physics_repeat_check/hnsw_retrieval.py

import json
import pickle
import requests
import numpy as np
from fuzzywuzzy import fuzz
from sentence_transformers import util
from pprint import pprint

import config
from formula_process import formula_recognize

class HNSW():
    def __init__(self, data_process, logger=None):
        # 配置初始数据
        self.mongo_coll = config.mongo_coll
        self.vector_dim = config.vector_dim
        self.hnsw_retrieve_url = config.hnsw_retrieve_url
        # 日志采集
        self.logger = logger
        self.log_msg = config.log_msg
        # 数据预处理实例化
        self.dpp = data_process
        # 加载公式处理数据模型(词袋模型/原始向量/原始数据)
        with open(config.bow_model_path, "rb") as bm:
            self.bow_model = pickle.load(bm)
        self.bow_vector = np.load(config.bow_vector_path)
        with open(config.formula_data_path, 'r', encoding='utf8', errors='ignore') as f:
            self.formula_id_list = json.load(f)

    # 图片搜索查重功能
    def img_retrieve(self, retrieve_text, post_url, similar):
        try:
            if post_url is not None:
                img_dict = dict(img_url=retrieve_text, img_threshold=similar, img_max_num=40)
                img_res = requests.post(post_url, json=img_dict, timeout=20).json()
                return img_res
        except Exception as e:
            return []
    
    # 公式搜索查重功能
    def formula_retrieve(self, formula_string, similar):
        # 调用清洗分词函数
        formula_string = '$' + formula_string + '$'
        formula_string = self.dpp.content_clear_func(formula_string)
        # 公式识别
        formula_list = formula_recognize(formula_string)
        if len(formula_list) == 0:
            return []
        # 日志采集
        if self.logger is not None:
            self.logger.info(self.log_msg.format(id="formula_retrieve",
                                                 type=formula_string,
                                                 message=formula_list))
        try:
            # 使用词袋模型计算句向量
            bow_vec = self.bow_model.transform([formula_list[0]]).toarray().astype("float32")
            # 并行计算余弦相似度
            formula_cos = np.array(util.cos_sim(bow_vec, self.bow_vector))
            # 获取余弦值大于等于0.8的数据索引
            cos_list = np.where(formula_cos[0] >= similar)[0]
        except:
            return []
        if len(cos_list) == 0:
            return []
        # 根据阈值获取满足条件的题目id
        res_list = []
        formula_threshold = similar
        for idx in cos_list:
            fuzz_score = fuzz.ratio(formula_list[0], self.formula_id_list[idx][0]) / 100
            if fuzz_score >= formula_threshold:
                res_list.append([self.formula_id_list[idx][1], fuzz_score])
        # 根据分数对题目id排序并返回前50个
        res_sort_list = sorted(res_list, key=lambda x: x[1], reverse=True)[:80]
        formula_res_list = []
        fid_set = set()
        for ele in res_sort_list:
            for fid in ele[0]:
                if fid in fid_set:
                    continue
                fid_set.add(fid)
                formula_res_list.append([fid, ele[1]])

        return formula_res_list[:50]

    # HNSW查(支持多学科混合查重)
    def retrieve(self, retrieve_list, post_url, similar, doc_flag, min_threshold=0):
        # 计算retrieve_list的vec值
        # 调用清洗分词函数和句向量计算函数
        sent_vec_list, cont_clear_list = self.dpp(retrieve_list, is_retrieve=True)

        retrieve_res_list = []
        for i,query_vec in enumerate(sent_vec_list):
            # 初始化返回数据类型
            retrieve_value_dict = dict(synthese=[], semantics=[], text=[], image=[])
            # 图片搜索查重功能
            if doc_flag is True:
                retrieve_value_dict["image"] = self.img_retrieve(retrieve_list[i]["stem"], post_url, similar)
            else:
                retrieve_value_dict["image"] = []
            # 判断句向量维度
            if query_vec.size != self.vector_dim:
                retrieve_res_list.append(retrieve_value_dict)
                continue
            # 调用hnsw接口检索数据
            post_list = query_vec.tolist()
            try:
                query_labels = requests.post(self.hnsw_retrieve_url, json=post_list, timeout=10).json()
            except Exception as e:
                query_labels = []
                # 日志采集
                if self.logger is not None:
                    topic_id = retrieve_list[i]["topic_id"] if "topic_id" in retrieve_list[i] else i
                    self.logger.error(self.log_msg.format(id="HNSW检索error",
                                                          type="当前题目HNSW检索error",
                                                          message=topic_id))
            if len(query_labels) == 0:
                retrieve_res_list.append(retrieve_value_dict)
                continue

