Clustering¶

class Clustering[source]¶

Bases: abc.ABC, etna.core.mixins.BaseMixin

Base class for ETNA clustering algorithms.

Inherited-members

Methods

`fit_predict`()	Fit clustering algo and predict clusters.
`get_centroids`()	Get centroids of clusters.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.

abstract fit_predict() → Dict[str, int][source]¶

Fit clustering algo and predict clusters.

Returns: dict in format {segment: cluster}
Return type: Dict[str, int]

abstract get_centroids() → pandas.core.frame.DataFrame[source]¶

Get centroids of clusters.

Returns: dataframe with centroids
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.