def figure2():
"""Make a figure for MCMC inference
"""
num_mcmc_iters = 10000
def stimulus(t):
return (1 * (t < 50)) + (-100 * (t >= 50) & (t < 75)) + (1 * (t >= 75))
# Generate data
y0 = np.array([0.0, 0.0])
m = diffeqinf.DampedOscillator(stimulus, y0, 'RK45')
m.set_tolerance(1e-8)
true_params = [1.0, 0.2, 1.0]
times = np.linspace(0, 100, 500)
y = m.simulate(true_params, times)
y += np.random.normal(0, 0.01, len(times))
# Run inference with correct model
problem = pints.SingleOutputProblem(m, times, y)
likelihood = pints.GaussianLogLikelihood(problem)
prior = pints.UniformLogPrior([0] * 4, [1e6] * 4)
posterior = pints.LogPosterior(likelihood, prior)
x0 = [true_params + [0.01]] * 3
mcmc = pints.MCMCController(posterior, 3, x0)
mcmc.set_max_iterations(num_mcmc_iters)
chains_correct = mcmc.run()
# Run inference with incorrect model
m.set_tolerance(1e-2)
problem = pints.SingleOutputProblem(m, times, y)
likelihood = pints.GaussianLogLikelihood(problem)
prior = pints.UniformLogPrior([0] * 4, [1e6] * 4)
posterior = pints.LogPosterior(likelihood, prior)
mcmc = pints.MCMCController(posterior, 3, x0)
mcmc.set_max_iterations(num_mcmc_iters)
chains_incorrect = mcmc.run()
# Plot MCMC chains
pints.plot.trace(chains_incorrect)
plt.show()
# Plot posteriors
diffeqinf.plot.plot_grouped_parameter_posteriors(
[chains_correct[0, num_mcmc_iters // 2:, :]],
[chains_incorrect[0, num_mcmc_iters // 2:, :]],
[chains_incorrect[1, num_mcmc_iters // 2:, :]],
[chains_incorrect[2, num_mcmc_iters // 2:, :]],
true_model_parameters=true_params,
method_names=[
'Correct', 'PoorTol_Chain1', 'PoorTol_Chain2', 'PoorTol_Chain3'
],
parameter_names=['k', 'c', 'm'],
fname=None)
plt.show()
def test_log_posterior(self):
# Create a toy problem and log likelihood
model = pints.toy.LogisticModel()
real_parameters = [0.015, 500]
x = [0.014, 501]
sigma = 0.001
times = np.linspace(0, 1000, 100)
values = model.simulate(real_parameters, times)
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.GaussianKnownSigmaLogLikelihood(problem, sigma)
# Create a prior
log_prior = pints.UniformLogPrior([0, 0], [1, 1000])
# Test
p = pints.LogPosterior(log_likelihood, log_prior)
self.assertEqual(p(x), log_likelihood(x) + log_prior(x))
y = [-1, 500]
self.assertEqual(log_prior(y), -float('inf'))
self.assertEqual(p(y), -float('inf'))
self.assertEqual(p(y), log_prior(y))
# Test derivatives
log_prior = pints.ComposedLogPrior(pints.GaussianLogPrior(0.015, 0.3),
pints.GaussianLogPrior(500, 100))
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
x = [0.013, 540]
y, dy = log_posterior.evaluateS1(x)
self.assertEqual(y, log_posterior(x))
self.assertEqual(dy.shape, (2, ))
y1, dy1 = log_prior.evaluateS1(x)
y2, dy2 = log_likelihood.evaluateS1(x)
self.assertTrue(np.all(dy == dy1 + dy2))
# Test getting the prior and likelihood back again
self.assertIs(log_posterior.log_prior(), log_prior)
self.assertIs(log_posterior.log_likelihood(), log_likelihood)
# First arg must be a LogPDF
self.assertRaises(ValueError, pints.LogPosterior, 'hello', log_prior)
# Second arg must be a log_prior
self.assertRaises(ValueError, pints.LogPosterior, log_likelihood,
log_likelihood)
# Prior and likelihood must have same dimension
self.assertRaises(ValueError, pints.LogPosterior, log_likelihood,
pints.GaussianLogPrior(0.015, 0.3))
def __init__(self, name):
super(TestAdaptiveCovarianceMCMC, self).__init__(name)
# Create toy model
self.model = toy.LogisticModel()
self.real_parameters = [0.015, 500]
self.times = np.linspace(0, 1000, 1000)
self.values = self.model.simulate(self.real_parameters, self.times)
# Add noise
noise = 10
self.values += np.random.normal(0, noise, self.values.shape)
self.real_parameters.append(noise)
# Create an object with links to the model and time series
self.problem = pints.SingleSeriesProblem(
self.model, self.times, self.values)
# Create a uniform prior over both the parameters and the new noise
# variable
self.prior = pints.UniformPrior(
[0.01, 400, noise * 0.1],
[0.02, 600, noise * 100]
)
# Create an un-normalised log-posterior (prior * likelihood)
self.log_likelihood = pints.LogPosterior(
self.prior, pints.UnknownNoiseLogLikelihood(self.problem))
# Select initial point and covariance
self.x0 = np.array(self.real_parameters) * 1.1
self.sigma0 = [0.005, 100, 0.5 * noise]
def _create_posterior(self, times, wd, wp):
"""
Runs the initial conditions optimisation routine for the PHE model.
Parameters
----------
times
(list) List of time points at which we have data for the
log-likelihood computation.
wd
Proportion of contribution of the deaths_data to the
log-likelihood.
wp
Proportion of contribution of the poritives_data to the
log-likelihood.
"""
# Create a likelihood
loglikelihood = PHELogLik(self._model, self._susceptibles_data,
self._infectives_data, times, self._deaths,
self._time_to_death, self._deaths_times,
self._fatality_ratio, self._total_tests,
self._positive_tests, self._serology_times,
self._sens, self._spec, wd, wp)
# Create a prior
log_prior = PHELogPrior(self._model, times)
# Create a posterior log-likelihood (log(likelihood * prior))
self._log_posterior = pints.LogPosterior(loglikelihood, log_prior)
def setUpClass(cls):
""" Prepare a problem for testing. """
# Random seed
np.random.seed(1)
# Create toy model
cls.model = toy.LogisticModel()
cls.real_parameters = [0.015, 500]
cls.times = np.linspace(0, 1000, 1000)
cls.values = cls.model.simulate(cls.real_parameters, cls.times)
# Add noise
cls.noise = 10
cls.values += np.random.normal(0, cls.noise, cls.values.shape)
cls.real_parameters.append(cls.noise)
cls.real_parameters = np.array(cls.real_parameters)
# Create an object with links to the model and time series
cls.problem = pints.SingleOutputProblem(cls.model, cls.times,
cls.values)
# Create a uniform prior over both the parameters and the new noise
# variable
cls.log_prior = pints.UniformLogPrior([0.01, 400, cls.noise * 0.1],
[0.02, 600, cls.noise * 100])
# Create a log likelihood
cls.log_likelihood = pints.GaussianLogLikelihood(cls.problem)
# Create an un-normalised log-posterior (log-likelihood + log-prior)
cls.log_posterior = pints.LogPosterior(cls.log_likelihood,
cls.log_prior)
def update_model(self, fixed_parameters_list):
"""
Update the model with fixed parameters.
Parameters
----------
fixed_parameters_list
List of fixed parameter values.
"""
# Create dictionary of fixed parameters and its values
name_value_dict = {
name: value
for (name, value
) in zip(self.model._parameter_names, fixed_parameters_list)
}
self.model.fix_parameters(name_value_dict)
# Setup the problem with pints,
# including likelihood, prior and posterior
print(self.model.n_parameters())
problem = pints.SingleOutputProblem(
model=self.model,
times=self.data['Time'].to_numpy(),
values=self.data['Incidence Number'].to_numpy())
log_likelihood = pints.GaussianLogLikelihood(problem)
priors = self.set_prior(name_value_dict)
self.log_prior = pints.ComposedLogPrior(*priors)
self.log_posterior = pints.LogPosterior(log_likelihood, self.log_prior)
# Run transformation
self.transformations = pints.LogTransformation(
self.log_posterior.n_parameters())
def test_model_that_gives_nan(self):
# This model will return a nan in the gradient evaluation, which
# originally tripped up the find_reasonable_epsilon function in nuts.
# Run it for a bit so that we get coverage on the if statement!
model = pints.toy.LogisticModel()
real_parameters = model.suggested_parameters()
times = model.suggested_parameters()
org_values = model.simulate(real_parameters, times)
np.random.seed(1)
noise = 0.2
values = org_values + np.random.normal(0, noise, org_values.shape)
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.GaussianKnownSigmaLogLikelihood(problem, noise)
log_prior = pints.UniformLogPrior([0.01, 40], [0.2, 60])
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
xs = [real_parameters * 1.1]
nuts_mcmc = pints.MCMCController(log_posterior,
len(xs),
xs,
method=pints.NoUTurnMCMC)
nuts_mcmc.set_max_iterations(10)
nuts_mcmc.set_log_to_screen(False)
nuts_chains = nuts_mcmc.run()
self.assertFalse(np.isnan(np.sum(nuts_chains)))
def setUpClass(cls):
""" Prepare problem for tests. """
# Load a forward model
model = pints.toy.LogisticModel()
# Create some toy data
real_parameters = [0.015, 500]
times = np.linspace(0, 1000, 1000)
org_values = model.simulate(real_parameters, times)
# Add noise
noise = 10
values = org_values + np.random.normal(0, noise, org_values.shape)
real_parameters = np.array(real_parameters + [noise])
# Create an object with links to the model and time series
problem = pints.SingleOutputProblem(model, times, values)
# Create an error measure
cls.score = pints.SumOfSquaresError(problem)
cls.boundaries = pints.RectangularBoundaries([0, 400], [0.05, 600])
# Create a log-likelihood function (adds an extra parameter!)
log_likelihood = pints.GaussianLogLikelihood(problem)
# Create a uniform prior over both the parameters and the new noise
cls.log_prior = pints.UniformLogPrior([0.01, 400, noise * 0.1],
[0.02, 600, noise * 100])
# Create a posterior log-likelihood (log(likelihood * prior))
cls.log_posterior = pints.LogPosterior(log_likelihood, cls.log_prior)
def _make_pints_posterior(self):
"""Rebuild the Pints posterior and save it.
"""
# Build a uniform model prior if it is not supplied
if self.model_prior is None:
num_model_params = self.problem.n_parameters()
model_prior = pints.UniformLogPrior([-1e6] * num_model_params,
[1e6] * num_model_params)
# Get the GP prior
kernel_prior = NonstatGPLogPrior(
self.gp_times,
self.kernel.num_parameters() // len(self.gp_times), self.mu,
self.alpha, self.beta)
# Combine the two priors
log_prior = pints.ComposedLogPrior(model_prior, kernel_prior)
# Build the likelihood
log_likelihood = flexnoise.KernelCovarianceLogLikelihood(
self.problem, self.kernel)
# Build the posterior
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
self.posterior = log_posterior
def test_build_tree_nan(self):
# This method gives nan in the hamiltonian_dash
# in the build_tree function
# Needed for coverage
model = pints.toy.LogisticModel()
real_parameters = np.array([0.015, 20])
times = np.linspace(0, 1000, 50)
org_values = model.simulate(real_parameters, times)
np.random.seed(1)
noise = 0.1
values = org_values + np.random.normal(0, noise, org_values.shape)
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.GaussianKnownSigmaLogLikelihood(problem, noise)
log_prior = pints.UniformLogPrior([0.0001, 1], [1, 500])
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
xs = [[0.36083914, 1.99013825]]
nuts_mcmc = pints.MCMCController(log_posterior,
len(xs),
xs,
method=pints.NoUTurnMCMC)
nuts_mcmc.set_max_iterations(50)
nuts_mcmc.set_log_to_screen(False)
np.random.seed(5)
nuts_chains = nuts_mcmc.run()
self.assertFalse(np.isnan(np.sum(nuts_chains)))
def run(model, real_parameters, noise_used, log_prior_used):
# Create some toy data
times = np.linspace(1, 1000, 50)
org_values = model.simulate(real_parameters, times)
# Add noise
noise = 10
values = org_values + np.random.normal(0, noise, org_values.shape)
real_parameters = np.array(real_parameters)
# Create an object with links to the model and time series
problem = pints.SingleOutputProblem(model, times, values)
# Create a log-likelihood function (adds an extra parameter!)
log_likelihood_used = pints.GaussianKnownSigmaLogLikelihood(problem, [noise_used])
# Create a uniform prior over both the parameters and the new noise variable
# Create a posterior log-likelihood (log(likelihood * prior))
log_posterior = pints.LogPosterior(log_likelihood_used, log_prior_used)
# Choose starting points for 3 mcmc chains
xs = [
real_parameters,
real_parameters * 1.01,
real_parameters * 0.99,
]
# Create mcmc routine with four chains
mcmc = pints.MCMCController(log_posterior, 3, xs, method=pints.HaarioACMC)
sample_size = 4000
# Add stopping criterion
mcmc.set_max_iterations(sample_size)
# Start adapting after 1000 iterations
mcmc.set_initial_phase_iterations(sample_size//4)
# Disable logging mode
mcmc.set_log_to_screen(False)
# Run!
print('Running...')
chains = mcmc.run()
print('Done!')
