HierarchicalClustering

class HierarchicalClustering(distance: etna.clustering.distances.base.Distance)[source]

Bases: etna.clustering.base.Clustering

Base class for hierarchical clustering.

Init HierarchicalClustering.

Inherited-members

Parameters

distance (etna.clustering.distances.base.Distance) –

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.

build_clustering_algo(n_clusters: int = 30, linkage: Union[str, etna.clustering.hierarchical.base.ClusteringLinkageMode] = ClusteringLinkageMode.average, **clustering_algo_params)[source]

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

Parameters

Notes

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

build_distance_matrix(ts: etna.datasets.tsdataset.TSDataset)[source]

Compute distance matrix with given ts and distance.

Parameters
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) pandas.core.frame.DataFrame[source]

Get centroids of clusters.

Returns

dataframe with centroids

Return type

pd.DataFrame