import warnings

warnings.filterwarnings("ignore")
from sklearn.linear_model import RidgeClassifierCV
from sklearn.pipeline import make_pipeline

from aeon.datasets import load_basic_motions
from aeon.transformations.collection import channel_selection
from aeon.transformations.collection.convolution_based import Rocket

X_train, y_train = load_basic_motions(split="train")
X_test, y_test = load_basic_motions(split="test")
X_train.shape, X_test.shape

((40, 6, 100), (40, 6, 100))

1 Channel Selection in a Pipeline

ElbowClassPairwise and ElbowClassSum are aeon transformers, so can be used in a pipeline with other transformers and suitable classifiers.

# cs = channel_selection.ElbowClassSum()  # ECS
cs = channel_selection.ElbowClassPairwise(prototype_type="mad")  # ECP
rocket_pipeline = make_pipeline(cs, Rocket(), RidgeClassifierCV())

rocket_pipeline.fit(X_train, y_train)
rocket_pipeline.score(X_test, y_test)

1.0

4 Identify channels selected

We can recover the selected channels from the transformer, and recover the centroids uses in the selection process. We can of course do this directly from the transform

X_selected = cs.fit(X_train, y_train)
cs.channels_selected_

[0, 1]

cs.distance_frame

	Centroid_badminton_running	Centroid_badminton_standing	Centroid_badminton_walking	Centroid_running_standing	Centroid_running_walking	Centroid_standing_walking
0	72.883920	37.486167	32.843063	95.139594	89.360656	9.786164
1	82.974076	19.586588	28.068407	87.661718	89.326726	21.778564
2	21.381712	18.196532	17.131596	26.885354	24.524588	4.834320
3	11.289843	9.402970	9.932407	7.750558	8.613671	3.744539
4	8.140510	6.623146	6.877631	6.112858	6.304372	1.546983
5	29.041896	9.025905	12.876751	27.785727	28.527294	9.650223

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