To train the BRIST model, first please unzip the PhysioNet data into raw folder, including the label file Outcomes-a.txt.
To run the model:
- make a empty folder named json, and run inpute_process.py.
- run different models:
- e.g., RITS_I: python main.py --model rits_i --epochs 1000 --batch_size 64 --impute_weight 0.3 --label_weight 1.0 --hid_size 108
- for most cases, using impute_weight=0.3 and label_weight=1.0 lead to a good performance. Also adjust hid_size to control the number of parameters
The data used in the experiments are public data. They can be found in the following links:
Air Quality Data: URL: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/STMVL-Release.zip Health-care Data: URL: https://physionet.org/challenge/2012/ We use the test-a.zip in our experiment. Human Activity Data: URL: https://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity