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https://github.com/wassname/pytorch-ts.git
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140 lines
3.6 KiB
Python
140 lines
3.6 KiB
Python
from abc import ABC, abstractmethod
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from typing import List
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import numpy as np
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import pandas as pd
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from .utils import get_granularity
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class TimeFeature(ABC):
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def __init__(self, normalized: bool = True):
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self.normalized = normalized
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@abstractmethod
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def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
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pass
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class MinuteOfHour(TimeFeature):
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"""
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Minute of hour encoded as value between [-0.5, 0.5]
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"""
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def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
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if self.normalized:
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return index.minute / 59.0 - 0.5
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else:
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return index.minute.map(float)
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class HourOfDay(TimeFeature):
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"""
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Hour of day encoded as value between [-0.5, 0.5]
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"""
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def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
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if self.normalized:
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return index.hour / 23.0 - 0.5
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else:
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return index.hour.map(float)
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class DayOfWeek(TimeFeature):
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"""
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Hour of day encoded as value between [-0.5, 0.5]
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"""
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def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
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if self.normalized:
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return index.dayofweek / 6.0 - 0.5
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else:
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return index.dayofweek.map(float)
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class DayOfMonth(TimeFeature):
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"""
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Day of month encoded as value between [-0.5, 0.5]
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"""
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def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
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if self.normalized:
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return index.day / 30.0 - 0.5
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else:
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return index.day.map(float)
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class DayOfYear(TimeFeature):
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"""
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Day of year encoded as value between [-0.5, 0.5]
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"""
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def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
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if self.normalized:
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return index.dayofyear / 364.0 - 0.5
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else:
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return index.dayofyear.map(float)
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class MonthOfYear(TimeFeature):
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"""
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Month of year encoded as value between [-0.5, 0.5]
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"""
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def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
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if self.normalized:
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return index.month / 11.0 - 0.5
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else:
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return index.month.map(float)
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class WeekOfYear(TimeFeature):
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"""
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Week of year encoded as value between [-0.5, 0.5]
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"""
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def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
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if self.normalized:
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return index.weekofyear / 51.0 - 0.5
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else:
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return index.weekofyear.map(float)
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def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]:
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"""
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Returns a list of time features that will be appropriate for the given frequency string.
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Parameters
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----------
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freq_str
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Frequency string of the form [multiple][granularity] such as "12H", "5min", "1D" etc.
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"""
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_, granularity = get_granularity(freq_str)
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if granularity == "M":
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feature_classes = [MonthOfYear]
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elif granularity == "W":
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feature_classes = [DayOfMonth, WeekOfYear]
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elif granularity in ["D", "B"]:
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feature_classes = [DayOfWeek, DayOfMonth, DayOfYear]
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elif granularity == "H":
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feature_classes = [HourOfDay, DayOfWeek, DayOfMonth, DayOfYear]
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elif granularity in ["min", "T"]:
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feature_classes = [MinuteOfHour, HourOfDay, DayOfWeek, DayOfMonth, DayOfYear]
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else:
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supported_freq_msg = f"""
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Unsupported frequency {freq_str}
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The following frequencies are supported:
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M - monthly
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W - week
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D - daily
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H - hourly
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min - minutely
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"""
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raise RuntimeError(supported_freq_msg)
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return [cls() for cls in feature_classes]
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