def test_likelihood_ratio(self):
objval, thetavals = self.pest.theta_est()
asym = np.arange(10, 30, 2)
rate = np.arange(0, 1.5, 0.25)
theta_vals = pd.DataFrame(list(product(asym, rate)),
columns=self.pest.theta_names)
obj_at_theta = self.pest.objective_at_theta(theta_vals)
LR = self.pest.likelihood_ratio_test(obj_at_theta, objval,
[0.8, 0.9, 1.0])
self.assertTrue(set(LR.columns) >= set([0.8, 0.9, 1.0]))
self.assertTrue(LR[0.8].sum() == 6)
self.assertTrue(LR[0.9].sum() == 10)
self.assertTrue(LR[1.0].sum() == 60) # all true
graphics.pairwise_plot(LR, thetavals, 0.8)
def test_bootstrap(self):
objval, thetavals = self.pest.theta_est()
num_bootstraps = 10
theta_est = self.pest.theta_est_bootstrap(num_bootstraps,
return_samples=True)
num_samples = theta_est['samples'].apply(len)
self.assertTrue(len(theta_est.index), 10)
self.assertTrue(num_samples.equals(pd.Series([6] * 10)))
del theta_est['samples']
# apply cofidence region test
CR = self.pest.confidence_region_test(theta_est, 'MVN',
[0.5, 0.75, 1.0])
self.assertTrue(set(CR.columns) >= set([0.5, 0.75, 1.0]))
self.assertTrue(CR[0.5].sum() == 5)
self.assertTrue(CR[0.75].sum() == 7)
self.assertTrue(CR[1.0].sum() == 10) # all true
graphics.pairwise_plot(theta_est)
graphics.pairwise_plot(theta_est, thetavals)
graphics.pairwise_plot(theta_est, thetavals, 0.8,
['MVN', 'KDE', 'Rect'])
def test_bootstrap(self):
objval, thetavals = self.pest.theta_est()
num_bootstraps = 10
theta_est = self.pest.bootstrap(num_bootstraps)
num_samples = theta_est['samples'].apply(len)
self.assertTrue(len(theta_est.index), 10)
self.assertTrue(num_samples.equals(pd.Series([6] * 10)))
filename = os.path.abspath(os.path.join(testdir, 'pairwise.png'))
if os.path.isfile(filename):
os.remove(filename)
graphics.pairwise_plot(theta_est, filename=filename)
#self.assertTrue(os.path.isfile(filename))
filename = os.path.abspath(
os.path.join(testdir, 'pairwise_bootstrap.png'))
if os.path.isfile(filename):
os.remove(filename)
graphics.pairwise_bootstrap_plot(theta_est,
thetavals,
0.8,
filename=filename)
def test_pairwise_plot(self):
graphics.pairwise_plot(self.A, alpha=0.8, distributions=['Rect', 'MVN', 'KDE'])
### Parameter estimation with entire data set
objval, thetavals = pest.theta_est()
if mpii.rank in [0, None]:
print("objective value=", str(objval))
print("theta-star=", str(thetavals))
### Parameter estimation with bootstrap
alpha = 0.8
num_bootstraps = 10
bootstrap_theta = pest.bootstrap(num_bootstraps)
if mpii.rank in [0, None]:
print("Bootstrap:")
print(bootstrap_theta)
grph.pairwise_plot(bootstrap_theta, filename="RB.png")
grph.pairwise_bootstrap_plot(bootstrap_theta,
thetavals,
alpha,
filename="RB_boot.png")
### Likelihood ratio
alpha = 0.8
search_ranges = {}
search_ranges["asymptote"] = np.arange(10, 30, 2) # np.arange(10, 30, 0.01)
search_ranges["rate_constant"] = np.arange(0, 1.5,
0.1) # np.arange(0, 1.5, 0.005)
SSE = pest.likelihood_ratio(search_ranges=search_ranges)
if mpii.rank in [0, None]:
print("Likelihood Ratio:")
print(SSE)
