jiangcang_vj/data_preprocess.py

import pandas as pd
import numpy as np
import bisect
import gc
from datetime import datetime, timedelta
from sklearn.preprocessing import StandardScaler
from config import city_to_country, vj_city_code_map, vi_flight_number_map, build_country_holidays
from utils import insert_df_col

COUNTRY_HOLIDAYS = build_country_holidays(city_to_country)


def preprocess_data(df_input, features, categorical_features, is_training=True, current_n_hours=36):
    print(">>> 开始数据预处理") 

    # 生成 城市对
    df_input['city_pair'] = (
        df_input['from_city_code'].astype(str) + "-" + df_input['to_city_code'].astype(str)
    )
    # 城市码映射成数字
    df_input['from_city_num'] = df_input['from_city_code'].map(vj_city_code_map)
    df_input['to_city_num'] = df_input['to_city_code'].map(vj_city_code_map)
    
    missing_from = (
        df_input.loc[df_input['from_city_num'].isna(), 'from_city_code']
        .unique()
    )
    missing_to = (
        df_input.loc[df_input['to_city_num'].isna(), 'to_city_code']
        .unique()
    )
    if missing_from:
        print("未映射的 from_city：", missing_from)
    if missing_to:
        print("未映射的 to_city：", missing_to)

    # 把 city_pair、from_city_code、from_city_num, to_city_code, to_city_num 放到前几列
    cols = df_input.columns.tolist()
    # 删除已存在的几列（保证顺序正确）
    for c in ['city_pair', 'from_city_code', 'from_city_num', 'to_city_code', 'to_city_num']:
        cols.remove(c)
    # 这几列插入到最前面
    df_input = df_input[['city_pair', 'from_city_code', 'from_city_num', 'to_city_code', 'to_city_num'] + cols]
    pass

    # 转格式
    df_input['search_dep_time'] = pd.to_datetime(
        df_input['search_dep_time'],
        format='%Y%m%d',
        errors='coerce'
    ).dt.strftime('%Y-%m-%d')
    # 重命名起飞日期
    df_input.rename(columns={'search_dep_time': 'flight_day'}, inplace=True)
    
    # 重命名航班号
    df_input.rename(
        columns={
            'seg1_flight_number': 'flight_number_1',
            'seg2_flight_number': 'flight_number_2'
        },
        inplace=True
    )
    # 分开填充
    df_input['flight_number_1'] = df_input['flight_number_1'].fillna('VJ')
    df_input['flight_number_2'] = df_input['flight_number_2'].fillna('VJ')

    # 航班号转数字
    df_input['flight_1_num'] = df_input['flight_number_1'].map(vi_flight_number_map)
    df_input['flight_2_num'] = df_input['flight_number_2'].map(vi_flight_number_map)

    missing_flight_1 = (
        df_input.loc[df_input['flight_1_num'].isna(), 'flight_number_1']
        .unique()
    )
    missing_flight_2 = (
        df_input.loc[df_input['flight_2_num'].isna(), 'flight_number_2']
        .unique()
    )
    if missing_flight_1:
        print("未映射的 flight_1：", missing_flight_1)
    if missing_flight_2:
        print("未映射的 flight_2：", missing_flight_2)
    
    # flight_1_num 放在 seg1_dep_air_port 之前
    insert_df_col(df_input, 'flight_1_num', 'seg1_dep_air_port')
    
    # flight_2_num 放在 seg2_dep_air_port 之前
    insert_df_col(df_input, 'flight_2_num', 'seg2_dep_air_port')

    df_input['baggage_level'] = (df_input['baggage'] == 30).astype(int)   # 30--> 1  20--> 0 
    # baggage_level 放在 flight_number_2 之前
    insert_df_col(df_input, 'baggage_level', 'flight_number_2')

    df_input['Adult_Total_Price'] = df_input['adult_total_price']
    # Adult_Total_Price 放在 seats_remaining 之前  保存缩放前的原始值
    insert_df_col(df_input, 'Adult_Total_Price', 'seats_remaining')

    df_input['Hours_Until_Departure'] = df_input['hours_until_departure']
    # Hours_Until_Departure 放在 days_to_departure 之前  保存缩放前的原始值
    insert_df_col(df_input, 'Hours_Until_Departure', 'days_to_departure')
    pass

