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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 time
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import numpy as np
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import pandas as pd
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from gensim.models import KeyedVectors
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import faiss
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import config
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from tqdm import tqdm
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import jieba
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from ltp import LTP
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import torch
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from config import data_path, word2vec_dim
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from config import ltp_model_path, subjects_keywords_path
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from config import JIEBA_TOKEN, LTP_TOKEN, logger, user_dict_path
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# 使用Faiss训练hnsw模型
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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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# 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 = re.sub(r"[\s+\.\!\/_,$%^*(+\"\')]+|[+——()?【】“”！，。？、~@#￥%……&*（）]+",
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# "", sentence)
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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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class HNSW(object):
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def __init__(self,
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w2v_path, # 通用word2vec词向量
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faiss_w2v_path, # 自己训练的word2vec词向量
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ef=config.ef_construction, # 搜索时保存最近邻的动态列表大小
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M=config.M, # 节点的邻结点的数量
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model_path=None, # hnsw模型保存路径
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data_path=None): # 数据文件路径
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# 加载词向量
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logger.info("加载Word2Vec词向量")
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self.w2v_model = KeyedVectors.load(w2v_path)
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self.fassi_w2v_model = KeyedVectors.load(faiss_w2v_path)
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# 加载hnsw模型
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if model_path and os.path.exists(model_path):
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logger.info("加载HNSW快速召回模型")
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self.data = pd.read_csv(data_path, sep='\t', encoding='utf-8')
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self.index = self.load_hnsw(model_path)
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# 训练hnsw模型
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elif data_path:
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logger.info("训练HNSW快速召回模型")
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self.data = self.load_data(data_path)
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self.index = self.build_hnsw(model_path, ef=ef, m=M)
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else:
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logger.error('No existing model and no building data provided.')
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def load_data(self, data_path):
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'''
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读取数据，并生成句向量
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:param data_path：数据所在路径
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:return: 包含句向量的dataframe
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'''
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data = pd.read_csv(data_path, sep='\t', encoding='utf-8')
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logger.info('生成所有课程向量')
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data['subject_name_vec'] = data['subject_name'].progress_apply(
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lambda x: sentence_embedding(x, self.w2v_model, self.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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# 保存生成好的句向量
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data['subject_id'] = data['subject_id'].astype(int)
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return data
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def evaluate(self, vecs):
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'''
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验证模型
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'''
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logger.info('Evaluating hnsw model')
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nq, d = vecs.shape
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t0 = time.time
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# 找top1个相似的
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D, I = self.index.search(vecs, 1)
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t1 = time.time
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missing_rate = (I == -1).sum() / float(nq)
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recall_at_1 = (I == np.arange(nq)).sum() / float(nq)
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print("\t %7.3f ms per query, R@1 %.4f, missing rate %.4f" % (
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(t1 - t0) * 1000.0 / nq, recall_at_1, missing_rate))
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def build_hnsw(self, to_file, ef=2000, m=64):
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"""
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训练hnsw模型
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"""
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logger.info('构建 HNSW 索引')
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# 所有的句向量拼接
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vecs = np.stack(self.data['subject_name_vec'].values).reshape(-1, word2vec_dim)
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vecs = vecs.astype('float32')
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dim = self.w2v_model.vector_size
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# 构建索引
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index = faiss.IndexHNSWFlat(dim, m)
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# 使用单个GPU资源
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res = faiss.StandardGpuResources()
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faiss.index_cpu_to_gpu(res, 0, index)
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index.hnsw.ef_construction = ef
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index.verbose = True
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index.add(vecs)
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# 保存hnsw模型
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faiss.write_index(index, to_file)
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return index
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def load_hnsw(self, model_path):
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hnsw = faiss.read_index(model_path)
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return hnsw
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def search(self, text, k=100):
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"""
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通过hnsw检索topk
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"""
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logger.info(f"Searching top {k} similarity for {text}.")
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# 转换句向量
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test_vec = sentence_embedding(text, self.w2v_model, self.fassi_w2v_model, verbose=True)
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test_vec = test_vec.astype('float32')
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# 搜索相似度最高的k个句向量
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# 多召回一些避免通过status过滤后不足k个
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D, I = self.index.search(test_vec, k * 2 )
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top_k_index = list(I.ravel())
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top_k_Item = []
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for index in top_k_index:
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subject_id = int(self.data.iloc[index]['subject_id'])
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subject_name = self.data.iloc[index]['subject_name']
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top_k_Item.append((subject_id, subject_name))
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# 只返回topk个
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return top_k_index[:k], top_k_Item[:k]
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def hnsw_search_test(hnsw):
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while True:
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topk = 100
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item_name = input('请输入您的课程名称：').strip()
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top_k_index, top_k_Item = hnsw.search(item_name, k=topk)
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df_top_k = pd.DataFrame(columns=['index', 'subject_recommend'])
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pd.set_option('colheader_justify', 'center')
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for i in range(topk):
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df_top_k.loc[i] = [top_k_index[i], top_k_Item[i]]
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print(df_top_k)
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if __name__ == '__main__':
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hnsw = HNSW(config.word2vec_model_path,
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config.subject_faiss_w2v_path,
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config.ef_construction,
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config.M,
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config.subjects_fassi_model_path,
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config.subjects_data_path) |
