def testFitBootstrap(self):
if IGNORE_TEST:
return
study = ModelStudy(th.ANTIMONY_MODEL, DATA_PATHS,
parametersToFit=PARAMETERS_TO_FIT,
dirStudyPath=SERIALIZE_DIR, isPlot=IS_PLOT, useSerialized=False)
study.bootstrap(numIteration=10)
for fitter in study.fitterDct.values():
self.assertIsNotNone(fitter.bootstrapResult)
def setUp(self):
self._remove()
self.parametersToFit = list(th.PARAMETER_DCT.keys())
self.study = ModelStudy(th.ANTIMONY_MODEL,
DATA_PATHS,
parametersToFit=PARAMETERS_TO_FIT,
dirStudyPath=SERIALIZE_DIR,
isPlot=IS_PLOT,
useSerialized=True)
def runVirus(self, values):
ANTIMONY_MODEL = '''
// Equations
E1: T -> E ; beta*T*V ; // Target cells to exposed
E2: E -> I ; kappa*E ; // Exposed cells to infected
E3: -> V ; p*I ; // Virus production by infected cells
E4: V -> ; c*V ; // Virus clearance
E5: I -> ; delta*I // Death of infected cells
// Parameters - from the Influenza article,
beta = 3.2e-5; // rate of transition of target(T) to exposed(E) cells, in units of 1/[V] * 1/day
kappa = 4.0; // rate of transition from exposed(E) to infected(I) cells, in units of 1/day
delta = 5.2; // rate of death of infected cells(I), in units of 1/day
p = 4.6e-2; // rate virus(V) producion by infected cells(I), in units of [V]/day
c = 5.2; // rate of virus clearance, in units of 1/day
// Initial conditions
T = 4E+8 // estimate of the total number of susceptible epithelial cells
// in upper respiratory tract)
E = 0
I = 0
V = 0.75 // the dose of virus in TCID50 in Influenza experiment; could be V=0 and I = 20 instead for a natural infection
// Computed values
log10V := log10(V)
'''
dataSource = self.mkTimeSeries(values, "log10V")
parameterDct = dict(
beta=(0, 10e-5, 3.2e-5),
kappa=(0, 10, 4.0),
delta=(0, 10, 5.2),
p=(0, 1, 4.6e-2),
c=(0, 10, 5.2),
)
if IGNORE_TEST:
logger = logs.Logger(logLevel=logs.LEVEL_MAX)
else:
logger = LOGGER
study = ModelStudy(ANTIMONY_MODEL, [dataSource],
parameterDct=parameterDct,
selectedColumns=["log10V"],
doSerialize=False,
useSerialized=False,
logger=logger)
study.bootstrap(numIteration=100)
fitter = list(study.fitterDct.values())[0]
if IS_PLOT and (fitter.bootstrapResult is not None):
study.plotFitAll()
fitter = study.fitterDct["src_1"]
for name in parameterDct.keys():
if fitter.bootstrapResult is not None:
value = fitter.bootstrapResult.params.valuesdict()[name]
self.assertIsNotNone(value)
def testConstructor1(self):
if IGNORE_TEST:
return
self.assertGreater(len(self.study.fitterDct.values()), 0)
# Ensure that ModelFitters are serialized correctly
study = ModelStudy(th.ANTIMONY_MODEL, DATA_PATHS,
parametersToFit=self.parametersToFit,
dirStudyPath=SERIALIZE_DIR, isPlot=IS_PLOT)
for name in self.study.instanceNames:
self.assertEqual(study.fitterDct[name].modelSpecification,
self.study.fitterDct[name].modelSpecification)
def runStudy(model, dirStudyPath):
study = ModelStudy(model, # Antimony model to evaluate
dataSourceDct, # Data sources to use for fitting
parameterDct=parameterDct, # Parameters and their value ranges
dirStudyPath=dirStudyPath, # Where to store the results of bootstrapping
selectedColumns=["log10V"], # Output column is computed in the assignment rule
doSerialize=DO_SERIALIZE, # Save the results of bootstrapping
useSerialized=USE_SERIALIZED) # Use previously calculated bootstrap results if they are present
study.bootstrap(numIteration=NUM_BOOTSTRAP_ITERATION) # Do bootstrapping
print("\n\n")
study.plotFitAll(ylim=[0, 9]) # Plot fitted and observed values with band plots for confidence
print("\n\n")
study.plotParameterEstimates() # Plot the parameter estimates for each data source