Transformer
ElbowClassPairwise
Elbow Class Pairwise (ECP) transformer to select a subset of channels.
Overview: From the input of multivariate time series data, create a distance matrix [1] by calculating the distance between each class centroid. The ECP selects the subset of channels using the elbow method that maximizes the distance between each class centroids pair across all channels.
Note: Channels, variables, dimensions, features are used interchangeably in literature.
Quickstart
python
from sktime.transformations.channel_selection import ElbowClassPairwise
estimator = ElbowClassPairwiseTags
Capabilities
- Multivariate
- Inverse transform: Not supported
- Missing values: Not supported
- Removes missing values: Not supported
- Unequal-length series: Not supported
- Equalizes series length: Not supported
Properties
- Input typescitype:transform-input
- Series
- Output typescitype:transform-output
- Series
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- No
- Keeps the time indextransform-returns-same-time-index
- No
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- Yes
- X and y need the same indexX-y-must-have-same-index
- No
Examples
>>> from sktime.transformations.channel_selection import ElbowClassPairwise
>>> from sktime.utils._testing.panel import make_classification_problem
>>> X, y = make_classification_problem (n_columns = 3, n_classes = 3, random_state = 42)
>>> cs = ElbowClassPairwise ()
>>> cs. fit (X, y) ElbowClassPairwise(
... )
>>> Xt = cs. transform (X)References
- ..[1]: Bhaskar Dhariyal et al. “Fast Channel Selection for Scalable Multivariate Time Series Classification.” AALTD, ECML-PKDD, Springer, 2021