    # gid：基于指定字段的分组标记（整数）
    df_input['gid'] = (
        df_input
        .groupby(
            ['city_pair', 'flight_day', 'flight_number_1', 'flight_number_2'],    # 'baggage' 先不进分组
            sort=False
        )
        .ngroup()
    )

    # 做一下时间段裁剪, 保留起飞前480小时之内且大于等于4小时的
    df_input = df_input[(df_input['hours_until_departure'] < 480) & 
                        (df_input['hours_until_departure'] >= 4)].reset_index(drop=True)
    
    # 在 gid 与 baggage 内按时间降序
    df_input = df_input.sort_values(
        by=['gid', 'baggage', 'hours_until_departure'],
        ascending=[True, True, False]
    ).reset_index(drop=True)

    # 价格变化掩码
    g = df_input.groupby(['gid', 'baggage'])
    diff = g['adult_total_price'].transform('diff')
    change_mask = diff.abs() >= 5   # 变化太小的不计入

    # 价格变化次数
    df_input['price_change_times_total'] = (
        change_mask.groupby([df_input['gid'], df_input['baggage']]).cumsum()
    )

    # 上次发生变价的小时数
    last_change_hour = (
        df_input['hours_until_departure']
        .where(change_mask)
        .groupby([df_input['gid'], df_input['baggage']])
        .ffill()  # 前向填充
    )

    # 当前距离上一次变价过去多少小时
    df_input['price_last_change_hours'] = (
        last_change_hour - df_input['hours_until_departure']
    ).fillna(0)
    pass

    # 想插入到 seats_remaining 前面的新列
    new_cols = [
        'price_change_times_total',
        'price_last_change_hours'
    ]
    # 当前所有列
    cols = df_input.columns.tolist()
    # 找到 seats_remaining 的位置
    idx = cols.index('seats_remaining')
    # 重新拼列顺序
    new_order = cols[:idx] + new_cols + cols[idx:]
    # 去重（防止列已经在原位置）
    new_order = list(dict.fromkeys(new_order))
    # 重新排列 DataFrame
    df_input = df_input[new_order]
    pass

    print(">>> 计算价格区间特征")
    #  1. 基于绝对价格水平的价格区间划分
    # 先计算每个(gid, baggage)的价格统计特征
    # g = df_input.groupby(['gid', 'baggage'])
    price_stats = df_input.groupby(['gid', 'baggage'])['adult_total_price'].agg(
        min_price='min',
        max_price='max',
        mean_price='mean',
        std_price='std'
    ).reset_index()

    # 合并统计特征到原数据
    df_input = df_input.merge(price_stats, on=['gid', 'baggage'], how='left')

    # 2. 基于绝对价格的价格区间划分 （可以删除，因为后面有更精细的基于频率加权的分类）
    # # 高价区间：超过均值+1倍标准差
    # df_input['price_absolute_high'] = (df_input['adult_total_price'] > 
    #                                   (df_input['mean_price'] + df_input['std_price'])).astype(int)

    # # 中高价区间：均值到均值+1倍标准差
    # df_input['price_absolute_mid_high'] = ((df_input['adult_total_price'] > df_input['mean_price']) & 
    #                                        (df_input['adult_total_price'] <= (df_input['mean_price'] + df_input['std_price']))).astype(int)

    # # 中低价区间：均值-1倍标准差到均值
    # df_input['price_absolute_mid_low'] = ((df_input['adult_total_price'] > (df_input['mean_price'] - df_input['std_price'])) & 
    #                                       (df_input['adult_total_price'] <= df_input['mean_price'])).astype(int)

    # # 低价区间：低于均值-1倍标准差
    # df_input['price_absolute_low'] = (df_input['adult_total_price'] <= (df_input['mean_price'] - df_input['std_price'])).astype(int)

    # 3. 基于频率加权的价格百分位数（改进版）
    # 计算每个价格出现的频率
    price_freq = df_input.groupby(['gid', 'baggage', 'adult_total_price']).size().reset_index(name='price_frequency')
    df_input = df_input.merge(price_freq, on=['gid', 'baggage', 'adult_total_price'], how='left')

