A repository to generate dataset with marginal efficiency for each actor from the evaluated network.
- Authors: Piotr Bródka, Michał Czuba, Adam Piróg, Mateusz Stolarski
- Affiliation: WUST, Wrocław, Lower Silesia, Poland
conda env create -f env/conda.yaml
conda activate infmax-simulator-icm-mln
The dataset is stored on DVC. In order to fetch it, please sent a request to get an access via
e-mail (michal.czuba@pwr.edu.pl). Then, simply execute dvc pull
in your shell.
.
├── _configs -> def. of the spreading regimes under which do computations
├── _data_set -> networks to compute actors' marginal efficiency for
├── _test_data -> examplary outputs of the dataset generator used in the E2E test
├── _output -> a directory where we recommend to save results
├── env -> a definition of the runtime environment
├── misc -> miscellaneous scripts helping in simulations
├── runners -> scripts to execute experiments according to provided configs
├── README.md
├── run_experiments.py -> main entrypoint to trigger the pipeline
└── test_reproducibility.py -> E2E test to prove that results can be repeated
To run experiments execute: run_experiments.py
and provide proper CLI arguments, i.e. a path to
configuration file and a runner type. See examples in _config/examples
for inspirations. As a
result, for each evaluated spreading case, a csv file will be obtained with a folllowing data
regarding each actor of the network:
actor: int # actor's id
simulation_length: int # nb. of simulation steps
exposed: int # nb. of infected actors
not_exposed: int # nb. of not infected actors
peak_infected: int # maximal nb. of infected actors in a single sim. step
peak_iteration: int # a sim. step when the peak occured
Results are supposed to be fully reproducable. There is a test for that: test_reproducibility.py
.