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graphkit-learn/notebooks/job_graphkernels.sl

job_graphkernels.sl 448 B
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* use global variable to tackle big read-only variables when they are used in subprocesses. * rewrite the implementation of the marginalized kernel. * implement four computing methods of the generalized random walk kernel. * in the path kernel up to length h, use trie to save all paths, saving tremendous memories; use the Deep-first search to get paths from graphs. * in model_selection_for_precomputed_kernel method, complete the part to do cross validation when Gram matrices are read from file. * in get_dataset_attributes methods, correct three sub-methods about getting node degrees, add sub-methods to get fill factors of graphs. * change default chunksize of pool.imap_unordered parallel method to 100. * remove try... except blocks in case they hidden bugs.
6 years ago
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  1. #!/bin/bash

  2. 

  3. #SBATCH --exclusive

  4. #SBATCH --job-name="graphkernels"

  5. #SBATCH --partition=tcourt

  6. #SBATCH --mail-type=ALL

  7. #SBATCH --mail-user=jajupmochi@gmail.com

  8. #SBATCH --output=output_graphkernels.txt

  9. #SBATCH --error=error_graphkernels.txt

  10. #

  11. #SBATCH --ntasks=1

  12. #SBATCH --nodes=2

  13. #SBATCH --cpus-per-task=56

  14. #SBATCH --time=24:00:00

  15. #SBATCH --mem-per-cpu=4000

  16. 

  17. srun hostname

  18. srun cd /home/2017018/ljia01/py-graph/notebooks

  19. srun python3 run_spkernel.py

A Python package for graph kernels, graph edit distances and graph pre-image problem.