    # 计算频率加权的百分位数
    def weighted_percentile(group):
        if len(group) == 0:
            return pd.Series([np.nan] * 4, index=['price_weighted_percentile_25', 
                                                'price_weighted_percentile_50', 
                                                'price_weighted_percentile_75', 
                                                'price_weighted_percentile_90'])
        
        # 按价格排序，计算累积频率
        group = group.sort_values('adult_total_price')
        group['cum_freq'] = group['price_frequency'].cumsum()
        total_freq = group['price_frequency'].sum()
        
        # 计算加权百分位数
        percentiles = []
        for p in [0.25, 0.5, 0.75, 0.9]:
            threshold = total_freq * p
            # 找到第一个累积频率超过阈值的价格
            mask = group['cum_freq'] >= threshold
            if mask.any():
                percentile_value = group.loc[mask.idxmax(), 'adult_total_price']
            else:
                percentile_value = group['adult_total_price'].max()
            percentiles.append(percentile_value)
        
        return pd.Series(percentiles, index=['price_weighted_percentile_25', 
                                             'price_weighted_percentile_50', 
                                             'price_weighted_percentile_75', 
                                             'price_weighted_percentile_90'])
        
    # 按gid和baggage分组计算加权百分位数
    weighted_percentiles = df_input.groupby(['gid', 'baggage']).apply(weighted_percentile).reset_index()
    df_input = df_input.merge(weighted_percentiles, on=['gid', 'baggage'], how='left')

    # 4. 结合绝对价格和频率的综合判断(改进版)
    freq_median = df_input.groupby(['gid', 'baggage'])['price_frequency'].transform('median')

    # 计算价格相对于90%百分位数的倍数，用于区分不同级别的高价
    df_input['price_relative_to_90p'] = df_input['adult_total_price'] / df_input['price_weighted_percentile_90']

    # 添加价格容忍度：避免相近价格被分到不同区间
    # 计算价格差异容忍度（使用各百分位数的1%作为容忍度阈值）
    # tolerance_90p = df_input['price_weighted_percentile_90'] * 0.01
    tolerance_75p = df_input['price_weighted_percentile_75'] * 0.01
    tolerance_50p = df_input['price_weighted_percentile_50'] * 0.01
    tolerance_25p = df_input['price_weighted_percentile_25'] * 0.01

    # 重新设计价格区间分类（确保无重叠）：
    # 首先定义各个区间的mask

    # 4.1 异常高价：价格远高于90%百分位数（超过1.5倍）且频率极低（低于中位数的1/3）
    price_abnormal_high_mask = ((df_input['price_relative_to_90p'] > 1.5) & 
                                (df_input['price_frequency'] < freq_median * 0.33))

    # 4.2 真正高位：严格满足条件（价格 > 90%分位数 且 频率 < 中位数）
    price_real_high_mask = ((df_input['adult_total_price'] > df_input['price_weighted_percentile_90']) & 
                            (df_input['price_frequency'] < freq_median) &
                             ~price_abnormal_high_mask)

    # 4.3 正常高位：使用容忍度（价格接近75%分位数）
    price_normal_high_mask = ((df_input['adult_total_price'] > df_input['price_weighted_percentile_75'] - tolerance_75p) & 
                               ~price_real_high_mask & ~price_abnormal_high_mask)

    # 4.4 中高价：使用容忍度（价格在50%-75%分位数之间）
    price_mid_high_mask = ((df_input['adult_total_price'] > df_input['price_weighted_percentile_50'] - tolerance_50p) & 
                           (df_input['adult_total_price'] <= df_input['price_weighted_percentile_75'] + tolerance_75p) &
                            ~price_normal_high_mask & ~price_real_high_mask & ~price_abnormal_high_mask)

    # 4.5 中低价：使用容忍度（价格在25%-50%分位数之间）
    price_mid_low_mask = ((df_input['adult_total_price'] > df_input['price_weighted_percentile_25'] - tolerance_25p) & 
                          (df_input['adult_total_price'] <= df_input['price_weighted_percentile_50'] + tolerance_50p) &
                           ~price_mid_high_mask & ~price_normal_high_mask & ~price_real_high_mask & ~price_abnormal_high_mask)
    
    # 4.6 低价：严格满足条件（价格 ≤ 25%分位数） 
    price_low_mask = ((df_input['adult_total_price'] <= df_input['price_weighted_percentile_25']) &
                 ~price_mid_low_mask & ~price_mid_high_mask & ~price_normal_high_mask & ~price_real_high_mask & ~price_abnormal_high_mask)

    # 使用np.select确保互斥性
    price_zone_masks = [
        price_abnormal_high_mask,  # 异常高价区（5级）
        price_real_high_mask,      # 真正高价区（4级）
        price_normal_high_mask,    # 正常高价区（3级）
        price_mid_high_mask,       # 中高价区（2级）
        price_mid_low_mask,        # 中低价区（1级）
        price_low_mask,            # 低价区（0级）
    ]  
    price_zone_values = [5, 4, 3, 2, 1, 0]  # 5:异常高价, 4:真正高价, 3:正常高价, 2:中高价, 1:中低价, 0:低价 

    # 使用np.select确保每个价格只被分到一个区间
    price_zone_result = np.select(price_zone_masks, price_zone_values, default=2)  # 默认中高价
    # 4.8 价格区间综合标记
    df_input['price_zone_comprehensive'] = price_zone_result

    # 5. 价格异常度检测
    # 价格相对于均值的标准化偏差
    df_input['price_z_score'] = (df_input['adult_total_price'] - df_input['mean_price']) / df_input['std_price']
    
    # 价格异常度：基于Z-score的绝对值
    df_input['price_anomaly_score'] = np.abs(df_input['price_z_score'])
    
    # 6. 价格稳定性特征
    # 计算价格波动系数（标准差/均值）
    df_input['price_coefficient_variation'] = df_input['std_price'] / df_input['mean_price']

    # 7. 价格趋势特征
    # 计算当前价格相对于历史价格的位置
    df_input['price_relative_position'] = (df_input['adult_total_price'] - df_input['min_price']) / (df_input['max_price'] - df_input['min_price'])
    df_input['price_relative_position'] = df_input['price_relative_position'].fillna(0.5)  # 兜底

    # 删除中间计算列
    df_input.drop(columns=['price_frequency', 'price_z_score', 'price_relative_to_90p'], inplace=True, errors='ignore')

    del price_freq
    del price_stats
    del weighted_percentiles
    del freq_median

    print(">>> 改进版价格区间特征计算完成")

    # 生成第一机场对
    df_input['airport_pair_1'] = (
        df_input['seg1_dep_air_port'].astype(str) + "-" + df_input['seg1_arr_air_port'].astype(str)
    )
    # 删除原始第一机场码
    df_input.drop(columns=['seg1_dep_air_port', 'seg1_arr_air_port'], inplace=True)
    # 第一机场对 放到 seg1_dep_time 列的前面
    insert_df_col(df_input, 'airport_pair_1', 'seg1_dep_time')

    # 生成第二机场对（带缺失兜底）
    df_input['airport_pair_2'] = np.where(
        df_input['seg2_dep_air_port'].isna() | df_input['seg2_arr_air_port'].isna(),
        'NA',
        df_input['seg2_dep_air_port'].astype(str) + "-" +
        df_input['seg2_arr_air_port'].astype(str)
    )
    # 删除原始第二机场码
    df_input.drop(columns=['seg2_dep_air_port', 'seg2_arr_air_port'], inplace=True)
    # 第二机场对 放到 seg2_dep_time 列的前面
    insert_df_col(df_input, 'airport_pair_2', 'seg2_dep_time')

    # 是否转乘
    df_input['is_transfer'] = np.where(df_input['flight_number_2'] == 'VJ', 0, 1)
    # 是否转乘 放到 flight_number_2 列的前面
    insert_df_col(df_input, 'is_transfer', 'flight_number_2')

    # 重命名起飞时刻与到达时刻
    df_input.rename(
        columns={
            'seg1_dep_time': 'dep_time_1',
            'seg1_arr_time': 'arr_time_1',
            'seg2_dep_time': 'dep_time_2',
            'seg2_arr_time': 'arr_time_2',
        },
        inplace=True
    )
    
