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)[source]

Bases: etna.transforms.base.IrreversibleTransform

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.

fit(ts: etna.datasets.tsdataset.TSDataset) etna.transforms.base.Transform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to fit the transform on.

Returns

The fitted transform instance.

Return type

etna.transforms.base.Transform

fit_transform(ts: etna.datasets.tsdataset.TSDataset) etna.datasets.tsdataset.TSDataset

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset

get_regressors_info() List[str][source]

Return the list with regressors created by the transform.

Return type

List[str]

inverse_transform(ts: etna.datasets.tsdataset.TSDataset) etna.datasets.tsdataset.TSDataset

Inverse transform TSDataset.

Do nothing.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to be inverse transformed.

Returns

TSDataset after applying inverse transformation.

Return type

etna.datasets.tsdataset.TSDataset

classmethod load(path: pathlib.Path) typing_extensions.Self

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters

path (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

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

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) etna.core.mixins.TMixin

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 – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

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()

Collect all information about etna object in dict.

transform(ts: etna.datasets.tsdataset.TSDataset) etna.datasets.tsdataset.TSDataset

Transform TSDataset inplace.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset