zwtinbuaa / birnn

biRNN in multi time series imputation.

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 brits

Data Format

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 #对模型没用

  '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 #对模型没用
  • evals: list, indicating x_t \in R^d (before elimination)
  • eval_masks: list, indicating whether each value is an imputation ground-truth

首先运行slovete.py进行数据的处理，此文件里面有个参数n代表连续时间步长度。 要训练模型： python main.py --epochs 1000 --batch_size 32 --model brits 这3个参数可以改变epochs，batch_size 和model