jiangcang_vj/result_keep_verify.py

import os
import datetime
import pandas as pd
from data_loader import mongo_con_parse, validate_keep_one_line, fill_hourly_crawl_date
from config import vj_flight_route_list_hot, vj_flight_route_list_nothot, \
    CLEAN_VJ_HOT_NEAR_INFO_TAB, CLEAN_VJ_HOT_FAR_INFO_TAB, CLEAN_VJ_NOTHOT_NEAR_INFO_TAB, CLEAN_VJ_NOTHOT_FAR_INFO_TAB


def _validate_keep_info_df(df_keep_info_part):
    client, db = mongo_con_parse()
    count = 0

    if "price_diff" not in df_keep_info_part.columns:
        df_keep_info_part["price_diff"] = 0
    if "time_diff_hours" not in df_keep_info_part.columns:
        df_keep_info_part["time_diff_hours"] = 0

    for idx, row in df_keep_info_part.iterrows():
        df_keep_info_part.at[idx, "price_diff"] = 0
        df_keep_info_part.at[idx, "time_diff_hours"] = 0

        city_pair = row['city_pair']
        flight_day = row['flight_day']
        flight_number_1 = row['flight_number_1']
        flight_number_2 = row['flight_number_2']
        baggage = row['baggage']
        # update_hour = row['update_hour']
        # update_dt = pd.to_datetime(update_hour, format='%Y-%m-%d %H:%M:%S')
        into_update_hour = row['into_update_hour']
        into_update_dt = pd.to_datetime(into_update_hour, format='%Y-%m-%d %H:%M:%S')
        del_batch_time_str = row['del_batch_time_str']
        del_batch_dt = pd.to_datetime(del_batch_time_str, format='%Y%m%d%H%M')
        del_batch_std_str = del_batch_dt.strftime('%Y-%m-%d %H:%M:%S')

        entry_price = pd.to_numeric(row.get('adult_total_price'), errors='coerce')
        if city_pair in vj_flight_route_list_hot:
            table_name_far = CLEAN_VJ_HOT_FAR_INFO_TAB
            table_name_near = CLEAN_VJ_HOT_NEAR_INFO_TAB
        elif city_pair in vj_flight_route_list_nothot: 
            table_name_far = CLEAN_VJ_NOTHOT_FAR_INFO_TAB
            table_name_near = CLEAN_VJ_NOTHOT_NEAR_INFO_TAB

        # 分别从远期表和近期表里查询
        df_query_far = validate_keep_one_line(db, table_name_far, city_pair, flight_day, flight_number_1, flight_number_2, 
                                              baggage, into_update_hour, del_batch_std_str)
        df_query_near = validate_keep_one_line(db, table_name_near, city_pair, flight_day, flight_number_1, flight_number_2, 
                                               baggage, into_update_hour, del_batch_std_str)
        # 合并
        df_query = pd.concat([df_query_far, df_query_near]).reset_index(drop=True)
        if (not df_query.empty) and pd.notna(entry_price):
            if ("adult_total_price" in df_query.columns) and ("crawl_date" in df_query.columns):
                df_query["adult_total_price"] = pd.to_numeric(df_query["adult_total_price"], errors="coerce")
                df_query["crawl_dt"] = pd.to_datetime(df_query["crawl_date"], errors="coerce")
                df_query = (
                    df_query.dropna(subset=["adult_total_price", "crawl_dt"])
                    .sort_values("crawl_dt")
                    .reset_index(drop=True)
                )
                mask_drop = df_query["adult_total_price"] < entry_price
                # mask_drop = (df_query["adult_total_price"] < entry_price) & (df_query["crawl_dt"] > update_dt)
                if mask_drop.any():
                    first_row = df_query.loc[mask_drop].iloc[0]
                    price_diff = entry_price - first_row["adult_total_price"]
                    time_diff_hours = (first_row["crawl_dt"] - into_update_dt) / pd.Timedelta(hours=1)
                    df_keep_info_part.at[idx, "price_diff"] = round(float(price_diff), 2)
                    df_keep_info_part.at[idx, "time_diff_hours"] = round(float(time_diff_hours), 2)
                    pass
        
        del df_query
        del df_query_far
        del df_query_near

        count += 1
        if count % 5 == 0:
            print(f"cal count: {count}")
    
    print(f"计算结束")
    client.close()

    return df_keep_info_part


def verify_process(min_batch_time_str, max_batch_time_str):
    object_dir = "./keep_0"

    output_dir = f"./validate/keep"
    os.makedirs(output_dir, exist_ok=True)

    timestamp_str = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
    save_scv = f"result_keep_verify_{timestamp_str}.csv"
    output_path = os.path.join(output_dir, save_scv)
    