s = sample_size//4+1
#HMC: s = 1
b = False
while s < sample_size:
chains_cut = chains[:,sample_size//4:s+1]
rhat = pints.rhat(chains_cut)
s+=1
if rhat[0] < 1.05:
b = True
break
print(s)
return chains[0][s:][:, 0]
def sample(self, x, parallel=False):
"""
Runs the sampler, this method:
(1) generates simulated data and adds noise
(2) sets up the sampler with the method given,
using an KnownNoiseLogLikelihood, and a UniformLogPrior
(3) runs the sampler
(4) returns:
- the calculated rhat value
- the average of ess across all chains, returning the
minimum result across all parameters
- the total time taken by the sampler
"""
the_model = self.model()
values = the_model.simulate(self.real_parameters, self.times)
value_range = np.max(values) - np.min(values)
values += np.random.normal(0, self.noise * value_range, values.shape)
problem = pints.MultiOutputProblem(the_model, self.times, values)
log_likelihood = pints.KnownNoiseLogLikelihood(
problem, value_range * self.noise)
# lower = list(self.lower) + [value_range *
# self.noise / 10.0]*the_model.n_outputs()
#upper = list(self.upper) + [value_range * self.noise * 10]*the_model.n_outputs()
lower = list(self.lower)
upper = list(self.upper)
middle = [0.5 * (u + l) for l, u in zip(lower, upper)]
sigma = [u - l for l, u in zip(lower, upper)]
log_prior = pints.UniformLogPrior(lower, upper)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
n_chains = int(x[-1])
xs = [[
np.random.uniform() * (u - l) + l for l, u in zip(lower, upper)
] for c in range(n_chains)]
mcmc = pints.MCMCSampling(log_posterior,
n_chains,
xs,
method=self.method)
[sampler.set_hyper_parameters(x[:-1]) for sampler in mcmc.samplers()]
if parallel:
mcmc.set_parallel(int(os.environ['OMP_NUM_THREADS']))
mcmc.set_log_interval(1000)
start = timer()
chains = mcmc.run()
end = timer()
rhat = np.max(pints._diagnostics.rhat_all_params(chains))
ess = np.zeros(chains[0].shape[1])
for chain in chains:
ess += np.array(pints._diagnostics.effective_sample_size(chain))
ess /= n_chains
ess = np.min(ess)
print('rhat:', rhat)
print('ess:', ess)
print('time:', end - start)
return rhat, ess, end - start
def mcmc_runner(temps, n_chains=nchains):
#
# Define the unnormalised tempered target density
#
x0 = np.loadtxt(cmaes_result_files + model_name + '-cell-' +
str(cell) + '-cmaes.txt')
# Define Likelihood
if args.discrepancy:
arma_start = armax_result.params
x0 = np.concatenate((x0, arma_start))
print('Initial values of chain from iid noise model')
timer.sleep(1.5)
tempered_log_likelihood = dsLogLikelihood.DiscrepancyLogLikelihood(
problem, armax_result, temperature=temps)
print(
'Experimental Warning: Running in discrepancy and thermodynamic mode'
)
timer.sleep(2.5)
else:
print('Running in iid noise mode')
timer.sleep(2.5)
tempered_log_likelihood = tiLogLikelihood.ThermoLogLikelihood(
problem, sigma_noise, temps)
tempered_log_posterior = pints.LogPosterior(tempered_log_likelihood,
log_prior)
# Define starting point for mcmc routine
print('Model parameters start point: ', x0)
xs = []
for _ in xrange(nchains):
xs.append(x0)
print('MCMC starting point: ')
for x0 in xs:
print(x0)
print('MCMC starting Log-Posterior: ')
for x0 in xs:
print(tempered_log_likelihood(x0))
print(tempered_log_likelihood(x0))
# Create sampler
mcmc = mcmcsampling.MCMCSampling(tempered_log_posterior,
n_chains,
xs,
method=pints.AdaptiveCovarianceMCMC)
mcmc.set_log_to_screen(False)
mcmc.set_max_iterations(iterations)
mcmc.set_parallel(False)
trace, LLs = mcmc.run(returnLL=True)
return trace, LLs
def setUpClass(cls):
""" Set up problem for tests. """
# Create toy model
cls.model = toy.LogisticModel()
cls.real_parameters = [0.015, 500]
cls.times = np.linspace(0, 1000, 1000)
cls.values = cls.model.simulate(cls.real_parameters, cls.times)
# Add noise
cls.noise = 10
cls.values += np.random.normal(0, cls.noise, cls.values.shape)
cls.real_parameters.append(cls.noise)
cls.real_parameters = np.array(cls.real_parameters)
# Create an object with links to the model and time series
cls.problem = pints.SingleOutputProblem(cls.model, cls.times,
cls.values)
# Create a uniform prior over both the parameters and the new noise
# variable
cls.log_prior = pints.UniformLogPrior([0.01, 400, cls.noise * 0.1],
[0.02, 600, cls.noise * 100])
# Create a log likelihood
cls.log_likelihood = pints.GaussianLogLikelihood(cls.problem)
# Create an un-normalised log-posterior (log-likelihood + log-prior)
cls.log_posterior = pints.LogPosterior(cls.log_likelihood,
cls.log_prior)
# Run MCMC sampler
xs = [
cls.real_parameters * 1.1,
cls.real_parameters * 0.9,
cls.real_parameters * 1.15,
]
mcmc = pints.MCMCController(cls.log_posterior,
3,
xs,
method=pints.HaarioBardenetACMC)
mcmc.set_max_iterations(200)
mcmc.set_initial_phase_iterations(50)
mcmc.set_log_to_screen(False)
start = time.time()
cls.chains = mcmc.run()
end = time.time()
cls.time = end - start
def run_figureS2(num_runs=3, output_dir='./'):
"""Run the Gaussian process on block noise data.
This function runs the simulations and saves the results to pickle.
"""
random.seed(12345)
np.random.seed(12345)
all_fits = []
iid_runs = []
sigmas = []
mult_runs = []
gp_runs = []
for run in range(num_runs):
# Make a synthetic time series
times, values, data = generate_time_series(model='logistic',
noise='blocks',
n_times=625)
# Make Pints model and problem
model = pints.toy.LogisticModel()
problem = pints.SingleOutputProblem(model, times, data)
# Initial conditions for model parameters
model_starting_point = [0.08, 50]
# Infer the nonstationary kernel fit
# Run an optimization assumming IID
log_prior = pints.UniformLogPrior([0] * 3, [1e6] * 3)
log_likelihood = pints.GaussianLogLikelihood(problem)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
opt = pints.OptimisationController(log_posterior,
model_starting_point + [2])
xbest, fbest = opt.run()
# Run the GP fit, using the best fit for initialization
gp_times = times[::25]
kernel = flexnoise.kernels.GPLaplacianKernel
gnp = flexnoise.GPNoiseProcess(problem, kernel, xbest[:2], gp_times)
gnp.set_gp_hyperparameters(mu=0.0, alpha=1.0, beta_num_points=200)
x = gnp.run_optimize(num_restarts=100, parallel=True, maxiter=150)
all_fits.append(x)
# Save all results to pickle
kernel = kernel(None, gp_times)
results = [all_fits, times, data, values, model, problem, kernel]
fname = os.path.join(output_dir, 'figS2_data.pkl')
with open(fname, 'wb') as f:
pickle.dump(results, f)
def mcmc_runner(temps):
nchains = 1
#print('temperature', temps)
tempered_log_likelihood = tiLogLikelihood(problem, sigma_noise, temps)
tempered_log_posterior = pints.LogPosterior(tempered_log_likelihood,
log_prior)
xs = log_prior.sample(1)
mcmc = mcmcsampling.MCMCSampling(tempered_log_posterior,
nchains,
xs,
method=pints.MetropolisRandomWalkMCMC)
#mcmc.set_log_to_file('log.txt')
mcmc.set_log_to_screen(False)
mcmc.set_max_iterations(niter)
mcmc.set_parallel(False)
chains, LL = mcmc.run(returnLL=True)
return chains, LL
def plot_likelihood(model, values, times):
# Create an object with links to the model and time series
problem = pints.SingleOutputProblem(model, times, values)
# Create a log-likelihood function (adds an extra parameter!)
log_likelihood = pints.GaussianLogLikelihood(problem)
# Create a uniform prior over both the parameters and the new noise variable
lower_bounds = model.non_dim([1e-3, 0.0, 0.4, 0.1, 1e-6, 8.0, 1e-4])
upper_bounds = model.non_dim([10.0, 0.4, 0.6, 100.0, 100e-6, 10.0, 0.2])
log_prior = pints.UniformLogPrior(lower_bounds, upper_bounds)
# Create a posterior log-likelihood (log(likelihood * prior))
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
# Choose starting points for 3 mcmc chains
param_names = ['k0', 'E0', 'a', 'Ru', 'Cdl', 'freq', 'sigma']
start_parameters = model.non_dim(
[0.0101, 0.214, 0.53, 8.0, 20.0e-6, 9.0152, 0.01])
scaling = (upper_bounds - lower_bounds)
minx = start_parameters - scaling / 1000.0
maxx = start_parameters + scaling / 1000.0
fig = plt.figure()
for i, start in enumerate(start_parameters):
print(param_names[i])
plt.clf()
xgrid = np.linspace(minx[i], maxx[i], 100)
ygrid = np.empty_like(xgrid)
for j, x in enumerate(xgrid):
params = np.copy(start_parameters)
params[i] = x
ygrid[j] = log_likelihood(params)
plt.plot(xgrid, ygrid)
plt.savefig('likelihood_' + param_names[i] + '.pdf')
sine_wave=False)
npar = model.n_params
#
# Define problem
#
problem_sine = pints.SingleOutputProblem(model, time_sine, current_sine)
problem_ap = pints.SingleOutputProblem(model_ap, time_ap, current_ap)
#
# Define log-posterior
#
log_likelihood = pints.KnownNoiseLogLikelihood(problem_sine,
sigma_noise_sine)
log_likelihood_ap = pints.KnownNoiseLogLikelihood(problem_ap,
sigma_noise_ap)
log_prior = prior.LogPrior(rate_dict, lower_conductance, npar, transform)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
log_posterior_ap = pints.LogPosterior(log_likelihood_ap, log_prior)
rate_checker = Rates.ratesPrior(transform, lower_conductance)
if args.mcmc:
model_metrics = np.zeros((5, 7))
root = os.path.abspath('mcmc_results')
param_filename = os.path.join(
root, model_name + '-cell-' + str(cell) + '-mcmc_traces.p')
trace = cPickle.load(open(param_filename, 'rb'))
burnin = 70000
points = burnin / args.points
samples_all_chains = trace[:, burnin:, :]
sample_chain_1 = samples_all_chains[0]
samples_waic = sample_chain_1[::10, :npar]
def __init__(self, name):
super(TestPlot, self).__init__(name)
# Create toy model (single output)
self.model = toy.LogisticModel()
self.real_parameters = [0.015, 500]
self.times = np.linspace(0, 1000, 100) # small problem
self.values = self.model.simulate(self.real_parameters, self.times)
# Add noise
self.noise = 10
self.values += np.random.normal(0, self.noise, self.values.shape)
self.real_parameters.append(self.noise)
self.real_parameters = np.array(self.real_parameters)
# Create an object with links to the model and time series
self.problem = pints.SingleOutputProblem(self.model, self.times,
self.values)
# Create a uniform prior over both the parameters and the new noise
# variable
self.lower = [0.01, 400, self.noise * 0.1]
self.upper = [0.02, 600, self.noise * 100]
self.log_prior = pints.UniformLogPrior(self.lower, self.upper)
# Create a log likelihood
self.log_likelihood = pints.GaussianLogLikelihood(self.problem)
# Create an un-normalised log-posterior (log-likelihood + log-prior)
self.log_posterior = pints.LogPosterior(self.log_likelihood,
self.log_prior)
# Run MCMC
self.x0 = [
self.real_parameters * 1.1, self.real_parameters * 0.9,
self.real_parameters * 1.05
]
mcmc = pints.MCMCController(self.log_posterior, 3, self.x0)
mcmc.set_max_iterations(300) # make it as small as possible
mcmc.set_log_to_screen(False)
self.samples = mcmc.run()
# Create toy model (multi-output)
self.model2 = toy.LotkaVolterraModel()
self.real_parameters2 = self.model2.suggested_parameters()
self.times2 = self.model2.suggested_times()[::10] # down sample it
self.values2 = self.model2.simulate(self.real_parameters2, self.times2)
# Add noise
self.noise2 = 0.05
self.values2 += np.random.normal(0, self.noise2, self.values2.shape)
# Create an object with links to the model and time series
self.problem2 = pints.MultiOutputProblem(self.model2, self.times2,
self.values2)
# Create a uniform prior over both the parameters and the new noise
# variable
self.log_prior2 = pints.UniformLogPrior([1, 1, 1, 1], [6, 6, 6, 6])
# Create a log likelihood
self.log_likelihood2 = pints.GaussianKnownSigmaLogLikelihood(
self.problem2, self.noise2)
# Create an un-normalised log-posterior (log-likelihood + log-prior)
self.log_posterior2 = pints.LogPosterior(self.log_likelihood2,
self.log_prior2)
# Run MCMC
self.x02 = [
self.real_parameters2 * 1.1, self.real_parameters2 * 0.9,
self.real_parameters2 * 1.05
]
mcmc = pints.MCMCController(self.log_posterior2, 3, self.x02)
mcmc.set_max_iterations(300) # make it as small as possible
mcmc.set_log_to_screen(False)
self.samples2 = mcmc.run()
# Create toy model (single-output, single-parameter)
self.real_parameters3 = [0]
self.log_posterior3 = toy.GaussianLogPDF(self.real_parameters3, [1])
self.lower3 = [-3]
self.upper3 = [3]
# Run MCMC
self.x03 = [[1], [-2], [3]]
mcmc = pints.MCMCController(self.log_posterior3, 3, self.x03)
mcmc.set_max_iterations(300) # make it as small as possible
mcmc.set_log_to_screen(False)
self.samples3 = mcmc.run()
df = pd.DataFrame(columns=columns)
for n in range(N):
print(n)
theta = log_prior.sample(n=1)[0]
times = np.linspace(1, 1000, 25)
org_values = model.simulate(theta, times)
# Add noise
noise = 10
ys = org_values + np.random.normal(0, noise, org_values.shape)
# Create an object with links to the model and time series
problem = pints.SingleOutputProblem(model, times, ys)
# Create a log-likelihood function (adds an extra parameter!)
log_likelihood = pints.GaussianKnownSigmaLogLikelihood(problem, [noise])
log_prior_incorrect = pints.UniformLogPrior([200], [800])
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
# Choose starting points for 3 mcmc chains
xs = [theta, theta * 1.01, theta * 0.99]
isinf = False
for x in xs:
if (math.isinf(log_posterior.evaluateS1(x)[0])):
isinf = True
d += 1
break
if (isinf == True):
continue
# Create mcmc routine with three chains
mcmc = pints.MCMCController(log_posterior, 3, xs, method=pints.HaarioACMC)
def run_figure2(num_mcmc_samples=20000,
num_mcmc_chains=3,
num_runs=8,
output_dir='./'):
"""Run the Gaussian process on multiplicative data.
