etna.clustering.HierarchicalClustering#

class HierarchicalClustering(distance: Distance)[source]#

Bases: Clustering

Base class for hierarchical clustering.

Init HierarchicalClustering.

Methods

`build_clustering_algo`([n_clusters, linkage])	Build clustering algo (see `sklearn.cluster.AgglomerativeClustering`) with given params.
`build_distance_matrix`(ts)	Compute distance matrix with given ts and distance.
`fit_predict`()	Fit clustering algorithm and predict clusters according to distance matrix build.
`get_centroids`(**averaging_kwargs)	Get centroids of clusters.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.

Attributes

This class stores its __init__ parameters as attributes.

Parameters:: distance (Distance) –

build_clustering_algo(n_clusters: int = 30, linkage: str | ClusteringLinkageMode = ClusteringLinkageMode.average, **clustering_algo_params)[source]#

Build clustering algo (see sklearn.cluster.AgglomerativeClustering) with given params.

Parameters:

n_clusters (int) – number of clusters to build
linkage (str | ClusteringLinkageMode) – rule for distance computation for new clusters, allowed “ward”, “single”, “average”, “maximum”, “complete”

Notes

Note that it will reset previous results of clustering in case of reinit algo.

build_distance_matrix(ts: TSDataset)[source]#

Compute distance matrix with given ts and distance.

Parameters:: ts (TSDataset) – TSDataset with series to build distance matrix

fit_predict() → Dict[str, int][source]#

Fit clustering algorithm and predict clusters according to distance matrix build.

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

get_centroids(**averaging_kwargs) → DataFrame[source]#

Get centroids of clusters.

Returns:: dataframe with centroids
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.