
else:
inp_tokens, _ , eop = data.next_dev1(dev_batch_size = 128, sort = False)
inp_lengths = get_lengths(inp_tokens, eos_idx)
raw_styles = torch.full_like(inp_tokens[:, 0], raw_style)
rev_styles = 1 - raw_styles
with torch.no_grad():
raw_log_probs = model_F(
inp_tokens,
None,
inp_lengths,
raw_styles,
generate=True,
differentiable_decode=False,
temperature=temperature,
)
with torch.no_grad():
rev_log_probs = model_F(
inp_tokens,
None,
inp_lengths,
rev_styles,
generate=True,
differentiable_decode=False,
temperature=temperature,
)
gold_text += tensor2text(data, inp_tokens.cpu())
raw_output += tensor2text(data, raw_log_probs.argmax(-1).cpu())
rev_output += tensor2text(data, rev_log_probs.argmax(-1).cpu())
if eop: break
return gold_text, raw_output, rev_output
if config.translate == True:
gold_text0, raw_output0, rev_output0 = inference(data, 0)
gold_text1, raw_output1, rev_output1 = inference(data, 1)
gold_text = (gold_text0, gold_text1)
raw_output = (raw_output0,raw_output1)
rev_output = (rev_output0, rev_output1)
valid_file_0 = os.path.join( config.save_folder + '/', str(model_name) + '_0')
valid_file_1 = os.path.join( config.save_folder + '/', str(model_name) + '_1')
out_file_0 = open(valid_file_0, 'w', encoding='utf-8')
out_file_1 = open(valid_file_1, 'w', encoding='utf-8')
for i in range(len(rev_output[0])):
line_0 = rev_output[0][i].strip()
out_file_0.write(line_0 + '\n')
out_file_0.flush()
for i in range(len(rev_output[1])):
line_1 = rev_output[1][i].strip()
out_file_1.write(line_1 + '\n')
out_file_1.flush()
out_file_0.close()
out_file_1.close()
else:
rev_output = []
valid_file_0 = config.valid_file_0
valid_file_1 = config.valid_file_1
with open(valid_file_0, 'r', encoding='utf-8') as f:
src_lines_0 = f.read().split('\n')
rev_output.append(src_lines_0)
with open(valid_file_1, 'r', encoding='utf-8') as f:
src_lines_1 = f.read().split('\n')
rev_output.append(src_lines_1)
evaluator = EvaluatorYelp()
ref_text = evaluator.yelp_ref
#acc_neg = evaluator.yelp_acc_0(rev_output[0])
acc_mod, _ = test(evaluator.classifier, data, 128, valid_file_0, config.dev_trg_file0, negate = True)
acc_cla, _ = test(evaluator.classifier, data, 128, valid_file_1, config.dev_trg_file1, negate = True)
#acc_pos = evaluator.yelp_acc_1(rev_output[1])
bleu_mod = evaluator.yelp_ref_bleu_0(rev_output[0])
bleu_cla = evaluator.yelp_ref_bleu_1(rev_output[1])
_ , ppl_mod = lm_ppl(evaluator.lm1, data, 128, valid_file_0, config.dev_trg_file0) #evaluator.yelp_ppl(rev_output[0])
_ , ppl_cla = lm_ppl(evaluator.lm0, data, 128, valid_file_1, config.dev_trg_file1) #evaluator.yelp_ppl(rev_output[1])
sim_mod = evaluator.ref_similarity_0(valid_file_0, str(model_name))
sim_cla = evaluator.ref_similarity_1(valid_file_1, str(model_name))
bartscore_mod = evaluator.ref_bartscore_0(rev_output[0])
bartscore_cla = evaluator.ref_bartscore_1(rev_output[1])
print(('[auto_eval] acc_cla: {:.4f} acc_mod: {:.4f} ' + \
'bleu_cla: {:.4f} bleu_mod: {:.4f} ' + \
'sim_cla: {:.4f} sim_mod: {:.4f} ' + \
'bartscore_cla: {:.4f} bartscore_mod: {:.4f} ' + \
'ppl_cla: {:.4f} ppl_mod: {:.4f}\n').format(
