def test_parameters(self):
"""
make sure that new parameter sets are being inserted into the
model before running the estimation
:return:
"""
CPE = tasks.ChaserParameterEstimations(self.MPE,
truncate_mode='ranks',
theta=range(2),
run_mode=True,
tolerance=1e-1,
iteration_limit=5)
## get parameters that were estimated in MPE
pe_data = viz.Parse(self.MPE).data
## get keys to ordered dict
keys = CPE.pe_dct.keys()
## key 0 should have best parameter set from MPE
zero = CPE.pe_dct[keys[0]]
pe_data0 = pe_data.iloc[0]
model_params = zero.model.parameters
pe_data.drop('RSS', inplace=True, axis=1)
for i in pe_data.keys():
self.assertAlmostEqual(float(model_params[i]), float(pe_data0[i]))
def test_run_true(self):
CPE = tasks.ChaserParameterEstimations(self.MPE,
truncate_mode='ranks',
theta=range(2),
run_mode=True,
tolerance=1e-1,
iteration_limit=5)
results = viz.Parse(CPE).data
self.assertEqual(results.shape[0], 2)
def do_profile_likelihood(self):
data = viz.Parse(self.MPE).data
pl = tasks.ProfileLikelihood(model=self.mod,
df=data,
index=0,
run=True,
tolerance=1e1,
iteration_limit=5)
viz.PlotProfileLikelihood(pl, savefig=True, y=['RSS', 'kADeg'])
return pl
def test_from_folder_generated_with_chaser_estimations(self):
MPE = tasks.MultiParameterEstimation(self.mod,
self.TC.report_name,
method='genetic_algorithm',
population_size=5,
number_of_generations=5,
run_mode=True,
copy_number=self.copy_number,
pe_number=self.pe_number)
MPE.write_config_file()
MPE.setup()
MPE.run()
CPE = tasks.ChaserParameterEstimations(MPE,
truncate_mode='ranks',
theta=list(range(2)),
tolerance=1e-2,
iteration_limit=5,
run_mode=True)
data = viz.Parse(CPE.results_directory,
copasi_file=CPE.model.copasi_file).data
print(data)
self.assertEqual(data.shape[0], 2)
def test_MPE_worked(self):
data = viz.Parse(self.MPE).data
self.assertEqual(data.shape[0], self.copy_number * self.pe_number)
def test_read_data(self):
data = viz.Parse(self.MPE).data
print(viz.TruncateData(data=data, mode='ranks', theta=[0, 1]).data)
def test(self):
print viz.Parse(self.MPE)