etna.transforms.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: str | None = None, in_column: str | None = None)[source]#

Bases: IrreversibleTransform

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 (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
in_column (str | None) – name of column to work with; if not given, index is used, only datetime index is supported

Raises:

ValueError: – if all features aren’t set in transform

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) → TimeFlagsTransform[source]#

Fit the transform.

Parameters:: ts (TSDataset) –
Return type:: TimeFlagsTransform

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.

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 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, 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