def test_compute_x_odd(self):
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'nl2sol')
context.set('tolerance', 1e-1)
context.set('iteration_limit', 5)
context.set('run_mode', True)
context.set('pe_number', 10)
config = context.get_config()
pl = tasks.ParameterEstimation(config)
data = viz.Parse(pl).data
p = viz.PlotProfileLikelihoods(self.pe_mod, pl, 1.2)
print(p.compute_x())
def test_parse(self):
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('tolerance', 1e-1)
context.set('iteration_limit', 5)
context.set('run_mode', True)
context.set('pe_number', 10)
config = context.get_config()
pe = tasks.ParameterEstimation(config)
data = viz.Parse(pe).data['A2B']
expected = (10, 5)
actual = data.shape
self.assertEqual(expected, actual)
def test_plotcl(self):
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('run_mode', True)
context.set('pe_number', 10)
context.set('prefix', '_')
context.set('nproc', 5)
config = context.get_config()
pl = tasks.ParameterEstimation(config)
p = viz.PlotProfileLikelihoods(self.pe_mod, pl, self.rss)
filename = os.path.join(os.path.dirname(__file__), 'plt.png')
p.plot(x='all', y='RSS', ncol=2, filename=None)
self.assertTrue(os.path.isfile(filename))
def test_compute_plot3(self):
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'nl2sol')
context.set('tolerance', 1e-1)
context.set('iteration_limit', 5)
context.set('run_mode', True)
context.set('pe_number', 10)
config = context.get_config()
pl = tasks.ParameterEstimation(config)
p = viz.PlotProfileLikelihoods(self.pe_mod, pl, self.rss)
filename = os.path.join(os.path.dirname(__file__), 'plt.png')
p.plot(x='all', y='C2A_k1', ncol=2, savefig=False, filename=filename)
self.assertTrue(os.path.isfile(filename))
def test_num_data_points(self):
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'nl2sol')
context.set('tolerance', 1e-1)
context.set('iteration_limit', 5)
context.set('run_mode', True)
context.set('pe_number', 10)
config = context.get_config()
pl = tasks.ParameterEstimation(config)
p = viz.PlotProfileLikelihoods(self.pe_mod, pl, 1.2)
expected = 99
actual = p.num_data_points()
self.assertEqual(expected, actual)
def test(self):
"""
Returns:
"""
with tasks.ParameterEstimation.Context(self.model,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('run_mode', True)
config = context.get_config()
pe = tasks.ParameterEstimation(config)
expected = 11
actual = viz.Parse(pe)['A2B'].shape[0]
self.assertEqual(expected, actual)
def test_run(self):
"""
Returns:
"""
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'nl2sol')
context.set('run_mode', True)
context.set('pe_number', 12)
config = context.get_config()
pe = tasks.ParameterEstimation(config)
expected = 12
data = viz.Parse(pe)['A2B']
actual = data.shape[0]
self.assertEqual(expected, actual)
def setUp(self):
super(ProfileLikelihoodTests, self).setUp()
self.fname = os.path.join(os.path.dirname(__file__), 'timecourse.txt')
self.data = self.model.simulate(0, 10, 1, report_name=self.fname)
with tasks.ParameterEstimation.Context(self.model,
self.fname,
context='s',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('run_mode', True)
context.set('randomize_start_values', True)
config = context.get_config()
self.pe = tasks.ParameterEstimation(config)
data = viz.Parse(self.pe).data
self.rss = data.loc[0, 'RSS']
self.pe_mod = self.pe.models['test_model'].model
self.pe_mod.insert_parameters(df=data, index=0, inplace=True)
def test_compute_x(self):
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'nl2sol')
context.set('tolerance', 1e-1)
context.set('iteration_limit', 5)
context.set('run_mode', True)
context.set('pe_number', 10)
config = context.get_config()
pl = tasks.ParameterEstimation(config)
data = viz.Parse(pl).data
p = viz.PlotProfileLikelihoods(self.pe_mod, pl, 1.2)
x = p.compute_x()
expected = (10, 5)
actual = x.shape
self.assertEqual(expected, actual)
def setUp(self):
super(BoxPlotTests, self).setUp()
fname = os.path.join(os.path.dirname(__file__), 'report1.txt')
data = self.model.simulate(0, 50, 1, report_name=fname)
with tasks.ParameterEstimation.Context(
self.model, fname, context='s', parameters='g',
) as context:
context.set('method', 'genetic_algorithm')
context.set('population_size', 2)
context.set('number_of_generations', 5)
context.set('copy_number', 2)
context.set('pe_number', 2)
context.set('run_mode', True)
config = context.get_config()
self.pe = tasks.ParameterEstimation(config)
def setUp(self):
super(ProfileLikelihoodTests, self).setUp()
ant_str = """
model new_model
R1: A -> B ; _k1*A;
R2: B -> A; k2*B;
R3: C -> D; _k3*C*B;
R4: D -> C; k4*D;
A = 100;
B = 0;
_k1=0.1;
k2 = 0.01
_k3 = 0.01
k4 = 1
end
"""
self.copasi_file = os.path.join(os.path.dirname(__file__),
'test_model.cps')
self.model = model.loada(ant_str, self.copasi_file)
self.fname = os.path.join(os.path.dirname(__file__), 'timecourse.txt')
self.data = self.model.simulate(0, 10, 1, report_name=self.fname)
with tasks.ParameterEstimation.Context(self.model,
self.fname,
context='s',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('run_mode', True)
context.set('prefix', '_')
context.set('randomize_start_values', True)
config = context.get_config()
self.pe = tasks.ParameterEstimation(config)
data = viz.Parse(self.pe).data['test_model']
self.rss = data.loc[0, 'RSS']
self.pe_mod = self.pe.models['test_model'].model
self.pe_mod.insert_parameters(df=data, index=0, inplace=True)
#context.set('randomize_start_values', True)
context.set('separator', ',')
context.set('method', 'hooke_jeeves')
context.set('pl_lower_bound', 1000)
context.set('pl_upper_bound', 1000)
#context.set('population_size', 50)
#context.set('number_of_generations', 300)
context.set('run_mode', True) ##defaults to False
context.set('pe_number', 3) ## number of repeat items in scan task
#context.set('copy_number', 2) ## number of times to copy model
#context.set('problem', 'Problem1')
#context.set('fit', 3)
#context.set('prefix', 'k')
config = context.get_config()
pe = tasks.ParameterEstimation(config)
#myPctMod.open()
data = viz.Parse(pe)
print(data)
myExMod = model.loada(model_string_ex, copasi_ex_filename)
def my_add_cols(inFName, outFName, sState, i1State, i2State):
myData = pd.read_csv(
os.path.join(os.path.dirname(working_directory), 'data', inFName))
myData.insert(len(myData.columns), 'S_indep', sState)
myData.insert(len(myData.columns), 'I1_indep', i1State)
myData.insert(len(myData.columns), 'I2_indep', i2State)
myData.rename(columns={
"pN": "Np",
def setUp(self):
ant1 = """
model first()
compartment Cell = 1;
R1: A => B ; Cell * k1 * A;
R2: B => C ; Cell * k2 * B;
R3: C => A ; Cell * k3 * C;
k1 = 0.1;
k2 = 0.1;
k3 = 0.1;
A = 100;
B = 0;
C = 0;
end
"""
ant2 = """
model second()
compartment Cell = 1;
R1: A => B ; Cell * k1 * A;
R2: B => C ; Cell * k2 * B;
R3: B => A ; Cell * k3 * B;
k1 = 0.1;
k2 = 0.1;
k3 = 0.1;
A = 100;
B = 0;
C = 0;
end
"""
self.fname1 = os.path.join(os.path.dirname(__file__), 'first.cps')
self.fname2 = os.path.join(os.path.dirname(__file__), 'second.cps')
with model.BuildAntimony(self.fname1) as loader:
self.mod1 = loader.load(ant1)
with model.BuildAntimony(self.fname2) as loader:
self.mod2 = loader.load(ant2)
self.fname1 = os.path.join(os.path.dirname(__file__), 'dataset1.txt')
self.fname2 = os.path.join(os.path.dirname(__file__), 'dataset2.txt')
self.mod1.simulate(0, 9, 1, report_name=self.fname1)
self.mod2.simulate(0, 9, 1, report_name=self.fname2)
with tasks.ParameterEstimation.Context(
[self.mod1, self.mod2], [self.fname1, self.fname2],
context='s', parameters='g'
) as context:
self.config = context.get_config()
config_dct = dict(
models=dict(
first=dict(
copasi_file=self.mod1.copasi_file,
),
second=dict(
copasi_file=self.mod2.copasi_file
)
),
datasets=dict(
experiments=dict(
first_exp=dict(
filename=self.fname1
),
second_exp=dict(
filename=self.fname2
)
)
),
items=dict(
fit_items='g'
),
settings=dict(
working_directory=os.path.dirname(__file__),
run_mode=True,
method='genetic_algorithm',
population_size=5,
number_of_generations=25
)
)
self.config = tasks.ParameterEstimation.Config(**config_dct)
self.pe = tasks.ParameterEstimation(self.config)
