DateFlagsTransform#

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

Bases: IrreversibleTransform, 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 (bool | None) – if True, add column with weekday info to feature dataframe in transform
day_number_in_month (bool | None) – if True, add column with day info to feature dataframe in transform
day_number_in_year (bool | None) – if True, add column with number of day in a year with leap year numeration (values from 1 to 366)
week_number_in_month (bool | None) – if True, add column with week number (in month context) to feature dataframe in transform
week_number_in_year (bool | None) – if True, add column with week number (in year context) to feature dataframe in transform
month_number_in_year (bool | None) – if True, add column with month info to feature dataframe in transform
season_number (bool | None) – if True, add column with season info to feature dataframe in transform
year_number (bool | None) – if True, add column with year info to feature dataframe in transform
is_weekend (bool | None) – 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 (str | None) –
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

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.

Attributes

This class stores its __init__ parameters as attributes.

fit(ts: TSDataset) → Transform[source]#

Fit the transform.

Parameters:: ts (TSDataset) – Dataset to fit the transform on.
Returns:: The fitted transform instance.
Return type:: Transform

fit_transform(ts: TSDataset) → TSDataset[source]#

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters:: ts (TSDataset) – TSDataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset

get_regressors_info() → List[str][source]#

Return the list with regressors created by the transform.

Return type:: List[str]

inverse_transform(ts: TSDataset) → TSDataset[source]#

Inverse transform TSDataset.

Do nothing.

Parameters:: ts (TSDataset) – TSDataset to be inverse transformed.
Returns:: TSDataset after applying inverse transformation.
Return type:: TSDataset

classmethod load(path: Path) → Self[source]#

Load an object.

Parameters:: path (Path) – Path to load object from.
Returns:: Loaded object.
Return type:: Self

params_to_tune() → Dict[str, BaseDistribution][source]#

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, BaseDistribution]

save(path: Path)[source]#

Save the object.

Parameters:: path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:: **params (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )

to_dict()[source]#: Collect all information about etna object in dict.

transform(ts: TSDataset) → TSDataset[source]#

Transform TSDataset inplace.

Parameters:: ts (TSDataset) – Dataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset