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 setUpClass(cls):
# Create test log-pdfs
model = pints.toy.ConstantModel(1)
problem = pints.SingleOutputProblem(model=model,
times=[1, 2, 3, 4],
values=[1, 2, 3, 4])
cls.log_pdf_1 = pints.GaussianLogLikelihood(problem)
problem = pints.SingleOutputProblem(model=model,
times=[1, 2, 3, 4],
values=[1, 1, 1, 1])
cls.log_pdf_2 = pints.GaussianLogLikelihood(problem)
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 test_stopping_on_ill_conditioned_covariance_matrix(self):
# Tests that ill conditioned covariance matrices are detected.
from scipy.integrate import odeint
#TODO: A quicker test-case for this would be great!
def OnePopControlODE(y, t, p):
a, b, c = p
dydt = np.zeros(y.shape)
k = (a - b) / c * (y[0] + y[1])
dydt[0] = a * y[0] - b * y[0] - k * y[0]
dydt[1] = k * y[0] - b * y[1]
return dydt
class Model(pints.ForwardModel):
def simulate(self, parameters, times):
y0 = [2000000, 0]
solution = odeint(
OnePopControlODE, y0, times, args=(parameters,))
return np.sum(np.array(solution), axis=1)
def n_parameters(self):
return 3
model = Model()
times = [0, 0.5, 2, 4, 8, 24]
values = [2e6, 3.9e6, 3.1e7, 3.7e8, 1.6e9, 1.6e9]
problem = pints.SingleOutputProblem(model, times, values)
score = pints.SumOfSquaresError(problem)
x = [3.42, -0.21, 5e6]
opt = pints.OptimisationController(score, x, method=method)
with StreamCapture() as c:
opt.run()
self.assertTrue('Ill-conditioned covariance matrix' in c.text())
def _problem(self):
import numpy as np
import pints
import pints.toy
# Load a forward model
model = pints.toy.LogisticModel()
# Create some toy data
xtrue = [0.015, 500]
times = np.linspace(0, 1000, 1000)
values = model.simulate(xtrue, times)
# Add noise
values += np.random.normal(0, 10, values.shape)
# Create problem
problem = pints.SingleOutputProblem(model, times, values)
score = pints.SumOfSquaresError(problem)
# Select some boundaries
boundaries = pints.RectangularBoundaries([0, 400], [0.03, 600])
# Select a random starting point
x0 = boundaries.sample(1)[0]
# Select an initial sigma
sigma0 = (1 / 6) * boundaries.range()
return score, xtrue, x0, sigma0, boundaries
def test_evaluateS1_two_dim_array_single(self):
# Convert data to array of shape (n_times, 1)
values = np.reshape(self.data_single, (self.n_times, 1))
# Create an object with links to the model and time series
problem = pints.SingleOutputProblem(self.model_single, self.times,
values)
# Create log_likelihood
log_likelihood = pkpd.ConstantAndMultiplicativeGaussianLogLikelihood(
problem)
# Evaluate likelihood for test parameters
test_parameters = [2.0, 0.5, 1.1, 1.0]
score, deriv = log_likelihood.evaluateS1(test_parameters)
# Check that likelihood score agrees with call
# There are floating point deviations because in evaluateS1
# log(sigma_tot) is for efficiency computed as -log(1/sigma_tot)
self.assertAlmostEqual(score, log_likelihood(test_parameters))
# Check that number of partials is correct
self.assertAlmostEqual(deriv.shape, (4, ))
# Check that partials are computed correctly
self.assertAlmostEqual(deriv[0], -2.0553513340073835)
self.assertAlmostEqual(deriv[1], -1.0151215581116324)
self.assertAlmostEqual(deriv[2], -1.5082610203777322)
self.assertAlmostEqual(deriv[3], -2.1759606944650822)
