etna.clustering.EuclideanDistance#

class EuclideanDistance(trim_series: bool = True)[source]#

Bases: Distance

Euclidean distance handler.

Init EuclideanDistance.

Parameters:: trim_series (bool) – if True, compare parts of series with common timestamp

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.
`__call__`(x1, x2)	Compute distance between x1 and x2.

Attributes

This class stores its __init__ parameters as attributes.

__call__(x1: Series, x2: Series) → float[source]#

Compute distance between x1 and x2.

Parameters:

x1 (Series) – timestamp-indexed series
x2 (Series) – timestamp-indexed series

Returns:

distance between x1 and x2

Return type:

float

get_average(ts: TSDataset, **kwargs: Dict[str, Any]) → DataFrame[source]#

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) → 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.