    # 第一段飞行时长
    df_input['fly_duration_1'] = (
        (df_input['arr_time_1'] - df_input['dep_time_1'])
        .dt.total_seconds() / 3600
    ).round(2)

    # 第二段飞行时长（无转乘为 0）
    df_input['fly_duration_2'] = (
        (df_input['arr_time_2'] - df_input['dep_time_2'])
        .dt.total_seconds() / 3600
    ).fillna(0).round(2)

    # 总飞行时长
    df_input['fly_duration'] = (
        df_input['fly_duration_1'] + df_input['fly_duration_2']
    ).round(2)

    # 中转停留时长（无转乘为 0）
    df_input['stop_duration'] = (
        (df_input['dep_time_2'] - df_input['arr_time_1'])
        .dt.total_seconds() / 3600
    ).fillna(0).round(2)

    # 裁剪,防止负数
    # for c in ['fly_duration_1', 'fly_duration_2', 'fly_duration', 'stop_duration']:
    #     df_input[c] = df_input[c].clip(lower=0)

    # 和 is_transfer 逻辑保持一致
    # df_input.loc[df_input['is_transfer'] == 0, ['fly_duration_2', 'stop_duration']] = 0
    
    # 一次性插到 is_filled 前面
    insert_before = 'is_filled'
    new_cols = [
        'fly_duration_1',
        'fly_duration_2',
        'fly_duration',
        'stop_duration'
    ]
    cols = df_input.columns.tolist()
    idx = cols.index(insert_before)
    # 删除旧位置
    cols = [c for c in cols if c not in new_cols]
    # 插入新位置（顺序保持）
    cols[idx:idx] = new_cols    # python独有空切片插入法
    df_input = df_input[cols]

    # 一次生成多个字段
    dep_t1 = df_input['dep_time_1']
    # 几点起飞（0–23）
    df_input['flight_by_hour'] = dep_t1.dt.hour
    # 起飞日期几号（1–31）
    df_input['flight_by_day'] = dep_t1.dt.day
    # 起飞日期几月（1–12）
    df_input['flight_day_of_month'] = dep_t1.dt.month
    # 起飞日期周几（0=周一, 6=周日）
    df_input['flight_day_of_week'] = dep_t1.dt.weekday
    # 起飞日期季度（1–4）
    df_input['flight_day_of_quarter'] = dep_t1.dt.quarter
    # 是否周末（周六 / 周日）
    df_input['flight_day_is_weekend'] = dep_t1.dt.weekday.isin([5, 6]).astype(int)

    # 找到对应的国家码
    df_input['dep_country'] = df_input['from_city_code'].map(city_to_country)
    df_input['arr_country'] = df_input['to_city_code'].map(city_to_country) 

    # 整体出发时间 就是 dep_time_1
    df_input['global_dep_time'] = df_input['dep_time_1']
    # 整体到达时间：有转乘用 arr_time_2，否则用 arr_time_1
    df_input['global_arr_time'] = df_input['arr_time_2'].fillna(df_input['arr_time_1'])

    # 出发日期在出发国家是否节假日
    df_input['dep_country_is_holiday'] = df_input.apply(
        lambda r: r['global_dep_time'].date()
        in COUNTRY_HOLIDAYS.get(r['dep_country'], set()),
        axis=1
    ).astype(int)

    # 到达日期在到达国家是否节假日
    df_input['arr_country_is_holiday'] = df_input.apply(
        lambda r: r['global_arr_time'].date()
        in COUNTRY_HOLIDAYS.get(r['arr_country'], set()),
        axis=1
    ).astype(int)

    # 在任一侧是否节假日
    df_input['any_country_is_holiday'] = (
        df_input[['dep_country_is_holiday', 'arr_country_is_holiday']]
        .max(axis=1)
    )

    # 是否跨国航线
    df_input['is_cross_country'] = (
        df_input['dep_country'] != df_input['arr_country']
    ).astype(int)

    def days_to_next_holiday(country, cur_date):
        if pd.isna(country) or pd.isna(cur_date):
            return np.nan

        holidays = COUNTRY_HOLIDAYS.get(country)
        if not holidays:
            return np.nan