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 setUpClass(cls):
""" Prepare for the test. """
# Create toy model
model = pints.toy.LogisticModel()
cls.real_parameters = [0.015, 500]
times = np.linspace(0, 1000, 1000)
values = model.simulate(cls.real_parameters, times)
# Add noise
np.random.seed(1)
cls.noise = 10
values += np.random.normal(0, cls.noise, values.shape)
cls.real_parameters.append(cls.noise)
# Create an object with links to the model and time series
problem = pints.SingleOutputProblem(model, times, values)
# Create a uniform prior over both the parameters and the new noise
# variable
cls.log_prior = pints.UniformLogPrior(
[0.01, 400],
[0.02, 600]
)
# Create a log-likelihood
cls.log_likelihood = pints.GaussianKnownSigmaLogLikelihood(
problem, cls.noise)
def test_basics(self):
# Test everything
model = pints.toy.LogisticModel()
times = [0, 1, 2, 3]
x = [1, 1]
values = model.simulate(x, times)
noisy = values + np.array([0.01, -0.01, 0.01, -0.01])
problem = pints.SingleOutputProblem(model, times, noisy)
self.assertTrue(np.all(times == problem.times()))
self.assertTrue(np.all(noisy == problem.values()))
self.assertTrue(np.all(values == problem.evaluate(x)))
self.assertEqual(problem.n_parameters(), model.n_parameters(), 2)
self.assertEqual(problem.n_outputs(), model.n_outputs(), 1)
self.assertEqual(problem.n_times(), len(times))
# Test errors
times[0] = -2
self.assertRaises(ValueError, pints.SingleOutputProblem, model, times,
values)
times = [1, 2, 2, 1]
self.assertRaises(ValueError, pints.SingleOutputProblem, model, times,
values)
times = [1, 2, 3]
self.assertRaises(ValueError, pints.SingleOutputProblem, model, times,
values)
# Multi-output problem not allowed
model = pints.toy.FitzhughNagumoModel()
self.assertEqual(model.n_outputs(), 2)
values = model.simulate([1, 1, 1], times)
self.assertRaises(ValueError, pints.SingleOutputProblem, model, times,
values)
def test_mean_squared_error_single(self):
""" Tests :class:`pints.MeanSquaredError` with a single output. """
# Set up problem
model = pints.toy.ConstantModel(1)
times = [1, 2, 3]
values = [1, 1, 1]
p = pints.SingleOutputProblem(model, times, values)
# Test
e = pints.MeanSquaredError(p)
self.assertEqual(e.n_parameters(), 1)
float(e([1]))
self.assertEqual(e([1]), 0)
self.assertEqual(e([2]), 1)
self.assertEqual(e([0]), 1)
self.assertEqual(e([3]), 4)
# Derivatives
for x in [1, 2, 3, 4]:
y, dy = e.evaluateS1([x])
r = x - 1
self.assertEqual(y, e([x]))
self.assertEqual(dy.shape, (1, ))
self.assertTrue(np.all(dy == 2 * r))
def test_arma11(self):
model = pints.toy.ConstantModel(1)
parameters = [0]
times = np.asarray([1, 2, 3, 4])
model.simulate(parameters, times)
values = np.asarray([3, -4.5, 10.5, 0.3])
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.ARMA11LogLikelihood(problem)
self.assertAlmostEqual(log_likelihood([0, 0.9, -0.4, 1]),
-171.53031588534171)
# multiple outputs
model = pints.toy.ConstantModel(4)
parameters = [0, 0, 0, 0]
times = np.arange(1, 5)
model.simulate(parameters, times)
values = np.asarray([[3.5, 7.6, 8.5, 3.4], [1.1, -10.3, 15.6, 5.5],
[-10, -30.5, -5, 7.6], [-12, -10.1, -4, 2.3]])
problem = pints.MultiOutputProblem(model, times, values)
log_likelihood = pints.ARMA11LogLikelihood(problem)
