近三年成功的营销案例快速排名优化系统

定义了一套与时间特征相关的类和函数，旨在从时间序列数据中提取有用的时间特征，以支持各种时间序列分析和预测任务 

from typing import Listimport numpy as np
import pandas as pd
from pandas.tseries import offsets
from pandas.tseries.frequencies import to_offset

1 TimeFeature 类

• 这是一个基础类，其他与时间特征相关的类都继承自它。
• 它提供了一个基本框架，但没有实现具体的功能。

class TimeFeature:def __init__(self):passdef __call__(self, index: pd.DatetimeIndex) -> np.ndarray:passdef __repr__(self):return self.__class__.__name__ + "()"

 2 时间特征类

SecondOfMinute、MinuteOfHour、HourOfDay、DayOfWeek、DayOfMonth、DayOfYear、MonthOfYear、WeekOfYear：这些类都继承自TimeFeature，每个类都实现了一个特定的时间特征提取方法。例如，HourOfDay类提取一天中的小时数并进行规范化处理，使得值在[-0.5, 0.5]之间。

class SecondOfMinute(TimeFeature):"""Minute of hour encoded as value between [-0.5, 0.5]"""def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:return index.second / 59.0 - 0.5class MinuteOfHour(TimeFeature):"""Minute of hour encoded as value between [-0.5, 0.5]"""def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:return index.minute / 59.0 - 0.5class HourOfDay(TimeFeature):"""Hour of day encoded as value between [-0.5, 0.5]"""def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:return index.hour / 23.0 - 0.5class DayOfWeek(TimeFeature):"""Hour of day encoded as value between [-0.5, 0.5]"""def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:return index.dayofweek / 6.0 - 0.5class DayOfMonth(TimeFeature):"""Day of month encoded as value between [-0.5, 0.5]"""def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:return (index.day - 1) / 30.0 - 0.5class DayOfYear(TimeFeature):"""Day of year encoded as value between [-0.5, 0.5]"""def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:return (index.dayofyear - 1) / 365.0 - 0.5class MonthOfYear(TimeFeature):"""Month of year encoded as value between [-0.5, 0.5]"""def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:return (index.month - 1) / 11.0 - 0.5class WeekOfYear(TimeFeature):"""Week of year encoded as value between [-0.5, 0.5]"""def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:return (index.week - 1) / 52.0 - 0.5

3 time_features_from_frwquency_str

def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]:"""根据给定的频率字符串（如"12H", "5min", "1D"等）返回一组适当的时间特征类实例"""features_by_offsets = {offsets.YearEnd: [],offsets.QuarterEnd: [MonthOfYear],offsets.MonthEnd: [MonthOfYear],offsets.Week: [DayOfMonth, WeekOfYear],offsets.Day: [DayOfWeek, DayOfMonth, DayOfYear],offsets.BusinessDay: [DayOfWeek, DayOfMonth, DayOfYear],offsets.Hour: [HourOfDay, DayOfWeek, DayOfMonth, DayOfYear],offsets.Minute: [MinuteOfHour,HourOfDay,DayOfWeek,DayOfMonth,DayOfYear,],offsets.Second: [SecondOfMinute,MinuteOfHour,HourOfDay,DayOfWeek,DayOfMonth,DayOfYear,],}'''特征映射字典 features_by_offsets:这个字典将pandas的时间偏移类（如YearEnd、QuarterEnd、MonthEnd等）映射到对应的时间特征类列表。例如，对于每月的数据（MonthEnd），它映射到MonthOfYear类；对于每小时的数据（Hour），它映射到HourOfDay、DayOfWeek、DayOfMonth和DayOfYear类。'''offset = to_offset(freq_str)#使用pandas的to_offset函数将频率字符串（如"12H"）转换为相应的pandas时间偏移对象。for offset_type, feature_classes in features_by_offsets.items():if isinstance(offset, offset_type):return [cls() for cls in feature_classes]'''遍历映射字典，检查提供的偏移对象是否属于字典中的某个偏移类型。如果找到匹配，为每个相关的特征类创建一个实例，并将这些实例作为列表返回。'''supported_freq_msg = f"""Unsupported frequency {freq_str}The following frequencies are supported:Y   - yearlyalias: AM   - monthlyW   - weeklyD   - dailyB   - business daysH   - hourlyT   - minutelyalias: minS   - secondly"""raise RuntimeError(supported_freq_msg)

4 time_features

'''
从日期数据中提取有用的时间特征
'''
def time_features(dates, timeenc=0, freq='h'):"""> `time_features` takes in a `dates` dataframe with a 'dates' column and extracts the date down to `freq` where freq can be any of the following if `timeenc` is 0:> * m - [month]> * w - [month]> * d - [month, day, weekday]> * b - [month, day, weekday]> * h - [month, day, weekday, hour]> * t - [month, day, weekday, hour, *minute]>> If `timeenc` is 1, a similar, but different list of `freq` values are supported (all encoded between [-0.5 and 0.5]):> * Q - [month]> * M - [month]> * W - [Day of month, week of year]> * D - [Day of week, day of month, day of year]> * B - [Day of week, day of month, day of year]> * H - [Hour of day, day of week, day of month, day of year]> * T - [Minute of hour*, hour of day, day of week, day of month, day of year]> * S - [Second of minute, minute of hour, hour of day, day of week, day of month, day of year]*minute returns a number from 0-3 corresponding to the 15 minute period it falls into."""if timeenc==0:dates['month'] = dates.date.apply(lambda row:row.month,1)dates['day'] = dates.date.apply(lambda row:row.day,1)dates['weekday'] = dates.date.apply(lambda row:row.weekday(),1)dates['hour'] = dates.date.apply(lambda row:row.hour,1)dates['minute'] = dates.date.apply(lambda row:row.minute,1)dates['minute'] = dates.minute.map(lambda x:x//15)freq_map = {'y':[],'m':['month'],'w':['month'],'d':['month','day','weekday'],'b':['month','day','weekday'],'h':['month','day','weekday','hour'],'t':['month','day','weekday','hour','minute'],}return dates[freq_map[freq.lower()]].values'''此模式下，函数直接从日期中提取特定的时间特征，如月份、日期、星期几、小时和分钟。freq参数指定要提取的时间特征的精度。例如，如果freq为'd'，则提取月、日和星期几。对于分钟，它被转换为一个从0到3的数字，表示15分钟的时间段。'''if timeenc==1:dates = pd.to_datetime(dates.date.values)return np.vstack([feat(dates) for feat in time_features_from_frequency_str(freq)]).transpose(1,0)'''此模式下，函数使用time_features_from_frequency_str函数来获取一组特征提取器，并应用它们来转换时间数据。这些特征提取器提取的特征被编码在[-0.5, 0.5]的范围内，以提供规范化的时间特征。
freq参数在这种情况下也指定了提取的时间特征的类型和精度。'''