This function runs the simulations and saves the results to pickle.
"""
random.seed(123)
np.random.seed(123)
all_fits = []
iid_runs = []
sigmas = []
mult_runs = []
gp_runs = []
for run in range(num_runs):
# Make a synthetic time series
times, values, data = generate_time_series(model='logistic',
noise='multiplicative',
n_times=251)
# Make Pints model and problem
model = pints.toy.LogisticModel()
problem = pints.SingleOutputProblem(model, times, data)
# Initial conditions for model parameters
model_starting_point = [0.08, 50]
# Run MCMC for IID posterior
likelihood = pints.GaussianLogLikelihood
x0 = model_starting_point + [2]
posterior_iid = run_pints(problem, likelihood, x0, num_mcmc_samples)
iid_runs.append(posterior_iid)
# Save standard deviations from IID runs
sigma = np.median(posterior_iid[:, 2])
sigmas.append(sigma)
# Run MCMC for multiplicative noise posterior
likelihood = pints.MultiplicativeGaussianLogLikelihood
x0 = model_starting_point + [0.5, 0.5]
posterior_mult = run_pints(problem, likelihood, x0, num_mcmc_samples)
mult_runs.append(posterior_mult)
# Infer the nonstationary kernel fit
# Run an optimization assumming IID
log_prior = pints.UniformLogPrior([0] * 3, [1e6] * 3)
log_likelihood = pints.GaussianLogLikelihood(problem)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
opt = pints.OptimisationController(log_posterior,
model_starting_point + [2])
xbest, fbest = opt.run()
# Run the GP fit, using the best fit for initialization
gp_times = times[::10]
kernel = flexnoise.kernels.GPLaplacianKernel
gnp = flexnoise.GPNoiseProcess(problem, kernel, xbest[:2], gp_times)
gnp.set_gp_hyperparameters(mu=0.0, alpha=1.0, beta_num_points=200)
x = gnp.run_optimize(num_restarts=100, parallel=True, maxiter=150)
all_fits.append(x)
# Run MCMC for multivariate normal noise
kernel = flexnoise.kernels.GPLaplacianKernel(None, gp_times)
kernel.parameters = x[2:]
cov = kernel.get_matrix(times)
likelihood = flexnoise.CovarianceLogLikelihood
x0 = model_starting_point
posterior_gp = run_pints(problem,
likelihood,
x0,
num_mcmc_samples,
likelihood_args=[cov])
gp_runs.append(posterior_gp)
# Save all results to pickle
results = [
iid_runs, mult_runs, all_fits, gp_runs, times, data, values, model,
problem, kernel, sigmas
]
fname = os.path.join(output_dir, 'fig2_data.pkl')
with open(fname, 'wb') as f:
pickle.dump(results, f)
def inference(model, values, times):
# Create an object with links to the model and time series
problem = pints.SingleOutputProblem(model, times, values)
# Create a log-likelihood function (adds an extra parameter!)
log_likelihood = pints.GaussianLogLikelihood(problem)
# Create a uniform prior over both the parameters and the new noise variable
lower_bounds = np.array([1e-3, 0.0, 0.4, 0.1, 1e-6, 8.0, 1e-4])
upper_bounds = np.array([10.0, 0.4, 0.6, 100.0, 100e-6, 10.0, 0.2])
log_prior = pints.UniformLogPrior(lower_bounds, upper_bounds)
# Create a posterior log-likelihood (log(likelihood * prior))
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
# Choose starting points for 3 mcmc chains
# params = ['k0', 'E0', 'a', 'Ru', 'Cdl', 'freq', 'sigma']
start_parameters = np.array(
[0.0101, 0.214, 0.53, 8.0, 20.0e-6, 9.0152, 0.01])
transform = pints.ComposedTransformation(
pints.LogTransformation(1),
pints.RectangularBoundariesTransformation(lower_bounds[1:],
upper_bounds[1:]),
)
sigma0 = [0.1 * (h - l) for l, h in zip(lower_bounds, upper_bounds)]
boundaries = pints.RectangularBoundaries(lower_bounds, upper_bounds)
found_parameters, found_value = pints.optimise(log_posterior,
start_parameters,
sigma0,
boundaries,
transform=transform,
method=pints.CMAES)
xs = [
found_parameters * 1.001,
found_parameters * 1.002,
found_parameters * 1.003,
]
for x in xs:
x[5] = found_parameters[5]
print('start_parameters', start_parameters)
print('found_parameters', found_parameters)
print('lower_bounds', lower_bounds)
print('upper_bounds', upper_bounds)
# Create mcmc routine with four chains
mcmc = pints.MCMCController(log_posterior,
3,
xs,
method=pints.HaarioBardenetACMC,
transform=transform)
# Add stopping criterion
mcmc.set_max_iterations(10000)
# Run!
chains = mcmc.run()
# Save chains for plotting and analysis
pickle.dump((xs, pints.GaussianLogLikelihood, log_prior, chains,
'HaarioBardenetACMC'), open('results.pickle', 'wb'))
def run_fit(
task,
optimisation_algorithm,
optimisation_arguments,
sampling_algorithm,
sampling_arguments,
):
print('Entering run_fit()')
if optimisation_algorithm != 'CMAES':
raise ValueError(
'Other optimisation algorithms are not yet supported.')
if sampling_algorithm != 'AdaptiveCovarianceMCMC':
raise ValueError('Other sampling algorithms are not yet supported.')
