base

Classes

ClusteringLinkageMode(value)	Modes allowed for clustering distance computation.
HierarchicalClustering(distance)	Base class for hierarchical clustering.

class ClusteringLinkageMode(value)

Modes allowed for clustering distance computation.

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

Base class for hierarchical clustering.

Init HierarchicalClustering.

Parameters

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

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

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

Parameters

• n_clusters (int) – number of clusters to build

• linkage (Union[str, etna.clustering.hierarchical.base.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: etna.datasets.tsdataset.TSDataset)

Compute distance matrix with given ts and distance.

Parameters

• ts (etna.datasets.tsdataset.TSDataset) – TSDataset with series to build distance matrix

• distance – instance if distance to compute matrix

fit_predict() → Dict[str, int]

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

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.