Algorithm for correcting sessions of users of large-scale peer-to-peer systems based on deep learning.
Complementary repository to the work available at: https://sol.sbc.org.br/index.php/sbrc/article/view/16766/16608
Torrent Trace Correct - Machine Learning
Arguments(run_SBRC21.py):
-h, --help | Show this help message and exit
--output | Full name of the output file with analysis results (default=sbrc21.txt)
--append | Append output logging file with analysis results (default=False)
--trials | Number of trials (default=1)
--start_trials | Start trials (default=0)
--skip_train | Skip training of the machine learning model
--campaign | Campaign [demo, sbrc21] (default=demo)
--verbosity | Verbosity logging level (INFO=20 DEBUG=10)
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Arguments(main.py):
-h,--help | Show this help message and exit
--original_swarm_file | File of ground truth.
--training_swarm_file | File of training samples
--corrected_swarm_file | File of correction
--failed_swarm_file | File of failed swarm
--analyse_file | Analyse results with statistics
--analyse_file_mode | Open mode (e.g. 'w' or 'a')
--model_architecture_file | Full model architecture file
--model_weights_file | Full model weights file
--num_epochs | Number of epochs
--threshold | i.e. alpha (e.g. 0.5 - 0.95)
--dense_layers | Number of dense layers (e.g. 1, 2, 3)
--pif PIF | pif (only for statistics)
--dataset DATASET | Dataset (only for statistics)
--seed SEED | Seed (only for statistics)
--skip_train, -t | Skip training of the machine learning model
--skip_correct, -c | Skip correction of the dataset
--skip_analyse, -a | Skip analyzis of the results
--verbosity VERBOSITY, -v | Verbosity logging level (INFO=20 DEBUG=10)
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Full traces available at: https://github.com/ComputerNetworks-UFRGS/TraceCollection/tree/master/01_traces
python3 run_sbrc21.py -c sbrc
python3 main.py
matplotlib 3.4.1
tensorflow 2.4.1
tqdm 4.60.0
numpy 1.18.5
keras 2.4.3
setuptools 45.2.0
h5py 2.10.0