# Get names of parameters --> They are not stored in order, so will need
# this a lot!
keys = task.parameters
# Use log transform
log_transform = task.prior.is_positive()
if log_transform:
print('Using log-transform for fitting')
else:
print('Unable to use log-transform')
# Select objective for Aidan's code to use
task.objFun = LogLikGauss()
# Wrap a LogPDF around Aidan's objective
class AidanLogPdf(pints.LogPDF):
def __init__(self, task, keys, log_transform=None):
self._task = task
self._keys = keys
self._log_transform = log_transform
self._dimension = len(keys)
self._p = {}
def n_parameters(self):
return self._dimension
def __call__(self, x):
# Untransform back to model space
if self._log_transform:
x = np.exp(x)
# Create dict
for i, key in enumerate(self._keys):
self._p[key] = x[i]
# Evaluate objective
return self._task.calculateObjective(self._p)
# Wrap a LogPrior around Aidan's prior
class AidanLogPrior(pints.LogPrior):
def __init__(self, task, keys, log_transform=None):
self._prior = task.prior
self._keys = keys
self._log_transform = log_transform
self._dimension = len(keys)
self._p = {}
def n_parameters(self):
return self._dimension
def __call__(self, x):
# Untransform back to model space
if self._log_transform:
x = np.exp(x)
# Create dict
for i, key in enumerate(self._keys):
self._p[key] = x[i]
# Evaluate prior and return
prior = self._prior.pdf(self._p)
if prior <= 0:
return -np.inf
return np.log(prior)
def sample(self, n=1):
assert n == 1
x = self._prior.draw()
x = [x[key] for key in self._keys]
# Transform to search space
if self._log_transform:
x = np.log(x)
return [x]
# Find a suitable starting point --> Will be the answer if no iterations
# are selected
log_prior = AidanLogPrior(task, keys, False)
x0 = log_prior.sample()[0]
del (log_prior)
print('Parameters: ')
print('\n'.join(' ' + x for x in parameters))
# If specified, run (repeated) CMA-ES to select a starting point
opt_repeats = optimisation_arguments['repeats']
print('CMA-ES runs: ' + str(opt_repeats))
if opt_repeats:
log_likelihood = AidanLogPdf(task, keys, log_transform)
boundaries = pints.LogPDFBoundaries(
AidanLogPrior(task, keys, log_transform))
x_best, fx_best = x0, -np.inf
for i in range(opt_repeats):
print(' CMA-ES run ' + str(1 + i))
# Choose random starting point (in search space)
x0 = boundaries.sample()[0]
f0 = log_likelihood(x0)
i = 0
while not np.isfinite(f0):
x0 = boundaries.sample()[0]
f0 = log_likelihood(x0)
i += 1
if i > 20:
print('Unable to find good starting point!')
break
# Create optimiser
opt = pints.OptimisationController(log_likelihood,
x0,
boundaries=boundaries,
method=pints.CMAES)
opt.set_max_iterations(None)
opt.set_parallel(True)
opt.set_max_unchanged_iterations(80)
# DEBUG
#opt.set_max_iterations(5)
# Run optimisation
try:
with np.errstate(all='ignore'):
x, fx = opt.run()
except ValueError:
fx = -np.inf
import traceback
traceback.print_exc()
# Check outcome
if fx > fx_best:
print('New best score ' + str(fx) + ' > ' + str(fx_best))
x_best, fx_best = x, fx
if log_transform:
# Transform back to model space
x_best = np.exp(x_best)
x0 = x_best
x0_obj = dict(zip(keys, x0))
# If specified, run MCMC
n_mcmc_iters = sampling_arguments.get('iterations', 0)
print('MCMC iterations: ' + str(n_mcmc_iters))
if n_mcmc_iters:
print('Starting MCMC')
log_likelihood = AidanLogPdf(task, keys)
log_prior = AidanLogPrior(task, keys)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
# Configure MCMC
mcmc = pints.MCMCSampling(log_posterior, 1, [x0])
mcmc.set_max_iterations(n_mcmc_iters)
mcmc.set_parallel(False)
# Run
chains = mcmc.run()
print('MCMC Completed')
# Get chain
chain = chains[0]
# Discard warm-up
warm_up = int(sampling_arguments.get('warm_up', 0))
if warm_up > 0:
print('Discarding first ' + str(warm_up) + ' samples as warm-up')
chain = chain[warm_up:]
else:
chain = [x0]
# Create distribution object and return
dist = []
for sample in chain:
d = {}
for i, key in enumerate(keys):
d[key] = sample[i]
dist.append(d)
return DiscreteParameterDistribution(dist)
def run(times, ax, bins):
values = m_true.simulate(true_params, times)
data = values + np.random.normal(0, 0.1, values.shape)
problem = pints.SingleOutputProblem(m_simple, times, data)
# Run MCMC for IID noise, wrong model
prior = pints.UniformLogPrior([0, 0], [1e6, 1e6])
likelihood = pints.GaussianLogLikelihood(problem)
posterior = pints.LogPosterior(likelihood, prior)
x0 = [[0.2, 1.0]] * 3
mcmc = pints.MCMCController(posterior, 3, x0)
mcmc.set_max_iterations(num_mcmc_iter)
chains_iid = mcmc.run()
freq_iid = chains_iid[0, :, 0][num_mcmc_iter // 2:]
# Run MCMC for AR(1) noise, wrong model
prior = pints.UniformLogPrior([0, 0, 0], [1e6, 1, 1e6])
likelihood = pints.AR1LogLikelihood(problem)
posterior = pints.LogPosterior(likelihood, prior)
x0 = [[0.2, 0.01, 1.0]] * 3
mcmc = pints.MCMCController(posterior, 3, x0)
mcmc.set_max_iterations(num_mcmc_iter)
chains_ar1 = mcmc.run()
freq_ar1 = chains_ar1[0, :, 0][num_mcmc_iter//2:]
# Run MCMC for IID noise, correct model
problem = pints.SingleOutputProblem(m_true, times, data)
prior = pints.UniformLogPrior([0, 0, 0], [1e6, 1e6, 1e6])
likelihood = pints.GaussianLogLikelihood(problem)
posterior = pints.LogPosterior(likelihood, prior)
x0 = [[0.2, 0.8, 1.0]] * 3
mcmc = pints.MCMCController(posterior, 3, x0)
mcmc.set_max_iterations(num_mcmc_iter)
chains_true = mcmc.run()
freq_true = chains_true[0, :, 0][num_mcmc_iter // 2:]
# Plot histograms of the posteriors
ax.hist(freq_true,
alpha=0.5,
label='Correct',
hatch='//',
density=True,
bins=bins,
histtype='stepfilled',
linewidth=2,
color='grey',
zorder=-20)
ax.hist(freq_ar1,
alpha=1.0,
label='AR1',
density=True,
bins=bins,
histtype='stepfilled',
linewidth=2,
edgecolor='k',
facecolor='none')
ax.hist(freq_iid,
alpha=0.5,
label='IID',
density=True,
bins=bins,
histtype='stepfilled',
linewidth=2,
color=plt.rcParams['axes.prop_cycle'].by_key()['color'][0],
zorder=-10)
ax.axvline(0.2, ls='--', color='k')
ax.set_xlabel(r'$\theta$')
ax.legend()
def run_model(model,
cell,
protocol,
time,
voltage,
current,
plot='unfold',
label=None,
axes=None):
# Select protocol file
protocol_file = os.path.join(root, protocol + '.mmt')
print(protocol_file)
myokit_protocol = myokit.load_protocol(protocol_file)
# Estimate noise from start of data
sigma_noise = np.std(current[:2000], ddof=1)
# fetch cmaes parameters
obtained_parameters = model.fetch_parameters()
# Cell-specific parameters
temperature = model.temperature(cell)
lower_conductance = model.conductance_limit(cell)
# Apply capacitance filter based on protocol
print('Applying capacitance filtering')
time, voltage, current = model.capacitance(myokit_protocol, 0.1, time,
voltage, current)
forward_model = model.ForwardModel(myokit_protocol,
temperature,
sine_wave=False)
problem = pints.SingleOutputProblem(forward_model, time, current)
log_likelihood = pints.KnownNoiseLogLikelihood(problem, sigma_noise)
log_prior = model.LogPrior(lower_conductance)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
# Show obtained parameters and score
obtained_log_posterior = log_posterior(obtained_parameters)
print('Kylie sine-wave parameters:')
for x in obtained_parameters:
print(pints.strfloat(x))
print('Final log-posterior:')
print(pints.strfloat(obtained_log_posterior))
# Simulate
simulated = forward_model.simulate(obtained_parameters, time)
if plot == 'unfold':
axes[0].plot(time, voltage, color='red') #, label='voltage')
#axes[0].legend(loc='upper right')
axes[1].plot(time, current, alpha=0.3,
color='red') #, label='measured current')
if label == 0:
model_name = 'circularCOIIC'
axes[1].plot(time,
simulated,
alpha=1,
color='blue',
label=model_name)
elif label == 1:
model_name = 'linearCOI'
axes[1].plot(time,
simulated,
alpha=1,
color='magenta',
label=model_name)
elif label == 2:
model_name = 'linearCCOI'
axes[1].plot(time,
simulated,
alpha=1,
color='seagreen',
label=model_name)
elif label == 3:
model_name = 'linearCCCOI'
axes[1].plot(time,
simulated,
alpha=1,
color='seagreen',
label=model_name)
#axes.subplot(2,1,1)
else:
IkrModel.fold_plot(protocol, time, voltage, [current, simulated])
def run_pints(problem,
likelihood,
x0,
num_mcmc_samples,
num_chains=3,
log_prior=None,
likelihood_args=None,
enforce_convergence=False,
mcmc_method=None):
"""Perform infernce with Pints using a specified model and likelihood.
