# Model Multiple Executions¶

dlib.utils.multi_runs allows the parallel execution of multiple instances of a given model starting from different initial infection conditions.

The initial infected nodes for each instance of the model can be specified either:

• by the “percentage_infected” model parameter, or
• explicitly through a list of n sets of nodes (where n is the number of executions required).

In the first scenario “percentage_infected” nodes will be sampled independently for each model execution.

Results of dlib.utils.multi_runs can be feed directly to all the visualization facilities exposed by ndlib.viz.

ndlib.utils.multi_runs(model, execution_number, iteration_number, infection_sets, nprocesses)

Multiple executions of a given model varying the initial set of infected nodes

Parameters: model – a configured diffusion model execution_number – number of instantiations iteration_number – number of iterations per execution infection_sets – predefined set of infected nodes sets nprocesses – number of processes. Default values cpu number. resulting trends for all the executions

## Example¶

Randomly selection of initial infection sets

import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics.SIRModel as sir
from ndlib.utils import multi_runs

# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)

# Model selection
model1 = sir.SIRModel(g)

# Model Configuration
config = mc.Configuration()
model1.set_initial_status(config)

# Simulation multiple execution
trends = multi_runs(model1, execution_number=10, iteration_number=100, infection_sets=None, nprocesses=4)


Specify initial infection sets

import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics.SIRModel as sir
from ndlib.utils import multi_runs

# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)

# Model selection
model1 = sir.SIRModel(g)

# Model Configuration
config = mc.Configuration()
model1.set_initial_status(config)

# Simulation multiple execution
infection_sets = [(1, 2, 3, 4, 5), (3, 23, 22, 54, 2), (98, 2, 12, 26, 3), (4, 6, 9) ]
trends = multi_runs(model1, execution_number=2, iteration_number=100, infection_sets=infection_sets, nprocesses=4)


Plot multiple executions

The ndlib.viz.mpl package offers support for visualization of multiple runs.

In order to visualize the average trend/prevalence along with its inter-percentile range use the following pattern (assuming model1 and trends be the results of the previous code snippet).

from ndlib.viz.mpl.DiffusionTrend import DiffusionTrend
viz = DiffusionTrend(model1, trends)
viz.plot("diffusion.pdf", percentile=90)


where percentile identifies the upper and lower bound (e.g. setting it to 90 implies a range 10-90).

The same pattern can be also applied to comparison plots.

Multiple run visualization.