etna.transforms.HolidayTransform#

class HolidayTransform(iso_code: str = 'RUS', mode: str = 'binary', out_column: str | None = None, in_column: str | None = None)[source]#

Bases: IrreversibleTransform

HolidayTransform generates series that indicates holidays in given dataset.

In binary mode shows the presence of holiday in a given timestamp.
In category mode shows the name of the holiday in a given timestamp, the value “NO_HOLIDAY” is reserved for days without holidays.
In days_count mode shows the frequency of holidays in a given period.
- If the frequency is weekly, then we count the proportion of holidays in a week (Monday-Sunday) that contains this day.
- If the frequency is monthly, then we count the proportion of holidays in a month that contains this day.
- If the frequency is yearly, then we count the proportion of holidays in a year that contains this day.

Transform can accept timestamp data in two forms:

As index. In this case the dataset index is used to compute features. The features will be the same for each segment.
As external column. In this case for each segment its in_column will be used to compute features. In days_count mode it is expected that for all segments only one frequency is used.

Notes

During fitting int days_count mode the transform saves frequency. It is assumed to be the same during transform.

Create instance of HolidayTransform.

Parameters:

iso_code (str) – internationally recognised codes, designated to country for which we want to find the holidays.
mode (str) – binary to indicate holidays, category to specify which holiday do we have at each day, days_count to determine the proportion of holidays in a given period of time.
out_column (str | None) – name of added column. Use self.__repr__() if not given.
in_column (str | None) – name of column to work with; if not given, index is used, only datetime index is supported

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 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) → HolidayTransform[source]#

Fit the transform.

Parameters:

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

Returns:

The fitted transform instance.

Raises:

ValueError: – if index timestamp is integer and in_column isn’t set
ValueError: – if external timestamp isn’t datetime
ValueError – if in days_count mode external timestamp doesn’t have frequency
ValueError – if in days_count mode external timestamp doesn’t have the same frequency for all segments

Return type:

HolidayTransform

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. :returns: 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.

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

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

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns:: Empty grid.
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 = 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