def test_algorithmic_bias_model(self):
g = nx.complete_graph(100)
model = opn.AlgorithmicBiasModel(g)
config = mc.Configuration()
config.add_model_parameter("epsilon", 0.32)
config.add_model_parameter("gamma", 1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_algorithmic_bias_model(self):
for g in get_graph():
model = opn.AlgorithmicBiasModel(g, seed=0)
config = mc.Configuration()
config.add_model_parameter("epsilon", 0.32)
config.add_model_parameter("gamma", 1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
_ = model.steady_state(max_iterations=100)
def test_opinion_viz(self):
g = nx.complete_graph(50)
model = op.AlgorithmicBiasModel(g)
# Model configuration
config = mc.Configuration()
config.add_model_parameter("epsilon", 0.32)
config.add_model_parameter("gamma", 0)
model.set_initial_status(config)
# Simulation execution
iterations = model.iteration_bunch(50)
viz = OpinionEvolution(model, iterations)
viz.plot("opinion_ev.png")
os.remove("opinion_ev.png")