#!/usr/bin/env python
# coding: utf-8

# 

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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.rocket 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


# ## 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.

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


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


# ## 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

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


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cs.distance_frame


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