Continuous Model Runner¶

Often, a model is not only executed once for n amount of iterations. Most of the time meaningful conclusions can be drawn after simulating the model multiple times and even using different inputs. This is made possible using the ContinuousModelRunner.

It takes as input a ContinuousModel object and a Configuration object (created by using ModelConfig).

After instantiating the runner object, two functions can be used; one runs the model multiple times for n amount of iterations using different parameters, the other performs sensitivity analysis based on specified measures.

Runner¶

The model can be executed N amount with different parameters using the run(N, iterations_list, initial_statuses, constants_list=None) function. If the length of a list is not equal to the amount of simulations, this is no problem, as the index of the list will be selected using iteration number % list_length. This means if you want to only use one value for every simulation, simply provide a list as argument with only one value.

Run parameters	Value Type	Default	Mandatory	Description
N	number		True	The amount of times to run the simulation
iterations_list	list[number]		True	A list containing the amount of iterations to use per simulation
initial_statuses	list[dictionary]		True	A list containing initial_status dictionaries to use per simulation
constants_list	list[dictionary]	None	False	A list containing constants dictionaries to use per simulation

Example:

import networkx as nx
import numpy as np
from ndlib.models.ContinuousModel import ContinuousModel
from ndlib.models.ContinuousModelRunner import ContinuousModelRunner
from ndlib.models.compartments.NodeStochastic import NodeStochastic
import ndlib.models.ModelConfig as mc

g = nx.erdos_renyi_graph(n=1000, p=0.1)

def initial_status_1(node, graph, status, constants):
    return np.random.uniform(0, 0.5)

def initial_status_2(node, graph, status, constants):
    return status['status_1'] + np.random.uniform(0.5, 1)

initial_status = {
    'status_1': initial_status_1,
    'status_2': initial_status_2,
}

model = ContinuousModel(g)

model.add_status('status_1')
model.add_status('status_2')

# Compartments
condition = NodeStochastic(1)

# Update functions
def update_1(node, graph, status, attributes, constants):
    return status[node]['status_2'] + 0.1

def update_2(node, graph, status, attributes, constants):
    return status[node]['status_1'] + 0.5

# Rules
model.add_rule('status_1', update_1, condition)
model.add_rule('status_2', update_2, condition)

config = mc.Configuration()
model.set_initial_status(initial_status, config)

# Simulation
runner = ContinuousModelRunner(model, config)
# Simulate the model 10 times with 100 iterations
results = runner.run(10, [100], [initial_status])

Sensitivity Analysis¶

Another important part of analysing a model is sensitivity analysis. Custom analysis can be done using the run function, but an integrated SALib version is included and can be ran using the analyze_sensitivity(sa_type, initial_status, bounds, n, iterations, second_order=True) function.

It requires the following parameters:

parameters	Value Type	Default	Mandatory	Description
sa_type	SAType		True	SAType enumerated value indicating what metric to use for sensitivity analysis
initial_status	dictionary		True	A dictionary containing the initial status per state
bounds	dictionary{status => (lower, upper)		True	A dictionary mapping a status string to a tuple in the form of [lower, upper]
n	integer		True	The amount of samples to get from the SALib saltelli sampler
iterations	integer		True	A list containing constants dictionaries to use per simulation
second_order	boolean	True	False	Boolean indicating whether to include second order indices

At the moment, after every simulation, the mean value for a state is taken over all the nodes, which is seen as one output for the model. After running the analysis, a dictionary is returned, mapping a state to a dictionary with the keys “S1”, “S2”, “ST”, “S1_conf”, “S2_conf”, and “ST_conf” which is acquired by using sobol.analyze() from SALib.

Note

Currently, the following sensitivity analysis metrics can be passed for the sa_type parameter (use the SAType enum):

SAType.MEAN

Example:

import networkx as nx
import numpy as np
from ndlib.models.ContinuousModel import ContinuousModel
from ndlib.models.ContinuousModelRunner import ContinuousModelRunner
from ndlib.models.compartments.NodeStochastic import NodeStochastic
from ndlib.models.compartments.enums.SAType import SAType
import ndlib.models.ModelConfig as mc

g = nx.erdos_renyi_graph(n=1000, p=0.1)

constants = {
    'constant_1': 0.5,
    'constant_2': 0.8
}

def initial_status_1(node, graph, status, constants):
    return np.random.uniform(0, 0.5)

def initial_status_2(node, graph, status, constants):
    return status['status_1'] + np.random.uniform(0.5, 1)

initial_status = {
    'status_1': initial_status_1,
    'status_2': initial_status_2,
}

model = ContinuousModel(g, constants=constants)

model.add_status('status_1')
model.add_status('status_2')

# Compartments
condition = NodeStochastic(1)

# Update functions
def update_1(node, graph, status, attributes, constants):
    return status[node]['status_2'] * constants['constant_1']

def update_2(node, graph, status, attributes, constants):
    return status[node]['status_1'] + constants['constant_2']

# Rules
model.add_rule('status_1', update_1, condition)
model.add_rule('status_2', update_2, condition)

config = mc.Configuration()
model.set_initial_status(initial_status, config)

# Simulation
runner = ContinuousModelRunner(model, config)
analysis = runner.analyze_sensitivity(SAType.MEAN, initial_status, {'constant_1': (0, 1), 'constant_2': (-1, 1)}, 100, 50)