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timefeatures.py
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timefeatures.py
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from typing import List
import numpy as np
import pandas as pd
from pandas.tseries import offsets
from pandas.tseries.frequencies import to_offset
class TimeFeature:
def __init__(self):
pass
def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
pass
def __repr__(self):
return self.__class__.__name__ + "()"
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 - 0.5
class 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 - 0.5
class 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 - 0.5
class 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 - 0.5
class 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 - 0.5
class 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 - 0.5
class 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 - 0.5
class 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
def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]:
"""
Returns a list of time features that will be appropriate for the given frequency string.
Parameters
----------
freq_str
Frequency string of the form [multiple][granularity] such as "12H", "5min", "1D" etc.
"""
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,
],
}
offset = to_offset(freq_str)
for offset_type, feature_classes in features_by_offsets.items():
if isinstance(offset, offset_type):
return [cls() for cls in feature_classes]
supported_freq_msg = f"""
Unsupported frequency {freq_str}
The following frequencies are supported:
Y - yearly
alias: A
M - monthly
W - weekly
D - daily
B - business days
H - hourly
T - minutely
alias: min
S - secondly
"""
raise RuntimeError(supported_freq_msg)
def time_features(dates, timeenc=1, 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
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)