pywatts.modules.feature_extraction package¶

Submodules¶

pywatts.modules.feature_extraction.calendar_extraction module¶

class pywatts.modules.feature_extraction.calendar_extraction.CalendarExtraction(name: str = 'CalendarExtraction', continent: str = 'Europe', country: str = 'Germany', features: Optional[List[pywatts.modules.feature_extraction.calendar_extraction.CalendarFeature]] = None)¶

Bases: pywatts_pipeline.core.transformer.base.BaseTransformer

This pipeline step will extract date features based on a timeseries defined by a DataArray input. It can calculate the year, month, month_sine, month_cos, day, day_sine, day_cos, hour, hour_sine, hour_cos, weekday, weekday_sine, weekday_cos, monday, tuesday, wednesday, thursday, friday, saturday, sunday, weekend, workday, and holiday. based on the timeseries. For the holidays it is importent to set the correct continent and country/region. E.g. ‘Europe’ and ‘BadenWurttemberg’ or ‘Germany’

Parameters:

name (str) – Name of this processing step.
continent (str) – Continent where the country or region is located (important for importing calendar module).
country (str) – Country or region to use for holiday calendar (default ‘Germany’)
features (Optional[List[CalendarFeature]]) – The features that should be extracted. The following features exist: year, month, month_sine, month_cos, day, day_sine, day_cos, hour, hour_sine, hour_cos, weekday, weekday_sine, weekday_cos, monday, tuesday, wednesday, thursday, friday, saturday, sunday, weekend, workday, holiday. (Default: month, day, weekday, hour)

Raises:

WrongParameterException – If ‘continent’ and/or ‘country’ is invalid.

set_params(**kwargs)¶

Set parameters of the calendar extraction processing step.

Parameters:	continent (str) – Continent where the country or region is located (important for importing calendar module). country (str) – Country or region to use for holiday calendar (default ‘Germany’) features (List[str]) – A list, which contains all features that should be calculated. Default all are calculated.
Raises:	AttributeError – If ‘continent’ and/or ‘country’ is invalid.

transform(x: xarray.core.dataarray.DataArray) → xarray.core.dataarray.DataArray¶

Add date features to xarray dataset as configured.

Parameters:	x – Xarray dataset containing a timeseries specified by the object’s ‘time_index’
Returns:	The xarray dataset with date features added.

class pywatts.modules.feature_extraction.calendar_extraction.CalendarFeature¶

Bases: enum.IntEnum

The available calendar features, that are extractable by the calendar extraction module:

year: Extracting the year of the time series element. month: Extracting the month of the time series element. month_sine: Extracting the month of the time series element and encodes it with sine. month_cos: Extracting the month of the time series element and encodes it with cos. day: Extracting the day of the time series element. day_sine: Extracting the day of the time series element and encodes it with sine. day_cos: Extracting the day of the time series element and encodes it with cos. hour: Extracting the hour of the time series element. hour_sine: Extracting the hour of the time series element and encodes it with sine. hour_cos: Extracting the hour of the time series element and encodes it with cos. weekday: Extracting the weekday of the time series element. weekday_sine: Extracting the weekday of the time series element and encodes it with sine. weekday_cos: Extracting the weekday of the time series element and encodes it with cos. monday: Extracting a flag indicating if the element of the time series element is a monday or not. tuesday: Extracting a flag indicating if the element of the time series element is a tuesday or not. wednesday: Extracting a flag indicating if the element of the time series element is a wednesday or not. thursday: Extracting a flag indicating if the element of the time series element is a thursday or not. friday: Extracting a flag indicating if the element of the time series element is a friday or not. saturday: Extracting a flag indicating if the element of the time series element is a saturday or not. sunday: Extracting a flag indicating if the element of the time series element is a sunday or not. weekend: Extracting a flag indicating if the element of the time series element is a weekend or not. workday: Extracting a flag indicating if the element of the time series element is a workday or not. holiday: Extracting a flag indicating if the element of the time series element is a holiday or not.