    # 获取今天的日期
    # today_str = pd.Timestamp.now().strftime('%Y-%m-%d')

    # 检查目录是否存在
    if not os.path.exists(object_dir):
        print(f"目录不存在: {object_dir}")
        return
                    
    # 获取所有以 keep_info_ 开头的 CSV 文件
    csv_files = []
    for file in os.listdir(object_dir):
        if file.startswith("keep_info_") and file.endswith(".csv"):
            csv_files.append(file)

    if not csv_files:
        print(f"在 {object_dir} 中没有找到 keep_info_ 开头的 CSV 文件")
        return
    
    csv_files.sort()
    # print(csv_files)

    min_batch_dt = datetime.datetime.strptime(min_batch_time_str, "%Y%m%d%H%M")
    min_batch_dt = min_batch_dt.replace(minute=0, second=0, microsecond=0)
    max_batch_dt = datetime.datetime.strptime(max_batch_time_str, "%Y%m%d%H%M")
    max_batch_dt = max_batch_dt.replace(minute=0, second=0, microsecond=0)

    if min_batch_dt is not None and max_batch_dt is not None and min_batch_dt > max_batch_dt:
        print(f"时间范围非法: min_batch_time_str({min_batch_time_str}) > max_batch_time_str({max_batch_time_str})，退出")
        return
    
    # 从所有的 keep_info 文件中
    for csv_file in csv_files:
        batch_time_str = (
            csv_file.replace("keep_info_", "").replace(".csv", "")
        )
        batch_dt = datetime.datetime.strptime(batch_time_str, "%Y%m%d%H%M")
        batch_hour_dt = batch_dt.replace(minute=0, second=0, microsecond=0)
        
        if min_batch_dt is not None and batch_hour_dt < min_batch_dt:
            continue
        if max_batch_dt is not None and batch_hour_dt > max_batch_dt:
            continue

        # 读取 CSV 文件
        csv_path = os.path.join(object_dir, csv_file)
        try:
            df_keep_info = pd.read_csv(csv_path)
        except Exception as e:
            print(f"read {csv_path} error: {str(e)}")
            df_keep_info = pd.DataFrame()
        
        if df_keep_info.empty:
            print(f"keep_info数据为空: {csv_file}")
            continue
        
        df_keep_info_del = df_keep_info[df_keep_info['keep_flag'] == -1].reset_index(drop=True)
        df_keep_info_del['del_batch_time_str'] = batch_time_str
        df_keep_info_del = _validate_keep_info_df(df_keep_info_del)

        # 根据价格变化情况, 移出时间与验证终点时间的对比, 计算 status_flag 状态
        price_diff_num = pd.to_numeric(df_keep_info_del.get("price_diff"), errors="coerce").fillna(0)
        del_batch_dt = pd.to_datetime(
            df_keep_info_del.get("del_batch_time_str"), format="%Y%m%d%H%M", errors="coerce"
        )
        valid_end_dt = pd.to_datetime(
            df_keep_info_del.get("valid_end_hour"), format="%Y-%m-%d %H:%M:%S", errors="coerce"
        )
        status_flag = pd.Series(0, index=df_keep_info_del.index, dtype="int64")   # 默认状态 0
        status_flag.loc[price_diff_num > 0] = 1   # 降价状态 1
        mask_zero = price_diff_num == 0
        mask_time_ok = mask_zero & del_batch_dt.notna() & valid_end_dt.notna() & (del_batch_dt >= valid_end_dt)
        status_flag.loc[mask_time_ok] = 2   # 超时状态 2
        df_keep_info_del["status_flag"] = status_flag
        
        write_header = not os.path.exists(output_path)
        df_keep_info_del.to_csv(output_path, mode="a", header=write_header, index=False, encoding="utf-8-sig")
        del df_keep_info_del
        print(f"批次:{batch_time_str} 检验结束")

    print("检验结束")
    print()


def verify_process_2(min_batch_time_str, max_batch_time_str):

    object_dir = "/home/node04/descending_cabin_files_vj"

    output_dir = f"./validate/keep"
    os.makedirs(output_dir, exist_ok=True)

    timestamp_str = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
    save_scv = f"result_keep_verify_{timestamp_str}.csv"
    output_path = os.path.join(output_dir, save_scv)

