def test_likelihood_ratio(self):
# tbd: write the plot file(s) to a temp dir and delete in cleanup
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, 0.85, 0.9, 0.95])
self.assertTrue(set(LR.columns) >= set([0.8, 0.85, 0.9, 0.95]))
filename = os.path.abspath(os.path.join(testdir, 'pairwise_LR_plot.png'))
if os.path.isfile(filename):
os.remove(filename)
parmest.pairwise_plot(LR, thetavals, 0.8, filename=filename)
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() == 7)
self.assertTrue(LR[0.9].sum() == 11)
self.assertTrue(LR[1.0].sum() == 60) # all true
parmest.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']
filename = os.path.abspath(os.path.join(testdir, 'pairwise_bootstrap.png'))
if os.path.isfile(filename):
os.remove(filename)
parmest.pairwise_plot(theta_est, filename=filename)
#self.assertTrue(os.path.isfile(filename))
filename = os.path.abspath(os.path.join(testdir, 'pairwise_bootstrap_theta.png'))
if os.path.isfile(filename):
os.remove(filename)
parmest.pairwise_plot(theta_est, thetavals, filename=filename)
#self.assertTrue(os.path.isfile(filename))
filename = os.path.abspath(os.path.join(testdir, 'pairwise_bootstrap_theta_CI.png'))
if os.path.isfile(filename):
os.remove(filename)
parmest.pairwise_plot(theta_est, thetavals, 0.8, ['MVN', 'KDE', 'Rect'],
filename=filename)
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
parmest.pairwise_plot(theta_est)
parmest.pairwise_plot(theta_est, thetavals)
parmest.pairwise_plot(theta_est, thetavals, 0.8,
['MVN', 'KDE', 'Rect'])
expr = sum((data.y[i] - model.response_function[data.hour[i]])**2
for i in data.index)
return expr
pest = parmest.Estimator(rooney_biegler_model, data, theta_names, SSE)
obj, theta = pest.theta_est()
print(obj)
print(theta)
### Parameter estimation with bootstrap resampling
bootstrap_theta = pest.theta_est_bootstrap(50, seed=4581)
print(bootstrap_theta.head())
parmest.pairwise_plot(bootstrap_theta, theta, 0.8, ['MVN', 'KDE', 'Rect'])
### Likelihood ratio test
asym = np.arange(10, 30, 2)
rate = np.arange(0, 1.5, 0.1)
theta_vals = pd.DataFrame(list(product(asym, rate)), columns=theta_names)
obj_at_theta = pest.objective_at_theta(theta_vals)
print(obj_at_theta.head())
LR = pest.likelihood_ratio_test(obj_at_theta, obj, [0.8, 0.85, 0.9, 0.95])
print(LR.head())
parmest.pairwise_plot(LR, theta, 0.8)
((float(data['cc1']) - model.cc)**2)*(1/2) + \
((float(data['cc2']) - model.cc)**2)*(1/2) + \
(float(data['cd']) - model.cd)**2
return expr
pest = parmest.Estimator(reactor_design_model, data, theta_names, SSE_multisensor)
obj, theta = pest.theta_est()
print(obj)
print(theta)
### Parameter estimation with bootstrap resampling
bootstrap_theta = pest.theta_est_bootstrap(50)
print(bootstrap_theta.head())
parmest.pairwise_plot(bootstrap_theta)
parmest.pairwise_plot(bootstrap_theta, theta, 0.8, ['MVN', 'KDE', 'Rect'])
### Likelihood ratio test
k1 = [0.83]
k2 = np.arange(1.48, 1.79, 0.05) # Only vary k2 and k3 in this example
k3 = np.arange(0.000155, 0.000185, 0.000005)
theta_vals = pd.DataFrame(list(product(k1, k2, k3)), columns=theta_names)
obj_at_theta = pest.objective_at_theta(theta_vals)
print(obj_at_theta.head())
LR = pest.likelihood_ratio_test(obj_at_theta, obj, [0.8, 0.85, 0.9, 0.95])
print(LR.head())
def test_pairwise_plot(self):
parmest.pairwise_plot(self.A,
alpha=0.8,
distributions=['Rect', 'MVN', 'KDE'])
solver_options = {"max_iter": 6000} # not really needed in this case
pest = parmest.Estimator(rooney_biegler_model, data, theta_names, SSE,
solver_options)
obj, theta = pest.theta_est()
print(obj)
print(theta)
### Parameter estimation with bootstrap resampling
bootstrap_theta = pest.theta_est_bootstrap(50, seed=4581)
print(bootstrap_theta.head())
parmest.pairwise_plot(bootstrap_theta, title='Bootstrap theta')
parmest.pairwise_plot(bootstrap_theta,
theta,
0.8, ['MVN', 'KDE', 'Rect'],
title='Bootstrap theta with confidence regions')
### Likelihood ratio test
asym = np.arange(10, 30, 2)
rate = np.arange(0, 1.5, 0.1)
theta_vals = pd.DataFrame(list(product(asym, rate)), columns=theta_names)
obj_at_theta = pest.objective_at_theta(theta_vals)
print(obj_at_theta.head())
LR = pest.likelihood_ratio_test(obj_at_theta, obj, [0.8, 0.85, 0.9, 0.95])
(float(data['cd']) - model.cd)**2
return expr
pest = parmest.Estimator(reactor_design_model, data, theta_names, SSE)
obj, theta = pest.theta_est()
print(obj)
print(theta)
### Parameter estimation with 'leave-N-out'
# Example use case: For each combination of data where one data point is left
# out, estimate theta
lNo_theta = pest.theta_est_leaveNout(1)
print(lNo_theta.head())
parmest.pairwise_plot(lNo_theta, theta)
### Leave one out/boostrap analysis
# Example use case: leave 50 data points out, run 75 bootstrap samples with the
# remaining points, determine if the theta estimate using the points left out
# is inside or outside an alpha region based on the bootstrap samples, repeat
# 10 times. Results are stored as a list of tuples, see API docs for information.
lNo = 50
lNo_samples = 10
bootstrap_samples = 75
dist = 'MVN'
alphas = [0.7, 0.8, 0.9]
results = pest.leaveNout_bootstrap_test(lNo,
lNo_samples,
bootstrap_samples,