day = 5¶

day_cos = 7¶

day_sine = 6¶

friday = 18¶

holiday = 23¶

hour = 8¶

hour_cos = 10¶

hour_sine = 9¶

monday = 14¶

month = 2¶

month_cos = 4¶

month_sine = 3¶

saturday = 19¶

sunday = 20¶

thursday = 17¶

tuesday = 15¶

wednesday = 16¶

weekday = 11¶

weekday_cos = 13¶

weekday_sine = 12¶

weekend = 21¶

workday = 22¶

year = 1¶

pywatts.modules.feature_extraction.rolling_base module¶

class pywatts.modules.feature_extraction.rolling_base.RollingBase(name: str = None, window_size=168, window_size_unit='d', group_by: pywatts.modules.feature_extraction.rolling_base.RollingGroupBy = <RollingGroupBy.No: 1>, continent: str = 'Europe', country: str = 'Germany', closed='left')¶

Bases: pywatts_pipeline.core.transformer.base.BaseTransformer, abc.ABC

Module which calculates a rolling mean over a specific window size. Note, currently the smallest resolution of the generated profile is one minute.

Parameters:	name (str) – Name of the new variable window_size (int) – Window size for which to calculate the mean window_size_unit (str) – Unit of the window size (default: “d” [day]) group_by – how the entries of the time series should be grouped

:type group_by. RollingGroupBy :param continent: If group_by is WorkdayAndHoliday: Continent where the country or region is located

(important for importing calendar module).

Parameters:	country (str) – If group_by is WorkdayAndHoliday: Country or region to use for holiday calendar (default ‘Germany’) closed (str) – If there array is closed left or right

get_min_data()¶: Returns how much data are at least needed by that transformer

set_params(**kwargs)¶

Set parameters of the rolling mean :param window_size: Window size for which to calculate the mean :type window_size: int :param window_size_unit: Unit of the window size (default: “d” [day]) :type window_size_unit: str :param groupy_by: how the entries of the time series should be grouped :type group_by. RollingGroupBy :param continent: If group_by is WorkdayAndHoliday: Continent where the country or region is located

(important for importing calendar module).

Parameters:	country (str) – If group_by is WorkdayAndHoliday: Country or region to use for holiday calendar (default ‘Germany’) closed (str) – If there array is closed left or right

transform(x: xarray.core.dataarray.DataArray, **kwargs) → xarray.core.dataarray.DataArray¶

Calculates a rolling mean

Parameters:	x – Xarray dataset containing a timeseries specified by the object’s ‘time_index’
Returns:	The xarray dataset with date features added.

class pywatts.modules.feature_extraction.rolling_base.RollingGroupBy¶

Bases: enum.IntEnum

An enumeration.

HourOnly = 4¶

No = 1¶

WorkdayWeekend = 2¶

WorkdayWeekendAndHoliday = 3¶

pywatts.modules.feature_extraction.rolling_kurtosis module¶

class pywatts.modules.feature_extraction.rolling_kurtosis.RollingKurtosis(name: str = None, window_size=168, window_size_unit='d', group_by: pywatts.modules.feature_extraction.rolling_base.RollingGroupBy = <RollingGroupBy.No: 1>, continent: str = 'Europe', country: str = 'Germany', closed='left')¶

Bases: pywatts.modules.feature_extraction.rolling_base.RollingBase

Module which calculates a rolling kurtosis over a specific window size. Note, currently the smallest resolution of the generated profile is one minute.

For the documentation of the methods see pywatts.modules.rolling_base.RollingBase.

Parameters:	name (str) – Name of the new variable window_size (int) – Window size for which to calculate the kurtosis window_size_unit (str) – Unit of the window size (default: “d” [day]) groupy_by – how the entries of the time series should be grouped

:type group_by. RollingGroupBy :param continent: If group_by is WorkdayAndHoliday: Continent where the country or region is located

(important for importing calendar module).

