EuclideanClustering

class EuclideanClustering

Bases: etna.clustering.hierarchical.base.HierarchicalClustering

Hierarchical clustering with euclidean distance.

Examples

>>> from etna.clustering import EuclideanClustering
>>> from etna.datasets import TSDataset
>>> from etna.datasets import generate_ar_df
>>> ts = generate_ar_df(periods = 40, start_time = "2000-01-01", n_segments = 10)
>>> ts = TSDataset(TSDataset.to_dataset(ts), freq="D")
>>> model = EuclideanClustering()
>>> model.build_distance_matrix(ts)
>>> model.build_clustering_algo(n_clusters=3, linkage="average")
>>> segment2cluster = model.fit_predict()
>>> segment2cluster
{'segment_0': 2,
 'segment_1': 1,
 'segment_2': 0,
 'segment_3': 1,
 'segment_4': 1,
 'segment_5': 0,
 'segment_6': 0,
 'segment_7': 0,
 'segment_8': 2,
 'segment_9': 2}

Create instance of EuclideanClustering.

Inherited-members

Methods

build_clustering_algo([n_clusters, linkage])	Build clustering algo (see sklearn.cluster.AgglomerativeClustering) with given params.
build_distance_matrix(ts)	Build distance matrix with euclidean 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_distance_matrix(ts: TSDataset)

Build distance matrix with euclidean distance.

Parameters

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