ElbowClassSum

Elbow Class Sum (ECS) transformer to select a subset of channels/variables.

Quickstart

from sktime.transformations.channel_selection import ElbowClassSum

estimator = ElbowClassSum(distance=None)

Parameters(1)

distance: sktime pairwise panel transform, str, or callable, optional, default=None

if panel transform, will be used directly as the distance in the algorithm default None = euclidean distance on flattened series, FlatDist(ScipyDist()) if str, will behave as FlatDist(ScipyDist(distance)) = scipy dist on flat series if callable, must be univariate nested_univ x nested_univ -> 2D float np.array

Examples

>>> from sktime.transformations.channel_selection import ElbowClassSum
>>> from sktime.utils._testing.panel import make_classification_problem
>>> X, y = make_classification_problem (n_columns = 3, n_classes = 3, random_state = 42)
>>> cs = ElbowClassSum ()
>>> cs. fit (X, y) ElbowClassSum(
... )
>>> Xt = cs. transform (X) Any sktime compatible distance can be used, e.g., DTW distance:
>>> from sktime.dists_kernels import DtwDist
>>> 
>>> cs = ElbowClassSum (distance = DtwDist ())
>>> cs. fit (X, y) ElbowClassSum(
... )
>>> Xt = cs. transform (X)

References

1. ..[1]: Bhaskar Dhariyal et al. “Fast Channel Selection for Scalable Multivariate Time Series Classification.” AALTD, ECML-PKDD, Springer, 2021