        # 找未来（含当天）的节假日，并排序
        future_holidays = sorted([d for d in holidays if d >= cur_date])
        if not future_holidays:
            return np.nan

        next_holiday = future_holidays[0]  # 第一个未来节假日
        delta_days = (next_holiday - cur_date).days
        return delta_days

    df_input['days_to_holiday'] = df_input.apply(
        lambda r: days_to_next_holiday(
            r['dep_country'],
            r['update_hour'].date()
        ),
        axis=1
    )
    
    # 没有未来节假日的统一兜底
    # df_input['days_to_holiday'] = df_input['days_to_holiday'].fillna(999)

    # days_to_holiday 插在 update_hour 前面
    insert_df_col(df_input, 'days_to_holiday', 'update_hour')

    # 训练模式
    if is_training:
        print(">>> 训练模式：计算 target 相关列")
        print(f"\n>>> 开始处理 对应区间: n_hours = {current_n_hours}")
        target_lower_limit = 4
        target_upper_limit = current_n_hours
        mask_targets = (df_input['hours_until_departure'] >= target_lower_limit) & (df_input['hours_until_departure'] < target_upper_limit) & (df_input['baggage'] == 30)
        df_targets = df_input.loc[mask_targets].copy()

        targets_amout = df_targets.shape[0]
        print(f"当前 目标区间数据量: {targets_amout}, 区间: [{target_lower_limit}, {target_upper_limit})")

        if targets_amout == 0:
            print(f">>> n_hours = {current_n_hours} 无有效数据，跳过")
            return pd.DataFrame()
        
        print(">>> 计算 price_at_n_hours")
        df_input_object = df_input[(df_input['hours_until_departure'] >= current_n_hours) & (df_input['baggage'] == 30)].copy()
        df_last = df_input_object.groupby('gid', observed=True).last().reset_index()   # 一般落在起飞前36\32\30小时
        
        # 提取并重命名 price 列
        df_last_price_at_n_hours = df_last[['gid', 'adult_total_price']].rename(columns={'adult_total_price': 'price_at_n_hours'})
        print(">>> price_at_n_hours计算完成，示例：")
        print(df_last_price_at_n_hours.head(5))

        # 新的计算降价方式
        # 先排序
        df_targets = df_targets.sort_values(
            ['gid', 'hours_until_departure'],
            ascending=[True, False]
        )

        # 在 gid 内计算价格变化
        g = df_targets.groupby('gid', group_keys=False)
        df_targets['price_diff'] = g['adult_total_price'].diff()
        
        VALID_DROP_MIN = 10
        LOWER_HOUR = 4
        UPPER_HOUR = 28

        valid_drop_mask = (
            (df_targets['price_diff'] <= -VALID_DROP_MIN) &
            (df_targets['hours_until_departure'] >= LOWER_HOUR) &
            (df_targets['hours_until_departure'] <= UPPER_HOUR)
        )
        # 有效的降价
        df_valid_drops = df_targets.loc[valid_drop_mask]

        # 找「第一次」降价（每个 gid）
        df_first_price_drop = (
            df_valid_drops
            .groupby('gid', as_index=False)
            .first()
        )

        # 简化列
        df_first_price_drop = df_first_price_drop[
            ['gid', 'hours_until_departure', 'adult_total_price', 'price_diff']
        ].rename(columns={
            'hours_until_departure': 'time_to_price_drop',
            'adult_total_price': 'price_at_d_hours',
            'price_diff': 'amount_of_price_drop',
        })

        # 把降价幅度转成正数（更直观）
        df_first_price_drop['amount_of_price_drop'] = (-df_first_price_drop['amount_of_price_drop']).round(2)
        pass

        # # 计算降价信息
        # print(">>> 计算降价信息")
        # df_targets = df_targets.merge(df_last_price_at_n_hours, on='gid', how='left')
        # df_targets['price_drop_amount'] = df_targets['price_at_n_hours'] - df_targets['adult_total_price']
        # df_targets['price_dropped'] = (
        #     (df_targets['adult_total_price'] < df_targets['price_at_n_hours']) &
        #     (df_targets['price_drop_amount'] >= 5)  # 降幅不能太小
        # )
        # df_price_drops = df_targets[df_targets['price_dropped']].copy()

