def test_options(self):
# Test setting acceptance rate
x0 = self.real_parameters
mcmc = pints.DramACMC(x0)
self.assertRaises(RuntimeError, mcmc.tell, 0.0)
x0 = self.real_parameters
mcmc = pints.DramACMC(x0)
mcmc.ask()
self.assertRaises(ValueError, mcmc.tell, -float('inf'))
self.assertNotEqual(mcmc.target_acceptance_rate(), 0.5)
mcmc.set_target_acceptance_rate(0.5)
self.assertEqual(mcmc.target_acceptance_rate(), 0.5)
mcmc.set_target_acceptance_rate(1)
self.assertRaises(ValueError, mcmc.set_target_acceptance_rate, 0)
self.assertRaises(ValueError, mcmc.set_target_acceptance_rate, -1e-6)
self.assertRaises(ValueError, mcmc.set_target_acceptance_rate, 1.00001)
# test hyperparameter setters and getters
self.assertEqual(mcmc.n_hyper_parameters(), 3)
self.assertRaises(ValueError, mcmc.set_hyper_parameters, [-0.1, 1, 3])
self.assertRaises(ValueError, mcmc.set_hyper_parameters, [0.5, 0, 3])
self.assertRaises(ValueError, mcmc.set_hyper_parameters, [0.5, 1, -1])
mcmc.set_hyper_parameters([0.1, 4, 3.5])
self.assertEqual(mcmc.eta(), 0.1)
self.assertEqual(mcmc.n_kernels(), 4)
self.assertEqual(mcmc.upper_scale(), 3.5)
mcmc.ask()
mcmc.set_sigma_scale()
scale = mcmc.sigma_scale()
a_min = np.log10(1)
a_max = np.log10(3.5)
scale1 = 10**np.linspace(a_min, a_max, 4)
scale1 = scale1[::-1]
self.assertTrue(np.array_equal(scale, scale1))
self.assertEqual(mcmc.name(),
('Delayed Rejection Adaptive Metropolis (Dram) MCMC'))
def test_options(self):
# Test setting acceptance rate
x0 = self.real_parameters
mcmc = pints.DramACMC(x0)
self.assertRaises(RuntimeError, mcmc.tell, 0.0)
x0 = self.real_parameters
mcmc = pints.DramACMC(x0)
mcmc.ask()
self.assertRaises(ValueError, mcmc.tell, -float('inf'))
self.assertNotEqual(mcmc.target_acceptance_rate(), 0.5)
mcmc.set_target_acceptance_rate(0.5)
self.assertEqual(mcmc.target_acceptance_rate(), 0.5)
mcmc.set_target_acceptance_rate(1)
self.assertRaises(ValueError, mcmc.set_target_acceptance_rate, 0)
self.assertRaises(ValueError, mcmc.set_target_acceptance_rate, -1e-6)
self.assertRaises(ValueError, mcmc.set_target_acceptance_rate, 1.00001)
# test hyperparameter setters and getters
self.assertEqual(mcmc.n_hyper_parameters(), 3)
self.assertRaises(ValueError, mcmc.set_hyper_parameters, [-0.1, 1, 3])
self.assertRaises(ValueError, mcmc.set_hyper_parameters, [0.5, 0, 3])
self.assertRaises(ValueError, mcmc.set_hyper_parameters, [0.5, 1, -1])
mcmc.set_hyper_parameters([0.1, 4, 3])
self.assertEqual(mcmc.eta(), 0.1)
mcmc.ask()
scale1 = [2, 3]
mcmc.set_sigma_scale(scale1)
scale = mcmc.sigma_scale()
self.assertTrue(np.array_equal(scale, scale1))
self.assertRaisesRegex(ValueError, 'must be of length 2',
mcmc.set_sigma_scale, [1])
self.assertEqual(mcmc.name(),
'Delayed Rejection Adaptive Metropolis (Dram) MCMC')
def test_method(self):
# Create mcmc
x0 = self.real_parameters * 1.1
mcmc = pints.DramACMC(x0)
# Configure
mcmc.set_target_acceptance_rate(0.3)
mcmc.set_initial_phase(True)
# Perform short run
rate = []
chain = []
for i in range(100):
x = mcmc.ask()
fx = self.log_posterior(x)
sample = mcmc.tell(fx)
while sample is None:
x = mcmc.ask()
fx = self.log_posterior(x)
sample = mcmc.tell(fx)
if i == 20:
mcmc.set_initial_phase(False)
if i >= 50:
chain.append(sample)
rate.append(mcmc.acceptance_rate())
if np.all(sample == x):
self.assertEqual(mcmc.current_log_pdf(), fx)
chain = np.array(chain)
rate = np.array(rate)
self.assertEqual(chain.shape[0], 50)
self.assertEqual(chain.shape[1], len(x0))
self.assertEqual(rate.shape[0], 100)
# Test with more kernels
x0 = self.real_parameters * 1.1
mcmc = pints.DramACMC(x0)
# Configure
mcmc.set_n_kernels(4)
# Perform short run
rate = []
chain = []
for i in range(100):
x = mcmc.ask()
fx = self.log_posterior(x)
sample = mcmc.tell(fx)
while sample is None:
x = mcmc.ask()
fx = self.log_posterior(x)
sample = mcmc.tell(fx)
if i == 20:
mcmc.set_initial_phase(False)
if i >= 50:
chain.append(sample)
rate.append(mcmc.acceptance_rate())
if np.all(sample == x):
self.assertEqual(mcmc.current_log_pdf(), fx)
chain = np.array(chain)
rate = np.array(rate)
self.assertEqual(chain.shape[0], 50)
self.assertEqual(chain.shape[1], len(x0))
self.assertEqual(rate.shape[0], 100)
def test_method(self):
# Create mcmc
x0 = self.real_parameters * 1.1
mcmc = pints.DramACMC(x0)
# Configure
mcmc.set_target_acceptance_rate(0.3)
mcmc.set_initial_phase(True)
# Perform short run
rate = []
chain = []
for i in range(100):
x = mcmc.ask()
fx = self.log_posterior(x)
reply = mcmc.tell(fx)
while reply is None:
x = mcmc.ask()
fx = self.log_posterior(x)
reply = mcmc.tell(fx)
y, fy, ac = reply
if i == 20:
mcmc.set_initial_phase(False)
if i >= 50:
chain.append(y)
rate.append(mcmc.acceptance_rate())
self.assertTrue(isinstance(ac, bool))
if ac:
self.assertTrue(np.all(x == y))
self.assertEqual(fx, fy)
chain = np.array(chain)
rate = np.array(rate)
self.assertEqual(chain.shape[0], 50)
self.assertEqual(chain.shape[1], len(x0))
self.assertEqual(rate.shape[0], 100)
# Test with more kernels
x0 = self.real_parameters * 1.1
mcmc = pints.DramACMC(x0)
# Configure
mcmc.set_n_kernels(4)
# Perform short run
rate = []
chain = []
for i in range(100):
x = mcmc.ask()
fx = self.log_posterior(x)
reply = mcmc.tell(fx)
while reply is None:
x = mcmc.ask()
fx = self.log_posterior(x)
reply = mcmc.tell(fx)
y, fy, ac = reply
if i == 20:
mcmc.set_initial_phase(False)
if i >= 50:
chain.append(y)
rate.append(mcmc.acceptance_rate())
self.assertTrue(isinstance(ac, bool))
if ac:
self.assertTrue(np.all(x == y))
self.assertEqual(fx, fy)
chain = np.array(chain)
rate = np.array(rate)
self.assertEqual(chain.shape[0], 50)
self.assertEqual(chain.shape[1], len(x0))
self.assertEqual(rate.shape[0], 100)