DateFlagsTransform

class DateFlagsTransform(day_number_in_week: Optional[bool] = True, day_number_in_month: Optional[bool] = True, day_number_in_year: Optional[bool] = False, week_number_in_month: Optional[bool] = False, week_number_in_year: Optional[bool] = False, month_number_in_year: Optional[bool] = False, season_number: Optional[bool] = False, year_number: Optional[bool] = False, is_weekend: Optional[bool] = True, special_days_in_week: Sequence[int] = (), special_days_in_month: Sequence[int] = (), out_column: Optional[str] = None)

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

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

Notes

Small example of week_number_in_month and week_number_in_year features

timestamp	day_number_in_week	week_number_in_month	week_number_in_year
2020-01-01	4	1	53
2020-01-02	5	1	53
2020-01-03	6	1	53
2020-01-04	0	2	1
…
2020-01-10	6	2	1
2020-01-11	0	3	2

Create instance of DateFlags.

Parameters

• day_number_in_week (Optional[bool]) – if True, add column with weekday info to feature dataframe in transform

• day_number_in_month (Optional[bool]) – if True, add column with day info to feature dataframe in transform

• day_number_in_year (Optional[bool]) – if True, add column with number of day in a year with leap year numeration (values from 1 to 366)

• week_number_in_month (Optional[bool]) – if True, add column with week number (in month context) to feature dataframe in transform

• week_number_in_year (Optional[bool]) – if True, add column with week number (in year context) to feature dataframe in transform

• month_number_in_year (Optional[bool]) – if True, add column with month info to feature dataframe in transform

• season_number (Optional[bool]) – if True, add column with season info to feature dataframe in transform

• year_number (Optional[bool]) – if True, add column with year info to feature dataframe in transform

• is_weekend (Optional[bool]) – if True: add column with weekends flags to feature dataframe in transform

• special_days_in_week (Sequence[int]) – list of weekdays number (from [0, 6]) that should be interpreted as special ones, if given add column with flag that shows given date is a special day

• special_days_in_month (Sequence[int]) – list of days number (from [1, 31]) that should be interpreted as special ones, if given add column with flag that shows given date is a special day

• 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

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: day_number_in_week, day_number_in_month, day_number_in_year, week_number_in_month, week_number_in_year, month_number_in_year, season_number, year_number, is_weekend. 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]