# ARMA1Logpdf((3.5,1.1,-10, -12)|mean=0, rho=0.5, phi=0.34 sigma=1) +
# ARMA1Logpdf((7.6,-10.3,-30.5, -10.1)|
# mean=0, rho=-0.25, phi=0.1, sigma=3) +
# ARMA1Logpdf((8.5,15.6,-5, -4)|mean=0, rho=0.9, phi=0.0, sigma=10) +
# ARMA1Logpdf((3.4,5.5,7.6, 2.3)|mean=0, rho=0.0, phi=0.9, sigma=2)
# = -116.009 -74.94 -14.32 -8.88
self.assertAlmostEqual(
log_likelihood(parameters + [
0.5, 0.34, 1.0, -0.25, 0.1, 3.0, 0.9, 0.0, 10.0, 0.0, 0.9, 2.0
]), -214.17034137601107)
def test_multiplicative_gaussian(self):
# Test single output
model = pints.toy.ConstantModel(1)
parameters = [2]
times = np.asarray([1, 2, 3, 4])
model.simulate(parameters, times)
values = np.asarray([1.9, 2.1, 1.8, 2.2])
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.MultiplicativeGaussianLogLikelihood(problem)
self.assertAlmostEqual(log_likelihood(parameters + [2.0, 1.0]),
-9.224056577298253)
# Test multiple output
model = pints.toy.ConstantModel(2)
parameters = [1, 2]
times = np.asarray([1, 2, 3])
model.simulate(parameters, times)
values = np.asarray([[1.1, 0.9, 1.5], [1.5, 2.5, 2.0]]).transpose()
problem = pints.MultiOutputProblem(model, times, values)
log_likelihood = pints.MultiplicativeGaussianLogLikelihood(problem)
self.assertAlmostEqual(
log_likelihood(parameters + [1.0, 2.0, 1.0, 1.0]),
-12.176330824267543)
def __init__(self, models: List[m.SingleOutputModel], times: List[np.ndarray], values: List[np.ndarray]):
"""Initialises a single output inference problem with default objective function pints.SumOfSquaresError and
default optimiser pints.CMAES. Standard deviation in initial starting point of optimisation as well as
restricted domain of support for inferred parameters is disabled by default.
Arguments:
models {List[m.SingleOutputModel]} -- Models, which parameters are to be inferred.
times {List[np.ndarray]} -- Times of data points for the different models.
values {List[np.ndarray]} -- State values of data points for the different models.
Return:
None
"""
# initialise problem container
self.problem_container = []
for model_id, model in enumerate(models):
self.problem_container.append(pints.SingleOutputProblem(model, times[model_id], values[model_id]))
# initialise error function container
self.error_function_container = []
for problem in self.problem_container:
self.error_function_container.append(pints.SumOfSquaresError(problem))
# initialise optimiser
self.optimiser = pints.CMAES
# initialise fluctuations around starting point of optimisation
self.initial_parameter_uncertainty = None
# initialise parameter constraints
self.parameter_boundaries = None
# initialise outputs
self.estimated_parameters = None
self.objective_score = None
def test_ar1(self):
# single outputs
model = pints.toy.ConstantModel(1)
parameters = [0]
times = np.asarray([1, 2, 3])
model.simulate(parameters, times)
values = np.asarray([1.0, -10.7, 15.5])
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.AR1LogLikelihood(problem)
self.assertAlmostEqual(log_likelihood([0, 0.5, 5]),
-19.706737485492436)
# multiple outputs
model = pints.toy.ConstantModel(4)
parameters = [0, 0, 0, 0]
times = np.arange(1, 5)
model.simulate(parameters, times)
values = np.asarray([[3.5, 7.6, 8.5, 3.4], [1.1, -10.3, 15.6, 5.5],
[-10, -30.5, -5, 7.6], [-12, -10.1, -4, 2.3]])
problem = pints.MultiOutputProblem(model, times, values)
log_likelihood = pints.AR1LogLikelihood(problem)