Parameters
----------
problem : pints.Problem
Pints problem holding the times and data
likelihood : pints.ProblemLogLikelihood
Pints likelihood for the data
x0 : array_like of float
Starting point of model parameters.
num_mcmc_samples : int
Total number of MCMC samples.
num_chains : int
Number of separate MCMC chains.
log_prior : pints.LogPrior
Prior distribution on all parameters in the likelihood. If None, a
uniform prior from 0 to 1e6 is chosen for all parameters.
likelihood_args : list
Any other arguments besides the pints problem which must be provided
when instantiating the likelihood.
enforce_convergence : bool
Whether to raise an error if the chains have not converged. After
finishing the MCMC chain, the Rhat value is calculated, and any value
of Rhat greater than 1.05 is assumed to indicate lack of convergence.
mcmc_method : str
Name of any MCMC sampler implemented in Pints.
Returns
-------
np.ndarray
MCMC samples of posterior. One chain is provided with the first half
discarded as burn-in.
"""
if likelihood_args is None:
log_likelihood = likelihood(problem)
else:
log_likelihood = likelihood(problem, *likelihood_args)
# Get the number of parameters to infer = model params plus noise params
num_params = len(x0)
if log_prior is None:
log_prior = pints.UniformLogPrior([0] * num_params, [1e6] * num_params)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
x0 = [np.array(x0), 1.1 * np.array(x0), 0.9 * np.array(x0)]
# Run MCMC
if mcmc_method is None:
mcmc = pints.MCMCController(log_posterior, num_chains, x0)
else:
mcmc = pints.MCMCController(log_posterior,
num_chains,
x0,
method=mcmc_method)
mcmc.set_max_iterations(num_mcmc_samples)
mcmc.set_log_to_screen(True)
chains = mcmc.run()
# Check convergence
rs = pints.rhat(chains[:, num_mcmc_samples // 2:, :])
if max(rs) > 1.05:
message = 'MCMC chains failed to converge, R={}'.format(str(rs))
if enforce_convergence:
pints.plot.trace(chains)
plt.show()
raise RuntimeError(message)
else:
warnings.warn(message)
# Get one chain, discard first half burn in
chain = chains[0][num_mcmc_samples // 2:]
return chain
)
LogPrior = {
'model_A': priors.ModelALogPrior,
'model_B': priors.ModelBLogPrior,
}
# Update protocol
model.set_fixed_form_voltage_protocol(protocol, protocol_times)
# Create Pints stuffs
problem = pints.SingleOutputProblem(model, times, data)
loglikelihood = pints.GaussianKnownSigmaLogLikelihood(problem, noise_sigma)
logprior = LogPrior[info_id](transform_to_model_param,
transform_from_model_param)
logposterior = pints.LogPosterior(loglikelihood, logprior)
# Check logposterior is working fine
priorparams = np.copy(info.base_param)
transform_priorparams = transform_from_model_param(priorparams)
print('Score at prior parameters: ', logposterior(transform_priorparams))
for _ in range(10):
assert(logposterior(transform_priorparams) ==\
logposterior(transform_priorparams))
# Run
try:
N = int(sys.argv[2])
except IndexError:
N = 3
def MCMC_routine(starting_point,
max_iter=4000,
adapt_start=None,
log_prior=None,
mmt_model_filename=None,
chain_filename=None,
pdf_filename=None,
log_likelihood='GaussianLogLikelihood',
method='HaarioBardenetACMC',
sigma0=None,
parallel=False):
"""
Runs a MCMC routine for the selected model
:param starting_point:
List of numpy.array. List of starting values for the MCMC for the
optimisation parameters. Must have the same length as
data_exp.fitting_parms_annot + 1 (for Noise). len(starting_point)
defines the amount of MCMC chains.
:param max_iter:
int. Maximal iterations for the whole MCMC. Should be higher than
adapt_start.
:param adapt_start:
int. Iterations before starting the adapting phase of the MCMC.
:param log_prior: pints.log_priors
Type of prior. If not specified, pints.UniformLogPrior
:param mmt_model_filename: str
location of the mmt model to run if different from the one loaded
previously. It will replace the sabs_pkpd.constants.s
myokit.Simulation() already present.
:param chain_filename:
str. Location of the CSV file where the chains will be written. If not
provided, the chains are not saved in CSV
:param pdf_filename:
str. Location of the CSV file where the log_likelihood will be written.
If not provided, it will not be saved in CSV.
:param log_likelihood: pints.LogLikelihood
Type of log likelihood. If not specified,
pints.UnknownNoiseLogLikelihood.
:param method: pints.method:
method of optimisation. If not specified, pints.HaarioBardenetACMC.
:param sigma0:
sigma0 for the desired MCMC algorithm. If not provided, sigma0 will be
computed automatically by the algorithm. See
https://pints.readthedocs.io/en/latest/mcmc_samplers/running.html for
documentation.
:param parallel:
Boolean. Enables or not the parallelisation of the MCMC among the available
CPUs. False as default.
:return: chains
The chain for the MCMC routine.
