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 testWolfBug(self):
if IGNORE_TEST:
return
trueParameterDct = {
"J1_n": 4,
"J4_kp": 76411,
"J5_k": 80,
"J6_k": 9.7,
"J9_k": 28,
}
parametersToFit = [
SBstoat.Parameter("J1_n", lower=1, value=1, upper=8), # 4
SBstoat.Parameter("J4_kp", lower=3600, value=36000,
upper=150000), #76411
SBstoat.Parameter("J5_k", lower=10, value=10, upper=160), # 80
SBstoat.Parameter("J6_k", lower=1, value=1, upper=10), # 9.7
SBstoat.Parameter("J9_k", lower=1, value=50, upper=50), # 28
]
ts = NamedTimeseries(csvPath=WOLF_DATA)
methods = []
for optName in ["differential_evolution", "leastsq"]:
methods.append(SBstoat.OptimizerMethod(optName, {cn.MAX_NFEV: 10}))
fitter = ModelFitter(WOLF_MODEL,
ts,
parametersToFit=parametersToFit,
fitterMethods=methods)
fitter.fitModel()
for name in [p.name for p in parametersToFit]:
expected = trueParameterDct[name]
actual = fitter.params.valuesdict()[name]
self.assertLess(np.abs(np.log10(expected) - np.log10(actual)), 1.5)
self.assertTrue(name in fitter.reportFit())
def testWolfBug(self):
if IGNORE_TEST:
return
fullDct = {
#"J1_n": (1, 1, 8), # 4
#"J4_kp": (3600, 36000, 150000), #76411
#"J5_k": (10, 10, 160), # 80
#"J6_k": (1, 1, 10), # 9.7
"J9_k": (1, 50, 50), # 28
}
for parameter in fullDct.keys():
logger = Logger(logLevel=LEVEL_MAX)
logger = Logger()
ts = NamedTimeseries(csvPath=WOLF_DATA)
parameterDct = {parameter: fullDct[parameter]}
fitter = ModelFitter(WOLF_MODEL, ts[0:100],
parameterDct=parameterDct,
logger=logger, fitterMethods=[
"differential_evolution", "leastsq"])
fitter.fitModel()
self.assertTrue("J9_k" in fitter.reportFit())
SproutyFunc: -> Spry2; HillTime(V_0, K_0, n_0, t)
// Species IVs
Spry2 = 0;
// Parameter values
V_0 = 19.9059673;
K_0 = 10153.3568;
n_0 = 2.52290790;
t := time
end
''')
# sim = model.simulate(0, 7200, 7201)
# model.plot()
# quit()
fitter = ModelFitter(model,
"spry2_2a.txt", ["V_0", "K_0", "n_0"],
fitterMethods='differential_evolution',
parameterDct={
"V_0": (10, 20, 40),
"K_0": (1800, 6000, 20000),
"n_0": (1, 2, 12)
})
fitter.fitModel()
print(fitter.reportFit())
