DistanceMatrix¶

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

Bases: etna.core.mixins.BaseMixin

DistanceMatrix computes distance matrix from TSDataset.

Init DistanceMatrix.

Parameters: distance (etna.clustering.distances.base.Distance) – class for distance measurement
Inherited-members

Methods

`fit`(ts)	Fit distance matrix: get timeseries from ts and compute pairwise distances.
`fit_predict`(ts)	Compute distance matrix and return it.
`predict`()	Get distance matrix.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.

fit(ts: TSDataset) → DistanceMatrix[source]¶

Fit distance matrix: get timeseries from ts and compute pairwise distances.

Parameters: ts (TSDataset) – TSDataset with timeseries
Returns: fitted DistanceMatrix object
Return type: self

fit_predict(ts: TSDataset) → numpy.ndarray[source]¶

Compute distance matrix and return it.

Parameters: ts (TSDataset) – TSDataset with timeseries to compute matrix with
Returns: 2D array with distances between series
Return type: np.ndarray

predict() → numpy.ndarray[source]¶

Get distance matrix.

Returns: 2D array with distances between series
Return type: np.ndarray

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.