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300 lines
9.2 KiB
300 lines
9.2 KiB
1 year ago
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/1/31 19:02
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@Author :
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@FileName:
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@Software:
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@Describe:
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"""
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import os
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# os.environ["TF_KERAS"] = "1"
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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import json
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import numpy as np
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from bert4keras.backend import keras, set_gelu
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from bert4keras.tokenizers import Tokenizer, load_vocab
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from bert4keras.models import build_transformer_model
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from bert4keras.optimizers import Adam, extend_with_piecewise_linear_lr
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from bert4keras.snippets import sequence_padding, DataGenerator
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from bert4keras.snippets import open
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from keras.layers import Lambda, Dense
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import tensorflow as tf
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from keras.backend import set_session
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from sklearn.metrics.pairwise import cosine_similarity
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from rouge import Rouge # pip install rouge
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from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
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from tqdm import tqdm
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import jieba
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from gensim.models import KeyedVectors, word2vec, Word2Vec
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import random
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import difflib
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import re
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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set_session(tf.Session(config=config)) # 此处不同
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class Word2vecModel:
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def __init__(self):
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self.path = "E:\pycharm_workspace\查重分析\word2vec_model\\word2vec_add_new_18.model"
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self.model = Word2Vec.load(self.path)
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def word2vec_res(self,seg_0_list, seg_1_list):
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sentence_0_list = []
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sentence_1_list = []
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for i in seg_0_list:
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a = self.model.wv[i]
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sentence_0_list.append(a)
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for i in seg_1_list:
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a = self.model.wv[i]
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sentence_1_list.append(a)
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return sentence_0_list, sentence_1_list
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class Evaluator(keras.callbacks.Callback):
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"""评估与保存
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"""
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def __init__(self):
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self.rouge = Rouge()
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self.smooth = SmoothingFunction().method1
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self.best_bleu = 0.
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# def on_epoch_end(self, epoch, logs=None):
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# metrics = self.evaluate(valid_data) # 评测模型
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# if metrics['bleu'] > self.best_bleu:
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# self.best_bleu = metrics['bleu']
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# model.save_weights('./best_model.weights') # 保存模型
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# metrics['best_bleu'] = self.best_bleu
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# print('valid_data:', metrics)
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def evaluate_t(self, data_1, data_2, topk=1):
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total = 0
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rouge_1, rouge_2, rouge_l, bleu = 0, 0, 0, 0
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scores = self.rouge.get_scores(hyps=[data_1], refs=[data_2])
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rouge_1 += scores[0]['rouge-1']['f']
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rouge_2 += scores[0]['rouge-2']['f']
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rouge_l += scores[0]['rouge-l']['f']
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bleu += sentence_bleu(
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references=[data_1.split(' ')],
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hypothesis=data_2.split(' '),
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smoothing_function=self.smooth
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)
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# rouge_1 /= total
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# rouge_2 /= total
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# rouge_l /= total
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# bleu /= total
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return [rouge_1, rouge_2, rouge_l, bleu]
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class bertModel:
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def __init__(self):
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# modelpath = "E:\pycharm_workspace\premodel\keras\chinese_simbert_L-12_H-768_A-12"
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# modelpath = "E:\pycharm_workspace\premodel\keras\chinese_roberta_wwm_ext_L-12_H-768_A-12"
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# modelpath = "E:\pycharm_workspace\premodel\keras\chinese_L-12_H-768_A-12"
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modelpath = "/home/majiahui/project/models-llm/keras/chinese_L-12_H-768_A-12"
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self.config_path = modelpath + r'/bert_config.json'
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self.checkpoint_path = modelpath + r'/bert_model.ckpt'
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self.dict_path = modelpath + r'/vocab.txt'
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self.token_dict, self.keep_tokens = load_vocab(
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dict_path=self.dict_path,
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simplified=True,
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startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'],
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)
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self.tokenizer = Tokenizer(self.token_dict, do_lower_case=True)
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self.buildmodel()
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def buildmodel(self):
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bert = build_transformer_model(
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config_path=self.config_path,
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checkpoint_path=self.checkpoint_path,
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return_keras_model=False,
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)
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output = Lambda(lambda x: x[:, 0], name='CLS-token')(bert.model.output)
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self.model = keras.models.Model(bert.model.input, output)
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self.model.summary()
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def predict(self,text):
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batch_token_ids, batch_segment_ids = [], []
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token_ids, segment_ids = self.tokenizer.encode(text, maxlen=256)
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batch_token_ids.append(token_ids)
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batch_segment_ids.append(segment_ids)
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return self.model.predict([batch_token_ids, batch_segment_ids])
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def predict_batch(self,text_list):
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batch_token_ids, batch_segment_ids = [], []
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for t in text_list:
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token_ids, segment_ids = self.tokenizer.encode(t, maxlen=256)
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batch_token_ids.append(token_ids)
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batch_segment_ids.append(segment_ids)
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batch_token_ids = sequence_padding(batch_token_ids)
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batch_segment_ids = sequence_padding(batch_segment_ids)
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return self.model.predict([batch_token_ids, batch_segment_ids])
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def simbert(data_1, data_2):
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pass
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def word2vec():
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pass
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def bleu():
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pass
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def bool_len_strsim(data_1, data_2):
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str_sim_value = difflib.SequenceMatcher(None, data_1, data_2).quick_ratio()
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if len(data_2) - len(data_1) < 0:
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if len(data_2) / len(data_1) > 0.8:
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num_yu = 1 - len(data_2) / len(data_1)
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str_sim_value = 1 - str_sim_value * num_yu
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else:
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return False, ""
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if str_sim_value < 0.65:
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return True, str_sim_value
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else:
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return False, ""
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def has_numbers(input_string):
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return any(char.isdigit() for char in input_string)
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def bool_num(data_1, data_2):
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bool_1 = has_numbers(data_1)
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bool_2 = has_numbers(data_2)
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if bool_1 == True and bool_2 == True:
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return True
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else:
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return False
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def is_contains_english(str):
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my_re = re.compile(r'[A-Za-z]', re.S)
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res = re.findall(my_re, str)
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if len(res):
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return True
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else:
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return False
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def is_contains_kongge(str):
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if " " in str or "\t" in str:
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return True
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else:
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return False
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if __name__ == '__main__':
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file = "../data/train_yy_pre.txt"
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# file = "../data/train_yy_zong_sim_99.txt"
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model = bertModel()
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eval_class = Evaluator()
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data_new = []
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data_1_list = []
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data_2_list = []
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# word2vecmodel = Word2vecModel()
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try:
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with open(file, 'r', encoding="utf-8") as f:
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lines = [x.strip() for x in f if x.strip() != '']
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except:
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with open(file, 'r', encoding="gbk") as f:
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lines = [x.strip() for x in f if x.strip() != '']
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bertsim_list = []
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bleusim_list = []
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word2vecsim_list = []
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data_train_text = []
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# random.shuffle(lines)
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print(len(lines))
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for txt in tqdm(lines):
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text = txt.split('\t')
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if len(text) == 3:
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data_1 = text[0]
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data_2 = text[2]
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# 判断是否包含数字
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bool_num_ = bool_num(data_1, data_2)
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if bool_num_ == False:
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continue
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# 判断是否包含英文
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# data_english_bool = is_contains_english(data_1)
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# if data_english_bool == True:
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# continue
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# 判断是否包含空格
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data_kongge_bool = is_contains_kongge(data_1)
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if data_kongge_bool == True:
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continue
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# 判断是否符合字符相似度标准
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bool_len_strsim_v, strsim = bool_len_strsim(data_1,data_2)
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if bool_len_strsim_v == True:
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continue
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# # 第一种方法
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# y1 = model.predict(data_1)[0]
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# y2 = model.predict(data_2)[0]
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# cos_sim = cosine_similarity(y1.reshape(1, -1), y2.reshape(1, -1))
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# # bertsim_list.append((cos_sim[0][0], strsim, data_1, data_2))
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# if cos_sim[0][0] > 0.9:
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# cos_sim_bool = True
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# else:
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# cos_sim_bool = False
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#
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# if cos_sim_bool == False:
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# continue
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#
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# data_new.append("\t".join([data_1, "to", data_2]))
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# data_train_text.append("\t".join([data_1, "to", data_2]))
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# 第二种方法
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y = model.predict_batch([data_1, data_2])
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y1 = y[0]
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y2 = y[1]
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cos_sim = cosine_similarity(y1.reshape(1, -1), y2.reshape(1, -1))
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# bertsim_list.append((cos_sim[0][0], strsim, data_1, data_2))
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if cos_sim[0][0] > 0.9:
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cos_sim_bool = True
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else:
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cos_sim_bool = False
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if cos_sim_bool == False:
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continue
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data_new.append("\t".join([data_1, "to", data_2]))
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# bertsim_list.sort(reverse=True)
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# with open("../data/tongji_len_strsim_nertsim_1.txt", "w", encoding="utf-8") as f:
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# for i in bertsim_list:
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# f.write(str(i[0]))
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# f.write(str("\t"))
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# f.write(str(i[1]))
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# f.write(str("\t"))
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# f.write(str(i[2]))
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# f.write(str("\t"))
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# f.write(str(i[3]))
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# f.write("\n")
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# print(len(data_train_text))
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fileName = '../data/train_new/train_yy_1.txt'
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# fileName = '../data/train_new/train_yy.txt'
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with open(fileName, 'w', encoding='utf-8') as f:
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for i in data_new:
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f.write(str(i) + '\n')
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f.close()
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