"""
sabs_pkpd.constants.n = len(starting_point[0]) - 1
if len(starting_point[0]) != \
len(sabs_pkpd.constants.data_exp.
fitting_instructions.fitted_params_annot) + 1:
raise ValueError('Starting point and Parameters annotations + Noise '
'must have the same length')
if mmt_model_filename is not None:
sabs_pkpd.constants.s = \
sabs_pkpd.load_model.load_simulation_from_mmt(mmt_model_filename)
# Then create an instance of our new model class
model = sabs_pkpd.pints_problem_def.MyModel()
# log_prior within [0.5 * starting_point , 2 * starting_point] if not
# specified
if log_prior is not None:
pass
else:
mini = np.array(np.min(starting_point, axis=0).tolist())
maxi = np.array(np.max(starting_point, axis=0).tolist())
log_prior = pints.UniformLogPrior(np.array(mini * 0.5).tolist(),
np.array(maxi * 2).tolist())
fit_values = np.concatenate(sabs_pkpd.constants.data_exp.values)
problem = pints.SingleOutputProblem(
model,
times=np.linspace(0, 1, len(fit_values)),
values=fit_values)
# Create a log-likelihood function (adds an extra parameter!)
log_likelihood = eval('pints.' + log_likelihood + '(problem)')
# Create a posterior log-likelihood (log(likelihood * prior))
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
method = eval('pints.' + method)
# Create mcmc routine
mcmc = pints.MCMCController(log_posterior,
len(starting_point),
starting_point,
method=method,
sigma0=sigma0)
# Allow parallelisation of computation when provided by user
mcmc.set_parallel(parallel)
# Add stopping criterion
mcmc.set_max_iterations(max_iter)
# Start adapting after adapt_start iterations
if adapt_start is not None:
mcmc.set_initial_phase_iterations(adapt_start)
if adapt_start > max_iter:
raise ValueError('The maximum number of iterations should be '
'higher than the adapting phase length. Got ' +
str(max_iter) + ' maximum iterations, ' +
str(adapt_start) + 'iterations in adapting phase')
if chain_filename is not None:
mcmc.set_chain_filename(chain_filename)
if pdf_filename is not None:
mcmc.set_log_pdf_filename(pdf_filename)
# Run!
print('Running...')
chains = mcmc.run()
print('Done!')
return chains
def load_problem(problem_dict):
"""
Returns a dictionary containing an instantiated PINTS problem
"""
problem_instance = copy.deepcopy(problem_dict)
model = problem_dict["model"]()
parameters = problem_dict['parameters']
# simulate problem
if 'simulation_noise_percent' in problem_dict:
values, times, noise_stds = emutils.simulate(
model,
parameters=problem_dict['parameters'],
times=problem_dict['times'],
noise_range_percent=problem_dict['simulation_noise_percent'],
)
else:
values, times = emutils.simulate(
model,
parameters=problem_dict['parameters'],
times=problem_dict['times'],
noise_range_percent=None,
)
noise_stds = None
# create instance of a problem and
if problem_dict['n_outputs'] == 1:
problem = pints.SingleOutputProblem(model, times, values)
else:
problem = pints.MultiOutputProblem(model, times, values)
# create likelihood with or without known noise
# log_likelihood = pints.UnknownNoiseLogLikelihood(problem)
log_likelihood = pints.KnownNoiseLogLikelihood(problem, noise_stds)
# should either provide the percentage range for parameters
# or the parameter range itself
if 'param_range_percent' in problem_dict:
param_range_percent = problem_dict['param_range_percent']
params_lower = parameters - param_range_percent * np.abs(parameters)
params_upper = parameters + param_range_percent * np.abs(parameters)
else:
params_lower, params_upper = problem_dict['param_range']
# add noise
# noise_lower, noise_upper = problem_dict['noise_bounds']
bounds = pints.RectangularBoundaries(
lower=params_lower,
upper=params_upper,
)
log_prior = problem_dict['prior'](bounds)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
# extend the dictionary with created variables
problem_instance.update({
'model': model,
'values': values,
'times': times,
'noise_stds': noise_stds,
'problem': problem,
'bounds': bounds,
'log_likelihood': log_likelihood,
'log_prior': log_prior,
'log_posterior': log_posterior
})
return problem_instance
def Algorithm1WithConvergence(L,
N,
model,
log_prior,
log_prior_used,
times,
noise,
noise_used,
MCMCmethod,
param=0):
time_start = time.time()
sum_p = 0
sum_p_theta = 0
sum_p_y = 0
c = 1
for i in range(c):
print(i)
res1 = []
res2 = []
thetatildeArray = [] #np.empty(N, dtype=float)
ytildeArray = [] #np.empty(N, dtype=float)
d = 0
for n in range(N):
print(n)
thetatilde = log_prior.sample(n=1)[0]
org_values = model.simulate(thetatilde, times)
ytilde_n = org_values + np.random.normal(0, noise,
org_values.shape)
problem = pints.SingleOutputProblem(model, times, ytilde_n)
log_likelihood_used = pints.GaussianKnownSigmaLogLikelihood(
problem, [noise_used])
log_posterior = pints.LogPosterior(log_likelihood_used,
log_prior_used)
#Start from thetatilde
xs = [thetatilde, thetatilde * 1.01, thetatilde * 0.99]
isinf = False
for x in xs:
#print(x)
if (math.isinf(log_posterior.evaluateS1(x)[0])):
isinf = True
d += 1
break
if (isinf == True):
print('isinf:', isinf)
continue
#Run Markov chain L steps from thetatilde'''
mcmc = pints.MCMCController(log_posterior,
len(xs),
xs,
method=MCMCmethod)
# Add stopping criterion
sample_size = 3000
mcmc.set_max_iterations(sample_size)
# Start adapting after sample_size//4 iterations
mcmc.set_initial_phase_iterations(sample_size // 4)
# Disable logging mode
mcmc.set_log_to_screen(False)
chains = mcmc.run()
s = sample_size // 4 + 1
b = False
while s < sample_size:
chains_cut = chains[:, sample_size // 4:s + 1]
#HMC: chains_cut = chains[:,0:s+1]
rhat = pints.rhat(chains_cut)
s += 1
if rhat[0] < 1.05:
print('converge')
b = True
break
if b == False:
d += 1
continue
print(s)
thetatilde_n = chains[0][(s + sample_size) // 2 - 1]
print(thetatilde)
thetatildeArray.append(thetatilde_n[param])
ytildeArray.append(ytilde_n[param])
res1.append((thetatilde_n[param], ytilde_n[param]))
thetaArray = np.empty(N - d, dtype=float)
yArray = np.empty(N - d, dtype=float)
for n in range(N - d):
theta_n = log_prior.sample(n=1)[0]
org_values = model.simulate(theta_n, times)
y_n = org_values + np.random.normal(0, noise, org_values.shape)
thetaArray[n] = theta_n[param]
yArray[n] = y_n[param]
res2.append((theta_n[param], y_n[param]))
p = ks2d2s(thetatildeArray, ytildeArray, thetaArray, yArray)
statistic_theta, p_theta = ks_2samp(thetatildeArray, thetaArray)
statistic_y, p_y = ks_2samp(ytildeArray, yArray)
sum_p += p
sum_p_theta += p_theta
sum_p_y += p_y
time_end = time.time()
duration = time_end - time_start
average_p = sum_p / c
average_p_theta = sum_p_theta / c
average_p_y = sum_p_y / c
print('average_p:', average_p)
print('average_p_theta:', average_p_theta)
print('average_p_y:', average_p_y)
return average_p, average_p_theta, average_p_y, duration, thetatildeArray, thetaArray, ytildeArray, yArray