# Test AR1Logpdf((3.5,1.1,-10, -12)|mean=0, rho=0.5, sigma=1) +
# AR1Logpdf((7.6,-10.3,-30.5, -10.1)|mean=0, rho=-0.25, sigma=3) +
# AR1Logpdf((8.5,15.6,-5, -4)|mean=0, rho=0.9, sigma=10) +
# AR1Logpdf((3.4,5.5,7.6, 2.3)|mean=0, rho=0.0, sigma=2)
# = -109.4752924909364 -93.58199 - 18.3833..
# -16.4988
self.assertAlmostEqual(
log_likelihood(parameters +
[0.5, 1.0, -0.25, 3.0, 0.9, 10.0, 0.0, 2.0]),
-237.93936126949615)
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 test_sum_of_squares_error_single(self):
""" Tests :class:`pints.MeanSquaredError` with a single output. """
# Set up problem
model = pints.toy.ConstantModel(1)
times = [1, 2, 3]
values = [1, 1, 1]
p = pints.SingleOutputProblem(model, times, values)
# Test
e = pints.SumOfSquaresError(p)
self.assertEqual(e.n_parameters(), 1)
float(e([1]))
self.assertEqual(e([1]), 0)
self.assertEqual(e([2]), 3)
self.assertEqual(e([0]), 3)
self.assertEqual(e([3]), 12)
# Derivatives
for x in [1, 2, 3, 4]:
ex, dex = e.evaluateS1([x])
r = x - 1
self.assertEqual(ex, e([x]))
self.assertEqual(dex.shape, (1, ))
self.assertEqual(dex[0], 2 * 3 * r)
def test_gaussian_integrated_uniform_log_likelihood_single(self):
# Tests GaussianIntegratedUniformLogLikelihood with single output
# problem
model = pints.toy.ConstantModel(1)
parameters = [0]
times = np.asarray([1, 2, 3])
model.simulate(parameters, times)
values = np.asarray([1.0, -10.7, 15.5])
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.GaussianIntegratedUniformLogLikelihood(
problem, 2, 4)
self.assertAlmostEqual(log_likelihood([0]), -20.441037907121299)
# test incorrect constructors
self.assertRaises(ValueError,
pints.GaussianIntegratedUniformLogLikelihood,
problem, -1, 2)
self.assertRaises(ValueError,
pints.GaussianIntegratedUniformLogLikelihood,
problem, 0, 0)
self.assertRaises(ValueError,
pints.GaussianIntegratedUniformLogLikelihood,
problem, 2, 1)
self.assertRaises(ValueError,
pints.GaussianIntegratedUniformLogLikelihood,
problem, [1, 2], [2, 3])
def setUpClass(cls):
cls.kernel = flexnoise.kernels.GPLaplacianKernel
cls.model = pints.toy.ConstantModel(1)
cls.times = np.linspace(1.0, 4.0, 15)
cls.data = np.random.normal(2.0, 1.0, 15)
cls.gp_times = np.array([1.0, 2.5, 4.0])
cls.problem = pints.SingleOutputProblem(cls.model, cls.times, cls.data)
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 main():
#constants
timeRangesToUse = [[1, 2499], [2549, 2999], [3049, 4999], [5049, 14999],
[15049, 19999], [20049, 29999], [30049, 64999],
[65049, 69999], [70049, -1]]
true_parameters = [
2.26E-04, 0.0699, 3.45E-05, 0.05462, 0.0873, 8.92E-03, 5.150E-3,
0.03158, 0.1524
]
model = ChannelModelPintsWrapper()
data = pd.read_csv("data/averaged-data.txt", delim_whitespace=True)
dat = extract_time_ranges(data.values, timeRangesToUse)
times = dat[:, 0]
values = dat[:, 1]
current = model.simulate(true_parameters, times)
plt.plot(times, values)
plt.plot(times, current)
plt.show()
problem = pints.SingleOutputProblem(model, times, values)
error = pints.SumOfSquaresError(problem)
boundaries = MarkovModelBoundaries()
x0 = np.array([0.1] * 9)
found_parameters, found_value = pints.optimise(error,
true_parameters,
boundaries=boundaries)
print(found_parameters, found_value)
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 test_sum_of_independent_log_pdfs(self):
# Test single output
model = pints.toy.LogisticModel()
x = [0.015, 500]
sigma = 0.1
times = np.linspace(0, 1000, 100)
values = model.simulate(x, times) + 0.1
