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import os
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import sys
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sys.path.append(os.getcwd())
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import pandas as pd
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import numpy as np
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from tqdm import tqdm
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from ltp import LTP
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import jieba
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import csv
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import torch
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from gensim.models import KeyedVectors
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from config import subjects_data_path, logger, user_dict_path
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from config import subjects_keywords_path
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from config import JIEBA_TOKEN, LTP_TOKEN, word2vec_dim
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from config import ltp_model_path
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from config import word2vec_model_path, subject_faiss_w2v_path, data_parent_path
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#生成句向量，实践课程包含字段："subject_name"，"sub_discipline_name"，"tag_names"
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tqdm.pandas()
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ltp = LTP(ltp_model_path)
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if torch.cuda.is_available():
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ltp.to("cuda")
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logger.info("加载Word2Vec词向量")
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w2v_model = KeyedVectors.load(word2vec_model_path)
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fassi_w2v_model = KeyedVectors.load(subject_faiss_w2v_path)
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# 加载用户自定义词典
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if os.path.exists(subjects_keywords_path):
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jieba.load_userdict(subjects_keywords_path)
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with open(subjects_keywords_path, 'r', encoding='utf-8') as f:
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user_dict_words = f.read().split()
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ltp.add_words(user_dict_words)
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if os.path.exists(user_dict_path):
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with open(user_dict_path, 'r', encoding='utf-8') as f:
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user_dict_words = f.read().split()
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ltp.add_words(user_dict_words)
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for word in user_dict_words:
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jieba.add_word(word)
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def tokenizer(sent, token_method=JIEBA_TOKEN, verbose=False):
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"""
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中文分词，支持jieba和ltp两种方式
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"""
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if token_method == JIEBA_TOKEN:
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seg = jieba.cut(sent)
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result = ' '.join(seg)
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elif token_method == LTP_TOKEN:
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content = []
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content.append(sent)
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seg = ltp.pipeline(content, tasks=['cws'])['cws']
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result = ''
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for word in seg[0]:
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if result == '':
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result = word
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else:
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result = result + ' ' + word
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if verbose == True:
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logger.info(f"分词方式：{token_method}, 分词结果：{result}")
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return result
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def sentence_embedding(sentence, w2v_model, fassi_w2v_model, verbose=False):
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'''
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通过词向量均值的方式生成句向量
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sentence: 待生成句向量的句子
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w2v_model: word2vec模型
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return: 句子中所有词向量的均值
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'''
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sentence = tokenizer(sentence, JIEBA_TOKEN,verbose)
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embedding = []
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for word in sentence.split():
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if (word not in w2v_model.wv.index_to_key) and (word not in fassi_w2v_model.wv.index_to_key):
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embedding.append(np.random.randn(1, word2vec_dim))
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else:
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if word in fassi_w2v_model.wv.index_to_key:
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embedding.append(fassi_w2v_model.wv.get_vector(word))
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else:
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embedding.append(w2v_model.wv.get_vector(word))
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# 所有词向量的均值为句向量
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return np.mean(np.array(embedding), axis=0).reshape(1, -1)
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def build_subjects_embedding():
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'''
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生成所有课程的句向量
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'''
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data = pd.read_csv(subjects_data_path, sep='\t', encoding='utf-8')
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# 准备数据
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subject_name = data["subject_name"]
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sub_dis_name = data["sub_discipline_name"]
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tags_name = data["tag_names"]
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subject_name.fillna(value="",inplace=True)
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sub_dis_name.fillna(value="",inplace=True)
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tags_name.fillna(value="",inplace=True)
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subject_text = subject_name+sub_dis_name+tags_name
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logger.info('生成所有课程向量')
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data['subject_name_vec'] = subject_text.progress_apply(
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lambda x: sentence_embedding(x, w2v_model, fassi_w2v_model))
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logger.info('检测所有课程向量的维度')
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data['subject_name_vec'] = data['subject_name_vec'].progress_apply(
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lambda x: x[0][0] if x.shape[1] != word2vec_dim else x)
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data['subject_id'] = data['subject_id'].astype(int)
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save_embedding_data(data)
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def save_embedding_data(datas):
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"""
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保存embedding
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"""
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logger.info("保存生成的课程向量")
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subjects_emb_data = open(data_parent_path + 'subjects_emb.csv', 'w', encoding='utf-8', newline="")
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csv_out = csv.writer(subjects_emb_data, delimiter='\t')
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# 先写入字段名
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headers = ['subject_id']
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subject_name_vec_100s = [("emb_" + str(i)) for i in range(100)]
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for i in subject_name_vec_100s:
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headers.append(i)
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csv_out.writerow(headers)
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# 再写入每行数据
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for index, row in datas.iterrows():
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subject_name_vec = row['subject_name_vec']
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subject_name_vecs = subject_name_vec[0]
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row_data = [row['subject_id']]
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for i in range(len(subject_name_vecs)):
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row_data.append(subject_name_vecs[i])
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csv_out.writerow(row_data)
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if __name__ == '__main__':
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build_subjects_embedding()
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