BIDI_EXTRACTION_AI/BiddingKG/dl/table_head/check_user_label_accuracy.py

import time
from BiddingKG.dl.table_head.pre_process import postgresql_util
from BiddingKG.dl.table_head.predict import predict


def user_label_accuracy(update_user):
    if update_user != 'test':
        sql = """
        select table_text, pre_label, post_label, id
        from label_table_head_info 
        where update_user='""" + update_user + "' order by update_time"
    else:
        sql = """
        select table_text, pre_label, post_label, id
        from label_table_head_info 
        where update_user='""" + update_user + "' and status = 1 and update_time >= '2022-01-23'"

    result_list = postgresql_util(sql, limit=1000000)
    right_cnt = 0
    error_cnt = 0
    error_id_list = []
    right_id_list = []
    i = 0
    start_time = time.time()
    for table in result_list:
        i += 1
        if i % 1000 == 0:
            if right_cnt + error_cnt != 0:
                print("Loop", i, right_cnt/(right_cnt+error_cnt), time.time()-start_time)
            else:
                print("Loop", i, time.time()-start_time)
            start_time = time.time()

        pre_label = eval(table[1])
        post_label = eval(table[2])
        _id = table[3]

        # table_text需要特殊处理
        try:
            table_text = table[0]
            if table_text[0] == '"':
                table_text = eval(table_text)
            else:
                table_text = table_text
            table_text = table_text.replace('\\', '/')
            table_text = eval(table_text)
        except:
            print("无法识别table_text", _id)
            continue

        if post_label:
            label_list = post_label
        else:
            label_list = pre_label

        predict_label_list = predict(table_text, model_id=3)
        if predict_label_list:
            if str(label_list) == str(predict_label_list):
                right_id_list.append(str(_id)+"\n")
                # right_cnt += 1
            else:
                error_id_list.append(str(_id)+"\n")
                # error_cnt += 1
            if len(label_list) == len(predict_label_list):
                for j in range(len(label_list)):
                    for k in range(len(label_list[j])):
                        if table_text[j][k] == "":
                            continue
                        if label_list[j][k] == "1" or predict_label_list[j][k] == "1":
                            if len(table_text[j][k]) >= 20:
                                continue
                            else:
                                if label_list[j][k] == predict_label_list[j][k]:
                                    right_cnt += 1
                                else:
                                    error_cnt += 1
            else:
                print("len(label_list) == len(predict_label_list)", _id,
                      len(label_list), len(predict_label_list))

    accuracy = right_cnt / (right_cnt + error_cnt)
    print(update_user + " accuracy:", accuracy, 'total:', len(result_list))
    print("error_id_list", len(error_id_list))

    save_path = "check_user_result/accuracy.txt"
    with open(save_path, 'a') as f:
        f.write(update_user + " "
                + "表头正确率-" + str(round(accuracy, 2)) + " "
                + "文章数-" + str(len(result_list)) + " "
                + "表格总数-" + str(right_cnt + error_cnt) + " "
                + "表头正确数-" + str(right_cnt)
                + "\n")

    save_path = "check_user_result/"+update_user+"_error.txt"
    with open(save_path, 'w') as f:
        f.writelines(error_id_list)

    save_path = "check_user_result/"+update_user+"_right.txt"
    with open(save_path, 'w') as f:
        f.writelines(right_id_list)

    return accuracy


def get_single_result(_id):
    sql = """
    select table_text, pre_label, post_label, id
    from label_table_head_info 
    where id=""" + str(_id)

    result_list = postgresql_util(sql, limit=1000000)
    table_text = result_list[0][0]
    if table_text[0] == '"':
        table_text = eval(table_text)
    else:
        table_text = table_text
    table_text = table_text.replace('\\', '/')
    table_text = eval(table_text)
    label_list = predict(table_text)
    for i in range(len(label_list)):
        print(i+1, label_list[i])


if __name__ == '__main__':
    # users = ["test9", "test11", "test12", "test25", "test26"]
    # users = ["test9", "test11", ]
    # users = ['test12', 'test25']
    # users = ["test20", "test27"]
    # users = ['test']
    users = [
        "test1",
        "test11",
        "test12",
        "test16",
        "test17",
        "test19",
        "test20",
        "test21",
        "test22",
        "test25",
        "test26",
        "test27",
        "test29",
        "test3",
        "test7",
        "test8",
        "test9",
    ]
    users = ["test"]
    users = ["test12", "test17", "test21", "test22", "test27", ]
    users = ["test27"]
    acc_list = []
    for user in users:
        acc = user_label_accuracy(user)
        acc_list.append([user, acc])

    print(acc_list)

    # get_single_result(161927)

    # import pandas as pd
    # df = pd.read_csv("C:\\Users\\Administrator\\Desktop\\4.csv")
    # _dict = {
    #     51: "公告变更",
    #     52: "招标公告",
    #     101: "中标信息",
    #     102: "招标预告",
    #     103: "招标答疑",
    #     104: "招标文件",
    #     105: "资审结果",
    #     106: "法律法规",
    #     107: "新闻资讯",
    #     108: "拟建项目",
    #     109: "展会推广",
    #     110: "企业名录",
    #     111: "企业资质",
    #     112: "全国工程人员",
    #     113: "业主采购",
    #     114: "采购意向",
    #     115: "拍卖出让",
    #     116: "土地矿产",
    #     117: "产权交易",
    #     118: "废标公告",
    #     119: "候选人公示",
    #     120: "合同公告",
    # }
    # data_list = []
    # for index, row in df.iterrows():
    #     if index % 100000 == 0:
    #         print("Loop", index)
    #         print(_dict[int(row['docchannel'])])
    #         print(df.iloc[index, 2])
    #     data = row.tolist()
    #     data[2] = _dict[int(data[2])]
    #     data_list.append(data)
    # df = pd.DataFrame(data_list)
    # df.columns = ['docid', '项目名称', '信息类型', '发布时间', '地区', '业主',
    #               '预算金额', '中标供应商', '成交金额', '代理机构']
    # df.to_csv("C:\\Users\\Administrator\\Desktop\\4-1.csv", index=False)