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[TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data [Paper] [Cite]

by: Mohamed Ragab*, Emadeldeen Eldele*, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen☨, Min Wu, Chee Kwoh, Xiaoli Li
* Equal contribution
☨ Corresponding author

Published in the ACM Transactions on Knowledge Discovery from Data (TKDD).

AdaTime is a PyTorch suite to systematically and fairly evaluate different domain adaptation methods on time series data.

Requirmenets:

  • Python3
  • Pytorch==1.7
  • Numpy==1.20.1
  • scikit-learn==0.24.1
  • Pandas==1.2.4
  • skorch==0.10.0 (For DEV risk calculations)
  • openpyxl==3.0.7 (for classification reports)
  • Wandb=0.12.7 (for sweeps)

Datasets

Available Datasets

We used four public datasets in this study. We also provide the preprocessed versions as follows:

Adding New Dataset

Structure of data

To add new dataset (e.g., NewData), it should be placed in a folder named: NewData in the datasets directory.

Since "NewData" has several domains, each domain should be split into train/test splits with naming style as "train_x.pt" and "test_x.pt".

The structure of data files should in dictionary form as follows: train.pt = {"samples": data, "labels: labels}, and similarly for test.pt.

Configurations

Next, you have to add a class with the name NewData in the configs/data_model_configs.py file. You can find similar classes for existing datasets as guidelines. Also, you have to specify the cross-domain scenarios in self.scenarios variable.

Last, you have to add another class with the name NewData in the configs/hparams.py file to specify the training parameters.

Domain Adaptation Algorithms

Existing Algorithms

Adding New Algorithm

To add a new algorithm, place it in algorithms/algorithms.py file.

Training procedure

The experiments are organised in a hierarchical way such that:

  • Several experiments are collected under one directory assigned by --experiment_description.
  • Each experiment could have different trials, each is specified by --run_description.
  • For example, if we want to experiment different UDA methods with CNN backbone, we can assign --experiment_description CNN_backnones --run_description DANN and --experiment_description CNN_backnones --run_description DDC and so on.

Training a model

To train a model:

python main.py  --phase train  \
                --experiment_description exp1  \
                --da_method DANN \
                --dataset HHAR \
                --backbone CNN \
                --num_runs 5 \

To test a model:

python main.py  --phase test  \
                --experiment_description exp1  \
                --da_method DANN \
                --dataset HHAR \
                --backbone CNN \
                --num_runs 5 \

Launching a sweep

Sweeps here are deployed on Wandb, which makes it easier for visualization, following the training progress, organizing sweeps, and collecting results.

python main_sweep.py  --experiment_description exp1_sweep  \
                --run_description sweep_over_lr \
                --da_method DANN \
                --dataset HHAR \
                --backbone CNN \
                --num_runs 5 \
                --sweep_project_wandb TEST
                --num_sweeps 50 \

Upon the run, you will find the running progress in the specified project page in wandb.

Note: If you got cuda out of memory error during testing, this is probably due to DEV risk calculations.

Upper and Lower bounds

  • To obtain the source-only or the lower bound you can choose the da_method to be NO_ADAPT.
  • To obtain the the target-only or the upper bound you can choose the da_method TARGET_ONLY

Results

  • Each run will have all the cross-domain scenarios results in the format src_to_trg_run_x, where x is the run_id (you can have multiple runs by assigning --num_runs arg).
  • Under each directory, you will find the classification report, a log file, checkpoint, and the different risks scores.
  • By the end of the all the runs, you will find the overall average and std results in the run directory.

Citation

If you found this work useful for you, please consider citing it.

@article{adatime,
  author = {Ragab, Mohamed and Eldele, Emadeldeen and Tan, Wee Ling and Foo, Chuan-Sheng and Chen, Zhenghua and Wu, Min and Kwoh, Chee-Keong and Li, Xiaoli},
  title = {ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data},
  year = {2023},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  issn = {1556-4681},
  url = {https://doi.org/10.1145/3587937},
  doi = {10.1145/3587937},
  journal = {ACM Trans. Knowl. Discov. Data},
  month = {mar}
}

Contact

For any issues/questions regarding the paper or reproducing the results, please contact any of the following.

Mohamed Ragab: mohamedr002{at}e.ntu.edu.sg

Emadeldeen Eldele: emad0002{at}e.ntu.edu.sg

School of Computer Science and Engineering (SCSE),
Nanyang Technological University (NTU), Singapore.