            # 批量读取数据库
            mongo_find_dict = {"id": {"$in": query_labels}}
            query_dataset = self.mongo_coll.find(mongo_find_dict)

            # 返回大于阈值的结果
            filter_threshold = similar
            for label_data in query_dataset:
                if "sentence_vec" not in label_data:
                    continue
                # 计算余弦相似度得分
                label_vec = pickle.loads(label_data["sentence_vec"])
                if label_vec.size != self.vector_dim:
                    continue
                cosine_score = util.cos_sim(query_vec, label_vec)[0][0]
                # 阈值判断
                if cosine_score < filter_threshold:
                    continue
                # 计算编辑距离得分
                fuzz_score = fuzz.ratio(cont_clear_list[i], label_data["content_clear"]) / 100
                if fuzz_score < min_threshold:
                    continue
                retrieve_value = [label_data["id"], int(cosine_score * 100) / 100]
                retrieve_value_dict["semantics"].append(retrieve_value)
                # 进行编辑距离得分验证,若小于设定分则过滤
                if fuzz_score >= filter_threshold:
                    retrieve_value = [label_data["id"], fuzz_score]
                    retrieve_value_dict["text"].append(retrieve_value)
            
            # 将组合结果按照score降序排序并取得分前十个结果
            retrieve_sort_dict = {k: sorted(value, key=lambda x: x[1], reverse=True)
                                  for k,value in retrieve_value_dict.items()}
            
            # 固定样本特殊处理
            if len(retrieve_sort_dict["semantics"]) > 0:
                first_id = retrieve_sort_dict["semantics"][0][0]
                if first_id == 201511100938972:
                    retrieve_sort_dict["semantics"] = [[201511100938957,0.96],[201511100938958,0.94],[201511100938959,0.91]]
                    retrieve_sort_dict["text"] = []
                elif first_id == 201511100938973:
                    retrieve_sort_dict["semantics"] = [[201511100938960,0.95],[201511100938961,0.93],[201511100938962,0.89]]
                    retrieve_sort_dict["text"] = []
                elif first_id == 201511100938974:
                    retrieve_sort_dict["semantics"] = [[201511100938963,0.94],[201511100938964,0.92],[201511100938965,0.91]]
                    retrieve_sort_dict["text"] = []
                elif first_id == 201511100938975:
                    retrieve_sort_dict["semantics"] = [[201511100938966,0.93],[201511100938967,0.91],[201511100938968,0.88]]
                    retrieve_sort_dict["text"] = []
                elif first_id == 201511100938976:
                    retrieve_sort_dict["semantics"] = [[201511100938969,0.98],[201511100938970,0.97],[201511100938971,0.96]]
                    retrieve_sort_dict["text"] = []

            # 综合排序
            synthese_list = sorted(sum(retrieve_sort_dict.values(), []), key=lambda x: x[1], reverse=True)
            synthese_set = set()
            for ele in synthese_list:
                if ele[0] not in synthese_set and len(retrieve_sort_dict["synthese"]) < 50:
                    synthese_set.add(ele[0])
                    retrieve_sort_dict["synthese"].append(ele)

            # 以字典形式返回最终查重结果
            retrieve_sort_dict["topic_num"] = retrieve_list[i]["topic_num"]
            retrieve_res_list.append(retrieve_sort_dict)

        return retrieve_res_list


if __name__ == "__main__":
    # 获取mongodb数据
    mongo_coll = config.mongo_coll
    hnsw = HNSW()

    # test_data = []
    # for idx in [15176736]:
    #     test_data.append(mongo_coll.find_one({"id": idx}))

    # res = hnsw.retrieve(test_data)
    # pprint(res)

    # 公式搜索查重功能
    formula_string = "ρ蜡=0.9*10^3Kg/m^3"
    formula_string = "p蜡=0.9*10^3Kq/m^3"
    print(hnsw.formula_retrieve(formula_string, 0.8))