DTWDistance

class DTWDistance(points_distance: typing.Callable[[float, float], float] = CPUDispatcher(<function simple_dist>), trim_series: bool = False)[source]

Bases: etna.clustering.distances.base.Distance

DTW distance handler.

Init DTWDistance.

Parameters

  • points_distance (Callable[[float, float], float]) – function to be used for computation of distance between two series’ points

  • trim_series (bool) – True if it is necessary to trim series, default False.

Notes

Specifying manual points_distance might slow down the clustering algorithm.

Inherited-members

Parameters

  • points_distance (Callable[[float, float], float]) –

  • trim_series (bool) –

Methods

get_average(ts, **kwargs)

Get series that minimizes squared distance to given ones according to the Distance.

set_params(**params)

Return new object instance with modified parameters.

to_dict()

Collect all information about etna object in dict.
get_average(ts: TSDataset, **kwargs: Dict[str, Any]) → pandas.core.frame.DataFrame

Get series that minimizes squared distance to given ones according to the Distance.

Parameters

  • ts (TSDataset) – TSDataset with series to be averaged

  • kwargs (Dict[str, Any]) – additional parameters for averaging

Returns

dataframe with columns “timestamp” and “target” that contains the series

Return type

pd.DataFrame

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.