TimeFlagsTransform

class TimeFlagsTransform(minute_in_hour_number: bool = True, fifteen_minutes_in_hour_number: bool = False, hour_number: bool = True, half_hour_number: bool = False, half_day_number: bool = False, one_third_day_number: bool = False, out_column: Optional[str] = None)

Bases: etna.transforms.base.IrreversibleTransform, etna.transforms.base.FutureMixin

TimeFlagsTransform is a class that implements extraction of the main time-based features from datetime column.

Initialise class attributes.

Parameters

• minute_in_hour_number (bool) – if True: add column with minute number to feature dataframe in transform

• fifteen_minutes_in_hour_number (bool) – if True: add column with number of fifteen-minute interval within hour with numeration from 0 to feature dataframe in transform

• hour_number (bool) – if True: add column with hour number to feature dataframe in transform

• half_hour_number (bool) – if True: add column with 0 for the first half of the hour and 1 for the second to feature dataframe in transform

• half_day_number (bool) – if True: add column with 0 for the first half of the day and 1 for the second to feature dataframe in transform

• one_third_day_number (bool) – if True: add column with number of 8-hour interval within day with numeration from 0 to feature dataframe in transform

• out_column (Optional[str]) –

  base for the name of created columns;

  • if set the final name is ‘{out_column}_{feature_name}’;

  • if don’t set, name will be transform.__repr__(), repr will be made for transform that creates exactly this column

Raises

ValueError – if feature has invalid initial params:

Inherited-members

Methods

fit(ts)	Fit the transform.
fit_transform(ts)	Fit and transform TSDataset.
get_regressors_info()	Return the list with regressors created by the transform.
inverse_transform(ts)	Inverse transform TSDataset.
load(path)	Load an object.
params_to_tune()	Get default grid for tuning hyperparameters.
save(path)	Save the object.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.
transform(ts)	Transform TSDataset inplace.

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Return type

List[str]

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get default grid for tuning hyperparameters.

This grid tunes parameters: minute_in_hour_number, fifteen_minutes_in_hour_number, hour_number, half_hour_number, half_day_number, one_third_day_number. Other parameters are expected to be set by the user.

There are no restrictions on all False values for the flags.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]