Parameters:	country (str) – If group_by is WorkdayAndHoliday: Country or region to use for holiday calendar (default ‘Germany’) closed (str) – If there array is closed left or right

pywatts.modules.feature_extraction.rolling_mean module¶

class pywatts.modules.feature_extraction.rolling_mean.RollingMean(name: str = 'RollingMean', window_size=168, window_size_unit='d', group_by: pywatts.modules.feature_extraction.rolling_base.RollingGroupBy = <RollingGroupBy.No: 1>, continent: str = 'Europe', country: str = 'Germany', closed='left', alpha=None)¶

Bases: pywatts.modules.feature_extraction.rolling_base.RollingBase

Module which calculates a rolling mean over a specific window size. Note, currently the smallest resolution of the generated profile is one minute.

For the documentation of the methods see pywatts.modules.rolling_base.RollingBase.

param name: Name of the new variable

type name: str

param window_size:

Window size for which to calculate the mean

type window_size:

int

param window_size_unit:

Unit of the window size (default: “d” [day])

type window_size_unit:

str

param groupy_by:

how the entries of the time series should be grouped

:type group_by. RollingGroupBy :param continent: If group_by is WorkdayAndHoliday: Continent where the country or region is located

(important for importing calendar module).

type continent: str

param country: If group_by is WorkdayAndHoliday: Country or region to use for holiday calendar (default ‘Germany’)

type country: str

param closed: If there array is closed left or right

type closed: str

param alpha: alpha value for weighting the most recent value if exponential weighting should be applied. If alpha is not set, then the mean of the sliding window is calculated.

set_params(alpha: Optional[float] = None, **kwargs)¶

Set parameters of the rolling mean :param window_size: Window size for which to calculate the mean :type window_size: int :param window_size_unit: Unit of the window size (default: “d” [day]) :type window_size_unit: str :param groupy_by: how the entries of the time series should be grouped :type group_by. RollingGroupBy :param continent: If group_by is WorkdayAndHoliday: Continent where the country or region is located

(important for importing calendar module).

Parameters:	country (str) – If group_by is WorkdayAndHoliday: Country or region to use for holiday calendar (default ‘Germany’) closed (str) – If there array is closed left or right alpha (float) – alpha value for weighting the most recent value if exponential weighting should be applied. If alpha is not set, then the mean of the sliding window is calculated.

pywatts.modules.feature_extraction.rolling_skewness module¶

class pywatts.modules.feature_extraction.rolling_skewness.RollingSkewness(name: str = None, window_size=168, window_size_unit='d', group_by: pywatts.modules.feature_extraction.rolling_base.RollingGroupBy = <RollingGroupBy.No: 1>, continent: str = 'Europe', country: str = 'Germany', closed='left')¶

Bases: pywatts.modules.feature_extraction.rolling_base.RollingBase

Module which calculates a rolling skewness over a specific window size. Note, currently the smallest resolution of the generated profile is one minute.

For the documentation of the methods see pywatts.modules.rolling_base.RollingBase.

Parameters:	name (str) – Name of the new variable window_size (int) – Window size for which to calculate the skewness window_size_unit (str) – Unit of the window size (default: “d” [day]) groupy_by – how the entries of the time series should be grouped

:type group_by. RollingGroupBy :param continent: If group_by is WorkdayAndHoliday: Continent where the country or region is located

(important for importing calendar module).

Parameters:	country (str) – If group_by is WorkdayAndHoliday: Country or region to use for holiday calendar (default ‘Germany’) closed (str) – If there array is closed left or right

pywatts.modules.feature_extraction.rolling_variance module¶

class pywatts.modules.feature_extraction.rolling_variance.RollingVariance(name: str = None, window_size=168, window_size_unit='d', group_by: pywatts.modules.feature_extraction.rolling_base.RollingGroupBy = <RollingGroupBy.No: 1>, continent: str = 'Europe', country: str = 'Germany', closed='left')¶

Bases: pywatts.modules.feature_extraction.rolling_base.RollingBase

Module which calculates a rolling variance over a specific window size. Note, currently the smallest resolution of the generated profile is one minute.

For the documentation of the methods see pywatts.modules.rolling_base.RollingBase.