        # price_drops_len = df_price_drops.shape[0]
        # if price_drops_len == 0:
        #     print(f">>> n_hours = {current_n_hours} 无降价信息")
        #     # 创建包含指定列的空 DataFrame
        #     df_price_drop_info = pd.DataFrame({
        #         'gid': pd.Series(dtype='int64'),
        #         'first_drop_hours_until_departure': pd.Series(dtype='int64'),
        #         'price_at_first_drop_hours': pd.Series(dtype='float64')
        #     })
        # else:
        #     df_price_drop_info = df_price_drops.groupby('gid', observed=True).first().reset_index()   # 第一次发生的降价
        #     df_price_drop_info = df_price_drop_info[['gid', 'hours_until_departure', 'adult_total_price']].rename(columns={
        #             'hours_until_departure': 'first_drop_hours_until_departure',
        #             'adult_total_price': 'price_at_first_drop_hours'
        #     })
        #     print(">>> 降价信息计算完成，示例：")
        #     print(df_price_drop_info.head(5))
        
        # # 合并信息
        # df_gid_info = df_last_price_at_n_hours.merge(df_price_drop_info, on='gid', how='left')
        # df_gid_info['will_price_drop'] = df_gid_info['price_at_first_drop_hours'].notnull().astype(int)
        # df_gid_info['amount_of_price_drop'] = df_gid_info['price_at_n_hours'] - df_gid_info['price_at_first_drop_hours']
        # df_gid_info['amount_of_price_drop'] = df_gid_info['amount_of_price_drop'].fillna(0)  # 区别    
        # df_gid_info['time_to_price_drop'] = current_n_hours - df_gid_info['first_drop_hours_until_departure']
        # df_gid_info['time_to_price_drop'] = df_gid_info['time_to_price_drop'].fillna(0)  # 区别

        # del df_input_object
        # del df_last
        # del df_last_price_at_n_hours
        # del df_price_drops
        # del df_price_drop_info

        df_gid_info = df_last_price_at_n_hours.merge(df_first_price_drop, on='gid', how='left')
        df_gid_info['will_price_drop'] = df_gid_info['time_to_price_drop'].notnull().astype(int)
        df_gid_info['amount_of_price_drop'] = df_gid_info['amount_of_price_drop'].fillna(0)
        df_gid_info['time_to_price_drop'] = df_gid_info['time_to_price_drop'].fillna(0)
        pass

        del df_input_object
        del df_last
        del df_last_price_at_n_hours
        del df_first_price_drop
        del df_valid_drops
        del df_targets
        gc.collect()
        
        # 将目标变量合并到输入数据中
        print(">>> 将目标变量信息合并到 df_input")
        df_input = df_input.merge(df_gid_info[['gid', 'will_price_drop', 'amount_of_price_drop', 'time_to_price_drop']], on='gid', how='left')
        # 使用 0 填充 NaN 值
        df_input[['will_price_drop', 'amount_of_price_drop', 'time_to_price_drop']] = df_input[
            ['will_price_drop', 'amount_of_price_drop', 'time_to_price_drop']].fillna(0)
        df_input = df_input.rename(columns={
            'will_price_drop': 'target_will_price_drop',
            'amount_of_price_drop': 'target_amount_of_drop',
            'time_to_price_drop': 'target_time_to_drop'
        })
        
        # 计算每个 gid 分组在 df_targets 中的 adult_total_price 最小值
        # print(">>> 计算每个 gid 分组的 adult_total_price 最小值...")
        # df_min_price_by_gid = df_targets.groupby('gid')['adult_total_price'].min().reset_index()
        # df_min_price_by_gid = df_min_price_by_gid.rename(columns={'adult_total_price': 'min_price'})
        # gid_count = df_min_price_by_gid.shape[0]
        # print(f">>> 计算完成，共 {gid_count} 个 gid 分组")

        # # 将最小价格 merge 到 df_inputs 中
        # print(">>> 将最小价格 merge 到输入数据中...")
        # df_input = df_input.merge(df_min_price_by_gid, on='gid', how='left')

        print(">>> 合并后 df_input 样例：")
        print(df_input[['gid', 'hours_until_departure', 'adult_total_price', 'target_will_price_drop', 'target_amount_of_drop', 'target_time_to_drop']].head(5))

