[1]:

import warnings

warnings.filterwarnings("ignore")

多元时间序列分类中的通道选择#

概述#

有时并非所有通道都用于执行分类；只有少数通道有用。[1] 提出了一种用于多元时间分类的快速通道选择技术。

[1] : 可扩展多元时间序列分类的快速通道选择链接

[2]:

from sklearn.linear_model import RidgeClassifierCV
from sklearn.pipeline import make_pipeline

from sktime.datasets import load_UCR_UEA_dataset
from sktime.transformations.panel import channel_selection
from sktime.transformations.panel.rocket import Rocket

1 初始化流水线#

[3]:

# cs = channel_selection.ElbowClassSum()  # ECS
cs = channel_selection.ElbowClassPairwise()  # ECP

[4]:

rocket_pipeline = make_pipeline(cs, Rocket(), RidgeClassifierCV())

2 加载并拟合训练数据#

[5]:

data = "BasicMotions"
X_train, y_train = load_UCR_UEA_dataset(data, split="train", return_X_y=True)
X_test, y_test = load_UCR_UEA_dataset(data, split="test", return_X_y=True)

[6]:

rocket_pipeline.fit(X_train, y_train)

[6]:

Pipeline(steps=[('elbowclasspairwise', ElbowClassPairwise()),
                ('rocket', Rocket()),
                ('ridgeclassifiercv',
                 RidgeClassifierCV(alphas=array([ 0.1,  1. , 10. ])))])

3 对测试数据进行分类#

[7]:

rocket_pipeline.score(X_test, y_test)

[7]:

1.0

4 识别通道#

[8]:

rocket_pipeline.steps[0][1].channels_selected_

[8]:

[0, 1]

[9]:

rocket_pipeline.steps[0][1].distance_frame_

[9]:

	Centroid_badminton_running	Centroid_badminton_standing	Centroid_badminton_walking	Centroid_running_standing	Centroid_running_walking	Centroid_standing_walking
0	39.594679	55.752785	48.440779	63.610220	57.247383	10.717044
1	57.681767	24.390543	27.770269	60.458125	62.339120	16.370347
2	20.175911	24.126969	22.331621	25.671979	22.991555	4.897452
3	12.546212	12.439152	12.741854	6.317654	6.695743	3.585273
4	10.101196	8.865871	9.221908	6.520172	6.715702	1.299989
5	23.464251	14.568685	13.953445	18.878429	19.768549	7.228389

5 独立运行#

[10]:

cs.fit(X_train, y_train)

[10]:

ElbowClassPairwise()

6 距离矩阵#

[11]:

cs.distance_frame_

[11]:

	Centroid_badminton_running	Centroid_badminton_standing	Centroid_badminton_walking	Centroid_running_standing	Centroid_running_walking	Centroid_standing_walking
0	39.594679	55.752785	48.440779	63.610220	57.247383	10.717044
1	57.681767	24.390543	27.770269	60.458125	62.339120	16.370347
2	20.175911	24.126969	22.331621	25.671979	22.991555	4.897452
3	12.546212	12.439152	12.741854	6.317654	6.695743	3.585273
4	10.101196	8.865871	9.221908	6.520172	6.715702	1.299989
5	23.464251	14.568685	13.953445	18.878429	19.768549	7.228389

[12]:

cs.train_time_

[12]:

使用 nbsphinx 生成。Jupyter 笔记本可在此处找到：here。