Parameters:	name (str) – Name of the new variable window_size (int) – Window size for which to calculate the variance window_size_unit (str) – Unit of the window size (default: “d” [day]) groupy_by – how the entries of the time series should be grouped

:type group_by. RollingGroupBy :param continent: If group_by is WorkdayAndHoliday: Continent where the country or region is located

(important for importing calendar module).

Parameters:	country (str) – If group_by is WorkdayAndHoliday: Country or region to use for holiday calendar (default ‘Germany’) closed (str) – If there array is closed left or right

pywatts.modules.feature_extraction.statistics_extraction module¶

class pywatts.modules.feature_extraction.statistics_extraction.StatisticExtraction(name: str = 'statistics', features: List[pywatts.modules.feature_extraction.statistics_extraction.StatisticFeature] = None, dim='horizon')¶

Bases: pywatts_pipeline.core.transformer.base.BaseTransformer

This module extracts statistical features based on samples of time series defined by a DataArray input. It can calculate the min, max, std, and mean of the samples.

Parameters:	name (str) – Name of this module. features (Optional[List[StatisticFeature]]) – The features that should be extracted. The following features exist: min, max, std, and mean. (Default: List[min, max, std, mean) dim (str) – The dimension on which the statistics should be extracted

transform(x: xarray.core.dataarray.DataArray) → xarray.core.dataarray.DataArray¶

Add statistic features to xarray dataarray as configured.

Parameters:	x – Xarray dataarray containing a time series
Returns:	The xarray dataarray with statistic features.

class pywatts.modules.feature_extraction.statistics_extraction.StatisticFeature¶

Bases: enum.IntEnum

The available statistic features, that are extractable by the statistic extraction module:

min: Extracting the min of the time series sample. max: Extracting the max of the time series sample. std: Extracting the std of the time series sample. mean: Extracting the mean of the time series sample.

max = 2¶

mean = 4¶

min = 1¶

std = 3¶

pywatts.modules.feature_extraction.trend_extraction module¶

This module contains the trend extraction

class pywatts.modules.feature_extraction.trend_extraction.TrendExtraction(period, length, indexes: List[str] = None, name: str = 'trend_extractor')¶

Bases: pywatts_pipeline.core.transformer.base.BaseTransformer

Module to extract a trend which can be specified through a period and a length, where the length indicates the length of the time step going back (ie 7, for a weekly trend with daily data) and period indicates the number of times this length is used (ie 10 to extract the last 10 weeks).

Parameters:	period (int) – Length of one period length (int) – Number of periods which should be extracted indexes (List[str]) – Index over which the trend is extracted (default: all time based indexes) name (str) – Name of the module

get_min_data()¶: Returns how much data are at least needed by that transformer

transform(x: xarray.core.dataarray.DataArray) → xarray.core.dataarray.DataArray¶

Extract trend values

Parameters:	x (xr.DataArray) – input xarray DataArray
Returns:	a dataset containing the trend information
Return type:	xr.DataArray

pywatts.modules.feature_extraction package¶

Submodules¶

pywatts.modules.feature_extraction.calendar_extraction module¶

pywatts.modules.feature_extraction.rolling_base module¶

pywatts.modules.feature_extraction.rolling_kurtosis module¶

pywatts.modules.feature_extraction.rolling_mean module¶

pywatts.modules.feature_extraction.rolling_skewness module¶

pywatts.modules.feature_extraction.rolling_variance module¶

pywatts.modules.feature_extraction.statistics_extraction module¶

pywatts.modules.feature_extraction.trend_extraction module¶

Module contents¶

PyWatts

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param window_size:
param name:	Name of the new variable
type name:	str
	Window size for which to calculate the mean
type window_size:
	int
param window_size_unit:
	Unit of the window size (default: “d” [day])
type window_size_unit:
	str
param groupy_by:
	how the entries of the time series should be grouped

type continent:	str
param country:	If group_by is WorkdayAndHoliday: Country or region to use for holiday calendar (default ‘Germany’)
type country:	str
param closed:	If there array is closed left or right
type closed:	str
param alpha:	alpha value for weighting the most recent value if exponential weighting should be applied. If alpha is not set, then the mean of the sliding window is calculated.