physics_repeat_check/db_train_app.py

import sys
import time
import json

import config
from data_preprocessing import DataPreProcessing
from dim_classify import Dimension_Classification
from physical_quantity_extract import physical_quantity_extract

"""
MongoDB数据类型: 
{ 
    "id" : 20231001, 
    "quesType" : {
        "quesType" : "单选题"
    }, 
    "quesBody" : "荔枝是一种岭南佳果，小明拿起一个荔枝，如题图所示，它的尺寸l大小约为（   ）<br/><img src=\"Upload/QBM/20231001.png\" /><br/>\nA. 0.1cm B. 3cm C. 0.3m D. 1m", 
    "quesParse" : "......", 
    "quesAnswer" : "【答案】见解析", 
    "difficulty" : "一般", 
    "knowledge" : [
        "长度的测量"
    ]
}
"""

# 数据清洗与句向量计算
def clear_embedding_train(mongo_coll, mongo_find_dict, sup, sub):
    origin_dataset = mongo_coll.find(mongo_find_dict, no_cursor_timeout=True, batch_size=5)
    dpp = DataPreProcessing(mongo_coll, is_train=True)
    start = time.time()
    dpp(origin_dataset[sup:sub])
    print("耗时:", time.time()-start)

# 知识点转换成id用于mongodb检索/计算物理量/计算求解类型
def convert_knowledge2id(mongo_coll, mongo_find_dict, sup, sub):
    # 加载知识点转ID数据
    with open(config.keyword_mapping_path, 'r', encoding="utf8") as f:
        knowledge2id = json.load(f)["knowledge2id"]
    dim_classify = Dimension_Classification(dim_mode=0)
    origin_dataset = mongo_coll.find(mongo_find_dict, no_cursor_timeout=True, batch_size=5)
    start = time.time()
    for data in origin_dataset[sup:sub]:
        condition = {"id": data["id"]}
        # 计算物理量
        physical_quantity_list = physical_quantity_extract(data["content_clear"])
        # 计算求解类型
        solution_list = dim_classify(data["content_clear"], data["quesType"])["solving_type"]
        # 知识点转ID
        knowledge_list = [knowledge2id[ele] for ele in data["knowledge"] if ele in knowledge2id]
        update_elements = {"$set": {"physical_quantity": physical_quantity_list, 
                                    "solving_type": solution_list,
                                    "knowledge_id": knowledge_list}}
        mongo_coll.update_one(condition, update_elements)
        print(physical_quantity_list, solution_list)
    print("耗时:", time.time()-start)

if __name__ == "__main__":
    # 获取shell输入参数
    argv_list = sys.argv
    if len(argv_list) == 1:
        sup, sub = None, None
    elif len(argv_list) == 2:
        sup, sub = argv_list[1].split(':')
        sup = None if sup == '' else int(sup)
        sub = None if sub == '' else int(sub)
    # 获取mongodb数据
    mongo_coll = config.mongo_coll
    # mongo_find_dict = {"sent_train_flag": {"$exists": 0}}
    mongo_find_dict = dict()
    # 清洗文本与计算句向量(train_mode=1表示需要进行文本清洗与句向量计算)
    clear_embedding_train(mongo_coll, mongo_find_dict, sup, sub)
    # 知识点转换成id用于mongodb检索
    convert_knowledge2id(mongo_coll, mongo_find_dict, sup, sub)