def testBootstrapAccuracy(self):
if IGNORE_TEST:
return
model = """
J1: S1 -> S2; k1*S1
J2: S2 -> S3; k2*S2
S1 = 1; S2 = 0; S3 = 0;
k1 = 0; k2 = 0;
"""
columns = ["S1", "S3"]
fitter = ModelFitter(model,
BENCHMARK_PATH, ["k1", "k2"],
selectedColumns=columns,
isPlot=IS_PLOT)
fitter.fitModel()
print(fitter.reportFit())
print(fitter.getParameterMeans())
fitter.bootstrap(numIteration=1000, reportInterval=500)
#calcObservedFunc=ModelFitter.calcObservedTSNormal, std=0.01)
fitter.plotParameterEstimatePairs(['k1', 'k2'], markersize=2)
print("Mean: %s" % str(fitter.getParameterMeans()))
print("Std: %s" % str(fitter.getParameterStds()))
fitter.reportBootstrap()
def evaluate(self, stdResiduals:float=0.1, relError:float=0.1,
endTime:float=10.0, numPoint:int=30,
fractionParameterDeviation:float=0.5,
numIteration=NUM_BOOTSTRAP_ITERATION):
"""
Evaluates model fitting accuracy and bootstrapping for model
Parameters
----------
stdResiduals: Standard deviations of variable used in constructing reiduals
relError: relative error of parameter used in evaluation
endTime: ending time of the simulation
numPoint: number of points in the simulatin
fractionParameterDeviation: fractional amount that the parameter can vary
"""
# Construct synthetic observations
if self.selectedColumns is None:
data = self.roadRunner.simulate(0, endTime, numPoint)
else:
allColumns = list(self.selectedColumns)
if not TIME in allColumns:
allColumns.append(TIME)
data = self.roadRunner.simulate(0, endTime, numPoint, allColumns)
bracketTime = "[%s]" % TIME
if bracketTime in data.colnames:
# Exclude any column named '[time]'
idx = data.colnames.index(bracketTime)
dataArr = np.delete(data, idx, axis=1)
colnames = list(data.colnames)
colnames.remove(bracketTime)
colnames = [s[1:-1] if s != TIME else s for s in colnames]
simTS = NamedTimeseries(array=dataArr, colnames=colnames)
else:
simTS = NamedTimeseries(namedArray=data)
synthesizer = ObservationSynthesizerRandomErrors(
fittedTS=simTS, std=stdResiduals)
observedTS = synthesizer.calculate()
# Construct the parameter ranges
parameterDct = {}
for name in self.parameterValueDct.keys():
lower = self.parameterValueDct[name]*(1 - fractionParameterDeviation)
upper = self.parameterValueDct[name]*(1 + fractionParameterDeviation)
value = np.random.uniform(lower, upper)
parameterDct[name] = (lower, upper, value)
# Create the fitter
fitter = ModelFitter(self.roadRunner, observedTS,
selectedColumns=self.selectedColumns, parameterDct=parameterDct,
**self.kwargs)
msg = "Fitting the parameters %s" % str(self.parameterValueDct.keys())
self.logger.result(msg)
# Evaluate the fit
fitter.fitModel()
self._recordResult(fitter.params, relError, self.fitModelResult)
# Evaluate bootstrap
fitter.bootstrap(numIteration=numIteration)
if fitter.bootstrapResult is not None:
if fitter.bootstrapResult.numSimulation > 0:
self._recordResult(fitter.bootstrapResult.params,
relError, self.bootstrapResult)
def main(numIteration):
"""
Calculates the time to run iterations of the benchmark.
Parameters
----------
numIteration: int
Returns
-------
float: time in seconds
"""
logger = logs.Logger(logLevel=logs.LEVEL_MAX, logPerformance=IS_TEST)
fitter = ModelFitter(MODEL, BENCHMARK_PATH,
["k1", "k2"], selectedColumns=['S1', 'S3'], isPlot=IS_PLOT,
logger=logger)
fitter.fitModel()
startTime = time.time()
fitter.bootstrap(numIteration=numIteration, reportInterval=numIteration)
elapsedTime = time.time() - startTime
if IS_TEST:
print(fitter.logger.formatPerformanceDF())
fitter.plotFitAll()
return elapsedTime
# time in days since volunteer exposure
# each line in the array is an individual volunteer
#SARS_CoV2_sputum.csv and SARS_CoV2_nasal.csv
# SARS-CoV-2 data - 9 patients,
# for each patient - viral loads from lungs (sputum) and from nasal cavity (swab)
# viral levels in log10(RNA copies / ml sputum), ...
# respectively log10(RNA copies / nasal swab)
# time in days since symptoms onset
# corresponding lines in the two arrays belong to an individual patient
#SARS.csv
# SARS data recorded from 12 patients;
# included them just for comparison, probably too few datapoints for model inference
# viral levels in log10(RNA copies / ml of nasopharingeal aspirate)
# time - only three samples per patient, at 5, 10 and 15 days post symptoms onset
# Fit parameters to ts1
from SBstoat.modelFitter import ModelFitter
fitter = ModelFitter(ANTIMONY_MODEL, "Influenza-1.txt",
["beta", "kappa", "delta", "p", "c"])
fitter.fitModel()
print(fitter.reportFit())
fitter.plotFitAll(numRow=2, numCol=2)
fitter.plotResiduals(numRow=2, numCol=2)
# Get estimates of parameters
fitter.bootstrap(numIteration=2000, reportInterval=500)
print(fitter.getFittedParameterStds())