Source code for ltsm.utils.timefeatures

from typing import List

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


[docs] class TimeFeature: def __init__(self): pass def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: pass def __repr__(self): return self.__class__.__name__ + "()"
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] class WeekOfYear(TimeFeature): """Week of year encoded as value between [-0.5, 0.5]""" def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: return (index.isocalendar().week - 1) / 52.0 - 0.5
[docs] 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)
[docs] def time_features(dates, freq='h'): return np.vstack([feat(dates) for feat in time_features_from_frequency_str(freq)])