problem = pints.SingleOutputProblem(model, times, values)
l1 = pints.GaussianKnownSigmaLogLikelihood(problem, sigma)
l2 = pints.GaussianLogLikelihood(problem)
ll = pints.SumOfIndependentLogPDFs([l1, l1, l1])
self.assertEqual(l1.n_parameters(), ll.n_parameters())
self.assertEqual(3 * l1(x), ll(x))
# Test single output derivatives
y, dy = ll.evaluateS1(x)
self.assertEqual(y, ll(x))
self.assertEqual(dy.shape, (2, ))
y1, dy1 = l1.evaluateS1(x)
self.assertTrue(np.all(3 * dy1 == dy))
# Wrong number of arguments
self.assertRaises(TypeError, pints.SumOfIndependentLogPDFs)
self.assertRaises(ValueError, pints.SumOfIndependentLogPDFs, [l1])
# Wrong types
self.assertRaises(ValueError, pints.SumOfIndependentLogPDFs, [l1, 1])
self.assertRaises(ValueError, pints.SumOfIndependentLogPDFs,
[problem, l1])
# Mismatching dimensions
self.assertRaises(ValueError, pints.SumOfIndependentLogPDFs, [l1, l2])
# Test multi-output
model = pints.toy.FitzhughNagumoModel()
x = model.suggested_parameters()
nt = 10
nx = model.n_parameters()
times = np.linspace(0, 10, nt)
values = model.simulate(x, times) + 0.01
problem = pints.MultiOutputProblem(model, times, values)
sigma = 0.01
l1 = pints.GaussianKnownSigmaLogLikelihood(problem, sigma)
ll = pints.SumOfIndependentLogPDFs([l1, l1, l1])
self.assertEqual(l1.n_parameters(), ll.n_parameters())
self.assertEqual(3 * l1(x), ll(x))
# Test multi-output derivatives
y, dy = ll.evaluateS1(x)
# Note: y and ll(x) differ a bit, because the solver acts slightly
# different when evaluating with and without sensitivities!
self.assertAlmostEqual(y, ll(x), places=3)
self.assertEqual(dy.shape, (nx, ))
y1, dy1 = l1.evaluateS1(x)
self.assertTrue(np.all(3 * dy1 == dy))
def test_known_noise_gaussian_single_and_multi(self):
"""
Tests the output of single-series against multi-series known noise
log-likelihoods.
"""
# Define boring 1-output and 2-output models
class NullModel1(pints.ForwardModel):
def n_parameters(self):
return 1
def simulate(self, x, times):
return np.zeros(times.shape)
class NullModel2(pints.ForwardModel):
def n_parameters(self):
return 1
def n_outputs(self):
return 2
def simulate(self, x, times):
return np.zeros((len(times), 2))
# Create two single output problems
times = np.arange(10)
np.random.seed(1)
sigma1 = 3
sigma2 = 5
values1 = np.random.uniform(0, sigma1, times.shape)
values2 = np.random.uniform(0, sigma2, times.shape)
model1d = NullModel1()
problem1 = pints.SingleOutputProblem(model1d, times, values1)
problem2 = pints.SingleOutputProblem(model1d, times, values2)
log1 = pints.GaussianKnownSigmaLogLikelihood(problem1, sigma1)
log2 = pints.GaussianKnownSigmaLogLikelihood(problem2, sigma2)
# Create one multi output problem
values3 = np.array([values1, values2]).swapaxes(0, 1)
model2d = NullModel2()
problem3 = pints.MultiOutputProblem(model2d, times, values3)
log3 = pints.GaussianKnownSigmaLogLikelihood(
problem3, [sigma1, sigma2])
# Check if we get the right output
self.assertAlmostEqual(log1(0) + log2(0), log3(0))
def setUpClass(cls):
cls.kernel = flexnoise.kernels.LaplacianKernel
cls.model = pints.toy.ConstantModel(1)
cls.times = np.arange(1, 91)
cls.data = np.hstack(
(np.random.normal(2.0, 0.1, 30), np.random.normal(2.0, 2.0, 30),
np.random.normal(2.0, 0.1, 30)))
cls.problem = pints.SingleOutputProblem(cls.model, cls.times, cls.data)
cls.model_prior = pints.UniformLogPrior([0] * 1, [1e6] * 1)
def figure1():
"""Make an interactive figure for numerical error.