acc_cla, acc_mod, bleu_cla, bleu_mod, sim_cla, sim_mod, bartscore_cla, bartscore_mod, ppl_cla, ppl_mod
))
# save output
eval_log_file = config.save_folder + '/eval_log.txt'
with open(eval_log_file, 'a') as fl:
print(('{:18s}: acc_cla: {:.4f} acc_mod: {:.4f} ' + \
'bleu_cla: {:.4f} bleu_mod: {:.4f} ' + \
'sim_cla: {:.4f} sim_mod: {:.4f} ' + \
'bartscore_cla: {:.4f} bartscore_mod: {:.4f} ' + \
'ppl_cla: {:.4f} ppl_mod: {:.4f}\n').format(
model_name, acc_cla, acc_mod, bleu_cla, bleu_mod, sim_cla, sim_mod, bartscore_cla, bartscore_mod, ppl_cla, ppl_mod
), file=fl)
if config.translate == True:
save_file = config.save_folder + '/' + str(model_name) + '.txt'
with open(save_file, 'w') as fw:
print(('[auto_eval] acc_cla: {:.4f} acc_mod: {:.4f} ' + \
'bleu_cla: {:.4f} bleu_mod: {:.4f} ' + \
'sim_cla: {:.4f} sim_mod: {:.4f} ' + \
'bartscore_cla: {:.4f} bartscore_mod: {:.4f} ' + \
'ppl_cla: {:.4f} ppl_mod: {:.4f}\n').format(
acc_cla, acc_mod, bleu_cla, bleu_mod, sim_cla, sim_mod, bartscore_cla, bartscore_mod, ppl_cla, ppl_mod
), file=fw)
for idx in range(len(rev_output[0])):
print('*' * 20, 'classic sample', '*' * 20, file=fw)
print('[gold]', gold_text[0][idx], file=fw)
print('[raw ]', raw_output[0][idx], file=fw)
print('[rev ]', rev_output[0][idx], file=fw)
print('[ref ]', ref_text[0][idx], file=fw)
print('*' * 20, '********', '*' * 20, file=fw)
for idx in range(len(rev_output[1])):
print('*' * 20, 'modern sample', '*' * 20, file=fw)
print('[gold]', gold_text[1][idx], file=fw)
print('[raw ]', raw_output[1][idx], file=fw)
print('[rev ]', rev_output[1][idx], file=fw)
print('[ref ]', ref_text[1][idx], file=fw)
print('*' * 20, '********', '*' * 20, file=fw)
def beam_eval(config, data, model_F, model_name, temperature=1):
model_F.eval()
vocab_size = len(data.tokenizer)
eos_idx = config.eos_id
config.save_folder = config.save_path + '/' + model_name
os.makedirs(config.save_folder)
print('Save Path:', config.save_folder)
def beam_inference(data, raw_style):
gold_text = []
rev_output = []
while True:
if raw_style == 0:
inp_tokens, _ , eop = data.next_dev0(dev_batch_size = 256, sort = False)
else:
inp_tokens, _ , eop = data.next_dev1(dev_batch_size = 256, sort = False)
inp_lengths = get_lengths(inp_tokens, eos_idx)
raw_styles = torch.full_like(inp_tokens[:, 0], raw_style)
rev_styles = 1 - raw_styles
hyps = []
batch_size = inp_tokens.size(0)
for i in range(batch_size):
x = inp_tokens[i,:].unsqueeze(0)
inp_length = inp_lengths[i].unsqueeze(0)
rev_style = rev_styles[i].unsqueeze(0)
hyp = model_F.translate_sent(x, inp_length, rev_style, temperature, max_len=config.max_length, beam_size=config.beam_size, poly_norm_m=1)[0]
hyps.append(hyp.y)
gold_text += tensor2text(data, inp_tokens.cpu())
rev_output += list2text(data, hyps)
if eop: break
return gold_text, rev_output
gold_text0, rev_output0 = beam_inference(data, 0)
gold_text1, rev_output1 = beam_inference(data, 1)
gold_text = (gold_text0, gold_text1)
rev_output = (rev_output0, rev_output1)
valid_file_0 = os.path.join( config.save_folder + '/' , str(model_name) + '_0')
valid_file_1 = os.path.join( config.save_folder + '/' , str(model_name) + '_1')
