This project has been executed via google colab.
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Download the code from github as a zip file.
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Extract the contents of the zip file and upload it into google drive.
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The code to be executed in google colab:
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Make sure to set the runtime to GPU before running the code.
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Make the necessary changes mentioned in the code to get the result.
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The dataset to be used has to be uploaded in the
data/datasets/folder(if not already present).
To train and evaluate Pyraformer on a dataset the following command is used in the google colab notebook :
!python -u long_range_main.py -data Battery -data_path new_data.csv -input_size 100 -predict_step 24 -n_head 6 -run_name new_data_trial_1_24
The detailed descriptions about the arguments are as following:
| Parameter name | Description of parameter |
|---|---|
| data | The dataset name |
| data_path | The name of the csv file to be used |
| predict_step | The prediction horizon |
| run_name | The folder name used to save the output. This can be set to any word |
After running the main code , the output can be found in results/run_name/ folder.
Any other changes such as epoch or batch_size can be done in the long_range_main.py script.
We appreciate the following github repos a lot for their valuable code base and datasets:
https://github.com/alipay/Pyraformer
Diao, W., Saxena, S., Pecht, M. Accelerated Cycle Life Testing and Capacity Degradation Modeling of LiCoO2 -graphite Cells. J. Power Sources 2019, 435, 226830
Calce Battery Research Group. [Online]. Available: https://web. calce.umd.edu/batteries/data.htm