data_process_example/README.md

How to Run

1. Execute process1.py to convert the csv file into a pkl file.

2. Run one of the process2 scripts based on your requirements:

   Note: We use -1000 to represent the position of package loss. You can apply various interpolation methods to fill these gaps. We highly encourage you to try our CSI-BERT model to recover the lost packages. (CSI-BERT, CSI-BERT2)

   (1) If you want to process each record into a long sequence, run process2.py. You can refer to dataset.py in CSI-BERT2 for guidance.

   (2) If you prefer to split each record into multiple fixed-length samples, run process2-split.py and modify the length parameter in the code to your desired length. You can refer to dataset.py in CSI-BERT, CrossFi, KNN-MMD, and LoFi for usage instructions.

   (3) process2-squeeze-split.py functions similarly to process2-split.py, but it excludes all lost packages.