The core function of the PBCT algorithm is included in the file utils/PBCT.py. Given the labeled and unlabeled training data as well as the test data, it triggers the training of the complete-view model and parital-view models, save the model parameters in the desired paths, and return the test error measured using RMSE. An example for utilizing the PBCT algorithm is provided in the main section of this file.

The source dataset under /data comes from

Severson, K.A., Attia, P.M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M.H., Aykol, M., Herring, P.K., Fraggedakis, D., et al. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4, 383–391

The original data can be found in link under the license of CC-BY. We extract the features according to the instruction from paper.