It is highly recommended that you install the SocialSim measurements package in a conda environment. SocialSim TA1 performers, please see the Wiki for information on how to run evaluation as a self-contained service.
conda create --name myenv python=3
source activate myenv Download the OS-specific distribution file here: http://snap.stanford.edu/snappy/release/beta/
# Snap installation
tar -xvzf snap_distribution.tar.gz
cd snap_distribution.tar.gz
python setup.py install
# iGraph installation
# Note: The iGraph installation process may vary from system to system
conda install -c conda-forge/label/gcc7 python-igraph
# For Mac OSX, you may to need install matplotlib with conda rather than
# relying on the pip install below
conda install matplotlibpip install socialsim.tar.gzimport socialsim as ss
# Load the simulation data
simulation = 'data/test_dataset.txt'
simulation = ss.load_data(simulation, ignore_first_line=True, verbose=False)
# Load the ground truth data
ground_truth = 'data/test_dataset.txt'
ground_truth = ss.load_data(ground_truth, ignore_first_line=True, verbose=False)
# Load the configuration file
# There are configuration files provided for CP1 and CP2 measurements
config = 'data/cp2_configuration.json'
config = ss.load_config(config)
# Get metadata
metadata = ss.MetaData(community_directory='data/communities/',
node_file='data/node_list.txt')
# Instantiate the task runner
task_runner = ss.TaskRunner(ground_truth, config, metadata=metadata, test=False)
# Run measurements and metrics on the simulation data
results, logs = task_runner(simulation, verbose=True)
# Get simulation measurements
results['simulation_results']
# Get ground truth measurements
results['ground_truth_results']
# Get metrics
results['metrics']There are several scripts to generate example test data and community and node files. These files are meant to illustrate the format for the input data and do not contain realistic interaction structure.
To generate example input data:
python data/generate_test_data.pyTo generate example community files:
python data/build_communities.pyTo generate an examples node list file:
python data/build_node_list.pyDataset files are made of individual json dictionaries on each line with a header json as the first entry.
The minimal format is shown below. Some measurements may require additional fields.
{"identifier": identifier, "team": team, "scenario": scenario}
{"nodeID": value, "nodeUserID": value, "actionType": value, "nodeTime": value, "platform": platform}
{"nodeID": value, "nodeUserID": value, "actionType": value, "nodeTime": value, "platform": platform}
{"nodeID": value, "nodeUserID": value, "actionType": value, "nodeTime": value, "platform": platform}
. .
. .
. .
{"nodeID": value, "nodeUserID": value, "actionType": value, "nodeTime": value, "platform": platform}
{"nodeID": value, "nodeUserID": value, "actionType": value, "nodeTime": value, "platform": platform}
{"nodeID": value, "nodeUserID": value, "actionType": value, "nodeTime": value, "platform": platform}All functions should follow the format shown below:
def f(x,y):
"""
Description: A plain English description of what this function does and
how it fits into the global package structure.
Inputs:
:x: (int) Plain English description of x.
:y: (int) Plain English description of y.
Outputs:
:z: (int) Plain English description of z.
"""
z = x**2 + y**2
return zOn line length: Lines have a hard limit of 80 characters.
On everything else: Try to follow PEP8 where possible. Always follow local conventions.
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