    # 检查目录是否存在
    if not os.path.exists(object_dir):
        print(f"目录不存在: {object_dir}")
        return
                    
    # 获取所有以 keep_info_end_ 开头的 CSV 文件
    csv_files = []
    for file in os.listdir(object_dir):
        if file.startswith("keep_info_end_") and file.endswith(".csv"):
            csv_files.append(file)
    
    if not csv_files:
        print(f"在 {object_dir} 中没有找到 keep_info_end_ 开头的 CSV 文件")
        return
    
    csv_files.sort()

    min_batch_dt = datetime.datetime.strptime(min_batch_time_str, "%Y%m%d%H%M")
    min_batch_dt = min_batch_dt.replace(minute=0, second=0, microsecond=0)
    max_batch_dt = datetime.datetime.strptime(max_batch_time_str, "%Y%m%d%H%M")
    max_batch_dt = max_batch_dt.replace(minute=0, second=0, microsecond=0)

    if min_batch_dt is not None and max_batch_dt is not None and min_batch_dt > max_batch_dt:
        print(f"时间范围非法: min_batch_time_str({min_batch_time_str}) > max_batch_time_str({max_batch_time_str})，退出")
        return
    
    list_df = []

    # 从所有的 keep_info_end_ 文件中
    for csv_file in csv_files:
        batch_time_str = csv_file.replace("keep_info_end_", "").replace(".csv", "")
        batch_dt = datetime.datetime.strptime(batch_time_str, "%Y%m%d%H%M%S")
        batch_hour_dt = batch_dt.replace(minute=0, second=0, microsecond=0)

        if min_batch_dt is not None and batch_hour_dt < min_batch_dt:
            continue
        if max_batch_dt is not None and batch_hour_dt > max_batch_dt:
            continue

        # 读取 CSV 文件
        csv_path = os.path.join(object_dir, csv_file)
        try:
            df_keep_info = pd.read_csv(csv_path)
        except Exception as e:
            print(f"read {csv_path} error: {str(e)}")
            continue

        if df_keep_info.empty:
            print(f"keep_info数据为空: {csv_file}")
            continue
        
        df_keep_info["batch_time_str"] = batch_hour_dt.strftime("%Y%m%d%H%M")
        # df_keep_info["src_file"] = csv_file
        list_df.append(df_keep_info)
        del df_keep_info

    if not list_df:
        print("时间范围内没有可用 keep_info_end_ 数据")
        return
    
    df_keep_all = pd.concat(list_df, ignore_index=True)
    del list_df
    
    sort_cols = ["city_pair", "flight_day", "flight_number_1", "flight_number_2", "into_update_hour"]
    df_keep_all = df_keep_all.sort_values(sort_cols, kind="mergesort").reset_index(drop=True)
    df_keep_all["gid"] = df_keep_all.groupby(sort_cols, sort=False).ngroup().astype("int64") + 1

    client, db = mongo_con_parse()
    list_base_row = []

    for gid, df_gid in df_keep_all.groupby("gid", sort=False):
        city_pair = df_gid["city_pair"].iloc[0]
        flight_day = df_gid["flight_day"].iloc[0]
        flight_number_1 = df_gid["flight_number_1"].iloc[0]
        flight_number_2 = df_gid["flight_number_2"].iloc[0]
        into_update_hour = df_gid["into_update_hour"].iloc[0]
        valid_end_hour = df_gid["valid_end_hour"].iloc[0]   

        into_update_dt = pd.to_datetime(
            df_gid.get("into_update_hour"), format="%Y-%m-%d %H:%M:%S", errors="coerce"
        ).min()    # 进入序列的小时数 
        batch_dt = pd.to_datetime(
            df_gid.get("batch_time_str"), format="%Y%m%d%H%M", errors="coerce"
        ).max()    # 离开序列的小时数

        valid_end_dt = pd.to_datetime(valid_end_hour, format="%Y-%m-%d %H:%M:%S", errors="coerce")  # 距离起飞72小时的节点
        
        flag = 0     # 等待标记
        if batch_dt >= valid_end_dt:
            flag = 2     # （距离起飞前72小时）超时标记
        elif batch_dt < max_batch_dt:
            flag = 3     # 弹出标记    

        if pd.isna(into_update_dt) or pd.isna(batch_dt):
            print(f"gid={gid} 时间字段解析失败，跳过")
            continue