    # 预测模式
    else:
        print(">>> 预测模式：补齐 target 相关列（全部置 0）")
        df_input['target_will_price_drop'] = 0
        df_input['target_amount_of_drop'] = 0.0
        df_input['target_time_to_drop'] = 0

    # 按顺序排列
    order_columns = [
        "city_pair", "from_city_code", "from_city_num", "to_city_code", "to_city_num", "flight_day", 
        "seats_remaining", "baggage", "baggage_level", 
        "price_change_times_total", "price_last_change_hours", "adult_total_price", "Adult_Total_Price", "target_will_price_drop", "target_amount_of_drop", "target_time_to_drop",
        "days_to_departure", "days_to_holiday", "hours_until_departure", "Hours_Until_Departure", "update_hour", "crawl_date", "gid",
        "flight_number_1", "flight_1_num", "airport_pair_1", "dep_time_1", "arr_time_1", "fly_duration_1", 
        "flight_by_hour", "flight_by_day", "flight_day_of_month", "flight_day_of_week", "flight_day_of_quarter", "flight_day_is_weekend", "is_transfer", 
        "flight_number_2", "flight_2_num", "airport_pair_2", "dep_time_2", "arr_time_2", "fly_duration_2", "fly_duration", "stop_duration", 
        "global_dep_time", "dep_country", "dep_country_is_holiday", "is_cross_country",
        "global_arr_time", "arr_country", "arr_country_is_holiday", "any_country_is_holiday",
        "price_weighted_percentile_25", "price_weighted_percentile_50", "price_weighted_percentile_75", "price_weighted_percentile_90",
        "price_zone_comprehensive", "price_relative_position",
    ]
    df_input = df_input[order_columns]
    
    return df_input


def standardization(df, feature_scaler, target_scaler=None, is_training=True, is_val=False):
    print(">>> 开始标准化处理")

    # 准备走标准化的特征
    scaler_features = ['adult_total_price', 'fly_duration', 'stop_duration', 
                       'price_weighted_percentile_25', 'price_weighted_percentile_50', 
                       'price_weighted_percentile_75', 'price_weighted_percentile_90']
    
    if is_training:
        print(">>> 特征数据标准化开始")
        if feature_scaler is None:
            feature_scaler = StandardScaler()
        if not is_val:
            feature_scaler.fit(df[scaler_features])
        df[scaler_features] = feature_scaler.transform(df[scaler_features])
        print(">>> 特征数据标准化完成")
    
    else:
        df[scaler_features] = feature_scaler.transform(df[scaler_features])
        print(">>> 预测模式下特征标准化处理完成")

    # 准备走归一化的特征
    # 事先定义好每个特征的合理范围
    fixed_ranges = {
        'hours_until_departure': (0, 480),       # 0-20天
        'from_city_num': (0, 38),
        'to_city_num': (0, 38),
        'flight_1_num': (0, 341),
        'flight_2_num': (0, 341),
        'seats_remaining': (1, 5),
        'price_change_times_total': (0, 30),     # 假设价格变更次数不会超过30次
        'price_last_change_hours': (0, 480), 
        'price_zone_comprehensive': (0, 5),    
        'days_to_departure': (0, 30),
        'days_to_holiday': (0, 120),             # 最长的越南节假日间隔120天
        'flight_by_hour': (0, 23),
        'flight_by_day': (1, 31),
        'flight_day_of_month': (1, 12),
        'flight_day_of_week': (0, 6),
        'flight_day_of_quarter': (1, 4),
    }
    normal_features = list(fixed_ranges.keys())

    print(">>> 归一化特征列: ", normal_features)
    print(">>> 基于固定范围的特征数据归一化开始")
    for col in normal_features:
        if col in df.columns:
            # 核心归一化公式: (x - min) / (max - min)
            col_min, col_max = fixed_ranges[col]
            df[col] = (df[col] - col_min) / (col_max - col_min)
            # 添加裁剪，将超出范围的值强制限制在[0,1]区间
            df[col] = df[col].clip(0, 1)
    print(">>> 基于固定范围的特征数据归一化完成")

    return df, feature_scaler, target_scaler