The processed data should be put into dataset/DATA_NAME/ so that each subject file can be located by dataset/DATA_NAME/Feature/feature_ID.npy, and the label file can be located by dataset/DATA_NAME/Label/label.npy.
The processed datasets can be manually downloaded at the following links.
- ADSZ dataset: https://drive.google.com/file/d/1jkqawkx1v_vlf5zFOSgIh2LJs_OJMrvf/view?usp=drive_link
- APAVA dataset: https://drive.google.com/file/d/1ARjYLQ-tSEu0r3bffnLn3tCWdUiz4qXI/view?usp=drive_link
- ADFD dataset: https://drive.google.com/file/d/1ACYmO2ai9ad4C2zP3-qJFMjDpTexD321/view?usp=drive_link
Since other three datasets in the paper are private datasets, we do not provide a download link here.
The recommended requirements are specified as follows:
- Python 3.8
- Jupyter Notebook
- einops==0.4.0
- matplotlib==3.7.0
- numpy==1.23.5
- pandas==1.5.3
- patool==1.12
- reformer-pytorch==1.4.4
- scikit-learn==1.2.2
- scipy==1.10.1
- sktime==0.16.1
- sympy==1.11.1
- torch==2.0.0
- tqdm==4.64.1
- wfdb==4.1.2
- neurokit2==0.2.9
- mne==1.6.1
- natsort~=8.4.0
The dependencies can be installed by:
pip install -r requirements.txtBefore running, make sure you have all the processed datasets put under dataset/. For Linux users, run each method's shell script in scripts/classification/.
You could also run all the experiments by running the meta-run.py/ file, which the method included in skip_list will not be run.
For Windows users, see jupyter notebook files. All the experimental scripts are provided cell by cell.
The gpu device ids can be specified by setting command line --devices (e,g, --devices 0,1,2,3).
You also need to change the visible gpu devices in script file by setting export CUDA_VISIBLE_DEVICES (e,g, export CUDA_VISIBLE_DEVICES=0,1,2,3).
The gpu devices specified by commend line should be a subset of visible gpu devices.
After training and evaluation, the saved model can be found incheckpoints/classification/;
and the results can be found in results/classification/.
You can modify the parameters by changing the command line.
The meaning and explanation of each parameter in command line can be found in run.py file.
Since APAVA is the smallest dataset and faster to run,
it is recommended to run and test our code with the APAVA dataset to get familiar with the framework.
This codebase is constructed based on the repo: Time-Series-Library. Thanks a lot for their amazing work on implementing state-of-arts time series methods!