【小笔记】用tsai库实现Rocket家族算法
2024.1.16
Rocket家族算法是用于时间序列分类的强baseline（性能比较参考【小笔记】时序数据分类算法最新小结），Rocket/MiniRocket/MultiRocket官方都有开源实现，相比较而言，用tsai来实现有三个好处：1是快速跑通模型；2是更简洁优雅；3是掌握一个框架能举一反三。
1.tsai简介
项目：https://github.com/timeseriesAI/tsai
简介：
用于处理时间序列的工具库，包含TCN、Rockert等众多时间序列处理算法
安装：
pip install tsai
2.Rocket：最优雅的实现
这个例子是基于UCR的Beef数据集，运行时，会自动下载数据集到项目的data路径下
from tsai.all import *
from sklearn.linear_model import RidgeClassifierCV
from dsets_build import get_my_dsets
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(device)
# 加载UCR数据集
X, y, splits = get_UCR_data('Beef', return_split=False, on_disk=True, verbose=True)
tfms = [None, [Categorize()]]
batch_tfms = [TSStandardize(by_sample=True)]
dsets = TSDatasets(X, y, tfms=tfms, splits=splits)
# 标准示例
dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=768, drop_last=False, shuffle_train=False,
device=device,
batch_tfms=[TSStandardize(by_sample=True)])
model = create_model(ROCKET, dls=dls)
# model = model.to(device)
print("构造特征...")
X_train, y_train = create_rocket_features(dls.train, model, verbose=False)
X_valid, y_valid = create_rocket_features(dls.valid, model, verbose=False)
print(X_train.shape, X_valid.shape)
print("基于特征开始训练...")
ridge = RidgeClassifierCV(alphas=np.logspace(-8, 8, 17))
ridge.fit(X_train, y_train)
print(f'alpha: {ridge.alpha_:.2E} train: {ridge.score(X_train, y_train):.5f} valid: {ridge.score(X_valid, y_valid):.5f}')
3.MiniRocket:（比Rocket更快）
待补充
4.MultiRocket:（比MiniRocket更强）
待补充
5.Hydra-MultiRocket:（Rocket家最强王者）
待补充
6.用自己的数据集训练模型
上面的例子都是用的UCR数据集，若要用自己的数据集进行训练怎么解决？
官方教程：
tsai-main\tutorial_nbs路径下的00c_Time_Series_data_preparation.ipynb
我总结了一下，基于单变量时间序列构建数据集就是下面这样：
dsets_build.py
from tsai.all import *
import numpy as np
import pandas as pd
def get_my_dsets():
# 导入数据集
train_data, valid_data, test_data = [[], []], [[], []], [[], []]
radio_train, radio_valid, radio_test = 0.6, 0.2, 0.2
# !这是我的读取读取例子,读者需要进行替换----------------------------------
path = "train.csc"
data = pd.read_csv(path)
train_data[0] = data['x'].tolist()
train_data[1] = data['y'].tolist()
# -----------------------------------------------------------------------
# 将数据转换为np.array即可，剩下的都是通用了
X_2d, y = np.array(train_data[0]), np.array(train_data[1])
print(X_2d.shape, y.shape) # (4000, 4096) (4000,)
splits = get_splits(y, valid_size=0.2, stratify=True, random_state=23, shuffle=True, show_plot=False)
print(splits)
tfms = [None, [Categorize()]]
dsets = TSDatasets(X_2d, y, tfms=tfms, splits=splits, inplace=True)
print(dsets)
return dsets
将数据集转换为tsai的dsets后，就可以直接用于训练模型了。
from tsai.all import *
from sklearn.linear_model import RidgeClassifierCV
from dsets_build import get_my_dsets
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(device)
# 加载UCR数据集
# X, y, splits = get_UCR_data('Beef', return_split=False, on_disk=True, verbose=True)
# tfms = [None, [Categorize()]]
# batch_tfms = [TSStandardize(by_sample=True)]
# dsets = TSDatasets(X, y, tfms=tfms, splits=splits)
# 加载自定义的数据集
dsets = get_my_dsets() # 和Rockert例子只有这里的区别
# 标准示例
dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=768, drop_last=False, shuffle_train=False,
device=device,
batch_tfms=[TSStandardize(by_sample=True)])
model = create_model(ROCKET, dls=dls)
# model = model.to(device)
print("构造rocket特征...")
X_train, y_train = create_rocket_features(dls.train, model, verbose=False)
X_valid, y_valid = create_rocket_features(dls.valid, model, verbose=False)
print(X_train.shape, X_valid.shape)
print("基于特征开始训练...")
ridge = RidgeClassifierCV(alphas=np.logspace(-8, 8, 17))
ridge.fit(X_train, y_train)
print(f'alpha: {ridge.alpha_:.2E} train: {ridge.score(X_train, y_train):.5f} valid: {ridge.score(X_valid, y_valid):.5f}')