Description

The source codes of RITS-I, RITS, BRITS-I, BRITS for health-care data imputation/classification

To run the code:

python main.py --epochs 1000 --batch_size 32 --model brist

Data Format

In json folder, we provide the sample data (400 patients). The data format is as follows:

• Each line in json/json is a string represents a python dict

• The structure of each dict is

  • forward
  • backward
  • label

  'forward' and 'backward' is a list of python dicts, which represents the input sequence in forward/backward directions. As an example for forward direction, each dict in the sequence contains:

  • values: list, indicating x_t \in R^d (after elimination)
  • masks: list, indicating m_t \in R^d
  • deltas: list, indicating \delta_t \in R^d
  • forwards: list, the forward imputation, only used in GRU_D, can be any numbers in our model
  • evals: list, indicating x_t \in R^d (before elimination)
  • eval_masks: list, indicating whether each value is an imputation ground-truth

Data Download 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