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207 lines
6.7 KiB
207 lines
6.7 KiB
2 years 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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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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self.config_path = '../chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_config.json'
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self.checkpoint_path = '../chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_model.ckpt'
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self.dict_path = '../chinese_roberta_wwm_ext_L-12_H-768_A-12/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 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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if __name__ == '__main__':
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file = "../data/train_yy_1.txt"
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model = bertModel()
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eval_class = Evaluator()
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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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str_sim_value = difflib.SequenceMatcher(None, data_1, data_2).quick_ratio()
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if len(data_2) - len(data_1) < 0 and 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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if str_sim_value < 0.70:
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data_train_text.append("\t".join([data_1, "to", data_2]))
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# eval_list = eval_class.evaluate_t(' '.join(data_1), ' '.join(data_2))
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# bleusim_list.append(eval_list[3])
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# word2vec
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# seg_0_list = jieba.cut(data_1, cut_all=False)
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# seg_1_list = jieba.cut(data_2, cut_all=False)
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# seg_0_list = [char for char in seg_0_list]
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# seg_1_list = [char for char in seg_1_list]
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#
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# sentence_0_list, sentence_1_list = word2vecmodel.word2vec_res(seg_0_list, seg_1_list)
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# sentence_0_result = np.array(sentence_0_list)
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# sentence_1_result = np.array(sentence_1_list)
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# sentence_0_array = sentence_0_result.sum(axis=0)
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# sentence_1_array = sentence_1_result.sum(axis=0)
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# print(sentence_1_array)
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# print(sentence_0_array)
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# cos_sim = cosine_similarity(sentence_0_array.reshape(1, -1), sentence_1_array.reshape(1, -1))
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# word2vecsim_list.append(cos_sim[0][0])
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# bertsim_list = sorted(bertsim_list)
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# zong_num = len(bertsim_list)
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# print(bertsim_list)
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# print(bertsim_list[int(zong_num/2)])
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# print(sum(bertsim_list)/zong_num)
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# bleusim_list = sorted(bleusim_list)
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# zong_num = len(bleusim_list)
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# print(bleusim_list)
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# print(bleusim_list[int(zong_num / 2)])
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# print(sum(bleusim_list) / zong_num)
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print(len(data_train_text))
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fileName = 'train_yy_1_sim_10.txt'
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with open(fileName, 'w', encoding='utf-8') as file:
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for i in data_train_text:
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file.write(str(i) + '\n')
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file.close()
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