Detach-ROCKET

Official repository for Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels and Classification of raw MEG/EEG data with detach-rocket ensemble: an improved rocket algorithm for multivariate time series analysis.

Overview

This repository contains Python implementations of Sequential Feature Detachment (SFD) for feature selection and Detach-ROCKET for time-series classification. Developed entirely in Python using primarly NumPy, PyTorch, Scikit-Learn and Sktime libraries, the core functionalities are encapsulated within the following classes:

• DetachRocket: Detach-ROCKET model class. It is constructed by pruning an initial ROCKET, MiniRocket or MultiROCKET model using SFD and selecting the optimal size.

• DetachMatrix: Class for applying Sequential Feature Detachment to any dataset matrix structured as (n_instances, n_features).

• DetachEnsemble: Detach-ROCKET Ensemble model class. It creates an ensemble of Detach models. We recommend using this class for multivariate time series, especially if they are high-dimensional. After training, these models are also able to provide channel relevance estimation and label probability.

For a detailed explanation of the models and methods please refer to the Detach-ROCKET article and the Detach-ROCKET Ensemble article.

Installation

To install the required dependencies, execute:

pip install numpy scikit-learn pyts torch matplotlib sktime==0.30.0
pip install git+https://github.com/gon-uri/detach_rocket --quiet

Usage - DetachRocket

The model usage is the same as in the scikit-learn library.

# Import Model
from detach_rocket.detach_classes import DetachRocket

# Instantiate Model
DetachRocketModel = DetachRocket('rocket', num_kernels=10000)

# Trian Model
DetachRocketModel.fit(X_train,y_train)

# Predict Test Set
y_pred = DetachRocketModel.predict(X_test)

For univariate time series, the shape of X_train should be (n_instances, n_timepoints).

For multivariate time series, the shape of X_train should be (n_instances, n_variables, n_timepoints).

Usage - DetachRocket Ensemble

This model is more suitable for Multivariate Time Series with a large number of channels/dimensions.

# Import Model
from detach_rocket.detach_classes import DetachEnsemble

# Instantiate Model
DetachRocketEnsemble = DetachEnsemble('pytorch_minirocket', num_kernels=10000)

# Trian Model
DetachRocketEnsemble.fit(X_train,y_train)

# Predict Test Set
y_pred = DetachRocketEnsemble.predict(X_test)

Notebook Examples

Detailed usage examples can be found in the included Jupyter notebooks in the examples folder.

Upcoming Features

• Built-in support for multilabel classification. (DONE!)
• Pytorch implementation of Detach-MiniRocket. (DONE!)
• Add channel releavance for Detach-MiniRocket. (DONE!)
• Implementation of Detach-ROCKET Ensemble. (DONE!)
• Add channel releavance and label probability for Detach-ROCKET Ensemble. (DONE!)
• Pytorch implementations of Detach-MultiRocket. (Coming soon...)
• Fully pytorch implementation of ROCKET with on-the-fly convolutions during training.
• Pytorch implementation of SFD for Multilayer Perceptrons.

License

This project is licensed under the BSD-3-Clause License.

Citation

If you find these methods useful in your research, please cite the following articles:

APA

Uribarri, G., Barone, F., Ansuini, A., & Fransén, E. (2024). Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels. Data Mining and Knowledge Discovery, 1-26.

Solana, A., Fransén, E., & Uribarri, G. (2024). Classification of raw MEG/EEG data with detach-rocket ensemble: an improved rocket algorithm for multivariate time series analysis. arXiv preprint arXiv:2408.02760.

BIBTEX

@article{uribarri2024detach,
  title={Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels},
  author={Uribarri, Gonzalo and Barone, Federico and Ansuini, Alessio and Frans{\'e}n, Erik},
  journal={Data Mining and Knowledge Discovery},
  pages={1--26},
  year={2024},
  publisher={Springer}
}

@article{solana2024classification,
  title={Classification of raw MEG/EEG data with detach-rocket ensemble: an improved rocket algorithm for multivariate time series analysis},
  author={Solana, Adri{\`a} and Frans{\'e}n, Erik and Uribarri, Gonzalo},
  journal={arXiv preprint arXiv:2408.02760},
  year={2024}
}