        crawl_date_begin = (batch_dt + pd.Timedelta(hours=0)).strftime("%Y-%m-%d %H:%M:%S")  # 出序列的那个时间段
        crawl_date_end = (batch_dt + pd.Timedelta(hours=8)).strftime("%Y-%m-%d %H:%M:%S")    # 出序列的那个时间段往后延申8小时

        if city_pair in vj_flight_route_list_hot:
            table_name_far = CLEAN_VJ_HOT_FAR_INFO_TAB
            table_name_near = CLEAN_VJ_HOT_NEAR_INFO_TAB
        elif city_pair in vj_flight_route_list_nothot:
            table_name_far = CLEAN_VJ_NOTHOT_FAR_INFO_TAB
            table_name_near = CLEAN_VJ_NOTHOT_NEAR_INFO_TAB
        else:
            print(f"gid={gid} 城市对{city_pair}不在热门/冷门列表，跳过")
            continue

        baggage = 0
        # 查远期表
        df_query_far = validate_keep_one_line(
            db,
            table_name_far,
            city_pair,
            flight_day,
            flight_number_1,
            flight_number_2,
            baggage,
            crawl_date_begin,
            crawl_date_end,
        )
        # 查近期表
        df_query_near = validate_keep_one_line(
            db,
            table_name_near,
            city_pair,
            flight_day,
            flight_number_1,
            flight_number_2,
            baggage,
            crawl_date_begin,
            crawl_date_end,
        )
        df_query = pd.concat([df_query_far, df_query_near], ignore_index=True)

        df_g1 = df_gid.copy()
        df_g2 = df_query.copy()

        df_g1["_batch_dt"] = pd.to_datetime(
            df_g1.get("batch_time_str"), format="%Y%m%d%H%M", errors="coerce"
        )

        last_price = float(df_g1.iloc[-1]["adult_total_price"])
        df_last_price = df_g1[df_g1["adult_total_price"] == last_price]
        base_row = df_last_price.iloc[0]
        # base_pos = int(df_last_price.index[0])
        base_dt = base_row["_batch_dt"]   # 出现最后价格的第一个批次
        base_price = float(base_row["adult_total_price"])  # 最后价格
       
        # drop_pos = pd.NA
        drop_crawl_date = pd.NA
        drop_price = pd.NA
        price_diff = 0.0
        time_diff_hours = 0.0

        if not df_g2.empty:
            df_g2["crawl_dt"] = pd.to_datetime(df_g2.get("crawl_date"), errors="coerce")
            mask_drop = df_g2["adult_total_price"] < base_price
            # 发生降价的场景
            if mask_drop.any():  
                drop_row = df_g2.loc[mask_drop].iloc[0]
                # drop_pos = int(drop_row.name)
                drop_crawl_date = drop_row.get("crawl_date")
                drop_price = float(drop_row["adult_total_price"])
                price_diff = round(base_price - drop_price, 2)
                time_diff_hours = round(
                    float((drop_row["crawl_dt"] - base_dt) / pd.Timedelta(hours=1)),
                    2,
                )
                flag = 1  # 发生降价标记
            # 没有发生降价的场景
            else: 
                pass

        base_row_cp = base_row.copy()
        base_row_cp["end_batch_dt"] = batch_dt
        base_row_cp["drop_crawl_date"] = drop_crawl_date
        base_row_cp["drop_price"] = drop_price
        base_row_cp["price_diff"] = price_diff
        base_row_cp["time_diff_hours"] = time_diff_hours
        if pd.notna(base_row_cp.get("end_batch_dt")) and pd.notna(base_row_cp.get("_batch_dt")):
            base_row_cp["time_diff_hours_2"] = round(
                float((base_row_cp["end_batch_dt"] - base_row_cp["_batch_dt"]) / pd.Timedelta(hours=1)),
                2,
            )
        else:
            base_row_cp["time_diff_hours_2"] = pd.NA
        base_row_cp["flag"] = flag

        list_base_row.append(base_row_cp)

        del df_g1
        del df_g2
        del df_last_price
        del df_query_far
        del df_query_near
        del df_query

    client.close()

    df_base = pd.DataFrame(list_base_row)
    df_base.to_csv(output_path, header=True, index=False, encoding="utf-8-sig")
    print(f"输出: {output_path}")
    return

if __name__ == "__main__":
    # verify_process("202604071700", "202604090900")
    # verify_process_2("202604211700", "202604220900")
    verify_process_2("202604281500", "202604291300")