Video demo

CIVis integrates a Contrastive Learing (CL) framework to enable modeling dataset with missing values, avoiding imputation, and is a visual analytics system to allow users with limited background CL knowledge to iteratively improve model training. Finally a accurate and trustworthy model makes prediction for downstream tasks, fed by incomplete data.

1. Install python packages (Exclude PyTorch if you already install.)

   cd backend
   pip install -r requirement.txt

2. Install frontend packages

   cd frontend
   npm install

3. Run backend

   cd backend
   /opt/conda/bin/flask run --host=0.0.0.0 --port=5000

4. Run frontend

   cd frontend
   npm run start

If this paper and tool helps your research projects, please considering citing our paper:

@article{xie2023towards,
  title={Towards Better Modeling With Missing Data: A Contrastive Learning-Based Visual Analytics Perspective},
  author={Xie, Laixin and Ouyang, Yang and Chen, Longfei and Wu, Ziming and Li, Quan},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2023},
  publisher={IEEE}
}