out_file_0 = open(valid_file_0, 'w', encoding='utf-8')
out_file_1 = open(valid_file_1, 'w', encoding='utf-8')
for i in range(len(rev_output[0])):
line_0 = rev_output[0][i].strip()
out_file_0.write(line_0 + '\n')
out_file_0.flush()
for i in range(len(rev_output[1])):
line_1 = rev_output[1][i].strip()
out_file_1.write(line_1 + '\n')
out_file_1.flush()
out_file_0.close()
out_file_1.close()
evaluator = EvaluatorYelp()
ref_text = evaluator.yelp_ref
#acc_neg = evaluator.yelp_acc_0(rev_output[0])
acc_mod, _ = test(evaluator.classifier, data, 128, valid_file_0, config.dev_trg_file0, negate = True)
acc_cla, _ = test(evaluator.classifier, data, 128, valid_file_1, config.dev_trg_file1, negate = True)
#acc_pos = evaluator.yelp_acc_1(rev_output[1])
bleu_mod = evaluator.yelp_ref_bleu_0(rev_output[0])
bleu_cla = evaluator.yelp_ref_bleu_1(rev_output[1])
_ , ppl_mod = lm_ppl(evaluator.lm1, data, 128, valid_file_0, config.dev_trg_file0) #evaluator.yelp_ppl(rev_output[0])
_ , ppl_cla = lm_ppl(evaluator.lm0, data, 128, valid_file_1, config.dev_trg_file1) #evaluator.yelp_ppl(rev_output[1])
sim_mod = evaluator.ref_similarity_0(valid_file_0, str(model_name))
sim_cla = evaluator.ref_similarity_1(valid_file_1, str(model_name))
bartscore_mod = evaluator.ref_bartscore_0(rev_output[0])
bartscore_cla = evaluator.ref_bartscore_1(rev_output[1])
for k in range(5):
idx = np.random.randint(len(rev_output[0]))
print('*' * 20, 'classic sample', '*' * 20)
print('[gold]', gold_text[0][idx])
print('[rev ]', rev_output[0][idx])
print('[ref ]', ref_text[0][idx])
print('*' * 20, '********', '*' * 20)
for k in range(5):
idx = np.random.randint(len(rev_output[1]))
print('*' * 20, 'modern sample', '*' * 20)
print('[gold]', gold_text[1][idx])
print('[rev ]', rev_output[1][idx])
print('[ref ]', ref_text[1][idx])
print('*' * 20, '********', '*' * 20)
print(('[auto_eval] acc_cla: {:.4f} acc_mod: {:.4f} ' + \
'bleu_cla: {:.4f} bleu_mod: {:.4f} ' + \
'sim_cla: {:.4f} sim_mod: {:.4f} ' + \
'bartscore_cla: {:.4f} bartscore_mod: {:.4f} ' + \
'ppl_cla: {:.4f} ppl_mod: {:.4f}\n').format(
acc_cla, acc_mod, bleu_cla, bleu_mod, sim_cla, sim_mod, bartscore_cla, bartscore_mod, ppl_cla, ppl_mod
))
# save output
eval_log_file = config.save_folder + '/eval_log.txt'
with open(eval_log_file, 'a') as fl:
print(('iter{:18s}: acc_cla: {:.4f} acc_mod: {:.4f} ' + \
'bleu_cla: {:.4f} bleu_mod: {:.4f} ' + \
'sim_cla: {:.4f} sim_mod: {:.4f} ' + \
'bartscore_cla: {:.4f} bartscore_mod: {:.4f} ' + \
'ppl_cla: {:.4f} ppl_mod: {:.4f}\n').format(
model_name, acc_cla, acc_mod, bleu_cla, bleu_mod, sim_cla, sim_mod, bartscore_cla, bartscore_mod, ppl_cla, ppl_mod
), file=fl)
def main():
config = Config()
data = DataUtil(config)
print('Vocab size:', config.src_vocab_size)
if config.valid_file_0:
assert config.translate == False
assert config.beam_size == 1
state_dict = torch.load(config.ckpt)
model_F = StyleTransformer(config, data).to(config.device)
model_F.load_state_dict(state_dict)
if config.beam_size == 1:
auto_eval(config, data, model_F, config.model_name)
else:
beam_eval(config, data, model_F, config.model_name)
if __name__ == '__main__':
main()