"""
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))
# Forward Euler method
m = diffeqinf.DampedOscillator(stimulus, y0, diffeqinf.ForwardEuler)
m.set_step_size(0.01)
problem = pints.SingleOutputProblem(m, times, y)
likelihood = pints.GaussianLogLikelihood(problem)
step_sizes = [0.2, 0.1, 0.01]
true_params = [1.0, 0.2, 1.0, 0.01]
diffeqinf.plot.plot_likelihoods(problem,
likelihood,
true_params,
step_sizes=step_sizes,
param_names=['k', 'c', 'm'])
# RK45 method
m = diffeqinf.DampedOscillator(stimulus, y0, 'RK45')
m.set_tolerance(1e-2)
problem = pints.SingleOutputProblem(m, times, y)
likelihood = pints.GaussianLogLikelihood(problem)
tolerances = [0.01, 0.0001, 0.000001]
diffeqinf.plot.plot_likelihoods(problem,
likelihood,
true_params,
tolerances=tolerances,
param_names=['k', 'c', 'm'])
def test_student_t_log_likelihood_single(self):
# Single-output test for Student-t noise log-likelihood methods
model = pints.toy.ConstantModel(1)
parameters = [0]
times = np.asarray([1, 2, 3])
model.simulate(parameters, times)
values = np.asarray([1.0, -10.7, 15.5])
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.StudentTLogLikelihood(problem)
# Test Student-t_logpdf(values|mean=0, df = 3, scale = 10) = -11.74..
self.assertAlmostEqual(log_likelihood([0, 3, 10]), -11.74010919785115)
def test_cauchy_log_likelihood_single(self):
# Single-output test for Cauchy noise log-likelihood methods
model = pints.toy.ConstantModel(1)
parameters = [0]
times = np.asarray([1, 2, 3])
model.simulate(parameters, times)
values = np.asarray([1.0, -10.7, 15.5])
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.CauchyLogLikelihood(problem)
# Test Cauchy_logpdf(values|mean=0, scale = 10) = -12.34..
self.assertAlmostEqual(log_likelihood([0, 10]), -12.3394986541736)
def test_bad_log_pdfs_parameters(self):
model = pints.toy.ConstantModel(1)
problem = pints.SingleOutputProblem(model=model,
times=[1, 2, 3, 4],
values=[1, 2, 3, 4])
log_pdf = pints.ConstantAndMultiplicativeGaussianLogLikelihood(problem)
log_pdfs = [self.log_pdf_1, log_pdf]
pooled = [True, True]
self.assertRaisesRegex(ValueError,
'All log-pdfs passed to PooledLogPDFs',
pints.PooledLogPDF, log_pdfs, pooled)
def test_not_implemented_error(self):
# Convert data to list
values = self.data_single.tolist()
# Create an object with links to the model and time series
problem = pints.SingleOutputProblem(self.model_single, self.times,
values)
# Create error measure
error = pints.RootMeanSquaredError(problem)
# Check that not implemented error is raised for evaluateS1
self.assertRaisesRegex(NotImplementedError, '', error.evaluateS1, 1)
